Sensors for ecology - Outil de Suivi des Contrats
Sensors for ecology - Outil de Suivi des Contrats
Sensors for ecology - Outil de Suivi des Contrats
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Présentation <strong>de</strong> l’éditeur<br />
Ecological sciences <strong>de</strong>al with the way organisms interact<br />
with one another and their environment. Using sensors<br />
to measure various physical and biological characteristics<br />
has been a common activity since long ago. However<br />
the advent of more accurate technologies and increasing<br />
computing capacities <strong>de</strong>mand a better combination<br />
of in<strong>for</strong>mation collected by sensors on multiple spatial,<br />
temporal and biological scales.<br />
This book provi<strong>de</strong>s an overview of current sensors <strong>for</strong><br />
<strong>ecology</strong> and makes a strong case <strong>for</strong> <strong>de</strong>ploying integrated<br />
sensor plat<strong>for</strong>ms. By covering technological challenges as<br />
well as the variety of practical ecological applications, this text is meant to be<br />
an invaluable resource <strong>for</strong> stu<strong>de</strong>nts, researchers and engineers in ecological<br />
sciences.<br />
This book benefited from the Centre National <strong>de</strong> la Recherche Scientifique<br />
(CNRS) funds, and inclu<strong>de</strong>s 16 contributions by leading experts in french<br />
laboratories.<br />
Key features<br />
• An overview of sensors in the field of animal behaviour and physiology,<br />
biodiversity and ecosystem.<br />
• Several case studies of integrated sensor plat<strong>for</strong>ms in terrestrial and aquatic<br />
environments <strong>for</strong> observational and experimental research.<br />
• Presentation of new applications and challenges in relation with remote<br />
sensing, acoustic sensors, animal-borne sensors, and chemical sensors.
<strong>Sensors</strong> <strong>for</strong> <strong>ecology</strong><br />
Towards integrated knowledge of ecosystems
Jean-François Le Galliard,<br />
Jean-Marc Guarini, Françoise Gaill<br />
<strong>Sensors</strong> <strong>for</strong> <strong>ecology</strong><br />
Towards integrated knowledge<br />
of ecosystems<br />
Centre National <strong>de</strong> la recherche scientifique (CNRS)<br />
Institut Écologie et Environnement (INEE)<br />
www.cnrs.fr
Photographie <strong>de</strong> couverture / Cover Picture<br />
© CNRS Photothèque – AMICE Erwan<br />
UMR6539 – Laboratoire <strong>de</strong>s sciences <strong>de</strong> l’environnement marin – LEMAR – PLOUZANE<br />
“A diver inspects a queen conch Strombus gigas during a scientific expedition<br />
in Mexico. The queen conch is equipped with acoustic sensors, here nearby a receptor,<br />
in or<strong>de</strong>r to collect in<strong>for</strong>mation on its behaviour and physiology in nature.”<br />
© CNRS, Paris, 2012<br />
ISBN : 978-2-9541683-0-2
Contents<br />
Foreword................................................................................................ 11<br />
I<br />
Ecophysiology and animal behaviour<br />
Chapter 1 :<br />
Bio-logging: recording the ecophysiology and behaviour of animals<br />
moving freely in their environment<br />
Yan Ropert-Cou<strong>de</strong>rt, Akiko Kato, David Grémillet, Francis Crenner.... 17<br />
Chapter 2 :<br />
Animal-borne sensors to study the <strong>de</strong>mography and behaviour<br />
of small species<br />
Olivier Guillaume, Aurélie Coulon, Jean-François Le Galliard,<br />
and Jean Clobert................................................................................... 43<br />
Chapter 3 :<br />
Passive hydro-acoustics <strong>for</strong> cetacean census and localisation<br />
Flore Samaran, Nadège Gandilhon, Rocio Prieto Gonzalez, Fe<strong>de</strong>rica<br />
Pace, Amy Kennedy, and Olivier Adam............................................... 63<br />
Chapter 4 :<br />
Bioacoustics approaches to locate and i<strong>de</strong>ntify animals in terrestrial<br />
environments<br />
Chloé Huetz, Thierry Aubin................................................................. 83
8 Contents<br />
II<br />
Biodiversity<br />
Chapter 1 :<br />
Global estimation of animal diversity using automatic acoustic<br />
sensors<br />
Jérôme Sueur, Amandine Gasc, Philippe Grandcolas, Sandrine Pavoine. 99<br />
Chapter 2 :<br />
Assessing the spatial and temporal distributions of zooplankton<br />
and marine particles using the Un<strong>de</strong>rwater Vision Profiler<br />
Lars Stemmann, Marc Picheral, Lionel Guidi, Fabien Lombard,<br />
Franck Prejger, Hervé Claustre, Gabriel Gorsky..................................... 119<br />
Chapter 3 :<br />
Assessment of three genetic methods <strong>for</strong> a faster and reliable<br />
monitoring of harmful algal blooms<br />
Jahir Orozco-Holguin, Kerstin Töbe, Linda K. Medlin.......................... 139<br />
Chapter 4 :<br />
Automatic particle analysis as sensors <strong>for</strong> life history studies in<br />
experimental microcosms<br />
François Mallard, Vincent Le Bourlot, Thomas Tully............................ 163<br />
III<br />
Ecosystem properties<br />
Chapter 1 :<br />
In situ chemical sensors <strong>for</strong> benthic marine ecosystem studies<br />
Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel................. 185<br />
Chapter 2 :<br />
Advances in marine benthic <strong>ecology</strong> using in situ chemical sensors<br />
Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel................. 209<br />
Chapter 3 :<br />
Use of global satellite observations to collect in<strong>for</strong>mation in marine<br />
<strong>ecology</strong><br />
Séverine Alvain, Vincent Vantrepotte, Julia Uitz, Lucile Du<strong>for</strong>êt-<br />
Gaurier................................................................................................. 227
Contents 9<br />
Chapter 4 :<br />
Tracking canopy phenology and structure using ground-based<br />
remote sensed NDVI measurements<br />
Jean-Yves Pontailler, Kamel Soudani.................................................... 243<br />
IV<br />
Integrated studies<br />
Chapter 1 :<br />
Integrated observation system <strong>for</strong> pelagic ecosystems and<br />
biogeochemical cycles in the oceans<br />
Lars Stemmann, Hervé Claustre, Fabrizio D’Ortenzio.......................... 261<br />
Chapter 2 :<br />
Tropical rain <strong>for</strong>est environmental sensors at the Nouragues<br />
experimental station, French Guiana<br />
Jérôme Chave, Philippe Gaucher, Maël Dewynter.................................. 279<br />
Chapter 3 :<br />
Use of sensors in marine mesocosm experiments to study the effect<br />
of environmental changes on planktonic food webs<br />
Behzad Mostajir, Jean Nouguier, Emilie Le Floc’h, Sébastien Mas,<br />
Romain Pete, David Parin, Francesca Vidussi....................................... 305<br />
Synthesis and conclusion<br />
Jean-François Le Galliard, Jean-Marc Guarini and Françoise Gaill...... 331<br />
NB: When cited in the text, a chapter of this book is i<strong>de</strong>ntified according<br />
to the part it belongs to. For example, (III, 3) refers to the chapter<br />
3 by Alvain et al. in the third (III) part of this book about Ecosystem<br />
properties.
Foreword<br />
Altogether explorer, scientist, philosopher and one of the first world citizen,<br />
the German naturalist Alexan<strong>de</strong>r von Humboldt (1769-1859) is often<br />
consi<strong>de</strong>red as a foun<strong>de</strong>r of ecological sciences, though the word “<strong>ecology</strong>”<br />
was only coined several <strong>de</strong>ca<strong>de</strong>s later by another German scientist, Ernst<br />
Haeckel (1834-1919). Equipped with the best sensors (thermometers,<br />
barometers, and so on) and familiar with advanced metrology techniques<br />
of its time, von Humboldt pioneered the field of plant biogeography, a<br />
discipline at the meeting point between botany, geography, climatology<br />
and geology. Von Humboldt major conceptual and methodological contributions<br />
consisted in collecting physical and geological data along with<br />
plant distribution maps to <strong>de</strong>termine the physical and historical conditions<br />
favouring specific plant assemblages all over the world. With this<br />
approach, he ventured into previously unsuspected complex interactions<br />
between plants and their physical surroundings. Two centuries later,<br />
researchers are still striving to un<strong>de</strong>rstand the ecological and evolutionary<br />
mechanisms that <strong>de</strong>termine the distribution of plant and animal species.<br />
In<strong>de</strong>ed, an accurate quantification of how organisms interact with each<br />
other and with their environment is at the heart of several grand challenges<br />
in mo<strong>de</strong>rn ecological sciences from the <strong>de</strong>scription of bio-geochemical<br />
balances to the prediction of ecosystem dynamics. However, contrary<br />
to von Humboldt and his followers, we can now explore thoroughly the<br />
natural world, thanks to major technological improvements in our ability<br />
to measure physical, chemical and biological quantities. <strong>Sensors</strong> are now<br />
part of the standard toolbox of most ecological studies, and play an important<br />
role in both exploratory studies of nature, experimental approaches,<br />
and the <strong>de</strong>velopment of predictive ecological mo<strong>de</strong>ls. With the advent<br />
of more advanced technologies and the strong opportunities offered by<br />
nowadays available computing capacities, we are in a better position to<br />
integrate ecological in<strong>for</strong>mation from sensors across multiple spatial, temporal<br />
and biological scales. This book, sponsored by the Centre National<br />
<strong>de</strong> la Recherche Scientifique (CNRS) in France, presents an up-to-date<br />
overview of the use sensors <strong>for</strong> <strong>ecology</strong> by some leading CNRS laborato-
12 Foreword<br />
ries. The book covers some of the main technological challenges in our<br />
field from bio-loggers attached on animals to remote sensing imaging<br />
capacities installed on satellites, and provi<strong>de</strong>s many examples of practical<br />
applications chosen from ongoing CNRS programs. It is also tightly<br />
connected with the current frontiers in <strong>ecology</strong> and evolution throughout<br />
the world. We hope that the book will become an invaluable resource to<br />
stu<strong>de</strong>nts, researchers and engineers in ecological sciences.<br />
Few books have reviewed methods and issues in the field of sensors <strong>for</strong><br />
<strong>ecology</strong>. The reason is easy to un<strong>de</strong>rstand: a great <strong>de</strong>al of techniques<br />
and sensor types do exist and are covered in specific reviews or journals.<br />
Keeping pace with the increasing number of sensors and technologies<br />
currently available is there<strong>for</strong>e a difficult task. Yet, this book provi<strong>de</strong>s<br />
in a synthetic way a balanced <strong>de</strong>scription of the new applications and<br />
challenges in ecological research that the use of remote, acoustic, animalborne,<br />
chemical and genosensors represents. Here, we adopted a broad<br />
view on sensors – usually <strong>de</strong>fined as a <strong>de</strong>vice that measures a physical<br />
quantity and converts it into a signal – to <strong>de</strong>scribe a quantity of tools<br />
including image and vi<strong>de</strong>o analyses, biodiversity and life history sensors,<br />
and other less traditional methods.<br />
Furthermore, this book contains technical <strong>de</strong>scriptions of some sensors<br />
even though it is not a handbook about sensor technologies. A technical<br />
treatment is crucial to un<strong>de</strong>rstand, <strong>de</strong>sign and integrate sensors <strong>for</strong><br />
the purpose of ecological research, but this issue is already addressed in<br />
many handbooks. Instead, it was <strong>de</strong>ci<strong>de</strong>d to focus this presentation on<br />
relevant applications and practical problems faced by ecologists during<br />
their research programs.<br />
Lastly, the book differs from a traditional presentation based on a standard<br />
classification among sensor types and a discussion of the specific<br />
issues within each category of sensors (e.g. remote sensing versus chemical<br />
sensors). Ecological studies often need to integrate physical, chemical and<br />
biological data obtained with various types of sensors to get a comprehensive<br />
view of the state and dynamics of ecosystems. We there<strong>for</strong>e chose to<br />
make a strong case <strong>for</strong> the <strong>de</strong>ployment of integrated, autonomous sensor<br />
plat<strong>for</strong>ms in the context of observational and experimental research infrastructures,<br />
and we present case studies where integrated data can in<strong>for</strong>m<br />
predictive mo<strong>de</strong>ls. The integration of various types of sensors <strong>for</strong> ecological<br />
studies poses a series of complex problems, from the engineering and<br />
autonomy of the plat<strong>for</strong>m to the access and use of data <strong>for</strong> mo<strong>de</strong>lling.<br />
These difficulties go far beyond the <strong>de</strong>sign of a single sensor and are often<br />
specific of the scale at which sensors should be <strong>de</strong>ployed.
Foreword 13<br />
The i<strong>de</strong>a of this project started un<strong>de</strong>r the patronage of the CNRS Institut<br />
Écologie et Environnement in 2010 with the aim to produce a state-ofthe-art<br />
review <strong>for</strong> sensors used in <strong>ecology</strong> in France, as well as to i<strong>de</strong>ntify<br />
strengths and weaknesses in this field <strong>for</strong> the future. Un<strong>de</strong>r the supervision<br />
of Jean-Marc Guarini and Karine Heerah, a workshop was organised at the<br />
University Pierre and Marie Curie in Paris in January 2011. Twenty four<br />
contributions were ma<strong>de</strong> and the workshop’s program addressed remote<br />
sensing techniques, the use of sensors <strong>for</strong> in situ studies, and the use of<br />
sensors <strong>for</strong> experimental studies. From these contributions and the discussions<br />
that followed, a few were selected to be published in the four<br />
sections of this book. The first section <strong>de</strong>als with animal behaviour and<br />
physiology, a very active field of research that raises both strong technical<br />
constraints and ethical issues. In a second section, we discuss the use of<br />
sensors in intra-specific and inter-specific biodiversity studies, and provi<strong>de</strong><br />
key examples ranging from the use of acoustic sensors or genetic methods<br />
to image analysis. We then present a series of ecosystem studies relying<br />
on advanced remote sensing and chemical sensors; those studies focus<br />
on measuring feedbacks between organisms and their geo-chemical environment.<br />
Our last section groups three integrated ecological studies. Two<br />
discuss observation plat<strong>for</strong>ms of ecosystems and one <strong>de</strong>scribes an experimental<br />
marine <strong>ecology</strong> infrastructure. The latter <strong>de</strong>monstrates how sensors<br />
can be used to manipulate environmental conditions and study the<br />
effect of environmental changes on ecosystems. Each chapter was organised<br />
so that it reviews existing methods and sensors, and discusses current<br />
difficulties and requirements <strong>for</strong> future technological <strong>de</strong>velopment.<br />
We would like to thank the authors <strong>for</strong> their participation and their kind<br />
patience during the editorial process. Angeline Perrot assisted us during<br />
the two last months of this project and was extremely efficient at organising<br />
reviews, editing all text and making the iconography tidy from an<br />
heterogeneous pool of figures and photographs. The editors would like to<br />
thank the CNRS and the Institut Écologie et Environnement (INEE) <strong>for</strong><br />
their financial support, as well as the University Pierre and Marie Curie<br />
<strong>for</strong> hosting the workshop. Jean-François Le Galliard acknowledges the support<br />
of the TGIR Ecotrons program and from the UMS 3194 CEREEP-<br />
Ecotron IleDeFrance as well as the financial support from ANR Equipex<br />
PLANAQUA coordinated by École normale superieure and CNRS (contract<br />
ANR-10-EQPX-13).<br />
Jean-François Le Galliard, Jean-Marc Guarini and Françoise Gaill<br />
January 16, 2012<br />
Paris, France
I<br />
EcoPHySIology<br />
AND ANIMAL bEHAVIoUR
Chapter 1<br />
Bio-logging: recording<br />
the ecophysiology and behaviour<br />
of animals moving freely in their<br />
environment<br />
Yan Ropert-Cou<strong>de</strong>rt, Akiko Kato, David Grémillet, Francis Crenner<br />
1. Setting the scene<br />
1.1 Sensing with bio-loggers<br />
Bio-logging refers to the fastening of autonomous <strong>de</strong>vices onto (mainly)<br />
free-ranging animals to collect physical and biological in<strong>for</strong>mation<br />
(Naito, 2004; 2010; Cooke et al., 2004; Ropert-Cou<strong>de</strong>rt et al., 2005; see<br />
also Costa, 1988, although the term bio-logging was not used in these<br />
times). It should be noted here that bio-logging is sometimes referred to<br />
as biologging. However, as Naito (2010) pointed out, the latter term is<br />
misleading as it is used in molecular biology. As such bio-logging differs<br />
from telemetry in the sense that data are stored locally in the memory<br />
of the <strong>de</strong>vices and not transferred via radio waves or other transmitting<br />
means. This move from biotelemetry to bio-logging was done in or<strong>de</strong>r<br />
to address practical difficulties related to data transmission. Thus, this<br />
comes, as no surprise that bio-logging was firstly used in ecological and<br />
physiological studies to investigate marine, far-ranging, diving species, as<br />
water represents a barrier to radio signals. Bio-logging studies were initially<br />
conducted on species with a body mass large enough to accommodate the<br />
large size of the very first loggers: seals and whales. As miniaturisation<br />
progressed, smaller species of seals and seabirds became target species <strong>for</strong><br />
bio-logging approaches. Among seabirds, penguins (Sphenscidae) represent<br />
an intensively studied family because of their adaptation to aquatic life
18 Ecophysiology and animal behaviour<br />
and their consequently <strong>de</strong>nser, larger and more robust body. Nowadays,<br />
bio-logging can be applied onto an impressive range of species, terrestrial<br />
or aquatic, whether these are mammals, birds or reptiles (see I, 2).<br />
Bio-logging <strong>de</strong>velopments are one step away from moving into the insect<br />
realm as radio-telemetry is already available to study terrestrial and flying<br />
insects (Vinatier et al., 2010; Wikelski et al., 2006).<br />
Immediate consequences of local storage are the necessity to retrieve the<br />
<strong>de</strong>vice to access the data and <strong>de</strong>velop appropriate sensors to gather data<br />
about physical and biological in<strong>for</strong>mation on relevant time scales. It there<strong>for</strong>e<br />
clearly appears that bio-logging primarily refers to a methodological<br />
approach and has generated research to improve existing technologies. Yet,<br />
bio-logging is more than a mere catalogue of tools and techniques. The<br />
possibility to obtain an uninterrupted flow of in<strong>for</strong>mation pertaining to<br />
both the activity and physiology of animal and its immediate, physical<br />
surroundings revolutionised the way we consi<strong>de</strong>r several fields in biology.<br />
We could draw a parallel with the field of genetics and how it evolved from<br />
Gregor Men<strong>de</strong>l crossing variety of peas to the advanced technologies of<br />
molecular sequencing. Similarly, the ecologist with its notebook possesses<br />
now a suite of approaches to examine animals living freely in their environment.<br />
In this context, bio-logging applications ranges from physiological<br />
investigations to the comprehension of the functioning of ecosystems,<br />
by relating a change in physical parameters of the environment to a change<br />
in the behaviour of both a predator and its prey, at the same spatial and<br />
temporal scales (Ropert-Cou<strong>de</strong>rt et al., 2009a).<br />
1.2. Bio-logging in the scientific community<br />
The word bio-logging was coined at the occasion of the first symposium<br />
about the topic held in 2003 in Tokyo, Japan. Over the past <strong>de</strong>ca<strong>de</strong>, three<br />
additional symposia took place: Saint Andrews (Scotland) in 2005, Pacific<br />
Grove (USA) in 2008, and Hobart (Tasmania) in 2011. The next bio-logging<br />
symposium will be organized in France and is tentatively scheduled<br />
<strong>for</strong> Strasbourg in September 2014. The number of manufacturers has<br />
steadily increased since the inception of Wildlife Computers (USA) in<br />
1986, the first – to the best of our knowledge – bio-logging company ever.<br />
Nowadays, the core of the bio-logging production is concentrated in the<br />
North America and Japan (Ropert-Cou<strong>de</strong>rt et al., 2009b), but emerging<br />
companies in the UK (CTL), Iceland (Star-Oddi) or Italy (Technosmart)<br />
are gaining worldwi<strong>de</strong> momentum (Table 1). A non-negligible proportion<br />
(a rough estimate of 20%) of bio-logging <strong>de</strong>vices is still produced in<br />
research institutions, the so-called custom-ma<strong>de</strong> bio-loggers, and is thus<br />
accessible only through collaborations between researchers. In Europe,<br />
<strong>for</strong> example, research-driven <strong>de</strong>velopments are found in the Sea Mammal
Part I – Chapter 1 19<br />
Table 1: A non exhaustive list of the most-used bio-loggers together<br />
with their weights and the sensors they inclu<strong>de</strong>, as well as the name<br />
of the manufacturers.<br />
Mo<strong>de</strong>l Weight (g) <strong>Sensors</strong> Manufacturers<br />
Mk 15 2.5 Light, wet or dry status British Antarctic Survey, UK<br />
Cefas G5 2.7 Depth, temperature Cefas technology Ltd, UK<br />
DST bird 1.7 Temperature, light<br />
Star-Oddi, Iceland<br />
DST magnetic 19 Depth, temperature, magnetometer, tilt<br />
ORI400-D3GT 9 Depth, temperature, acceleration<br />
W1000-3MPD3GT 130 Depth, temperature, speed, acceleration, magnetometer<br />
Little Leonardo, Japan<br />
W400-ECG 57 ECG<br />
DSL400-VDT II 82 Depth, temperature, image<br />
GiPSy I 22 GPS TechnoSmart, Italy<br />
CatTraQ 22 GPS Mr. Lee, USA<br />
Mk9 30 Depth, temperature, light, acceleration, magnetometer<br />
Wildlife Computers, USA<br />
SPLASH10-F-400 225 Depth, temperature, light, GPS, Argos (data transmission)<br />
DTAG 300 Depth, audio, pitch, roll, heading<br />
SRDL tag 370 Depth, temperature, speed, Argos (data transmission)<br />
CTD tag 545 Depth, temperature, conductivity<br />
GPS Phone tag 370 Depth, temperature, GPS, GMS (data transmission)<br />
Daily Diary 42<br />
Depth, temperature, light, speed, acceleration,<br />
magnetometer<br />
Woods Hole Oceangraphic<br />
Institution, USA<br />
Sea Mammal Research Unit,<br />
UK<br />
Swansea University, UK
20 Ecophysiology and animal behaviour<br />
Research Unit of the University of St. Andrews, which organized the 2nd<br />
bio-logging symposium. In France, the only openly <strong>de</strong>clared bio-logging<br />
<strong>de</strong>velopment team is found at the Institut Pluridisciplinaire Hubert Curien<br />
in Strasbourg. The next big step <strong>for</strong> the bio-logging community will be<br />
to <strong>for</strong>m a society so as to reach an official status and help structuring the<br />
community. Bio-logging is especially expected to play an important role<br />
in the <strong>for</strong>thcoming <strong>de</strong>ca<strong>de</strong> regarding conservation issues and will represent<br />
a crucial tool to assess large vertebrate species distribution and links<br />
between the physical environment and the biological response of animals<br />
to its variation (see Cooke, 2008).<br />
2. Overview of bio-logging applications<br />
2.1. Reconstructing the movement and feeding behaviour<br />
The ancestors of all bio-loggers are probably time-<strong>de</strong>pth-recor<strong>de</strong>rs, commonly<br />
referred to as TDR in several instances. These <strong>de</strong>vices record<br />
hydrostatic pressure according to time so as to reconstruct diving activity<br />
of sea animals. Oddly, the very first incarnation of a TDR, which was<br />
attached to a freely-diving Wed<strong>de</strong>ll seal Leptonychotes wed<strong>de</strong>lli, consisted<br />
in coupling a kitchen timer with a pressure transducer (Kooyman, 1965;<br />
1966). Subsequent <strong>de</strong>vices also functioned on a mechanical basis, such as<br />
miniature pencils that were animated by pressure changes and drew the<br />
profiles of dives onto a miniature paper (e.g. Naito et al., 1990). The emergence<br />
of solid-state memories put an end to this era of clever handcrafting.<br />
Nowadays, TDR can weigh as less as 2.7g and are able to capture <strong>de</strong>pth<br />
and temperature data every second <strong>for</strong> around 10 days. When associated<br />
with GPS, they provi<strong>de</strong> localisation onto both the horizontal and vertical<br />
dimensions, on a large range of species.<br />
Originally, TDR <strong>de</strong>livered only a 2D view of the diving activity (<strong>de</strong>pth<br />
according to time) but progresses in behaviour reconstruction came from<br />
the utilisation of accelerometers. Accelerometers record gravity-related<br />
and dynamic acceleration signals and can be used to provi<strong>de</strong> specific<br />
in<strong>for</strong>mation about the movements of the body, such as walking gait (e.g.<br />
Halsey et al., 2008) or head-jerking (e.g. Viviant et al., 2010). The potential<br />
of accelerometers to reconstruct time budget activity was <strong>de</strong>monstrated<br />
in several instances (e.g. Yoda et al., 1999; Ropert-Cou<strong>de</strong>rt et al.,<br />
2004a; Watanabe et al., 2005). The addition of gyroscopes and magnetometers<br />
makes it possible to reconstruct the precise path of animals in<br />
the three dimensions. This approach, called “<strong>de</strong>ad reckoning” (Wilson<br />
et al., 1991), is very prone to making substantial errors. For example, a<br />
Wed<strong>de</strong>ll seal diving <strong>for</strong> ca. 17mn would accumulate an error in its posi-
Part I – Chapter 1 21<br />
tion calculated via <strong>de</strong>ad reckoning of nearly 100m over this period (see<br />
figure 5a in Mitani et al., 2003). While methods exist to take this error<br />
into account (Mitani et al., 2003), <strong>de</strong>ad reckoning is yet to be implemented<br />
at time scales longer than a few days. Anyway, the precision of<br />
tracking techniques thanks to GPS <strong>de</strong>velopment makes it unlikely that<br />
<strong>de</strong>ad reckoning will become a major approach. Small body movements,<br />
such as limb movements (Wilson and Liebsch, 2003), can also be finely<br />
reconstructed using Hall sensors, i.e. sensors measuring the intensity of<br />
the magnetic field. In this case, a magnet placed on one mandibular plate<br />
facing a Hall sensor glued onto the other mandibular plate (figure 1)<br />
allows researchers to <strong>de</strong>termine when a prey has been swallowed and,<br />
following a proper calibration, the size and type of prey (Wilson et al.,<br />
2002; Ropert-Cou<strong>de</strong>rt et al., 2004b).<br />
Figure 1: Schematic representation of the jaw movement recor<strong>de</strong>r on a gentoo<br />
penguin Pygoscelis papua (top) and a young wild boar Sus scrofa (bottom). A magnet<br />
and a Hall sensor, sensitive to the strength of the magnetic field are placed on the<br />
two mandibles, facing each other. When the mouth opens the Hall sensor senses<br />
a reduction in the intensity of the magnetic field and sends this in<strong>for</strong>mation via a<br />
cable to the bio-logger attached on the body.<br />
Finally, in the context of assessing animal movements on a world-wi<strong>de</strong><br />
scale, the two major <strong>de</strong>velopments of the recent <strong>de</strong>ca<strong>de</strong>s feature the
22 Ecophysiology and animal behaviour<br />
advent of GLS (global location sensors) and of GPS (global positioning<br />
system). Global location sensors are miniaturised units that store light<br />
measurements at regular intervals, from which position can be estimated<br />
(using day length and noon time). Initially <strong>de</strong>scribed by Wilson et al.<br />
(1992a), this method revolutionised migration studies because <strong>de</strong>vices are<br />
particularly small (around 1g), cheap, and are able to record data up to<br />
several years. They can there<strong>for</strong>e be <strong>de</strong>ployed year-round on a wi<strong>de</strong> range<br />
of individuals and species (Fort et al., in press). Miniaturised GPS (the<br />
smallest ones currently weigh 5g or less) usually have shorter recording<br />
times yet far higher spatial resolution than GLS (a few meters versus a few<br />
tens of km). Their generalised use triggered a quantum leap in the spatial<br />
<strong>ecology</strong> of free-ranging animals (Ryan et al., 2004)<br />
2.2. Reconstructing the internal temperature and heat flux<br />
Animal-borne bio-loggers also benefited physiological studies as these<br />
bio-loggers allowed researchers to investigate internal adjustments to the<br />
constraints of, <strong>for</strong> example, experiencing extremely low temperatures<br />
(Gilbert et al., 2008; Eichhorn et al., 2011). These feats cannot be realized<br />
in the confines of a laboratory. Reduced core temperature in the<br />
body of <strong>de</strong>ep divers like the king penguins Aptenodytes patagonicus shed<br />
a new light on the physiological mechanisms involved in energy savings<br />
at great <strong>de</strong>pths (e.g. Handrich et al., 1997). In parallel to the externallyattached<br />
bio-loggers that recor<strong>de</strong>d mandibular activity (see above), measurements<br />
of temperature in the stomach (Wilson et al., 1992b; Grémillet<br />
and Plos, 1994) or the oesophagus of endotherms (Ancel et al., 1997;<br />
Ropert-Cou<strong>de</strong>rt et al., 2001) also permitted to explore when these animals<br />
fed onto their exothermic prey as their swallowing induced a drop in the<br />
temperature (see figure 2 and additional discussions around the principle<br />
and the limitations of this method in Hedd et al., 1995; Ropert-Cou<strong>de</strong>rt<br />
et al., 2006a). Heat flux measurement bio-loggers may also be used to<br />
study homeothermy in animals swimming in cold waters (e.g. Willis and<br />
Horning, 2005).<br />
2.3. Reconstructing the heart ef<strong>for</strong>t: ECG vs. heart rate<br />
One challenge in ecophysiology is to <strong>de</strong>termine energy expenditures of<br />
free-ranging animals. Field methods based on doubly-labelled water exist<br />
but these are long-term methods that integrate the energy expen<strong>de</strong>d over a<br />
period of few days (Speakman, 1997). Further to the point, these methods<br />
require multiple capture and handling, which are not always easy to implement<br />
in the field, especially <strong>for</strong> shy and sensitive species. Cormorants, <strong>for</strong><br />
example, respond to handling with intense overheating. Last but not least,
Part I – Chapter 1 23<br />
Figure 2: Two temperature signals (°C) recor<strong>de</strong>d by sensors placed in the upper<br />
part of the oesophagous (top) and in the stomach (bottom) of an Adélie penguin<br />
Pygoscelis a<strong>de</strong>liae fed with cold food items. Each ingestion is visualised as a sud<strong>de</strong>n<br />
drop in the temperature, followed by a slow recovery.<br />
the doubly-labelled water method is expensive and implies a laboratory<br />
specifically equipped with isotope analysis facilities. In contrast, the measurement<br />
of heart rate can give an i<strong>de</strong>a of the energy expen<strong>de</strong>d, as heart<br />
rates are linked with metabolic rates (Nolet et al., 1992; Green et al., 2001;<br />
Weimerskirch et al., 2002). Although the shape of the relationship is often<br />
unclear (Froget et al., 2002; Ward et al., 2002; McPhee et al., 2003), measuring<br />
heart rate still enables the estimation of the ef<strong>for</strong>t allocated to basal<br />
versus non-basal (e.g. locomotor) activities.<br />
Among the bio-logging approaches <strong>for</strong> measuring heart rate, two techniques<br />
have emerged: i) heart rate recor<strong>de</strong>rs (HRR) that <strong>de</strong>tect the heart<br />
beat and store in their memory the interval between each heartbeat or<br />
the number of heart beats per certain time period; ii) electrocardiogram<br />
recor<strong>de</strong>rs (ECGR) that monitor and store the complete electric signals<br />
allowing to access the complete PQRS profile of a heartbeat. both systems<br />
measure the electrical activity of the heart transmitted via 2 or 3<br />
electro<strong>de</strong>s placed in different parts of an animal’s body. HRR have an<br />
exten<strong>de</strong>d autonomy since they only count intervals (Grémillet et al.,<br />
2005) but are prone to error because the ability to distinguish heartbeats<br />
from electric noise due to muscular activity <strong>de</strong>pends solely on an onboard<br />
algorithm. In contrast, ECGR requires a processing of the signal<br />
but this ensures that only heartbeats are counted (Ropert-Cou<strong>de</strong>rt et al.,<br />
2006b; 2009c). However, commercially available ECGR have limited<br />
autonomy.
24 Ecophysiology and animal behaviour<br />
Figure 3: Recordings of heart rate on a captive mandrill Mandrillus sphinx.<br />
A. Photograph of the collar where <strong>de</strong>vices are attached and the electro<strong>de</strong>s<br />
protruding from it. B. Collar mounted on the mandrill with the electro<strong>de</strong>s plugged<br />
on the skin and secured by bolts. C. A comparison of the heart rate recor<strong>de</strong>d by two<br />
different <strong>de</strong>vices: a heart rate counter (Polar Watch, blue) and an electrocardiogram<br />
(ECG recor<strong>de</strong>r, red). The latter allows the user to visualize each heart beat as a<br />
PQRS complex and is thus much more reliable than heart rate directly given by the<br />
counter (calculated via an internal algorythm to which the user generally cannot<br />
access). The heart rate given by the counter shows large variation that are absent on<br />
the signals <strong>de</strong>rived from the ECG. © Jacques-Olivier Fortrat.<br />
The comparison of the heart rate signals of a sleeping mandrill Mandrillus<br />
sphinx directly <strong>de</strong>rived from a commercially-available heart rate monitor<br />
(© Polar Electro, France) and the one calculated from an ECGR (Little<br />
Leonardo, Japan) illustrates well the risk of applying tools that are <strong>de</strong>veloped<br />
<strong>for</strong> a specific use (here, the Polar Watch is inten<strong>de</strong>d <strong>for</strong> measuring<br />
heart rate during human exercise) onto an animal mo<strong>de</strong>l without prior
Part I – Chapter 1 25<br />
calibration work (figure 3). The need to reduce the risk of storing electromyograms<br />
generally leads researchers to implant the HRR in the body,<br />
while ECGR can either be implanted or externally attached. Implantation<br />
is not trivial as it involves anaesthesia and surgery, with all the associated<br />
risks, and is not always easy to per<strong>for</strong>m in the field (see Green et al., 2004;<br />
Beaulieu et al., 2010).<br />
2.4. Viewing the environment: image data logger<br />
Data contained in bio-loggers are used to reconstruct the activity and, in<br />
some cases, the environment in which the animals move. But the dream<br />
of all users is to be able to visualise directly what the animals are seeing.<br />
Images, if they do not give access to physiological in<strong>for</strong>mation per se, are a<br />
smart and in<strong>for</strong>mative way of studying behaviour. Images are also attractive<br />
to a large audience as they do not always require specific knowledge to<br />
be interpreted. As communication towards the public becomes paramount<br />
to Science, this is a non negligible asset <strong>for</strong> bio-logging approaches that<br />
use digital-still picture recor<strong>de</strong>rs or even vi<strong>de</strong>o recor<strong>de</strong>rs. The National<br />
Geographic Crittercam project was a pioneer in merging the scientific<br />
community with common people. However, their usefulness to answer<br />
scientific questions was often questioned. Digital-still cameras take images<br />
following a <strong>de</strong>finite sampling interval which is not always a<strong>de</strong>quate <strong>for</strong><br />
short time events like prey capture.<br />
Yet, these techniques can provi<strong>de</strong> unravelled insights into prey i<strong>de</strong>ntification<br />
(figure 4, see also Davis et al., 1999; Watanabe et al., 2006), prey<br />
<strong>de</strong>nsity (Watanabe et al., 2003), group behaviour (Takahashi et al., 2004a;<br />
Rutz et al., 2007) or the biomechanics of flight (Gillies et al., 2011). Vi<strong>de</strong>o<br />
recording systems have limited autonomy and are still rather bulky to<br />
be used without the risk of impairing the per<strong>for</strong>mances and health of<br />
some animal mo<strong>de</strong>ls (see the bulkiness of a vi<strong>de</strong>o recor<strong>de</strong>r mounted on<br />
an emperor penguin in the figure 1 from Ponganis et al., 2000). Recent<br />
advances in miniaturisation allowed <strong>for</strong> these <strong>de</strong>vices to be placed on the<br />
head of a flying seabird (Sakamoto et al., 2009). In an applied context, it<br />
has been recently proposed to use newly-<strong>de</strong>veloped, highly miniaturised<br />
digital-still picture recor<strong>de</strong>rs mounted on seabirds to monitor pirates fishing<br />
boat (Grémillet et al., 2010).<br />
2.5. Reconstructing the environment: animals as bio-plat<strong>for</strong>ms<br />
The pioneers of bio-logging soon realised that this technology not only<br />
allowed the study of animals in their natural surroundings, but also to<br />
access their biotic and abiotic environment. Especially in the oceans,<br />
where sampling through the water column is impossible from satellites
26 Ecophysiology and animal behaviour<br />
and expensive from research vessels, this approach led to remarkable<br />
advances. As soon as time-<strong>de</strong>pth-recor<strong>de</strong>rs were coupled with positioning<br />
<strong>de</strong>vices and temperature sensors, the thermal structure of water masses<br />
could be assessed. This was first conducted in Antarctica by Wilson et al.<br />
(1994), which used penguins equipped with data loggers to map thermal<br />
gradients across the 100m of the Maxwell Bay. Not only did they assess<br />
this abiotic parameter, but they also cross-checked this in<strong>for</strong>mation with<br />
an estimation of krill biomass in this water mass, which was based upon<br />
the predatory per<strong>for</strong>mance of the birds. This approach was revolutionary.<br />
Yet, temperature measurements were too coarse to be a<strong>de</strong>quate <strong>for</strong> proper<br />
oceanography work. It is only a <strong>de</strong>ca<strong>de</strong> later that seabirds were equipped<br />
with loggers measuring ocean temperature to 0.005K and <strong>de</strong>pth to 0.06m,<br />
values accurate enough to track the vertical movements of the thermocline<br />
off Scotland in the North Sea (Daunt et al., 2003). However, this<br />
approach was then only used to investigate areas that had been already<br />
studied, and had been sampled using conventional, ship-based surveys.<br />
Figure 4: Image data loggers. A. A digitial-still-picture logger (Little Leonardo, Japan)<br />
mounted on a great cormorant Phalacrocorax carbo in Greenland (left, © David Grémillet)<br />
together with a view of the logger itself (B) and four examples of pictures taken by<br />
the logger (C). The examples show fish prey caught in the beak of the cormorant.
Part I – Chapter 1 27<br />
The next step consisted in using free-ranging marine animals fitted<br />
with bio-loggers to sample unknown areas. For instance, Charrassin et<br />
al. (2002) used temperature data collected by diving king penguins to<br />
i<strong>de</strong>ntify a previously-unknown water mass off Kerguelen in the Southern<br />
Ocean. However, operational oceanography requires real-time assessments<br />
of biotic and abiotic parameters, <strong>for</strong> instance to parameterise mo<strong>de</strong>ls of<br />
ocean circulation and climatic processes (IV, 1). This was not possible<br />
using ancient archival tags fitted to marine predators, since those had<br />
to be recovered to download the data, sometimes weeks or months after<br />
the actual measurement. Such problem was solved by the use of a system<br />
integrating bio-physical sensors of the environment (e.g. water colour,<br />
temperature, salinity) and sensors of the animal’s movements (3D acceleration,<br />
<strong>de</strong>pth and speed) with the Argos positioning and transmission<br />
system. Such tools are large, require substantial battery power, and can<br />
only be <strong>de</strong>ployed on large marine mammals <strong>for</strong> the time being, in particular<br />
elephant seals (Mirounga leonina). However, they allowed a major step<br />
<strong>for</strong>ward because elephant seals cruise the Southern Ocean in areas that<br />
are beyond the reach of satellite or vessel-based oceanography, especially<br />
in the marginal ice zone off Antarctica and at <strong>de</strong>pths of more than 1000m<br />
(Charrassin et al., 2008). From these areas, <strong>de</strong>vices fitted to these large,<br />
record-breaking divers can send new data which are now being routinely<br />
integrated into ocean physics mo<strong>de</strong>ls (Roquet et al., 2011).<br />
2.6. Multi-in<strong>for</strong>mation sensors: the special case of accelerometry<br />
A single parameter may not always be sufficient to address a scientific<br />
question, such as in the case of the <strong>de</strong>ad reckoning technique that we<br />
mentioned earlier (section 2.1). However, the use of multiple sensors is not<br />
always possible since it generally leads to an increase in the bulkiness of<br />
the <strong>de</strong>vices. Fortunately, accelerometry can be used to <strong>de</strong>rive more in<strong>for</strong>mation<br />
than only the posture or the activity of animals. For example,<br />
with sensitive accelerometers, it is possible to <strong>de</strong>tect the faint signal of<br />
the heart rate in the movements of the cloacae of a bird and thus address<br />
physiological questions without the need <strong>for</strong> electro<strong>de</strong>s and/or implanted<br />
materials (Wilson et al., 2004). In addition, since a rough 70% estimate of<br />
the energy is expen<strong>de</strong>d through movements, overall dynamic body acceleration<br />
(ODBA) or partial dynamic body acceleration (PDBA), <strong>de</strong>rived<br />
from 3-axes or 2-axes accelerometers, respectively, was proposed as an<br />
in<strong>de</strong>x of energy expenditures (Wilson et al., 2006). ODBA and PDBA are<br />
in<strong>de</strong>ed significantly related to oxygen consumption in a variety of species,<br />
and both offer a good proxy of metabolic activity when combined with<br />
heart rate loggers (Halsey et al., 2008). Apart from accessing physiological<br />
parameters, these sensors can also be used to infer prey availability in the
28 Ecophysiology and animal behaviour<br />
environment. Changes in wing beat frequency and amplitu<strong>de</strong> are increasingly<br />
used to infer prey encounter in birds (Ropert-Cou<strong>de</strong>rt et al., 2006b),<br />
while <strong>de</strong>tection of head jerking movement are related to prey capture in<br />
marine mammals (Suzuki et al., 2009; Viviant et al., 2010).<br />
3. The road to bio-logging is paved with good intentions but…<br />
3.1. The standard bio-logging tra<strong>de</strong>-off<br />
Increasing the life-time of a bio-logger while keeping the same level of per<strong>for</strong>mances<br />
leads to the following paradox. On the one hand, the amount<br />
of in<strong>for</strong>mation stored is increased, and consequently the memory capacity<br />
has to increase too; on the other hand, the energy required to power<br />
the electronic circuit is increased, and so should be the battery size and<br />
weight in or<strong>de</strong>r to address this extra <strong>de</strong>mand. Based on the power consumption<br />
of a unit, it is possible to adapt batteries of different capacities<br />
to the <strong>de</strong>vices in or<strong>de</strong>r to adjust the working-time to the specific needs of a<br />
study. However, a longer working-time means larger and heavier batteries<br />
and bio-loggers, which may have an impact on the health of the species<br />
targeted or even become inappropriate (I, 2). This balance between small<br />
units with a lesser impact on the animal but reduced life time, and larger<br />
<strong>de</strong>vices with enhanced functionalities but restrictions on their applicability,<br />
is a major problem seriously <strong>de</strong>alt with by the bio-logging community<br />
i) <strong>for</strong> ethical reasons, and ii) to ensure that the data collected are reliable<br />
and are as close to the norm as possible (Ropert-Cou<strong>de</strong>rt et al., 2007).<br />
Regarding the impact of bio-logger, one must be aware that animals<br />
are generally shaped to optimise their movement through a medium.<br />
Swimmers are hydrodynamically featured, while flying animals present<br />
a specific adaptations to reduce their body mass. Thus, any externallyattached<br />
item may impair these features and lead to an increase in energy<br />
expen<strong>de</strong>d or a change in behaviour. In parallel, we already mentioned the<br />
negative consequences of implanting bio-loggers. Gui<strong>de</strong>lines are regularly<br />
produced to reduce the negative impact of bio-loggers (Casper, 2009). Biologgers,<br />
<strong>for</strong> example, should weigh less than 3% of the body mass of flying<br />
birds (Phillips et al., 2003) and less than 4-5% of the cross-section of the<br />
animal (Bannasch et al., 1994).<br />
Despite these gui<strong>de</strong>lines, we believe that the scientific community should<br />
move <strong>for</strong>ward to adopt a common co<strong>de</strong> of conduct. In<strong>de</strong>ed, the bio-logging<br />
community is very mindful about the need to reduce the impact of <strong>de</strong>vices,<br />
but newcomers may not always be aware of gui<strong>de</strong>lines specifically <strong>de</strong>signed<br />
<strong>for</strong> bio-logger <strong>de</strong>ployments (see above). In some instances, referees are not<br />
aware of them and accept papers that present ethical concerns or which
Part I – Chapter 1 29<br />
results are questionable due to the negative influence of a bulky <strong>de</strong>vice on<br />
the per<strong>for</strong>mances of the animals. Which institution could be in charge of<br />
ensuring that the appropriate gui<strong>de</strong>lines are followed? Some scientific journals<br />
have taken the lead in addressing this problem: <strong>for</strong> example, Animal<br />
Behaviour has very strict ethics regulations and ask the authors to address<br />
them be<strong>for</strong>e submission to peer review. The pressure to produce attractive<br />
results could, however, hin<strong>de</strong>r these ef<strong>for</strong>ts as it sometimes pushes<br />
researchers to emphasise outputs against rigor (see Ropert-Cou<strong>de</strong>rt et al.,<br />
2007). Conversely, en<strong>for</strong>cements of strict rules would also be <strong>de</strong>trimental<br />
without consi<strong>de</strong>ration of the benefits that overstepping them could bring<br />
in terms of new scientific results.<br />
3.2. Beyond sensors and <strong>de</strong>vices: homogenising analyses and sharing data<br />
Originally, each research group using bio-logging approaches <strong>de</strong>veloped<br />
its own method <strong>for</strong> analysing the data generated by bio-loggers. This led<br />
to the emergence of several analytical programming co<strong>de</strong>s that tackled the<br />
same question and there<strong>for</strong>e, to a divergence in the way bio-logging data<br />
were processed. For example, the bottom phase of a dive can be <strong>de</strong>fined in<br />
several different manners, leading to values that are not comparable from<br />
one study to another. The trend of diversifying the analytical methods is<br />
also enhanced by the presence of free software like R that allows users to<br />
create and disseminate their own co<strong>de</strong>s and thus their own <strong>de</strong>finitions<br />
<strong>for</strong> various parameters. In addition, the possibility offered by most biologgers<br />
of selecting the frequency at which the sampling is done also leads<br />
to diversification and ren<strong>de</strong>rs comparisons across data sets difficult. In<br />
physics, the “sampling theorem” states that the sampling frequency must<br />
be at least twice that of the signal’s highest component frequency (<strong>for</strong> a<br />
periodic signal) to avoid aliasing. Similarly, biologists suggested that the<br />
sampling interval should not represent more than 10% of the duration of<br />
the biological event that one wishes to measure (e.g. the lowest sampling<br />
frequency to measure a 600sec dive of a Wed<strong>de</strong>ll seal is 60sec, Boyd et<br />
al., 1993; Wilson et al., 1995). Not adopting a proper sampling protocol<br />
may lead to misinterpretation of the data and false biological conclusions<br />
(Ropert-Cou<strong>de</strong>rt and Wilson, 2004).<br />
Recently, the question has become a topic of reflexion on the occasion<br />
of various workshops. Can we (and should we) homogenise bio-logging<br />
data analysis? The difficulty to <strong>de</strong>fine the best practice in that case is<br />
twofold. First, <strong>de</strong>vices always evolve and become more efficient or collect<br />
new types of data. Consequently new analytical methods are required to<br />
handle these novelties. Secondly, the analytical method <strong>de</strong>pends upon the<br />
questions sought. In that sense, the currently best practice would not stay<br />
best <strong>for</strong> very long. Yet, we need to be able to compare datasets taken in
30 Ecophysiology and animal behaviour<br />
different locations, time and using different means, especially if we are to<br />
tackle large-scale questions. Methods like down-sampling, although necessarily<br />
frustrating, are keys to address such issues. We strongly advocate<br />
<strong>for</strong> working groups to explore paths <strong>for</strong> the homogenisation of analytical<br />
procedures within the framework of, <strong>for</strong> example, the Expert Group in<br />
Birds and Marine Mammals of the Scientific Committee <strong>for</strong> Antarctic<br />
Research (SCAR), or the newly-<strong>for</strong>med group of experts in accelerometry<br />
that was constituted on the last bio-logging symposium in Hobart.<br />
In addition to this issue, the use and share of data from bio-logging must<br />
be optimised. A whole book could be filled with the issue of data sharing,<br />
but only the surface will be scratched here. The million of data points<br />
that are now routinely recor<strong>de</strong>d by data loggers and the multiplicity of<br />
the research teams using such an approach make it necessary to centralise,<br />
archive, and ultimately share the data. Some researchers had been collecting<br />
bio-logging in<strong>for</strong>mation over several <strong>de</strong>ca<strong>de</strong>s and onto a large range<br />
of individuals and species. Upon retirement, their data would be lost if no<br />
system stores them. This is only recently that specific data repository have<br />
emerged. The ten<strong>de</strong>cy is now to multiply storage points, each scientific<br />
society recognizing the need <strong>for</strong> a database on their specific topic. For<br />
example, marine researchers studying the localisation and diving activity<br />
of polar top predators can store their data into the database managed by<br />
the SCAR (SCAR-marBIN and Antabif) that are themselves linked to<br />
marine databases at a larger scale (OBIS, SeaWiFS, etc.). This multiplication<br />
and cross-sharing of datasets among databases, while duplicating the<br />
work, guarantee the permanence of a dataset as it will still be available<br />
even if one database is closed. An incentive to sharing the data is found<br />
in the recent ef<strong>for</strong>t to consi<strong>de</strong>r data sharing as a genuine publication, associating<br />
a DOI to a data set. As such, institutions evaluating a researcher’s<br />
output can value his/her ef<strong>for</strong>t towards the scientific community through<br />
this marker.<br />
3.3. Bio-logging: an aca<strong>de</strong>mic and commercial en<strong>de</strong>avour<br />
Efficient bio-logging equipment is generally achieved through a close<br />
collaboration between engineers and users. However, research institutions<br />
able to combine both expertises un<strong>de</strong>r the same roof are scarce. In<br />
some privileged situations, an aca<strong>de</strong>mic collaboration can be <strong>de</strong>veloped<br />
between universities so as to link a <strong>de</strong>partment of biology and an engineering<br />
<strong>de</strong>partment <strong>for</strong> example. The highest technical sophistication can<br />
then be attained and complex and specific questions be answered. Once a<br />
prototype is created, engineers face more practical duties that may be less<br />
intellectually satisfying. Among those, the issue of proper conditioning<br />
and packaging of the <strong>de</strong>vice is critical. Most dysfunctions of bio-loggers
Part I – Chapter 1 31<br />
are due to practical packaging problems. Solving these problems requires<br />
a multidisciplinary and complex engineering approach. Once the equipment<br />
has finally been validated, biologists would request a large number<br />
of units and this is precisely when aca<strong>de</strong>mic systems reach their limits.<br />
In<strong>de</strong>ed, aca<strong>de</strong>mic bodies are (and probably should) not be involved into<br />
mass production as this would mean adopting an industrial approach to<br />
bio-loggers production. Industrial production implies that electronics<br />
hardware, software, connectic systems and batteries, circuit <strong>de</strong>sign and<br />
protection, casing and packaging, tests and validation, are all inclu<strong>de</strong>d<br />
at once in the reflexion process. Additionally at each stage of <strong>de</strong>velopment,<br />
costs are balanced and they influence <strong>de</strong>cisions at the next stage.<br />
Industries usually aim at producing the best <strong>de</strong>vice according to the cost<br />
it represents <strong>for</strong> them; and this is generally <strong>de</strong>ci<strong>de</strong>d with consi<strong>de</strong>ration of<br />
the market, the number of potential customers and the most reasonable<br />
price per unit. Real and viable situations generally lay between these two<br />
positions. Subcontracting industrial fabrication could be an alternative<br />
<strong>for</strong> aca<strong>de</strong>mic <strong>de</strong>velopers. Aca<strong>de</strong>mic engineers and/or researchers could<br />
also create a start-up company based on what they <strong>de</strong>veloped to initially<br />
address their scientific needs. However, this involves an optimal knowledge<br />
of the scientific and technical need, as well as of the practical problems<br />
that may be encountered in the field while using the equipment.<br />
In a nutshell, everything reverts to the following question: is the <strong>de</strong>mand<br />
originating from users asking <strong>for</strong> specific <strong>de</strong>velopments (greater per<strong>for</strong>mance,<br />
new sensors…) or from the engineers anticipating the application<br />
of new technologies? Both stimulations are probably necessary to draw an<br />
ambitious but realistic product specification.<br />
4. Where do we go from here?<br />
4.1. Going toward large-scale <strong>de</strong>ployment<br />
For <strong>de</strong>ca<strong>de</strong>s, the paucity of manufacturers, the expensive price of biologgers,<br />
their restricted memory or battery capacity, as well as the lack of<br />
adapted analytical tools preclu<strong>de</strong>d the <strong>de</strong>ployment of numerous units at a<br />
time. Thanks to technological advances, such as those taking place in the<br />
mobile phone industry, some cheap, low consumption and consequently<br />
small bio-loggers have started to appear on the market. With these, largescale<br />
<strong>de</strong>ployments have become achievable. While occasionally dozen<br />
of <strong>de</strong>vices had been <strong>de</strong>ployed simultaneously to explore cooperative diving<br />
(Takahashi et al., 2004b), the first large-scale <strong>de</strong>ployments, in both<br />
space and time, originated through programs like the Tagging of Pacific<br />
Pelagics (Topp, Block et al., 2003, see also http://www.topp.org/). Since
32 Ecophysiology and animal behaviour<br />
the inception of the Topp programs, thousands of tags have been attached<br />
to 22 top predator species in the Pacific, including whales, sharks, sea<br />
turtles, seabirds, pinnipeds and even squids. Mass production of <strong>de</strong>vices is<br />
now a reality: it allows researchers to work at unprece<strong>de</strong>nted spatial scales<br />
and on entire populations of studied animals. In this field, the United<br />
Kingdom has taken a huge step <strong>for</strong>ward. For example, the long-life, minute<br />
geolocators <strong>de</strong>veloped by the British Antarctic Survey are <strong>de</strong>ployed on<br />
a worldwi<strong>de</strong> scale (e.g. Conklin et al. 2010). Recently, mass-production of<br />
GPS <strong>for</strong> mobile phone also created an alternative market where cheap GPS<br />
can be purchased by researchers who can re-conditioned them specifically<br />
to their needs. As an illustration of this, the IPHC bio-logging unit<br />
is modifying commercially-available GPS units (Cat Traq from Perthold<br />
Inc., http://www.mr-lee-catcam.<strong>de</strong>/ct_in<strong>de</strong>x_en.htm) to make them suitable<br />
<strong>for</strong> use on wild animals. However, there is a negative si<strong>de</strong> to this<br />
large-scale enthusiasm: cheap <strong>de</strong>vices do not always meet the usual scientific<br />
criteria. Lesser reliability or lower <strong>de</strong>gree of technical in<strong>for</strong>mation<br />
must be balanced with the benefits that can arise from the use of these<br />
mass-production bio-loggers. In other words, caution in the use of cheap<br />
<strong>de</strong>vices must be taken to avoid impacting scientific excellence. Thorough<br />
calibration must be a premise to large-scale <strong>de</strong>ployments.<br />
4.2. Importance of multiple sensors<br />
As evoked briefly earlier in this chapter, the use of multiple sensors –<br />
when applicable – offers an ad<strong>de</strong>d value by providing a much complete<br />
picture of the behaviour and physiology of the animals in their environment.<br />
The combination of simple sensors (e.g. pressure sensor and<br />
temperature sensor) became a standard in even the simplest data loggers,<br />
but genuinely multi-sensor loggers are still few. Among those, it is worth<br />
mentioning the “daily diary” unit <strong>de</strong>veloped by Prof. Rory Wilson at the<br />
University of Swansea. Despite their relatively small size ranging between<br />
21 and 90g according to the size of animal, these bio-loggers can contain<br />
up to 14 different channels of both slow and fast sampling sensors working<br />
simultaneously (Wilson et al., 2008). Apart from the daily diary unit,<br />
multi-sensing <strong>de</strong>vices, either <strong>de</strong>veloped by research teams or commercially<br />
available (Wildlife Computers, Little Leonardo, Greeneridge Science,<br />
etc.), are used in large body sized mo<strong>de</strong>ls, e.g. fin whales Balaenoptera<br />
physalus (Goldboegen et al., 2006). To extend the applicability of multisensing<br />
<strong>de</strong>vices to smaller animals, special <strong>de</strong>velopments are nee<strong>de</strong>d (I,<br />
2); <strong>for</strong> example, a drastic reduction in the consumption is a pre-requisite<br />
to a generalisation of multi-sensing to species smaller than a 1-2 kg animal.<br />
In addition, new chemical sensors to <strong>de</strong>tect <strong>for</strong> example the level of<br />
oxygen in the water or the blood will pave the way <strong>for</strong> new generations of
Part I – Chapter 1 33<br />
multi-sensing bio-loggers with new requirements and constraints <strong>for</strong> the<br />
<strong>de</strong>velopers. Here, a distinction must be ma<strong>de</strong> <strong>de</strong>pending on the acquisition<br />
rates of these new sensors. The <strong>de</strong>ployment of sensors <strong>for</strong> quasi-static<br />
parameters <strong>for</strong> which the sampling interval is equal or longer than 1s (e.g.<br />
temperature, light, pressure…) would not cause any trouble as transducers<br />
use low power and the volume of data is small. However, the use of sensors<br />
<strong>for</strong> medium speed parameters sampled typically between 10 and 100Hz<br />
(e.g. accelerometers, gyroscopes, etc.) requires larger memory volume and<br />
greater energy to store the data. Even stronger difficulties are faced <strong>for</strong><br />
sensors that acquire high speed parameters (more than 100Hz) like electrocardiograms,<br />
electromyograms, or electroencephalograms. Numerous<br />
technical problems occur, and a special electronic architecture is nee<strong>de</strong>d<br />
to manage the high volume of memory, high speed communication <strong>for</strong><br />
data transfer, and so on.<br />
With the million of data points that the daily diary units can generate, the<br />
next challenge will be to <strong>de</strong>velop a software able to handle, display and<br />
summarise the complex in<strong>for</strong>mation <strong>de</strong>livered by the next generation of<br />
bio-loggers. Prof. Wilson thus invested an important amount of energy,<br />
resources and time in <strong>de</strong>veloping such a tool and did it in such a way that<br />
its utilisation can reach a larger public than the scientific community<br />
alone (Wilson et al., 2008, http://www.swan.ac.uk/biosci/research/smart/<br />
smartsoftware/). The software allows users to interpret the data from the<br />
bio-loggers so as to truly reconstruct behaviour and visualise it. For example,<br />
data points from the magnetometer, gyroscope, accelerometer and<br />
altitu<strong>de</strong> sensors are combined and the result on the screen is an albatross<br />
(a computer graphic one, of course) flying in three dimensions following<br />
the exact paths that the original albatross flew. Beyond the example of<br />
the daily diary, visualisation software to accommodate complex and large<br />
datasets and display them in a pleasant and efficient manner is becoming<br />
increasingly available. The statistical free software R is of course powerful<br />
and readily accessible but its lack of user-friendliness may sometimes<br />
limit its popularity <strong>for</strong> complex analyses and representations. Alternatives<br />
to R are numerous and we can only mention Igor Wavemetrics, which<br />
was recurrently presented at the last bio-logging symposium (http://www.<br />
wavemetrics.com/).<br />
4.3. Combining the best of biotelemetry and bio-logging<br />
Biotelemetry – at least in theory – clearly has its advantages, especially as<br />
long as securing data is concerned. However, real-time data transmission<br />
is practically hampered by numerous factors leading to a temporary interruption<br />
in communication, which in turn means a <strong>de</strong>finite loss of measurements<br />
(Vincent et al., 2002; Costa et al., 2010). These blank periods
34 Ecophysiology and animal behaviour<br />
are generally due to technical limitations (e.g. signal attenuation, wave’s<br />
absorption by the environment, electromagnetic interferences, etc.) and<br />
to the behaviour of the animal to which the transmitter is attached (e.g.<br />
relative position of body and antenna, immersion in water or in a burrow,<br />
etc.). In comparison, bio-logging seems the perfect solution. Yet it<br />
suffers from an important drawback: the bio-logger has to be connected<br />
back to a computer at the end of the experiment to retrieve the data,<br />
which means that the animal has to be still alive, re-localised, re-captured<br />
and should still be carrying a bio-logger that is still functioning! In other<br />
word, <strong>de</strong>ploying a bio-logger represents a binary game: if only one point<br />
goes wrong in the chain, no data are collected.<br />
Obviously, combining the capabilities of the two methods seems to be<br />
the solution. An i<strong>de</strong>al <strong>de</strong>vice would record permanently the data in an<br />
embed<strong>de</strong>d memory, and would then transmit them regularly to a base<br />
station. Of course, this basic principle needs to be adjusted to each experimental<br />
situation. Data may be transferred following a fixed schedule, <strong>for</strong><br />
instance when an animal returns to a fixed location in space and time.<br />
As a consequence this would only require a single base station installed<br />
within radio range of such a site where the animal is known to be found at<br />
regular interval, and with a bidirectional connection between the logger<br />
and the station. The base station would be filled gradually with data from<br />
the logger, and be downloa<strong>de</strong>d by the user when nee<strong>de</strong>d. If the animal disappears<br />
only the data collected after the last transfer with the base station<br />
are lost. Alternatively, the base station can interrogate the environment at<br />
fixed schedules or be triggered manually to search <strong>for</strong> a telemetric logger<br />
within its reception range (see the approach <strong>de</strong>veloped by the University<br />
of Amsterdam, Shamoun-Baranes et al., 2011). The reverse strategy consists<br />
in asking the telemetric logger to regularly scan the radio-frequency<br />
environment, in or<strong>de</strong>r to search <strong>for</strong> a base station. In this case, scattering<br />
numerous base stations in a given experimental area would enhance<br />
the success rate of data transfer. These stations can also communicate<br />
between each other to optimise data organization and synchronisation.<br />
Additionally, each base station can communicate with a large number of<br />
telemetric loggers. The last step in this concept consists in bio-loggers able<br />
to communicate not only with base stations, but also among themselves,<br />
leading to a genuine network of communicating <strong>de</strong>vices. Data would then<br />
be shared with all the loggers coming within communication range and<br />
then transferred to a base station when one logger is close to it. A nonnegligible<br />
si<strong>de</strong> aspect of such an approach is the possibility to investigate<br />
proximity between animals, including time, duration and possibly<br />
distance of encounters. While theoretically attractive, a fair amount of<br />
<strong>de</strong>velopment has to be done to reach this grand challenge. Both advances<br />
in electronics and data communication protocols are required. Progresses
Part I – Chapter 1 35<br />
in theoretical studies over software that could be able to manage such<br />
complex sets of interactions are paramount to the future success of these<br />
bio-logging networks and cannot involve only one type of institutions.<br />
5. Conclusion<br />
Bio-logging has gone through several steps from mechanical to digital,<br />
and from bulkiness to miniaturisation. The field is now moving towards<br />
globalisation and large scale coverage. In the marine realm, bio-logging<br />
coupled to automatic i<strong>de</strong>ntification and weighing systems such as those<br />
that exist in the Antarctic could serve as a basis <strong>for</strong> long term monitoring<br />
programs. Such observatories would thus act in parallel with weather<br />
or oceanographic stations to <strong>de</strong>liver data on Antarctic biodiversity. This<br />
concept can be exten<strong>de</strong>d to the terrestrial realm with a network of sensing<br />
no<strong>de</strong>s monitoring the state of terrestrial ecosystems over time. With the<br />
rapid modifications affecting all ecosystems on Earth, monitoring programs<br />
such as these are urgently nee<strong>de</strong>d. The diversification of the data<br />
collected, the increase in the temporal coverage and accessibility of biologged<br />
data, and the possibility <strong>for</strong> large number of units to be <strong>de</strong>ployed in<br />
a given environment concur to promote bio-logging as the key approach<br />
<strong>for</strong> ecological sciences in the future.<br />
Authors’ references<br />
Yan Ropert-Cou<strong>de</strong>rt, Akiko Kato, Francis Crenner:<br />
Université <strong>de</strong> Strasbourg, Institut pluridisciplinaire Hubert Curien, UMR<br />
7178, Strasbourg, France<br />
David Grémillet:<br />
Centre d’écologie fonctionnelle et évolutive, UMR 5175, Montpellier,<br />
France<br />
University of Cape Town, FitzPatrick Institute, DST/NRF Excellence<br />
Centre, Ron<strong>de</strong>bosch, South Africa<br />
Corresponding author: Yan Ropert-Cou<strong>de</strong>rt, yan.ropert-cou<strong>de</strong>rt@iphc.<br />
cnrs.fr
36 Ecophysiology and animal behaviour<br />
Aknowledgement<br />
We thank Prof. Y. Naito <strong>for</strong> his <strong>de</strong>dication to promote bio-logging even<br />
after his retirement. We also thank Prof. Rory Wilson <strong>for</strong> sharing his passion<br />
<strong>for</strong> the <strong>de</strong>velopment and application of novel bio-loggers to a wi<strong>de</strong><br />
range of species.<br />
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Shamoun-Baranes J., Bouten W., Camphuysen C. J., Baaij E., 2011. Riding the<br />
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40 Ecophysiology and animal behaviour<br />
Speakman, J.R., 1997. Doubly labelled water: theory and practice, Chapman &<br />
Hall, London.<br />
Suzuki I., Naito Y., Folkow L. P., Miyazaki N., Blix A. S., 2009. Validation of<br />
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Takahashi A., Sato K., Naito Y., Dunn M., Trathan P. N., Croxall J. P.,<br />
2004a. Penguin-mounted cameras glimpse un<strong>de</strong>rwater group behaviour.<br />
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Takahashi A., Sato K., Nishikawa J., Watanuki Y., Naito Y., 2004b. Synchronous<br />
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Vinatier F., Chailleux A., Duyck P.-F., Salmon F., Lescourret F., Tixier P.,<br />
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Biology, 202, pp. 3121-3126.
Chapter 2<br />
Animal-borne sensors to study<br />
the <strong>de</strong>mography and behaviour<br />
of small species<br />
Olivier Guillaume, Aurélie Coulon, Jean-François Le Galliard, and Jean Clobert<br />
1. Introduction<br />
One of the main characteristics of ecological systems is their hierarchical<br />
organisation – communities are collections of species interacting with<br />
each other, species are groups of populations distributed spatially and<br />
connected through dispersal, and populations are ma<strong>de</strong> up of individuals.<br />
Despite the wi<strong>de</strong>spread opinion that individual variation is the raw<br />
material of ecological and evolutionary dynamics, ecological approaches<br />
at the level of communities or ecosystems have ten<strong>de</strong>d to ignore the large<br />
variation among individuals seen in their morphology, behaviour, or<br />
life histories (Bolnick et al., 2003). One of the reasons <strong>for</strong> this is that<br />
there are serious methodological constraints in our ability to i<strong>de</strong>ntify and<br />
track individuals of most animal species within complex communities.<br />
In<strong>de</strong>ed, most communities are ma<strong>de</strong> up of relatively small species, which<br />
are extremely challenging to mark and equip with sensors. For example,<br />
a large part of the world’s mammals weigh less than 100g (see figure 1,<br />
Gar<strong>de</strong>zi and da Silva, 1999) and the median body size of birds is around<br />
30-40g (Blackburn and Gaston, 1994). Yet, small animal species contributed<br />
a lot to our un<strong>de</strong>rstanding of ecological and evolutionary processes<br />
within natural populations, including population <strong>de</strong>mography, dispersal<br />
<strong>ecology</strong> and evolutionary <strong>ecology</strong> (e.g. Clobert et al., 2001). They also<br />
play an important part in many terrestrial and aquatic ecosystems on<br />
Earth, where they inclu<strong>de</strong> a large number of herbivores, small predators,<br />
as well as parasitoids, pollinators and plant mutualists.
44 Ecophysiology and animal behaviour<br />
Figure 1: Relationship between species diversity (number of species) and species<br />
body mass (log-trans<strong>for</strong>med, kg) in a large data set of the world’s mammal species<br />
after Gar<strong>de</strong>zi and da Silva (1999). The distribution is significantly skewed to the<br />
right and indicates maximal species diversity <strong>for</strong> body mass around 25-63g. The<br />
dashed arrow represents the range of body mass (more than 100g) where animals can<br />
currently be fitted with some of the smallest bio-tracking and bio-logging <strong>de</strong>vices<br />
(assuming the <strong>de</strong>vice must weigh less than 3-5% of the body mass, see table 1).<br />
This review focuses on animals weighing less than 100g, which represents most<br />
vertebrate species on earth and almost all invertebrate species. © The University of<br />
Chicago Press, 1999.<br />
Several challenges <strong>for</strong> the <strong>de</strong>velopment and proper implementation of<br />
animal-borne sensors on small terrestrial and aquatic animal species are<br />
i<strong>de</strong>ntified. Appropriate <strong>de</strong>tection and marking techniques are nee<strong>de</strong>d <strong>for</strong><br />
the population <strong>ecology</strong> of many animal species, especially when rare and<br />
elusive ones are involved. In the field of movement <strong>ecology</strong>, gathering<br />
data on individual behaviour and movements of individuals in space and<br />
time, by using appropriate tracking technologies (also sometimes called<br />
bio-telemetry and referred to here as bio-tracking), is also fundamental.<br />
Finally, ecophysiological studies require gathering in<strong>for</strong>mation on individual<br />
physiological state as well as on environmental conditions using<br />
micrometeorological and physiological sensors. This chapter aims at<br />
i) <strong>de</strong>scribing methods and sensors that have been used to collect such<br />
behavioural and <strong>de</strong>mographic data on individuals of small animal species<br />
and ii) i<strong>de</strong>ntifying the main current limitations and challenges of existing<br />
technologies and the most urgent areas of <strong>de</strong>velopment in this domain.
Part I – Chapter 2 45<br />
Note that the methods reviewed here are also relevant <strong>for</strong> the juvenile<br />
<strong>for</strong>ms of many large species.<br />
We <strong>de</strong>fine and review sensors in the broad sense so as to inclu<strong>de</strong> individual<br />
marking techniques, bio-tracking techniques, and micrometeorological<br />
and physiological sensors sensu stricto. Animal-borne sensors may<br />
be <strong>de</strong>fined as any <strong>de</strong>vice installed on an animal that measures a physical<br />
or chemical quantity and converts it into a signal which can be read<br />
by an observer or by an instrument, <strong>for</strong> example a micrometeorological<br />
quantity (e.g. temperature) or a physiological quantity (e.g. glucose level).<br />
Some tag systems that give in<strong>for</strong>mation on the i<strong>de</strong>ntity and <strong>de</strong>vices that<br />
provi<strong>de</strong> in<strong>for</strong>mation on location of study animals, <strong>for</strong> example through<br />
the use of radio emitters or harmonic radar tags carried by the individuals,<br />
can be also consi<strong>de</strong>red as a type of biological sensors. Note that the coupled<br />
system of sensors and data loggers installed on the animal is called<br />
a bio-logger, and differs from simple bio-telemetry tools in the sense that<br />
data are stored locally in the memory of the <strong>de</strong>vices and not transferred in<br />
real time via radio waves or other transmitting means. The <strong>de</strong>velopment<br />
and use of bio-loggers with large animals is reviewed in chapter 1 of this<br />
book by Ropert-Cou<strong>de</strong>rt et al., and we will briefly review its applications<br />
with small animals here.<br />
2. Methodological issues<br />
Studying small animals in their natural environment or in experimental<br />
infrastructures without interfering with their normal behaviour presents<br />
major challenges to ecologists. This is especially true when studies require<br />
equipping mo<strong>de</strong>l species with animal-borne sensors to measure and record<br />
environmental or physiological parameters. The main methodological<br />
issue with small animals lies in their weight and volume, which poses an<br />
upper limit on the size of sensors and constrains any marking and attachment<br />
technique. In<strong>de</strong>ed, tags and sensors must obviously neither interfere<br />
with the natural behaviour, nor influence the survival of the animals that<br />
carry them. Gui<strong>de</strong>lines generally recommend that sensors must weight less<br />
than 5% of the animal body mass <strong>for</strong> vertebrates, and less than 10% <strong>for</strong><br />
invertebrates (Cochran, 1980; Cant et al., 2005). However, those figures<br />
must be adjusted according to the study species and populations. For<br />
example, in species <strong>for</strong> which running, flying or swimming is essential,<br />
the maximum tolerated weight of the <strong>de</strong>vice should even be lighter (e.g.,<br />
Bedrosian and Craighead, 2007). The evaluation of the impacts of sensors,<br />
of their components, like the antenna, and of the fixation procedure<br />
has been per<strong>for</strong>med in various contexts, such as meta-analyses of survival,<br />
growth or fecundity, which showed some significant <strong>de</strong>trimental effects
46 Ecophysiology and animal behaviour<br />
(<strong>for</strong> example Bridger and Booth, 2003; Weatherhead and Blouin-Demers,<br />
2004; Whid<strong>de</strong>n et al., 2007). Hence, careful tests of animal-borne sensors<br />
and associated protocols should be conducted prior to <strong>de</strong>ployment in<br />
field or experimental conditions.<br />
In addition, small animals or juvenile <strong>for</strong>ms are often characterised by fast<br />
growth and high mortality, and some of these species may also be difficult<br />
to capture even when their populations are abundant. These <strong>de</strong>mographic<br />
facts put some challenges on the ability to recapture animals, relocate<br />
their tags and <strong>de</strong>sign appropriate attachment techniques. For example,<br />
juvenile <strong>for</strong>ms of many lizards must be equipped with sensors that must<br />
not interfere with their fast growth and strong susceptibility to predators,<br />
but at the same time should be inexpensive because of the high chance to<br />
loose sensors in the course of a standard <strong>de</strong>mographic study (Le Galliard<br />
et al., 2011). In the following section, we review available animal-borne<br />
sensors used to study the <strong>de</strong>mography and behaviour of animals, and discuss<br />
their applications and limits in the context of their implementation<br />
on small species.<br />
3. Specifications of animal-borne sensors <strong>for</strong> small species<br />
We list in table 1 some animal-borne sensors that can be commonly used<br />
to study <strong>de</strong>mography and behaviour including bio-tracking, where the<br />
focus is on animal movements and data are often obtained remotely<br />
using telemetry, and bio-logging, where combined in<strong>for</strong>mation on animal<br />
behaviour, physiology and habitat are typically recor<strong>de</strong>d and data<br />
are stored locally on an autonomous <strong>de</strong>vice installed on free-ranging<br />
animals. Bio-tracking technologies compatible with <strong>de</strong>mographic and<br />
behavioural studies of small animals inclu<strong>de</strong> small tags used to mark and<br />
i<strong>de</strong>ntify individuals from a short distance like magnetic wire tags and<br />
passive Radio Frequency I<strong>de</strong>ntification tags (RFID, Canner and Spence,<br />
2011; Courtney et al., 2000; Bergman et al., 1992), also called passive<br />
integrated transpon<strong>de</strong>rs (PIT). Other tracking <strong>de</strong>vices such as harmonic<br />
radars, VHF transmitters or satellite based transmitters allow the localisation<br />
of small animals from a longer distance and there<strong>for</strong>e were used more<br />
often to study movement behaviour. Moreover, a range of more advanced<br />
bio-loggers can be used on small animals when remote transmission is<br />
not feasible and data should be stored locally. We review and discuss the<br />
use of these animal-borne sensors and techniques <strong>for</strong> small species. Other<br />
methods like vi<strong>de</strong>o or camera traps have been used in some instances to<br />
obtain in<strong>for</strong>mation on the abundance and <strong>ecology</strong> of small animals but<br />
they are less flexible and in<strong>for</strong>mative than bio-tracking and bio-logging<br />
technologies (see IV, 2, <strong>for</strong> a case study).
Part I – Chapter 2 47<br />
3. 1. Marking and tracking small animals with passive RFID tags<br />
It may be difficult to i<strong>de</strong>ntify small animals with the help of techniques<br />
that avoid undue pain and stress, have no effect on fitness traits, and produce<br />
marks that are not easily lost. Non-invasive techniques, such as paint<br />
marks or bead-tags, do not ensure the i<strong>de</strong>ntification of a large number of<br />
individuals and are often only temporary, except <strong>for</strong> rings. Thus, most<br />
<strong>de</strong>mographic studies of small species involve more invasive techniques<br />
such as branding, toe-clipping or scale-clipping. To avoid the potential<br />
pain and stress caused by these techniques, passive integrated transpon<strong>de</strong>rs<br />
(PIT) tags, also called passive RFID (radio frequency i<strong>de</strong>ntification)<br />
tags, have been recommen<strong>de</strong>d because these ones provi<strong>de</strong> permanent and<br />
reliable individual marks. This technology uses communication via radio<br />
waves to exchange data (i<strong>de</strong>ntification number) between a rea<strong>de</strong>r and<br />
a passive electronic tag attached to the animal (Gibbons and Andrews,<br />
2004). Examples of their use inclu<strong>de</strong> tracking studies where “antennas”<br />
positioned in the natural habitat are connected to the rea<strong>de</strong>r. However,<br />
because the animal can only be <strong>de</strong>tected at a short distance (see table 1),<br />
antennas must be located at places of maximal use by animals (e.g. dispersal<br />
corridors, nests or burrows, runways, etc.). We used this technology<br />
to study the spatial <strong>ecology</strong> of small mammals in northern Europe,<br />
even during the winter snow period (Hoset et al., 2008; Le Galliard et<br />
al., 2007). The custom-ma<strong>de</strong> system was <strong>de</strong>veloped by Harald Steen and<br />
Lars Korslund from the University of Oslo (Korslund and Steen, 2006).<br />
It consists of a tube-shaped single coil antenna (20 × 4cm) placed on<br />
the ground along runways to maximize recording rates, and attached<br />
to Trovan® LID665 OEM PIT tag <strong>de</strong>co<strong>de</strong>rs (LID665, EID Aalten BV,<br />
Aalten, Netherlands) that record PIT-tag number, date and hour each time<br />
a tagged vole passed through the antenna (see figure 2). There are however<br />
potential difficulties with PIT tags: they cannot be injected on the<br />
juvenile <strong>for</strong>ms of most species, tags may get lost through the injection site,<br />
and injection as well as retention of these tags can cause small species pain<br />
and harm (e.g. Le Galliard et al., 2011).<br />
3. 2. Harmonic radars <strong>for</strong> tracking very small animals<br />
Harmonic radar is becoming common to track small animals. Several<br />
studies recor<strong>de</strong>d trajectories of hundreds of metres of very small animals,<br />
especially flying insects like beetles, honeybees or butterflies. Those<br />
studies focused on migration, dispersal, <strong>for</strong>aging, or flight behaviour and<br />
assessed environmental factors influencing movements (e.g. Chapman et<br />
al., 2011 and references therein). The harmonic radar system consists of<br />
an emitter that generates a micro-wave signal of a <strong>de</strong>termined frequency.<br />
The tag – composed of a dio<strong>de</strong> connected to a wire antenna – receives
48 Ecophysiology and animal behaviour<br />
Category Sensor type In<strong>for</strong>mation Data acquisition Detection range<br />
Passive RFID tag<br />
I<strong>de</strong>ntity and<br />
location<br />
Scanning with a specific<br />
radiofrequency source<br />
< 1 m with onground<br />
antenna<br />
Passive wire tag<br />
I<strong>de</strong>ntity<br />
I<strong>de</strong>ntity and<br />
location<br />
Visual reading after<br />
extraction<br />
Scanning with a specific<br />
magnetic source<br />
Requires recapture<br />
3 cm with handheld<br />
antenna<br />
Biotracking<br />
Harmonic radar<br />
Location<br />
Scanning with a specific<br />
radiofrequency source<br />
Up to hundreds<br />
of meters with onground<br />
radars<br />
Radio<br />
transmitter<br />
I<strong>de</strong>ntity and<br />
location<br />
VHF receiver connected<br />
to an antenna<br />
Up to 50 m with onground<br />
antenna<br />
GPS tracking<br />
I<strong>de</strong>ntity and<br />
location<br />
Satellite relay or VHF<br />
receiver connected to an<br />
antenna<br />
Global with<br />
satellites, hundreds<br />
meters with VHF<br />
Satellite PTT<br />
I<strong>de</strong>ntity and<br />
location<br />
Satellite relay (Argos)<br />
Global<br />
GLS logger<br />
Location<br />
Direct communication<br />
port to data logger<br />
Requires recapture<br />
GPS data logger<br />
Location<br />
Direct communication<br />
port to data logger<br />
Requires recaptures<br />
Biologging<br />
Data storage tag<br />
Light,<br />
temperature;<br />
humidity, tilt;<br />
pressure and<br />
<strong>de</strong>pth; magnetic<br />
field strength;<br />
pitch and roll;<br />
conductivity,<br />
salinity, dissolved<br />
oxygen, pH,<br />
imaging, EEG,<br />
ECG, EMG, etc<br />
Direct communication<br />
port to data logger<br />
Requires recapture<br />
Combined<br />
<strong>de</strong>vices<br />
Data storage<br />
tags, and GPS<br />
and GLS loggers<br />
combined with<br />
VHF and/or<br />
Argos <strong>for</strong> remote<br />
transmission<br />
Bio-logging + biotracking<br />
Satellite relay or VHF<br />
receiver connected to an<br />
antenna<br />
Global with<br />
satellite, hundreds<br />
meters with VHF<br />
relay<br />
Table 1. Specifications of some available sensors used in <strong>de</strong>mographic and<br />
behavioural studies of small animals. Bio-tracking refers to the process of<br />
gathering remotely i<strong>de</strong>ntity and/or location data on the study animal using<br />
passive or active <strong>de</strong>vices, even if data may be stored locally prior to retrieval.<br />
Bio-logging differs from telemetry because data (location, animal physiology,<br />
or environment) are stored locally in the memory of the <strong>de</strong>vices and must<br />
be downloa<strong>de</strong>d via a communication port. Combined <strong>de</strong>vices allow to store
Part I – Chapter 2 49<br />
Accuracy<br />
Smallest <strong>de</strong>vice<br />
Approximate lifespan<br />
of smallest <strong>de</strong>vice<br />
< 1m 1 × 6 mm, 7.15 mg Unlimited<br />
- 0.25 × 0.5 mm, small weight Unlimited<br />
Few meters 12 mm long, 3 mg Unlimited<br />
Few meters 10 × 5 mm, antenna 70 mm, 0.2 g Ten days<br />
Few meters<br />
15g<br />
From hours in real-time to months<br />
<strong>de</strong>pending on the data acquisition<br />
and retrieval procedures<br />
Hundreds of meters 5 g Several months<br />
Hundred kilometres 0.5 g Up to several years<br />
Meters<br />
22 × 14 mm, antenna 7 mm, 2 g<br />
From hours to months <strong>de</strong>pending<br />
on the numbers of locations<br />
iButton (temperature): 17 × 6 mm, 1.49 g<br />
> 1 year<br />
-<br />
Meters with GPS,<br />
ten meters with<br />
VHF, hundred<br />
meters with Argos<br />
Geolocating archival tags (geolocation,<br />
internal and external temperature,<br />
pressure, light, sea water switch):<br />
8 × 20 × 6.7 mm, 1.9 g<br />
Archival Tag (temperature and pressure):<br />
8 × 32 mm, 3.4 g<br />
EEG + GPS: 66 × 36 × 18 mm, 35g<br />
Argos + temperature sensor: 7.5 × 24 mm,<br />
antenna 200 mm, 5 g<br />
Argos + GPS + Temperature + activity:<br />
64 × 23 × 16.5 mm, antenna 178 mm, 22g<br />
Vi<strong>de</strong>o AVED + VHF: 14 g<br />
6 months<br />
1 year<br />
< 47 hours<br />
Up to months <strong>de</strong>pending on data<br />
acquisition and retrieval procedures<br />
Up to 3 years<br />
Ten minutes<br />
together both location, physiological and environmental data from various<br />
sensors and to transmit the stored data via a VHF radio or a satellite data relay<br />
network. RFID : RadioFrequency IDentification ; VHF : Very High Frequency ;<br />
GPS : global positioning system ; PTT : Plat<strong>for</strong>m Terminal Transmitter ; GLS :<br />
Global Location Sensing, AVEDs : animal-borne vi<strong>de</strong>o and environmental data<br />
collection systems.
50 Ecophysiology and animal behaviour<br />
Figure 2: Use of passive radiofrequency i<strong>de</strong>ntification (RFID) tags <strong>for</strong> <strong>de</strong>mographic<br />
studies of small mammals. A. Subcutaneous injection of a RFID tag on the back of<br />
a juvenile root vole (Microtus oeconomus). B, C. Custom ma<strong>de</strong> rea<strong>de</strong>r connected to<br />
a battery and an antenna, tube-shaped single coil antenna placed on the ground.<br />
© J.-F. Le Galliard.<br />
the signal and reemits at a doubled frequency that can be picked up by<br />
the receiver antenna. Two different transmitter-receiver systems are used<br />
to <strong>de</strong>tect the tags (see figure 3). The first is a hand-held unit, originally<br />
<strong>de</strong>signed to locate avalanche victims who wear tags on their clothes (e.g.<br />
Recco). This system is efficient to locate more or less 10cm tags from 50m<br />
above ground, less than 10 m on the ground and about 10cm below the<br />
ground surface (Mascanzoni and Wallin, 1986; o’Neal et al., 2005). The<br />
second, a ground-based scanning station, uses conventional radar plan<br />
position indicator technology (PPI) that gives the coordinates (range and<br />
azimuth) of the dio<strong>de</strong>s (see Riley and Smith, 2002 <strong>for</strong> a <strong>de</strong>tailed <strong>de</strong>scription).<br />
This system can be used to track smaller tags (more than 1cm) on
Part I – Chapter 2 51<br />
horizontal landscapes (Cant et al., 2005; ovaskainen et al., 2008) or <strong>for</strong><br />
vertical looking up to 900m (Riley et al., 2007).<br />
The advantages of the harmonic radar system are multiple. Since tags are<br />
passive and do not require batteries, they have a potentially unlimited<br />
lifespan, their weight can be very low (down to a few milligrams), and<br />
they are rather cheap (less than 1€). However, this system also presents<br />
drawbacks. All dio<strong>de</strong>s reemit at the same frequency. There<strong>for</strong>e, <strong>for</strong> studies<br />
that need to individually i<strong>de</strong>ntify the target, a complementary method<br />
is required (e.g. a visual mark to i<strong>de</strong>ntify the individual once found). In<br />
addition, the radar signals can be absorbed or disturbed by landscape<br />
components that may work as barriers or reemit a background noise. The<br />
best per<strong>for</strong>mances have been obtained with PPI in agricultural landscapes<br />
(Cant et al., 2005; Ovaskainen et al., 2008) but this was achieved with the<br />
use of a heavy and complex system ma<strong>de</strong> out of a trailer, which is often<br />
uneasy to use in the field. Finally, the external antenna of the <strong>de</strong>vice can<br />
hin<strong>de</strong>r the movements of individuals, especially <strong>for</strong> ground-dwelling species,<br />
and it also complicates the fixation process (Langkil<strong>de</strong> and Al<strong>for</strong>d,<br />
2002; o’Neal et al., 2005; Pellet et al., 2006).<br />
Figure 3: Use of harmonic radar to track flying insects. Left to right, a tag attached<br />
on a honeybee (from Riley et al., 2007), the ground-based scanning station used to<br />
track flying insects (from ovaskainen et al., 2008). © J.R. Riley/outlooks on Pest<br />
Management, © O. Ovaskainen.<br />
To summarise, harmonic radars represent one of the most promising<br />
opportunities to study movements on small spatial scales <strong>for</strong> small species,<br />
but only a limited number of research groups were able to use this
52 Ecophysiology and animal behaviour<br />
technology and few succee<strong>de</strong>d on other fauna than flying insects (Lovei<br />
et al., 1997; Langkli<strong>de</strong> and Al<strong>for</strong>d, 2002; O’Neal et al., 2005; Pellet et<br />
al., 2006). Technical advances are still necessary to improve the per<strong>for</strong>mances<br />
of these radars and to extend the field of investigations to new species<br />
and various ecological problems. Harmonic radars provi<strong>de</strong> to date<br />
the best method to track very small animals (Chapman et al., 2011), but<br />
their use may require complex equipments that are not necessarily easy to<br />
implement un<strong>de</strong>r field conditions. Another major drawback of this system<br />
is that tracking simultaneously several individuals is difficult because<br />
signals from different individuals can not be differentiated.<br />
3. 3. VHF radio tracking of small species<br />
Very high frequency (VHF, 30-300MHz) radio tracking systems consist of<br />
three parts: i) an emitter attached to an animal; ii) an antenna that picks<br />
up the signal sent by the emitter; iii) a receiver, to which the antenna<br />
is plugged, that <strong>de</strong>co<strong>de</strong>s the signal to the operator. The antenna can be<br />
located on a car or hand held. The closer the orientation of the antenna<br />
is to the direction of the signal, the stronger the signal is received. The<br />
strength of the signal is used to calculate the location of the monitored<br />
animal using triangulation. VHF radio tracking (also called radio telemetry)<br />
has been used to monitor large animals since the early 1960s (e.g. large<br />
carnivores or large mammals, Lemunyan et al., 1959), because the size<br />
of the first emitters preclu<strong>de</strong>d its use on smaller species. Miniaturisation<br />
ef<strong>for</strong>ts have then allowed the application of the system on smaller species,<br />
e.g. bats and birds. The smallest emitters currently available weigh circa<br />
0.2g, allowing thus the monitoring of species as small as arthropods, small<br />
reptiles and amphibians (e.g. Naef-Daenzer et al., 2005, Rock and Cree,<br />
2008). For example, recent advances in radio telemetry allowed tracking<br />
movements of a Neotropical orchid bee, which is a small insect pollinator<br />
(Wikelski et al., 2010, see figure 4). This technology provi<strong>de</strong>d new and<br />
valuable knowledge to un<strong>de</strong>rstand the <strong>ecology</strong> and evolution of pollination<br />
of orchids.<br />
However, the miniaturisation also comes at a cost since the duration of<br />
the miniaturised emitter is generally short (i.e. a few days to a few weeks<br />
<strong>for</strong> emitters < 1g), and the range of <strong>de</strong>tectability of the emitter (i.e. the<br />
maximum distance at which the signal can be <strong>de</strong>tected by the antennareceptor<br />
system) is reduced to a few hundred meters <strong>for</strong> emitters < 1g<br />
compared to several kilometres <strong>for</strong> the biggest emitters assuming groundto-ground<br />
conditions where the signal from an emitter on the ground<br />
picked up by a hand-held antenna. In the study by Wikelski et al. (2010)<br />
cited above, the lifespan of the emitters was around 10 days, which preclu<strong>de</strong>d,<br />
<strong>for</strong> example, to draw firm conclusions on the size of the home
Part I – Chapter 2 53<br />
ranges of the bees. Yet, the monitoring of the signal of fast moving animal<br />
can be improved by <strong>de</strong>tecting the signal from the air with mobile<br />
antenna installed on e.g. an helicopter or with a fixed network of antenna<br />
installed on towers (Kays et al., 2011, Wikelski et al., 2010). For example,<br />
an automated radio telemetry system was built from receivers mounted<br />
on 40m towers topped with arrays of directional antennas and was later<br />
used to track the activity and location of radio-collared animals in a tropical<br />
rain<strong>for</strong>est (Kays et al., 2011; IV, 2) This automated plat<strong>for</strong>m can be<br />
installed on a permanent study plot <strong>for</strong> long-term monitoring of medium<br />
and large sized animals, but the system encounters difficulties when it<br />
comes to <strong>de</strong>tect activity and movements of the smallest species (around<br />
4-100g., Kays et al., 2011). Thus, there is <strong>de</strong>finitely room <strong>for</strong> technological<br />
improvements of radio tracking systems <strong>for</strong> most juveniles of the small<br />
species still cannot be fitted with emitters, and because the lifespan of the<br />
smallest emitters preclu<strong>de</strong>s any long-term monitoring of individuals and<br />
reduces long range <strong>de</strong>tection.<br />
Figure 4: VHF tracking of small species. The transmitter (300mg) is glued to the<br />
bee thorax. © C. Ziegler from Wikelski et al. (2010).<br />
3. 4. Satellite-based tracking <strong>for</strong> small species<br />
Satellite-based telemetry like Argos system utilises a plat<strong>for</strong>m transmitter<br />
terminal (PTT) attached to the animal that transmits an ultra high<br />
frequency (UHF, 300-3000MHz) signal by pulses according to pre-programmed<br />
time laps. This UHF signal can be <strong>de</strong>tected by more than one<br />
of the satellites from the Argos network when these satellites pass over the<br />
tags. During this measurement window lasting a few minutes, the satellites<br />
can calculate the animal’s location based on the Doppler effect (i.e.,<br />
the shift in pulse radio frequency due to the movement of the satellite
54 Ecophysiology and animal behaviour<br />
relative to the tag) and the satellite then relays this in<strong>for</strong>mation to receiving-interpreting<br />
sites located on the ground like the Argos system data<br />
processing centres. Satellites network from the Argos system allow theoretically<br />
locating a PTT anywhere on the earth with an accuracy of about<br />
150 meters (Wikelski et al., 2007; Bridge et al., 2011).<br />
For <strong>de</strong>termination of more accurate locations down to a few meters, a<br />
GPS (global positioning system) can be installed on the animal. The GPS<br />
tag sends an UHF signal to a specific satellites network different from<br />
the Argos satellite constellation. These satellites return then the signal to<br />
the tag which calculates its own position by trilateration. The number of<br />
location records per unit time is pre-programmed by users and data are<br />
then sent to a satellite relay and available using an internet interface <strong>for</strong><br />
example (e.g. the Argos network). This GPS technology is more precise<br />
than direct telemetry by the Argos system (less than 10m accuracy) and<br />
now Argos <strong>de</strong>vices often integrate a GPS tag that sends locations within<br />
the Argos transmission. However, the GPS system coupled to the Argos<br />
relay is also more energy-consuming, especially <strong>for</strong> real-time location.<br />
Thus, GPS <strong>de</strong>vices require powerful batteries and have a relatively shorter<br />
lifespan than PTT <strong>de</strong>vices of the same weight. Currently, the smallest<br />
available Argos transmitter weights approximately 5g, while the smallest<br />
<strong>de</strong>vice <strong>for</strong> Argos satellite-relay GPS tracking weights 22g with a maximum<br />
lifespan of several months <strong>de</strong>pending on the frequency of data sampling<br />
and retrieval (Guil<strong>for</strong>d et al., 2011, see table 1). There<strong>for</strong>e, the use of satellite-based<br />
tracking technologies is still restricted to animals weighting<br />
more than 100g especially when it comes to research projects that require<br />
long-term tracking data (Wilkeski et al., 2007). To fulfill the lack of technology<br />
applicable to smaller animals, the Icarus initiative, abbreviation of<br />
the International cooperation <strong>for</strong> animal research using space hea<strong>de</strong>d by<br />
Martin Wikelski at the Max Planck Institute in Germany, aims at establishing<br />
a remote sensing plat<strong>for</strong>m <strong>for</strong> tracking over large spatial scales<br />
with transmitters as small as 1g (www.icarusinitiative.org). The project<br />
will require the <strong>de</strong>ployment of low orbit altitu<strong>de</strong> satellites to track the<br />
weak UHF signals of low weight transmitters located on the ground. A<br />
test of the method using an antenna attached to the International space<br />
station will start by 2014.<br />
An alternative to the very energy-consuming GPS tracking in real time is<br />
GPS data logging, where location data are stored locally on the tag attached<br />
to the animal. Several manufacturers <strong>de</strong>velop GPS data loggers <strong>for</strong> wildlife<br />
tracking with a total weight starting at values as small as 2 grams and<br />
with an accuracy within 2.5m <strong>for</strong> on-ground measurements (table 1). The<br />
main limitation of these small GPS loggers is that the retrieval of the data<br />
requires recapturing the animals to download from the data logger. Thus,<br />
some of these <strong>de</strong>vices also allow downloading the location data using
Part I – Chapter 2 55<br />
remote communication via satellites or via VHF, which brings the total<br />
weight of the equipment up to about respectively 15g and 30g. In addition,<br />
solar panels can be used to power the GPS directly in direct sunlight<br />
and to charge the battery of the logger. This technology helps reduce the<br />
weight of the battery while at the same time increasing the overall lifetime<br />
possibly up to several years and increasing the capacity to collect more<br />
location data. Nevertheless, the solar panels also cause some overload and<br />
the smallest available <strong>de</strong>vices of this type weigh approximately 5g, which<br />
is still too heavy to fit on numerous small animal species. Irrespective of<br />
the method used (satellite Argos PTT or GPS tracking), another important<br />
limiting factor of positioning systems using satellites is that marine species<br />
cannot be tracked during the diving phase because the UHF signal used<br />
<strong>for</strong> satellite communication propagates badly in the sea <strong>de</strong>pths.<br />
3. 5. Bio-logging <strong>for</strong> small species<br />
More complex environmental and physiological parameters can be<br />
collected with the help of various types of data loggers (see table 1).<br />
Autonomous bio-loggers like data storage tags and iButton® <strong>de</strong>vices can<br />
be set up on some small animals because of their relatively low weight<br />
(around 2g), but the data from these loggers cannot be retrieved from a<br />
distance. Animals must there<strong>for</strong>e be recaptured to upload the data, which<br />
may be time-consuming and difficult to practice un<strong>de</strong>r field conditions.<br />
For example, the iButton thermal loggers were used to gather data on<br />
body temperatures of small reptiles in the laboratory because these loggers<br />
are small and cheap (Lovegrove, 2009; Robert and Thompson, 2003).<br />
Similar thermal data can be obtained by using temperature-sensitive passive<br />
integrated transpon<strong>de</strong>rs. In addition to this, data storage tags offer<br />
the possibility to measure a great variety of parameters including light<br />
intensity, tilt, pressure, <strong>de</strong>pth, magnetic field strength, pitch and roll, conductivity,<br />
dissolved oxygen, pH, electroencephalograms, electrocardiograms,<br />
or electromyograms (Walsh and Morgan, 2004; Van <strong>de</strong>r Kooij et<br />
al., 2007; Jonsson et al., 2010) with minimum weights starting at approximately<br />
several grams. For example, a 3.4 g archival tag allows to measure<br />
both temperature and pressure during a minimum period of one year<br />
(table 1). However, more integrated and complex systems become inevitably<br />
heavier. For example, a neurologger used in combinaison with a GPS<br />
has been <strong>de</strong>veloped by Vyssotski et al. (2008) to analyse the neuronal<br />
activity of navigating homing pigeons and weights 35 grams.<br />
Data loggers combined with GPS, VHF or satellite transmitters are essential<br />
to our un<strong>de</strong>rstanding of the behaviour of large animals (see I, 1) since<br />
these technologies offer the possibility to correlate location or movement<br />
with various bio-physical and physiological parameters (Ropert-Cou<strong>de</strong>rt
56 Ecophysiology and animal behaviour<br />
and Wilson, 2005). Yet, these loggers can be difficult to mount on small<br />
animals due to their large volume and their weight typically larger than<br />
10-20g, which is the minimum weight <strong>for</strong> a current GPS data logger (see<br />
section 3.4 above). When the sole focus is on location, geolocators (or<br />
GLS logging) can provi<strong>de</strong> an alternative lightweight technology <strong>for</strong> tracking<br />
individual movements on large spatial scales. The method consists to<br />
measure ambient light level during the day from which sunset and sunrise<br />
times are estimated from thresholds in light curves. The latitu<strong>de</strong> and<br />
longitu<strong>de</strong> are then <strong>de</strong>rived from characteristics of daytime light cycles.<br />
Archival GLS loggers are now as small as 0.5g and can be fitted on small<br />
animal species, but the main limitations against the <strong>de</strong>ployment of this<br />
technique are that i) animals must be recaptured to retrieve the data and<br />
ii) the accuracy of the measurements is rarely better than 100 km (Phillips<br />
et al., 2004). Nevertheless, this technology is a<strong>de</strong>quate <strong>for</strong> tracking sea<br />
birds, migratory passerines or pelagic species (e.g. marine mammals, penguins)<br />
in or<strong>de</strong>r to <strong>de</strong>termine long-distance movements, breeding season<br />
<strong>for</strong>aging ranges or broad-scale habitat preference from several months to<br />
multiple years.<br />
In addition to this range of bio-loggers, animal-borne vi<strong>de</strong>o and environmental<br />
data collection systems (AVEDs) have been used <strong>for</strong> a long time to<br />
study large marine or terrestrial species (Moll et al., 2007), and have been<br />
recently upgra<strong>de</strong>d to be suitable <strong>for</strong> a wi<strong>de</strong>r range of smaller species (Rutz<br />
and Bluff, 2008). For example, a <strong>de</strong>vice weighting about 14g and adapted<br />
to crows seems promising to collect animals’ eye view of resource use and<br />
social interactions along a known movement trajectory. Un<strong>for</strong>tunately,<br />
the system is still too heavy to fit on many small species and its current<br />
recording capacity does not exceed 1 hour. Advances are obviously necessary<br />
to improve the per<strong>for</strong>mances of this technology and to extend the<br />
field of investigations to smaller mo<strong>de</strong>l species.<br />
4. Designing the future generation of sensors<br />
<strong>for</strong> small animals<br />
Our comparative analysis of available sensors highlights that current systems<br />
present major limits. The first one is that even basic sensors constitute<br />
a real bur<strong>de</strong>n <strong>for</strong> the small animals that have to carry them. There<strong>for</strong>e,<br />
the main technical challenge is the miniaturisation of the components.<br />
An extra challenge is that this miniaturisation must be done without too<br />
much loss of capacity in the number of measured parameters, autonomy,<br />
or <strong>de</strong>tectability. Another necessary improvement is to <strong>de</strong>velop compatible<br />
fixation procedures. For large animals, various techniques (including glues,<br />
harnesses, subcutaneous or intraperitoneal implants, or ingestible tags) have
Part I – Chapter 2 57<br />
been experimented to attach the animal-borne <strong>de</strong>vice with a minimum hindrance.<br />
However, ecologists who work on small species must often <strong>de</strong>velop<br />
their own attachment techniques and materials since few standard are available<br />
or adapted to their mo<strong>de</strong>ls, and this process generally adds annoyances<br />
to a bur<strong>de</strong>n yet significant. The miniaturisation of sensors and associated<br />
equipments could limit this problem; however, technological advancements<br />
<strong>for</strong> harmless and weakly invasive fixation techniques are necessary. In addition,<br />
to address further questions in the field of animal <strong>ecology</strong>, we may<br />
wish to obtain novel kinds of in<strong>for</strong>mation. This could be done by combining<br />
the “classical” data obtained with standard animal-borne sensors with<br />
new data obtained from other environmental sensors. In particular, a wi<strong>de</strong><br />
range of techniques are now available to assess the thermal environments<br />
(thermal imagery techniques, Lavers et al., 2005; Hristov et al., 2008), the<br />
weight of individuals (<strong>for</strong> automatic measurement of growth, body mass<br />
regulation and feeding activity, Rands et al., 2006), or the acoustic environment<br />
(<strong>for</strong> characterisation of behaviour and social interactions, see chapter<br />
by Huetz and Aubin). With such an integration in mind, the next generation<br />
of sensors <strong>for</strong> small animals will there<strong>for</strong>e allow investigating more<br />
accurately the internal and external factors influencing the responses of<br />
organisms to environmental variations.<br />
Authors’ references<br />
Olivier Guillaume, Jean Clobert:<br />
Station d’Écologie Expérimentale du CNRS à Moulis, CNRS USR 2936,<br />
Moulis, Saint-Girons, France<br />
Aurélie Coulon:<br />
Muséum National d’Histoire Naturelle, Département Écologie et Gestion<br />
<strong>de</strong> la Biodiversité, Unité Conservation <strong>de</strong>s Espèces, Restauration et <strong>Suivi</strong><br />
<strong>de</strong>s Populations (CERSP), MNHN-CNRS UMR 7204, Brunoy, France<br />
Jean-François Le Galliard:<br />
Université Pierre et Marie Curie, Laboratoire Écologie et Évolution,<br />
CNRS UMR 7625 Paris, France<br />
École Normale Supérieure, Centre <strong>de</strong> recherche en écologie expérimentale<br />
et prédictive (CEREEP) – Ecotron Ile <strong>de</strong> France, CNRS UMS 3194,<br />
St-Pierre-Lès-Nemours, France<br />
Corresponding author: Olivier Guillaume, olivier.guillaume@EcoExmoulis.cnrs.fr
58 Ecophysiology and animal behaviour<br />
Acknowledgement<br />
Jean-François Le Galliard acknowledges the support of the ANR grant<br />
Extinction (07-JCJC-0120) and a Marie Curie Fellowship.<br />
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Korslund L., Steen H., 2006. Small ro<strong>de</strong>nt winter survival: snow conditions<br />
limit access to food resources. Journal of Animal <strong>ecology</strong>, 75, pp. 156-166.<br />
Langkil<strong>de</strong> T., Al<strong>for</strong>d R., 2002. The tail wags the frog: harmonic radar<br />
transpon<strong>de</strong>rs affect movement behaviour in Litoria lesueuri. Journal of<br />
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Lavers C., Franks K., Floyd M., Plowman A., 2005. Application of remote<br />
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humans. Journal of Physics: Conference Series, 15, pp. 207-212.<br />
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do not affect natal dispersal in a small ro<strong>de</strong>nt. Behavioural Ecology, 18,<br />
pp. 665-673.<br />
Le Galliard J.-F., Paquet M., Pantelic Z., Perret S., 2011. Effects of miniature<br />
transpon<strong>de</strong>rs on physiological stress, locomotor activity, growth and<br />
survival in small lizards. Amphibia-Reptilia, 32, pp. 177-183.<br />
Lemunyan C. D., White W. M., Nyberg E., Christian J. J., 1959. Design of a<br />
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Management, 23, pp. 107-110.<br />
Lovegrove B. G., 2009. Modification and miniaturization of thermochron<br />
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Lovëi G. L., Stringer I. A. N., Devine C. D., Cartellieri M., 1997. Harmonic<br />
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60 Ecophysiology and animal behaviour<br />
Mascanzoni D., Wallin H., 1986. The harmonic radar: a new method of tracing<br />
insects in the field. Ecological Entomology, 11, pp. 387-390.<br />
Moll R. J., Millspaugh J. J., Beringer J., Sartwell J., He Z., 2007. A new ‘view’ of<br />
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Ecology and Evolution, 22, pp. 660-668.<br />
Naef-Daenzer B., Früh D., Stal<strong>de</strong>r M., Welti P., Weise E., 2005. Miniaturization<br />
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O’Neal M. E., Landis D. A., Rothwell E., Kempel L., Reihard D., 2005. Tracking<br />
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pp. 19090-19095.<br />
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Part I – Chapter 2 61<br />
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Moskowitz D., Roubik D. W., Kays R., 2010. Large-range movements of<br />
neotropical orchid bees observed via radio telemetry. PLoS One, 5, pp. 6.
Chapter 3<br />
Passive hydro-acoustics <strong>for</strong> cetacean<br />
census and localisation<br />
Flore Samaran, Nadège Gandilhon, Rocio Prieto Gonzalez, Fe<strong>de</strong>rica Pace,<br />
Amy Kennedy, and Olivier Adam<br />
1. Introduction<br />
Scientific observations of cetacean species (whales, dolphins, and relatives)<br />
are nowadays intensively collected by biologists <strong>for</strong> several purposes: i<strong>de</strong>ntifying<br />
the species to monitor their population, locating individuals to collect<br />
data on the abundance and seasonal distribution of each species, obtaining<br />
<strong>de</strong>tailed in<strong>for</strong>mation about individuals (e.g. individual behaviour, food<br />
and health) and <strong>de</strong>scribing interactions among individuals (social group<br />
relationships, cross-species interaction). These observations are critical to<br />
measure the impacts of anthropogenic activities (e.g. un<strong>de</strong>rwater noise,<br />
ship strikes, fishery tools and bycatch) on populations and communities<br />
of cetaceans. Recently, Simmonds and Isaac (2007) and Simmonds and<br />
Eliott (2009) suggested to use whale observations to inclu<strong>de</strong> relevant indicators<br />
in prediction mo<strong>de</strong>ls that <strong>de</strong>scribe the global warming of the planet.<br />
Odontocetes are top predators and mysticetes eat krill. Global warming<br />
will impact their alimentary resources since we know, <strong>for</strong> example, that<br />
krill mass is <strong>de</strong>creasing in the Austral ocean. Moreover, some species use<br />
hot-spots <strong>for</strong> <strong>for</strong>aging and breeding. Observations of changes in the use of<br />
these hot-spots by cetaceans will give further insights into changes of their<br />
environment.
64 Ecophysiology and animal behaviour<br />
Figure 1: Common observation methods <strong>for</strong> cetaceans. These methods inclu<strong>de</strong><br />
visual approaches by human observers, animal-borne sensors (I, 1), genetic methods<br />
and acoustic tools. The methods allow to obtain <strong>de</strong>mographic, behavioural and<br />
genetic in<strong>for</strong>mation. © Gandilhon/Breach, © Kennedy/NMML.<br />
There are several methods available to observe and collect scientific data on<br />
cetaceans (figure 1). A classical and often-used method is based on visual<br />
observations, when cetaceans are i<strong>de</strong>ntified by sight from a distance by a<br />
trained human observer. This technique is simple and trained observers can<br />
easily i<strong>de</strong>ntify species, count the number of individuals or even i<strong>de</strong>ntify individuals<br />
within a group using photo-i<strong>de</strong>ntification techniques. Behavioural<br />
observations can also be conducted with this technique. However, this<br />
approach has also some drawbacks, such as the <strong>de</strong>pen<strong>de</strong>nce on weather and<br />
availability of observation sites (shore, boat, etc), the impossibility to collect<br />
data at night, and the potential bias from the observers. Another and<br />
more advanced solution to observe and collect scientific data on cetaceans<br />
is to use automatic animal-borne sensors. This approach is newer and faces<br />
numerous technical challenges, including, <strong>for</strong> example, the need <strong>for</strong> sensors<br />
able to resist pressure differentials due to the water <strong>de</strong>pth, and autonomous<br />
enough in terms of power supply and data storage (see chapter I, 1).<br />
We <strong>de</strong>scribe the principles and challenges of advanced <strong>de</strong>tection methods<br />
based on passive acoustics monitoring (PAM). PAM consists in passively<br />
listening to the sounds emitted by animals. It is an attractive alternative to<br />
classical visual observations because cetaceans are vocally active and their<br />
sound can travel far un<strong>de</strong>rwater (Samaran et al., 2010b). In addition, species
Part I – Chapter 3 65<br />
of cetaceans can be <strong>de</strong>tected remotely, according to the main features of<br />
their sounds (acoustic intensity, bandwidth), the acoustic propagation and<br />
the ambient noise level. Note that the extraction of individual acoustic signature<br />
is still a scientific challenge. The first step with acoustic methods<br />
consists in choosing the right electronic instrumentation to make recordings<br />
(hydrophone, amplifier, filters, data acquisition board, data transmission,<br />
storage unit). The following steps are <strong>de</strong>dicated to signal processing<br />
and pattern recognition to <strong>de</strong>tect cetacean sounds in the recordings, to<br />
i<strong>de</strong>ntify, date, and localise species observations, and estimate the number of<br />
individuals. These acoustic events can then be used by biologists to map the<br />
presence of even distribution (locations). The diversity of sounds produced<br />
by cetaceans is overviewed in the first part of this chapter. Then, a <strong>de</strong>tailed<br />
account of the technological solutions to record and to analyse these sounds<br />
<strong>for</strong> the purpose of ecological studies of cetaceans is presented. Lastly, the<br />
application of these techniques is illustrated with case studies taken from<br />
our current projects.<br />
2. Acoustic signatures of cetaceans’ species<br />
Cetaceans are marine mammals that inclu<strong>de</strong> 83 species from two major taxonomic<br />
units named Mysticetes (cetaceans without teeth) and Odontocetes<br />
(cetaceans with teeth). Cetaceans are present in all oceans, and their conservation<br />
statuses are different according to the species (http://www.iucnredlist.org/).<br />
The sound repertoire of different cetacean species may vary<br />
from highly stereotyped repetitive sounds like the ones emitted by fin<br />
whales (Balaenoptera physalus) and the monotonous clicks of sperm whales<br />
(Physeter macrocephalus) to more complex communication calls, like those of<br />
bottlenose dolphins (Tursiop truncatus), killer whales (Orcinus orca) and humpback<br />
whales (Megaptera novaeangliae). A summary of the frequency range<br />
and source levels of sounds emitted by common cetacean species is given in<br />
table 1. These sounds are hypothesised to play a crucial role <strong>for</strong> communication<br />
among cetaceans, including group cohesion, searching <strong>for</strong> food, strategy<br />
<strong>for</strong> eating, mother-calf contact, individual recognition (acoustic signature),<br />
hunting, socialising, looking <strong>for</strong> a partner, <strong>de</strong>lineating a territory, establishing<br />
a hierarchy, <strong>de</strong>tecting predators and dangers, and orientation (Frankel, 1998).<br />
The intrinsic sound characteristics are non-linearly distorted from the<br />
acoustic propagation. Acoustic sound attenuation <strong>de</strong>pends on distance<br />
between the cetacean source and the hydrophone and is also proportional<br />
to the frequency squared. Leroy’s equation gives the attenuation coefficient<br />
α (dB/km) <strong>de</strong>pending on the frequency:<br />
( f )= 6.10 3 f 2 + 0.16 f f 2<br />
r<br />
f 2 r<br />
+ f , 2
66 Ecophysiology and animal behaviour<br />
where f (kHz) is the frequency of the acoustic wave and fr is the relaxation<br />
frequency of the boric acid B(OH) 3<br />
present in the sea water. The fr<br />
value <strong>de</strong>pends on the location and the empirical value is 1.6 kHz in the<br />
Mediterranean Sea <strong>for</strong> example.<br />
In addition, acoustic waves can be reflected at the sea surface and bottom,<br />
where the level of absorption <strong>de</strong>pends on the nature of the seabed.<br />
Furthermore, the propagation speed of the sound in seawater is not constant:<br />
it <strong>de</strong>pends importantly on salinity, pressure and temperature. All<br />
these parameters can be inclu<strong>de</strong>d in a mathematical mo<strong>de</strong>l to <strong>de</strong>scribe<br />
acoustic propagation, based on the Helmholtz equation, where P is the<br />
pressure, and c 0<br />
is the propagation speed of the sound in the seawater:<br />
2 P 1 2 P<br />
2<br />
c 0<br />
t = 0. 2<br />
For harmonic solution where P = pe i t and ω is the angular frequency<br />
(ω=2πF with F the frequency), the Helmholtz equation writes like:<br />
2 p + k 2 p = 0, with the wavenumber k = = 2 . c<br />
To solve this equation, different mathematic mo<strong>de</strong>ls and associated softwares<br />
could be applied including ray tracing (e.g. Bellhop software), normal<br />
mo<strong>de</strong>s mo<strong>de</strong>ls (e.g. Kraken software), the parabolic equation (e.g.<br />
RAM software) or the wavenumber integration (e.g. oases software). All<br />
of these can be downloa<strong>de</strong>d on the Ocean acoustic library website (http://<br />
oalib.hlsresearch.com).<br />
Table 1: Features of sounds emitted by some common cetaceans’ species<br />
(adapted from Simmonds et al., 2004). To compare, a large tanker generates<br />
low frequency noise (
Part I – Chapter 3 67<br />
The analysis of these sounds is difficult because of the presence of noise<br />
and non-linear distortion. Usually, different approaches are used to analyse<br />
vocalisations or short transient sounds (e.g. odontocete clicks, right<br />
whale gunshots). The first step consists in using the bandpass filter corresponding<br />
to the species studied, and possibly applying an enhancement of<br />
the signal if the amplifier gain is too low <strong>for</strong> the recordings. To <strong>de</strong>tect the<br />
emitted sounds, one method is to set a threshold on the temporal energy<br />
signal. The use of the Teager-Kaiser operator represents an alternative that<br />
takes into account the fact that acoustic signals vary within the same species<br />
or individual, where signal variation is given by equation:<br />
( s) = s 2 ( n) sn ( 1)sn+1<br />
( ),<br />
where s(i) is the i th sample of the recor<strong>de</strong>d signal (voltage corresponding<br />
to the acoustic pressure on the hydrophone, V/dB unit). The main advantages<br />
of this non-linear operator (in addition to its easy implementation)<br />
is that only three successive samples are nee<strong>de</strong>d to calculate Ψ. This operator,<br />
taking into account the signal variations, is used to track the instantaneous<br />
energy of the signal (<strong>for</strong> example <strong>for</strong> amplitu<strong>de</strong> modulation and<br />
frequency modulation).<br />
Parametric mo<strong>de</strong>ls as autoregressive (AR) or/and mean average (MA)<br />
mo<strong>de</strong>ls are also a solution, especially <strong>for</strong> the <strong>de</strong>tection of vocalisations or<br />
non-stationarity in the acoustic recordings. To analyse cetacean sounds,<br />
specialists in marine biology make extensive use of the Fourier trans<strong>for</strong>m<br />
or the spectrogram <strong>for</strong> the time evolution of frequency variations. More<br />
up-to-date methods exist such as the wavelet trans<strong>for</strong>m or the Hilbert<br />
Huang trans<strong>for</strong>m (Adam, 2008).<br />
3. Acoustic observatories<br />
Passive acoustics is based on the use of at least one hydrophone to record<br />
un<strong>de</strong>rwater sounds during a given time. These recordings typically inclu<strong>de</strong><br />
a large variety of sounds such as natural sounds (waves, rain, wind…), biological<br />
sounds (fish, shrimp, coral reef…) or sounds from human activities<br />
(sonar, airgun, marine traffic…). The choice of the recording equipment<br />
<strong>de</strong>pends on several factors including the acoustic intensity and frequency<br />
bandwidth of sounds emitted by the cetacean species un<strong>de</strong>r investigation,<br />
the bathymetry of the area where it is <strong>de</strong>ployed, and the ambient<br />
noise level, including those resulting from human activities. The choice<br />
of hydrophone also <strong>de</strong>pends on the objectives of the study including the<br />
willingness to <strong>de</strong>tect one or more specific cetacean species. If the target<br />
of the study is to <strong>de</strong>tect cetaceans’ vocalisations over very long distances,<br />
the <strong>de</strong>vice will be chosen to maximise the amplitu<strong>de</strong> of the signal up to
68 Ecophysiology and animal behaviour<br />
the saturating level that may result from marine noise and the minimum<br />
amplitu<strong>de</strong> of vocalisations that are to be <strong>de</strong>tected. The solution is to<br />
record continuously and keep the whole signal in memory to provi<strong>de</strong> an<br />
a posteriori analysis. This recordings can be continuous time or during<br />
a specific period every day to optimise the memory size and the power<br />
consuming. The other solution is to process cetacean sounds immediately<br />
in situ, such that only time corresponding to cetaceans’ presence is stored<br />
in memory. This approach can save substantial energy and memory space.<br />
3.1 Instantaneous acoustic observatories<br />
Here, the objective is to make recordings from a ship. There<strong>for</strong>e, the<br />
acoustic system should be light and easily manoeuvrable. Typically, the<br />
equipments required are one or more hydrophones, their amplifier, the<br />
digital recor<strong>de</strong>r or the data acquisition board and a computer. It is possible<br />
to buy this material <strong>for</strong> 6,000 to 7,000€ <strong>for</strong> a single hydrophone (sensitivity<br />
-170dB re 1V/uPa, [10-90kHz] omnidirectionnal). Digital recor<strong>de</strong>rs<br />
can now record at 192kHz on 24 bits and it is possible to use data<br />
acquisition cards with higher sampling rates and the possibility of different<br />
synchronised channels. Instantaneous acoustic observatories are usually<br />
used <strong>for</strong> opportunistic <strong>de</strong>tections of cetaceans in a specific area. This<br />
approach may be supplemented by visual observations. The objective is to<br />
give in<strong>for</strong>mation about the cetaceans’ distribution and localise potential<br />
hot-spots in a local region. We used this approach in Gua<strong>de</strong>loupe (French<br />
West Indies) to confirm the presence of elusive species like beaked whales,<br />
and in Madagascar to record different humpback whale singers.<br />
3.2 Semi-permanent and permanent acoustic stations<br />
Semi-permanent or permanent acoustic stations are used <strong>for</strong> the purpose<br />
of monitoring a specific area (hot-spot of whale activity, a channel, a<br />
strait, or a harbour…). This system can be <strong>de</strong>ployed with a buoy at the<br />
sea surface or can be anchored on the seabed. A good example of the<br />
first case is a new prototype that we <strong>de</strong>ployed in Gua<strong>de</strong>loupe (French<br />
West Indies) around the end of 2010 (figure 2). This system was built<br />
by CeSigma (www.cesigma.com) and PLK Marine (www.plkmarine.com),<br />
and was implemented in a Fish Aggregating Device (FAD) in agreement<br />
with the fisheries committee of Gua<strong>de</strong>loupe. Technical features were a<br />
sampling frequency of 200kHz, samples co<strong>de</strong>d on 24 bits, 4 channels,<br />
and 700Gb of storage, a Wifi transmission capacity, and continuous<br />
recordings (Gandilhon et al., 2010). This so-called “sonobuoy” is used to<br />
observe different species including sperm whales, humpback whales, bottlenose<br />
dolphins, spotted dolphins, rough-toothed dolphins.
Part I – Chapter 3 69<br />
Figure 2: Design of the acoustic system <strong>de</strong>veloped by CeSigma and PLK Marine<br />
<strong>for</strong> the scientific program Gualiba I coordinated by the team “Dynamique <strong>de</strong>s<br />
écosystèmes Caraïbes” <strong>de</strong> l’Université <strong>de</strong>s Antilles et <strong>de</strong> la Guyane and the Centre<br />
<strong>de</strong> neurosciences Paris Sud of University Paris Sud orsay. The sonobuoy consists<br />
of an autonomous recording and transmission <strong>de</strong>vice connected to a hydrophone<br />
recording ocean sounds continuously.<br />
Another <strong>de</strong>vice is the autonomous un<strong>de</strong>rwater acoustic recor<strong>de</strong>r <strong>for</strong> listening,<br />
manufactured and distributed by Multi-Electronique (www.multielectronique.com).<br />
The entire system is based on a hydrophone, a data<br />
acquisition card, an external hard drive and power batteries. An acoustic<br />
latch releases the material from its anchor, back to the surface by a buoy.<br />
This type of material was used to monitor populations of great whales<br />
and some odontocetes <strong>for</strong> a year in the Mozambique Channel and on the<br />
south of Tromelin Island in the Indian Ocean. The buoy was <strong>de</strong>ployed<br />
during the mission Eparses 2009 fun<strong>de</strong>d by the Marine protected areas,<br />
Terres australes et antarctiques françaises (Taaf), Centre d’étu<strong>de</strong>s biological<br />
Chizé (CEbC-CNRS) and the Laboratoire domaines océaniques <strong>de</strong><br />
l’université <strong>de</strong> Bretagne occi<strong>de</strong>ntale (LDO-UBO). The analysis was done<br />
a posteriori to automatically <strong>de</strong>tect the cetacean sounds and extract the<br />
presence rate during the different months of the year. Other instruments<br />
of the same type have been employed since 2007 in collaboration with
70 Ecophysiology and animal behaviour<br />
the Pacific marine environment laboratory (NoAA), CNRS-CEbC, and<br />
the LDO-UBO to monitor large whales as part of the Subantarctic Indian<br />
ocean Program (see figure 3).<br />
Figure 3: Design of the autonomous hydrophone of the Pacific marine environment<br />
laboratory (NoAA) and used <strong>for</strong> the scientific mission DEFLo-HyDRo. The<br />
sample frequency of the hydrophone is 250Hz and the data is co<strong>de</strong>d 16 bits with<br />
a storage capacity of 80Go. The autonomy can be chosen from 18 to 24 months<br />
<strong>de</strong>pending on if the recordings are continuous or during a short specific period<br />
of the day (10 min recordings every 6 hours <strong>for</strong> example). © NoAA/PML Vents<br />
program/Acoustics group (http://www.pmel.noaa.gov/vents/multimedia.html).<br />
Regarding permanent observatories, marine ecologists using acoustic<br />
methods have exploited permanent infrastructures from other disciplines,<br />
like those of geophysics or physical oceanography. For example, in Europe,<br />
the ESONET project (European Seafloor Observatories NETwork, www.<br />
esonet-emso.org) brings together specialists in geophysics, chemistry,<br />
biochemistry, oceanography, marine biology and fisheries. Different<br />
un<strong>de</strong>rwater observatories are distributed all around the European coasts.<br />
In 2007, in collaboration with Prof. H. Glotin (LSIS, www.lsis.org), we<br />
proposed to add the marine mammal observation task to the neutrino<br />
<strong>de</strong>tector installed on the ANTARES observatory (http://antares.in2p3.<br />
fr) <strong>de</strong>ployed in the Mediterranean Sea (Hyères, France). The advantage of<br />
this system is that it provi<strong>de</strong>s permanent acoustic recordings throughout<br />
the whole year. The objective is to give an indication of the presence of<br />
sperm whales and fin whales even during winter, when weather conditions<br />
are not optimal <strong>for</strong> visual observations. Passive acoustics, in this case, is a<br />
unique solution to provi<strong>de</strong> this kind of in<strong>for</strong>mation of cetacean presence.<br />
There are other permanent observatories using acoustic <strong>de</strong>tectors, such as<br />
the Mars ocean observatory testbed in Monterrey Bay (www.mbari.org/<br />
mars) and the Neptune project in the Northeast Pacific ocean (www.<br />
neptune.washington.edu). The modules of these infrastructures are placed<br />
on the seabed, and power supply and data can be transmitted by a cable<br />
connected to facilities on shore. These projects have a section <strong>de</strong>dicated to<br />
marine biology and especially to cetacean observations.
Part I – Chapter 3 71<br />
4. Case studies<br />
We worked on several cetacean species and the diversity of sounds that we<br />
<strong>de</strong>alt with explains the different methods we investigated.<br />
4.1 Analysis of Sperm whale clicks<br />
Sperm whales emit two major types of clicks: regular clicks characterised<br />
by high intensities and inter-click intervals greater than 0.5ms, and creaks<br />
that are successions of clicks of variable amplitu<strong>de</strong> and spacing of less<br />
than 0.5sec. The bio-mechanic of click generation is pretty well <strong>de</strong>scribed<br />
in the literature by the mo<strong>de</strong>l first introduced by Mohl et al. (2003) and<br />
subsequently modified by Laplanche et al. (2006). However, automatic<br />
<strong>de</strong>tection of clicks can be challenging because the inter-click interval fluctuates<br />
over time, and the signal-to-noise ratio varies consi<strong>de</strong>rably according<br />
to the ambient noise and the position of the whale relative to the<br />
recording <strong>de</strong>vice (figure 4). There<strong>for</strong>e, one needs to tra<strong>de</strong> <strong>de</strong>tection capacity<br />
off the risks of false alarms when setting and adjusting the <strong>de</strong>tector.<br />
Figure 4: Sperm whale clicks with un<strong>de</strong>rwater noise (upper panel) <strong>de</strong>tected by using<br />
the Teager-Kaiser operator <strong>de</strong>scribed previously (lower panel). Sperm whale clicks<br />
were recor<strong>de</strong>d off the coast of Toulon (France) in August 2004. one sperm whale<br />
was presented the data shows regular clicks.
72 Ecophysiology and animal behaviour<br />
Figure 5: Data retrieved from the acoustic sensing of Sperm whale clicks.<br />
A. Estimation of the Sperm whale length by measurement of the <strong>de</strong>lay between<br />
the first pulse and the second pulse. The y-axis measures the relative amplitu<strong>de</strong> of<br />
the pulse (normalized correlation between the original pulse and its reflection) and<br />
the z-axis corresponds to the number of successives clicks (here, 50 clicks). Sperm<br />
whale clicks are multipulsed signals. The first pulse comes from the “monkey lips”<br />
close to the blowhole and the other pulses come from reflections from the distal sac
Part I – Chapter 3 73<br />
and the frontal sac respectively located at the beginning and the end of the head.<br />
We can measure the time between 2 successive pulses: this <strong>de</strong>lay is the time it takes<br />
<strong>for</strong> the acoustic wave to cross the head. From the known celerity of the acoustic<br />
wave in the head, this allows to estimate the head length and there<strong>for</strong>e the body<br />
length (Lopakta et al., 2006). B. Classification of Sperm whale clicks. Clicks were<br />
classified with the use of three parameters calculated after the Schur coefficients<br />
(<strong>for</strong> <strong>de</strong>tails, see Lopatka et al., 2006). Black circles: sperm whale clicks. Red circles:<br />
clicks from striped dolphins. C. This figure <strong>de</strong>scribes the different steps during the<br />
Sperm whale dives. Step 1, Sperm whale emits regular clicks at time intervals greater<br />
than 0.5sec to <strong>de</strong>tect the presence of preys. Step 2, Sperm whale emits buzz at time<br />
intervals less than 0.5sec to get a better resolution of the “acoustic image” of this<br />
volume. Step 3, Sperm whale stops emitting clicks at the end of this part of the dives<br />
and will go back to step A <strong>for</strong> another prey research.<br />
Sperm whale clicks have high frequency components (greater than<br />
5kHz), so it is possible to use a high pass filter cutoff frequency exceeding<br />
1kHz or more to overcome the ambient noise mostly due to the presence<br />
of maritime traffic. We tested several approaches to <strong>de</strong>tect sperm whale<br />
clicks: Teager-Kaiser operator (see figure 4), autoregressive and moving<br />
average mo<strong>de</strong>ls, Schur algorithm, spectrogram and wavelet <strong>de</strong>composition<br />
(Adam et al., 2005; Lopatka et al., 2005). The goals were firstly to<br />
minimise the false alarm rate, and secondly to obtain accurate estimates<br />
of the time of occurrence of clicks in or<strong>de</strong>r to localise the individuals by<br />
triangulation. We found that sperm whale clicks were characterised the<br />
best by reflection coefficients of the Schur mo<strong>de</strong>l (Lopatka et al., 2006).<br />
By using the Schur mo<strong>de</strong>l, it was then possible to characterise the morphology<br />
of individuals (the size of the sperm whale can be <strong>de</strong>duced from<br />
the inter-pulse), distinguish clicks of sperm whales from other transient<br />
sounds, and locate the individual with precision enough to rebuild its<br />
dive profiles (figure 5).<br />
4.2 Detection and localisation of blue whales<br />
We have also used passive acoustic monitoring to study Antarctic blue<br />
whale populations in the Southern Ocean. Most of the year, Antarctic<br />
blue whale emits a low frequency call (from 28Hz to 20Hz, see figure 6A)<br />
that lasts <strong>for</strong> 15-20 sec with high intensity every minute. Algorithms<br />
<strong>for</strong> automatic whale call <strong>de</strong>tection, extraction and discrimination were<br />
<strong>de</strong>veloped and used on a one-year continuous acoustic dataset (2003-<br />
2004) recor<strong>de</strong>d in the station located in sub Antarctic area near Crozet<br />
Islands un<strong>de</strong>r the framework of the International monitoring system of<br />
the comprehensive-test-ban treaty organisation (IMS-CTBTO). All data<br />
are available un<strong>de</strong>r contract with the Direction <strong>de</strong>s applications militaires<br />
du commissariat à l’énergie atomique (CEA-DAM). The aim was to assess
74 Ecophysiology and animal behaviour<br />
Figure 6: Acoustic study of Antarctic blue whale. A. Two successive Antarctic blue<br />
whale calls. Calls are stereotypical sounds, with 2 main frequencies at 28Hz and<br />
20Hz. Antarctic blue whales emit these calls between 2 breathing phase. From<br />
this specific time-frequency pattern, these calls can be automatically <strong>de</strong>tected.<br />
b. Number of Antarctic blue whale calls <strong>de</strong>tected from the recordings of one<br />
hydrophone <strong>de</strong>ployed in the North of Crozet Island from May 2003 to April 2004.<br />
the seasonal occurrence of blue whale in a specific area. The <strong>de</strong>tection<br />
procedure was based on a matched filter mo<strong>de</strong>l (Samaran et al., 2008).<br />
More than 170,000 blue whale calls were <strong>de</strong>tected all year-round indicating<br />
their continuous presence in the region (figure 6B). Results revealed<br />
the seasonal occurrence and migration patterns of blue whales, providing<br />
in<strong>for</strong>mation about <strong>ecology</strong> and habitats in this <strong>for</strong>mer commercial whaling<br />
area (Samaran et al., 2010c).<br />
A mathematical mo<strong>de</strong>l RAM (Range-<strong>de</strong>pen<strong>de</strong>nt Acoustic Mo<strong>de</strong>l) was used<br />
to predict how sound levels changed with distance between vocalising<br />
whales and IMS receivers. This approach allowed estimating the size of the<br />
monitored area, which was estimated to be a radius of 200km. The distri-
Part I – Chapter 3 75<br />
bution of the estimated distances confirmed the presence of whales close<br />
to the Crozet Islands, showing the importance of this sub-Antarctic area<br />
<strong>for</strong> these endangered species especially during the austral summer feeding<br />
season (Samaran et al., 2010b). In addition, the triangular configuration of<br />
the calibrated hydrophones of the station allowed localising and tracking<br />
calling whales from observed differences in arrival times of the same signals<br />
at the three hydrophones. We could there<strong>for</strong>e estimate the movement<br />
and <strong>de</strong>tection range between the recording system and the animals, which<br />
are critical data to un<strong>de</strong>rstand the habitat of calling whales without human<br />
disturbance. The sound levels of received calls may also be used to estimate<br />
the level of sound emitted by the vocalising whales (Samaran et al., 2010a).<br />
The last objective of our analysis was to estimate the total number of<br />
vocalising whales. This task was not trivial and could be conducted along<br />
two methodological approaches. First, we could search <strong>for</strong> individual<br />
acoustic signature. Un<strong>for</strong>tunately, this is difficult <strong>for</strong> cetaceans in their<br />
natural environment, especially when they are far from the hydrophone,<br />
because of the non-linear distortion of sound due to the acoustic propagation<br />
(Musikas et al., 2009). Second, we could use the distance sampling<br />
method. This method is well-known <strong>for</strong> a wi<strong>de</strong> range of applications with<br />
visual observations of wildlife, but it is particularly difficult to apply when<br />
little or no in<strong>for</strong>mation is available about the call rate (Marques et al.,<br />
2009). There<strong>for</strong>e, we suggested a third method based on a joint estimation<br />
of the number of calls emitted by each individual and the number of individuals<br />
in a specific area (Valsero et al., 2010). The two estimates are given<br />
by their probability functions, assumed to follow a Poisson distribution:<br />
( ) k<br />
s<br />
P( B()= s k)= e s k!<br />
( ) k<br />
μt<br />
PCt ( ()= k)= e μt k!<br />
where B(s) is the number of whales in the area s (around the hydrophone)<br />
and C(t) the number of <strong>de</strong>tected calls during time t. The estimated number<br />
of individuals in the study area at a time t can then be obtained by the<br />
method of moments using the maximum likelihood estimation (Valsero et<br />
al., 2010):<br />
ˆμ = N 2<br />
N<br />
N 2<br />
N<br />
ˆ =<br />
( 2 N<br />
N )s
76 Ecophysiology and animal behaviour<br />
where N is the number of <strong>de</strong>tected calls in the area s during t, N is the<br />
mean and<br />
2 N is the variance. The method gives estimates of the number<br />
of individuals present in the Crozet Archipelago during the year between<br />
0 and 4 individuals (95% confi<strong>de</strong>nce interval) based on the distribution of<br />
the time between successive calls and between 2 and 12 individuals based<br />
on the number of calls. This type of results is only possible with passive<br />
acoustics because visual observations cannot be conducted throughout<br />
the whole year in this area, highlighting the importance of having a permanent<br />
recording system installed there.<br />
Table 2: Estimation of the number [min, max] of whale individuals<br />
around the Crozet Archipelagos by 2 distributions based<br />
on the distribution 1) of the time between successive calls and 2)<br />
of the number of calls in a specific area (Valsero et al., 2010).<br />
Confi<strong>de</strong>nce interval (1) Confi<strong>de</strong>nce interval (2)<br />
Month 90% 95% 90% 95%<br />
May [1, 8] [1, 8] [3, 12] [2, 13]<br />
June [1, 8] [1, 9] [3, 12] [2, 13]<br />
July [1, 7] [0, 7] [2, 10] [2, 11]<br />
August [1, 7] [0, 7] [3, 11] [2, 12]<br />
September [0, 6] [0, 6] [3, 11] [2, 12]<br />
October [1, 6] [0, 7] [3, 10] [2, 11]<br />
November [1, 7] [0, 8] [3, 11] [2, 12]<br />
December [0, 6] [0, 7] [3, 11] [2, 12]<br />
January [0, 5] [0, 6] [3, 11] [2, 12]<br />
February [1, 7] [1, 8] [2, 10] [2, 11]<br />
March [0, 3] [0, 3] [2, 10] [2, 11]<br />
Avril [0, 6] [0, 7] [2, 10] [2, 11]<br />
Mean [0, 3] [0, 4] [3, 11] [2, 12]<br />
4.3 Analysis of Humpback whale songs<br />
During the breeding season, Humpback whale males emit songs structured<br />
in a hierarchical manner where the basic building blocks are called<br />
sound units according to Payne and McVay (1971). Until now, whale<br />
experts classified these sounds manually by listening and labeling their<br />
recordings on spectrograms. Their objective was to extract the leitmotiv<br />
of the song <strong>for</strong> a specific population in a specific area <strong>for</strong> the breeding<br />
season (Noad et al., 2000; Cerchio et al., 2001). Of course, it is clear that
Part I – Chapter 3 77<br />
the choice of the well-adapted hydrophones <strong>for</strong> this application is crucial<br />
to improve the correct classification.<br />
To help the biologists, our objective was to <strong>de</strong>velop an automatic analysis<br />
of Humpback whales songs (figure 7). This goal is quite difficult because,<br />
even when the hydrophone was just positioned in front of one singer, the<br />
recordings simultaneously inclu<strong>de</strong>d a lot of vocalisations from the other<br />
singers consi<strong>de</strong>red as background noise. So, to go further in the automatic<br />
classification task, we extracted different features of each sound unit, as<br />
duration, bandwith, harmonics, and frequency slopes. In addition, we<br />
proposed an original approach to increase the per<strong>for</strong>mance of our classifier:<br />
we <strong>de</strong>fined the concept of subunits, which means that the sound<br />
units <strong>de</strong>fined by Payne and McVay (1971) can be <strong>de</strong>composed into one or<br />
more subunits. By this <strong>de</strong>finition, we are willing to use the tonal in<strong>for</strong>mation<br />
and the sound prosody (variation of the features in the frequency<br />
domain) to increase the correct classification. Our un<strong>de</strong>rlying i<strong>de</strong>a is to<br />
show that the diverse number of sound unit types per<strong>for</strong>med by the singers<br />
can be explained by a limited short number of subunits.<br />
Figure 7: Segmentation of Humpback whale song recording into units (green area)<br />
and ambient un<strong>de</strong>rwater noise (red area).<br />
The second step of our work was to extract in<strong>for</strong>mation <strong>for</strong> each <strong>de</strong>tected<br />
sound units. We first focused on the variation of the <strong>de</strong>rivative of the five<br />
first main energetic frequencies. We then also <strong>de</strong>fined different types of<br />
sound unit shape: sound unit with constant harmonic signals or chirps having<br />
increased or <strong>de</strong>creased, convex, concave or linear frequency variations.<br />
These features were extracted with different methods largely used in the<br />
Speech processing method (Kay, 1988) and based on the presence of at least<br />
one frequency in the sound units (Pace et al., 2009a). Classes were obtained<br />
by applying the unsupervised k-means algorithm, a statistical classification<br />
method, and the Davies-Bouldin criterion was used to evaluate the similarity<br />
within and between classes. This criterion ma<strong>de</strong> it possible to <strong>de</strong>termine
78 Ecophysiology and animal behaviour<br />
the optimal number of classes <strong>for</strong> all vocalisations present in our dataset.<br />
Based on this method, 18 different groups of subunits were <strong>de</strong>tected in a<br />
sample of 424 vocalisations (Picot et al., 2008; Pace et al., 2009b). We finally<br />
worked on hid<strong>de</strong>n Markov mo<strong>de</strong>ls to take into account the possible variant<br />
duration of the subunits and to characterise the link between successive<br />
subunits (i.e. the syntax, see figure 8). The complete method <strong>for</strong> automatic<br />
classification of these sound units is a valuable tool <strong>for</strong> biologists willing to<br />
investigate the song evolution and the interactions between or within populations.<br />
In the future, we expect that this method will also make it possible<br />
to assign an acoustic signature to a specific Humpback whale individual.<br />
Figure 8: Segmentation and classification of Humpback whale sound units. The<br />
sound units are not emitted in a random or<strong>de</strong>r by the singers. Thus we used the<br />
hid<strong>de</strong>n markov mo<strong>de</strong>l (HMM) to take advantage of the specific or<strong>de</strong>r of these sound<br />
units to <strong>de</strong>tect and classify them. MFCC: Mel-frequency cepstrum coefficients, Δs:<br />
a measure the temporal rates of change of the MFCCs.<br />
5. Conclusion and future work<br />
Using passive acoustic monitoring to assess cetacean populations has several<br />
benefits in comparison with conventional survey methods such as
Part I – Chapter 3 79<br />
visual sightings. The animals can be studied continuously without any<br />
negative impact. This method is also less <strong>de</strong>pen<strong>de</strong>nt on weather conditions<br />
than visual methods, and does not rely on animals surfacing in<br />
or<strong>de</strong>r to be <strong>de</strong>tected. It can be applied globally, including remote areas<br />
where visual sightings are usually either too sparse, difficult to gather, or<br />
costly. Other advantages of passive acoustic monitoring are that it helps<br />
to i<strong>de</strong>ntify areas of cetacean concentration, seasonal occurrence and distribution<br />
patterns; it can facilitate the long-term monitoring of cetacean<br />
abundance through variations in call rates over the years, and in<strong>for</strong>m on<br />
where to establish marine protected areas. Passive acoustics is there<strong>for</strong>e an<br />
interesting complementary method <strong>for</strong> the cetacean observations.<br />
Table 3: Advantages and drawbacks of two permanent acoustic systems<br />
System with<br />
buoy at the<br />
sea surface<br />
System<br />
<strong>de</strong>ployed<br />
on the sea<br />
bottom<br />
Advantages<br />
Electrical power by solar panels<br />
and/or wind turbines<br />
Setting parameters (amplifier<br />
gain, sampling frequency)<br />
Data transmission via HF, Wifi<br />
Real-time application<br />
Discreet<br />
Less susceptible to surface<br />
activities<br />
Drawbacks<br />
Weather conditions:<br />
movement of waves, wind,<br />
bad weather<br />
Risk of damage and theft<br />
Electrical power<br />
Access to instrument<br />
(parameters settings)<br />
Data transmission<br />
Several passive acoustic approaches are possible, from instantaneous<br />
observations with a light <strong>de</strong>ployable hydrophone to continuous observations<br />
with sonobuoys <strong>de</strong>ployed either at the sea surface or on the seabed.<br />
The solutions offered by permanent observatories have advantages and<br />
drawbacks listed in table 3, and future work could be <strong>de</strong>dicated to build a<br />
system that retains the advantages of the two main techniques and eliminates<br />
the drawbacks. For this purpose, the power supply, data storage,<br />
and data transmission limits must be circumvented, especially <strong>for</strong> realtime<br />
applications. The best feasible solution is probably to set up fully<br />
cabled systems (data and power) at the sea bottom, even if this solution is<br />
quite expensive. Another important task is to <strong>de</strong>velop automatic real-time<br />
analysis <strong>for</strong> the <strong>de</strong>tection and, if possible, the localisation of cetaceans.<br />
These analyses would be best conducted in situ and the results could be<br />
directly sent to the biologists and the managers of marine protected areas<br />
and/or coastal areas. This could provi<strong>de</strong> authorities with real-time monitoring<br />
tools to diminish the risk of collision between ships and cetaceans<br />
<strong>for</strong> example.
80 Ecophysiology and animal behaviour<br />
Authors’ references<br />
Flore Samaran:<br />
Université <strong>de</strong> la Rochelle, Observatoire Pelagis, Centre <strong>de</strong> Recherche <strong>de</strong>s<br />
Mammifères Marins, ULR-CNRS UMS 3419, La Rochelle, France<br />
Nadège Gandilhon:<br />
Université <strong>de</strong>s Antilles et <strong>de</strong> la Guyane, Laboratoire <strong>de</strong> Biologie Marine,<br />
Equipe dynamique <strong>de</strong>s écosystèmes Caraïbes, Pointe-à-Pitre, Gua<strong>de</strong>loupe,<br />
France<br />
Rocio Prieto Gonzalez:<br />
Universidad <strong>de</strong> Valladolid, Departamento <strong>de</strong> Estadística e Investigación<br />
Operativa, Valladolid, Spain<br />
Rocio Prieto Gonzalez, Amy Kennedy, Olivier Adam:<br />
Université Paris Sud Orsay, Centre <strong>de</strong> Neurosciences Paris Sud, UPS-<br />
CNRS UMR 8195, Orsay, France<br />
Fe<strong>de</strong>rica Pace:<br />
University of Southampton, Institute of Sound and Vibration Research,<br />
Southampton England<br />
Amy Kennedy:<br />
Alaska Fisheries Science Center, National Marine Mammal Laboratory,<br />
Seattle, USA<br />
Olivier Adam:<br />
Université Pierre et Marie Curie, Institut Jean d’Alembert, Lutheries<br />
acoustique musicale, UPMC-CNRS UMR 7190, Paris, France<br />
Corresponding author: Olivier Adam, olivier.adam@u-psud.fr<br />
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area <strong>for</strong> two Southern Hemisphere blue whale subspecies. Endangered<br />
Species Research, 12, pp. 157-165.<br />
Simmonds M. P., Dolman S., Weilgart L. (Eds) 2004. Oceans Of Noise. The<br />
Whale and dolphin conservation society, Chippenham, UK.<br />
Simmonds M. P., Isaac S., 2007. The impact of climate change on marine<br />
mammals: early signs of significant problems. Oryx, 41, pp. 1-8.<br />
Simmonds M. P., Eliott W. J., 2009. Climate change and cetaceans: concerns<br />
and recent <strong>de</strong>velopment. Journal of the Marine Biological Association of<br />
the United Kingdom, 89, pp. 203-210.<br />
Valsero, M. C., Prieto Gonzalez, R., Samaran, F., Adam, O., 2010. A spatiotemporal<br />
Poisson mo<strong>de</strong>l to estimate the <strong>de</strong>nsity of the Antartic blue whales<br />
in the Austral Ocean. International Workshop on Applied Probability,<br />
Espagne.
Chapter 4<br />
Bioacoustics approaches to locate<br />
and i<strong>de</strong>ntify animals in terrestrial<br />
environments<br />
Chloé Huetz, Thierry Aubin<br />
1. Needs <strong>for</strong> non-invasive methods to i<strong>de</strong>ntify and locate<br />
animals<br />
Population assessment and a proper un<strong>de</strong>rstanding of behavioural strategies<br />
are central and urgent tasks in conservation biology. Nevertheless, up<br />
to now, field-based biological researches are held back by the difficulty,<br />
cost and intrusiveness of marking and tagging animals, and the relative<br />
ineffectiveness of manual data collection and analysis thereafter. In<strong>de</strong>ed,<br />
the monitoring of wild animals almost systematically presupposes their<br />
catching first. This invasive stage is not necessary when animals are acoustically<br />
monitored (Gilbert et al., 1994; Hartwig, 2005). Almost all vocal<br />
species possess unique acoustic patterns that differ significantly from one<br />
to another individual, while following a common structure typical of the<br />
species. By using acoustic analysis methods, it is then possible to i<strong>de</strong>ntify<br />
individuals or species emitting vocalisations (insects, frogs, birds and<br />
mammals). Sound sources have also the property to be localisable. Until<br />
recently, localisation of wild animals by acoustic methods was not wi<strong>de</strong>ly<br />
used. This was mainly due to technical limitations, as the monitoring of<br />
simultaneous acoustic sources is problematic in the field. In<strong>de</strong>ed, among<br />
several requirements, simultaneous field recordings <strong>de</strong>vices have to share<br />
features such as being wireless, waterproof, easily transportable, and with<br />
large memory capacities. Now that technologies exist to overcome these<br />
limitations, it has become possible to localise and track the movements of<br />
animals that generate sounds. Such systems have been first called acoustic
84 Ecophysiology and animal behaviour<br />
location systems (ALS) by McGregor et al. (1997), but are now currently<br />
named automatic acoustic survey systems (AASS).<br />
An accurate AASS must fulfil two conditions: it must allow to localise the<br />
sound source with precision and i<strong>de</strong>ntify the emitter. Until now, AASS<br />
have been used mostly to <strong>de</strong>tect marine mammals (e.g. Staf<strong>for</strong>d et al.,<br />
1998; Mellinger and Clark, 2003; Clark and Clapham, 2004; see chapter<br />
I, 3). In contrast, AASS <strong>de</strong>dicated to the monitoring of terrestrial species<br />
are rather uncommon (even though Mennill et al. 2006 used it <strong>for</strong> a case<br />
study with birds). For cetaceans, sensors are hydrophones and AASS spatial<br />
precision <strong>for</strong> animal localisation is in the kilometre range. Animals<br />
can also be tagged with localisation systems such as Argos <strong>for</strong> more accurate<br />
localisation, but even these systems cannot provi<strong>de</strong> an accuracy below<br />
the metre range. For terrestrial species, more accurate locations are often<br />
required, <strong>for</strong> example, to <strong>de</strong>termine the relative positions of neighbouring<br />
birds. In addition, tagging small terrestrial animals is often impossible due<br />
to several technical reasons (small size, difficulty of catching animals, see<br />
chapter I, 2). Moreover, marine and terrestrial environments differ from<br />
an acoustic point of view, the latter being often less homogeneous, with<br />
more obstacles.<br />
Here we review and discuss the accuracy of AASS <strong>for</strong> monitoring the<br />
position of animals vocalising in different terrestrial environments. We<br />
first introduce the principles and purposes of acoustic location systems.<br />
Then, we propose a non-exhaustive review of the different methods that<br />
can be implemented in an AASS in or<strong>de</strong>r to automatically locate animals<br />
from the sounds they produce. We also present several existing<br />
methods used to extract from a vocalisation the individual and the species<br />
signatures. Finally, we illustrate the use of these technologies with<br />
some recent field applications concerning the location of birds in <strong>for</strong>est<br />
habitats.<br />
2. Localisation system: time <strong>de</strong>lay of sound arrival estimation<br />
and triangulation<br />
2.1. Principle of Automatic Acoustic Survey Systems<br />
Acoustic location systems use simultaneous recordings from an array of<br />
acoustic sensors (microphones or hydrophones) scattered over a particular<br />
area. From these recordings, two measurements can be extracted to<br />
compute the sound-source location: the level (or amplitu<strong>de</strong>) differences<br />
between recordings, and the time <strong>de</strong>lays between the sound arrival times<br />
at spatially separated microphones (see <strong>for</strong> review McGregor et al., 1997;<br />
Mennill et al., 2006). The latter, usually <strong>de</strong>signated as the time-of-arrival
Part I – Chapter 4 85<br />
difference (TDoA) is estimated either i) by pair-wise cross-correlations<br />
of the sound wave<strong>for</strong>ms recor<strong>de</strong>d from the time-synchronised microphones<br />
or ii) by beam<strong>for</strong>ming algorithms, which can be <strong>de</strong>fined as the<br />
sum of all signals or their energy in the time domain (Valin et al., 2004)<br />
or in the frequency domain (Chen et al., 2006) properly time-<strong>de</strong>layed.<br />
The cross-correlation between two recordings shows one peak, corresponding<br />
to the TDoA of the same sound source at the two microphone<br />
positions.<br />
Figure 1: Representation of our automatic acoustic survey system (AASS)<br />
localisation procedure. The first step consists in setting up the microphones and<br />
the wireless recording system (1). All pairwise distances between microphones are<br />
measured with a lasermeter (1.1), and all channels are recor<strong>de</strong>d synchronously<br />
on a laptop computer (1.2). The offline analysis (2) follows three steps: from the<br />
6-channels recordings, the animals’ vocalisations are extracted and band-pass<br />
filtered (2.1) to extract putative noise sources, then pairwise cross-correlations of<br />
the wave<strong>for</strong>ms are per<strong>for</strong>med <strong>for</strong> each vocalisation (2.2) in or<strong>de</strong>r to compute the<br />
time-of-arrival differences, which are then used to locate each vocalisation (2.3).<br />
Sound source location can then be <strong>de</strong>termined using the so-called triangulation<br />
principle. In<strong>de</strong>ed, the TDoAs of a sound between each microphone<br />
pair constrain the emitter’s location to a hyperboloid, and its
86 Ecophysiology and animal behaviour<br />
precise location can be resolved by intersecting hyperboloids from many<br />
pairs of receivers. Another possibility is to divi<strong>de</strong> the space around the<br />
microphones into cells by a grid and compute all possible TDoAs <strong>for</strong><br />
each cell. The location of a sound source is then computed by minimising<br />
the difference between the measured TDoAs with the theoretical<br />
ones. When using the beam<strong>for</strong>ming algorithms, TDoA are estimated by<br />
searching the <strong>de</strong>lays between recordings that maximise the output signal<br />
of the beam<strong>for</strong>mer. Lastly, some methods use simultaneously the TDoA,<br />
the level (amplitu<strong>de</strong>) difference, and the reflection of the sound on the<br />
surface and ground to locate with a greater precision the emitter (Cato,<br />
1998).<br />
Figure 2: Example of the output of our localisation system. In this case, a<br />
loudspeaker was placed outsi<strong>de</strong> the microphone configuration (red star) and was<br />
emitting seven vocalisations. Each vocalisation was then localised (blue circle). As<br />
shown on the zoom, the localisation error is small, on the centimetre range.<br />
our AASS consisted of an array of six wireless omnidirectional microphones<br />
recording simultaneously up to six-channel sound files on a laptop<br />
computer via an emitter-receiver station (figure 1). The microphones<br />
were set up throughout a given area in the field. Exploiting the speed of<br />
sound propagation through air, the system triangulates the position of<br />
sound sources on the basis of TDoAs at the microphones. These TDoAs
Part I – Chapter 4 87<br />
are estimated using a cross-correlation of the band-pass filtered wave<strong>for</strong>ms.<br />
Instead of using the standard triangulation equations, we applied<br />
a simple exhaustive search in space like Hammer and Barrett (2001). Test<br />
experiments were conducted in the field in brazilian rain<strong>for</strong>est and in<br />
European temperate <strong>for</strong>ests in or<strong>de</strong>r to probe the accuracy of the system.<br />
A loudspeaker was placed at a given location around the microphones,<br />
and its distance to the microphones was measured with a lasermeter.<br />
Several sounds were played and recor<strong>de</strong>d on the AASS. Figure 2 shows<br />
an example of the localisation system’s result <strong>for</strong> the playback of seven<br />
bird songs (computed source in blue circles). The “real position” of the<br />
emitter (the loudspeaker) is indicated with a red star and it can be seen<br />
that all songs are localised very close to the “real” position measured with<br />
the lasermeter.<br />
2.2. Limits of Automatic Acoustic Survey Systems<br />
Acoustic localisation systems are prone to the same constraints and usually<br />
present the same limits as any kind of acoustic survey system. In<strong>de</strong>ed,<br />
sound localisation of animals is difficult when emitters are distributed<br />
wi<strong>de</strong>ly in the open and when reverberations <strong>de</strong>gra<strong>de</strong> temporal patterns in<br />
vocalisations (Spiesberger, 1999). Selective filtering due to obstacles in the<br />
environment can also modify the signal during propagation (Spiesberger,<br />
1999; 2005). In noisy environments, multiple sounds coming from different<br />
species or individuals, and their background, can overlap with the<br />
signal of interest in the temporal and frequency domains, producing a<br />
jamming effect and leading to false localisations. An insufficient signalto-noise<br />
ratio in turn impairs the localisation process (Quazi and Lerro,<br />
1985) and methods to enhance the signal-to-noise ratio (e.g. a band-pass<br />
prefiltering) must be applied when possible. In general, any factor <strong>de</strong>teriorating<br />
the cross-correlations between recordings strongly affects the<br />
TDOA estimation and there<strong>for</strong>e can diminish the accuracy of the localisation<br />
process (Spiesberger, 1999; 2005).<br />
In addition, several constraints are specific to the use of an AASS <strong>for</strong> the<br />
purpose of localisation. The first necessary prerequisite of any localisation<br />
system is to know accurately the relative positions of the microphones<br />
(Quazi and Lerro, 1985). In<strong>de</strong>ed, in or<strong>de</strong>r to make use of the TDOAs<br />
and convert them into distances relative to the microphones, the whole<br />
microphone configuration has to be known, and subsequently, several<br />
pairwise distances between microphones have to be measured. The accuracy<br />
and the space coverage of the localisation process strongly <strong>de</strong>pend<br />
on the inter-microphone distances. In fact, the optimisation of the spatial<br />
configuration of microphones faces tra<strong>de</strong>-offs between a large spacing,<br />
an accurate measurement of their relative positions, and a good signal-to-
88 Ecophysiology and animal behaviour<br />
noise ratio of the recor<strong>de</strong>d vocalisation on the different microphones. If<br />
most of the studies assess that, in theory, three microphones are necessary<br />
to localise in 2D, and four in 3D, it has been shown that one additional<br />
microphone is nee<strong>de</strong>d to obtain accurate estimates of sound location (4 in<br />
2D and 5 in 3D, Spiesberger, 2001).<br />
Moreover, some redundancy can help the localisation process (Chen<br />
et al., 2003). Depending on the nee<strong>de</strong>d accuracy of the output localisation<br />
and on the studied environment, several methods can be used to<br />
measure the relative microphone positions. The position of the microphones<br />
can be estimated by the coordinates of a global-positioning system<br />
– GPS (Mennill et al., 2006). However, GPS positioning can hardly<br />
be used when animals need to be localised with great accuracy (less<br />
than 1m), or in obstructed environments such as <strong>de</strong>nse <strong>for</strong>ests. In these<br />
cases, a lasermeter can be used un<strong>de</strong>r the condition that no obstacle<br />
stops the laser beam and thus prevents the distance measures between<br />
pairs. This constrains the inter-microphone distances in thick vegetation<br />
or in irregular topography. Another constraint on the microphone<br />
configuration is the perfect time-synchronization that must be achieved<br />
between <strong>de</strong>vices. A wireless or a wired synchronization system is there<strong>for</strong>e<br />
nee<strong>de</strong>d.<br />
3. I<strong>de</strong>ntification systems: review of acoustic methods<br />
to extract species and individual signatures from animals’<br />
vocalisations<br />
3.1. Principles of i<strong>de</strong>ntification systems<br />
The fact that animals can recognise one another by voice alone has been<br />
<strong>de</strong>monstrated repeatedly, especially in birds (<strong>for</strong> a review see Falls, 1982;<br />
Dhondt and Lambrechts, 1992) and mammals (<strong>for</strong> example Balcombe<br />
and McCracken, 1992, <strong>for</strong> bats; Caldwell et al., 1990, <strong>for</strong> dolphins; Sèbe et<br />
al., 2010, <strong>for</strong> lambs; Tooze and Harrington, 1990, <strong>for</strong> wolves). The in<strong>for</strong>mation<br />
content of a signal is represented by structural sound features such<br />
as its spectrum or the temporal evolution of its amplitu<strong>de</strong> and frequency<br />
modulations (see figure 3). A signal would be i<strong>de</strong>al <strong>for</strong> individual or species<br />
recognition if it is highly stereotyped within each individual or species,<br />
and if it significantly differs between individuals or species. Different<br />
methods are available <strong>for</strong> <strong>de</strong>termining which parameters enco<strong>de</strong> acoustic<br />
i<strong>de</strong>ntity and allow recognition between species or between individuals.
Part I – Chapter 4 89<br />
Figure 3: Examples of display calls (above: spectrograms; below: oscillograms)<br />
emitted by three individuals of King penguins Aptenodytes patagonicus (from Aubin<br />
and Jouventin, 2002).
90 Ecophysiology and animal behaviour<br />
Among the possible means to i<strong>de</strong>ntify animal vocalisations, the most<br />
classical method is the visual inspection and labelling of oscillographs<br />
or spectrographs. This process is time consuming and <strong>de</strong>pen<strong>de</strong>nt upon<br />
the judgement of one observer (Kogan and Margoliash, 1998). Besi<strong>de</strong><br />
this “manual” approach, some more or less automatic classification methods<br />
can be used. A first method is based on variance calculations realised<br />
on the temporal, amplitu<strong>de</strong> and frequency parameters of a given signal.<br />
For each call parameter, the between-individual and within-individual<br />
coefficients of variation (CVbi and CVwi) can be calculated as follows:<br />
CV = 100 (SD/X<br />
1<br />
mean<br />
)(1 + [ 4n ]) ,<br />
where SD is the standard <strong>de</strong>viation, Xmean the mean of the sample and<br />
n the sample size (Sokal and Rohlf, 1995). To assess the potential <strong>for</strong> the<br />
coding of individual i<strong>de</strong>ntity (potential of individual coding: PIC) <strong>for</strong><br />
each parameter, the ratio CVbi E(CVwi), where E(CVwi) is the mean value<br />
of the CVwi of all individuals, is calculated (Scherrer, 1984; Robisson<br />
et al., 1993). For a given parameter, a PIC value greater than 1 means<br />
that this parameter may correspond to an individual parameter as its<br />
intra-individual variability is smaller than its inter-individual variability.<br />
In the same way, the potential of specific coding (PSC) can be evaluated<br />
with the same <strong>for</strong>mula by using the between-species and withinspecies<br />
coefficients of variation. Besi<strong>de</strong> this univariate approach using<br />
parameters with high PIC or PSC values, multivariate analyses (discriminant<br />
function analysis, DFA, principal component analysis, PCA, and<br />
artificial neural network analysis, ANN) can be per<strong>for</strong>med (figure 4).<br />
All these methods provi<strong>de</strong> classification procedures that assign each<br />
recor<strong>de</strong>d call to its appropriate emitter (correct assignment) or reject the<br />
assignment (see Terry and McGregor, 2002).<br />
Other existing vocalisation classification techniques are based on traditional<br />
automatic speech recognition methods (Rabiner and Juang, 1993).<br />
one conventional method, the dynamic time warping (DTW; An<strong>de</strong>rson<br />
et al., 1996), is well suited <strong>for</strong> the <strong>de</strong>tection of pure tones such as those<br />
in bird, bat and cetacean songs. This method compares the spectrograms<br />
(frequency versus time representation, see figure 3) of input sounds with<br />
those of a training data set of pre<strong>de</strong>fined templates (representative of<br />
sounds to <strong>de</strong>tect and chosen by the investigator) by successive cross-correlations<br />
(Clark et al., 1987). These templates are the targeted database <strong>for</strong><br />
the matching process. In contrast to the <strong>de</strong>terministic template matching<br />
of DTW, another method, the hid<strong>de</strong>n markov mo<strong>de</strong>l (HMM, Rabiner,<br />
1989) uses a statistical representation. Briefly, an HMM is typically a collection<br />
of finite sets of states. Each state represents spectral properties<br />
in the <strong>for</strong>m of Gaussian mixtures of spectral features, while temporal<br />
properties are represented by state transition probabilities. Each state has
Part I – Chapter 4 91<br />
a probability distribution over the possible output cases. There<strong>for</strong>e, the<br />
sequence of cases generated by a HMM provi<strong>de</strong>s in<strong>for</strong>mation about the<br />
sequence of states and are thus especially adapted <strong>for</strong> temporal pattern<br />
recognition of sounds such as sequences of successive notes in songs. This<br />
list of classifiers presented here is not exhaustive and it is possible to find<br />
in the literature a lot of other acoustic i<strong>de</strong>ntification methods such as <strong>for</strong><br />
example the spectral peak tracks method (SPTM) recently suggested by<br />
Chen and Maher (2006).<br />
Figure 4: A principal component analysis taking into account 2 factors (F1 + F2<br />
= 54% of the total variance) and based upon 18 acoustic parameters measured in<br />
the song of a tropical bird, the White-browed Warbler Basileuterus leucoblepharus.<br />
on this basis, the PCA separates 71% of the 21 individuals analysed. Each polygon<br />
corresponds to one individual (from Aubin et al., 2004).<br />
3.2. Limits of i<strong>de</strong>ntification systems<br />
All the methods <strong>de</strong>scribed above are well suited to classify sounds, but<br />
they also have some limitations. The most important requirement <strong>for</strong> the<br />
reliability of the i<strong>de</strong>ntification of vocal signature is that emitters produce<br />
individualised and stable vocalisations. Another necessity is to have a prior<br />
knowledge of the structure of the vocalisations emitted by the individuals
92 Ecophysiology and animal behaviour<br />
who will be then automatically i<strong>de</strong>ntified. For example, as un<strong>de</strong>rlined by<br />
Terry et al. (2005), discriminant function analysis may assign all vocalisations<br />
to particular individuals if all individuals are known; thus this<br />
method cannot accommodate vocalisations from new individuals. All<br />
these methods, qualified as “supervised” methods, use in fact a training<br />
data set or templates that must be “learned” by the classifier. Instead, the<br />
HMM mo<strong>de</strong>l is based on a statistical calculation; it can there<strong>for</strong>e accumulate<br />
more in<strong>for</strong>mation and possibly generalise better than techniques<br />
based on fixed templates. The ANN classifiers often require a very high<br />
computational complexity. The DTW method does not use amplitu<strong>de</strong><br />
normalisation, so the results may be sensitive to amplitu<strong>de</strong> differences<br />
between signals. The DTW and HMM methods do not per<strong>for</strong>m well in<br />
noisy environments or <strong>for</strong> sounds with short duration and variable amplitu<strong>de</strong>.<br />
In a word, all of the proposed methods accommodate well tonal or<br />
harmonic sounds, but are inappropriate when vocalisations containing<br />
aperiodic or noise-like components are involved.<br />
4. Field applications<br />
Studying animals in their natural habitat with a minimised influence on<br />
their behaviour is a key issue in <strong>ecology</strong> and ethology. Locating animals by<br />
the sounds they produce has the first advantage to be passive, meaning that<br />
the effect of the AASS on animal behaviour is insignificant. The second<br />
benefit is that terrestrial AASS can be used in habitats where visual location<br />
is difficult, such as <strong>de</strong>nse vegetation (tropical <strong>for</strong>ests, bushes, reed-beds).<br />
These systems may be also useful <strong>for</strong> studying secretive animals, difficult<br />
to observe because of large home ranges or nocturnal activity (see chapter<br />
IV, 2). They also ensure an accurate investigation of biological questions<br />
such as territorial <strong>de</strong>fence, nest site, and mate fi<strong>de</strong>lity. For example, they<br />
may be used to monitor multiple individuals simultaneously and, there<strong>for</strong>e,<br />
study behaviours such as territorial interactions between neighbours and<br />
duetting (Bower and Clark, 2005; Burt and Vehrencamp, 2005; Mennill<br />
and Vehrencamp, 2008). Thus, these systems seem particularly suitable <strong>for</strong><br />
studying communication networks. For example, we are currently studying<br />
in the Amazonian <strong>for</strong>est the acoustic network of a typical bird of this habitat,<br />
the screaming piha Lipaugus vociferans. This species shows a remarkable<br />
<strong>for</strong>m of lek display: females are attracted by singing assemblies of males and<br />
come <strong>for</strong> mating. Up to 25 males distributed on an area of about 600m of<br />
diameter are usually observed at a single lek. Each male has its own song<br />
posts and it counter-sings the other males. The use of an AASS will enable<br />
us to <strong>de</strong>cipher this complex vocal organisation and particularly, to examine<br />
the singing interactions and movements of all the birds from a lek.
Part I – Chapter 4 93<br />
In a more general way, AASSs appear extremely useful to provi<strong>de</strong>, with very<br />
little human interference, quantitative and qualitative indices of animal<br />
diversity in poorly accessible environments. Starting from this method,<br />
it is possible to <strong>de</strong>velop concrete applications with an acoustic plat<strong>for</strong>m<br />
<strong>de</strong>signed <strong>for</strong> the estimation of biodiversity of the fauna in different categories<br />
of environments. An automatic acoustic survey may help in the evaluation<br />
of the biological quality of a given habitat and appears as a useful tool<br />
<strong>for</strong> measuring the abundance of species or the impact of human activity<br />
on biodiversity (Sueur et al, 2008; see also II, 1). This method should substitute<br />
to the traditional human visual or hear sampling realised on point<br />
counts (i.e. Uezu et al., 2005) to estimate the abundance of vocal species in<br />
remote or obstructed environments, such as rain<strong>for</strong>ests. Thus, we believe<br />
that acoustic location and i<strong>de</strong>ntification technology may provi<strong>de</strong> a valuable<br />
and versatile tool <strong>for</strong> ecologists and ethologists.<br />
5. Conclusion: future orientations, <strong>de</strong>velopments<br />
and needs <strong>for</strong> new sensors?<br />
AASSs offer one of the best solutions <strong>for</strong> a non-invasive sensor that will<br />
enable biologists both to locate and i<strong>de</strong>ntify individuals of a large number<br />
of “singing” species within a population in cheap, fast and automatic manner<br />
and in a wi<strong>de</strong> range of environments. The emergence of this method<br />
is sped up by recent technological advances. Thus, commercially available<br />
autonomous digital recor<strong>de</strong>rs are now able to collect thousands of hours of<br />
audio data. The automatic i<strong>de</strong>ntification of vocalisations is not always perfectly<br />
accurate, but the <strong>de</strong>velopment of new algorithms methods in automatic<br />
human speech recognition has recently improved the process. Source<br />
localisation algorithms <strong>for</strong> monitoring vocal wildlife populations are now<br />
efficient, the limitations being mainly due either to imprecise microphone<br />
coordinates or the presence of a particular constellation of competing sound<br />
sources in the field. In the first case, the <strong>de</strong>velopment of more accurate GPS<br />
would be an alternative to laser measurements to geolocate more precisely<br />
microphones. With such systems, it would also be possible to increase the<br />
distance between the microphone and thus monitor a wi<strong>de</strong>r area. In the<br />
second case, the noise generated by parasitic sounds overlapping the target<br />
sound, should be more or less removed by using specific artificial neural<br />
network analysis. Despite significant progress in source localisation theory<br />
and sensor network systems, progress toward <strong>de</strong>veloping prototype AASSs<br />
has been greatly slowed down by the absence of integrated plat<strong>for</strong>ms suitable<br />
<strong>for</strong> monitoring wild animals. An emphasis must be now placed on<br />
fully integrated systems (hardware and software) that are robust enough to<br />
be <strong>de</strong>ployed in all kind of environments, and user-friendly enough to be<br />
used by biologists with little or no technical expertise.
94 Ecophysiology and animal behaviour<br />
Authors’ reference:<br />
Chloé Huetz, Thierry Aubin:<br />
Université Paris-Sud, Centre <strong>de</strong> Neurosciences Paris-Sud, UPS-CNRS<br />
UMR 8195, Orsay, France<br />
Corresponding author: Thierry Aubin, thierry.aubin@u-psud.fr<br />
Aknowledgement<br />
We thanks F. Sèbe, H. Courvoisier and M.L. da Silva <strong>for</strong> technical support<br />
and help in the field. The work in the Amazonian <strong>for</strong>est (Brazil) was<br />
financially supported by a FP7-people-IEF grant.<br />
References<br />
An<strong>de</strong>rson S. E., Dave A. S., Margoliash D., 1996. Template-based automatic<br />
recognition of birdsong syllables from continuous recordings. Journal of<br />
the Acoustical Society of America, 100, pp. 1209-1219.<br />
Aubin T., Jouventin P., 2002. How to i<strong>de</strong>ntify vocally a kin in a crowd? The<br />
penguin mo<strong>de</strong>l. Advances in the Study of Behaviour, 31, pp. 243-277.<br />
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II<br />
BIODIVERSITY
Chapter 1<br />
Global estimation of animal diversity<br />
using automatic acoustic sensors<br />
Jérôme Sueur, Amandine Gasc, Philippe Grandcolas, Sandrine Pavoine<br />
1. Introduction<br />
The estimation of biodiversity can be consi<strong>de</strong>red as one of the main challenges<br />
in mo<strong>de</strong>rn biology. When <strong>de</strong>aling with <strong>ecology</strong>, evolutionary biology<br />
and conservation biology, there is an inescapable need to <strong>de</strong>scribe<br />
the composition and dynamics of biological diversity (Magurran, 2004).<br />
In <strong>ecology</strong>, the concept of biological diversity is mainly species-oriented,<br />
even if other evolutionary units or traits can also be used. In this context,<br />
biodiversity potentially refers to all species encountered in a given area<br />
at a specified time, including every potential species from un<strong>de</strong>rground<br />
bacteria to giant trees. There<strong>for</strong>e, biodiversity assessment can turn out to<br />
be a time-consuming and complex task, as it relies on species inventory<br />
that may involve very different taxonomic groups.<br />
Exhaustive approaches such as the all taxa biodiversity inventory (ATBI)<br />
programs aim at inventorying the whole biodiversity mainly in tropical<br />
habitats (Gewin, 2002), but these programs are highly sensitive to the<br />
logistic and time-constraints of most inventory studies. An alternative to<br />
these approaches is to focus on one or a few taxa and consi<strong>de</strong>r them as<br />
biodiversity indicators (Pearson, 1994), but the choice of representative<br />
taxa is not trivial (Lawton et al., 1998). In addition, it is well known that<br />
patterns of species diversity <strong>for</strong> different taxa are sensitive to the observation<br />
scale. More precisely, there is a general congruence <strong>for</strong> species diversity<br />
between different taxa at a large area scale (more than 1km 2 ) but not<br />
at a fine scale (less than 1km 2 , Weaver, 1995). This ren<strong>de</strong>rs difficult the<br />
<strong>de</strong>finition of an indicator taxon or even of several indicator taxa supposedly<br />
representative of the diversity in other <strong>for</strong>ms of organisms (Ricketts
100 Biodiversity<br />
et al., 1999). Irrespective of the taxonomic breadth of any biodiversity<br />
assessment, the estimation of species biodiversity relies on inventories and<br />
species examination by one or several taxonomic experts that can be supported<br />
with genetic barcoding techniques (see chapter II, 3). Sampling<br />
in the field and i<strong>de</strong>ntification in museum collections can require a consi<strong>de</strong>rable<br />
ef<strong>for</strong>t when the objective is to sample a large region <strong>for</strong> a long<br />
time period. To improve the rate of specimen collection, non-specialist<br />
taxonomic workers – or para-taxonomists – can separate morpho-species<br />
instead of i<strong>de</strong>ntifying valid species. This solution is advocated by the rapid<br />
biodiversity assessment (RBA) programs that have been especially <strong>de</strong>veloped<br />
<strong>for</strong> the rapid exploration of biodiversity in tropical habitats (Oliver<br />
and Beattie, 1993; 1996; Oliver et al., 2000).<br />
Biodiversity assessment is often restricted to species richness, i.e. to the<br />
counting of the total number of species. However, a collection of species<br />
cannot be <strong>de</strong>scribed solely by the number of items it inclu<strong>de</strong>s. The<br />
abundance of each species has to be assessed to provi<strong>de</strong> an estimation of<br />
species evenness. Evolutionary, ecological and life history characters of<br />
the species also <strong>de</strong>scribe facets of biodiversity (Brooks and McLennan,<br />
1991; Vane-Wright et al., 1991; Grandcolas, 1998; Pavoine et al., 2009;<br />
Petchey et al., 2009). Species turnover along time and/or spatial scales<br />
is also required to take into account biodiversity dynamics. All of these<br />
requirements led to a plethora of biodiversity indices that have been <strong>de</strong>veloped<br />
<strong>for</strong> <strong>de</strong>ca<strong>de</strong>s (Magurran, 2004; Buckland et al., 2005; Pavoine and<br />
Bonsall, 2010).<br />
In practice, a measure of biodiversity can be achieved with direct or indirect<br />
sampling. In the latter case, the use of a sensor should be i<strong>de</strong>ally consi<strong>de</strong>red<br />
by employing a simple tool that returns an in<strong>de</strong>x of biodiversity. A<br />
human observer or a network of human observers might be consi<strong>de</strong>red as<br />
a “biodiversity sensor”, but may be biased by the experience of the observers<br />
and cannot be “<strong>de</strong>ployed” in rough terrains <strong>for</strong> long periods of time.<br />
Another possibility is to work with local image, vi<strong>de</strong>o capture instruments<br />
or with global satellite imagery. Satellite-based earth observations,<br />
or remote sensing, can produce environmental parameters from biophysical<br />
characteristics that can be indirectly used to assess species ranges and<br />
species richness patterns (Kerr et al., 2003, Turner et al., 2003; Wang et<br />
al., 2010; see also IV, 2). These methods are undoubtedly very attractive,<br />
but they rely on extremely expensive equipment and are difficult to adapt<br />
to small spatial scales. Like other methods, remote sensing often requires<br />
a time-consuming validation step. For instance, vegetation mapping with<br />
satellite images is based on a colorimetric calibration of pixels with a large<br />
set of direct vegetation samples.<br />
The use of a given sensor should be ma<strong>de</strong> according to a sampling strategy<br />
<strong>de</strong>signed and evaluated carefully with respect to the type of data to
Part II – Chapter 1 101<br />
be collected. It is particularly important to i<strong>de</strong>ntify the precision of the<br />
measurement system (data quality) and the level of accuracy that has to<br />
be reached along both time and space scales (data quantity). In most sampling<br />
strategies, there is a basic tra<strong>de</strong>-off between precision and accuracy.<br />
In this context, we are currently <strong>de</strong>veloping an acoustic sensor that would<br />
produce a biodiversity in<strong>de</strong>x by analysing the sound produced by local<br />
animal communities. This approach could provi<strong>de</strong> a portable, cheap, reasonably<br />
accurate and non-invasive animal diversity sensor that could be<br />
used at different space and time scales.<br />
2. Sensing diversity through bioacoustics<br />
Some animal species, including taxa often used in biodiversity studies,<br />
produce active sounds during their social interactions or in other contexts.<br />
For example, some fish and reptiles, most amphibians, birds, mammals,<br />
insects and other arthropods use sound <strong>for</strong> communication, navigation or<br />
predation acts. These acoustic signals generally produce a species-specific<br />
signature and several techniques in bioacoustics were <strong>de</strong>veloped to exploit<br />
these signals as an indication of species occurrence and as a tool <strong>for</strong> biodiversity<br />
studies (Obrist et al., 2010). The most elementary application is<br />
sensing by observers. This is usually achieved when following animal populations<br />
through aural listening and i<strong>de</strong>ntification (e.g., Cano-Santana<br />
et al., 2008, <strong>for</strong> crickets). When based on a massive network of listeners,<br />
such a census method can generate large datasets of strong interest to<br />
ecologists (e.g. Devictor et al., 2008, <strong>for</strong> birds). Nonetheless, volunteerbased<br />
call surveys tend to be replaced by the automated digital recording<br />
system (ADRS), which is an electronic equipment that allows automatic<br />
data collection and generates a large amount of high-quality in<strong>for</strong>mation<br />
about species biodiversity (e.g. Acevedo and Villanueva-Rivera, 2006, <strong>for</strong><br />
amphibians).<br />
The problems allu<strong>de</strong>d above <strong>for</strong> species i<strong>de</strong>ntification with museum<br />
specimens is also true <strong>for</strong> species i<strong>de</strong>ntification with sound. Acoustic<br />
i<strong>de</strong>ntification is based on the experience of the observers, which can be<br />
biased due to sensory or training differences. As any other i<strong>de</strong>ntification,<br />
it also relies on a taxonomic database providing in<strong>for</strong>mation on the correspon<strong>de</strong>nce<br />
between every species and its acoustic signature. Automatic<br />
i<strong>de</strong>ntification of the different songs embed<strong>de</strong>d in the recording is rather<br />
complex and still suffers errors (e.g. Skowronski and Harris, 2006, <strong>for</strong><br />
bats). These approaches are also difficult to <strong>de</strong>ploy in complex acoustic<br />
environments like tropical <strong>for</strong>est soundscapes, where tens of signals mix<br />
up and many species still remain unknown (Rie<strong>de</strong>, 1993). Reliable results<br />
can be obtained only when focusing on a single species with a rather
102 Biodiversity<br />
simple and loud call as <strong>de</strong>monstrated with the neotropical bird Lipaugus<br />
vociferans (see I, 4) and the Blue Whale Balaenoptera musculus in a marine<br />
context (see I, 3).<br />
Keeping in mind these constraints, we applied the concept of RBA to<br />
sounds produced by animals and even pushed the concept one step further.<br />
We recently suggested tackling the problem of diversity assessment at<br />
the community level by using bioacoustic methods (Sueur et al., 2008a).<br />
In the case of bioacoustics, the unit to work with is the acoustic community,<br />
which is <strong>de</strong>fined as the sum of all sounds produced by animals<br />
at given location and time. The signals produced by different species can<br />
overlap, interfere and consequently reduce signal transmission between<br />
the emitter and the receiver of a focal species. Sound produced by other<br />
species is in<strong>de</strong>ed consi<strong>de</strong>red as noise <strong>for</strong> the focal species and acts as a<br />
severe constraint on the evolution of conspecific signals (Brumm and<br />
Slabbekoorn, 2005). Consequently, species sharing the same acoustic<br />
space are supposed to show an over-dispersion of the frequency and timeamplitu<strong>de</strong><br />
parameters of their songs reducing the risk of interference. This<br />
has been reported in several acoustic communities (e.g., Lüd<strong>de</strong>cke et al.,<br />
2000, <strong>for</strong> amphibians; Sueur, 2002, <strong>for</strong> cicadas; Luther, 2009, <strong>for</strong> birds).<br />
A measure of sound complexity could then work as a proxy of community<br />
richness and composition. The acoustic indices we are <strong>de</strong>veloping<br />
are mainly based on this concept of acoustic partitioning. We hereafter<br />
review the recording equipment and analysis we used to try and build an<br />
animal diversity acoustic sensor.<br />
3. Listening and measuring acoustic diversity<br />
A biodiversity sensor provi<strong>de</strong>s a measure of a single or a set of variables<br />
characterising biodiversity. Even if a sensor is composed of several probes<br />
and data analysers, it is often viewed as a all-in-one equipment that senses<br />
and analyses the environment concomitantly. Our method currently<br />
relies on two different equipments that are not used at the same time.<br />
However, we here consi<strong>de</strong>r that these sub-units constitute together a single<br />
sensor (figure 1). The first sub-unit is a digital sound-recor<strong>de</strong>r that can be<br />
settled outdoor. The second sub-unit is a computer installed with software<br />
specifically <strong>de</strong>veloped to analyse sound diversity. Further statistical<br />
analyses on the acoustic indices, i.e. the biodiversity variables measured<br />
by the sensor, are not consi<strong>de</strong>red as part of the sensor but as part of data<br />
analysis processes. We hereafter <strong>de</strong>tail the sampling protocols based on<br />
a single recor<strong>de</strong>r or an array of recor<strong>de</strong>rs, the properties of the autonomous<br />
recor<strong>de</strong>r currently in use and, eventually, the algorithm <strong>de</strong>veloped<br />
to compute the diversity indices from sound files.
Part II – Chapter 1 103<br />
Figure 1: Diagram showing the successive steps of the global estimation of animal<br />
diversity. Here, the biodiversity sensor is consi<strong>de</strong>red as the combination of different<br />
processes: recording, audio file conservation, signal analysis, and indices<br />
computation. These processes – in situ recording step with autonomous equipment<br />
and ex situ calculation of indices from stored acoustic data – are currently separated<br />
from each other. However, a portable all-in-one system might be <strong>de</strong>veloped in the<br />
future.<br />
3.1. From a single manual recording spot to a network of autonomous<br />
recor<strong>de</strong>rs<br />
The sampling protocol is mainly constrained by the recording equipment<br />
available. Our method was first tested with a comparison between two<br />
closely spaced dry lowland coastal <strong>for</strong>ests in Tanzania. The recording<br />
of the animal communities inhabiting these <strong>for</strong>ests was achieved with a<br />
digital recor<strong>de</strong>r (Edirol© R09) equipped with an omnidirectional microphone<br />
(Sennheiser© K6/ME62). Recordings were done by a single person<br />
at three times of the day and successively in the two <strong>for</strong>ests. This procedure<br />
limited the sampling to a few days and to only two sampling sites.<br />
Such digital recor<strong>de</strong>rs also provi<strong>de</strong> internal microphones that can be used<br />
to reduce costs. In this case, several items can be purchased to cover a<br />
wi<strong>de</strong>r area and a longer period of time. However, the recor<strong>de</strong>rs still have<br />
to be triggered and stopped manually, a condition that makes field work<br />
rather challenging.<br />
Unatten<strong>de</strong>d recor<strong>de</strong>rs were not available since the North American company<br />
Wildlife Acoustics© provi<strong>de</strong>d an autonomous digital field recor<strong>de</strong>r<br />
(see <strong>de</strong>tails about this recording package in section 3.2). An autonomous<br />
system was absolutely necessary to <strong>de</strong>sign sampling protocols with synchronised<br />
units such as regular, cluster, multi-level, or stratified protocols.<br />
We first used three of these recor<strong>de</strong>rs to assess animal diversity within temperate<br />
woodlands by simultaneously recording a mature <strong>for</strong>est, a young<br />
<strong>for</strong>est and an edge <strong>for</strong>est (figure 2A, Depraetere et al., 2012). We then<br />
increased the number of recor<strong>de</strong>rs to estimate biodiversity en<strong>de</strong>mism of<br />
three New-Caledonian sites. We planned a stratified sampling with four<br />
recor<strong>de</strong>rs set in each site. This ensured a repetition per site and allowed<br />
comparisons within and between study sites (figure 2B). Later, we tracked<br />
acoustic diversity of a typical tropical <strong>for</strong>est by <strong>de</strong>ploying a network of 12
104 Biodiversity<br />
recor<strong>de</strong>rs regularly spaced on a 100 ×100m grid in French Guiana (see IV,<br />
2). Each recor<strong>de</strong>r was equipped with a microphone settled 1.5m high and<br />
a second microphone placed 20m high in the canopy (figure 2C). This 3D<br />
regular sampling covered 12ha of <strong>for</strong>est <strong>for</strong> more than 40 days. Eventually,<br />
we tried to transfer our method to freshwater habitats like <strong>for</strong>est ponds.<br />
This was achieved by adapting the autonomous recor<strong>de</strong>r with a Reson©<br />
hydrophone and an Avisoft© pre-amplifier (figure 2D). This high-quality<br />
equipment is expensive (about 2700€ per unit) and sampling was there<strong>for</strong>e<br />
limited to three recording units. We there<strong>for</strong>e <strong>de</strong>signed a rotating<br />
sampling by regularly moving the hydrophone position along transects.<br />
Figure 2: The autonomous Wildlife Acoustics© recor<strong>de</strong>r installed <strong>for</strong> outdoor<br />
studies. A. First version of the recor<strong>de</strong>r (SM1) settled in a temperate woodland<br />
to estimate local bird acoustic community (Rambouillet, France). B. Second<br />
version of the recor<strong>de</strong>r (SM2) with a single microphone in action (Mandjelia, New-<br />
Caledonia). C. The same recor<strong>de</strong>r with two microphones, one 2 m high and the<br />
other one ready to be set 20 m up in the canopy (Nouragues experimental station,<br />
French Guiana). D. Recor<strong>de</strong>r connected to an hydrophone to record un<strong>de</strong>rwater<br />
sound of a pond (Rambouillet, France).<br />
3.2. The Song Meter: an autonomous acoustic sensor<br />
Wildlife Acoustics© <strong>de</strong>veloped two generations of autonomous digital<br />
recor<strong>de</strong>rs, namely the Song Meter SM1 and SM2 (figure 3). These stereo<br />
recor<strong>de</strong>rs, which weigh 1.6kg each and measure 20.3 × 20.3 × 6.4cm,
Part II – Chapter 1 105<br />
possess a stereo recording system with omnidirectional microphones<br />
that have a flat frequency response between 0.02 and 20kHz. These<br />
microphones can be directly connected to the main box, where data are<br />
stored, or can be settled up to 50m away from it. Given that terrestrial<br />
animals produce sound with an intensity of ca. 80dB at 1m re. 2×10 -5<br />
μPa (Sueur, unpublished data) and given that the microphones have a<br />
sensitivity of -36 ± 4dB, we can estimate that in a closed habitat, such<br />
as a <strong>for</strong>est, the microphone <strong>de</strong>tects sounds up to around 100m from the<br />
source. A SM2 plat<strong>for</strong>m would then cover an area of approximatively<br />
3,1ha.<br />
The recording sampling rate can be set from 4 to 48kHz with the standard<br />
SM2 and up to 384kHz with the ultrasonic SM2 option. The SM2<br />
recor<strong>de</strong>rs are currently working with a lossless compression <strong>for</strong>mat (.wac)<br />
that can be written on four secure digital (SD) cards. The four SD slots<br />
provi<strong>de</strong> 128Go storage space. Choosing an a<strong>de</strong>quate sampling rate is not<br />
an easy task as it results from a tra<strong>de</strong>-off between cost, data storage and<br />
the sound frequency used by animals. Increasing the sampling rate to high<br />
frequency requires a specific and expensive motherboard and, above all,<br />
generates very large sound files that are difficult to handle and to analyse.<br />
However, this is the only solution to record the acoustic activity of<br />
some insects and bats that emit ultrasound signals <strong>for</strong> communication or<br />
navigation. Up to now, we sampled the animal acoustic communities at<br />
a 44.1kHz sampling rate. A network of recor<strong>de</strong>rs generates thousands of<br />
files that need to be stored and analysed (see section 4.2). Using a higher<br />
sampling rate will certainly preclu<strong>de</strong> the estimation of acoustic diversity<br />
by generating too high an amount of data.<br />
Electrical power is provi<strong>de</strong>d by four alkaline or LR20 batteries ensuring<br />
a maximum of 240 hours of recordings. Energy can also come from an<br />
external 12V battery potentially connected to a solar panel. Eventually,<br />
the SM2 plat<strong>for</strong>m provi<strong>de</strong>s also an internal temperature sensor and a<br />
connection <strong>for</strong> an external sensor. The additional data are written on<br />
the SD cards together with sound files. The main advantage of the<br />
Song Meter is that it can be easily programmed to record on simple<br />
time-of-day schedules or to implement complex monitoring protocols,<br />
even scheduling recordings relative to local sunrise, sunset and twilight.<br />
For instance, a schedule can be programmed to record regularly all day<br />
and night long, but also to record more intensively around sunrise and<br />
sunset, when dawn and dusk choruses of birds, insects and amphibians<br />
occur.
106 Biodiversity<br />
Figure 3: The second version of the recor<strong>de</strong>r (SM2) opened to show the main<br />
characteristics. A cable can be used to set the microphones away from the main<br />
box. Detailed characteristics can be obtained at http://www.wildlifeacoustics.com.<br />
© Wildlife Acoustics.<br />
3.3. Computing the acoustic indices<br />
Biodiversity is traditionally <strong>de</strong>composed into two levels, the average<br />
diversity within communities, or α diversity, and the diversity between<br />
communities, or β diversity. We there<strong>for</strong>e <strong>de</strong>veloped two acoustic indices<br />
aiming at estimating these two components of biodiversity (Sueur et al.,<br />
2008a). Both indices can be computed with the package seewave (Sueur et<br />
al., 2008b) of the free R environment (R Development Core Team, 2012).<br />
The first in<strong>de</strong>x, named H, is a Shannon-like in<strong>de</strong>x. The in<strong>de</strong>x H gives a<br />
measure of the entropy of the acoustic community by consi<strong>de</strong>ring both<br />
temporal and frequency entropy. H is computed according to:<br />
H = Ht × Hf with 0 ≤ H ≤ 1, and<br />
Ht = - ∑ (A(t) × log(A(t)) / log (n)), and<br />
Hf = - ∑ (S( f ) × log(S( f )) / log (N)),<br />
where n = length of the signal in number of digitized points, A(t) = probability<br />
mass function of the amplitu<strong>de</strong> envelope, S( f ) = probability mass<br />
function of the mean spectrum calculated using a short term Fourier<br />
trans<strong>for</strong>m (STFT) along the signal with a non-overlapping Hamming window<br />
of N = 512 points (figure 4).
Part II – Chapter 1 107<br />
Figure 4: The main two trans<strong>for</strong>ms used on raw recordings. A. Wave<strong>for</strong>m or<br />
oscillogram of a sound recording. b. Amplitu<strong>de</strong> envelope, A(t), obtained through<br />
the Hilbert trans<strong>for</strong>m. C. Mean spectrum, S( f ), obtained through a Fourier<br />
trans<strong>for</strong>m. Note the different x axes and that all y axes are in relative amplitu<strong>de</strong><br />
along a linear scale.<br />
The H in<strong>de</strong>x increases logarithmically from 0 to 1 with species richness<br />
and evenness when consi<strong>de</strong>ring species-specific calls (Sueur et al., 2008a).<br />
The in<strong>de</strong>x will be particularly high <strong>for</strong> a signal that has a flat amplitu<strong>de</strong><br />
envelope and a flat frequency spectrum. When only consi<strong>de</strong>ring the spectral<br />
component of the in<strong>de</strong>x, a flat or multi-peak spectrum will give a<br />
higher Hf in<strong>de</strong>x than a single peak spectrum (figure 5 A, b). The H in<strong>de</strong>x<br />
was applied in Tanzania, and correctly revealed a higher acoustic diversity<br />
in the preserved part of the <strong>for</strong>est than in the disturbed part (Sueur et al.,<br />
2008a). However, Hf is not reliable when <strong>de</strong>aling with recordings ma<strong>de</strong> in<br />
the temperate woodland, where the acoustic activity is low and polluted
108 Biodiversity<br />
with environmental noise. In this particular case, we <strong>de</strong>veloped another<br />
in<strong>de</strong>x, named Acoustic Richness AR which was computed according to:<br />
AR = rank(Ht) × rank(M) × n -2 , with 0 ≤ AR ≤ 1,<br />
where rank is the value position along the or<strong>de</strong>red samples, M is the median<br />
of the amplitu<strong>de</strong> envelope and n the number of recordings (Depraetere et<br />
al., 2012).<br />
The second in<strong>de</strong>x, named D, is a simple acoustic dissimilarity measure.<br />
D is similarly composed of two sub-indices based on a difference between<br />
amplitu<strong>de</strong> envelopes and frequency spectra respectively (figure 5 C). D is<br />
calculated like following:<br />
D = Dt × Df with 0 ≤ D ≤ 1, and<br />
Dt = 0.5 × ∑ |A 1<br />
(t) – A 2<br />
(t)|, and<br />
Df = 0.5 × ∑ |S 1<br />
( f ) – S 2<br />
( f )|,<br />
where A 1<br />
(t), A 2<br />
(t) are probability mass functions of the amplitu<strong>de</strong> envelope<br />
<strong>for</strong> the two recordings un<strong>de</strong>r comparison, and S 1<br />
( f ), S 2<br />
( f ) are probability<br />
mass functions of the mean spectrum <strong>for</strong> the two recordings to be<br />
compared. The D in<strong>de</strong>x increases linearly with the number of unshared<br />
species between the two recordings, or communities (Sueur et al., 2008a).<br />
Both indices may suffer a bias as some species naturally produce signals<br />
with high temporal and/or spectral entropy. This is particularly the case<br />
of cicadas whose noise-like sound can be mistakenly interpreted as a high<br />
local diversity. Such bias can be buffered with a large sampling including<br />
a high number of time and space repetitions. The indices can also produce<br />
false values when background noise overlaps with the sound produced by<br />
the animal community (see section 4.1). Both indices are currently tested<br />
in different temperate and tropical habitats in this respect.<br />
Other acoustic indices have been <strong>de</strong>veloped elsewhere to monitor habitat<br />
state or community activity. Qi et al. (2008) divi<strong>de</strong>d the soundscape of an<br />
ecosystem following three frequency bands: the anthrophony, between<br />
0.2 and 1.5kHz, the biophony, which starts at 2kHz with a peak at 8kHz,<br />
and the geophony, which can cover the entire spectrum with dominant<br />
low frequency. By computing a ratio between biological and anthropogenic<br />
signals, they coined an ecological estimator of ecosystem health.<br />
This original procedure does not give an estimation of local diversity but<br />
assesses the level of biological sound activity relative to anthropogenic<br />
activities. Pierreti et al. (2010) and Farina et al. (2011) <strong>de</strong>signed an acoustic<br />
complexity in<strong>de</strong>x (ACI). This in<strong>de</strong>x computes time and frequency variability<br />
of a sound extrapolated from a spectrogram. The ACI appears to<br />
be correlated with the number of vocalisations produced by a bird community.<br />
However, this in<strong>de</strong>x assesses neither species diversity nor community<br />
turnover. The ACI in<strong>de</strong>x proved to be poorly sensitive to invariant<br />
noise, such as continuous noise from cars or aircrafts, but can be impacted
Part II – Chapter 1 109<br />
by unpredictable noise such as wind, running water or irregular human<br />
activity. All these acoustic indices, including H, D, and others in current<br />
<strong>de</strong>velopment probably do not quantify the same facet of animal acoustic<br />
diversity.<br />
Figure 5: Illustration of a spectral analysis on recordings ma<strong>de</strong> in two sites in New-<br />
Caledonia (France). A. A recording showing a broadband frequency spectrum<br />
with a high H f<br />
in<strong>de</strong>x and a high number of peaks. b. A recording with a single<br />
dominant frequency peak generating a lower H f<br />
in<strong>de</strong>x and less frequency peaks. C.<br />
The difference between the two spectra used to compute the D f<br />
in<strong>de</strong>x.
110 Biodiversity<br />
4. Sensitivity to noise level, sensor size and autonomy<br />
4.1. Everything but noise<br />
Background noise is probably the primary issue in bioacoustics. Noise can<br />
significantly impairs acoustic observations and experiments by masking<br />
or distorting both time-amplitu<strong>de</strong> and frequency parameters (Hartmann,<br />
1998; Vaseghi, 2000). There are three main sources of noise to consi<strong>de</strong>r<br />
when recording outdoor: i) anthropogenic noise due to machinery, car,<br />
boat, plane, train traffic, or any other human activity, ii) biotic noise due<br />
to the activity of surrounding species, and iii) environmental noise due to<br />
rain, wind, river stream, waterfall, or sea wave (Brumm and Slabbekoorn,<br />
2005; Laiolo, 2010). The estimation of animal diversity through acoustics<br />
is based on the recording of a whole community and as such does not face<br />
the classical problems encountered when trying to record a single species in<br />
the background noise generated by surrounding active species. However,<br />
anthropogenic and environmental noise can have negative effects on the<br />
results. In a few instances, anthropogenic noise can be removed by applying<br />
classical frequency filters (Stoddard, 1998). Recordings ma<strong>de</strong> close to<br />
an airport or a road with a regular traffic can be cleaned with a high-pass<br />
filter that will remove the low frequency band generated by plane or car<br />
engines. Such filters might exclu<strong>de</strong> low pitch animal calling songs, but<br />
this can be accounted <strong>for</strong> when computing diversity indices. The main<br />
difficulty arises when recordings are polluted with unpredictable and/or<br />
broadband noise that can be interpreted erroneously as animal sounds.<br />
Removing such chaotic sound is a challenge to be solved in bioacoustics<br />
as well as in other acoustic disciplines (Rumsey and McCormick, 1992;<br />
Hartmann, 1998; Stoddard, 1998; Vaseghi, 2000). Usual frequency filters<br />
cannot be used as noise may overlap the frequency band used by the animal<br />
community. Other noise reduction algorithms use noise spectrum as<br />
a reference to be convoluted with the original signal. This solution might<br />
appear elegant but still suffers important limitations. First, the noise has<br />
to be constant in its frequency content, a condition rarely met in a natural<br />
acoustic environment. Second, it is necessary to i<strong>de</strong>ntify accurately a time<br />
window where only noise occurs. This latter condition is very difficult to<br />
meet when faced with hundreds or thousands of recordings.<br />
Fortunately, some upstream solutions can be consi<strong>de</strong>red to reduce the<br />
anthropogenic and environmental noise (Obrist et al., 2010). When using<br />
an outdoor acoustic sensor, the most important parameter to consi<strong>de</strong>r<br />
is the direction and the protection of the microphone. The microphone<br />
can be oriented in a horizontal or vertical position as soon as its directivity<br />
pattern is omnidirectional. A vertical upward position should be<br />
avoi<strong>de</strong>d when possible, as rain drops might directly strike the microphone
Part II – Chapter 1 111<br />
membrane. A vertical upsi<strong>de</strong>-down orientation might be the best solution<br />
in avoiding rain and lateral wind effects. More generally, adapting<br />
the orientation of the microphone to the local main sources of noises is<br />
usually advocated. For instance, the noise of running water or passing-by<br />
cars can be reduced by orienting the microphone perpendicularly to the<br />
source, and windscreens should be used to attenuate wind noise. Another<br />
upstream solution is to exclu<strong>de</strong> data potentially corrupted with environmental<br />
noise. This can be achieved in three ways. The first option consists<br />
in cutting off the recording session when weather conditions are too bad.<br />
It is not yet available but could certainly be implemented quickly, given<br />
the availability of climate sensors in sound meter <strong>de</strong>vices. The second<br />
option is to apply a signal-to-noise algorithm that indicates the occurrence<br />
of an important background noise. A threshold could be used as a reference<br />
to keep or to remove the files from the dataset. This solution is un<strong>de</strong>r<br />
<strong>de</strong>velopment in our group. The third and last option, which is currently in<br />
use, is to gather climatic parameters from a local station and i<strong>de</strong>ntify the<br />
time periods when the weather was too bad to allow a correct estimation<br />
of the acoustic diversity. This i<strong>de</strong>ntification can be achieved automatically<br />
with a threshold applied on the climatic parameters or by running<br />
a redundancy analysis (RDA, Rao, 1964) to the acoustic indices with the<br />
climatic parameters as factors (Depraetere et al., 2012).<br />
4.2. Optimal size of recor<strong>de</strong>rs<br />
As <strong>de</strong>scribed above, the SM2 recor<strong>de</strong>r weighs around 1.6kg and can<br />
be fitted with two microphones (figure 3). Hence, handling several of<br />
these units in a hard-to-reach environment requires a significant ef<strong>for</strong>t.<br />
A reduced size and weight would make field work easier and could also<br />
allow settling more units in the habitat. However, this has to be tra<strong>de</strong>d<br />
off against the size of the data that needs to be stored and analysed. A<br />
typical .wav file, which is the most popular uncompressed audio <strong>for</strong>mat,<br />
has a size of around 690kb/s (= 84ko/s) when sampled at a 44.01kHz rate.<br />
This means that one minute of recording is roughly equivalent to 5Mo<br />
<strong>for</strong> a single channel (mono) or around 10Mo <strong>for</strong> two channels (stereo).<br />
Sampling quickly generates x×10 2 hours of recording in x×10 3 files <strong>for</strong> a<br />
total x×10 2 Go data. As <strong>de</strong>tailed above, the recor<strong>de</strong>rs have storage capacity<br />
of 128Go, which is enough <strong>for</strong> most applications sampled at 44.01kHz,<br />
but might appear limited <strong>for</strong> an over-month or over-year survey or <strong>for</strong> a<br />
long ultrasound monitoring. The next step of data transfer onto a hard<br />
disk <strong>for</strong> storage and conservation can take a significant time as writing<br />
speed is usually slow (around 6Mb/s = 0.7Mo/s). Eventually, the longterm<br />
storage of teraoctets of data can encounter some limits with a standard<br />
hard disk or server capacity.
112 Biodiversity<br />
Regarding the calculation of indices, the larger the file, the slower the<br />
analysis process. Even if automated with R scripts, the analysis of thousands<br />
sound files is time-consuming. This is due to three main factors:<br />
i) the number of files to be analysed, ii) the size of each file, and iii) the<br />
time taken by R to work with large files. There is no easy way yet to counteract<br />
these three caveats. The number of files will increase as samples will<br />
be larger. The size of each file cannot be reduced. Compressed <strong>for</strong>mats<br />
in particular, such as .mp3, cannot be used <strong>for</strong> obvious reasons of signal<br />
quality. The plat<strong>for</strong>m R is very convenient as it is free and open-source. It<br />
makes it a perfect tool <strong>for</strong> sharing our research and transferring our techniques<br />
to other laboratories. However, it may be relevant to look <strong>for</strong> other<br />
software solutions (see section 5.2).<br />
4.3. Energy<br />
The SM2 recor<strong>de</strong>r was <strong>de</strong>veloped to consume as less energy as possible,<br />
but current batteries ensure 240 hours of recording and there<strong>for</strong>e put a<br />
strong limit on the duration of sampling. A solution is to connect the<br />
recor<strong>de</strong>r with a 12V battery fuelled with solar energy. However, if such<br />
autonomous energy system properly works in sunny areas, it is not adapted<br />
to cloudy or sha<strong>de</strong>d areas like the un<strong>de</strong>rstory of a tropical <strong>for</strong>est where a<br />
very low percentage of solar radiation reaches the ground.<br />
5. What’s next?<br />
5.1. Sampling<br />
Our method needs to be tested, validated and eventually applied in several<br />
acoustic conditions from different habitats. So far, we have tested it<br />
with both simulated and field acoustic communities (Sueur et al., 2008a;<br />
Depraetere et al., 2012). Tests on field communities concerned African<br />
tropical coast <strong>for</strong>est and temperate <strong>for</strong>est habitats. The latter test implied<br />
a modification of the indices to take into account the background noise<br />
and the low activity of the acoustic community. We are currently sampling<br />
several other places including mountain tropical <strong>for</strong>ests in New<br />
Caledonia, neotropical evergreen <strong>for</strong>est in French Guiana, and evergreen<br />
monsoon <strong>for</strong>est in India. We are also transferring the technique to freshwater<br />
habitats by using hydrophones immerged in ponds.<br />
One of the aims of our method is to provi<strong>de</strong> a long-term and large-scale<br />
sampling. We are currently sampling species diversity with a network of<br />
10-16 sensors working about 40 days long over approximately 16 ha of<br />
tropical <strong>for</strong>est in French Guiana and India. This time period is too short
Part II – Chapter 1 113<br />
to track seasonal variations of species diversity. We would like to extend it<br />
to at least one year or even longer periods. Moreover, we plan to increase<br />
the number of sensors to monitor a larger area. Increasing the sampling<br />
time and network size will generate serious storage issues. A cut-off system<br />
that stops recordings when the meteorological conditions are not good<br />
enough could constitute a nice and cheap solution to overcome this difficulty.<br />
Another way could consist in sending directly the data from the<br />
recor<strong>de</strong>r to a server through a satellite connection, as wireless connection<br />
to a base radio may be too slow <strong>for</strong> heavy sound files (see IV, 2 section<br />
2.3). However such technological improvement mainly <strong>de</strong>pends on<br />
the industry and may take some time to emerge.<br />
5.2. Improving the indices<br />
As explained earlier, background noise is a central issue, and our indices,<br />
especially the in<strong>de</strong>x H, are particularly sensitive to noise. It is there<strong>for</strong>e<br />
necessary to <strong>de</strong>velop new indices that are noise-resistant. Current research<br />
is ongoing in our laboratory to <strong>de</strong>velop a new measurement of the richness<br />
based on the frequency peaks of the Fourier spectrum (figure 5).<br />
The spectrum can be smoothed or residual peaks due to noise can be<br />
filtered out so as to improve the measure in case of rain or wind noise.<br />
Amplitu<strong>de</strong> or frequency threshold will be also applied on the envelope<br />
and the frequency spectrum respectively, to try to increase the signal-tonoise<br />
ratio. Whatever the in<strong>de</strong>x in use is, we also need to exactly i<strong>de</strong>ntify<br />
which biodiversity in<strong>for</strong>mation is collected by using the acoustic community<br />
as a proxy of animal diversity. Does the H in<strong>de</strong>x only embed a<br />
richness-evenness value or does it inclu<strong>de</strong> phylogenetic and/or functional<br />
diversity in<strong>for</strong>mation? Eventually, as outlined above, the signal analysis<br />
can be slow due to R process. Software directly written in C language<br />
will be <strong>de</strong>veloped on the next years to significantly speed up the analysis<br />
process.<br />
5.3 Sharing the method with other scientists and citizens<br />
There is an important ethical requirement <strong>for</strong> making available the bioacoustic<br />
sensor and primary biodiversity data <strong>for</strong> later uses in terms of<br />
knowledge, engineering or conservation (e.g. Graham et al., 2004; Suarez<br />
and Tsutsui, 2004). The recording equipment we used so far can be purchased<br />
to the company Wildlife Acoustics©. The H and D in<strong>de</strong>x can be<br />
computed with the free R package seewave. The sensor and integrated bioacoustic<br />
system is there<strong>for</strong>e available to anyone. However, R does not have<br />
a user-friendly interface and we plan there<strong>for</strong>e to share the method soon<br />
through an interactive website. Any user will be able to upload recording
114 Biodiversity<br />
files <strong>for</strong> analysis. The acoustic indices will be returned to the user together<br />
with an optional graphical representation of the sound analysed (e.g.,<br />
wave<strong>for</strong>m, envelope, spectrogram, spectrum). On a long-term scale, the<br />
recor<strong>de</strong>r and the signal analysis would not be separated but associated in<br />
a small and light all-in-one system. This system could be a ‘smartphone’<br />
including a free application that computes the indices. Smartphones were<br />
proved to work as nice sensors <strong>for</strong> mapping the noise level of European cities<br />
(Maisonneuve et al., 2010). A similar citizen-science experience could<br />
be un<strong>de</strong>rtaken to assess animal acoustic diversity insi<strong>de</strong> or around cities.<br />
Authors’ references<br />
Jérôme Sueur, Amandine Gasc, Philippe Grandcolas:<br />
Muséum national d’Histoire naturelle, Département Systématique et<br />
Évolution, UMR 7205 Paris, France<br />
Sandrine Pavoine:<br />
Muséum national d’Histoire naturelle, Département Écologie et Gestion<br />
<strong>de</strong> la Biodiversité, UMR 7204 Paris, France<br />
Corresponding author: Jérôme Sueur, sueur@mnhn.fr<br />
Aknowledgement<br />
This research has been supported by the INEE (CNRS) with a PEPS program<br />
and a PhD grant awar<strong>de</strong>d to AG. Sampling in French Guiana was<br />
achieved thanks to a CNRS Nouragues 2010 grant. Sampling in New<br />
Caledonia was realised thanks to the ANR BIONEOCAL grant to PG.<br />
Main part of research was financed with the FRB BIOSOUND grant<br />
(Fondation pour la Recherche sur le Biodiversité). Marion Depraetere,<br />
Vincent Devictor, Stéphanie Duvail, Olivier Hamerlynck, Frédéric Jiguet,<br />
Isabelle Leviol, Pierre-Yves Martel, and Raphaël Pélissier participated at<br />
different steps to the <strong>de</strong>velopment of this new sensing method or provi<strong>de</strong>d<br />
field data with which to compare our acoustic indices.<br />
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Biology, 9, pp. 939-942.
Chapter 2<br />
Assessing the spatial and temporal<br />
distributions of zooplankton and<br />
marine particles using the Un<strong>de</strong>rwater<br />
Vision Profiler<br />
Lars Stemmann, Marc Picheral, Lionel Guidi, Fabien Lombard, Franck Prejger,<br />
Hervé Claustre, Gabriel Gorsky<br />
1. Introduction<br />
The last two <strong>de</strong>ca<strong>de</strong>s, international multidisciplinary programs such as<br />
the Census Of Marine Life (COML), Joint GlObal Flux Studies (JGOFS),<br />
Global Ocean Ecosystem Dynamics (GLOBEC), Integrated Marine<br />
Biogeochemistry and Ecosystem Research (IMBER) conducted numerous<br />
cruises and sampled large areas of the oceans, often focusing on<br />
the first hundred meters of the water column. In parallel, advances in<br />
remote sensing technologies from satellites allowed synoptic <strong>de</strong>scriptions<br />
of some physical and optical properties of the ocean surface used to assess<br />
epipelagic particle biomasses and primary production at a global scale<br />
(see III, 3). By contrast, pelagic ecosystems of mesopelagic water layers –<br />
also known as mid-water (100-1000m) – and <strong>de</strong>eper water layers remain<br />
wi<strong>de</strong>ly unknown. Observing these pelagic ecosystems requires the use of<br />
large and often costly instruments launched from research vessels such<br />
as pumps, multinets, remotely operated vehicles (ROV), or submersibles.<br />
Furthermore, fragile zooplankton (ctenophores, medusae, siphonophores,<br />
appendicularians) or fragile aggregates are <strong>de</strong>stroyed during collection<br />
with plankton nets, in situ water pumps, and/or sediment traps, which prevents<br />
the analysis of their spatial distribution. This challenge can partly<br />
be overcome by using non intrusive un<strong>de</strong>rwater optical and imaging technologies<br />
that appear to be promising tools <strong>for</strong> the study and quantifica-
120 Biodiversity<br />
tion of zooplankton community structures, diversity, as well as marine<br />
particles size spectra.<br />
The <strong>de</strong>scription of the meso- and bathypelagic fauna began to emerge<br />
with the use of ship-tethered cameras hooked on ROV (Lindsay et al.,<br />
2004; Lindsay and Hunt, 2005; Robison, 2004; Robison et al., 2005a;<br />
Steinberg et al., 1997). However, the <strong>de</strong>ployment of these cameras is timeconsuming<br />
and financially expensive, which prevents their wi<strong>de</strong> use.<br />
Smaller instruments hooked on conventional gears – such as a rosette<br />
– or on autonomous plat<strong>for</strong>m – such as gli<strong>de</strong>rs and profilers (see IV, 1),<br />
may be more cost efficient and would provi<strong>de</strong> valuable dataset on the<br />
spatial and temporal distributions of organisms and non living particles.<br />
Relatively few available instruments allow simultaneous in situ measurements<br />
of oceanic particles and zooplankton. Particles can be <strong>de</strong>tected and<br />
measured by the laser in situ scattering and transmissometry (Agrawal and<br />
Pottsmith, 2000) based on scattering intensity. However, this instrument<br />
does not provi<strong>de</strong> in<strong>for</strong>mation on the shape of the particles and limits<br />
its use <strong>for</strong> zooplankton i<strong>de</strong>ntification. The laser optical plankton counter<br />
records a shape approximation of particles crossing an array of light<br />
beams and can hardly set one particle apart another among various classes<br />
of particles and organisms (Herman et al., 2004).<br />
More recently, several instruments that employ image analysis to characterise<br />
and enumerate oceanic zooplankton have been <strong>de</strong>veloped and<br />
tested in the field (Benfield et al., 2007), including i) the vi<strong>de</strong>o plankton<br />
recor<strong>de</strong>r (Davis et al., 2005), ii) the shadowed imaged particle profiling<br />
and evaluation recor<strong>de</strong>r (Sipper, Samson et al., 2001), iii) the in situ ichthyoplankton<br />
imaging system (Isiis, Cowen and Guigand, 2008), and iv)<br />
the zooplankton visualisation and imaging system (Zoovis, Benfield et al.,<br />
2007). Most of these instruments <strong>de</strong>tect relatively large organisms (more<br />
than 100µm); however, there is an increasing interest in quantifying nanoand<br />
microplankton particles (Olson and Sosik, 2007; Sosik and Olson,<br />
2007). Several systems using holographic imaging have been <strong>de</strong>veloped<br />
<strong>for</strong> this purpose (Alexan<strong>de</strong>r et al., 2000; Hobson et al., 1997; Katz et al.,<br />
1999; Pfitsch et al., 2007). Whether <strong>de</strong>signed <strong>for</strong> small or large plankton,<br />
all these instruments collect images of a <strong>de</strong>fined volume of water that can<br />
be processed to obtain unique in<strong>for</strong>mation about the distribution, abundance,<br />
and behaviour of plankton on scales that cannot be investigated by<br />
conventional sampling systems such as nets and pumps. Most of the time,<br />
these instruments were used to document the in situ behaviour, taxonomic<br />
diversity, spatial distribution, and relative abundance of planktons. They<br />
were also used in<strong>de</strong>pen<strong>de</strong>ntly to study the dynamic of non-living particles<br />
in the water column.<br />
I<strong>de</strong>ally, both plankton and non-living particles should be studied simultaneously<br />
because of their interactions in the pelagic realm. These interac-
Part II – Chapter 2 121<br />
tions inclu<strong>de</strong> <strong>for</strong> example zooplankton feeding on <strong>de</strong>tritus produced at<br />
the surface leading to particle aggregation, fragmentation, and remineralisation<br />
in the water column. These interactions affect the transfer of large<br />
amounts of carbon from the surface to the <strong>de</strong>ep sea – a process known as<br />
the “biological pump” – and contribute significantly to climate variability<br />
(Sarmiento and Le Quere, 1996; Volk and Hoffert, 1985). There<strong>for</strong>e, in<br />
or<strong>de</strong>r to better un<strong>de</strong>rstand the biological pump, it is crucial to evaluate<br />
simultaneously the distribution of the particulate matter and the zooplankton<br />
in the water column. The un<strong>de</strong>rwater vision profilers (UVPs)<br />
were <strong>de</strong>signed and constructed in our laboratory at Villefranche-sur-Mer<br />
in or<strong>de</strong>r to achieve this goal (figure 1). Yet, particle and plankton-imaging<br />
systems present new challenges to the studies of aquatic biota. In this<br />
paper, we <strong>de</strong>scribe the fifth generation of the UVP (UVP5) <strong>de</strong>sign and calibrations.<br />
Moreover, we expose experimental results from different cruises<br />
showing the possibility of studying the biodiversity of zooplankton and<br />
the size spectra of particles.<br />
Figure 1: Pictures of the un<strong>de</strong>rwater vision profiler UVP4 (A) and UVP5 as stand<br />
alone (b) and picture of UVP 5 in a 24 bottles Rosette CTD system (conductivity,<br />
temperature and <strong>de</strong>pth, C). UVP4 is a large stand-alone package of nearly 1 m 3 (300<br />
kg) and incorporates a CTD, fluorometer and nephelometer sensors (Gorsky et al.,<br />
1992; Gorsky et al., 2000). The latest version called UVP5 (Picheral et al., 2010) is a<br />
smaller instrument (30kg) that can equip a standard rosette frame, interfaced with<br />
the CTD, and used down to 6000m <strong>de</strong>ep instead of 1000m <strong>de</strong>ep <strong>for</strong> UVP4.
122 Biodiversity<br />
2. Description of the un<strong>de</strong>rwater vision profiler (UVP)<br />
2.1. Main characteristics<br />
The un<strong>de</strong>rwater vision profilers (UVPs) were <strong>de</strong>signed and constructed at<br />
the laboratory of Villefranche-sur-Mer to quantify simultaneously large<br />
particles (more 100 μm) and zooplankton in a known volume of water<br />
(Picheral et al., 2010). The UVP versions 2 to 4 had been operating since<br />
1991 and they provi<strong>de</strong>d a database of more than 1300 inter-calibrated<br />
profiles of particle size distribution covering the global ocean. However<br />
these instruments required <strong>de</strong>dicated winch time on research ships, their<br />
maximum operating <strong>de</strong>pth was 1000m, and the image acquisition at the<br />
ocean surface was limited because of daytime light saturation. In addition,<br />
their complexity required an onboard trained technician, which limited<br />
spreading their use over the oceanographic community. Nowadays,<br />
the UVP5 overcomes these limitations and can be set up <strong>for</strong> short or<br />
long-term <strong>de</strong>ployments either as an autonomous system or as a complement<br />
to CTD (conductivity, temperature and <strong>de</strong>pth) system. The UVP5<br />
dimensions allow its incorporation into autonomous un<strong>de</strong>rwater vehicles<br />
(AUV), remotely operated vehicles (ROV), or drifting or geostationary<br />
mooring. In the near future, the ongoing miniaturisation of the sensors<br />
Table 1: Un<strong>de</strong>rwater Vision Profiler 5 <strong>de</strong>tails<br />
Housing<br />
Data storage<br />
Camera and image<br />
analysis<br />
Lighting<br />
Piloting board<br />
Connection (camera<br />
housing)<br />
Embed<strong>de</strong>d <strong>Sensors</strong><br />
Power<br />
Camera housing pressure rated 6000 m<br />
2 in<strong>de</strong>pen<strong>de</strong>nt glass cylin<strong>de</strong>rs <strong>for</strong> the lighting<br />
Camera 8 with internal memory storage<br />
Optional external drive<br />
1.3 Megapixelup to 11fps processed images<br />
9 mm fixed focal lens<br />
Pass band Filter centered on 625 nm<br />
Flash duration down to 100 μs<br />
Persistor CF2 piloting processor<br />
Analog to digital conversion <strong>for</strong> external sensors<br />
Digital to analog output to CTD<br />
Power management<br />
Serial interface 100Mb network<br />
Pressure digital sensor with 0.01% accuracy<br />
Pitch sensor<br />
Internal temperature sensor<br />
Rechargeable lithium-ion 6.3 A/29 V battery pack<br />
Continuous monitored during data acquisition
Part II – Chapter 2 123<br />
will lead to the <strong>de</strong>velopment of autonomous camera systems that could<br />
be mounted on drifters and gli<strong>de</strong>rs working in network allowing real time<br />
“visual” monitoring of the biogeochemistry and the biology of the ocean<br />
(see IV, 1).<br />
The UVP5 instrumental package contains an intelligent camera and a<br />
lighting system encompassed into in<strong>de</strong>pen<strong>de</strong>nt housings (figure 1). In<br />
addition, pressure and angle sensors are inclu<strong>de</strong>d to the system in or<strong>de</strong>r<br />
to monitor the UVP5 <strong>de</strong>ployments and data acquisition. The hardware is<br />
also composed of an acquisition and piloting board, internet switch, hard<br />
drive, and <strong>de</strong>dicated electronic power boards whose <strong>de</strong>tails and characteristics<br />
are presented in table 1. Images can be recor<strong>de</strong>d in fields of view<br />
ranging from 8 × 6 to 22 × 18cm at a distance of 40cm from the camera in<br />
red light environment in or<strong>de</strong>r to reduce zooplankton phototactic behaviour<br />
and to prevent contamination by the sunlight at the surface.<br />
2.2 Calibration<br />
The manufacturing process of the UVP5 produces light-emitting dio<strong>de</strong>s<br />
(LED) lighting systems and glass housings with unique optical characteristics.<br />
There<strong>for</strong>e, each instrument requires individual calibration. In or<strong>de</strong>r<br />
to be able to estimate accurate concentrations and sizes of in situ marine<br />
particles, calibrations of the water volume and the size of particle within<br />
an image have to be done prior to the first <strong>de</strong>ployment. A short <strong>de</strong>scription<br />
of the method is presented below but <strong>de</strong>tails can be found in Picheral<br />
et al. (2010).<br />
The calibration of the volume of the image has to be done in<strong>de</strong>pen<strong>de</strong>ntly<br />
<strong>for</strong> each of the two lights. A white sheet of paper, immerged in a tank with<br />
seawater, is placed at different distances from the LEDs. Pictures of the<br />
light field projected on the white paper are recor<strong>de</strong>d and gathered in or<strong>de</strong>r<br />
to reconstruct the volume in 3D (Picheral et al., 2010, figure 2C). The<br />
size calibration protocol <strong>de</strong>fines the equation and enables the conversion<br />
from a particle <strong>de</strong>fined by a number of pixels to size (area) in metric unit.<br />
Due to light-scattering in the water, this relationship is not linear <strong>for</strong> small<br />
targets. It follows the rule<br />
Sm=A×Sp B ,<br />
where Sp is the surface of the particle in pixels and Sm is the surface<br />
in squared-millimetres. The calibration and <strong>de</strong>termination of A and B<br />
involves diverse objects sorted into three major qualitative optical groups<br />
(dark, transparent, and heterogeneous) in or<strong>de</strong>r to represent the diversity<br />
of natural particles present in the environment.
124 Biodiversity<br />
2.3. Zooplankton i<strong>de</strong>ntification<br />
Since 2001, the UVP4 and UVP5 have provi<strong>de</strong>d images of macrozooplankton<br />
over the globe. All profiles have been analysed following the<br />
same protocol and using custom software routines to extract large objects<br />
(i.e. 500µm in maximum length). This size threshold was selected because<br />
most of the organisms cannot be i<strong>de</strong>ntified below that size due to current<br />
insufficient resolution of the images. The sorting of the objects is<br />
computer-assisted as <strong>for</strong> the laboratory Zooscan system (Gorsky, 2010)<br />
and the computer prediction is visually validated by specialists to i<strong>de</strong>ntify<br />
taxa. The size of the organisms is reported as well as its area or major and<br />
minor axes of the best fitted ellipse. This measure is best suited <strong>for</strong> dark<br />
and opaque organisms such as chaeognathes, radiolarians, fish, and large<br />
crustaceans, but cannot be used <strong>for</strong> gelatinous organisms.<br />
3. Study of particle dynamics and zooplankton community<br />
structures at different spatial scales<br />
3.1. Marine particles<br />
The UVPs were <strong>de</strong>ployed more than 3000 times covering almost all oceans<br />
on Earth (figure 2). The first versions of the UVPs (2 and 3) were not<br />
able to efficiently distinguish the non-living particles from the zooplanktonic<br />
organisms. There<strong>for</strong>e, earlier studies focused on the size spectra of<br />
all particles, assuming that most of them were nonliving particles. This<br />
hypothesis was then confirmed by the use of UVPs 4 and 5 showing that<br />
zooplanktons account <strong>for</strong> only 0.1 to 10% of the total number of particles<br />
in the water column (see next section).<br />
The most important biogeochemical in<strong>for</strong>mation provi<strong>de</strong>d by the UVPs<br />
consists on the size spectra of large particles (more than 100µm). These<br />
particles, in the <strong>for</strong>m of aggregates of individual particles of different<br />
origins, are the main vector of the vertical flux of carbon to the <strong>de</strong>ep<br />
sea. In or<strong>de</strong>r to correctly estimate this flux, the concentration of particle<br />
per size bin (number per centimetre) must be converted to biovolume<br />
(cm 3 .cm -3 ) and to biomass (mg DryWeight.cm -3 ) assuming relationships<br />
between size and mass (Stemmann et al., 2008a). Then, the known relationship<br />
between size and settling speed can be used to estimate vertical<br />
flux (Guidi et al., 2008; Stemmann et al., 2004b).<br />
The coupling between small and meso-scale (scales from 5km to 100km)<br />
physical and biological processes in highly dynamic environments such as<br />
frontal zones, filaments, and equatorial systems was shown to influence<br />
the spatial patterns of carbon export. Vertical profiles of particle flux can
Part II – Chapter 2 125<br />
be analysed in a spatial context in or<strong>de</strong>r to provi<strong>de</strong> estimates of carbon<br />
sequestration by the oceans at different scales. Previous <strong>de</strong>ployments of<br />
the UVPs at high spatial resolution revealed that particle spatial patterns<br />
can be observed at scales as small as 10 to 100 km (Gorsky et al., 2002a;<br />
Gorsky et al., 2002b; Guidi et al., 2007; Stemmann et al., 2008c). Particle<br />
size spectra were also used in time series to constrain mathematical mo<strong>de</strong>ls<br />
of particle flux to the interior of the ocean (Stemmann et al., 2004a;<br />
2004b). These analyses led to <strong>for</strong>mulate the hypothesis that zooplankton<br />
organisms can <strong>de</strong>tect large settling particles and can fragment them in<br />
numerous smaller parts that have slower settling speed. This process may<br />
generally affect carbon sequestration in the <strong>de</strong>ep ocean.<br />
Figure 2: Global map showing the location of sites that were studied using the<br />
different versions of the UVP (dark blue = UVP2, green = UVP3, light blue =<br />
UVP4, red = UVP5).<br />
3.2. Comparison between zooplankton and non living particle size spectra<br />
The improvements of the optics and illumination of UVP4 and UVP5<br />
enabled simultaneous estimations of the vertical distributions of both<br />
particles and zooplankton size spectra (figure 3).
126 Biodiversity<br />
Figure 3: A. Vertical abundance (relative units) of two size classes of large particulate<br />
matter (LPM lines) and vertical day (upper right) and night (upper left) distributions<br />
of copepods during the Cali<strong>for</strong>nia current ecosystem long-term ecological research<br />
(CCELTER) cruise off the Cali<strong>for</strong>nian coast in autumn 2008. b. Typical UVP5<br />
images of individuals from different macrozooplankton groups including copepoda<br />
(1), radiolarian (2), chaetognate (3), medusae (4), appendicularia (5), and euphausid<br />
(6).
Part II – Chapter 2 127<br />
Acoustic, optical, and imaging systems all face the same challenge when<br />
trying to distinguish between plankton and other particles in the water<br />
column. Plankton larger than 500µm inclu<strong>de</strong>s crustacean (e.g. copepods<br />
and euphausiids), gelatinous taxa (e.g. medusae, tunicates), and eggs and<br />
fish larvae. Other particles of the same size range inclu<strong>de</strong> aggregates,<br />
abandoned houses of larvaceans, mucous webs of pteropods and all associated<br />
material, including living (protozoa and bacteria) and <strong>de</strong>ad materials.<br />
Many of these “other particles” are fragile and are not retained and/<br />
or preserved by filters or nets meshes (Gonzalez-Quiros and Checkley,<br />
2006). There<strong>for</strong>e, the contributions of organisms to the total number or<br />
the biomass of particles is not well known. Misrecognition between organisms<br />
and particles can have <strong>de</strong>ep implication <strong>for</strong> the estimation of available<br />
biomass <strong>for</strong> higher trophic levels and <strong>for</strong> the estimation of vertical<br />
carbon fluxes. The laser optical plankton counter (LOPC) potentially distinguishes<br />
automatically zooplankton from particles based on the opacity<br />
and size of the recor<strong>de</strong>d objects (Checkley et al., 2008; Gonzalez-Quiros<br />
and Checkley, 2006; Jackson and Checkley, 2011). However, results provi<strong>de</strong>d<br />
by this instrument consist in a proxy <strong>for</strong> zooplankton since the<br />
recognition cannot be validated nor the taxa recognised. The UVP’s distinction<br />
is based on the automatic sorting of particles larger than 500µm<br />
followed by manual image analysis and visual verification of the plankton<br />
i<strong>de</strong>ntifications by experts (Stemmann et al., 2008b; Stemmann et al.,<br />
2008d).<br />
During the Boum cruise on the Mediterranean Sea (summer 2008), the<br />
UVP was <strong>de</strong>ployed on a longitudinal transect from the East to West basin<br />
<strong>for</strong> short-term stations and 3 sites were selected <strong>for</strong> their oligotrophic cha–<br />
racteristic (figure 4). The comparison between particles and zooplankton<br />
size spectra <strong>for</strong> the same size range (500µm-few mm) shows that the<br />
dominant zooplankton in abundance were radiolaria. More interesting,<br />
the results show almost <strong>for</strong> the first time that living organisms were only<br />
1-15% of total particles <strong>de</strong>tected by the UVP in the more than 500µm<br />
size range. These ratios are slightly lower than those reported earlier <strong>for</strong><br />
the oPC (25%) and LOPC (20+/-14%) in the Cali<strong>for</strong>nian Current system<br />
(Gonzalez-Quiros and Checkley, 2006; Jackson and Checkley, 2011).<br />
More data of such type should be acquired in different oceans to test<br />
whether the strong dominance of non-living particles is a common feature<br />
of pelagic ecosystem.
128 Biodiversity<br />
Figure 4: Particles and zooplankton normalised number spectra obtained by the<br />
un<strong>de</strong>rwater vision profiler at 3 locations in the Eastern (left), Central (middle) and<br />
Western (right) Mediterranean Sea during the BOUM cruise in July 2008 (adapted<br />
from Stemmann and Boss, 2012). Particles were counted automatically from 60µm<br />
in equivalent spherical diameter (ESD) and thus inclu<strong>de</strong> non living particles and<br />
zooplankton organisms. The different taxa were counted manually on the images<br />
only <strong>for</strong> size larger than 500µm from which they can be i<strong>de</strong>ntified.<br />
3.3. Appendicularians and the biological pump<br />
Appendicularians are zooplanktonic pelagic tunicates. They produce a<br />
mucous external filtration <strong>de</strong>vice called “the house” which allow them<br />
to filter small particles (0.2-50µm, see Lombard et al., 2011) from the<br />
seawater. Up to 26 houses can be produced within a day by a single individual<br />
(Sato et al., 2003), and once clogged, are discar<strong>de</strong>d contributions<br />
to marine snow (Alldredge, 2005; Alldredge and Silver, 1988). Thus, the<br />
biogeochemical action of appendiculiarians inclu<strong>de</strong>s mostly “repackaging”<br />
by filtering small particles and producing large ones. This effect<br />
on the biogeochemistry of particles and there<strong>for</strong>e on carbon fluxes was<br />
shown to be potentially important (Berline et al., 2011; Robison et al.,<br />
2005b). However, these organisms have been largely un<strong>de</strong>rstudied until<br />
now mainly because of instrument limitations.<br />
Imaging systems such as the UVP overcome these limitations and provi<strong>de</strong><br />
simultaneous observations of their distribution and relation to particle<br />
stocks and fluxes. Appendicularians repackaging action were estimated<br />
from observations in the northeastern Atlantic ocean by the UVP4.<br />
Combined data of appendicularians and associated fluxes from UVP<br />
observations and from sediment traps suggested that the estimated pro-
Part II – Chapter 2 129<br />
duction of particulate matter by sub-surface appendicularians excee<strong>de</strong>d<br />
the observed total sinking flux at 200m (Lombard et al., 2010). This study<br />
supports the hypothesis that appendicularians play an important role in<br />
the production of particle fluxes (Alldredge, 2005). In addition, laboratory<br />
observation on discar<strong>de</strong>d houses showed that empty appendicularian<br />
houses un<strong>de</strong>rgo a rapid <strong>de</strong>flation and compression process, <strong>de</strong>creasing<br />
their size and increasing their sinking speed (Lombard and Kiørboe,<br />
2010). This process, combined with the previous estimation of discar<strong>de</strong>d<br />
houses production, leads to the conclusion that up to 20-40% of the<br />
300-500µm particles observed by the UVP in the upper 100m of the water<br />
column may be of appendicularian origin.<br />
In addition to producing discar<strong>de</strong>d houses in the epipelagic layers,<br />
appendicularians are also supposed to be efficient at repackaging small<br />
particles by grazing into larger aggregates (more than 1mm) in the <strong>de</strong>ep<br />
ocean (Alldredge, 2005). Using the UVP4 observations, the relationship<br />
between the changes in the vertical distributions of particles and<br />
zooplankton, including appendicularians, was investigated during the<br />
Mareco cruise in the North Atlantic (Stemmann et al., 2008b). The<br />
gelatinous fauna were consistently the most numerous between 400-<br />
900m and in particular the appendicularians, that occurred mostly<br />
below 300m (figure 5). Particles vertical profiles showed that the equivalent<br />
spherical volume of particles (100µm
130 Biodiversity<br />
Figure 5: Vertical distribution of appendicularians (upper panel, bars are mean<br />
abundance and the stems are the standard <strong>de</strong>viation) and particles (lower panel,<br />
100µm
Part II – Chapter 2 131<br />
We tested if the zoogeography of macrozooplankton in the mesopelagic<br />
layer corresponds to these biogeochemical provinces (Stemmann et al.,<br />
2008d). The zooplankton community was sorted in 21 morphotypes and<br />
more than 5000 organisms were i<strong>de</strong>ntified in the 100-1000 m <strong>de</strong>pth layer.<br />
The numerically dominant groups were crustaceans (24%) followed by the<br />
medusae (18%), appendicularians (14%) chaetognathes (11%), fish (7%) and<br />
single-cell sarcodines of the group Star (6%, see figure 6). The other taxonomic<br />
groups were less than 5% of the total count each. However, pooling<br />
all single-cell sarcodines moved this group to second rank (23%) in term<br />
of frequency of occurrence. From a trophic perspective, the assemblages<br />
Figure 6: Frequency of occurrence <strong>for</strong> the 20 taxonomic groups in the 9 regions.<br />
Note that the numerically dominant group of Crustacean has been removed<br />
from the list to increase the <strong>de</strong>tails in the other groups. Appendicularians (App.),<br />
Thaliacae (Thal.), Fish, Haliscera spp. medusa (Hal.), S. bittentaculata (Sol.), Aglantha<br />
spp. (Agl.), Aeginura grimaldii (Grim.), “other medusae” (Med.), chaetognath<br />
(Chaet.), lobate ctenophore (Lob.), cydippid ctenophore (Cyd.), siphonophore<br />
(Sipho.), single-cell sarcodine grouped by four (Radio CS.), colonial radiolarians<br />
(Radio C.), colonial radiolarians with double line (Radio CD.), Phaedorian (Phaeo.),<br />
single-cell sarcodine with spines (Spine.), double-cell sarcodine with spines (Spine<br />
2.), spheres (Sphere.), and sarcodine with hairs (Star.). The regions are <strong>de</strong>fined as:<br />
Northeast Atlantic shelves (NECS), Atlantic Arctic (ARCT), North Atlantic drift<br />
(NADR), Atlantic Subarctic (ARC), Subantarctic, ocean (SANT), North Atlantic<br />
Subtropical ocean, (NAST), South Pacific Subtropical Gyre (SPSG), Western<br />
Australia (AUSW), Mediterranean Sea (MEDI). The or<strong>de</strong>r of the region is set so the<br />
proportion of carnivorous organisms (in grey from Chaet. to Sipho.) <strong>de</strong>creases from<br />
left to right (modified from Stemmann et al., 2008).
132 Biodiversity<br />
of zooplankton could be lumped into three categories: gelatinous carnivores<br />
(cydippid stenophores, lobate ctenophores, medusae, siphonophores,<br />
chaetognathes), filter fee<strong>de</strong>r <strong>de</strong>tritivores (appendicularians and salps) and<br />
omnivores (sarcodines, crustaceans and fish). Interestingly, the proportion<br />
of carnivores <strong>de</strong>creased from 95% to 15%, from the high latitu<strong>de</strong> regions<br />
(Northest Atlantic shelves, Atlantic Arctic, North Atlantic drift, Atlantic<br />
Subarctic, Subantarctic ocean) to the low latitu<strong>de</strong> regions (Mediterranean<br />
sea, western Australia, South Pacific subtropical Gyre). The similarity in<br />
the community assemblages of zooplankton in the layer between 100 and<br />
1000m was significantly higher within regions than between regions, <strong>for</strong><br />
most cases. The regions with comparable compositions were located in the<br />
North Atlantic with adjacent water masses, suggesting that the assemblages<br />
were either mixed by advective transport or that environmental conditions<br />
were similar in mesopelagic layers. The data suggest that the spatial structuring<br />
of mesopelagic macrozooplankton occurs at large scales (e.g. basin<br />
scales) but not necessarily at smaller scales (e.g. oceanic front).<br />
4. Conclusion<br />
Results obtained using the UVP but also several other in situ imaging instruments<br />
have shown that bio-imagery techniques can provi<strong>de</strong> useful data on<br />
plankton and particles spatial and temporal distribution in the upper kilometre<br />
of the ocean. In the next <strong>de</strong>ca<strong>de</strong>, rapid technological evolution toward<br />
miniaturisation in the optical sensors is expected, and will make possible<br />
the use of these sensors on autonomous plat<strong>for</strong>ms. Their extensive use may<br />
set a revolution in ocean plankton sciences equivalent to the revolution in<br />
medical practices <strong>for</strong> the last 15 years. Broa<strong>de</strong>r spatial and longer temporal<br />
coverage of plankton size spectra will soon be possible <strong>for</strong> global monitoring<br />
programs (see chapter IV, 1). Mathematical mo<strong>de</strong>ls <strong>for</strong> individual<br />
physiological and population change rates, biomasses flow between trophic<br />
levels, and functions of organisms or particle size, were also <strong>de</strong>veloped in<br />
the last <strong>de</strong>ca<strong>de</strong>. The new sets of data obtained by the wi<strong>de</strong> use of imaging<br />
instruments are well adapted to calibrate and validate these mo<strong>de</strong>ls.<br />
Authors’ references<br />
Lars Stemmann, Marc Picheral, Franck Prejger, Hervé Claustre, Gabriel<br />
Gorsky:<br />
Université Pierre et Marie Curie, Laboratoire d’Océanographie <strong>de</strong><br />
Villefranche, UPMC-CNRS UMR 7093, Villefranche-sur-Mer, France.
Part II – Chapter 2 133<br />
Lionel Guidi:<br />
University of Hawaii, Department of Oceanography, C-MORE, Center<br />
<strong>for</strong> Microbial Oceanography: Research and Education, Honolulu, USA<br />
Fabien Lombard:<br />
Université <strong>de</strong> la Méditerranée, Laboratoire d’Océanographie Physique et<br />
Biogéochimique, UMR 6535, Campus <strong>de</strong> Luminy, Marseille, France<br />
Corresponding author: Lars Stemmann, stemmann@obs-vlfr.fr<br />
Aknowledgement<br />
The authors would like to thank the many colleagues who helped us to<br />
<strong>de</strong>velop our knowledge on plankton and particles dynamics as <strong>de</strong>tected<br />
using their optical properties. Lars Stemmann was supported by funding<br />
from the 7th European Framework Program (JERICO) and by the PICS<br />
program of the CNRS. Fabien Lombard was supported by the French program<br />
ANR-10-PDOC-005-01 ‘Ecogely’. Lionel Guidi was supported by<br />
Center <strong>for</strong> Microbial Oceanography, Research and Education (C‐MORE;<br />
NSF grant EF-349 0424599), the HOT program (NSF grant OCE09‐26766)<br />
and the Gordon and Betty Moore Foundation (P.I. Pr David M. Karl).<br />
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Gorsky G., Ohman M. D., Picheral M., Gasparini S., Stemmann L., Romagnan<br />
J.-B., Cawood A., Pesant S., Garcia-Comas C., Prejger F., 2010. Digital<br />
zooplankton image analysis using the ZooScan integrated system. Journal<br />
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Gorsky G., Picheral M., Stemmann L., 2000. Use of the un<strong>de</strong>rwater vi<strong>de</strong>o<br />
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Gorsky G., Prieur L., Taupier-Letage I., Stemmann L., Picheral M., 2002b. Large<br />
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Guidi L., Jackson G. A., Stemmann L., Miquel J., Picheral M., Gorsky G.,<br />
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Guid L., Stemmann L., Legendre L., Picheral M., Prieur L., Gorsky G., 2007.<br />
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Picheral M., Guidi L., Stemmann L., Karl D. M., Iddaoud G., Gorsky G., 2010.<br />
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2008b. Vertical distribution (0-1000 m) of macrozooplankton, estimated<br />
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distributions and fluxes in the midwater column that inclu<strong>de</strong>s biological<br />
and physical processes – Part II: application to a three year survey in<br />
the NW Mediterranean Sea. Deep Sea Research Part I: Oceanographic<br />
Research Papers, 51, pp. 885-908.<br />
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Part II – Chapter 2 137<br />
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Chapter 3<br />
Assessment of three genetic methods<br />
<strong>for</strong> a faster and reliable monitoring<br />
of harmful algal blooms<br />
Jahir Orozco-Holguin, Kerstin Töbe, Linda K. Medlin<br />
1. General introduction<br />
Harmful marine algal taxa are globally distributed from tropical to polar<br />
latitu<strong>de</strong>s, and occupy ecological niches ranging from brackish water, such<br />
as the Baltic Sea, to oceanic environments. It is generally acknowledged<br />
that the occurrence of harmful algal blooms is increasing and that pollution<br />
in coastal waters has contributed to this increase. Harmful algae can<br />
produce secondary compounds that are toxic to fish and shellfish (e.g.<br />
oysters, mussels) who feed on toxic algae. These compounds can cause<br />
amnesic, paralytic or diarrhetic traumas when fish and shellfish are eaten<br />
by humans (Hallegraeff, 1993).<br />
To mitigate health problems <strong>for</strong> human populations and negative effects<br />
to fisheries, aquaculture and tourism, accurate and cost-effective systems<br />
<strong>for</strong> i<strong>de</strong>ntification and <strong>de</strong>tection of toxic algae are urgently required. The<br />
monitoring of harmful algal blooms (HABs) is an European Union<br />
requirement and usually relies on visual confirmation of water discoloration,<br />
fish kills, and laborious cell counts. These techniques are very<br />
time-consuming, require specialised or trained personnel, expensive<br />
equipment, and are ineffective when many samples have to be routinely<br />
analysed. Currently, up to five working days may be nee<strong>de</strong>d between<br />
specimen collection and the <strong>de</strong>livery of a diagnostic report of the species<br />
present, leaving little time <strong>for</strong> mitigation responses, which usually involve<br />
moving the caged pinfish or the mussel rafts to a new location. Other
140 Biodiversity<br />
mitigation ef<strong>for</strong>ts, such as precipitation of the bloom with clay particles,<br />
are not practised in Europe.<br />
Molecular methods are potentially faster and more accurate than traditional<br />
light microscopy methods, and have been used <strong>for</strong> i<strong>de</strong>ntification of<br />
phytoplankton (Ayers et al., 2005; Diercks et al., 2008a; 2009; Gescher et<br />
al., 2008; Greenfield et al., 2006; O’Halloran et al., 2006). Recently, the<br />
analysis of small-subunit (SSU) ribosomal RNA (rRNA) genes has been<br />
established as an efficient way to characterise complex microbial samples<br />
(Amann et al., 1990). The method has proven to be of special value <strong>for</strong> the<br />
analysis of picophytoplankton samples, which are difficult to monitor by<br />
conventional methods because of their small size (Moon-van <strong>de</strong>r Staay et<br />
al., 2001). This method also circumvents the selective step of laboratory<br />
cultivation (Giovannoni et al., 1990). Direct cloning and sequencing of<br />
the small subunit (SSU) ribosomal DNA (rDNA) from natural samples<br />
provi<strong>de</strong> a broa<strong>de</strong>r view of the structure and composition of communities<br />
(López-Garcia et al., 2001). Because the rRNA database is increasing<br />
continually, it is possible to <strong>de</strong>sign probes from higher taxonomic groups<br />
down to the species level (Guillou et al., 1999; Groben et al., 2004). The<br />
species-specific probes can be applied <strong>for</strong> the analysis of phytoplankton<br />
communities with <strong>de</strong>tection by flow cytometry, epifluorescence microscopy<br />
(Miller and Scholin, 1998; Lim et al., 1999), or other methods that<br />
take advantage of the hybridisation principle (Metfies and Medlin, 2004).<br />
These well-established approaches have the major disadvantage that they<br />
can only be used to i<strong>de</strong>ntify one or a few organisms at a time, which<br />
makes it very time-consuming to get a broad view of a microbial sample.<br />
Thus, other methods have been <strong>de</strong>veloped in which multiple species can<br />
be i<strong>de</strong>ntified at the same time (Metfies and Medlin, 2004).<br />
The i<strong>de</strong>ntification of a region of the rRNA molecule that is species or<br />
group specific is essentially a genetic barco<strong>de</strong>. However most people working<br />
in the barco<strong>de</strong> field do not take into consi<strong>de</strong>ration how they could<br />
possible apply their barco<strong>de</strong> in a real life situation. For monitoring purposes,<br />
rRNA barco<strong>de</strong>s are put to use, either as a probe <strong>for</strong> hybridisation<br />
to a target or as a primer to amplify the target in a PCR reaction (see<br />
below). In <strong>de</strong>signing a barco<strong>de</strong>, the position of the mismatch must be<br />
taken into account if using the barco<strong>de</strong> as a probe (mismatch in the middle)<br />
or as a primer (mismatch at the 3’ end) is chosen. The use of rRNA<br />
probes in fluorescence in situ hybridisation (FISH) reactions has often<br />
been used <strong>for</strong> i<strong>de</strong>ntification of harmful microalgal species in field samples<br />
(e.g., Groben and Medlin, 2005), although it is not regularly inclu<strong>de</strong>d in<br />
monitoring programmes. FISH enables the direct visualisation of target<br />
cells by epi-fluorescence microscopy or by automated cytometric techniques.<br />
The whole cell stays intact and co-occurring phytoplankton species<br />
can be discriminated when counterstained with an overall DNA stain.
Part II – Chapter 3 141<br />
However, weak probe penetration, loss of cells during preparatory steps<br />
and autofluorescence of the target cells can mask the fluorescence signal.<br />
Thus, an unambiguous species differentiation may be difficult to achieve.<br />
Moreover, an extensive analysis of environmental samples with FISH is<br />
very time consuming, thus being ina<strong>de</strong>quate to achieve the high sample<br />
throughput nee<strong>de</strong>d in routine monitoring programs (Touzet et al., 2009).<br />
The limited number of fluorochromes available also restricts the use of<br />
multiple probes at one time. Several cell free <strong>for</strong>mats (DNA only) are currently<br />
available and have overcome the problems associated with FISH<br />
and the whole cell <strong>for</strong>mat. These inclu<strong>de</strong> real-time quantitative polymerase<br />
chain reaction (qPCR), biosensors and microarrays. In the following<br />
sections, we will address issues related to the assessment of these laboratory<br />
methods <strong>for</strong> the monitoring of toxic algae and their possible use in<br />
automated <strong>de</strong>vices and in situ monitoring programs of HAB. The monitoring<br />
of aquatic pathogens is essential to guarantee the safety and health<br />
of aquatic resources and the application of cell free methods to routine<br />
monitoring programs offers the best solution to assess good environmental<br />
status of all waters in a rapidly changing environment.<br />
2. Quantitative polymerase chain reaction-based method<br />
2.1. General principles<br />
The polymerase chain reaction (PCR) is one of the most powerful technologies<br />
in molecular biology. With PCR specific sequences, the number<br />
of DNA target molecules is amplified exponentially with each amplification<br />
cycle. The direct sequence amplification in PCR approaches<br />
enables the <strong>de</strong>tection of low abundance targets and the <strong>de</strong>tection of<br />
“hid<strong>de</strong>n” DNA like target species consumed by a predator. However, an<br />
adverse aspect of PCR is the impossibility to visualise the target species<br />
to ensure unequivocally their presence in the sample and to assess any<br />
cross-reaction to any non-target organism (Ku<strong>de</strong>la et al., 2010). With<br />
traditional qualitative “endpoint” PCR, no in<strong>for</strong>mation about the quantity<br />
of starting material in the sample is available. Whereas in qPCR<br />
approaches, data are collected over the entire PCR cycle by using fluorescent<br />
markers that are incorporated into the PCR product during<br />
amplification. The quantity of the amplified product is proportional to<br />
the fluorescence generated during each cycle, which is monitored with<br />
an integrated <strong>de</strong>tection system during the linear exponential phase of<br />
the PCR (Saun<strong>de</strong>rs, 2004). The accumulation of the PCR amplicon is<br />
measured as a change in fluorescence and is directly proportional to the<br />
amount of starting material (see figure 1).
142 Biodiversity<br />
Figure 1: Amplification plot of 28S rDNA from Alexandrium tamarense in two<br />
different environmental samples from the Scottish East Coast (TaqMan approach).<br />
The excited fluorescence is plotted against the cycle number. The <strong>de</strong>lta Rn is<br />
the magnitu<strong>de</strong> of the signal generated by the given PCR conditions relative to a<br />
standard.<br />
The qPCR can single out base pair differences: thus closely related species<br />
or populations can be distinguished. For environmental samples, an<br />
external standard <strong>for</strong> quantifying the amplified DNA is used. This could<br />
be a dilution of plasmids or DNA <strong>de</strong>rived from laboratory cultures with<br />
a known concentration of the target template. To infer concentrations of<br />
the target species in an unknown sample, a standard curve must be ma<strong>de</strong><br />
<strong>for</strong> each target species because of differences in DNA content per cell<br />
(Handy et al., 2008). When using a plasmid standard <strong>for</strong> quantifying cell<br />
numbers, it is essential to know the copy number of the ribosomal gene<br />
of the target species. In addition, one should take into account that the<br />
copy number of the rDNA genes may vary among different strains of an<br />
organism and species (Erdner et al., 2010).<br />
The most common used qPCR method is the SybR Green approach. In<br />
this assay, the fluorescent dye SYBR Green binds to the minor groove of<br />
double stran<strong>de</strong>d DNA (dsDNA), which results in an increase of the fluorescent<br />
emission proportional to an increase in the dsDNA PCR amplicon<br />
<strong>for</strong>mation after each cycle. Proper primer <strong>de</strong>sign is critical to avoid<br />
primer-dimers, which would be counted as amplified DNA because of the<br />
unspecific binding of SybR Green to all dsDNAs. There<strong>for</strong>e, a melting<br />
curve analysis is per<strong>for</strong>med that i<strong>de</strong>ntifies primer-dimers by their lower
Part II – Chapter 3 143<br />
melting temperature compared to that of the target amplicon (Nolan,<br />
2004). In other more sensitive and specific qPCR approaches, specific<br />
or non-specific primers together with a specific fluorigenic oligonucleoti<strong>de</strong><br />
probe are applied (e.g., TaqMan approach, molecular beacon and<br />
hybridisation probe assay). These assays apply fluorescence resonance<br />
energy transfer (FRET), which is the transfer of energy from an excited<br />
fluorophore, the donor, to another fluorophore, the acceptor, to generate<br />
enhanced fluorescence upon binding of the specific probe to its target<br />
(Cardullo et al., 1988).<br />
The use of specific primers and oligonucleoti<strong>de</strong> probes, labelled with<br />
unique fluorescent dyes with different excitation wavelength, enables<br />
a rapid and quantitative enumeration of several organisms within one<br />
sample (multiplex PCR). The number of <strong>de</strong>tectable target genes in one<br />
sample is limited by the number of available fluorescence reporter dyes<br />
<strong>for</strong> the separate probes. Consequently the <strong>de</strong>tection is limited to six species<br />
in one sample. However, multiplex qPCR experiments have to be<br />
carefully optimised, and often require an elaborate adaptation, notably<br />
with increasing target species in one assay (Ku<strong>de</strong>la et al., 2010). Potential<br />
drawbacks and limitations of qPCR could be, <strong>for</strong> instance, different DNA<br />
extraction yields <strong>de</strong>pending on the extraction method used and the presence<br />
of humic substances that could influence the PCR reaction. These<br />
problems could be resolved or at least minimised by applying a highquality<br />
DNA isolation method.<br />
Quantitative PCR can be easily per<strong>for</strong>med immediately after in situ sampling<br />
onboard ship or on shore, but preserved samples can also be used.<br />
However, the preservation method can influence the results or even<br />
inhibit the reaction. The sensitivity of qPCR is consi<strong>de</strong>rably lower with<br />
<strong>for</strong>malin and glutaral<strong>de</strong>hy<strong>de</strong> preservation than with no preservation.<br />
Preservation using ethanol and freezing is preferred because it is still possible<br />
to <strong>de</strong>tect and quantify target cells from fixed field samples after three<br />
years (Hosoi-Tanabe and Sako, 2005). Another commonly used fixative<br />
<strong>for</strong> phytoplankton samples is Lugol’s iodine, which has been reported<br />
to lower the sensitivity of some qPCR experiments (Bowers et al., 2000)<br />
but has also been successfully applied in others (Kavanagh et al., 2010).<br />
In HAB studies, multiplex qPCR experiments are applied less frequently,<br />
because of the extensive required optimisations to apply different primers<br />
and/or probes together in one environmental sample. Handy et al. (2006)<br />
successfully tested multiprobing using a single primer set with species specific<br />
probes in one assay versus multiplexing using specific primers and<br />
specific probes. They found that multiplexing was more efficient, albeit<br />
both methods were successful in <strong>de</strong>tecting multiple raphidophyte species.<br />
More recently a new technology <strong>for</strong> qPCR has emerged. It is termed droplet<br />
qPCR and involves the Illumina® or 454 sequencing method. Tewhey
144 Biodiversity<br />
et al. (2009) were able to per<strong>for</strong>m 1.5 million PCR with primers targeting<br />
435 exons of 47 genes to screen genetic variation in large human populations.<br />
In this method, the genomic DNA template mixture contains all<br />
of PCR components except <strong>for</strong> the primers. The template is prepared by<br />
fragmenting genomic DNA using DNaseI to produce 2-4kb fragments.<br />
The template mixture is ma<strong>de</strong> into droplets and paired with primer pair<br />
droplets and both droplets enter the microfluidic chip at a rate of about<br />
3,000 droplets per second. As the primer pair droplets are smaller than<br />
the template droplets, they move faster through the channels until they<br />
contact the preceding template droplet. Field-induced coalescence of these<br />
droplet pairs results in the two droplets merging to produce a single PCR<br />
droplet, which is collected and processed as an emulsion PCR reaction<br />
(Tewhey et al., 2009).<br />
2.2. Case studies of harmful algae<br />
Quantitative PCR has been used as a sensitive and accurate alternative to<br />
microscopic cell counts <strong>for</strong> estimating changes in cell <strong>de</strong>nsities of harmful<br />
algal species in natural phytoplankton samples. In particular, qPCR<br />
enables the differentiation between morphologically similar species, such<br />
as the dinoflagellate Cryptoperidiniopsis brodyi (Steidinger et al., 2006),<br />
which co-occurs with Pfiesteria species and is indistinguishable by light<br />
microscopy, but is easily i<strong>de</strong>ntified by using qPCR (Park et al., 2007).<br />
Another benefit over microscopic counts is the sensitive enumeration<br />
and i<strong>de</strong>ntification of fragile species, which might not be easily preserved,<br />
such as raphidophytes (Handy et al., 2008) or species that can only be<br />
reliably i<strong>de</strong>ntified with electron microscopy, such as Prymnesium cells.<br />
Multiplexing has also been successfully applied to <strong>de</strong>tect the harmful species<br />
Prymnesium parvum (Manning and La Claire, 2010).<br />
Moreover, qPCR can be much faster and more reliable than traditional<br />
counting methods and cryptic species can be more easily i<strong>de</strong>ntified<br />
(Manning and La Claire, 2010). There<strong>for</strong>e, qPCR has become a standard<br />
method in <strong>de</strong>tecting harmful algae (Fitzpatrick et al., 2010). The target<br />
genes <strong>for</strong> the primers and probes in HAb qPCR applications are the<br />
internal transcribed spacer I-5.8S rRNA gene, or the 18S/28S rRNA gene,<br />
<strong>de</strong>pending on the DNA sequence divergence between closely related species.<br />
A variety of primers targeting these ribosomal genes are hitherto available<br />
<strong>for</strong> the <strong>de</strong>tection and i<strong>de</strong>ntification of various HAb species in qPCR<br />
applications (table 1).
Part II – Chapter 3 145<br />
Table 1: Summary of qPCR studies <strong>for</strong> the <strong>de</strong>tection of HAB species<br />
and the toxins they produce.<br />
Taxon<br />
Toxins<br />
QPCR<br />
Approach<br />
On Midtal<br />
Phylochip<br />
Genus Alexandrium SYBR Green Yes<br />
Toxic North<br />
American cla<strong>de</strong> of<br />
the A. catenella/<br />
fundyense /tamarense<br />
species complex<br />
Pfiesteria shumwayae<br />
Saxitoxin SYBR Green Yes<br />
Unknown<br />
fish killer<br />
SYBR Green<br />
No<br />
Prymnesium parvum prymensins SYBR Green Yes<br />
Genus Pseudonitzschia<br />
Cysts of toxic North<br />
American cla<strong>de</strong> of<br />
the A. catenella/<br />
fundyense /tamarense<br />
species complex<br />
Pfiesteria species<br />
Toxic North<br />
American and<br />
Temperate Asian<br />
cla<strong>de</strong> of the A.<br />
catenella/fundyense<br />
/tamarense species<br />
complex<br />
A. minutum,<br />
A. ostenfeldii,<br />
A. tamutum,<br />
Mediterranean,<br />
North American and<br />
Western European<br />
ribotypes of the A.<br />
catenella/ fundyense<br />
/tamarense species<br />
complex from<br />
European waters<br />
Cysts of the<br />
Temperate Asian<br />
ribotype of A.<br />
catenella/ fundyense/<br />
tamarense species<br />
complex<br />
Domoic<br />
acid<br />
SYBR Green<br />
Yes<br />
Saxitoxins SYBR Green Yes<br />
Unknown<br />
fish killer<br />
Saxitoxin<br />
Saxitoxins<br />
and<br />
sprilloids<br />
Saxitoxins<br />
Taqman<br />
probes<br />
Taqman<br />
probes<br />
Taqman<br />
probes<br />
Taqman<br />
probes<br />
No<br />
Yes<br />
Yes<br />
Yes<br />
Study<br />
Galluzzi et al.<br />
2004<br />
Dyhrman et<br />
al. 2006 and<br />
2010<br />
Zhang and<br />
Lin 2005<br />
Zamor et al.<br />
2011, Galluzzi<br />
et al. 2008<br />
Fitzpatrick et<br />
al. 2010<br />
Erdner et al.<br />
2010<br />
Bowers et al.<br />
2000<br />
Hosoi-Tanabe<br />
and Sako<br />
2005<br />
Töbe et al.<br />
unpublished<br />
Kamikawa et<br />
al. 2007<br />
Table 1 – to be continued
146 Biodiversity<br />
Table 1 – continued<br />
Taxon<br />
Harmful<br />
raphidophytes<br />
Dinophysis species<br />
Toxins<br />
Unknown<br />
Fish killer<br />
Okadaic<br />
acid<br />
A. minutum Saxitoxins<br />
Lingulodinium<br />
polyedrum<br />
Not toxic<br />
QPCR<br />
Approach<br />
Taqman<br />
probes<br />
Hybridization<br />
probes<br />
Hybridization<br />
probes<br />
Hybridization<br />
probes<br />
On Midtal<br />
Phylochip<br />
Yes<br />
Yes<br />
Yes<br />
No<br />
Study<br />
Bowers et<br />
al. 2006,<br />
Coyne et al.<br />
2005, Handy<br />
et al. 2006,<br />
Kamikawa et<br />
al. 2006<br />
Kavanagh et<br />
al. 2010<br />
Touzet et al.<br />
2009<br />
Moorthi et al.<br />
2006<br />
2.3. Future prospects<br />
Multiplex qPCR assays should be improved <strong>for</strong> routine testing in HAB<br />
studies, with several probes recognising different HAB species in one<br />
single environmental sample, in or<strong>de</strong>r to accelerate the i<strong>de</strong>ntification of<br />
several species and lower substantially analysis costs and time per sample.<br />
Further <strong>de</strong>velopment of primer and probes <strong>for</strong> HAB species and the<br />
application of the variety of available probes will alleviate the <strong>de</strong>ployment<br />
of the method, circumventing long primer and probe testing procedures.<br />
Costs of real-time PCR instruments are <strong>de</strong>creasing, so that in future qPCR<br />
instruments will be standard tools in HAB studies. New high throughput<br />
technologies, such as the open array technology using the qPCR method,<br />
are available. Here, the benefits from microarrays and the data quality of<br />
PCR are combined. The open array is a new nanoliter fluidics plat<strong>for</strong>m <strong>for</strong><br />
low volume solution phase reactions, which enables the analysis of thousands<br />
of samples in parallel. The application of such a high throughput<br />
method will also consi<strong>de</strong>rably alleviate the analysis of large amounts of<br />
environmental samples in HAB studies in future. In addition, qPCR has<br />
now been adapted <strong>for</strong> use in a buoy (Preston et al., 2011) and it is only a<br />
matter of time until qPCR primers <strong>for</strong> toxic algae are ad<strong>de</strong>d because the<br />
environmental sample processor buoy is already capable of <strong>de</strong>tecting toxic<br />
algae using a sandwich hybridisation method with chemiluminescence<br />
<strong>de</strong>tection (see section 3.3 below).
Part II – Chapter 3 147<br />
3. Electrochemical biosensor-based methods<br />
3.1. General principles<br />
DNA (RNA)-based biosensors have been used in numerous fields ranging<br />
from medical diagnosis to <strong>for</strong>ensic and environmental research<br />
(Hei<strong>de</strong>nreich et al., 2010; Liu et al., 2010). An electrochemical biosensor<br />
is a self-contained integrated <strong>de</strong>vice capable of providing specific (semi)-<br />
quantitative analytical in<strong>for</strong>mation using a biological recognition element<br />
(biochemical receptor), which is retained in direct spatial contact with an<br />
electrochemical transduction element (Thévenot et al., 1999). This transducer<br />
trans<strong>for</strong>ms the recognition event into a measurable signal by means<br />
of a potentiostat (see complete set-up in figure 2). Whereas a chemiluminescent<br />
biosensor requires a spectrophometer or a camera to record a<br />
change of colour, electrochemical genosensors use molecular probes to<br />
<strong>de</strong>tect target nucleic acids in a sample by recording changes in an electrochemical<br />
signal. DNA (RNA) strands are used as the recognition element<br />
that can discriminate any target within a mixed assemblage by specific<br />
hybridisation of the capture and signal probes to the complementary target<br />
strand. The method provi<strong>de</strong>s a high selectivity, sensitivity and accuracy,<br />
typically from the millimolar to the femtomolar range with more or<br />
less 5% accuracy.<br />
Figure 2: Principles of a lab-benched electrochemical genosensor. A. Experimental<br />
set-up including the electrochemical sensor interface, the chip connector and<br />
the chip. B. Design of the three electro<strong>de</strong> cell printed on a ceramic substrate<br />
(© Dropsens, http://www.dropsens.com/).
148 Biodiversity<br />
Biosensors are also powerful tools <strong>for</strong> species <strong>de</strong>tection. Although some<br />
chemiluminescent biosensors have been <strong>de</strong>veloped (Scholin et al., 1996),<br />
those based on the direct electrochemical <strong>de</strong>tection of nucleic acid target<br />
molecules have successfully been applied by linking DNA or RNA<br />
hybridisation events onto an oligonucleoti<strong>de</strong>-modified electro<strong>de</strong> surface<br />
(Drummond et al., 2003). The simplicity, low power requirements, speed<br />
and accuracy of electrochemical biosensors have ma<strong>de</strong> them attractive<br />
candidates to overcome traditional limitations in HAB studies (Diercks<br />
et al., 2008b; 2011; Metfies et al., 2005). Moreover, the ability of electrochemical<br />
or chemiluminescent sensors to i<strong>de</strong>ntify directly nucleic acids<br />
in complex samples is a valuable advantage over other approaches, such<br />
as qPCR that requires target purification and amplification (Liao et al.,<br />
2007).<br />
The study of toxic algal blooms with genosensors is greatly facilitated by<br />
the use of DNA (rRNA) probes. The <strong>de</strong>tection strategy is usually based<br />
on a sandwich hybridisation assay (SHA) in which a target DNA or<br />
RNA is bound by both a capture and a signal probe (Rautio et al., 2003;<br />
Zammatteo et al., 1995). Only one of the two probes needs to be specific<br />
<strong>for</strong> the target species. A capture probe is immobilised on a semiconducting<br />
transducer plat<strong>for</strong>m, e.g. carbon or gold. If the target sequence binds<br />
to the capture probe in the first hybridisation event, its <strong>de</strong>tection takes<br />
place via a second hybridisation event with a signal probe linked to a<br />
recor<strong>de</strong>r molecule, such as fluorochromes or digoxigenin. An antibody<br />
linked to the recor<strong>de</strong>r molecule is coupled to a horseradish-peroxidase<br />
(HRP) enzyme <strong>for</strong> electrochemical signal amplification. HRP converts<br />
electrochemically inactive substrates to an electroactive product that<br />
can be <strong>de</strong>tected amperometrically, where the measured current is proportional<br />
to the analyte concentration in a sample (Metfies et al., 2005).<br />
Figure 3 shows the typical amperometric signal expected <strong>for</strong> a DNAbased<br />
biosensor using gold as transducer plat<strong>for</strong>m (signal c) as compared<br />
to the negative control and background (signals b and a, respectively).<br />
For monitoring of water samples, a calibration curve has to be <strong>de</strong>termined<br />
<strong>for</strong> each probe set to assess the current <strong>de</strong>nsity (nA.mm -2 ) <strong>for</strong> 1 ng<br />
RNA. For each target species, the RNA concentration per cell has to<br />
be investigated. Subsequently the cell concentration of the target<br />
species in a water sample can be calculated from the electrochemical<br />
signals. By using a different substrate the anti-digoxigenin antibody<br />
conjugated to HRP reacts to produce a green coloured product,<br />
the intensity of the reaction can be measured in a spectrophotometer<br />
or captured by a camera, thus <strong>for</strong>ming a chemiluminescent<br />
bio sensor.
Part II – Chapter 3 149<br />
Figure 3: Analytical signal recor<strong>de</strong>d by a DNA-based biosensor at a fix potential of<br />
-0.15 V. The signal inclu<strong>de</strong>s (a) background noise, (b) negative control current, and<br />
(c) positive control current with a DNA-based biosensor using gold as transducer<br />
surface. The signal of the positive control relative to the negative control is<br />
proportional to DNA concentration in the sample.<br />
Detection assays of oligonucleoti<strong>de</strong> probes involving the amplification of<br />
hybridisation signals through enzyme tracer molecules have the advantage<br />
of being ultrasensitive (Ronkainen-Matsuno et al., 2002). The assay <strong>for</strong>mat<br />
maximizes discrimination of the target sequences, and RNA purification<br />
is not required. Reactions are rapid, easy to execute and amenable to automation.<br />
Quantification of the target species can be per<strong>for</strong>med by using<br />
smaller, portable and inexpensive instrumentation and several probe sets<br />
have already been <strong>de</strong>veloped <strong>for</strong> this purpose. Such probes can simultaneously<br />
be measured by using the multichip connector shown in Figure 2.<br />
However, few reliable genosensors have been applied so far because one of<br />
their main drawbacks is their lack of robustness. Conditions, such as pH,<br />
temperature and ionic strength, and short-term stability, must be consi<strong>de</strong>red.<br />
3.2 Case studies of harmful algae<br />
The sandwich hybridisation assay with chemiluminescent <strong>de</strong>tection<br />
was first introduced by Scholin et al. (1996) <strong>for</strong> the <strong>de</strong>tection of Pseudonitzschia<br />
species in Cali<strong>for</strong>nia waters. A benchtop <strong>de</strong>vice is available and<br />
other prototypes operated from a buoy are currently being tested. Probes<br />
were initially <strong>de</strong>signed with sequence data from local populations, which<br />
proved to be non-specific when applied to other areas. This difficulty<br />
un<strong>de</strong>rlies the need to use a sequence database based on global isolates
150 Biodiversity<br />
when probes are <strong>de</strong>signed to make them universal. Currently, DNA<br />
probes <strong>for</strong> Pseudo-nitzschia spp., Alexandrium spp., Heterosigma akashiwo,<br />
Chattonella spp., Fibrocapsa japonica, a variety of Karenia spp., Karlodinium<br />
veneficum and Gymnodinium aureolum are available using the chemiluminescent<br />
<strong>de</strong>tection system in a semiautomatic robotic system (Scholin et<br />
al., 2003). Other species <strong>for</strong> whom sandwich hybridisation probes have<br />
been <strong>de</strong>veloped (e.g. Coccholodinium polykrikoi<strong>de</strong>s, Mikulski et al., 2008),<br />
have yet to be applied in a semiautomatic system. In New Zealand, this<br />
method has gained national accreditation and is used to monitor shellfish<br />
harvests (Ayers et al., 2005). A chemiliuminescent SHA <strong>de</strong>tection in a<br />
microliter plate <strong>for</strong>mat was also adapted as a rapid means to test probe<br />
specificity and some probe sets <strong>for</strong> 10 toxic algae were validated (Diercks<br />
et al., 2008c).<br />
Fibre optic genosensors have been applied to harmful algal cell enumeration<br />
of Alexandrium fundyense, Pseudo-nitzschia australis and Alexandrium<br />
ostenfeldii (An<strong>de</strong>rson et al., 2006). Biosensors <strong>for</strong> the <strong>de</strong>tection and i<strong>de</strong>ntification<br />
of the toxic dinoflagellate Alexandrium ostenfeldii and A. minutum<br />
were <strong>de</strong>veloped by Metfies et al. (2005). Development and adaptation of<br />
a multiprobe biosensor <strong>for</strong> the simultaneous <strong>de</strong>tection of 16 target species<br />
of toxic algae has been conducted but is not commercially available<br />
(Diercks et al., 2008a; Diercks et al., 2011). More recently, elucidation<br />
of the different steps of the biosensor fabrication process from the electrochemical<br />
point of view, proof of concept with different algal species,<br />
and evaluation of the influence of the transducer plat<strong>for</strong>m geometry and<br />
material has been published (Orozco et al., 2011a). Probe orientation and<br />
effect of the digoxigenin-enzymatic label in a sandwich hybridization <strong>for</strong>mat<br />
to <strong>de</strong>velop toxic algae biosensors have also been evaluated (Orozco<br />
et al., 2011b). However, a system enabling the i<strong>de</strong>ntification of a broad<br />
spectrum of toxic algal species and the in situ quantification of very low<br />
cell concentrations of cells is still unavailable and very much nee<strong>de</strong>d.<br />
3.3 Future prospects<br />
In the past <strong>de</strong>ca<strong>de</strong>, the application of biosensor technology has gained<br />
significant impact in microbial <strong>ecology</strong>. In the European Union (EU)<br />
FP6-project Alaga<strong>de</strong>c, a portable semi-automated electrochemical biosensor-system<br />
was <strong>de</strong>veloped in or<strong>de</strong>r to facilitate the <strong>de</strong>tection of toxic<br />
algae in the field. This <strong>de</strong>vice enables the electrochemical <strong>de</strong>tection of<br />
microalgae from water samples in less than two hours, without the need<br />
of expensive equipment. In the future, autonomous biosensors will be<br />
combined with in situ measurement systems <strong>for</strong> monitoring of the marine<br />
environment. The Scholin chemiluminescent SHA has been adapted <strong>for</strong><br />
real time measurements in a buoy, the environmental sample processor
Part II – Chapter 3 151<br />
(ESP, Greenfield et al., 2006). Toxin analysis by antibody/antigen <strong>de</strong>tection<br />
methods (Elisa) and most recently qPCR (Preston et al., 2011) have<br />
also been ad<strong>de</strong>d to this plat<strong>for</strong>m, which will serve the need <strong>for</strong> high resolution<br />
monitoring of marine phytoplankton in or<strong>de</strong>r to evaluate consequences<br />
of environmental change in the oceans.<br />
Whereas the chemiluminescent robotic system is already commercialised,<br />
the hand held electrochemical <strong>de</strong>vice is still a prototype and is<br />
not available <strong>for</strong> purchase. Nevertheless, probes exist only <strong>for</strong> a limited<br />
number of phytoplankton and must be validated <strong>for</strong> each region where<br />
they are applied, calibration curves must be generated <strong>for</strong> each probe<br />
set, and high sample volume (ca. 5L) are required if the cell <strong>de</strong>nsities are<br />
relatively low (un<strong>de</strong>r the limit of <strong>de</strong>tection of the methods). Validation<br />
of probe signals against total rRNA and over the growth cycle of the<br />
algae un<strong>de</strong>r different environmental conditions has to be carried out to<br />
infer cell numbers be<strong>for</strong>e the method can be applied to wild samples. In<br />
addition, manual RNA isolation should be done by a trained molecular<br />
scientist, because a large amount of good quality target rRNA is required<br />
<strong>for</strong> these assays. The manual isolation of RNA is currently the limiting<br />
factor of all systems. It has been found out that different users can isolate<br />
different qualities of rRNA from the same sample. An automated RNA<br />
isolation, <strong>de</strong>veloped during the Alga<strong>de</strong>c-project and the lysis methods<br />
available in Scholin’s environmental sample processor should overcome<br />
these difficulties.<br />
4. Microarrays-based method<br />
4.1. General principles<br />
At the core of the DNA microarray technology is a DNA microchip that<br />
contains an array of oligonucleoti<strong>de</strong>s, PCR-products, or cDNAs spotted<br />
onto a small surface, e.g. a glass sli<strong>de</strong>. Recently <strong>de</strong>veloped DNAmicroarray-technology<br />
allows the simultaneous analysis of up to 250,000<br />
probes at a time (Lockhart et al., 1996). DNA-microarray technology has<br />
enormous potential to be used as a method to analyse samples from complex<br />
environments, because it provi<strong>de</strong>s a rapid tool without a cultivation<br />
step. Target nucleic acids are labelled with a fluorescent dye prior to their<br />
hybridisation to the probes on the DNA chip. The fluorescence pattern<br />
on the DNA-chip after the hybridisation of the target DNA is then analysed<br />
with a fluorescent laser-scanner (DeRisi et al., 1997, see figure 4).<br />
When these probes <strong>de</strong>tect species, the microarray has been termed a phylochip<br />
and these are essentially barco<strong>de</strong>s <strong>for</strong> species and their automated<br />
application.
152 Biodiversity<br />
Figure 4: Spotting scheme <strong>for</strong> the first generation Midtal microarray. The microarray<br />
consists in two supergrids ma<strong>de</strong> out of four grids installed on a microscopic sli<strong>de</strong>.<br />
Each position in the grid represents a spot of ca. 50µm in diameter where a given<br />
probe is immobilised. Each probe (colour co<strong>de</strong>d) is spotted four times. This<br />
generation of the microarray has 960 spots, covering 112 probes <strong>for</strong> toxic algal<br />
species and higher taxon levels, and various positive and negative control probes.<br />
The basis of the immuno-microarray is an immunoassay that has been miniaturised.<br />
In immunoassays, a competitive <strong>for</strong>mat is applied where the antigens<br />
are miniaturised in diminutive spots on a small surface. Fluorescently<br />
labelled antibodies compete with the analytes in the sample to conjugate<br />
with the miniaturised antigen. Unbound antibodies from the sample are<br />
then free to bind to the microarray, and the lower the signal, the higher the<br />
concentration of analytes (toxins) present in the field sample. Fluorescence<br />
emission is measured with techniques such as confocal or CCD microarray<br />
scanners. For optimal use in a monitoring program, it is absolutely necessary<br />
to validate the signal intensity against known cell counts un<strong>de</strong>r various<br />
environmental conditions, because absolute cell numbers are the basis <strong>for</strong><br />
HAb studies. The principal advantage of the method is that thousands of<br />
probes can be miniaturised on a single chip. The primary disadvantage is
Part II – Chapter 3 153<br />
the high cost, which is a ca. 25€ per sample (Gescher et al., 2010) and the<br />
need to make a calibration curve <strong>for</strong> each probe on the chip.<br />
The first DNA-microchip to study microbial diversity was to analyse samples<br />
from nitrifying bacteria, which are difficult to study by cultivation<br />
(Guschin et al., 1997). A hierarchical set of oligonucleoti<strong>de</strong> probes targeting<br />
the 16S ribosomal RNA was created to analyse the bacterial samples on the<br />
DNA-chip. Hierarchical ribosomal RNA probes are now available <strong>for</strong> many<br />
species of algae (Groben and Medlin, 2005), and some of them are available<br />
in a microarray <strong>for</strong>mat (Metfies and Medlin, 2004, Gescher et al., 2008a;<br />
2008b, Midtal: www.midtal.com). These techniques have been tested in the<br />
field and have shown congruence with results obtained by flow cytometry<br />
and FISH hybridisation (Metfies et al., 2010; Gescher et al., 2008b).<br />
4.2. Case studies of harmful algae<br />
There are no published case studies directly applying microarrays to toxic<br />
algae. However, microarrays have been the subject of several EU projects.<br />
In FP5 Picodiv and Micropad projects, microarrays were <strong>de</strong>veloped <strong>for</strong><br />
algae and protozoa, and results from chip hybridisation were favourably<br />
compared to other measurements of diversity, i.e., direct cell counts and<br />
clone libraries (Medlin et al., 2006). In these two projects, the microarrays<br />
were in early stages of <strong>de</strong>velopment and proof of principle was the major<br />
outcome because it was discovered that probes ma<strong>de</strong> <strong>for</strong> fluorescence in situ<br />
hybridization (FISH) could not be directly transferred to a microarray chip<br />
<strong>for</strong>mat (Metfies and Medlin, 2008). With a few exceptions, nearly every<br />
probe had to be modified <strong>for</strong> a successful use in the microarray chip <strong>for</strong>mat.<br />
Problems with transferring FISH probes to a microarray chip <strong>for</strong>mat<br />
led workers in the EU project Midichip to modify their probes and microarrays<br />
<strong>for</strong> cyanobacteria. The updated method involved additional steps as<br />
compared to the one step hybridisation found on most microarrays. The<br />
FP6 project FISH AND CHIPS ma<strong>de</strong> use of prototype findings to <strong>de</strong>velop<br />
a microarray chip <strong>for</strong> phytoplankton at the class level. Field data were analysed<br />
over three years with rRNA as the preferred target molecule (Gescher<br />
et al., 2008b, Metfies et al., 2010). A microarray <strong>for</strong> toxic species in the<br />
dinoflagellate genus Alexandrium was also <strong>de</strong>veloped but not field tested<br />
(Gescher et al., 2008a). In the EU project Aquachip, pathogenic bacteria<br />
were the target of interest. This project <strong>de</strong>veloped a chip <strong>for</strong> five bacteria<br />
but they were not wi<strong>de</strong>ly tested with environmental samples. In addition,<br />
the <strong>de</strong>tection system <strong>de</strong>veloped <strong>for</strong> this chip was based on a microtiter plate<br />
system with <strong>de</strong>tection un<strong>de</strong>r a fluorescent microscope. This is not a standard<br />
protocol that can be used in a commercial microarray chip rea<strong>de</strong>r, and<br />
there<strong>for</strong>e was never commercialised. In EU FP7 project Midtal (www.midtal.com),<br />
a species microarray with 163 hierarchical probes <strong>for</strong> species of
154 Biodiversity<br />
toxic algae is in early stages of <strong>de</strong>velopment (figure 4). Field testing has just<br />
begun with high correlation between microarray signal intensity and field<br />
counts. The toxin microarray in Midtal, which is based on surface plasmon<br />
resonance, <strong>de</strong>tects changes in mass when the antibody binds to the toxin.<br />
This microarray can simultaneously <strong>de</strong>tect 4 different toxins (saxitoxins,<br />
neosaxitoxin, okadaic acid, and domoic acid) in a competitive assay <strong>for</strong>mat.<br />
4.3. Future prospects<br />
A common problem in all of the phylochip assays is the wi<strong>de</strong> variation in<br />
signal strength of the various probes. In Midtal, the signal of the probes<br />
on the microarrays was enhanced by increasing probe length to 25 nucleoti<strong>de</strong>s<br />
instead of 18 and also by adding a longer spacer region to lift the<br />
probes above the surface of the microarray so that there is more space <strong>for</strong><br />
the hybridisation to occur. A fragmentation protocol to break the RNA<br />
into small pieces has also been optimised to prevent strong secondary<br />
structure <strong>for</strong>mation <strong>for</strong> signal enhancement (figure 5). Calibration curves<br />
will be produced <strong>for</strong> each probe on the microarray. This is the most timeconsuming<br />
step nee<strong>de</strong>d to make the microarray quantitative because culture<br />
experiments have to be established to measure the amount of RNA<br />
per cell un<strong>de</strong>r different abiotic conditions and to equate RNA content to<br />
cell numbers accurately. However, once these calibrations are done, the<br />
microarray becomes a very valuable and fast tool to measure community<br />
responses over broad ranges in space and time.<br />
Figure 5: Hybridisation of fragmented RNA in increasing fragmentation temperature.<br />
The probes tested in this study are: bathy01 (a), Pra507 (b), Chlo02 (c), Crypt01 (d),<br />
CryptoA (e), Crypt01-25A (f), Crypt03-26 (g), ATWE03 (h), DinoE-12 (i), LPoLyJ<br />
(j), Prym01-A (k), Pela02 (l), PNFRAGA (m), Psnmulti A-17 (n), Psn seriA+11 (o), and<br />
NS04 (p). Probes with lower signals are enhanced by fragmentation by increasing<br />
temperatures from 40°C to 70°C. Data shown are the signal to noise ratio of the<br />
hybridisation signal according to the probes tested at different temperatures.
Part II – Chapter 3 155<br />
Authors’ references<br />
Linda K. Medlin:<br />
Université Pierre et Marie Curie, Laboratoire d’Océanographie Microbienne,<br />
UPMC-CNRS UMR 7621, Banyuls-sur-mer, France<br />
Jahir Orozco-Holguin:<br />
University of Cali<strong>for</strong>nia, San Diego (UCSD), Department of Nano-<br />
Engineering, La Jolla, USA<br />
Kerstin Toebe:<br />
Alfred Wegeneer Institute <strong>for</strong> Polar and Marine Research, Bremerhaven,<br />
Germany<br />
Corresponding author: Linda K. Medlin, medlin@obs-banyuls.fr<br />
Aknowledgement<br />
J.O. was supported by a Postdoctoral Fellowship from The Institut National<br />
<strong>de</strong>s Sciences <strong>de</strong> l’Univers (INSU), France. This work was partially supported<br />
by EU FP7 MIDTAL.<br />
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Metfies K., Medlin L. K., 2008. Feasibility of transferring FISH-probes to an<br />
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Metfies K., Medlin L. K., 2007. Refining cryptophyte i<strong>de</strong>ntification with DNAmicroarrays.<br />
Journal of Plankton Research, 29, pp. 1071-1076.<br />
Mikulski C. M., Park Y. T., Jones K. L., Lee C. K., Limb W. A., Lee Y., Scholin<br />
C. A., Doucette G. J., 2008. Development and field application of rRNAtargeted<br />
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Moon-van <strong>de</strong>r Staay S. Y., De Wachter R., Vaulot D., 2001. Oceanic 18S rDNA<br />
sequences from picoplankton reveal unsuspected eukaryotic diversity.<br />
Nature, 409, pp. 607-610.<br />
Moorthi S. D., Countway P. D., Stauffer b. A., Caron D. A., 2006. Use<br />
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ti<strong>de</strong> dinoflagellate Lingulodinium polyedrum. Microbial Ecology, 52,<br />
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Jolla, pp. 527-544.<br />
O’Halloran C., Silver M. W., Holman T. R., Scholin C. A., 2006. Heterosigma<br />
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Orozco J., Medlin L. K., 2011a. Electrochemical per<strong>for</strong>mance of a DNA-based<br />
sensor <strong>de</strong>vice <strong>for</strong> <strong>de</strong>tecting toxic algae. <strong>Sensors</strong> and Actuators B: Chemical,<br />
153, pp. 71-77.<br />
Orozco J., Baudart J., Medlin L. K., 2011b. Evaluation of probe orientation<br />
and effect of the digoxigenin-enzymatic label in a sandwich hybridization<br />
<strong>for</strong>mat to <strong>de</strong>velop toxic algae biosensors. Harmful Algae, 10, pp. 489-494.<br />
Park T. G., <strong>de</strong> Salas M. F., Bolch C. J. S., Hallegraeff G. M., 2007. Development<br />
of a real-time PCR probe <strong>for</strong> quantification of the heterotrophic<br />
dinoflagellate Cryptoperidiniopsis brodyi (Dinophyceae) in environmental<br />
samples. Applied and Environmental Microbiology, 73, pp. 2552-2560.<br />
Penna A., Magnani M., 1999. I<strong>de</strong>ntification of Alexandrium (Dinophyceae)<br />
species using PCR and rDNA-targeted probes. Journal of Phycology, 35,<br />
pp. 615-621.<br />
Preston C. M., Harris A., Ryan J. P., Roman B., Marin III R., Jensen S., Everlove<br />
C., Birch J., Dzenitis J. M., Pargett D., Adachi M., Turk K., Zehr J. P.,<br />
Scholin C. A., 2011. Un<strong>de</strong>rwater application of quantitative PCR on an<br />
ocean mooring. PloS One, 6, e22522.<br />
Rautio J., Barken K. B., Lah<strong>de</strong>npera J., Bretenstein A., Molin S., Neubauer P.,<br />
2003. Sandwich hybridization assay <strong>for</strong> quantitative <strong>de</strong>tection of yeast<br />
RNAs in cru<strong>de</strong> cell lysates. Microbial Cell Factories, 2, pp. 4.<br />
Ronkainen-Matsuno N. J., Thomas J. H., Halsall H. B., Heinemann W. R.,<br />
2002. Electrochemical immunoassay moving into the fast lane. Trends in<br />
Analytical Chemistry, 21, pp. 213-225.<br />
Saun<strong>de</strong>rs N. A., 2004. Introduction to Real-Time PCR, in: Edwards K. Logan<br />
J., Saun<strong>de</strong>rs N. A., (Eds.), Real-Time PCR. An Essential Gui<strong>de</strong>. Horizon<br />
Bioscience, Norfolk, pp. 1-11.<br />
Scholin C. A., Buck K. R., Britschig T., Cangelosi G., Chavez F. P., 1996.<br />
I<strong>de</strong>ntification of Pseudo-nitzschia australis (Bacillariophyceae) using<br />
rRNA-targeted probes in whole cell and sandwich hybridization <strong>for</strong>mats.<br />
Phycologia, 35, pp. 190-197.<br />
Scholin C. A., Marin III R., Miller P. E., Doucette G. J., Powell C. L., Haydock<br />
P., Howard J., Ray J., 1999. DNA probes and a receptor-binding assay<br />
<strong>for</strong> <strong>de</strong>tection of Pseudo-nitzschia (Bacillariophyceae) species and domoic<br />
acid activity in cultured and natural samples. Journal of Phycology, 35,<br />
pp. 1356-1367.<br />
Scholin C. A., Vrieling E., Peperzak L., Rho<strong>de</strong>s L., Rublee P., 2003. Detection<br />
of HAB species using lectin, antibody and DNA probes, in: Hallegraeff<br />
G. M., An<strong>de</strong>rson D. M., Cembella A. D. (Eds), Manual on harmful marine<br />
microalgae 11. Intergovernmental oceanographic commission of Unesco,<br />
Paris, pp. 131-164.<br />
Steidinger K. A., Landsberg J. H., Mason P. L., Vogelbein W. K., Tester P. A.,<br />
Litaker R. W., 2006. Cryptoperidiniopsis brodyi gen. et sp. nov. (Dinophyceae),<br />
a small lightly armoured dinoflagellate in the Pfiesteriaceae. Journal of<br />
Phycology, 42, pp. 951-961.
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Thévenot D., Toth K., Durst R. A., Wilson G., 1999. Electrochemical biosensors:<br />
recommen<strong>de</strong>d <strong>de</strong>finitions and classification. Pure and Applied Chemistry,<br />
71, pp. 2333-2348.<br />
Touzet N., Keady E., Raine R., Maher M., 2009. Evaluation of taxa-specific<br />
real time PCR, whole-cell FISH and morphotaxonomy analyses <strong>for</strong> the<br />
<strong>de</strong>tection and quantification of the toxic microalgae Alexandrium minutum<br />
(Dinophyceae), Global Cla<strong>de</strong> ribotype. FEMS Microbiology Ecology, 67,<br />
pp. 329-341.<br />
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Kotsopoulos S. K., Samuels M. L., Hutchison J. B., Larson J. W., Topol E.<br />
J., Weiner M. P., Harismendy O., Olson J., Link D. R, Frazer K. A., 2009.<br />
Microdroplet-based PCR enrichment <strong>for</strong> large-scale targeted sequencing.<br />
Nature Biotechnology, 27, pp. 1025-1031.<br />
Tyrrell J. V., Connell L. B., Scholin C. A., 2002. Monitoring <strong>for</strong> Heterosigma<br />
akashiwo using a sandwich hybridization assay. Harmful Algae, 1,<br />
pp. 205-214.<br />
Wang J., 2006. Electrochemical biosensors: Towards point-of-care cancer<br />
diagnostics. Biosensors and Bioelectronics, 21, pp. 1887-1892.<br />
Zammatteo N., Moris P., Alexandre I., Vaira D., Piette J., Remacle J., 1995.<br />
DNA probe hybridization in microwells using a new bioluminescent<br />
system <strong>for</strong> the <strong>de</strong>tection of PCR-amplified HIV-1 proviral DNA. Journal<br />
of Virological Methods, 55, pp. 185-197.<br />
Zamor R. M., Glenn K. L., Hambright K. D., 2011. Incorporating molecular<br />
tools into routine HAB monitoring programs: using qPCR to track<br />
invasive Prymnesium. Harmful Algae, in press.<br />
Zhang H., Lin S., 2005. Development of a cob-18S rDNA real-time PCR assay<br />
<strong>for</strong> quantifying Pfiesteria shumwayae in the natural environment. Applied<br />
and Environmental Microbiology, 71, pp. 7053-7063.
Chapter 4<br />
Automatic particle analysis as sensors<br />
<strong>for</strong> life history studies in experimental<br />
microcosms<br />
François Mallard, Vincent Le Bourlot, Thomas Tully<br />
1. Introduction<br />
Biodiversity is expected to be heavily damaged by the alarming effects of<br />
climate change in the next <strong>de</strong>ca<strong>de</strong>s and centuries (Leadley et al., 2010).<br />
Assessing how populations will respond to environmental change is crucial<br />
if one wants to predict the consequences of global change on biodiversity.<br />
The <strong>de</strong>nsity, phenotypic structure and genetic composition of a<br />
population are shaped by extrinsic variations of the environment, intrinsic<br />
regulatory mechanisms such as <strong>de</strong>nsity <strong>de</strong>pen<strong>de</strong>nce mechanisms, and<br />
complex interactions between both extrinsic and intrinsic factors. These<br />
processes <strong>de</strong>termine whether <strong>de</strong>leterious environmental changes can lead<br />
either to a local population extinction (Drake and Griffen, 2010; Griffen<br />
and Drake, 2008; Sinervo et al., 2010) or its rescue through plastic or<br />
genetic adaptation (Bell and Gonzalez, 2009; 2011; Chevin and Lan<strong>de</strong>,<br />
2010). Thus, it is especially important to monitor population changes and<br />
get accurate measurements of the factors that regulate the size and structure<br />
of a population so as to un<strong>de</strong>rstand how they will react to environmental<br />
changes.<br />
Studying how life history traits (individual fitness or <strong>de</strong>mographic components)<br />
respond to environmental changes is wi<strong>de</strong>ly done in evolutionary<br />
<strong>ecology</strong>. In this context, the most common traits un<strong>de</strong>r study in<br />
population dynamics are age and size at maturity, reproductive output<br />
(fecundity, egg size, intervals between reproductive events), longevity<br />
and mortality rates (Braendle et al., 2011). These traits are <strong>de</strong>termined
164 Biodiversity<br />
by a combination of genetic and environmental effects. When the value<br />
of a trait changes according to an environmental factor, it is consi<strong>de</strong>red<br />
as phenotypically plastic (Scheiner, 1993). The shape of its relationship<br />
with the environmental factor (also called a reaction norm) is essential to<br />
assess the population responses to environmental change (Flatt, 2005).<br />
By linking life-history variation with the genetic makeup of an organism,<br />
the interplay between population dynamics and evolutionary dynamics<br />
can also be addressed (Saccheri and Hanski, 2006). As a matter of fact,<br />
most populations are composed of a mixture of different categories of<br />
individuals and, even in an extreme case of a clonal population where all<br />
individuals share the same genotype, there is variation in age, size or body<br />
condition <strong>for</strong> instance.<br />
Life-history variation has to be taken into account and measured to better<br />
un<strong>de</strong>rstand how a population behaves (De Roos, 2008; Tuljapurkar<br />
et al., 2009). Thus, one of the dreams of a population ecologist would<br />
be to follow in parallel the dynamics and structure of a population,<br />
and the life-histories of every individual the population is ma<strong>de</strong> up of.<br />
This would enable the un<strong>de</strong>rstanding of how population growth, population<br />
dynamics, individual phenotype and life-history traits influence<br />
one another (Pelletier et al., 2007; Coulson et al., 2006; ozgul et al.,<br />
2009; Pettorelli et al., 2011). Un<strong>for</strong>tunately, following simultaneously<br />
the dynamic of a whole population and the growth and reproductive<br />
trajectory of its individuals remains a Holy Grail quest especially <strong>for</strong><br />
animal populations in the wild because of the mobility and elusiveness<br />
of the tracked individuals. In this chapter, we discuss the use of<br />
a new generation of sensors, based on automated image analysis from<br />
microcosm experiments, to address limitations of ecological methods in<br />
previous studies.<br />
2. State of the art and objectives<br />
Several studies in the wild have quantified to which extent the population<br />
growth and dynamics are controlled by the un<strong>de</strong>rlying life-history strategies<br />
of individuals within the population (Coulson et al., 2006; Pettorelli<br />
et al., 2011). These studies are based on longitudinal follow-up both at<br />
the population and individual levels, the individuals being marked and<br />
recaptured. A longitudinal follow-up of individuals is crucial if one wants<br />
to address fundamental questions about the patterns of life-history traits<br />
throughout life. However, a longitudinal follow-up of wild animals is in<br />
general very time-consuming and can only be applied to a part of the<br />
studied population.
Part II – Chapter 4 165<br />
Given the difficulty of gathering relevant <strong>de</strong>mographic data on wild<br />
animals both at the population and individual levels, researchers have<br />
looked <strong>for</strong> complementary and more convenient experimental mo<strong>de</strong>l<br />
approaches, in particular microcosms experiments (Benton et al., 2007).<br />
Because of their relatively short generation time and ease of rearing, several<br />
small arthropods have been used as mo<strong>de</strong>l organisms in microcosm<br />
experiments in or<strong>de</strong>r to study in parallel population dynamics and lifehistory<br />
traits including Daphnia (Drake and Griffen, 2009; Hebert, 1978),<br />
Drosophila (Mueller et al., 2005), mites (Benton and Beckerman, 2005),<br />
and collembola (Ellers et al., 2011; Tully and Ferrière, 2008). Data collection,<br />
however, is often ma<strong>de</strong> visually in most microcosm experiments.<br />
This may be accurate enough when populations are sufficiently small and<br />
close to extinction (Drake and Lodge, 2004; Pike et al., 2004) but it<br />
becomes very time-consuming or impossible to do <strong>for</strong> larger populations.<br />
For instance, experimental mite populations are studied by daily<br />
counts of individuals using a binocular microscope and hand-held<br />
counter. When the <strong>de</strong>nsity is too high to be measured visually, it is<br />
estimated by extrapolating measurements ma<strong>de</strong> on a sub-sample of the<br />
population (Bowler and benton, 2011; Plaistow and benton, 2009). This<br />
procedure not only takes time but it is also prone to errors, including<br />
differences among observers, and it only gives coarse measurement of<br />
the life-history.<br />
Alternatively, measurements can be ma<strong>de</strong> on digital images of individuals<br />
or populations. Digital images are i<strong>de</strong>al sources of in<strong>for</strong>mation<br />
<strong>for</strong> phenotypic and <strong>de</strong>mographic studies in microcosm experiments,<br />
because images can be collected very rapidly, they are cheap and can be<br />
stored and re-observed if necessary, and the procedure of taking images<br />
is generally harmless. For example, Stemman et al. (II, 2) <strong>de</strong>scribes how<br />
image analysis can be used to i<strong>de</strong>ntify and separate particles and plankton<br />
species by size in pelagic, marine environments. In images from<br />
microcosm experiments, the extraction of life-history data can be ma<strong>de</strong><br />
by hand on a computer using appropriate image analysis software such<br />
as ImageJ (Abramoff et al., 2004) to estimate <strong>for</strong> instance egg and body<br />
sizes (Tully and Ferrière, 2008). However, this non-automated procedure<br />
is time-consuming and quickly becomes impractical when one wants<br />
to closely follow hundreds of individuals each laying hundreds of eggs,<br />
or dozens or more populations composed of hundreds of individuals<br />
each. Automatic counting and measuring is then nee<strong>de</strong>d. Some commercial<br />
softwares provi<strong>de</strong> such services but they are often unaf<strong>for</strong>dable<br />
and closed source.<br />
Previous studies <strong>de</strong>signed and proposed image analysis methods to automatically<br />
track or count small organisms such as small arthropods in the<br />
laboratory (Krogh et al., 1998, Auclerc et al., 2010; Lukas et al., 2009).
166 Biodiversity<br />
The method of Krogh et al. (1998) is adapted <strong>for</strong> white collembolan<br />
species and requires immobilising the individuals by CO 2<br />
anaesthesia<br />
and transferring them on an even, black surface. The method there<strong>for</strong>e<br />
requires in practice long and <strong>de</strong>licate manipulations. Other image analysis<br />
methods usually require a very contrasted and even background,<br />
which is rarely the case in microcosms, or a polarised filter to prevent<br />
light reflection on the background. Hooper et al. (2006) <strong>de</strong>veloped an<br />
image analysis setup <strong>for</strong> measuring Daphnia population size, but this<br />
method does not allow recursive automatic counting because nondaphnid<br />
objects (noise and impurities on the glass) had to be manually<br />
<strong>de</strong>leted be<strong>for</strong>e automatic enumeration of the Daphnia on their pictures.<br />
Using these different methods as a routine <strong>for</strong> counting and measuring<br />
individuals in an experimental population must there<strong>for</strong>e be banished.<br />
Some authors have used morphological image processing tools to reduce<br />
the noise in the background and help to i<strong>de</strong>ntify the individuals (here<br />
collembolan) on the images (Marçal and Carida<strong>de</strong>, 2006). However, this<br />
method is prone to errors: it does not permit the elimination of particles<br />
that look like collembolans and <strong>de</strong>ad collembolans will be counted and<br />
measured.<br />
We present hereafter a method that we <strong>de</strong>veloped to automate the measurements<br />
of i) some fundamental life-history traits (size, growth, fecundity)<br />
of a collembolan that is used as a mo<strong>de</strong>l organism and ii) the<br />
<strong>de</strong>nsity and fine scale size structure of collembolan populations reared<br />
in microcosms. Our method can be applied in general to count and<br />
measure some animals (or some particles) that are moving on a motionless<br />
background. It requires simple material (a digital camera, a stand<br />
and a good lighting <strong>de</strong>vice) and the freely available, open-source image<br />
processing software ImageJ (Abràmoff et al., 2004). We first give some<br />
<strong>de</strong>tails about our mo<strong>de</strong>l organism be<strong>for</strong>e presenting the principle of<br />
our method and the <strong>de</strong>vice settings that we use. We then explain more<br />
precisely how the images are processed to extract relevant in<strong>for</strong>mation<br />
from the background. Lastly, examples of analyses with large data sets<br />
collected a on large number of individuals are presented, such as the<br />
follow-up of growth trajectory of isolated individuals, the growth of a<br />
cohort, or the fluctuations of both population size and structure.
Part II – Chapter 4 167<br />
3. Description of the methods<br />
3.1. The biological mo<strong>de</strong>l<br />
In our study, we used the springtail Folsomia candida (Collembola,<br />
Isotomidae) as a mo<strong>de</strong>l organism. This species is convenient to breed in<br />
the laboratory (Fountain and Hopkin, 2005) and is used as a mo<strong>de</strong>l organism<br />
in ecotoxicology, <strong>ecology</strong> and evolution (Fountain and Hopkin, 2001;<br />
Tully and Lambert, 2011). The collembolans are bred in small boxes of<br />
about 5cm in diameter whose bottoms have been filled with a 2 to 3cm<br />
thick layer of plaster of Paris. Plaster of Paris is perfect <strong>for</strong> growing collembolans<br />
since it keeps a high level of moisture in the boxes. It also has<br />
the advantage of providing a flat two-dimensional environment, which<br />
is i<strong>de</strong>al to observe and count all the creepy-crawlies wan<strong>de</strong>ring in the<br />
box. To enhance the contrast between the collembolans that are white<br />
and their background, the plaster was darkened with some Indian ink.<br />
However, a completely dark and homogeneous background is not necessary<br />
<strong>for</strong> efficient image processing (see below). More <strong>de</strong>tails about our<br />
rearing conditions of the collembolan can be found in Tully and Ferrière<br />
(2008) and Tully and Lambert (2011).<br />
3.2. Camera settings and lighting unit<br />
We used a digital camera (Nikon D300) equipped with a 60mm macro<br />
lens and fixed on a camera stand. The camera is connected to a computer<br />
and is driven through the software Camera Control Pro from Nikon that<br />
enables the adjustment and control of the camera settings (figure 1). We<br />
took some 8 bit grey pictures of 4,288 × 2,848 pixels, saved as slightly<br />
compressed .JPEG files. We used several LED bulbs such as powerful<br />
Pikaline bulbs (16W, 650 lumens) that generate relatively constant, homogeneous<br />
and strong lighting. The lighting unit has to provi<strong>de</strong> a light as<br />
homogeneous as possible but our image analysis can compensate <strong>for</strong> some<br />
heterogeneity in the lighting as discussed below. However, the stability of<br />
lighting conditions between pictures in a stack is more important. The<br />
powerful lighting unit enables to shoot with short aperture time (1/100)<br />
and small aperture (F36), which ensures sharp pictures with large <strong>de</strong>pth of<br />
field. Artificial lighting using fluorescent light bulbs is not recommen<strong>de</strong>d<br />
because the light intensity fluctuates at 50Hz frequency, which generates<br />
substantial lighting heterogeneity between pictures when using an aperture<br />
time shorter than 1/50s. We also avoid using incan<strong>de</strong>scent light bulbs<br />
since they produce a lot of heat that can harm or disturb our organisms.
168 Biodiversity<br />
Figure 1: The camera stand with the camera and the LED lighting unit on, ready to<br />
take pictures of the rearing box in the centre.<br />
3.3 Principle of analysis<br />
Our method consists in taking a set of several (usually three to five) images<br />
of our rearing boxes un<strong>de</strong>r the same conditions. Between each image of a<br />
box, we blow lightly in it to ensure that each living individual has moved<br />
between the first and the last shot. These images are then compared using<br />
the ImageJ software (http://rsbweb.nih.gov/ij/) to generate a new image<br />
composed of all elements that remained motionless within the set (figure<br />
2). This generated image, called the still background, is then subtracted<br />
to each picture. This produces a stack of images that only contains the<br />
mobile elements, here the collembolans that have moved between pictures.<br />
These elements are then counted and measured after scaling the images.
Part II – Chapter 4 169<br />
Figure 2: The principle of image analysis using the ImageJ multitracker procedure.<br />
A. one of the images of the original set. Left, the rearing box contains a population<br />
of collembolans and can be scaled relative to a graph paper (on the top) or a black<br />
square automatically recognised by the plugin. Right, a <strong>de</strong>tailed view of one area<br />
of the rearing box. B. The motionless image obtained by comparing four different<br />
pictures from the same set to keep the elements that did not move. This motionless<br />
image is then used to <strong>de</strong>tect the outline of the box – the area of interest to <strong>de</strong>tect<br />
particles (C). D. A subtraction between the original image and the motionless<br />
picture followed by a thresholding procedure yields a new image where the particles<br />
can be counted and measured.
170 Biodiversity<br />
The structure and size of the population is stored in a text file. The principle<br />
of this analysis is inspired by the particle analysis procedure <strong>de</strong>veloped<br />
in the ImageJ multitracker plugin (Kuhn, 2001). An almost perfectly even<br />
and contrasted background is not nee<strong>de</strong>d to calculate the still background,<br />
which allows the measurements to be taken directly in the rearing boxes<br />
and minimise disturbances (see figures 2, 3 and 4). Only the moving particles<br />
are measured so that <strong>de</strong>ad animals are discar<strong>de</strong>d from the counting.<br />
3.4. Image analysis<br />
Once the still background is removed from the set of pictures, the ImageJ<br />
software can be used to count and measure the particles. A thresholding<br />
procedure is then nee<strong>de</strong>d to trans<strong>for</strong>m our 8-bits image that contains 256<br />
levels of grey into a black and white 2-bits image. One has to choose a<br />
threshold value that is the grey level above which the pixels will become<br />
white and un<strong>de</strong>r-which they will become dark. It is then possible to count<br />
and measure the white particle on the black background (figure 3). To<br />
facilitate this thresholding procedure when a single picture is analysed,<br />
users have to i) control the overall luminosity on each picture and to<br />
ii) maximise the contrast between the particles of interest and its background.<br />
The first constraint ensures selecting particles with the same precision<br />
within a picture (homogeneous lighting). The second one allows<br />
getting a straight discrimination between particles and the background<br />
such that precise and repeatable measurements can be obtained with different<br />
thresholds in a broa<strong>de</strong>r range of values.<br />
Our measurement method guarantees great precision while allowing a<br />
reliable automatic thresholding: removing the motionless background<br />
corrects relative lack of enlightenment homogeneity and increases the<br />
contrast between moving particles and the background that becomes<br />
homogeneously black (figure 3). The user no longer needs to choose by<br />
hand an appropriate threshold and the software can be programmed to<br />
automatically run the analyses. However, our method is sensitive to local<br />
variations of lighting between pictures within the stack. In some cases,<br />
the moving particles can create shadows that darken their surrounding<br />
substrate, which may reduce the efficiency of the removal of the motionless<br />
pixels. This may generate some noise in the background. Providing<br />
omnidirectional lighting reduces the <strong>for</strong>mation of shadows and easily<br />
prevents these annoying effects.
Part II – Chapter 4 171<br />
Figure 3: Comparison of two particle analysis methods. A. The original picture to be<br />
analysed: the collembolans lay on an inhomogeneous substrate. B, C and D. The same<br />
picture from which a thresholding procedure has been applied. In B, the motionless<br />
background has been removed be<strong>for</strong>e the process and the particles are reliably<br />
<strong>de</strong>tected. Without removal of the motionless background, no threshold level gives<br />
satisfying results: in C, the threshold level is too low and some unwanted background<br />
elements are kept (red arrow); whereas in D, the level is both too high to select all the<br />
individuals (red arrow) and too low to remove all the non-living elements.<br />
3.5. Time requirement and versatility<br />
This automatic particle measuring and counting method enabled us to<br />
analyse a large number of samples in a relatively short amount of time.<br />
For instance, it takes about a little less than two hours to shoot a hundred<br />
populations (5 pictures/populations) and to sort these pictures on<br />
the computer. It then takes about one hour <strong>for</strong> the plugin to analyse the<br />
whole 500 pictures and count and measure all the individuals in these<br />
populations (20 to 30sec per set of 5 pictures on a 2.5GHz computer).<br />
Although the procedure was <strong>de</strong>veloped and adapted to our collembolan<br />
system, it is versatile enough to be tuned and adapted to many different<br />
systems as long as there is a still background on which some particles<br />
randomly move. To scale our measurements into millimetres rather than
172 Biodiversity<br />
pixels we <strong>de</strong>signed an automatic scaling script based on the recognition<br />
of a black square of a known area (see figure 2). Including the camera<br />
(Nikon D300s, about 1,200€), the lens (Nikon 60mm f/2.8G ED AF-S<br />
Micro NIKKOR, about 550€), the stand (starting at about 90€ <strong>for</strong> the<br />
Kaiser 5361) and the software Camera Control Pro (about 130€, running<br />
on both Mac and Windows computer), our measurement system costs<br />
about 2,000€.<br />
4. Case studies<br />
We use our automated particle analysis as a routine procedure in the<br />
laboratory. We <strong>de</strong>veloped several specific java-co<strong>de</strong>d plugins that we can<br />
directly run within ImageJ. The software allows us to choose between a<br />
large number of measurements per<strong>for</strong>med on each particle including position<br />
on the picture, area, and centre of mass, but also bounding rectangles<br />
or fitted ellipse parameters and many more. We present below different<br />
types of analyses based on this method to suggest i<strong>de</strong>as <strong>for</strong> broa<strong>de</strong>r applications.<br />
We recommend using the program R to manipulate and analyse<br />
such data (R Development Core Team, 2011).<br />
4.1. Movement analysis<br />
Our automated particle analysis was used to track an individual. We collected<br />
a set of pictures (1 every 6sec during about 3/4h) with a fixed webcam<br />
controlled by a webcam capture application (Dorgem, Fesevur). The<br />
different steps of the analyses are <strong>de</strong>scribed on figure 4. Some difficulties<br />
may arise from temporal variation in the still background, which is likely<br />
to occur from variation in light intensity or from some changes in the<br />
background due <strong>for</strong> instance to the drying of the plaster in long term<br />
follow-ups. One way to circumvent these difficulties is to calculate the<br />
motionless background picture and analyse images on shorter periods.<br />
But this is very time-consuming and is prone to error when an individual<br />
does not move during this period. The image analysis gives the different<br />
particle positions during the follow-up. The few tiny particles that<br />
may also be <strong>de</strong>tected can be easily discriminated from our collembolan<br />
by setting <strong>for</strong> instance a size threshold in ImageJ be<strong>for</strong>e the analysis.<br />
In figure 4, one can see that the animals first explore relatively exhaustively<br />
the rearing box be<strong>for</strong>e finding refuge in the upper left si<strong>de</strong> of the<br />
box. The method was used to track a single animal, but tracking several<br />
animals at the same time is also possible using the MultiTracker plugin<br />
from ImageJ.
Part II – Chapter 4 173<br />
Figure 4: Image analysis method customised <strong>for</strong> tracking individuals. A. The first<br />
picture of the set shows the starting position of the collembola. B. This picture<br />
shows the still background. C. This picture is obtained from first picture substracted<br />
from the background. D. This picture is the sum of the 300 pictures in the stack<br />
from which the background has been removed. The particle analysis is robust to the<br />
substrate or lighting heterogeneities. E. The successive positions of the individual<br />
are measured by particle analyses.
174 Biodiversity<br />
4.2. Life-history analysis<br />
One of the major advantages of our automated image analysis method is<br />
that it provi<strong>de</strong>s a straight<strong>for</strong>ward estimate of the number and the size of the<br />
particles without altering their shape by smoothing or other image treatments.<br />
We used it as a routine procedure to measure and counts cohorts of<br />
collembolan, but also to get fecundity measurements by isolating the eggs<br />
and then counting the active juveniles once the eggs have hatched. The<br />
method helps characterise life-history traits (Tully and Ferrière, 2008): it<br />
allows measuring growth trajectories and reproductive events <strong>for</strong> instance.<br />
On figure 5, we illustrate some data collected from a cohort of more than<br />
3,500 individuals that has been kept and followed un<strong>de</strong>r controlled conditions<br />
of temperature, food and <strong>de</strong>nsity. The adults were transferred regularly<br />
to fresh rearing boxes and the eggs kept separately until hatching to be<br />
counted. Body length (figure 5B) and fecundity (figure 5C) were measured<br />
using our automatic particle analysis. Although the counting measurements<br />
is pretty accurate (figure 2 and 3), the body length measurements<br />
can be biased when some individuals are curved or are measured in weird<br />
positions. To avoid that, we scored each particle using ratios of its width,<br />
length, perimeter and area (figure 5A). We used this score to select the collembolan<br />
on which a reliable measurement can be ma<strong>de</strong>. This allows us to<br />
reach a precision of about 0.1 to 0.15mm on the mean cohort body length<br />
(figure 5B). These two types of measurements (number and size of particles)<br />
can also be coupled together to follow populations ma<strong>de</strong> of a mixture<br />
of cohorts. The data can be conveniently arranged to draw histograms of<br />
the relative abundance by size class (figure 5D), which allows calculating<br />
the size distribution of the population.<br />
4.3. Population dynamics<br />
Another application of our method is the survey of a population dynamics<br />
by following the population’s size and structure. This was done on a weekly<br />
basis <strong>for</strong> about a year on our collembolan populations. A standard population<br />
survey focuses on counting the number of individuals at each time<br />
step to produce a time series of the population size or <strong>de</strong>nsity. Although<br />
the in<strong>for</strong>mation is incomplete to embrace all the richness of a population<br />
dynamic, it is an essential first step. This kind of in<strong>for</strong>mation is easily<br />
obtained from the results of the automatic particle measuring process by<br />
calculating the average number of particles per date. Figure 6A shows such<br />
a time series. Large fluctuations of the population size can be observed. It<br />
rises from 366 individuals to more than 2000 with no specific temporal<br />
pattern. Rapid increase in the number of individuals can be explained by<br />
the simultaneous hatching of numerous clutches. But it is not possible with<br />
this method to tell if the observed <strong>de</strong>creases are caused by <strong>de</strong>ath of adults or<br />
of juveniles, even though these two mortality processes have very different<br />
meanings regarding the future dynamic of the population.
Part II – Chapter 4 175<br />
3.5<br />
3.0<br />
2.5<br />
A<br />
Particle Score<br />
0 7<br />
1 8<br />
2 9<br />
3 10<br />
4 11<br />
5 12<br />
6<br />
3.0<br />
2.5<br />
2.0<br />
B<br />
Length (mm)<br />
2.0<br />
1.5<br />
Length (mm)<br />
1.5<br />
1.0<br />
1.0<br />
0.5<br />
0.5<br />
0.0<br />
0.0<br />
0.0 0.2 0.4 0.6 0.8 1.0<br />
0 50 100 150 200 250 300<br />
Width (mm)<br />
Age (days)<br />
50<br />
C<br />
500<br />
D<br />
Pictures<br />
Mean<br />
Fecundity (eggs hatched / individual / week)<br />
20<br />
10<br />
5<br />
2<br />
Number of individuals<br />
200<br />
100<br />
50<br />
20<br />
10<br />
5<br />
2<br />
1<br />
1<br />
0 50 100 150 200 250 300<br />
Age (days)<br />
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6<br />
Length (mm)<br />
Figure 5: Examples of life-history trait measurements on a cohort of individuals<br />
kept in a constant environment. A. The measurements of about 28,000 50-days old<br />
individuals are scored on their <strong>for</strong>m (width versus length area). This score enables<br />
unreliable results to be removed in or<strong>de</strong>r to reach high precision measurements<br />
of body length in the cohort. b. Growth trajectory of a cohort. A subset of 100<br />
individuals is measured regularly and particles with an unreliable score have<br />
been removed. C. Fecundity measurements (log scale). The adults of the cohort<br />
are regularly transferred to fresh rearing boxes. The eggs laid in the old boxes are<br />
stored in controlled conditions and the juveniles are counted after hatching. Each<br />
blue point is a measure of fecundity based on 10 individuals during one week and<br />
red points are mean values. D. Population structure measured on a collembolan<br />
population (number of individuals on log scale). The population structure was<br />
measured in<strong>de</strong>pen<strong>de</strong>ntly on six pictures of the population (grey lines), which<br />
illustrates the good repeatability of our measurement method. The black line<br />
represents the estimated average structure.
176 Biodiversity<br />
Figure 6: Population dynamics of collembolan population assessed by automated<br />
image analysis. A. Time series of the number of individuals in a collembolan<br />
population during about 300 days. The number of individuals is on a log scale.<br />
B. Time series of the measured total biosurface (on a log scale) of the same<br />
population.<br />
Another way to represent the population dynamics is to calculate the<br />
total biosurface rather than the total number of individuals. The biosurface<br />
is the surface occupied by a living individual on a given area,<br />
here the rearing box, and is proportional to the biomass of the population.<br />
Figure 6B displays the time series of the biosurface measured<br />
by summing the surface of all particles at a given date. The observed<br />
biosurface oscillations are smoother than the <strong>de</strong>nsity ones (figure 6).<br />
Biosurface tend to rise throughout the year which suggests that although<br />
individuals are hatching and dying regularly, some juveniles are growing<br />
and reach adulthood, thus increasing the whole population’s<br />
biosurface.<br />
To encompass the richness of <strong>de</strong>tails provi<strong>de</strong>d by our method and to<br />
provi<strong>de</strong> insights into the dynamics of the size structure of the population,<br />
we plotted the population’s size structure during the year (figure<br />
7A). The population size-structure is clearly bimodal with a group<br />
of small and another group of large individuals (figure 5D). At the<br />
beginning of the follow-up, the two groups are close to one another: the<br />
small newly hatched individuals are about 0.2 to 0.3mm long and the<br />
larger individuals (adults) are 0.5 to 1.25mm long. Thereafter, the mean
Part II – Chapter 4 177<br />
length of juveniles remains stable while birth events are clearly visible<br />
as orange and red spots. These birth events correspond to the spikes<br />
in the number of individuals shown in figure 6A and the abrupt drops<br />
following the spikes can now be attributed to events of high newlyhatched<br />
juvenile mortality. The group of larger individuals manage to<br />
grow from about 0.75mm at the beginning of the follow-up to 1.15mm<br />
after 200 days whereas their number remains almost stable. This suggests<br />
that the increasing trend in biosurface (figure 6B) is due to adult<br />
growth rather than recruitment of young individuals into adulthood.<br />
However this does not mean that young individuals do not recruit at all<br />
but either that the time scale of the measure is too large to capture those<br />
recruitment events or that too few individuals are reaching adulthood<br />
simultaneously.<br />
When the population is grown at a lower temperature, the population<br />
dynamic is different (figure 7B): adults reach a much higher size (ca.<br />
1.75mm) and some cohorts of juveniles succeed in recruiting into the<br />
adult population. These waves of recruitment can be clearly seen around<br />
100 days, after 300 days, and be<strong>for</strong>e 400 days. This shows that our measurement<br />
method can be used to analyse the <strong>de</strong>tailed dynamics of population<br />
structures and to extract life-history data from these dynamics<br />
(average size at birth and adult size, maximum body length, average<br />
growth rate, etc.).<br />
5. Conclusion<br />
Our automated particle analysis method was initially <strong>de</strong>signed to count<br />
and measure small moving organisms in experimental microcosms. This<br />
method relies on the i<strong>de</strong>a of analysing pictures of the same sample and<br />
removing the motionless background to count and measure the moving<br />
particles. This method is simple, cheap and efficient and can readily<br />
be ma<strong>de</strong> automatic using the freely available software ImageJ. Although<br />
we have applied the method to experimental collembolan populations<br />
<strong>for</strong> studying movements, life-history traits and population dynamics, it<br />
is clear that this kind of sensor can be adapted to many other experimental<br />
systems and research issues. It is possible to apply more sophisticated<br />
image treatments such as smoothing filters once the background<br />
has been removed in or<strong>de</strong>r to improve the efficiency of particle recognition<br />
especially when some particles are so close that they are in contact<br />
or partially overlaping. Using colour pictures could also brings out<br />
new possibilities to increase the contrasts and discriminate particles of<br />
interest from some background noise (Harman, 2011). In addition, we
178 Biodiversity<br />
Figure 7: Size-structured population dynamics. A. Dynamics of the population<br />
size structure at a given temperature. The colour score scales with the number of<br />
individuals (on a log scale) of a size class at a given time. The number of individuals<br />
can be less than one if an individual is not counted on all of the slices during<br />
the picture analysis. b. Dynamics of the population size structure at another<br />
temperature. Here, we can observe larger adults (more than 1.5mm) and several<br />
waves of birth events followed by recruitment.
Part II – Chapter 4 179<br />
anticipate that several improvements could be implemented such as the<br />
discrimination of several species or types of individuals mixed together<br />
based on shape and colour recognition. In this way, the behaviour of phenotypically<br />
contrasted individuals (with some colour mark <strong>for</strong> instance)<br />
could be followed insi<strong>de</strong> a population and the invasion dynamics of two<br />
competing species or phenotypically different strains could be tracked<br />
down.<br />
Authors’ references<br />
François Mallard, Vincent Le Bourlot, Thomas Tully:<br />
École normale supérieure, Laboratoire Écologie et Évolution, UPMC-<br />
CNRS-ENS UMR 7625, Paris, France<br />
Thomas Tully:<br />
Université Paris 4 – Sorbonne, IUFM <strong>de</strong> Paris, Paris, France<br />
Corresponding author: Thomas Tully, tully@biologie.ens.fr<br />
Acknowledgement<br />
This research was supported by a french grant from the Programme interdisciplinaire<br />
du vivant Longévité et vieillissement fun<strong>de</strong>d by the Centre<br />
National <strong>de</strong> la Recherche Scientifique (CNRS).<br />
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III<br />
EcosySTEM PRoPERTIES
Chapter 1<br />
In situ chemical sensors <strong>for</strong> benthic<br />
marine ecosystem studies<br />
Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel<br />
1. Introduction<br />
Benthic marine environments concentrate living and non-living entities<br />
that rule major biogeochemical processes such as the remineralisation of<br />
carbon, inorganic carbon fixation by phototrophs or chemoautotrophs,<br />
the recycling of nitrogen, phosphorus, sulphur or metals, and the precipitation<br />
and burial of minerals. These processes generate steep physical<br />
and chemical gradients within sedimentary or porous rocky substrates,<br />
as well as in the boundary layer above them. The quantification of the<br />
rates of these trans<strong>for</strong>mations and the resulting fluxes across the benthicpelagic<br />
interface has prompted the <strong>de</strong>velopment of in situ chemical sensors<br />
over the last three <strong>de</strong>ca<strong>de</strong>s. Yet the initial momentum on chemical sensor<br />
application <strong>for</strong> benthic environment came from functional <strong>ecology</strong><br />
studies in naturally “extreme” environments, such as microbial mats in<br />
hypersaline lakes (Jørgensen and Revsbech, 1983) or <strong>de</strong>ep-sea hydrothermal<br />
vents (Johnson et al., 1986; Luther III et al., 2001a). Here, the main<br />
requirement was to assess the distribution of micro- or macro-organisms<br />
in relation to in situ chemical gradients, while minimising disturbance of<br />
these gradients. Some of these tools were later converted to the monitoring<br />
of a variety of marine environments, including environments impacted by<br />
human activities (Moore et al., 2009).<br />
This cross-fertilisation led to the <strong>de</strong>velopment of different techniques<br />
available <strong>for</strong> benthic ecologists. Some of these techniques have been<br />
commercialised (e.g.: Unisense, AIS, SAtlantic) and became accessible to<br />
non-specialised users. However, <strong>de</strong>spite their advantages over sampling<br />
approaches, in situ sensors still remain un<strong>de</strong>r-used. In this chapter, we
186 Ecosystem properties<br />
intend to provi<strong>de</strong> an overview of available sensing techniques and present<br />
their respective advantages <strong>for</strong> various applications in benthic <strong>ecology</strong>.<br />
Existing gaps and promising new technologies are also briefly presented.<br />
Chemical sensors <strong>de</strong>dicated to the monitoring of water masses in coastal<br />
observatories (e.g. Johnson et al., 2007) are out of the scope of this review.<br />
Instead of discussing analytical per<strong>for</strong>mances of sensors per se, which are<br />
<strong>de</strong>tailed in several review papers and books (Taillefert et al., 2000; Buffle<br />
and Horvai, 2000; Moore et al., 2009), we intend to clarify the capabilities<br />
and limitations of these tools <strong>for</strong> the assessment of chemical constraints<br />
on biodiversity. There<strong>for</strong>e, the focus is on the <strong>de</strong>tection or quantification<br />
of chemical compounds and their interaction with biological processes<br />
in the benthic ecosystem, including compounds such as oxygen, carbon<br />
dioxi<strong>de</strong> (CO 2<br />
), hydrogen sulphi<strong>de</strong> (H 2<br />
S), or other biologically relevant<br />
parameters like pH. Quantifying fluxes of nutrients and carbon budgets<br />
in marine environments is often indirectly <strong>de</strong>rived from the measurements<br />
of these compounds. This specific application of sensors was consi<strong>de</strong>red<br />
more broadly in a previous review (Viollier et al., 2003).<br />
2. Why is in situ chemical sensing nee<strong>de</strong>d?<br />
The dynamic mixing zone between sulphi<strong>de</strong>-rich fluids and seawater at<br />
<strong>de</strong>ep-sea hydrothermal vents provi<strong>de</strong>s a striking illustration of the need <strong>for</strong><br />
in situ chemical sensing in marine environment. Using a prototype un<strong>de</strong>rwater<br />
colorimetric chemical analyser, Johnson et al. (1988) first <strong>de</strong>scribed<br />
the spatial and temporal heterogeneity of this particular benthic habitat<br />
where oxygen and sulphi<strong>de</strong> can coexist, whereas they are consi<strong>de</strong>red<br />
as mutually exclusive in most marine environments. Water sampling in<br />
this habitat is unable to preserve the chemical disequilibrium. The relaxation<br />
of chemical systems to more stable thermodynamic states during<br />
sample recovery results in strong biases in the characterisation of habitats<br />
chemistry (Le Bris et al., 2006). Such sampling biases are not restricted<br />
to <strong>de</strong>ep-sea hydrothermal vents, but can occur wherever local hydrodynamics<br />
promotes diffusive or advective transport of reduced compounds<br />
into oxygenated water, creating chemically unstable conditions. These<br />
metastable chemical conditions, which are supporting chemosynthetic<br />
metabolic pathways <strong>for</strong> microbial communities, can only be accurately<br />
characterised by using in situ sensors (e.g. Laurent et al., 2009; Vopel et<br />
al., 2005).<br />
On the other hand, it is generally assumed that pore water chemical gradients<br />
that are governed by slow molecular diffusion rates are stable in<br />
sediment cores. Most of the time, these profiles are there<strong>for</strong>e measured<br />
on cores, shortly after sampling. Cores are sometimes maintained un<strong>de</strong>r
Part III – Chapter 1 187<br />
controlled temperature, close to in situ conditions, in or<strong>de</strong>r to minimise<br />
changes. Despite this, differences between in situ and laboratory pore water<br />
chemical profiles from sediment cores were highlighted, which illustrated<br />
the sensitivity of these profiles to sampling conditions and encouraged<br />
direct in situ sensing approaches (Preisler et al., 2007). The risk of disturbance<br />
is obviously more critical when the sample un<strong>de</strong>rgoes large pressure<br />
and temperature changes. Sediment cores collected in <strong>de</strong>ep-sea environments,<br />
in which dissolved gases (e.g. CO 2<br />
, methane) can be enriched, are<br />
subject to <strong>de</strong>gassing during recovery, affecting chemical profiles and leading<br />
to significant biases (De Beer et al., 2006).<br />
In addition, one of the main advantages of in situ sensing relative to direct<br />
sampling is to provi<strong>de</strong> a better view of temporal and spatial variability<br />
from larger data sets. This is particularly crucial in extreme and remote<br />
environments where sampling is difficult and time-constrained. The capacity<br />
to monitor environmental fluctuations also provi<strong>de</strong>s a much more relevant<br />
view of the chemical constraints exerted on communities and on<br />
the processes ruling these fluctuations (III, 2). For all these reasons, studies<br />
of <strong>de</strong>ep-sea benthic ecosystems have ma<strong>de</strong> important contributions to<br />
metho dological advances in sensors and analysers (figure 1). As reviewed<br />
in Moore et al. (2009), many of the technologies and analytical principles<br />
available to date <strong>for</strong> coastal monitoring and shallow water studies<br />
(e.g. colorimetric analysers or micro-profilers) originated from early prototypes<br />
<strong>de</strong>veloped <strong>for</strong> extreme and remote environments. Nowadays, the<br />
need is expanding to polar and sub-seafloor environments, bringing new<br />
constraints on the use of in situ sensors and supporting new <strong>de</strong>velopments.<br />
Figure 1: Examples of <strong>de</strong>ep-sea chemical sensing <strong>de</strong>vices. A. Microprofiler <strong>for</strong> highresolution<br />
sediment oxygen, pH, T and sulfi<strong>de</strong> profiles at a methane seepage area<br />
(© Ifremer/ MEDECO-cruise, F. Wenzhöfer). B. Electrochemical probe integrating<br />
both pH and H2S and temperature sensors in the mixing plume of ‘CrabSpa’, a<br />
diffuse of hydrothermal vent on the top of a lava pillar 2500m <strong>de</strong>ep, colonized by<br />
bacterial mats and Bythogreid crabs (© WHOI /S. Sievert). These extreme benthic<br />
environments have contributed importantly to technological advances in the field<br />
of benthic chemical sensors.
188 Ecosystem properties<br />
3. A variety of techniques with different capabilities<br />
3.1. Spectrophotometric flow analyzers<br />
Continuous-flow analysis (CFA) and flow-injection-analysis (FIA) <strong>de</strong>vices<br />
are routinely used in laboratory to analyse large series of aquatic samples,<br />
and have been adapted <strong>for</strong> use on various oceanographic plat<strong>for</strong>ms,<br />
including lan<strong>de</strong>rs and submersibles operating on the seafloor. The <strong>de</strong>tection<br />
principle is usually based on spectrophotometric and fluorimetric<br />
methods after mixing with reagents or dyes (table 1). For hydrothermal<br />
habitat studies, the first in situ measurements of sulphi<strong>de</strong> were obtained at<br />
2500m <strong>de</strong>ep from a submersible <strong>de</strong>vice based on the Cline colorimetric<br />
method (Johnson et al., 1986). In these chemically diverse environments,<br />
multichannel instruments ma<strong>de</strong> it possible to per<strong>for</strong>m sulphi<strong>de</strong> analysis<br />
in combination with silicate (Johnson et al., 1994; Le Bris et al., 2000),<br />
nitrate (Le Bris et al., 2000), iron (Chapin et al., 2002; Sarradin et al.,<br />
2005) and manganese (Sarrazin et al., 1999).<br />
The main advantage of these methods is the possibility of in situ calibration<br />
using standards (Le Bris et al., 2000). In<strong>de</strong>ed, chemical methods require<br />
repeated calibration, as close as possible to measurement conditions, in or<strong>de</strong>r<br />
to correct drifts and account <strong>for</strong> interference effects. In an attempt to avoid<br />
the use of reagents and standards, an UV spectrophotometry method based<br />
on the <strong>de</strong>convolution of complex spectra reflecting the mixture of several<br />
absorbing species (e.g. nitrate, sulphi<strong>de</strong>, iodine) was suggested (Johnson and<br />
Coletti, 2002). However, although the UV nitrate sensor ISUS is now commercially<br />
available and has achieved a certain level of success in the field of<br />
coastal monitoring, the use of this <strong>de</strong>vice <strong>for</strong> benthic ecosystem studies has<br />
not yet been reported. A number of studies confirmed the per<strong>for</strong>mances<br />
of flow <strong>de</strong>vices <strong>for</strong> the mapping of steep mixing fluid-seawater interfaces<br />
above the seafloor (Johnson et al., 1988; 1994; Sarrazin et al., 1999; Le bris<br />
et al., 2000; 2006). New generation <strong>de</strong>vices were built using miniaturised<br />
flow actuators solenoid valves and optimised <strong>for</strong> application in environmental<br />
monitoring (e.g. Johnson et al., 2007; Vuillemin et al., 2009).<br />
Flow-measurement methods using a colorimetric or fluorescent dye that<br />
acts as a pH indicator have been successfully implemented from ships<br />
and buoys to investigate the CO 2<br />
-carbonate system at the air-sea interface<br />
(Feely et al., 1998). The transfer of such techniques to pH, DIC, alkalinity<br />
and pCO 2<br />
measurement in benthic systems has not yet been achieved.<br />
For applications at the interface between the seafloor and the water column,<br />
other techniques are being preferred. In<strong>de</strong>ed, in flow analysis, the<br />
risk of clogging is large when transferring the sample across the reaction<br />
pathway to the <strong>de</strong>tection cell. This restricts its use to the aqueous phase<br />
only, excluding environments with high particle load or colloids, and the<br />
application of flow analysis in close vicinity to animals or bacterial mats is<br />
there<strong>for</strong>e <strong>de</strong>licate. For this reason and <strong>de</strong>spite the wi<strong>de</strong> range of methods
Part III – Chapter 1 189<br />
available, the use of flow analysers in benthic environments remained<br />
limited. If the chemical parameter to be measured can be converted into<br />
a physical (electrical or optical) signal, electro<strong>de</strong>s or opto<strong>de</strong>s provi<strong>de</strong> a<br />
more a<strong>de</strong>quate solution, circumventing the problem of sample drawing by<br />
<strong>de</strong>porting the measurement point to the environment itself (see below).<br />
Table 1: Summary of the sensing techniques used in benthic environments,<br />
their respective advantages and drawbacks.<br />
Group of<br />
sensors<br />
Flow analyzers<br />
Johnson et al,<br />
1986, Le Bris<br />
et al 2000,<br />
Vuillemin et<br />
al. 2009<br />
Single analyte<br />
electrochemical<br />
sensors<br />
Revsbech et al<br />
1983, De Beer<br />
2002, Cai and<br />
Reimers 2003,<br />
Le Bris et al.<br />
2001<br />
Multi-analyte<br />
electrochemical<br />
sensors<br />
Luther et al<br />
1999, Buffle<br />
and Tercier<br />
2000<br />
Opto<strong>de</strong>s<br />
Glud et al<br />
2003, Konig et<br />
al 2005<br />
New spectrometry<br />
methods.<br />
Wankel et al<br />
2010<br />
Operating<br />
Principle<br />
Spectrophotometry<br />
and<br />
micro-flow<br />
techniques<br />
after mixing<br />
the sample with<br />
reagents/dyes<br />
Potentiometry<br />
and<br />
amperometry<br />
with selective<br />
membrane<br />
electro<strong>de</strong>s<br />
Voltammetry,<br />
working<br />
electro<strong>de</strong><br />
surface<br />
generally<br />
modified (e.g.<br />
gold amalgam<br />
electro<strong>de</strong>)<br />
Measurement<br />
of fluorescence<br />
intensity and<br />
lifetime of a dye<br />
sensitive to the<br />
parameter to be<br />
measured<br />
Raman<br />
and mass<br />
spectrometry<br />
Available chemical<br />
species in marine<br />
environments<br />
S(-II), NO 2-<br />
+NO 3-<br />
,<br />
silicates, Fe(II),<br />
Mn(II),<br />
DIC, alkalinity, pH<br />
pH, Ca 2+ , CO 2<br />
, H 2<br />
S,<br />
S 2- , N 2<br />
O, O 2<br />
, H 2<br />
O 2<br />
, H 2<br />
S, S x<br />
2-<br />
, Fe(II),<br />
Mn(II), S 2<br />
O 3<br />
2-<br />
Cu(II), Pb(II), Zn(II),<br />
Cd(II)<br />
O 2<br />
, pH<br />
CH 4<br />
, H 2<br />
S, H 2,<br />
pCO 2<br />
organics<br />
Benthic use<br />
Mixing zone<br />
above the<br />
seafloor at<br />
hydrothermal<br />
vents<br />
Only in the<br />
water column<br />
From shallow to<br />
<strong>de</strong>ep sediments;<br />
including<br />
hydrothermal<br />
vents and<br />
methane seeps.<br />
Interfaces<br />
between<br />
organisms and<br />
the environment<br />
Vent habitats,<br />
sediments,<br />
Metal-rich<br />
coastal waters<br />
Methane<br />
seeps, shallow<br />
and <strong>de</strong>ep-sea<br />
sediments<br />
Deep-sea<br />
hydrothermal<br />
vents and<br />
methane seeps<br />
Key Advantages<br />
/ Limitation<br />
In situ calibration,<br />
high precision,<br />
wi<strong>de</strong> range<br />
of analytes /<br />
sensitivity to<br />
clogging, not<br />
selective to<br />
chemical speciation<br />
High spatial<br />
resolution, low<br />
energy use /<br />
long-term drifts,<br />
microsensor<br />
fragility<br />
Access to chemical<br />
speciation / long<br />
term instability,<br />
sensitivity<br />
to electro<strong>de</strong><br />
poisoning<br />
No analyte<br />
consumption,<br />
2D mapping /<br />
<strong>de</strong>crease sensitivity<br />
over time, high<br />
<strong>de</strong>tection limit<br />
and low precision<br />
Detection of new<br />
analytes / cost,<br />
limited to aquatic<br />
media<br />
The list of tools and associated references are not exhaustive. The cited advantages and limitations<br />
are within the context of this review article.
190 Ecosystem properties<br />
3.2 Single-parameter potentiometric sensors<br />
The simplest electrochemical technique, potentiometry, uses the potential<br />
difference between a reference electro<strong>de</strong> and an ion sensitive electro<strong>de</strong><br />
(ISE). The logarithmic relationship between the potential and the concentration<br />
(or activity) of the <strong>de</strong>tected ion by the ISE is referred as the Nernst<br />
law. The pH glass electro<strong>de</strong> is by far the most wi<strong>de</strong>ly used potentiometric<br />
sensor in benthic environments. Potentiometric pH measurements were<br />
obtained from the shallowest to the <strong>de</strong>epest environments, both insi<strong>de</strong><br />
the sediment (Wenzhöfer et al., 2000; De beer et al., 2006; Reimers,<br />
2007) and above it, in the acidic plume of hydrothermal vents (Le Bris<br />
et al., 2001), within the tube of an extremophilic polychaete worm living<br />
on the wall of hydrothermal chimneys (Le Bris et al. 2003, see figure 2),<br />
or on the surface of <strong>de</strong>grading organic falls (Laurent et al., 2009). The<br />
low sensitivity of miniaturized pH electro<strong>de</strong>s to pressure is an advantage<br />
to work at various <strong>de</strong>pths (up to at least 3000m <strong>de</strong>ep), but temperature<br />
effects are larger and should be accounted <strong>for</strong> (Le bris et al., 2001). A wi<strong>de</strong><br />
range of analytes can be similarly measured by potentiometry based on<br />
ion-specific membranes although only a few were used in situ (De Beer,<br />
2002). The Ca 2+ sensor, which was used to assess saturation thresholds <strong>for</strong><br />
calcification is of particular interest (Cai and Reimers, 2003). pCO 2<br />
was<br />
also quantified by a glass sensor covered with a gas permeable membrane<br />
(the so-called Severinghaus sensor), in combination with pH (Cai and<br />
Reimers, 2000; Al-Horani et al., 2003).<br />
Figure 2: Measurement of pH insi<strong>de</strong> the tube of an Alvinella pompejana hydrothermal<br />
worm tube. The diameter of the pH glass electro<strong>de</strong> is 2mm. It is combined with a<br />
temperature sensor and a fluid flow sampler inlet (red arrow).
Part III – Chapter 1 191<br />
The sulphi<strong>de</strong> potentiometric sensor is the most wi<strong>de</strong>ly used and best studied<br />
of these sensors. A potentiometric sulphi<strong>de</strong> sensor based on a silver<br />
wire coated with silver sulphi<strong>de</strong> was first suggested by Berner (1963). It<br />
was later miniaturised by Revsbech et al. (1983) who were the first to<br />
study sediment profiles with a potentiometric sulphi<strong>de</strong> microelectro<strong>de</strong><br />
of 200µm tip diameter, enhancing spatial resolution and reducing disturbance<br />
on in situ gradients. From this, the use of electrochemical techniques<br />
<strong>de</strong>veloped rapidly, because of their capacity to achieve the spatial<br />
resolution required <strong>for</strong> biogeochemical studies within microbial mats and<br />
sediments (Taillefert et al., 2000; De Beer, 2002 <strong>for</strong> review). Asi<strong>de</strong> from<br />
the capacity to be miniaturised, potentiometric sulphi<strong>de</strong> electro<strong>de</strong>s have<br />
several advantages <strong>for</strong> benthic <strong>ecology</strong> studies, such as relatively low sensitivity<br />
to temperature and pressure and large <strong>de</strong>tection range (e.g. from<br />
less than 1µM to more than 1mM <strong>for</strong> sulphi<strong>de</strong>, Vismann et al., 1996). At<br />
higher concentrations, they suffer from low accuracy due to their logarithmic<br />
response, but these electro<strong>de</strong>s still respond reproducibly to sulphi<strong>de</strong><br />
at least up to 20mM. Conversely, <strong>de</strong>viations from the Nernst law and long<br />
response times can affect the in situ per<strong>for</strong>mance of the potentiometric<br />
sulphi<strong>de</strong> electro<strong>de</strong>s at low sulphi<strong>de</strong> levels (Müller and Stierli, 1999; Ye<br />
et al., 2008). To solve this problem, these authors proposed epoxy-based<br />
silver sulphi<strong>de</strong> sensors with a much larger nernstian measurement range,<br />
but the in situ per<strong>for</strong>mances of these sensors are not known.<br />
A main difficulty arising from the use of potentiometric sensors comes<br />
from potential drifts, which result from the fact that sensors can only be<br />
calibrated in laboratory conditions be<strong>for</strong>e <strong>de</strong>ployment. Drift may occur<br />
when the reactive surfaces of the reference and sensing electro<strong>de</strong>s un<strong>de</strong>rgo<br />
fouling by bacterial biofilms or by adsorption of mineral and organic<br />
colloids. The conversion of the AgCl surface of the reference electro<strong>de</strong><br />
to silver sulphi<strong>de</strong> upon exposure to H 2<br />
S, or the ageing of the crystalline<br />
layer of the sensing surface are also i<strong>de</strong>ntified as possible causes of<br />
drift (Janz and Ives, 1968). Furthermore, since Ag/Ag 2<br />
S electro<strong>de</strong>s are<br />
sensitive to S 2- , pH measurements need to be per<strong>for</strong>med along with in<br />
situ <strong>de</strong>ployments, raising similar constraints with pH electro<strong>de</strong> responses.<br />
Ecological studies, however, do not always require highly accurate chemical<br />
data, and the temporal and spatial variability of analytes, in a semiquantitative<br />
approach, may be more in<strong>for</strong>mative than few quantitative<br />
values to elucidate the dynamics of natural systems (Le Bris et al., 2008).<br />
However, when quantitative assessment of fluxes at the sediment water<br />
interface is required, potentiometric sensors are often replaced by other<br />
techniques.
192 Ecosystem properties<br />
3.3 Single-parameter electrochemical sensors by amperometry<br />
Amperometry is one of the most used techniques <strong>for</strong> microsensors nowadays<br />
(table 1). Amperometric microsensors measure the current generated<br />
at the surface of a working electro<strong>de</strong> to which a fixed difference of potential<br />
is applied according to a reference electro<strong>de</strong>. In the so-called Clark<br />
sensor <strong>for</strong> oxygen, the applied potential enables the reduction of oxygen<br />
at the working electro<strong>de</strong> ma<strong>de</strong> of gold or platinum. A gas-permeable membrane<br />
prevents other potential oxidants from reacting on the electro<strong>de</strong>,<br />
while maintaining a fixed pH and ionic strength at the electro<strong>de</strong> surface.<br />
Un<strong>de</strong>r these conditions, the measured current is proportional to the rate<br />
of the reaction, controlled by the flux of oxygen passing the membrane,<br />
and ultimately the concentration of oxygen in the medium. Revsbech<br />
(1989) presented an oxygen microsensor built on the principle of the Clark<br />
electro<strong>de</strong>. The addition of a third electro<strong>de</strong>, the guard catho<strong>de</strong>, enabled<br />
the improvement of the accuracy, the response time and the stability of<br />
measurements, by preserving the miniaturised reference electro<strong>de</strong> from<br />
reduction or oxidation currents. On the same principle, an amperometric<br />
microsensor was <strong>de</strong>veloped to measure H 2<br />
S (Jeroschewski et al., 1996).<br />
This sensor was used in a wi<strong>de</strong> range of sulphidic environments such as<br />
coastal sediments (Kühl et al., 1998), hypersaline lakes (Wieland and<br />
Kühl, 2000), shallow water hydrothermal vents systems (Wenzhöfer et<br />
al., 2000) and <strong>de</strong>ep-sea methane seeps (De Beer et al., 2006). The H 2<br />
S<br />
microsensor is sensitive to temperature and salinity changes, but unlike<br />
the potentiometric sensor, its response is linearly related to the concentration<br />
of H 2<br />
S (Jeroschewski et al., 1996). For this reason, amperometric sensors<br />
are particularly useful un<strong>de</strong>r acidic conditions where the H 2<br />
S <strong>for</strong>m of<br />
free sulphi<strong>de</strong> is dominant. It can also be utilised un<strong>de</strong>r mo<strong>de</strong>rate alkaline<br />
conditions (pH< 8.5), but when pH increases, the proportion of H 2<br />
S over<br />
HS - <strong>de</strong>creases to such an extent that it becomes more advantageous to use<br />
the potentiometric sulphi<strong>de</strong> ISE method (Taillefert et al., 2000).<br />
Amperometric microsensors were ma<strong>de</strong> commercially available to a wi<strong>de</strong><br />
range of users by the group that <strong>de</strong>signed them at the Aarhus University,<br />
Denmark (Unisense, http://www.unisense.dk). Amperometic sensors<br />
are also available <strong>for</strong> hydrogen, hydrogen sulphi<strong>de</strong> and N 2<br />
O resulting of<br />
nitrate reduction by microbes. For sediments, a new bio-electrochemical<br />
sensor has been <strong>de</strong>signed, which combines an amperometric N 2<br />
O sensor<br />
and a microbial community that converts nitrate into this compound<br />
(Larsen et al., 1996).<br />
Despite the success of these microsensors, an important limitation should<br />
be kept in mind: the fragility of the glass tip usually less than 100µm<br />
makes the acquisition of profiles over 10 cm or more extremely difficult<br />
in sediments inhabited by macrofauna <strong>for</strong>ming carbonate shells or chi-
Part III – Chapter 1 193<br />
tin tubes (De Beer et al., 2006). The pressure sensitivity of amperometric<br />
sensors is also much higher than those potentiometric sensors, since<br />
the measurement is related to the partial pressure of a volatile compound<br />
(Glud et al., 2003). Strong temperature variations also influence the permeability<br />
of the membrane and have to be corrected (Wenzhöfer et al.,<br />
2000). Drift and limited lifetime is another limitation of amperometric<br />
sensors. Particularly, electrochemical reactions occurring at the electro<strong>de</strong>s<br />
results in changes in the chemical composition of the internal electrolyte.<br />
The <strong>de</strong>gree of this change <strong>de</strong>pends on the current intensity and on the<br />
electrolyte volume. Such changes are particularly significant in the case<br />
of microsensors, due to their very small size, and can cause signal drift<br />
and limit the operational lifetime. For example, reduction of sensitivity<br />
has been reported after a prolonged use of the H 2<br />
S sensor (Jeroschewski<br />
et al., 1996), and the lifetime of the commercial sensor is consi<strong>de</strong>red to be<br />
limited to several months<br />
3.4 Multiparameter voltammetric techniques<br />
Compared to potentiometry and amperometry, voltammetric techniques<br />
are attractive <strong>for</strong> benthic studies because they can <strong>de</strong>tect several analytes<br />
with the same electro<strong>de</strong> and have fast response times (less than 10sec),<br />
there<strong>for</strong>e avoiding problems due to the use of multiple electro<strong>de</strong>s in<br />
spatially or temporally heterogeneous environments (Wang et al., 1998;<br />
Luther III et al., 1999; 2001b). In voltammetry, a variable potential is<br />
applied between a working electro<strong>de</strong> and a reference electro<strong>de</strong>. In cyclic<br />
voltammetry, the most in situ used method, this potential is swept from a<br />
minimum to a maximum value, and reverse. At an appropriate potential,<br />
an analyte is oxidized or reduced at the working electro<strong>de</strong> resulting in a<br />
current peak recor<strong>de</strong>d on a so-called voltammogram (see figure 3 <strong>for</strong> an<br />
example). Reviews of the voltammetric techniques inclu<strong>de</strong> those by Tercier<br />
and Buffle (1993) and Luther III et al. (2008). Like in amperometry, the<br />
current is measured between the working electro<strong>de</strong> and an auxiliary (or<br />
guard) electro<strong>de</strong>. By using an un<strong>de</strong>rwater voltammetric <strong>de</strong>vice, Luther III<br />
et al. (2001a) investigated sulphi<strong>de</strong> speciation (i.e. different electroactive<br />
<strong>for</strong>ms of sulphur(-II)) down to 2500m <strong>de</strong>ep and temperature up to 80°C,<br />
and emphasised significant differences in the chemical environment of<br />
hydrothermal fauna. The working electro<strong>de</strong> used by these authors was<br />
ma<strong>de</strong> of a mercury film <strong>for</strong>med on a gold disc (diameter 100 µm). One of<br />
the main advantages of this gold amalgam electro<strong>de</strong> lies in its high overpotential<br />
<strong>for</strong> the reduction of water, expanding the range of analytes that<br />
can be measured (Kühl and Steuckart, 2000). Concentrations of thiosulfate,<br />
polysulphi<strong>de</strong>s and iron(II, III) and manganese(II), oxygen and iodine<br />
were also obtained with the use of voltammetric methods (Luther III et
194 Ecosystem properties<br />
al., 2001b; 2008). For sulphi<strong>de</strong> measurements, solid electro<strong>de</strong>s such as Pt,<br />
Au, Ag and various types of carbon substrates were also used without Hg<br />
film (review in Buffle and Tercier-Weber, 2005) and have the advantage<br />
of simplicity and ruggedness although their analytical per<strong>for</strong>mances are<br />
more limited.<br />
Figure 3: A typical voltammogram obtained using a solid state gold-amalgam<br />
microelectro<strong>de</strong> (100µm tip diameter) maintained in a hole insi<strong>de</strong> a <strong>de</strong>grading woody<br />
substrate in seawater. The figure illustrates the oxidation (positive) and reduction<br />
(negative) currents recor<strong>de</strong>d while the potential is scanned from -0.1 V to -1.8 V<br />
and back at a rate of 1V/s (cyclic voltammetry). The height of the negative current<br />
intensity peak is proportional to the concentration of sulphi<strong>de</strong> (about 440µM here)<br />
after several weeks of immersion of the wood in a seawater aquarium.<br />
Several metals like manganese, copper, silver, mercury, arsenic, and other<br />
toxic metals can be measured in laboratory by using voltammetric techniques.<br />
An automated flow-through <strong>de</strong>vice has been <strong>de</strong>signed <strong>for</strong> monitoring<br />
of Mn, Cu, Pb and Cd in natural waters (buffle and Tercier-Waeber,<br />
2000). However, since the methods usually require pH buffering, their in<br />
situ use <strong>for</strong> benthic ecosystem studies is not straight<strong>for</strong>ward, <strong>de</strong>spite their<br />
interest <strong>for</strong> the study of contaminated sediments. The recent <strong>de</strong>velopment<br />
of nanomaterials is one of the promising ways to <strong>de</strong>velop new electrochemical<br />
and optical sensors, especially <strong>for</strong> organic molecules that require<br />
higher oxidation potential. For example, the simultaneous <strong>de</strong>tection of<br />
inorganic sulphi<strong>de</strong> and organic sulphi<strong>de</strong> species in a single voltammetric<br />
scan may be possible with carbon nanotubes. This electro<strong>de</strong> enables H 2<br />
S<br />
and thiols signals to be discriminated, while they overlap on other electro<strong>de</strong><br />
materials like gold-amalgam <strong>for</strong> example (Lawrence et al., 2004a;<br />
2004b).
Part III – Chapter 1 195<br />
3.5 Opto<strong>de</strong>s<br />
Opto<strong>de</strong>s are based on the measurement of fluorescence intensity or lifetime<br />
of a dye that is modulated by a chemical substance to be measured.<br />
This fluorescent molecule is embed<strong>de</strong>d in a polymeric membrane integrated<br />
in an optical measurement system able to induce (using a pulsed<br />
UV light source) and to quantify (using a CCD camera or a <strong>de</strong>tector with<br />
high temporal resolution) the fluorescent signal (Klimant et al., 1995;<br />
Glud et al., 1996; 2003). The first oxygen opto<strong>de</strong>s were <strong>de</strong>veloped with the<br />
ruthenium(II)-tris-(4,7-diphenyl-1,10-phenantrolin) complex, which fluorescence<br />
is quenched by oxygen favouring the relaxation of the excited<br />
<strong>for</strong>m, but similar dyes are now available providing a higher resolution<br />
(König et al., 2005). Other fluorescent pH indicators are used <strong>for</strong> pH<br />
measurements (Stahl et al., 2006).<br />
One of the main limitations of opto<strong>de</strong>s is the progressive <strong>de</strong>gradation<br />
of the dye while exposed to the excitation light (Glud et al. 2003). To<br />
improve accuracy, the fluorescent lifetime, in<strong>de</strong>pen<strong>de</strong>nt of the dye concentration<br />
in the matrix, is generally used instead of the intensity signal.<br />
Fibre optics allowed microopto<strong>de</strong>s to be <strong>de</strong>signed with advantages <strong>for</strong><br />
high-resolution profiles similar to those achieved with microelectro<strong>de</strong><br />
(Glud et al., 1999). The main advantage of opto<strong>de</strong>s, over amperometric<br />
electro<strong>de</strong>s, is that they do not consume the analyte. As a result the size of<br />
the opto<strong>de</strong> is not limited and planar opto<strong>de</strong>s of several square centimetres<br />
were <strong>de</strong>veloped <strong>for</strong> an implementation in aquaria (Glud et al., 1996;<br />
Stahl et al., 2006) and recently <strong>for</strong> in situ measurements down to great<br />
<strong>de</strong>pth (Glud et al., 2005). However, the time response of these planar<br />
opto<strong>de</strong>s is in the range of minutes, much longer than the fast-responding<br />
microopto<strong>de</strong>s.<br />
3.6 New in situ techniques<br />
Advanced analytical techniques are <strong>de</strong>veloping rapidly <strong>for</strong> in situ marine<br />
applications (Moore et al., 2009 <strong>for</strong> review). The adaptation <strong>for</strong> un<strong>de</strong>rwater<br />
use of a new set of spectrometric methods, such as Raman spectroscopy<br />
(Brewer et al., 2004), surface plasmon resonance (SPR, Boulart et al.,<br />
2008) or membrane inlet mass spectrometry (MIMS, Schluter and Gentz,<br />
2008; Wankel et al., 2010) further expan<strong>de</strong>d the potential of in situ <strong>de</strong>tection<br />
of less reactive chemical compounds such as methane, hydrocarbons,<br />
or organic pollutants. Their adaptation to various immersion <strong>de</strong>pths will<br />
provi<strong>de</strong> a better view of the fine-scale gradients of these compounds at<br />
the scale of patchy macrophyte and macrofauna aggregations on the seafloor.<br />
The technique has been used recently from remote operated vehicles<br />
(ROVs) <strong>for</strong> the measurement of a variety of analytes on the seafloor,
196 Ecosystem properties<br />
ranging from simple volatiles like H 2<br />
S, CO 2<br />
and methane down to 2500m<br />
(Wankel et al., 2010).<br />
These new techniques also hold the promise of disentangling dissolved<br />
organic carbon complexity directly in situ with spatial resolution similar<br />
to inorganic compounds profiles. While amino acids, sugars, volatile<br />
organic acids are commonly measured in laboratory, there are still no<br />
tools to access these biochemical tracers directly in situ. <strong>Sensors</strong> should<br />
also target organic pollutants or signaling molecules that are likely to<br />
play a significant role in marine benthic ecosystems at low concentration<br />
levels, but very low <strong>de</strong>tection thresholds are a major challenge and<br />
will likely require highly selective biosensing techniques to be <strong>de</strong>veloped.<br />
Carbon nanotube-based biosensors <strong>de</strong>veloped <strong>for</strong> other purposes could<br />
be adapted to <strong>de</strong>scribe benthic marine ecosystems (see the review by<br />
Merkoci, 2006). Current sensors are however also lacking a number of<br />
ecologically important inorganic components, the major one being sulphate,<br />
which is a main electron acceptor <strong>for</strong> organic matter <strong>de</strong>gradation<br />
in anoxic environment.<br />
4. Per<strong>for</strong>mances of in situ chemical sensing techniques<br />
<strong>for</strong> benthic studies<br />
4.1. Measurement ranges<br />
The tools mentionned previously were generally <strong>de</strong>veloped <strong>for</strong> a given<br />
application, i.e. tightly linked to the ecological issue that was addressed.<br />
Their application to different contexts may suffer from significant limitations.<br />
For instance, while amperometric sensors are selective to H 2<br />
S,<br />
less than 90% of the free sulphi<strong>de</strong> is present un<strong>de</strong>r this acidic <strong>for</strong>m<br />
in typical seawater and most pore waters of the benthic environment,<br />
where pH is above 7.5. De Beer et al. (2006) reported that the <strong>de</strong>tection<br />
limit of this microsensor is about 10µM sulphi<strong>de</strong> at pH 8. In such conditions,<br />
the sensitivity of this sensor is there<strong>for</strong>e insufficient to <strong>de</strong>tect<br />
significant exposure to this toxic compound, consi<strong>de</strong>ring that a few<br />
micromolar is already <strong>de</strong>leterious <strong>for</strong> marine aerobes (Vismann et al.,<br />
1991). Potentiometry is better adapted to characterise the potential toxicity<br />
of low sulphi<strong>de</strong> content in the environment. Conversely, voltammetry<br />
offers a much lower <strong>de</strong>tection limit (0.2µM according to Luther<br />
III et al., 2008) but also accounts <strong>for</strong> the less toxic ionic fraction, HS - .<br />
Assessing the toxicity of the environment required pH to be measured<br />
in parallel, in or<strong>de</strong>r to assess the ratio of H 2<br />
S and HS - in the total concentration<br />
of free sulphi<strong>de</strong>.
Part III – Chapter 1 197<br />
A similar issue was raised about oxygen. Amperometric microsensors<br />
were first <strong>de</strong>veloped to <strong>de</strong>scribe oxygen sediment profiles from which the<br />
consumption of oxygen in sediment can be quantified (Revsbech et al.,<br />
1983). These microsensors use the Clark amperometric electro<strong>de</strong> principle<br />
and have a <strong>de</strong>tection limit estimated to be 1µM. While this is suitable to<br />
quantify oxygen consumption rates, this <strong>de</strong>tection limit is insufficient to<br />
establish true anoxic conditions in which anaerobic ammonium oxidation<br />
or another strict anaerobic microbial processes could occur. This limit led<br />
to the <strong>de</strong>velopment and commercialisation of a new tool, the STOx sensor<br />
with enhanced sensitivity. The <strong>de</strong>tection limit of this sensor is much<br />
lower (0.01µM, www.unisense.com). In comparison, the other methods<br />
used to <strong>de</strong>tect oxygen in sediment or overlying water have a much higher<br />
<strong>de</strong>tection threshold: 10µM <strong>for</strong> voltammetry and 7µM <strong>for</strong> opto<strong>de</strong>s (Moore<br />
et al., 2009).<br />
Conversely, the application range of a chemical sensor may be limited by<br />
its saturation threshold. For in situ colorimetric sulphi<strong>de</strong> analysers, the<br />
maximum concentration lies around 200µM (Sarrazin et al., 1999) and<br />
was exten<strong>de</strong>d to about 1mM using a non linear calibration method (Le<br />
Bris et al., 2003). Similarly, amperometric sulphi<strong>de</strong> sensors are expected<br />
to have a maximum concentration range of 200µM <strong>for</strong> H 2<br />
S (Borum<br />
et al., 2005), expanding its limit to a few millimolar of free sulfi<strong>de</strong> in<br />
alkaline conditions. This is important to be acknowledged given that the<br />
concentration of sulphi<strong>de</strong> often exceeds 1mM in organic rich sediments.<br />
Potentiometric sensors with logarithmic response are sensitive over a<br />
much larger measurement range, although their precision is limited at<br />
high concentration.<br />
4.2 Temperature <strong>de</strong>pen<strong>de</strong>nce and other interferences<br />
For the purpose of habitat comparison or rate quantification, chemical<br />
concentrations may require to be quantified with maximum accuracy.<br />
In this case, besi<strong>de</strong> the classical issues of sensitivity and reproducibility,<br />
measurement conditions are to be accounted in the choice of the sensor.<br />
When temperature is prone to change, which is often the case in natural<br />
environments, the temperature influence on the sensor response is<br />
an important criterium. Colorimetric <strong>de</strong>tection methods are sensitive to<br />
temperature (and pressure) changes due to their influence on flow rates<br />
and reaction kinetics (Le Bris et al., 2000). In comparison, electrochemical<br />
techniques with solid state electro<strong>de</strong> are less affected by temperature<br />
change, unless a selective membrane is used. Diffusion through a<br />
membrane is highly <strong>de</strong>pen<strong>de</strong>nt on temperature, and correction is nee<strong>de</strong>d<br />
while using amperometric microsensors in thermally variable environments<br />
(Wenzhöfer et al., 2000). Glass electro<strong>de</strong>s also display a signifi-
198 Ecosystem properties<br />
cant temperature <strong>de</strong>pen<strong>de</strong>nce that needs to be accounted (Le Bris et al.,<br />
2001). The sensitivity of the technique used to potential interferences of<br />
seawater major ions should additionally be consi<strong>de</strong>red in the calibration<br />
protocol, especially in environments with variable salinities (estuaries,<br />
brines, salt-marshes).<br />
4.3 Chemical speciation<br />
The capacity of sensing techniques to measure various chemical species<br />
of the same compound is also of major importance while consi<strong>de</strong>ring the<br />
toxicity or bioavailability of a chemical compound. Rather than quantifying<br />
the total concentration of a dissolved element encompassing both<br />
biologically active and inactive <strong>for</strong>ms, as done in reagent-based colorimetry,<br />
electrochemistry and selective membrane-based spectrometry<br />
target single species or group of reactive species. For example, among<br />
all sulphi<strong>de</strong> species, the H 2<br />
S <strong>for</strong>m has the strongest <strong>de</strong>leterious effect on<br />
non-adapted organisms, due to its capacity to pass gas exchange membrane.<br />
Bulk sulphi<strong>de</strong> analysis on water or pore water samples with the<br />
Cline or the iodometric methods does not enable the discrimination of<br />
this toxic sulphi<strong>de</strong> <strong>for</strong>m from iron complexes, which are abundant in<br />
most sulfidic marine environments and much less toxic. While in situ<br />
colorimetric analysers based on laboratory methods typically measure<br />
the total concentration of soluble sulphi<strong>de</strong> (Le Bris et al., 2003), electrochemical<br />
techniques give access to various <strong>for</strong>ms of free sulphi<strong>de</strong>,<br />
and amperometric microsensors are selective to H 2<br />
S (Jeroschewski et<br />
al., 1996). Potentiometric sulphi<strong>de</strong> sensors are sensitive to S 2- and combined<br />
with pH measurement allows the calculation of both HS - and H 2<br />
S.<br />
Voltammetry does not discriminate these two <strong>for</strong>ms but provi<strong>de</strong>s a much<br />
better view of the different labile sulphi<strong>de</strong> species in the environment,<br />
including dissolved or colloidal iron sulphi<strong>de</strong>s (FeS aq<br />
or FeS°) and polysulphi<strong>de</strong>s<br />
(S n<br />
2-<br />
). The total concentration of sulphi<strong>de</strong> (i.e. all labile sulphur<br />
compounds in the redox state S-II) is also directly accessible in cyclic<br />
voltammetry.<br />
5. Expanding the temporal stability of chemical sensors<br />
Today, temporal variability of marine environments over days to months<br />
is still mostly addressed using physical sensors (temperature, salinity, see<br />
IV, 1). Chemical sensors were applied to the harshest benthic environments,<br />
but the typical measurement durations were often less than a few<br />
hours and successful examples of long-term <strong>de</strong>ployments are quite rare<br />
in the literature. The principle of unatten<strong>de</strong>d in situ measurement over
Part III – Chapter 1 199<br />
several months in the surrounding of hydrothermal vent macrofauna<br />
has been <strong>de</strong>monstrated <strong>for</strong> ferrous iron Fe(II) by flow colorimetry using<br />
low energy osmotic pumps and solenoid valves (Chapin et al., 2002).<br />
Since that time, similar <strong>de</strong>vices have been <strong>de</strong>signed based on solenoid<br />
valves and peristaltic pumps (e.g. Vuillemin et al., 2009 <strong>for</strong> Fe(II)) <strong>for</strong><br />
unatten<strong>de</strong>d long-term <strong>de</strong>ployment. Be<strong>for</strong>e these systems can be used routinely,<br />
several issues are still to be solved including power, mechanical<br />
and electronic ruggedness, in addition to standard solution and reagent<br />
instability.<br />
Tools with higher ruggedness and simpler operation procedures were proposed<br />
<strong>for</strong> this purpose. For example, UV spectrometry overcomes the<br />
addition of reagents and has been consi<strong>de</strong>red as an alternative, though it<br />
has not been validated <strong>for</strong> benthic environment studies. Electrochemistry<br />
is another option, which offers the advantage of mechanical ruggedness<br />
(no moving parts like pumps and valves) and low energy requirements.<br />
However, in this case, calibration issues are important since calibration is<br />
not done in situ but in laboratory and there<strong>for</strong>e raise the problem of longterm<br />
stability of measurement during <strong>de</strong>ployment. Currently, the availability<br />
of integrated sensor systems <strong>for</strong> long term continuous monitoring<br />
from shallow to great <strong>de</strong>pth is scarce, but a few examples of autonomous<br />
<strong>de</strong>ployments over several days to weeks are known. A first <strong>de</strong>ep-sea<br />
autonomous voltammetric analyzer, the ISEA system, <strong>de</strong>signed by AIS<br />
(www.aishome.com), was successfully <strong>de</strong>ployed to record the concentration<br />
of H 2<br />
S <strong>for</strong> up to 3 days on an animal assemblage of the East Pacific<br />
Rise (Luther III et al., 2008; Lutz et al., 2008). These series revealed that<br />
H 2<br />
S can change over two or<strong>de</strong>rs of magnitu<strong>de</strong> (from non-<strong>de</strong>tectable levels<br />
to 30μM) within minutes to hours several times during a few days.<br />
Using potentiometric electro<strong>de</strong>s, Ye et al. (2008) found a similar pattern<br />
in the same <strong>de</strong>ep-sea environment with total dissolved H 2<br />
S fluctuating<br />
periodically over about 12 hours, reflecting tidal influence. The <strong>de</strong>tection<br />
system was integrated in a flow <strong>de</strong>vice allowing calibration of the<br />
electro<strong>de</strong> in situ.<br />
Autonomous miniaturised potentiometers (<strong>de</strong>signed by NKE, France)<br />
and equipped with lab-ma<strong>de</strong> pH and sulphi<strong>de</strong> electro<strong>de</strong>s were adapted<br />
<strong>for</strong> the purpose of long-term <strong>de</strong>ployment (Le Bris et al., 2001; 2008; see<br />
figure 4). Although they were mostly used <strong>for</strong> short term measurements<br />
during submersible dives of a few hours (Le Bris et al., 2001; 2006), their<br />
unatten<strong>de</strong>d use over 3 days in a shallow water mangrove swamp revealed<br />
the potentiality of these simple and easy-to-handle tools <strong>for</strong> autonomous<br />
monitoring in various environments (Laurent et al., 2009). Solid-state<br />
voltammetric and potentiometric electro<strong>de</strong>s are particularly suitable<br />
<strong>for</strong> harsh conditions within the environment of benthic fauna. Using<br />
microelectro<strong>de</strong>s, these techniques are also well suited <strong>for</strong> low cost, energy
200 Ecosystem properties<br />
Figure 4: Deployment of chemical sensors <strong>for</strong> benthic studies. A. Potentiometric<br />
sensors <strong>de</strong>ployed <strong>for</strong> continuous sulfi<strong>de</strong> and pH measurements over two weeks on<br />
a hydrothermal mussel bed at 2500 meter <strong>de</strong>pth (9°50’N, East Pacific Rise) © Le<br />
bris/MESCAL/Ifremer. b. Programmation of autonomous sensors <strong>for</strong> unatten<strong>de</strong>d<br />
in situ monitoring of a wood fall experiment over 4 months 500m <strong>de</strong>ep (Lacaze<br />
Duthiers canyon, Mediterranean sea) ©Le bris/LECob/UPMC. C. Autonomous<br />
un<strong>de</strong>rwater potentiometers (NKE) equipped with laboratory-ma<strong>de</strong> pH and sulfi<strong>de</strong><br />
electro<strong>de</strong>s similar to those used in A and b. The length of the ruler is 30 cm.<br />
and size-limited monitoring <strong>de</strong>vices (Pizeta et al., 2003). Since ef<strong>for</strong>ts<br />
to integrate both sensors and their electronics in shallow water housings<br />
(Unisense and MPI divers system) have expan<strong>de</strong>d, the capacity <strong>for</strong><br />
in situ <strong>de</strong>ployments increased (Vopel et al., 2005). In addition to commercialised<br />
one-dimensional sediment profilers (Unisense), recently<br />
<strong>de</strong>veloped systems offer access to three-dimensional mapping and a
Part III – Chapter 1 201<br />
wi<strong>de</strong>r range of application, such as coral reef microhabitats (Weber et<br />
al., 2007).<br />
Expanding the autonomy of these chemical sensors over several days to<br />
months is the most crucial need today, but the most difficult task is to<br />
ensure the stability of electro<strong>de</strong> response. Uncontrollable matrix interferences,<br />
lack of specificity or selectivity, problems of reversibility, fouling<br />
and drift highlighted by analytical chemists impe<strong>de</strong>d the transfer of<br />
electrochemical techniques <strong>for</strong> in situ long-term monitoring (Tercier and<br />
Buffle, 1993; Muller and Stierli, 1999). In this prospect, promising laboratory<br />
works showed that the potentiometric Ag/Ag 2<br />
S electro<strong>de</strong> could<br />
have a stable response to a certain level over two months (Lacombe et al.,<br />
2007), at least when operated with a laboratory instrument in artificial<br />
seawater and ambient temperature. However the stability of electrochemical<br />
analyses in situ when sensors are exposed to colloids, biofilms, and<br />
varying pH and temperature has to be better comprehen<strong>de</strong>d. In situ testing<br />
of completely autonomous and submersible <strong>de</strong>vices over several months<br />
on <strong>de</strong>grading wood in a mangrove waters, confirmed their relative stability<br />
in a microbiologically active environment (Le Bris et al., submitted,<br />
Yücel et al., submitted). Deployments over two to three weeks in <strong>de</strong>ep<br />
sea hydrothermal environments confirmed calibration stability at ambient<br />
pressure but in situ stability still needs to be address un<strong>de</strong>r simulated<br />
pressure and temperature conditions (Contreira et al., 2011).<br />
6. Conclusion<br />
Compared to the profusion of sensor principles and techniques available<br />
<strong>for</strong> ex situ analyses of benthic marine environments, only a few are operational<br />
in situ today. Among the sensors adapted to un<strong>de</strong>rwater use even<br />
fewer sensors have achieved sufficient reliability and ruggedness <strong>for</strong> common<br />
use in benthic <strong>ecology</strong>. Microelectro<strong>de</strong>s <strong>de</strong>veloped since the 1980s<br />
have significantly improved our capacity to characterise physicochemical<br />
gradients at the sediment-water interface in relation with the structure and<br />
activities of microbial communities. However, the inherent fragility of<br />
microelectro<strong>de</strong>s resulted in a biased sampling of benthic habitats toward<br />
soft sediments, excluding the habitat of macro-organisms with hard shells<br />
or tubes or rocky substrates. A significant step <strong>for</strong>ward is still awaited <strong>for</strong><br />
making in situ sensors operational <strong>for</strong> a wi<strong>de</strong>r range of ecosystem studies.<br />
There is a great need to encourage the integration of new sensing techniques<br />
<strong>de</strong>veloped by analytical chemists as part of experimental <strong>de</strong>signs<br />
in <strong>ecology</strong>. For this, however, a paradigm change is required: high frequency<br />
but low precision data obtained in situ in natural environments<br />
can be of much higher relevance to the ecological processes studied than
202 Ecosystem properties<br />
few high quality analyses per<strong>for</strong>med on samples in controlled laboratory<br />
conditions. The challenge is to gather interdisciplinary expertise, at the<br />
interface of geochemistry, <strong>ecology</strong> and analytical chemistry in or<strong>de</strong>r to<br />
appreciate the advantages and drawbacks of the various techniques and<br />
their possible combination. In<strong>de</strong>ed no standard set of in situ sensors in<br />
benthic <strong>ecology</strong> exists, but rather a bunch of available techniques that<br />
need to be combined to answer specific scientific issues and <strong>de</strong>velop comprehensive<br />
mechanistic mo<strong>de</strong>ls.<br />
Authors’ references<br />
Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel:<br />
Université Pierre et Marie Curie, Laboratoire d’écogéochimie <strong>de</strong>s environnements<br />
benthiques, CNRS-UPMC FRE3350, Banyuls-sur-Mer, France.<br />
Corresponding author: Nadine Le Bris, lebris@obs-banyuls.fr<br />
Acknowledgement<br />
This review was supported by UPMC, CNRS, the TOTAL fundation<br />
and the European commission, within the SENSEnet ITN network<br />
(International sensor <strong>de</strong>velopment network GA 237868). The TOTAL<br />
fundation supports the post-doctoral grant of Mustafa Yücel, as part of the<br />
chair “Extreme marine environments, biodiversity, and global change”.<br />
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distribution<br />
in benthic communities. Marine Ecology Progress Series, 140, pp. 217-226.<br />
Glud R. N., Klimant I., Holst G., Kohls O., Meyer V., Kühl M., Gun<strong>de</strong>rsen<br />
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on benthic lan<strong>de</strong>rs. DeepSea Research Part I: Oceanographic Research<br />
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the <strong>de</strong>termination of H 2<br />
S in aquatic environments. Analytical Chemistry,<br />
68, pp. 4351-4357.<br />
Johnson K. S., Beelher C.L., Sakamoto-Arnold C. M., 1986. A submersible flow<br />
analysis system. Analytica Chimica Acta, 179, pp. 245-257<br />
Johnson K. S., Childress J. J., Hessler R. R., Sakamoto-Arnold C. M., Beelher<br />
C. L., 1988. Chemical and biological interactions in the Rose Gar<strong>de</strong>n<br />
hydrothermal vent field, Galapagos spreading center. Deep Sea Research<br />
Part I: Oceanographic Research Papers, 35, pp. 1723-1744.<br />
Johnson K. S., Childress J. J., Beelher C. L., Sakamoto C. M., 1994.<br />
Biogeochemistry of hydrothermal vent mussel communities: the <strong>de</strong>ep sea<br />
analogue to the intertidal zone. Deep Sea Research Part I: Oceanographic<br />
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Johnson K. S., Needoba J. A., Riser S. C., Showers W. J., 2007. Chemical<br />
sensor networks <strong>for</strong> the aquatic environment. Chemical Reviews, 107,<br />
pp. 623-640.<br />
Johnson K. S., Coletti L. J., 2002. In situ ultraviolet spectrophotometry <strong>for</strong> high<br />
resolution and long-term monitoring of nitrite, bromi<strong>de</strong> and bisulfi<strong>de</strong> in<br />
the ocean. Deep Sea Research Part I: Oceanographic Research Papers, 49,<br />
pp. 1291-1305.<br />
Jørgensen B. B., Revsbech N. P., 1983. Photosynthesis and structure of benthic<br />
microbial mats: microelectro<strong>de</strong> and SEM studies of four cyanobacterial<br />
communities. Limnology and Oceanography, 28, pp. 1075-1093.<br />
Klimant I., Meyer V., Kühl M., 1995. Fiber-optic oxygen microsensors, a new<br />
tool in aquatic biology. Limnology and Oceanography, 40, pp. 1159–1165.<br />
König B., Kohls O., Holst G., Glud R. N., Kühl M., 2005. Fabrication and<br />
test of sol-gel based planar oxygen opto<strong>de</strong>s <strong>for</strong> use in aquatic sediments.<br />
Marine Chemistry, 97, pp. 262-276.<br />
Kühl M., Steuckart C., 2000. <strong>Sensors</strong> <strong>for</strong> in situ analysis of sulfi<strong>de</strong> in aquatic<br />
systems, in Buffle J., Horvai G., (Eds) In situ monitoring of aquatic
Part III – Chapter 1 205<br />
systems. Chemical analysis and speciation. IUPAC Series on analytical<br />
and physical chemistry of environmental systems, 6, pp. 121-159.<br />
Kühl M., Steuckart C., Eickert G., Jeroschewski P., 1998. A H 2<br />
S microsensor <strong>for</strong><br />
profiling biofilms and sediments: application in an acidic lake sediment.<br />
Aquatic Microbial Ecology, 15, pp. 201-209.<br />
Lacombe M., Garcon V., Comtat M., Oriol L., Sudre J., Thouron D., Le Bris N.,<br />
Provost C., 2007. Silicate <strong>de</strong>termination in sea water: toward a reagentless<br />
electrochemical method. Marine Chemistry, 106, pp. 489-497.<br />
Laurent M. C. Z., Gros O., Brulport J.-P., Gaill F., Le Bris N., 2009. Sunken<br />
wood habitat <strong>for</strong> thiotrophic symbiosis in mangrove swamps. Marine<br />
Environmental Research, 67, pp. 83–88.<br />
Larsen L. H., Revsbech N. P., Binnerup S. J. A., 1996. Microsensor <strong>for</strong> nitrate<br />
based on immobilized <strong>de</strong>nitrifying bacteria. Applied and Environmental<br />
Microbiology, 62, 4, pp. 1248-1251 .<br />
Lawrence N. S., Deo R. P., Wang J., 2004a. Electrochemical <strong>de</strong>termination<br />
of hydrogen sulfi<strong>de</strong> at carbon nanotube modified electro<strong>de</strong>s. Analytica<br />
Chimica Acta, 517, pp. 131-137.<br />
Lawrence N. S., Deo R. P., Wang J., 2004b. Detection of homocysteine at<br />
carbon nanotube paste electro<strong>de</strong>s. Talanta, 63, pp. 443-449.<br />
Le bris N., Sarradin P.-M., birot D., Alayse-Danet A.-M., 2000. A new<br />
chemical analyzer <strong>for</strong> in situ measurement of nitrate and total sulfi<strong>de</strong> over<br />
hydrothermal vent biological communities. Marine Chemistry, 72, pp. 1-15.<br />
Le Bris N., Sarradin P.-M., Pennec S., 2001. A new <strong>de</strong>ep-sea probe <strong>for</strong> in situ<br />
pH measurement in the environment of hydrothermal vent biological<br />
communities. Deep Sea Research Part I: Oceanographic Research Papers,<br />
48, pp. 1941-1951.<br />
Le Bris N., Sarradin P.-M., Caprais J.-C., 2003. Contrasted sulphi<strong>de</strong> chemistries<br />
in the environment of 13°N EPR vent fauna. Deep Sea Research Part I:<br />
Oceanographic Research Papers, 50, pp. 737-747.<br />
Le Bris N., Govenar B., Le Gall C., Fisher C. R., 2006. Variability of physicochemical<br />
conditions in 9°N EPR diffuse flow vent habitats. Marine<br />
Chemistry, 98, pp. 167-182.<br />
Le Bris N., Brulport J.-P., Laurent M., Lacombe M., Garcon V., Gros O., Comtat<br />
M., Gaill F., 2008. Autonomous potentiometric sensor <strong>for</strong> in situ sulfi<strong>de</strong><br />
monitoring in marine sulfidic media. Geophysical Research Abstracts, 10,<br />
EGU2008-A-11476.<br />
Luther III G. W., Reimers C. E., Nuzzio D. b. Lovalvo D., 1999. In situ<br />
<strong>de</strong>ployment of voltammetric, potentiometric, and amperometric<br />
microelectro<strong>de</strong>s from a ROV to <strong>de</strong>termine dissolved O2, Mn, Fe, S(-II),<br />
and pH in porewaters. Environmental Science and Technology, 33,<br />
pp. 4352-4356.<br />
Luther III G. W., Rozan T. F., Taillefert M., Nuzzio D. B., Di Meo C.,<br />
Shank T. M., Lutz R. A., Cary S. C., 2001a. Chemical speciation drives<br />
hydrothermal vent <strong>ecology</strong>. Nature, 410, pp. 813-816.
206 Ecosystem properties<br />
Luther III G. W., Glazer b. T., Hohmann L., Popp. J. I., Taillefert M., Rozan T.<br />
F., bren<strong>de</strong>l P. J., Theberge S. M., Nuzzio D. b., 2001b. Sulfur speciation<br />
monitored in situ with solid state gold amalgam voltammetric microelectro<strong>de</strong>s:<br />
polysulfi<strong>de</strong>s as a special case in sediments, microbial mats and hydrothermal<br />
vent waters. Journal of Environmental Monitoring, 3, pp. 61-66.<br />
Luther III G. W., Glazer B. T., Ma S., Trouwborst R. E., Moore T. S., Metzger E.,<br />
Kraiya C., Waite T. J., Druschel G., Sundby B., Taillefert M., Nuzzio D. B.,<br />
Shank T. M., Lewis B. L., Bren<strong>de</strong>l P. J., 2008. Use of voltammetric solidstate<br />
(micro) electro<strong>de</strong>s <strong>for</strong> studying biogeochemical processes: laboratory<br />
measurements to real time measurements with an in situ electrochemical<br />
analyzer (ISEA). Marine Chemistry, 108, pp. 221- 235.<br />
Lutz R.A., Shank T.M., Luther III G.W., Vetriani C., Tolstoy M., Nuzzio<br />
D.B., Moore T.S., Waldhauser F., Crespo-Medina M., Chatziefthimiou<br />
A.D., Annis E.R., Reed A.J., 2008. Interrelationships between vent fluid<br />
chemistry, temperature, seismic activity, and biological community<br />
structure of mussel-dominated <strong>de</strong>ep-sea vent along the East Pacific Rise.<br />
Journal of Shellfish Research, 27, 177-190.<br />
Merkoçi A., 2006. Carbon nanotubes in analytical sciences. Microchimica<br />
Acta, 152, pp. 157-174.<br />
Moore T. S., Mullaugh K. M., Holyoke R. R., Madison A., Yucel M., Luther III<br />
G. W., 2009. Marine chemical technology and sensors <strong>for</strong> marine waters:<br />
potentials and limits. Annual Review of Marine Science, 1, pp. 91-115.<br />
Müller B., Stierli R., 1999. In situ <strong>de</strong>termination of sulfi<strong>de</strong> profiles in sediment<br />
porewaters with a miniaturized Ag/Ag2S electro<strong>de</strong>. Analytica Chimica<br />
Acta, 401, pp. 257-264.<br />
Pizeta I., Billon G., Fischer J. C., Wartel M., 2003. Solid microelectro<strong>de</strong>s <strong>for</strong> in<br />
situ voltammetric measurements. Electroanalysis, 15, pp. 1389-1396.<br />
Preisler A., De Beer D., Litchschlag A., Lavik G., Boetius A., Jørgensen B. B.,<br />
2007. Biological and chemical sulfi<strong>de</strong> oxidation in Beggiatoa inhabited<br />
marine sediment. The ISME Journal, 1, pp. 341-353.<br />
Reimers C. E., 2007. Applications of microelectro<strong>de</strong>s to problems in chemical<br />
oceanography. Chemical Reviews, 107, pp. 590-600.<br />
Revsbech N. P., Jørgensen B. B., Blackburn T. H., Cohen Y., 1983. Microelectro<strong>de</strong><br />
studies of the photosynthesis and O 2<br />
, H 2<br />
S and pH profiles of a microbial<br />
mat. Limnology and Oceanography, 28, pp. 1062-1074.<br />
Revsbech N. P., 1989. An oxygen microsensor with a guard catho<strong>de</strong>. Limnology<br />
and Oceanography, 34, pp. 474-478.<br />
Sarradin P.-M., Le Bris N., Le Gall C., Rodier P., 2005. Fe analysis by the ferrozine<br />
method: adaptation to FIA towards in situ analysis in hydrothermal<br />
environment. Talanta, 66, pp. 1131-1138.<br />
Sarrazin J., Juniper S. K., Massoth G., Legendre P., 1999. Physical and chemical<br />
factors influencing species distribution on hydrothermal sulfi<strong>de</strong> edifices<br />
of the Juan <strong>de</strong> Fuca Ridge, northeast Pacific. Marine Ecology Progress<br />
Series, 190, pp. 89-112.
Part III – Chapter 1 207<br />
Schluter M., Gentz T., 2008. Application of membrane inlet mass spectrometry<br />
<strong>for</strong> online and in situ analysis of methane in aquatic environments.<br />
Journal of the American Society of Mass Spectrometry, 19, pp. 1395-<br />
1402.<br />
Stahl H., Glud A., Schrö<strong>de</strong>r C. R., Klimant I., Tengberg A. Glud R. N., 2006.<br />
Time-resolved pH imaging in marine sediments with a luminescent planar<br />
opto<strong>de</strong>. Limnology and Oceanography: Methods, 4, pp. 336-345.<br />
Taillefert M., Luther III G. W., Nuzzio D. B., 2000. The application of<br />
electrochemical tools <strong>for</strong> in situ measurements in aquatic systems: a review.<br />
Electroanalysis, 12, pp. 401-412.<br />
Tercier M.-L., Buffle J. 1993. In situ voltammetric measurements in natural<br />
waters: future prospects and challenges. Electroanalysis, 5, pp. 187-200.<br />
Viollier E., Rabouille C., Apitz S. E., Breuer E., Chaillou G., Dedieu K.,<br />
Furukawa Y., Grenz C., Hall P., Janssen F., Mor<strong>for</strong>d J. L., Poggiale J.,<br />
Roberts S., Shimmield T., Taillefert M., Tengberg A., Wenzhofer F., Witte<br />
U., 2003. Benthic biogeochemistry: state of the art technologies and<br />
gui<strong>de</strong>lines <strong>for</strong> the future of in situ survey. Journal of Experimental Marine<br />
Biology and Ecology, 285, pp. 5-31.<br />
Vismann B., 1991. Sulfi<strong>de</strong> tolerance: physiological adaptation and ecological<br />
implication. Ophelia, 34, pp. 1-27.<br />
Vismann B., 1996. Sulfi<strong>de</strong> exposure experiments: The sulfi<strong>de</strong> electro<strong>de</strong> and a<br />
set-up automatically controllling sulfi<strong>de</strong>, oxygen and pH. The Journal of<br />
Experimental Biology, 204, pp. 131-140.<br />
Vopel K., Thistle D., Ott J., Bright M., Roy H., 2005. Wave-induced H 2<br />
S flux<br />
sustains a chemoautotrophic symbiosis. Limnology and Oceanography,<br />
50, pp. 128-133<br />
Vuillemin R., Sanfilippo L., Moscetta P., Zudaire L., Carbones E., Maria E.,<br />
Tricoire C., Oriol L., Blain S., Le Bris N., Lebaron P., 2009. Continuous<br />
nutrient automated monitoring on the Mediterranean Sea using in situ<br />
flow analyser. OCEANS 2009 MTS/IEEE, Biloxi-Marine technology <strong>for</strong><br />
our future: global and locate challenges, pp. 1-8.<br />
Wang F., Tessier A., Buffle J., 1998. Voltammetric <strong>de</strong>termination of elemental<br />
sulfur in pore waters. Limnology and Oceanography, 43, pp. 1353-1361.<br />
Wankel S. D., Joye S. B., Samarkin V. A., Shah S. R., Frie<strong>de</strong>rich G., Melas-<br />
Kyriazi J., Girguis P. R. 2010. New constraints on methane fluxes and<br />
rates of anaerobic methane oxidation in a Gulf of Mexico brine pool via<br />
in situ mass spectrometry. Deep Sea Research Part II: Topical Studies in<br />
Oceanography, 57, pp. 2022-2029.<br />
Weber M., Faerber P., Meyer V., Lott C., Eickert G., Fabricius K. E., De beer<br />
D., 2007. In situ applications of a new diver-operated motorized<br />
micro sensor profiler. Environmental Science and Technology, 41,<br />
pp. 6210-6215.<br />
Wenzhöfer F., Holby O., Glud R. N., Nielsen H. K., Gun<strong>de</strong>rsen J. K., 2000. In<br />
situ microsensor studies of a shallow water hydrothermal vent at Milos,<br />
Greece. Marine Chemistry, 69, pp. 43-54.
208 Ecosystem properties<br />
Wieland A., Kühl M., 2000. Short-term temperature effects on oxygen and<br />
sulfi<strong>de</strong> cycling in a hypersaline cyanobacterial mat (Solar Lake, Egypt).<br />
Marine Ecology Progress Series, 196, pp. 87-102.<br />
Ye Y., Huang X., Pan Y., Han C. H., Zhao W., 2008. In situ measurement of<br />
the dissolved S 2− in seafloor diffuse flow system: sensor preparation and<br />
calibration. Journal of Zhejiang University, 3, pp. 423-428.
Chapter 2<br />
Advances in marine benthic <strong>ecology</strong><br />
using in situ chemical sensors<br />
Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel<br />
1. Introduction<br />
Seafloor ecosystems play a significant role in energy transfer, nutrient<br />
recycling and carbon storage in marine environments, while sustaining<br />
diverse biological communities in a variety of benthic habitats. The abiotic<br />
properties of benthic environments reflect available resources and<br />
physical or chemical constraints on biodiversity, and are themselves<br />
modulated by biological activity. However, insufficient knowledge of the<br />
interplay between organisms and communities and the dynamic components<br />
of their environment prevents their role in ecosystem functioning<br />
to be fully un<strong>de</strong>rstood. Opening this black box is particularly crucial to<br />
un<strong>de</strong>rstand how these complex and dynamic interactions governs ecosystem<br />
responses to disturbances. In situ chemical sensing techniques are<br />
of primary importance in this context. They have provi<strong>de</strong>d the ability<br />
to <strong>de</strong>scribe how hydrodynamics, microbial activity, engineer species and<br />
chemical kinetics combine and shape in situ gradients in marine benthic<br />
systems. Complementing the inventory of available techniques and their<br />
respective advantages and limitations (see chapter III, 1), this chapter illustrates<br />
major insights in marine benthic <strong>ecology</strong> gained from the use of in<br />
situ sensors, and discusses the potentialities offered by the <strong>de</strong>velopment of<br />
autonomous sensing <strong>de</strong>vices <strong>for</strong> in situ experimental approaches.
210 Ecosystem properties<br />
2. Biological activity and habitat chemical heterogeneity<br />
2.1 Chemical gradients at interfaces<br />
Opposite vertical gradients in electron acceptors (dissolved oxygen,<br />
nitrate, sulphate as well as particulate iron and manganese oxi<strong>de</strong>s) and<br />
electron donors (ammonium, sulphi<strong>de</strong>), characterise marine redox interfaces<br />
where organic matter is <strong>de</strong>gra<strong>de</strong>d by microbes using redox reactions<br />
as an energy source (Schulz and Zabel, 2006). These gradients exist both<br />
in benthic and in pelagic environments. In the latter, <strong>de</strong>ep suboxic layer<br />
(tens to hundreds of meter thick) may <strong>for</strong>m like the one probed using in situ<br />
sensors in the black Sea (Glazer et al., 2006). This transition occurs along<br />
much more shorter distances in benthic systems. A particular example is<br />
the redox gradients occurring over <strong>de</strong>cimetre to meter-scale in the plumes<br />
of hydrothermal vents, where fluids are mixed with seawater above the<br />
seafloor. Within these narrow interfaces, specialised chemolithotrophic<br />
communities settle and grow, exploiting the energy provi<strong>de</strong>d by the combination<br />
of inorganic electron donors and acceptors in their environment.<br />
Steep changes in sulphi<strong>de</strong>, pH, as well as nitrate, oxygen, and iron, have<br />
been documented at the scale of chemosynthetic assemblages, with the<br />
use of sensors operated by <strong>de</strong>ep-sea submersibles (see figure 1, Johnson et<br />
al., 1988; Le Bris et al., 2000; 2003). The co-variation of chemical parameters<br />
with temperature, taken as a dilution tracer, revealed the combined<br />
influence of physical process (mixing) and biological activity (consumption<br />
or biologically induced heat exchange) on these gradients (Johnson<br />
et al., 1988; 1994; Le Bris et al., 2005; 2006a).<br />
Figure 1: In situ sensing of sulphi<strong>de</strong> and pH gradients at the scale of an aggregation<br />
of hydrothermal tubeworms Riftia pachyptila at 2015 meter <strong>de</strong>pth in the Gulf of<br />
Cali<strong>for</strong>nia. © S. Sievert, Woods Hole Oceanographic Institute.
Part III – Chapter 2 211<br />
Due to the slow diffusion of solutes in sediment pore waters, gradients in<br />
sediment pore waters often occur over even shorter distances (e.g. <strong>de</strong>cimetre<br />
to sub-millimeter scales). Besi<strong>de</strong>s being indicative of the rate of<br />
carbon and nutrients remineralisation processes in the sediment, these<br />
gradients reflect the environmental constraints exerted on microbial communities<br />
and on benthic fauna. Particularly, the oxygen penetration <strong>de</strong>pth<br />
can extend from only a few millimetres to several centimetres below the<br />
sediment-water interface. This penetration layer <strong>de</strong>fines the habitat of<br />
microbial aerobes. As reviewed in Stockdale et al. (2009), positioning of<br />
the lower limit of the oxic layer can only be resolved using in situ microsensing<br />
techniques. While oceanographic chemical probes (mostly <strong>for</strong> pH<br />
and oxygen) have been available since the mid-twentieth century, their use<br />
by microbial ecologists <strong>for</strong> sediment studies only traces back to the early<br />
eighties (Revsbech et al., 1983). Miniaturisation of electro<strong>de</strong> tips down to<br />
c.a. 10µm <strong>for</strong> o 2<br />
offered significant advantage over sampling, as it allowed<br />
resolving chemical changes over distances as short as 25µm, which is<br />
required to highlight the processes sustaining microbial mats at the sediment<br />
water-interface. Strong gradients were thus revealed and related to<br />
the vertical zonation of bacteria and diatoms over a few millimetre-thick<br />
microbial mat from core samples of sediments (Jørgensen and Revsbech,<br />
1983).<br />
The technique is still wi<strong>de</strong>ly used, most of the time combined with the<br />
measurement of other major tracers of biogeochemical processes in marine<br />
sediments, such as sulphi<strong>de</strong>. Sulphi<strong>de</strong> is produced by the reduction of<br />
sulphate diffusing from seawater and has strong ecological implications.<br />
Besi<strong>de</strong>s being toxic to aerobes, sulphi<strong>de</strong> is used as an electron donor by<br />
chemolithotrophic microbes to gain energy. Amperometric microsensors<br />
provi<strong>de</strong>d high-resolution profiles of the most toxic sulphi<strong>de</strong> <strong>for</strong>m, H 2<br />
S, in<br />
addition to oxygen, and sometimes pH. By using these sensors, microprofiles<br />
were obtained directly in situ through microbial mats and the un<strong>de</strong>rneath<br />
sediments at shallow hydrothermal vents (Wenzhöfer et al., 2000)<br />
or greater <strong>de</strong>pth (Gun<strong>de</strong>rsen and Jørgensen, 1992; De Beer et al., 2006).<br />
These data illustrated steep sulphi<strong>de</strong> concentration increases from zero to<br />
as high as millimolar levels.<br />
Beyond assessing sulphate and oxygen consumption rates, and related<br />
carbon remineralization rates, these profiles offered useful in<strong>for</strong>mation<br />
on the drivers of microbial diversity and distribution in marine sediments.<br />
Chemical profiles of oxygen and sulphi<strong>de</strong> in different sulphidic<br />
microbial mats of a <strong>de</strong>ep-sea mud volcano in the eastern Mediterranean<br />
Sea, revealed strong habitat preferences <strong>for</strong> different sulphi<strong>de</strong>-oxidising<br />
microbe types (Grünke et al., 2011). Besi<strong>de</strong> their catabolic requirements,<br />
it was also proposed that sulphi<strong>de</strong> gradients are used by motile microbes<br />
as chemical cue to position themselves within the sediment. A steep
212 Ecosystem properties<br />
sulphi<strong>de</strong> concentration increase would thus prevent the sulphi<strong>de</strong> oxidising<br />
Beggiatoa strains, which stores nitrate from shallow sediment layers,<br />
to get lost in <strong>de</strong>eper sulphi<strong>de</strong>-rich regions of the sediment (Preisler et<br />
al., 2007). The use of an amperometric nitrate microsensor in combination<br />
with oxygen, pH and sulphi<strong>de</strong> microsensors greatly improved the<br />
un<strong>de</strong>rstanding of the <strong>ecology</strong> of these wi<strong>de</strong>ly distributed chemolithotrophic<br />
microbes in marine sediments. For example, a comparison of in situ<br />
nitrate profiles in mats of Beggiatoa, a sulphi<strong>de</strong>-oxidizing bacterium, with<br />
the total concentration of nitrate after bacterial cell lease confirmed the<br />
storage of nitrate in intracellular vacuoles. In turn, this storage capacity<br />
allows sulphi<strong>de</strong>-oxidising bacteria to survive <strong>for</strong> days in the sediments,<br />
outsi<strong>de</strong> of the upper part of the vertical redox zonation where nitrate is<br />
supplied (Preisler et al., 2007).<br />
Sedimentary profiles of various iron species is also of major ecological<br />
relevance. The coupling of iron and sulphi<strong>de</strong> chemistries in sediment is<br />
known to control sulphi<strong>de</strong> toxicity and bioavailability. In Beggiatoa mats,<br />
chemical oxidation of H 2<br />
S with iron oxi<strong>de</strong>s unexpectedly appeared to be<br />
the main sulphi<strong>de</strong> consuming process (Preisler et al., 2007). Furthermore,<br />
the <strong>for</strong>mation of less labile precipitated <strong>for</strong>ms of iron sulphi<strong>de</strong> exerts a major<br />
control on biogeochemical cycling and habitat conditions (Stockdale et<br />
al., 2009). In situ voltammetry on gold-amalgam micro-electro<strong>de</strong>s proved<br />
to be a very powerful technique to access the dissolved or colloid <strong>for</strong>ms<br />
of reduced and oxidised iron and sulphi<strong>de</strong> involved in these processes. G.<br />
W. Luther III from the University of Delaware and his group ma<strong>de</strong> this<br />
technique fully operational <strong>for</strong> in situ use up to 3000m <strong>de</strong>ep within redox<br />
interfaces above the seafloor (Bren<strong>de</strong>l and Luther III, 1995; Luther III et<br />
al., 2008). A first <strong>de</strong>ep sea in situ sediment profiling voltammetric system<br />
has recently been applied in the hydrothermal environment of the Loihi<br />
Seamount, <strong>de</strong>scribing the intricate iron cycling in this iron-rich environment<br />
(Glazer and Rouxel, 2009).<br />
2.2. Habitat heterogeneity on and below the seafloor<br />
By offering a much better spatial coverage and resolution than achievable<br />
from sampling, in situ measurements allowed to document horizontal<br />
changes in chemical gradients. Changes occurring at scales ranging from<br />
centimetres to metres or tens of metres are often related to the distribution<br />
of fauna and macrophyte assemblages. For example, higher sulphi<strong>de</strong><br />
levels in seagrass bed sediments compared to non-vegetated surrounding<br />
sediments were documented by Hebert and Morse (2003). The higher<br />
sulphi<strong>de</strong> level was attributed to the enrichment of organic material in<br />
sediment. Furthermore, Borum et al. (2005) highlighted one to two or<strong>de</strong>r<br />
of magnitu<strong>de</strong> increases in sulphi<strong>de</strong> from 2 to 6cm <strong>de</strong>ep (up to more than
Part III – Chapter 2 213<br />
500µM) between <strong>de</strong>nsely vegetated areas and Thalassia testidum (a seagrass)<br />
die-off patches.<br />
At hydrothermal vents, the mapping of oxygen, sulphi<strong>de</strong>, iron and manganese<br />
and other chemical factors at the surface of faunal assemblages, using<br />
both colorimetric analysers and electrochemical sensors, revealed steep<br />
transitions between the habitats of different dominant chemosynthetic<br />
species (Sarrazin et al., 1999; Le Bris et al., 2000; Le Bris et al., 2006;<br />
Podowski et al., 2010). Organic fall <strong>de</strong>gradation on the seafloor, such as<br />
whale skeletons, similarly induces marked lateral chemical zonation with<br />
distinct sulphi<strong>de</strong> maxima in the sediment that were <strong>de</strong>scribed using in situ<br />
amperometric microprofilers (Treu<strong>de</strong> et al., 2009). At kilometre scale (i.e.<br />
the diameter of the Haakon Mosby mud volcano), Nieman et al. (2006)<br />
and De Beer et al. (2006) found strong differences in the sulphi<strong>de</strong> and<br />
oxygen gradients characterising areas occupied by tubeworms, microbial<br />
mats, as well as the centre of the volcano without visible colonisation.<br />
Interestingly, these authors also pointed <strong>de</strong>eper sulphate penetration and<br />
sulphi<strong>de</strong> production in the sediments colonised by tubeworms illustrating<br />
their large influence on sediment profiles.<br />
The effect of bioturbation and bioirrigation on the fluxes at the sedimentwater<br />
interface is often accounted <strong>for</strong> by <strong>de</strong>fining a specific diffusion coefficient,<br />
which is used to extrapolate fluxes over larger areas. In reality,<br />
faunal activity results in very complex and diverse gradient shapes, which<br />
cannot be reduced to a simple diffusion gradient according to the Fick’s<br />
first law (Glud et al., 2005; Bertic and Ziebis, 2009). Even when classical<br />
diffusion profiles are obtained, significant changes in microsensor<br />
profiles are reported within centimeter distances, reflecting the heterogeneous<br />
organisation of meiofaunal and microbial communities within<br />
sediments (Glud et al., 2005; Stahl et al., 2006). Similar to macrofauna,<br />
macrophytes create marked zonation around their rhizosphere, both due<br />
to the enrichment in labile organic compounds and the oxygen release in<br />
the sediment by roots (Hebert and Morse, 2003, Borum et al., 2005). By<br />
using a gold-amalgam electro<strong>de</strong>, Hebert and Morse (2003) not only documented<br />
large differences between adjacent vegetated and unvegetated<br />
sediments, but they also found up to 80 µM difference in the maximum<br />
sulphi<strong>de</strong> content between two cores collected 1.5cm apart in the rhizosphere<br />
region of a vegetated sediment. Despite these studies, the relevance<br />
of spatial heterogeneity on nutrients and carbon fluxes has been largely<br />
overlooked in marine benthic <strong>ecology</strong>.<br />
The lateral heterogeneity that was already recognised from multiple series<br />
of sediment cores can now be captured at centimetres scale with microsensors<br />
and down to even millimetres with planar opto<strong>de</strong>s. Assessing<br />
the influence of habitat heterogeneity on microbial activities will help<br />
to better integrate this unevenness into ecological mo<strong>de</strong>ls. As recently
214 Ecosystem properties<br />
illustrated by Bertic and Ziebis (2009), microsensors offer the opportunity<br />
to link benthic biogeochemical processes with biological diversity,<br />
thus addressing the control of burrowing organisms on the diversity and<br />
activity of microbial communities. These tools are undoubtedly opening<br />
new ways to un<strong>de</strong>rstand the mechanisms un<strong>de</strong>rlying the control of bioengineers<br />
on microbial processes, and ultimately on the functioning and<br />
function of benthic ecosystems.<br />
Planar opto<strong>de</strong>s provi<strong>de</strong> access to sediment heterogeneity in two dimensions,<br />
and are very efficient tools to address the relationships between<br />
chemical gradients in sediments and the behaviour of benthic organisms<br />
(figure 2). Oxygen planar opto<strong>de</strong>s, in particular, revealed steep changes<br />
due to the filling of macrofauna burrows with oxygenated water contrasting<br />
with the surrounding sediment pore waters, and the diffusion<br />
through their wall from and to the surrounding sediment (König et al.,<br />
2005; Glud et al., 2005). Similarly, planar opto<strong>de</strong>s revealed unexpected<br />
pH heterogeneity resulting from carbon remineralisation within the sediment.<br />
Steep pH changes were observed across burrow walls, and within<br />
microniches in the sediment (Stahl et al., 2006). For example, the photosynthetic<br />
activity of a small benthic diatom at the sediment surface<br />
was characterised by a remarkable pH increase from 8.0 in the water to<br />
up to 8.6. Today, however, most of these techniques are applied ex situ<br />
in mesocosms and have only been rarely operationally used in situ (Glud<br />
et al., 2005).<br />
Figure 2: A two dimensional image of the oxygen variability at the sediment<br />
water interface showing the irrigation of burrows with oxygenated water by marine<br />
invertebrates. © F. Wenzhöfer and R. Glud.
Part III – Chapter 2 215<br />
Lateral heterogeneity is a major problem when two or more chemical sensors<br />
are nee<strong>de</strong>d to assess a chemical factor from equilibrium calculation.<br />
This is particularly the case <strong>for</strong> assessing total CO 2<br />
(or dissolved inorganic<br />
carbon, DIC) from the in situ measurement of pH and pCO 2<br />
, or the<br />
total sulphi<strong>de</strong> concentration from pH and H 2<br />
S microsensors. A mismatch<br />
between the profiles of different chemical parameters obtained within<br />
several centimetres distance can lead to strong biases in the <strong>de</strong>finition of<br />
the chemical factor of interest (De Beer et al., 2006). The same problem<br />
arises when addressing the constraints exerted on calcifying organisms at<br />
the scale of their microhabitat, which requires to combine the measurement<br />
of pH, pCO 2<br />
and Ca 2+ (Cai and Reimers, 2003). In the heterogeneous<br />
media characterising most benthic habitats, the <strong>de</strong>sign of integrated<br />
in situ probes combining different sensors within a short distance is there<strong>for</strong>e<br />
a critical need. A compromise has to be found, since disturbance<br />
of the environmental gradient increases with the diameter of the probe,<br />
particularly in sediments.<br />
3. Temporal dynamics of benthic ecological processes<br />
3.1. Reactive chemical species and chemical speciation<br />
Aquatic chemical systems are dynamic molecular assemblages, whose<br />
composition and properties are governed by kinetic and thermodynamic<br />
laws (Stumm and Morgan, 1996). The influence of living organisms on<br />
chemical assemblages often results in non-equilibrium states allowing<br />
reactive chemical species to coexist (e.g. O 2<br />
, H 2<br />
S, NH 4+<br />
). These reactive<br />
chemicals can be used as energy sources, but they also constitute potential<br />
stress factors and constrain the ability of organisms to settle and grow.<br />
Quantifying the concentration of reactive chemical species within characteristic<br />
microniches is there<strong>for</strong>e of main relevance to ecological studies.<br />
Conventional colorimetric analytical methods allow the quantification of<br />
total concentrations of elements, sometimes selecting a given redox states<br />
(e.g. Fe II , S -II ), but they are unable to discriminate between reactive <strong>for</strong>ms<br />
even when operated in situ. By using in situ amperometric and potentiometric<br />
microelectro<strong>de</strong>s, it is possible to selectively <strong>de</strong>tect a single reactive<br />
<strong>for</strong>m of a chemical element (see III, 1). In situ voltammetry can further<br />
discriminate different chemical species and has been particularly useful<br />
in distinguishing various sulphi<strong>de</strong>-rich environments in terms of toxicity<br />
and energy availability <strong>for</strong> chemoautotrophs by comparing the respective<br />
contents of free sulphi<strong>de</strong> and of sulphi<strong>de</strong>s complexed with iron (Luther III<br />
et al., 2001a; 2001b). Furthermore, the technique allows to measure other<br />
<strong>for</strong>ms of reduced sulfur, thiosulphates (S 2<br />
O 3<br />
2-<br />
) or polysulphi<strong>de</strong>s (S n<br />
2-<br />
) and
216 Ecosystem properties<br />
can there<strong>for</strong>e assess the importance of these intermediate compounds in<br />
the biological sulphi<strong>de</strong> oxidation (Waite et al., 2008; Gartman et al., 2011).<br />
Colloidal <strong>for</strong>ms of metals are other reactive species playing a significant<br />
role in benthic <strong>ecology</strong> and biogeochemistry. Cyclic voltammetry revealed<br />
that iron sulphi<strong>de</strong> colloids are electrochemically active and can be particularly<br />
abundant in metal rich reducing environments. These labile<br />
macromolecules <strong>de</strong>termine not only sulphi<strong>de</strong> speciation – thus limiting<br />
the toxicity of sulphi<strong>de</strong> – but also iron speciation with significant effects<br />
on its mobility (Rickard and Luther III, 2007). Taillefert et al. (2000a)<br />
showed that soluble Fe(III) colloids <strong>de</strong>tected in sediment pore waters can<br />
have important implications on the mineralisation rate of organic matter.<br />
This highly reactive Fe(III) can diffuse in and out of sediments, supplying<br />
an electron acceptor at locations where particulate iron oxi<strong>de</strong>s are no<br />
more available. In addition, reduction of soluble Fe(III) by sulphi<strong>de</strong> was<br />
i<strong>de</strong>ntified as a major removal process <strong>for</strong> toxic sulphi<strong>de</strong> in the sediments<br />
(Preisler et al., 2007).<br />
2.2 Short-term dynamics: from laboratory to natural conditions<br />
Physical and biological factors not only constrain the spatial distribution of<br />
in situ chemical gradients, they also generate substantial temporal variability<br />
that has long been difficult to investigate in benthic ecosystems. Day-night<br />
transitions are of particular ecological relevance. With the help of chemical<br />
sensing techniques, the challenge of capturing light-dark variability was<br />
first tackled from ex situ experiments on sediment cores. Jørgensen et al.<br />
(1983) thus <strong>de</strong>scribed how the interplay of diatoms with photosynthetic<br />
and non-photosynthetic sulphur bacterial communities shaped chemical<br />
gradients over day-night cycles within a benthic microbial mat collected in<br />
a hypersaline lake. Monitoring the oxygen gradients at the sediment interface<br />
in response to light exposure revealed profound <strong>de</strong>viation from oxygen<br />
saturation, due to the activities of benthic phototrophic primary producers<br />
(Revsbech et al., 1986). In a narrow photosynthetic layer – less than a<br />
few millimetres thick – oxygen reached up to three times the atmospheric<br />
saturation level in light conditions, in complete opposition with the steep<br />
oxygen consumption profile in the dark. Daily changes in oxygen within<br />
algae microhabitat with temporary anoxia and hyperoxia were similarly<br />
documented by Pöhn et al. (2001) in artificial light exposure conditions.<br />
<strong>Sensors</strong> have moreover fostered the investigation of the reciprocal relationships<br />
between key bioengineers and chemical factors variation in their<br />
environment. As an example, seagrass beds are known not only <strong>for</strong> their<br />
carbon storage efficiency and the nutritional and sheltering role of their<br />
canopy above the sediment, but also <strong>for</strong> the diurnal control they exert<br />
on biogeochemical processes insi<strong>de</strong> the sediment. Significant light-dark
Part III – Chapter 2 217<br />
changes in microsensor vertical profiles were documented from a series of<br />
sediment cores collected at different times of the day on a seagrass bed, highlighting<br />
the influence of the rhizosphere activity on oxygen profiles and<br />
subsequent biogeochemical processes (Hebert and Morse, 2003; Borum et<br />
al., 2005). The most important ecological consequence of this process is<br />
the reduction of toxic sulphi<strong>de</strong> in the upper layer of the sediment. In<strong>de</strong>ed,<br />
the oxygen released from the roots during the day promotes the oxidation<br />
of sulphi<strong>de</strong>, and protect the plants from toxic sulphi<strong>de</strong> invasion as it was<br />
<strong>de</strong>monstrated by using microsensors in microcosms (Borum et al., 2005).<br />
Using similar microsensors, these authors monitored in situ daily changes<br />
in oxygen and sulphi<strong>de</strong> in healthy patches of seagrass Thalassia testidum<br />
and adjacent areas including die-off patches. These direct measurements<br />
confirmed that, without <strong>de</strong>toxification by oxygen, the sulphi<strong>de</strong> produced<br />
in non-vegetated sediment can reach higher levels and diffuse outward,<br />
further increasing <strong>de</strong>leterious effects on adjacent seagrass beds.<br />
The fast response of amperometric microsensors is particularly well suited<br />
to address temporal variability at even shorter scales. For example, the monitoring<br />
of oxygen use of an microelectro<strong>de</strong> over one hour allowed to resolve<br />
fluctuation in oxygen content from 0 to 80% of the overlying water saturation<br />
within a benthic polychaete burrow in a mesocosm (Kristensen, 2000).<br />
This study highlighted the influence of burrow ventilation by the worm<br />
not only on the burrow habitat itself but also on the surrounding sediment,<br />
through diffusion of oxygen across the burrow wall. Kristensen (2000) also<br />
pointed out the need <strong>for</strong> more experimental testing of the impact of such<br />
burrowing activity, which implicitly called <strong>for</strong> an extension of the use of<br />
microsensors directly in situ as done by Volkenborn et al. (2007). From<br />
2D vi<strong>de</strong>o imaging on sediment cores obtained with planar opto<strong>de</strong>s, these<br />
authors have recently quantified the influence of Arenicola marina, a common<br />
burrowing annelid, on oxygen penetration in sediment through an<br />
ex situ approach. Oxygenated seawater pumping by the worm through its<br />
burrow promotes lateral diffusion of oxygen through burrow walls to the<br />
surrounding sediment. The use of in situ microsensors to compare chemical<br />
gradients between a 400m 2 exclusion zone and a <strong>de</strong>nsely colonized adjacent<br />
area confirmed the influence of this process over large scales, but it also<br />
revealed that burrow irrigation is not the sole influence of these organisms<br />
on oxygen transport rates in the sediment. The combination of local heterogeneities<br />
<strong>for</strong>med by burrow openings and excreted material at the sediment<br />
surface with hydrodynamic <strong>for</strong>cing by currents and waves promoting diffuse<br />
fluxes at the sediment-water interface also appeared as a major control<br />
on oxygen penetration in sediment. This example illustrates well the importance<br />
of in situ studies, in combination to ex situ approaches using sensors.<br />
Another example is provi<strong>de</strong>d by Vopel et al. (2001; 2005), who studied<br />
the two transport mechanisms enabling sulphi<strong>de</strong> and oxygen to be sup-
218 Ecosystem properties<br />
plied to the chemosynthetic bacterial symbionts of Zoothamnium niveum, a<br />
colonial ciliate typically found in mangrove peat sulphidic habitats. Using<br />
microsensors in aquarium, these authors <strong>de</strong>scribed how the contraction and<br />
extension of the ciliate stalk enhanced advective transport of alternatively<br />
sulphi<strong>de</strong> and oxygen over durations a few seconds (Vopel et al., 2001). In<br />
situ measurements on mangrove rootlets colonized by these ciliates, further<br />
documented the capacity of this symbiosis to exploit natural hydrodynamics.<br />
In high flow conditions, boundary-layer modulations over <strong>de</strong>grading<br />
peat results in fluctuating sulphi<strong>de</strong> and oxygen concentrations over ciliate<br />
groups, whereas in quiescent periods advective transport generated by the<br />
ciliate is nee<strong>de</strong>d <strong>for</strong> the supply of these compounds to the symbionts.<br />
Most continuous in situ monitoring studies remained limited to very shallow<br />
environments, like salt marshes or mangrove swamp, where the use of<br />
in situ sensors is facilitated because electronics can be maintained out of<br />
the water and only the electro<strong>de</strong> is immersed and can be positioned by an<br />
operator (Taillefert, 2000b; Vopel et al., 2005) Today, however, un<strong>de</strong>rwater<br />
sensing <strong>de</strong>vices allowing autonomous <strong>de</strong>ployments are becoming available<br />
<strong>for</strong> a variety of environmental conditions, including remote <strong>de</strong>ep-sea conditions<br />
(III, 1),. As far as tracers of ecological processes are concerned, the<br />
need <strong>for</strong> highly accurate and precise measurements may be balanced by the<br />
benefits gained from the characterisation of temporal changes. The study of<br />
wood <strong>de</strong>gradation and colonisation in tropical shallow waters provi<strong>de</strong>d an<br />
illustration of the advantages of continuous autonomous monitoring over<br />
durations exceeding a few hours. Large sulphi<strong>de</strong> and pH variation on the<br />
surface of a natural wood fall in a mangrove swamp were <strong>de</strong>termined over<br />
almost three days in correlation with ti<strong>de</strong> (figure 3 and Laurent et al., 2009).<br />
The succession of high sulphi<strong>de</strong> and low pH periods with low sulphi<strong>de</strong><br />
and higher pH periods was attributed to the influence of tidal currents<br />
on local hydrodynamics. This study emphasized the need <strong>for</strong> continuous<br />
measurement series over several days capturing tidal fluctuation in or<strong>de</strong>r<br />
to fully <strong>de</strong>scribe habitat redox conditions. Similar wi<strong>de</strong> temporal fluctuations<br />
partly attributed to the <strong>for</strong>cing of tidal current on vent fluid mixing<br />
have been recently highlighted from the use of autonomous voltammetric<br />
and potentiometric sensing <strong>de</strong>vice <strong>de</strong>ployed over days to weeks in <strong>de</strong>ep-sea<br />
hydrothermal vent habitats (Luther III et al., 2008; Contreira et al., 2011).<br />
The availability of fully autonomous systems that can be operated unatten<strong>de</strong>d<br />
over longer periods will enlarge the capacity to tackle the transient<br />
dynamics of ecological processes directly in situ in the future. With the<br />
objectives of documenting the dynamics of establishment of sulphidic conditions<br />
suitable <strong>for</strong> chemoautotrophy on organic falls, pioneer works have<br />
just explored the capacity to per<strong>for</strong>m continuous chemical monitoring on<br />
experimentally immersed organic substrates over duration exceeding two<br />
months (figure 4 and Le Bris et al. submitted; Yücel et al., submitted).
Part III – Chapter 2 219<br />
Figure 3: Fluctuation of sulphi<strong>de</strong> (A) and pH (b) at the surface of a woody substrate<br />
naturally immersed in a tropical mangrove swamp as measured with sulphi<strong>de</strong><br />
(green signal) and pH sensors (red signal). The ti<strong>de</strong> is represented by the blue curve<br />
(adapted from Laurent et al., 2009).
220 Ecosystem properties<br />
Figure 4: Autonomous sulphi<strong>de</strong> and pH sensors used <strong>for</strong> an experimental study of<br />
the <strong>de</strong>gradation and colonisation of woody <strong>de</strong>bris over three months in a mangrove<br />
swamp. The electro<strong>de</strong> tips are attached to the surface (green arrow) and inserted<br />
(red arrow) into a piece of Coco nuciphera (© o. Gros UAG).<br />
4. Conclusion<br />
In situ chemical sensing supported a significant step <strong>for</strong>ward in the<br />
un<strong>de</strong>rstanding of the interaction of microbial consortia and macrofaunal<br />
assemblages in marine benthic habitats, as well as their role in the spatial<br />
and temporal dynamics of chemical gradients. Processes governing nutrient<br />
recycling, organic matter trans<strong>for</strong>mation and energy transfer, environmental<br />
toxicity, and the resulting chemical constraints on biological<br />
diversity are important ecological issues that have been addressed by using<br />
chemical sensors. These tools hold great promises <strong>for</strong> the investigation<br />
of marine biodiversity and ecosystem functioning relationships, whose<br />
un<strong>de</strong>rlying mechanisms remain largely unknown. Modularity, robustness,<br />
small size, and cost effectiveness of sensors largely improved, which<br />
maximised their capacity to be implemented as part of in situ experimental<br />
<strong>de</strong>signs. Analogous to the miniaturised modules that equip Mars lan<strong>de</strong>rs<br />
(Kounaves et al., 2010), new generations of integrated and autonomous<br />
sensing <strong>de</strong>vices are to be <strong>de</strong>veloped by combining different techniques<br />
to access the full range of relevant parameters <strong>for</strong> specific questions.<br />
Exchange of knowledge and technical expertise between soil and terrestrial<br />
aquatic sciences and marine benthic <strong>ecology</strong> will undoubtedly foster<br />
these <strong>de</strong>velopment ef<strong>for</strong>ts.
Part III – Chapter 2 221<br />
Authors’ references<br />
Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel:<br />
Université Pierre et Marie Curie, Laboratoire d’écogéochimie <strong>de</strong>s environnements<br />
benthiques, CNRS-UPMC FRE3350, observatoire océanologique,<br />
Banyuls-sur-Mer, France.<br />
Corresponding author: Nadine Le Bris, lebris@obs-banyuls.fr<br />
Acknowledgement<br />
This review was supported by UPMC, CNRS, the TOTAL foundation<br />
and the European Commission, within the SENSEnet ITN network<br />
(International sensor <strong>de</strong>velopment network GA 237868). The TOTAL<br />
foundation supports the post-doctoral grant of Mustafa Yücel, as part<br />
of the Chair “Extreme marine environments, biodiversity, and global<br />
change”.<br />
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Grünke S., Fel<strong>de</strong>n J., Lichtschlag A., Girnth A.-C. De Beer D., Wenzhöfer<br />
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Gun<strong>de</strong>rsen J. K., Jørgensen B. B., Larsen E., Jannasch H. W., 1992. Mats of giant<br />
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flow. Nature, 360, pp. 454-456.<br />
Hebert A. B., Morse J. W., 2003. Microscale effect of light on H 2<br />
S and Fe2+<br />
in vegetated (Zoostera marina) sediments. Marine Chemistry, 81, pp. 1-9.<br />
Johnson K. S., Childress J. J., Hessler R. R., Sakamoto-Arnold C. M., Beelher<br />
C. L., 1988. Chemical and biological interactions in the Rose Gar<strong>de</strong>n<br />
hydrothermal vent field, Galapagos spreading center. Deep-Sea Research<br />
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Johnson K. S., Childress J. J., Beelher C. L., Sakamoto-Arnold C. M., 1994.<br />
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analogue to the intertidal zone. Deep Sea Research Part I: Oceanographic<br />
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Jörgensen B. B., Revsbech N. P., 1983. Photosynthesis and structure of benthic<br />
microbial mats: microelectro<strong>de</strong> and SEM studies of four cyanobacterial<br />
communities. Limnology and Oceanography, 28, pp. 1075-1093.<br />
König B., Kohls O., Holst G., Glud R. N., Kühl M., 2005. Fabrication and<br />
test of sol-gel based planar oxygen opto<strong>de</strong>s <strong>for</strong> use in aquatic sediments.<br />
Marine Chemistry, 97, pp. 262-276.<br />
Kounaves S. P., Hecht M. H., Kapit J., Gospodinova K., DeFlores L., Quinn<br />
R. C., boynton W. V., Clark b. C., Catling D. C., Hredzak P., Ming D.
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W., Moore Q., Shusterman J., Stroble S., West S. J., young S. M. M., 2010.<br />
Wet chemistry experiments on the 2007 Phoenix Mars Scout Lan<strong>de</strong>r<br />
mission: Data analysis and results. Journal of Geophysical Research, 115,<br />
pp. 1-10.<br />
Kristensen E. 2000. Organic matter diagenesis at the oxic/anoxic interface in<br />
coastal marine sediments, with emphasis on the role of burrowing animals.<br />
Hydrobiologia, 426, pp. 1-24.<br />
Laurent M. C. Z., Gros O., Brulport J.-P., Gaill F., Le Bris N., 2009. Sunken<br />
wood habitat <strong>for</strong> thiotrophic symbiosis in mangrove swamps. Marine<br />
Environmental Research, 67, pp. 83-88.<br />
Le Bris N., Sarradin P.-M., Birot D., Alayse-Danet A.-M., 2000. A new<br />
chemical analyzer <strong>for</strong> in situ measurement of nitrate and total sulfi<strong>de</strong><br />
over hydrothermal vent biological communities. Marine Chemistry, 72,<br />
pp. 1-15.<br />
Le Bris N., Sarradin P.-M., Caprais J.-C., 2003. Contrasted sulphi<strong>de</strong> chemistries<br />
in the environment of 13°N EPR vent fauna. Deep Sea Research Part I:<br />
Oceanographic Research Papers, 50, pp. 737-747.<br />
Le Bris N., Zbin<strong>de</strong>n M., Gaill F., 2005. Processes controlling the physicochemical<br />
micro-environments associated with Pompeii worms. Deep Sea<br />
Research Part I: Oceanographic Research Papers, 52, pp. 1071-1083.<br />
Le Bris N., Govenar B., Le Gall C., Fisher C. R., 2006. Variability of physicochemical<br />
conditions in 9°N EPR diffuse flow vent habitats. Marine<br />
Chemistry, 98, pp. 167-182.<br />
Le Bris N, Yücel M, Laurent M, Gros O. Submitted. Dynamics of sulfur redox<br />
cycling from <strong>de</strong>grading wood in a tropical marine environment.<br />
Luther III G. W., Rozan T. F., Taillefert M., Nuzzio D. B., Di Meo, C.,<br />
Shank T. M., Lutz R. A., Cary S. C., 2001a. Chemical speciation drives<br />
hydrothermal vent <strong>ecology</strong>. Nature, 410, pp. 813-816.<br />
Luther III G. W., Glazer B. T., Hohmann L., Popp. J. I., Taillefert M.,<br />
Tozan T. F., Bren<strong>de</strong>l P. J., Theberge S. M., Nuzzio D. B., 2001b. Sulfur<br />
speciation monitored in situ with solid state gold amalgam voltammetric<br />
microelectro<strong>de</strong>s: polysulfi<strong>de</strong>s as a special case in sediments, microbial mats<br />
and hydrothermal vent waters. Journal of Environmental Monitoring, 3,<br />
pp. 61-66.<br />
Luther III G. W., Glazer B. T., Ma S., Trouwborst R. E., Moore T. S., Metzger E.,<br />
Kraiya C., Waite T. J., Druschel G., Sundby B., Taillefert M., Nuzzio D. B.,<br />
Shank T. M., Lewis B. L., Bren<strong>de</strong>l P. J., 2008. Use of voltammetric solidstate<br />
(micro)electro<strong>de</strong>s <strong>for</strong> studying biogeochemical processes: laboratory<br />
measurements to real time measurements with an in situ electrochemical<br />
analyzer (ISEA). Marine Chemistry, 108, pp. 221-235.<br />
Nieman H., Lösekann T., De Beer D., Elvert M., Nadalig T., Knittel K., Amann<br />
R., Sauter E. J., Schlüter M., Klages M., Foucher J. P., Boetius A., 2006.<br />
Novel microbial communities of the Haakon Mosby mud volcano and<br />
their role as a methane sink. Nature, 443, pp. 854-858.
224 Ecosystem properties<br />
Podowski E. L., Ma S., Luther III G.W., Wardrop D., Fisher C. R., 2010. Biotic<br />
and abiotic factors affecting realized distributions of mega-fauna in diffuse<br />
flow on an<strong>de</strong>site and basalt along the Eastern Lau Spreading Center,<br />
Tonga. Marine Ecology Progress Series, 418, pp. 25-45.<br />
Pöhn M., Vopel K., Grünberger E., Ott J., 2001. Microclimate of the brown alga<br />
Feldmannia caespitula interstitium un<strong>de</strong>r zero-flow conditions. Marine<br />
Ecology Progress Series, 210, pp. 285-290.<br />
Preisler A., De Beer D., Litchschlag A., Lavik G., Boetius A., Jørgensen B. B.,<br />
2007. Biological and chemical sulfi<strong>de</strong> oxidation in Beggiatoa inhabited<br />
marine sediment. The ISME Journal, 1, pp. 341-353.<br />
Revsbech N. P., Jørgensen B. B., Blackburn T. H., Cohen Y., 1983. Microelectro<strong>de</strong><br />
studies of the photosynthesis and O 2<br />
, H 2<br />
S and pH profiles of a microbial<br />
mat. Limnology and Oceanography, 28, pp. 1062-1074.<br />
Revsbech N. P., Madsen B., Jørgensen B. B., 1986. Oxygen production and<br />
consumption in sediments <strong>de</strong>termined at high spatial resolution by<br />
computer simulation of oxygen microelectro<strong>de</strong> data. Limnology and<br />
Oceanography, 31, pp. 293-304.<br />
Rickard D., Luther III G.W., 2007. Chemistry of iron sulfi<strong>de</strong>s. Chemical<br />
Reviews, 107, pp. 514-562.<br />
Sarrazin J., Juniper S. K., Massoth G., Legendre P., 1999. Physical and chemical<br />
factors influencing species distribution on hydrothermal sulfi<strong>de</strong> edifices<br />
of the Juan <strong>de</strong> Fuca Ridge, northeast Pacific. Marine Ecology Progress<br />
Series, 190, pp. 89-112.<br />
Schulz H. D., Zabel M. (Eds), 2006. Marine Geochemistry, 2nd edition.<br />
Springer Berlin Hei<strong>de</strong>lberg, New York, USA.<br />
Stahl H., Glud A., Schrö<strong>de</strong>r C. R., Klimant I., Tengberg A. Glud R. N., 2006.<br />
Time-resolved pH imaging in marine sediments with a luminescent planar<br />
opto<strong>de</strong>. Limnology and Oceanography: Methods, 4, pp. 336-345.<br />
Stockdale A., Davison W., Zhang H., 2009. Micro-scale biogeochemical<br />
heterogeneity in sediments: A review of available technology and observed<br />
evi<strong>de</strong>nce. Earth-Science Reviews, 92, pp. 81-97.<br />
Stumm W., Morgan J. J., 1996. Aquatic Chemistry, 3rd edition. Wiley, New<br />
York, USA.<br />
Taillefert M., bono A. b., Luther III G. W., 2000a. Reactivity of freshly<br />
<strong>for</strong>med Fe(III) in synthetic solutions and (pore)waters: voltammetric<br />
evi<strong>de</strong>nce of an aging process. Environmental Science and Technology,<br />
34, pp. 2169-2177.<br />
Taillefert M., Luther III G. W., Nuzzio D. B., 2000b. The application of<br />
electrochemical tools <strong>for</strong> in situ measurements in aquatic systems: a review.<br />
Electroanalysis, 12, pp. 401-412.<br />
Treu<strong>de</strong> T., Smith C. R., Wenzhoefer F., Carney E., Bernardino A. F., Hanni<strong>de</strong>s<br />
A. K., Kruger M., Boetius A., 2009. Biogeochemistry of a <strong>de</strong>ep-sea whale<br />
fall: sulfate reduction, sulfi<strong>de</strong> efflux and methanogenesis. Marine Ecology<br />
Progress Series, 382, pp. 1-21.
Part III – Chapter 2 225<br />
Volkenborn N., Polerecky L., Hedtkamp S. I. C., Van Beusekom J. E. E., De<br />
Beer D., 2007. Bioturbation and bioirrigation extend the open exchange<br />
regions in permeable sediments. Limnology and Oceanography, 52,<br />
pp. 1898-1909.<br />
Vopel K., Pöhn M., Sorgo A., Ott J., 2001. Ciliate-generated advective seawater<br />
transport supplies chemoautotrophic ectosymbionts. Marine Ecology<br />
Progress Series, 210, pp. 93-99.<br />
Vopel K., Thistle D., Ott J., Bright M., Roy H., 2005. Wave-induced H 2<br />
S flux<br />
sustains a chemoautotrophic symbiosis. Limnology and Oceanography,<br />
50, pp. 128-133<br />
Waite T. J., Moore T. S., Childress J. J., Hsu-Kim H., Mullaugh K. M., Nuzzio D.<br />
B., Paschal A. N., Tsang J., Fisher C. R., Luther III G. W., 2008. Variation<br />
in sulfur speciation with shellfish presence at a Lau Basin diffuse flow vent<br />
site. Journal of Shellfish Research, 27, pp. 163-168.<br />
Wenzhöfer F., Holby O., Glud R. N., Nielsen H. K., Gun<strong>de</strong>rsen J. K., 2000. In<br />
situ microsensor studies of a shallow water hydrothermal vent at Milos,<br />
Greece. Marine Chemistry, 69, pp. 43-54.<br />
Yücel M., Galand P. E., FagervoldS. K., Le Bris N. Submitted. Transition<br />
from suboxic to sulfidic conditions during early wood-fall <strong>de</strong>gradation in<br />
seawater.
Chapter 3<br />
Use of global satellite observations<br />
to collect in<strong>for</strong>mation in marine<br />
<strong>ecology</strong><br />
Séverine Alvain, Vincent Vantrepotte, Julia Uitz, Lucile Du<strong>for</strong>êt-Gaurier<br />
1. Introduction: ocean colour features, a state of the art<br />
The term “ocean colour” encompasses the retrieval and <strong>de</strong>scription of<br />
parameters linked with oceanic phytoplankton from optical measurements.<br />
The remote sensing of ocean colours has been used <strong>for</strong> more than<br />
30 years and now provi<strong>de</strong>s key in<strong>for</strong>mation on the dynamics of the oceanic<br />
phytoplankton (Morel and Prieur, 1977; Mobley et al., 1993; Antoine<br />
et al., 1996; bricaud et al., 1998; Loisel et al., 2006). Phytoplankton comprises<br />
microscopic plant-like organisms living in the illuminated surface<br />
layers of the ocean. The existence of phytoplankton is of a fundamental<br />
interest as they <strong>for</strong>m the base of the aquatic food webs, providing an<br />
essential ecological function <strong>for</strong> all aquatic life. Like terrestrial plants,<br />
phytoplankton uses pigment antennae to capture the energy of photons.<br />
Among these phytoplankton pigments, total chlorophyll-a (i.e. the<br />
sum of chlorophyll-a, divinyl-chlorophyll-a, and chlorophylli<strong>de</strong> a) is a<br />
commonly used proxy of total phytoplankton biomass. Chlorophyll-a<br />
selectively modifies the flux of photons that penetrates the ocean surface<br />
layer. It absorbs the red and blue wavelengths and scatters the green<br />
ones. For this reason, the colour of the ocean changes from blue to green<br />
<strong>de</strong>pending on the concentration and type of phytoplankton populations.<br />
Thus, by studying the colour of light scattered from the oceans, in other<br />
words ocean colour, optical sensors onboard satellites enable to quantify<br />
the chlorophyll concentration and observe its interactions with other<br />
constituents (Mobley et al., 1993; Antoine et al., 1996; bricaud et al.,<br />
1998).
228 Ecosystem properties<br />
Visible and near-infrared passive radiometers onboard spacecrafts<br />
provi<strong>de</strong> useful data on spatial and temporal scales, unattainable by<br />
shipboard sampling. This was well <strong>de</strong>monstrated by the first satellite<br />
<strong>de</strong>dicated to the observation of ocean colour, the coastal zone colour<br />
scanner (CZCS) launched in 1978. Since then, a number of advanced<br />
ocean-colour satellites have been launched, including SeaWiFS (sea<br />
viewing wi<strong>de</strong> field of view sensor, from August 1997 to December 2010),<br />
Modis (mo<strong>de</strong>rate resolution imaging spectroradiometer) and Meris<br />
(medium-spectral resolution imaging spectrometer), which are still in<br />
activity. However, the ocean colour observation from space faces some<br />
important limitations. In<strong>de</strong>ed, the in<strong>for</strong>mation obtained from satellite<br />
observation is restricted to the near-surface layer of the ocean (Gordon<br />
and McCluney, 1975). The thickness of this layer typically varies from<br />
a few metres to about 60m, <strong>de</strong>pending on the presence of optically-significant<br />
constituents in the water and the wavelength consi<strong>de</strong>red (Smith<br />
and baker, 1978). Products <strong>de</strong>rived from satellite data are there<strong>for</strong>e integrated<br />
content over the first penetration <strong>de</strong>pth. Another limitation is<br />
that a large part ocean colour measurements in the visible spectrum is<br />
caused by the atmosphere and aerosols that diffuse and absorb light.<br />
The atmosphere is responsible <strong>for</strong> about 90% of the blue light <strong>de</strong>tected<br />
by a satellite sensor. However, the portion of the signal that carries in<strong>for</strong>mation<br />
from the ocean and the atmosphere can be <strong>de</strong>-convoluted. This<br />
is currently done by using atmospheric correction algorithms that are<br />
still being improved. In the past few years, the analysis of ocean colour<br />
satellite data has moved beyond the estimation of chlorophyll-a concentration<br />
to inclu<strong>de</strong> new parameters. This inclu<strong>de</strong>s the ability to <strong>de</strong>termine<br />
the dominant phytoplankton groups in the surface waters (Aiken<br />
et al., 2009; Alvain et al., 2005: Uitz et al., 2006; Raitsos et al., 2008;<br />
Kostadinov et al., 2009; Brewin et al., 2010), to obtain in<strong>for</strong>mation on<br />
particle size distribution (Loisel et al., 2006), or to retrieve in<strong>for</strong>mation<br />
about other biogeochemical components such as particulate organic carbon<br />
(POC) and coloured <strong>de</strong>trital matter (Stramski et al., 1999; Loisel<br />
et al., 2002; Siegel et al., 2002). This chapter presents an overview of<br />
these newly available parameters from remote sensing of ocean colour.<br />
We conclu<strong>de</strong> by a synthesis of most important challenges and ongoing<br />
<strong>de</strong>velopments.
Part III – Chapter 3 229<br />
2. Overview of newly available parameters<br />
from remote sensing<br />
2.1. Particulate organic carbon<br />
Inherent optical properties (IOPs) <strong>de</strong>scribe the absorption and scattering<br />
properties of ocean water and its constituents. A recent method to<br />
analyse remote sensing data consists in <strong>de</strong>riving the surface content of<br />
particulate organic carbon (POC surf<br />
) from the inherent optical properties,<br />
as presented in Loisel et al. (2002). The natural variations of opticallysignificant<br />
substances in seawater can be <strong>de</strong>duced from the measurements<br />
of the total backscattering coefficient of seawater, b b<br />
, which is not sensitive<br />
to the presence of dissolved material. The b b<br />
coefficient can be partitioned<br />
into two components,<br />
b b<br />
=b bp<br />
+b bw<br />
where b bw<br />
is the backscattering coefficient of seawater (Morel and Prieur,<br />
1977) and b bp<br />
, is the backscattering coefficient of particles. The b bp<br />
variability<br />
is <strong>de</strong>termined primarily by changes in the abundance of the particle<br />
assemblage and also, secondarily, by the composition of the assemblage.<br />
In a remote-sensing context, the backscattering coefficient of seawater<br />
is not measured directly, but is <strong>de</strong>rived by the inversion of the natural<br />
light field reflected back from the ocean and <strong>de</strong>tected by satellite<br />
ocean colour sensors (Loisel and Stramski, 2000; Loisel and Poteau,<br />
2006). A simple linear relationship calibrated <strong>for</strong> a study area is then<br />
used between POC surf<br />
and b bp<br />
(Claustre et al., 1999; Loisel et al., 2001).<br />
Previous studies at regional (Stramski et al., 1999; Loisel et al., 2001)<br />
and global scales (Loisel et al., 2002) have <strong>de</strong>monstrated the feasibility<br />
of estimating POC from b bp<br />
, and figure 1 displays global maps of the<br />
near-surface concentration (POC surf<br />
) <strong>for</strong> the SeaWiFS period 1997-2008<br />
in June and January.<br />
The global distribution of POC surf<br />
follows the major gyre system and<br />
other large scale circulation features of the ocean. Low surface POC<br />
concentrations are encountered in subtropical gyres, where large scale<br />
downwelling is expected. For example, POC surf<br />
is less than 50mg.m -3 in<br />
the South Pacific gyre. Elevated near-surface POC concentration in the<br />
range 100-200mg.m -3 are encountered at high and temperate latitu<strong>de</strong>s<br />
(e.g. Antarctic circumpolar current, subarctic gyres, or temperate North<br />
Atlantic). Compared to subtropical gyres, these areas are characterized<br />
by a high chlorophyll concentration supported by inputs of nutrients<br />
injected from below the euphotic layer by advection or vertical mixing, or<br />
from terrestrial sources.
230 Ecosystem properties<br />
Figure 1: Global maps of the particulate organic plankton near-surface concentration<br />
calculated from SeaWiFS observations, during the period 1997-2008 in June and<br />
January using the method of Loisel et al. (2002).<br />
2.2. Phytoplankton functional types<br />
Phytoplankton plays an important role in many global biogeochemical<br />
cycles. However, the photosynthetic efficiency and biogeochemical<br />
impacts of phytoplankton <strong>de</strong>pend strongly on the functional types of<br />
phytoplankton species. Thus, monitoring the spatial and temporal distribution<br />
of dominant phytoplankton functional groups is of critical importance.<br />
For a given chlorophyll-a concentration (Chl-a), phytoplankton<br />
groups scatter and absorb light differently according to their pigments<br />
composition, shape and size. However, the first or<strong>de</strong>r signal retrieved from<br />
ocean colour sensors in open oceans, the normalized water leaving radiance<br />
(nL w<br />
), varies with Chl-a (Gordon et al., 1983; Morel et al., 1988) and<br />
cannot be easily used to extract in<strong>for</strong>mation about phytoplankton groups
Part III – Chapter 3 231<br />
present in the oceanic surface layer. To circumvent this difficulty, different<br />
approaches have been <strong>de</strong>veloped in the past few years. When changes<br />
in nL w<br />
are significant enough between phytoplankton groups, they can be<br />
<strong>de</strong>tected from their specific radiances measurements (Sathyendranath et<br />
al., 2004; Ciotti et al., 2006). When reflectance changes are not significant<br />
enough to separate one group from another, empirical or semi-empirical<br />
methods have to be <strong>de</strong>veloped. This last case is particularly relevant<br />
when the objective is to <strong>de</strong>tect phytoplankton groups <strong>de</strong>fined from a biogeochemical<br />
or size point of view at global scale. The Physat algorithm<br />
(Alvain et al., 2005; Alvain et al., 2008) has been <strong>de</strong>veloped based on an<br />
empirical relationship between coinci<strong>de</strong>nt in situ phytoplankton observations<br />
and remote sensing measurements anomalies. The Physat method<br />
has been applied to the SeaWiFS satellite archive, and more recently to<br />
MODIS. Monthly Physat data have been used to retrieve the monthly<br />
climatology maps <strong>for</strong> January and June, shown in figure 2.<br />
Figure 2: Dominant phytoplankton groups climatology maps over 1997-2008<br />
period (SeaWiFS), from Physat, <strong>for</strong> June and January. Physat method allows to<br />
separate dominant phytoplankton groups from remote sensing measurements.
232 Ecosystem properties<br />
Physat has a domain of applicability ranging from concentrations of Chl-a<br />
higher than 0.04mg m -3 , so as to discard ultra-oligotrophic waters where<br />
it is unlikely that a dominant group can be found using ocean-colour<br />
data, to Chl-a lower than 3mg.m -3 so that waters possibly contaminated<br />
by coastal material are exclu<strong>de</strong>d. The Physat approach is based on the<br />
i<strong>de</strong>ntification of specific signatures in spectra classically measured by<br />
ocean colour sensors. It has been established by comparing two kinds of<br />
simultaneous and coinci<strong>de</strong>nt measurements: normalised SeaWiFS water<br />
leaving measurements (nL w<br />
) and in situ measurements of phytoplankton<br />
biomarker pigments per<strong>for</strong>med in the framework of the Gep&Co program<br />
(Dandonneau et al., 2004). Five dominant phytoplankton groups are currently<br />
i<strong>de</strong>ntified: diatoms, nanoeukaryotes, Synechococcus, Prochlorococcus<br />
and Phaeocystis-like. Note that the Physat method allows the <strong>de</strong>tection of<br />
these groups only when they are dominant.<br />
The key step in the success of methods such as Physat is to associate in situ<br />
measurements with remote sensing measurements after having removed<br />
the first or<strong>de</strong>r variations due to the Chl-a concentration and classically<br />
used in previous ocean colour products. This step is done by dividing the<br />
actual nL w<br />
by a mean nL w<br />
mo<strong>de</strong>l (nL w<br />
ref) <strong>for</strong> each wavelength (λ), established<br />
from a large remote sensing dataset of nL w<br />
(λ) and Chl-a:<br />
nL w<br />
*(λ) = nL w<br />
(λ) / nL w<br />
ref(λ, Chl a)<br />
By dividing nL w<br />
by this reference, we obtain a new product, noted nL w<br />
*,<br />
which is used in Physat. In<strong>de</strong>ed, it was shown that main dominant phytoplankton<br />
groups sampled during the GeP&Co program were associated<br />
with a specific nL w<br />
* spectrum. It is thus possible to <strong>de</strong>fine a set of criteria<br />
to characterise each group as a function of its nL w<br />
* spectrum. These<br />
criteria can thus be applied to the global daily SeaWiFS archive in or<strong>de</strong>r<br />
to obtain global monthly maps synthesis of the most frequently <strong>de</strong>tected<br />
dominant group, as shown in figure 2. Note that when no group prevails<br />
over the period of one month, the pixels are associated with an “uni<strong>de</strong>ntified”<br />
group. The geographical distribution and seasonal succession<br />
of major dominant phytoplankton groups were studied in Alvain et al.<br />
(2008) and are in good agreement with previous studies and in situ observations<br />
(Zubkov et al., 2000; DuRand et al., 2001; Marty and Chivérini,<br />
2002; Dandonneau et al., 2004; Longhurst, 2007; Alvain et al., 2008).<br />
However, as <strong>for</strong> all empirical ocean colour methodology, validation based<br />
on in situ measurements has to be pursued every time a suitable dataset<br />
is available. In situ observations are indispensable in any stage of satellite<br />
<strong>de</strong>velopment. There<strong>for</strong>e, constructing and maintaining fully consistent<br />
coupled biogeochemical and optical records is a high priority.
Part III – Chapter 3 233<br />
2.3. Phytoplankton size classes and associated primary production<br />
Another approach to discriminating phytoplankton groups from space<br />
consists in using the surface Chl-a concentration (Chl-a surf<br />
) retrieved from<br />
ocean colour measurements as an in<strong>de</strong>x of phytoplankton community<br />
composition. Chlorophyll-based approaches typically rely on the general<br />
knowledge that, in open oceans, large phytoplankton cells (mostly diatoms)<br />
<strong>de</strong>velop in eutrophic regions (e.g. upwelling systems) where new<br />
nutrients are available, whereas small phytoplankton are preferentially<br />
associated with the presence of regenerated <strong>for</strong>ms of nutrients and dominate<br />
phytoplankton assemblage in oligotrophic environments. On the<br />
basis of such trends, Uitz et al. (2006) proposed a method <strong>for</strong> <strong>de</strong>riving the<br />
contributions of three pigment-based size classes (micro-, nano-, and picophytoplankton)<br />
to <strong>de</strong>pth-resolved chlorophyll-a biomass using Chl-a surf<br />
as<br />
input parameter. This method was <strong>de</strong>veloped through the statistical analysis<br />
of an extensive phytoplankton pigment database (2419 sampling stations)<br />
obtained from high per<strong>for</strong>mance liquid chromatography (HPLC)<br />
analysis of samples from a variety of oceanic regions. Using an improved<br />
version of the diagnostic pigment criteria of Vidussi et al. (2001), Uitz et<br />
al. (2006) computed phytoplankton class-specific vertical profiles of Chl-a<br />
<strong>for</strong> each station inclu<strong>de</strong>d in the pigment database, from which the <strong>de</strong>sired<br />
statistical relationships were established. Essentially, seven pigments were<br />
selected as biomarkers of specific taxa, which were then assigned to one of<br />
the three size classes according to the average size of the organisms.<br />
Some limitations of this method were recognised in the past (Vidussi et al.,<br />
2001; Uitz et al., 2006). For example, certain diagnostic pigments are shared<br />
by various phytoplankton taxa and some taxa may have a wi<strong>de</strong> range of cell<br />
size. yet, this method enables characterising the taxonomic composition of<br />
the entire phytoplankton assemblage while providing relevant in<strong>for</strong>mation<br />
on its size structure (Bricaud et al., 2004). For example, microphytoplankton<br />
essentially inclu<strong>de</strong> diatoms, nanophytoplankton inclu<strong>de</strong> primarily<br />
prymnesiophytes, and picophytoplankton are often prokaryotes (cyanobacteria)<br />
and small eukaryotic species. The approach of Uitz et al. (2006)<br />
provi<strong>de</strong>s quantitative in<strong>for</strong>mation on the composition of phytoplankton<br />
community within the entire upper water column rather than just the surface<br />
layer accessible to ocean colour satellites. In addition, this approach can<br />
be exten<strong>de</strong>d to the estimation of primary production associated with the<br />
pigment-based size classes, using a bio-optical mo<strong>de</strong>l (Morel, 1991) coupled<br />
to class-specific photo-physiological properties (Uitz et al., 2008). In a nutshell,<br />
Uitz et al. (2008) investigated relationships between phytoplankton<br />
photo-physiology and community composition by analysing a large database<br />
of HPLC pigment <strong>de</strong>terminations and measurements of phytoplankton<br />
absorption spectra and photosynthesis vs. irradiance curve parameters<br />
collected in various open ocean waters. An empirical mo<strong>de</strong>l that <strong>de</strong>scribes
234 Ecosystem properties<br />
Figure 3: Seasonal climatology (1998-2007) of total and phytoplankton classspecific<br />
primary production <strong>for</strong> the December-February period (boreal winter/<br />
austral summer; left-hand si<strong>de</strong> panels) and <strong>for</strong> the June-August period (boreal<br />
summer/austral winter; right-hand si<strong>de</strong> panels). (a, e) Total primary production in<br />
absolute units of gC.m -2 .d -1 . (b-d) and (f-h) percent contribution of class-specific<br />
production to total primary production. Total primary production, attributed to<br />
the entire algal biomass, was obtained by summing the contributions of each class.<br />
(adapted from Uitz et al., 2010).
Part III – Chapter 3 235<br />
the <strong>de</strong>pen<strong>de</strong>nce of algal photo-physiology on the community composition<br />
and <strong>de</strong>pth within the water column, essentially reflecting photoacclimation,<br />
was proposed. The application of the mo<strong>de</strong>l to the set of in<br />
situ data enabled the i<strong>de</strong>ntification of vertical profiles of photo-physiological<br />
properties <strong>for</strong> each phytoplankton size class.<br />
Figure 3 illustrates the seasonal climatology of phytoplankton class-specific<br />
and total primary production obtained by applying the class-specific<br />
approach to a 10-year time series of Chl-a surf<br />
data from SeaWiFS (Uitz et<br />
al., 2010). Temperate and subpolar latitu<strong>de</strong>s in each hemisphere exhibit<br />
high total primary production values in summer, especially in the North<br />
Atlantic Ocean. In contrast, oligotrophic subtropical gyres are associated<br />
with low values and show weak seasonality. Microphytoplankton appear<br />
as a major contributor to primary production in temperate and subpolar<br />
latitu<strong>de</strong>s in spring-summer, especially in the North Atlantic (more than<br />
50%) and in the Southern Ocean (30-50%). Their contribution reaches<br />
a maximum of about 70% in near-coastal upwelling systems year-round,<br />
but is reduced drastically in subtropical gyres. Nanophytoplankton appear<br />
ubiquitous and account <strong>for</strong> a significant fraction of total primary production<br />
(30-60%). The relative contribution of picophytoplankton to primary<br />
production represents up to 40-45% in subtropical gyres and <strong>de</strong>creases to<br />
15% in the northernmost latitu<strong>de</strong>s in summer. The proposed approach<br />
enabled to produce ocean colour-<strong>de</strong>rived climatology of primary production<br />
at a phytoplankton class-specific level in the world’s open oceans<br />
(Uitz et al., 2010). Such in<strong>for</strong>mation represents a significant contribution<br />
to our ability to un<strong>de</strong>rstand and quantify marine carbon cycle. It also provi<strong>de</strong>s<br />
a benchmark <strong>for</strong> monitoring the responses of oceanic ecosystems to<br />
climate change in terms of modifications of phytoplankton biodiversity<br />
and associated carbon fluxes.<br />
3. Challenges posed by current <strong>de</strong>velopments<br />
3.1. Dissolved organic matter (DOM)<br />
Besi<strong>de</strong>s the latter parameters, new <strong>de</strong>velopments in ocean colour remote<br />
sensing are nee<strong>de</strong>d especially <strong>for</strong> studying the dynamics of the dissolved<br />
organic matter. The dissolved organic carbon (DOC) is operationally<br />
<strong>de</strong>fined as the fraction of organic carbon smaller than 0.2µm. It accounts<br />
<strong>for</strong> almost all the organic carbon of the ocean (Chen and Borges, 2009)<br />
being equivalent in magnitu<strong>de</strong> to the atmospheric CO 2<br />
stock. DOC can<br />
be <strong>de</strong>gra<strong>de</strong>d by microbial activity and sunlight action and converted into<br />
CO 2<br />
. Hedges (2002) reported that an increase of 1% in the DOC <strong>de</strong>gradation<br />
rates would lead to a source of CO 2<br />
equivalent or greater than that
236 Ecosystem properties<br />
represented by the fossil fuel combustion. There<strong>for</strong>e it appears crucial, <strong>for</strong><br />
un<strong>de</strong>rstanding the global carbon cycle, to investigate the dynamics of this<br />
biogeochemical compartment, which is still poorly constrained. This is<br />
particularly true <strong>for</strong> the coastal ocean, where DOC fluxes are potentially<br />
large and highly variable in time and space, due to numerous driving<br />
factors taking effect on these very heterogeneous ecosystems, such as biological<br />
activity, land-sea interactions, and strong hydrodynamic <strong>for</strong>cing.<br />
In that context, ef<strong>for</strong>ts are nee<strong>de</strong>d to <strong>de</strong>velop research activities aiming to<br />
estimate DOC concentrations and fluxes from space.<br />
Recent studies have emphasised the potential of satellite imagery <strong>for</strong><br />
retrieving DOC concentrations with a satisfying accuracy (Mannino et<br />
al., 2008; Del Castillo et al., 2008; Fichot and Benner, 2011). The current<br />
main limitation <strong>for</strong> estimating DOC concentrations from radiative<br />
measurements stands in the crucial need of a relevant correlation between<br />
DOC concentration, which is uncoloured, and CDOM (coloured dissolved<br />
organic matter), which represents the coloured part of the marine<br />
dissolved material and is there<strong>for</strong>e measurable from space. Significant<br />
DOC-CDOM relationships were documented <strong>for</strong> various coastal ecosystems,<br />
especially those influenced by rivers discharges (e.g. Ferrari, 2000;<br />
Mannino et al., 2008; Del Castillo et al., 2008). However, the diversity in<br />
the origin of the dissolved material as well as the potential <strong>de</strong>coupling in<br />
the sensitivity of DOC and CDOM to various environmental factors (e.g.<br />
biological activity, photo-<strong>de</strong>gradation processes…) induces regional and<br />
seasonal variations in the CDOM-DOC relationships. Environmental<br />
effects can significantly alter or preclu<strong>de</strong> the establishment of a significant<br />
link between CDOM and DOC, <strong>for</strong> example, in the oceanic waters<br />
and coastal ecosystems not influenced by terrestrial inputs. There<strong>for</strong>e,<br />
our ability to <strong>de</strong>rive dissolved organic carbon contents from satellite measurements<br />
is still limited. The un<strong>de</strong>rstanding of environmental effects<br />
through <strong>de</strong>dicated in situ or laboratory studies represents the major challenge<br />
<strong>for</strong> <strong>de</strong>veloping DOC inversion algorithms in the next years. These<br />
algorithms will provi<strong>de</strong>, in a near future, relevant insights <strong>for</strong> global ocean<br />
carbon cycle study.<br />
3.2. Scaling down and up from regional scales to global scale<br />
Coastal oceans have fast changing and contrasted optical properties,<br />
which prevents the <strong>de</strong>velopment of a “simple”, general algorithm to <strong>de</strong>rive<br />
in-water bio-optical and biogeochemical parameters <strong>for</strong> the whole ocean<br />
from satellite in<strong>for</strong>mation. There<strong>for</strong>e, open ocean or coastal algorithms<br />
are usually <strong>de</strong>veloped to focus on an area-specific range of optical variability.<br />
However, these algorithms have some limitations related to their<br />
high <strong>de</strong>pen<strong>de</strong>ncy upon the data set used <strong>for</strong> their <strong>de</strong>velopment, as well
Part III – Chapter 3 237<br />
as to the difficulty to capture the numerous high frequency processes<br />
affecting regional bio-optical relationships. Moreover, the scaling-up of<br />
such regional approaches to <strong>de</strong>rive biogeochemical parameters at large<br />
scale (i.e. global) would require to consi<strong>de</strong>r a patchwork of algorithms<br />
<strong>de</strong>veloped on a mosaic of regions. This seems to be difficult to set up<br />
in practice. Another approach consists in taking explicitly into account<br />
the optical diversity of the marine environment within the algorithms<br />
<strong>de</strong>velopment procedure. This was shown to be crucial <strong>for</strong> explaining the<br />
dispersion found around the bio-optical relationships (Loisel et al., 2010).<br />
In practice, this original approach aims to classify the different regions<br />
according to their optical properties as <strong>de</strong>scribed by the marine reflectance<br />
spectra. Further, region-specific algorithms (empirical or semi-analytical)<br />
are <strong>de</strong>veloped and applied to the <strong>de</strong>fined optical regions. The main<br />
advantage of this classification-based approach is that it is in<strong>de</strong>pen<strong>de</strong>nt of<br />
the location and time period, being thus more universal than classical<br />
approaches and potentially applicable to large-scale studies. The potential<br />
of this classification-based approach <strong>for</strong> improving the per<strong>for</strong>mance of<br />
the inversion procedure has been recently emphasised <strong>for</strong> the retrieval of<br />
the Chl-a (Mélin et al., 2011) and SPM concentrations (Vantrepotte et al.,<br />
submitted).<br />
3.3. Theoretical studies<br />
If the last years have seen the <strong>de</strong>velopment of different approaches to distinguish<br />
phytoplankton groups from space, the current techniques are<br />
usually based on empirical methods (see above). Despite the fact that<br />
remotely sensed measurements are generally well matched with in situ measurements,<br />
the un<strong>de</strong>rlying theoretical foundation is still to be addressed.<br />
In a recent study published by Alvain et al. (2011), a radiative transfer<br />
mo<strong>de</strong>l called Hydrolight (Mobley et al., 1993) was used to reconstruct the<br />
signals used in Physat. A sensitivity analysis of the method to the following<br />
three mo<strong>de</strong>l parameters was conducted: the specific phytoplankton<br />
absorption, the dissolved organic matter absorption, and the particle backscattering<br />
coefficients. This last parameter explained the largest part of the<br />
variability in the radiative anomalies. Our results also showed that specific<br />
environments associated with each group must be consi<strong>de</strong>red imperatively.<br />
This study represents a first step toward a better un<strong>de</strong>rstanding and future<br />
improvement of phytoplankton groups <strong>de</strong>tection methods based on specific<br />
signal i<strong>de</strong>ntification. In a near future, further advances are expected<br />
from the improvement of the optical sensors themselves, especially their<br />
spectral and spatial resolution. This may also pave the way <strong>for</strong> the <strong>de</strong>velopment<br />
of new algorithm based on both phytoplankton groups and their<br />
environmental conditions (such as the content in dissolved organic matter).
238 Ecosystem properties<br />
3.4. Geostationary sensors<br />
The recent <strong>de</strong>velopment of geostationary ocean colour sensors will increase<br />
the precision of the remote sensing measurements and will provi<strong>de</strong> relevant<br />
insights <strong>for</strong> the study of marine biogeochemical cycles. Geostationary satellites<br />
continuously view the same region of the Earth’s surface. The size of<br />
the observed region <strong>de</strong>pends on the spacecraft specification. It thus allows<br />
obtaining high quality and frequent observations of a <strong>de</strong>fined area. Such an<br />
instrument is there<strong>for</strong>e useful in or<strong>de</strong>r to follow the response of marine ecosystems<br />
to short-term variations in the environmental conditions. In particular,<br />
it is of interest <strong>for</strong> monitoring the effects of rivers plumes, tidal front,<br />
and mixing on the biotic and abiotic material present in coastal areas or<br />
assessing the effects of exceptional events (storms, red ti<strong>de</strong>s, dissemination of<br />
sediments or pollutants). Data <strong>de</strong>rived from geostationary satellites will also<br />
provi<strong>de</strong> relevant in<strong>for</strong>mation <strong>for</strong> biogeochemical mo<strong>de</strong>lling purposes as well<br />
as <strong>for</strong> research activities related to the biogeochemical cycles at daily scales.<br />
The South Korean instrument on board the COMS-1 satellite (GOCI, geostationary<br />
ocean colour imager), launched in 2010, is the first ocean-colour<br />
sensor in a geostationary orbit. The target area of GOCI covers a large region<br />
(2500 × 2500km) around the Korean peninsula. It resolution is of 500 m<br />
while it acquires data at a 1-hour frequency. The other ocean colour geostationary<br />
missions that are currently planned (oCAPI-CNES, GeoCAPE-<br />
NASA) will increase the spatial coverage and the number of in<strong>for</strong>mation<br />
<strong>de</strong>livered by such sensors.<br />
3.5 Cross-using remote sensing data<br />
Consi<strong>de</strong>ring the recent variety of new ocean colour products, cross using<br />
studies will open a large range of new applications. For example, in<strong>for</strong>mation<br />
on dominant phytoplankton groups could be analysed concomitantly<br />
with maps of POC and particle size distribution, hence providing<br />
new insights into biogeochemical or ecological processes. An illustration<br />
is shown in figure 1 and 2 <strong>for</strong> a Northern area (45°N-52°N, 30°W-15°W)<br />
and a Southern area (47°S-40°S, 65°E-80°E). The two areas are almost<br />
i<strong>de</strong>ntical in terms of Chl-a concentration but distinct in terms of POC<br />
concentration. This difference also exists in terms of phytoplankton<br />
groups. The region in the Southern Ocean is dominated by diatoms<br />
whereas the region in the northern Atlantic is dominated by nanoeukaryotes.<br />
The comparison of the spatial distribution of Chl-a, POC and<br />
dominant phytoplankton groups prompts the following question. Is it<br />
possible to i<strong>de</strong>ntify from space, and at a global scale, some differences in<br />
the “POC vs Chl-a” relationship based on the dominant phytoplankton<br />
group as <strong>de</strong>tected by the Physat tool? Further investigations are required<br />
to fully answer this question but our simple example illustrates how
Part III – Chapter 3 239<br />
much cross-using of remote sensing data will be necessary and useful in<br />
a near future.<br />
Authors’ references<br />
Séverine Alvain, Vincent Vantrepotte, Lucile Du<strong>for</strong>et-Gaurier:<br />
Université du Littoral <strong>de</strong> la Côte d’Opale-Lille Nord, Laboratoire d’Océanologie<br />
et Géosciences, Lille1-ULCO-CNRS UMR 8187, Lille, France<br />
Julia Uitz:<br />
Université Pierre et Marie Curie, Laboratoire d’Océanographie <strong>de</strong><br />
Villefranche, UPMC-CNRS UMR 7093, Villefranche-sur-Mer, France<br />
Corresponding author: Séverine Alvain, severine.alvain@univ-littoral.fr<br />
Acknowledgement<br />
The authors would like to thank the NASA SeaWiFS project and the<br />
NASA/GSFC/DAAC <strong>for</strong> the production and distribution of the SeaWiFS<br />
data.<br />
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Science, 285, pp. 239-242.<br />
Uitz J., Claustre H., Morel A., Hooker S. B., 2006. Vertical distribution of<br />
phytoplankton communities in open ocean: an assessment based on<br />
surface chlorophyll. Journal of Geophysical Research, 111, CO8005.<br />
Uitz J., Huot Y., Bruyant F., Babin M., Claustre H., 2008. Relating phytoplankton<br />
photophysiological properties to community structure on large scale.<br />
Limnology and Oceanography, 53, pp. 614-630.<br />
Uitz J., Claustre H., Gentili B., Stramski D., 2010. Phytoplankton class-specific<br />
primary production in the world’s oceans: seasonal and interannual<br />
variability from satellite observations. Global Biogeochemical Cycles, 24,<br />
pp. 3016-3035.<br />
Vidussi F., Claustre H., Manca B. B., Luchetta A., Marty J.-C., 2001.<br />
Phytoplankton pigment distribution in relation to upper thermocline<br />
circulation in the eastern Mediterranean Sea during winter. Journal of<br />
Geophysical Research, 106, pp. 19939-19956.<br />
Zubkov M. V., Sleigh M. A., Burkill P. H., Leakey R. J. G., 2000. Picoplankton<br />
community structure on the Atlantic meridional transect: a comparison<br />
between seasons. Progress in Oceanography, 45, pp. 369-386.
Chapter 4<br />
Tracking canopy phenology<br />
and structure using ground-based<br />
remote sensed NDVI measurements<br />
Jean-Yves Pontailler, Kamel Soudani<br />
1. Introduction and general context<br />
The NDVI (normalised-difference vegetation in<strong>de</strong>x) is the most popular<br />
remotely sensed spectral vegetation in<strong>de</strong>x. It was <strong>de</strong>fined in the early<br />
seventies (Kriegler et al., 1969; Rouse et al., 1973; Tucker, 1977; 1979) <strong>for</strong><br />
remote sensing purposes, and has been mostly used to track green vegetation<br />
from satellites, airplanes and ground-based spectral measurements.<br />
The first measurements of vegetation indices from space were conducted<br />
in 1972 thanks to Landsat 1 and its embed<strong>de</strong>d MSS (multispectral scanner)<br />
and this technique carries on nowadays with satellites such as Spot<br />
or Modis Terra. The NDVI is an estimate of the amount of visible light<br />
absorbed by canopies and can correlate with gross primary photosynthesis<br />
(Running et al., 2004). The measurement of NDVI exploits the fact<br />
that green vegetation absorbs largely the inci<strong>de</strong>nt red radiation but reflects<br />
a large part of the infrared radiation. By contrast, a bare soil does not<br />
exhibit such differences. Thus, NDVI is <strong>de</strong>fined as a normalised difference<br />
between red (RED) and near-infrared (NIR) reflectance given by the<br />
equation:<br />
NDVI = (NIR – RED) / (NIR + RED)<br />
The NDVI value ranges from -1.0 to 1.0: negative values indicates water<br />
and snow, values close to zero indicates bare soil, and high positive values<br />
indicates sparse vegetation (0.2 to 0.5) or <strong>de</strong>nse green vegetation (up<br />
to 0.85). NDVI can also be related to the leaf area in<strong>de</strong>x (LAI), green
244 Ecosystem properties<br />
biomass, photosynthetic activity or to ground cover, which are relevant<br />
parameters in canopy and <strong>for</strong>est ecosystem mo<strong>de</strong>lling (Bréda et al., 2002).<br />
Several factors affect the accuracy of satellite-based NDVI measurements,<br />
<strong>for</strong> example, the presence of clouds or atmospheric dust in the solar and<br />
sensor viewing directions and topography. These impose geometric and<br />
atmospheric corrections (Soudani et al., 2006). Surprisingly, very few<br />
studies make use of ground-based NDVI measurements to record vegetation<br />
dynamics in situ in spite of the limited constraints imposed by this<br />
technique. This chapter <strong>de</strong>scribes a new low-cost laboratory-ma<strong>de</strong> NDVI<br />
sensor <strong>de</strong>voted to track in situ temporal variations in canopy structure and<br />
plant phenology.<br />
2. Designing a new NDVI sensor<br />
2.1. General concept<br />
Most authors agree on the fact that an accurate NDVI estimate requires<br />
measurements in a narrow red band centred around the chlorophyll<br />
absorption band (between 650 and 680nm) and in a narrow or broad<br />
near infrared band placed in the 750-1100mm range (Thenkabail et al.,<br />
2000; Elvidge and Chen, 1995). In these conditions, a relevant choice<br />
<strong>for</strong> the light <strong>de</strong>tectors of an on-ground NDVI sensor would be to couple<br />
silicon photodio<strong>de</strong>s that operate in the red and near-infrared ranges with<br />
interference filters that transmit the required bandpass only. This operating<br />
mo<strong>de</strong> is sound but such <strong>de</strong>tectors typically show a low sensitivity due<br />
to the necessity of collimating inci<strong>de</strong>nt radiation (i.e. removing oblique<br />
beams) to allow the interference filters to work properly. The narrow red<br />
band is more affected by very low output levels, especially over <strong>de</strong>nse<br />
canopies that absorb a large part of incoming red light. In these conditions,<br />
we chose not to use interference filters but instead ma<strong>de</strong> use of<br />
long-pass filters providing a sharp cut-off. Also, we took benefit of the<br />
peculiar shape of the spectral response curve of gallium arseni<strong>de</strong> phosphi<strong>de</strong><br />
photodio<strong>de</strong>s (GaAsP, Hamamatsu Photonics, Japan) when <strong>de</strong>signing<br />
the red <strong>de</strong>tector.<br />
2.2. Sensor <strong>de</strong>sign and calibration<br />
The red <strong>de</strong>tector uses a large GaAsP photodio<strong>de</strong> (TO-8 package) coupled<br />
with a Schott RG645 glass-tinted long-pass filter (Schott Glaswerke,<br />
Mainz, Germany). Thanks to this combination, only the portion of the<br />
GaAsP response curve corresponding to the longest wavelengths is used<br />
(figure 1A). As a consequence, the <strong>de</strong>tector has a narrow response curve
Part III – Chapter 4 245<br />
centred on 655nm. The near-infrared <strong>de</strong>tector is equipped with a silicon<br />
photodio<strong>de</strong> (TO-5 package) and a Schott RG780 glass-tinted long-pass filter.<br />
It has there<strong>for</strong>e a broad response curve centred on 825nm.<br />
Figure 1: Characteristics of RED and NIR <strong>de</strong>tector of the custom-ma<strong>de</strong> NDVI<br />
sensor. A. Relative response of the RED <strong>de</strong>tector. The relative response results from<br />
the shape of a gallium arseni<strong>de</strong> phosphi<strong>de</strong> photodio<strong>de</strong> (GaAsP) and from the cut-off<br />
of a long-pass filter. B. Relative response of RED and NIR (near infrared) <strong>de</strong>tectors.<br />
The relative spectral response of the two channels (figure 1B) was monitored<br />
using a halogen stabilised light source and a monochromator (H10,<br />
Jobin Yvon, France), combined with simultaneous energy measurement<br />
with a pyranometer (CE 180, Cimel, France).<br />
The body of the sensor is 85mm long and 38mm in diameter. It is ma<strong>de</strong> of<br />
polytetrafluoroethylene (Teflon) surroun<strong>de</strong>d by a stainless steel housing<br />
(figure 2A). The two <strong>de</strong>tectors face a 5mm thick acrylic diffuser (Altuglass<br />
740, Altulor, France). Current is converted to voltage with shunt resistors.<br />
To provi<strong>de</strong> a high sensitivity, the value of the resistors was selected as<br />
high as possible without affecting signal linearity. As a result, no amplification<br />
is necessary contrary to similar sensors using silicon dio<strong>de</strong>s and<br />
interference filters (Methy et al., 1987). The or<strong>de</strong>r of magnitu<strong>de</strong> of the<br />
output level is about 25mV so the sensor can be connected directly to<br />
most commercial data loggers. Other characteristics of the sensor can be<br />
found in Pontailler et al. (2003). Prior to its <strong>de</strong>ployment in situ, the sensor<br />
is calibrated against a spectroradiometer (Li-1800, Li-Cor, Nebraska,<br />
USA), consi<strong>de</strong>ring bandwidths equal to 640-660 nm and 780-920 nm <strong>for</strong><br />
RED and NIR, respectively.
246 Ecosystem properties<br />
Figure 2: The custom-ma<strong>de</strong> NDVI sensor <strong>de</strong>veloped at the Laboratoire Écologie-<br />
Systématique-Évolution (CNRS, Université Paris Sud, Agro Paris Tech , UMR 8079,<br />
Orsay, France). A. Cross-section of the NDVI sensor. b. NDVI sensor per<strong>for</strong>ming<br />
routine measurements at barbeau Carboeurope site.© J.-y. Pontailler.<br />
2.3. Operating mo<strong>de</strong><br />
The sensor has a view angle of 100° with a greater sensitivity in the centre<br />
of the field of view and is “looking” downwards. Users can restrict<br />
this angle by using a black hood, which <strong>de</strong>creases both channels outputs<br />
equally so that NDVI values remain unaffected. The fact that a single sensor<br />
provi<strong>de</strong>s no reflectance data but measures radiance should be kept in<br />
mind. Computing NDVI from radiance assumes that the RED/NIR ratio<br />
of inci<strong>de</strong>nt light is constant, which is not totally true due to the presence<br />
of clouds or variations in solar angle. Of course, it is possible to add a second<br />
sensor looking upwards to obtain true reflectance data. However, this<br />
second sensor has to be cosine-corrected and must be frequently cleaned<br />
because it is exposed to bad weather. That is why we promote the use of a<br />
single sensor looking downwards. It is a robust maintenance-free solution,<br />
well adapted to routine measurements. Data filtering based on time, sun<br />
elevation, or inci<strong>de</strong>nt radiation may be helpful to increase accuracy.
Part III – Chapter 4 247<br />
3. In situ <strong>de</strong>ployment to monitor vegetation phenology<br />
Up to now, 47 sensors were manufactured and installed on various sites<br />
including flux towers within Carboeurope network, crops, meadows,<br />
Mediterranean vegetation and shrubs or rain <strong>for</strong>est. All sensors were i<strong>de</strong>ntical<br />
and calibrated in the same way in our laboratory be<strong>for</strong>e shipping. During<br />
measurements, sensors were linked to Campbell CR10x or CR1000 data loggers<br />
(Campbell Scientific, Utah, USA) using 0-50 or 0-250mV input ranges.<br />
Zeroes (night values) were checked and offsets were adjusted if necessary.<br />
Measurements were per<strong>for</strong>med at one minute interval and half-hourly average<br />
values were recor<strong>de</strong>d. Data obtained when inci<strong>de</strong>nt radiation was low<br />
were rejected (below 250Wm -2 ) and filtered data were then averaged daily.<br />
3.1. Measurements over <strong>for</strong>ests<br />
In <strong>for</strong>ests stands, NDVI sensors were installed on top of towers measuring<br />
carbon and water vapour fluxes by eddy covariance. <strong>Sensors</strong> had a view<br />
angle restricted to approximately 60° and were obliquely set up at 20° from<br />
vertical (figure 2B). They were located from 4 to 20m over the top of the<br />
canopy. The NDVI sensor nicely tracks phenological events over <strong>de</strong>ciduous<br />
<strong>for</strong>ests. In an oak stand located in Barbeau Carboeurope site (Quercus<br />
sessiliflora, Seine-et-Marne, France), the course of NDVI over time highlights<br />
several phases (figure 3A). be<strong>for</strong>e budburst, the sensor mostly <strong>de</strong>tects<br />
branches, trunks, leaf litter and bare soil. NDVI values are stable around 0.4<br />
and display some slight variations due to modifications of litter moisture<br />
and direct versus diffuse radiation regimes. After budburst, NDVI increases<br />
rapidly during leaf expansion and maturation that lasts about 30 days until<br />
it reaches its maximum value in the late spring at day-of-the-year 130. Then,<br />
NDVI is almost constant during summer from early May to the end of<br />
September. This period constitutes the main season of growth in temperate<br />
<strong>de</strong>ciduous <strong>for</strong>ests. The NDVI plateau during the growing season is characterised<br />
by a very slow and regular <strong>de</strong>crease of NDVI values from 0.9 to 0.8,<br />
which may be caused by imperceptible colour changes in the canopy (slight<br />
yellowing) and probably sun-view geometry variations throughout summer.<br />
Our research team per<strong>for</strong>med eddy-covariance measurements in<br />
Carboeurope <strong>de</strong>ciduous sites, and observed at the same period a similar<br />
regular <strong>de</strong>crease of carbon uptake probably due to early foliage senescence<br />
(Granier et al., 2000). This phenomenon is not always taken into account<br />
by process-based mo<strong>de</strong>ls of canopy functioning. During this stage of<br />
NDVI maximum, the radiance measured by the red channel is very low<br />
and NDVI signal approaches saturation. Later in the season, leaf yellowing<br />
and fall both cause a strong <strong>de</strong>crease of NDVI values. Colour and <strong>de</strong>nsity<br />
changes partly overlap, and the <strong>de</strong>convolution of signal components
248 Ecosystem properties<br />
is not easy. We can notice that the NDVI curve matches pretty well with<br />
the evolution of the fraction of photosynthetically active radiation (PAR)<br />
transmitted by the canopy during the same period, indicating that NDVI<br />
is a good indicator of the amount of absorbed visible light (figure 3A).<br />
Figure 3: Seasonal and inter-annual variations in NDVI measurements over a<br />
<strong>de</strong>ciduous <strong>for</strong>est study plot. A. Temporal profiles of NDVI and transmitted PAR<br />
(photosynthetically active radiation) in Carboeurope oak <strong>for</strong>est site in Barbeau<br />
(France) in 2006. B, C. NDVI temporal profiles at Barbeau site over six years (2005-<br />
2010) and annual carbon net exchange of the stand from 2006 to 2010. Year 2007<br />
was characterised by a stronger carbon storage flux, which was <strong>de</strong>tected by NDVI<br />
measurements (red arrow)
Part III – Chapter 4 249<br />
Figure 4: Seasonal variation in NDVI measurements over two evergreen <strong>for</strong>est<br />
study plots. A. NDVI temporal profile at le bray Carboeurope pine site in 2007. b.<br />
NDVI temporal profile in Paracou site (French Guyana, rain <strong>for</strong>est) in 2007 during<br />
two dry seasons (yellow bands).<br />
The sensor also highlights inter-annual variations of phenological events.<br />
Figure 3B shows the evolution of NDVI over a 6-year period in Barbeau<br />
Carboeurope flux site. Few inter-annual changes are noticeable between<br />
day-of-the-year 130 and day-of-the-year 275 but spring and autumn greatly<br />
differ from one year to another. These changes are of importance. For<br />
instance, an early budburst was observed during the warm spring of 2007,
250 Ecosystem properties<br />
inducing an exceptional annual carbon uptake as shown by eddy-covariance<br />
measurements on the same site (Delpierre et al., 2009). Carbon<br />
assimilation is consi<strong>de</strong>rable at this period of the year because canopy photosynthetic<br />
capacity is intact. By contrast, a late foliage senescence has a<br />
limited impact on carbon budget because aged leaves have a lower photosynthetic<br />
capacity and because there is less available light at this period of<br />
the year. Of course, the length of the leafy period is not the only parameter<br />
that influences the carbon uptake of <strong>de</strong>ciduous <strong>for</strong>ests. We can notice<br />
on figure 3B that year 2009 had a reduced net carbon budget in spite of a<br />
relatively long leafy period. This may be due both to the negative impact<br />
of several cold weeks that occurred immediately after leaf expansion and<br />
an increase of ecosystem respiration in autumn because of a particularly<br />
mild weather.<br />
A NDVI sensor can also track the phenology of evergreen tree species,<br />
but with small variations of NDVI due to subtle phenological changes<br />
in these ecosystems. figure 4A shows NDVI values measured in a pine<br />
<strong>for</strong>est in Le Bray Carboeurope site (Pinus pinaster, Giron<strong>de</strong>, France). The<br />
sensor can monitor the rise and growth of new shoots during the spring<br />
from day-of-the-year 90 to day-of-the-year 160. In the tropical rain <strong>for</strong>est<br />
in Paracou Carboeurope site (French Guiana), NDVI varies little over the<br />
year, as one would expect in evergreen moist <strong>for</strong>ests. Nevertheless, we<br />
observe two periods with a <strong>de</strong>cline in NDVI of variable magnitu<strong>de</strong>. These<br />
two periods of NDVI <strong>de</strong>cline are concomitant with the two dry seasons,<br />
the short dry season called “the little summer of March” and the major<br />
dry season from July to November (figure 4B).<br />
3.2. Measurements over savannahs and crops<br />
In a savannah (Congo), a NDVI sensor was useful to <strong>de</strong>scribe the evolution<br />
of the herbaceous cover (figure 5A). A mo<strong>de</strong>rate wet season was<br />
observed from day-of-the-year 60 to day-of-the-year 160, followed by a<br />
dryer period. A fire occurred on day-of-the-year 185, lowering NDVI values<br />
to zero. A fast regrowth took place from day-of-the-year 300 onwards<br />
after a fairly long dry period. Similarly, crops (figure 5B) exhibit large<br />
temporal changes from ploughing to harvest. The colour and ground<br />
cover of crops there<strong>for</strong>e both vary intensely, and NDVI sensors may be<br />
useful to characterise these changes. figure 5B shows the temporal pattern<br />
of NDVI in a winter wheat field in Belgium. NDVI reaches a maximum<br />
value in May (day-of-the-year 130) and drops to zero within two months<br />
due to an intense yellowing. The harvest does not modify the signal. Later<br />
sparse vegetation appears up to the end of the vegetation period (day-ofthe-year<br />
260).
Part III – Chapter 4 251<br />
Figure 5: Seasonal variation in NDVI measurements over a savannah and a winter<br />
wheat study plot. A. NDVI temporal profile in Tchizalamou site (Congo, savannah)<br />
in 2008 during wet and dry seasons. b. NDVI temporal profile in Lonzée site<br />
(Belgium, winter wheat) in 2007, be<strong>for</strong>e and after havest.<br />
4. Use of the NDVI sensor to estimate the leaf area in<strong>de</strong>x<br />
and plant biomass<br />
4.1. Leaf area in<strong>de</strong>x<br />
The leaf area in<strong>de</strong>x (LAI) is <strong>de</strong>fined as the total one-si<strong>de</strong>d area of leaf tissue<br />
per unit ground surface area. It is a key parameter to <strong>de</strong>scribe the state<br />
of a <strong>for</strong>est ecosystem and a relevant input to productivity mo<strong>de</strong>ls. Several
252 Ecosystem properties<br />
methods have been <strong>de</strong>vised <strong>for</strong> estimating LAI. Destructive methods<br />
that require direct sampling and allometric methods – which establishes<br />
quantitative relations between several easy to measure dimensions in<br />
the canopy (tree size and/or <strong>de</strong>nsity, leaf characteristics…) and leaf area<br />
in<strong>de</strong>x (Ceulemans and al., 1993) – are cumbersome, time consuming and<br />
site <strong>de</strong>pen<strong>de</strong>nt. Conversely, optical methods are fast and appropriate <strong>for</strong><br />
monitoring spatial and temporal dynamics of canopy structure (Bréda et<br />
al., 2002).<br />
Our NDVI sensor was used to keep track of LAI in an elevated CO 2<br />
experiment<br />
conducted in Florida (Pontailler et al., 2003). The vegetation studied<br />
was a scrub-oak natural ecosystem ma<strong>de</strong> up of three main tree species<br />
(Quercus myrtifolia, Q. geminata and Q. chapmannii). Four 4m 2 contrasted<br />
calibration plots were marked out in February and four similar plots were<br />
marked out in June. The vegetation was low in stature and we used a<br />
handheld version of the sensor equipped with a dual LCD display to measure<br />
NDVI (36 measurements per plot). Immediately after these measurements,<br />
the plots were manually <strong>de</strong>foliated, the area of a sub-sample of<br />
the leaves – ca. 0.1m 2 – was measured using a leaf area meter (LI-3100,<br />
Li-Cor, Nebraska, USA), and all leaves, including those sub-sampled, were<br />
dried to constant weight at 80°C and weighed. In these conditions, we<br />
could assess “true LAI” values from direct, <strong>de</strong>structive samples. The best<br />
fit between “true LAI” and the NDVI measurements was obtained using<br />
a negative exponential function (r 2 =0.987, figure 6A).<br />
We observed a mo<strong>de</strong>rate saturation effect in the range of values <strong>de</strong>termined<br />
here, making it possible to obtain reliable estimations of LAI up<br />
to 3 or even more. The explanation <strong>for</strong> this mo<strong>de</strong>rate saturation is that<br />
even if the canopy reflectance in the red band reaches an asymptote at<br />
LAI values of 2-3, its reflectance in the near-infrared continues to increase<br />
with an increase in leaf area (Peterson and Running, 1989; Soudani et<br />
al., 2006). Nevertheless, the accuracy of this technique <strong>de</strong>creases when<br />
LAI increases, and NDVI is also affected by background materials when<br />
LAI is low. Contrary to other field techniques used to assess LAI, the<br />
approach based on NDVI measurement has the advantage to consi<strong>de</strong>r<br />
green plant parts only, ignoring branches and woody stems. Also, groundbased<br />
NDVI is well adapted to low canopies where other techniques may<br />
be difficult to implement.<br />
4.2. Estimating biomass and ground cover<br />
The NDVI measurements are also useful to estimate biomass non-<strong>de</strong>structively.<br />
A good linear relationship was obtained between NDVI and harvested<br />
green biomass by Buttler and Landolt (unpublished data) in a peat<br />
bog stand in the East of France (figure 6B). As observed when per<strong>for</strong>ming
Part III – Chapter 4 253<br />
Figure 6: Use of NDVI <strong>for</strong> plant biomass studies. A. Relationship between NDVI<br />
and “true” LAI (leaf area in<strong>de</strong>x, total leaf tissue area per unit ground surface area)<br />
in a scrub oak ecosystem in Florida. B Relationship between NDVI and biomass in<br />
a peat bog stand.<br />
LAI measurements, saturation also occurs when <strong>de</strong>termining biomass in<br />
<strong>de</strong>nse plots. The ability of NDVI to discriminate between green vegetation<br />
and bare soil makes it also useful to <strong>de</strong>termine the ground cover of<br />
stands. This has to be consi<strong>de</strong>red carefully because <strong>de</strong>nsity insi<strong>de</strong> green<br />
spots often varies together with ground cover, and both are taken into<br />
account when per<strong>for</strong>ming measurements. This technique appears more<br />
adapted to assess trends rather than absolute values, even when a cautious<br />
calibration process is applied.
254 Ecosystem properties<br />
5. Comparison with satellite-based remote sensing data<br />
Despite the technological maturity of remote sensing technologies<br />
onboard of numerous satellites and the significant progress achieved <strong>for</strong><br />
the last <strong>de</strong>ca<strong>de</strong> in this field, the potential use of remote sensing to monitor<br />
phenology and vegetation dynamics remains severely limited by atmospheric<br />
conditions in general and especially by the cloud cover (Soudani<br />
et al., 2008). Thus, ground-based NDVI measurements provi<strong>de</strong> the data<br />
nee<strong>de</strong>d <strong>for</strong> the calibration and validation of satellite observations and<br />
products. Figure 7 shows the NDVI temporal profiles measured <strong>for</strong> three<br />
consecutive years over a <strong>de</strong>ciduous mature <strong>for</strong>est in Barbeau Carboeurope<br />
site using our in situ NDVI sensor. These data are compared with those<br />
obtained from bands 1 (red) and 2 (near infrared) of Modis onboard Terra<br />
satellite plat<strong>for</strong>m <strong>for</strong> the same area. Both sensors show similar patterns and<br />
may be used to extract the main phenological markers that characterise<br />
the seasonality of vegetation in <strong>de</strong>ciduous <strong>for</strong>ests. Discrepancies between<br />
in situ NDVI sensor and Modis Terra based NDVI may be explained by<br />
numerous factors. First, an important reason <strong>for</strong> the observed discrepancies<br />
is the differences in the spectral responses of the two sensors. Shapes<br />
and bandwidths of spectral responses of Modis bands 1 and 2 are different<br />
from those of our in situ NDVI sensor. Second, other contributing<br />
factors inclu<strong>de</strong> atmospheric effects, and view and illumination conditions.<br />
Finally, differences in spatial resolution may also be important here<br />
because the area observed by the in situ NDVI sensor (around 100m²) is<br />
rather small compared to Modis pixel size (a few hectares).<br />
Figure 7: NDVI temporal profiles measured using a NDVI sensor located on top of<br />
a flux tower (squares, red lines) and <strong>de</strong>rived from Modis satellite data (circles, blue<br />
line) over three years in Barbeau Carboeurope oak site.
Part III – Chapter 4 255<br />
6. Conclusion<br />
The sensor <strong>de</strong>scribed in this chapter is rugged and maintenance-free. We<br />
<strong>de</strong>signed it ten years ago and we have used it since then in a variety of<br />
conditions where it always showed a satisfying reliability. For example,<br />
several units per<strong>for</strong>med continuous measurements on top of flux towers<br />
<strong>for</strong> six years without failure or noticeable drift. The cost of this instrument<br />
is af<strong>for</strong>dable, about 200€ without labour costs, and the instrument<br />
can be purchased from ESE laboratory (www.ese.u-psud.fr/). Our onground<br />
NDVI sensor appears well adapted to track vegetation phenology<br />
routinely, to assess leaf area in<strong>de</strong>x or ground cover in relatively sparse<br />
canopies and estimate standing biomass provi<strong>de</strong>d calibration is done.<br />
This sensor partly fills the lack of low-cost sensors available to scientists<br />
that are not ready to invest in expensive spectrometers to per<strong>for</strong>m simple<br />
spectral measurements.<br />
Authors’ references<br />
Jean-Yves Pontailler, Kamel Soudani:<br />
Université Paris-Sud 11, Écophysiologie végétale, bilan carboné et fonctionnement<br />
<strong>de</strong>s écosystèmes, Laboratoire Écologie, systématique, évolution,<br />
CNRS-UPS-AgroParisTech UMR 8079, Orsay, France<br />
Corresponding author: Kamel Soudani, kamel.soudani@u-psud.fr<br />
Aknowledgement<br />
The authors thank GIP ECOFOR and F-ORE-T « Observatoires <strong>de</strong><br />
Recherche en Environnement (ORE) sur le Fonctionnement <strong>de</strong>s<br />
Écosystèmes Forestiers » ECOFOR, INSU, Ministère <strong>de</strong> l’Enseignement<br />
Supérieur et <strong>de</strong> la Recherche <strong>for</strong> funding the NDVI project. We express<br />
our thanks to Eric Dufrêne who has strongly supported this project. We<br />
would like to express our profound gratitu<strong>de</strong> to Laurent Vanbostal and<br />
Daniel Berveiller <strong>for</strong> their help in manufacturing NDVI sensors and per<strong>for</strong>ming<br />
measurements. We also thank all our collaborators who were<br />
involved in the data collection process in all study sites.
256 Ecosystem properties<br />
References<br />
Bréda N., Soudani K., Bergonzini J.-C., 2003. Mesure <strong>de</strong> l’indice foliaire en<br />
<strong>for</strong>êt. Eco<strong>for</strong>, Paris, France.<br />
Ceulemans R., Pontailler J.-Y., Mau F., Guittet J., 1993. Leaf allometry in young<br />
poplar stands: reliability of leaf area in<strong>de</strong>x estimation, site and clone<br />
effects. Biomass and Bioenergy, 4, pp. 315-321.<br />
Delpierre N., Soudani K., François F., Köstner b., Pontailler J.-Y., Aubinet<br />
M., bernhofer C., Granier A., Grunwald T., Heinesch b., Longdoz b.,<br />
Misson L., Nikinmaa E., ourcival J.-M., Rambal S., Vesala T., Dufrêne<br />
E., 2009. Exceptional carbon uptake in European <strong>for</strong>ests during the<br />
warm spring of 2007: a data-mo<strong>de</strong>l analysis. Global Change Biology, 15,<br />
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Elvidge C. D., Chen Z., 1995. Comparison of broad-band and narrow-band red<br />
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Granier A., Ceschia E., Damesin C., Dufrêne E., Epron D., Gross P., Lebaube<br />
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Pontailler J.-Y., Saugier B., 2000. The carbon balance of a young beech<br />
<strong>for</strong>est. Functional Ecology, 14, pp. 312-325.<br />
Kriegler F. J., Malila W. A., Nalepka R. F., Richardson W., 1969. Preprocessing<br />
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Environment. University of Michigan, Ann Arbor, pp. 97-131.<br />
Methy M., Fabreguettes J., Jardon F., Roy J., 1987. Design of a simple instrument<br />
<strong>for</strong> the measurement of red/far red ratio. Acta Oecologica, 8, pp. 281-290.<br />
Peterson D. L., Running S. W., 1989. Applications in <strong>for</strong>est science and<br />
management, in: Asrar G. (Ed.) Theory and applications of optical remote<br />
sensing. Wiley, New York, pp. 429-473.<br />
Pontailler J.-Y., Hymus G. J., Drake B. G., 2003. Estimation of leaf area in<strong>de</strong>x<br />
using ground-based remote sensed NDVI measurements: validation and<br />
comparison with two indirect techniques. Canadian Journal of Remote<br />
Sensing, 29, pp. 381-387.<br />
Rouse J. W., Haas R. H., Schell J. A., Deering D. W., 1973. Monitoring vegetation<br />
systems in the Great Plains with ERTS, in: Proceedings of the Third ERTS<br />
Symposium, 1, pp. 309-317.<br />
Running S. W., Nemani R. R., Heinsch F. A., Zhao M., Reeves M., Hashimoto<br />
H., 2004. A continuous satellite-<strong>de</strong>rived measure of global terrestrial<br />
primary production. BioScience, 54, pp. 547-560.<br />
Soudani K., le Maire G., Dufrêne E., François C., Delpierre N., Ulrich E.,<br />
Cecchini S., 2008. Evaluation of the onset of green-up in temperate<br />
<strong>de</strong>ciduous broadleaf <strong>for</strong>ests <strong>de</strong>rived from Mo<strong>de</strong>rate Resolution Imaging<br />
Spectroradiometer (Modis) data. Remote Sensing of Environment, 112,<br />
pp. 2643-2655.
Part III – Chapter 4 257<br />
Soudani K., François C., le Maire G., Le Dantec V., Dufrêne E., 2006.<br />
Comparative analysis of Ikonos, Spot and ETM+ data <strong>for</strong> Leaf Area In<strong>de</strong>x<br />
estimation in temperate coniferous and <strong>de</strong>ciduous <strong>for</strong>est stands. Remote<br />
Sensing of Environment, 102, pp. 161-175.<br />
Thenkabail P. S., Smith R. B., De Pauw E. 2000. Hyperspectral vegetation<br />
indices and their relationship with agricultural crop characteristics.<br />
Remote Sensing of Environment, 71, pp. 158-182.<br />
Tucker, C.J. 1977. Use of near infrared/red radiance ratios <strong>for</strong> estimating<br />
vegetation biomass and physiological status, NASA/GSFC Report X-923-<br />
77-183, NASA Goddard Space Flight Center, USA.<br />
Tucker C. J. 1979. Red and photographic infrared linear combinations <strong>for</strong><br />
monitoring vegetation. Remote Sensing of Environment, 8, pp. 127-150.
IV<br />
INTEGRATED STUDIES
Chapter 1<br />
Integrated observation system<br />
<strong>for</strong> pelagic ecosystems and<br />
biogeochemical cycles in the oceans<br />
Lars Stemmann, Hervé Claustre, Fabrizio D’Ortenzio<br />
1. Exploring the un<strong>de</strong>r-sampled ocean at a time<br />
of global changes<br />
Our climate is changing at unprece<strong>de</strong>nted rates, and there is an urgent need<br />
to improve the observation at global scales of pelagic ecosystems biodiversity<br />
and functioning. The oceans constitute the largest habitats on Earth <strong>for</strong><br />
a highly diverse and numerous flora and fauna, and play a major role in the<br />
carbon cycle and climate. Ocean carbon sources and sinks are controlled<br />
by both physical (Sabine et al., 2004) and biological (Volk and Hoffert,<br />
1985) processes that take place at various temporal and spatial scales. Based<br />
on global biogeochemical mo<strong>de</strong>lling and on the use of paleoproxies from<br />
sedimentary archives, the sedimentation of biogenic particulate matter<br />
from the euphotic zones of the ocean, a process named biological carbon<br />
pump, was shown to contribute significantly to climate variability<br />
(Sarmiento and Le Quere, 1996; Volk and Hoffert, 1985). However, the<br />
uncertainties in our un<strong>de</strong>rstanding of the biological pump’s functioning<br />
in today’s oceans remain important. For example, recent reviews about<br />
the export of biogenic particles to the <strong>de</strong>ep ocean showed that there is no<br />
consensus on the mechanisms controlling its spatial and temporal variability<br />
(Boyd and Trull, 2007; Burd et al., 2010). In particular, the roles of<br />
zooplankton and bacteria are not well un<strong>de</strong>rstood.<br />
Large temporal and spatial scales of marine surface production can be<br />
studied using satellite data (see III, 3). Alternatively, un<strong>de</strong>rwater autonomous<br />
floats technology has improved, thus allowing the exploration of<br />
the <strong>de</strong>eper layers of pelagic environments (Claustre et al., 2010b; Johnson
262 Integrated studies<br />
et al., 2009). Run since almost a <strong>de</strong>ca<strong>de</strong>, the international Argo project,<br />
which currently has an array of about 3,000 floats <strong>de</strong>ployed in the world<br />
ocean, has proven to be an invaluable tool in mo<strong>de</strong>rn physical oceanography<br />
(figure 1). The Argo project provi<strong>de</strong>s, on a routine basis and<br />
with unprece<strong>de</strong>nted <strong>de</strong>tail, the heat and salt content of the upper kilometre<br />
ocean, as well as water mass circulation. In addition, the BIO-Argo<br />
research group intends to add biogeochemical sensors to the current floats<br />
(Claustre et al., 2010a; 2010b; IOCCG, 2011).<br />
To summarise individual researchers as well as agencies have recognised<br />
the fact that autonomous plat<strong>for</strong>ms array could provi<strong>de</strong> 3D in<strong>for</strong>mation<br />
not attainable by satellite plat<strong>for</strong>ms, where the vertical dimension is missing.<br />
Such a plat<strong>for</strong>m can also <strong>de</strong>termine near surface properties when<br />
cloud cover impe<strong>de</strong>s observations from space. There<strong>for</strong>e, it seems useful<br />
and timely to coordinate the work of different groups to obtain coherent<br />
data sets to <strong>de</strong>termine global patterns of nutrients, plankton and marine<br />
particles distribution in the oceans. Until now, studies of the biological<br />
pump based on Argo floats have used chemical or physical sensors, and<br />
have there<strong>for</strong>e overlooked the effects of living organisms. This is because<br />
available sensors were not adapted to record individual organisms but<br />
rather did bulk measurements of their biomasses. Bio-optical and imaging<br />
sensors <strong>de</strong>dicated to the i<strong>de</strong>ntification and quantification of organisms<br />
living in pelagic environments are now being <strong>de</strong>veloped (see II, 2). The<br />
current limitation of Argo floats shall soon be overcome thanks to the<br />
miniaturisation of these sensors, which make them compatible <strong>for</strong> implementation<br />
on the Argo floats.<br />
The abundance and size distribution of plankton and particles are among<br />
the relevant variables of pelagic environments that are not measured with<br />
standard floats (but see III, 3 <strong>for</strong> remote sensing techniques). The analysis<br />
of the size distribution of planktonic taxa is important because metabolic<br />
processes and many ecological traits, including population abundance,<br />
growth rate and productivity, spatial niche, trophic, competitive and<br />
facilitative relationships between species, are influenced by the body<br />
size of the organism (Brown et al., 2004; Gillooly, 2000; 2001; 2002). In<br />
addition, most marine organisms are opportunistic fee<strong>de</strong>rs and their prey<br />
size is limited by the diameter of their mouth. There<strong>for</strong>e, predator–prey<br />
relationships are, in many marine systems, importantly <strong>de</strong>termined by<br />
size (Hansen et al., 1997; Jennings and Warr, 2003). Furthermore, the<br />
particle diameter can <strong>de</strong>scribe multiple particle properties such as mass<br />
and settling speed or flux (Alldredge and Gotschalk, 1988), rate of colonisation<br />
by microbes and zooplankton (Kiørboe, 2000; Kiørboe et al.,<br />
2002, 2004) and coagulation rate (Jackson, 1990). The rate of biogeochemical<br />
activity such as aggregate remineralisation by bacterial activity<br />
or zooplankton consumption can also be proportional to the size of
Part IV – Chapter 1 263<br />
Figure 1: The Argo float is a global array of autonomous floats measuring pressure,<br />
temperature and salinity. A. Map showing the location of 3161 Argo floats in the<br />
world ocean (in May 2010, http://www.argo.ucsd.edu/). B. Schematic operating<br />
sequence of an Argo float. See section 2 <strong>for</strong> a full explanation of the sampling<br />
strategy and see figure 2 <strong>for</strong> an image of the <strong>de</strong>ployment of a Provor float. © Argo,<br />
© jcommops.<br />
particles and plankton (Kiørboe and Thygesen, 2001; Ploug and Grossart,<br />
2000). Hence, because size of organisms or particles captures so many<br />
aspects of ecosystem functioning, the size distribution of plankton and
264 Integrated studies<br />
particles in a volume of water can be used to synthesise a succession of<br />
co-varying traits into a single dimension (Woodward et al., 2005).<br />
Based on autonomous vehicles equipped with bio-optical sensors, pilot<br />
projects were launched or are planed <strong>for</strong> studying the size distribution<br />
of pelagic communities at different temporal and spatial scales (Boss et<br />
al., 2008; Kortzinger et al., 2004; Niewiadomska et al., 2008). If networks<br />
and arrays are implemented from these pilot projects to coordinate<br />
the ef<strong>for</strong>ts at the international level, a revolution in biological<br />
and biogeochemical oceanography will happen. The community will<br />
have access to an unprece<strong>de</strong>nted observational array of vertically<br />
resolved “biogeochemical” and ecological variables (see next section<br />
<strong>for</strong> <strong>de</strong>tails). Developing such an in situ automated observation system<br />
will constitute an essential step towards a better un<strong>de</strong>rstanding of biogeochemical<br />
cycles and ecosystem dynamics, especially at spatial and<br />
temporal scales that have been unexplored until now. Two main outcomes<br />
can be expected from a well-<strong>de</strong>signed integrated observation<br />
system (Claustre et al., 2010a). The scientific outcomes inclu<strong>de</strong> a better<br />
exploration and an improved un<strong>de</strong>rstanding of both present state<br />
and change and variability in ocean biology and biogeochemistry over<br />
a large range of spatial and temporal scales (see figure 1). Associated<br />
with this, the reduction of uncertainties in the estimation of biogeochemical<br />
fluxes is an obvious target and the assessment of zooplankton<br />
resources <strong>for</strong> higher trophic levels (fish) is also an achievable important<br />
goal.<br />
Besi<strong>de</strong> these scientific objectives, the operational and long-term outcomes<br />
are the <strong>de</strong>velopment of sound predictions of ocean biogeochemistry<br />
and ecosystem dynamics as well as the <strong>de</strong>livery of real-time<br />
and open-access data to scientists, users and <strong>de</strong>cision makers. It is also<br />
expected that reduced uncertainties will result in better policy. The present<br />
chapter reviews recent sensor technical <strong>de</strong>velopments and scientific<br />
results from pilot projects that investigated pelagic ecosystems using<br />
autonomous vehicles. It is a synthesis of two recent articles (Claustre<br />
et al., 2010b; Stemmann and Boss, 2012). The following sections were<br />
set to i) <strong>de</strong>scribe autonomous vehicles, ii) <strong>de</strong>scribe miniaturised present<br />
and future sensors that can <strong>de</strong>scribe habitats (physical and geochemical<br />
environment) and plankton communities (phyto and zooplankton), iii)<br />
suggest framework <strong>for</strong> data control and quality and iv) propose their<br />
integration in mo<strong>de</strong>lling and observing systems.
Part IV – Chapter 1 265<br />
2. The various plat<strong>for</strong>ms in support of a pelagic autonomous<br />
observation system<br />
Autonomous floats spend most of their life drifting at <strong>de</strong>pth where they<br />
are stabilised by being neutrally buoyant at the “parking <strong>de</strong>pth” pressure<br />
where they have a <strong>de</strong>nsity equal to the ambient pressure and a compressibility<br />
that is less than that of sea water (figure 2A). In the Argo mo<strong>de</strong>, the<br />
floats pump fluid into an external blad<strong>de</strong>r at typically 10-day intervals,<br />
and rise to the surface <strong>for</strong> about 6 hours while measuring temperature and<br />
salinity. Satellites <strong>de</strong>termine the position of the floats when they surface,<br />
and receive the data transmitted by the floats. The blad<strong>de</strong>r then <strong>de</strong>flates<br />
and the float returns to its original <strong>de</strong>nsity and sinks to drift until the<br />
cycle is repeated. The floats can also be configured remotely to another<br />
prescribed resting <strong>de</strong>pth. In the Argo array, floats are <strong>de</strong>signed to make<br />
about 150 such cycles. With their long lasting capacities (3 years in the<br />
Argo array), floats are particularly useful to follow the temporal dynamics<br />
of the pelagic ecosystems in large-scale physical structures such as long<br />
lasting gyres.<br />
In contrast to floats, gli<strong>de</strong>rs can be steered and maintained in particular<br />
areas providing the spatial structure <strong>for</strong> all variables measured by the<br />
sensors on-board at relatively slow speed (30km.day -1 horizontally, see<br />
figure 2B). They are suitable plat<strong>for</strong>ms <strong>for</strong> any sustained observational<br />
system aimed at monitoring bio-physical coupling at the coastal interface<br />
between shelf and open ocean because they can operate at sub- to<br />
meso-scale (1km to 100km). The improvements in gli<strong>de</strong>r technology were<br />
accompanied by the emergence of gli<strong>de</strong>r ports or centres. These logistical<br />
centres, very often in the proximity of a laboratory, are key to the success<br />
of these systems. The <strong>de</strong>velopment of a “global bio gli<strong>de</strong>r network”<br />
in the near future will have to rely on a cluster of these local, national or<br />
international (e.g. Everyone’s gliding observatories) centres. The endurance<br />
(around 4 months) and range (2000km) of gli<strong>de</strong>rs constrain the procedure<br />
by requiring repetitive <strong>de</strong>ployments, but gli<strong>de</strong>rs are already capable<br />
to cover large parts of the global ocean. On a longer term and with the<br />
continuing improvement of technology (e.g. increasing endurance and<br />
range), transoceanic and repeated transects from gli<strong>de</strong>r port to gli<strong>de</strong>r port<br />
will likely become possible. Animal-borne systems (see figure 2C) can<br />
nicely complement gli<strong>de</strong>rs and floats (Teo et al., 2009). Recently, animalborne<br />
instruments have been <strong>de</strong>signed and implemented to provi<strong>de</strong> in situ<br />
hydrographic data from parts of the oceans where little or no other data<br />
are currently available such as <strong>for</strong> example the Southern ocean (Bailleul<br />
et al., 2010; Roquet et al., 2011). The animal-plat<strong>for</strong>m community is only<br />
beginning and no continuous <strong>de</strong>ployments is un<strong>de</strong>rway (see I, 1 <strong>for</strong> more<br />
<strong>de</strong>tails).
266 Integrated studies<br />
Figure 2: Three plat<strong>for</strong>ms used to study the pelagic environment. A. PRoVoR<br />
float about to be immerged in the ocean <strong>for</strong> a three years journey (© Ifremer). B.<br />
Gli<strong>de</strong>r (© DR). C. Elephant seal equipped with sensors glued at on the top of his<br />
head (©C. Guinet/CNRS).
Part IV – Chapter 1 267<br />
3. Relevant pelagic ecosystem variables at global scales<br />
3.1. Monitoring the pelagic habitats with core physical and geochemical<br />
variables<br />
The broad-scale global array of temperature and salinity profiling floats,<br />
known as Argo, has already grown to be a major component of the ocean<br />
observing system. The final array of 3,000 floats now provi<strong>de</strong>s 100,000<br />
temperature/salinity (T/S) profiles and velocity measurements per year<br />
distributed over the global oceans at an average 3-<strong>de</strong>gree spacing (figure<br />
1). Floats cycle at a 2,000m <strong>de</strong>pth every 10 days, with 3-4 year lifetimes<br />
<strong>for</strong> individual instruments. All Argo data are publically available<br />
in near real-time via the global data assembly centers (GDACs) after an<br />
automated quality control (QC), and in scientifically quality controlled<br />
<strong>for</strong>m, <strong>de</strong>layed mo<strong>de</strong> data, via the GDACs within six months of collection.<br />
Hence, basic physical data about the salinity and temperature of<br />
the pelagic habitat are readily available. In addition to these physical sensors,<br />
geochemical sensors are now being <strong>de</strong>veloped and <strong>de</strong>ployed on Argo<br />
floats. Oxygen sensors are currently being installed on floats <strong>for</strong> multiyear<br />
periods with little or no drift in sensor response (Kortzinger et al.,<br />
2004; Riser and Johnson, 2008). As <strong>for</strong> June 2009, more than 200 floats<br />
have been <strong>de</strong>ployed with oxygen sensors, with about 150 currently active<br />
(Johnson et al., 2009). Nitrate sensors based on direct optical <strong>de</strong>tection<br />
are now also <strong>de</strong>ployed on floats, and they operate <strong>for</strong> more than 500 days<br />
(Johnson and Coletti, 2002). Detection limits are on the or<strong>de</strong>r of 0.5 to<br />
1μM. Although not sufficient to measure euphotic zone nitrate concentrations<br />
in many regions, these sensors can resolve annual cycles in mesotrophic,<br />
bloom-<strong>for</strong>ming regions. Measurements with gas tension <strong>de</strong>vices<br />
(McNeil et al., 2006) can be combined with oxygen concentrations to<br />
<strong>de</strong>termine the partial pressure of molecular nitrogen (N 2<br />
) in seawater.<br />
Finally, prototype pCO 2<br />
sensors were tested on floats but several technical<br />
problems (long time constants of sensors) have to be solved be<strong>for</strong>e<br />
their operational use. Yet, major improvements in the pCO 2<br />
sensors can<br />
be expected in the near future.<br />
Bio-optical sensor technologies have also been refined so that they can<br />
now be <strong>de</strong>ployed on autonomous plat<strong>for</strong>ms (Bishop and Wood, 2009;<br />
Boss et al., 2008). Particle load is the main driver of water turbidity or<br />
transparency in the open ocean. Turbidity can be quantified by the<br />
measurement of the backscattering coefficient using a backscattering<br />
metre, while transparency is measured by the particle attenuation coefficient<br />
using a transmissometer. In open ocean waters, particulate organic<br />
carbon (POC) is the main source of particles, and both optical measurements<br />
can be converted to a concentration of POC with reasonable
268 Integrated studies<br />
accuracy (Bishop and Wood, 2009). The long-term <strong>de</strong>ployment of biooptical<br />
sensors is possible on Argo floats because these plat<strong>for</strong>ms spend<br />
much of their time <strong>de</strong>ep down in cold and dark waters. Consequently,<br />
biofouling is less of an issue than when sensors are permanently fixed<br />
in the upper ocean, <strong>for</strong> example, on moorings, or on benthic surfaces<br />
(see III, 1).<br />
3.2. Monitoring the plankton communities<br />
The BIO-Argo community has already implemented multispectral optical<br />
sensors to estimate chlorophyll-a as a proxy <strong>for</strong> phytoplankton biomass.<br />
It can be measured by fluorescence, and miniature fluorescence<br />
sensors are now available to equip a variety of plat<strong>for</strong>ms (e.g. gli<strong>de</strong>rs,<br />
floats, animals). When converting chlorophyll-a data into biomass data,<br />
one must however take into account the issues of variable pigment to<br />
carbon ratios and variable fluorescence to chlorophyll concentration<br />
ratios, which are caused by non photochemical quenching, changes in<br />
species composition, and changes in temperature. Chlorophyll fluorescence<br />
and light scattering (proxy <strong>for</strong> POC) in the upper 1000m were<br />
measured in the North Atlantic <strong>for</strong> three years (Boss et al., 2008). In the<br />
future, coccolithophorids carbonate shells might be <strong>de</strong>tected from the<br />
background of nano-sized phytoplankton cells by their specific optical<br />
birefringence properties (Guay and Bishop, 2002).<br />
Much less work has been carried out to characterise the zooplankton,<br />
which constitutes a major trophic level of pelagic ecosystems. Checkley<br />
et al. (2008) combined a sounding oceanographic lagrangian observer<br />
float with a laser optical plankton counter (LOPC) and a fluorometer to<br />
make an autonomous biological profiler, the SOLOPC. The instrument<br />
senses plankton and other particles over a size range of 100µm to 1cm<br />
in profiles to 300m in <strong>de</strong>pth and sends data ashore via satellite. Objects<br />
sensed by the LOPC inclu<strong>de</strong> aggregates and zooplankton, the larger of<br />
which can be distinguished from one another by their transparency. The<br />
instrument was <strong>de</strong>ployed during several weeks in the Cali<strong>for</strong>nian current<br />
system (Checkley et al., 2008). In the future, these imaging systems will<br />
make monitoring particles and zooplankton at the same time possible<br />
(see II, 2).<br />
Acoustic sensors from gli<strong>de</strong>rs (Davis et al., 2008) or from floats were<br />
also used to <strong>de</strong>tect plankton and non living particles (Jaffe et al., 2007).<br />
But the interpretation of acoustic backscatter at a single frequency is<br />
complicated by several factors. Marked spatial changes in intensity of<br />
acoustic backscatter do not necessarily imply changes in zooplankton or<br />
fish biomasses, as differences in body size, species composition, elastic<br />
properties of the animals, or orientation, can also markedly influence
Part IV – Chapter 1 269<br />
acoustic signals (Roberts and Jaffe, 2007; 2008). These instruments have<br />
not yet been <strong>de</strong>ployed over long periods of time but we expect that a<br />
strong <strong>de</strong>velopment and wi<strong>de</strong> use of these instruments will be seen the<br />
next <strong>de</strong>ca<strong>de</strong>.<br />
The use of flow cytometry <strong>for</strong> plankton organisms smaller than about<br />
20μm (pico and nano-size range) provi<strong>de</strong>s an alternate way to automatically<br />
obtain taxonomic in<strong>for</strong>mation in this size range (Olson and<br />
Sosik, 2007). Molecular sensors are also now being <strong>de</strong>veloped <strong>for</strong> coastal<br />
observatories to remotely <strong>de</strong>tect marine microbes and small invertebrate<br />
(Scholin et al., 2009). In situ flow cytometers and molecular sensors represent<br />
a promising avenue in this respect, although their size and energy<br />
consumption prevent them, <strong>for</strong> the moment, from being part of an operational<br />
open ocean observation system, <strong>for</strong> which long term autonomy and<br />
cost efficient sensors are important.<br />
3.3. From observations to predictions using mo<strong>de</strong>lling framework<br />
Building a global observation system to <strong>de</strong>scribe and predict the functioning<br />
of the pelagic ecosystem requires a stepwise approach with regionalscale,<br />
pilot projects. Pilot studies that combine in situ sensors <strong>de</strong>ployed<br />
on long-endurance plat<strong>for</strong>ms with satellite sensors, ship cruises, and data<br />
assimilation of biogeochemical-ecological mo<strong>de</strong>ls must be carried on to<br />
obtain a proof of the concept. In particular, data assimilation into different<br />
types of mo<strong>de</strong>ls is essential to interpret spatial and temporal variability,<br />
and to convert the sum of local measurements into quantitative rate<br />
estimates over large regions of the ocean.<br />
For more than two <strong>de</strong>ca<strong>de</strong>s, pelagic ecosystem mo<strong>de</strong>ling has focused on<br />
the role and functioning of ecosystem components <strong>de</strong>scribed as “boxes”.<br />
In these box mo<strong>de</strong>ls, the marine ecosystem is divi<strong>de</strong>d into several<br />
dynamic compartments. The first mo<strong>de</strong>ls of marine ecosystems dynamics<br />
contained only one variable, the phytoplankton (Fleming, 1939; Riley<br />
and Bumpus, 1946), but mo<strong>de</strong>ls including three variables – nutrient, phytoplankton,<br />
zooplankton – were quickly <strong>de</strong>veloped (Riley et al., 1949).<br />
Thereafter, the <strong>de</strong>velopment of computers enabled the number of variables<br />
to increase up to seven by adding bacteria, particulate and dissolved<br />
<strong>de</strong>tritus and ammonia as a second source of nutriment (Fasham et al.,<br />
1990). This latter mo<strong>de</strong>l became a standard <strong>for</strong> the subsequent <strong>de</strong>velopment<br />
of biogeochemical mo<strong>de</strong>ls, including the <strong>de</strong>velopment of biogeochemical<br />
mo<strong>de</strong>ls with up to 11 compartments (Aumont et al., 2003; Le<br />
Quere et al., 2005) or mo<strong>de</strong>ls involving a greater number of phytoplankton<br />
types from which merging communities can be extracted (Follows<br />
et al., 2007). Despite these improvements, little attention has been paid
270 Integrated studies<br />
Figure 3: Conceptual scheme showing how data from the new generations of sensors<br />
can be integrated into improved mo<strong>de</strong>ls of the pelagic ecosystems. Improved mo<strong>de</strong>ls<br />
inclu<strong>de</strong> coupled dynamics of nutrients (blue ellipse), size-structured populations of<br />
phytoplankton (P, green symbols) and zooplankton (Z, dark orange symbols) and a<br />
size-structured pool of <strong>de</strong>tritus (D, orange symbols) in the upper ocean layer and the<br />
midwater layer. Black arrows represent the flow of mass from one compartment to<br />
the other. The flow of mass of <strong>de</strong>tritus due to physical processes (mixing and particle<br />
settling) from the upper to the midwater layer <strong>de</strong>pends on the size of <strong>de</strong>tritus. Grey<br />
arrows represent mass transfer between the size classes within the same group. In<br />
the future, <strong>de</strong>tritus and some zooplankton groups <strong>de</strong>tected using imaging systems<br />
may be replaced by a size-based <strong>de</strong>scription. However, not all compartments may be<br />
<strong>de</strong>scribed with size when size is less important <strong>for</strong> bio-geochemical processes or less<br />
variable. This is particularly true <strong>for</strong> the phytoplankton because these species don’t<br />
prey on each other or they have very specific functions (N 2<br />
fixators, calcifiers) and<br />
several zooplankton taxa (tunicates, jellyfish). Crustaceans may be good candidate<br />
<strong>for</strong> a size based <strong>de</strong>scription because they share many metabolic pathways and life<br />
cycle dynamics. The proposed mo<strong>de</strong>l should be simple enough to be inclu<strong>de</strong>d in<br />
3D biogeochemical mo<strong>de</strong>ls. Only vertical processes are represented here and landoceans<br />
transports or bentho-pelagic coupling are not represented.
Part IV – Chapter 1 271<br />
to zooplankton because of the complexity of this group and the small<br />
number of global data sets (Carlotti and Poggiale, 2010). The zooplankton<br />
is often represented as a closure variable with fixed rates in<br />
compartment mo<strong>de</strong>ls while their dynamic trophic interactions with<br />
the phytoplankton may be important to un<strong>de</strong>rstand the ecosystem<br />
dynamics. Robust mo<strong>de</strong>ls relating climate change to fish production<br />
require also an a<strong>de</strong>quate <strong>de</strong>scription of the zooplanktonic intermediaries<br />
between phytoplankton and fish in end-to-end mo<strong>de</strong>ls. There<strong>for</strong>e,<br />
acquisition of in situ data is nee<strong>de</strong>d <strong>for</strong> testing mechanistic end-to-end<br />
mo<strong>de</strong>ls and optimising the balance between fi<strong>de</strong>lity and simplicity in<br />
the zooplankton component.<br />
At the same time, the <strong>de</strong>eper ocean (below the mixed layer) was treated<br />
as a black box because of the lack of data. In particular, the <strong>de</strong>scription<br />
of particle sinking to the <strong>de</strong>ep oceans still mostly relies on exponential<br />
or power law functions (Armstrong et al., 2002; betzer et al., 1984;<br />
Martin et al., 1987; Suess, 1980). However, marine particle fluxes display<br />
strong regional and temporal variability in response to production<br />
regimes and their seasonality. The relationship between surface ocean<br />
ecosystem structure and variability is not captured by these simplified<br />
approaches.<br />
A recent mo<strong>de</strong>l (Gehlen et al., 2006; Kriest and Evans, 2000) provi<strong>de</strong>s<br />
an interesting alternative suitable <strong>for</strong> global scale applications (figure 3).<br />
It relies on the explicit parameterisation of particle interactions (aggregation/disaggregation)<br />
where particle number and size are state variables.<br />
The sinking speed is computed as a function of particle size distribution.<br />
This approach relies however on simplifying assumptions that have<br />
not been fully validated by comparing with data on particle dynamics.<br />
For instance, the <strong>de</strong>scription of the particle size spectrum by a constant<br />
exponent contradicts observations where variability with <strong>de</strong>pth of<br />
the latter was reported (Guidi et al., 2009). This variability most likely<br />
reflects the impact of zooplankton feeding and microbial <strong>de</strong>gradation<br />
on particle size spectra (Stemmann et al., 2004a; 2004b). In this case,<br />
measuring organisms and particles size distribution would also lead to<br />
general improvement of the <strong>de</strong>scription and dynamics of zooplankton<br />
in mo<strong>de</strong>ls.
272 Integrated studies<br />
Figure 4: Schematic representation of the outcomes of a future array of oceanic<br />
floats equipped with biological sensors to measure various pelagic ecosystems<br />
components as well as standard physical and chemical sensors. In the future, the<br />
nutrient(N)-phytoplankton(P)-zooplankton(Z)-<strong>de</strong>tritus(D) conceptual scheme<br />
of most biogeochemical mo<strong>de</strong>ls will be quantified by the observation of new<br />
ecosystem components.<br />
4. The key to success: agreed procedures, data management<br />
and distribution<br />
In principle, the different observational approaches from ships, satellites,<br />
or autonomous vectors can be regar<strong>de</strong>d as stand-alone initiatives with<br />
their own rationales, objectives, analysis tools and synthesis products. In<br />
fact, this was the path followed previously, even though many scientists<br />
are often involved in more than one approach. Overcoming the separa-
Part IV – Chapter 1 273<br />
tion between the different observational approaches is a major objective<br />
<strong>for</strong> the scientific community <strong>for</strong> the next <strong>de</strong>ca<strong>de</strong>.<br />
The technology <strong>for</strong> observing key oceanic biogeochemistry and ecosystem<br />
variables has progressively matured to the point where it is now amenable to<br />
a global dissemination (figure 4). Additionally, data sources will be much<br />
more diverse than today, going essentially from ship-based data acquisition<br />
to an increased contribution of data acquired through remotely operated<br />
plat<strong>for</strong>ms. Within a few years, our community will thus acquire tremendous<br />
amounts of biological data in addition to the standard physical and<br />
chemical data. An integrated observation system will be operationally useful<br />
and scientifically relevant if and only if this huge data acquisition ef<strong>for</strong>t<br />
is supported by an efficient data management system, able to meet both<br />
basic scientific and operational goals. In<strong>de</strong>ed, the success in implementing<br />
these new cost effective technologies in our observation strategy will heavily<br />
rely on our capacity to make all data easily available.<br />
Nevertheless, such data management system is still to be <strong>de</strong>signed and implemented.<br />
The important criteria that preclu<strong>de</strong> this implementation are, notably,<br />
the availability of real-time quality-controlled (QC) data <strong>for</strong> operational<br />
applications and the production of <strong>de</strong>layed-mo<strong>de</strong>l QC data required <strong>for</strong><br />
climate-related studies. In some ways, these prerequisites are orthogonal to<br />
the historic habits or constraint in relation with biological data management.<br />
First of all, with the exception of satellite data, biologists were not used to<br />
the mana gement of very large datasets because most biological data acquisition<br />
was done during discrete measurements per<strong>for</strong>med from ship-based<br />
plat<strong>for</strong>ms. Secondly, there are generally some hurdles to make biological<br />
data publically available. While ef<strong>for</strong>ts in this direction are un<strong>de</strong>rway, much<br />
remains to be done and the community has to consi<strong>de</strong>r this aspect of data<br />
management as a priority. Finally, and in relation with the preceding point,<br />
the biological oceanographer community is even less used to the constraints<br />
involved in the production and distribution of data in near-to-real-time. A<br />
technological evolution is thus required in the way we manage data to guarantee<br />
public access and to <strong>de</strong>liver real-time data and products, when required.<br />
This likely represents the most challenging issue <strong>for</strong> our community, at least<br />
as challenging as the required technological <strong>de</strong>velopments themselves.<br />
Authors’ references<br />
Lars Stemmann, Hervé Claustre, Fabrizio D’Ortenzio:<br />
Université Pierre et Marie Curie, Laboratoire d’Océanographie <strong>de</strong> Villefranche,<br />
UPMC-CNRS UMR 7093, Villefranche-sur-Mer, France.<br />
Corresponding author: Lars Stemmann, stemmann@obs-vlfr.fr
274 Integrated studies<br />
Aknowledgement<br />
This work is a synthesis of two recent articles (Claustre et al., 2010b;<br />
Stemmann and Boss, 2012). Authors were supported by funding from<br />
7th European Framework Program (Research Infrastructure JERICO and<br />
GRooM projects). Lars Stemmann is supported by the CNRS/UPMC<br />
Chaire on Biodiversity and Functioning of Pelagic Ecosystems.<br />
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Chapter 2<br />
Tropical rain <strong>for</strong>est environmental<br />
sensors at the Nouragues experimental<br />
station, French Guiana<br />
Jérôme Chave, Philippe Gaucher, Maël Dewynter<br />
1. Introduction<br />
Although they only cover about 7-10% of landmasses, tropical <strong>for</strong>ests<br />
hold over two thirds of terrestrial biological diversity, and over 40%<br />
of its carbon stocks. It is there<strong>for</strong>e unsurprising that scientists and the<br />
public alike show so much interest in this biome. Historically, however,<br />
active research in tropical <strong>ecology</strong> was restricted to a few well-equipped<br />
research sites where scientists – mostly permanently based in North<br />
America and in Europe – could settle down from a few months to a few<br />
years to conduct their research program. In tropical rain<strong>for</strong>ests, large<br />
scale logging programs have been initiated only since the 1960s, as much<br />
of Europe and North America had already been <strong>de</strong><strong>for</strong>ested centuries<br />
ago. Thus, when it comes to <strong>for</strong>ested ecosystems, the fundamental difference<br />
between the tropical and the temperate zone is that the tropics<br />
still hold unlogged <strong>for</strong>ests, whereas the overall area of unlogged <strong>for</strong>ests<br />
in Europe and North America today cover less than a few hundreds<br />
of hectares. Ecological processes that take place in mixed old-growth,<br />
tropical rain<strong>for</strong>ests are different from that of plantations, and they call<br />
<strong>for</strong> different research methods and measurement techniques. In<strong>de</strong>ed, in<br />
plantations one or a few tree species dominate the ecosystem dynamics,<br />
trees are even-aged, and <strong>de</strong>composition and tree mortality are much<br />
lower than in natural <strong>for</strong>ests. Hence, spatial and temporal heterogeneity<br />
are both lower in plantations than in unmanaged tropical <strong>for</strong>ests<br />
(Ghazoul and Sheil, 2010).
280 Integrated studies<br />
Another peculiarity of the tropics is the prevalence of harmful diseases,<br />
a major impediment to welfare <strong>de</strong>velopment over the 20 th century, with<br />
diseases such as mosquito-borne viruses (yellow fever, <strong>de</strong>ngue fever,<br />
chikungunya) or various malaria strains. Thus epi<strong>de</strong>miological research<br />
has been a major impetus <strong>for</strong> the <strong>de</strong>velopment of research programs in<br />
the tropics. In French Guiana, Institut Pasteur has been established as<br />
early as 1940, when research infrastructure in this French <strong>de</strong>partment<br />
was altogether lacking. Major tropical <strong>ecology</strong> programs were initiated<br />
based on the infrastructure provi<strong>de</strong>d by these epi<strong>de</strong>miological research<br />
programs, such as Panama’s antenna of the Smithsonian Institute first<br />
established on Barro Colorado Island, in an area that was disrupted in<br />
1913 by the flooding of Gatun lake, central Panama. An emerging concern<br />
<strong>for</strong> biological conservation during the 1970s motivated other programs,<br />
such as the Manu Biosphere Reserve, along a tributary of the<br />
rio Madre <strong>de</strong> Dios, South-east Peru, which was originally established to<br />
study the biology of the then endangered black caiman. None of these<br />
initiatives were primarily driven by issues in environmental science.<br />
Capacity building in tropical environmental science was prompted by<br />
a succession of international research programs, which started with the<br />
International biological program (Golley, 1993). Our expertise in this<br />
question lays in the management of the Nouragues ecological research<br />
station, a scientific research station managed by the CNRS since 1986<br />
and located in the tropical rain <strong>for</strong>est of French Guiana, within the<br />
Nouragues natural reserve. At the Nouragues station, research programs<br />
have been <strong>de</strong>ployed to monitor relevant biological and physical<br />
measures to un<strong>de</strong>rstand the ecological dynamics of this important and<br />
endangered ecosystem.<br />
The structural heterogeneity of tropical <strong>for</strong>est ecosystems explain the<br />
difficulties of implementing quantitative research programs in the tropics.<br />
Because of rapid advance in environmental sensors and sampling<br />
technologies, it appears important to review progress in environmental<br />
monitoring and sensing applied to tropical <strong>for</strong>ests. Environmental sensors<br />
have a long history and they have been intensively researched <strong>for</strong><br />
a wi<strong>de</strong> range of applications. Although the focus of this contribution<br />
is on tropical <strong>for</strong>est <strong>ecology</strong> applications of sensors, it seems relevant<br />
to emphasise the major classes of sensors as used in research programs<br />
recently. <strong>Sensors</strong> fall into three main categories (see table 1): physical<br />
environmental sensors, chemical sensors, and biological sensors.<br />
Physical sensors measure micrometeorological variables, including light<br />
intensity, wind speed, and air moisture. Chemical sensors were mostly<br />
<strong>de</strong>veloped <strong>for</strong> monitoring carbon dioxi<strong>de</strong> concentration. However, more<br />
recent applications have expan<strong>de</strong>d its range to nitrous oxi<strong>de</strong>s and to
Part IV – Chapter 2 281<br />
volatile organic compounds. Biological sensors measure a wi<strong>de</strong> array of<br />
biological phenomena (see below).<br />
Table 1. Examples of major sensor modalities with comments<br />
on cost, reliability, and power requirements.<br />
Adapted from Run<strong>de</strong>l et al., 2009.<br />
Sensor<br />
category<br />
Physical<br />
Chemical<br />
Biological<br />
Temperature<br />
Example<br />
Relative humidity<br />
Leaf wetness<br />
Soil moisture<br />
Wind speed and direction<br />
(sonic anemometer)<br />
Atmospheric carbon dioxi<strong>de</strong><br />
Soil carbon dioxi<strong>de</strong><br />
Soil carbon dioxi<strong>de</strong> efflux<br />
Nitrate<br />
Phosphorus<br />
Digital imagers<br />
Minirhizotron cameras<br />
Sap flow sensors<br />
Acoustic sensors<br />
Comments<br />
Inexpensive to intermediate cost,<br />
reliable, low power requirements<br />
Intermediate, reliable, low power<br />
Inexpensive, reliable, low power<br />
Inexpensive to mo<strong>de</strong>rate, issues<br />
with calibration and measurement<br />
units, low power<br />
Intermediate, very reliable,<br />
mo<strong>de</strong>rate power<br />
Expensive, reliable, mo<strong>de</strong>rate<br />
power, requires careful calibration<br />
Intermediate, reliable, low power<br />
Expensive, reliable, mo<strong>de</strong>rate<br />
power, requires careful calibration<br />
Expensive, un<strong>de</strong>r <strong>de</strong>velopment <strong>for</strong><br />
reliable terrestrial <strong>de</strong>ployments<br />
Not available <strong>for</strong> terrestrial<br />
<strong>de</strong>ployments<br />
Mo<strong>de</strong>rately expensive, reliable,<br />
mo<strong>de</strong>rate power<br />
Expensive, variable power<br />
requirements<br />
Commercial probes: mo<strong>de</strong>rately<br />
expensive, calibration issues<br />
Mo<strong>de</strong>rate, reliable, mo<strong>de</strong>rate power<br />
There are several constraints to the implementation of environmental sensors<br />
in the tropics. First, the conditions are not always favourable to a<br />
sensor because of very high moisture levels, temperature exceeding 28°C<br />
most of the day, and the occurrence of frequent thun<strong>de</strong>rstorms – climatic<br />
conditions that may have an adverse effect on electronic components.<br />
Second, electricity and internet connection are not always ensured. At the
282 Integrated studies<br />
Nouragues station, a dual solution of a hydroelectric power plant and solar<br />
panels was chosen, yet we have experienced several episo<strong>de</strong>s of electric<br />
power failure over the past few years. Finally, the maintenance of most<br />
instruments is expensive and difficult because on-site support is lacking <strong>for</strong><br />
many of the sensors. In the <strong>for</strong>thcoming sections, possible options <strong>for</strong> environmental<br />
sensor <strong>de</strong>ployment in the tropical rain <strong>for</strong>est of the Nouragues<br />
station are reviewed.<br />
2. Environmental sensors<br />
2.1. Meteorology<br />
Many projects in <strong>ecology</strong> make direct use of environmental variables.<br />
These encompass climate, major elements in the environment (especially<br />
carbon and nitrogen), the soil, water and air chemistry, and light radiation.<br />
Environmental sensors need to be <strong>de</strong>ployed when these values are<br />
expected to vary at a sufficiently high frequency so as to impact ecological<br />
processes. While the measurement of climatic variables sounds like<br />
a trivial task, specific challenges occur in tropical <strong>for</strong>est environments.<br />
In<strong>de</strong>ed, norms specify that a rainfall gauge should not be placed closer<br />
than 10 times the height of the closest obstacle. In tropical <strong>for</strong>ests, trees<br />
are obvious obstacles, and they commonly grow 40m high or even higher.<br />
Hence, a clear cut of almost 1km² area should be ma<strong>de</strong> to ensure that<br />
the specifications are met. For this reason, it is virtually impossible to<br />
set up rainfall gauges at ground level in closed <strong>for</strong>est environments. One<br />
solution would be to set up the meteorological station atop a tower taller<br />
than the canopy, a difficult challenge in itself. Light measurement (either<br />
photosynthetically active radiation or total intensity), and the other main<br />
meteorological measures (air moisture, air temperature) suffers from the<br />
same limitation.<br />
A direct consequence of this limitation is that climate variables now routinely<br />
used in ecological mo<strong>de</strong>lling are poorly interpolated in many tropical<br />
areas. For instance, the large Worldclim database inclu<strong>de</strong>s only some<br />
10 reference points <strong>for</strong> the whole 83000km² of French Guiana (http://<br />
www.worldclim.org/; Hijmans et al., 2005). The university of East Anglia<br />
CRU Global climate dataset inclu<strong>de</strong>s mean climate variables <strong>for</strong> the<br />
period between 1960 and 1990, and is <strong>de</strong>rived from a global dataset of climatic<br />
normals, numbering 19,295 stations <strong>for</strong> precipitation and 12,092 <strong>for</strong><br />
temperature, and interpolated onto a 0.5° latitu<strong>de</strong> by 0.5° longitu<strong>de</strong> grid<br />
(New et al. 2002). This dataset inclu<strong>de</strong>d less than 30 points of temperature<br />
measurement over Amazonia, and a significantly sparser sampling of rainfall<br />
over the same area. The absence of fine-grained meteorological data
Part IV – Chapter 2 283<br />
limits our capability to draw conclusions about climate change effects in<br />
tropical rain<strong>for</strong>ests. For example, Fonty et al. (2009) reported changes in<br />
floristic composition at the top of the Nouragues inselberg, central French<br />
Guiana, and a staggering 2°C increase of the annual temperature mean in<br />
the village of Régina, around 40 km North East of Nouragues, from the<br />
the Meteo France data set of 1955-2005. These authors conclu<strong>de</strong>d that<br />
climate change was a potential trigger <strong>for</strong> the floristic changes observed<br />
at Nouragues. However, temperatures recor<strong>de</strong>d by the meteorological station<br />
in Regina may have been biased upward by land-use change and<br />
population increase around the municipality of Régina. Temperature<br />
measurements at the Nouragues station failed to <strong>de</strong>tect any significant<br />
trend over the period 1999-2009 (figure 1).<br />
Figure 1: Daily temperature from 1999 to 2009 at the inselberg site, Nouragues<br />
experimental Station. In blue: minimal temperature; in red: maximal temperature;<br />
in black: mean temperature (estimated as the arithmetic mean of minimal and<br />
maximal daily temperatures). Measurements <strong>for</strong> the period 1999-2002 were taken<br />
by an automated meteorological station. Measurements reported <strong>for</strong> the period<br />
2003-2009 were recor<strong>de</strong>d by permanent staff of the station (gaps correspond to<br />
periods of inactivity at the station).
284 Integrated studies<br />
2.2 Eddy flux covariance<br />
Over the past two <strong>de</strong>ca<strong>de</strong>s, an extension of micrometeorological stations<br />
has met with a great success in relation with questions in ecosystem science.<br />
This method, called eddy-flux correlation, makes use of micrometeorological<br />
sensors (including anemometers) coupled with CO 2<br />
concentration<br />
sensors (<strong>for</strong> a <strong>de</strong>tailed theoretical account, see Baldocchi 2003). The i<strong>de</strong>a<br />
is to measure CO 2<br />
concentration below and above any vegetated surface<br />
and eventually to measure the carbon balance of a range of vegetation<br />
types, thus bridging the gap between ecophysiological theories – applied<br />
at the leaf level – and whole ecosystem processes. The gradient in CO 2<br />
concentration is directly related to the net flux of CO 2<br />
between the turbulent<br />
air layer and the insi<strong>de</strong> of the vegetation. More precisely, the vertical<br />
CO 2<br />
flux is proportional to the covariance between fluctuations in the<br />
vertical component of air velocity, w, and the CO 2<br />
mixing ratio c (c = ρc/<br />
ρa where ρc is CO 2<br />
<strong>de</strong>nsity and ρa is air <strong>de</strong>nsity). For a tropical rain <strong>for</strong>est,<br />
it is there<strong>for</strong>e necessary to set up CO 2<br />
sensors below and above the<br />
canopy, and this can only be achieved by the construction of a tower<br />
climbing well above the canopy.<br />
Early attempts to implement this strategy were carried out in Brazil, near<br />
Manaus, by a British group led by Prof John Grace (Grace et al., 1995).<br />
Some technical problems ma<strong>de</strong> the long-term operation of such an instrument<br />
quite difficult (see Carswell et al., 2002). One of them is that the<br />
above-canopy air layer is not turbulent at night, violating one of the main<br />
assumptions of the eddy flux covariance method at night, when the <strong>for</strong>est<br />
is expected to release CO 2<br />
through respiration. The day-night change<br />
in the above-canopy turbulent air profile is far more pronounced in the<br />
tropics than in the temperate zone, making this problem even more acute<br />
than in temperate <strong>for</strong>ests. This bias would result in an un<strong>de</strong>restimation of<br />
the CO 2<br />
out flux at night time, resulting in an overestimation of the CO 2<br />
storage capacity of tropical <strong>for</strong>ests. Statistical methods have been <strong>de</strong>vised<br />
to <strong>de</strong>al with such anomalies, but it is to be expected that they are more<br />
serious when the terrain is hilly than when it is flat, a serious concern in<br />
many cases. In<strong>de</strong>ed, the horizontal flux of CO 2<br />
(CO 2<br />
leakage effect) may<br />
confound the vertical CO 2<br />
balance if the above-canopy air layer is not<br />
turbulent at night. Despite these problems, the technology has met with<br />
a great success over the past <strong>de</strong>ca<strong>de</strong> and eddy-flux covariance towers are<br />
now part of a worldwi<strong>de</strong> network of sites called Fluxnet to measure global<br />
patterns of exchanges of CO 2<br />
between terrestrial ecosystems and the<br />
atmosphere (Baldocchi et al., 2001).<br />
Because of the logistical <strong>de</strong>mands of such an instrumentation and associated<br />
manpower, an eddy-flux tower has not been established at Nouragues,<br />
but one is operating near the coast, at the Paracou research station, oper-
Part IV – Chapter 2 285<br />
ated by the Institut National <strong>de</strong> la Recherche Scientifique (INRA, Bonal<br />
et al., 2008). Less than 10 eddy-flux covariance towers are now operating<br />
in Amazonia (almost all of them in Brazil), and their number is far less<br />
per unit area than in temperate areas, due to establishment cost and challenging<br />
logistics. Currently, only three sites have sufficient in<strong>for</strong>mation to<br />
fully resolve the carbon balance of an Amazonian rain <strong>for</strong>est.<br />
2.3 Distributed sensor networks<br />
The strategy of measuring meteorological variables by means of a single<br />
station is akin to assuming that fine scale spatial variation in micrometeorology<br />
may be ignored. However, the tropical <strong>for</strong>est environment is<br />
highly heterogeneous and <strong>for</strong> some applications it is critically important<br />
to <strong>de</strong>scribe and monitor this fine-grained variability. Montgomery and<br />
Chazdon (2002), <strong>for</strong> instance, measured with great <strong>de</strong>tails the amount<br />
of light that is available <strong>for</strong> the seedlings of sha<strong>de</strong> tolerant tree species<br />
in the un<strong>de</strong>rstory of a tropical <strong>for</strong>est of Costa Rica. They showed<br />
that light availability was essential to seedling survival and growth,<br />
but that only <strong>de</strong>tailed monitoring of long-term light availability could<br />
<strong>de</strong>termine the status of the plant. The light environment of a <strong>for</strong>est<br />
un<strong>de</strong>rstory is however extremely difficult to measure. One often uses<br />
the technique of hemispherical photographs of the canopy taken from<br />
the ground. These photographs are then analysed so as to threshold<br />
obstacles and measure the overall amount of sky (usually a few percent,<br />
Nor<strong>de</strong>n et al., 2007). This requires consi<strong>de</strong>rable manpower, as these photographs<br />
should be taken at dawn or dusk, so as to avoid the problem of<br />
saturating the image with direct sunlight. Photosynthesis theory tells us<br />
that other variables – among which temperature, soil hygrometry, and<br />
soil chemistry – should be also important in <strong>de</strong>termining the status of<br />
a seedling.<br />
One solution to this problem is to <strong>de</strong>vise networks of environmental<br />
sensors. When sensing a rough environment, it is important to have<br />
many in<strong>de</strong>pen<strong>de</strong>nt points of measurement. The challenges associated to<br />
this goal are that the no<strong>de</strong>s need to have a good autonomy (low energy<br />
<strong>de</strong>mand and/or solar power availability). They also should preferably be<br />
versatile and allow wireless communication. A cheap technical implementation<br />
of this i<strong>de</strong>a consists in placing small sensors at many points on<br />
the ground level and conducting periodic data collection campaigns. Allin-one<br />
probes are easy to set up and retrieval may be achieved through<br />
a simple USB port. One example is the EL-USB-2 (Lascar Electronics,<br />
Salisbury, UK), a sensor module that sells at about 50€ apiece. Another<br />
promising instrument is the iButton, a rugged and small sensor that easily<br />
withstands the moist and warm conditions of the tropical rain <strong>for</strong>est
286 Integrated studies<br />
environment (produced and sold by Maxim Inc., Sunnyvale, CA, USA,<br />
figure 2). The iButton may measure basic environmental variables, but<br />
a much larger range of such sensors is available in the market. In<strong>de</strong>ed,<br />
this market is driven by the need <strong>for</strong> continuous monitoring of the storage<br />
conditions <strong>for</strong> food products. Such a technology has a long history,<br />
although its application to a wi<strong>de</strong> array of problems (crop health status,<br />
medical tracking of patients in hospitals, <strong>de</strong>tection of hazards in remote<br />
areas) is recent.<br />
A more advanced version of a distributed sensor network would be to<br />
have the probes exchange in<strong>for</strong>mation between themselves and with a<br />
central monitoring unit. It would then be possible to extend the network<br />
to some arbitrarily large spatial scales and have the measured data<br />
propagate from one sensor to the next up until it reaches the ‘master’<br />
unit. Although the technique <strong>de</strong>scribed seems appealing, because it can<br />
be ma<strong>de</strong> redundant and may be <strong>de</strong>signed to spread over large sampling<br />
areas, no convincing examples are available as of now in the literature.<br />
One company, Crossbow (now purchased by Memsic Andover MA,<br />
USA), has <strong>de</strong>veloped easily <strong>de</strong>ployable sensor networks, the most recent<br />
version being the eko Pro series system (figure 2). Thus far, it has mostly<br />
been used <strong>for</strong> precision agriculture and crop monitoring (especially<br />
wine in Cali<strong>for</strong>nia). one great advantage of the eko Pro series no<strong>de</strong>s is<br />
their versatility: the no<strong>de</strong> can be fitted with four sensors that measure<br />
soil moisture and temperature, soil water content, ambient temperature<br />
and humidity, leaf wetness and solar radiation. A test of this instrument<br />
is un<strong>de</strong>rway in the tropical rain <strong>for</strong>est environment of the Nouragues<br />
experimental research Station.<br />
An extension of the above concept of environmental sensor networks<br />
may be using a relatively low number of sensors but retaining the capacity<br />
to sample large areas by moving sensors insi<strong>de</strong> the study site. One<br />
implementation of this i<strong>de</strong>a is cable-based robotic systems in long term<br />
and rapidly <strong>de</strong>ployable configurations, called networked info-mechanical<br />
systems (Run<strong>de</strong>l et al., 2009). This solution has the advantage of covering<br />
a large area with just one sensor, but it requires a lot of engineering and<br />
wiring, which may make its implementation clumsy in the un<strong>de</strong>rstory<br />
of a tropical rain <strong>for</strong>est. A related i<strong>de</strong>a has been implemented in sensorbearing<br />
robots. However, given the complex environment of tropical rain<br />
<strong>for</strong>ests, it is unlikely that robots will be a useful solution in a <strong>for</strong>eseeable<br />
future.
Part IV – Chapter 2 287<br />
Figure 2: A. The iButton rugged environmental sensor with data transfer <strong>de</strong>vice.<br />
b. The eko Pro series no<strong>de</strong> (yellow box) installed at the blue oak Ranch Reserve,<br />
University of Cali<strong>for</strong>nia. No<strong>de</strong>s read data from the local weather station and<br />
wirelessly transmit the data to the base radio. The data sent from all no<strong>de</strong>s is<br />
received at the base radio and is <strong>for</strong>war<strong>de</strong>d to the gateway. The gateway then stores<br />
data from the sensor network and makes it accessible via a web GUI interface. © B.<br />
Decencière, © M. Hamilton.
288 Integrated studies<br />
3. Biological monitoring<br />
3.1 Monitoring plant physiology<br />
Plants have also been a prime target <strong>for</strong> monitoring physiological status.<br />
Photosynthetic exchange capacity is now measured through very precise<br />
instrumentation, which gives access to the short-term response of the leaf<br />
to changes in the light and CO 2<br />
microenvironment (LiCOR 6400 photosynthesis<br />
analyser, Lincoln, USA). It is possible to monitor continuously<br />
the leaf assimilation rate of a plant using such instrument. Of course,<br />
such leaf-level measurements are critical to interpret correctly the outputs<br />
of eddy-flux covariance sensors (see section 2.2 above). At a coarser grain<br />
of resolution, it is also useful to get a simple normalised measurement<br />
<strong>for</strong> photosynthetic capacity that may be repeated <strong>for</strong> a large number of<br />
plants or across many plants, so as to assess the acclimation to environmental<br />
conditions. Portable instruments such as the Spad-meter measure<br />
the activity of the photosystem, thus the concentration in chlorophyll<br />
(Coste et al., 2010), in just a few seconds. A related portable instrument,<br />
the Dualex 4, commercialised by the French company Force-A (http://<br />
www.<strong>for</strong>ce-a.eu/) jointly measures chlorophyll content and polyphenol<br />
concentration (especially flavonoids).<br />
It is also possible to measure how fast sap flows from the roots to the leaves<br />
in a woody plant by using the difference of temperature between two thermocouples.<br />
This clever i<strong>de</strong>a was first implemented by André Granier from<br />
INRA Nancy (Granier, 1985), and is now commonly referred to as a Granier<br />
sapflow probe. This method uses two sensors, each containing a thermocouple<br />
inserted perpendicularly into the sapwood. The downstream sensor<br />
is heated and the measured difference in temperature between the sensors<br />
narrows as sap flux <strong>de</strong>nsity increases. Granier (1985) established empirically<br />
the relationship between temperature difference and sap flux <strong>de</strong>nsity<br />
by testing the system in <strong>de</strong>tached stem segments through which water was<br />
allowed to flow at known rates. The Granier probes are now commonplace<br />
in tropical ecosystem science (see O’Brien et al., 2004), and are routinely<br />
coupled with the study of sites equipped with eddy-flux towers. They are<br />
especially useful in tracking the water status of large trees. In<strong>de</strong>ed, plants<br />
un<strong>de</strong>r water stress close their stomata and thus lead to a reduction in sap<br />
flow, resulting in an increase of the plant’s water use efficiency.<br />
A third emerging application in monitoring the biological activity of plants<br />
from the ground is related to the emission of chemical volatile organic compounds<br />
(VOCs). Plants emit VOCs when they are stressed either by peculiar<br />
environmental conditions or by predation. Their flowers also often emit<br />
VOCs to signal their presence to cohorts of pollinators, which may highly be<br />
specialised. The diversity of VOCs is wi<strong>de</strong> and represents a fascinating way
Part IV – Chapter 2 289<br />
by which plants may exchange in<strong>for</strong>mation with their ecological partners<br />
(Raguso, 2008). However, a few molecules contribute to a disproportionate<br />
amount to the chemistry of the atmosphere and <strong>for</strong> this reason are being<br />
studied far more intensively. It is the case of isoprene, a terpene molecule<br />
with five carbon atoms whose half-life in the atmosphere is over 1 hour. It<br />
has been estimated that isoprene alone contributes to almost half of VOC<br />
emissions by vegetation (in mass) at a global scale (Guenther et al., 1995).<br />
Classical methods to monitor VOCs have not ma<strong>de</strong> use of sensors, but<br />
rely on the i<strong>de</strong>a of trapping (in fact adsorbing) VOCs in a physical matrix,<br />
and <strong>de</strong>sorbing the VOCs at high temperature to funnel them into a gas<br />
spectrometer coupled with a molecular analyser. This somewhat cumbersome<br />
procedure does not easily un<strong>de</strong>rgo automation. However, some recent<br />
progress was ma<strong>de</strong>. First, fast isoprene sensors have been <strong>de</strong>veloped based<br />
on the principle of chemiluminescence (Guenther and Hills, 1998) and are<br />
now used <strong>for</strong> monitoring in tropical environments. The second category<br />
of VOC sensors <strong>de</strong>tects and quantifies the presence of all monoterpenes<br />
(carbon based volatiles with 10 carbon atoms). A frontier in this research<br />
on VOC sensors is to retrieve the full in<strong>for</strong>mation about VOC emitted<br />
by the vegetation in real time and in the field. Recent advances inclu<strong>de</strong><br />
the automation of VOC trapping techniques including one method called<br />
relaxed eddy accumulation (REA). Instruments such as those are now being<br />
tested in tropical <strong>for</strong>est environments and one has been mounted atop the<br />
eddy-flux tower in Paracou, French Guiana. Up to now, these methods still<br />
require a downstream <strong>de</strong>sorption of the trapped molecules into a gas chromatographer,<br />
but technological advances may soon make it possible to analyse<br />
the VOCs in real time and without resorting to adsorption.<br />
3.2 Monitoring animal movement and physiological status<br />
Un<strong>de</strong>rstanding the home range and displacement of elusive tropical rain<br />
<strong>for</strong>est animals is one of the most exciting questions in tropical <strong>ecology</strong>. For<br />
this reason, a tremendous amount of research has been <strong>de</strong>voted to <strong>de</strong>velop<br />
technological and statistical techniques to estimate the <strong>de</strong>nsity and biology<br />
of these animals. At the Nouragues experimental research Station, these<br />
animals inclu<strong>de</strong> large mammals such as tapirs, peccaries, <strong>de</strong>er, monkeys<br />
and large birds such as black curassows or gray-winged trumpeters. One<br />
classic method is to survey transects at a slow speed (less than 1km/h)<br />
and observe animals. The distance to the animal, together with the angle,<br />
may be used to estimate population <strong>de</strong>nsity. Of course, other techniques<br />
may be <strong>de</strong>vised. One of them consists in randomly establishing camera<br />
traps in the survey area. Each time an animal crosses the line of a laser<br />
(or other <strong>de</strong>tection techniques), a photograph of the animal is taken. In some<br />
cases, it is possible to i<strong>de</strong>ntify the photographed individual. A long-term
290 Integrated studies<br />
monitoring program <strong>for</strong> large vertebrates based on this technology has<br />
been <strong>de</strong>veloped at the Nouragues station by Cecile Richard-Hansen and<br />
her colleagues from the Office national <strong>de</strong> la chasse et <strong>de</strong> la faune sauvage<br />
(see figure 3). This research group was able to successfully i<strong>de</strong>ntify tapirs<br />
and jaguars multiple times in the study area.<br />
Figure 3: A. C. Richard-Hansen setting up a camera trap. b. Photograph of a tapir<br />
moving in font of a camera trap. C. Camer traps to record animal presence and<br />
movements in the Nouragues experimental station. The camera traps were installed<br />
along walking paths (red lines). © C. Richard-Hansen.
Part IV – Chapter 2 291<br />
In a similar way, vi<strong>de</strong>o surveillance equipment was adapted to record the<br />
behaviour of animals living in the canopy. The equipment used at the<br />
Nouragues station consists of a small waterproof camera connected to a<br />
portable vi<strong>de</strong>o recor<strong>de</strong>r, settled to motion <strong>de</strong>tection, and fitted in a waterproof<br />
case. The electric power is supplied by lihium-ion batteries because<br />
of their lightness with regard to lead-ion batteries at comparable capacity.<br />
This equipment has been successfully used to study the nesting behaviour<br />
of the Red-throated Caracara, Ibycter americanus, one of the rare raptors<br />
living socially in small groups of less than ten individuals. The scientific<br />
knowledge concerning this relatively common species was so poor that<br />
even its nest was unrecor<strong>de</strong>d. It was found out that the dominant female<br />
of the group lays and incubates a unique egg. In addition, all individuals<br />
of the group feed the chick mainly with wasp nests, large millipe<strong>de</strong>s and<br />
fruits (McCann et al. 2010). The next step will be using this vi<strong>de</strong>o equipment<br />
in or<strong>de</strong>r to find out how this bird can avoid the stings when it is<br />
attacking wasp nests.<br />
The equipment was also tested to study the breeding behaviour of nocturnal<br />
and arboreal frogs Trachycephalus resinifictrix which are breeding in<br />
the water holes of canopy trees (Gaucher, unpublished results). A bucket<br />
simulating a water hole used by the male singers in trees was settled 5m<br />
high and equipped with this vi<strong>de</strong>o materiel. A continuous recording <strong>for</strong> a<br />
week shows that the bucket can attract up to four males together which are<br />
fighting to exclu<strong>de</strong> each other. Also, two different species of small snakes<br />
(one diurnal, one nocturnal) were <strong>de</strong>tected, and these fed upon tadpoles.<br />
Finally, one individual of Dendrobates tinctorius <strong>de</strong>posited its tadpole in<br />
the water bucket where it was also feeding on the eggs and tadpoles of<br />
T. resinifictrix.<br />
In non-tropical environments, the preferred method <strong>for</strong> monitoring individuals<br />
of large vertebrate species consists in capturing them and equipping<br />
them with a collar so that the animals can be located by radio-tracking<br />
or satellite tracking (see I, 2). This i<strong>de</strong>a is faced with several problems in<br />
the tropical <strong>for</strong>est environment, among which those caused by missing<br />
data from tracking <strong>de</strong>vices. First, it is quite hard to capture a wild animal,<br />
being rare and skilled at <strong>de</strong>tecting humans. Second, not all sensors can be<br />
used to geo-locate the animal because of the <strong>de</strong>nse <strong>for</strong>est un<strong>de</strong>rstory. One<br />
solution has been implemented by the group of Martin Wikelski, then at<br />
Princeton University. They installed a number of large towers on Barro<br />
Colorado Island, Panama, to follow the movement of a range of animals.<br />
The i<strong>de</strong>a is to set up transmitters on an animal, and to monitor its movement<br />
by triangulation between the antenna-receptor systems. The transmitters<br />
used by this team are less than 1cm long (http://www.princeton.<br />
edu/~wikelski/research/transmitters.htm) and the signal is propagated<br />
through FreeWave radios that ensure proper propagation from the sensors
292 Integrated studies<br />
to the lab (figure 4). Other examples of animal geolocation in tropical<br />
rain <strong>for</strong>ests inclu<strong>de</strong> birds (such as toucan, again in Panama), and bats.<br />
Because of their wi<strong>de</strong> ecological spectrum, their fascinating biology, and<br />
their high diversity in the tropics, bats have especially attracted a lot of<br />
research. Transmitter-equipped bats were followed to <strong>de</strong>tect their nesting<br />
sites, their <strong>for</strong>aging strategy and their metabolic phases (<strong>for</strong> an overview<br />
of the bats of French Guiana, see Charles-Dominique et al., 2001). Similar<br />
techniques could be applied to track the displacement of even tiny objects<br />
such as plants’ seeds (see Wright et al., 2008).<br />
Figure 4: Example of the movement of one radio-tracked agouti (in green), and<br />
of one ocelot (in yellow) in the ca. 15 km2 barro Colorado Island, Panama. The<br />
trajectory of both animals distinctively shows that the agouti was killed and eaten<br />
by the ocelot (red area). Vertical black bar on the background represent the location<br />
of the triangulation antennae. © R. Kays.<br />
Several applications of environmental sensors in animal <strong>ecology</strong> involve<br />
the continuous monitoring of the physiological status of a study animal,<br />
such as heart beat, transpiration, or movement speed (see I, 1). For example,<br />
it is now possible to continuously monitor the blood chemistry in<br />
diving emperor penguins (blood gases, O 2<br />
content, hemoglobin concen-
Part IV – Chapter 2 293<br />
tration, lactate concentration, Ponganis et al., 2007). To our knowledge,<br />
these involved applications <strong>for</strong> monitoring animal biological status still<br />
have to be implemented in tropical terrestrial animals, as these species<br />
need to move in complex environments and cannot be equipped with<br />
heavy instrumentation.<br />
3.3 Monitoring biodiversity<br />
A major frontier in biodiversity research is that long-term species monitoring<br />
was restricted. Long-term data usually are limited to a small number<br />
of emblematic species, often birds or terrestrial mammals, and these data<br />
are almost totally lacking in the tropics. Long term monitoring in the<br />
tropics has focused on frogs (especially in Central America, a hotspot <strong>for</strong><br />
<strong>de</strong>ndrobatid frogs), birds (inspired by initiatives that began in temperate<br />
areas such as the breeding bird survey), and trees. Yet, these monitoring<br />
programs require tremendous amounts of manpower and are extremely<br />
expensive. For instance, the census of a large permanent tree sampling<br />
plot with some 10,000 trees mapped, tagged, and measured requires at<br />
least 2 weeks of work <strong>for</strong> an experienced team of approximately 10 technicians.<br />
In French Guina, the most recent census of the 22ha of permanent<br />
plots at Nouragues was conducted in November 2008, and more regular<br />
ones are now being carried out in the Paracou station and at other places<br />
through the Guya<strong>for</strong> project, a joint ef<strong>for</strong>t of Cirad-ONF and CNRS.<br />
Recently, a two-year long research project was conducted at the Nouragues<br />
station to assess the feasibility of a sampling method of abundance and<br />
diversity of common birds based on the French protocol of the “<strong>Suivi</strong><br />
temporel <strong>de</strong>s oiseaux oommuns” (Stoc program). The Stoc program has<br />
been <strong>de</strong>veloped in mainland France during the last <strong>de</strong>ca<strong>de</strong> (Julliard et al.,<br />
2004), but had never been implemented in a tropical environment. Mist<br />
nets were set up in the un<strong>de</strong>rstory and monitored since October 2008 and<br />
until April 2010. A total of 453 birds were caught, 367 were ringed (all species<br />
except hummingbirds), and 86 marked animals were recaptured, <strong>for</strong> a<br />
total of 65 bird species. The first results of the Stoc, with a recapture rate<br />
about 20%, are very encouraging. However, this valuable survey had to be<br />
discontinued because of limited manpower at the station.<br />
Would it be possible to <strong>de</strong>vise autonomous instruments to monitor species<br />
diversity at one site in a tropical rain <strong>for</strong>est environment? The challenge is<br />
to screen at a fast pace signals that could be unambiguously assigned to a<br />
species or a taxon. Several options have recently opened up. One of them<br />
is based on the impressive advances offered by high-throughput DNA<br />
sequencing. Sequencing technologies are currently evolving at a fast pace<br />
and should offer unique opportunities to make a significant progress <strong>for</strong><br />
biodiversity surveys in the near future. One i<strong>de</strong>a <strong>de</strong>veloped recently in
294 Integrated studies<br />
the ANR (Agence nationale <strong>de</strong> la recherche) project Metabar led by Pierre<br />
Taberlet from the University of Grenoble and implemented partly in the<br />
tropical environment of Les Nouragues is to combine concepts from<br />
metagenomics (analysis of cellular microbial DNA from the soil) and from<br />
the recently emerged field of DNA barcoding (use of small DNA fragments<br />
that serve to discriminate among species). Virtually any soil contains<br />
enough extracellular DNA from <strong>de</strong>composed tissues (even <strong>de</strong>gra<strong>de</strong>d<br />
and in small quantities) to be extracted, amplified, and sequenced using<br />
next generation sequencers. An analysis of sequence repositories shows<br />
that, <strong>for</strong> most taxonomic groups, it is possible to find small DNA regions<br />
(i<strong>de</strong>ally around 30bp) that will efficiently discriminate across the taxa<br />
(Jørgensen et al., 2011). Using this approach, it should be possible to survey<br />
the diversity of taxa that make a substantial portion of the biomass<br />
in the soil environment, such as termites, earthworms, plants or fungi.<br />
Based on this strategy, a routine protocol could be established to evaluate<br />
the long-term trends, but also inter-annual variations in biodiversity, <strong>for</strong> a<br />
broad range of taxa. Such a DNA-based biodiversity sensor would require<br />
the <strong>de</strong>velopment of semi-automated procedures to extract and preserve<br />
environmental DNA be<strong>for</strong>e the laboratory analysis phase. It would also<br />
need a substantial and long-term financial investment to secure the funds<br />
<strong>for</strong> regular high-throughput sequencing runs.<br />
Another application relies on the sound produced by active animals<br />
such as amphibians, fish, birds, mammals, insects and other arthropods,<br />
all of which use sound <strong>for</strong> communication, navigation or predation.<br />
Bioacoustics is the discipline concerned with the <strong>de</strong>velopment of sensors<br />
specialised in <strong>de</strong>tecting animal sounds (see I, 4) and analysing them <strong>for</strong><br />
their ecological relevance, or as an indication of species occurrence and<br />
as a tool <strong>for</strong> biodiversity studies. Applications of these techniques are routinely<br />
ma<strong>de</strong> in tropical <strong>for</strong>est environments <strong>for</strong> birds (some ornithologists<br />
are able to i<strong>de</strong>ntify virtually all the species based on their song alone),<br />
but also <strong>for</strong> anurans. Automated methods <strong>for</strong> recording were <strong>de</strong>veloped<br />
and implemented in tropical <strong>for</strong>est environments (see II, 1; Acevedo and<br />
Villanueva-Rivera, 2006; see also Pijanowski et al., 2011 <strong>for</strong> a review). One<br />
company is now selling a fully packed solution <strong>for</strong> frog monitoring, called<br />
the froglogger (http://www.frogloggers.com/). We should also emphasise<br />
here that the method may be exten<strong>de</strong>d to the study of ultrasounds emitted<br />
by bats, an important component of the mammal community in neotropical<br />
rain <strong>for</strong>ests. However, more than twenty years after a hallmark publication<br />
of Fenton and Bell (1981), the acoustic i<strong>de</strong>ntification of neotropical<br />
bats is still at the stage of the research. The main problem is to build a reference<br />
library of ultrasound that would characterise the bat species, while<br />
taking in account intraspecific variability, which constitutes a consi<strong>de</strong>rable<br />
challenge. Studies by Michel Barataud and the Groupe chiroptères <strong>de</strong>
Part IV – Chapter 2 295<br />
Guyane have contributed to the <strong>de</strong>velopment of a large data base of sound<br />
sequences in natural conditions of flight. The data collection method uses<br />
<strong>de</strong>tectors of ultrasounds heterodyne–time expansion Pettersson D1000X,<br />
D980 and D 240X (Pettersson Elektronik AB TM ), and digital recor<strong>de</strong>rs on<br />
card (Edirol TM ) or minidisc (Sharp TM ).<br />
4. Remote sensing of the environment<br />
Remote sensing has played a key role in ef<strong>for</strong>ts to un<strong>de</strong>rstand ecological,<br />
hydrological and land-use processes in the tropics. The main advantage<br />
with remote sensing is that observations are ma<strong>de</strong> at a spatial scale and<br />
temporal resolution that captures the regional-level effects, and yet, can<br />
offer an outstanding level of <strong>de</strong>tail. The disadvantage is that the measurements<br />
are sometimes difficult to validate at a scale that matches the<br />
patterns expressed in the satellite observations. Also, it is often difficult<br />
to establish the causal mechanisms contributing to the remotely sensed<br />
patterns.<br />
4.1 Measuring rainfall from space<br />
One approach to bypass the lack on ground-based meteorological measurements<br />
makes use of remote sensing instrumentation embarked in the<br />
tropical rainfall monitoring mission (TRMM, Kummerow et al., 2000),<br />
with a wall-to-wall coverage, but a relatively coarse spatial resolution.<br />
Gaining a better knowledge on the climatic environment of the tropical<br />
<strong>for</strong>est biome still represents an important challenge because many of<br />
the climatic processes observed in the tropical belt have no equivalent in<br />
temperate zones, and are thus un<strong>de</strong>rstudied, in spite of their critical relevance<br />
to human welfare. This notably inclu<strong>de</strong>s quantitative data on the<br />
El Nino southern oscillation phenomenon, which causes serious droughts<br />
and associated fires quite regularly in South East Asia and America.<br />
Consequences <strong>for</strong> the flora and the fauna of tropical rain <strong>for</strong>ests are also<br />
important, as the lack of rain may result in a limited fruit <strong>de</strong>velopment in<br />
many keystone species, and thus lead the animal that feed upon it to starvation,<br />
as was first documented by Robin Foster (see Wright et al., 1999<br />
<strong>for</strong> a more quantitative and updated <strong>de</strong>scription). The TRMM produces<br />
a best-estimate precipitation rate within grids of 0.25° by 0.25° in a global<br />
band extending from 50 <strong>de</strong>grees South to 50 <strong>de</strong>grees North latitu<strong>de</strong>. The<br />
satellite has onboard a 2.3cm wavelength radar, called precipitation radar,<br />
that provi<strong>de</strong>s a global image of rainfall accumulation every three hours in<br />
pixels of about 1° in latitu<strong>de</strong> and longitu<strong>de</strong>. These values may be cumulated<br />
at a daily or monthly basis (see figure 5 <strong>for</strong> a long-term average).
296 Integrated studies<br />
Figure 5: Average rainfall in millimeters per day over the period 1998-2011 across<br />
the tropics. High rainfall zones are evi<strong>de</strong>nt in the Choco area in Colombia, in<br />
Papua New Guinea, and western borneo. A mean rainfall of 8mm/day (or about<br />
3,000mm/yr) is observed over most of Amazonia. © Tropical Rainfall Monitoring<br />
Mission’s website (http://trmm.gsfc.nasa.gov/).<br />
4.2 Measuring <strong>for</strong>est status from space<br />
Remote sensing of the biosphere has a long history which may be traced<br />
back to the very birth of the history of photography itself. In<strong>de</strong>ed the<br />
photographer Gaspard-Félix Tournachon (also known as Nadar) photographed<br />
the Petit bicêtre (nowadays Petit Clamart) from a captive balloon<br />
as early as 1858. However, the technology was largely <strong>de</strong>veloped <strong>for</strong> military<br />
purposes during the great wars, and resulted in a range of applications<br />
in the visible range, that culminated with Landsat and Spot instruments<br />
in the 70s. Both instruments passively record a few bands in the visible<br />
range (plus one in the infrared) and this signal may be related to the<br />
activity of the vegetation because of the absorbance spectrum of photosynthesis.<br />
One measurement of the vegetation activity is the normalised<br />
difference vegetation in<strong>de</strong>x (NDVI), which was successfully used to map<br />
the world-wi<strong>de</strong> extent of terrestrial biomes over the 1980s and well into<br />
the 1990s (see III, 4). Mapping ecosystems is of crucial importance, especially<br />
to un<strong>de</strong>rstand potential routes of dispersal, suitability of habitats,<br />
and other ecological features. One outstanding contribution to geographers<br />
and ecologists alike is due to the group led by Valéry Gond from<br />
Montpellier, who recently provi<strong>de</strong>d a <strong>de</strong>tailed map of the Guiana Shield<br />
based on optical remotely sensed instruments (figure 6, adapted from<br />
Gond et al., 2011). Not only does this map clearly shows the great heterogeneity<br />
of the Guiana Shield, which expands from Southern Colombia<br />
to French Guiana, and inclu<strong>de</strong>s a significant part of the Amazon and<br />
Orinoco watersheds, but it also evi<strong>de</strong>nces the presence of dry areas along<br />
the so-called Roraima corridor, which connects the Venezuelan llanos to<br />
the Serra dos Carajás in brazil, through the Rupununi of Guyana and the<br />
Sipaliwini savanna, South of Surinam.
Part IV – Chapter 2 297<br />
Low <strong>de</strong>nse <strong>for</strong>est / inclu<strong>de</strong>d savanna<br />
on poor drainage soils (RSLC 18)<br />
Tree / savanna<br />
(RSLC 25 and RSLC 26)<br />
High <strong>for</strong>est with regular canopy<br />
mostly on terra firme (RSLC 19)<br />
High <strong>for</strong>est with disrupted canopy<br />
(RSLC 20)<br />
Agriculture settlement / city<br />
(RSLC 32)<br />
Mixed high and open <strong>for</strong>est<br />
(RSLC 21)<br />
Open <strong>for</strong>est / euterpe palm <strong>for</strong>est<br />
(RSLC 22)<br />
Permanent / temporary waterbody<br />
(from RSLC 2 to RSLC 13 and RSLC 27,<br />
RSLC 29, RSLC 31 and RSLC 33)<br />
Figure 6: Map of landscape types of the Guiana Shield based on a classification<br />
of 1-km 2 remotely sensed pixels recor<strong>de</strong>d by the VEGETATIoN sensor onboard<br />
the Spot-4 satellite covering a period of 12 months. In the legend, RSLC stands <strong>for</strong><br />
remotely sensed landscape classes. The maroon and green color scale represents the<br />
five dominant <strong>for</strong>est landscape types. © V. Gond (see Gond et al., 2011 <strong>for</strong> a full<br />
review).<br />
Another important application of remote sensing is monitoring the status<br />
of vegetation as a response to changing climates. This is a difficult task<br />
however, as the controversy initiated by the publication by Scott Saleska<br />
and colleagues (Saleska et al., 2007) emphasises. These authors used<br />
NASA’s Terra satellite to argue that the canopy of the Amazon rain <strong>for</strong>est<br />
greened up during the 2005 drought, which affected much of the Amazon<br />
watershed. They inferred that the rain <strong>for</strong>est could be resilient to dryness,<br />
at least <strong>for</strong> short periods, and un<strong>de</strong>rmined in<strong>de</strong>pen<strong>de</strong>nt evi<strong>de</strong>nce that
298 Integrated studies<br />
Amazonian trees may have suffered directly from this drought (Phillips<br />
et al., 2009). However, Saleska et al’s discovery has been criticised because<br />
their methodology and data were inappropriate, and led to flawed conclusions<br />
(Samanta et al., 2010). In<strong>de</strong>ed, interpretation of satellite imagery<br />
in the visible spectrum may be contaminated by atmospheric aerosol,<br />
water vapour and sub-pixel cloud hazes.<br />
Because most remote sensors are able to <strong>de</strong>tect the roughness of a surface<br />
or its optical spectrum, what happens below the canopy of a <strong>for</strong>est is seldom<br />
visible. This causes serious problems <strong>for</strong> applications involving GPS<br />
units (though these have significantly improved over the past years), and<br />
also <strong>for</strong> measuring <strong>for</strong>est structure. A technique to scan below the canopy<br />
is the light <strong>de</strong>tection and ranging application (Lidar), which measures the<br />
distance from a source to a point based on the return time of a laser pulse,<br />
at a high frequency. Aircraft-borne instruments have been used to measure<br />
the topography of terrain below the canopy, because a fraction of the<br />
pulses reach the ground level. In 2007, canopy height has been measured<br />
over 600ha at the Nouragues station through a simple difference of canopy<br />
altitu<strong>de</strong> and ground altitu<strong>de</strong> as measured by a helicopter-borne Lidar<br />
instrument. Using these data, one can monitor the large-scale <strong>for</strong>est structure,<br />
locate treefall gaps and even the dynamics of treefall gap <strong>for</strong>mation,<br />
and better un<strong>de</strong>rstand the landscape-scale variation of ecosystem features.<br />
One of the grails of tropical <strong>for</strong>est science is to be able to measure biomass<br />
stocks in tropical <strong>for</strong>ests, because tropical <strong>for</strong>ests represent an important<br />
carbon stock and these stocks are in the process of being incorporated<br />
in financial markets through schemes such as reducing emissions from<br />
<strong>de</strong><strong>for</strong>estation and <strong>for</strong>est <strong>de</strong>gradation. Some proposals were ma<strong>de</strong> to<br />
relate canopy height to biomass stocks, and they show some promise<br />
at the landscape scale (Asner et al., 2010). However, other techniques<br />
were proposed as alternatives, including a radar sensor operating at the<br />
P band that may equip a future satellite currently in discussion at the<br />
European space agency, and called BIOMASS (http://www.esa.int/esaLP/<br />
SEMFCJ9RR1F_in<strong>de</strong>x_0.html). Some preliminary tests of this instrument<br />
were per<strong>for</strong>med in 2009 in French Guiana, through collaboration<br />
between Onera, ESA and the local French research teams. Flights over the<br />
Paracou station and over the Nouragues station yiel<strong>de</strong>d fascinating results,<br />
some of which are still being processed.<br />
4.3 Measuring biodiversity from space<br />
One of the dreams of remote sensing science is to <strong>de</strong>tect more subtle patterns<br />
than geographical or physical ones. This may be more than sciencefiction<br />
today but the use of hyperspectral sensors may help to achieve<br />
this goal. Hyperspectral sensors retrieve the reflection of a surface, not
Part IV – Chapter 2 299<br />
with a few wavelengths like in classical remote sensing imagery, but with<br />
hundreds of wavelengths. In section 3.2, we mentioned that the absorption<br />
pattern of light in the UV spectrum and in red could be jointly used<br />
to assess the concentration of chlorophyll and of flavonoids, one important<br />
class of secondary compounds in plant tissue. In fact, the continuous<br />
absorption spectrum of vegetation may yield bands that are typical to<br />
other compounds, and there<strong>for</strong>e be able to <strong>de</strong>tect part of the chemical<br />
composition of the canopy from a remote sensor. A research program on<br />
this issue has been implemented by Asner’s group at the Carnegie institution<br />
<strong>for</strong> science (Asner and Martin, 2011). by focusing a beam to single<br />
canopy, their technology is now able to retrieve part of the chemical composition,<br />
but also biophysical parameters, and possibly provi<strong>de</strong> a fast in<strong>de</strong>x<br />
of functional diversity in tropical <strong>for</strong>est environments (Asner et al., 2011).<br />
5. Conclusion<br />
Many of the techniques briefly presented here are by no means unique to<br />
tropical <strong>for</strong>est environments, but they share some features. These environmental<br />
sensors are comparatively simpler and more rugged than in many<br />
temperate applications. One critical factor is obviously the moisture of<br />
the tropical environment that has a <strong>de</strong>leterious effect on the electronics<br />
of many components of the sensor. Another constraint is the topic of<br />
research. While much of mo<strong>de</strong>rn <strong>ecology</strong> in the temperate zone <strong>de</strong>als with<br />
ecosystem science (with a strong focus on the measurement of fluxes),<br />
and experimental approaches (hypothesis testing on mo<strong>de</strong>l species), the<br />
breadth of research topics in the tropics inclu<strong>de</strong>s also an important but<br />
exceedingly difficult part: the documentation of basic patterns of diversity.<br />
Thus programs aimed at <strong>de</strong>veloping environmental sensors in the<br />
tropics should acknowledge this fact and strive to provi<strong>de</strong> tools adapted<br />
to documenting the environment in which species live (i.e. their niche).<br />
Many of these sensors are aimed at measuring physiological or chemical<br />
processes <strong>for</strong> specific species. While the <strong>de</strong>velopment of such sensors<br />
would seem like a natural outgrowth of fundamental research programs,<br />
it appears that the link between basic science and sensor <strong>de</strong>velopment<br />
remains loose in <strong>ecology</strong> and evolutionary biology (with a few noticeable<br />
exceptions). Finally, we have emphasised several possibilities towards the<br />
<strong>de</strong>velopment of biodiversity sensors, i.e. semi-automated instruments that<br />
would provi<strong>de</strong> partial in<strong>for</strong>mation on the status of biodiversity, but that<br />
could be maintained <strong>for</strong> long time scales, so that early-warning signals to<br />
radical changes on biodiversity could be <strong>de</strong>tected early on.
300 Integrated studies<br />
Authors’ references<br />
Jérôme Chave:<br />
Université Paul Sabatier, Laboratoire Évolution et diversité biologique,<br />
UPS-CNRS-ENFA UMR 5174, Toulouse, France<br />
Philippe Gaucher:<br />
CNRS-Guyane, USR 3456, Cayenne, France<br />
Maël Dewynter:<br />
Office National <strong>de</strong>s Forêts, Réserve <strong>de</strong> Montabo, Cayenne, France<br />
Corresponding author: Jérôme Chave, jerome.chave@univ-tlse3.fr<br />
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Ecology 80, pp. 1632-1647.
Chapter 3<br />
Use of sensors in marine mesocosm<br />
experiments to study the effect of<br />
environmental changes on planktonic<br />
food webs<br />
Behzad Mostajir, Jean Nouguier, Emilie Le Floc’h, Sébastien Mas, Romain Pete,<br />
David Parin, Francesca Vidussi<br />
1. Introduction<br />
Marine ecosystems, and their biological components in particular, play<br />
a crucial role in the regulation of Earth climate and in the biogeochemical<br />
cycles of major elements such as carbon, nitrogen, phosphorus, or<br />
sulphur (Pielke, 2008). Moreover, marine ecosystems are reservoirs<br />
of biodiversity that remain partially undiscovered (Webb et al., 2010).<br />
In addition, these ecosystems, in particular coastal zones, provi<strong>de</strong> a<br />
large variety of ecological services to our society. In the context of a<br />
changing environment at global and local scales, studying the ecological<br />
and evolutionary responses of marine organisms, from cell to population<br />
level and from communities to food web level, becomes more than ever<br />
essential.<br />
The observation of marine ecosystem from permanent fixed observatory<br />
systems provi<strong>de</strong>s essential in<strong>for</strong>mation about ecosystem variability<br />
at local or regional scales. These observatory systems can be linked<br />
together and <strong>for</strong>m a web to gather data that can be interpreted at large<br />
spatial and temporal scales and coupled with remote-sensing data from<br />
satellites (see III, 3 and II, 2). However, it is necessary to complement<br />
these large-scale field studies with observations and experiments of isolated<br />
assemblages of marine micro-organisms. The isolation of a portion<br />
of water mass including associated plankton assemblages can be realised
306 Integrated studies<br />
into an enclosed experimental unit (e.g. a mesocosm) to study ecological<br />
processes from cells to communities and food webs. The confined<br />
organisms and associated water mass can also be subjected to different<br />
experimental treatments in or<strong>de</strong>r to study responses to known <strong>for</strong>cing<br />
factors.<br />
This mesocosm experimental approach, complementary to the observational<br />
approach, provi<strong>de</strong>s additional fundamental in<strong>for</strong>mation about<br />
the <strong>ecology</strong> of organisms and their interactions with biotic and abiotic<br />
factors. In<strong>for</strong>mation from field observation and from experimentation<br />
can be merged and used <strong>for</strong> predictive mo<strong>de</strong>lling. The main objective<br />
of this chapter is to illustrate and discuss the use of sensors to study<br />
physico-chemical and biological variables as well as sensors used to<br />
apply treatments in marine mesocosm experiments. These sensors can<br />
also be used as feedback controls of the applied treatments in marine<br />
mesocosm experiments. Some of these sensors are also used in marine<br />
observatories and have been presented elsewhere in the context of other<br />
methodological approaches (see II, 2 and III, 1). However, we focus<br />
here on constraints from using these sensors in mesocosm experimentation<br />
to study confined planktonic assemblages and obtain <strong>de</strong>tailed<br />
in<strong>for</strong>mation on ecosystem functioning. It is necessary to stress out that<br />
some of the sensors and automated systems presented in this chapter<br />
were <strong>de</strong>veloped <strong>for</strong> specific objectives within the framework of our<br />
scientific projects.<br />
2. Experimental marine <strong>ecology</strong> using mesocosm approach<br />
A major concern in environmental marine science is to <strong>de</strong>termine and<br />
predict the response of pelagic ecosystems to factors such as increasing<br />
pCO 2<br />
and consequent <strong>de</strong>crease of water pH as well as increasing temperature<br />
and stratification, influencing both light and nutrient availabilities.<br />
At the same time, in applied aquatic science, the means to preserve or<br />
restore coastal and marine environments to ensure their sustainable ecological<br />
services attract an increasing attention. In addition, it is becoming<br />
more and more important to un<strong>de</strong>rstand how some marine resources can<br />
be newly produced and sustainably exploited, including microalgae used<br />
<strong>for</strong> biofuel production, <strong>for</strong> example.<br />
The water column of marine ecosystems is a natural habitat <strong>for</strong> many<br />
organisms that have adopted the planktonic life mo<strong>de</strong> <strong>for</strong> the main part<br />
of their life cycle (see II, 2). Although the planktonic organisms can<br />
move in the water column, these organisms cannot actively swim like<br />
fish and their movement is rather dictated by water mass advection.
Part IV – Chapter 3 307<br />
Plankton microbial organisms ensure the most fundamental functions<br />
in marine ecosystems such as primary production and remineralisation,<br />
and play a key role in the biogeochemical cycles of major elements on<br />
Earth (Falkowski et al., 2008; Falkowski, 1994; Falkowski and Woodhead,<br />
1992). Study of microbial planktonic processes such as nutrient assimilation,<br />
production, growth, and respiration, as well as investigation of their<br />
interaction with other plankton assemblages are most of the time carried<br />
out in the microcosms.<br />
Figure 1: Relationship between the size of a marine organism (adapted from<br />
Sieburth et al., 1978) and the water volume required <strong>for</strong> studying a community<br />
assemblage. The most important organisms in the marine ecosystem functioning<br />
are Femto-plankton (0.02-0.2µm, viruses and small heterotrophic Archea and<br />
bacteria), Pico-plankton (0.2-2µm, larger heterotrophic bacteria, cyanobacteria,<br />
small eukaryotic phototrophs, and heterotrophic flagellates), Nano-plankton<br />
(2-20µm, most of phytoplankton assemblages, large heterotrophic flagellates<br />
and small ciliates), Micro-plankton (20-200µm, large phytoplankton, large<br />
ciliates including thintini<strong>de</strong>s, and different stages of metazooplankton including<br />
copepods), Meso-plankton (0.2-20mm, large phytoplankton, protozooplankton<br />
and metazooplankton), Macro-plankton (2-20cm) and Mega-plankton<br />
(20-200cm).<br />
As an operational way to study planktonic assemblages, Sieburth et<br />
al. (1978) distinguished these assemblages according to their sizes (see
308 Integrated studies<br />
figure 1). To investigate physiology, life cycle and interaction between<br />
femto-, pico-, nano- and micro-plankton the experiments can be conducted<br />
in the laboratory using some litres of water containing all these<br />
assemblages (i.e. microcosm experiments). However, experimental systems<br />
of community responses to perturbations should inclu<strong>de</strong> most<br />
of the components of the food web to investigate both direct effects<br />
of the studied stressors on organisms and indirect effects through trophic<br />
responses, <strong>for</strong> example trophic casca<strong>de</strong>s (e.g., Vidussi et al., 2011).<br />
For this, an experimental water volume of at least thousand litres (i.e.<br />
mesocosm) that contain all or most of the biological components of<br />
the plankton food web (virus, bacteria, phyto and zoo-plankton, fish<br />
larvae) is necessary (figure 1). Mesocosms can also host benthic organisms<br />
interacting with the plankton (i.e. filters fee<strong>de</strong>rs) or small fish (i.e.<br />
plankton fee<strong>de</strong>rs).<br />
The utilisation of mesocosms has proved essential to provi<strong>de</strong> new results<br />
on the topics of global change effect on marine systems, such as the<br />
study of the effects of increasing ultraviolet B radiation (e.g. Mostajir<br />
et al., 1999), the increase of CO 2<br />
and acidification (e.g. Riebesell et<br />
al., 2007), the increase of water temperature (e.g. Lewandowska and<br />
Sommer, 2010), and simultaneous increases of water temperature and<br />
ultraviolet B radiation (Vidussi et al., 2011). New fundamental concepts<br />
have emerged from mesocosm experiments (i.e. concerning food web<br />
functioning, Thingstad et al., 2007), and mesocosm experiments are<br />
also an essential tool in eco-toxicology (e.g. Sargian et al., 2005; Pestana,<br />
2009). Until now, studies of marine ecosystems in the water column of<br />
the mesocosms have been generally per<strong>for</strong>med from discrete samplings<br />
at different time interval from hours to days or weeks. Yet, ecological<br />
processes at the microbial level can occur at short time scales in the<br />
or<strong>de</strong>r of hours, and the absence of a<strong>de</strong>quate high-frequency sampling<br />
can hi<strong>de</strong> some fundamental processes taking place between two discrete<br />
sampling episo<strong>de</strong>s. Thus, automated sensors in mesocosm experiments<br />
have been used recently to monitor physicochemical and biological variables<br />
and hence provi<strong>de</strong> fundamental in<strong>for</strong>mation at high frequency<br />
with little perturbation of mesocosms and a reduced human ef<strong>for</strong>t<br />
(Vidussi et al., 2011). Another fundamental advantage of using automated<br />
sensors is that they allow regulation and realistic simulation of<br />
some experimental treatments including climatic variables (Nouguier et<br />
al., 2007). We present in this chapter different sensors that can be used<br />
in mesocosm experiments. Most examples and related data presented<br />
here are from experiments carried out in the Mediterranean centre <strong>for</strong><br />
marine ecosystem experimental research operated jointly by the CNRS<br />
and the University of Montpellier 2 (Medimeer, http://www.medimeer.<br />
univ-montp2.fr/, see figure 2).
Part IV – Chapter 3 309<br />
Figure 2: In situ moored mesocosms (up to 12) around the Medimeer floating<br />
plat<strong>for</strong>m (ca. 30m) installed in Thau lagoon (Mediterranean coastal lagoon, South<br />
of France).<br />
3. <strong>Sensors</strong> to monitor ecological variables in marine mesocosms<br />
Several variables in the water column can be studied during the mesocosm<br />
experiments. These environmental variables inclu<strong>de</strong> the water column<br />
physical properties (e.g. temperature, salinity, and light radiation), the biogeochemical<br />
properties (e.g. dissolved nutrients, oxygen concentration,<br />
and particle load), and proxies of biological components (e.g. fluorescence<br />
of phytoplankton). These variables can be continuously monitored at a<br />
high resolution by a set of automated in situ sensors.<br />
3.1. Monitoring of the water column physico-chemical properties<br />
and variability<br />
3.1.1. Water temperature<br />
The monitoring of temperature along a vertical profile within a mesocosm<br />
gives in<strong>for</strong>mation on the <strong>de</strong>gree of stratification or homogenisation of the<br />
water column. Moreover, because biological processes are temperature<strong>de</strong>pen<strong>de</strong>nt,<br />
monitoring of water temperature can contribute to un<strong>de</strong>rstand
310 Integrated studies<br />
responses of organisms to experimental treatments. Among the usual temperature<br />
sensors, such as thermocouple and platinum resistor (Pt100 and<br />
Pt1000), thermistors are well adapted to the measurement of natural water<br />
temperature. Their resistance varies inversely with temperature, but, more<br />
importantly, their baseline resistance value and coefficient of variation are<br />
generally large. This permits the use of long cables and allows an easy half<br />
bridge measurement on data loggers <strong>for</strong> the long term recording of numerous<br />
temperature channels. An improvement by a software correction is<br />
used to minimise linearisation errors and obtain measurements from 0 to<br />
40°C with an accuracy from 0.01 to 0.02°C. Figure 3A shows an example<br />
of water temperature monitored at three <strong>de</strong>pths during a mesocosm experiment.<br />
This shows firstly the daily variations of water temperature in the<br />
mesocosm, and also <strong>de</strong>monstrates the efficiency of water mass mixing done<br />
with a pump in the mesocosm. Diel variations of water column temperature<br />
tend to follow the variation of atmospheric temperature.<br />
3.1.2. Conductivity<br />
The conductivity of water is its ability to transmit the electric current.<br />
The electrically charged ions move in presence of an electrical field or an<br />
alternative magnetic field (see III, 1). To measure the water conductivity<br />
in aquatic environments, two techniques are available. The first one is<br />
the electro<strong>de</strong> cell method that is mostly used <strong>for</strong> measuring low conductivities<br />
in pristine environments. Here, the conductivity is obtained from<br />
measurement of the electric current generated by an alternative electric<br />
field between the electro<strong>de</strong>s of the cell. The second one relies on the electromagnetic<br />
induction method, which is used <strong>for</strong> high conductivity measurements<br />
in various types of marine environments. <strong>Sensors</strong> using this<br />
method are rather immune to biofouling, and the measured electric current<br />
flowing through the water is generated by the alternative electric field<br />
from a ring trans<strong>for</strong>mer. As the relative proportions of the main ionic constituents<br />
are nearly constant, the conductivity can be directly converted<br />
to salinity <strong>for</strong> a given temperature using available standards (Weyl, 1964).<br />
During the mesocosm experiments, conductivity measurements were conducted<br />
with AADI Aan<strong>de</strong>raa CS3119 AIW (Aan<strong>de</strong>raa Data Instrument<br />
AS, Norway) to monitor the temporal variations of salinity (figure 3B).<br />
The trend after one day of measurement shows a salinity increase in surface<br />
waters, indicating the occurrence of evaporation.<br />
3.1.3. Nutrients concentrations<br />
The nutrients commonly measured during experiments are nitrate, nitrite,<br />
ammonia, phosphate, silicate, dissolved organic nitrogen and phosphorus<br />
species. Nutrients are one of the essential elements <strong>for</strong> the growth of
Part IV – Chapter 3 311<br />
Figure 3: In situ sensors installed on the Medimeer plat<strong>for</strong>m. A. Monitoring of<br />
daily variations of water temperature measured every 2 minutes at three <strong>de</strong>pths<br />
during a mesocosm experiment. B. Monitoring of salinity in the surface water of a<br />
mesocosm measured every 2 minutes.<br />
phytoplankton and bacteria. The continuous nutrient concentration<br />
monitoring in real time by in situ nutrient analyser probes allows <strong>de</strong>termining<br />
sources, sinks, and dynamics of different nutrients in natural environments<br />
(Vuillemin et al., 2009) and can be adapted <strong>for</strong> the mesocosm<br />
experiment. This permits <strong>for</strong> example long-term study of water quality<br />
regarding pollutants, effluents and nutrient loads. In<strong>de</strong>ed, a continuous<br />
measurement of nutrient concentrations helps to i<strong>de</strong>ntify the effect of<br />
anthropogenic nutrient sources on planktonic communities including the<br />
triggering of blooms by the nutrient load of continental waters (Lefebvre,<br />
2006). Most in situ nutrients analyser-probes, such as SubChem Analyzer<br />
and Wiz probes, measure automatically various dissolved chemical species,<br />
using wet chemical techniques of in-flow analysis based on standard<br />
laboratory analytical methods (spectrophotometry and fluorimetry)<br />
<strong>de</strong>veloped as long as a century ago (Varney, 2000; Thouron et al., 2003).<br />
These analytical probes are equipped with one or multiple reagent-<strong>de</strong>livering<br />
modules and standards, and one or mutiple electro-optical <strong>de</strong>tectors<br />
– their number <strong>de</strong>pends on the measurement of one or several nutrients<br />
(nitrate, nitrite, phosphate, ammonia, see Blain et al., 2004; Lefebvre,<br />
2006). The frequency of measurement <strong>de</strong>pends on the amount of reagent<br />
and standard loa<strong>de</strong>d in the probe, and the number of nutrients measured<br />
by the probes. To avoid the drifts and the <strong>de</strong>gradation of optics, reagents,<br />
calibration standards, and also biofouling in the sensor sampling line, a<br />
frequent maintenance is necessary, which inclu<strong>de</strong>s reagents and standards<br />
change, complete clean up and in situ recalibration of the probe.
312 Integrated studies<br />
3.1.4. Turbulence<br />
In natural aquatic environments, turbulence is created by wind shear at<br />
the surface, horizontal shear at the pycnocline (i.e., <strong>de</strong>nsity gradient) and<br />
sediment surface, breaking surface and internal waves, and buoyancy<br />
effects such as convection. Turbulent mixing at a small scale can influence<br />
predator-prey interactions (Rothschild and Osborn, 1988; Browman,<br />
1996; Dower et al., 1997), particle capture (Shimeta and Jumars, 1991),<br />
aggregation and disaggregation (MacIntyre et al., 1995), small-scale<br />
patchiness (Moore et al., 1992; Squires and Yamazaki, 1995), and species-specific<br />
growth inhibition (Gibson and Thomas, 1995). However,<br />
turbulence is also important <strong>for</strong> mixing large water masses with distinct<br />
physical properties or nutrient flux in the same water column (Jørgensen<br />
and Revsbech, 1985; Da<strong>de</strong>, 1993; Denman and Gargett, 1995; Karp-Boss<br />
et al., 1996). There<strong>for</strong>e, in the aquatic ecosystems, turbulence is one of the<br />
significant environmental factors influencing the plankton <strong>ecology</strong>.<br />
In mesocosm experiments, turbulence regimes can be simulated to adjust<br />
fluxes of nutrients, dissolved gases and particle aggregations and disintegrations.<br />
There are various techniques ensuring turbulent mixing in the<br />
mesocosms such as rotating paddles and oscillating grids in tanks, flexiblewalled<br />
in situ enclosures (Menzel and Case, 1977; Grice and Reeve, 1982)<br />
and other mixing schemes including bubbling (Eppley et al., 1978; Sonntag<br />
and Parsons, 1979) and pumping (Kotak and Robinson, 1991). These techniques<br />
can provi<strong>de</strong> reasonable representation of some aspects of natural<br />
turbulent mixing but usually not all of them (San<strong>for</strong>d, 1997). In<strong>de</strong>ed, it is<br />
not expected that any single mixing <strong>de</strong>sign <strong>for</strong> a mesocosm experiment will<br />
reproduce all aspects of natural turbulence. Hence significant differences<br />
between natural turbulence and that generated in the water column of a<br />
mesocosm are frequent (Brumley and Jirka, 1987; Fernando, 1991).<br />
Turbulent mixing can be monitored during experiments by using several<br />
types of sensors including laser doppler velocimeters (Hill et al., 1992),<br />
small acoustic (Kraus et al., 1994; Trivett and Snow, 1995) and electromagnetic<br />
velocity sensors (Howarth et al., 1993), flow meters and particle<br />
image velocimetry. In particular, the particle image velocimetry system,<br />
<strong>de</strong>veloped <strong>for</strong> fluid dynamics studies (Gray and Bruce, 1995) and based<br />
on the assumption that the particles follow the flow, allows estimating<br />
accurately the velocity field and the small-scale distribution of flow and<br />
turbulence by tracking small particle movement through series of frame.<br />
Fluorometers are also wi<strong>de</strong>ly used to study processes of diffusion and<br />
mixing based on dye dispersion technique, since fluorescent dyes such<br />
as rhodamine and fluoresceine are <strong>de</strong>tectable at very low concentrations.<br />
This dye dispersion technique consists in injecting a small amount of a<br />
fluorescent neutrally buoyant dye either into the surface or mid-<strong>de</strong>pth,<br />
and measuring fluorescence at several locations in the mesocosms over
Part IV – Chapter 3 313<br />
time. The mixing time and velocity are <strong>de</strong>fined by tracer concentration<br />
reaching a specified <strong>de</strong>gree of uni<strong>for</strong>mity insi<strong>de</strong> the mesocosm.<br />
3.2. Monitoring the plankton food web components<br />
3.2.1. Chlorophyll and phycoerythrin concentrations<br />
Measurements of fluorescence have been used <strong>for</strong> many years in marine<br />
science to measure concentrations of chlorophyll (Lorenzen, 1966; Mignot<br />
et al., 2011). Phytoplankton species have specific pigments and the <strong>de</strong>tection<br />
of some of these pigments gives in<strong>for</strong>mation about their concentrations,<br />
as well as phytoplankton biomasses and diversity. The fluorescence<br />
level can be monitored by sensors and mo<strong>de</strong>rn compact ones have a wi<strong>de</strong><br />
range of optical configurations and a wi<strong>de</strong> range of wavebands to measure<br />
chlorophyll a (phycoerythrin and phycocyanin) and other pigments. This<br />
has paved the way <strong>for</strong> new <strong>de</strong>velopments such as use of multiples excitation<br />
wavelengths <strong>for</strong> discriminating between phytoplankton taxa (Paresys<br />
et al., 2005). In the mesocosm experiment, the continuous measurement<br />
of chlorophyll a and phycoerythrin concentrations by fluorescence sensors<br />
is used to monitor the presence and the temporal dynamic of the algal<br />
bloom (chlorophyll a measurement) or cyanobacteria (phycoerythrin<br />
measurement). It is also used to <strong>de</strong>termine the diel variations of pigment<br />
concentration (particularly chlorophyll a), which is affected by change of<br />
phytoplanktonic biomass and photoacclimation processes. Figure 4 shows<br />
an example of diel variations <strong>for</strong> chlorophyll a concentration monitored<br />
<strong>for</strong> the first time during a mesocosm experiment.<br />
Figure 4: Diel variations of chlorophyll a concentration monitored every 2 minutes<br />
by a Wetlabs sensor in a mesocosm experiment.
314 Integrated studies<br />
3.2.2. Particulate organic carbon of small particles<br />
In open ocean waters, particulate organic carbon (POC) is the main<br />
source of particles (see III, 3 and IV, 1). Particle load is the main driver<br />
of water turbidity in the open ocean, which can be quantified by the<br />
backscattering coefficient measurement with backscattering meter. The<br />
bulk backscattering coefficient (b bp<br />
) measurements are affected by particle<br />
size distribution and composition of seawater constituents (colloids,<br />
bacteria, phytoplankton, biogenic <strong>de</strong>tritus, minerogenic particles, and<br />
bubbles). Recent studies have shown that the POC concentration of<br />
small particles can be estimated with reasonable accuracy from particle<br />
backscattering coefficient at 470 or 510nm, provi<strong>de</strong>d mineral particles<br />
are absent (Stramski et al., 1999; Loisel et al., 2001; Balch et al., 2005).<br />
Some optical sensors measure the backscattering coefficient at different<br />
wavelengths and, through some calculations, give the spectral slope γ of<br />
the particulate backscattering coefficient. Based on theoretical and in situ<br />
studies (e.g. Reynolds et al., 2001; Stramska et al., 2003), the γ slope has<br />
been proposed as a proxy of the suspen<strong>de</strong>d marine particle size distribution<br />
<strong>for</strong> particles smaller than 10µm (Loisel et al., 2006). backscattering<br />
coefficient measurements at high frequency can be per<strong>for</strong>med during<br />
mesocosm experiments using commercial sensors such as the Wetlabs<br />
Eco-Triplet sensors, which operates at 470, 532, 650 and/or 880nm. These<br />
sensors were used to <strong>de</strong>termine the dynamic of organic particles smaller<br />
than 10µm during two mesocosm experiments in the Medimeer plat<strong>for</strong>m<br />
(data not shown).<br />
3.2.3. Plankton i<strong>de</strong>ntification, size and abundance<br />
Plankton is the main component of the pelagic food web and is an indicator<br />
of the ecological status of water masses (Manizza et al., 2005; Wyatt<br />
and Ribera d’Alcala, 2006). Plankton is also an indicator of water quality<br />
and is influenced by natural and anthropogenic changes in the environment<br />
(Belin and Berthome, 1991). Apart from the possibility to use in<br />
situ imagery system <strong>for</strong> observations and i<strong>de</strong>ntifications of marine zooplankton<br />
and particles (see II, 2), in situ flow cytometry is a powerful<br />
tool to investigate microorganisms such as phytoplankton in their natural<br />
environment (Dubelaar et al., 1999; Olson et al., 2003; Sosik et al.,<br />
2003; Dubelaar et al., 2004; Thyssen et al., 2008). One of the first in<br />
situ flow cytometer, the FlowCytobot was <strong>de</strong>signed to cover the full-size<br />
range of phytoplankton (from 1µm up to several 100µm) during several<br />
month <strong>de</strong>ployments (Olson et al., 2003; Sosik et al., 2003). The first generation<br />
of FlowCytobot was optimised <strong>for</strong> the analysis of pico- and small<br />
nanoplankton (around 1 to 10μm) and used fluorescence and light scattering<br />
signals from a laser beam (Olson et al., 2003; Sosik et al., 2003).
Part IV – Chapter 3 315<br />
To complement this <strong>de</strong>vice, a submersible imaging FlowCytobot (Olson<br />
and Sosik, 2007) has been <strong>de</strong>veloped to i<strong>de</strong>ntify the taxonomy of natural<br />
plankton assemblages (nano- and microplanktonic organisms and <strong>de</strong>tritus)<br />
in the size range from around 10 to 100μm. This new generation combines<br />
a vi<strong>de</strong>o and image in-flow system to cover the size range from 10 to<br />
100µm (Olson and Sosik, 2007; Sosik and Olson, 2007) and a flow cytometric<br />
technology to both capture images of organisms <strong>for</strong> i<strong>de</strong>ntification<br />
and measure chlorophyll fluorescence associated with each image. Like<br />
the laboratory-based FlowCam (Sieracki et al., 1998), the automatic classification<br />
procedures of the various plankton types uses an approach based<br />
on a combination of image feature types including size, shape, symmetry,<br />
and texture characteristics, as well as orientation invariant moments, diffraction<br />
pattern sampling, and co-occurrence matrix statistics (Sosik and<br />
Olson, 2007). Moreover, the measurements of chlorophyll fluorescence<br />
allow to discriminate phototrophic (i.e. phytoplankton) from heterotrophic<br />
cells.<br />
The fully automated CytoBuoy is another submersible flow cytometer<br />
that <strong>de</strong>livers data in real-time when operated online (Dubelaar et al.,<br />
1999; Cunningham et al., 2003; Dubelaar and Jonker, 2000). CytoBuoy<br />
allows estimating phytoplankton biomass and discriminating between<br />
different phytoplankton groups with an approach that combines the<br />
morphological, pulse-shape (large horizontal to vertical aspect ratio)<br />
and high-frequency analysis of particle crossing a laser beam. With<br />
additional ef<strong>for</strong>ts in sensor miniaturisation and in reduction of power<br />
consumptions, the continuous and long-term use of a submersible flow<br />
cytometer will be soon possible during mesocosm experiments.<br />
We have not used a submersible flow cytometer yet in the Medimeer’s<br />
mesocosms. This instrument is still too large and it is difficult to immerse<br />
it in the mesocosms where different treatments are applied without<br />
increasing disturbance or contamination. Also, the cost of the system is<br />
still prohibitive when one wants to replicate sensors across mesocosms.<br />
An alternative solution could be to use an automated pumping system to<br />
provi<strong>de</strong> the water sample from different mesocosms to a flow cytometer<br />
installed in the laboratory nearby the mesocosm plat<strong>for</strong>m. This would<br />
allow to per<strong>for</strong>m continuous plankton monitoring with minimal cost<br />
and disturbance.<br />
3.3. Assessment of the food web functioning<br />
In aquatic <strong>ecology</strong>, mesocosm experimentations are per<strong>for</strong>med in or<strong>de</strong>r<br />
to assess the functioning of the food web and the web energy transfer. To<br />
characterise the resilience of the ecosystem studied, the direct and indirect<br />
effects of biological and environmental interactions (trophic casca<strong>de</strong>s,
316 Integrated studies<br />
feedbacks) on the ecosystem functioning (e.g. CO 2<br />
exchanges, nitrogen<br />
or phosphorus cycles, aerosols production) should be assessed. Currently,<br />
only few sensors are <strong>de</strong>veloped and used to automatically monitor such<br />
parameters and these sensors focus on net O 2<br />
exchanges or bivalve filtration<br />
activity.<br />
3.3.1. Dissolved oxygen concentration and community oxygen<br />
metabolism<br />
Oxygen is involved in most of biological and chemical processes in aquatic<br />
systems. The net O 2<br />
production and respiration rates can be calculated by<br />
measuring the changes in the dissolved O 2<br />
concentrations in the course<br />
of the experiment during day-time and night-time respectively. There<strong>for</strong>e,<br />
the dissolved O 2<br />
can be used as a proxy of the net community oxygen production.<br />
Moreover, as photosynthesis and respiration stoichiometrically<br />
relate on oxygen and carbon fluxes, the net O 2<br />
production and respiration<br />
rates can be converted into net carbon production and respiration rates by<br />
using equations with photosynthetic and respiratory quotients of conversion<br />
(Dickson and Orchardo, 2001). The photosynthetic quotients are<br />
various, because they have been calculated on the basis of the biochemical<br />
composition of phytoplankton (Laws, 1991), direct measurements of<br />
natural communities (Williams and Robertson, 1991; DeGrandpre et al.,<br />
1997) and mo<strong>de</strong>lling studies (Williams, 1993).<br />
Two families of sensors are used to measure dissolved oxygen (see III, 1).<br />
The sensors based on galvanic or polarographic oxygen electro<strong>de</strong>s are generally<br />
used <strong>for</strong> short-term measurements because frequent changes of the<br />
membrane and calibration are necessary. Thus, most of the oxygen sensors<br />
used in marine mesocosm experiments are based on oxygen dynamic<br />
luminescence quenching of a platinum porphyries complex. These opto<strong>de</strong><br />
sensors are used <strong>for</strong> long term recording of dissolved O 2<br />
in marine and<br />
brackish waters because of their strength, their immunity to biofouling<br />
and their easiness of cleaning. Since salinity and water temperature influence<br />
the dissolved O 2<br />
concentration in marine system, O 2<br />
sensor is generally<br />
associated with a temperature sensor and a conductivity or salinity<br />
sensor via a data logger in or<strong>de</strong>r to realise the necessary correction in real<br />
time.<br />
In flow-through mesocosms, flow-respirometry technique is applied by<br />
measuring difference in concentration in the outflowing water compared<br />
to the inflowing water (Griffith et al., 1987). In open-top mesocosms,<br />
sea-air exchange has to be taken into account and the assumption<br />
that the respiration rate is constant in the light and in the dark has to<br />
be ma<strong>de</strong> (Leclerc et al., 1999). The common gross photosynthesis and<br />
respiration <strong>for</strong> a community can then be calculated based on ampero-
Part IV – Chapter 3 317<br />
polarographic electro<strong>de</strong> measurements over 24 hours and by using a<br />
multiple regression method. Figure 5 shows an example of dissolved<br />
O 2<br />
concentrations monitoring at high frequency during a mesocosm<br />
experiment per<strong>for</strong>med in Medimeer. The dissolved O 2<br />
concentration<br />
increase during light period indicates that phytoplanktonic O 2<br />
production<br />
excee<strong>de</strong>d plankton community respiration, whereas the O 2<br />
concentration<br />
<strong>de</strong>crease during dark period suggests an important community<br />
respiration activity.<br />
Figure 5: Monitoring of oxygen saturation every 2 minutes in the water column of<br />
a mesocosm. Oxygen saturation was measured with an opto<strong>de</strong> sensor.<br />
3.3.2. Bivalve activity in relation with plankton communities<br />
In coastal marine environments, filter fee<strong>de</strong>rs such as bivalves can control<br />
the planktonic communities by filtering their surrounding water including<br />
small living and non-living particles. To study the relationship between<br />
plankton dynamics and bivalve filtration, it is necessary to be sure that<br />
the bivalves are alive and physiologically active. The bivalve physiological<br />
activity can be assessed continuously by measuring the frequency of valve<br />
gape. For mussels, valve gape is measured by automated remote-sensing<br />
technologies such as fiber-optic imaging (Franck et al., 2007) or Hall<br />
effect sensor system (Robson et al., 2007). Robson et al. (2009) also used a<br />
Hall effect sensor system to measure the pumping flux from the exhalant<br />
siphon. A Hall effect sensor is a transducer that varies its output voltage<br />
in response to changes in a magnetic field. These changes are induced by<br />
changes in the distance between the sensor and a magnet positioned few<br />
millimetres away.
318 Integrated studies<br />
Figure 6: Monitoring of oyster valve gape every 2 seconds at the beginning (day 1,<br />
dash black line) and at the end (day 9, plain grey line) of a mesocosm experiment.<br />
The oyster Crassostrea gigas remains open most of the time at the beginning while its<br />
periods of activity are shortened at the end of the experiment (with fewer opening<br />
period).<br />
The Hall effect sensor was used in a gape activity measurement system<br />
during a mesocosm study carried out on Medimeer in or<strong>de</strong>r to measure<br />
oysters (Crassostrea gigas) filtration pressure (Mostajir et al., unpublished<br />
data). During this experiment, each oyster resting on its bottom shell was<br />
glued on a PVC plate where a hall sensor was inserted. The other part of<br />
the hall sensor system was a magnet attached on a PVC stick glued on the<br />
oyster top shell. In this way, changes in magnetic field were produced by<br />
variations in the distance between the sensor and this magnet. Twenty<br />
oysters were introduced to two mesocosms and sensors were connected to<br />
a data logger (CR23X, Campbell Scientific). The signal (output voltage)<br />
was sampled every 2 seconds. The physiological activity of the twenty<br />
oysters, related to the <strong>de</strong>gree of valve aperture at two levels (open and<br />
close), was monitored continuously over the nine days of the experiment<br />
(figure 6). The results highlighted that oyster behaviour of particle filtration,<br />
as indicated by duration of valve aperture, correlated with plankton<br />
abundances. At the beginning of the experiment (day 1), plankton was<br />
abundant and valves were opened all day long. When food became scarce<br />
at the end of the experiment (day 9) valves remained closed some hours<br />
per days (Mostajir et al., unpublished data).
Part IV – Chapter 3 319<br />
4. Automated sensors to regulate marine mesocosm experiments<br />
<strong>Sensors</strong> in aquatic mesocosm studies can also used <strong>for</strong> the automated<br />
feedback control of the experimental conditions. Automatic regulation is<br />
crucial <strong>for</strong> the long-term and continuous control of marine mesocosms,<br />
and to ensure a good repeatability between treatments. Automated sensors<br />
are wi<strong>de</strong>ly used in indoor mesocosm facilities. For example, automated<br />
sensors control the chemical doses of pollutants and stressors in<br />
the Experimental stream facility (U.S. Environmental protection agency,<br />
USA), the water level in the Multiscale experimental ecosystem research<br />
center facility (University of Maryland, USA), and the water motion insi<strong>de</strong><br />
a mesocosm (Falter et al., 2006).<br />
In outdoor mesocosms, especially those immersed in the sea, regulation<br />
of the experimental conditions is more problematic than in indoor mesocosms<br />
because of their higher temporal variability and because of stronger<br />
constraints on accessing an energy source. Still, the mixing turn-over time<br />
generated by a pump of which flow rate is controlled by voltage input<br />
can be automated using the feedback of a flow meter set in the outflow<br />
circuitry (Medimeer team, unpublished data). In addition, recent <strong>de</strong>velopments<br />
have focused on sensor-based regulation systems of different global<br />
changes scenarios. Up to now, the feedback control <strong>de</strong>vice focused on<br />
increase of carbon dioxi<strong>de</strong> (CO 2<br />
), temperature and ultraviolet B radiation<br />
(UVBR: 280-320nm). Regulation of these variables is important to<br />
elucidate the response of marine organisms as well as those of the whole<br />
food web to these stressors, as they are expected to change in the near<br />
feature from global anthropogenic changes. Mo<strong>de</strong>ls predict an increase<br />
of average atmospheric pCO 2<br />
which induces marine acidification (IPCC,<br />
2007). Water temperature is also expected to increase as a consequence of<br />
global warming, and inci<strong>de</strong>nt UVBR at the sea surface are expected to be<br />
modified as a concomitant effect of ozone <strong>de</strong>pletion and global warming<br />
(Weatherhead and An<strong>de</strong>rsen, 2006; IPCC, 2007).<br />
Kim et al. (2008) <strong>de</strong>signed an in situ mesocom facility <strong>for</strong> CO 2<br />
enhancement<br />
studies by adapting the set up used in previous experiments (Engel et<br />
al., 2005; Delille et al., 2005; Kim et al., 2006). The continuous feedback<br />
control only concerned the gas concentration in the headspace above the<br />
mesocosm measured with online infrared analysers (LI-COR 820). Other<br />
experimental setups focused only either on temperature (Liboriussen et al.,<br />
2005) or on UVBR (Roy et al., 2006). Here, we present a set-up of sensors<br />
used in the combined regulation of UVBR and water temperature increase<br />
in the outdoor immersed mesocosms of Medimeer. The experiment and<br />
technical set up are thoroughly <strong>de</strong>scribed in our previously published articles<br />
(Nouguier et al., 2007; Vidussi et al., 2011). The <strong>de</strong>veloped setup aimed<br />
to maintain a proportional enhancement between the control mesocosms
320 Integrated studies<br />
and the treatment mesocosms, where an increase of 3.1°C was applied<br />
<strong>for</strong> the temperature and an increase of 20% was applied <strong>for</strong> the UVBR.<br />
The main objective was to ensure that the enhanced treatments perfectly<br />
tracked natural temporal variations in temperatures and inci<strong>de</strong>nt UVBR<br />
in real-time. A data logger (CR23X, Campbell Scientific) monitored the<br />
reference measurements, the feedback control measurements, calculated the<br />
<strong>de</strong>viation between the measured values and the target values, and sent a<br />
proportional command to heating elements and UV lamps (figure 7).<br />
For the two temperature-increased mesocosms, the reference temperature<br />
was calculated from the mean of the temperature measured every 30 seconds<br />
at three <strong>de</strong>pths in the control mesocosms using thermistor probes<br />
(Campbell Scientific 107). Changes in the reference temperature comman<strong>de</strong>d<br />
the functioning of a submersible heating element insi<strong>de</strong> treatment<br />
mesoscosms (Galvatec, France). The feedback control temperature was calculated<br />
<strong>for</strong> warmed mesocosms from the mean temperature measured at 3<br />
<strong>de</strong>pths by using thermistors probes. As shown on figure 8A, the variations<br />
Figure 7: Scheme of the experimental <strong>de</strong>sign <strong>for</strong> increasing UVBR and water<br />
temperature in the mesocosm relative to water temperature and inci<strong>de</strong>nt UVBR<br />
measured in the control mesocosms (without heating and UVBR increase). For<br />
further in<strong>for</strong>mation see Nouguier et al. (2007).
Part IV – Chapter 3 321<br />
of the temperature in the warmed mesocosm matched the variations in the<br />
control mesocosms and a constant difference of 3.1°C was maintained.<br />
For the UVBR enhanced treatments, the reference inci<strong>de</strong>nt irradiance<br />
was measured every 30 seconds by an ultraviolet B sensor SKU 430 (Skye<br />
Instruments) situated outsi<strong>de</strong> and corrected with a set coefficient taking<br />
into account shading and transmission reduction at the centre of the<br />
mesocosms. Variations in inci<strong>de</strong>nt UVBR comman<strong>de</strong>d the regulation of<br />
ultraviolet b fluorescent lamps (Philips TL20RS/01) controlled by electronic<br />
ballasts (bAG electronics AD18.22310) and allowed to maintain<br />
a constant +20% increase of UVBR in UVBR enhanced treatments. The<br />
stability of the +20% constraint is achieved thanks to a ultraviolet B sensor<br />
SKU 430 (Skye Instruments) positioned indoor in a structure imitating<br />
the upper part of the treatment mesocosms. This latter structure<br />
and controlling sensor receive the same regulation command from the<br />
immersed mesocosms. It was placed in the workshop of Medimeer to<br />
accurately measure the radiation <strong>de</strong>livered by the ultraviolet B lamps as<br />
the sensor needs to be set on a stable plane surface. In addition, the correct<br />
functioning of the ultraviolet B lamps is checked in real-time <strong>for</strong> each<br />
lamp by a custom-built photodio<strong>de</strong> system (sglux TW30DZ). As shown<br />
on figure 8B, the variations of the UVBR in the illuminated mesocosm<br />
matched the variations in the control mesocosm and a constant enhancement<br />
of 20% was maintained during the day.<br />
Figure 8: Use of sensors to manipulate environmental conditions in a mesocosm<br />
experiment. A. In the mesocosm experiment carried out in April 2005, after the<br />
heating started (1), the temperature gradually increased over 2 hours until an<br />
enhancement of 3°C is reached. The temperature regulation (2) was then fully<br />
in operation maintaining a difference on 3.1°C on average and tracking perfectly<br />
the variations of the temperature in the control mesocosm. Water temperature of<br />
mesocosms was monitored every 30 seconds. B. Data from a mesocosm experiment<br />
carried out in April 2005 <strong>de</strong>monstrate that the UV regulation running from 9:45<br />
to 17:45 controlled the 20% enhancement <strong>de</strong>livered by UVB lamps: the artificial<br />
UVBR intensity (black line) followed the short-term variations in the natural<br />
inci<strong>de</strong>nt UVBR. UVBR was monitored every 30 seconds.
322 Integrated studies<br />
5. Towards a new generation of sensors<br />
In marine mesocosm experiments, several environmental parameters can<br />
be monitored with the use of automated sensors as <strong>de</strong>scribed in section 3.<br />
Some of these sensors are voluminous (sometimes 0.5m high) and difficult<br />
to <strong>de</strong>ploy in relatively small mesocosms (around 1m of diametre<br />
and 1m 3 water volume). There is there<strong>for</strong>e a need to reduce the size of<br />
the current sensors <strong>for</strong> future applications in mesocosm experiments. In<br />
addition, several measurement biases may arise from biofouling. This is<br />
the case in particular <strong>for</strong> fluorescence sensor, which will require frequent<br />
cleaning especially in eutrophic waters where biofilm <strong>de</strong>velopment can<br />
be very fast. In this case, a sensor with a wiper should be used such as<br />
the Wetlabs fluorescence sensors. Additional ef<strong>for</strong>ts in the equipment of<br />
copper wired filter avoiding the creation of biofouling may also be recommen<strong>de</strong>d.<br />
Finally, a reduction of the power and reagents consumption of<br />
some chemical sensors is necessary to enable their routine use in oceanic<br />
monitoring during long-term <strong>de</strong>ployments.<br />
As previously mentioned, data coming from the use of several sensors<br />
insi<strong>de</strong> mesocosms can be combined to investigate the global characteristics<br />
or responses of the food web components un<strong>de</strong>r a simulated perturbation.<br />
Un<strong>for</strong>tunately, there are still obvious lacks in the availability<br />
of sensors providing key in<strong>for</strong> mation about the diversity and dynamics<br />
of aquatic microorganisms. Some miniaturised geno-sensors <strong>de</strong>dicated to<br />
the i<strong>de</strong>ntification of microorganisms can distinguish species or groups of<br />
microorganisms, and would provi<strong>de</strong> very useful in<strong>for</strong>mation about the<br />
food web dynamics in the mesocom experiments (see II, 3 <strong>for</strong> toxic algae).<br />
Miniaturised camera sensors with associated software allowing image<br />
analysis would also be useful to study the interaction between microorganisms<br />
or between microorganisms and metazooplankton such as predation<br />
(see II, 2). More generally, the <strong>de</strong>velopment of the next generation<br />
of biological sensors and their use in mesocosm experiments would help<br />
to bring a huge step <strong>for</strong>ward regarding our knowledge in marine <strong>ecology</strong>.<br />
This is likely to be the case regarding urgent scientific questions about the<br />
consequences of anthropogenic changes on the local and global scales.<br />
Authors’ references<br />
Behzad Mostajir, Emilie Le Floc’h, Sébastien Mas, David Parin:<br />
Université <strong>de</strong> Montpellier 2, Centre d’écologie marine expérimentale<br />
Medimeer (Mediterranean centre <strong>for</strong> marine ecosystem experimental<br />
research), CNRS UMS 3301, Montpellier, France.
Part IV – Chapter 3 323<br />
Behzad Mostajir, Jean Nouguier, Romain Pete, Francesca Vidussi:<br />
Université <strong>de</strong> Montpellier 2, Écologie <strong>de</strong>s systèmes marins côtiers<br />
(Ecosym), CNRS, IRD, IFREMER, Université Montpellier 1 UMR 5119,<br />
Montpellier, France<br />
Corresponding author: Behzad Mostajir, bmostajir@univ-montp2.fr<br />
Aknowledgement<br />
MEDIMEER was fun<strong>de</strong>d by ECOSYM laboratory, CNRS INEE, Institut<br />
Fédératif <strong>de</strong> Recherche 129 Armand Sabatier, CNRS-GDR 2476, and<br />
Région Languedoc-Roussillon. We acknowledge support of the European<br />
project MESOAQUA, Grant agreement n° 22822, to provi<strong>de</strong> some of the<br />
sensors and some of the related mesocosm experiments from which some<br />
data are presented here. The UV-B and temperature increase mesocosm<br />
experiment (UVTEMP project) and the Oyster mesocosm experiment<br />
(Oyster and Fish project) were foun<strong>de</strong>d by the French National Program<br />
in Coastal Science (PNEC-Chantier Lagunes Mediterréennes). Special<br />
thanks to Eric Fouilland <strong>for</strong> the constructive comments in the first version<br />
of the manuscript.<br />
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Synthesis and conclusion<br />
Jean-François Le Galliard, Jean-Marc Guarini and Françoise Gaill<br />
1. Finale<br />
Advances in ecological sciences <strong>de</strong>pend on a tight interaction between<br />
observation of nature, experimentation, and mo<strong>de</strong>lling. Relevant to all<br />
three approaches is the availability of high-quality data about physical,<br />
chemical and biological variables, and there<strong>for</strong>e the <strong>de</strong>velopment of<br />
appropriate instruments and sensors. The content of this book <strong>de</strong>monstrates<br />
that the use of sensors has allowed great conceptual progresses<br />
including un<strong>de</strong>rstanding animal physiology and behaviour, assessing<br />
biodiversity and quantifying states and processes of entire ecosystems.<br />
<strong>Sensors</strong> are now critical to explore ecological systems that remain difficult<br />
to access or sample. For example, sensors were necessary to the discovery<br />
and <strong>de</strong>scription of new life supports un<strong>de</strong>r “extreme” environmental<br />
conditions such as <strong>de</strong>ep-sea ecosystems, the analysis of physiological and<br />
behavioural adaptations of animals in polar conditions, or the observation<br />
from space of global surface compartments of marine ecosystems.<br />
One of the most recent outcomes concerns the quantification of the biological<br />
diversity in its broa<strong>de</strong>st ecological sense, encompassing both the<br />
number and abundance of species and intra-specific variation in the <strong>for</strong>m<br />
and life-history of organisms (see section II). The ability to i<strong>de</strong>ntify automatically,<br />
to locate and to count organisms is clearly a major improvement<br />
in the “toolbox” of <strong>ecology</strong>. However, there is still a long way to<br />
go to <strong>de</strong>sign shared procedures and fully autonomous plat<strong>for</strong>ms as those<br />
required <strong>for</strong> marine pelagic environments. <strong>Sensors</strong> play a critical role in<br />
ecosystem studies focusing on measuring stocks (e.g., plant, soil, or carbon<br />
biomass), fluxes of molecules in solid, liquid or gaseous media, and functional<br />
processes (e.g., photosynthesis). In these perspectives, sensors have<br />
greatly improved both in terms of quality and diversity over the past last<br />
years. They enable faster, more accurate and less invasive quantification<br />
of whole-ecosystem components in the field (section III). Once again, the<br />
trends and needs are in the <strong>de</strong>velopment of autonomous plat<strong>for</strong>ms based
332 Synthesis and conclusion<br />
on networks of sensors. This is clearly an important step that needs to<br />
be fulfilled to be able to <strong>de</strong>scribe and mo<strong>de</strong>l entire ecosystems at local,<br />
regional or global scales, or un<strong>de</strong>r experimental conditions (section IV).<br />
2. Next generation sensors: synthesis<br />
2.1. Bio-logging and bio-tracking<br />
Technological advances in the fields of material <strong>de</strong>sign, optics and<br />
imagery, chemistry, communications, and computers have sped up the<br />
<strong>de</strong>velopment of next generation sensors providing higher quality as<br />
well as new types of data (Benson et al., 2010; Porter et al., 2009). Yan<br />
Ropert-Cou<strong>de</strong>rt et al. (I, 1) have <strong>de</strong>monstrated this very clearly regarding<br />
bio-logging, which focuses on measuring physiological and behavioural<br />
variables of animals in their natural environment. For this, they used sensors<br />
and loggers that were directly attached on the animal (i.e. bio-loggers).<br />
The quantity and quality of variables that mo<strong>de</strong>rn bio-loggers can<br />
record has steadily increased, because technology improved at the same<br />
time power sources, data storage capacities, sensors characteristics and<br />
communication <strong>de</strong>vices, allowing trends towards smaller dimensions and<br />
lighter weight. New technologies recently <strong>de</strong>veloped in other disciplines<br />
(image and vi<strong>de</strong>o acquisition <strong>de</strong>vices, miniaturised GPS, biomedical sensors)<br />
are also ad<strong>de</strong>d on mo<strong>de</strong>rn bio-loggers. Nowadays, the community<br />
of bio-logging is equipped with a broad range of sensors that started a<br />
conceptual revolution in eco-physiological studies. The next steps in this<br />
field of <strong>ecology</strong> will be to <strong>de</strong>velop smaller bio-loggers able to track and<br />
study smaller animals, as stressed by Guillaume et al. (II, 2). This is a<br />
relevant point <strong>for</strong> a large community of researchers who addresses already<br />
many key issues (e.g., surveys of avian migration or <strong>de</strong>cline of pollinators),<br />
<strong>for</strong> which mo<strong>de</strong>l species are still too small to be equipped with the<br />
bio-loggers currently available (Bridge et al., 2011). Another important<br />
step <strong>for</strong>ward in eco-physiology is the <strong>de</strong>velopment of non invasive remote<br />
sensing techniques that can <strong>de</strong>tect movement, behaviour, or physiological<br />
state of animals without interfering with their normal behaviour. Samaran<br />
et al. (I, 3) as well as Huetz and Aubin (I, 4) have <strong>de</strong>monstrated how<br />
passive acoustic sensors can be used to i<strong>de</strong>ntify species, track animals<br />
and study their behaviour in both aquatic and terrestrial environments.<br />
Similar progresses in animal behaviour and physiology have been ma<strong>de</strong><br />
recently using other techniques like remote image sensing, vi<strong>de</strong>o analyses<br />
and thermography (McCafferty et al., 2011).
Synthesis and conclusion 333<br />
2.2. Biodiversity and ecosystem functioning<br />
Working out accurate biodiversity scenarios and un<strong>de</strong>rstanding the coupling<br />
between biodiversity and ecosystem functioning are two of the great<br />
challenges of mo<strong>de</strong>rn <strong>ecology</strong> (Leadley et al., 2010; Loreau, 2010). A large<br />
range of advanced physical, chemical and biological sensors are now available<br />
to measure eco-geochemical processes (Benson et al., 2010; Porter et<br />
al., 2009). Today, technological advances emphasise autonomy, ruggedness,<br />
and resistance to drift of standard “lab-benched” analytical sensors<br />
be<strong>for</strong>e their <strong>de</strong>ployment in the field. The chapters by Le Bris et al. (III,<br />
1 and III, 2) and that of Mostajir et al. (IV, 3) advocate <strong>for</strong> more autonomous<br />
and less invasive sensors because it is critical to study the short<br />
scale spatial heterogeneity and temporal variability of chemical gradients.<br />
However, these authors also pointed out that there are still critical limits<br />
against the long-term <strong>de</strong>ployment of chemical sensors in the field, and the<br />
quantity of environmental factors that can be recor<strong>de</strong>d <strong>for</strong> the purpose of<br />
long-term monitoring programs often remains small.<br />
The i<strong>de</strong>a to measure and monitor proxies of biodiversity automatically has<br />
led to the <strong>de</strong>velopment of a new generation of “biodiversity sensors” during<br />
the last <strong>de</strong>ca<strong>de</strong> (this book). I<strong>de</strong>ally, these sensors should be able to track<br />
automatically species or morph abundance and diversity in space and time,<br />
and could also be used to quantify biodiversity below the species taxonomic<br />
level accurately. As exemplified in the four chapters specifically <strong>de</strong>dicated to<br />
biodiversity and in other chapters alluding to the same matter throughout<br />
the book, this i<strong>de</strong>a will require the use of multi-source data from remote<br />
sensing methods, image analyses, or genosensors. Sueur et al. (II, 1) reviewed<br />
advanced acoustic methods to estimate the species diversity of singing<br />
amphibians, birds and insects. Imagery and spectrometry sensors carried<br />
into aircrafts and satellites are also used routinely to study biodiversity (see<br />
III, 3 and IV, 2). The more advanced instruments are characterised by greater<br />
spatial and spectral resolutions (Ustin et al., 2004; Wang et al., 2010). For<br />
example, imaging spectroscopy of ground surfaces at high resolutions (less<br />
than 10m and with bandwidths of 10-20 nms) can be used to i<strong>de</strong>ntify tree<br />
species, to map vegetation and land covers, or to track invasive plant species.<br />
With standard multispectral sensors such as those implemented on<br />
Landsat or Modis satellites, a new range of analytical imagery solutions<br />
help to <strong>de</strong>convulate the spectral signals in or<strong>de</strong>r to <strong>de</strong>tect additional biological<br />
items. Alvain et al. (III, 3) have <strong>de</strong>monstrated how such analytical<br />
progresses have led to build accurate maps of major phytoplanktonic<br />
groups at the surface of the global ocean. Moreover, the use of remote<br />
sensing imagery is not limited to the taxonomic i<strong>de</strong>ntification and biodiversity<br />
mapping only, but expands rapidly toward the <strong>de</strong>velopment of<br />
indices of carbon or dry matter stocks, estimates of biochemical variables,
334 Synthesis and conclusion<br />
and calculation of functional processes (e.g., Kokaly et al., 2009). This<br />
trend is discussed using data from satellite imagery, light <strong>de</strong>tection and<br />
ranging application (LIDAR), or radar satellites in two chapters, the first<br />
one by Alvain et al. (III, 3) on oceanic ecosystems and the second one<br />
by Chave et al. (IV, 2) proposing a review on tropical rain<strong>for</strong>ests studies.<br />
Pontallier and Soudani (III, 4) have shown that remote sensing technologies<br />
from space also stimulate the <strong>de</strong>velopment of in situ sensors that can<br />
be <strong>de</strong>ployed above plant canopy, on flux towers <strong>for</strong> example. These in situ<br />
sensors have a great potential to characterise the dynamics of vegetation<br />
in real time with limited costs and maintenance.<br />
Another application of “digital eyes” technology is the pattern recognition<br />
in natural samples. The cameras <strong>de</strong>veloped by Gorsky, Stemman and their<br />
research group (II, 2) are, <strong>for</strong> instance, capable of sorting particles from<br />
pelagic oceanic samples by type and by size, including <strong>de</strong>tecting nonliving<br />
particles and i<strong>de</strong>ntifying a substantial fraction of marine zooplankton.<br />
This technological improvement has led to a tremendous change in<br />
the capacity to quantify marine particles compared to what was done with<br />
sediment traps or discrete sampling in the water column. Pattern recognition<br />
techniques are also used in plant physiology, agronomy and eco-morphology<br />
to measure the <strong>for</strong>m of plants and animals, and are sometimes<br />
grouped together with eco-physiology, functional genomics and metabolomics<br />
into the so-called “phenomics” techniques of biotechnology. Plant<br />
phenomics is un<strong>de</strong>r rapid <strong>de</strong>velopment in agronomy but relies on greenhouse<br />
protocols that are ina<strong>de</strong>quate <strong>for</strong> ecological studies and use imagery<br />
techniques that still require to be tested in the field (Eberius and Lima-<br />
Guerra, 2009). Yet, the transfer of lab-benched phenomics into field conditions<br />
holds much promise <strong>for</strong> those interested in the un<strong>de</strong>rstanding of<br />
phenotypic diversity and biological adaptations (Houle, 2010). Mallard et<br />
al. (II, 4) <strong>de</strong>scribes how a simple pattern recognition method can be used<br />
in microcosms to locate, count and measure the size of individual arthropods.<br />
Similar approaches need to be tested and implemented in the field<br />
and should be exten<strong>de</strong>d to study other taxonomic groups.<br />
Concerning the characterisation of biodiversity, an alternative to remote<br />
sensing and image analysis mentioned above is the use of genetic barco<strong>de</strong>s<br />
from water or soil samples, or living tissues. A first approach, called<br />
environmental metabarcoding, requires the sampling, extraction and<br />
amplification of multiple barco<strong>de</strong>s and the subsequent sequencing with<br />
high throughput technologies (Jørgensen et al., 2012). However, metabarcoding<br />
techniques are difficult to adapt into autonomous sensors in the<br />
field. Some laboratory-based alternatives allow near-real-time <strong>de</strong>tection<br />
of toxic marine algae and other microorganisms. They have the potential<br />
to be <strong>de</strong>ployed in the field in a near future, and were <strong>de</strong>scribed here by<br />
Orozco et al. (II, 3). These alternatives rely on the existence of taxon-
Synthesis and conclusion 335<br />
specific genetic barco<strong>de</strong>s, but seek <strong>for</strong> <strong>de</strong>tecting these barco<strong>de</strong>s (using<br />
amplification or hybridisation techniques) rather than sequencing them<br />
exhaustively. The field portable versions of these lab-benched techniques<br />
are called genosensors and some of them are un<strong>de</strong>r <strong>de</strong>velopment <strong>for</strong> few<br />
years in marine <strong>ecology</strong> (Paul et al., 2007; Scholin, 2010). This inclu<strong>de</strong>s<br />
the autonomous microbial genosensor (AMG) and the environmental<br />
sample processor (ESP) used <strong>for</strong> real-time studies of bacterioplankton and<br />
invertebrates (figure 1). Jahir orozco et al. (II, 3) have <strong>de</strong>monstrated that<br />
it is still extremely difficult to standardise genetic protocols when the<br />
interest is to calculate real cell counts. In addition, the method is suitable<br />
<strong>for</strong> fluids, but it is not easy to implement in hard substrates, such as soils<br />
and benthic sediments. There<strong>for</strong>e, much progress remains to be done to<br />
import genetic methods into autonomous sensors installed in the field.<br />
Figure 1: This field-portable genosensor, called the environmental sample processor<br />
(ESP), can be <strong>de</strong>ployed during several months to monitor the diversity and toxicity<br />
of marine bacterioplankton. The ESP is a collaborative project coordinated by<br />
Christopher Scholin and fun<strong>de</strong>d by the Monterey bay Aquarium Research Institute,<br />
National Science Foundation, and NASA. It is based on the electrochemical biosensor<br />
based methods <strong>de</strong>scribed by Jahir orozco et al. in this book, and allows real-time<br />
monitoring of a diversity of marine species. © Kim Fulton-bennet/2006 MAbRI.
336 Synthesis and conclusion<br />
2.3. Towards integrated knowledge of ecosystems<br />
Despite strong differences among sensor types and technologies, several<br />
authors of this book drew similar perspectives <strong>for</strong> the future. The first<br />
shared opinion is that recent progresses in <strong>ecology</strong> have been achieved<br />
through similar technological improvements including increased miniaturisation,<br />
increased autonomy, and increased communication capacities.<br />
The need <strong>for</strong> sensors that present these three traits is critical in <strong>ecology</strong><br />
when observations must be conducted <strong>for</strong> long periods of time in remote<br />
areas (Porter et al., 2009) and must be as non-invasive as possible. There<br />
is however an obvious tra<strong>de</strong>-off between miniaturisation and increased<br />
communication capacities on one si<strong>de</strong> and autonomy on the other si<strong>de</strong>.<br />
Various alternatives exist to reduce consumption (including pre-programmation<br />
of sampling and communication rates) and to increase power<br />
capacities (including renewable energy sources as solar panels and wind or<br />
hydro-turbines). Miniaturisation is also important because the smaller the<br />
sensors the less they interfere with the dynamics or the behaviour of the<br />
studied systems. Most <strong>ecology</strong> laboratories in France are far from reaching<br />
the ability to address all of these technological challenges when <strong>de</strong>signing<br />
and constructing sensors. There<strong>for</strong>e, a stronger priority must be given to<br />
collaborative research and <strong>de</strong>velopment projects involving environmental<br />
scientists, engineers and specialists in in<strong>for</strong>matics or telecommunication.<br />
Most studies also emphasise the need <strong>for</strong> a<strong>de</strong>quate and representative spatial<br />
coverage to study spatial ecological processes. Even if it is not a major<br />
issue <strong>for</strong> remote sensing from space, the question of ina<strong>de</strong>quate spatial coverage<br />
raises serious concerns to interpretation and analysis of data collected<br />
by in situ sensors. The challenge rests on the fact that most ecological phenomena<br />
exhibit spatial structures at multiple scales from a few millimetres<br />
to hundreds of kilometres, and that most ecological systems <strong>de</strong>monstrate<br />
characteristic patterns only at certain spatial scales (Levin, 1992; Wiens,<br />
1989). Several technological solutions are now available to cope with this<br />
problem, <strong>de</strong>pending on the spatial scale of investigation. At broad spatial<br />
scales (i.e. hundreds of kilometres range), ecological patterns are best<br />
addressed by remote sensing and network of autonomous stations such as<br />
buoys on oceans and towers on continents. These types of measurements<br />
have a long tradition in oceanography, <strong>for</strong>estry and hydrology. At regional<br />
and local scales, standard networks of sensors typically had small spatial<br />
and temporal resolution until the recent <strong>de</strong>velopment of wireless technologies<br />
(Porter et al., 2005). The wireless network technology, of which Chave<br />
et al. discuss one potential application (IV, 2), allows high-frequency observations,<br />
intensive and inexpensive sampling over large areas up to tens of<br />
kilometres, non-intrusive sampling of study sites, and real-time reactions<br />
(figure 2). Generally speaking, long-term approaches conducted at large<br />
spatial scales are beyond the scope of a single laboratory program and
Synthesis and conclusion 337<br />
require coordination and shared procedures among an entire international<br />
community of scientists. This is well <strong>de</strong>monstrated by the Argos oceanographic<br />
network <strong>de</strong>scribed by Stemmann et al. (IV, 1).<br />
Figure 2. A wireless network of sensors <strong>de</strong>veloped by the Fraunhofer Institute <strong>for</strong><br />
Microelectronic Circuits and Systems (IMS, Germany) installed on the grounds of<br />
the Northwest German Forestry Testing Facility in Göttingen. The wireless network<br />
enables real-time measurements of environmental data during a <strong>de</strong>ployment period<br />
of up to 12 months. © Fraunhofer IMS.<br />
In addition to specific requirements of temporal and spatial coverage,<br />
most ecologists emphasise the strong need to integrate multiple types of<br />
in<strong>for</strong>mation, which usually implies to collect at the same time and in<br />
the same place both physical, chemical and biological data (e.g. Sagarin<br />
and Pauchard, 2010). The integration of multiple sensors is an extremely<br />
difficult task, given the costs, amount of data types and need <strong>for</strong> evolvability<br />
of multi-sensor plat<strong>for</strong>ms. Modular equipments provi<strong>de</strong> the best<br />
solution to accommodate multiple sensor types and anticipate technological<br />
changes. Strong improvements in data streaming, data processing and<br />
storing capacities must be achieved compared to standard procedures to<br />
customise multi-sensors network. Priority must be given to real-time acquisition<br />
and processing, open source tools, and freely available data sets,<br />
which requires a strong cooperation with computer scientists (Benson et<br />
al., 2010). This book <strong>de</strong>monstrates the feasibility and usefulness of some<br />
sensor networks that collect physical and chemical data in oceanography
338 Synthesis and conclusion<br />
or <strong>for</strong>estry (Le Bris et al., III, 1; Stemman et al., IV, 1; Chave et al., IV, 2).<br />
There is now a clear need to expand these networks in or<strong>de</strong>r to collect at<br />
the same time biological data on the presence and abundance of species<br />
of interest (see section IV).<br />
4. Next generation sensors: perspectives <strong>for</strong> infrastructures<br />
Well-organised networks of observational and experimental infrastructures<br />
are critical <strong>for</strong> the <strong>de</strong>velopment and use of next generation sensors.<br />
These infrastructures can help to <strong>de</strong>sign and test new types of sensor and<br />
are i<strong>de</strong>al places to promote the long-term <strong>de</strong>ployment of sensors and to<br />
test the relevance of integrated in<strong>for</strong>mation from multiple sensors types.<br />
For example, observational and experimental programs conducted in the<br />
Long-term Ecological Research (LTER) network, National Ecological<br />
Observatory Network (NEON) and ocean observatories initiative in the<br />
USA have all been <strong>de</strong>voted to the <strong>de</strong>velopment of new sensors technology<br />
(Benson et al., 2010; Porter et al., 2009).<br />
Several authors of this book are involved in similar infrastructures at a<br />
national or international level. For the last <strong>de</strong>ca<strong>de</strong>, the CNRS in France<br />
has promoted a range of observational and experimental infrastructures,<br />
which should <strong>for</strong>m the basis <strong>for</strong> the <strong>de</strong>velopment of next generation sensors<br />
<strong>for</strong> <strong>ecology</strong>. Regarding observational infrastructures, various specialised<br />
environmental science observatory networks managed or supported<br />
by the CNRS exist, including a network <strong>de</strong>dicated to greenhouse gas<br />
monitoring (Icos, http://www.icos-infrastructure.eu/), a critical zone<br />
exploration network <strong>de</strong>dicated to geochemical and hydrological processes<br />
on continents (http://www.czen.org/), and several national and international<br />
oceanographic networks such as the Naos project coordinated by<br />
Ifremer (http://www.naos-equipex.fr/). In addition, the CNRS is strongly<br />
involved in the management and support of a LTER-like network that<br />
inclu<strong>de</strong>s 10 study sites scattered in France and overseas (http://www.cnrs.<br />
fr/inee/outils/za.htm). This network inclu<strong>de</strong>s regional study sites where<br />
the coupled dynamics of ecosystems and societies are investigated on the<br />
long-term with a multidisciplinary approach. All these study sites rely<br />
strongly on sensor networks <strong>for</strong> monitoring the environmental and ecosystem<br />
processes.<br />
Regarding experimental infrastructures, the project ANAEE (analysis and<br />
experimentation on ecosystem), supported by the CNRS in partnership<br />
with INRA, is registered on the roadmap of the European ESFRI program<br />
with a preparation phase planned from 2011 to 2014 (http://www.anaee.<br />
com/). It will bring together the major experimental facilities in continental
Synthesis and conclusion 339<br />
Figure 3: Infrastructures in experimental <strong>ecology</strong> managed by the Institute Ecology<br />
and Environment of the CNRS in France belong to the ANAEE European research<br />
infrastructure. In ANAEE, research infrastructures encompass in vivo approaches<br />
(small scale, high manipulative power of Ecotrons), semi-natural situations<br />
(intermediate scales, medium manipulative power), and natural situations (larger<br />
spatio-temporal scales, low manipulative power). A-b. Ecotrons are newly created<br />
infrastructures that enable highly controlled manipulation of terrestrial and aquatic<br />
organisms, communities and ecosystems. In France, this infrastructure consists in the<br />
Ecotron <strong>de</strong> Montpellier (A) specialised in the analysis of terrestrial ecosystems, and the<br />
Ecotron Ile-<strong>de</strong>-France (B) specialised in the analysis of aquatic ecosystems. C-D. The<br />
semi-natural plat<strong>for</strong>m inclu<strong>de</strong>s facilities that enable manipulation of terrestrial and<br />
aquatic organisms, communities and ecosystems un<strong>de</strong>r partially controlled situations.<br />
In France, it inclu<strong>de</strong>s of the national aquatic <strong>ecology</strong> plat<strong>for</strong>m (C) located nearby Paris,<br />
and a large infrastructure of experimental meta-ecosystems located nearby Toulouse<br />
(D). E-F. In natura experimental sites are long-term experimental facilities allowing<br />
simultaneous measurements of key ecosystem variables and parameters through<br />
a multi-disciplinary approach. They inclu<strong>de</strong> experimental grasslands and <strong>for</strong>ests<br />
supported by the CNRS and several other institutional partners such as the Nouragues<br />
tropical rain<strong>for</strong>est (E) and the alpine grasslands of the Lautaret (F). © CNRS.
340 Synthesis and conclusion<br />
ecosystem sciences in a distributed and coordinated network (Lemaire,<br />
2008). Selected experimental plat<strong>for</strong>ms from ANAEE will allow simultaneous<br />
manipulation and monitoring of ecosystem processes by collaborative<br />
approaches. Meeting the challenges of ANAEE requires a sustained<br />
research ef<strong>for</strong>t with a range of complementary approaches from highly<br />
controlled facilities to natural systems (see Figure 3). We anticipate that<br />
the financial support provi<strong>de</strong>d <strong>for</strong> these sites will stimulate collaborations<br />
and programs to <strong>de</strong>velop sensors that ecologists will use to explore natural<br />
ecosystems, test ecological theories, and <strong>de</strong>velop predictive mo<strong>de</strong>ls.<br />
Authors’ references<br />
Jean-François Le Galliard:<br />
Université Pierre et Marie Curie, Laboratoire Écologie et Évolution,<br />
CNRS-UPMC-ENS UMR 7625, Paris, France École normale supérieure,<br />
CEREEP – Écotron Ile-<strong>de</strong>-France, CNRS-ENS, UMS 3194, Saint-Pierrelès-Nemours,<br />
France<br />
Jean-Marc Guarini:<br />
Université Pierre et Marie Curie, Laboratoire d’Écogéochimie <strong>de</strong>s<br />
Environnements Benthiques, CNRS-UPMC, FRE 3350, Banyuls-sur-Mer,<br />
France<br />
Françoise Gaill:<br />
CNRS, Institut Écologie et Environnement, Paris, France<br />
Corresponding author: Jean-François Le Galliard, galliard@biologie.<br />
ens.fr<br />
Acknowledgement<br />
Jean-François Le Galliard acknowledges the support of the TGIR Eco trons<br />
program, and of the ANR program “Investissement d’avenir: Equipement<br />
d’excellence” (ANR PLANAQUA 10-EQPX-13-01) coordinated by UMS<br />
3194 CEREEP-Ecotron IleDeFrance.
Synthesis and conclusion 341<br />
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