Reading the code of coral reef sponge community ... - Porifera Brasil

Porifera Research: Biodiversity, Innovation and Sustainability - 2007
Reading the code of coral reef sponge community
composition and structure for environmental biomonitoring:
some experiences from Cuba
Pedro M. Alcolado
Instituto de Oceanología, Ave. 1ra, No. 18406, Playa, La Habana, Cuba. alcolado@ama.cu
Abstract: The structure of exposed (non-cryptic) coral reef sponge communities could be considered as a potentially readable
coded message reflecting their physical environment. The present paper describes explorations in Cuba of the potential use of
sponge communities as bio-indicators. Clathria venosa is the sponge that most consistently has proved to be a bioindicator of
urban based pollution in Cuban coral reefs due to its stenotopic character with regard to this stress source. Iotrochota birotulata
forma musciformis was abundant close to the polluted Havana Bay, but not in other polluted sites, making it inconsistent as
indicator. It has been quite rare in non-polluted waters. Cliona delitrix was represented in an area with great sewage influence.
However it did not appear in some polluted sites probably because corals were extremely scarce and small. Scopalina ruetzleri
was well represented close to bays with different degrees of urban based pollution. Cliona varians was well represented only
in one polluted place. Multivariate analyses (cluster analysis, non-metric multidimensional scaled analysis) have proved to be
very useful tools to clearly segregate sites with regard to level of pollution, and to identify factors and interactions determining
community structure and composition. Abundance or dominance of Tectitethya crypta and Cliona vesparia (alpha stage) were
typical of heavy sedimentation conditions; while Aplysina cauliformis tended to dominate in sites affected by both hurricanes
and sedimentation (abundance increased by fragmentation). Meta-analysis of Shannon’s heterogeneity index H’ and Pielou’s
equitability index J’ is proposed as a useful tool to classify and compare sites with regard to the way that sponges interpret
their environment (degree of severity and predictability). Meta-analysis by means of a scatter graph with ranges of H’ at
different depths provides a spatial framework for comparing and classifying sponge communities with regard to environment
severity.
Keywords: sponges, bio-indicators, coral reefs, Cuba
Introduction
Many papers have dealt with the factors and interactions that
determine sponge distribution and community characteristics
(partly reviewed by Sarà and Vacelet 1973, Bergquist
1978, Wulff 2006), but few have been explicitly devoted to
exploring the potential usefulness of sponge communities as
bio-indicators for environmental bio-monitoring purposes.
In the last few decades, the search for bio-indicators
has become an urgent need in a world environment that is
changing at an unprecedented rate. According to Alcolado
(1984; with some added arguments), sessile taxa are suitable
as potential environmental bio-indicators because:
- They must be adapted to the environment due to their
immobility. Thus, their abundance or their presence (or even
absence) must reflect the average ecological conditions, or
very recent strong stressful events.
- Their composition and community structure are not affected
by migrations or local displacements.
- The exposed (non-cryptic) sponge communities, having
passed the fish predation filter thanks to deterrence (Wulff
1997), are influenced more by the physical environment
than by ecological interactions within themselves (sensu
Bradbury 1977). Cooperation rather than competition
seems to be the rule among sponge populations (Sarà 1970)
and, according to Rützler (1970), sponges are able to solve
competition by entering into complex epizoic relationships,
without detriment to their pumping and filtering activities.
Reiswig (1973) adds that small sponge individuals (during
the first year after settlement) are subject to severe mortality
by competition with other sessile organisms, but when
sponges reach greater volume competitors have little further
effect. On the other hand, sponges overgrow corals much
more frequently than the reverse, although when the reverse
occurs, the sponge tissue shows no adverse effect (Jackson
and Buss 1975).
- The absence of food partitioning mechanisms influencing
community structure.
Such features favor sponges over many other zoological
groups as potential indicators. That does not mean that
there could not be some degree of influence of biological
interactions, but apparently to a much lower extent than the
physical environment (light, waves, sediments, pollution) in
building up the community structure and composition. This

also makes community structure and composition easier to
analyze and to understand in a bio-monitoring context.
For these reasons, the structure of exposed (non-cryptic)
coral reef sponge communities could be considered as a
potentially readable coded message reflecting how sponges
interpret their physical environment. Indeed, sponges have
been suggested as potential environmental bio-indicators by
Alcolado (1984, 1985, 1990, 1992, 1994, 1999), Alcolado and
Herrera (1987), Muricy (1989, 1991), Zea (1994), Alcolado et
al. (1994), Carballo et al. (1994, 1996), Carballo and Naranjo
(2001), and Vilanova et al. (2004). Some attempts and
successes in Cuba and other countries exploring the potential
use of sponge communities as simpler, faster and lower cost
bio-indicators (from a sponge life perspective) are discussed
below. The results compiled in this review come from a great
number of coral reef sites sampled around Cuba since 1976.
Discussion
Indicator species
A few sponge species have been found to be associated
with polluted or relatively unpolluted conditions in coral reefs
(Table 1). Particularly, Clathria venosa (Alcolado, 1984)
and Iotrochota birotulata forma musciformis (Duchassaing
and Michelotti, 1864) have only been observed dominating
in fore-reefs (10-20 m deep) affected by organic pollution
(Alcolado and Herrera 1987) (Table 1; Fig. 1). The first
species appeared to be markedly stenotopic of enriched
inshore and coral reef waters and its occurrence has been
very consistent in all polluted reefs evaluated or visited in
northwestern Cuba (close to Havana Bay, Almendares and
Quibú rivers, and the town of Santa Fé), and according to
Zea (1994), also in Santa Marta, Colombia. This species
was previously reported by Hechtel (1965) on shells and
piling in the enriched waters of Port Royal (southern shore
of Kingston Harbor), Jamaica (as Microciona microchela
n. sp.); and by van Soest (1984) in the fouling community
on dead corals and gorgonians at the bay and Hilton Hotel
Landing of Curaçao (as Rhaphidophlus raraechelae n. sp.).
It was also found in the fouling communities of the concrete
dock of Marina Barlovento (organically enriched site) and
the seawall of a small organically polluted cove (Rada del
Instituto de Oceanología), both in the western Havana City,
Cuba. However, I. birotulata forma musciformis was not
consistently dominant or abundant in the visited Cuban
polluted sites.
Mycale microsigmatosa Arndt, 1927, which has been
found dominating under domestic sewage stress in Brazil
(Muricy 1989), was also found in a very polluted coastal
lagoon at Jaimanitas Town, west of Havana city (muddy/
algal bottom) together with well developed Suberites
aurantiaca (Duchassaing and Michelotti, 1964), Chondrilla
aff. nucula Schmidt, 1862 and Halichondria melanadocia de
Laubenfels, 1936. Both S. aurantiaca and C. aff. nucula are
“bacteriosponges” (Rützler 2002), which could explain their
abundance in this lagoon.
Holmes (1997, 2000), Holmes et al. (2000) and Rützler
(2002), comment on the increased abundance and activity of
boring sponges in areas affected by urban based pollution.
Indeed, Cliona delitrix Pang, 1973, a species reported as
abundant in areas submitted to sewage pollution (Rose and
Risk 1985, Chávez-Fonnegra and Zea 2006), was observed
during four years by Marcos and Alcolado (unpublished
observations) with significant relative abundance (%) at a
fore-reef site close to both the polluted Quibú River and a
nearby sewage outfall (western Havana City). However, it
was not found by Alcolado and Herrera (1987) at stations
near Havana Bay, maybe due to the scarcity and small size of
corals (dominated by Siderastrea radians Pallas, 1766).
Another boring sponge, Cliona varians (Duchassaing and
Michelotti, 1864), was well represented only in a polluted
fore-reef close to both the Quibú River and a nearby sewage
outfall at western Havana City (Marcos and Alcolado
unpublished observations). However, it was also common
Table 1: Potential indicator species and their respective inferred condition according to authors. D and M = Duchassaing and Michelotti.
Dominant or abundant species Indicated condition Author
Clathria venosa (Alcolado, 1984) Organic pollution Alcolado and Herrera (1987)
Iotrochota birotulata f. musciformis (D. and M., 1864) Organic pollution Alcolado and Herrera (1987)
Scopalina ruetzleri (Wiedenmayer, 1977) Moderate organic pollution Alcolado and Herrera (1987);
Muricy (1989); Zea (1994)
Sewage pollution Muricy (1989)
Cliona delitrix Pang, 1973 Sewage (bacterial) pollution Rose and Risk (1985)
Mycale microsigmatosa Arndt, 1927 Sewage pollution Muricy (1989)
Amphimedon viridis D. and M., 1864 Sewage pollution Muricy (1989)
Aplysina fistularis (Pallas, 1766) Comparatively non-polluted Alcolado (present paper, Fig. 1)
Cliona caribbaea D. and M., 1864 Comparatively non-polluted López-Victoria and Zea (2004)
Cliona vesparia (Lamarck, 1815) (alpha stage) Sedimentation plus wave stress Alcolado (present paper)
Tectitethya crypta (de Laubenfels, 1949) Sedimentation stress Alcolado and Gotera (1985)
Aplysina fulva (Pallas, 1766) Strong waves Wulff (1995)
Aplysina cauliformis (Carter, 1882) Eventual strong waves and sedimentation Alcolado (present paper)

Fig. 1: Relative abundances of
potential pollution bioindicators
species, presented as percentages
of total sponge abundance (number
of individuals), at stations located
at different distances from two
main pollution sources in the
north-western Cuba (Havana Bay
and Almendares River). Mariel
Bay is not significantly polluted.
in non-polluted reef areas, which makes it inconsistent as a
potential bio-indicator.
López-Victoria and Zea (2004) showed that the abundance
of Cliona caribbaea is not related to pollution in San Andrés
Archipelago, Colombia. Indeed, this species did not occur at
sites close to the organically polluted Quibú River and the
nearby sewage outfall, but only in more distant sites (Marcos
and Alcolado, unpublished observations).
Other sponge species have been associated with factors
other than pollution, namely sedimentation and wave stress
(Table 1). Particularly, Aplysina cauliformis is apparently
tolerant to strong waves, as can be deduced from its dominance
in coral reef sites exposed to more frequent tropical storms
(keys Juan García and Cantiles, southwestern Cuba). This can
be due to its branching morphology, flexibility and elasticity,
similar to what was suggested by Wulff (1995) for Aplysina
fulva, also branching and with rather similar consistency.
The suggested usefulness of the presence or abundance
of some sponges as environmental indicators has been
based much on expert observation and on inferences
related to distance from known pollution sources, wave
and wind exposure, visual evidence of varying intensity of
sedimentation, etc. For that reason, to validate these results
and make further progress, more evidence is necessary,
obtained both from well designed experiments and from
multivariate analysis in which factors are directly measured
on appropriate temporal and spatial scales. Additionally,
more sites in the Wider Caribbean, suffering various degrees
of pollution, tropical storm frequency, exposure to waves and
dominant winds, etc., are worth being researched to test the
generality of the mentioned findings. It would be of particular
interest to determine if Clathria venosa feeds on bacteria with
emphasis on enteric taxa, as does Clathria prolifera (Ellis and
Solander, 1786) according to Claus et al. (1967).
Community indices
In agreement with other authors (Muricy 1989, Carballo et
al. 1996, among others), Alcolado and Herrera (1987) found
that species richness and Shannon´s heterogeneity index H’
were lower at more polluted sites (Fig. 2). Pielou’s equitability
index J’ was also lower in the more polluted sites close to the
mouth of Almendares River (Fig. 2).
Given that a condition of significant stress can be inferred
only when the dominance of some of the mentioned indicator
species (Table 1) is coupled with low values of species
richness or species heterogeneity (Alcolado et al. 1994),
these univariate indices have to be taken into account as an
important complement for environmental monitoring.
The summing up of the numerical percentages of
individuals belonging to species that are tolerant to the same
kind of stressor (e.g., pollution, sedimentation, turbulence,
etc.) could be useful as another potential community index
for monitoring purposes, as done by Alcolado (1981) with
gorgonians to infer relative turbulence intensity, and by
Herrera-Moreno (1991), also with gorgonians, to infer relative
organic pollution level.
The usefulness and conceptual validity of diversity indices
has been controversial (Hurlbert 1971, Peet 1974). However
(without disregarding potential pitfalls), the herein explored
diversity indices can be used and tested pragmatically and
heuristically for bio-monitoring purposes in the context of
environmental management, not specifically for advancing

Fig. 2: H’ and J’ in stations located
at different distances from two
main pollution sources in the
north-western Cuba (Havana Bay
and Almendares River). Mariel
Bay is not significantly polluted.
science (but being increasingly supported by scientific
research). The same validation and progress efforts can be
applied to community indices.
Environmental severity and predictability inference
graph
Preston and Preston (1975) deserve the credit for
integrating and applying theoretical criteria from classic
community ecology in a simple and practical scheme to infer
the environmental severity and predictability (constancy) for
comparative purposes. According to Margalef (1963, 1968)
and Odum (1969), high species diversity and high equitability
are generally associated with mature, late successional
stages. On the other hand, Sanders (1969) and Slobodkin
and Sanders (1969) hypothesize that severe environments
generally permit less diversity to develop than favorable
ones do. As suggested by Slobodkin and Sanders (1969)
and supported by Preston and Preston (1975), the degree
of stress is determined primarily by the degree of temporal
predictability of environmental conditions and the degree of
physiological stress imposed by the physical environment.
After Preston and Preston (1975), by means of the values
of H’ and J’ three different ecological situations can be
inferred: high values of both indices suggest a favorable
and predictable environment; a low H’ coupled to a high J’
indicates a constantly severe environment; and low values of
both indices reflect an unpredictably severe environment.
Due to the fact that this scheme excessively reduces the
real variety of situations and leads to misinterpretation of
the specific case of extremely low values of both indices, it
was modified by Alcolado (1992) for sponges (Fig. 3). This
modification consisted of an inference diagram obtained from
a scatter graph of pairs of H’ and J’ values from 112 sites.
The resulting scatter area was divided into 11 “environmental
inference zones or classes” reflecting corresponding
ecological situations instead of Preston and Preston’s (1975)
original three zones or classes (constantly favorable, constant
or temporally predictable stress and unpredictable stress).
Except for the class 1 of the scale, which is a qualitative
addition to the original scheme, the remaining classes resulted
from subdividing the original overly inclusive classes. This
resulted in a finer grain of environmental situations to infer,
matching more closely the relatively wide range of withinclass
environmental variability perceived in the field by the
author within the original three classes (e.g., sponge size, coral
size and cover, wind and wave exposure, etc.). The number
of subdivisions preferred among different persons would
certainly vary, as happens with the different temperature
measurement scales (Celsius, Fahrenheit and Kelvin). The
new scale comprises the following environmental severitypredictability
classes: 1 = environment extremely stressed by
both, a constant basal level of disturbance and intermittent
unpredictable strong events (H’ = 0-1.3 natural bells; J’ = 0-
0.5); 2 = very severe and unpredictable environment (H’ =
0-1.3 natural bells; J’ = 0.5-0.69); 3 = severe and unpredictable
environment (H’ = 1.3-2.0 natural bells; J’ = 0.5-0.69);
4 = almost constantly severe environment (H’ = 1.3-2.0
natural bells; J’ = 0.7-0.8); 5 = constantly severe environment

(e.g., Cliona aprica, among sponges, Gorgonia flabellum
Linnaeus, 1758, among gorgonians, and Acropora palmata
Lamarck, 1816, among scleractinians). For that reason, both
J’ an H’ show extremely low values. This combination, within
Preston and Preston’s (1976) original scheme, would suggest
an unpredictable environment (with its constant component
omitted).
Alcolado’s (1992) inference diagram also differentiates
the very favorable and constant environments of the deep
reefs (e.g., at 20-30 m) within the rank 11, from those that are
simply favorable and quasi-constant (rank 10). Nevertheless,
it is advisable to be aware of specific situations of very longterm
environmental stability where some species can escape
from demographic control and become excessively dominant,
and consequently diminishing H’ and J’. This situation is
common at reef sites deeper than 25 m. This phenomenon of
community senescence is not contemplated in either of the
two mentioned inference methods and has to be taken into
account in supposedly extremely constant environments (e.g.,
deep reef zones, and reefs where hurricanes are very rare, as
those of Bonaire and Tobago).
The author’s scale is proposed as an alternative reference
(among other possible ones) and could be tested and improved
with further research. More sites across the Wider Caribbean
should be studied and included in the scatter graph to refine
its spatial contour.
Fig. 3: Inference diagram reflecting eleven ways in which sponges
interpret their physical environment, derived from a meta-analysis
with 112 coral reef stations. 1 = extremely severe with mixture
of constant and unpredictable environment; 2 = very severe
and unpredictable; 3 = severe and unpredictable; 4 = quasiconstantly
severe; 5 = constantly severe; 6 = moderately severe
and unpredictable; 7 = moderately severe and quasi-constant; 8 =
moderately severe and constant; 9 = favorable and quasi-constant;
10 = constantly favorable; and 11 = very favorable and constant.
(H’ = 1.3-2.0 natural bells; J’ = 0.8-1); 6 = moderately and
unpredictably severe environment (H’ = 2.0-2.5 natural bells;
J’ = 0.5-0.69); 7 = moderately and almost constantly severe
environment (H’ = 2.0-2.5 natural bells; J’ = 0.7-0.8); 8 =
moderately and constantly severe environment (H’ = 2.0-2.5
natural bells; J’ = 0.8-1); 9 = favorable and almost constant
environment (H’ = 2.5-2.9 natural bells; J’ = 0.7-0.8); 10 =
favorable and constant environment (H’ = 2.5-2.9 natural
bells; J’ = 0.8-1); and 11 = very favorable and constant
environment (H’ >2.9 natural bells; J’ = 0.8-1).
Rank 1 shows a (qualitative) situation that is not
considered by Preston and Preston (1975). This is the case
of the surf zones of some Cuban reefs, which have constant
average (basal or chronic) conditions of fairly strong wave
action, but which are also unpredictably affected by severe
impacts of tropical storms. Under these circumstances, there
is only one predominant species within each sessile taxon
Scatter graphs for comparing community indices at
different depths
Scatter graphs of variability of sponge diversity indices,
population density and cover with regard to depth were
obtained for many Cuban reef sites (Alcolado 1994, 1999).
These graphs, which display the area (range) of variation of
those indices with regard to depth, can be used as a reference
pattern to infer in a comparative way the community condition
within stress gradients, taking into account site depth, given
that such indices do not necessarily behave in the same way
along depth gradients. What is normally a moderate value of
H’ for a given depth could be considered a high value for a
lower depth.
The upper border of the variation area (an ascending convex
line with a slight diminution at depths greater than 25 m)
reflects the best conditions registered at different depths for
sponge species richness and species heterogeneity (Fig. 4),
while the lower border (an asymptotically ascending curve)
shows the worst environmental conditions at different depths
(Alcolado 1994).
Care must be taken at deep reef stations (about 30 m depth
or more), as lower diversities can be caused by extremely
constant and favorable conditions that lead to the dominance
of competitively stronger species, and not by any stressor. The
same recommendations given for the environmental severity
and predictability inference graph are applicable here.
Classification and ordination
Classification (Fig. 5) and ordination (Fig. 6) analyses
have proved to be useful when using sponge communities
to separate sites with regard to degree of pollution and

Fig. 4: Example of meta-analysis as a scatter graph of H’ values at
different depths, with classification bands of inferred environmental
conditions (from 112 coral reef sites of Cuba). Care must be taken
at stations about 30 m depth or more, as lower diversities can be
determined by extremely constant and favorable conditions leading
to dominance of very competitive species, and not by severe
environmental conditions. Arrows indicate more polluted stations
(10 m depth) and stations affected by sediments (deeper stations).
Fig. 6: MDS analysis segregating stations located at different
distances from two main pollution sources (Havana Bay and
Almendares River) with regard to degree of pollution (from left to
right: very polluted, polluted, and little polluted). This analysis was
done with quadratic transformation of sponge densities and Bray
Curtis similarity Index.
Fig. 5: Cluster analysis segregating stations located at different
distances from two main pollution sources (Havana Bay and
Almendares River) with regard to degree of pollution (going
downwards: very polluted, polluted, and little polluted). This analysis
was done with quadratic transformation of sponge densities, Bray
Curtis similarity Index, and un-weighted paired average clustering.
to explore the factors impinging on their structure and
composition (Alcolado and Herrera 1987, Muricy 1989,
1991, Carballo et al. 1994, 1996, Carballo and Naranjo
2001, Bell and Barnes 2003, Vilanova et al. 2004, Marcos
and Alcolado unpublished observations). Particularly, the
Multidimensional Scaled analysis (MDS) has provided
clear results (PRIMER version 5). Multivariate techniques
are useful tools for identifying factors and interactions, and
as such have to be applied complementarily with simpler,
faster and lower cost univariate inference approaches in
environmental monitoring. Multivariate analyses have to
serve also to strengthen the validation and to reduce the pitfalls
of potential indicator species and ecological indices that have
been proposed, to a great extent based on observational and
inference approaches.
In the context of the application of the suggested biomonitoring
methods, an aspect that deserves future effort is
to assess the convenience of using sponge cover instead of
sponge density, both from practical and scientific points of
view.
Finally, another matter of concern could be the need
of sponge taxonomy skills for the implementation of the
proposed bio-monitoring methods. In this sense, the potential
indicator species are easy to identify in situ, and sampling
for calculation of community indices would only require
differentiation of species, and not necessarily identification to
species level. With some practice, the identification of most
common species can be learned and the sampling work can
become even easier.
Acknowledgements
I would like to thank the National Museum of Rio de
Janeiro, PETROBRAS, the UNDP/GEF Project Sabana-
Camagüey and Dr. Robert N. Ginsburg (Ocean Research and
Education Foundation) for making possible my participation
in the 7 th International Sponge Symposium. I am grateful to
Dr. Janie Wulff, Dr. Georgina Bustamante and Marta Rivero
for their valuable comments to the manuscript.
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