Periphyton and Phytoplankton Response to Reduced Dry Season ...

FINAL MILESTONE REPORT
National River Health Program – Environmental Flows Initiative
Periphyton and Phytoplankton Response
to Reduced Dry Season Flows in the Daly River
Project ID: 22963
Project Investigator: Dr Simon Townsend (NT Department of Infrastructure,
Planning and Environment, formerly Department of Lands, Planning &
Environment).
Commonwealth Officer: Gayle Stewart, Environment Australia.
Project team:
Dr Peter Gell, University of Adelaide
Dr Sophie Bickford, University of Adelaide
Dr John Tibby, Monash University
Assoc. Prof. Roger Croome, La Trobe University
Malgorzata Przybylska, La Trobe University
Dr Simon Townsend, Department of Infrastructure, Planning and Environment
Armando Padovan, Department of Infrastructure, Planning and Environment
Rodney Metcalfe, Department of Infrastructure, Planning and Environment
1

FORWARD
This Environmental Flows Initiative project examines the relationship between flow
and algae in the Daly River. It is one of five projects funded by Environment Australia
for the N.T. The project comprises of three studies, each one evaluating a different
group of algae (non-vascular plants) found in the river. These are the (1)
phytoplankton (microscopic algae suspended in the river water), benthic diatoms
(microscopic algae belonging to the class Bacillariophyceae that grow on river
substrates), and benthic macroalgae (algae visible to the naked eye growing on the
river bed).
2

Table of Contents
SUMMARY 6
RECOMMENDATIONS 7
1 INTRODUCTION 10
1.1 Daly River catchment hydrography, aquifers and water quality 10
1.2 Water allocation for the environmental and project objectives 14
1.3 References 14
2 RIVER PHYTOPLANKTON 15
2.1 Summary 15
2.2 Introduction 16
2.3 Methods 16
2.3.1 Sample collection (see also Chapter 3) 16
2.3.2 Taxonomic texts 17
2.3.3 Enumeration 17
2.3.4 Estimation of biomass 18
2.4 Results and Discussion 18
2.4.1 Algal taxa observed 18
2.4.2 Total algal numbers and biomass 32
2.4.3 Contribution of principal individual taxa 52
2.4.4 Downstream changes in principal taxa 54
2.5 Additional remarks 56
2.6 References 57
3 CORRELATION BETWEEN FLOW AND OTHER ENVIRONMENTAL
VARIABLES WITH PHYTOPLANKTON ASSEMBLAGES 60
3.1 Introduction 60
3.2 Methods 60
3.2.1 Sample sites 60
3.2.2 Water sample collection, and in situ measurements 62
3.2.3 Statistical Analyses 64
3.3 64
3.4 Results 64
3.4.1 River flow in 2000 64
3.4.2 Water Quality 65
3.4.3 Phytoplankton 70
3.4.4 Phytoplankton assemblages in Donkey Camp Pool inflow and outflow 73
3.5 Discussion 78
3.6 Implications for environmental flow allocation 79
3.7 Recommendations 80
3.8 References 80
3.9 Appendix 3. 1 82
3.10 Appendix 3.2 Water quality figures (presented after Chapter 7) 83
4 DIATOM ASSEMBLAGES ON RIVER SUBSTRATES 83
4.1 Introduction 83
4.2 Methods 84
4.2.1 Site selection criteria 84
4.2.2 Field measurements 87
3

4.2.3 Chemical analyses 87
4.2.4 Diatom sample collection 87
4.2.4 Diatom identification and enumeration 89
4.2.5 Data analysis 90
4.3 Results 91
4.3.1 River substrates sampled 91
4.3.2 Water quality 92
4.3.3 Comparison of substrate diatom assemblages by ordination. 93
4.3.4 Comparison of substrate species richness 96
4.3.5 Species counting effort and distribution of species relative abundance 97
4.3.6 Comparison of common taxa 98
4.3.7 Species unique to a substrate 100
4.4 Discussion 101
4.5 References 102
5 THE RELATIONSHIP BETWEEN BENTHIC DIATOM ASSEMBLAGES
AND WATER QUALITY 104
5.1 Introduction 104
5.2 Methods 104
5.2.1 Site selection and sample frequency 104
5.2.2 Diatom Identification and Enumeration 109
5.2.3 Data analysis 109
5.3 Results 110
5.3.1 Temporal Study 110
Total 111
5.3.2 Taxon response to TDS 116
5.3.3 Longitudinal Study 117
5.4 Discussion 127
5.5 Conclusion and Implications for the Allocation of Environmental Flows for
the Daly River 128
5.6 References 129
5.7 Appendix 4. 1 132
6 THE RELATIONSHIP BETWEEN FLOW, GROWTH OF SPIROGYRA
AND LOSS OF HABITAT IN THE DALY RIVER 136
6.1 Introduction 136
6.2 Methods 136
6.2.1 Study Species 136
6.2.2 Study Reach 137
6.2.3 Water Quality 139
6.2.4 Biomass Measurements 139
6.2.5 Spirogyra-Velocity Relationship 139
6.2.6 Flow-Biomass Model 140
6.2.7 Water Extraction Simulations 142
6.3 Results 142
6.3.1 Water Quality 142
6.3.2 Chlorophyll-a, Ash Free Dry Weight and Biomass Scores 145
6.3.3 Seasonal Biomass Changes 145
6.3.4 Flow-Biomass Model 146
6.3.5 Water Extraction Simulations 153
6.4 Discussion 155
4

6.4.1 General 155
6.4.2 Habitat Preference 155
6.4.3 Flow-Biomass Relationship 156
6.4.4 Water Extraction Simulations 157
6.5 Conclusions 159
6.6 Recommendations 160
6.7 References 160
7 COMMUNICATION OF TECHNOLOGY TRANSFER ACTIVITIES,
AND PROJECT ACTIVITIES TO THE COMMUNITY AND
STAKEHOLDERS 164
5

SUMMARY
Algae in the Daly River are present as phytoplankton, suspended in the river, and
periphyton, attached to a river substrate. Owing to their rapid replication rate of a
couple of days, the algae are responsive over a time scale of weeks to changes in the
aquatic environment, notably flow and water quality. This project evaluates whether
phytoplankton, benthic diatoms and macroalgae are directly, or indirectly, responsive
to dry season river flow, and provide information for the allocation of water for the
environment.
Flow in the Daly River and its major tributaries, during the dry season, is maintained
by groundwater. The extraction of water directly from these rivers or from the
groundwater during the “dry” will reduce flows in the Daly River and its tributaries.
There is also potential for the river’s water quality to be directly affected. In the upper
reaches of the catchment, dry season flows originate predominantly from aquifers
within Cretaceous sediments. With groundwater inflow from the Daly River Basin,
the conductivity of the Daly River increases 20-30 fold, pH and the carbonate
buffering capacity increases at least an order of magnitude, whilst soluble phosphorus
and nitrate concentrations more then double. In the Douglas River, inflow from the
Tindal Limestone results in an almost 100 fold increase in nitrate concentrations but
has not resulted in high phytoplankton concentrations due probably to phosphorus
limitation. Such a marked increase in nitrate concentrations was not measured
elsewhere in the catchment, and may be due to modified land-use and management
practices. Extraction from the Daly River Basin for consumptive use would be
expected to alter, in addition to flow, river water quality, depending of the change in
the mix of river sources.
Diatoms (microscopic algae) are an abundant component of periphyton (algae that
grow on surfaces) in rivers, and an important primary producer. They provide a simple
and time-efficient means of biomonitoring. The diatom assemblage (the number of
species and their relative abundances) on river substrates was investigated to
determine the best sampling strategy for diatom sample collection. The diatom
assemblage on different river substrates was not always similar, underpinning the
requirement for a monitoring program to use a single substrate, or substrates shown to
be equivalent. Epilthic (rock) and epidendronic (woody debris) substrates occurred
commonly in the river, featured similar diatom flora, and were recommended for
sample collection in the Daly River and its tributaries.
A total of 252 diatom species were identified, comprising both cosmopolitan and
tropical taxa. Epilithic diatom assemblages in the Daly River are responsive to the
ionic composition of river water, as well as nitrate, soluble phosphorus, dissolved
oxygen, temperature and turbidity. This sensitivity is independent of flow. The diatom
flora will respond indirectly to reduced dry season flow caused by water extraction,
through its impact on water quality. Diatoms ought to be one of several biomonitoring
tools considered to determine the significance of any anthropogenic impacts on the
aquatic ecosystem.
Phytoplankton in the river are highly diverse, with most species representing a small
percentage (

taxonmic groups including the blue-greens (Cyanobacteria). The concentration of
phytoplankton (suspended algae growing in the river’s water), measured either as
chlorophyll a (a photosynthetic pigment) or biovolume, in the Daly River and its
tributaries was limited by river flow, rather than light, nutrients or zooplankton
grazing. Under lower flows, however, due to either climatic or anthropogenic
influences, phytoplankton concentrations may no longer become limited by flow, and
instead become nutrient limited. Under such a scenario, where soluble nitrogen and
phosphorus enter the river from the Daly River Basin, phytoplankton concentrations
could be expected to increase.
Flow, as well as ionic chemistry, influences the assemblage (species composition and
their relative abundances) of phytoplankton in the river. A possible mechanism for the
influence of flow is through the volume and proportion of waters with a retention time
longer than the river’s average, for example back-flow waters and “dead” zones where
there is minimal exchange with the main river. If this mechanism occurs, as it does in
other rivers, then the assemblage of phytoplankton would be expected to differ with
lower river flows.
The macroalgae (algae visible to the naked eye), Spirogyra, is present on gravel and
rock substrates, and along the banks, of the Daly River and some tributaries during the
dry season. From being absent early in the dry season (May-June), the alga undergoes
rapid growth to reach a maximum biomass in July-August that is likely to represent a
significant portion of the river’s plant biomass and primary productivity. Algal
biomass then declines, finally being removed by storm flow early in the wet season. In
addition to a substrate preference, Spirogyra also has an optimal range of shear
velocity (force parallel to the substrate that can remove the algae). At the upper range
of shear velocity, the algae is physically removed, whereas below this range the alga is
unable to grow as well, possibly by not receiving adequate nutrients. The biomass of
Spirogyra, over a wide range of flows that included ones below the historic range, was
modelled. Above 12 m 3 /s, algal biomass depended primarily on the shear velocity
(and thereby flow) above river gravel and rock substrates. Below this value, though,
the loss of habitat (rock and gravel substrate) through drying and stagnation became
important. Moreover, the rate of biomass loss with reduced flows below the 12 m 3 /s
threshold was three times greater, than rates of loss above the threshold. Simulations
of water extraction from the flow record shows that a proportional extraction regime
better maintained the natural interannual variability than a fixed regime. These
simulations suggest that a proportional extraction rate below 8% not adversely affect
the natural variability of Spirogyra biomass if historical minimum river flows are not
to be maintained. If minimum river levels are to be preserved, then simulations show
that if no extraction is to occur below 10 m 3 /s, then a proportional extraction rate of
less than 9% will not affect the natural variability of Spirogyra biomass.
RECOMMENDATIONS
The final recommendations for water allocation to the environment need to be based
on a whole-of-ecosystem approach. To achieve this, three activities should be
considered. Firstly, the nature of the anthropogenic impact on the river’s hydrography
and water quality needs to be stated and understood. Secondly, our understanding and
knowledge of the river’s ecosystem needs to be brought together to identify processes,
7

habitats and biota vulnerable to altered flow regime. This could be summarised by a
conceptual model of ecosystem function. Thirdly, the ecological impacts should be
ranked, and the risk to the ecosystem’s fucntion and sustainability assessed.
Assuming the impact of consumptive use will be on dry season flows of the the Daly
River and its tributaries, this project makes the following recommendations:
(1) the altered dry season flow regime for the daly River and its tributaries is
maintained above historic minimum flows. For example. at Mt Nancar
hydrographic station, this approximates 7 cumecs (Fig. 0.1), though a more
conservative flow could be set (e.g. 10 cumecs) in recognition that this represents
an extreme.
(2) inter-annual variation in dry season flows is mimiced, with a proportion allocated
to the environment and the remainder made available for the consumptive uses.
(This amount allocated to the environment is in addtion to the minimum flow as
stated above). The altered dry season flow regime will then still be maintained
within the natural flow variation. The impact on the river’s productivity, however,
should be assessed as it is unlikely to be directly proportional to flow based on the
river’s wetted area and benthic algae biomass (see Chaper 6). On the basis of
simulations on Spirogyra biomass it is recommended that proportional extraction
should not exceed 8%.
(3) that consideration be given to meeting consumptive water needs that exceed the
amount available during the dry season, to be taken from wet season runoff and
flows that have been stored in off-stream reservoirs (i.e no streams or rivers are
dammed).
Flow (cumecs)
40
35
30
25
20
15
10
5
0
1960 1970 1980 1990 2000
Legend:
G8140041
upstream of Nt Nancar
G8140040
Mt Nancar
Year
Figure 0.1 Minimum annual flows in the Daly River, upstream of the Daly River
crossing.
8

Figure 0.2 Daly River catchment aquifers and rivers
9

1 INTRODUCTION
Townsend, S.A., N.T. Department of Infrastructure, Planning and Development.
1.1 Daly River catchment hydrography, aquifers and water quality
The Daly River catchment is located in the wet/dry tropics of northern Australia
(Figure 0.2) where rainfall is highly seasonal. At Katherine township, annual rainfall
averages 980mm, with 83% falling during the wet season between December and
March due to monsoonal activity, cyclones and rain depressions. In the dry season,
between May and September, rainfall averages only 17 mm. Not surprisingly, river
and stream flow is also highly seasonal (Fig. 1.1), with wet season flow more than 10
times that in the dry season. Flow patterns in the rivers and streams of the Daly River
catchment are either perennial (e.g. Daly, Katherine and Flora Rivers) or seasonal,
with predictable wet season flow but ceasing to flow sometime during the dry season.
Median monthly flow (cumecs)
1000
750
250
125
100
75
50
25
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Figure 1.1 Daly River flow at Mt Nancar (G8140040), 10 km upstream of the
road crossing to Port Keats (note logarithmic scale for y-axis)
Flow in the dry season is maintained by groundwater flow. This reduces over the dry
season (Fig. 1.2), as the water table reduces and groundwater stored in close proximity
to the river is depleted (see Jolly et al. 2000). This decline in river flow over the dry
season is referred to as seasonal recession flow. The three major tributaries of the
Daly River flowing through the dry season are the Katherine, Flora and Douglas
Rivers. The Katherine and Flora Rivers meet to become the Daly River, each
contributing about half the flow in the Daly River.
Some rivers may flow throughout the dry season only when the groundwater table is
high, but in years when the water table is lower, these rivers cease flowing during the
dry season. In general, such rivers contribute a small amount of flow in the Daly
10

Median flow (cumecs)
200
150
100
50
0
Daly River,
Mt Nancar
Daly River,
Dorisvale
Katherine River,
Railway Bridge
May Jun Jul Aug Sep Oct Nov
Month
Figure 1.2 Dry season flows in the Katherine (G8140001, 435 km upstream of the
daly River mouth) and Daly Rivers (Dorisvale, G8140067 (290 km stream of
mouth); Mt. Nancar G8140040 (105 km upstream of mouth)).
River. For example, in October 2000 when river flows were historically high, the
King River contributed only 0.004 m 3 /s (400 µS/cm), and dominated by
calcium, magnesium and bicarbonate. The Jinduckin aquifer contributes substantially
less to dry season flows than the other two aquifers.
Dry season flow in the upper reaches of the Katherine River and its tributaries is
supplied from the Cretaceous sandstone aquifer. At Katherine township, substantial
groundwater from the Tindal Limestone aquifer enters the river. The Flora River, in
contrast, is supplied by the Tindal Limestone aquifer (Jolly et al. 2000). Flow in the
Daly River increases downstream due to springs, seepages and direct inputs to the
river (White 2001, Tickell 2002), rather than tributary contributions with the
exception of the Douglas which increases Daly River flow about 10% (Fig. 1.4).
Significant groundwater inflow to the Daly River occurs between Dorisvale Crossing
and the river’s junction with Jinduckin Creek (White 2001, Tickell 2002). For
example, in October 2000 flow along this reach increased 150% (White 2001). The
higher conductance waters of the Daly River Basin increased the conductivity of the
11

Katherine River (Fig. 1.4) to about 600 µS/cm in October 2000. River conductivity
then fluctuated between 500 and 620 µS/cm.
Mg
pH
Ca
Na+K
6
7
8
9
Cl
SO4
HCO3
Salinity, Total Dissolved Solids (mg/L)
Figure 1.3 Ionic chemistry of the upper Katherine River (open circles), supplied
by the Cretaceous sandstone aquifer, and the Daly, Douglas and Flora Rivers
(closed circles), supplied by the Daly River Basin aquifers.
Flow (m3/s)
Conductivity (µS/cm)
25
20
15
10
5
0
800
600
400
200
1 2 3 4 5
20
0
0 100 200 300
River distance from Donkey Camp Pool outflow,
Katherine River(km)
6
7
50
Tributaries:
(1) King River
(2) Flora River
(3) Fergusson River
(4) Bradshaw Creek
(5) Stray Creek
(6) Cattle Creek
(7) Douglas River
Figure 1.4 Flow and conductivity in the Katherine and Daly Rivers, October 4-
19, 2000. Sources: White (2001) and DIPE unpublished data.
100
200
12

Conductivity
(µS/cm)
Nitrate
(µg/L as N)
600
400
200
0
150
100
50
0
Boundary of
Tindal Limestone
Legend
2001
1999
0 10 20 30 40 50 60
River distance from Butterfly Gorge (km)
Figure 1.5 Longitudinal conductivity and nitrate gradient along the Douglas
River in 1999 and 2001.
The Douglas River is supplied principally from the Oolloo and Tindal formations, but
also the sandstone aquifers its headwaters. Near the Oolloo Road bridge, the water
quality of the Douglas River (Fig. 1.5) changes markedly where waters from the
Tindal aquifer enter the river.
About 50 km downstream of the confluence with the Douglas River, the Daly River
ceases traversing the Daly River Basin to no longer receive significant groundwater or
tributary inflow.
The Daly River rises 55 m, over a river distance of 354 km and at an almost uniform
rate (15m/100 km), between the river’s mouth and the junction between the Flora and
Katherine Rivers (Faulks 1998). The lower reaches of the Katherine and Flora River
rise, respectively, at about 50 and 80 m/100 km river distance. A detailed survey of
the rivers bed elevation over a 130 km reach downstream of Dorisvale (Tickell 2001)
has an average slope of 18m/100 km.
The Daly River and its major tributaries river comprise a series of pools and runs,
with occasional rapids. The pools are typically 700 m long, 60 m wide and 1.5 - 4 m
deep in the dry season (Faulks 1998). Some pools, however, can be as much as 8 m
deep (see Tickell 2001).
13

1.2 Water allocation for the environmental and project objectives
The allocation of water for agricultural, potable water supply, and the environment is
a water resource management priority for the Daly River Basin. It is part of a broader
natural resource management strategy that underpins agricultural and other natural
resource development in the Daly River Basin.
Current consumptive use of Daly River Basin surface and groundwater is considered
low, but has the potential to increase with agricultural development. The most likely
consumptive use of water is surface and ground water extraction during the dry
season, resulting in reduced dry season flows. Wet season harvesting of water,
however, is another option.
The DIPE current interim water allocation for the environment is 80% of
instantaneous river flow. This is made recognising the paucity of ecological
information directly relevant to the Daly River and its tributaries.
This project seeks to provide information to contribute to the allocation of water for
the environment in the Daly River Basin.
1.3 References
Faulks, J. (1988) Daly River catchment. Part 1 An Assessment of the physical and
ecological condition of the Daly River and its major tributaries. Northern Territory
Department of Lands, Planning and Environment. Darwin.
Jolly, P. (2001) Daly River catchment water balance. Draft Report. Northern territory
Department of Infrastructure, Planning and Environment. Darwin.
Jolly, P., George, D., Jolly, I. And Spiers, Z. (2000) Analysis of groundwater fed
flows for the Flora, Katherine, Douglas and Daly Rivers. Report 26/2000D.
N.T.Department of Lands, Planning and Environment. Darwin.
Tickell, S.J. (2002) A survey of springs along the Daly River. Report 06/2002.
Northern territory Department of Infrastructure, Planning and Environment. Darwin.
White, E. (2001) A late dry season survey of the Katherine and Daly Rivers. Report
24/2001D. Northern territory Department of Infrastructure, Planning and
Environment. Darwin.
14

2 RIVER PHYTOPLANKTON
Przybylaka, M. and Croome, R. La Trobe University.
2.1 Summary
Some 202 algal taxa were observed during the study. Of these, 36 had not been
reported previously from the Northern Territory, and 5 had not been reported
previously from Australia. The Bacillariophyceae (63 taxa), the Desmidiaceae (47)
and the Chlorophyta (34) were dominant with respect to the number of taxa present.
Samples occasionally contained up to 640,000 cells/L (due for instance to the
presence of Urosolenia eriensis and Fragilaria zasuminensis) but overall, all but 6
samples contained less than 150,000 cells/L. These algal densities are well below
those commonly recorded in the larger rivers of the world, but are similar to those
observed in moderately sized rivers in south-eastern Australia, and suggest a trophic
status within the Katherine/Daly system of oligotrophic to mesotrophic.
Relatively few species contributed significantly to the algal biomass: only four taxa
(Peridinium inconspicuum, Cryptomonas sp., Synedra ulna and Encyonema
silesiacum) usually contributed more than 1% to total biovolume. Peridinium
inconspicuum was the most substantial consistent contributor, occurring at up to
195,000 cells/L, and usually comprising 2-15% of total biovolume (maximum 74%).
Four taxa (Spirogyra spp., Urosolenia eriensis, Fragilaria zasuminensis and
Peridinium umbonatum) usually comprised less than 1% of the biomass, but
occasionally occurred in high numbers, contributing substantially to biovolume. The
occurrence of one of these, Spirogyra spp. was something of a confounding factor.
Presumed to be present within the water column due to detachment from its usual
benthic state, it comprised up to 96% of the calculated biovolume on occasion, but
appeared to contribute little to biomass in terms of Chlorophyll a concentrations. A
potential explanation of this is the relatively large proportion of cells present from
which the chloroplast had been lost.
Although as a general trend, higher numbers of algae were recorded towards the end
of the dry season, this trend was not particularly striking and did not appear, for
instance, at the most downstream site.
With respect to lateral distribution within the Katherine and Daly Rivers, no marked
trend of increasing numbers downstream was observed. Indeed, several of the higher
peaks occurred at the sites furthest upstream, particularly due to Urosolenia eriensis
and Fragilaria zasuminensis. Cell numbers within the Flora and Douglas Rivers were
generally similar to those recorded in the upper reaches of the Daly River.
An attempt to quantify downstream increases/decreases in algal “loadings” of four key
taxa was inconclusive, except to suggest increased loadings downstream for certain
taxa in July, and to indicate substantial tributary contribution on occasion by both the
Flora and Douglas Rivers.
15

2.2 Introduction
This report has been prepared for the Northern Territory Department of Lands
Planning and Environment as part of an Environment Australia sponsored project
within the National River Health Program - Environmental Flows Initiative (EFI). The
overall project is titled "Periphyton and phytoplankton response to reduced dry season
flows in the Daly River", and this (sub)report is concerned with the phytoplankton
aspect of the project, detailing in particular the phytoplankton species and biomass
present in the Katherine R. / Daly R. and two tributaries (Flora R. and Douglas R.) on
six occasions from June - November 2000.
The report includes a phytoplankton species list for each site, and graphical displays
of algal numbers and biovolume month by month, site by site, and in terms of the 18
principal taxa identified. Its greatest value undoubtedly lies in the raw data generated
within this relatively acute study, and these data are provided in electronic form to
facilitate their future publication and use, especially within the more predictive
aspects of the current project.
2.3 Methods
2.3.1 Sample collection (see also Chapter 3)
Samples were received from 9 sites (see Table 2.1, Fig. 2.1). One site, Stray Creek,
was sampled once only, on 7 June 2000. The other 8 sites were sampled monthly from
June – November 2000.
Table 2.1. Listing of sampling sites and year 2000 samples analysed.
Numbers indicate date of sampling (ns – no sample received)
Site Description June July Aug Sep Oct Nov
SK1 Katherine R. @ Donkey Camp Pool - inflow 07 18 15 12 17 21
SK2 Katherine R. @ Donkey Camp Pool - outflow 07 18 15 12 17 21
SF3 Flora River ns 17 14 11 16 20
SD4 Daly R. @ Claravale Crossing 07 19 16 13 18 22
SSt Stray Creek 07 ns ns ns ns ns
SD5 Daly R. @ Oolloo Crossing ns 20 17 14 19 23
SDo6 Douglas River ns 20 17 14 19 22
SD7 Daly R. @ Beeboom ns 21 18 15 ns 23
SD8 Daly R. @ Daly R. Township 05 21 18 15 20 24
16

Fig 2.1. Phytoplankton sampling sites on Daly River and its tributaries.
2.3.2 Taxonomic texts
The individual algae observed were identified utilising the following taxonomic
texts:
Baker (1991, 1992)
Baker & Fabbro (1999)
Day et al. (1995)
Gell et al. (1999)
Huber-Pestalozzi (1968, 1969, 1974, 1975, 1976, 1982, 1983)
Lind & Brook (1980)
Ling & Tyler (1980, 2000)
Prescott (1951, 1978)
Sonneman et al. (2000).
2.3.3 Enumeration
Samples received had been preserved in Lugol’s Iodine and concentrated by
sedimentation from 1L to approx. 50ml in the Northern Territory. The samples were
17

then further concentrated by sedimentation to 10ml, giving a 100 fold concentration
overall.
The algae in these concentrated samples were counted using a Lund cell (capacity
0.55ml) at a magnification of 200x on a Zeiss Axioskop microscope. Forty transects
across the Lund cell were examined during each count, ensuring that 150 or more
individuals of the most frequently occurring taxa were enumerated, giving a counting
precision of ±20% or better for the dominant organisms.
2.3.4 Estimation of biomass
Algal biovolumes were estimated by measuring a minimum of 200 cells of each
taxon, relating their size to known geometric shapes, and determining volumes after
Plinski et al. (1984).
2.4 Results and Discussion
2.4.1 Algal taxa observed
For the sake of convenience, the taxa observed were categorised into eight groupings:
. Cyanophyta
. Chlorophyta
. Desmidiaceae (technically part of the Chlorophyta)
. Euglenophyta
. Pyrrophyta
. Cryptophyta
. Chrysophyta
. Bacillariophyceae (technically part of the Chrysophyta).
[The taxonomic groupings were further modified during graphical presentation in
order to more clearly display the data, the categories Euglenophyta, Cryptophyta and
Chrysophyta being merged as “Flagellates”].
Number of taxa:
An unexpectedly high total of 202 algal taxa were observed during the study (2.2), the
number observed at any one site ranging from 76 taxa at the Flora River site (SF3) to
146 taxa at the upper Katherine River site (SK1). (Only 51 taxa were recorded for
Stray Creek, but this site was sampled on one occasion only).
76 taxa were identified to species level or beyond. Of these, 36 have not been reported
previously as occurring in the Northern Territory, and 5 are reported for the first time
in Australia (Ankyra lanceolata, Coelastrum astroideum, Spirogyra aequinoctialis,
Fragilaria zasuminensis and Stenopterobia pelagica – as determined via comparison
18

with species listings by Day et al. (1995), Ling & Tyler (2000) and Sonneman et al.
(2000)).
Table 2.2 List of algal taxa observed in phytoplankton samples from all year 2000
sampling sites.
* denotes not recorded previously from Northern Territory.
# denotes not recorded previously from Australia.
Taxa / Sampling site SK1 SK2 SF3 SK4 SSt SK5 SDo6 SD7 SD8
Cyanophyta
Aphanizomenon cf. gracile 1 1 1 1 1 1 1
Anabaena sp.1 1 1 1 1 1 1 1 1 1
Chroococcus sp. 1
Merismopedia sp. 1
Microcystis sp. 1 1
Oscillatoria sp. 1 1 1 1 1 1 1
Phormidium sp. 1 1 1 1 1 1 1 1
Planktolyngbya cf. subtilis 1 1 1 1 1 1 1
Planktolyngbya sp.1 1 1 1
Pseudanabaena cf. limnetica 1 1 1 1 1 1 1 1
Blue-green 1
- unidentified colony ( ∅ 2 µm) 1 1
Total 12 6 6 5 9 2 6 8 5 7
Chlorophyta
Ankyra lanceolata * # 1 1 1 1
Ankistrodesmus convolutus * 1 1 1 1 1 1 1 1 1
Ankistrodesmus falcatus 1 1 1 1 1 1 1 1 1
Coelastrum astroideum * # 1 1 1 1 1 1
Eudorina sp. 1
Gonium sp. 1 1 1
Micractinium sp. 1
Monoraphidium arcuatum * 1 1 1 1 1 1 1 1
Monoraphidium mirabile * 1 1 1 1 1 1 1 1 1
Oocystis gigas * 1 1 1
Oocystis sp. 1 1 1 1
Pediastrum sp.1 1 1 1 1
Pteromonas sp. 1 1 1
Scenedesmus acuminatus 1
Scenedesmus acutus * 1 1 1 1
Scenedesmus bijuga * 1 1 1 1 1 1 1 1
Scenedesmus bijuga var. alternans 1 1 1
Scenedesmus denticulatus 1 1 1 1 1 1 1 1
Scenedesmus opoliensis * 1 1 1 1 1 1 1 1
Scenedesmus sp.1 1 1 1 1
Scenedesmus sp.2 1
Spirogyra aequinoctialis * # 1 1 1 1
Spirogyra condensata * 1 1 1
Conjugales I 1 1 1
Conjugales II 1 1
Conjugales III 1
Green 1 1 1 1 1 1
Green 2 1 1 1 1
Green 3 cf. Ankyra sp. 1 1 1 1 1 1
Green 4 1 1 1 1 1 1 1 1
Green 5 cf. Kirchneriella sp. 1 1 1 1 1 1 1 1
Green 6 1 1 1
Green 7 1 1
Green 8 1 1
Total 34 21 20 12 21 5 16 21 16 20
Desmidiaceae
19

Actinotaenium cucurbitum 1 1
Closterium acutum 1 1 1 1 1 1 1 1
Closterium dianae var. minor * 1 1 1 1 1 1
Closterium gracile var. elongatum * 1 1 1
Closterium idiosporum * 1
Closterium kutzingii 1 1 1 1
Closterium limneticum 1 1 1 1
Closterium cf. Macilentum 1 1 1 1 1 1 1 1
Closterium sp. 1 1
Closterium sp.5 1
Closterium sp.7 1 1 1
Cosmarium binum 1 1 1 1
Cosmarium depressum var. planktonicum 1
*
1 1 1 1 1 1 1
Cosmarium excavatum 1 1
Cosmarium galeritum * 1 1 1 1
Cosmarium granatum 1 1 1 1 1 1 1 1 1
Cosmarium impressulum 1 1 1
Cosmarium lundellii var. coruptum 1 1
Cosmarium punctulatum 1 1 1 1 1 1 1 1 1
Cosmarium spinuliferum 1 1
Cosmarium trilobulatum var. depressum 1 1 1 1 1
Cosmarium sp.2 1
Cosmarium sp.5 1
Cosmarium sp.7 1 1
Cosmarium sp.9 1
Cosmarium sp.11 1
Euastrum denticulatum 1 1
Euastrum sp.1 1 1
Micrasterias sp.1 1 1
Spondylosium sp. 1 1 1
Staurastrum cf. avicula 1 1 1 1 1
Staurastrum bifidum 1 1
Staurastrum chaetoceras * 1 1 1 1 1
Staurastrum gladiosum 1
Staurastrum longibrachiatum 1 1 1
Staurastrum pinnatum var. subpinnatum 1 1 1 1 1 1
Staurastrum sp. 2 1 1
Staurastrum sp.3 1 1 1 1 1 1
Staurastrum sp. 4 1 1 1
Staurastrum sp. 5 1 1
Staurastrum sp. 6 1
Staurodesmus cf. glabrus 1 1 1
Staurodesmus megacanthus 1 1
Xanthidium armatum var. anguliferum 1 1 1 1 1 1 1
Xanthidium hastiferumvar. javanicum 1 1
Xanthidium sp. 1 1 1 1
Xanthidium sp. 2 1
Total 47
Euglenophyta
42 33 13 20 11 9 7 17 12
Euglena acus 1 1 1 1 1 1 1 1
Euglena spirogyra 1 1 1 1 1 1 1
Euglena sp. 1 1 1 1 1 1
Euglena sp. 1 1 1 1 1 1 1 1 1
Phacus sp.1 1 1 1 1 1 1
Strombomonas sp. 1 1
Trachelomonas
var.rectangularis
australica 1 1 1 1 1
Trachelomonas cf. eurystoma 1 1 1
Trachelomonas cf. oblonga var. australica 1 1 1 1 1 1
Trachelomonas sp. 1 1 1 1 1 1 1 1 1 1
Trachelomonas sp. 2 1 1 1 1 1 1 1
20

Trachelomonas sp. 3 1 1
Trachelomonas sp. 4 1 1
Trachelomonas sp. 5 1 1
Total 14 11 14 7 9 4 6 7 8 7
Pyrrophyta
Cystodinium sp. 1
Glenodinium sp. 1 1 1 1
Gymnodinium cf. aeruginosum 1
Gymnodinium sp.1 1 1 1 1 1
Gymnodinium sp.2 1 1 1 1 1 1 1 1
Peridinium inconspicuum 1 1 1 1 1 1 1 1 1
Peridinium umbonatum var. remotum * 1 1 1 1 1 1
Peridinium sp. 2 1 1 1 1 1 1 1 1
Peridinium sp. 3 1 1 1 1
Peridinium sp. 5 1
Total 10 5 8 6 7 1 5 5 5 5
Cryptophyta
Cryptomonas sp. 1 1 1 1 1 1 1 1 1
Nephroselmis sp. 1 1
Total 2 2 1 1 1 1 1 1 1 2
Chrysophyta
Dinobryon sp. 1 1 1
Mallomonas splendens 1 1 1
Mallomonas sp. 1 1 1 1
Mallomonas sp. 3 1 1
Pyramidomonas cf. inconstans 1 1 1
unidentified spher. cells (∅ 13 µm) 1 1 1 1
unidentified spher. cells ( ∅ 8 µm) 1 1
unidentified spher. cells ( ∅ 7 µm) 1 1
Chryso. 1 1 1 1 1
Chryso. 2 1 1
Chryso. 3 1 1 1 1
Chryso. 4 1 1 1 1 1 1
Chryso. 5 1 1 1 1
Chryso. 6 1
Chryso. 7 1 1 1
Chryso. 8 1
Chryso. 9 1
Chryso. 10 1 1 1
Chryso. 11 1
Chryso. 12 1
Total 20 7 6 3 13 0 8 4 4 8
Bacillariophyceae
Acanthoceros sp.1 1 1
Aulacoseira cf. ambigua 1
Aulacoseira granulata 1 1 1 1
Aulacoseira sp.1 1 1 1
Cyclotella meneghiniana 1 1 1 1 1 1 1 1
Melosira sp. 1 1 1
Urosolenia eriensis var. morsa * 1 1 1 1 1
Urosolenia longiseta 1 1
Achnanthes oblongella * 1 1 1 1 1 1 1 1 1
Achnanthes cf. subexigua 1 1 1 1 1 1 1 1
Amphora sp. 1 1 1 1 1 1 1 1 1
Cymbella sp. 1 1 1 1 1
Encyonema cf. gracile 1 1 1 1 1 1 1 1 1
Encyonema silesiacum * 1 1 1 1 1 1 1 1 1
Encyonema sp.1 1 1 1 1 1 1 1 1
Encyonema sp.2 1 1 1
Eunotia minor * 1 1 1 1 1 1 1 1
Eunotia cf. pectinalis 1 1 1 1 1
Fragilaria capucina var. capucina * 1 1 1 1 1 1 1 1 1
21

Fragilaria capucina var. vaucheria * 1 1 1 1 1 1 1
Fragilaria zasuminensis * # 1 1 1
Gomphonema affine * 1 1 1 1 1 1 1 1
Gomphonema lagenula * 1 1 1 1 1 1 1 1 1
Gomphonema cf. parvulum 1 1 1 1 1 1 1 1
Gyrosigma attenuatum 1 1 1 1 1 1
Gyrosigma sp. 1 1 1 1
Navicula cf. cryptocephala 1 1 1 1 1 1 1 1 1
Navicula cryptotenella * 1 1 1 1 1 1 1 1 1
Navicula radiosa 1 1 1 1 1 1 1 1
Navicula cf. recens 1 1 1 1 1 1 1 1 1
Navicula sp.6 1 1 1 1 1 1 1
Navicula sp.7 1 1 1 1 1 1 1 1 1
Neidium iridis 1 1 1 1 1 1
Nitzschia cf.agnita 1 1 1 1 1 1 1 1 1
Nitzschia cf. dissipata 1 1 1 1
Nitzschia cf. filiformis 1 1 1 1 1 1 1 1
Nitzschia cf. gracilis 1 1 1 1 1 1 1 1
Nitzschia linearis 1 1 1 1 1 1 1 1 1
Nitzschia longissima 1 1 1 1 1 1 1
Nitzschia sigmoidea 1 1 1 1 1 1 1 1
Nitzschia cf. tubicola 1 1 1 1 1 1
Nitzschia sp.2 1 1
Pinnularia subcapitata 1 1 1 1 1 1 1
Pinnularia subcapitata cf. subcapitata * 1 1 1 1 1 1 1
Pinnularia sp. 1 1 1
Staurophora salina 1 1 1 1
Stenopterobia pelagica * # 1 1 1 1 1
Surirella angusta * 1 1 1
Surirella brebissoni * 1 1 1 1 1 1 1 1
Surirella minuta * 1 1 1 1 1
Surirella sp.1 1 1
Synedra ulna 1 1 1 1 1 1
Synedra ulna var. amphirhynchus * 1 1 1 1 1 1 1 1 1
Tryblionella apiculata * 1 1 1 1 1 1
Tryblonella sp.1 1 1 1 1 1 1 1
Diatom 2 1 1 1 1 1
Diatom 9 1 1 1 1
Diatom 10 1 1 1
Diatom 11 1 1 1 1 1 1
Diatom 12 1 1 1 1 1
Diatom 13 1 1 1 1 1
Diatom 14 1 1
Diatom 15 1 1
Total 63 52 44 29 49 27 41 44 43 46
Summary of totals SK1 SK2 SF3 SK4 SSt SK5 SDo6 SD7 SD8
Cyanophyta
12
6 6 5 9 2 6 8 5 7
Chlorophyta
34
21 20 12 21 5 16 21 16 20
Desmidiaceae
47
42 33 13 20 11 9 7 17 12
Euglenophyta 14 11 14 7 9 4 6 7 8 7
Pyrrophyta 10 5 8 6 7 1 5 5 5 5
Cryptophyta 2 2 1 1 1 1 1 1 1 2
Chrysophyta
20
7 6 3 13 0 8 4 4 8
Bacillariophyceae
63
52 44 29 49 27 41 44 43 46
Total 202 146 132 76 129 51 92 97 99 107
22

Diversity:
The green algae (here the Chlorophyta and the Desmidiaceae) dominated with respect
to the number of taxa present, totalling 81 of the 202 taxa observed (Fig. 2.2). The
Bacillariophyceae (diatoms) were next with 63 taxa, followed by the Chrysophyta
with 20 taxa. The Cyanophyta, Euglenophyta and Pyrrophyta each contained 10-14
taxa, and the Cryptophyta (although relatively important on occasion in terms of cell
density) 2 taxa only.
No of species
70
60
50
40
30
20
10
0
Cyanophyta
Chlorophyta
Desmidiaceae
Euglenophyta
Fig. 4.2. Number of taxa within the eight main groups of algae in the Daly R.
and its tributaries during the survey period (June-November 2000).
Most significant individual taxa
In terms of cell numbers and biomass, 18 taxa were predominant (listed and pictured
below in Fig. 2.3), occurring either consistently or occasionally as peaks:
Pyrrophyta
Cyanophyta
Anabaena
Planctolyngbya cf. subtilis
Chlorophyta
Ankistrodesmus convolutus
cf. Kirchneriella sp.
Pteromonas sp.
Scenedesmus bijuga
Scenedesmus denticulatus
Spirogyra spp. (S. aequinoctialis and S. condensata – indistinguishable
in absence of cell contents)
Pyrrophyta
Peridinium inconspicuum
Peridinium umbonatum tab. remotum
Cryptophyta
Chrysophyta
Bacillariophyceae
23

Cryptophyta
Cryptomonas sp.
Bacillariophyceae
Encyonema silesiacum
Fragilaria zasuminensis
Navicula cryptotenella
Navicula cf. agnita
Navicula cf. recens
Synedra ulna var. amphirhynchus
Urosolenia eriensis var. morsa
24

Fig 2.3. Picture and drawing gallery of 18 predominant taxa found in Daly River
during the 2000 dry season.
Cyanophyta - Anabaena sp.
Cyanophyta – Planktolyngbya cf. subtilis
Chlorophyta – Ankistrodesmus convolutus
25

Chlorophyta - cf. Kirchneriella sp.
Chlorophyta – Pteromonas sp.
Chlorophyta – Scenedesmus bijuga
Chlorophyta – Scenedesmus denticulatus
26

Chlorophyta - Spirogyra aequinoctialis
Chlorophyta – Spirogyra condensata
Pyrrophyta – Peridinium inconspicuum
Pyrrophyta – Peridinium umbonatum tab. remotum
27

Cryptophyta – Cryptomonas sp.
Bacillariophyceae – Encyonema silesiacum
Bacillariophyceae – Fragilaria zasuminenzis
Bacillariophyceae – Navicula cryptotenella
28

Bacillariophyceae
Navicula cf. recens Nitzschia cf. agnita
Bacillariophyceae – Synedra ulna var. morsa
Bacillariophyceae – Urosolenia eriensis var. morsa
29

cell/L
600,000
500,000
400,000
300,000
200,000
100,000
[mm³/L]
0
2.5
2
1.5
1
0.5
0
SK1
SK2
Cell number
SF3
Jun-00
Oct -00
Aug-00
Biovolume
Fig 2.4. Total algal cells number and biovolume within the Daly River
and its major tributaries.
SD4
SD5
SDo6
SD7
SD8
Jun-00
Oct-00
Aug-00
30

Fig 2.5. Chlorophyll-a values in the Daly River system during dry season 2000.
µg/L
3
2.5
2
1.5
1
0.5
0
SK1
SK2
SF3
SD4
Fig 2.6. Water flows in the Daly River system during dry season 2000.
cumecs
80
70
60
50
40
30
20
10
0
SK1
SK2
SF3
SD5
SD4
SDo6
SD5
SD7
SDo6
SD8
SD7
Nov-00
Oct-00
Sep-00
Aug-00
Jul-00
Jun-00
SD8
Oc t -00
Nov-00
Aug-00
Sep-00
Jun-00
Jul-00
31

2.4.2 Total algal numbers and biomass
Algal numbers and algal biovolume data across all sites over the entire study period
are summarised in Fig. 2.4, and the corresponding Chlorophyll a data (kindly
provided by the NT Dept. of Lands Planning and Environment) are shown in Fig. 2.5.
All but 6 samples contained less than 150,000 cells/L: the two largest cell number
peaks (480-640,000 cells/L: Sites SK1 and SK2 in October) comprised mainly two
diatom species – Urosolenia eriensis and Fragilaria zasuminensis; the other 4 larger
peaks were due to the presence of Peridinium inconspicuum, Pteromonas sp.,
Cryptomonas sp., cf. Kirchneriella sp. and assorted diatoms.
The flows at the time of each sampling are given in Fig. 2.6, which generally shows
flows gradually diminishing as the study progressed (but note the absence of data
during October and November). Lowest flows were recorded in the Flora and
Douglas Rivers, and for any one monthly sampling, discharge within the Katherine
and Daly Rivers generally increased with distance downstream.
Although as a general trend, higher numbers of algae were recorded towards the end
of the dry season (Fig. 2.4), this trend was not particularly striking and did not appear,
for instance, at the most downstream site SD8.
With respect to lateral distribution within the Katherine and Daly Rivers, no marked
trend of increasing numbers downstream was observed. Indeed, several of the higher
peaks occurred at the sites furthest upstream, particularly due to Urosolenia eriensis
and Fragilaria zasuminensis at SK1 and SK2 in October.
Cell numbers within the Flora (SF3) and Douglas (SDo6) Rivers were similar to those
recorded, for instance, at the Daly River SD4 site, with the exception of a value for the
Flora River of 302,000 cells/L in November, due in large part to Peridinium
inconspicuum.
Table 2.3 has been prepared to allow a comparison of the planktonic algae population
of the Daly system with those of other rivers, principally larger rivers in Europe (from
the literature) and smaller rivers in south-eastern Australia (unreported data from
Hotzel and Croome).
While the algal densities of the Daly system (20 – 480 cells/ml) are well below those
commonly recorded for the larger rivers shown here (

Table 2.3 Algal densities and biomass data from various rivers. (1) Interpreted from
Friedrich 1991, (2) Dokulil 1991, (3) Coste et al. 1991, (4) Hotzel & Croome 1996, at
Torrumbarry, (5) Hotzel & Croome, unpublished data, 8-16 samples per river, taken
Spring/Autumn 1995/96, (6) This study, (7) Interpreted from Reynolds & Descy
(1996), (8) Marker & Collett 1991.
2.4.2.1.1 River 2.4.2.1.2 Algal density
Rhine at Bimmen (1)
Danube (2)
Tributaries of Danube (2)
Lot (3)
Murray – A. granulata (4)
Murray above L. Hume (5)
Mitta Mitta (5)
Kiewa (5)
Broken (5)
Ovens (5)
Buffalo (5)
Goulburn (5)
Billabong Ck. (5)
Katherine (6)
Flora (6)
Daly (6)
Douglas (6)
Seine (3)
Meuse (7)
Soft-water streams (8)
Chalk streams (8)
Hypereutrophic streams (8)
cells/ml
2,000 – 66,000
Often > 25,000
< 1000 – 2000
1,000 – 30,000
2,000 – 20,000
< 5 – 95
30 – 280
< 5 – 85
< 5 – 2,600
< 5 – 270
< 5 – 140
5 – 5,700
65 – 2,200
20 – 480
25 – 300
30 – 205
60 - 125
Biomass as Chlorophyll a
mg/m 3 mg/m 2
5 – 100

Algal biovolume:
The summary data for Total biovolume (Fig. 2.4) show no obvious trend in biomass
either with time or distance downstream. Sporadic peaks in biomass exist, but are due
primarily to the benthic alga Spirogyra becoming suspended in the water column (all
the peaks downstream of SD4 are in this category), or to the presence of the relatively
large Peridinium umbonatum, and Urosolenia eriensis and Fragilaria zasuminensis
again (sites SK1 and SK2 in October).
Biovolume values were typically less than 0.25 mm 3 /L, and this value corresponds to
something like 2.5 mg/m 3 of Chlorophyll a (using a cell volume/chlorophyll ratio of
10 ug Chl a/mm 3 of algae – see Reynolds 1984, p. 38). Actual Chlorophyll a values
(Fig. 2.5, Table 2.3) did not exceed 3 mg/m 3 on any occasion, and the application of
Table 2.4 to this data gives a classification of oligotrophic.
[It should be noted here that cell volume/chlorophyll calculations gave (theoretical)
values up to 26 mg/m 3 when Spirogyra was present (eg. in the Daly R. at Oolloo
Crossing in October) but that the majority of these cells were devoid of cell content
and therefore would not have contributed to chlorophyll levels].
In summary, planktonic densities for the algae of the Daly system are towards the
lower end of the spectrum of densities reported from rivers, and no marked trend in
biomass was observed during this particular study with respect to either time or
distance downstream. Several peaks observed in the data were due to the presence of
the benthic Spirogyra becoming suspended in the water column. Other, smaller peaks
were due to the presence of Peridinium umbonatum, Urosolenia eriensis and
Fragilaria zasuminensis. Algal numbers and biomass estimates infer a trophic status
of oligo/mesotrophic.
Monthly data for each taxonomic grouping
The relative importance of each taxonomic group along the river system in terms of
both algal density and biomass is shown in Fig. 2.7. (Please note differences in scale
between individual months.)
Density:
In terms of cell numbers, the samples in June were dominated by the
Bacillariophyceae (Navicula cryptotenella, Navicula cf. recens, Nitzschia cf. agnita,
Synedra ulna) except for the most downstream Daly R. Site SD8, where the flagellate
Cryptomonas was also important. The Cyanophyta contribution at the Daly R. at
Claravale Crossing (SD4) was due to Phormidium sp. and Planktolyngbya cf. subtilis.
In July and August several groups contributed significally to cell numbers: the
Bacillariophyceae (Encyonema silesiacum, Navicula cf. recens in July: Nitzschia
longissima, Nitzschia cf. agnita, Navicula cryptonella in August), Flagellates
(Cryptomonas sp., plus two unidentified Chrysophytes), Pyrrophyta (Peridinium
34

inconspicuum), and Chlorophyta (Ankistrodesmus convolutus, cf. Kirchneriella,
Pteromonas sp.).
In September, the Pyrrophyta were a particularly significant grouping, with
Peridinium inconspicuum pre-dominating. The Bacillariophyceae (Fragilaria
zasuminensis, Urosolenia eriensis) were again important, especially at the Katherine
R. Sites SK1 and SK2. The Flagellate Euglena sp. was also significant at Site SK2, as
was the Cyanophyte Phormidium at the Daly R. at Claravale Crossing (SD4) and the
Douglas R. (SDo6).
In October, particularly high values of Bacillariophyceae (Fragilaria zasuminensis –
up to 386,000 cells/L, Urosolenia eriensis – up to 115,800 cells/L, and Acanthoceros
sp. – up to 23,000 cells/L) were recorded at SK1 and SK2. Contributions by the
Pyrrophyta at all sites were due to Peridinium inconspicuum, and the Cyanophyte
contribution at SD8 was due to Phormidium sp. at 19,000 cells/L.
In November many groups again contributed significantly to total cell number.
Significant values were recorded for Pyrrophyta (Peridinium inconspicuum),
Chlorophyta (Ankistrodesmus convolutus, Ankistrodesmus falcatus, Scenedesmus
bijuga, Scenedesmus opolienses), Bacillariophyceae (Aulacoseira granulata, Navicula
cf. agnita), and Flagellates (Cryptomonas sp.) in the Flora River. A contribution by
Cyanophyta (Pseudanabaena cf. limnetica) was also apparent at most sites.
35

36
June 2000
July 2000
August 2000
SK1
SK2
SF
SD4
SD5
SDo6
SD7
SD8
Cyanophyta
Chlorophyta
Desmidiaceae
Pyrrophyta
Flagellates
Bacillariophyceae
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
cells/L
SK1
SK2
SF
SD4
SD5
SDo6
SD7
SD8
Cyanophyta
Chlorophyta
Desmidiaceae
Pyrrophyta
Flagellates
Bacillariophyceae
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
cells/L
SK1
SK2
SF
SD4
SD5
SDo6
SD7
SD8
Cyanophyta
Desmidiaceae
Flagellates
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
cells/L
SK1
SK2
SF
SD4
SD5
SDo6
SD7
SD8
Cyanophyta
Desmidiaceae
Flagellates
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
Biovolume [mm³/L]
SK1
SK2
SF
SD4
SD5
SDo6
SD7
SD8
Cyanophyta
Desmidiaceae
Flagellates
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Biovolume [mm³/L]
SK1
SK2
SF
SD4
SD5
SDo6
SD7
SD8
Cyanophyta
Desmidiaceae
Flagellates
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Biovolume [mm³/L]

cells/L
cells/L
cells/L
200000
180000
160000
140000
120000
100000
80000
60000
40000
20000
600000
500000
400000
300000
200000
0
100000
200000
180000
160000
140000
120000
100000
80000
60000
0
40000
20000
0
SK1
SK1
SK1
SK2
SK2
SK2
SF
SF
SF
SD4
SD4
SD4
SD5
SD5
SD5
SDo6
SDo6
SDo6
SD7
SD7
SD7
SD8
SD8
SD8
September 2000
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
October 2000
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
November 2000
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Fig 2.7 Monthly changes in the cell density and biovolume of the main algal
groups comprising the phytoplankton community of the Daly R. over the dry
season
Biovolume [µm³/L]
Biovolume [µm³/L]
Biovolume [µm³/L]
0.3
0.3
0.2
0.2
0.1
2.5
0.1
0.0
2.0
1.5
1.0
1.4
1.2
0.5
1.0
0.0
0.8
0.6
0.4
0.2
0.0
SK1
SK1
SK1
SK2
SK2
SK2
SF
SF
SF
SD4
SD4
SD4
SD5
SD5
SD5
SDo6
SDo6
SDo6
SD7
SD7
SD7
SD8
SD8
SD8
37
Desmidiaceae
Cyanophyta
Cyanophyta
Flagellates
Flagellates
Desmidiaceae
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta

In terms of algal biovolume, the samples in June were dominated by the
Bacillariophyceae, with a significant contribution by the Flagellates (Cryptomonas
sp.) at the most downstream site, SD8.
The largest peak in each subsequent monthly figure is due to Spirogyra, presumably
benthic cells becoming detached and drifting downstream. If Spirogyra had not been
present, dominance would have been by the Bacillariophyceae and Pyrrophyta.
Site by site data for each taxonomic grouping
Fig. 2.8 shows the relative importance of each taxonomic group, site by site, over the
entire study period.
SK1 – Katherine R. at Donkey Camp Pool – inflow
SK1 – Katherine R. at Donkey Camp Pool – outflow
The data from these two sites are all but identical (note however, a small difference in
scales). While there is in fact a contribution from each of the taxonomic groupings,
the figures are dominated by the contribution of the Bacillariophyceae in October in
particular. Maximum biomass contributions at both sites were by the
Bacillariophyceae in all months, and by the Pyrrophyta, Desmidiaceae (Cosmarium,
Staurodesmus) and Chlorophyta in October and November.
SF3 – Flora R.
At SF3, three groups were predominant – the Pyrrophyta, Bacillariophyceae, and
Flagellates – the Pyrrophyta in particular (Peridinium inconspicuum) being important
each month. Some 10,000 – 13,000 cells/L of Cyanophyta were present in October
(Anabaena sp.) and November (Pseudanabaena sp.), but made little contribution to
biomass as a consequence of their (small) size.
SD4 – Daly R. at Claravale Crossing
Cell densities at SD4 indicate many taxa contributing to the population. The
biovolume data, however, is dominated again by the Bacillariophyceae, Pyrrophyta,
and Flagellates, with Desmidiaceae (relatively large cells of Closterium dianae var.
minor, Cosmarium granatum and Staurastrum longibrachiatum) and Chlorophyta
(Scenedesmus bijuga, Scenedesmus opoliensis) contributing on occasion.
The greatest biomass contribution overall at SD4 was by the Bacillariophyceae
(Encyonema, Navicula, Nitzschia, Synedra).
38

cells/L
cells/L
cells/L
600,000
500,000
400,000
300,000
200,000
100,000
450,000
400,000
350,000
300,000
250,000
200,000
150,000
0
100,000
50,000
200,000
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
SK1 - Kathrine R.- Donkey Camp Pool inflow
Sep-00
Oct-00
Nov-00
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
SK2 - Kathrine R.- Donkey Camp Pool outflow
Sep-00
Sep-00
Oct-00
Oct-00
Nov-00
Nov-00
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Biovolume [mm³/L]
SF3 - Flora River
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Biovolume[mm³/L]
Biovolume [ mm³/L]
0.6
0.5
0.4
0.3
0.2
0.5
0.4
0.1
0.4
0.0
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
Nov-00
39
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta

cells/L
cells/L
cells/L
100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
45,000
40,000
35,000
30,000
25,000
20,000
15,000
0
0
10,000
5,000
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
SD4 - Daly R. - Claravale Crossing
Oct-00
Oct-00
Oct-00
Nov-00
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Biovolume [mm³/L]
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
SD5 - Daly R. - Oolloo Crossing
Nov-00
Nov-00
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
SDo6 - Douglas River
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Biovolume [mm³/L]
Biovolume [mm³/L]
2.5
2.0
1.5
1.0
0.5
0.0
0.25
0.20
0.15
0.10
0.05
0.00
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
Nov-00
40
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta

cells/L
cells/L
200,000
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
0
0
Jun-00
Jun-00
Jul-00
Jul-00
Aug-00
Aug-00
Sep-00
Sep-00
Oct-00
Oct-00
Nov-00
Nov-00
SD7 -Daly R. - Beeboom
SD8 - Daly R. - Daly R. Township
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Fig 2.8. Algal cell density and biovolume fluctuations in the Daly R. and its
tributaries during the dry season 2000.
Biovolume [mm³/L]
Biovolume [mm³/L]
0.14
0.12
0.10
0.08
0.06
1.4
0.04
1.2
0.02
1.0
0.00
0.8
0.6
0.4
0.2
0.0
Jun-00
Jun-00
Jul-00
Jul-00
Aug-00
Aug-00
Sep-00
Sep-00
Oct-00
Oct-00
Nov-00
Nov-00
41
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta
Bacillariophyceae
Flagellates
Pyrrophyta
Desmidiaceae
Chlorophyta
Cyanophyta

SD5 – Daly R. at Oolloo Crossing
The figure for SD5 differs from that of the previous upstream site (SD4) in generally
having fewer algae in each taxonomic grouping. In addition, algal numbers in July,
August and September are relatively low, giving the impression of increased numbers
at this site with progression of the dry season. The biomass graph is dominated by
Chlorophyta (Spirogyra) to the extent that it is not readily comparable to eg. the
equivalent diagram for SD4.
SDo6 – Douglas R.
All groups but Desmidiaceae contributed significantly to cell densities on occasion. In
terms of biomass, the Bacillariophyceae were the most consistent contributors
(Encyonema, Synedra), with occasional peaks by the Flagellates and Pyrrophyta.
Chlorophyte peaks in August and November were dominated by Spirogyra.
SD7 – Daly R. at Beeboom
SD8 – Daly R. at Daly R. Township
All groups but the Desmidiaceae made substantial contributions to cell densities.
The diagrams for SD7 and SD8 are not as dissimilar as first appears. Compare, for
instance, the data for November and July. The largest dissimilarity occurred in August
when high numbers of Flagellates (Cryptomonas sp.) were present at SD8 but not
SD7, and likewise for Chlorophyta (Pteromonas, Kirchneriella).
A difference between the two sites is also apparent for the Cyanophyta. Excepting
November, cell numbers for Cyanophyta were higher at SD8 than SD7. Cyanophyta
cell numbers at SD8 in August, for instance, were due to Planktolyngbya and
Aphanizomenon, and in September to Phormidium.
The several large Pyrrophyta peaks at both SD7 and SD8 were due to Peridinium
inconspicuum.
The biomass diagrams for SD7 and SD8 are difficult to compare due to differences in
scale, caused by the presence of relatively few (5,200 cells/L) of the Chlorophyte
Spirogyra in November in particular.
At SD7 biomass was dominated by the Bacillariophyceae and Pryyophyta: at SD8
these groups also contributed, but their contribution was dwarfed by that of Spirogyra.
In summary, a diverse algal population was found at each site. With respect to algal
biomass and the contribution to it by the individual taxonomic groupings:
• the mainstream sites SK1 ( Katherine R. at Donkey Camp Pool – inflow) and SK2
(Katherine R. at Donkey Camp Pool – outflow) were similar to each other,
42

dominated by Bacillariophyceae with occasional peaks of Pyrrophyta and
Chlorophyta
• the mainstream site SD4 (Daly R. at Claravale Crossing) was dominated by
Bacillariophyceae and Pyrrophyta, with occasional peaks of Flagellates and
Chlorophyta
• the mainstream sites further downstream – SD5 (Daly R. at Oolloo Crossing), SD7
(Daly R. at Beeboom) and SD8 (Daly R. at Daly R. Township) – also displayed a
similar pattern of contribution to algal biomass (Bacillariophyceae, Pyrrophyta
plus occasional peaks of Flagellates) except that at SD5 and SD8 the data were
swamped by the contribution of the Chlorophte Spirogyra.
• the tributary site SF3 (Flora R.) showed a similar pattern to that seen from SD4
downstream i.e. Bacillariophyceae plus Pyrrophyta, with occasional Flagellate
peak. The relative contribution of the Pyrrophyta is somewhat larger in proportion
here however, and may have provided important seeding downstream?
• the tributary site SDo6 (Douglas R.) was found to be dominated by the
Bacillariophyceae, but with contributions also by the Pyrrophyta and Flagellates,
and peaks of the Chlorophyte Spirogyra.
• although not particularly significant in terms of biomass, members of the
Cyanophyta were present at all sites, and in comparable numbers to other major
groupings at sites SD4-SD8, SF3 and SDo6.
3.6 Distribution of principal individual taxa
The cell density and biovolume data for the 18 most significant taxa (as listed in
Section 3.2) are given in Fig. 2.9, and will be discussed below in taxonomic order.
Two Cyanophytes occurred in significant proportions: (1) Planktolyngbya subtilis was
present over most of the study period at up to 5,000 cells/L, occurring at one time or
another at all sites except SF3 – the Flora R. (2) Anabaena sp. occurred more
sporadically, in particular being found in October in the Flora R. at 10,500 cells/L, and
in the Daly R. at Claravale Crossing (SD4) and Oolloo Crossing (SD5) (but not
further downstream). Anabaena sp. was also recorded in the Douglas R. in November.
The Chlorophytes Ankistrodesmus convolutus, Scenedesmus bijuga and Scenedesmus
denticulatus occurred consistently at all sites. Ankistrodesmus convolutus peaked at
24,300 cells/L in August in the Daly R. at Claravale Crossing (SD4), Scenedesmus
bijuga peaked at 20,200 cells/L in November at the same site, and Scenedesmus
denticulatus peaked at 3,500 cells/L in August in the most upstream site, the
Katherine R. at Donkey Camp Pool inflow (SK1).
43

The Chlorophyte taxa cf. Kirchneriella sp., Pteromonas sp. and Spirogyra spp. again
occurred sporadically. The organism cf. Kirchneriella was present in August at up to
34,100 cells/L in the main stem of the Katherine/Daly (but not in the Flora or
Douglas). Pteromonas sp. was significant at one site only, the Daly R. at the Daly. R.
44

45
Cyanophyta - Planktolyngbya subtilis
Cyanophyta - Anabaena sp.
Chlorophyta - Ankistrodesmus convolutus
Jun-00
Jul-00
Aug-00
Sep-00
Oct-00
Nov-00
SK1
SF3
SD5
SD7
0
2,000
4,000
6,000
8,000
10,000
12,000
cell/L
Jun-00
Jul-00
Aug-00
Sep-00
Oct-00
Nov-00
SK1
SF3
SD5
SD7
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
Biovolume [µm³/L]
Jun-00
Jul-00
Aug-00
Sep-00
Oct-00
Nov-00
SK1
SF3
SD5
SD7
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
cell/L
Jun-00
Jul-00
Aug-00
Sep-00
Oct-00
Nov-00
SK1
SF3
SD5
SD7
0
10,000
20,000
30,000
40,000
50,000
60,000
Biovolume [µm³/L]
Jun-00
Jul-00
Aug-00
Sep-00
Oct-00
Nov-00
SK1
SF3
SD5
SD7
0
5,000
10,000
15,000
20,000
25,000
cell/L
Jun-00
Jul-00
Aug-00
Sep-00
Oct-00
Nov-00
SK1
SF3
SD5
SD7
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
Biovolume [µm³/L]

cell/L
cell/L
cell/L
25,000
20,000
15,000
10,000
5,000
3,500
3,000
2,500
2,000
1,500
1,000
500
35,000
30,000
25,000
0
0
20,000
15,000
10,000
5,000
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Chlorophyta - Scenedesmus bijuga
Nov-00
SD7
SD5
SF3
SK1
Biovolume [µm³/L]
4,000,000
3,500,000
3,000,000
2,500,000
2,000,000
1,500,000
1,000,000
Chlorophyta - Scenedesmus denticulatus
Nov-00
SD7
SD5
SF3
SK1
Chlorophyta - cf. Kirchneriella sp.
Nov-00
SD7
SD5
SF3
SK1
Biovolume [µm³/L]
Biovolume [µm³/L]
500,000
100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
700,000
600,000
500,000
400,000
300,000
200,000
0
100,000
0
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
Nov-00
46
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1

cell/L
cell/L
cell/L
30,000
25,000
20,000
15,000
10,000
5,000
14000
12000
10000
8000
6000
4000
2000
200,000
0
0
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Chlorophyta - Pteromonas sp.
SD7
SD5
SF3
SK1
Chlorophyta - Spirogyra spp.
Nov-00
Biovolume [µm³/L]
250,000
200,000
150,000
100,000
50,000
Pyrrophyta - Peridinium inconspicuum
Nov-00
SK1
SF3
SD5
SD7
SD7
SD5
SF3
SK1
Biovolume [mm³/L]
Biovolume [mm³/L]
2.5
2.0
1.5
1.0
0.5
0.14
0.0
0.12
0.10
0.08
0.06
0.04
0.02
0.00
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
Nov-00
47
SD7
SD5
SF3
SK1
SK1
SF3
SD5
SD7
SD7
SD5
SF3
SK1

cell/L
cell/L
cell/L
12,000
10,000
8,000
6,000
4,000
2,000
70,000
60,000
50,000
40,000
30,000
0
20,000
10,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Pyrrophyta - Peridinium umbonatum tab. remotum
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
SD7
SD5
SF3
SK1
Biovolume [mm³/L]
Flagellates - Cryptomonas sp.
SD7
SD5
SF3
SK1
Bacillariophyceae - Encyonema silesiacum
Nov-00
SD7
SD5
SF3
SK1
Biovolume [mm³/L]
Biovolume [mm³/L]
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0.018
0.016
0.014
0.012
0.010
0.008
0.006
0.004
0.002
0.000
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
Nov-00
48
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1

Fig 2.9. Seasonal cell density and biovolume fluctuations of the principal 18
algal taxa within Daly R. and its major tributaries.
Bacillariophyceae - Navicula cryptotenella
cell/L
cell/L
cell/L
12,000
10,000
8,000
6,000
4,000
2,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
0
6,000
5,000
4,000
3,000
2,000
1,000
0
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
SD7
SD5
SF3
SK1
Biovolume [mm³/L]
0.006
0.005
0.004
0.003
0.002
0.001
0.000
Bacillariophyceae - Navicula cf. recens
Nov-00
SD7
SD5
SF3
SK1
Biovolume [mm³/L]
0.004
0.003
0.003
0.002
0.002
0.001
0.001
0.000
Bacillariophyceae - Nitzschia cf. agnita
Nov-00
SD7
SD5
SF3
SK1
Biovolume [mm³/L]
0.003
0.003
0.002
0.002
0.001
0.001
0.000
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
Nov-00
49
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1

cell/L
cell/L
cell/L
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
400,000
350,000
300,000
250,000
200,000
150,000
100,000
50,000
120,000
100,000
80,000
60,000
40,000
Jun-00
0
20,000
0
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Bacillariophycea - Synedra ulna
SD7
SD5
SF3
SK1
Bacillariophyceae - Fragilaria zasuminensis
Nov-00
SD7
SD5
SF3
SK1
Bacillarophyceae - Urosolenia eriensis var. morsa
Nov-00
SD7
SD5
SF3
SK1
Fig 2.9. Seasonal cell density and biovolume fluctuations of the principal 18
algal taxa within Daly R. and its major tributaries.
Biovolume [mm³/L]
Biovolume [mm³/L]
Biovolume [mm³/L]
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Jun-00
Jun-00
Jun-00
Jul-00
Jul-00
Jul-00
Aug-00
Aug-00
Aug-00
Sep-00
Sep-00
Sep-00
Oct-00
Oct-00
Oct-00
Nov-00
Nov-00
Nov-00
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1
SD7
SD5
SF3
SK1
50

Township (SD8) in August and September, being present at 28,500 cells/L, but even
then contributing little to overall biomass (

2.4.3 Contribution of principal individual taxa
The 18 principal taxa are listed in Table 2.5, together with their contribution to cell
densities and biomass.
Table 2.5. Daly R. study – relative contribution to the algal population of the 18
principal taxa.
Max cell Max bio-
2.4.3.1.1 Taxa
no. volume
2.4.3.1.2 C mm
e
l
l
s
/
L
3 Usual Maximum
contribution contribution
/L to biovol % to biovol %
2.4.3.1.2.1
2.4.3.1.2.2 Consistent
contributors
2.4.3.2 Peridinium
inconspicuum
Cryptomonas sp.
2.4.3.3 Synedra ulna
2.4.3.4 Encyonema
silesiacum
Nitzschia cf. agnita
Navicula cf. recens
Navicula cryptotenella
Scenedesmus bijuga
Ankistrodesmus convolutus
Scenedesmus denticulatus
Planktolyngbya subtilis
2.4.3.4.1.1 Occasional
substantial
contributors
Spirogyra spp.
Urosolenia eriensis
Fragilaria zasuminensis
195,000
41,000
8,000
7,000
6,000
7,000
10,000
20,000
24,000
4,000
5,000
5,000
116,000
386,000
11,000
10,000
34,000
29,000
0.129
0.067
0.057
0.017
0.003
0.003
0.005
0.004

Peridinium umbonatum
2.4.3.4.1.2 Occasional,
lesser
contributors
Anabaena sp.
cf. Kirchneriella sp.
Pteromonas sp.
While 11 taxa were “consistent contributors” to the algal population, only four
(Peridinium inconspicuum, Cryptomonas s., Synedra ulna and Encyonema silesiacum)
consistently contributed more than 1% to total biovolume. Moreover, of these
consistent contributors, only the same four taxa ever contributed more than 5% to total
biovolume. The Pyrrophyte Peridinium inconspicuum was the most substantial
consistent contributor, usually comprising 2-15% of the biomass (maximum 74%) and
being present at up to 195,000 cells/L.
Four taxa (Spirogyra spp., Urosolenia eriensis, Fragilaria zasuminensis and
Peridinium umbonatum) usually contributed little to biovolume, but occurred in
substantial biomass on occasion. The taxon Spirogyra spp. is taken to comprise
organisms detached from their usual benthic state, and contained many cells from
which the chloroplast had been lost, thus contributing little to measures of
Chlorophyll a for example.
53

Most noticeable in this category of “occasional substantial contributors” then is
Urosolenia eriensis, contributing up to 51% to total biovolume on one occasion (in
October at SK1 – Katherine R. @ Donkey Camp Pool – inflow).
Three “occasional, lesser contributors” to biovolume (Anabaena sp., cf. Kirchneriella
sp. and Pteromonas sp.) are included in Table 2.5, by virtue of their occasional
occurrence in high cell number, although they never contributed substantially to total
biovolume (as a consequence of their relatively small size).
Future work on these waters could well be concentrated on these 18 taxa, with
presence/absence data alone being recorded for the remaining organisms, unless a
substantial change is noticed.
2.4.4 Downstream changes in principal taxa
An attempt was made to quantify the total number of cells of the four most consistent
taxa (Cryptomonas sp., Peridinium inconspicuum, Encyonema silesiacum, Synedra
ulna) present at each sampling site, by determining individual loadings i.e. the product
of cell concentration and instantaneous flow. The flow data provided enabled this to
be done for July and August only, and the results are shown in Fig. 2.10.
The intent was to see if there might be a readily discernible pattern with respect to
downstream increase/decrease of algal loads with, for instance, travel (but note no
travel-time data available) or contribution from the tributaries.
July
Cryptomonas sp. appeared to originate in the Flora R. and loads generally increased
along the Daly system. Given the load present in the Douglas (Sdo6) a greater increase
might have been expected between SD5 and SD7 (refer Fig. 2.1).
54

Fig 2.10. Daly R. study– algal “loading” in July and August for the four most
Cells/sec
Cells/sec
Cells/sec
Cells/sec
40
35
30
25
20
15
10
5
0
70
60
50
40
30
20
10
8
7
6
5
4
3
2
1
0
0
12
10
8
6
4
2
0
Cryptomonas sp.
July August
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
d
Cells/sec
Peridinium inconspicuum
July August
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
Encyonema silesiacum
July August
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
Synedra ulna
July August
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
Cells/sec
Cells/sec
Cells/sec
100
90
80
70
60
50
40
30
20
10
45
40
35
30
25
20
15
10
5
0
30
25
20
15
10
5
0
18
16
14
12
10
8
6
4
2
0
0
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
SK1 SK2 SF3 SD4 SD5 SDo6 SD7 SD8
55

consistent taxa. Values = cells/sec x 10 −7 .
Loadings of Peridinium inconspicuum generally increased downstream, with small but
significant contributions from the Flora and Douglas. A similar but more variable
pattern was observed for Encyonema silesiacum and Synedra ulna.
August
Isolated population peaks were apparent in the data for August, particularly for
Cryptomonas sp., Peridinium inconspicuum and Synedra ulna at SD4 and SD8.
A substantial population of Encyonema silesiacum appeared to enter the Daly system
from the Douglas R.
Comment:
The above was attempted to indicate population increases/decreases downstream, and
could be accomplished for July and August only. No data are available to hand on
travel times, and the variability of algal sampling/counting has to be taken into
account.
Overall, the data in Fig. 2.10 are too variable to determine definitive trends, other than
to suggest increased loadings downstream for certain taxa e.g. in July, and to indicate
substantial tributary contribution on occasion by both the Flora and Douglas.
2.5 Additional remarks
The above work was commissioned to detail the planktonic algae present over a six
month period in the Katherine/Daly River and two of its tributaries.
The results have shown, in algal terms, the relatively constant presence of planktonic
algae at all sites, of substantial diversity. Calculations made of biovolume agree well
with independently determined Chlorophyll a concentrations, in both magnitude and
consistency.
Despite the work being limited by the frequency of sampling (monthly vs. the more
desirable weekly sampling), we believe it has adequately determined the nature of the
algal populations present, and the extent of their fluctuations in time and space.
The extent to which the planktonic algal populations may vary from year to year can
only be determined by further sampling, and we understand this is under way.
It would also be interesting to conduct a one-off sampling at an appropriate time to
more clearly determine the origin of the organisms present in such a diverse
population of algae, but we realise that physical access to sampling sites further up the
catchment would make this difficult.
56

2.6 References
Baker, P. (1991). Identification of common noxious cyanobacteria. Part I –
Nostocales. Research Report No. 29, Urban Water Research Association of Australia.
Baker, P. (1992). Identification of common noxious cyanobacteria. Part II –
Chroococcales Oscillatoriales. Research Report No. 46, Urban Water Research
Association of Australia.
Baker, P.D. & Fabbro, L.D. (1999). A guide to the identification of common bluegreen
algae (Cyanoprokaryotes) in Australian freshwaters. Identification Guide No.
25, Australian Cooperative Research Centre for Freshwater Ecology.
Coste, M., Bosca, C. 7 Dauta, A. (1991). Use of algae for monitoring rivers in France.
pp. 75-88 in Whitton, B.A., Rott, E. & Friedrich, G. (1991). Use of algae for
monitoring rivers. E. Rott, Innsbruck, 193pp.
Day, S.A., Wickham, R.P., Entwisle, T.J. & Tyler, P.A. (1995). Bibliographic
checklist of non-marine algae in Australia. Flora of Australia Supplementary Series
Number 4. Australian Biological Resources Study, Canberra.
Dokulil, M. (1991). Review of recent activities, measurements and techniques
concerning phytoplankton algae of large rivers in Austria. pp. 53-58 in Whitton, B.A.,
Rott, E. & Friedrich, G. (1991). Use of algae for monitoring rivers. E. Rott, Innsbruck,
193pp.
Felfoldy, L. (1987). Biological methods for the classification of water quality. Vizugyl
Hidrobiologia 16., VIZDOK Budapest (in Hungarian).
Friedrich, G. (1991). Use of phytoplankton in monitoring rivers in the Federal
Republic of Germany. pp. 97-102 in Whitton, B.A., Rott, E. & Friedrich, G. (1991).
Use of algae for monitoring rivers. E. Rott, Innsbruck, 193pp.
Gell, P.A., Sonneman, J.A., Reid, M.A., Illman, M.A. & Sincock, A.J. (1999). An
illustrated key to common diatom genera from southern Australia. Identification
Guide No. 26, Australian Cooperative Research Centre for freshwater Ecology.
Hötzel, G. & Croome, R. (1996). Population dynamics of Aulacoseira granulata (Ehr.)
Simonson (Bacillariophyceae, Centrales), the dominant alga in the Murray River,
Australia. Archiv fur Hydrobiologie 136/2: 191-215.
Huber-Pestalozzi, G. (1968). Das Phytoplankton des Susswassers, Systematik und
Biologie. Die Binnengewasser, Band XVI, Teil 3. Cryptophyceae,
Chloromonadophyceae, Dinophyceae. Schweizerbart’sche Verlagsbuchhandlung,
Stuttgart.
57

Huber-Pestalozzi, G. (1969). Das Phytoplankton des Susswassers, Systematik und
Biologie. Die Binnengewasser, Band XVI, Teil 4. Euglenophyceen.
Schweizerbart’sche Verlagsbuchhandlung, Stuttgart.
Huber-Pestalozzi, G. (1974). Das Phytoplankton des Susswassers, Systematik und
Biologie. Die Binnengewasser, Band XVI, Teil 5. Chlorophyceae (Grunalgen),
Ordnung: Volvocales. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart.
Huber-Pestalozzi, G. (1975). Das Phytoplankton des Susswassers, Systematik und
Biologie. Die Binnengewasser, Band XVI, Teil 2. Diatomeen. Schweizerbart’sche
Verlagsbuchhandlung, Stuttgart.
Huber-Pestalozzi, G. (1976). Das Phytoplankton des Susswassers, Systematik und
Biologie. Die Binnengewasser, Band XVI, Teil 2. Chrysophyceen, Farblose
Flagellaten, Heterokonten. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart.
Huber-Pestalozzi, G. (1982). Das Phytoplankton des Susswassers, Systematik und
Biologie. Die Binnengewasser, Band XVI, Teil 8. Conjugatophyceae, Zygnematales
und Desmidiales (excl. Zygnemataceae). Schweizerbart’sche Verlagsbuchhandlung,
Stuttgart.
Huber-Pestalozzi, G. (1983). Das Phytoplankton des Susswassers, Systematik und
Biologie. Die Binnengewasser, Band XVI, Teil 7. Chlorophyceae (Grunalgen),
Ordnung: Chlorococcales. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart.
Lind, E.M. & Brook, A.L. (1980). A key to the commoner desmids of the English lake
district. Scientific Publication No. 42, Freshwater Biological Association. Ferry
House, Ambleside, Cumbria.
Ling, H.U. & Tyler, P.A. (1980). Freshwater algae of the Alligator Rivers Region,
Northern Territory of Australia. A report to the Office of the Supervising Scientist.
University of Tasmania, September 1980.
Ling, H.U. & Tyler, P.A. (2000). Australian freshwater algae (exclusive of diatoms).
Bibliotheca Phycologia, Band 105. J. Cramer, Stuttgart.
Marker, A.F.H. & Collett, G.D. (1991). Biomass, pigment and species composition.
pp. 21-24 in Whitton, B.A., Rott, E. & Friedrich, G. (1991). Use of algae for
monitoring rivers. E. Rott, Innsbruck, 193pp.
Padisak, J., Acs, E., Rajczy, M. & Kiss, M.T. (1991). Use of algae for monitoring
rivers in Hungary. pp. 123-128 in Whitton, B.A., Rott, E. & Friedrich, G. (1991). Use
of algae for monitoring rivers. E. Rott, Innsbruck, 193pp.
Plinski, M., Picinska, J. & Targonski, L. (1984). Computer analysis methodology for
marine phytoplankton. Publications of the School of Biology and Earth Sciences,
Gdansk University, No. 10, pp. 131-154.
58

Prescott, G.W. (1951). Algae of the Western Great Lakes Area (1982 reprint). O.
Koeltz Science Publishers, Germany.
Prescott, G. W. (1978). How to know the freshwater algae. Wm. C. Brown Company
Publishers, Duduque, Iowa.
Reynolds, C.S. (1984). The ecology of freshwater phytoplankton. Cambridge
University Press, Cambridge.
Reynolds, C.S. & Descy J.-P. (1996). The production, biomass and structure of
phytoplankton in large rivers. Arch. Hydrobiol. Supp. 103, Large Rivers 10 (1-4),
161-187.
Sonneman, J.A., Sincock, A., Fluin, J. Reid, M., Newall, P., Tibby, J. & Gell, P.
(2000). An illustrated guide to common stream diatom species from temperate
Australia. Identification Guide No. 33, Australian Cooperative Research Centre for
Freshwater Ecology.
59

3 CORRELATION BETWEEN FLOW AND OTHER ENVIRONMENTAL
VARIABLES WITH PHYTOPLANKTON ASSEMBLAGES
Townsend, S.A., N.T. Dept. of Infrastructure, Planning and Environment.
3.1 Introduction
The unidirectional flow of rivers dominates the composition and biomass of
phytoplankton. To maintain a population in a river, recruitment rates of phytoplankton
need to exceed the river’s mean travel time; otherwise populations will decline
(Reynolds 1996). River flow also has a direct influence on turbulence, and hence the
light climate phytoplankton are exposed to, whilst stratification can indirectly affect
river nutrient dynamics (e.g. Donnelly et al. 1997; Sherman et al. 1998). The biotic
control of river phytoplankton, for example by zooplankton grazing, can only take
place when the physical constraints of flow are reduced (Gosselain et al. 1998). These
influences combine to make phytoplankton a potentially sensitive component of a
river’s ecosystem to flow. A dramatic example was the 1991 blue-green algal bloom
along the 1000 km length of the Darling-Barwon River in southern Australia
(Bowling and Baker 1996; Donnelly et al. 1997). The bloom was primarily caused by
a combination of drought conditions, and significant anthropogenic impacts on the
river’s water budget, hydrodynamics and flow regime.
The objective of this chapter is to evaluate the relationship between phytoplankton
composition and biovolume, with flow and other environmental variables in the Daly
River and its tributaries.
3.2 Methods
3.2.1 Sample sites
Eight sites were sampled between June and November, 2000, at monthly intervals,
though not all sites could be sampled in June (4 sites) and October (1 site). In the
upper catchment, samples were collected from the inflow and outflow of Donkey
Camp Pool (DCP) on the Katherine River (Figure 3.1; Table 3.1). This is a natural
pool, though its water level has been raised 1 m by a weir to facilitate extraction for
potable water use for Katherine township. The pool is 4.6 m km long, and has an
average depth of 3 m and width of 40 m during the dry season. River flow during the
dry season at DCP is supplied from the Cretaceous sandstone aquifer, whilst the other
six sites, all located further downstream, receive groundwater from the Daly River
Basin aquifers. Four sites are located along the length of the Daly River, each about
70 km apart (Figure 3.1; Table 3.1). The other two sites were located on the tributaries
of the Flora and Douglas Rivers, within 4 km of their confluence with the Daly River.
60

Figure 3.1 Sample sites and hydrographic stations
61

Table 3.1 Phytoplankton sample site information
Site Abbreviated
name
Donkey Camp Pool (DCP)
inflow, Katherine River
Donkey Camp Pool outflow,
Katherine River
Daly River, 0.25 km upstream
of Dorisvale Crossing. Also
known as Claravale Crossing.
Daly river, 14.5 km
downstream of Oolloo
Crossing, adjacent to the Daly
River Conservation Esplanade
Daly River, Beeboom
Crossing
Daly River causeway to Port
Keats, near Daly River Police
Station
Douglas River at Crystal
Falls, 0.87 km upstream of the
confluence with the Daly
River.
Flora River, 3.2 km upstream
of the confluence with the
Daly River
ite
Number#.
Hydsys
site
number*
Type Depth:-
Average;
min -
max
DCP inflow (SK1) G8145381 Pool 3.7;
2.6 - 4.6
DCP outflow (SK2) G8145382 Pool 5.2;
3.7 - 7.4
Dorisvale (SD4) G8145384 Pool 2.6;
2.0 – 3.0
Esplanade (SD5) G8145385 Run 1.0;
0.6 – 1.5
Beeboom (SD7) G8145387 Pool 3.2;
2.6 – 5.6.
DR Crossing (SD8) G8145388 Run 1.6;
1.0 - 2.3
Douglas
River
(Sdo6) G8145386 Run 0.7;
0.5 - 1.1
Flora River (SF3) G8145383 Pool 2.8;
1.9 - 4.2
River
distance
from DCP
inflow (km)
0
4.6
145.5
226.2
268.2
342
206.5 km to
the confluence
of the Douglas
and Daly
Rivers.
97.4 km to
the confluence
of the Flora
and Daly
Rivers
# In brackets are the sample site labells use din Chapter 2. * DIPE water resource
database
3.2.2 Water sample collection, and in situ measurements
Flow data was obtained from either a nearby hydrographic station (sites 3, 4, 8), or
individual gaugings between June and September and hydrographic data from a
distant station. River water levels were continuously recorded at the hydrographic
stations, though with occasional periods of missing data, and converted to flow using
a rating table. Flows at Beeboom were estimated from flow data at Mt Nancar
hydrographic station, based on a regression of dry season gaugings at the two sites
(Appendix 3.1), because at low water levels the rating is inaccurate.
At distances of ¼, ½ and ¾ across the river, water samples were collected 15 cm
below the surface and in situ measurements undertaken. A composite sample was also
collected for ionic chemical analysis, and phytoplankton identification and
enumeration. The latter was preserved with Lugols Iodine immediately after
collection. Water samples were analysed for nitrogen, phosphorus, silicon, gilvin (a
measure of colour), total and dissolved organic carbon, suspended sediment and
chlorophyll a by standard methods (Table 3.2).
62

Samples for chlorophyll a were first passed through a 1 mm pore size sieve to remove
large strands of benthic Spirogyra, to better evaluate the chlorophyll a concentration
of planktonic algae. The samples were filtered in the field, refrigerated for a maximum
Table 3.2 Analytical methods for water samples. Parentheses contain the APHA (1998)
method number.
Parameter Method
Ammonia Automated phenate method (4500-NH3 G)
Nitrate, nitrite Automated cadmium reduction method (4500-
NO3 - F.)
Total Kjeldahl nitrogen Sulphuric acid digestion, automated phenate
method (4500-Norg B,4500-NH3 G)
Filterable reactive phosphorus Filteration through a 1 µm pore size filter.
Automated ascorbic acid reduction method
(4500-P F)
Total phosphorus Persulphate acid digestion, ascorbic acid method
(4500-P B.,4500-P F).
Soluble reactive silicon Automated method for molybdate-reactive
silicon (4500-SiO2 C).
PH Electrometric Method (4500-H + B.)
Conductivity Laboratory Method (2510 B.)
Chlorophyll a Extraction in 90% acetone by ultra-sonication,
fluorometric determination corrected for
phaeophytin (10200 H 3.)
Total and dissolved organic carbon High-Temperature Combustion Method (5310
B.) (Sample for dissolved portion passed through
a 1 µm pore size filter).
Total and volatile suspended sediment Total Suspended Solids Dried at 103-105 o C
(2540 D.) and Fixed and Volatile Solids Ignited
at 550 o C (2540 E.)
Gilvin Filtration through a 1 µm membrane, absorption
at 440 nm relative to a distilled water blank in a
4 cm quartz cell
of 5 days, then frozen for later analysis. This was necessary because it was not
possible to freeze the filter immediately after filtration. This procedure is unlikely to
have compromised the integrity of the analysis, as trials conducted by DIPE have
shown that the chlorophyll a concentrations of filters stored this way were not
statistically different from samples filtered and frozen with 6 hours of collection.
Depth profiles of temperature, dissolved oxygen, pH and conductivity were measured,
at 0.5 m intervals, with a multi-parameter probe (Hydrolab instrument). Profiles of
photosynthetically available radiation (PAR) was also measured, at 0.25 cm depth
intervals, with a Licor 188B sensor. These instruments were weighted to ensure the
63

cable was perpendicular to the water’s surface, and measured depth accurately. The
turbidity of surface samples was measured with a Hach 2100 nephelometric turbidity
meter, calibrated with Formazin standards.
Light attenuation was determined from a regression of the natural log transformed
PAR and depth, where the slope of the regression equals the attenuation coefficient,
and is expressed as the euphotic depth, Zeu (ln(100)/attenuation coefficient). This is
the depth to which 1% of incident PAR penetrates, though on most samples dates the
euphotic depth exceeded the river’s depth.
3.2.3 Statistical Analyses
Pearson product correlations were undertaken using the software program “Sigmastat”
(Fox et al. 1994).
Patterns in the phytoplankton community, as measured by taxon biovolume
concentrations, were examined using the multi-variate analyses of the PATN Software
(Belbin 1993). The Bray-Curtis dissimilarity measure was used to assess the similarity
of samples. This matrix was classified using the FUSE routine and UPGMA option.
The patterns of phytoplankton assemblages are presented as a semi-strong hydrib
(SSH) ordination, and a dendrogram. PCC (principle component correlation) was used
to evaluate the correlation between the ordination and environmental variables that
included all the chemical variables, as well as the Julian day, flow (cumecs) and a
standardised flow parameter where flow is expressed as a percentage of flow
measured in mid-June. The significance of each correlation was assessed by the
Monte-Carlo procedure.
3.3 Results
3.3.1 River flow in 2000
Inter-annual variation in dry season flow of the Daly River and its tributaries is
primatily dependent on the elevation of the groundwater table. In 2000, the
groundwater table of the Oolloo aquifer was historically high, being 15 m higher than
the minimum level recorded over a 20 year period (Tickell 2002). Consequently,
flows in Daly River and its tributaries in 2000 were also historically high. For
example, Daly River flow at Dorisvale in May and June 2000 were the highest on
record, whilst other monthly average flows were ranked amongst the top 10% (Figure
3.2). Flow at each of the sites declined over the dry season until October, when storm
runoff entered the river (Fig. 3.3), marking the transition to the wet season and
cessation of river flow supplied only by groundwater.
64

Cumulative frequency of flows
(equal to or less than nominated value)
100
80
60
40
20
Oct.
Sept.
Aug.
July
June
0
1 10 100 1000
May
Mean monthly flow (cumecs)
2000 dry season
Figure 3.2 Frequency distribution of mean monthly flows for Dorisvale Station.
3.3.2 Water Quality
The water quality of the Daly River and its tributaries, over the dry season, reflect the
relative contributions from the Cretaceous sediment and Daly River Basin aquifers.
The temporal patterns for each water quality parameter are shown in Appendix 3.2
(following chapter 7). In the upper reaches of the catchment, the water quality of the
Katherine River DCP sites differs from sites further downstream (Figs. 3.4, 3.5)
because the DCP is supplied only from the Cretaceous aquifer. Conductivity in the
river is low (20 µS/cm), and the pH slightly acidic (pH 6.5; Figure 3.4). The euphotic
depth in DCP averaged 5.0 m (Figure 3.4), turbidity approximated 2 NTU, suspended
sediment was less than 2 mg/L. Colour (gilvin 0.8 m -1 ) and concentrations of
dissolved organic carbon (1.2 mg/L) were low.
With the introduction of higher conductance waters from the Oolloo aquifer, clarity
increased about 50% due to a reduction in dissolved organic substances that imparts
colour to the water. Turbidity remains largely unchanged, except at the DR Crossing
where turbidity increased. The exceptionally clear waters of the Flora River are
noteworthy; for example, in July the euphotic depth was 15.04 m and turbidity 0.9
NTU. This must rank the Flora River amongst clearest rivers globally.
65

Flow (cumecs)
Flow (cumecs)
Flow (cumecs)
Stage (m)
Stage (m)
1000
100
10
1
1000
100
10
100
10
1
10
1
10
1
Dorisvale
Katherine River
upstream of DCP
Daly River
Flora River
Mt Nancar
Daly River
at Beeboom Crossing
Douglas River
near Oolloo Road Bridge
Apr May Jun Jul Aug Sep Oct Nov Dec
2000
Esplanade

pH
Euphotic depth
(m)
Turbidity (NTU)
DOC (mg/L)
9
8
7
6
15
10
5
0
4
2
0
2
1
0
1 2 3 4 5 6 7 8
Sample site
1 2 3 4 5 6 7 8
Sample sites: 1 Katherine River, Donkey Camp Pool inflow
2 Katherine River, Donkey Camp Pool outflow
3 Flora River
4 Daly River, Dorisvale
5 Daly River, Conservation Esplanade
6 Douglas River, Crystal Falls
7 Daly River, Beeboom Crossing
8 Daly River, Township Crossing
Conductivity
(µS/cm)
Figure 3.4 Average dry season (July, August and September) chemical and
optical water quality. Open circles represent tributaries (Flora and Douglas
Rivers) to the Katherine-Daly Rivers.
800
600
400
20
10
10
5
0
1.0
0.5
0.0
2
1
0
Z eu :depth ratio
Gilvin (m -1 )
Total suspended
sediment (mg/L)
67

Nitrate (as N µg/L)
TKN (µg/L)
FRP (µg/L)
TP (µg/L)
Chl a (µg/L)
100
98
96
10
5
0
8
4
0
8
6
4
2
0
200
100
0
2
1
0
1 2 3 4 5 6 7 8
Sample site
Figure 3.5 Average dry season (July, August and September) nutrient concentrations.
Sample site numbers are the same as those in Figure 3.4. Open circles represent
tributaries (Flora (site 3) and Douglas Rivers (site 6)) to the Katherine-Daly Rivers.
Groundwater inflow from the Daly River Basin to the Daly River and its tributaries is
also a source of dissolved nitrogen and phosphorus, resulting in at least a doubling of
concentrations compared to DCP (Figure 3.5). Although the Douglas River is supplied
principally from the Oolloo and Tindal aquifers, nitrate concentrations are
nevertheless almost twenty times that measured in the Flora and Daly Rivers. These
high concentrations are first evident immediately downstream of the Oolloo Road
bridge where Tindal Limestone groundwaters enter the Douglas River (see Chapter 1).
The high concentrations of nitrate may be a feature of the Tindal aquifer in the region,
and/or result from anthropogenic activity; for example, past fertiliser applications, and
alterations to the catchment’s nitrogen budget caused by clearing and agricultural
practices such as growing legumes.
68

The impact of high nitrate concentrations from the Douglas River on the Daly River
water quality is not apparent at Beeboom, 62 km further downstream from the
confluence of the two rivers. Flow in the Douglas River is diluted an estimated 5-10
fold when it mixes fully with the Daly River. For example, in early October 2000,
flow in the Douglas River was 3 m 3 /s (unpublished data), compared to 20 m 3 /s in the
Daly River 200 m upstream of the Douglas/Daly junction (White 2001), providing a 7
fold dilution. Note though, flow from the Douglas River may not fully mix with the
Daly River for many kilometres downstream of the confluence.
Concentrations of total phosphorus were much the same along the length of the Daly
and Katherine Rivers (Figure 3.5). Total Kjeldahl nitrogen concentrations however
were higher in the Katherine River (Figure 3.5) than sites further downstream. Silicon
concentrations approximated 10 mg/L at all sites, except DCP where concentrations
were slightly lower at 7 mg/L (Appendix 3.2). These concentrations are unlikely to
limit diatom growth.
The euphotic depth was more than double the water column depth for all sites, except
DCP, prior to storm runoff entering the river (Figure 3.4). The lower ratios for DCP
result from the greater depth of pool, compared to other sites, as well as shallower
euphotic depths.
Despite the input of dissolved nutrients and increased water clarity, chlorophyll a
concentrations in the Daly River were always low (

Vertical water quality gradients occurred at the DCP outflow, becoming more
pronounced as the dry season progressed, until storm flow entered the pool in
October. Vertical gradients were greatest in September, when dissolved oxygen
concentrations declined from 6.3 mg/L at the surface to 0.9 mg/L at 4.6 m depth.
Conductivity (µS/cm)
Euphotic depth (m)
Temperature ( o C)
700
500
300
100
32
28
24
20
16
12
8
4
0
Jun Jul Aug Sep Oct Nov
2000
Figure 3.6 Temporal trends of (a) conductivity (Daly River sites 4,5,7,8); (b)
temperature (Daly River sites), and (c) euphotic depth (grey diamond = Flora River;
grey triangle = Douglas River; open circle = Daly (Daly River Crossing).
3.3.3 Phytoplankton
3.3.3.1 Spirogyra
Spirogyra is a benthic alga that grows on all Daly River substrates excluding sand and
mud (see Chapter 6). The alga has been observed to colonise the river early in the dry
season, though this date is likely to be dependent on river flow and vary between
years. In 2000 and 2001 the alga became visible in June, then grew rapidly over a 3-4
week period. In some places, the extent of Spirogyra resembled a “carpet” of green on
70

the river bed. Most of the algae then remained attached to the river bed and banks
until it is sloughed off by the first few storm runoff events of the wet season.
Otherwise, small areas (e.g. 20m 2 ) of the algae have also been observed to be removed
in regions of very high flow, whilst long strands, some up to 1 m in length, are broken.
The alga represented a large proportion (14-97%) of the phytoplankton biomass at the
DR Crossing and Esplanade sites, the Douglas River (Crystal Falls), and, on one
occasion, in DCP (Table 3.4). At the Daly River Crossing site, Spirogyra occurred
between July and November, representing 32-97% of the biomass. The absence of
Spirogyra in June, at any of the sites, concurs with field observations that the algae
colonises early in the dry season.
Spirogyra was not detected at Beeboom, Dorisvale or the Flora River pool sites. The
alga was only detected once in DCP, even though the waters immediately upstream
are fast flowing, indicating the alga may occur less frequently in the low conductance
waters of the Katherine River, compared to higher conductance waters further
downstream. The intermittent occurrence of Spirogyra at the Esplanade and Douglas
River sites may be due to its random distribution in the river, as benthic Spirogyra
was observed upstream of both these sites between July and October. It seems, the
concentration of Spirogyra suspended in the river is dependent on the river turbulence
to keep the alga suspended. When the alga enters pools, it settles to longer remain part
of the suspended algal flora.
Table 3.4 Spirogyra as a percentage of the total biovolume concentration (dash
indicates no sample collection).
Site June July Aug Sept Oct Nov
Katherine River, DCP inflow 1 0 0 0 0 0 0
Katherine River, DCP outflow 2 0 0 0 0 0 50
Flora River 3 - 0 0 0 0 0
Daly River, Dorisvale 4 0 0 0 0 0 0
Daly River, Esplanade 5 - 0 92 0 96 0
Douglas River 6 - 0 14 0 0 62
Daly River, Beeboom 7 - 0 0 0 - 0
Daly River, Crossing 8 0 83 67 76 32 97
3.3.3.2 Seasonal and longitudinal distribution of phytoplankton
biovolume
As flow decreased between June and September, when dry season conditions
persisted, there was no obvious trend in phytoplankton biomass (Figure 3.7), except in
the Douglas River, where the total phytoplankton biovolume and proportion of
diatoms increased. The influence of storm runoff (October and November sample
dates) is difficult to assess because samples were collected at different times over the
storm hydrograph and intervening periods. Nevertheless, samples collected during
storm runoff events (e.g. DCP and Dorisvale in November; Fig. 3.3) still maintained a
phytoplankton biovolume comparable to dry season flows despite likely dilution
effects.
71

Nor were there any obvious longitudinal trends in phytoplankton biovolume (Fig. 3.8).
Concentrations approximated 100 mm 3 /L, with no increase in concentration
downstream as reported for some rivers (see Reynolds 1995).
Biovolume (mm 3/L)
700
650
200
100
0
400
200
0
400
200
0
400
200
Katherine River,
DCP inflow
Katherine River,
DCP outflow
Flora River Douglas River
Daly River, Dorisvale
Daly River, Esplanade
Daly River, Beeboom Daly River, Crossing
0
0
Jun Jul Aug Sep Oct Nov Dec Jun Jul Aug Sep Oct Nov Dec
Figure 3.7 Phytoplankton biovolume concentrations for each site. Open circle =
excludes Spirogyra; closed circle = excludes Spirogyra and diatoms.
Biovolume (mm 3 /L)
200
100
0
200
100
0
June July
August
1 2 3 4 5 6 7 8
Sample site
September
1 2 3 4 5 6 7 8
Figure 3.8 Phytoplankton biovolume concentrations for dry season months.
Open symbol excludes Spirogyra, filled symbol excludes Spirogyra and diatoms.
Site number information is provided in Table 3.1.
700
650
200
100
0
400
200
0
400
0
200
0
400
200
200
100
200
100
0
72

3.3.4 Phytoplankton assemblages in Donkey Camp Pool inflow and outflow
To gain an insight into the effect of pool retention on phytoplankton assemblage,
samples were collected from the inflow and outflow of DCP. These assemblages,
however, did not differ markedly, and were more similar on the day of sample
collection, than between sample dates (Figure 3.9).
0.4550 0.5780 0.7010 0.8240 0.9470 1.0700
| | | | | |
6DCPin _
6DCPout |_______________
7DCPin _______________|_____
7DCPout ____________________|______________________
8DCPin _________________ |
8DCPout ________________|_______ |
9DCPin ___________ | |
9DCPout __________|____________|__________________|____
11DCPin _____________________________________ |
11DCPout____________________________________|_________|______________
10DCPin __ |
10DCPout_|__________________________________________________________|
| | | | | |
0.4550 0.5780 0.7010 0.8240 0.9470 1.0700
Figure 3.9 Dendrogram of Katherine River, Donkey Camp Pool (DCP)
phytoplankton assemblages (exclusive of Spirogyra), based on biovolume
concentration. (Code: numbers refer to sample month; in, inflow; out, outflow).
3.3.4.1 Correlation between environmental variables and phytoplankton
assemblages
Algae suspended in the water may be of benthic, lentic (lakes and reservoirs) or truly
riverine origin. The first multi-variate analysis of the suspended algae data excluded
Spirogyra owing to its benthic origin.
UPGMA classification of the suspended algae data identified four discrete groups
(Figure 3.10). The first group (13 samples) comprises mainly Katherine River DCP
inflow and outflow samples, collected throughout the study with the exception of
October, and samples collected during August from the Flora River and two Daly
River sites. The second group (13 samples) comprises all sites, excluding DCP,
between June and November, though predominantly during the first three months of
the study (June: 2 samples; July: 6 samples; August: 3 samples). The third group
consists of five samples collected during each of the latter three months of the study
from all sites excluding DCP. The fourth group is represented by two samples
collected from DCP in October.
The groups were characteristed by a small number of species abundant in relatively
high concentrations in all samples (Table 3.5). Peridinium inconspicuum was the only
species to occur in high concentrations, at each site, throughout the dry season.
73

Sample* Measure of similarity
0.2310 0.4208 0.6106 0.8004 0.9902 1.1800
| | | | | |
1-6 ( 1)_______________
2-6 ( 2)______________|__________
1-7 ( 5)________________________|____
2-7 ( 6)____________________________|___
5-8 ( 17)_________________ |
7-8 ( 19)________________|______________|_____ Group 1
3-8 ( 15)____________________________________|________
1-8 ( 13)__________________________ |
2-8 ( 14)_________________________|____ |
1-9 ( 21)______________________ | |
2-9 ( 22)_____________________|_______|______________|_
1-11 ( 36)_______________________________________ |
2-11 ( 37)______________________________________|______|_____ ……………………………………..
4-6 ( 3)__________________________ |
4-8 ( 16)_____________________ | |
6-8 ( 18)__________________ | | |
8-8 ( 20)_________________|__|____|____ |
4-7 ( 8)__________________ | |
5-7 ( 9)_______ | | | Group 2
8-7 ( 12)______|________ | | |
7-7 ( 11)______________|__|___________|_____ |
6-9 ( 26)____________________________ | |
5-11 ( 40)___________________________|______|_ |
8-6 ( 4)_______________________________ | |
3-7 ( 7)_________ | | |
6-7 ( 10)________|_____________________|____|___________ | ……………………………………….
3-9 ( 23)______________________ | |
8-9 ( 28)__________________ | | |
6-11 ( 41)_______ | | | |
7-11 ( 42)______|__________|_ | | |
8-11 ( 43)__________________|__|___ | |
4-9 ( 24)____________ | | |
5-9 ( 25)___________|__ | | |
5-10 ( 33)_____________|__________|_____________ | | Group 3
3-10 ( 31)_________________________________ | | |
4-10 ( 32)_________________________ | | | |
8-10 ( 35)________________________|_______|__ | | |
4-11 ( 39)__________________________________|__|_____ | |
7-9 ( 27)_ | | |
3-11 ( 38)|________________ | | |
6-10 ( 34)________________|_________________________|___|___|__________ ……………
1-10 ( 29)________________ | Group 4
2-10 ( 30)_______________|____________________________________________| ………..
| | | | | |
0.2310 0.4208 0.6106 0.8004 0.9902 1.1800
• Sample legend. Site number first, month second separated by a dash.
Figure 3.10 Dendrogram of phytoplankton, exclusive of Spirogyra.
Linear correlations between twenty environmental variables and the similarity matrix
were conducted. Five variables were significant at the 5% level. These were flow
(r=0.46), conductivity (r=0.55), filterable reactive phosphorus (r=0.54) and gilvin
(r=0.51) (Figure 3.11). Of these variable, flow was not correlated to any other, but
conductivity was correlated to FRP and gilvin, whilst FRP and gilvin were correlated
(Table 3.6). These cross-correlations confound the interpretation of the ecological
importance of the water quality variables.
74

SSH2
2
1
0
-1
-2
Flow
Cond.
FRP
Gilvin
-2 -1 0
SSH1
1 2
Legend: Group 1= circle (black)
Group 2=circle (grey)
Group 3=circle (white)
Group 4=square (black)
FRP=filterable reactive phosphorus
Cond. = conductivity
Figure 3.11 Vectors for significant environmental variables, superimposed on
ordination of the phytoplankton assemblage (exclusive of Spirogyra), with
dendogram groupings.
Table 3.5 Commonly occurring species characteristic of each dendrogram groups
(see Figure 3.10)
Group Phytoplankton species
1 Navicula radiosa (Bacillariophyceae)
1 Ankistrodesmus convolutus (Chlorophyta)
2 Navicula cryptotenella (Bacillariophyceae)
2 Navicula cf. recens (Bacillariophyceae)
2 Nitzschia cf. agnita (Bacillariophyceae)
2 Synedra ulna var. amphirhynchus (Bacillariophyceae)
3 Achnanthes cf. subexigua (Bacillariophyceae)
4 Acanthoceros sp.1 (Bacillariophyceae)
4 Fragilaria zasuminensis (Bacillariophyceae)
4 Urosolenia eriensis var. morsa (Bacillariophyceae)
4 Cosmarium binum (Desmidaceae)
4 Staurastrum bifidum (Desmidaceae)
4 Staurastrum pinnatum var. subpinnatum (Desmidaceae)
4 Trachlomonas sp. 1
4 Trachlomonas sp. 5
4 Peridinium umbonatum var. remotum
75

Table 3.6 Correlation between statistically significant environmental variables
that explain phytoplankton assemblages (exclusive of Spirogyra).
Conductivity FRP Gilvin
Flow r=0.24; p=0.12 r=0.05; p=0.73 r= -0.08; p=0.67
Conductivity r=0.53; p=

Sample* Measure of similarity
0.1370 0.3336 0.5302 0.7268 0.9234 1.1200
| | | | | |
1-6 ________________________________________
2-8 ___________________________________ |
5-8 __________________________________|____|___
2-6 ___________________ | Group 1
6-8 __________________|________ |
4-6 __________________________|_____ |
2-7 _______________________________|_ |
1-7 ________________________________|_________|___________ …………………………………..
4-7 ______________________ |
5-7 ________ | |
5-11_______|___ | |
8-7 __________|____ | |
7-7 ______________|______|___ |
4-8 ____________________ | |
8-8 ___________________|____|_________________ |
3-9 _________ | |
4-9 _ | | |
8-9 |_______|___ | |
5-10___________|____ | |
6-11_______ | | | Group 2
7-11______|________|______ | |
5-9 ______ | | |
8-11_____|_______________|___________________|__ |
1-8 ______________________________ | |
1-9 ___________________ | | |
2-9 __________________|__________|_____ | |
3-10_________________________________ | | |
4-10_______________________ | | | |
4-11______________________|_________|_|__ | |
3-8 ___________________ | | |
7-8 __________________|______ | | |
6-9 _____________ | | | |
8-10____________|___________|___________|______|_________|_______ ………………..
8-6 ___________________________ |
3-7 __________ | |
6-7 _________|________________|_______________________________ | Group 3
1-11__________________________________________________ | |
2-11_________________________________________________|_______|__| ………………..
7-9 __ ||
3-11_|___________ ||
6-10____________|_____________________________________ || Group 4
1-10_______________________ | ||
2-10______________________|__________________________|_________||
| | | | | |
0.1370 0.3336 0.5302 0.7268 0.9234 1.1200
• Sample legend. Site number first, month second separated by a dash.
Figure 5.12 Dendogram of phytoplankton, exclusive of Spirogyra and diatoms.
SSH2
2
1
0
-1
-2
Cond
Si
pH
Flow
Gilvin
-2 -1 0
SSH2
1 2
Legend: diamond (black) = Group 1
diamond (grey) = Group 2
diamond (white) = Group 3
triangle (black) = Group 4
Figure 5.13 Vectors for significant environmental variables, superimposed on
ordination of the phytoplankton assemblage (excludive of Spirogyra and
diatoms), with dendrogram groupings.
77

3.4 Discussion
The principal factors selecting phytoplankton in rivers are: (1) nutrient limitation
(N,P, Si), (2) light limitation, (3) river discharge, and (4) river mixing dynamics.
These factors combine to determine the total biomass of phytoplankton, and its
species assemblage.
Nutrient limitation of phytoplankton biovolume is rare in river systems, owing to the
dominance of physical constraints (Reynolds 1995; Wehr and Descay 1998).
Concentrations of phytoplankton are low in the Daly River and its tributaries
compared to large rivers, such as the lower reaches of the Murray River, but similar or
greater than smaller rivers in southern Australia (see Chapter 2).
The concentration of phytoplankton is unlikely to be limited by nutrients, as the
addition of soluble nitrogen and phosphorus from the Oolloo aquifer is not
accompanied by elevated concentrations of chlorophyll a and biovolume.
Nevertheless, the addition of nutrients may have altered the phytoplankton
assemblage, based on the relative abilities of species to utilise these nutrients.
Filterable reactive phosphorus was found to be a significant correlate when the
phytoplankton data set included diatoms, though notably not when diatoms were
excluded from the data set. This probably reflects the significance of reactive
phosphorus in explaining the assemblage of benthic diatoms (see chapter 5).
Diatoms in the Daly River and its tributaries are unlikely to be limited by silica, an
essential element for diatoms for their cell frustules. Concentrations exceeded 0.6
mg/L Si, found to limit the growth of some diatoms (see Kilham 1988). The nutrient
was not significantly correlated to the phytoplankton assemblages when the multivariate
analysis included diatoms. Silica, however, was a significant correlate for the
analysis that excluded diatoms, but itself was correlated to conductivity, pH and
gilvin. This correlation is not considered to have ecological significance, instead it is
attributed to silica’s correlation to other water quality variables of potential ecological
significance.
The waters of the Daly River, when groundwater fed, are clear. Furthermore, incident
radiation levels are relatively high at a latitude of 13°S. The river is directly exposed
to sunlight between about 10 am and 3 pm when daytime incident radiation levels are
greatest. In the early morning and late afternoon portions of the river were in shadow.
Consequently, light is not likely to limit phytoplankton biovolume or influence its
assemblage at any of the sites. This conclusion is supported by the multi-variate
analyses, which found no significant correlation between the euphotic depth and
phytoplankton assemblage.
Gilvin was significantly correlated to the phytoplankton assemblages in both statistical
analyses. Gilvin is a quantitative measure of dissolved organic substances (e.g. humic
acids) that absorb light with wavelengths of greater than 400 nm. In waters with very
high gilvin values (e.g. 20 m -1 , Wrigley et al. 1988), the absorption of light by humic
substances significantly reduces light penetration. This is clearly not the case in the
Daly River and its tributaries. The correlation between gilvin and conductivity best
78

explains gilvin’s statistical significance, and, like silica, is unlikely to provide
selective pressure on the river’s phytoplankton species or limit its biovolume.
Conductivity and pH contributed to explaining the variability in phytoplankton
assemblages. Phytoplankton cells tolerate a range of salinities and pH, specific to a
species. When the salinity exceeds this range, the cells die, due to water movement
into or out of the cell. If a cell cannot maintain its plasma pH, critical enzymes may
not function. The salinity and pH tolerances of benthic diatoms is known for some
temperate species, but poorly known for phytoplankton except over large salinity
(freshwater-brackish-marine) and pH ranges. The ionic chemistry of the Daly River
and its tributaries may exert selective pressure on the assemblage of phytoplankton.
Both correlation analyses identified flow as a factor that explained the phytoplankton
assemblage of the Daly River and its tributaries. Flow will affect river phytoplankton
indirectly through the following (Reynolds 1995):
(1) current speed, and hence the rate of removal of phytoplankton.
(2) The occurrence of zones where water is retained for periods longer than the
average travel time. These are likely to be more common in the lower reaches of a
river. However, the deep pools of Katherine Gorge and further downstream (e.g.
Dorisvale-Beeboom reach) may have zones of long retention time.
(3) The growth of phytoplankton in these low retention zones, that are separated from
the main flow by a boundary where water is exchanged with the river.
These hydrodynamic factors, along with water quality and grazing, combine to shape
the size and structure of the phytoplankton assemblage. The results of the statistical
analysis suggest that the mixing dynamics of the Daly River vary with flow, which in
turn influences the phytoplankton assemblage. The actual mechanism, however, was
not been examined by study.
3.5 Implications for environmental flow allocation
The study has shown that the phytoplankton assemblage of the river was responsive to
flow. The extent to which flow determines the assemblage over the historic range is
unknown, though it is reasonable to speculate that the hydraulic mechanisms that
influenced phytoplankton assemblage in 2000 would also in years of lower river flow.
Consequently, a reduction in dry season flow, due to water allocation for consumptive
uses, could affect the size and nutritional value of the phytoplankton assemblage. This
may have broader ecological implications for zooplankton grazing, and the river’s
carbon (energy) dynamics.
The concentration of phytoplankton, measured as either chlorophyll a or biovolume,
in the Daly River and its tributaries was limited by river flow, rather than light,
nutrients or zooplankton grazing. Under lower flows, however, due to either climatic
or anthropogenic influences, phytoplankton concentrations may no longer become
limited by flow, and instead become nutrient limited. Under such a scenario, where
soluble nitrogen and phosphorus enter the river from the Daly River Basin,
phytoplankton concentrations could be expected to increase.
79

Phytoplankton biovolume is often reported to increase as river flow reduces, though
there was no evidence of this in the 2000 dry season. This may occur, though, when
river flows are lower.
In summary, the assemblage of phytoplankton is responsive to flow in the Daly River
and its tributaries, and ought to be a useful biological indicator in determining the
significance of any anthropogenic impacts on the rivers’ ecosystem. These, of course,
are not limited to the consumptive use of water. Changes in land-use and catchment
hydrology will also potentially impact the river’s ecosystem.
To incorporate phytoplankton (and other biological indicators) in a monitoring
program, as part of an adaptive management strategy, there needs to be (1) collection
of pre-impact data, and (2) evaluation of the effect of consumptive water use on river
flow, and (3) identification of other anthropogenic impacts on the river’s ecosystem.
3.6 Recommendations
The physical mechanisms that determine phytoplankton assemblage be assessed.
The ecological implications, in particular for zooplankton grazing, of phytoplankton
assemblages is assessed.
Further monitoring be undertaken, to evaluate the effect of the historic the range of
flows under on phytoplankton assemblages and biovolumes.
The effect of river pools, such as that at Beeboom Crossing, on phytoplankton
assemblage and biovolume be investigated, and its utility for long term monitoring be
assessed.
3.7 References
Bowling, L. C. and P. D. Baker (1996). “Major cynanobacterial bloom in the Barwon-
Darling River, Australia, in 1991, and underlying limnological conditions.” Aust. J.
Mar. Freshw. Res. 47: 643-657.
Donnelly, T. H., M. R. Grace, et al. (1997). “Algal blooms in the Darling-Barwon
River, Australia.” Water, Air and Soil Pollution 99: 487-496.
Faulks, J. (1988) Daly River catchment. Part 1 An Assessment of the physical and
ecological condition of the Daly River and its major tributaries. Northern Territory
Department of Lands, Planning and Environment. Darwin.
Gosselain, V., L. Viroux, et al. (1998). “Can a community of small-bodied grazers
control phytoplankton in rivers?” Freshwater Biology 39: 9-24.
80

Jolly, P. (2001) Daly River catchment water balance. Northern Territory Department
of Infrastructure, Planning and Environment. Darwin.
Kilham, P. (1988). Ecology of Melosira species in the great lakes of Africa. Large
lakes: ecological structure and function. M. M. Tilzer and C. Serruya. New York,
Springer-Verlag: 414-427.
Reynolds, C. S. (1995). River plankton: The paradigm regained. The ecological basis
for river management. D. M. Harper and A. J. D. Ferguson, John Wiley & Sons Ltd:
161-174.
Reynolds, C. S. (1996). “The 1996 founders' lecture: potamoplankters do it on the
side.” Eur. J. Phycol. 31: 111-115.
Sherman, B. S., I. T. Webster, et al. (1998). “Transitions between Aulacoseira and
Anabaena dominance in a turbid weir pool.” Limnol. Oceanogr. 1998: 1902-1915.
Tickell, S.J. (2002) A survey of springs along the Daly River. Report 06/2002.
Northern Territory Department of Infrastructure, Planning and Environment. Darwin.
Wehr, J. D. and Descay. J.P. (1998). “Use of phytoplankton in large river
management.” J. Phycol. 34: 741-749.
White, E. (2001) A late dry season survey of the Katherine and Daly Rivers. Report
24/2001D. Northern Territory Department of Infrastructure, Planning and
Environment. Darwin.
Wrigley, T. J., Chambers, J. M. and McComb, A.J.(1988). “Nutrient and gilvin levels
in waters of coastal-plain wetlands in an agricultural area of Western Australia.” Aust.
J. Marine Freshwater Res. 39: 685-694.
81

3.8 Appendix 3. 1
Dry season gaugings (cumecs) at Beeboom and Mt Nancar Daly River
hydrographic stations
Beeboom Mt
Difference
Nancar
in flow
Date Time Flow Date Time Flow Mt Nancar-
Beeboom
26/07/80 10:10 22 20/07/80 12:00 25.4 3.4
16/08/80 16:04 18.1 12/08/80 16:35 20.8 2.7
3/06/83 8:00 22.7 2/06/83 10:35 23.2 0.5
16/08/83 16:40 19.7 12/08/83 15:07 20.3 0.6
22/09/83 15:32 18.8 18/09/83 12:38 19.8 1
7/10/83 10:00 16.5 8/10/83 9:45 15 -1.5
26/10/83 7:55 16.5 23/10/83 9:52 17 0.5
30/06/84 11:20 29.1 27/06/84 12:40 35.6 6.5
21/08/84 11:15 24.3 21/08/84 18:10 25.9 1.6
19/06/87 12:15 19.3 17/06/87 10:00 20.4 1.1
24/09/87 8:55 18.2 22/09/87 11:35 14.5 -3.7
7/06/88 14:35 15.9 8/06/88 10:30 15.5 -0.4
20/07/88 9:50 18 21/07/88 10:30 13.9 -4.1
7/09/88 16:50 11.9 6/09/88 13:53 13.7 1.8
18/07/89 13:30 21.6 20/07/89 11:30 16.9 -4.7
31/08/89 16:13 18.4 31/08/89 9:12 16.6 -1.8
11/10/89 15:30 15.6 12/10/89 11:45 20.2 4.6
30/08/90 12:10 11.1 29/08/90 15:15 13 1.9
22/10/90 15:00 10.7 22/10/90 15:00 12.3 1.6
Beeboom Crossing flow (cumces)
40
35
30
25
20
15
10
10 15 20 25 30 35 40
Mt Nancar flow (cumecs)
y=0.69X + 5.3 (r 2 =0.75, p

3.9 Appendix 3.2 Water quality figures (presented after Chapter 7)
4 DIATOM ASSEMBLAGES ON RIVER SUBSTRATES
Townsend, S.A. ♦ , Gell, P.A. * and Bickford, S. *
♦ N.T. Dept. of Infrastructure, Planning & Environment,
* University of Adelaide.
4.1 Introduction
Benthic diatoms are an abundant component of periphyton in rivers and streams, and
important primary producers in many lotic food webs. Their ubiquitous distribution in
the aquatic environment and responsiveness to water quality have made diatoms a
popular biological indicator of river health in Europe (Whitton et al. 1995) and North
America (Porter et al. 1993, Barbour et al. 1999), and increasingly in Australia.
Diatoms grow on a wide range of substrates, classified into the following categories:
epilithon - periphyton growing on rocks.
epidendron - periphyton growing on submerged wood.
epipelon - periphyton growing on fine sediment (e.g. mud).
epipsammon - periphyton growing on sand.
epiphyton - periphyton growing on submerged aquatic plants, including macroalgae.
epizoon - periphyton growing on aquatic animals.
In addition to these natural substrates, diatoms will also grow on artificial substrates,
such as glass and plastic surfaces.
Three approaches are generally considered in selecting substrates for benthic diatom
sample collection, each with its own advantages and disadvantages. These are:
(1) Artificial substrates. The principal advantages of these substrates are (1) the
uniform nature of the substrate, (2) known period of colonisation, (3) the potential
to be deployed in any water body, and (4) easy determination of sample area for
quantitative evaluations. Glass slides are the most commonly used artificial
substrate. The main disadvantage is the requirement for two field trips (deployment
and retrieval), thereby doubling field time compared to sampling natural substrates.
Whilst the period of colonisation is known for artificial substrates, other factors
(e.g. water quality and temperature) also influence colonisation and may need to be
considered (Cattaneo and Amireault 1992). The deployment time for artificial
substrates is usually fixed to standardise the effect of time, which reduces the
flexibility to schedule field trips. Moreover, artificial substrates are vulnerable to
natural (e.g. storm runoff) and anthropogenic disturbances (e.g. removal and
vandalism). Also, artificial substrates may not have an assemblage typical of the
natural environment, which could limit their ecological relevance and value as
indicators of diatom biodiversity. In a large catchment such as the Daly River
(48,400 km 2 at the Daly River township), the use of artificial substrates and
83

equirement for two field trips would have added considerably to the cost of
sampling and was not investigated.
(2) Multi-substrate sampling. This sampling protocol best characterises benthic
diatoms in a reach, and provides the most complete information about diatom
diversity compared to a single substrate. There is an assumption, though, that
diatom relative abundance will vary with river substrate. Where a wide range of
substrates are sampled along a reach, there is potential for any heterogeneity in
water quality to confound the ecological interpretation of the diatom assemblages.
The main disadvantage is the necessity for all substrates to be present at a river site.
(3) Single substrate sampling. The variability between substrates may be reduced by
using a single substrate, enhancing the comparability between sites. The principal
disadvantage of this sampling protocol is the dependency of site selection on
substrate availability. This, however, may be partly overcome if two or more
substrates can be shown to have similar diatom assemblages. Species richness of a
single substrate may be lower than multi-substrate sampling.
The third protocol was selected, and a study undertaken to assess (1) availability of
river substrates, and (2) the similarity of diatom assemblages on river substrates. If
diatom assemblages on different substrates are similar, this would increase the number
of substrates available for sampling.
In addition to evaluating diatom assemblages on river substrates, the number of
diatoms required to evaluate species richness has also been evaluated. This is referred
to as the counting effort.
4.2 Methods
4.2.1 Site selection criteria
Five sites were selected in the Daly River catchment (Fig, 4.1), and two sites in the
adjacent Roper River catchment (Table 4.1). The sites were selected according to the
following criteria:
• ease of vehicle access,
• a river reach relatively undisturbed by anthropogenic activity,
• a riffle or run section of the river with homogenous water quality,
• at least three substrates,
• a wide water quality range.
• a water depth of no more than 50 cm.
Pool habitats were not sampled primarily due to their depth, and potential for water
quality heterogeneity due to “dead” zones of water which are hydraulically isolated
from the main flow, and stratification. The two sites from the Roper River catchment
were included to extend the water quality range. The sites were sampled during the
dry season of 2000, in July and August (Table 4.1), and more than 8 weeks after the
last major storm runoff that may have disturbed diatom colonisation (Figure 4.2).
84

Figure 4.1 Sample sites in the Daly River catchment, and hydrographic stations.
85

Flow (cumecs)
Flow (cumecs)
Flow (cumecs)
Stage (m)
Stage (m)
1000
100
10
1
100
10
1
100
10
1
10
1
10
1
Katherine River,
upstream of Donkey Camp Pool
(G8140022)
Roper River, G9030176
Flora River (G8140044)
Daly River,
at Beeboom Crossing
(G8140042)
Douglas River,
near Oolloo Road Bridge
(G8140063)
Apr May Jun Jul Aug
2000
Crystal
Falls
Near Hot
Springs
Figure 4.2 Sample collection dates during the seasonal recession of the Daly,
Douglas, Flora and Katherine Rivers. (No hydrographic information is available for
Salt Creek)
86

Table 4.1 Diatom sample sites and dates.
Abbreviated Site Sample HYDSYS Latitude S Longitude E
name number date number *
Flora River, Kathleen Falls Kathleen Falls 1 17/7/00 G8145406 14 o
45.305'
131 o 35.817'
Salt Creek, Roper Highway
bridge
Salt Ck 4 18/7/00 G9035399 15 o 0.763' 133 o 14.241'
Roper River, Moroak Roper River 5 18/7/00 G9035398 14
Station causeway
o
133
50.281'
o 38.703'
Katherine River, Donkey Katherine 2 16/7/00 G8145403 14
Camp Pool (DCP) inflow River, DCP
o
132
22.465'
o 21.787'
Douglas River, 200 m Douglas River 12 11/8/00 G8145409 14
upstream of Douglas hot
springs
o
131
45.988'
o 26.646'
Douglas River,
Crystal Falls 9 20/7/00 G8145386 13
Crystal Falls
o
131
50.508'
o 08.853'
Daly River,
Beeboom Crossing
Beeboom Hydrographic
station
downstream
of site 9
21/7/00 G8145387 13 o
51.650'
131 o 04.631'
* Sample location registered in DIPE HYDSYS water resource database.
Epilithic samples were collected to evaluate counting effort from the Katherine River at Donkey Camp Pool
inflow (Table 2.1), and the Daly River at Dorisvale Crossing (Figure 4.1)
4.2.2 Field measurements
Temperature, dissolved oxygen, pH and conductivity were measured with a Horiba instrument, calibrated
before each field trip. Field and laboratory measurements of conductivity were comparable, in contrast to
pH, which varied by up to one pH unit (Figure 4.3). In the field, it was observed that some pH
measurements took a long time to equilibrate (10 minutes or more) in waters of low buffering capacity, and
may not have reached equilibrium. For this reason, laboratory measurements of pH have been used.
Turbidity was measured with a Hach turbidity meter, calibrated with Formazin standards.
4.2.3 Chemical analyses
Water samples were analysed for the parameters listed in Table 4.2 by standard methods. Gilvin is a
measure of colour imparted to water by dissolved humic substances (Cuthbert & del Giorgio 1992).
4.2.4 Diatom sample collection
At each site, a diatom sample was collected from each of three replicates of a substrate. At least three
substrates were sampled at each site. Epilithon, epidendron and firm epipelon samples were first shaken
under the water, to remove any diatoms growing amongst silt covering these hard surfaces. The diatom flora
were then
87

Figure 4.3 Comparison of field and laboratory measurements of conductivity and
pH for the Daly River catchment sites.
(a) Laboratory versus field measurement of conductivity
Laboratory
conductivity (µS/cm)
Laboratory pH
800
600
400
200
0
9
8
7
6
Y=0.94X+21 r 2 =0.976
0 200 400 600 800 1000
Field conductivity (µS/cm)
(b) Laboratory versus field measurement of pH
Y=1.037X-0.12 r 2 =0.58
5
5 6 7 8 9
Field pH
sampled by scraping the surface with a clean wooden spatula. Substrates were selected
from waters of no more than 50 cm depth, with most samples collected in water less than
25 cm deep.
Epipsammon were collected by moving the sample container, a small plastic test-tube
(mouth diameter 1 cm), along the sand bed to collect the surface film together with a
small amount of sand. Epiphytic samples were collected from Vallisneria and Juncus
leaves by scaping the leaf with a blade. Macroalgae samples were collected by taking a
subsample of the filamentous algal strand, and placing it directly in a container. Algal
strands were also squeezed by hand, when there was sufficient quantity, and the water and
other material collected. Porter et al. (1993) has suggested this method of epiphytic
88

sample collection. Bacterial slime and Chara samples were placed whole in the sample
container. All samples were preserved in Lugols iodine.
Table 4.2 Analytical methods for water samples. Parentheses contain the APHA (1998)
method number (except for gilvin).
Parameter Method
Gilvin Absorption at 440 nm on a UV/VIS
spectrophotometer with a 4 cm quartz
cell and distilled water blank (Kirk
1976).
Nitrate, nitrite Automated cadmium reduction method (4500-
NO3 - F.)
Total Kjeldahl nitrogen Sulphuric acid digestion, automated phenate
method (4500-Norg B,4500-NH3 G)
Filterable reactive phosphorus Filteration through a 1 µm pore size filter.
Automated ascorbic acid reduction method
(4500-P F)
Total phosphorus Persulphate acid digestion, ascorbic acid method
(4500-P B.,4500-P F).
Soluble reactive silicon Automated method for molybdate-reactive
silicon (4500-SiO2 C).
PH Electrometric Method (4500-H + B.)
Conductivity Laboratory Method (2510 B.)
Alkalinity Titration Method (2320 B.)
Bicarbonate Alkalinity and HCO3 - ions Carbon Dioxide and Forms of Alkalinity by
Calculation (2320 B.)
Sulphate ion Automated Methylthymol Blue Method (4500-
SO4 2- F.)
Ca, Mg, K, Na ions Direct Air-Acetylene Flame Method (3111 B.)
Chloride ion Automated Ferricyanide Method (4500-Cl - E.)
4.2.4 Diatom identification and enumeration
In the laboratory, samples were treated with dilute HCl and H2O2 following the methods
of Battarbee (1986) and Gell et al. (1999). Sub-samples of 400 µL were placed on each of
two coverslips and allowed to dry. These were inverted on drops of warm Naphrax
mountant on a microscope slide, gently pressed, and allowed to set. Diatoms were viewed
under a Nikon Axiolab microscope with Differential Interference Contrast at 1500x
magnification using immersion oil. Species were identified using the standard taxonomic
texts of Krammer and Lange-Bertalot (1986-1991) and regional floras such as John
89

(1983). Images of most taxa were produced using a poloroid and Sony video camera and
captured electronically using miraVideo to ensure taxonomic consistency between
operators. A minimum of 300 valves was counted from each of the two coverslips along
set vertical transects. The names used here have been updated following the review of
Fourtanier and Kociolek (1999).
To evaluate the species-effort relationship, a total of 797 and 658 diatoms were counted,
respectively, for the Dorisvale and DCP samples.
4.2.5 Data analysis
Two complementary analytical approaches were employed to examine the relationship
between substrate and diatom assemblages. Univariate analyses of variance (ANOVAs)
were used to test hypotheses about the equality of the relative abundance of common taxa
and species richness on substrates. The advantage of univariate analyses is their
inferential technique to test and disprove null hypotheses. However, such an approach can
only be applied to a single variable, precluding an analysis of diatom assemblage as a
single data set.
To examine the relationship between diatom assemblages, simultaneously, a
non-parametric approach of ordination has been used. This technique is useful in
identifying any patterns in the data.
Univariate analyses Several ANOVAs were performed for a site, followed by Tukey
pair-wise comparisons when the ANOVA was significant at the 5% level. The maximum
number of tests undertaken, at a site, was nine. The tests undertaken for each site are
considered independent of each other. When making multiple tests of hypotheses, the
Type I error (wrongly rejecting the null hypothesis) rate for a family of comparisons
increases beyond the chosen level of significance (∝) for a single test, so that for c
comparisons, the family-wise rate is less than or equal to 1-(1-∝) c . Consequently, it can
be argued that the Type I error rate should be corrected (reduced) for each comparison to
maintain an acceptable family-wise error rate.
In this report, several ANOVAs have been performed for species relative abundances at a
site. Analyses for each of these variables used separate data, as a result univariate F-tests
were statistically independent, though they may not be biologically independent at a site
because the abundance of one species may influence the abundance of another. A reduced
individual level of significance could be used to maintain an acceptable family-wise Type
I error. In such situations, Stewart-Oaton (1995) emphasise that the interpretation of the
ANOVAs be made with care, and that the level of correction required is based on
judgement rather than any mathematical grounds. Without detailed knowledge of the
interactions between species, it is not possible to quantify the degree of biological
90

independence and hence to determine the correction required. In this report, therefore,
error rates have not been adjusted. The results from the sites have been pooled to give an
overview of the similarity of diatom assemblages on river substrates.
The statistical power of each ANOVA and pair-wise test was assessed. This is the
probability of accepting the null hypothesis when it is false. Tests undertaken with low
power may mistakenly accept the null hypothesis when, in fact, it is false due to
inadequate replication and relatively small actual differences between the sample means
being tested. A desirable power of 0.8 was chosen, meaning the probability of correctly
accepting the null hypothesis was equal to or greater than 0.8.
To satisfy the assumptions of normality and equal variance, the data was log10(x+1) or
square root (x + 1) transformed. Statistically significant ANOVAs were followed by
Tukey pair-wise comparisons. All statistical analyses, including tests for normality, equal
variance and power, were conducted using the software “Sigma-Stat” (Fox et al. 1994).
Multivariate analyses Patterns in diatom assemblages were examined using the PATN
multi-variate analysis software package (Belbin 1993). A matrix of similarity between
diatom assemblages, comprising species relative abundances, was constructed using the
Bray Curtis measure, then ordinated by the semi-strong hybrid (SSH) option.
4.3 Results
4.3.1 River substrates sampled
Epilithic substrates were present at each of the seven sites selected (Table 4.3). Other
commonly occurring substrates were epiphyton (macroalgae), epidendron (woody debris)
and epipsammon (sand). Some macroalgae were small, and could not be squeezed by
hand. Aquatic vegetation (Chara sp., Vallisneria nana, Juncus sp.) and epipelon (firm
mud surfaces) were not common. Soft surfaces, such as detritus and sediment, were
absent.
91

Table 4.3 River substrates sampled
Site Epilithon
Epipsammon
Epidendron
Epiphyton
Epiphyton
(sq.)
Epiphyton
(plants)
Bacterial
slime
Kathleen Falls X X(1) X
Salt Creek X X X*
Roper River X X X(2) X
Katherine
River, DCP
X X X(3)
Douglas River X X X X (4) X
Crystal Falls X X X X(5) X
Beeboom
Crossing
X X X X(6) X X#
Epipelon
(mud)
Total 7 4 5 6 3 3 1 1
sq, squeezed macro-algae
* Juncus sp, and Chara sp.; # Vallisneria nana leaves
(1) Batrachospermum australicum, (2) Spirogyra, Oedogonium, Vaucheria and
Cladophora complex, (3) Zygnematales, (4) two macroalgae sampled; Cyanobacteria,
Chlorophyta (genera not identified) (5) Spirogryra sp., (6) Spirogryra sp.
4.3.2 Water quality
At each site, the waters were clear, with low turbidity (

Donkey Camp
Pool inflow
(97%)
Douglas River, 0.4 3.6 7.6 24.9 14 3 5 1 80
u/s hot springs
(94%)
Crystal Falls, 0.42 2.0 8.2 24.4 10 91 3 1 50
Douglas River
(100%)
Daly River, 0.27 2.6 8.8 24.9 10 11 3 1 50
Beeboom
crossing
(109%)
g, gilvin; Turb, turbidity; DO, dissolved oxygen with percentage saturation in parentheses; Temp,
temperature; NOx, nitrate and nitrite; TP, total phosphorus; FRP. filterable reactive phosphorus; TKN, total
Kjeldahl nitrogen.
4.3.3 Comparison of substrate diatom assemblages by ordination.
Ordination of the diatom assemblages are presented in Figure 4.4, with commentary
below for each site.
Flora River at Kathleen Falls. Replicates for each substrate lie close to each other in
ordination space, and separated from the other substrates.
Katherine River at Donkey Camp Pool inflow. The rock triplicate samples are separated
from algae and epipsammon samples which exhibited minor overlap.
Salt Creek at Roper River Highway bridge. There is no clear separation of the substrates.
Roper River at Moroak Station. The rock and epidendron replicates overlapped in
ordination space, whilst the algae and squeezed algae overlapped. These two groups (rock
and epidendron , algae and squeezed algae) however were clearly separated.
Daly River at Beeboom Crossing. The rock, algae, squeezed algae, and epidendron
replicates tended to overlap, but were distant from the Vallisneria substrate assemblage.
Douglas River at Crystal Falls. Replicates of the epipsammon , algae and squeezed algae
substrata grouped separately, and were distant from the rock and epidendron substrates
which overlapped in ordination space.
Douglas River, 200m upstream of Douglas Hot Springs. The replicates grouped together,
separated from the other substrates. The epipelon substrate exhibited poor replication and
overlapped with the epidendron assemblage
93

Table 4.5 Ionic chemistry at substrate sample sites. All concentrations % meq/L
Cond. Na K Ca Mg Dominance
(based on percentage meq/L)
Flora River
(µS/cm)
776 15 3.7 70 41 Mg=Ca>>Na>K
Salt Creek 6520 910 80 137 282 Na>>Mg>>Ca>K
Roper River 1300 110 14 68 51 Na>Mg>Ca>>K
Katherine River,
DCP
27 2.6 0.5 0.9 1.3 Na=Mg>>Ca>Na
Douglas River, u/s
hot springs
43 2.0 0.5 3.9 1.7 Ca>Mg>Na>K
Douglas River,
Crystal Falls
481 4.6 2.1 56 25 Ca>Mg>Na>K
Daly River,
Beeboom Crossing
538 7.7 2.2 55 35 Ca=Mg>Na>K
pH Total
alkalini
ty
Cl SO4 HCO3 NO3 Dominance
Flora River 7.6 344 19 27 419 0.10 HCO3> SO4≈Cl
Salt Creek 8.0 572 1260 1510 699 0.02 Cl≈ SO4> HCO3
Roper River 8.2 292 153 159 356 0.02 HCO3>Cl>SO4
Katherine River,
DCP
6.2 9.8 3 1 12 0.02 HCO3>Cl> SO4
Douglas River, u/s
hot springs
6.3 20 3 2 24 0.06 HCO3> Cl> SO4
Douglas River,
Crystal Falls
8.2 254 6 11 311 1.9 HCO3> SO4≈Cl
Daly River,
Beeboom Crossing
8.2 283 10 18 346 0.044 HCO3> SO4≈Cl
94

SSH2
2
1
0
-1
Kathleen Falls
SSH2
2
1
0
-1
Salt Creek
-2
-2 -1 0 1 2
Katherine River,
DCP
Roper River
-2
-2
-2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2
SSH2
Legend
2
1
0
-1
Beeboom Crossing
-2
-2 -1 0 1 2
Circle, grey: epilithon
Circle, filled: epidendron
Circle, open: Chara
Triangle, grey: Vallisneria
Triangle, filled: epipelon
Triangle, open: bacterial slime
Crystal Falls
-2 -1 0 1 2
Douglas River
-2 -1 0 1 2
Figure 4.4 Ordination of diatom assemblages. Each symbol represents the
assemblage for a single sample. Triplicate samples were collected from each
substrate.
2
1
0
-1
-2
-1
-2
Diamond, grey: Macroalgae 1
Diamond, filled: Macroalgae 2
Diamond, open: Macroalgae, squeezed
Square, grey: episammon
Square, filled: Juncus
2
1
0
Y Data
2
1
0
-1
95

4.3.4 Comparison of substrate species richness
Species richness was not uniform between sites (Table 4.6), varying between 42 (Salt
Creek) and 87 (Roper River). There was no clear trend between the number of substrates
sampled, and species richness indicating other factors (e.g. water quality) influence
species richness between sites.
Substrate species richness, at each site, was evaluated by ANOVA (Appendix 4.1). The
null hypothesis that the number of species was equal for each substrate was rejected for
five of the seven sites, and followed by pair-wise comparisons. ANOVAs for Salt Creek
and Crystal Falls were not significant, though this should be viewed cautiously as the
power of these tests were less than the desirable 0.8. Of the statistically significant
ANOVAs, few pair-wise comparisons were significant (9 of 60 tests; Table 4.7). At
Beeboom, the species richness of the Vallisneria leaves were significantly different to all
other substrates at the site (epilithon, epipsammon, epidendron, macroalgae). At the
Douglas River site, the species richness of the two macro-algae species differed by a
factor of two.
Table 4.6 Site and substrate total species richness. (Total derived from three
replicate samples).
Epilithon
Epidendron
Epipsammon
Algae Sq.
Algae
Macropytes
Other Number
of substrates
site
species
richness
(all substrates)
Kathleen
Falls
40 23 29 (5) 3 57
Salt Ck 26 24 31 (2)
22 (3)
4 41
Roper
River
55 68 31 50 4 87
Katherine
River, DCP
37 51 52 3 69
Douglas 49 37 49 32, 60
46 (6) 5 84
River
(1)
Crystal
Falls
30 37 48 53 39 5 77
Beeboom 38 33 44 52 30 14 (4) 6 74
Sq, squeezed; (1) two macroalgae species sampled; (2) Chara; (3) Juncus; (4) Vallisneria leaves; (5)
Bacterial slime, (6) epipelon.
The species richness of macro-algae would be expected to equal or exceed the richness of
the squeezed macroalgae, as the latter constitutes a sub-sample. This was the case in only
two of the three comparisons (Table 4.5). At the Roper River, the species richness of
macroalgae was two-thirds less than the squeezed algae; one macroalgae replicate had
96

only nine species. The most likely explanation for this is the variability of diatom species
richness amongst macroalgae, and possibly heterogenous nature of the macroalgae. At the
Roper River, a single macroalgae sample comprised four species (Table 4.1). It is
possible that diatom species richness of macroalgae substrates is partly dependent on the
macroalgae species present. This supposition is
supported by the different species richness of the two macroalgae substrates sampled at
the Douglas River. The sloughing off of macroalgae with a diverse assemblage, and
exposure of new algae within the algal mat and yet to be colonised, may also confound
the use of macroalgae and explain the variability of assemblages.
When species richness is compared between substrates, and across sites, the following
generalisations are made:
(1) epilithon and epidendron species richness was similar;
(2) the species richness of epipsammon is greater than epilithon (average 10 spp.),
(3) the species richness of macroalgae is highly variable, representing both the
most and least diverse of substrates, even at a single site. As a result, the
squeezed macroalgae sample can also be variable, with respect to both
macroalgae and other substrates.
Table 4.7 Statistically significant pair-wise comparisons of species richness between
common substrates. The total number of tests is presented in parentheses. Test
undertaken for Roper River, Beeboom, Douglas River, Kathleen Falls, and
Katherine River.
Epidendron Epipsammon Algae Squeezed
algae
Epilithon 0 (3) 1 (3) 2 (6) 0 (2)
Epidendron x 0 (2) 1 (5) 0 (2)
Epipsammon x x 0 (4) 0 (1)
Algae x x x 0 (2)
4.3.5 Species counting effort and distribution of species relative abundance
With increasing counting effort, the number of new species identified declined
exponentially (Figure 4.5). A total of 52 from DCP and 41 species from Dorisvale were
identified from counts of, respectively, 658 and 797. Of the total number of species
identified, half were identified during the first 104 and 83 valves, respectively, for DCP
and Dorisvale. To identify 90% of species, counts of 427 and 263 were required,
respectively, for these two sites.
97

Species
50
40
30
20
10
0
Katherine River, DCP
Daly River, Dorisvale Crossing
0 100 200 300 400 500 600 700 800
Valve count
Figure 4.5 Species counting effort for two samples. Each symbol represents a new
species, excluding the last symbol for each curve which indicates the last diatom
identified.
The relative abundance most species (>90%) was less than 10%, with approximately 75%
of species having relative abundances of less than 2% (Fig 4.6). There were few species
of relative abundances greater than 10% (maximum 9). This distribution of species
relative abundances was common to all substrates, at all sites.
Relative abundance (%)
50
40
30
20
10
0
Episammon
Macroalgae
Epilithon
0 20 40 60 80 100
Species (Percentage of total number)
Figure 4.6 Distribution of diatom species relative abundances for three substrates
sampled at Donkey Camp Pool, Katherine River. For clarity, the macroalgae and
epipsammon plots have been raised 10% and 20% points, respectively. The dotted lines
equate to 0% for either macroalgae or epipsammon. Relative abundances are based on
aggregated replicate data for each substrate.
4.3.6 Comparison of common taxa
The relative abundances of common taxa were assessed by ANOVAs, followed by
pair-wise comparisons between substrates. Taxa with relative abundances of greater than
10%, on a single substrate replicate, were chosen for evaluation. The statistical power to
detect significant differences was satisfactory for most tests ANOVAs, with the exception
98

of Salt Creek, Kathleen Falls and Katherine River analyses where the power was below
0.8 for the majority of tests (11 of 19) and were not significant at the 5% level.
About one-quarter of all possible comparisons were significant at the 5% level (Table
4.8). The relative abundance of all common species on the Vallisneria leaves differed
from all other substrates. The relative abundance of common taxa on the epipelon
(Douglas River) and bacterial slimes (Flora River), however, did not markedly differ from
the other substrates. No comparisons of epilithon and epidendron common taxa were
significant, despite this being one of the most numerous comparisons. Nor were any of
the squeezed algae and epidendron, and the squeezed algae and algae comparisons
significant. The number of common species with significant different relative abundances
on river substrates, at a single site, ranged between 2 and 8 (Table 4.9).
Table 4.8 Summary of pair-wise comparisons of the relative abundance of
common species on substrates at each site. The percentage of significant tests is
shown, with the total number in parentheses. Level of significance for each test
was 5%.
Substrate Comparison % significant
Epidendron vs. epilithon 0% (26)
Epidendron vs. squeezed algae 0% (11)
Algae vs. squeezed algae 0% (17)
Macrophyte vs. macrophyte 0% (6)
Epidendron vs. algae 13% (23)
Epilthon vs. epipsammon 13% (15)
Epilthon vs. macrophyte 17% (18)
Epipsammon vs. squeezed algae 17% (12)
Epilthon vs. squeezed algae 18% (17)
Epidendron vs. epipsammon 25% (12)
Epipsammon vs. algae 26% (23)
Epidendron vs. macrophyte 33% (18)
Epilithon vs. algae 43% (35)
Epipsammon vs. macrophyte 50% (6)
Squeezed algae vs. macrophyte 83% (6)
Algae vs. macrophyte 100% (6)
Total 23% (251)
Table 4.9 The number of common taxa at each site, and the number with
statistically significant relative abundances between at least two substrates.
Kathleen
Falls
Salt
Creek
Roper
River
Katherine
River,
DCP
Douglas
River
Crystal
Falls
Beeboom
Crossing
Commonly occurring species 8 7 7 5 9 9 7
Number of common spp. with
relative abundances
significantly different between
at least two substrates
5 2 5 2 7 8 6
99

4.3.7 Species unique to a substrate
A maximum of 20 species were unique to a substrate at a site (Table 4.10). These species
constituted, on average, one-third of the site’s species richness, and totalled between
3.7% and 16.9% of the relative abundance (Table 4.10). The maximum relative
abundance for a single species, unique to a substrate, varied between 0.59% and 2.0%.
One-way ANOVAs of the number of species unique to a substrate were not significant for
the six sites, excluding Kathleen Falls where the number of unique epilithic species was
significantly different from that identified from other substrata. No commonly occurring
taxa were unique to a substrate, though on occasion these species were absent on a single
substrate replicate.
Table 4.10 Number of species unique to a substrate. In parentheses is the highest
relative abundance (%) of a substrate unique species, and the total relative
abundance (%) of all species unique to a substratum.
Kathleen Salt Ck. Roper Katherine Douglas Crystal Falls Beeboom
Falls
River River, DCP River
Epilithon 20 6 5 3
0 2
5
(2.0%, (0.59%, (2.0%, (0.21%, (0%, (0.09%, (0.36%,
11.9%) 1.6%) 3.3%) 3.1%) 0%) 0.2%) 1.2%)
Epidendron 3 13
2 3
3
(0.33%, (0.86%,
(0.22%, (0.18%, (0.75%,
0.8%) 2.7%)
0.4%) 0.5%) 1.2%)
Epipsammon 13 5 6
6
(1.5%, (0.29%, (0.72%, (0.38%,
5.9%) 1.0%) 1.7%) 1.6%)
Algae 5
1 11 11 10 7
(0.58%,
(0.10%, (0.85%, (0.56%, (0.74%, (0.38%,
1.1%)
0.1%) 0.41%) 1.6%) 2.8%) 1.3%)
Squeezed
7
2
2
Algae
(0.56%,
(0.24%, (0.21%,
2.2%)
0.4%) 0.4%)
Other 9 5 #
8
0
(1.4%, (1.3%,
(1.7%,
(0%,
Total number
3.9%) 2.7%)
3.6%)
0%)
of spp. unique
to a
substratum,
and percentage
of spp.
richness
Total relative
34
(60%)
14
(34%)
26
(30%)
27
(39%)
26
(31%)
23
(29%)
23
(31%)
abundance of
substratum
unique species
16.9% 5.1% 8.4% 9.4% 6.6% 3.7% 5.8%
# Chara sp. , no unique taxa on Juncus sp.
100

4.4 Discussion
The selection criteria for a sample site resulted in all sites being located where either a
road crossed or ended at a river. Causeways were located, not surprisingly, where the
river was shallow with a stable bed, generally comprising cobbles and boulders.
Otherwise, roads tended to end at sites of recreation value, such as the Crystal Falls,
Kathleen Falls and Douglas Hot Springs. All the sites included epilithic substrata, and
featured fast flowing water which seemed to favour the presence of macro-algae. Woody
debris were also a common substrate, as the Daly River and its tributaries have not been
de-snagged as have some southern Australian rivers. Owing to the weathered nature of
the catchments, and high loads of sand carried by the rivers, epipsammic substrates were
common. Macrophytes and soft sediment, however, occurred less frequently than other
substrata.
The following observations are made:
• The similarity of diatom assemblages was, in general, greater between replicate
samples collected from the same substrate, than replicates collected from other
substrates.
• Diatom assemblages between substrates varied due to sometimes significantly
different relative abundances of commonly occurring species. There was no evidence
of commonly occurring species being substrate specific.
• Diatom assemblages between substrates also varied due to the occurrence of species
unique to a substrate. These taxa, however, were rare (relative abundance

substrata have hard surfaces, and differed little in their relative abundances of common
taxa and species richness. Epidendric substrates are relatively common in the streams and
rivers of the Daly River catchment. These two hard substrata are also recommended by
Gell et al. (1999). Epilithon are the recommended substrate for European rivers (Whitton
and Rott 1995), though other substrates are used frequently used.
The diatom flora of macrophyte leaf surfaces can vary substantially from epilithon and
epidendron, and not recommended for catchment-wide monitoring. This substrate is the
least preferred substrate recommended by Gell et al. (1999). Diatom communities on
macrophytes can vary between species, and even on the same plant (Whitton et al. 1995).
4.5 References
APHA (1998) Standard methods for the examination of water and wastewater. 20th
edition. American Public Health Assoc., American Water Works Assoc. and American
Water Pollution Control Federation, New York.
Battarbee, R.W. 1986. Diatom Analysis: In ‘Handbook of Holocene Palaeoecology and
Palaeohydrology’. (Ed. B.E. Berglund) pp. 527-570. (John Wiley: Chichester).
Barbour, M.T., Gerristen, J., Synder, B.D. and Stribling, J.B. (1999) Rapid bioassessment
protocols for use in streams and wadeable rivers: periphyton, macroinvertebrates and fish.
Second edition. US EPA, Office of Water. Washington, D.C.
Belbin, L. (1993) PATN software Package. CSIRO. Canberra.
Cattaneo, A. and Amireault, M.C. (1992) How artificial are artificial substrata for
periphyton? J. Nth. American Benthol. Soc. 11, 244-256.
Cuthbert & del Giorgio (1992) Toward a standard method of measuring colour in
freshwater Limnol.Oceanogr. 92, 1319-26.
Fourtanier, E. & Kociolek, P. 1999. Catalogue of the diatom genera. Diatom Research 14,
1-190.
Fox, E., Kuo, J. Tilling, L. and Ulrich, C. (1994) Sigma Stat User’s Manual. Jandel
Scientific Software. U.S.A.
Gell, P.A., Sonneman, J.A., Reid, M.A., Illman, M.A. and Sincock, A.J. (1999) An
illustrated key to common diatom genera from southern Australia. for Collaborative
Research Centre for Freshwater Ecology (CRCFC) Identification Guide No. 26. CRCFE,
Thurgoona, NSW.
102

John (1983) The Diatom flora of the Swan River Estuary Western Australia. Vaduz,
Bibliotheca Phycologica 64, J. Cramer.
Krammer, K. and Lange-Bertalot, H. 1986. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae, Teil i: Naviculaceae. 876 pp (Gustav Fischer Verlag: Stuttgart.).
Krammer, K. and Lange-Bertalot, H. 1988. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae Teil ii: Bacillariaceae, Epithemiaceae, Surirellaceae. 576 pp. (Gustav
Fischer Verlag: Stuttgart.).
Krammer, K. and Lange-Bertalot, H. 1991a. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae Teil iii: Centrales, Fragilariaceae, Eunotiaceae. 596 pp. (Gustav
Fischer Verlag: Stuttgart.).
Krammer, K. and Lange-Bertalot, H. 1991b. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae Teil iv: Achnanthaceae. 437 pp. (Gustav Fischer Verlag: Stuttgart.).
Porter, S.D., Cuffney, T.F., Gurtz, M.E., Meador, M.R. (1993) Methods for collecting
algal samples as part of the national water-quality assessment program. Raleigh, U.S.A.
U.S.Geological Survey.
Stewart-Oaton, A. (1995) Rules and judgements in statistics: three examples. Ecology,
76: 2001-2009.
Whitton, B.A., Rott, E. and Friedrich, G. (1995) Use of algae for monitoring rivers.
Proceedings of an International Symposium. Düsseldorf, Germany.
103

5 THE RELATIONSHIP BETWEEN BENTHIC DIATOM ASSEMBLAGES
AND WATER QUALITY
Gell, P.A. * , Tibby, J. # & Townsend, S.A. ♦
* University of Adelaide, # Monash University, ♦ N.T. Dept. of Infrastructure,
Planning & Environment.
5.1 Introduction
Diatoms provide a simple and time-efficient means of biomonitoring streams as they are
abundant and are easily to collect (Dixit et al., 1992; Reid et al., 1995). This facilitates
regular sampling that can provide insights of seasonal variation and, where support is
maintained over longer periods, of inter-annual variability. Diatoms have advantages over
macroinvertebrates for biomonitoring (Dixit et al., 1992; Gell et al., 2002) and, after a
period of being underutilized in Australia (Reid et al., 1995), are increasingly being
valued in Australia as a biomonitoring organism. Diatom data sets have been established
in Australia to infer lake and reservoir salinity (Gell, 1997), pH (Fluin, unpublished;
Tibby et al.; unpublished,) and phosphorous (Tibby and Olley, submitted). Stream
diatoms have been applied to the AUSRIVAS model with limited success (Chessman et
al., 1999) and have been used to demonstrate the impact of urbanization (Sonneman et
al., 2001), regulation (Growns & Growns, 2001) and nutrient (Chessman, 1985a; Gell et
al., in press) and thermal (Chessman, 1985b; 1986) pollution.
By virtue of their abundance and diversity, diatoms were considered to be one of the most
appropriate indicators groups for a study of the impact of flow on the ecology on the Daly
River system. A wide range of samples was collected from 18 sites and from a range of
substrates. Epilithic samples (see Chapter 4) were used to evaluate the relations between
diatoms and water quality both over time and position in the catchment. These
sub-studies are identified as the temporal and longitudinal studies respectively. A total of
252 species were identified, comprising both cosmopolitan and tropical taxa.
5.2 Methods
5.2.1 Site selection and sample frequency
During 2000, twelve sites (Table 5.1; Fig. 5.1) were sampled for the temporal study. The
selection criteria were :
• Ease of vehicle access
• A riffle or run reach of the river
• Downstream of a reach not significantly impacted by anthropogenic activity
• Presence of epilthic substratum
• Cover a wide range of water qualities.
104

Fig. 5.1 Diatom sample sites and hydrographic stations.
105

Temporal sites were sampled at one monthly intervals, between June and October
2000. The commencement date for sampling, however, varied between June and
August (Table 5.1), with most sites first visited in June.
Samples for the longitudinal studies were collected in October 1999 and October 2000
for the Douglas River, and July 2000 and October 2001 for the Daly River. Details are
provided in Tables 5.2 and 5.3.
Table 5.1 Sample sites for diatom temporal study
Site HYDSYS
Number *
Description Latitude S Longitude E Date of first
site visit
1 G8145406 Flora River, Kathleen
Falls
14 o 45.305' 131 o 35.817' 17/7/00
2 G8145403 Katherine River, 14
Donkey
inflow
Camp pool
o 22.465' 132 o 21.787' 7/6/00
3 G8145404 Katherine
Knotts crossing
River, 14 o 26.193' 132 o 16.569' 7/6/00
4 G9035399 Salt creek, Roper 15
River catchment
o 0.763' 133 o 14.241' 8/6/00
5 G9035398 Roper river, Moroak
Station
14 o 50.281' 133 o 38.703' 8/6/00
6 G8140067 Daly River, Dorisvale 14
crossing
o 21.939' 131 o 34.465' 19/6/00
7 G81l45388 Daly River, police 13
station crossing
o 46.123' 130 o 42.784' 5/6/00
8 G8145418 Middle creek, 50m d/s
bridge
13 o 48.594' 131 o 20.491' 6/6/00
9 G8145386 Douglas River, Crystal
Falls
13 o 50.508' 131 o 08.853' 6/6/00
10 G8145414 Douglas R., 50m d/s
bridge.
13 o 47.422' 131 o 21.161' 6/6/00
11 G8145417 Douglas River, 2m d/s 13
weir
o 47.890' 131 o 20.310' 17/8/00
12 G8145409 Douglas River, 200m
u/s hot springs
14 o 45.988' 131 o 26.646' 11/8/00
* HYDSYS is the DIPE water resource database.
106

Table 5.2 Douglas River longitudinal study sites
HYDSYS
number
Site description,
Douglas River
River
distance
down-
107
Latitude Longitude Year
sampled
G8145408 Butterfly Gorge
stream
0 13 o 44.579' 131 o 34.390' 1999,
2001
G8145409 300 m u/s hot 16.3 14
springs
o 45.988' 131 o 26.646' 1999,
2001
G8145422 4.4 km u/s Oolloo 24.3 13
Rd. bridge.
o 46.57' 131 o 23.04' 1999,
2001
G8145412 2.6 km u/s Oolloo
Rd. bridge.
24.7 13 o 47.08' 131 o 22.31' 1999
G8145413 2.3 km u/s Oolloo
Rd. bridge.
26.2 13 o 47.030' 131 o 22.170' 1999
G8145414 50 m d/s Oolloo 28.1 13
Road bridge.
o 47.422' 131 o 21.161' 1999,
2001
G8145416 25 m u/s weir, near
hydrographic station
29.8 13 o 47.800' 131 o 20.380' 1999
G8145417 2 m d/s weir, near 30 13
hydrographic station
o 47.890' 131 o 20.310' 1999,
2001
G8145386 Crystal Falls 54.2 13 o 50.508' 131 o 08.853' 1999,
2001
Table 5.3 Katherine-Daly River longitudinal study sites
HYDSYS
number
G8145403
G8145404
G8145384
G8145387
G8145388
Site River
distance
down-
Katherine River,
Donkey Camp Pool
inflow.
Katherine River,
Knotts crossing
Daly River,
Dorisvale crossing
Daly River,
Beeboom Crossing
Daly River, Police
Station crossing
stream
Latitude Longitude Year
sampled
0 14 o 22.465' 132 o 21.787' 2000,
2001
10.4 14 o 26.193' 132 o 16.569' 2000,
2001
146 14 o 21.872' 131 o 33.731' 2000,
2001
268 13 o 51.650' 131 o 04.631' 2000,
2001
342 13 o 46.123' 130 o 42.784' 2000,
2001

5.2.2 Water sample collection, in situ measurements and chemical analyses
A water sample was collected at each site and analysed for the parameters listed in
Table 5.4. In situ measurements of temperature, dissolved oxygen, pH and
conductivity were made with a Horiba instrument, calibrated before each field trip.
Turbidity was measured with a Hach turbidity meter, calibrated with Formazin
standards. Laboratory measurements of pH have been used in preference to field
Horiba measurements (See Chapter 3). Flow was measured at a nearby hydrographic
station for most sites, further details are given in Chapter 3.
Table 5.4 Analytical methods for water samples. Parentheses contain the APHA
(1998) method number (excluding gilvin).
Parameter Method
Nitrate, nitrite Automated cadmium reduction method (4500-
NO3 - F.)
Total Kjeldahl nitrogen Sulphuric acid digestion, automated phenate
method (4500-Norg B,4500-NH3 G)
Filterable reactive phosphorus (herein
referred to as reactive phosphorus)
Filteration through a 1 µm pore size filter.
Automated ascorbic acid reduction method
(4500-P F)
Total phosphorus Persulphate acid digestion, ascorbic acid method
(4500-P B.,4500-P F).
Soluble reactive silicon Automated method for molybdate-reactive
PH
silicon (4500-SiO2 C).
Electrometric Method (4500-H + B.)
Conductivity Laboratory Method (2510 B.)
Alkalinity Titration Method (2320 B.)
Bicarbonate Alkalinity and HCO3 -
Carbon Dioxide and Forms of Alkalinity by
Calculation (2320 B.)
Sulphate ions Automated Methylthymol Blue Method (4500-
SO4 2- F.)
Ca, Mg, K, Na ions Direct Air-Acetylene Flame Method (3111 B.)
Chloride ions Automated Ferricyanide Method (4500-Cl - E.)
Total and dissolved organic carbon High-Temperature Combustion Method (5310
B.) (Sample for dissolved portion passed
through a 1 µm pore size filter).
Total and volatile suspended solids Total Suspended Solids Dried at 103-105 o C
(2540 D.) and Fixed and Volatile Solids Ignited
at 550 o C (2540 E.)
Gilvin Filtration through 1 µm filter, absorbance at 440
nm.
108

5.2.2 Diatom Identification and Enumeration
Three epilithic samples were collected from each site, using the method presented in
Chapter 3. One hundred diatoms identified and enumerated as outlined in Chapter 3.
Photographs of some taxa shown in Appendix 4.1.
5.2.3 Data analysis
The Temporal Study
The data from 64 epilithic samples (Appendix 4.2) collected between 1999 and 2001
were assembled, into an EXCEL file. The water chemistry data from the time of
sampling was assembled into a separate file with corresponding site codes. The
diatom data set was screened to eliminate rare taxa (> 1% in > 2 samples) and that set,
and the water quality data, were entered as Cornell Condensed files for canonical
correspondence analysis (CCA) in CANOCO v3.12 (ter Braak, 1988; 1990). Output
files were transferred to CALIBRATE (Juggins and ter Braak, 1993) for plotting of
the ordination outcomes.
We undertook Canonical Correspondence Analysis (CCA) ordination to investigate
multivariate relationships between diatoms and environment data (Appendix 4.3) in
the Daly River catchment. CCA is an appropriate method for assessing the response of
(multivariate) biological assemblages to multivariate environmental parameters where
species exhibit an approximate unimodal relationship to those variables. Hill’s scaling
in DCA was used to assess whether the species data had a gradient length greater than
three, which indicates that a unimodal-based technique is appropriate (ter Braak and
Prentice, 1988). This was true in all cases.
To assess the significance of the explanatory power of environmental variables, we
used Monte Carlo testing of the strength of species-environment relationships. In this
analysis, 9999 unrestricted permutations of the data were used and significance taken
at p

Data from samples collected on the same survey along the main arms of the Daly and
Douglas Rivers were collated to demonstrate downstream variation in diatom
assemblages. These were transformed into files suitable for TILIA (Grimm, 1992) for
graphical presentation of floristic changes downriver. These data were analysed using
the stratigraphically constrained option in the program CONISS which provided
dendrograms to demarcate points of greatest assemblage change.
The diatom data set was screened to eliminate rare taxa (> 1% in > 2 samples) and
that set, and the water quality data, were entered as Cornell Condensed files for
canonical correspondence analysis (CCA) in CANOCO v3.12 (ter Braak, 1988; 1990).
Output files were transferred to CALIBRATE (Juggins and ter Braak, 1993) for
plotting of the ordination outcomes.
5.3 Results
5.3.1 Temporal Study
The results of four CCAs are reported here, which assessed different (sub)sets of
environmental variables and samples. They were:
• CCA 1, with all available environment data (29 variables) in all samples (64
samples at 12 sites) and is reported in table 5.5.
• CCA 2 used the same data as CCA1, except that the concentration of major
ions was not included in analysis (although ionic charge was included). This
analysis is reported in table 5.6 and in Figs 5.2 & 5.3.
• CCA 3 utilised a further reduced data set where samples from saline Salt
Creek and Roper River were excluded as were the concentrations of major
ions (Table 5.7, Figs 5.4, 5.5).
• (partial) CCA 4 firstly determined the statistical significance of flow (data
available for 42 samples) and then assessed the degree to which the
significance of measured variables resulted from co-variance with flow
CCA 1 [“full” analysis: 64 samples, 29 variables, 275 taxa]
Seven variables explain a significant amount of variance in this data set (Table 5.??).
Essentially one third of diatom variance is explained by these variables, a figure which
is relatively high for ecological data sets. This analysis highlights the importance of
the concentration of major ions in explaining diatom community composition.
Potassium, magnesium and bicarbonate are three most important variables in
explaining diatom composition in the study samples. Nitrate is the only micronutrient
which explains significant variance in this data set, while the physical variables
temperature and turbidity also explain significant, though relatively small amounts of
variance.
110

Table 5.5. Significance level and proportion of diatom variance explained by
variables which explain a significant proportion of change in the Daly River
catchment diatom flora.
Variable p-value diatom variance explained
K (mg/L) 0.0001 12.5
Mg (mg/L) 0.0001 7.1
HCO3 (mg/L) 0.0001 3
DO (mg/L) 0.0001 3
Nitrate (meq/L) 0.0002 2.7
Temp (ºC) 0.0001 2.4
Turbidity (NTU) 0.0002 2.2
Total 33%
CCA 2 [64 samples, 21 variables (ion concentrations excluded), 275 taxa]
Given the significant explanatory power of individual ions, a second CCA was
conducted with the ion concentration data excluded from the analysis. Information
about diatom response to ionic concentration and composition was still assessed in
this analysis through inclusion of total dissolved solids (TDS) and millequivalents of
major ions. In CCA 2, a greater number of variables explained statistical significant
variance in the diatom data in CCA 2 (Table 2). Once again the importance of ionic
composition was highlighted in the analysis. TDS explained the greatest proportion of
diatom variance, though this was marginally lower than that explained by potassium
(mg/L) in CCA 1. Sodium (meq/L) replaces magnesium (mg/L) as the second most
important explanatory variable, while alkalinity, Julian day and reactive phosphorus
were also significant in this analysis.
Table 5.6 Significant variables and proportion of variance explained in CCA2.
Variable p-value diatom variance explained
TDS 0.0001 11.7
Na (meq/L) 0.0001 7.4
Nitrate (meq/L) 0.0001 3.0
Alk (mg/L) 0.0001 3.0
Julian Day 0.0001 2.7
Temp (ºC) 0.0001 2.5
Turbidity (NTU) 0.0001 2.5
DO (mg/L) 0.0027 1.9
Reactive phosphorus 0.0027 2.2
Total 36.9
111

2.0
1.0
Axis 2
(7.2%)
0.0
HS191000
DC070600
HS041001
KC150800
OB051099
DC120900
KC180700
VN130900
Na %
Turb. DO
Nitrate %
Temp
RP
MC041099
KF140800
KF170600
PO150900
PO180800
112
TDS
RR150800
RR180700
RR120900 SC181001
Alk (mg/L)
RR181000
SC150800
RR080600
SC120900
SC180700
SC080600
-1.0
-2.0 -1.0 0.0 1.0 2.0 3.0
Axis 1 (12.5%)
Fig. 5.2. Plot of CCA sample scores and environmental variable vectors for
CCA2 (ions expressed as mg/L excluded). RP=reactive phosphorus,
DO=dissolved oxygen . Only environmental variables which explained a
significant amount of diatom variance are displayed. A short vector for Julian
Day which largely opposes that for DO is not labelled. Shown in parentheses is
the amount of diatom variance explained by each axis.
In the plot of sample scores and environmental variable vectors (Fig. 5.2) samples
from the saline sites in Roper River and Salt Creek (prefixed by RR and SC) are
located highest on the TDS vector. The plot of species location in the ordinations
indicate that taxa commonly which are often abundant in saline waters are strongly
associated with Salt Creek and Roper River, located in the upper right part of the
ordination. These include Mastogloia recta (the most abundant taxon in these
samples), Tabularia fasciculata, Amphora coffaeformis (including a elongate form)
and Navicella pusilla (see also Fig. 4.2.5). In terms of nutrients, a number of taxa
associated with enriched environments plot along the nitrate vector. These taxa
include Mayamea minima, Sellaphora pupula, S. seminulum and Navicula
crytocephala. The good agreement between the independent positioning of taxa along
these key gradients and what could be expected from their ecology suggests that CCA
has provided a robust indication of factors influencing diatom composition in the Daly
River catchment.

Axis 2
5.0
4.0
3.0
Nav even
2.0
1.0
0.0
-1.0
-2.0
Eun form
Gom exil
Frag ten
Brach a
Sel semi
Maya min
Sel pup
Nav a ra
Frag cap
Eun a ac
Ach sace
Stn deli
Eun flex
Neid dub
Cym fala
Stn ance
Nit TF 4
Pla lanc
Rhop bre
Cyc ocel
Dip oblo
Stph cha
Gyro sca
Brach se
113
Cyc parv
Dip elli
Nit soli
Han amph
Am coffL
Nav pusi
Tab fasc
Nit a mi
Mast rec
Nit micr
Nav rece
Nav subm
Ple sali
-3.0
-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
Axis 1
Am coff
Fig. 5.3 Plot of species scores for CCA2. Environmental variable vectors are not
shown on this plot, but species positioning is related to their strength and
direction as shown in Figure 5.2.
CCA 3 [54 samples (Roper River and Salt Creek excluded), 21 variables (ion
concentrations excluded), 249 taxa]
Results of CCA 3 are shown in table 3. In this analysis, despite the exclusion of saline
sites from Roper River and Salt Creek have been, TDS remains the most important
variable. Nutrients (nitrate and reactive phosphorus) and DO, which may be
associated with nutrient mediated oxygen demand), are significant in combination
explain the same amount of diatom variance as TDS. Turbidity, temperature and
Julian Day (independent of temperature effects) also explain significant variance.
The most saline sites in this analysis, those from Kathleen Falls are located low on
axis 1 (far left of diagram), associated with the highest position on the TDS vector
(Fig. 4.2.3). From examination of the plot of species scores, the high relative
abundance of taxa such as Mastogloia recta, Stephanocostis chantaicus and
Encynopsis perborealis are strong influences on the positioning of these samples.
There appears to be a less strong association between taxa usually associated with
high nutrient environments and the nitrate and reactive phosphorus vectors. For
example, nutrient indicator taxa such as Sellaphora seminulum and Mayamaea
minima (located high on axis 1 and 2), are not associated with elevated positioning on
either the nitrate of reactive phosphorus vectors. A partial explanation may be the
overriding influence of salinity since the variance explained by high nitrate is largely

correlated, the plots with that explained by low TDS (which explains a much greater
proportion of data set variance). Hence abundant taxa which plot high on the nitrate
vector, such as Eunotia rhomboides are associated with a strong preference for
freshwaters.
Table 5.7 Significant variables and proportion of variance explained in CCA3.
diatom variance
Variable p-value explained
TDS 0.0001 11.13
DO (mg/L) 0.0001 4.26
Nitrate (meq/L) 0.0001 3.6
Temp (ºC) 0.0002 3.6
Reactive phosphorus 0.0016 3.27
Julian Day 0.0001 3.27
Turbidity (NTU) 0.0002 2.95
Total 32.08
DO has the strongest association with axis 2 four samples from a mixture of samples
(PO210700, CL190700, CF200700, CF311000) plot high on this vector. These
samples are dominated by the commonly occurring taxa Achnanthidium minutissimum
which has been associated with high DO, however this taxon is also found in a
number of samples with low DO. Of those taxa located high on the DO vector,
Navicula vandamii has the strongest association with these samples, largely restricted
to these sites, though at low relative abundances. The lack of any strong association of
taxon position with DO is likely to relate to the low proportion of variance explained
by Axis 2.
Partial CCA (examining the effect of flow in a reduced data set):
An analysis was undertaken on the effect of flow (where measured) on the diatom
assemblages. This analysis focussed on 42 samples (with 236 taxa) where flow data
were available. Excluded samples were: 9, 15, 16, 17, 18, 19, 33, 34, 35, 36, 37, 38,
48, 53, 54, 55, 59, 60, 61, 62, 63, 64
In this reduced data set, flow when tested alone, significantly (p=0.0017) accounts for
4.9% of the diatom variance. As a result the variance associated with flow was
removed from the data set to examine whether its co-variance with other parameters
may confound their explanatory power (a partial CCA)
The results of this analysis are presented in table 4. Firstly, a full CCA was undertaken
on the 42 samples with flow data to see which variables were significant. These
results were then compared to those where the flow variance was removed. This
analysis shows that only here is very little shared variance between flow and TDS,
sodium (meq/L), alkailinity and Julian Day and these variables explain significant
variance independent of flow. Turbidity is the only significant variable which is not
independent of flow effects.
114

2.0
1.0
Axis 2
(5.6%)
0.0
KF140800
KF170600
KF110900
KF161000
MC041099
CF041099
MC031001
MC311000
CF311000
CL190700
CF200700
PO210700
OB051099
OB311000
OB191000
PO180800 Temp.
React.P Day
TDS
Turb.
Nitrate%
DO mg/L
115
DC120900
KC070600
KC120900
HS041099
KC180700
DC021001
DC180700
HS191000
-1.0
-1.0 0.0 1.0 2.0
Axis 1 (11.2%)
Fig. 5.4 Plot of CCA sample scores and environmental variable vectors for
CCA3 (ions expressed as mg/L and Roper River/Salt Creek samples excluded).
Shown in parentheses is the amount of diatom variance explained by each axis.
10
8
6
4
Axis 2
Cal sp o
2 Brach se
Eny perbo
0 Tryb deb
Stph cha
Cym cymb
-2 Nit micr
Nav vand
-4 Pin viri Nit dist
Eun a ac
Nav a br Nav sp t
Frag cap
Cym deli
Cym lanc
Nav a sc
Nit soli
Sel pup
Maya min
Sel semi
Stn ance
Sur line
Rhop bre Eun flex
Brach st
Eun rhomb
Pin brau Eun form Sur bis
-6
Cal TE o
-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0
Axis 1
Fig 5.5 Plot of species scores for CCA3.

Table 5.8 Significant variables and proportion of variance explained (with and
without flow effects removed) in CCA from 42 samples.
"Full" analysis on 42 samples with flow
data With flow variance removed
Variable
pvalue
% explained Variable p-value % explained
Lab TDS 0.0001 12.12 Lab TDS 0.0001 11.79
Na cat
Na cat
%meq/L 0.0001 7.86
%meq/L 0.0001 8.19
Alk mg/L 0.0001 5.57
Turb
Alk mgpL 0.0001 5.24
NTU 0.0004 3.93 Julian D 0.0005 3.60
Julian day0.0006 3.27 React P 0.0016 3.27
Nitrate p 0.0008 3.27
5.3.2 Taxon response to TDS
Given the result of CCA which indicate that ionic concentration is a highly important
factor in explaining diatom distribution in the Daly and Douglas River systems, we
have illustrated the response of key taxa to TDS (Figure 5.6). It is clear that some taxa
exhibit a strong preference for freshwater (Eunotia rhomboides, Navicula
heimansoides) with others apparently competitive in saline waters (Amphora
coffaeformis, Encyonema perborealis). A number of other taxa exhibit greatest
abudance between these extremes (i.e. Achnanthes exilis, Planothidium
frequentissimum and Synedra ulna ). Given the strongly preferences of these taxa for
particular salinities, it appears they may be particularly useful in future biomonitoring
of the Daly and Douglas systems.
116

%
40
0
5.0
0.0
40
0
Ach exil
Am coff
Ecy perb
1.0 4.0
25
0
75
0
15
0
Eun rhom
Mast rec
Nav heim
1.0 4.0
Log10 TDS
117
40
0
25
0
12
0
Pla freq
Syn ulna
Tabu fas
1.0 4.0
Fig. 5.6. Plots of taxon relative abundance vs. Log10 total dissolved solids. Note
that the Y-axes are scaled to the maximum relative abundance of the taxa shown.
Taxa shown are Achnanthes exilis (Ach exilis), Eunotia rhomboides (Eun rhom),
Planothidium frequentissimum (Pla freq), Amphora coffaeformis (Am coff),
Mastogloia recta (Mast rec), Synedra ulna (Syn ulna), Encyonema perborealis
(Ecy perb), Navicula heimansoides (Nav heim) and Tabularia fasciculata (Tabu
fas).
5.3.3 Longitudinal Study
Diatom profiles were assembled for surveys on Douglas (October 1999, October
2001) and Daly Rivers (July 2000; October 2001). Despite the tropical setting of the
rivers the vast majority of the flora are cosmopolitan species. CCA was undertaken to
investigate downstream diatom-environment relationships in the Daly (2000 and
2001) and Douglas Rivers (2000 and 2001). There was a small number of diatom
samples (4-10) in these data set and as a result the number of environmental variables
was reduced to ensure reliable plotting of samples and species in ordination space.
We selected the most important chemistry variables from the second CCA analysis
(which excluded Salt Creek and Roper River) as explanatory variables in the
longitudinal study. These were total dissolved solids, dissolved oxygen (mg/L),
reactive phosphorus, turbidity and nitrate (meq/L). Where these measures strongly covaried
only one of the co-variants was used. In the Daly River reactive phosphorus did
not exceed 0.001 mg/L. TP was selected to use instead, however it was excluded from
the Daly 2001 analysis as it strongly covaried with TDS. Non-significant variables

were plotted in CCA as this analysis was undertaken to highlight taxon associations
with environmental variables.
5.3.3.1 Douglas River
October 1999
Seventy-five taxa were recorded from 9 samples collected down a 55 km stretch of the
Douglas River in 1999. The most common of these were Fragilariforma virescens,
Planothidium frequentissimum, Achnanthidium minutissimum, Encyonema gracile,
Encyonema minuta, Luticola goeppertiana, Mayamaea atomus and Sellaphora
seminulum. Most species were littoral forms although epiphytic types were common
in the upper 30 km and facultative planktonic taxa were common in the lower 25 km
of the River. There was considerable variation in the relative abundance of taxa
downstream (Figure 5.6) and four diatom zones were identified with the assistance of
the CONISS dendrogram. These can be interpreted by reference to the water
chemistry data and the CCA (Figure 5.7) for 10 samples (site 9 at 35.9 km was
excluded from the TILIA plot owing to the diatoms being from a different substrate).
Zone DG99-1.
This zone, represented by the uppermost site Butterfly Gorge, is distinctive to the flora
at the next site. While it supported two taxa that were also common in the lower
stretches of the River (Achnanthidium minutissimum and Encyonema gracile), it was
characterized by a diverse array of species that were found in relatively low
abundance. In particular many species of usually acidophilous Eunotia spp. (van Dam
et al. 1994) were identified, consistent with the pH of 5.6. Navicula cryptotenella and
Stenopterobia curvula were also most abundant in this sample.
The proximity of the Butterfly Gorge site to site 3 at 24.3 km in the CCA plot (Figure
4.2.8) shows that its isolation in the TILIA plot is due to the stratigraphic constraining
function of the dendrogram. So, site 2 at 16.3 km is the more distinctive flora
dominated by Mayamaea atomus and Sellaphora seminulum. The CCA plot
demonstrates that Butterfly Gorge and site 3 are correlated to turbid waters with high
DO concentrations.
118

Douglas River Longitudinal Study 1999
0
5
10
15
20
25
30
35
40
45
50
55
Distance (Km)
Fragilaria capucina
Fragilariforma viriscens
20 40
FP Epiphytic Littoral
Gomphonema spp
Planothidium frequentissimum
20 40 60
Synedra ulna
Achnanthidium minutissimum
20 40
Brachys ira brachys ira
Cymbella cistula
Cymbella falaisensis
Encyonema gracile
20
20
20
Encyonema minuta
20 40
20
119
Encyonema silesiaca
Epithemia cistula
Eunotia spp
Mayamaea minima
Navicula aff radiosa
Navicula cryptotenella
Nitzs chia palea
Sellaphora seminulum
Stenopterobia curvula
Luticola goeppertiana
20
20 40 60
Facultative Planktonic
Epiphytic
Littoral
20 40 60 80 100
Aerophilous
Total
377
390
384
309
389
385
391 424
406
Zone
DG99-1
DG99-2
DG99-3
DG99-4
CONISS
2 4 6
Total sum of squares
Figure 5.6. A downstream profile of diatom assemblages collected on the
Douglas River, October 1999.
Zone DG99-2.
This zone comprises two samples that, in fact, supported quite distinct diatom
assemblages as revealed by the relatively high CONISS dissimilarity value. The
uppermost site, ‘upstream of the Douglas Hot Springs’, supported the epiphytic
Planothidium frequentissimum and the littoral forms Brachysira brachysira,
Mayamaea minima and Sellaphora seminulum. The shift to almost circumneutral
conditions (pH = 6.3) accounts for the reduction in numbers of Eunotia spp. yet the
acidophilous Brachysira brachysira remains. Mayamaea minima, Sellaphora
seminulum and Nitzschia palea are recognized as indicators of eutrophic conditions
(van Dam et al., 1994) consistent with the highest TP values for the transect. The
CCA plot (Figure 4.2.8) shows that this correlates with low reactive phosphorus.
Within 8 km of this site these nutrient indicators had declined and the assemblage had
shifted to one dominated by Encyonema minuta and Navicula aff. radiosa. At this
point Epithemia cistula, usually a alkaliphilous taxon, appears perhaps reflecting the
elevated proportion of calcium.

3.0
2.0
1.0
16.3
26.2
24.3
Axis 2 25.7
Sel sem Turb.
May min DO
0.0
28.1
0
Lut goe
35.9
120
RP
TDS
30
Ach min
54.2
Enc min
Enc gra
Pla fre
Cym cis
-1.0
-2.0 -1.0 0.0 1.0 2.0
Axis 1
Figure 5.7 Sample scores (●), species scores (π) and environmental vectors in a
CCA of the October 1999 Douglas River longitudinal study. Numbers refers to
distances downstream. Note only the positions of the most abundant indicator
taxa are shown.
Zone DG99-3.
These three samples showed considerable variation but were characterized by
Achnanthes (sensu lato) types (Gell et al., 1999) and the aerophilous (often growing
on soil and so reflective of erosion), nutrient indicator Luticola goeppertiana. This
reflects a chemical hinge point in the systems with substantial changes in
conductivity, cation and chloride proportions, pH and gilvin. Within 1.5 kilometres of
the previous site Luticola goeppertiana came to represent 50% of the diatom flora
bringing the site into proximity with the origin in the CCA plot (4.2.2). This however,
is difficult to explain from the chemistry as there is no substantial shift in nutrients or
turbidity. The reduction in gilvin may have provided it with the light to better exploit
the available phosphorus. The reduction in TP within 0.5 km may explain its
replacement by Planorthidium frequentissimum and the rise in TP again at Oolloo
Road Crossing may account for its return in association with circumneutral species
such as Cymbella cistula. Other than TP, there is little to explain the reappearance of
Achnanthidium minutissimum as the waters at Butterfly Gorge are chemically distinct
to those at the Crossing.
Zone DG99-4.
This zone is marked by a reappearance of Encyonema minuta but this is restricted to
the ‘upstream weir’ site. It is also marked by the first appearance of Fragilariforma
virescens that remains a common taxon through to the lowermost site at Crystal Falls.
The weir itself appears to impact on the diatom flora with a shift away from Cymbella
cistula and Encyonema minuta and to Achnanthidium minutisimum, Encyonema
gracile, Encyonema silesiaca and briefly to the eutrophic indicators Sellaphora
seminulum and Luticola goeppertiana. This can only be explained chemically in the
29.8

increase in TP and sudden drop in TKN. The Crystal Falls site is dominated by largely
the same taxa as at Butterfly Gorge other than the replacement of Eunotia spp. and
Stenopterobia curvula, with Fragilarifoma virescens and Cymbella falaisensis. These
lowermost sites correlate best with clear waters of elevated TDS (Figure 4.2.8).
October 2001
Eighty taxa were recorded from 6 samples collected down a 55 km stretch of the
Douglas River in October 2001. Several taxa commonly found in the 1999
(Fragilariforma virescens, Sellaphora seminulum) survey were largely absent in 2001
however Achnanthidium minutissimum, Encyonema gracile and Encyonema minuta
remained among the most common (Figure 4.2.9). Other forms commonly found at
points along the transect include Eunotia rhomboides, Navicula radiosa, Navicula
TEEF3 1 , small Navicula TEEF 2 and Nitzschia angustata. Again most species were
littoral forms although epiphytic types were common in the upper 30 km. Very few
planktonic or facultative planktonic taxa were identified. As in 1999 there was
considerable variation in the relative abundance of taxa downstream and three diatom
zones and two sub-zones were identified with the assistance of the CONISS
dendrogram.
Zone DG01-1
The Butterfly Gorge and ‘upstream Douglas Hot Springs’ flora were considerably
more similar in 2001 than in 1999, almost certainly due to the shift from a pH of 5.6
to 7.0 in the former. This has largely eliminated several of the taxa that made Butterfly
Gorge distinctive in 1999. Also, the lower TP values in the latter have subdued the
response of nutrient indicator species. Eunotia rhomboides, not observed in the 1999
samples, was a relatively common species in this zone and was restricted to it. Their
similarity is supported by their proximity in the CCA plot (Figure 4.2.10). The vectors
of environmental variables again demonstrate that they are correlated with dilute,
turbid waters.
Zone DG01-2
Again the point the zone from the Oolloo Road Crossing and 4.5 km above it marks
the principal hinge point in the chemistry and this is reflected in the variable nature of
the flora. There is no taxon typical of the zone and so is demarcated by common
combinations of taxa. At ‘4.5 km upstream Oolloo Road Crossing small Navicula
TEEF 2 and Planothidium frequentissimum are common. The sub-zone is
characterized by almost unique occurrences of several taxa including Amphora libyca,
Epithemia cistula, Navicula shadei, Navicula TEEF1, Nitzschia liebetruthii and
Synedra acus. Several of these are known to prefer circumneutral to alkaline and may
have responded to the elevated proportions of calcium and bicarbonate. Downstream
from Oolloo Road Crossing, Achnanthidium minutissimum, Cymbella cymbiformis,
Encyonema silesiaca and Navicula radiosa peak. Several taxa disappear (Amphora
libyca, Epithemia cistula, Navicula cryptotenella, small Naviculas TEEF 1 and 2 and
Nitzschia liebetruthii) and are not recorded downstream of this point. This is likely to
be related to a step in conductivity and a decline in the proportion of sodium. Within 2
km, at the weir, several new taxa appear including Navicula TEEF3, Nitzschia
angustata and Stauroneis TEEF1. This is despite no substantial change in ionic
1 TEEF (Top End Environmental Flows) is a project name given to undetermined species.
121

chemistry. The aerophilous, eutrophic indicator Luticola goeppertiana reaches almost
10% of valves at the weir perhaps responding to abrupt increases in nitrate and
reactive phosphorus. The CCA plot (Figure 4.2.10) shows site 3 (at 24.27 km) to be
distinctive and correlated with higher DO concentrations. It is clearly demarcated
from sites 4 (at 28.09 km) and 5 (at 30.04 km) along axis 2 correlated with elevated
TDS and reactive phosphorus concentrations.
Douglas River Longitudinal Study - October 2001
0
5
10
15
20
25
30
35
40
45
50
55
Distance (km)
Achnanthes exilis
Achnanthidium minutissimum
20 40
Amphora libyca
Cymbella cymbiformis
Encyonema gracile
percentage
20
Encyonema minuta
20 40
20
20
20
Encyonema silesiaca
Encyonopsis perborealis
Encyonopsis ruttneri
Epithemia cistula
Eunotia rhomboides
Navicula cryptotenella
Navicula gallica
Navic ula heimansioides
Navicula radiosa
Navic ula shadei
Navicula schroeteri
Navicula TEEF1
Navic ula TEEF3
Naviculadicta pseudosubtillissima
Nitzschia angustata
Nitzschia liebetruthii
Nitzschia palea
Navicula TEEF1s
Navic ula TEEF2s
20
Littoral Epiphytic
20
20
122
20
Stauroneis TEEF1
Cocconeis placentula
Gomphonema auritum
Gomphonema gracile
Gomphonema lagenula
Planothidium frequentissima
Synedra acus
Synedra ulna
Luticola goeppertiana
Planktonic
Facultative Planktonic
Littoral
20 40 60 80 100
Epiphytic
Aerophilous
Total
383
111
DG01-2i
317
408
348
407
Zone
DG01-1
DG01-2ii
DG01-3
CONISS
0.2 0.4 0.6 0.8 1.0 1.2
Total sum of squares
Figure 5.8. A downstream profile of diatom assemblages collected on the Douglas
River, October 2001.
Zone DG01-3
As in 1999, the Crystal Falls flora is dominated by Achnanthidium minutissimum and
Enconema gracile but in this instance they shared dominance with Encyonopsis
ruttneri. Twenty three of the taxa commonly found at or above the weir were not
amongst those recorded at Crystal Falls, most likely owing to its shift to conductive
(513 µS/cm), alkaline (pH=8.1) waters. The CCA pot shows this site to be at the end
of a gradient from the uppermost sites correlated with elevated TDS and reactive
phosphorus concentrations.

Axis 2
3.0
2.0
1.0
0.0
-1.0
Pla fre
24.27
28.09
Nit ang
30.04
Nav TF3
54.23
TDS RP
DO
Turb
123
Nav rad
Ach min
Enc gra
0
Enc min
16.31
Syn uln
-2.0
-2.0 -1.0 0.0 1.0 2.0
Axis 1
Figure 5.9. Sample scores (●), species scores (π) and environmental vectors in a CCA of
the 2001 Douglas River longitudinal. Note only the positions of the most abundant
indicator taxa are shown.
5.3.3.2 Daly River
July 2000
One hundred and twenty-four taxa were recorded from five samples collected down a
345 km stretch of the Daly River in July 2000. The most common of these were
Achnanthidium minutissimum and Encyonopsis perborealis while Achnanthes exilis,
Encyonema gracile, Encyonema minuta and Encyonopsis ruttneri were common at
select sites. Most species were littoral forms with some facultative planktonic and
epiphytic types restricted to the upper 10.4 km. There was considerable variation in
the relative abundance of taxa downstream (Figure 4.2.11) and four diatom zones
were identified with the assistance of the CONISS dendrogram. An interpretation of
the patterns of change down the system is supported by the water chemistry data and
the CCA plot of the five samples (Figure 4.2.12).
Zone DR00-1
The flora at the uppermost site, ‘Katherine River Donkey Camp Pool Inflow’ were
identified by the dendrogram as being the most distinctive. This is due to unique
occurrences of several taxa including Cymbella cistula, Eunotia spp. (other than E.
naegelii), Navicella pusilla, Navicula gregaria, Navicula heimansioides and
Rhopalodia spp. and the absence of Achnanthidium minutisimum, Encyonema spp and
Encyonopsis spp. The only species commonly recorded here and at Claravale
Crossing, 145.6 km downstream or below, was Cymbella cymbiformis. This flora is
most likely associated with the low conductivity waters in association with relatively
high proportions of sodium and chloride at this site and the lowest calcium

proportions (16.2%) of any site reported in the longitudinal study. The site plots away
from the TDS vector as well as turbidity.
Zone DR00-2
Knotts Crossing on the Katherine River, only 10.4 km below the top site, marks the
beginning of the diatom flora typical of the greater Daly River, despite it only plotting
part way toward the origin from site 1 (Figure 4.2.12). Achnanthidium minutissimum,
Encyonema minuta and Fragilaria capucina are the dominant taxa along with the first
occurrences of Gomphonema gracile, Gomphonema parvulum, Synedra ulna,
Encyonema gracile, Eunotia naegelii, Nitzschia palea and Sellaphora pupula. It most
likely relates to the shift to calcium-magnesium bicarbonate waters, which correlate
with increasing TDS, and which persist to the end of the transect. The TP vector is
consistent with the appearances of Gomphonema parvulum, Nitzschia palea and
Sellaphora pupula.
Daly River Longitudinal Study - July 2000
0
50
100
150
200
250
300
350
Distance (km)
Fragilaria capucina
Gomphonema gracile
Gomphonema parvulum
Synedra ulna
Achnanthes exilis
20 40
percentage
Epiphytic Littoral
Achnanthidium minutissima
20 40
Cymbella cistula
Cymbella cymbiformis
20
Encyonema gracile
20
Encyonema mesiana
Encyonema minuta
Encyonopsis perborealis
20
20 40
20
20
124
Encyonopsis ruttneri
Eunotia naegelii
Eunotia spp.
Navicella pusilla
Navicula gregaria
Navicula heimansoides
Navicula notha
Nitzschia angustata
Nitzschia lacuum
Nitzschia palea
Rhopalodia spp
Sellaphora pupula
Planktonic
Facultative Planktonic
Littoral
Epiphytic
Total
20 40 60 80 100
906
363
367
1101
374
Zone
DY00-1
DY00-2
DY00-3
DY00-4
CONISS
0.5 1.0 1.5 2.0 2.5 3.0
Total sum of squares
Figure 5.10. A downstream profile of diatom assemblages collected on the Daly
River, July 2000.

Axis 2
2.0
1.0
0.0
-1.0
0
Enc mes
10.4
125
Enc min
TP
TDS
Turb.
Enc gra
268.2
Ach min
342.1
Ach exl
Nav not
Eny rut
Eny per
145.6
-2.0
-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
Axis 1
Figure 5.11. Sample scores (●), species scores (π) and environmental vectors in a
CCA of the 2000 Daly River longitudinal study. Note only the positions of the
most abundant indicator taxa are shown.
Zone DR00-3
Despite being 123 km apart the dendrogram demonstrates that the flora of the Daly
River at Claravale and Beeboom Crossing are the most similar of the five sites. They
are distinct from the Knotts Crossing flora as Encyonopsis perborealis, Encyonopsis
ruttneri and Navicula notha have replaced Fragilaria capucina, Gomphonema spp.
and Eunotia naegelii. This is best attributable to the step in pH (6.4 – 8.2) and
conductivity (55 – 426) between Knotts and Claravale Crossings although nitrate
levels are the highest through this stretch. Being more influenced by less common
species the CCA separates these two sites across axis 2, but they remain distinct from
the uppermost sites across the stronger axis 1. Also, Beeboom Crosing distinguishes
itself in the high proportions of Encyonema gracile and plots to the top of the CCA
diagram accordingly.
Zone DR00-4
While the Police Station Crossing shares several common taxa with the other crossing
sites upstream, it is distinguished by the elevated proportions of Achnanthes exilis, the
return of Cymbella cistula and the absence of Encyonema gracile and Encyonopsis
ruttneri and accordingly plots near to Claravale Crossing in the CCA. This is despite
little change in ionic chemistry. Nitrate however, declines markedly here and TP,
while still low, is at maximum concentrations at this site.
October 2001

One hundred and forty-nine taxa were recorded from five samples collected down a
345 km stretch of the Daly River in October 2001. Again among the most common of
these were Achnanthidium minutissimum and Encyonopsis perborealis while
Encyonema gracile and Encyonopsis ruttneri, and in this case, Synedra acus, were
common at select sites. Most species were littoral forms. There was considerable
variation in the relative abundance of taxa downstream (Figure 5.12) and three diatom
zones were identified with the assistance of the CONISS dendrogram. These are
interpreted with the assistance of the CCA plot (Figure 5.13) that, due to the low
number of samples, only shows two water quality variables.
Zone DR01-1
Despite the shift in ionic chemistry between Donkey Camp Pool Inflow and Knotts
Crossing being as abrupt as in July 2000, the diatom flora of these two Katherine
River samples are considerably more similar than those collected the previous year.
Again several taxa occur only in the uppermost site, namely Eunotia spp.,
Naviculadicta spp. and Stenopterobia spp however, much more of the flora is
common to both sites (e.g. Achnanthidium minutissimum, Encyonema gracile,
Navicula heimansioides). This is possibly due to the slightly lesser proportions of
sodium and chloride in the uppermost site in October 2001. The flora at Knotts
Crossing are distinguished by high proportions of Synedra acus and the site plots
accordingly in the CCA..
Daly River Longitudinal Study - October 2001
0
50
100
150
200
250
300
350
Distance (km)
Fragilaria capucina
Achnanthes exilis
Achnanthidium minutissimum
20 40
percentage
Encyonema gracile
20
20
20
Littoral Epiphytic
Encyonema minuta
Encyonema silesiaca
Encyonopsis perborealis
Encyonopsis ruttneri
Eunotia spp.
Eunotia zasumensis
Mayamaea atomus
Navicula cryptotenella
Navicula heimansoides
Navicula notha
Navicula veneta
Naviculadicta spp.
Nitzschia angustata
Nitzs chia liebetruthii
Stenopterobia spp.
Gomphonema gracile
Planothidium frequentissimum
Rhoicosphenia abbreviata
Synedra acus
20
126
20 40
Synedra ulna
Fallacia tenera
Sellaphora pupula
Planktonic
Facultative Planktonic
20 40 60 80 100
Littoral
Epiphytic
Aerophilous
Total
3048
320
2538
365
314
Zone
DY01-1
DY01-2
DY01-3
CONISS
0.2 0.4 0.6 0.8
Total sum of squares
Figure 5.12. A downstream profile of diatom assemblages collected on the Daly
River, October 2001.

2.0
1.0
0.0
Axis 2
-1.0
Enp rut
268.2
TDS
342.1
DO
Ach min
145.6
Enc gra
127
Nav not
10.4
Syn acu
0
Nav hei
Nit lie
-2.0
-2.0 -1.0 0.0 1.0 2.0 3.0
Axis 1
Figure 5.13. Sample scores (●), species scores (π) and environmental vectors in a
CCA of the 2001 Daly River longitudinal study. Note only the positions of the
most abundant indicator taxa are shown.
Zone DR01-2
Again, despite being 123 km apart the dendrogram demonstrates that the flora of the
Daly River at Claravale and Beeboom Crossing are the most similar of the five sites.
As in July 2000 both sites share Achnanthidium minutissimum, Encyonema gracile,
Encyonopsis perborealis, Encyonema ruttneri as the most common taxa. The higher
Claravale Crossing is distinguished by higher percentages of Achnanthes exilis,
Encyonema silesiaca while Synedra ulna is characteristic of the lower site of
Beeboom Crossing. Again Claravale Crosing marks the shift to the waters typical of
the lower Daly River having elevated conductivity and pH and are dominated by
calcium, magnesium and bicarbonate ions. Being more influenced by less common
species the CCA again separates these two sites across axis 2, but they remain distinct
from the uppermost sites across the stronger axis 1.
Zone DR01-3
While Achnanthidium minutissimum and Encyonopsis perborealis remain as common
taxa this zone, represented by the lowermost site Police Station Crossing, is
characterised by peaks in Navicula veneta, Nitzschia liebetruthii, Planothidium
frequentissimum and Fallacia tenera all of which reflect either brackish or alkaline
conditions (van Dam et al., 1994).
5.4 Discussion
The diatom assemblages are clearly related to the water chemistry of the waters of the
Daly, and in particular, the Douglas River systems. The most influential parameters

appear to be conductivity or TDS, ionic ratio, pH, turbidity, dissolved oxygen and
nutrients. The first four of these, and perhaps also the others, are influenced by the
relative contributions of surface and groundwater contribution to the streams.
It appears clear from the transect data from the Douglas River in 1999 in particular
that, despite considerable changes in the pH, conductivity and ionic chemistry, that
nutrients play an important role in defining the diatom flora at a site. Where TP
concentrations are generally lower in the Daly River, by contrast, the diatom flora
responds to the abrupt changes in pH, conductivity and ionic chemistry.
Outside the flood period of the wet season, diversion of surface waters, abstraction of
surface waters, pumping of groundwaters, and the release of return water from
irrigation development are all likely to impact upon these key water quality
parameters by reducing the contribution of one source over the other or by introducing
elements in concentrations not typical of the Daly-Douglas system. The establishment
of this baseline data set has characterised the variability of the diatom flora
longitudinally down these system and identified the hinge points where the influence
of groundwaters takes exceeds that of surface waters. Ongoing monitoring of the
systems will provide further insights into the inter-annual variation of this balance and
will strengthen the capacity to demonstrate the significance of the impact of catchment
development on the aquatic microflora.
Several of the more common taxa are reliably found on the middle and lower reaches
of the rivers and so appear to be broadly tolerant of the chemical changes within. The
abundance of many, in fact most, other taxa is highly variable and they constitute
useful indicator taxa for future monitoring. While a comprehensive monitoring regime
would provide greatest levels of confidence, ongoing sampling undertaking small
counts from single epilithic diatom samples from the established sites may be a costefficient
means of overseeing changes of the interaction between the water and the
biota in the future.
5.5 Conclusion and Implications for the Allocation of Environmental Flows for the
Daly River
The water chemistry and diatom data shows that the Daly and Douglas Rivers exhibit
biologically critical trends from turbid, dilute, acid to circumneutral waters to those
which are clear, alkaline and of elevated conductivities. Critical hinge-points exist
above Oolloo Road Crossing on the Douglas and below Knotts Crossing on the
Katherine-Daly where there is an abrupt shift from water dominated by sandstone
aquifers water to one dominated by Daly River Basin aquifers. These shifts are clearly
differentiated by the diatom flora and they represent an efficient means of tracking any
longitudinal changes associated with catchment development. By virtue of their
abundance and diversity, diatoms ought to be one of the more useful biomonitoring
tools in determining the significance of any impact on the aquatic biota of the system.
Having established the link between a general diatom community pattern, water
quality (particularly ionic composition) and the relative contributions of surface and
groundwater, it is conceivable that diatoms will provide an adequate means of
128

assessing the adequacy of future environmental flows to the system. This assessment
would relate mostly to the maintenance of community assemblages or changes in
indicator taxa. The relationship between one diatom assemblage and that of another
biological group is presently unknown. So is the more critical question of the link
between one of the most instructive and efficient biomonitoring indicator groups and
changes to aquatic ecosystem functioning that is likely to be of key issues in
determining environmental flows. The abundance and palatability of diatoms suggests
that this ought to be a key area of future research so as to assess the degree to which
changes identified in diatom assemblages translate into the functioning of the greater
system.
5.6 References
Battarbee, R.W. 1986. Diatom Analysis: In ‘Handbook of Holocene Palaeoecology
and Palaeohydrology’. (Ed. B.E. Berglund) pp. 527-570. (John Wiley: Chichester).
Chessman, B.C. 1985a. Phytoplankton of the LaTrobe River, Victoria. Australian
Journal of Marine and Freshwater Research 36, 115-122.
Chessman, B. C. 1985b. Artificial-ssubstratum periphyton and water quality in the
Lower LaTrobe River, Victoria. Australian Journal of Marine and Freshwater
Research 36, 115-122.
Chessman, B.C. 1986. Diatom flora of an Australian River system: spatial patterns
and environmental relationships. Freshwater Biology 16: 805-819.
Chessman, B., Growns, I., Currey, J. & Plunkett_Cole, N. 1999. Predicting diatom
communities at the genus level for the rapid biological assessment of rivers.
Freshwater Biology, 41, 317-331.
Cumming, B.F., Wilson, S.E., Hall, R.I. & Smol, J.P. 1995. Diatoms from British
Columbia (Canada) lakes and their relationship to salinity, nutrients and other
limnological variables. Bibliotheca Diatomologica 31, 1-207.
Dixit, S.S., Smol, J.P., Kingston, J.C. & Charles, D.F. 1992. Diatoms: Powerful
indicators of environmental change. Environmental Science and Technology 26(1),
23-33.
Fourtanier, E. & Kociolek, P. 1999. Catalogue of the diatom genera. Diatom Research
14, 1-190.
Fritz, S.C., Juggins, S, & Battarbee, R.W. (1993). Diatom assemblages and ionic
characterization of lakes of the northern Great Plains, N.A.: a tool for reconstructing
past salinity and climate fluctuations. Canadian Journal of Fisheries and Aquatic
Sciences 50, 1844-1856.
Gasse, F. 1986. East African diatoms. Taxonomy and ecological distribution.
Bibliotheca Diatomologica 2, 1-201.
129

Gell, P.A. 1997. The development of a diatom data base for inferring lake salinity:
towards a quantitative approach for reconstructing past climates. Australian Journal
of Botany 45 (3), 389-423.
Gell, P.A., Sluiter, I.R. & Fluin, J. in press. Seasonal and inter-annual variations in
diatom assemblages in Murray River-connected wetlands in northwest Victoria,
Australia. Marine and Freshwater Research.
Grimm, E.C. 1992. ‘TILIA version 1.12.’ (Illinois State Museum: Springfield.)
Growns, I.O. & Growns, J.E. 2001.Ecological effects of flow regulation on
macroinvertebrate and periphytic diatom assemblages in the Hawkesbury-Nepean
River, Australia. Regulated Rivers: Research and Management 17, 275-293.
Hill, M.O. 1973. Diversity and evenness: a unifying notation and its consequences.
Ecology 54, 427-432.
Krammer, K. and Lange-Bertalot, H. 1986. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae, Teil i: Naviculaceae. 876 pp (Gustav Fischer Verlag: Stuttgart.).
Krammer, K. and Lange-Bertalot, H. 1988. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae Teil ii: Bacillariaceae, Epithemiaceae, Surirellaceae. 576 pp.
(Gustav Fischer Verlag: Stuttgart.).
Krammer, K. and Lange-Bertalot, H. 1991a. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae Teil iii: Centrales, Fragilariaceae, Eunotiaceae. 596 pp. (Gustav
Fischer Verlag: Stuttgart.).
Krammer, K. and Lange-Bertalot, H. 1991b. ‘Susswasserflora von Mitteleuropa’.
Bacillariophyceae Teil iv: Achnanthaceae. 437 pp. (Gustav Fischer Verlag: Stuttgart.).
Macumber, P.G. 1991. ‘Interaction Between Ground Water and Surface Systems in
Northern Victoria’. 345 pp. (Department of Conservation and Environment:
Melbourne.).
Reid, M.A., Tibby, J., Penny, D. & Gell, P. 1995. The use of diatoms to assess past
and present water quality. Australian Journal of Ecology 20, 57-64.
Round F. E., Crawford, R. M., & Mann, D. G. 1990. ‘The Diatoms: Biology and
Morphology of the Genera’. 774 pp. (Cambridge University Press: Cambridge.).
Sonneman, J., Sincock, A., Fluin, J., Reid, M., Newall, P., Tibby, J. & Gell, P. 2000.
‘An Illustrated Guide to Common Stream Diatom Species from Temperate Australia’.
Cooperative Research Centre for Freshwater Ecology Identification Guide No. 33. 166
pp. (CRCFE: Thurgoona.).
Sonneman, J.A., Walsh, C.J., Breen, P.F. & Sharpe, A.K. 2001. Effects of
urbanisation on streams of the Melbourne region, Victoria, Australia. II. Benthic
diatom communities. Freshwater Biology 46, 1-13.
130

ter Braak, C.J.F. & Prentice, I.C. 1988. A theory of gradient analysis. Advances
in Ecological Research 18(271-317.).
ter Braak, C.J.F. & Smilauer, P, 1999. Canoco for Windows Version 4.02.
Centre for Biometry, Wageningen, Wageningen, The Netherlands.
Tibby, J. & Olley, J. (submitted). Development and application of a diatom-based
total phosphorus transfer function for south-eastern Australian reservoirs. For
Freshwater Biology.
131

5.7 Appendix 4. 1
Caloneis_lauta Mastologia_recta Gomphonema_vibroides Gomphonema_lagenula
Fragillaria capucina Gomphonema lagenula Luticola TEEF2 Gomphonema_vibroides
Mastologia_recta. Navicula_tridentula Nitzschia_pumilla Gomphonema TEEF2.
(TEEF, unidentified diatom (Top End Environmental Flows))
132

Appendix 4.2
Sample codes used in CCA plots.
Site Date Code
Flora River at Kathleen Falls 17-Jul-00KF170600
14-Aug-
Flora River at Kathleen Falls
00
11-Sep-
KF140800
Flora River at Kathleen Falls
00
16-Oct-
KF110900
Flora River at Kathleen Falls
00 KF161000
DC07060
Katherine River, Donkey Camp Pool inflow 7-Jun-00 0
DC18070
Katherine River, Donkey Camp Pool inflow 18-Jul-000
15-Aug- DC15080
Katherine River, Donkey Camp Pool inflow 00 0
12-Sep- DC12090
Katherine River, Donkey Camp Pool inflow 00 0
DC02100
Katherine River, Donkey Camp Pool inflow 2-Oct-01 1
KC07060
Katherine River, Knotts Crossing 7-Jun-00 0
KC18070
Katherine River, Knotts Crossing 18-Jul-000
15-Aug- KC15080
Katherine River, Knotts Crossing 00 0
12-Sep- KC12090
Katherine River, Knotts Crossing 00 0
KC02100
Katherine River, Knotts Crossing 2-Oct-01 1
Salt Creek, Roper Hwy bridge crossing 8-Jun-00 SC080600
Salt Creek, Roper Hwy bridge crossing 18-Jul-00SC180700
15-Aug-
Salt Creek, Roper Hwy bridge crossing 00
12-Sep-
SC150800
Salt Creek, Roper Hwy bridge crossing 00
18-Oct-
SC120900
Salt Creek, Roper Hwy bridge crossing 00 SC181001
Roper River, Moroak Station, road crossing 8-Jun-00 RR080600
Roper River, Moroak Station, road crossing 18-Jul-00RR180700
15-Aug-
Roper River, Moroak Station, road crossing 00
12-Sep-
RR150800
Roper River, Moroak Station, road crossing 00
18-Oct-
RR120900
Roper River, Moroak Station, road crossing 00 RR181000
133

Daly River, Claravale crossing 19-Jul-00CL190700
16-Aug-
Daly River, Claravale crossing
00
13-Sep-
CL160800
Daly River, Claravale crossing
00 CL130900
Daly River, Claravale crossing 3-Oct-01 CL031000
Daly River, Police station crossing 21-Jul-00PO210700
18-Aug-
Daly River, Police station crossing 00
15-Sep-
PO180800
Daly River, Police station crossing 00 PO150900
Daly River, Police station crossing 1-Oct-01 PO011001
MC04109
Middle Creek, 50 m d/s Oolloo bridge 4-Oct-99 9
MC06060
Middle Creek, 50 m d/s Oolloo bridge 6-Jun-00 0
MC19070
Middle Creek, 50 m d/s Oolloo bridge 19-Jul-000
17-Aug- MC17080
Middle Creek, 50 m d/s Oolloo bridge 00 0
14-Sep- MC14090
Middle Creek, 50 m d/s Oolloo bridge 00 0
31-Oct- MC31100
Middle Creek, 50 m d/s Oolloo bridge 00 0
MC03100
Middle Ck., 50 m d/s Oolloo Rd bridge 3-Oct-01 1
Douglas River, 50 m d/s Oolloo bridge 5-Oct-99 OB051099
Douglas River, 50 m d/s Oolloo bridge 6-Jun-00 OB060600
Douglas River, 50 m d/s Oolloo bridge 19-Jul-00OB190700
17-Aug-
Douglas River, 50 m d/s Oolloo bridge 00
13-Sep-
OB170800
Douglas River, 50 m d/s Oolloo bridge 00
19-Oct-
OB130900
Douglas River, 50 m d/s Oolloo bridge 00
31-Oct-
OB191000
Douglas River, 50 m d/s Oolloo bridge 00 OB311000
Douglas River, 50 m d/s Oolloo bridge 3-Oct-01 OB031001
Douglas River, Crystal Falls 4-Oct-99 CF041099
Douglas River, Crystal Falls 6-Jun-00 CF060600
Douglas River, Crystal Falls 20-Jul-00CF200700
17-Aug-
Douglas River, Crystal Falls
00
14-Sep-
CF170800
Douglas River, Crystal Falls
00
19-Oct-
CF140900
Douglas River, Crystal Falls
00 CF191000
Douglas River, Crystal Falls 31-Oct- CF311000
134

Douglas River, Crystal Falls
00
4-Oct-01 CF041001
Douglas River, V-notch weir d/s Oolloo17-Aug-
VN17080
Road bridge
00 0
Douglas River, V-notch weir d/s Oolloo13-Sep-
VN13090
Road bridge
00 0
Douglas River, V-notch weir d/s Oolloo31-Oct-
VN31100
Road bridge
00 0
Douglas River, u/s hot springs 4-Oct-99 HS041099
11-Aug-
Douglas River, u/s hot springs
00
13-Sep-
HS110800
Douglas River, u/s hot springs
00
19-Oct-
HS130900
Douglas River, u/s hot springs
00
31-Oct-
HS191000
Douglas River, u/s hot springs
00 HS311000
Douglas River, u/s hot springs 4-Oct-01 HS041001
135

6 THE RELATIONSHIP BETWEEN FLOW, GROWTH OF SPIROGYRA
AND LOSS OF HABITAT IN THE DALY RIVER
Padovan, A.V. and Townsend, S.A.
NT Department of Infrastructure, Planning and Environment.
6.1 Introduction
This study examines the relationship between the benthic alga Spirogyra and flow
along a 17 km reach of the Daly River. Spirogyra is an obvious and widespread
species that grows on the river bottom and banks attached to rock, gravel, snags and
living plants. Being a plant it has obvious importance both as a food source for
animals (grazers) and as a habitat for smaller organisms. Although the contribution of
Spirogyra to the ecology of the river is unknown, its significance to river ecology may
be deduced by its status as a primary producer and its abundance.
The relationship between Spirogyra and flow was assessed using two methods. The
first was to determine the habitat preference of Spirogyra on the riverbed and model
the effect of flow reduction on the loss of this habitat. The second approach was to
determine the relationship between water flow (velocity) and biomass, and model the
effect of reduced flows on algal biomass. Both approaches required the development
of a two-dimensional hydrodynamic model of the study reach.
Simulations on the effects of different water extraction regimes were undertaken to
determine the effect this may have on reach biomass. These simulations utilised the
flow-biomass relationship and the historical dry season low flow record for the study
reach. Simulations involved ‘extracting’ water from the flow record to determine the
effect this has on biomass variability when compared to the no extraction case.
6.2 Methods
6.2.1 Study Species
Spirogyra is a genus in Division Chlorophyta (the green algae) and in the family
Zygnemataceae (Ling and Tyler, 1986). It is commonly found in waterways in the Top
End of Australia forming extensive dense stands covering the bottom of rivers and
creeks attached to gravel, rock, plants, roots and snags in flowing waters (Plate 1). An
association between Spirogyra and flow is deduced from observations in many
waterways: Spirogyra is usually found in flowing water, and is either absent or in poor
health in calm or still waters.
Spirogyra has been found growing in the Daly River as far upstream as Knotts
Crossing above the township of Katherine and downstream at Daly River Township
crossing near the tidal zone. Samples collected from the study reach shows the species
to be a mixture of Spirogyra rivularis/schmidtii and Spirogyra aff. inflata. A more
136

definitive identification was hampered by the absence of sexual structures in the
specimens collected that are key taxonomic features of this group.
6.2.2 Study Reach
137
Plate 1. Spirogyra attached to
different substrates. Clockwise from
top left. Extensive coverage over a
gravel bed; attached to gravel but
not sand; attached to surface of rock
and crevices; and attached to
Vallisneria nana and bedrock.
The study reach extends 17 km from 740155E, 8444644N at the upstream end to
735734E, 8454640N at the downstream end (Figure 6.1). This section lies between,
but does not cover all the distance between the Stray Creek confluence upstream to
the Douglas River confluence downstream. The reach is located downstream of the
agricultural development area, and is therefore the area most likely to be affected by
the extraction of water. The subdivision of land and clearing has commenced (Figure
6.1) although the total area to date may be considered small compared to the potential
of the region as a whole.

The study reach is located downstream of major springs (Tickell, 2002). Although
seepage of groundwater is evident throughout the study reach, this section may be
considered to be relatively uniform in terms of freshwater inputs along its length. The
contribution of groundwater to dry season flows is substantial, and results in the
perennial flow of the river. Groundwater discharge to the Daly River above this reach
in September 2000 was estimated to be 19 m 3 s -1 (Tickell 2002).
Lower
Middle
Upper
Site 2
Site 4
138
Site 3
Figure 6.1 Satellite image of the 17 km study reach on the Daly River. Solid bars
across the river marks the upstream and downstream boundaries of the study
reach and the length modelled (740155E, 8444644N and 735734E, 8454640N,
respectively). Upper, Middle and Lower are three water quality sampling
locations. Sites 2, 3 and 4 are river gauging sites used to calibrate the RMA-2
hydrodynamic model. Flow is to the north
The study reach offers a range of water velocity, depth and substrate type, primarily
sand, gravel and rock (see Appendix A for particle size analysis of typical riverbed
sediment). This provides many combinations of growing environment to examine the
N
5 km

elationship between flow and biomass of Spirogyra, and to determine the habitat
preference for this alga.
6.2.3 Water Quality
Physical and chemical measurements were made at three sites in the upper, middle
and lower reaches of the study site (Figure 6.1). At the middle site water samples were
collected at approximately two-week intervals between April and November, and at
monthly intervals at the upper and lower sites between July and November. Samples
were analysed for nitrate, nitrite, total Kjeldhal nitrogen, total and reactive phosphorus
and alkalinity. In situ measurements were made for temperature, pH, conductivity,
light attenuation and turbidity.
Water samples were collected and stored on ice in washed high-density polyethylene
(HDPE) containers until delivered to the laboratory for analysis using standard
methods (APHA 1992). Temperature, pH and conductivity were measured using
either a calibrated Hydrolab Surveyor II multi-parameter probe or a Horiba U10 Water
Checker. Turbidity was measured using a portable Hach Turbidity meter. Light was
measured using a Licor Quantum PAR light sensor at several depths.
6.2.4 Biomass Measurements
Algal biomass was estimated by visually scoring the abundance of algae in a 0.5 x
0.5-meter quadrat. Algal biomass scores assigned were: (percent Spirogyra coverage
in brackets): 0 (Spirogyra absent), 1 (1-10%), 2 (11-30%), 3 (31-60%), 4 (61-80%)
and 5 (>80%).
To enable the conversion of algal biomass scores to chlorophyll-a and ash free dry
weight, 0.5 x 0.5 m quadrats were placed in accessible regions of the river. A biomass
score was assigned and all algae within the quadrat was harvested. Quadrats were
distributed to ensure three of each biomass score was measured. The harvested
Spirogyra was stored in HDPE bottles on ice. Chlorophyll-a was analysed using
standard methods (APHA 1992). Ash free dry weight was determined as loss on
ignition using standard methods (APHA 1992).
6.2.5 Spirogyra-Velocity Relationship
The relationship between the abundance of Spirogyra on the riverbed and velocity
was investigated by establishing twelve transects across the river that represented
different combinations of depth, velocity and substrate type. Transect location was
established after evaluating 24 potential transects and selecting twelve that were
typical for the study reach.
Transects consisted of a cable marked at one-meter intervals attached at two fixed
points on either side of the river. The zero point of each transect was aligned with the
left-hand bank (facing upstream). The same point across the river could therefore be
139

evisited and measured repeatedly over time. Points along the transect were spaced at
approximately 2 meter intervals and were measured on six occasions during the dry
season (5 July, 2 August, 29 August, 2 October, 24 October, and 20 November)
At each point water depth, velocity at 0.1 m above the bottom (bottom velocity) and
algal biomass was measured. Velocity was measured using a calibrated fan meter
(model OTT Type V) over a 40-second interval and converting revolutions to velocity
using calibration tables. Depth was measured against the steel rod supporting the fan
unit.
The abundance of Spirogyra on the edge of the river was measured at the same time
as the riverbed. At each transect site five quadrats were assessed on each side of the
river. The quadrats were spaced at 2 meter intervals in a downstream direction, with
the first quadrat placed where the transect cable intersected the bank. Each quadrat
extended over a 0.5 m length along the edge to a depth of 0.5 m. Algal biomass was
scored as described above. The available substrates of vegetation (trailing roots,
snags, and plants), sand, silt, rock and mud was recorded, as was the presence or
absence of Spirogyra on each substrate.
The calculation of total biomass for the study each requires an estimate of algal
biomass on different substrates as well as an estimate of the area of each substrate.
Riverbed substrates were characterised as either sand and gravel (which also includes
rock). This is explained in detail in section 6.2.6.1. Substrates along the edge of the
river were measured by making observations of both sides of the river at 50 m
intervals and recording substrate type as either vegetated (trailing roots or plants),
sand, mud or rock. This was used to estimate the proportion of river edge providing
each habitat.
6.2.6 Flow-Biomass Model
6.2.6.1 Hydrodynamic Model
Flow through the study reach was modelled using a two-dimensional RMA (RMA-2)
hydrodynamic model. Data for the model network (bathymetry) was obtained by
measuring over 100 transects at approximately 150 m intervals. At seven points along
each transect depth and substrate type (as sand, gravel or rock) was recorded. The
location of each record was measured using continuously recording differential GPS
(mobile mode). The location of each traverse was marked with a uniquely numbered
steel spike driven into a tree, and the height of the spike above the water was
measured. At a later date each spike was surveyed against a registered survey point,
and the elevation of the river bottom (bottom elevation) above Australian Height
Datum was calculated.
The bathymetric survey data was used to construct the model geographic files for the
river reach that describes location (easting, northing), bottom elevation (AHD) and
substrate type (sand, gravel). These files were used to construct a model network
consisting of elements (polygons) representing segments of the river bottom that are 8
to 10 m wide and 16 to 20 meters long (half this size in areas where flow is fast eg
140

apids). Elements were characterised as having either a sand or hard (rock or gravel)
bottom. The river reach was described using 4,722 elements defined by 15,713 nodes
(individual points on the bottom where depth and velocity are calculated).
The model was calibrated using hydrographic data measured at three locations within
the reach where water level (in AHD) and discharge (depth, cross-sectional area and
velocity) were measured three times during the dry season (see Figure 6.1 for site
locations). Discharge values used to calibrate the model were 26, 31 and 49 m 3 s -1 .
Model calibration involved adjusting the surface elevation of water at the downstream
boundary (boundary elevation) of the study reach and changing the friction
coefficients of the sand and gravel substrate so that modelled water surface elevation
at each site matched the surface elevations measured in the field. This was repeated
for all three discharge values and the best combination of friction coefficients and
boundary elevation was selected. Model performance was further assessed using
additional data collected from these sites at other times.
6.2.6.2 Shear Velocity Calculations
The field measurement of flow associated with Spirogyra biomass is velocity at 0.1 m
from the bottom (bottom velocity). However, the RMA-2 hydrodynamic model can
only output depth-averaged velocity and depth for each node. However, the model is
able to calculate shear velocity using the following equation:
Shear velocity = n x g 0.5 x V x Z -0.1667
where n is Manning’s roughness coefficient (in this case 0.025), g is the acceleration
due to gravity, V is the depth-averaged velocity and Z is the depth. To link model
outputs to field measurements a relationship between bottom velocity and shear
velocity was determined by measuring 19 velocity-depth profiles in the river at
differing flows. Shear velocity was calculated for each profile using the above
equation. The advantage of using shear velocity as a measure of flow is that in
hydrological terms it is a measurement with defined meaning that may be compared
with measurements from the literature, as opposed to velocity at 0.1 m which is an
arbitrary measure.
6.2.6.3 Spirogyra-Flow Simulations
To simulate the effects of varying flows on the abundance of Spirogyra, the RMA-2
model was used to calculate depth and depth-averaged velocity for nodes along the
study reach. Flows of 0.5, 1, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25, 27.5 and 30
m 3 s -1 were simulated. Using a separate program this output was used to calculate
shear velocity. This result was viewed in RMAPLT and areas corresponding to
different shear velocity classes were calculated (see Table 6.2 for shear velocity
classes used). As Spirogyra was only observed to grow on gravel substrate only these
areas were used for aerial calculations (ie sand substrate was ignored). These areas
were multiplied by the corresponding algal biomass (expressed as mass of
chlorophyll-a per meter square) and summed to derive a total biomass value for the
141

study reach. These calculations take into account the gravel portions of the river which
have dried under low flows by assigning a biomass value of zero.
6.2.7 Water Extraction Simulations
The relationship between Spirogyra biomass and discharge (Figure 6.12) was applied
to the historical dry season low flow record for the study reach (Figure 6.3) to
determine the historical variability in biomass. The relationship between biomass (as
kg chlorophyll-a) and flow (m 3 s -1 ) shown in Figure 6.12 can be described using the
following equation:
Biomass = (0.9969 x ln(Q) + 1.6243) 2 , r 2 = 0.99
This relationship only applies to discharge in the range 0.5 to 30 m 3 s -1 .
The effects of water extraction on biomass may be determined by applying different
water extraction regimes to the historical flow record, and calculating how biomass is
changed for each year of the record. This new annual biomass frequency distribution
can then be compared to the historical distribution to determine the effect of water
extraction.
Two extraction regimes were simulated. The first is a fixed regime where a set
volume of water (eg 1 m 3 s -1 ) is removed from each year of the historical low flow
record. Fixed extraction rates of 1 to 7 m 3 s -1 were simulated. The second extraction
regime is a proportional one where a percentage of the flow is removed (eg 10%). The
proportional extraction rates were selected to correspond to the fixed rates with
respect to historical median flow. For example, a 1 m 3 s -1 reduction in flow in each
year of the historical record corresponds to an 8% reduction in the historical median
flow.
6.3 Results
6.3.1 Water Quality
Dry season recessional flow commenced in May in 2001 Figure 6.2a). Discharge at
this time was 50 m 3 s -1 , which continued to gradually decrease with time to 24 m 3 s -1 in
October. Towards the end of October (21 and 31) two small flow events associated
with local rainfall is evident, marking the onset of the next wet season. On November
15-16 the first major flow event occurred where discharge peaked at 200 m 3 s -1 .
142

Photic Depth (m)
Alkalinity (mg/L CaCO 3 )
Discharge (m 3 s -1 )
pH
Total Nitrogen (mg/L)
250
200
150
100
50
0
12
10
8
6
4
2
0
350
300
250
200
150
100
8.4
8.2
8.0
7.8
7.6
7.4
0.4
0.3
0.2
0.1
0.0
a
b
c d
e f
g h
i j
Apr May Jun Jul Aug Sep Oct Nov
143
Middle
Upper
Lower
Apr May Jun Jul Aug Sep Oct Nov
Figure 6.2 Water quality changes measured at three locations (upper, middle and
lower) in the study reach during the 2001 dry season. Discharge was measured at
the middle site. Arrows in plot ‘a’ indicate period in which biomass
measurements were made. Refer to Figure 6.1 for site location.
The minimum flow measured during 2001 of 24 m 3 s -1 is one of the highest on record
and is markedly greater than the lowest flow of 8 m 3 s -1 and median flow of 15 m 3 s -1
(Figure 6.3). The minimum discharge at Site 3 is similar to the lowest flow measured
at G8140041, which is approximately 50 km downstream of Site 3.
20
15
10
5
0
700
600
500
400
300
200
100
0
34
32
30
28
26
24
22
0.12
0.10
0.08
0.06
0.04
0.02
0.00
0.03
0.02
0.01
0.00
Turbidity (NTU)
Conductivity (uS/cm)
Temperature ( o C)
Nitrate (mg/L)
Total Phosphorus (mg/L)

Predicted minimum flow (m 3 s -1 )
35
30
25
20
15
10
5
0
1960 1965 1970 1975 1980 1985 1990 1995 2000
Figure 6.3 Predicted minimum dry season flow along the study site (data from
Rea et al, 2002).
Most water quality measurements remained relatively uniform throughout the study.
Measurements of Spirogyra biomass occurred during the recessional flow period
where discharge ranged from 40 to 25 m 3 s -1 . This period is associated with
consistently low turbidity in the range of 2-5 NTU and high photic depth (5-10 m)
relative to the deepest (approximately 3m) areas measured (Figure 6.2 b and c).
Conductivity (533-596 uS/cm), alkalinity (277-316 mg/L CaCO3) and pH (7.6-8) did
not vary greatly during this period. The nutrients nitrate (

6.3.2 Chlorophyll-a, Ash Free Dry Weight and Biomass Scores
Figure 6.4 summarises the relationship between ash free dry weight (AFDW),
chlorophyll-a and biomass score. There is a good linear relationship between all these
measures of biomass allowing each to be used interchangeably. Biomass scores of 1
and 5 correspond to 0.6 and 5.4 g m -2 AFDW, respectively. The equivalent values as
chlorophyll-a is15 and 73 mg m -2 .
Chlorophyll-a (mg m -2 )
100
90
80
70
60
50
40
30
20
10
Chlorophyll-a = 12.09.AFDW + 7.04
r 2 = 0.99
1
2
3
0
0 1 2 3 4 5 6
Ash Free Dry Weight (g m -2 )
Figure 6.4 Relationship between ash-free dry weight, chlorophyll-a and biomass
score (numbered). Error bars represent standard deviation.
6.3.3 Seasonal Biomass Changes
Biomass is presented as the total mass of chlorophyll-a for the study reach (Figure
6.5). This was calculated for the riverbed as the product of the mean chlorophyll-a
measure for all observations on suitable Spirogyra substrates (gravel) on each
sampling date multiplied by the area of gravel substrate in the study reach. Edge
biomass was estimated as the product of mean chlorophyll-a measure of all
observations (for each date) and the area of river edge providing suitable habitat for
Spirogyra growth (trailing vegetation and rock). Edge measurements of habitat found
vegetation and rock made up 74 and 17.2 %, respectively, of observations along the
study reach, which corresponds to 25 and 6 km (of the available 34 km) of edge length
for both sides of the river. The calculation of area assumed a depth of 0.5 m. Sand
and mud accounted for 5.9 % (or 3 km) of the edge length and this was found to be an
unsuitable habitat for Spirogyra.
145
4
5

Total and Riverbed Biomass (kg chlorophyll-a)
10
8
6
4
2
0
May Jun Jul Aug Sep Oct Nov
146
Edge
Riverbed
Total Biomass
Figure 6.5 Seasonal changes in biomass as mass of chlorophyll-a in the study
reach on the riverbed and edges (note different scales on y-axes).
The growth cycle of Spirogyra in 2001 commenced in May and ended in November
(Figure 6.5). Although biomass measurements did not commence until July, weekly
observations of the study reach first detected Spirogyra on May 14 2001. Highest
biomass on the riverbed occurred from early July to late August, and early July to
early August for edge biomass. The interruption of the smooth decline in edge and
riverbed biomass in October occurs one week after two small flushes in the river
(Figure 6.2a). The absence of all Spirogyra in November follows a major flow event.
The majority of Spirogyra biomass in the study reach occurs as growth on the river
bottom. This compartment accounts for between 95 to 98 percent of the total biomass
for the reach.
6.3.4 Flow-Biomass Model
6.3.4.1 Shear Velocity and Flow
There is a good linear relationship between bottom velocity and shear velocity (Figure
6.6). This equation enables measurements of bottom velocity associated with
observations of algal biomass to be converted to values of shear velocity. Therefore,
the model output of shear velocity may be related to an effect on algal biomass.
0.5
0.4
0.3
0.2
0.1
0.0
Edge Biomass (kg chlorophyll-a)

Shear velocity (m/s)
0.10
0.08
0.06
0.04
0.02
Shear velocity = 0.1128 x (bottom velocity) + 0.0059
r 2 = 0.91
0.00
0.0 0.2 0.4 0.6 0.8 1.0
Velocity (m/s) 0.1 m from bottom
Figure 6.6 Relationship between velocity 0.1 m from the river bottom (bottom
velocity) and shear velocity (as calculated by the RMA-2 model using total depth,
depth-averaged velocity and Manning’s roughness of 0.025 for sand and gravel).
6.3.4.2 Hydrodynamic Model
Table 6.1 summarises the performance of the calibrated two-dimensional RMA model
by comparing water levels measured in the field at Sites 2, 3 and 4 to water levels
predicted by the calibrated model.
Table 6.1 Summary of performance of calibrated model using water surface
elevations at three sites along the study reach. Refer to Figure 6.1 for site
locations.
Discharge
(m 3 s -1 )
Bound Elev.
(AHD)
Site 2
Site 3
Site 4
(AHD)
(AHD)
(AHD)
Actual Model Actual Model Actual Model
23.2 19.72 23.00 23.00 21.04 21.04 20.83 20.86
30.7 19.95 23.17 23.19 21.20 21.19 21.02 21.01
49.7 20.95 23.66 23.56 21.69 21.61 21.56 21.45
147

Using a friction coefficient (Manning’s roughness) of 0.025 for sand and gravel
substrates resulted in good agreement between measured and modelled water surface
levels at 23.17 and 30.7 m 3 s -1 , and a reasonable agreement at 49.7 m 3 s -1 .
The performance of the model was further evaluated by comparing measured and
predicted cross-sectional averaged depths and velocities at the three sites measured at
different flows. There was good agreement between mean velocity measured in the
field to that predicted by the calibrated model with a slope of almost one and a small
y-intercept (Figure 6.7).
Mean Modelled Velocity (m/s)
0.8
0.6
0.4
0.2
Modelled = 0.958 x Measured - 0.0113
r 2 = 0.95
0.0
0.0 0.2 0.4 0.6 0.8
Mean Measured Velocity (m/s)
Figure 6.7 Comparison of measured and modelled cross-sectional mean
velocities.
There is reasonable agreement between mean depth measured in the field with
predicted values, where there is a tendency with the model to overestimate depth
(Figure 6.8).
The calibration data was used to construct a stage discharge relationship for flow at
the downstream boundary of the modelled reach (Figure 6.9). This relationship was
used to calculate the model-input parameter of boundary elevation for simulating
different flows.
148

Mean Modelled Depth (m)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Modelled = 1.118 x Measured - 0.1037
r 2 = 0.98
0.0 0.4 0.8 1.2 1.6 2.0
Mean Measured Depth (m)
Figure 6.8 Comparison of measured and modelled cross-sectional mean depths.
Boundary Surface Elevation (AHDm)
22
21
20
19
18
17
BSE = -0.0006367172.D 2 + 0.0963045493.D + 17.7151017662
r 2 = 0.99
0 10 20 30 40 50 60
Discharge (m 3 s -1 )
Figure 6.9 Flow-height relationship at the downstream model boundary.
149

6.3.4.3 Spirogyra-Velocity Relationship
Algal biomass data is presented as the maximum biomass score. This value is derived
from the raw data and is a measure of the maximum biomass observed at each point
(ie at each location along all transects) during the dry season. This value therefore
does not consider seasonal changes in biomass, rather it is the highest biomass
attained at each point that is of interest.
Maximum Biomass Score
5
4
3
2
1
0
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Shear velocity (m/s)
Figure 6.10 Relationship between Maximum Biomass Score measured at each
point during the 2001 dry season and shear velocity. The solid line shows
Maximum Potential Biomass.
Figure 6.10 shows the relationship between maximum biomass score and shear
velocity (the bottom velocity measured at the time that maximum biomass was
measured and converted to shear velocity using the equation in Figure 6.6). The solid
outline in Figure 6.10 represents the maximum potential biomass (MPB). The highest
MPB (score 5, representing >80% coverage) corresponded to shear velocities in the
range 0.025-0.055 ms -1 . Below a shear velocity of 0.025 ms -1 there is a rapid decline
in algal biomass with decreasing shear velocity. No Spirogyra was found at a shear
velocity of less than 0.005 ms -1 . Above a shear velocity of 0.055 ms -1 there is a
decline (although not as rapid as seen below a shear velocity of 0.025 ms -1 ) in algal
biomass with increasing shear velocity. At 0.11 ms -1 no Spirogyra was observed.
From Figure 6.10 shear velocity classes were derived and are summarised in Table 6.2
along with the corresponding MPB scores and equivalent algal biomass as
chlorophyll-a.
The results presented in Figure 6.10 are for gravel substrates only. Throughout two
years of observations Spirogyra has never been observed growing on sand. Therefore
for the purposes of this study sand is eliminated as a suitable substrate for Spirogyra
150

and the focus is on gravel and rock (combined in this model as gravel) as suitable
anchoring substrates for this alga.
Table 6.2 Summary of shear velocity classes and corresponding maximum
potential biomass (MPB) scores and equivalent biomass as chlorophyll-a. Refer
to Figure 6.10 for source data.
Shear velocity
Class (ms -1 )
Max. Potential
Biomass Score
151
Chlorophyll-a
(mg m -2 )
0.000 – 0.005 0 0
0.005 – 0.010 1 14.65
0.010 – 0.015 2 18.84
0.015 – 0.020 3 28.16
0.020 – 0.025 4 42.84
0.025 – 0.055 5 72.65
0.055 – 0.070 4 42.84
0.070 – 0.085 3 28.16
0.085 – 0.095 2 18.84
0.095 – 0.110 1 14.65
> 0.110 0 0
6.3.4.4 Flow-Biomass Simulations
Figure 6.11 and Figure 6.12 summarises the output of the modelling simulations
showing changes in gravel area and biomass, respectively, for a range of river
discharge. Only areas suitable for the growth of Spirogyra (gravel beds) are used in
these simulations. Gravel beds cover an area of 40.5 hectares out of a total area (sand
and gravel) of 82.3 hectares in the study reach.
The highest critical shear value modelled in the study reach was 0.12 ms -1 (Figure
6.11). Between discharges of 12.5 and 30 m 3 s -1 the largest area of gravel are subjected
to shear velocities in the range 0.025-0.055 ms -1 , followed to a lesser extent by shear
velocities in the classes 0.02-0.025 and 0.015-0.02 ms -1 . These shear velocity classes
are associated with the highest algal biomass (corresponding to biomass scores of 5, 4
and 3 respectively, ). At discharges of less than 15 m 3 s -1 the dominant shear velocities
over gravel beds are associated with the lower levels of algal biomass (scores of 1 and
2, Table 6.2). The area of dry gravel (ie exposed) increases with decreasing discharge,
accounting for 17 Ha (or 42%) of the total gravel area at 0.5 m 3 s -1 . The area of gravel
subjected to shear velocities too low for algal growth (class 0.005-0.010 ms -1 )
increases markedly at discharges below 5 m 3 s -1 (Figure 6.11).
Algal biomass for each discharge was calculated using the area of gravel exposed to
each shear velocity class from Figure 6.11 and corresponding biomass as chlorophylla
from Table 6.2. This is shown plotted in Figure 6.12. At a discharge of 30 m 3 s -1 a
maximum biomass of 25 kg of chlorophyll-a was derived. The majority of this
biomass is from gravel beds subject to shear velocities of 0.025-0.055 ms -1 . Gravel

substrate exposed to this class of shear velocity contributes the greatest proportion of
chlorophyll-a to the total at most of the discharges simulated.
Area (hectares)
45
40
35
30
25
20
15
10
5
dry gravel
shear vel. classes with zero biomass
shear vel. 0.015-0.020 ms -1
shear vel. 0.020-0.025 ms -1
shear vel. 0.025-0.055 ms -1
all other shear vel. classes
total area
0
0 5 10 15 20 25 30
Discharge (m 3 s -1 )
Figure 6.11 Calculated area of gravel substrate for shear velocity classes and
exposed (dry) beds at different discharges. Shear velocity classes with no biomass
refers to the total area of gravel where shear velocity is either too slow or fast for
growth of Spirogyra.
Biomass (kg chlorophyll-a)
25
20
15
10
5
0.005-0.010
0.010-0.015
0.015-0.020
0.020-0.025
0.025-0.055
0.055-0.070
0.070-0.085
0.085-0.095
0.095-0.110
>0.110
Total biomass
0
0 5 10 15 20 25 30
Discharge (m 3 s -1 )
Figure 6.12 Estimated biomass of Spirogyra on gravel substrate subjected to the
different shear velocity classes (units ms -1 ).
152

As discharge is reduced from 30 to 0.5 m 3 s -1 there is a continuous reduction in total
biomass from 25 to 1 kg of chlorophyll-a (Figure 6.12). The rate of reduction in total
biomass is not uniform across the discharge range simulated. There is a steady
reduction in total biomass between discharges of 30 to 15 m 3 s -1 . From 10 to 0.5 m 3 s -1
the reduction in biomass is also uniform, however the rate of reduction is higher. The
rate of reduction in total biomass between 30 and 15 m 3 s -1 is 0.4 kg of chlorophyll-a
for each 1 m 3 s -1 reduction in discharge. For discharge in the range of 10 to 0.5 m 3 s -1
the rate of biomass reduction was almost four times greater at 1.5 kg of chlorophyll-a
for each 1 m 3 s -1 reduction in flow.
6.3.5 Water Extraction Simulations
Figure 6.13 and Figure 6.14 summarises the effects of a range of fixed and
proportional extraction rates, respectively, on the annual biomass frequency
distribution of chlorophyll-a biomass. Biomass distribution is presented as box and
whisker plots which shows the median biomass associated with the historical flow
record, and an indication of the spread (range) of this distribution. Note that the
proportional extraction rates were selected to be comparable to the fixed extraction
rates on the basis of the historical median flow. For example, a fixed extraction rate of
4 m 3 s -1 from the historical flow record equates with a 31% reduction in the median
flow.
There are marked differences on the effect of the two extraction regimes on the annual
biomass frequency distribution. The fixed extraction regime, in addition to leading to
a reduction in median biomass with increasing extraction, results in an increase in the
distribution (range) of biomass (Figure 6.13). The increase in variability is due mainly
to an accelerating decline in the minimum biomass with increasing extraction. At 7
m 3 s -1 the minimum biomass reaches zero, that is, for at least some of the years the
biomass is expected to be zero. The effect of increasing fixed extraction rates is a
biomass distribution quite different to the expected historical distribution in that the
median is reduced, the minimum biomass is markedly reduced, and the spread of the
distribution of biomass values is almost doubled.
In the case of a proportional extraction regime, the reduction in median, minimum and
maximum biomass for each extraction rate decreases at approximately the same rate
(Figure 6.14). The effect is that the variability of the distribution remains constant,
however, the biomass frequency distribution as a whole moves towards zero and
becomes progressively more different to the expected historical distribution
153

Biomass (kg Chl-a)
25
20
15
10
5
0
Max
Min
75%
25%
Median
0 1 2 3 4 5 6 7
Extraction rate (m 3 s -1 )
Figure 6.13 The effect of a fixed water extraction rate on the expected historical
distribution of chlorophyll-a biomass in the study reach. Estimates were derived
by applying the biomass-discharge relationship in Figure 6.12 to the historical
dry season low flow record in Figure 6.3. The extraction rate of 0 m 3 s -1
represents biomass distribution based on the natural historical flow record.
Biomass (kg Chl-a)
25
20
15
10
5
0
Max
Min
75%
25%
Median
0 8 16 23 31 39 47 55
Extraction rate (%)
Figure 6.14 The effect of a proportional water extraction rate on the expected
historical distribution of chlorophyll-a biomass in the study reach. Estimates
were derived by applying the biomass-discharge relationship in Figure 6.12 to
the historical dry season low flow record in Figure 6.3. The extraction rate of 0
m 3 s -1 represents biomass distribution based on the natural historical flow record.
154

6.4 Discussion
6.4.1 General
Water quality remained relatively constant during the survey period in 2001 and along
the study reach (Figure 6.2). Although the effect of these variables on the growth of
Spirogyra is unknown, their stability over time and space resulted in a uniform growth
environment through which field measurements were made.
Dry season flow during the study was at historically high levels (Figure 6.3). The
effect of this on the relationship between biomass and flow (represented by shear
velocity) presented in Figure 6.10, which is used as the basis of all simulations, is
unknown. However, the approach adopted in this study in measuring field data by
establishing transects over a broad range of substrate, depth and velocity
(approximately 250 quadrats measured per survey date) is expected to negate any
effects of high flows during the study. In terms of determining the loss of gravel
habitat for Spirogyra the historical high flow is not of concern. This is a purely
physical approach using a hydrodynamic model to calculate water level and
incorporating riverbed substrate type.
Field measurements were undertaken throughout the growth cycle of Spirogyra
(Figure 6.5). This provided the greatest opportunity of observing the highest biomass
attained within each quadrat. The trend in seasonal changes on the riverbed and edge
shows a period of rapid establishment at the commencement of dry season recessional
flow (Figure 6.2a) followed by a period of sustained high biomass over 2-3 months.
The decline in biomass between August and October coincides with rising water
temperature. Temperature was the most fluctuating water quality measure. The
interruption in the decline of edge and riverbed biomass in October follows two small
flushes down the river by a week. It is likely that nutrients transported by these flushes
stimulated the growth of Spirogyra. The removal of all biomass by November was
due to the destructive nature of the first major flow event (200 m 3 s -1 ) 5 days prior to
the survey (Figure 6.2).
6.4.2 Habitat Preference
The 17 km river reach selected for this study covers an area of 82 hectares, half of
which consists of a sand substrate and the remainder a gravel/rock (predominantly
gravel) substrate. Throughout this study Spirogyra was not observed growing on sand
substrate, presumably due to the unstable nature of this substrate allowing algal
filaments to be easily dislodged. This highlights the importance of gravel/rock
substrates as a stable substrate allowing the development of extensive Spirogyra
strands on the river bottom. Similarly, stable substrates along the edges of the river
such as exposed roots, plants and rock allows the development of trailing stands of
Spirogyra.
155

Although both the riverbed and edges provide suitable substrate for the growth of
Spirogyra, the riverbed supports over 95% of the total biomass of the study reach
(Figure 6.5). This is presumably due to the greater area of suitable substrate the
riverbed provides relative to the edges. Because the bulk of the biomass was on the
riverbed, this study focussed on this area of the river to establish a flow-biomass
relationship. It should be acknowledged that the edges of the river provide important
habitats for other plants and animals, and that these habitats may be lost through
declining water levels as a result of water extraction. An investigation into this region
of the river provides another measure of assessing a relationship between flow and
habitat loss.
The proportion of gravel substrate exposed at 12.5 m 3 s -1 is 3%. The proportion lost at
10 m 3 s -1 is 5%, at 7.5 m 3 s -1 it is 9%, and at 5 m 3 s -1 the loss is 13% (Figure 6.11). The
increase in the area of exposed gravel as flow declines below 12.5 m 3 s -1 is of
importance not only for the growth of Spirogyra but also for other organisms utilising
this habitat. The drying of this substrate represents a loss of available habitat from the
river and a corresponding loss of life on these areas. Examples of organisms able to
utilise this substrate are diatoms and other attached microscopic plants, and
macroinvertebrates.
6.4.3 Flow-Biomass Relationship
The relationship between algal biomass and flow (as shear velocity, calculated using
bottom velocity) on gravel substrate was based on a derived measure of maximum
potential biomass (Figure 6.5). It was beyond the scope of this study to examine in
detail seasonal changes in the growth and biomass of Spirogyra and what may be
driving these changes (Figure 6.10). This will be examined elsewhere. Of more
importance was the maximum extent to which Spirogyra was able to grow under
different flow conditions. Maximum potential biomass was used as a measure of this
extent, and allows a measure of flow to be related to biomass.
The relationship seen in Figure 6.10 is consistent with observations made on the
distribution of Spirogyra in the river. In slow flowing areas high biomass (scores
greater than 2) was never observed. Spirogyra appears to require a minimum flow to
enable dense stands to become established. In very high flow environments dense
stands are also not observed. This is presumably due to the drag acting on filaments
being sufficiently high to cause tearing or dislodgment. The relationship between
maximum potential biomass and shear velocity in Figure 6.10 is consistent with these
observations, and suggests that the optimal range for the growth of Spirogyra
corresponds to shear velocities of 0.025 to 0.055 ms -1 . This relates to a bottom
velocity (at 0.1 m) of between 0.17 and 0.44 ms -1 .
The establishment of a relationship between maximum potential biomass of Spirogyra
and flow (as shear velocity) for gravel substrates has enabled the calculation of river
biomass (Figure 6.12) when used in conjunction with a hydrodynamic model to
simulate a range of discharge scenarios. This estimate of biomass may therefore be
considered as the maximum biomass that may be produced in the study reach in a
growing season.
156

Simulations on the effect of reducing flow through the 17 km reach shows a
corresponding reduction in algal biomass (Figure 6.12). This reduction does not occur
at a constant rate across the range of discharges investigated. The rate of decline in
biomass with reducing discharge above 12.5 m 3 s -1 (0.4 kg/m 3 s -1 ) was markedly less
than the rate of biomass decline below this level (1.5 kg/m 3 s -1 ,Figure 6.12). This
almost four-fold difference can be explained in terms of the relative changes in the
area of gravel subject to the different shear velocity classes, the maximum potential
biomass for each class, and the area of gravel exposed as river levels fall.
At discharges above 12.5 m 3 s -1 the proportion of exposed gravel is small, accounting
for 3% of the total gravel area (Figure 6.11). The main factor affecting total biomass
in this range is the area of gravel exposed to shear velocities in the range 0.025-0.055
ms -1 . This range accounts for the greatest area of gravel beds and is also associated
with the highest biomass levels (Table 6.2). Below 12.5 m 3 s -1 two factors contribute
to the accelerated decline in algal biomass. Firstly, the area of gravel beds subjected to
flows less favourable to algal growth is greater than the area of gravel subjected to
more productive flows. This includes shear velocities of 0.00-0.005 ms -1 that are too
low to enable the growth of Spirogyra. This area is markedly greater at a discharge of
less than 5 m 3 s -1 (Figure 6.11). Secondly, there is a marked increase in the area of
gravel exposed by falling water level (Figure 6.11). Therefore there is the additive
impact of less favourable flows and habitat loss at discharges below 12.5 m 3 s -1 that
results in an almost four-fold increase in the rate of loss of biomass compared to flows
greater than 12.5 m 3 s -1 .
6.4.4 Water Extraction Simulations
The relationship between flow and biomass was applied to the dry season historical
low flow record at the study reach to determine the annual biomass frequency
distribution for the 42 years for which flow records are available. This provides a
comparative assessment of how water extraction may change the distribution of
biomass levels from the expected natural state (Figure 6.13 and Figure 6.14). These
figures show that a proportional extraction regime has less of an effect on the natural
historical distribution of biomass as opposed to a fixed extraction rate.
Although this approach provides a measure of the effects water extraction may have
on Spirogyra biomass, it must be treated with caution as the absolute consequences of
biomass changes for river ecology are unknown. It is worthwhile at this stage to put
this study into context and state what the assumptions and unknowns are.
With respect to the flow-biomass model:
• The accuracy of the hydrodynamic model for low flows is unknown.
• The relationship between flow and velocity is based on results from a single dry
season, and a record high year as well. It is unknown whether low flow years are
associated with other changes in river condition (eg water quality) that also affect
biomass of Spirogyra.
• It is assumed that the historical low flow record for the study reach is
representative of the longer-term flow record.
157

• It is assumed that the distribution of riverbed substrate (especially gravel) remains
unchanged over time and between high and low flow years.
With respect to the ecological significance of Spirogyra:
• Being a plant Spirogyra is considered an important component of river ecology, as
it is a primary producer and a habitat for smaller fauna. Therefore its ecological
significance is assumed.
• The true role of Spirogyra in river ecology is unknown. Therefore the effect of
changes in Spirogyra biomass on other organisms is unknown.
• A broader unknown is the consequence of low river flows on river ecology as a
whole. It can be assumed that under historical low flow years additional stresses
may be imparted to river organisms through loss of wetted area, changes in water
quality, changes in flow and fragmentation of river into pools. The historical flow
record shows that a succession of low years is possible (Figure 6.3).
Given these assumptions and unknowns a conservative approach in setting water
extraction levels should be adopted in the first case. As our understanding of model
accuracy and ecological significance of Spirogyra develops, water extraction rates
may be changed.
Two guiding rules are proposed for a conservative water allocation approach. These
are:
1. Maintain the historical annual biomass frequency distribution of Spirogyra.
2. Recognise that periods of historical low flows may place additional natural
stresses on river ecology where extraction of water may have added consequences
for river life.
The best approach for maintaining the frequency distribution of biomass over time is
through a proportional water extraction regime (Figure 6.13). When presented as a
frequency distribution, an extraction rate of 8% does not result in a new biomass
distribution that is different to the historical biomass distribution ie the upper and
lower biomass classes are retained (Appendix B-1). The reduction in median biomass
over the record is estimated to be in the order of 4%. At extraction rates above 8% the
annual biomass frequency distribution is changed to the extent that the upper biomass
class is lost and a new minimum biomass class created. On the basis of the guiding
rules proposed above this change in biomass distribution is unacceptable.
These extraction rates do not consider the second guiding rule that recognises the
potential effects of low flow periods, and the extra stresses imposed by water
extraction during these periods. However, the setting of a minimum discharge level
below which extraction is not to occur is difficult as the true effects of single and
successive low flow periods are unknown. The setting of a minimum threshold level
at this stage is an arbitrary decision. An example of a simulation where a minimum
threshold is considered is shown in Appendix B-2. In this example an arbitrary
minimum threshold of 10 m 3 s -1 is set. This figure shows that at above an extraction
rate of 9% the biomass frequency distribution is changed from the historical state due
to the loss of the highest biomass class.
158

The proportional rate of extraction is similar with and without a minimum flow
threshold being set. However, a minimum threshold flow below which extraction is
not allowed offers some protection to river ecology at the expense of surety of water.
As extraction of water is most likely to be from aquifers and not directly from the
river, the issue of surety of supply needs to consider the relationships between
previous years rainfall, movement of groundwater and effects on river flow. Distance
between groundwater extraction point and river is also a factor that needs to be
considered.
6.5 Conclusions
1. The species of Spirogyra growing in the study reach of the Daly River was only
observed attached to gravel or rock substrates.
2. A relationship between flow (as bottom velocity and shear velocity) and algal
biomass was quantified by expressing algal biomass as maximum potential
biomass (ie the highest level of biomass that may be expected at a certain flow).
There was an optimal range of shear velocity that could sustain high levels of
biomass (0.025-0.055 ms -1 ). There was a reduction in biomass if shear velocity
was either above or below this range.
3. Simulation of flow through the study reach using a two dimensional
hydrodynamic (RMA-2) model enabled shear velocity to be calculated and areas
of gravel substrate subjected to different shear velocities to be determined. This
output was used to estimate total algal biomass for the study reach for a range of
discharge values.
4. There is a reduction in total algal biomass of the study reach (from 25 to 1 kg
chlorophyll-a) with decreasing flows (from 30 to 0.5 m 3 s -1 , respectively). This
reduction is not linear but two staged. Above 12.5 m 3 s -1 the reduction in biomass
is primarily due to the effects of reduced velocity. Below 12.5 m 3 s -1 the reduction
in total biomass is due to a combination of reduced velocity and loss of habitat
through drying of gravel beds with decreasing water level.
5. The rate of decline of total algal biomass in the study reach with decreasing
discharge is almost four times greater between 0.5 and 12.5 m 3 s -1 when to the rate
of decline between 12.5 and 30 m 3 s -1 .
6. Gravel substrate in a major habitat in the study reach making up 50% of the total
area.
7. The area of gravel substrate lost through a reduction in water level from 30 to 12.5
m 3 s -1 is estimated to be 3%. As flow is further reduced to below 12.5 m 3 s -1 the
area of gravel that dries increases. The proportion lost at 10 m 3 s -1 is 5%, at 7.5
m 3 s -1 9%, and at 5 m 3 s -1 the loss is 13%.
8. A proportional extraction rate is preferred over a fixed extraction rate to maintain
as much as possible the historical biomass frequency distribution ie to maintain
the natural variability.
9. Based on simulations of the effects of water extraction on the annual biomass
frequency distribution, and adopting a conservative approach, the proportional
extraction rate should not exceed 8% of the minimum dry season flow at the study
reach.
159

6.6 Recommendations
1. Verify the relationship between water level and discharge at the downstream
model boundary (Figure 6.9). This relationship was estimated and needs to be
tested by undertaking flow and height measurements across a range of flows. This
can only be achieved after a number of successively drier years as flow decreases
with decreasing water table.
2. Test the accuracy of the model in predicting velocity and depth at flows below the
minimum used to calibrate the model (24 m 3 s -1 ). A simulation of flows much less
than this (ideally less than 10 m 3 s -1 ) is required. This can only be achieved after a
number of successively drier years as flow decreases with decreasing water table.
3. Confirm the response of Spirogyra biomass to different shear velocities in lowerflow
years. A subset of the original field measurements should be repeated to
ensure that the relationship between algal biomass and shear velocity holds true
across a wide range of river discharge to build confidence in the predictions of
biomass at much reduced flows.
4. Investigate the importance of Spirogyra to river ecology. The importance of this
species is deduced by its status as a primary producer and its widespread
occurrence.
5. Reduce the current water extraction regime from a maximum of 20% of
instantaneous flow to 8% of the minimum dry season flow.
6. Recognise the importance of low flow years and develop a minimum threshold
below which extraction is not permitted. As an example a minimum threshold of
10 m 3 s -1 will allow 9% of the minimum dry season flow to be extracted for 70%
of years.
7. Expand the range of species examined for their response to changes in river flows.
The model developed in this study can be directly applied to other indicator
organisms such as Chara, another riverbed plant of widespread occurrence in the
study reach. This would require the establishment of a shear velocity-biomass
relationship specific to this species.
8. Utilise the model to assess the effect of reducing water levels on the loss of edge
habitat. This is an important habitat for a range of plant and animal species. The
hydrodynamic model can be applied to examine the area of edge lost through
drying as water levels are decreased.
6.7 References
APHA (1992). Standard Methods for the Examination of Water and Wastewater. 18 th
Edition. American Public Health Association. American Water Works Association
and American Water Pollution Control Federation, New York.
Ling, H.U. and Tyler, P.A. (1986). A limnological survey of the Alligators Rivers
Region. II Freshwater Algae, exclusive of diatoms. Research Report 3. Australian
Government Publishing Service, Canberra.
Rea, N., Dostine, P.L., Cook, S., Webster, I. And Williams, D. (2002). Environmental
Flow Requirements of Vallisneria nana. Natural Resources Division, NT Department
of Infrastructure, Planning and Environment. Report.
160

Tickell, S.J. A survey of springs along the Daly River. Report 06/2002. Department of
Infrastructure, Planning and Environment, NT Government.
161

Appendix A
Sieve size Classification Site 1
Site 2 Site 5A
Site 14
Site 17
Site 24
(mm) (sand and gravel)
(sand) (sand) (sand and gravel)
(gravel)
(fine gravel)
152.4 Cobbles -
60 Gravel coarse
53 Gravel coarse 100 100 100
37.5 Gravel coarse 90 92 86
26.5 Gravel coarse 100 100 78 57 58
19 Gravel medium 99 100 100 97 68 39 39 100
13.2 Gravel medium 97 97 100 94 91 100 58 32 29 98
9.5 Gravel medium 93 93 98 90 81 98 52 29 24 98 100 100
6.7 Gravel medium 88 89 92 87 73 94 45 24 21 96 98 98
4.75 Gravel fine 78 80 83 81 64 100 87 100 37 20 18 83 84 89
2.36 Gravel fine 58 60 59 60 47 95 100 67 91 23 14 12 49 50 53
1.18 Sand coarse 37 37 35 37 32 88 100 99 51 54 16 10 8 32 34 28
0.6 Sand coarse 20 18 17 18 15 67 99 87 34 35 11 6 5 20 22 15
0.425 Sand medium 13 11 11 11 7 42 97 46 18 32 7 5 4 11 10 7
0.3 Sand medium 8 8 7 7 4 26 84 10 10 31 5 4 3 6 4 2
0.15 Sand fine 1 2 1 2 1 9 33 0 4 23 2 1 1 4 2 1
0.075 Silt - 0 1 0 1 0 1 9 0 1 7 1 1 1 4 1 1
Appendix A. Particle size analysis (as percent passing through designated sieve size, wet sieving) of riverbed samples from six selected sites.
Description in brackets refers to subjective visual assessment made in the field.
Site Locations: Site 1 (740924, 8451132); Site 2 (741304, 8450957); Site 5A (741582, 8450837); Site 14 (739359, 8447864); Site 17 (738541, 8445792); Site 24 (737783, 8454635).
162

Appendix B1
Comparison of biomass frequency distribution for an 8% proportional extraction rate
with the expected historical distribution.
Frequency
9
8
7
6
5
4
3
2
1
0
1
3
5
7
9
11
13
15
Appendix B2
Comparison of biomass frequency distribution for a 9% proportional extraction rate
when discharge exceeds 10 m 3 s -1 with the expected historical distribution.
Frequency
9
8
7
6
5
4
3
2
1
0
1
3
5
163
17
19
Biomass Class (kg chlorophyll-a)
7
9
11
13
15
17
19
Biomass Class (kg chlorophyll-a)
21
21
23
23
25
25
Historical
8% extraction
Historical
9% extraction

7 COMMUNICATION OF TECHNOLOGY TRANSFER ACTIVITIES, AND
PROJECT ACTIVITIES TO THE COMMUNITY AND STAKEHOLDERS
Community familiarity with the project, and Environmental Flows Initiative (EFI) in
general, has been achieved primarily by person-to-person contact in the Douglas/Daly
River region. Whilst officers have been in the field, they have discussed the project
with land-holders, tourist operators and businesses, tourists, recreational fishing
people and Government extension officers. In May 2001, a poster outlining this, and
the other projects, was presented at a public open day at the Douglas Daly Research
Farm. The poster informed the public of the issue, and those scientific studies that
were being undertaken.
In July 2000, a field day was conducted by the Department, as part of the Australian
Society for Limnology Annual Congress, to inform scientists and water resource
managers of the Daly River Basin environmental flow issues. The field trip visited the
Douglas-Daly River region, provided written and oral information, and provided a
forum to informally discuss the scientific and management issues.
During 2001, preliminary findings of the project were presented, as part of a series of
EFI presentations. These were well attended, though mainly by professionals and
resource managers from a range of organisations.
In September 2002, the Department of Infrastructure, Planning and Environment will
hold a workshop for all NT EFI project members. The objectives of the workshop are
primarily facilitate information exchange between the projects, and to provide a forum
to discuss the ecological issues that need to be addressed when making a water
allocation to the environment.
The primary modes of technology transfer are this report, a NT EFI workshop report,
and publications in international journals.
164

Appendix 3.2 Water quality figures
Water quality parameter Page
Chlorophyll a 166
Conductivity 167
Gilvin 168
Nitrate 169
Turbidity 170
pH 171
Silica 172
Temperature 173
Total organic carbon 174
Total Kjedahl nitrogen 175
Total phosphorus 176
Total suspended sediment 177
Euphotic depth 178
165

Chlorophyll a (µg/L)
2
1
0
2
1
0
(a) Daly River tributaries
(b) Daly River
Jun Jul Aug Sep Oct Nov Dec
2000
166
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Claravale Crossing
Lukies Farm
Beeboom Crossing
Township

Conductivity (µS/cm)
800
600
400
40
30
20
10
700
600
500
400
300
200
100
(a) Daly River tributaries
(b) Daly River
Jun Jul Aug Sep Oct Nov Dec
2000
167
Katherine R., DCP inflow
Katherine R., DCP outflow
Douglas River
Flora River
Claravale Crossing
Lukies Farm
Beeboom Crossing
Township

Dissolved Gilvin oxygen (m (mg/L)
-1 )
93
8
2
7
1
6
50
(a) Daly River tributaries tributaries
93 (b) Daly River
Claravale Crossing
Lukies Farm
8
2
Beeboom Crossing
Township
7
1
6
50
Jun Jul Aug Sep Oct Nov Dec
2000
168
Katherine R., DCP inflow
Katherine R., DCP outflow outflow
Flora River
Douglas River

Nitrate (as N, µg/L)
110
100
90
80
4
2
0
(a) Daly River tributaries
(b) Daly River
30 Claravale Crossing
Lukies Farm
20
10
0
Jun Jul Aug Sep Oct Nov Dec
2000
169
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Beeboom Crossing
Township

Turbdity (NTU)
8
6
4
2
0
(a) Daly River tributaries
(b) Daly River
8 Claravale Crossing
Lukies Farm
6
4
2
0
Jun Jul Aug Sep Oct Nov Dec
2000
170
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Beeboom Crossing
Township

pH
9
8
7
6
5
(a) Daly River tributaries
9
(b) Daly River
Claravale Crossing
Lukies Farm
8
Beeboom Crossing
Township
7
6
5
Jun Jul Aug Sep Oct Nov Dec
2000
171
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River

Silica (mg/L)
20
10
0
(a) Daly River tributaries
(b) Daly River
20 Claravale Crossing
Lukies Farm
10
0
Jun Jul Aug Sep Oct Nov Dec
2000
172
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Beeboom Crossing
Township

Temperature ( 0 C)
32
28
24
20
32
28
24
20
(a) Daly River tributaries
(b) Daly River
Jun Jul Aug Sep Oct Nov Dec
2000
173
Katherine R., DCP inflow
Katherine R., DCP outflow
Douglas River
Flora River
Claravale Crossing
Lukies Farm
Beeboom Crossing
Township

Total organic carbon (mg/L)
2
1
0
2
1
0
(a) Daly River tributaries
(b) Daly River
Jun Jul Aug Sep Oct Nov Dec
2000
174
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Claravale Crossing
Lukies Farm
Beeboom Crossing
Township

TKN (µg/L)
300
200
100
0
(a) Daly River tributaries
(b) Daly River
300 Claravale Crossing
Lukies Farm
200
100
0
Jun Jul Aug Sep Oct Nov Dec
2000
175
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Beeboom Crossing
Township

Total phosphorus (µg/L)
20
10
0
(a) Daly River tributaries
(b) Daly River
20 Claravale Crossing
Lukies Farm
10
0
Jun Jul Aug Sep Oct Nov Dec
2000
176
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Beeboom Crossing
Township

Total suspended sediment (mg/L)
6
4
2
0
(a) Daly River tributaries
(b) Daly River
6 Claravale Crossing
Lukies Farm
4
2
0
Jun Jul Aug Sep Oct Nov Dec
2000
177
Katherine R., DCP inflow
Katherine R., DCP outflow
Flora River
Douglas River
Beeboom Crossing
Township
Note, 17 mg/L in November
at Daly River township site

Euphotic depth (m)
16
12
8
4
0
12
8
4
0
(a) Daly River tributaries
(b) Daly River
Jun Jul Aug Sep Oct Nov Dec
2000
178
Katherine R., DCP outflow
Katherine R., DCP inflow
Flora River
Douglas River
Claravale Crossing
Lukies Farm
Beeboom Crossing
Township