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Macroinvertebrate responses
to low-flow conditions
in New South Wales rivers
Bruce Chessman, Tim Haeusler and Andrew Brooks
NSW Office of Water
Low flows report series, June 2012
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© Commonwealth of Australia 2012
This work is copyright.
Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by
any process without prior written permission.
Requests and enquiries concerning reproduction and rights should be addressed to the
Communications Director, National Water Commission, 95 Northbourne Avenue, Canberra
ACT 2600 or email bookshop@nwc.gov.au.
Online/print: ISBN: 978-1-921853-73-9
Published by the National Water Commission
95 Northbourne Avenue
Canberra ACT 2600
Tel: 02 6102 6000
Email: enquiries@nwc.gov.au
Date of publication: June 2012
An appropriate citation for this report is:
Chessman B et al 2012, Macroinvertebrate responses to low-flow conditions in New South
Wales rivers, National Water Commission, Canberra
Disclaimer
This paper is presented by the National Water Commission for the purpose of informing
discussion and does not necessarily reflect the views or opinions of the Commission or the
NSW State Government.
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Low flows report series
This paper is part of a series of works commissioned by the National Water Commission on
key water issues. This work has been undertaken by the NSW Office of Water on behalf of
the National Water Commission.
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Contents
Executive summary
Report context
1.
Introduction
2.
Methods
2.1.
Macroinvertebrate data
2.2.
Site selection
2.3.
Hydrological analysis
2.4.
Macroinvertebrate traits
2.5.
Statistical analysis
3.
Results
3.1.
Canonical correspondence analysis
3.2.
Principal components analysis
4.
Discussion
5.
Conclusion
Shortened forms
References
viii
ix
1
2
2
2
2
4
4
7
7
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12
14
15
16
Tables
Table 1: Seasonal and habitat distribution of macroinvertebrate samples..................................3
Table 2: Summary of canonical correlation analysis for the full set of riffle
samples (n = 40) with hydrological metrics calculated over various
antecedent periods. The correlation and loadings are for the first canonical
variable. ................................................................................................................................7
Table 3: Summary of canonical correlation analysis for the reduced set of riffle
samples (n = 36) with hydrological metrics calculated over various
antecedent periods. The correlation and loadings are for the first canonical
variable. ................................................................................................................................8
Table 4: Summary of canonical correlation analysis for the full set of edge
samples (n = 289) with hydrological metrics calculated over various
antecedent periods. The correlation and loadings are for the first canonical
variable. ................................................................................................................................8
Table 5: Summary of canonical correlation analysis for the reduced set of edge
samples (n = 273) with hydrological metrics calculated over various
antecedent periods. The correlation and loadings are for the first canonical
variable. ................................................................................................................................9
Table 6: Summary of least squares regression of invertebrate traits and low-flow
conditions derived from the first principal component of PCA of the
hydrological metrics calculated over various antecedent periods. The full set
of riffle samples (n = 40) were used for one- to 12-month antecedent periods
and a reduced set (n = 36) was used for the 24-month period. .........................................10
Table 7: Summary of least squares regression of invertebrate traits and low-flow
conditions derived from the first principal component of PCA of the
hydrological metrics calculated over various antecedent periods. The full set
of edge samples (n = 289) were used for one- to 12-month antecedent
periods and a subset (n = 273) was used for the 24-month period....................................10
Figures
Figure S1: Context of reports produced for the Low Flow Ecological Response
and Recovery Project. Each circle represents the location of individual case
studies and the size of each circle represents the spatial extent of each case
study. ................................................................................................................................... ix
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Figure 1: Location of macroinvertebrate sampling sites with hydrological data. .........................3
Figure 2: Example relationships between invertebrate traits of aerophily,
rheophily and thermophily and a gradient of low-flow conditions derived from
the first principal component of a PCA of hydrological statistics. The low-flow
gradient in this figure represents flow conditions for the preceding 12
months. ...............................................................................................................................11
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Acknowledgements
Macroinvertebrate and associated data were kindly provided by Greg Long and Alison
Reardon (Murray-Darling Basin Authority), Sonia Claus, Jan Miller and Chris Rush (NSW
Office of Environment and Heritage), Minal Khan (Queensland Department of Environment
and Resource Management), Peter Goonan (South Australian Environment Protection
Authority) and Lisa Singleton (Victorian Environment Protection Authority). Hydrographic data
were obtained from the NSW Office of Water Hydsys database. We thank Simon Williams,
Nick Marsh and Stephen Balcombe for comments on a draft version of this report.
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Executive summary
This study is part of a larger project being undertaken by the National Water Commission to
investigate the ecological response to and recovery from low flows in Australian rivers.
The response of aquatic macroinvertebrates to low-flow conditions in New South Wales rivers
was investigated using existing data collected as part of the Murray-Darling Basin Authority’s
Sustainable Rivers Audit and the state’s Monitoring, Evaluation and Reporting program. The
study examines flow regimes at aquatic macroinvertebrate sampling sites for up to 24 months
before sampling, and relates the flow data to flow-relevant biological traits of the
macroinvertebrates sampled.
Three traits that have a logical conceptual connection to sensitivity or tolerance to low flow
were considered: aerophily (preference for high versus low concentrations of dissolved
oxygen), rheophily (preference for fast-flowing versus still waters) and thermophily
(preference for high versus low temperatures). A combination of canonical correspondence
analysis and principal components analysis was used to investigate relationships.
The study results supported our a priori hypotheses that aquatic macroinvertebrate
assemblages that had been exposed to severe flow reduction or cessation during the period
before sampling would be dominated by taxa tolerant of low oxygen concentrations, low water
velocities and high temperatures, whereas assemblages not exposed to very low flows would
be dominated by taxa that favour aerated, fast-flowing and cool conditions. The relationships
were strongest in riffle habitats that are characterised by faster flowing, well-oxygenated water
and tend to be the first habitat type to be impacted by reduced river discharge.
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Report context
This report is part of a larger series of reports produced for the National Water Commission’s
Low Flow Ecological Response and Recovery Project (Figure S1). This report presents one of
11 hydro-ecological case studies. The purpose of the case studies is to test hypotheses that
relate ecological process and function and biological traits to key hydrological measures that
are affected by low flows. A summary of the findings in this report and the other case studies
are contained in Synthesis of case studies quantifying ecological responses to low flows
(Marsh et al. 2012).
Guidance on ecological response and hydrological modelling for low-flow
water planning
Low-flow hydrological classification of Australia
Review of literature quantifying ecological responses to low flows
Early warning, compliance and diagnostic monitoring of ecological
responses to low flows
Synthesis of case studies quantifying ecological responses to low flows
Figure S1: Context of reports produced for the Low Flow Ecological Response and Recovery
Project. Each circle represents the location of individual case studies and the size of each
circle represents the spatial extent of each case study.
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1. Introduction
This study is part of a national project being undertaken to investigate the ecological response
to and recovery from low flows in Australian rivers. The project’s aim is to support water
planning by developing a contemporary, Australia-wide understanding and quantification of
thresholds of response to the onset of, and recovery from, single and successive low-flow
events by individual species, biotic assemblages and ecosystem processes.
As part of the project’s aim to develop a national understanding, each state or territory is
assessing existing ecological and hydrological datasets to investigate the ecological response
to low flows.
The NSW Office of Water has been engaged by the National Water Commission to
investigate the relationships between biota and low flows. This study uses existing aquatic
macroinvertebrate data collected across News South Wales by state government agencies
and partner organisations as part of the Murray-Darling Basin Authority’s Sustainable Rivers
Audit (SRA) (Davies et al. 2010) and the Monitoring, Evaluation and Reporting (MER)
program of New South Wales.
The study examines flow regimes at aquatic macroinvertebrate sampling sites for up to 24
months before sampling, and considers the longer-term flow regime at each site. The study
relates the flow data to flow-relevant biological traits of the macroinvertebrates sampled. The
analysis was based on the trait composition of local assemblages rather than their taxonomic
composition because taxa that share certain trait states are likely to be affected similarly by
low flows regardless of their degree of phylogentic relatedness (Bonada et al. 2007; Brooks et
al. 2011). In addition, an analysis of traits provides insights into the mechanisms by which
flow reduction may impact on sensitive taxa.
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2. Methods
2.1. Macroinvertebrate data
A total of 3267 SRA and MER samples collected from 1405 sites across New South Wales
and the Australian Capital Territory from spring 2004 to autumn 2010 were considered for
possible inclusion in the analysis. Each sample was collected with a hand net (250 μm mesh)
from either fast-flowing water (‘riffles’) or slow-flowing or still water (‘edges’). Most samples
were collected by New South Wales agency staff according to the state’s sampling manual of
the Australian River Assessment System (AusRivAS) (Turak et al. 2004), but some were
taken by staff of interstate agencies according to the Queensland, South Australian and
Victorian AusRivAS manuals. Collected invertebrates were generally identified only to family
or higher taxonomic levels. The AusRivAS sampling and subsampling procedures are not
quantitative (e.g. sampling and subsampling effort varies among samples and operators),
thus the data were transformed to recorded presence or absence of each identified taxon per
sample.
2.2. Site selection
The macroinvertebrate sampling locations were mapped in ArcGIS and compared with the
locations of 791 active hydrometric (gauging) stations across New South Wales. Gauges
located on storages, weirs and irrigation offtakes were excluded from consideration.
Macroinvertebrate samples were considered potentially suitable for analysis if collected within
approximately five river kilometres of a gauging station, with no major tributary inflow or
anabranch outflow between the macroinvertebrate sampling location and the gauge.
The record of each gauging station was assessed for its suitability to analyse flow history
before each macroinvertebrate sampling event. For each gauge matched to at least one
macroinvertebrate sample, a gap analysis in the River Analysis Package (RAP) (Marsh et al.
2003) determined whether there was an unbroken daily flow record for the 12 months
preceding each sampling date. The flow record and associated macroinvertebrate sample or
samples were excluded from the analysis if a gap in the record was greater than four
consecutive days. Gaps smaller than this were filled with the linear fill option in the RAP.
A total of 103 of the 1405 macroinvertebrate sampling locations had suitable associated
hydrological data (Figure 1) and form the basis of the statistical analysis. Sampling of these
sites between October 2004 and June 2010 resulted in 329 samples, collected from both riffle
and edge habitats and primarily in spring and autumn (Table 1).
2.3. Hydrological analysis
The RAP was used to calculate several metrics to describe the hydrology at each of the
sampling locations.
The full flow record for each gauge was used to calculate general statistics such as flow
percentiles, daily mean and median discharge. Any gaps in the flow record were ignored for
the calculation of these metrics, provided they did not occur within the 12 months preceding
the sampling dates.
The flow record at each sampling location was analysed for four time periods before each
sampling occasion: one, three, six and 12 months. We chose these periods to determine
whether invertebrate responses to antecedent low-flow conditions were rapid or gradual. The
RAP was used to calculate general statistics such as mean and total flow for each of these
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periods, as well as distribution statistics such as standard deviation, coefficient of variation,
and the mean daily baseflow. The baseflow calculation in RAP employs the Lyn and Holick
digital filter method to separate the component of the hydrograph attributable to recent runoff
events from the more consistent low flows resulting from groundwater inflow (Marsh et al.
2003). A spell analysis for low-flow periods was also calculated for each of the antecedent
periods. For the spell analysis, the low-flow threshold for each gauge was defined as 10 per
cent of the mean for the full period of record. This figure was selected because sharp declines
in wetted area and velocity are typically observed once flow falls below about 10 per cent of
the mean for a particular river (Tennant 1976; Reinfelds et al. 2004). Thus, this figure
represents a level below which considerable stress on the in-stream fauna can be expected
as a result of loss of habitat.
Sample Site
Major River Catchment
0
90
180
360 Kilometres
Figure 1: Location of macroinvertebrate sampling sites with hydrological data.
Table 1: Seasonal and habitat distribution of macroinvertebrate samples.
Habitat
Edge
Autumn
Spring
Summer
Winter
Total
128
119
20
22
289
Riffle
17
19
2
2
40
Total
145
138
22
24
329
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2.4. Macroinvertebrate traits
Three traits that have a logical conceptual connection to sensitivity or tolerance to low flow
were considered for this analysis:

aerophily – preference for high versus low concentrations of dissolved oxygen (DO)

rheophily – preference for fast-flowing versus still waters

thermophily – preference for high versus low temperatures.
We hypothesised that conditions of severe flow reduction or cessation of flow would be
detrimental to aerophilous macroinvertebrates with high oxygen requirements, because decay
of organic matter in sluggish or stagnant streams would lower DO concentrations, and reaeration of stream water from the atmosphere would be less effective without fast, turbulent
flow. Similarly, we expected that low-flow and no-flow conditions would not favour rheophilous
macroinvertebrates that prefer fast currents, because such habitats would be rare or absent
under these conditions. Conversely, we hypothesised that low or zero flows would benefit
thermophilic macroinvertebrates, which prefer warmer conditions, because daytime stream
temperatures would be likely to rise under these conditions through greater solar heating of
shallower waters.
Levels of each trait applicable to each macroinvertebrate family were estimated on continuous
scales according to the occurrence of the families in 8928 biomonitoring samples collected in
New South Wales and the Australian Capital Territory between 1994 and 2010, plus
associated environmental measurements (Chessman in review). The aerophily of each family
was estimated from the average DO concentration associated with all samples in which the
family was collected for which contemporaneous DO data were available, divided by the
average DO concentration of all samples with DO data. The thermophily of each family was
estimated in a similar manner from the average instantaneous water temperature associated
with samples in which that family was detected. Because data on associated current velocity
were not available for any of the samples, rheophily was estimated from the habitats from
which samples were collected. Habitats were scored in order of increasing energy: 0 for pool
edge-waters and other still waters, 1 for glides, 2 for runs, 3 for riffles, 4 for rapids and 5 for
cascades and waterfalls. The average hydraulic score of samples in which each family was
detected was calculated and divided by the average score of all hydraulically rated samples.
2.5. Statistical analysis
2.5.1. Preparation of data for analysis
The statistical analysis related the trait signatures of macroinvertebrate samples from each of
the edge and riffle habitats to hydrological metrics characterising the hydrological regime of
each sampling site for various periods before sampling.
The average aerophily, rheophily and thermophily of the families recorded in each
macroinvertebrate sample were calculated, ignoring any taxa not identified to family level.
A data screening procedure was used to select a small number of hydrological metrics from
the large number generated by the RAP. Metrics were assessed according to their conceptual
relevance to likely impacts of low flows on invertebrate assemblages, and by examining
bivariate scatterplots of relationships of individual metrics to trait averages. Five metrics were
selected as a result of this screening.
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1. Minimum discharge: the minimum flow was considered relevant as it was likely to
characterise the greatest hydrological stress to which the resident fauna were exposed
during the antecedent period under consideration.
2. Number of low-flow spells: this metric was considered relevant because of the likelihood
that repeated excursions of flow into the defined low-flow range (i.e. flows below 10 per
cent of long-term mean flow) during the antecedent period could result in cumulative
stress on the fauna.
3. Length of the longest low-flow spell: this metric was also considered a useful measure of
low-flow stress, since it is possible that some invertebrates might tolerate brief periods of
low flow but would be unable to cope with extended low flow.
4. Total duration of low-flow spells: this metric was considered a further potentially useful
predictor of cumulative stress imposed by low-flow conditions.
5. Mean daily baseflow: this metric was considered a useful summary measure of the
persistence of flow during the entire antecedent period, excluding high-flow events.
Before the analysis, the variables minimum discharge and mean daily baseflow were
standardised to account for differences in stream size among sites by dividing each value by
the long-term average discharge for the applicable site. The resulting standardised variables
were highly positively skewed and were therefore transformed to log(100x+1), which largely
removed the skew. The other hydrological variables were not highly skewed and did not
require transformation.
2.5.2. Canonical correspondence analysis
The relationships between the traits and hydrological metrics were analysed with canonical
correlation analysis (CCA). This method was chosen because a high degree of correlation
existed among the three traits and among the five hydrological metrics, which could have
created problems of multi-colinearity in many statistical methods. CCA deals with intercorrelations among both dependent variables (in this case, traits) and independent variables
(hydrological metrics) by creating linear combinations of both sets of variables (called
canonical variables) that are maximally correlated with each other. Thus the first pair of
canonical variables (CVx1 and CVy1) has the relationship:
CVx1 = ax1 + bx2 … + kxn
CVy1 = my1 + ny2 … + wyn
where y1 … yn are dependent variables, x1 … xn are independent variables, and a, b, … w
are coefficients with values that result in the maximum possible correlation between CV x1
and CVy1.
After the first pair of canonical variables is formed, subsequent pairs are created from the
residual variance; that is, the variance remaining after the variance accounted for by previous
pairs has been removed from the original variables.
CCA was performed separately for each antecedent flow period, ranging from one to 12
months. Analyses were also done separately for riffles and edges because of the well-known
differences in biota and hydraulics between the two habitats.
Because the analyses showed strong relationships with the maximum antecedent period, an
additional analysis was done with hydrological metrics calculated over an antecedent period
of 24 months. This required that a few samples were excluded because of gaps in the flow
record over the longer period. The analyses for the shorter antecedent periods were repeated
for this reduced set of samples to permit comparisons of all antecedent periods, including 24
months, for the same set of sites.
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2.5.3. Principal components analysis
We also analysed the relationship between individual traits and a gradient of low-flow
conditions. A principal components analysis (PCA) was performed on the hydrological metrics
to obtain a single gradient of low flow. The relationship between each trait and the first
principal component axis for each antecedent period was tested using least squares linear
regression.
The PCA and subsequent regression analyses were performed separately for each
antecedent flow period, ranging from one to 24 months. Analyses were also done separately
for riffles and edges.
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3. Results
3.1. Canonical correspondence analysis
For the CCA, results for only the first pair of canonical variables are reported for each
analysis. Subsequent pairs were not informative as they generally had low correlation
coefficients and few strong loadings for (i.e. correlations with) the original dependent and
independent variables.
For the riffle analysis including the full set of samples, all CCAs were statistically significant
except for the analysis with a one-month antecedent flow period, which was marginally nonsignificant (P = 0.075) (Table 3). The R2 values and first canonical correlation coefficient were
highest for the analysis with an antecedent period of 12 months, though not greatly different
from the values for periods of three and six months.
Results for the riffle analysis including the reduced set of samples were generally similar to
those for the full set for antecedent periods of one to 12 months (Table 4). For the additional
analysis with an antecedent period of 24 months, the R2 values and first canonical correlation
coefficient were slightly lower than for the 12-month analysis.
For the edge analysis including the full set of samples, R2 values and values of first canonical
correlation coefficient were much lower than for the riffle analysis, but statistical significance
was greater because the number of edge samples much exceeded the number of riffle
samples (Table 5). The strength of the relationships showed little variation according to the
length of the antecedent flow period considered. Results were similar for the analysis of the
reduced set of samples that included analysis for a 24-month antecedent flow period.
Table 2: Summary of canonical correlation analysis for the full set of riffle samples (n = 40)
with hydrological metrics calculated over various antecedent periods. The correlation and
loadings are for the first canonical variable.
1 month
3 months
6 months
12 months
R2
0.494405
0.668095
0.666695
0.699076
P
0.074606
0.000926
0.000972
0.000289
Canonical correlation
0.662490
0.721544
0.698583
0.737324
Aerophily
0.963160
-0.855161
-0.804215
0.851099
Rheophily
0.990991
-0.923541
-0.961875
0.979526
Thermophily
-0.850558
0.892701
0.737281
-0.776689
Minimum discharge
0.901596
0.727399
-0.767455
-0.758168
Number of low-flow spells
-0.828480
-0.378084
0.441923
0.369613
Length of the longest low-flow spell
-0.799949
-0.977211
0.872529
0.923550
Total duration of low-flow spells
-0.873585
-0.902993
0.900177
0.902297
Mean daily baseflow
0.930447
0.683078
-0.952488
-0.899817
Loadings for dependent variables:
Loadings for independent variables:
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Table 3: Summary of canonical correlation analysis for the reduced set of riffle samples (n =
36) with hydrological metrics calculated over various antecedent periods. The correlation and
loadings are for the first canonical variable.
1 month
3 months
6 months
12 months
24 months
R2
0.476161
0.690818
0.724976
0.752287
0.720548
P
0.185162
0.001980
0.000598
0.000197
0.000706
Canonical correlation
0.638020
0.734494
0.713110
0.758536
0.748183
Aerophily
0.954474
-0.811449
-0.945562
-0.903783
0.881082
Rheophily
0.993554
-0.888271
-0.989604
-0.987626
0.995955
Thermophily
-0.839299
0.877186
0.862013
0.833560
-0.758029
Minimum discharge
0.909009
0.696785
-0.905937
0.824217
-0.692462
Number of low-flow
spells
-0.820431
-0.287950
0.532724
-0.355764
0.431928
Length of the longest
low-flow spell
-0.830412
-0.985057
0.811971
-0.887776
0.879578
Total duration of lowflow spells
-0.909498
-0.880176
0.870674
-0.874552
0.876606
Mean daily baseflow
0.932894
0.623955
-0.873532
0.877540
-0.893573
Loadings for
dependent variables:
Loadings for
independent
variables:
Table 4: Summary of canonical correlation analysis for the full set of edge samples (n = 289)
with hydrological metrics calculated over various antecedent periods. The correlation and
loadings are for the first canonical variable.
1 month
3 months
6 months
12 months
R2
0.234082
0.219761
0.208149
0.242797
P
0.000000
0.000000
0.000000
0.000000
Canonical correlation
0.461693
0.428750
0.427388
0.459200
Aerophily
0.885208
0.922630
0.926448
0.885508
Rheophily
0.572350
0.652876
0.675874
0.582183
Thermophily
-0.909880
-0.936482
-0.950059
-0.919992
Minimum discharge
-0.894570
-0.944712
-0.963885
0.939346
Number of low-flow spells
0.668224
0.324554
0.297190
-0.157640
Length of the longest low-flow spell
0.775575
0.813728
0.734371
-0.769803
Total duration of low-flow spells
0.789473
0.801203
0.831521
-0.904530
Mean daily baseflow
-0.928194
-0.735519
-0.764754
0.881112
Loadings for dependent variables:
Loadings for independent variables:
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Table 5: Summary of canonical correlation analysis for the reduced set of edge samples (n =
273) with hydrological metrics calculated over various antecedent periods. The correlation
and loadings are for the first canonical variable.
1 month
3 months
6 months
12 months
24 months
R2
0.251106
0.225719
0.227034
0.255047
0.275948
P
0.000000
0.000000
0.000000
0.000000
0.000000
Canonical correlation
0.459028
0.431135
0.454099
0.478620
0.493079
Aerophily
0.877343
-0.916273
0.916579
0.873191
0.881573
Rheophily
0.579601
-0.616429
0.629034
0.551564
0.544291
Thermophily
-0.919671
0.896523
-0.913397
-0.892860
-0.862958
Minimum discharge
-0.883056
-0.935813
0.954021
0.923033
0.888585
Number of low-flow
spells
0.681297
0.319102
-0.263049
-0.117507
-0.201847
Length of the longest
low-flow spell
0.774602
0.850391
-0.759068
-0.791070
-0.748848
Total duration of lowflow spells
0.780598
0.821824
-0.832076
-0.906723
-0.923730
Mean daily baseflow
-0.918626
-0.760594
0.750702
0.890125
0.937617
Loadings for
dependent variables:
Loadings for
independent
variables:
3.2. Principal components analysis
We found significant relationships between all traits and low-flow conditions derived from the
first principal component of the PCA of the hydrological statistics (tables 6 and 7). These
relationships were also significant for all antecedent periods and both riffle and edge habitats
(tables 6 and 7). In general, invertebrates that favour cool, fast-flowing, well-oxygenated water
dominated assemblages where low-flow conditions were not prevalent (Figure 2). Conversely,
invertebrates tolerant of high water temperatures, slow flow and reduced oxygen were
common in assemblages exposed to very-low-flow conditions (Figure 2).
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Table 6: Summary of least squares regression of invertebrate traits and low-flow conditions
derived from the first principal component of PCA of the hydrological metrics calculated over
various antecedent periods. The full set of riffle samples (n = 40) were used for one- to 12month antecedent periods and a reduced set (n = 36) was used for the 24-month period.
1 month
3 months
6 months
12 months
24 months
82.8
73.7
71.2
71.1
68.6
Linear regression
y = 1.03 +
0.01x
y = 1.03 +
0.01x
y = 1.03 +
0.01x
y = 1.03 +
0.01x
y = 1.03 +
0.01x
R2
0.38
0.36
0.35
0.42
0.46
P
<0.01
<0.01
<0.01
<0.01
<0.01
Linear regression
y = 1.60 +
0.1x
y = 1.60 +
0.11x
y = 1.60 +
0.11x
y = 1.60 +
0.12x
y = 1.60 +
0.13x
R2
0.38
0.40
0.43
0.49
0.50
P
<0.01
<0.01
<0.01
<0.01
<0.01
Linear regression
y = 0.98 0.01x
y = 0.98 0.01x
y = 0.98 0.01x
y = 0.98 0.01x
y = 0.98 0.01x
R2
0.31
0.33
0.33
0.36
0.37
P
<0.01
<0.01
<0.01
<0.01
<0.01
% variation
explained by
PC1
Aerophily
Rheophily
Thermophily
Table 7: Summary of least squares regression of invertebrate traits and low-flow conditions
derived from the first principal component of PCA of the hydrological metrics calculated over
various antecedent periods. The full set of edge samples (n = 289) were used for one- to 12month antecedent periods and a subset (n = 273) was used for the 24-month period.
1 month
3 months
6 months
12 months
24 months
79.0
71.2
66.0
65.5
64.7
Linear regression
y = 0.98 +
0.003x
y = 0.98 +
0.003x
y = 0.98 +
0.003x
y = 0.98 +
0.004x
y = 0.98 +
0.004x
R2
0.14
0.12
0.14
0.16
0.19
P
<0.01
<0.01
<0.01
<0.01
<0.01
Linear regression
y = 0.71 +
0.02x
y = 0.71 +
0.02x
y = 0.71 +
0.03x
y = 0.71 +
0.03x
y = 0.71 +
0.03x
R2
0.05
0.05
0.07
0.08
0.08
P
<0.01
<0.01
<0.01
<0.01
<0.01
Linear regression
y = 1.02 0.005x
y = 1.02 0.005x
y = 1.02 0.005x
y = 1.02 0.006x
y = 1.02 0.006x
R2
0.15
0.11
0.13
0.17
0.17
P
<0.01
<0.01
<0.01
<0.01
<0.01
% variation
explained by
PC1
Aerophily
Rheophily
Thermophily
NATIONAL WATER COMMISSION — Low flows report series
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preference
for high
D.O.
conditions
Riffles
Edges
1.08
1.08
y = 1.03 + 0.01x
R2 = 0.42
P <0.01
1.04
Aerophily
y = 0.98 + 0.004x
R2 = 0.16
P <0.01
1.06
1.04
1.02
1.00
1.00
0.98
0.96
0.96
0.94
tolerance of
low D.O
conditions
Rheophily
preference
for fastflowing
waters
preference
for still
waters
0.92
-5
-4
-3
-2
-1
0
1
2
3
2.40
2.00
-2
-1
0
1
2
3
4
5
1.20
1.20
0.80
0.80
0.40
0.40
-4
-3
-1
0
1
2
3
4
5
-1
0
1
2
3
4
5
y = 0.71 + 0.03x
R2 = 0.08
P <0.01
2.00
1.60
-5
-3
2.40
y = 1.60 + 0.12x
R2 = 0.49
P <0.01
1.60
tolerance of
1.08
high
temperatures
-2
-1
0
1
2
3
4 -4
-3
-2
1.08
y = 0.98 - 0.01x
R2 = 0.36
P <0.01
1.04
1.04
Thermophily
0.92
4 -4
1.00
1.00
0.96
y = 1.02 - 0.01x
R2 = 0.17
P <0.01
0.96
0.92
0.92
preference
for low
temperatures
-5
-4
more extreme
low flow
conditions
-3
-2
-1
0
1
Low flow gradient (PC1)
2
3
0.88
4 -4
less prevalent
low flow
conditions
-3
more extreme
low flow
conditions
-2
Low flow gradient (PC1)
less prevalent
low flow
conditions
Figure 2: Example relationships between invertebrate traits of aerophily, rheophily and
thermophily and a gradient of low-flow conditions derived from the first principal component of
a PCA of hydrological statistics. The low-flow gradient in this figure represents flow conditions
for the preceding 12 months.
NATIONAL WATER COMMISSION — Low flows report series
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4. Discussion
The three biological traits that we considered – aerophily, rheophily and thermophily – were
highly correlated with one another. Thus invertebrates that appear to favour high oxygen
levels also tend to favour fast-flowing habitats and cooler waters. This association is to be
expected because there is a characteristic longitudinal gradient in river systems throughout
the world from cool, turbulent and well-aerated headwater streams to warm, sluggish and less
oxygenated lowland rivers (Vannote et al. 1980). Since particular combinations of
environmental temperature, flow velocity and aeration are common, adaptation to these
environments will favour specific trait combinations. Different invertebrates will have
characteristic suites of interrelated traits according to the portions of the abiotic continuum
that they inhabit and are adapted to.
The study results supported our a priori hypotheses that invertebrate assemblages that had
been exposed to severe flow reduction or cessation during the period before sampling would
be dominated by taxa tolerant of low oxygen concentrations, low water velocities and high
temperatures. Conversely, assemblages not exposed to very low flows would be dominated
by taxa that favour aerated, fast-flowing and cool conditions. One way in which this
association could arise is through shifts in the composition of assemblages at the same site
over time in response to temporal changes in flow regime, with invertebrates that are
adversely affected by the changes dying or emigrating, and those that are suited to the
changes multiplying or colonising the site. It could also arise through long-term biogeographic
processes whereby some streams frequently suffer low-flow stress, and therefore have
developed persistent assemblages that are adapted to cope with that stress, whereas other
streams rarely have low-flow stress and therefore support persistent stress-intolerant
assemblages.
Our analysis does not explicitly differentiate the role of these temporal and spatial processes,
but some clues are provided by the observation that the strength of association between the
low-flow regime and the trait signature of the invertebrate fauna varied with the length of the
antecedent period over which flow was considered. If the flow/trait association was due
mainly to temporally stable faunal differences between streams with different long-term flow
regimes, we would expect a stronger association with longer antecedent periods – because
the calculation of flow metrics over longer periods would better characterise the long-term
flow regime of a site. Alternatively, if the flow/trait association was mainly due to short-term
assemblage adjustments to recent flow conditions, we would expect the association to be
stronger for analysis based on shorter antecedent periods.
The flow/trait association that we demonstrated was generally strongest when calculated for
the longer antecedent flow periods (12 and 24 months), and weakest for very short
antecedent periods. This suggests that assemblage adjustment to the flow regime of a stream
is a relatively long-term phenomenon, at least in relation to the typical life cycle length of
aquatic invertebrates, rather than being dominated by rapid faunal tracking of fluctuations in
flow regimes. Similarly, Finn et al. (2009) found that changes in macroinvertebrate
assemblages within an individual stream in New South Wales were correlated more strongly
with the number of low-flow events over the long term (12 months) than with the number over
shorter terms (less than three months). It would be useful to assess the temporal pattern of
within-site adjustment of invertebrate faunas to flow regimes more widely, by monitoring
changes in flow and trait signatures at individual sites over time. Data from the routine stream
macroinvertebrate monitoring program of New South Wales are not well suited to this task
because sites are sampled only once or at intervals of about two years. However, other
datasets may be available in which the same site was sampled at much shorter intervals over
an extended period. It might also be useful to consider relationships of invertebrate trait
signatures to antecedent flows calculated within particular seasons, as Extence et al. (1999)
NATIONAL WATER COMMISSION — Low flows report series
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did when relating an invertebrate index based on velocity preferences to antecedent flow
regimes for rivers in the United Kingdom.
Flow/trait relationships in this study were much stronger for the riffle fauna than for the edge
fauna, suggesting that the biota in riffles are most at risk from extended periods of low flow.
This is probably because the abiotic environment in riffles is more strongly affected by
changes in flow than that of pools. In riffles, flow reduction will sharply reduce both velocity
and wetted area, whereas in pools velocities are typically low even during periods of
moderately high flow. In addition, low flows will only slightly lower water levels in pools, unless
flow stops altogether and pools begin to dry out through evaporation or seepage. Moreover,
the effects of low flow may be variable in pools depending on the characteristics of a
particular pool and stream (Boulton & Lake 2008; Brooks et al. 2011). For example, pools with
large amounts of organic matter may suffer deoxygenation during low flow, whereas other
pools may contain low quantities of organic matter and maintain high oxygen levels when flow
is low.
There was no evidence of a threshold response to increasing severity of low flow, as
relationships between traits and flow were essentially linear (Figure 2). This implies that any
increase in the severity of low flows as a result of water abstraction (‘artificial drought’) will
impact on the macroinvertebrate fauna, especially in fast-flowing habitats such as riffles.
However, there was considerable variability in trait averages for any given flow regime,
suggesting that these traits also respond to other environmental factors. In Denmark and the
United Kingdom, Dunbar et al. (2010) found that an invertebrate index based on velocity
preferences related to both antecedent flow regime and the physical structure of stream
channels. In further development of our approach, it could be useful to incorporate other
independent variables (e.g. stream morphology and temperature regime) in statistical models
to strengthen the ability to predict invertebrate responses to particular changes in flow regime.
Long-term studies with frequent sampling at individual sites would also be useful to assess
the degree and pace of recovery after low-flow and zero-flow events. This would help
formulate recommendations for management of water abstraction because greater impact
might be tolerated if the impact is only transient.
Previous studies have related flow regimes to a variety of macroinvertebrate traits other than
the ones that we considered (e.g. lifespan, fecundity, voltinism; Bonada et al. 2007; Arscott et
al. 2010). Our ability to test other traits (such as those listed above) was constrained by the
scarcity of published lists of the traits of Australian freshwater invertebrates (though see
Schäfer et al. 2011). Trait-based approaches would benefit from the compilation of a
comprehensive trait database for Australian freshwater invertebrate taxa, using consistent
trait definitions and measurement scales or categories. Such a database could probably not
be compiled from published information alone, and would require substantial input from
specialists in different invertebrate groups.
NATIONAL WATER COMMISSION — Low flows report series
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5. Conclusion
This study investigated the response of macroinvertebrates to low-flow conditions in New
South Wales using existing datasets for both macroinvertebrates and hydrology. The
macroinvertebrate traits of aerophily, rheophily and thermophily were significantly related to
the antecedent flow conditions, with the strongest relationships evident for flow conditions of
12 months before each of the samples being collected. The results support our a priori
hypotheses that invertebrate assemblages that had been exposed to severe flow reduction or
cessation during the period before sampling would be dominated by taxa tolerant of low
oxygen concentrations, low water velocities and high temperatures, whereas assemblages
not exposed to very low flows would be dominated by taxa that favour aerated, fast-flowing
and cool conditions. The results also showed that these relationships were strongest in riffle
habitats that are characterised by faster flowing, well-oxygenated water and tend to be the
first habitat type to be impacted by reduced flow.
Although the study found significant relationships, the use of existing datasets was less than
ideal. Approximately only 10 per cent of samples collected under routine monitoring and
evaluation programs were able to be used in the study due to the lack of appropriate
hydrological data. It is also noted that very little site-specific information on channel
morphology is collected during routine sample programs, making it very difficult to establish
how flow conditions translate into the hydraulic conditions being experienced by biota in
various habitats. We recommended that future efforts be directed to locating routine biological
monitoring sites near hydrological monitoring sites where possible, and that greater effort be
directed to collecting channel morphology or hydraulic information at the time of sampling.
These minor refinements to routine MER programs at a statewide level would maximise their
potential usefulness in evaluating water allocation management actions and policies.
NATIONAL WATER COMMISSION — Low flows report series
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Shortened forms
ACT
Australian Capital Territory
AusRivAS
Australian River Assessment System
CCA
canonical correlation analysis
DO
dissolved oxygen
MER
Monitoring, Evaluation and Reporting
NSW
New South Wales
PCA
principal components analysis
RAP
River Analysis Package
SRA
Sustainable Rivers Audit
NATIONAL WATER COMMISSION — Low flows report series
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NATIONAL WATER COMMISSION — Low flows report series
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NATIONAL WATER COMMISSION — Low flows report series
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