Fish responses to low flows in dryland
rivers of western Queensland
Stephen R Balcombe and David Sternberg
Australian Rivers Institute, Griffith University
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: 978-1-921853-77-7
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:
Balcombe, SR & Sternberg D 2012, Fish responses to low flows in dryland rivers of western
Queensland, 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.
NATIONAL WATER COMMISSION — Low flows report series
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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 Griffith University on behalf of the National Water
Commission.
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Contents
Executive summary
Report context
1. Introduction
2. Methods
2.1.
Moonie River
2.2.
Cooper Creek
3. Results
3.1.
Moonie River
3.2.
Cooper Creek
4. Discussion
5. Conclusion
References
viii
ix
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25
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Tables
Table 1: Summary of sampling regime for the Moonie River between February 2006 and
April 2010 .......................................................................................................................................... 5
Table 2: Summary and description of low-flow response variables ......................................................... 6
Table 3: Summary of results ( r2 (P-value) direction of relationship) from Least Squares
Regression between flow metrics and ecological response variables. Significant
results are highlighted in bold. ........................................................................................................ 13
Table 4: Summary of analysis of variance results between low flows and normal flows.
Significant results are highlighted in bold. ...................................................................................... 15
Table 5: Summary of ANCOVA results comparing condition factor of the four most
abundant fish species in Cooper Creek waterholes between September 2001 and
December 2004. Sampling occasion as per Figure 10. For significant pairwise
comparisons among sampling occasions bold indicates significantly better condition
(higher Fulton’s condition). .............................................................................................................. 20
Figures
Figure S1: Context of reports produced for the National Water Commission's Low Flow
Ecological Response and Recovery Project. The circles represent the location of
individual case studies and the size of each circle represents the spatial extent of
each case study. .............................................................................................................................. ix
Figure 1: Location of the Moonie River catchment and associated fish sampling sites.
KIL=Killawarra; KUR=Kurmala; VER=Verena; KOO=Kooroon; ATL=Altonvale;
CAR=Carbeens; APP=Appletree; NIN=Nindi Gauge; NUL=Nullera; FEN=Fenton. ......................... 3
Figure 2: Long-term flow record for the Moonie River at Nindigully gauging station
(417201B). ........................................................................................................................................ 4
Figure 3: Hydrograph of mean daily discharge in ML/day at Nindigully Gauge (417201B)
between June 2005 and June 2010. Arrows indicate sampling trips. Dashed line
indicates discharge >2000 ML/d (see text for explanation). Periods where low flows
have been identified are shown by horizontal arrows. ...................................................................... 7
Figure 4: Sampling sites within the Cooper Creek catchment for the analysis of fish
response to low flows (Note: the Currareeva gauge is located in the waterhole
directly upstream of Murken). ........................................................................................................... 9
Figure 5: Mean daily discharge of the Thomson and Barcoo Rivers combined (Cooper
Creek) and the eight sampling occasions with time since flow superimposed where
short = flow <10 in Cooper Creek (summed from discharge measured at Retreat,
Barcoo River and Thomson River) at the sum of the at the hydrograph ........................................ 12
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Figure 6: Significant correlations between flow metrics and fish ecological response
variables. Horizontal lines show linear trendlines. Summary statistics are shown in
Table 3. ........................................................................................................................................... 14
Figure 7: Summary plots of Least Squares Means showing differences in Shannon
Index, Simpson’s Index, bony bream Young-Adult Ratio, and yellowbelly Fulton’s K
between normal flow conditions and low-flow conditions. Summary statistics are
provided in Table 4. ........................................................................................................................ 16
Figure 8: Principle Co-ordinate Analysis (PCoA) of 13 sampling trips according to
ecological response variables for normal and low-flow conditions. ................................................ 17
Figure 9: Eigenvector plots of ecological response variables with significant (P = <0.01)
loadings on the first two principle components. .............................................................................. 17
Figure 10: Non-metric multidimensional scaling plots of fish assemblage structure based
on log10(x + 1) transformed CPUE data (a) and presence/absence of species (b) for
eight sampling occasions at the four Windorah waterholes. Note: sites and sampling
times grouped according to time since flow categories, where closed symbols =
Short (S) time since flow and open squares = Long (L) time since flow. ........................................ 19
Figure 11: Mean (+ S.E.) of fish body condition for N. erebi, N. hyrtlii and P. argenteus
across seven sampling times in four Cooper Creek waterholes ..................................................... 21
Figure 12: Mean (+ S.E.) of fish body condition for Macquaria sp. B, L. unicolor and M.
splendida tatei across seven sampling times in four Cooper Creek waterholes. ........................... 22
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Acknowledgments
The fish data used in this report was collected during the CRC for Freshwater Ecology Dryland
Refugium Project and the eWater Ecological Management Project. We want to thank all research staff
and field volunteers for their assistance. Fish were collected under the Queensland Fisheries Permit
PRM00157K and the Griffith University Animal Experimentation Ethics Committee Permit
AES/14/05/AEC. We would also like to thank the various landholders who kindly provided access to
their properties in both the Moonie River and Cooper Creek catchments. Hydrographic data was
provided by the Department of Environment and Resource Management, Queensland.
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Executive summary
Understanding the role low flows play in the long-term maintenance of fish populations is important for
understanding the ecology of dryland rivers. While floods no doubt provide the mechanisms for fish
populations to bounce back after dry episodes, the actual long-term persistence of a given species
will be dependent on individuals surviving through low-flow or drought periods.
To gain some understanding of how fish respond to low flow in intermittent rivers we examined
relationships between flow metrics and fish assemblage characteristics such as abundance, biomass
and individual body condition in two dryland systems – the Moonie River (upper Murray-Darling Basin)
and Cooper Creek (Lake Eyre drainage). We had predicted that under periods of low-flow stress fish
abundance, richness and body condition would decrease. We examined relationships using both
multivariate and univariate techniques but only found reliable and consistent relationships in Cooper
Creek. There was no consistency in the results found in the Moonie River, which we attribute to
dampened responses to the cycles of ‘boom’ and ‘bust’. These cycles appear to be more pronounced
in more arid systems like Cooper Creek.
The use of simple metrics for evaluating low-flow stress in semi-arid to arid dryland rivers that
experience contrasting ‘boom’ and ‘bust’ productivity appears to have potential. In dryland systems
that do not exhibit such stark extremes in production, finding ways to measure low-flow stress is likely
to be far more difficult.
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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 hydroecological 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 National Water Commission's Low Flow Ecological
Response and Recovery Project. The circles represent 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
Dryland rivers drain approximately one third of the earth’s land area (Walker et al. 1995) and service
around 20 per cent of the world’s population (Bull & Kirby 2002). These unique systems are found in
large areas of Australia, Africa, Asia, America, and Europe. Despite their widespread distribution and
ecological and anthropogenic importance, dryland rivers are not as well understood, nor studied with
the same intensity as rivers characteristic of wetter climates (Kingsford & Thompson 2006; Nanson et
al. 2002).
Dryland river systems are typically characterised by low gradient landscapes and a climatic regime
dominated by isolated, but often intense, rainstorm events which produce less than 500 mm of mean
annual rainfall (Kingsford & Thompson 2006). As a result, many dryland rivers show extreme variation
in the timing, frequency, magnitude and duration of flow events (sensu Poff et al. 1997; Puckridge et
al. 1998) and exist for much of the year as a series of unconnected waterholes (Arthington et al. 2005;
Balcombe et al. 2005; Biggs et al. 2005; Bunn et al. 2003). Typically, these systems are dominated by
long periods without flow, irregularly punctuated by channel flow and flood events. Although the more
frequent but smaller channel flows may be functionally important for recruitment processes
(Humphries et al. 2002), it is thought that the much larger flood events, which inundate lateral
floodplain areas and generate high rates of aquatic production, ultimately sustain biotic assemblages
in these highly variable systems (Balcombe & Arthington 2009; Kingsford et al. 1999; Puckridge et al.
2000; Sternberg et al. 2011). This temporary ‘boom’ of aquatic production is inevitably followed by a
period of ‘bust’ owing to long periods without flow when production in isolated waterholes is limited to
a thin, marginal band of algae (Bunn et al. 2003). This cycling of ‘boom and bust’ conditions has been
well documented and is a widely accepted model of hydro-ecological processes in large dryland rivers
(Balcombe et al. 2007; Balcombe & Arthington 2009; Bunn et al. 2006; Kingsford et al. 1999).
Populations of dryland river fish are able to persist through the cycles of ‘boom and bust’ and the
associated fluctuation of available food resources due to their generalist ecological traits. Such traits
include high tolerance of harsh environmental conditions (Gehrke & Fielder 1988; Kingsford et al.
2006; Wager & Unmack 2000), opportunistic spawning strategies (Balcombe et al. 2007; King et al.
2003) and adaptive feeding strategies (Balcombe et al. 2005; Sternberg et al. 2008). Species with
these attributes can often reach high levels of abundance and biomass in large dryland rivers, where
the productivity cycles of ‘boom and bust’ are mirrored by wide variations in fish abundance from
‘boom’ (high fish abundance) to ‘bust’ (low fish abundance) periods. For example, Balcombe and
Arthington (2009) reported elevated fish abundances following periods of widespread floodplain
inundation in Cooper Creek, Australia, and concluded that antecedent hydrology, combined with high
seasonal temperatures, had a marked influence on fish abundances. Balcombe et al. (2007) reported
high rates of fish production on the shallow floodplain waters of the same system and concluded that
most members of the fish fauna are adapted to take advantage of the resource and growth
opportunities associated with extensive floodplain inundation (‘boom’ period). Furthermore, Arthington
et al. (2005) reported a 93 per cent decrease in total fish abundance in isolated waterholes during the
2001 dry season in Cooper Creek, associated with decreasing waterhole volume, habitat alterations
and declining water quality. Given that inundated floodplain habitats represent areas of high resource
potential, whereas drying river channels and waterholes do not generally provide resource rich
habitats, it follows that the body condition of individual fish can be expected to vary with pronounced
cycles of ‘boom and bust’.
Previous studies in dryland rivers have clearly demonstrated that fish respond quickly to changes in
hydrology (e.g. flooding and high flow) and such changes have been measured by metrics relating to
fish abundance and richness (Arthington et al. 2005; Balcombe et al 2007). This study was
undertaken to examine the effects of low flow on fish assemblages. In permanent flowing streams,
low flow places aquatic ecosystems under stress. However, in dryland rivers this stress is more likely
to be encountered during periods of zero flow where disconnected waterholes are on a drying
trajectory. The nature of low flow in dryland rivers is dictated by the hydrological regime and can
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range from rivers that flow for large parts of the year with only short zero-flow spells to those that may
not flow in any given year, with long zero-flow spells common. In this context we examined fish
assemblages in two contrasting dryland rivers: the least arid Moonie River in the upper MurrayDarling Basin, and the more arid Cooper Creek in the Lake Eyre Basin. We were constrained by the
amount of data in both catchments and thus we were able to use data from nine waterholes in the
Moonie River on 13 sampling occasions over four years, and from four waterholes sampled eight
times over four years in Cooper Creek. Our primary aim was to test the prediction that fish
populations under low-flow stress will have poorer body condition, while at an assemblage level,
abundance and richness will be reduced in periods of extended low (or zero) flow compared with
times where flow has only recently ceased.
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2. Methods
2.1. Moonie River
The Moonie River is a dryland system situated east of St George in south-western Queensland
(Figure 1). The river drains an area of 14,870 km2 and is a tributary of the Barwon River in the MurrayDarling Basin. The Moonie River experiences spatially and temporally variable rainfall averaging
between 500 to 600 mm/year-1. The Moonie River is subject to moderate water regulation, with
several weirs and off-channel storages. There are also a significant number of sites where water is
pumped for unregulated stock and domestic use.
Figure 1: Location of the Moonie River catchment and associated fish sampling sites. KIL=Killawarra;
KUR=Kurmala; VER=Verena; KOO=Kooroon; ATL=Altonvale; CAR=Carbeens; APP=Appletree;
NIN=Nindi Gauge; NUL=Nullera; FEN=Fenton.
2.1.1. Low-flow classification
Low-flow conditions are a natural phenomenon in dryland rivers. Many biota have developed specific
life-history strategies to cope with prolonged dry periods and the closely linked aquatic processes that
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accompany them. For many dryland rivers, any flow that connects previously isolated waterholes can
be considered ecologically important to obligate aquatic biota. Higher flows that not only link
waterholes together, but also inundate more complex in-channel habitats, are likely to achieve greater
ecological responses by fish as they move into newly inundated areas including among waterholes. In
the Moonie River, a daily discharge greater than approximately 2000 ML/day is known to achieve this
connection and has been shown to elicit migratory responses by a number of species (Balcombe,
unpublished data). Hence flow above this magnitude could be considered to achieve both
hydrological and ecological connectivity. On average, the Moonie River experiences discharge of this
magnitude every 235 (±35 SE) days or around every eight months. Therefore, we conservatively
define ‘low-flow conditions’ for the Moonie River to be any period greater than 10 months (304 days)
without the minimum flow required for biological connectivity. This low-flow classification was
undertaken a priori after dissecting the 42-year flow record for the Moonie River (Figure 2).
Figure 2: Long-term flow record for the Moonie River at Nindigully gauging station (417201B).
2.1.2. Data collection
Data on fish distribution, abundance, length and weight were collected from nine waterholes on 13
occasions over a 50-month period between February 2006 and April 2010 (Figure 3). Sampling
occurred in both the ‘wet’ season (December–April), and the ‘dry’ season (May–November). All wet
season samples had recently experienced some degree of channel flow before sampling. This
information is summarised in Table 1. Fish were sampled using the same protocols as per Balcombe
et al. (2011).
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Table 1: Summary of sampling regime for the Moonie River between February 2006 and April 2010
2.1.3. Flow variables
Due to the relationship between antecedent flow conditions and dryland river species, we defined four
flow variables that describe recent flow magnitude and duration. Recent flow magnitude was
quantified as the number of days since a flow event greater than 0 ML/day and 2000 ML/day. Recent
flow duration was quantified as the number of days since a flow event greater than 10 days and 80
days. These values were also defined a priori from the flow record where we found that the average
flow duration for an event is about 80 days. Redundancy analysis revealed that days since flow
greater than 0 ML/day and days since flow duration greater than 10 days were highly correlated
resulting in the removal of the 10-day flow-duration metric.
2.1.4. Biotic variables
We collated data for six variables that describe fish abundance, richness and diversity, as well as the
total biomass of the two most abundant species (yellowbelly, Macquaria ambigua; and bony bream,
Nematolosa erebi) present in the system. We collated data for two variables that describe the relative
‘health’ of fish in the system. The first of these, ‘water content’, is known to be inversely proportional
to fish lipid content – a measure of how ‘fat’ fish are, under the assumption that ‘fatter’ fish are in
better condition. The second variable, ‘Fulton’s K’, describes the cubic-length-weight-relationship in
fish under the assumption that for a given length, variation in fish weight can be attributed to variation
in fish condition. Three life-history variables that describe the age structure of yellowbelly and bony
bream were calculated: ‘Young-Adult Ratio’ defines the ratio of juveniles to adults in the population;
‘Juvenile-Population Ratio’ defines the proportion of juveniles in the population; and ‘Adult-Population
Ratio’ defines the proportion of adults in the population.
All variables were selected a priori given their likely utility for assessing the ecological response of fish
to low flows. A summary of all variables used is provided in Table 2.
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Table 2: Summary and description of low-flow response variables
2.1.5. Data analysis
Relationships between continuous flow metrics and low-flow response variables were analysed
between sampling trips using least squares regression. Response variables were averaged across all
waterholes for each sampling trip and regressed against the antecedent flow metrics. Analysis of
Variance (ANOVA) was used to test for differences in ecological response variables between low-flow
conditions and normal flow conditions. We used a reduced dataset for this analysis for two reasons.
Firstly, ANOVA assumes there is homogeneity of variance within each of the sampling units. This
assumption can be violated by unequal sample sizes in the analysis. Secondly, as recent antecedent
flow conditions are the main hydrologic factor driving changes in dryland rivers systems, we chose to
analyse the ecological response variables during low-flow sampling and on the two sampling
occasions before low-flow conditions being realised (Figure 3). This meant that our ANOVA was
performed between trips 1, 2, 8, 9 (pre- low-flow samples) and trips 3, 4, 10, 11 (samples obtained
during low-flow conditions. Principle Co-ordinate Analysis (PCoA) was used to summarise similarities
in fish response between low-flow conditions and normal conditions. PERMANOVA was used to test
for multivariate differences in the ecological response of fish between flow conditions in
multidimensional space. BETADISPER was performed to test for homogeneity of dispersion (i.e. the
multidimensional area occupied by sampling trips) in ecological response of fish between flow
conditions.
All analyses were performed in R using the Vegan library (R Development Core Team 2008) with
alpha significance set to 0.05.
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Figure 3: Hydrograph of mean daily discharge in ML/day at Nindigully Gauge (417201B) between June 2005 and June 2010. Arrows indicate sampling trips.
Dashed line indicates discharge >2000 ML/d (see text for explanation). Periods where low flows have been identified are shown by horizontal arrows.
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2.2. Cooper Creek
Cooper Creek in the Lake Eyre Basin
(
Figure 4)
has one of the most variable flow regimes in the world (Puckridge et al. 1998) driven by the
unpredictable timing and volume of rain events in the catchment. While large floods in this system
cover vast tracts of land, such events occur irregularly and the river mostly exists as a series of
disconnected waterholes joined by dry anastomosing channels. During these extended dry periods
many waterholes dry down completely and such periods could last up to 21 months without flow
(Bunn et al. 2003). Hence, to examine low flows in this system we really are referring to zero flows.
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Figure 4:
Sampling sites within the Cooper Creek catchment for the analysis of fish response to low flows
(Note: the Currareeva gauge is located in the waterhole directly upstream of Murken).
2.2.1. Data collection
The same sampling methods used in the Moonie River were also used in Cooper Creek, except these
were only collected from four waterholes.
2.2.2. Flow
We classified flow a priori according to how our data were aligned in relation to time since no flow. In
this case we classified flow as any flow greater than 10m3s-1 at the Currareeva gauge
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(
Figure 4)
because previous experience demonstrated this was the amount of flow needed to connect the
waterholes downstream of Currareeva (Balcombe & Arthington 2009). The flow record throughout the
sampling period is provided in Figure 5. Our eight sampling occasions were thus, 56, 209, 215, 67,
12, 40, 184, and 212 days post-flow, respectively. Based on these measures there were clearly two
antecedent flow groups: short (S) time elapsed (< 3mo.) and long (L) time elapsed (> 6mo.) since flow
exceeded 10 m3s-1.
2.2.3. Biotic variables
We collated data for fish abundance and species presence and absence and length and weight data
to calculate condition factor for the six most abundant species (bony bream, Nematolosa erebi; Hyrtl’s
tandan, Neosilurus hyrtlii; silver tandan, Porochilus argenteus; Lake Eyre yellowbelly, Macquaria sp.
B; spangled perch, Leiopotherapon unicolor; desert rainbowfish, Melanotaenia splendida tatei). For
the first four species these were grouped into a small (new recruits) size class while the other larger
individuals were grouped as a separate size class to account for differences in body condition owing
to maturity. These length cut-offs were determined as per Balcombe and Arthington (2009). We
caught very few small juveniles of either L. unicolor or M. splendida tatei, therefore only the large
individuals of these two fish were analysed.
Before statistical analyses, fish data were grouped according to antecedent hydrology and season.
Antecedent hydrology was measured in relation to time since flow exceeded 10 m3s-1 based on our
earlier experience in this system whereby flow needed to be greater than 10 m3s-1 to provide
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connection between waterholes below Currareeva waterhole (the waterhole directly above Murken
(
Figure 4).
2.2.4. Data analysis
Assemblage patterns were analysed using ordination based on hybrid non-metric multi-dimensional
scaling (MDS). MDS plots were generated from Bray-Curtis similarity matrices produced from log10
(CPUE + 1) and species presence/absence data. One-way analyses of similarities (ANOSIM) based
on the same similarity matrices were used to identify differences in assemblage structure in relation to
antecedent hydrology for the period April 2001 to December 2004. ANOSIMS were considered
statistically significant at P < 0.05.
Variation in fish condition was also tested through time by examining differences in mean condition
(Fulton’s condition) among sampling occasions using Analysis of Covariance (ANCOVA) with
sampling occasion as a fixed effect and fish standard length as a covariate where appropriate using
Systat for Windows 11.00.01 (SSI 2004). To meet the assumptions of ANCOVA, the study tested for
heterogeneity of slopes between the covariate and water content among the four sampling occasions
as per (Quinn & Keogh 2005). Tukey’s HSD post-hoc tests were used to establish the nature of any
differences in fish condition among each possible pair of sampling occasions. One-way ANOVAS
were undertaken for those variables where standard length was not significant as the covariate. It
must be noted that although we analysed eight sampling occasions for the assemblage data, we were
only able to analyse a maximum of seven occasions for body condition as there was no weight data
collected for time one. Statistical significance was accepted at P < 0.05 for all analyses.
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Figure 5: Mean daily discharge of the Thomson and Barcoo Rivers combined (Cooper Creek) and the
eight sampling occasions with time since flow superimposed where short = flow <10 in Cooper Creek
(summed from discharge measured at Retreat, Barcoo River and Thomson Rivers) at the sum of the
at the hydrograph
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3. Results
3.1. Moonie River
3.1.1. Linear regression
Overall we found relatively few correlations between flow statistics and ecological response variables
(Table 3). A significant positive relationship between the proportion of adult yellowbelly in the
population to the number of days since a flow event greater than 0 ML/day was found (Table 3). This
indicates the longer this system goes without flow, the greater the proportion of larger yellowbelly in
that species population. Similarly, a significant positive relationship was observed between yellowbelly
biomass and the number of days since a flow of greater than 80 days’ duration (Table 3). This result
is a reflection of the life-history adaptations of this species to flow (see Section 2.1.2) wherein
biomass will increase over time following a flow event – presumably as a result of new recruits to the
population spawned after the flow event. Finally, a significant negative relationship was shown
between the body condition of bony bream (measured by Fulton’s K) and the number of days since
discharge greater than 0 ML/day. This is an interesting result in that it shows the importance of flow to
the health of this species and potentially offers some insight into the ecological response of this
species to low flows. These significant regressions are plotted in Figure 6.
There were also a number of regressions which, while not significant, did hint at a relationship
between the flow metrics and ecological response variables. For example, the proportion of juvenile
bony bream in the population and the number of days since a flow of duration greater than 80 days
showed a trend towards a relationship. The number of days since flow greater than 0 ML/day and
catch per unit effort (CPUE) also showed a trend towards a relationship. Given the scope of this
report, it is not unreasonable to note that in the context of exploring low-flow ecology relationships,
increased temporal data may yield significant results, and that CPUE may be a useful metric for
highlighting the effects of low-flow episodes in this river.
Table 3: Summary of results ( r2 (P-value) direction of relationship) from Least Squares Regression
between flow metrics and ecological response variables. Significant results are highlighted in bold.
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Figure 6: Significant correlations between flow metrics and fish ecological response variables.
Horizontal lines show linear trendlines. Summary statistics are shown in Table 3.
3.1.2. Analysis of variance
We found a number of significant differences between ecological response variables in low-flow
periods and normal flow conditions using the reduced dataset (Table 4). Both measures of community
diversity were significantly different between sampling trips during low-flow episodes and sampling
trips during normal flow conditions before the low-flow event (Table 4). Essentially, this indicates that
during low-flow periods the richness and evenness of the fish community increased. This indicates
two things. Firstly, the increase in richness may be a result of increased sampling efficiency due to the
concentrating effects of the low flows. Secondly, the increase in evenness may be a result of the fish
community approaching an equilibrium point following an increase in production and juvenile
survivorship around the earlier flow event. Yellowbelly body condition measured with Fulton’s K also
showed a significant difference between low-flow and normal flow conditions (Table 4). Simply put,
this shows that during low-flow episodes the body condition and therefore ‘health’ of yellowbelly is
lower. Finally, the ratio of juvenile bony bream to adult bony bream was significantly different between
low-flow and normal flow conditions. This result indicates that the bony bream population is
dominated by more adults than juveniles in the low-flow periods compared with normal flow
conditions. These significant results are plotted in Figure 7.
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Table 4: Summary of analysis of variance results between low flows and normal flows. Significant
results are highlighted in bold.
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Figure 7: Summary plots of Least Squares Means showing differences in Shannon Index, Simpson’s
Index, bony bream Young-Adult Ratio, and yellowbelly Fulton’s K between normal flow conditions and
low-flow conditions. Summary statistics are provided in Table 4.
3.1.3. Principle Co-ordinate Analysis
Ordination of sampling trips based on the ecological response variables revealed two gradients of trait
variation represented by the first two PCoA axes which explained 27.5 per cent and 19.6 per cent of
variation respectively (Figure 8). With respect to the low-flow sampling times there was some
grouping on the second PC towards the negative end which indicates these sampling times were
dominated by high proportions of larger-bodied individuals of the two species (Figure 9). There was a
similar gradient for the sampling trips performed under normal flow conditions, however these were
much more variable on the second PC. Low-flow sampling trips were more tightly clustered than
normal flow sampling trips which may reflect the homogenous trajectory of drying waterholes during
low-flow conditions (Figure 8). Alternatively, normal flow sampling trips were much more variable on
both axes presumably as a result of the ecological response to flow before sampling (Figure 8).
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Figure 8: Principle Co-ordinate Analysis (PCoA) of 13 sampling trips according to ecological response
variables for normal and low-flow conditions.
Figure 9: Eigenvector plots of ecological response variables with significant (P = <0.01) loadings on
the first two principle components.
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3.2. Cooper Creek
3.2.1. Fish assemblage structure
Ordination plots revealed that fish assemblage structure based on CPUE data was arrayed in relation
to antecedent hydrology with S flow groups in the middle of the plot and the L groups trending
towards the upper left corner (Figure 10a). The difference in structure between the S and L fish
groups was statistically significant (ANOSIM: Global R = 0.244, P < 0.001). Fish assemblage structure
based on presence/absence data was also differentiated between the two antecedent hydrology
groups with the S group tending towards the top left diagonal of the plot and the L groups towards the
bottom left diagonal (Figure 10b). ANOSIM revealed the two flow groups were significantly different
(Global R= 0.17, P < 0.001). Although not analysed in this report, using the same dataset Balcombe
and Arthington (2009)found that mean CPUE and species richness were significantly higher in the
‘short (S) time since flow’ compared with ‘long (L) time since flow’ groups, which corroborates the
assemblage patterns.
3.2.2. Fish condition
Mean fish condition was significantly variable through time for all groups analysed (Table 5). There
was a general trend of fish being in better condition when sampled after recent flow events, although
this was not universal (Table 5; figures 5, 11, 12). Those fish that largely met our predictions were
small N. erebi, large N. hyrtlii and L. unicolor (Figure 11) and both size classes of P. argenteus and
Macquaria sp. B (Figure 12). The main anomaly for these particular species was that fish condition on
sampling occasion 6 (June 2004) tended to be lower than expected and appeared to be grouping out
with occasions 7 and 8 (October and December 2004). This might be explained by waterholes in June
which, even though only 40 days post-flow, were on a significant drying trajectory.
The fish groups that did not meet our predictions were large N. erebi, small N. hyrtlii and M. splendida
tatei (figures 11 and 12). These species tended to show higher body condition following significant
duration without flow.
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Figure 10: Non-metric multidimensional scaling plots of fish assemblage structure based on log 10(x +
1) transformed CPUE data (a) and presence/absence of species (b) for eight sampling occasions at
the four Windorah waterholes. Note: sites and sampling times grouped according to time since flow
categories, where closed symbols = Short (S) time since flow and open squares = Long (L) time since
flow.
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Table 5: Summary of ANCOVA results comparing condition factor of the four most abundant fish
species in Cooper Creek waterholes between September 2001 and December 2004. Sampling
occasion as per Figure 10. For significant pairwise comparisons among sampling occasions bold
indicates significantly better condition (higher Fulton’s condition).
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Figure 11: Mean (+ S.E.) of fish body condition for N. erebi, N. hyrtlii and P. argenteus across seven
sampling times in four Cooper Creek waterholes
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Figure 12: Mean (+ S.E.) of fish body condition for Macquaria sp. B, L. unicolor and M. splendida tatei
across seven sampling times in four Cooper Creek waterholes.
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4. Discussion
We had predicted that fish would be responsive to low-flow stress in dryland rivers. We found
reasonably strong results that largely met our predictions in the Cooper Creek waterholes. In contrast,
there was limited evidence that fish were responding differently to low flows than to ‘normal’ or ‘high’
flows in the Moonie River. There are two possible explanations for this that we will elaborate on. First,
although we observed some periods of waterhole drying in the Moonie River, it is possible this was
not to such an extent that it was having a profound effect on fish condition and breeding, thereby
waterhole abundances were largely maintained. If so, monitoring fish further into low-flow periods
where waterholes reach complete drying might offer greater insight into fish-flow relationships. A
second point may be the large difference in productivity between the two systems. Given the massive
numbers of fish found in Cooper Creek waterholes following both small and large floods (Arthington et
al. 2005; Balcombe & Arthington 2009), there is likely to be intense competition for resources as soon
as the waterholes start receding, hence die-offs of over 90 per cent of fish are not uncommon
(Arthington et al. 2010). As such, the responses of fish population decline using even simple metrics
such as abundance and richness is easy to detect. Conversely, in a relatively simple system such as
the Moonie River, fluctuations in abundance are less pronounced leading to difficulties detecting
population decline. Balcombe et al (2011) also found a similar problem when trying to fit fish habitat
models for the same species in the Warrego and Moonie rivers. They found the models were not
transferrable between the two rivers largely due to the weak response of biota in the Moonie River to
either flow or drying brought about largely by low system productivity. On the other hand, system
productivity was significantly higher in the Warrego River, resulting in strong fish biomass
relationships with habitat diversity. With the more arid rivers such as the Warrego, where the starting
condition post-flow is a ‘boom’ in fish productivity, changes in fish condition and biomass would be
easier to detect as waterholes dry and resources become limited. Hence if such distinct ‘booms’ in
fish productivity do not occur in response to flow, then it could be expected that drying will also result
in only gradual change in both fish condition and biomass, thus rendering detection of a biological
response problematic.
The Cooper Creek dataset demonstrated the potential for using fish metrics to monitor low-flow
stress. As indicated above, the ‘boom’ and ‘bust’ ecology of this system allows for the use of simple
metrics to measure response to drying. With a larger time series it may be possible to have more
refined measures than ‘short- and long-time since flow’ periods, or even the cut-off discharge where a
flow is considered to be a no-flow. For example, we categorised sampling time 6 as a short (S) time
since flow, because the system had a small in-channel flow of 10 m3s-1 some 40 days earlier
(Balcombe & Arthington 2009). For a number of fish metrics (condition factor) time 6 was more similar
to times 7 and 8 than to time 5 (see figures 12 and 13). As the waterholes were on a drying trajectory
(from the large January 2004 flood, Figure 6), the very small-channel flow although providing
hydrological connectivity probably did not evince a biological response by the fish.
There were some differences noted in the response to antecedent hydrology by the different fish-size
classes that probably demonstrate a need to better understand the assemblage as a whole. For
example, the two N. erebi size classes had opposite responses to low flow. The larger-sized
individuals were gaining condition as waterholes dried while the new recruits were losing condition.
This is probably best explained by examining the whole assemblage dataset shown in Balcombe and
Arthington (2009). Although waterholes were drying off, there were also massive die-offs of all other
species apart from bony bream, so it is conceivable the large-bodied individuals were released from
interspecific competition as other species sharing similar resource requirements died off. In contrast,
the rapid increase in N. erebi abundance through the last three sampling occasions was largely due to
the massive recruitment of juveniles which were likely to be undergoing intense intraspecific
competition for limited resources. This might explain the loss in juvenile fish condition during
waterhole drying. Hence, while we advocate the use of simple metrics (such as length and weight) for
ease of collection and analysis, it would also be prudent to concurrently collect assemblage data to
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provide supporting information for other potential factors that affect fish condition (such as breeding
and die-off).
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5. Conclusion
This study examined the response of fish assemblages to low flows in two dryland rivers using
metrics such as condition and abundance. Our prediction was that fish condition, species diversity
and species abundance would fall with waterhole drying as a consequence of low-flow conditions. We
found strong evidence of low-flow stress in fish assemblages exposed to drying pools after flow
events in the more arid of the two rivers (Cooper Creek). For this ‘boom’ and ‘bust’ system, simple
metrics such as species richness and abundance, as well as individual body condition, are well suited
for measuring responses to extended low-flow stress. Our predictions were not met for the more
temperate river (Moonie River) and it was not possible to tell whether there had been any impact of
low flows on the fish assemblage, or if in fact the low flows themselves were of such a magnitude to
have caused any impacts on fish assemblages. This suggests the approach we used to investigate
low-flow stress in fish will be applicable to other dryland rivers that exhibit clear ‘boom’ and ‘bust’
responses to floods and droughts.
For dryland rivers at the benign end of the arid scale such as the Moonie River, simple metrics are
unlikely to reveal responses to low flows or prolonged drying. This is likely to require a greater
understanding of the key drivers of fish distribution and abundance in these systems and indeed
whether low flow is one of them.
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