Macroinvertebrate and fish
NATIONAL WATER COMMISSION — Low flows report series
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Macroinvertebrate and fish responses
to low flows in South Australian rivers
David Deane
South Australian Department for 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: 978-1-921853-71-5
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:
Deane D 2012, Macroinvertebrate and fish responses to low flows in South Australian 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, nor
does it represent South Australian State Government opinion or policy.
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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 South Australian Government on
behalf of the National Water Commission.
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Contents
Report context
1. Introduction
1.1 Purpose
1.2 Significance of low flows for South Australian streams
1.3 Introduction to the case studies
2. The influence of streamflow on aquatic macroinvertebrate community traits in
South Australia (case study 1)
2.1 Summary
2.2 Introduction
2.3 Methods
2.4 Results
2.5 Discussion
2.6 Conclusions and further work
3. Changes in the macroinvertebrate community over a decadal period of drying
(case study 2)
3.1 Summary
3.2 Introduction
3.3 Methods
3.4 Results
3.5 Discussion
3.6 Conclusions and further work
4. Preliminary analysis of the flow – recruitment response of mountain galaxias in
the Marne River, South Australia (case study 3)
4.1 Summary
4.2 Introduction
4.3 Methods
4.4. Results
4.5 Discussion
5. Biological monitoring of South Australian aquatic biota
5.1 Current programs
Appendix 1: Table of macroinvertebrate traits
Appendix 2: Trait group sample membership
Appendix 3: Highly prevalent South Australian macroinvertebrate families in
dataset
Appendix 4: Relevant findings for the Low Flow Ecological Response and
Recovery Project
Shortened forms
References
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Tables
Table 1: Summary characteristics of indicative traits within the seven traits groups
shown in Figure 7. Low flow for this analysis is indicative of mean daily
streamflow less than half the volume of the non-zero median. .......................................... 6
Table 2: Site names and summary statistics, 1979–2007 ....................................................... 10
Table 3: Streamflow metrics used to characterise flow regime at the 12 sampling
sites .................................................................................................................................. 13
Table 4: Results of Mantel tests for correlation (r value) between the flow, traits,
family and geographical proximity matrices. .................................................................... 18
Table 5: Traits identified through indicator analysis as being significantly associated
(p<0.01) with rivers in group 1 (Figure 2). ........................................................................ 18
Table 6: Selected characteristics of the different trait groups ................................................. 21
Table 7: Indicator species analysis results for groups shown in Figure 7. Statistical
significance: ** p <0.01; * p <0.05. ................................................................................... 24
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Table 8: Indicator species analysis trait states for the two sample groupings shown in
figures 19 and 20. ............................................................................................................. 41
Table 9: Count of the trait states within each functional group ............................................... 43
Table 10: Selected annual streamflow summary statistics (2002–08) and autumn fish
census data* (2002–10) for the Marne River catchment .................................................. 55
Table 11: Traits and trait states used in analysis. ................................................................... 64
Table 12: Assignment of macroinvertebrate samples to trait groups as discussed in
Section 2 ........................................................................................................................... 65
Table 13: Families observed in more than half of riffle samples collected at more
than half of the sites (total sites number = 11). ................................................................ 67
Table 14. As above, but for edge samples (total sites = 12) ................................................... 67
Figures
Figure S1: Context of reports produced for the Low Flow Ecological Recovery and
Response Project. Each circle represents the location of individual case studies
and the size of each circle represents the spatial extent of each case study. .................. xi
Figure 1: Location of study catchments for case studies presented in this chapter
(Cooper Creek not shown). .............................................................................................. 11
Figure 2: Dendrogram showing Ward’s method clustering of the trait prevalence
dissimilarity matrix. ........................................................................................................... 15
Figure 3: Dendrogram showing Ward’s method clustering of the flow statistics
distance matrix ................................................................................................................. 16
Figure 4: Mean value for selected flow statistics for the two major groupings in
Figure 2. Flow statistic codes and descriptions shown in Table 3. .................................. 16
Figure 5: NMDS ordination (stress = 7%) of the trait prevalence dataset. Convex
hulls enclosing the two groups shown in Figure 2 are drawn in red, with group 1
on the left. Centroids of trait prevalence are shown in blue, and significant (p <
0.01) flow vectors in green (see Table 3 for descriptions of flow variables, and
Appendix 1 for traits). ....................................................................................................... 17
Figure 6: Distribution of functional dispersion values for the two broad groupings
shown in Figure 2. Notches indicate the approximate 95 per cent confidence
intervals. ........................................................................................................................... 19
Figure 7: Ward’s minimum variance classification of the traits by abundance matrix.
Groupings reflect the optimal pruning height suggested by indicator species
analysis. Indicative traits were identified for each group. For group membership
see Appendix 2. ................................................................................................................ 20
Figure 8: Boxplot summary of the rank order of the 30-day antecedent flow as a ratio
of the non-zero median by traits group............................................................................. 22
Figure 9: Boxplot summary of the rank order of the 90-day antecedent flow as a ratio
of the non-zero median by traits group............................................................................. 22
Figure 10: NMDS ordination (stress = 13 per cent) of the riffle trait prevalence data.
Shown are the trait-based groupings (red polygons enclose samples within each
group); environmental vectors and factors with a significant correlation with
ordination axes (p < 0.01) are shown in green; and centroids for trait abundance
are shown in blue. ............................................................................................................ 23
Figure 11: Boxplot showing the distribution of functional dispersion index scores for
the seven trait grouping samples. .................................................................................... 25
Figure 12: Log transformed mean daily flow at A2390519, at Mosquito Creek near
Struan, 1994–2011. Zero values are not defined on a log scale, hence cease-toflow periods are indicated by breaks in the hydrograph. Macroinvertebrate
sampling dates are marked as black triangles, with the final sample being
collected in autumn 2007. ................................................................................................ 33
Figure 13: Classification of macroinvertebrate edge samples from Mosquito Creek,
autumn 1997–autumn 2007. Codes indicate season and year (e.g. ‘A02’ refers
to Mosquito Creek, 2002 edge habitat sampled in autumn)............................................. 35
Figure 14: NMDS ordination of edge samples from Mosquito Creek. Colours indicate
the groups shown in Figure 13. Environmental variables significant at 0.05 are
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shown: zero.q.90 = number of zero-flow days in the 90 days before sampling;
days.ctf is the count of days since flow ceased; ave.90 is the average daily flow
for the 90 days before sampling; is.spring is a logical variable indicating if the
sample was collected in spring. Codes indicate season and last two digits of the
year sampled (i.e. A05 = autumn 2005 sample; S05 is spring 2005). ............................. 36
Figure 15: Ordination of Mosquito Creek autumn edge samples indicating trajectory
over time, and with flow variables of statistical significance (p<0.05) shown:
Nnzq.90 = median non-zero daily flow for 90 days before sampling. .............................. 37
Figure 16: Spring edge macroinvertebrate community dissimilarity 1994–2006, with
trajectory shown for sequential annual samples 1997–2006. Environmental
variables shown were significant (p = 0.05) and represent the number of zeroflow days in the 12 months before sampling (z.q.12m) and sample electrical
conductivity (EC). ............................................................................................................. 38
Figure 17: Field readings of electrical conductivity (EC) taken at time of
macroinvertebrate sampling at Mosquito Creek, 1994–2007........................................... 38
Figure 18: Linear regression of log transformed spring conductivity at Mosquito
Creek on square root transformed 90-day mean daily antecedent streamflow
(ML/d). .............................................................................................................................. 39
Figure 19: Ward’s minimum variance classification of the trait-abundance matrix. Site
codes are explained in Figure 14. Group 2 is on the right. .............................................. 40
Figure 20: NMDS ordination of the trait prevalence data for Mosquito Creek, 1997–
2007. Sample group 1 is on the right of the figure. zq.90 = zero-flow days in the
90 days before sampling. ................................................................................................. 41
Figure 21: Functional groups based on Ward’s clustering of the Gower dissimilarity
trait by family matrix to form three functional groups. ...................................................... 42
Figure 22: Proportion of functional group 1 in the edge data time-series, 1994–2007. .......... 44
Figure 23: Proportion of functional group 2 in the edge data time-series, 1994–2007 ........... 45
Figure 24: Proportion of functional group 3 in the edge data time-series, 1994–2007 ........... 45
Figure 25: The proportion of richness for edge samples for functional group 2
families as a function of streamflow on the day of sampling. The curve is a
lowess smooth of the data. ............................................................................................... 46
Figure 26: The proportion of richness for edge samples for functional group 3
families as a function of streamflow on the day of sampling. The curve is a
lowess smooth of the data. Similar patterns were evident for the 30- and 90-day
antecedent flow statistics. ................................................................................................ 47
Figure 27: Family richness of South Australian edge samples (n=289) as a function
of electrical conductivity (EC) measured in the field on the day of sampling. The
fitted curve is a lowess smooth of the data which suggests a slight linear
increase in richness up to a value of around natural log 7 (1000 EC). After this
point richness declines in a linear manner according to the lowess smoother,
although there is some evidence for a step change at ln(8), or ~3000 EC. ..................... 50
Figure 28: Linear regression model of Marne River mountain galaxias YOY in
autumn as a function of non-zero median flow. The least squares fit to the data
and 95 per cent confidence intervals are shown. ............................................................. 56
Figure 29: Modelled no-development and current development ranked daily flows for
exceedence percentile indicated (note percentiles calculated on no-development
scenario and hence are not applicable to the current flow).............................................. 57
Figure 30: Predicted mountain galaxias YOY numbers in three sites for the Marne
River under current and no-development scenarios, 1975–2006. ................................... 58
Figure 31: Recruitment success of mountain galaxias for 32 years under scenarios
of current water resource development and modelled no-development. ......................... 58
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Acknowledgements
An enormous effort is represented by the collection of the data analysed here – especially
from biologists from the Australian Water Quality Centre (SAWater) and the Environment
Protection Authority which undertook the painstaking work of sampling, sorting and identifying
macroinvertebrate samples. The efforts of regional hydrographers who have maintained the
South Australian streamflow-gauging network since the 1940s are also of note. In recent
years the South Australian Murray-Darling Basin natural resources management board has
taken a lead role in biological data collection. In supporting detailed native fish time-series
data in eastern Mount Lofty Ranges streams, the board has generated an invaluable dataset
for use in flow-related ecology studies. Provision of this data for this study was greatly
appreciated.
Analysis of this type would not be possible without the foresight of numerous state and
consulting biologists, who collected data from sites where flow comparison was possible. The
contribution to knowledge made by those who organised, archived, sorted or collected data
used in these case studies is acknowledged and respected by the author.
Paul McEvoy and Peter Goonan are thanked for sharing their many years of experience in
studying macroinvertebrate communities of South Australia. Mardi van der Wielen and Mike
Hammer are thanked for providing Marne River fish census data. Glen Scholz and Michelle
Bald are thanked for their support throughout the project.
Rob Rolls provided the low flows coded-traits database used in the analysis (adapted from
Schafer et al. 2011). Chris Madden provided a technical review on an early draft and two
reviewers from Griffith University are thanked for their consideration of the report. Scott
Hardie provided a detailed review of the technical content of the draft report, which was
greatly appreciated. Other state jurisdictional representatives and the delivery agents (notably
Nick Marsh) from the Low Flow Ecological Response and Recovery Project Team are also
thanked for sharing their knowledge and experience over the course of the project.
Finally, Clare Taylor from the National Water Commission provided support and copious
levels of understanding (not to mention patience) in helping enable the collaboration of South
Australia to happen.
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Executive summary
In this report three flow-related ecological case studies are presented from South Australia.
Case studies 1 and 2 examine streamflow as a factor in structuring macroinvertebrate
communities using data collected under the AusRivAS program, while case study 3 examines
the effects of flow on recruitment success in a rheophilic fish species.
Overall this study suggests the flow regime exerts the overriding influence on
macroinvertebrate community structure, which was assessed in terms of functional diversity
via a traits database. At the site scale, macroinvertebrate communities separated into two
groups that had a statistically-significant positive correlation with a division of the same sites
based on long-term flow statistics. Hydrologically, groups largely reflected differences in the
baseflow index, inter-annual variation in baseflow and the duration of the annual cease-toflow period. Hence, the division tended to represent sites with ephemeral or near-perennial
flow regimes. Functional diversity was clearly greater in perennial sites than ephemeral sites.
While nearly a third of the trait states used in the analysis were indicative of perennial sites,
none were indicative of ephemeral sites. These findings are consistent with the ecological
theory that ephemeral conditions only support macroinvertebrates with traits tolerant to
variable flow and physical habitat conditions.
Classification of the trait prevalence of 161 riffle samples from across the state identified
seven trait groups. Groups showed a strong affinity for flow regime types, with five groups
dominated by samples collected from either ephemeral or perennial rivers. Only two groups
contained samples collected from both perennial and ephemeral sites in comparable
numbers.
In contrast with flow regime, only weak relationships were evident between trait prevalence
and flow conditions immediately before sampling. Two groups included a relatively high
proportion of samples collected following low-flow periods, but in general all trait groups come
from a range of antecedent flow magnitudes. For the two groups with relatively low-flow or
cease-to-flow conditions, the observed functional diversity in the two trait groups differed
considerably, with the ephemeral low-flow traits group having the lowest median functional
diversity of all groups. This was consistent with the low functional diversity of the ephemeral
group in the site-scale analysis. In contrast, the low-flow group comprising samples from
perennial sites had a similar range of trait states to groups with moderate to high antecedent
flows, and a mixture of trait states associated with flowing and non-flowing habitat (e.g.
univoltinism and rapid maturation). Similar mixtures of indicative trait states between those
adapted to variable conditions and those more associated with perennial flow were observed
for all trait groups, even those predominantly comprising samples from perennial systems.
This suggests a macroinvertebrate fauna that is generally adapted to variable flow conditions.
Six of the seven groups suggested some dependence on season in structuring dominant
traits. However, the combination of high ambient salinity and near-perennial flow at the Light
River appears to support a unique community of relatively low functional diversity (trait group
7 comprised 88 per cent of Light River samples – the only such dominance by one site in any
group).
Functional diversity is associated with ecosystem resilience to environmental change. In case
study 1, there was a strong association between functional diversity in macroinvertebrate
communities and the flow regime. Managing for high functional diversity will require managers
to ensure sites do not transition along a gradient of flow permanence from perennial to
ephemeral. The surface expression of groundwater is clearly a critical influence on stream
ecology in generating persistent streamflow conditions and supporting refuge habitat.
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Protecting critical low-flow characteristics requires consideration of surface as well as
groundwater development.
Case study 2 examined a situation where precisely the transition discussed above has
occurred. Before 2001, Mosquito Creek flowed perennially. Since then, cease-to-flow periods
have increased in duration each year, to the point where the creek is now effectively
ephemeral. Macroinvertebrate communities respond to this transition in flow regime in several
ways, including:

Family richness decreases from a mean of 34 before the drying phase to a mean of
23 for the post-impact samples. Both streamflow and conductivity had similar size, but
opposite direction correlations with richness.

Of three identified trait-based functional groups, differential flow dependencies are
indicated by the proportional representation in samples with positive, negative and
neutral responses evident. A linear decline in the rheophilic group with declining
streamflow was offset by increases in the proportion of negative flow-responding
families.
Examples of non-linear community dynamics were also observed, with sharp changes in
functional group proportions evident in the first autumn sample collected following antecedent
cease-to-flow conditions. Proportions later return to pre-impact dynamics for the neutral
group. A step change in spring community composition with a sharp increase in streamflow
conductivity was evident in taxonomic data.
Case study 3 indicates the importance of flow for a rheophilic fish species, Galaxias olidus, in
the Marne River. In this catchment, G. olidus appears to have a linear flow dependence for
recruitment, at least over the range of observed conditions from 2002 to 2010. This linear
response was used to model recruitment success as a function of streamflow using observed
and modelled no-development flow data. Modelling indicated the major difference was a
decrease in the frequency of years with moderate recruitment success. Decreases in the
number of moderate recruitment years were due to a reduction in flow duration and
magnitude, which was previously attributed to farm dam development in the catchment. This
case study illustrates the importance of flow magnitude for higher trophic levels in riverine
ecosystems that was not evident in the macroinvertebrate data.
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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 Recovery and Response
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
1.1 Purpose
This report was prepared for the National Water Commission as a component of the Low
Flow Ecological Response and Recovery Project funded under the Raising National Water
Standards Program. This project aims to help 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. The material in this report relates to Component 2 of
the project, which seeks to examine low-flow ecological response thresholds and recovery
trajectories. Marsh et al. (2012) presents a synthesis of current ecological understanding of
the effects of low flows and associated ecological stressors on aquatic communities. The
work in this chapter is discussed in the context of the understanding summarised in that
review and also through work on water allocation planning in South Australian conditions.
1.2 Significance of low flows for South
Australian streams
South Australian climatic conditions vary from Mediterranean in the south, grading to semiarid and arid to the north. Prevailing influences on rainfall are frontal systems moving inland
from the Southern Ocean, and the southerly movement of tropical moisture from the north of
the continent. Latitude and elevation influence observed rainfall patterns, particularly in the
south, where rainfall from northern migration of moisture bearing oceanic frontal systems
dominates. As a result, the highest and most reliable rainfall totals are associated with the
relatively significant relief of the Mount Lofty Ranges and higher latitudes of the state in southeastern areas towards the Victorian border.
In southern regions, the Mediterranean influence is most apparent, with warm to hot and dry
summers and wet, cool winters. The resulting seasonal rainfall excess over winter and spring
provides the most reliable streamflow conditions. It is important to note that even in the
wettest regions of the state, average potential evaporation exceeds average rainfall. This
means that seasonal droughts with associated cease-to-flow periods are expected in South
Australian stream ecosystems.
Consistent with these climatic characteristics, streamflow regimes across virtually all South
Australian watercourses are typified by a seasonal cease-to-flow period over summer and
autumn. The duration of this cease-to-flow period varies depending on factors such as
latitude, elevation of headwaters, groundwater input and catchment size. Wherever it is found
in the landscape, permanent surface water represents a significant ecological asset, the value
of which increases with the size, quality and relative proximity of other refugia and the
duration of annual flow. Land use naturally exerts an ecological influence, but the major
limiting factor on South Australian streams is the presence of standing, and particularly
flowing water. Managing for low flows, usually including cease-to-flow periods, is thus a key
focus of water allocation planning processes.
In ephemeral and intermittent (also known as seasonal) streams, groundwater/surface water
interactions assume a high level of importance (Hughes 2005). These temporary systems are
characterised by a high degree of spatial heterogeneity in terms of wetted areas. Owing to the
relatively dry South Australian climate, groundwater discharge is extremely influential on the
presence of surface water refugia and observed streamflow duration, especially low flows
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(Green & Stewart 2008; Banks 2010). Although Lake Eyre Basin refugia waterholes are
thought to entirely depend on surface flows to persist (Costello et al. 2007), virtually any
permanent surface water outside Lake Eyre Basin rivers are in the first instance generally
assumed to be groundwater-dependent. While groundwater supports riverine assets from
permanent pools to seasonal and perennial reaches, permanently-flowing stream reaches are
uncommon, and where these occur, groundwater discharge is invariably the source. Even in
higher rainfall areas, groundwater supports much of the extended duration of low-flow periods
and highly influences the duration of seasonal dry periods in streams.
Under South Australian conditions, an average annual rainfall of 500–600 mm is recognised
as the threshold where reliable streamflow can be expected in most years. Areas where
rainfall exceeds this threshold are more likely to support aquatic communities where the
ecology reflects this reliability. A reliable water resource also attracts human activities such as
irrigated horticulture and forestry, bringing the more flow-sensitive ecosystems into direct
competition with human extraction. Resource development, especially small on-stream farm
dam storages, has been demonstrated to influence the length of seasonal no-flow (e.g.
Savadamuthu 2002). In more arid regions, streamflow is so unreliable that farm dam
development is largely limited to smaller storages used for stock watering. Aquatic
communities may still be impacted by the interception of low flows through farm dam capture,
but as the natural hydrology is highly variable, these communities are likely to comprise highly
prevalent taxa broadly adapted to extended drought periods common under South Australian
conditions. For example, in data analysed for this study, eight families were present in at least
half of the riffle samples collected at every site (see Appendix 3 for a list of families).
Given the strong winter and spring seasonal flow dominance, ecological theory would predict
a profound influence on stream ecology (Poff et al. 1997; Bunn & Arthington 2002). Much of
South Australia’s aquatic flora and fauna has evolved in conditions of seasonal or longer
drought periods. Biological traits such as high physiological tolerance to salinity, desiccationresistant life-history phases, high mobility or multivoltinism enabling rapid response to flow
periods are common in, for example, the macroinvertebrate fauna (see case study 1).
However, more sensitive taxa are still found, often in small permanently-flowing refugia.
The location of these assets for most Mount Lofty Ranges catchments can be unpredictable
as they are generally sourced from groundwater springs where the underlying hydrogeology
is dominated by fractured rock (Green & Stewart 2008; Banks 2010). This creates the
situation where reaches of permanent flow may be located anywhere in a catchment (a
common observation in ephemeral and intermittent systems), making the determination of
environmental water requirements spatially complex (Hughes 2005). The more flowdependent biological components of South Australian rivers are particularly sensitive to the
loss of the more amenable conditions associated with perennial or near-perennial flow, which
are often small in size and spatially isolated. Protecting good water quality and long-flow
duration assets is thus arguably the most critical focus for water allocation planning in South
Australia.
1.3 Introduction to the case studies
To investigate ecological responses to low-flow conditions in riverine ecosystems in South
Australia, three case studies are presented. Case studies 1 and 2 use macroinvertebrate data
collected for the AusRivAS program, while case study 3 relies on native fish data collected by
the regional natural resources management (NRM) board to investigate recruitment success
as a function of flow in the Marne River, a tributary to the South Australian Murray-Darling
Basin (SAMDB).
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All streamflow data used in this chapter was collected by the South Australian Government at
gauging stations forming part of the State Surface Water network, with some stations
maintained in a funding partnership between the state government and NRM boards. The
report concludes with a summary of current aquatic biological monitoring being undertaken in
South Australia. Existing data, and that being collected at present, are discussed in terms of
how suitable these may be to support research to fill current gaps in flow ecology
understanding.
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2. The influence of streamflow on
aquatic macroinvertebrate community
traits in South Australia (case study 1)
2.1 Summary
In this largely exploratory meta analysis, two scales of investigation were undertaken
incorporating macroinvertebrate samples and daily flow data from 12 sites across South
Australia. Catchments ranged over four orders of magnitude in size and represented
latitudinal, land use and climatic variations in the state, while flow regimes included examples
from ephemeral to perennial conditions. Data provide a good indication of temporal variations
in macroinvertebrate communities likely to be observed within refugial riverine habitat in
South Australia.
2.1.1 Flow regime and site trait prevalence
Undertaken at site scale, the first analysis characterised macroinvertebrate communities and
flow regimes across the total period of sampling to examine long-term correlations. Data
consisted of the prevalence of 50 macroinvertebrate families collected in edge habitat
samples at the 12 sites from 1994 to 2007 (21–25 samples per site). The family prevalence
data matrix was multiplied with a binary trait matrix of 36 states among nine biological traits to
provide an indication of trait prevalence within macroinvertebrate communities at each site
over a decadal time-scale. Observed streamflow near reaches where macroinvertebrate
samples were collected were characterised using summary statistics derived from daily
gauged flows. Selected statistics largely measured variability over daily, annual and interannual periods. An association matrix was generated for the trait prevalence and streamflow
data and cluster analysis was undertaken using hierarchical classification methods (Ward’s
minimum variance). Grouping of sites in the two classifications was then compared, and
analysed for indicative trait states and flow regime characteristics.
Classification of sites based on trait prevalence produced two broad groupings distinguishable
in terms of flow regime by: mean baseflow indices (proportional groundwater contribution to
total streamflow); inter-annual variability in baseflow indices; and mean duration of the annual
cease-to-flow period. Hence the broad division represents relatively reliable and highly
variable flow regimes. The high-variability site group is referred to as the ‘ephemeral group’,
with on average annual cease-to-flow periods exceeding 130 days more consistent with
ephemeral conditions. The lower variability sites were a mixture of perennial and intermittent
reaches, but cease-to-flow periods averaged only 22 days across the group and this group is
referred to as ‘near-perennial’.
Indicator species analysis suggested no trait states were indicative of the ephemeral group,
while 10 of 36 states were significantly associated with the near-perennial group (p <0.01).
This was interpreted as an indication that highly variable flow regimes in the ephemeral group
could only support a subset of the trait states supported under less variable conditions,
consistent with previously published theory and study findings. Evidence for this conclusion
was also apparent in the distribution of functional dispersion index scores (a measure of
spread in community traits). The near-perennial group had a higher median value and smaller
spread of values than observed for the more variable sites consistent with an increased
diversity of trait states.
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While site classification based on streamflow statistics produced three groups, with few
exceptions groups formed were consistent with the trait prevalence classification. Mantel tests
indicated that trait prevalence and community composition (taxonomic) dissimilarity matrices
had statistically-significant (p <0.01) positive correlations with the flow statistics matrix.
Geographical and flow distance matrices were also significantly correlated (p <0.01). High r
values for Mantel tests of geographical distance with both trait prevalence and taxonomic
dissimilarity matrices suggested some positive association, but this relationship was not
statistically significant in either case (p >0.05).
2.1.2 Antecedent flows and sample-scale trait composition
The second scale of investigation examined individual riffle samples (n = 161) collected at the
same 12 sites. The aim of this analysis was to obtain an indication of the short-term influence
of flow on dominant trait states and trait diversity in macroinvertebrate communities. Data
were prepared and analysed largely in the manner described above for catchment-based
analyses: statistical summaries of antecedent flow conditions for 7, 30, 90 and 180 days
before sampling were generated for comparison with a trait-abundance matrix describing the
macroinvertebrate community. The trait-abundance dissimilarity matrix was classified
(hierarchical classification) and ordinated (non-metric multidimensional scaling – NMDS), and
flow regime statistics were compared with the resulting macroinvertebrate community patterns
using statistical summaries and vector analysis.
Using stopping rules based on provision of maximum ecological information (Dufrene &
Legendre 1997), pruning of the trait-abundance classification yielded seven trait groupings.
For each trait group, characteristic antecedent flow conditions and indicative trait states were
identified (Table 1). In addition to antecedent flow magnitude, duration and cease-to-flow
periods, sampling season and site were clearly influential in structuring sample groupings.
Trait groups also reflected flow regime as determined in the site-scale analysis to a large
degree. Greater than 75 per cent of samples in three trait groups were from the perennial
flow-regime grouping, while samples from ephemeral flow-regime group sites comprised
greater than 85 per cent of samples in two trait groups. The two remaining trait groups
featured roughly even proportions of samples from across the two flow regime types.
Each trait group contained samples from three to nine sites, indicating a degree of consistent
community trait structure between rivers. The most numerically-dominant trait group
contained nearly a quarter of all samples. This group was also most broadly representative,
including samples from nine sites (although more than three-quarters of samples were from
the six near-perennial sites). Group 7 was the most unique grouping, dominated by samples
from the Light River (88 per cent). This site was unique in having very high ambient salinity
despite near-perennial streamflow (electrical conductivity values were typically 8000–10,000
µS cm-1).
No trait group contained samples collected exclusively following low-flow conditions, though
some evidence of the influence of antecedent flow conditions was apparent. Relatively high
proportions of samples collected following antecedent low flows or cease-to-flow periods were
evident in four of seven groups (groups 2–5). Low and cease-to-flow conditions were most
dominant in groups 3 and 5, which were predominantly from ephemeral and near-perennial
sites respectively. Although indicative trait states for both groups included those predicted to
be favoured under low-flow conditions, these were more apparent in group 3. The distribution
of functional diversity within groups differed. Group 3 (86 per cent ephemeral site samples)
had the lowest median and second highest within-group variability but group 5 (94 per cent
near-perennial site samples) were among the highest median value and had the lowest
within-group variability.
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At the whole-of-sample scale, vector analysis suggested the magnitude of recent flows and
the number of zero-flow days over the previous three months were influential in structuring
trait diversity. Flows over the seven days before sampling loaded in the opposite direction to
the same statistic over a 90-day period, which may represent the impact of short-term highflow events impacting on trait diversity. Season was also statistically significant (p <0.01), as
suggested by the distribution of samples between the trait groupings.
Table 1: Summary characteristics of indicative traits within the seven traits groups shown in
Figure 7. Low flow for this analysis is indicative of mean daily streamflow less than half the
volume of the non-zero median.
Trait
Characteristics of antecedent flow or flow
Indicative group traits
group
regime
(indicator species analysis p <0.05)
1
Generally continuous, though low/moderate
antecedent flow magnitude (10% of samples
collected following low-flow conditions); mostly
spring samples; mostly perennial flow-regime
site groups.
Long time to reach maturity;
semivoltinism; respire via
pneumostome; second-highest
median functional diversity.
2
Relatively high proportion of samples collected
following low-flow periods, but generally
continuous flow; mostly spring samples;
mixture of flow regime site groups.
3
Highest proportion of low-flow and second
highest cease-to-flow antecedent conditions.
Largely autumn samples from ephemeral flowregime site groups.
Predatory taxa; high dispersal
capability for drift as well as flight;
lowest functional diversity.
4
No low-flow samples, and recent flow typically
higher than the long-term median. Some
cease-to-flow samples; mixture of flow regime
site groups.
Filter-feeding; low salinity tolerance.
5
High proportion of low-flow samples; high
proportion of cease-to-flow samples; most
samples in autumn; mostly perennial flowregime site groups.
Highest trait functional dispersion;
terrestrial reproduction; diverse
feeding strategies; fast maturing;
univoltine.
6
No cease-to-flow, very few low-flow samples;
mostly autumn samples; all perennial flowregime site groups.
Respire via gills; plant-based
feeding strategies; highest median
functional diversity.
7
Perennial-flow dominant; no low-flow and few
cease-to-flow samples; almost entirely from the
Light River (ephemeral site group).
Multivoltine; high salinity tolerance;
detritivores; low functional diversity.
Respire via plastron; low, but highly
variable functional diversity.
2.1.3 Conclusions
A consistent finding across the two scales of analysis was the decreased functional diversity
and indicative trait states with ephemeral flow regimes. The effects of low flows on
macroinvertebrate communities appear to depend firstly on the prevailing flow regime for the
river system under consideration, which is consistent with ecological theory (Townsend &
Hildrew 1994; Poff 1997; Poff et al. 1997). Ephemeral sites are capable of supporting a
limited number of biological attributes (traits) and as a result, flow persistence is associated
with communities exhibiting higher levels of functional diversity at site as well as sample
scales. High salinity and highly modified land use appear influential and likely impose
constraints even under conditions of near-perennial flow. Conversely, perennial headwaters
and/or high proportions of catchment native vegetation result in the intermittent Rocky River
macroinvertebrate community being more consistent with perennial sites.
No association between reduced functional diversity and antecedent low-flow or cease-to-flow
conditions were apparent for sites from the near-perennial flow-regime group. In a near-
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perennial South Australian river system this analysis suggests that functional diversity may
not be measurably impacted by flow magnitude, at least for the range of flow conditions,
taxonomic resolution and range of traits examined. An association between low functional
diversity and low antecedent flow conditions was apparent for ephemeral sites, but at present
there is an insufficient number of sites or samples to be able to quantify this.
Similarly, while observed differences in functional diversity and favoured trait states were
consistent across the two scales of analysis, establishing any possible flow-based transition
threshold between these end-points is not possible. Mean annual cease-to-flow durations
and/or inter-annual variability in this appear to play a role, but this did not universally
distinguish between sites or community traits.
The results strongly suggest the role of groundwater discharge to streams in creating more
perennial flow conditions. Sites where groundwater input is reliable are most likely those
where near-perennial conditions occur. It is important the spatial variability of groundwater
input is considered in flow-restoration planning, especially where a biological response is
predicted. Structuring future studies of aquatic flora and fauna around gradients in
groundwater/surface water interaction is an area worthy of further research in South Australia.
This will build understanding essential to informing water allocation planning for realistic flowbased ecological restoration. Given the prevailing fractured rock environment over much of
the state, however, simply mapping the groundwater/surface water physical interaction is
challenging and insights gained through research may be difficult to incorporate into resource
planning. Without the ability to model changes in groundwater discharge to streams for a
given development scenario, the value in understanding changes in biological patterns that
may result is reduced.
Although a number of findings align with previously published studies on macroinvertebrate
trait flow relationships, many more indicative trait states for drought-tolerant taxa such as
body size, armouring and desiccation resistance of propagules (Poff 1997; Bonada et al.
2007; Diaz et al. 2008; Robson et al. 2011) were not available. Development of databases
with this information is critical if predictive models of macroinvertebrate communities are to be
developed for climate change adaptation or resource development planning. Notwithstanding
these limitations, this work demonstrates the usefulness of trait data as a means to link
patterns of diversity (macroinvertebrate family abundance) to habitat conditions (streamflow).
These analyses provide a more powerful means for managers to interpret what changes in
the flow regime may mean for macroinvertebrates, or any biota for which trait data are
available. The usefulness and insight which can be generated will only increase as genuslevel trait data becomes available. Investment in developing a trait database for all aquatic
flora and fauna should be a national priority if predictive capacity to inform management
decisions based on ecological processes is seen as important.
2.2 Introduction
Aquatic macroinvertebrates have been widely used as determinants of stream condition, and
there is a long history of their use in flow-related ecological investigations. The database used
in this study is made available to all researchers and the public by the data custodians, the
South Australian Environmental Protection Authority (EPA). The data itself represents
macroinvertebrate samples collected from across South Australia, which were used to
construct the AusRivAS models of macroinvertebrate community structure. Data from 12 sites
that were co-located with gauging stations and had a period of record from 1994 to 2007 (with
the exception of 1996) were selected for further analysis as part of this project. Streamflow
data (also freely available) for the relevant sites was obtained from the State Surface Water
archive maintained by the South Australian Department for Water.
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Macroinvertebrate data were collected according to AusRivAS protocols, and involved
sampling a 10-m linear section of either edge (pool; i.e. still water environments) and riffle
(where present) habitats using a standard 250-micron sweep net. Data used in this study is
lumped to family-level taxonomic resolution to allow comparisons across results from other
state jurisdictions. In the original datasets, samples were identified to the maximum possible
resolution, usually to genus or species level. A brief exploratory analysis of the differences
between using family and genus-level data suggested that where strong trends were
indicated in data, no change in conclusion resulted when switching between the two levels of
taxonomic resolution. Trait data were however not available at resolution below family, but
once available will likely be of much higher value given intra-family variation in traits (Robson
et al. 2011).
The value of using biological traits as a means to link ecological patterns to mechanistic
processes has long been recognised and used as a basis to formulate some well-tested
theories of aquatic ecosystem function (Townsend & Hildrew 1994; Poff 1997). Trait-based
analysis of macroinvertebrate communities has over recent years particularly resulted in the
development of a significant body of knowledge (e.g. Lamouroux et al. 2004; Bêche et al.
2006; Diaz et al. 2008; Miller et al. 2010; Kefford et al. 2011; Schäfer et al. 2011; Walters
2011). The aim of this analysis was to link macroinvertebrate communities, in terms of the
trait structure and diversity, to streamflow at two broad scales. Sites were firstly characterised
in terms of the dominant traits across all samples and linked to the flow regime at the site.
Individual samples were then analysed for similarities in trait structure between sites and any
relationship between trait-based groups of samples and the antecedent flow conditions were
investigated.
2.2.1 Limitations of the dataset
A number of methodological factors associated with the approach to sampling within the
AusRivAS protocols need consideration when interpreting the results of analysis conducted
on this, or any other AusRivAS dataset – especially when jurisdictions are compared. Firstly,
the absence of a riffle sample at a given site does not mean no flowing water habitat was
present. Consistent with the protocol, in South Australia riffle habitat was not sampled unless
at least a 10-m section of habitat could be accessed at the site. This means data are biased
towards conditions where flow was present. Another confounding feature of the data for this
study may be present where samples with no or few macroinvertebrates were collected may
have been discarded without being quantitatively recorded. In South Australia most sites
feature only seasonal flow, and following the onset of flow, a period of time is required before
the system transitions from a terrestrial habitat to a riverine system. This requires time for the
necessary biofilms to establish, creating the basal sources for the foodweb, and allowing true
aquatic species to proliferate through the system (P McEvoy & P Goonan, personal
communications 2011). While a sample that found no macroinvertebrates at a given site
would have provided valuable data to provide interpretation in this project, for the purposes it
was collected (to characterise the macroinvertebrate fauna at a site) it was of no value and
was not recorded. Sampling trips were similarly undertaken once the onset of flow was known
to have occurred, hence late cease-to-flow season samples that would have characterised
this phase of the trajectory in wetter regions of the state are under-represented.
Despite these limitations, the dataset represents an invaluable annual time-series of the
status of macroinvertebrate communities over time and analysis undertaken for this study
represents one of few attempts to link the observations to flow.
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2.2.2 Previous work considering South Australian
macroinvertebrate flow dependencies
The major previous effort to assess macroinvertebrate flow-related ecology in South Australia
was undertaken in the context of establishing an optimal flow regime for water allocation
planning purposes in the Mount Lofty Ranges (see Vanlaarhoven & van der Wielen 2009 for
references and the process to determine these). Consistent with the climatic limitations on
habitat extent (especially lotic habitat), ecological values for different sites were based on the
presence of flowing habitat. Twelve different flow-related habitat value classes were assigned
to different reaches within the Mount Lofty Ranges based on the presence, absence and
reliability of permanent still and flowing water habitat (e.g. a site that always had edge as well
as riffle habitat in autumn and spring would be assigned the highest ecological value). In this
manner, time-series data for a subset of sites included in this study were used to assess the
flow characteristics (metrics in the water allocation planning sense) that supported optimal
macroinvertebrate community structure.
As indicated in Section 1.2 above, the nature of South Australian aquatic ecology is very
much driven by refugia from the seasonal drought conditions. Even very small springs with
perennial flow are critical habitats.
2.3 Methods
2.3.1 Study sites
Time-series data from 12 sites were available for analysis (Table 2 and Figure 1; note Cooper
Creek is not shown in Figure 1 to improve clarity). The geographical spread of sites is largely
along the central high rainfall areas associated with the Mount Lofty Ranges, and these
capture the latitudinal variation well (Figure 1). Data presented for the Cooper Creek provides
an extreme in north-south variation, though this site perhaps should be considered unique
owing to the large-scale non-local influences which drive the hydrology. East-west gradients
are not representative of the entire state, but only limited surface water is found beyond the
central region and the higher rainfall areas are well covered.
Flow data from the South Australian State Surface Water archive from 1979 to 2007 was
obtained, with any gaps in the record infilled using linear interpolation based on correlations
with nearby sites. These data were used to calculate all daily and annual streamflow statistics
for macroinvertebrate sample flow-dependence interpretation. Table 2 presents summary
statistics on the catchments used in the study.
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Table 2: Site names and summary statistics, 1979–2007
Site code
Site name
Area
(km2)
Elev
(mAHD)
Annual
Median
BFI
Ann.
rainfall
daily
2
zero-
(mm)1
flow
flow
(ML)
days
CV3
Typ
EC4
A2390519
Mosquito Creek,
Struan
1215
54
614
5.25
0.16
8
4.0
2389
A0030503
Cooper Creek,
Cullyamurra
23000
43
250
110.3
0.19
123
5.9
204
A4260504
Finniss River, E
of Yundi
193
203
851
6.93
0.17
5
3.8
1310
A5010500
Hindmarsh
River
56
90
884
3.54
0.24
11
2.9
1090
A5020502
Myponga River
76
214
865
6.17
0.25
3
2.7
736
A5040512
Torrens River,
Mt Pleasant
26
415
684
0.11
0.09
119
6.4
2813
A5040517
Torrens River,
First Creek
5
284
972
0.71
0.32
0
2.3
350
A5050532
Light River,
Mingays W'Hole
838
226
504
1.90
0.14
2
8.0
8265
A5070500
Hill R, near
Andrews
235
322
556
0.38
0.12
176
7.1
6630
A5090503
Kanyaka Creek
187
246
300
0.18
0.08
176
25.9
9586
A5130501
Rocky River, u/s
gorge falls
189
77
783
8.45
0.28
80
2.7
2389
A4260533
Bremer River,
near Hartley
49
39
425
2.86
0.15
135
5.6
3150
1. Mean catchment rainfall or visual estimate from annual rainfall isohyets generated from Bureau of Meterology data.
2. Baseflow index, the ratio of mean annual baseflow to mean annual flow – an indication of groundwater input.
3. Coefficient of variation in annual streamflow.
4. Average of field electrical conductivity readings at the site of macroinvertebrate sampling. Data – EPA.
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Figure 1: Location of study catchments for case studies presented in this chapter (Cooper
Creek not shown).
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2.3.2 Data processing
Macroinvertebrate data
Abundance data
Macroinvertebrate abundance data from the South Australian EPA was sourced at a range of
taxonomic levels, many of a greater precision than required for this project. All data were
firstly summarised at family level to allow use of traits data at this resolution and to allow interjurisdictional comparison. Further processing was undertaken as required for each analysis.

For the meta analysis at a river scale, the probability of detection, rather than
abundance data was adopted following the philosophy employed by Chessman
(2009) in analysing AusRivAS datasets from New South Wales. Probability of
sampling of each family at each river was estimated as the simple proportion of total
samples that taxon was observed within. No effort was made to stratify by season,
but habitat was restricted to edge samples. Although this would likely under-represent
the more rheophilic taxa, numbers of riffle samples were highly variable between
sites, while edge samples were relatively consistent. The total number of edge
samples was 289 and replication at each site ranged from 21 to 25. Prevalence data
for each family with traits data available was used in the analysis at the river scale.

Biological sampling data was processed in different ways depending on the intended
uses. For traits analysis, data was first sub-sampled to select only those families for
which trait data were available (n= 50). Data were then relativised to sample total
abundance following the methods presented in Miller et al. (2010) to prepare the sites
by species matrix for further pre-processing (see Section 2.3.3 below for data
analysis).
Traits data
A recently-published traits database (Schäfer et al. 2011) was adapted for use as a low-flow
traits database in the current study as a component of the project (Rolls, unpublished data
2011). Additional information on functional feeding groups provided by the EPA (Goonan,
unpublished data 2011) was also incorporated to provide an additional level of local
relevance. While the Schäfer et al. (2011) traits (four levels) provide a high-level indication of
trophic status within samples, the functional feeding groups (six levels) allowed for a finerscale interpretation of relative trait abundances.
Flow variables
Flow regime summary variables for river-scale analysis
A range of flow variables were calculated to describe the flow regime at each river. Table 3
describes these and the rationale behind selection of each variable. Summary statistics were
calculated using the River Analysis Package ‘RAP’ (Version 3.0.3) (Marsh et al. 2003) or
using Microsoft Excel.
Antecedent flow conditions for sample analysis
Simple duration and magnitude statistics were calculated for antecedent flow conditions
immediately before sampling based on periods of 7, 30, 90 and 180 days. Magnitude was
represented by the average daily flow, while duration was recorded in two ways: the number
of zero-flow days in the antecedent period and the duration in days of the current-flow or
cease-to-flow period.
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Finally, to allow for an indication of the context of antecedent flow magnitudes in terms of the
historical flow conditions at each site, 30 and 90 day values for each river were re-scaled
according to the non-zero median daily flow for each. These two flow variables are thus in
units of non-zero median daily flow and an indicated flow of 1 represents non-zero-flow for the
site concerned. This allowed for simple sub-sampling of antecedent flow conditions based on
a ratio of the non-zero median daily flow at all sites. For the purposes of establishing if
antecedent flow conditions could be considered to be low flows, an effectively arbitrary
threshold of half the non-zero median was adopted unless otherwise discussed.
Table 3: Streamflow metrics used to characterise flow regime at the 12 sampling sites
Variable
Description
Rationale
rank.ave
Ranked average daily flow
Ranks used to compensate for the large
range in flow magnitude across sites
rank.med
Ranked median daily flow
Similar to above
mean.ann.
bfi
The average of the annual baseflow index
Used to indicate the reliance of the site on
groundwater discharge in terms of volume
cv.ann.bfi
Coefficient of variation in the above
To provide an indication of inter-annual
variability in groundwater-sourced streamflow
cv.d.bf
Coefficient of variation in daily baseflow
To provide an indication of short-term
variability
col.p.m
Colwells Predictability on a monthly time
step
Index of monthly variability
cv.zqd
Coefficient of variation in zero-flow days
per year
To provide an indication of the variability in
the duration of the seasonal drought
ave.zqd
Mean number of zero-flow days per year
To provide an indication of the magnitude of
the annual cease-to-flow period
ave.P10
Mean of the annual 10th percentile flow
calculated on daily streamflow
To provide an indication of inter-annual
variability in low-flow magnitude
cv.ave.q
Mean of annual coefficient of variation in
daily flow
To provide an indication of short-term
variability in streamflow
name
2.3.3 Data analysis
Data analysis involved the following stages: combining trait and abundance data; classifying
groups using hierarchical clustering methods; characterising flow conditions correlated with
each of the grouping patterns; and testing multivariate, factor and vector correlations on
community dissimilarities. All data analysis was conducted in the statistical programming
language R 2.13.0 (R Development Core Team 2010), using packages ‘vegan’ (Oksanen et
al. 2011), ‘labdsv’ (Roberts 2011) and FD (Laliberté & Shipley 2011).
Trait-based classification and grouping
Abundance and trait datasets were firstly combined following the methods of Miller et al.
(2010), where the river (or sample) by family matrix was firstly relativised by rows (either river
or sample) before matrix multiplication with the binary-coded traits matrix to create a river-bytrait (or sample-by-trait) prevalence matrix (Miller et al. 2010). The resulting matrix was
arcsine square root transformed and a Bray Curtis dissimilarity matrix (Bray & Curtis 1956)
was then generated for use in classification and ordination.
Ward’s minimum variance method was used in clustering as this approach minimises the
distances from group centroids across the total sample space (Legendre & Legendre 1998),
resulting in tight clusters within multidimensional (trait–abundance) space. This tends to
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create a dendrogram structure well-suited to pruning into trait functional groups (for an
example of this approach see Laliberte et al. 2011).
Optimal ecological pruning heights for dendrograms to group trait combinations were
established based on either visual inspection (river scale) or minimising the average p-value
in indicator species analysis as suggested by Dufrene & Legendre (1997). Non-metric
multidimensional scaling was used to ordinate data into two-dimensional space to allow for
vector/factor correlation analysis with ordination axes.
The classification based on flow regime summary statistics (Table 3) was used as a basis to
investigate the dominant traits in the river-scale analysis. The dendrogram was pruned at
heights giving equal size groupings.
Characterising trait groups
A number of methods were used to characterise the flow environments correlated with each
of the groups identified using graphical techniques and simple proportions.
The functional dispersion index (FDis) of Laliberté & Legendre (2010) provides an indication
of the variance in trait space by calculating the mean distance in trait space distance for each
species to the group centroid. A higher value for FDis indicates higher dispersion of trait
characteristics within trait space, which can be viewed as a more functionally-diverse
community. The FDis statistic provided a comparison between the different trait groups and
for the families present within each group for a given sample (community).
Flow correlations
Mantel tests of matrix correlation were used to compare dissimilarity matrices of taxonomic,
flow regime, trait prevalence and geographical proximity for the river-scale analysis.
Techniques used to determine correlations between flow and traits at sample scales
employed vector analysis for correlations of non-metric multidimensional scaling ordination
axes. In these analyses, typically around half the variation was explained simply by the factor
variable ‘river’. Permutation tests to determine correlation were thus stratified by river (i.e.
permutations only occurred within rivers, rather than between) to test the null model that
antecedent flow conditions have no effect on community dissimilarity within a river system.
2.4 Results
2.4.1 Flow–trait relationships at site scale
Ward’s minimum variance hierarchical clustering applied to the Bray Curtis matrix of trait
prevalence resulted in a dendrogram with two equal-sized clusters at the highest dissimilarity
level (groups 1 and 2, Figure 2). Comparison with a purely flow-based classification (Figure 3,
Euclidean distance matrix on the summary statistics in Table 3) suggests some similarities in
small-scale clustering, but broadscale structure differs somewhat, with three higher level
groups evident based on flow alone. Closer inspection indicates that group 1 in Figure 2 is
effectively intact in the flow-based classification as well (Figure 3), with the exception that
based on the hydrological statistics, Mosquito Creek groups with the Light River and Cooper
Creek. The major structural difference is the closer affinity of the Light, Mosquito and Cooper
group with the group 1 rivers from the trait classification, though these clearly form a separate
group based on hydrology.
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Differences in flow regimes for the trait prevalence classification groups are evident in the
respective means for the flow statistics (Figure 4). The division largely represents the
variability in streamflow and duration of cease-to-flow periods. Rivers in group 1 (First, Rocky,
Finniss, Hindmarsh, Mosquito and Myponga) are all located in relatively high rainfall areas
and have higher baseflow contributions (mean baseflow index of 0.27 vs 0.17 in group 2
rivers; Figure 4). Baseflow is also of relatively low inter-annual variability at these sites
(cv.ann.bfi in Figure 4) than in group 2 rivers (Cooper, Light, Hill, Kanyaka, Bremer and
Torrens). An exception to this pattern is the coefficient of variation in the number of zero-flow
days per year. Most group 1 rivers generally had very short, if any, cease-to-flow periods but
Rocky River had longer durations, increasing the mean value of this variable across the nearperennial-group rivers (group 1).
Group 2
0.3
Group 1
Light
Hill
Torrens
Bremer
Cooper
Myponga
Mosquito
Hindmarsh
Finniss
Rocky
First
0.0
0.1
Kanyaka
0.2
Height
0.4
0.5
0.6
Group 2 rivers have a mean annual cease-to-flow of 135 days, compared with a mean of 22
days for the group 1 rivers. Along with the baseflow statistics, this indicates the classifications
largely differentiate between ephemeral sites with long annual cease-to-flow periods (e.g. Hill,
Kanyaka, Cooper) and perennial or intermittent sites with only a short annual cease-to-flow
(e.g. First, Hindmarsh, Myponga). However, there are exceptions to this, with the Rocky River
site clustering with the low variability rivers despite a mean number of zero-flow days per year
of 106. Similarly the Light River is almost perennial, yet it clusters with the high-variability
rivers. Variation in baseflow is the predominant reason for the groupings, as the Light River is
highly variable from year to year, while Rocky River has a consistently high baseflow index
and low inter-annual variability. If these two rivers are removed, the average number of zeroflow days in the group 1 rivers is reduced to six, while group 2 rivers without the Light River
average 161 days per year of no-flow.
Figure 2: Dendrogram showing Ward’s method clustering of the trait prevalence dissimilarity
matrix.
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3.5
3.0
2.5
2.0
Mosquito
Myponga
Finniss
Hindmarsh
Rocky
First
Torrens
Bremer
Hill
Kanyaka
0.5
0.0
Light
Cooper
1.5
1.0
Height
Figure 3: Dendrogram showing Ward’s method clustering of the flow statistics distance matrix
1.6
Mean Group Value
1.4
1.2
1
0.8
0.6
Group 1
0.4
Group 2
0.2
0
Figure 4: Mean value for selected flow statistics for the two major groupings in Figure 2. Flow
statistic codes and descriptions shown in Table 3.
The NMDS ordination of the trait prevalence data (Figure 5) indicates two flow variables were
significantly correlated (p < 0.01) with site dissimilarities, and these load in a similar fashion.
The coefficient of variation of daily streamflow and annual baseflow indices are aligned
closely1 along NMDS axis 1, which also aligns with the maximum dissimilarity gradient
(Oksanen et al. 2011). Hence vector analysis suggests this major gradient is correlated with
the variability of the rivers at daily and annual scales. Centroids for trait prevalence
1 Pre-processing removed statistics with correlations exceeding 0.7, though these variables were close to this value r
= 0.68
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0.2
0.3
(analogous to species abundance in this analysis) are also shown on the ordination, and are
also largely distributed along the axis 1 gradient.
ffg.pred
matur.quart
cv.ave.q
0.1
cv.ann.bfi
terr.month
univoltine
matur.semi.ann
reprod.terr
resp.plast
0.0
NMDS2
ffg.shdr
ec.med.low
matur.month
resp.pneu
ffg.scrpr
ffg.gath
-0.1
reprod.ovo
matur.ann
terr.surf terr.semi
ec.med
disp.flight
ec.high
terr.quart terr.edge
ffg.coll
reprod.aq
semivoltine
matur.sup.ann
terr.aqua
ec.low
-0.2
resp.gill
disp.drift
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
NMDS1
Figure 5: NMDS ordination (stress = 7%) of the trait prevalence dataset. Convex hulls
enclosing the two groups shown in Figure 2 are drawn in red, with group 1 on the left.
Centroids of trait prevalence are shown in blue, and significant (p < 0.01) flow vectors in
green (see Table 3 for descriptions of flow variables, and Appendix 1 for traits).
Matrix correlations
Mantel tests were used to evaluate the correlation between the trait abundance, taxonomic,
flow and geographical association matrices (Table 4). Dissimilarity matrices based on traits as
well as taxonomic data were positively and significantly correlated with flow (p < 0.01).
Despite having reasonably high r values, neither matrix was significantly correlated with
geography (p > 0.05). A partial mantel test of the correlation between traits and flow
constrained to control for geographical proximity had a Mantel r value of 0.38 and was
statistically significant (p < 0.01). The abundance (i.e. taxonomic) data had a higher matrix
correlation with flow variables than the trait prevalence matrix. This may be a result of the
incomplete nature of the trait database.
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Table 4: Results of Mantel tests for correlation (r value) between the flow, traits, family and
geographical proximity matrices.
Traits by
Family
Streamflow
prevalence
abundance
summary
data
data
statistics
Streamflow
summary
statistics
0.46**
0.59**
-
Spatial
proximity
0.27
0.34
0.48**
(** p < 0.01, otherwise p > 0.05)
Differences in trait composition
With reference to the two trait-based site classification groups shown in Figure 2, indicator
species analysis of the trait by prevalence dataset did not result in any traits diagnostic of
group 2 (the highly variable rivers). In contrast 10 trait states were significantly (p < 0.01)
associated with group 1 rivers (Table 5). These represent a range of traits and multiple states
within these, suggesting a more diverse range of traits is supported under the more reliable
flow conditions.
Table 5: Traits identified through indicator analysis as being significantly associated (p<0.01)
with rivers in group 1 (Figure 2).
Trait state
Indicator value
Functional feeding group
shredder
0.766
Functional feeding group
gatherer
0.6954
Respiration pneumostome
0.5972
Surface-dwelling
0.6571
Semi-terrestrial life-history
0.6554
Time to maturity 1 year
0.6497
Time to maturity <4 weeks
0.6044
Reproduction via ovoviviparity
0.5994
Low to moderate salinity
tolerance
0.7318
Moderate salinity tolerance
0.6601
Direct evidence for a greater range of supported trait states under the more reliable conditions
is provided in the relative distribution of functional dispersion scores between the individual
edge samples (n = 289) used to generate the trait prevalence dataset. When stratified by the
river groups from Figure 2, samples collected from group 1 rivers consistently have a higher
functional dispersion index, and are less variable in terms of this measure of functional
complexity2 .
2 The higher the value observed for the functional dispersion index, the broader the range of traits represented in the
community.
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6.0
5.5
5.0
4.5
4.0
3.5
Functional Dispersion Index
1
2
River Group
Figure 6: Distribution of functional dispersion values for the two broad groupings shown in
Figure 2. Notches indicate the approximate 95 per cent confidence intervals.
2.4.2 Flow–trait relationships within samples
Data for all riffle samples across the 12 rivers (n=161) were analysed using similar methods to
those employed for sites (see Section 2.4.1 above). The aim of sample analysis was to gain a
better understanding of how short-term variations in streamflow, as opposed to long-term flow
regimes, affect trait expression in macroinvertebrate communities.
The trait by abundance dissimilarity matrix for the riffle samples was classified using Ward’s
minimum variance method. The dendrogram pruning height to provide optimal ecological
information as determined by indicator species analysis (Dufrene & Legendre 1997) was a
Bray Curtis dissimilarity of 1.2, which generated seven groups (Figure 7). These are referred
to as ‘trait groups’ to distinguish from the river groups in the previous analysis. Trait group
membership was driven by a range of factors including river, season, flow regime type and
antecedent flow conditions (Table 6). Each trait group had at least two trait states indicative at
statistically-significant levels (p < 0.05; Table 7). Simple summaries of indicative trait states
for each group are described in Table 1 in the Summary of this chapter along with a verbal
description of the key observed flow regime characteristics. Additional detail is provided
below.
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7
6
5
4
3
2
Height
1
4
3
2
6
5
1
0
7
Figure 7: Ward’s minimum variance classification of the traits by abundance matrix.
Groupings reflect the optimal pruning height suggested by indicator species analysis.
Indicative traits were identified for each group. For group membership see Appendix 2.
Streamflow and other characteristics of groups
Differences between trait groups suggest cease-to-flow events, sampling season and site
were all apparently involved in structuring community traits as indicated by sample groupings
(see Table 6). Trait groupings reflected the influence of flow regime as determined in the sitescale analysis to a large degree. Greater than 75 per cent of samples in three trait groups
(groups 1, 5 and 6) were from the perennial flow-regime grouping, while samples from
ephemeral flow-regime group sites comprised greater than 85 per cent of samples in two trait
groups (groups 3 and 7). The two remaining trait groups (groups 2 and 4) featured roughly
even proportions of samples from across the two flow regime types (Table 6).
Each trait group contained samples from three to nine sites. Trait group 1 was numerically
dominant containing nearly a quarter of all samples. This group was also the most broadly
representative, including samples from nine sites (although over 75 per cent of samples were
from the perennial flow-regime sites). Antecedent flow conditions characterising this group
were low to moderate in magnitude but generally continuous, while relatively high functional
diversity was a feature of the macroinvertebrate community (Table 1, Figure 11). Group 7 was
the most distinctive grouping, dominated by samples from the Light River (88 per cent). This
sample group was unique in having near-perennial streamflow despite the high ambient
salinity in the catchment (electrical conductivities typically c. 8000 µS cm-1).
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Table 6: Selected characteristics of the different trait groups
Traits group
Characteristic
1
2
3
4
5
6
7
Number of
samples in group
39
19
22
18
16
22
25
Number of rivers
represented
9
6
6
6
4
4
3
Most common
river
First
Torrens
Cooper
Rocky
Rocky
Hindmarsh/
Myponga
Light
(% of samples
from above river)
26%
42%
41%
39%
50%
36%
88%
Proportion of
samples collected
in spring
67%
63%
36%
67%
13%
23%
52%
Proportion of
samples collected
following low-flow
conditions1
10%
21%
27%
0%
19%
9%
0%
Proportion of
samples collected
following cease-toflow conditions2
8%
16%
50%
22%
56%
0%
8%
Proportion of
samples from
group 1 sites 3
79%
42%
14%
44%
94%
100%
4%
Median duration of
antecedent flow
events
>365
169
112
170
51
>365
>365
1: 30-day antecedent mean daily flows of less than a quarter that of the long-term non-zero median daily flow are
considered indicative of low-flow conditions.
2: A cease-to-flow period occuring in the 90 days before sampling.
3: Group 1 sites as defined in the site-scale analysis, with broadly perennial flow regimes (see Figure 2).
Seasonal dominance is evident in many groups: trait groups 3, 5 and 6 predominantly
comprise autumn samples; groups 1, 2, and 4 are around two-thirds spring samples; while
group 7 shows no seasonal dominance (Table 6).
No trait group contained samples collected exclusively following low-flow conditions, but
relatively high proportions of samples collected following antecedent low or cease-to-flow
periods were evident in four of seven groups (groups 2–5). Trait groups 3 and 5 are
distinguished by having relatively high proportions of zero-flow days in the three months
before sampling. Group 3 had around one-third of samples collected following low-flow
periods3 and is the most clearly dominated by antecedent low flows and cease-to-flow
periods.
Traits groups 1, 6 and 7 were mostly collected following periods of continuous streamflow for
at least a year, meaning sites from where these were collected were effectively perennial over
the sampling period (Table 6).
The distribution of antecedent flow conditions in terms of median flow magnitude show limited
differentiation. Most trait groups included samples collected following a range in flow
conditions over 30-day (Figure 8) and 90-day (Figure 9) antecedent periods. Trait group 1
3 Low flows were defined for this purpose as being less than one quarter of the median non-zero daily flow. As no
allowance was made for cease-to-flow periods in the calculation of average flow statistics, any such period during the
calculation window would also contribute to lower values for the daily mean flow.
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100
50
0
Rank order of 30d antecedent flow/nz median
150
samples had the lowest median ranking for the month before sampling, and second-lowest
median ranking for the 90-day antecedent period, though only 10 per cent of samples were
deemed as collected following low-flow conditions. Trait group 7 is distinguished in the figures
for a relatively low variation in antecedent flow magnitudes for both time windows.
1
2
3
4
5
6
7
Traits group
100
50
0
Rank order of 90d antecedent flow/nz median
150
Figure 8: Boxplot summary of the rank order of the 30-day antecedent flow as a ratio of the
non-zero median by traits group.
1
2
3
4
5
6
7
Traits group
Figure 9: Boxplot summary of the rank order of the 90-day antecedent flow as a ratio of the
non-zero median by traits group.
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In ordination space the trait groups exhibit different degrees of overlap (Figure 10). Trait
groups 5 to 7 occupy distinct areas of the plot. Groups 1 to 4 share considerable common
area, with groups 2 and 4 having substantial overlap (Figure 10). Environmental correlations
with NMDS axes (stratified by site to control for inter-basin variation) indicated antecedent
flow magnitude and cease-to-flow periods for 90 days before sampling were significantly
correlated (p < 0.01). Antecedent flows for seven days before sampling was also a significant
factor at the same level, but loaded in the opposite direction to the 90-day flow variable
(Figure 10). Season was also significantly correlated with the ordination. As with Figure 5,
NMDS axes have been rotated to align the maximum gradient along axis 1. None of the
significant flow variables are oriented directly along this gradient.
f f g.shdr
zq.90
0.5
reprod.terr
f ood.gen
univ oltine
sqrt.7
semiv oltine
5
ec.med.low
matur.ann
terr.quart
f matur.sup.ann
f g.gath
matur.semi.ann
resp.pneu
matur.month
1
is.spring0
terr.surf
3
reprod.aq
resp.plast
disp.drif t
terr.semi
0.0
NMDS2
f ood.pred
f f g.pred
terr.month
4
f f g.f ilt
ec.low
lightis.spring1
2disp.f
f f g.coll
terr.edge matur.quart
f ood.det
ec.med
f f g.scrpr
f ood.plant
resp.gill
multiv oltine
6
terr.aqua
reprod.ov o
7
-0.5
sqrt.90
ec.high
-0.5
0.0
0.5
1.0
NMDS1
Figure 10: NMDS ordination (stress = 13 per cent) of the riffle trait prevalence data. Shown
are the trait-based groupings (red polygons enclose samples within each group);
environmental vectors and factors with a significant correlation with ordination axes (p < 0.01)
are shown in green; and centroids for trait abundance are shown in blue.
Trait characteristics and functional diversity of groups
Indicator species analysis of the trait-abundance matrix yielded trait states indicative of each
trait group (Table 7). The maximum number of indicative states was observed for groups 3
and 6, each having a total of 7. Groups 2 and 4 had the lowest number of statistically-distinct
trait states, with two each.
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Trait states associated with flowing conditions were observed in all groups with the exception
of group 7, and were particularly dominant in group 1 (long time to maturity, semivoltinism)
and also prevalent in group 6 (lack of terrestrial phase, gills).
Indicative trait states that would be predicted under low-flow conditions (Robson et al. 2011)
were also observed in every group, but dominated group 7 (multivoltinism, rapid maturity, high
salinity tolerance) and group 3 (flight dispersal, dominance of predatory feeding strategies).
Table 7: Indicator species analysis results for groups shown in Figure 7. Statistical
significance: ** p <0.01; * p <0.05.
Trait
Description
Group
Indicator
value
matur.ann
Time to maturity of 1 year
1
0.31**
resp.pneu
Respiration by pneumostome
1
0.30**
terr.quart
Terrestrial life phase lasting 1–3 months
1
0.28**
matur.sup.ann
Time to maturity exceeding 1 year
1
0.19*
semivoltine
Less than one generation per year
1
0.16**
resp.plast
Respiration by plastron, spiracle
2
0.25**
ffg.coll
Feeding group collector
2
0.21**
disp.flight
Dispersal via flight phase
3
0.24**
terr.month
Terrestrial life phase lasting less than 1 month
3
0.22**
food.pred
Trophic level predator
3
0.21**
ffg.pred
Predatory feeding group
3
0.21**
ec.med.low
low–moderate salinity tolerance
3
0.21**
reprod.aq
Reproduces via aquatic eggs
3
0.20**
disp.drift
Dispersal via drift
3
0.20**
ec.low
low salinity tolerance
4
0.33**
ffg.filt
Feeding group filterer
4
0.31**
ffg.shdr
Feeding group shredder
5
0.53**
reprod.terr
Reproduces via terrestrial eggs
5
0.49**
univoltine
One generation per year
5
0.36**
food.gen
Generalist trophic status
5
0.34**
matur.month
Time to maturity less than 1 month
5
0.27**
ffg.gath
Feeding group gatherer
5
0.26**
food.plant
Plant-based feeding strategy
6
0.45**
ffg.scrpr
Feeding group scraper
6
0.43**
reprod.ovo
Reproduces via ovoviviparity
6
0.33**
matur.semi.ann
Time to maturity 6–12 months
6
0.30**
terr.aqua
Obligate aquatic (no terrestrial phase)
6
0.24**
ec.med
Moderate salinity tolerance
6
0.20**
resp.gill
Respiration via gills
6
0.20**
ec.high
High salinity tolerance
7
0.56**
food.det
Detritus-based feeding strategy
7
0.19**
matur.quart
Time to maturity 1–3 months
7
0.19**
multivoltine
Multiple generations per year
7
0.16**
In terms of the range in functional diversity represented by the sample groups, the highest
median levels were observed in samples for trait groups 1, 5 and 6, although group 2 samples
exhibit a similar top quartile (Figure 11) . These trait groups are dominated by samples
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5.0
4.5
4.0
3.5
Functional dispersion of samples
5.5
6.0
collected in sites with the perennial flow-regime type. Trait group 3 clearly has the lowest
median functional dispersion and along with group 2 a higher within-group variation in
functional dispersion index scores. Groups 3 and 7 have the two lowest median and upper
quartile values and are the two groups dominated by samples from the ephemeral flowregime sites.
1
2
3
4
5
6
7
Trait group number
Figure 11: Boxplot showing the distribution of functional dispersion index scores for the seven
trait grouping samples.
2.5 Discussion
2.5.1 The role of flow regime
At the broadest classification level, there is considerable similarity in the division of
macroinvertebrate communities between the site and sample-scale analyses. Comparison
between the hydrological and biological site-scale classifications is generally good and along
with the results of the Mantel tests indicate that flow regime is correlated with both taxonomy
and trait prevalence, even when geographical proximity is controlled for. Classification of the
trait-abundance data for the riffle samples divides into two groups which display a high degree
of fidelity with the site grouping. For the right-most cluster of Figure 7 (trait groups 1, 5 and 6)
NATIONAL WATER COMMISSION — Low flows report series
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88 per cent of samples are from the perennial site group. Five of the seven trait groups
comprise 75 per cent of samples or more from within a single-flow regime grouping. Only two
trait groups have roughly equal numbers of samples from both flow regime site groups. This
provides an indication that the long-term flow regime exerts the dominant influence on
macroinvertebrate communities, hardly a finding new to stream ecology (Walker et al. 1995;
Poff et al. 1997; Bunn & Arthington 2002; Sheldon 2006; Bonada et al. 2007).
The most apparent difference in the flow regimes of the river groups is the duration of the
mean annual cease-to-flow periods. Flow permanence has previously been shown to be
correlated with macroinvertebrate community structure and biological traits in Mediterranean
rivers across a perennial to ephemeral gradient in Spain (Bonada et al. 2007). The duration of
dry seasons (Williams 1996) and the harshness of intermittent streams (Fritz & Dodds 2005)
have also been associated with decreased levels of diversity. Similar patterns can evidently
be assigned to macroinvertebrate communities in South Australia.
It is unsurprising that a group of rivers with a mean annual cease-to-flow period of around
three weeks will favour a different suite of biological traits than rivers where the same statistic
takes a value of over four months. However the two exceptions to this otherwise clear
distinction between sites require further consideration. Rocky River has a mean annual
cease-to-flow duration of over 100 days, yet in both the hydrological and biological
classifications it groups with the near-perennial sites. Although cease-to-flow conditions are
observed at the stream gauge (and site of macroinvertebrate sampling), this is located in a
losing section of the catchment (Banks 2010b). The pristine headwaters of the river are a
gaining system, where groundwater enters the stream. Perennial baseflow is supplied via
shallow sedimentary aquifers and large volume surface storages (Banks 2010b). Despite the
longer average cease-to-flow periods at Rocky River, it has the second-lowest value of all
sites for inter-annual variability in baseflow and the second-highest baseflow index; hence
baseflow is persistent and highly reliable. Moreover, perennial headwaters in the pristine
upper catchment will provide ample permanently-flowing habitat. Land use is also highly likely
to play a role, as discussed below.
The Light River grouping is also an anomaly, being an almost perennial reach that was
clustered with the ephemeral group in both hydrological and biological classifications. In terms
of hydrology, although perennial through many years, the Light River site has high interannual variability in both the baseflow index and number of zero-flow days. This resulted in
the river being grouped with the Cooper and Mosquito creeks in the hydrological
classification, although this does not reflect the relative reliability of flow at the site. Traitbased clustering with ephemeral sites is likely due to the high ambient salinity, with electrical
conductivity (EC) point readings averaging over 8000 EC over the sampling period. A degree
of positive covariance in trait states adapted to ephemeral flow with those for high
physiological tolerance to salinity is likely given the usual co-occurrence of these conditions.
Salinity is not thought to severely constrain the range of taxon richness observed in South
Australian macroinvertebrate communities (McEvoy & Goonan 2003), an observation
attributed to the presence of a suite of salinity tolerant taxa. It appears the combination of
near-perennial flow and high salinity selects for a unique combination of traits given the
dominance of Light River samples in trait group 7, likely representing a high level of diversity
among the salinity tolerant taxa.
In comparisons of the trait-based river grouping with the hydrological grouping, only Mosquito
Creek changes clustering affinity (compare figures 2 and 3). This likely represents the
influence of the linear drying trajectory in flow regime that has occurred at the site and is
discussed in the following case study. The prevalence of different traits observed at the
Mosquito Creek site has altered over the period of data collection, responding to changes in
flow regime (case study 2). However, since only the last few samples in this study were
collected after the onset of the drier conditions, when averaged over the whole dataset as in
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this case study, trait prevalences observed reflect those characteristic of both ephemeral and
perennial rivers.
2.5.2 Trait responses to flow conditions
The long-term flow regime is an example of a regional-scale selective force (or filter; Poff
1997) influencing the range of biological attributes (traits) that allow biota to persist under
prevailing conditions. Filters at this scale dictate trait diversity, in turn determining the regional
species pool from which communities are assembled in response to shorter-term and smallerscale variations (Poff 1997). Near-perennial flow regimes are associated with increased
functional diversity in both scales of investigation in this case study (figures 6 and 11, tables 5
and 7), though this general pattern appears to be modified by salinity and possibly land use.
An increase in the diversity of traits observed under less variable flow regimes is consistent
with theoretical predictions (Townsend & Hildrew 1994; Poff 1997). Under the Poff (1997)
framework an ephemeral flow regime applies additional filters around life-history, body size
and drought response of macroinvertebrate biota, reducing the suite of traits suited to
persistence. Under the Townsend and Hildrew (1994) river system habitat templet, a
combination of factors may lead to higher functional diversity in less variable habitat.
Examples of this include limitations in the range of biological traits suitably adapted to harsher
conditions and the influence of competition leading to increased niche differentiation (e.g.
allowing for more specialised feeding or other strategies) under more constant flow regimes
(Townsend & Hildrew 1994).
Many indicative trait states in perennial-site-dominant groups (groups 1, 5 and 6) are more
readily associated with ephemeral conditions, but few perennial traits were associated with
ephemeral-site-dominant groups (groups 3 and 7). Although antecedent flow conditions were
not highly informative, overall trait states are in accord with predictions from ecological theory
(Townsend & Hildrew 1994; Poff 1997; Williams 1996; Robson et al. 2011) once flow regime
associations are considered. Some examples of this are:

A predatory feeding strategy, high tolerance to salinity and high dispersal capability
were indicative for trait group 3 , which are all linked to low or temporary flow
conditions (Boulton & Lake 1992; Williams 1996; Bogan & Lytle 2007; Bonada et al.
2007; Diaz et al. 2008; Robson et al. 2011; Walters 2011). This group comprised 86
per cent of samples from ephemeral sites.

Groups 1, 5 and 6 were largely (79 to 100 per cent) comprised of samples associated
with perennial flow regimes and tended to have indicative trait states more likely to be
favoured under reliable streamflow conditions (Townsend & Hildrew 1994; Bonada et
al. 2007) including diverse feeding strategies, long times to reach maturity, semivoltinism and respiration via gills.
In addition to being consistent with ecological theory, findings are also supported by previous
studies. Bonada et al. (2007) in a study of rivers in north-eastern Spain identified distinctive
traits for perennial, intermittent and ephemeral regimes, which are supported by the samplescale analysis. While no indicative traits were identified for ephemeral conditions at the site
scale in the present study, they were evident at sample scale, and this appears to reflect
methodological differences between this current study and Bonada et al. (2007). However,
conclusions of both studies are effectively the same and consistent with theoretical prediction:
rather than highly variable rivers having a unique suite of indicative trait states corresponding
only to these conditions, these represent subsets of the traits observed in the less variable
rivers.
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Season, although undoubtedly correlated with flow, is suggested as partially structuring
communities. Although not as evident in trait group membership as flow regime, the NMDS
ordination suggests this influence is important.
In addition to flow regime, other selective filters which operate at regional scales and below
are land use and resource development (Poff 1997). Semi-arid catchments are especially
sensitive to water resource development, even small farm dam storages (Hughes 2005) and
both land use and development exert an influence on macroinvertebrate communities (Brown
et al. 1997; Kay et al. 2001; Townsend et al. 2003; Bonada et al. 2007; Collier 2008; Diaz et
al. 2008; Miller et al. 2010; Schäfer et al. 2011). While geographically remote, First Creek and
Rocky River are more or less dominated by woody native vegetation cover. This represents
the most unique land use, with catchments for all other sites largely under agriculture and
widely cleared. These sites grouped together in both classifications, despite the differences in
cease-to-flow conditions. The hydrological affinity between the two sites relates to their high
baseflow index and low inter-annual variability in this, while the biological classification
suggests similarly high functional diversity. Trait group 5 was predominantly comprised of
samples from Rocky River, and the indicative feeding group state of shredder suggests land
use at least in part is attributable to the elevated functional diversity by providing more
vegetative matter.
While antecedent flow magnitude did not discriminate well between trait groups, correlations
were observed over the sample as a whole. The manner in which 90-day and seven-day
antecedent flow magnitudes loaded in opposite directions on the NDMS ordination suggests
opposite influences were involved. The short-term influence of flow magnitude may reflect
larger events in the period immediately before sampling, removing taxa without adequate
behavioural or other strategies to resist high-flow events. Flood events were observed to alter
community structure in a study of an intermittent stream in Victoria, which recovered to preevent levels within two weeks (Boulton & Lake 1992). The 90-day flow vector loaded in the
same direction as season, and may partly reflect the influence on community structure of
higher flow conditions associated with spring.
The effect of low flows on macroinvertebrate communities appears to depend firstly on the
prevailing flow regime for the river system under consideration consistent with ecological
theory (Townsend & Hildrew 1994; Poff 1997; Poff et al. 1997). In a relatively reliable South
Australian river system it appears likely from the analysis that flow magnitude will not
necessarily impact functional diversity, at least at the taxonomic resolution available here.
Flow persistence is associated with the highest levels of trait diversity at both site and sample
scales, although high salinity and highly modified land uses likely impose constraints even
under perennially-flowing conditions.
2.5.3 Implications for management
The question of how much flow can be removed from a system without impacting the ecology
is closely aligned with questions of low-flow impact. A number of recently-published field
experiments where water was diverted to create an artificial low-flow disturbance have shown
some influence of the diversion, although responses were quite limited (Miller et al. 2010;
Walters 2011; Walters & Post 2011). Diversions of streamflow of up to 80 per cent in some
instances resulted in few changes in macroinvertebrate community structure other than those
related to contraction in habitat and resulting faunal densities (Miller et al. 2010; Walters
2011; Walters & Post 2011). Where detecting changes in macroinvertebrate communities has
been unsuccessful for such dramatic flow decreases, managers are clearly faced with a
serious challenge in establishing a defensible allocation limit.
Trait-based analysis appears to offer a viable approach for water allocation planning purposes
where macroinvertebrates are concerned. Changes were evident in trait prevalence in the
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above studies, including shifts in feeding groups where predatory strategies increased while
filterers and gatherers decreased (Miller et al. 2010; Walters & Post 2011). Trait-based
analysis has also recently been used to demonstrate an impact of water abstraction in New
South Wales (Brooks et al. 2011), although these authors noted a similar study from the
north-east of that state which failed to detect a difference (Chessman et al. 2011). Brooks et
al. (2011) note the influence of trait combinations, rather than individual traits, on whether a
given functional group of macroinvertebrates will be tolerant or susceptible to the impacts of
abstraction (Brooks et al. 2011).
The presence of reliable riffle and edge habitat in autumn and spring is correlated with high
ecological value macroinvertebrate communities in the Mount Lofty Ranges (P McEvoy,
personal communications 2011), reflecting recognition of the importance of near-perennial
flow. Evidence presented in this study suggests that clearer guidance can be offered to river
managers on duration impacts than those relating to flow magnitude. Cease-to-flow periods
create step changes in conditions, for example resulting in the total loss in riffle habitat (Lake
2003). Linking changes in macroinvertebrate communities to the duration of cease-to-flow
periods may offer more promise than thresholds based on reductions in magnitude. Given the
dominance of intermittent streams under South Australian conditions, focusing on preserving
the natural cease-to-flow characteristics of a given system may be the most critical goal
ecologically.
More broadly, for South Australian river managers this would mean ensuring a given system
does not transition along the continuum of conditions from perennial to ephemeral. Transition
to a more variable flow regime will at some critical point be predicted to lead to a loss of trait
functional diversity (see case study 2), with implications for biodiversity (Hooper et al. 2005)
as well as ecosystem resilience (Folke et al. 2004). Determining acceptable durations for any
transitional thresholds will require dedicated research, as existing data are weighted towards
sampling during flow periods and are not of sufficient precision or replication to undertake
such an analysis. In the absence of further information, any development levels that lead to
an increase in the duration or variability in the seasonal cease-to-flow, or cause a perennial
flow regime for a given reach to transition to an intermittent regime would be expected to also
produce transition within the macroinvertebrate community involving loss of functional
diversity and resilience. Case study 2 presents a preliminary analysis of just such an event.
From the low-flow ecology perspective this analysis highlights the significance of groundwater
discharges. Under climatic conditions prevailing in South Australia, with its notable seasonal
rainfall deficit, both the spatial distribution of refugia and characteristics of the streamflow
regime relating to flow persistence and variability are largely driven by the influence of
groundwater (Risby et al. 2003; Deane et al. 2008; Green & Stewart 2008; Banks 2010a). As
the functional diversity of macroinvertebrate communities would appear to be largely driven
by flow permanence and reliability, surface expression of groundwater is likely to be a major
factor leading to differences in community composition.
While it is ecologically critical to ensure streamflow variability does not increase dramatically
as a result of surface or groundwater resource development, it may be technically challenging
to achieve. The spatial distribution and discharge flux of groundwater springs is critical in
determining the number, proximity and to some extent connectivity of refugia. These patterns
are not easily predicted across much of South Australia owing to the prevailing fractured rock
hydrogeological environment (Green & Stewart 2008; Banks 2010a). In the Mount Lofty
Ranges, for example, while surface water development is relatively straightforward to model,
the influence of groundwater development on the stream environment is more difficult to
determine owing to the fractured rock environment.
Interactions with groundwater present a difficult challenge for river managers and any
additional uncertainty associated with increased climatic variability will add to the complexity
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of the task. Irrespective of management controls on development, managing stream ecology
for a decrease in groundwater expression at the surface will likely be necessary. In terms of
ecosystem function, major impacts can be anticipated on the extent and connectivity of
permanent habitat. To ensure maximum trait diversity (and therefore ecological resilience) in
macroinvertebrate communities is maintained, additional understanding is required on both
physical and biological factors.
2.6 Conclusions and further work
This case study has demonstrated that the prevalence of different trait states of aquatic
macroinvertebrates and the overall functional diversity this represents is largely structured by
characteristics of the flow regime, in particular cease-to-flow periods. At the river scale, the
most highly-correlated characteristics are those relating to variability of the flow regime, in
turn a reflection of the groundwater contribution.
Short-term streamflow dynamics exert an influence on functional diversity that can only be
predicted based on knowledge of the prevailing flow regime. For the ephemeral sites, low and
cease-to-flow conditions are associated with reduced functional diversity and indicative trait
states consistent with predictions for flow restriction. The lack of clear links between
antecedent flow conditions and trait responses indicates these short-term fluctuations will
require a higher degree of sampling effort and trait data resolution to identify any flowmagnitude-related thresholds of probable concern for South Australian systems.
This work has demonstrated the usefulness of trait data as a means to link patterns of
diversity (family abundance) to mechanistic processes (streamflow). Such analyses provide a
more powerful means for managers to interpret what changes in the flow regime may mean
for macroinvertebrates – or any biota for which trait data are available. Although results are
consistent with previously published ecological theory, many of the more indicative trait states
for drought-tolerant taxa such as body size, armouring and desiccation resistance of
propagules (Poff 1997; Bonada et al. 2007; Diaz et al. 2008; Robson et al. 2011) were not
available. Development of databases with this information will be critical if predictive models
of macroinvertebrate communities are to be developed for use in climate change adaptation
or resource development-planning purposes. Investment in the development of a trait
database for all aquatic flora and fauna should be a national priority if predictive capacity to
inform management decisions based on processes is seen as important.
Many future uses for data of this nature present themselves, especially as additional trait data
becomes available. A logical next step for research in this area on macroinvertebrate
communities is to determine if any threshold of change exists where a small increase in the
duration or variability of cease-to-flow periods results in a major change in the trait
expression. In researching such a topic, the critical importance of groundwater must be
considered. Without the ability to model changes in groundwater discharge to surface
systems for a given development scenario, there may be limited value in understanding the
biological implications.
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3. Changes in the macroinvertebrate
community over a decadal period of
drying (case study 2)
3.1 Summary
From 1994 to 2007, Mosquito Creek in south-eastern South Australia changed from a
perennially-flowing to intermittent stream, with steadily increasing annual cease-to-flow
periods from 2001. Before autumn 2007 no streamflow was observed at the gauge for over
270 days. It is now considered to be a highly-ephemeral system. This drying event provided
an ideal opportunity to investigate concurrent changes in the biological community.
Macroinvertebrate samples collected in autumn and spring during this period were analysed
for changes in taxonomy (family resolution) and biological traits. There was a marked shift in
the community composition in the spring edge samples but a more gradual change in
composition in autumn edge samples. Although the antecedent cease-to-flow period was
correlated with compositional changes over time, trajectory of samples in multidimensional
space was most clearly associated with changes in EC. Typical EC readings over the record
were in the region of 2500 µS cm-1, but field sample data indicated a step change in
conductivity to almost 6000 µS cm-1 occurred in 2005. A conductivity threshold of around 3000
µS cm-1 was evident in the macroinvertebrate dataset with samples collected at conductivities
above this value generally having lower family richness. Flow was positively correlated with
richness and may have contributed directly to decreased richness, but flow also had a highly
significant impact on conductivity. Increased conductivity in turn appears to have significantly
influenced community composition including reduced richness in the post-impact samples.
Trait data was analysed using a multivariate approach and analysis of trait-based functional
groups. Multivariate analysis suggests two major groupings of samples largely comprising pre
(before 2002) and post (2002–07) impact periods. Indicative traits for the two sample groups
are in accord with theoretical expectations. Three functional groups identified through
classification of the project trait database were determined, each representing a distinct trait
combination. Each identified functional group indicated a different flow response, with
positive, negative and neutral relationship with flow evident. Evidence of declines in the
proportion of total richness attributable to the rheophilic functional group concurrent with the
reduction in flow was apparent. This relationship was used to determine the flow conditions
likely to lead to a proportion of total richness consistent with pre-impact samples for the
rheophilic trait group. A flow of 0.35 ML/d was identified as a minimum threshold likely to
support this functional group. This flow corresponds to a first percentile pre-impact flow, but
under the existing flow regime is exceeded only 30 per cent of the time.
An increased number of functional groups present within a community are indicative of a
more functionally-diverse community. The number of families present from within each
functional group is a direct measure of the functional redundancy (i.e. resilience) of the
community for the combination of traits represented by that functional group. This analysis
suggests that optimising functional diversity and resilience through the use of trait groups may
offer some potential as a basis for developing water allocation rules. This approach would
enable decisions on environmental water provision based on functional aspects (rather than
taxonomic aspects) of the community, potentially improving generality and predictive
capability.
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3.2 Introduction
Mosquito Creek was unique among the 12 sites examined in case study 1, as it showed an
extreme drying trend in the hydrograph over the study period (Figure 12). In the mid-1990s,
flow was perennial and most early macroinvertebrate sampling trips acquired edge and riffle
samples in both spring and autumn. Annual baseflow began to decline around 1998, which
was followed by gradual drying trend (Figure 12). In recent years this has resulted in
increasing annual periods of cease-to-flow starting around summer 2000. The resulting
changes to streamflow are obvious and the median flow from 1994 to 2001 of 4.1 ML/d was
reduced from 2002 to 2007 to 0.1 ML/d – decreasing by a factor of 40. The cause of the
drying is believed to be related to land use and presents a good opportunity to investigate the
effects of a perennial to ephemeral transition in a flow regime on macroinvertebrate
communities.
10000
1000
100
10
1
0.1
0.01
0.001
Mean daily flow (ML)
Sample date
Figure 12: Log transformed mean daily flow at A2390519, at Mosquito Creek near Struan,
1994–2011. Zero values are not defined on a log scale, hence cease-to-flow periods are
indicated by breaks in the hydrograph. Macroinvertebrate sampling dates are marked as
black triangles, with the final sample being collected in autumn 2007.
3.3 Methods
3.3.1 Taxonomic analysis
For multivariate analyses involving taxonomic data, abundance data were prepared as
follows. Raw data were firstly sub-sampled to remove families observed in less than 5 per
cent of samples. The remaining data (n=80) were log(x + 1) transformed and then BrayCurtis (Bray & Curtis 1956) dissimilarities were calculated. Flow data was prepared as
described in case study 1.
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Data was analysed using similar methods to case study 1 by creating a distance matrix of all
samples and then investigating the composition between groups of samples that clustered
together. An additional exploratory approach of plotting temporal trajectories in NMDS
ordinations of the log(x+1) Bray Curtis dissimilarity matrices was also employed, with
separate distances calculated for the sequential autumn and spring samples. Insufficient riffle
data were available in the later record to enable comparisons of riffle fauna over the period of
impact, thus data for this habitat were not examined further.
3.3.2 Traits-based analysis
Two different methods were employed to examine the flow–trait relationship: a multivariate
analysis analogous to the methods used in case study 1 (see Section 2.3) and a functional
group analysis. For the latter, the species by traits dataset was classified using a Gower
dissimilarity matrix and Ward’s minimum variance to create functional groups with a
comparable range of trait states. The proportion of each group present in a given sample
provides an indirect indication of the suitability of the habitat for the traits represented by the
group, and a direct measure of the functional redundancy (resilience) for that particular suite
of trait states (Laliberté et al. 2010). Group proportions for each sample were calculated as
the simple ratio of the number of families within the sample from each group divided by the
total sample richness. These were then plotted as time-series and against antecedent flow
conditions to assess trends.
For the rheophilic functional group, sample proportion as a function of antecedent flow was
modelled to provide an indication of a minimum flow to support this functional group at the
site. Binomial logistic regression was used to model this relationship using function ‘glm’ in R
(R Development Core Team 2010). The binary-dependent variable was created where
success (coded as binary 1) represented typical pre-impact proportions (>0.4 of total sample
richness). Square root transformed mean daily antecedent flow was the independent variable.
3.4 Results
3.4.1 Taxonomic analysis
Classification of all edge samples is shown in Figure 13. Three major clusters are evident at
distance h = 7, and this height was used to group samples for additional analysis. Group 1
contains only four samples, all post-2004 and comprises two samples each collected in
autumn and spring which cluster together. Group 2 clusters with group 3 at a height of around
0.8, and contains five spring samples, mostly from late in the record (2000–04), along with the
1997 spring sample. The remaining group is the largest, containing mostly autumn samples,
and two spring samples from 1998 and 1999.
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1.2
1.0
0.8
0.6
A99
A97
S99
A01
A98
A05
S98
A07
A03
A02
S04
S02
S01
S03
S97
A00
A06
S00
S06
S05
0.2
A04
0.4
Height
Figure 13: Classification of macroinvertebrate edge samples from Mosquito Creek, autumn
1997–autumn 2007. Codes indicate season and year (e.g. ‘A02’ refers to Mosquito Creek,
2002 edge habitat sampled in autumn).
When the grouping pattern is shown in NMDS two-dimensional space, group 1 samples
(shown in green) score consistently high axis 1 values, and are well separated across axis 2
(Figure 14). Environmental variables associated with cease-to-flow periods before sampling
are significantly associated with axis 1 (p < 0.05). Spring samples from group 2 plot in a tight
cluster with high axis 2 values, and slightly negative axis 1 scores. Axis 2 is significantly (p <
0.05) and positively correlated with season and with the magnitude of flow 90 days before
sampling.
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5
is.spring
S97ave.90
S00
S04
S98
S03 S01
S06
S99
habitatedge
S05
0
A02
S02
NMDS 2
A98
A01
A99
A05
days.ctf
A97
A00
zero.q.90
-5
A07
A06
A03
-10
A04
-5
0
5
10
NMDS 1
Figure 14: NMDS ordination of edge samples from Mosquito Creek. Colours indicate the
groups shown in Figure 13. Environmental variables significant at 0.05 are shown: zero.q.90 =
number of zero-flow days in the 90 days before sampling; days.ctf is the count of days since
flow ceased; ave.90 is the average daily flow for the 90 days before sampling; is.spring is a
logical variable indicating if the sample was collected in spring. Codes indicate season and
last two digits of the year sampled (i.e. A05 = autumn 2005 sample; S05 is spring 2005).
NMDS ordination and vector analysis of the seasonal samples analysed independently
(figures 15 and 16) show different trajectories through time and different environmental
associations. Autumn samples (Figure 15) over recent years indicate a directional trajectory
towards lower axis 2 scores, and higher axis 1 scores. Significant (p < 0.05) environmental
vector overlays suggest salinity and decreased flow duration are driving the recent trajectory.
While 1996 samples were not collected, the early record seems to indicate a trajectory from
higher antecedent flow conditions, based on the non-zero-flow 90 days before sampling
vector.
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A00
flow.dur
5
A03
A04
A01
A98
A99
A97
0
NMDS Axis 2
A02
A95
A05
A06
EC
-5
A07
Nnzq.90
A94
-20
-15
-10
-5
0
5
10
NMDS Axis 1
Figure 15: Ordination of Mosquito Creek autumn edge samples indicating trajectory over time,
and with flow variables of statistical significance (p<0.05) shown: Nnzq.90 = median non-zero
daily flow for 90 days before sampling.
In contrast, spring samples (Figure 16) imply a sudden step change in population composition
from 2004 to 2005 sampling events, and the vector analysis suggests this is attributable to an
increase in salinity. When spot salinity samples are plotted over time (Figure 17), a clear
increase in salinity coincides with the period between the 2005 autumn and spring samples.
While ambient salinities up to this point in the record are around 2500 µS cm-1, the spring
2005 sample had a value of 4270 µS cm-1. Conductivity remains high for the following two
samples, reaching a maximum of over 5700 µS cm-1, before returning to more typical value in
autumn 2007 (Figure 17).
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6
S04
S97
S03
4
S01
S02
S00
0
S99
-2
NMDS Axis 2
2
z.q.12m
S98
-4
EC
S05
-6
S95
S06
S94
-10
-5
0
5
NMDS Axis 1
Figure 16: Spring edge macroinvertebrate community dissimilarity 1994–2006, with trajectory
shown for sequential annual samples 1997–2006. Environmental variables shown were
significant (p = 0.05) and represent the number of zero-flow days in the 12 months before
sampling (z.q.12m) and sample electrical conductivity (EC).
Electrical conductivity (microSiemens)
7000
6000
5000
4000
3000
2000
1000
0
Nov-93
Aug-96
May-99
Feb-02
Nov-04
Aug-07
Figure 17: Field readings of electrical conductivity (EC) taken at time of macroinvertebrate
sampling at Mosquito Creek, 1994–2007.
The relationship between antecedent streamflow and EC was log-linear for all samples,
strongly so for the spring samples. A regression of the natural log of the spring sample EC on
square root transformed flow data was highly significant (p < 0.001) and explained almost 80
per cent of the variation in conductivity. The model, shown in Figure 18, was:
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8.0
7.5
log(EC)
8.5
Ln(EC) = 8.35 – 0.1213*SQRT(90day mean flow)
0
2
4
6
8
Mean 90 day antecedent flow (ML/d^0.5)
Figure 18: Linear regression of log transformed spring conductivity at Mosquito Creek on
square root transformed 90-day mean daily antecedent streamflow (ML/d).
3.4.2 Trait-based analysis
Multivariate analysis
Two trait-based groups are evident in the classification of the trait by abundance dissimilarity
matrix (Figure 19). The right grouping is predominantly composed of samples collected after
2002 when the drying period had started. Eight of the 36 trait states were indicative of this
division in samples, with five indicative for the left-hand group (group 1 in Table 8) and three
states indicative of the right-hand group. For the left-hand grouping, indicative traits include
drift dispersal, filter-feeding and gills, while the right-hand grouping has indicative trait states
of semi-terrestrial life-history, moderate salinity tolerance and plastron respiration.
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Figure 19: Ward’s minimum variance classification of the trait-abundance matrix. Site codes
are explained in Figure 14. Group 2 is on the right.
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ec.low
0.15
ffg.scrpr
food.gen
0.10
ec.med.low
disp.drift
terr.month
reprod.aq
0.05
univoltine
resp.gill
0.00
food.plant
matur.semi.ann terr.aqua
food.det
terr.quart
multivoltine
-0.05
NMDS2
ffg.gath
food.pred
terr.semi
ffg.coll
matur.quart
disp.flight
ec.med
ffg.pred
-0.10
zq.90
matur.ann
reprod.ovo
-0.15
resp.plast
-0.20
reprod.terr
-0.2
-0.1
0.0
0.1
NMDS1
Figure 20: NMDS ordination of the trait prevalence data for Mosquito Creek, 1997–2007.
Sample group 1 is on the right of the figure. zq.90 = zero-flow days in the 90 days before
sampling.
Table 8: Indicator species analysis trait states for the two sample groupings shown in figures
19 and 20.
Trait
Description
Group
Indicator value
ffg.filt
Filter-feeder
1
0.7802
food.gen
Generalist feeding strategy
1
0.6948
terr.quart
Terrestrial phase < 3 months
1
0.6582
disp.drift
Dispersal via drift
1
0.6379
resp.gill
Respires via gills
1
0.5791
terr.semi
Semi-terrestrial
2
0.6506
resp.plast
Respires via plastron
2
0.6083
ec.med
Moderate salinity tolerance
2
0.5611
state
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Functional groups analysis
Classification of the families by traits matrix indicated three functional groups (Figure 21). This
represents groups of families with distinct combinations of trait states. The number of families
classified within each functional group was not consistent, with group 1 representing only
seven families, while groups 2 and 3 had 17 and 19 members respectively.
Figure 21: Functional groups based on Ward’s clustering of the Gower dissimilarity trait by
family matrix to form three functional groups.
Binary trait states in each grouping were tabulated to determine trait characteristics (Table 9).
Functional group 1 does not have any unique trait states, but is mostly comprised of gilled
predators with limited dispersal capability; functional group 2 contains a combination of
respiratory strategies, are of either predatory or collector-feeding group, reproduce largely via
terrestrial eggs, are mostly multivoltine and disperse via a flight phase; and functional group 3
are all gilled (noting the adults of some beetle families are air-breathers, the assumption is
made that these were larval phases), disperse mostly as part of the drift, are filterers,
gatherers or collectors and reproduce via aquatic eggs.
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Table 9: Count of the trait states within each functional group
Trait state
Group 1
Group 2
Group 3
food.pred
6
7
3
food.det
1
4
9
food.plant
0
5
2
food.gen
0
1
5
resp.gill
7
0
19
resp.plast
0
16
0
terr.quart
4
1
3
terr.aqua
1
5
6
terr.month
1
7
8
terr.surf
0
1
0
terr.semi
0
1
1
terr.edge
0
3
0
ec.low
0
1
3
ec.med.low
2
5
6
ec.med
5
8
7
ec.high
0
3
3
univoltine
5
4
6
semivoltine
0
0
1
multivoltine
2
12
12
matur.month
0
1
1
matur.quart
2
8
6
matur.semi.ann
3
7
7
matur.ann
2
2
2
matur.sup.ann
0
0
3
disp.drift
0
2
8
disp.flight
2
5
3
reprod.terr
5
4
0
reprod.aq
2
12
13
reprod.ovo
0
0
6
ffg.pred
7
7
0
ffg.coll
0
6
8
ffg.scrpr
0
3
3
ffg.shdr
0
0
0
ffg.filt
0
0
4
ffg.gath
0
2
4
Functional group 1 families form a small proportion of the total sample across the sampling
period reflecting the low number of total families classified within this category. While in the
early record numbers vary following seasonal trends, samples from latter years were
characterised by increasing variability (Figure 22). Proportional abundance dropped to zero in
the autumn 2004 sample, which was the first sample collected following zero-flow periods in
the 90 days before sampling.
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0.25
0.20
0.15
0.10
0.00
0.05
Proportion of Group 1
5
10
15
20
25
Sample number
Figure 22: Proportion of functional group 1 in the edge data time-series, 1994–2007.
Functional group 2 families also form an increasingly variable proportion of the total richness
in the edge samples but have exhibited an increasing contribution to overall richness across
the time-series (Figure 23). The proportion of functional group 3 families decreased in the
most recent samples, falling to 20 per cent of total richness in the autumn 2006 sample
(Figure 24). The increased variability in all proportions coincides with the samples collected
during or after autumn 2003. No families from functional group 1 were recorded in the autumn
2004 sample, which was also extremely low for group 3. In contrast, this sample was the
highest proportion in the time-series for functional group 2, which represented 75 per cent of
total richness. While the creek was flowing on the day of sampling in autumn 2004, 60 of the
previous 90 days were zero-flow, the first antecedent period of this type in the dataset.
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0.7
0.6
0.5
0.4
0.2
0.3
Proportion of Group 2
5
10
15
20
25
Sample number
0.5
0.4
0.2
0.3
Proportion of Group 3
0.6
Figure 23: Proportion of functional group 2 in the edge data time-series, 1994–2007
5
10
15
20
25
Sample number
Figure 24: Proportion of functional group 3 in the edge data time-series, 1994–2007
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When the proportion data is plotted against antecedent flow conditions differential flow
responses are evident between the groups. The proportion of families from group 2 as a
function of streamflow on the day of sampling indicates a negative flow preference (Figure
25), while group 3 had a positive response to flow (Figure 24). Observed patterns were
consistent across the other antecedent periods (30 and 90 days). Group 1 proportions were
evidently not affected by flow and were randomly distributed (data not shown).
0.7
Proportion Group 2
0.6
0.5
0.4
0.3
0.2
0
1
2
3
4
5
Streamflow (sqrt(ML/d)
Figure 25: The proportion of richness for edge samples for functional group 2 families as a
function of streamflow on the day of sampling. The curve is a lowess smooth of the data.
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Proportion Group 3
0.6
0.5
0.4
0.3
0.2
0
1
2
3
4
5
Streamflow (sqrt(ML/d)
Figure 26: The proportion of richness for edge samples for functional group 3 families as a
function of streamflow on the day of sampling. The curve is a lowess smooth of the data.
Similar patterns were evident for the 30- and 90-day antecedent flow statistics.
The flow response of functional group 3 families suggests a threshold response, where above
a given flow threshold proportional richness remains consistently above 0.4. To gain an
indication of the likely flow requirement to support a proportional richness of this level, the
flow–proportion relationship was modelled using binomial logistic regression. The aim of the
analysis was to objectively identify a minimum flow likely to provide conditions suitable for
these families at the site. The predicted log odds for functional group 3 families to exceed a
proportion of 0.4 of total families in a given sample was:
Logit (group 3 proportion > 0.4) = -1.67 + 2.814*SQRT.30d.FLOW
The model was statistically significant (p < 0.001, Chi square test) and explained around 57
per cent of the deviance. The model suggests a 50 per cent probability of observing a
proportion of 0.4 of group 3 families would require an average daily flow for the antecedent 30
days of 0.35 ML/d. From 1994 to 2001, this represents the first percentile flow, while in post2001 this corresponds to the 69th flow percentile, indicating the magnitude of the flow
decrease.
3.5 Discussion
The hydrological environment at the Mosquito Creek site has undergone profound changes
from the late-1990s to 2010 (Figure 12). Concurrent changes to the macroinvertebrate
community are apparent, with spring sample dissimilarities particularly reflecting a probable
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step change in composition. A shift in dominant traits is evident between sampling groups that
are characteristic of pre- and post-impact, and these are consistent with theoretical
expectations (Townsend & Hildrew 1994; Williams 1996; Bonada et al. 2007). The nature of
the change in the community is readily evident in the indicative traits for the pre- and postimpact sample groups, and in the trajectory in proportions of two of the three functional
groups, which exhibit inverse flow preferences.
The changes in flow regime at the site represent a shift from perennial to ephemeral
streamflow and changes in the macroinvertebrate community in terms of taxonomy and
functional diversity are clearly apparent. Different trait states have been shown to be more
suited to different points on this flow continuum (Bonada et al. 2007) and the findings of this
case study provide an indication of the nature of transition from one community to another.
Positive and negative linear trends in the time-series for functional group proportions follow
the declining streamflow trajectory, but non-linear dynamics are also apparent:

Following the first enduring antecedent cease-to-flow period proportional
representation of the three functional groups show sharp responses with group 2
proportions rising to 75 per cent and group 1 proportions falling to zero, the
respective maxima and minima in the time-series for both groups.

A step change in spring community composition is evident in the trajectory from 2004
to 2005, concurrent with a spike in EC readings.
In terms of the dynamics of change, the relative recovery following the initial post cease-toflow sample is of interest. While proportions for group 2 continue to remain above pre-impact
levels, they do rebound from the peak values. Group 1 proportions appear to return to the
pre-impact proportions.
As the duration of annual cease-to-flow periods at Mosquito Creek has transitioned from zero
to over 200 days per year, it is to be expected that profound shifts in invertebrate and other
aquatic fauna (and flora) would occur. Even though the most dramatic changes have actually
occurred since macroinvertebrate monitoring ceased at the site, impacts are evident in the
dataset examined in this study.
This case study has demonstrated some benefit in assessing impacts based on combinations
of traits (functional groups) rather than focusing entirely on how individual traits are
expressed, or simply examining the responses of taxonomic community metrics. A further
advantage of this approach is the ability to gauge the resilience of the system. Any decline in
functional group proportion represents a loss of resilience in the macroinvertebrate
community for this suite of traits (Laliberté et al. 2010). A functional group approach has
previously been used (albeit unsuccessfully) to evaluate flow dependence in experimental
flow manipulations (Miller et al. 2010) and also to demonstrate impacts on plant communities
across a land use intensity gradient (Laliberté et al. 2010). Complex combinations of
macroinvertebrate traits have been linked to impacts due to abstraction in Australia (Brooks et
al. 2012), raising the possibility that a priori trait groupings may yield taxa indicative of
impacts.
An approach focusing on functional groups appears to offer scope for water resource
planners to develop environmental flow recommendations based on transparent links to
ecosystem processes for any biotic groups for which traits are available. As an example of
how these links could be made, trait analysis in this study allowed for predictions of a flow
threshold that would provide a 50 per cent probability of group 3 families achieving pre-impact
proportions in the community. The flow threshold occurred over the pre-impact period on 99
per cent of days and would have been meaningless before impact. The functional
relationship, however, suggests this ecosystem property could possibly have been maintained
at much lower streamflow volumes than were observed historically. This information could
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have been used to establish a maximum extraction limit. The advantage of this approach is
the transparent link to process and the ease that success or otherwise could be determined.
As it now represents a flow exceeded on only 30 per cent of days over recent times, it seems
unlikely it could be achieved.
Changes in the flow regime have predictably resulted in major concurrent changes to water
quality, exemplified by the strong influence of declining streamflow on observed EC during
spring. The decrease in functional group 3 families is clearly associated with streamflow
conditions, but major shifts in macroinvertebrate community composition appear to be
associated with the increase in EC according to sample trajectory in Figure 16.
When considering the importance of conductivity it should be borne in mind that the
taxonomic data contained almost twice the number of families than the trait data, and may
provide a better indication of the level of impacts. Although many macroinvertebrate families
found in South Australia are adapted to high salinity (McEvoy & Goonan 2003), there is a
general negative correlation with richness. Figure 27 was generated using the entire South
Australian AusRivAS dataset employed in this study, and implies something of a step change
in family-level richness as a function of increasing conductivity at levels above 3000 µS cm-1 –
this threshold was exceeded in autumn and spring 2005 when ambient conductivities of
around 3000 µS cm-1 spiked to almost twice this value before returning to lower levels in
2007. In response, family richness of the 2005 and 2006 spring samples were 25 and 19
respectively, compared with a mean of 34 before these. Species-level data indicates a greater
loss of richness where the 2005–06 sample mean is 35.5, compared with a mean of 57 for the
previous record.
The magnitude of the drought at the site in Mosquito Creek has continued to increase since
the final macroinvertebrate sample was collected in 2007, and the flow regime now appears
essentially ephemeral. Even if the question of how to restore some streamflow could be
addressed, restoration of the functional group within the system would rely on a source of
colonists being within dispersal distance. Given the group favours drift dispersal this may not
be likely to occur. Nonetheless it is an interesting situation and a community in transition in
response to such a drastic change in flow regime presented a rare research opportunity. Any
restoration attempts will present even greater opportunities to study the dynamics of a
recovery phase.
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35
30
edge.fam.rich
25
20
15
10
6
7
8
9
10
log(ec)
Figure 27: Family richness of South Australian edge samples (n=289) as a function of
electrical conductivity (EC) measured in the field on the day of sampling. The fitted curve is a
lowess smooth of the data which suggests a slight linear increase in richness up to a value of
around natural log 7 (1000 EC). After this point richness declines in a linear manner according
to the lowess smoother, although there is some evidence for a step change at ln(8), or ~3000
EC.
It is perhaps unfortunate that macroinvertebrate sampling ended when it did in the present
study, as the transition to a more drought-tolerant invertebrate fauna was likely incomplete.
With the benefit of hindsight, this period presented a rare opportunity to gather data that could
be used to investigate potential early warning indicators of change. It is worth noting this may
still be possible with available data – to best interpret the pattern in community shift with
existing data, the use of species-level information is warranted, but this was not attempted in
this analysis. Genus or species-level data should more clearly indicate changes in community
composition through an indicator species approach, and may also provide improved insights
into the nature of any threshold response. However, trait information for Australian
macroinvertebrate species and genera is presently limited.
3.6 Conclusions and further work
This case study has provided an example of the usefulness of trait databases to enable the
objective grouping of macroinvertebrate taxa at family level into flow-response groups. It has
also illustrated one way this information can be used to support environmental flow decisions.
Other than the fairly predictable change in community dynamics which might be expected
when the median streamflow is reduced by a factor of 40, the analysis suggests two things of
general interest. Firstly, a possible threshold response in proportional representation was
evident in the rheophilic trait group and modelling suggests the flow-dependent functional
group may have required only a modest flow volume to remain at pre-impact levels. Secondly,
the impacts of salinity increase were driven by the change in streamflow, but appear to have
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resulted in a step change impact on the community as a whole, as point conductivity readings
exceeded 5000 µS cm-1.
The rare nature of the documented loss of streamflow, such as the drying event in this study,
warrants increased analytical effort with the existing trait database and additional effort in
seeking datasets from other agencies or researchers collected over the drying period. Other
biological data such as fish population dynamics may exist, and would allow for some
investigation of trophic effects associated with decreases in flow duration and magnitude.
Since macroinvertebrate sampling ended at Mosquito Creek, cease-to-flow periods have
further increased. It is likely that extant taxa at the site are more characteristic of semi-arid,
generalist South Australian macroinvertebrate taxa. Assuming any aquatic environment
persists, the current state of the macroinvertebrate community if re-sampled would indicate if
additional shifts in community composition have occurred under the enduring ephemeral
conditions. This would also establish baseline conditions to measure any possible
improvement in condition should flow be returned through management intervention or
through changes in climatic conditions.
Whether the impact on the flow regime observed at the gauging station is common to the
entire Mosquito Creek drainage network is unknown, but being a large catchment, it is likely
that some upper catchment flow refugia may persist. This may present opportunities to
examine meta-population dynamics at the site, assuming flow will at some point return
towards a more perennial flow. Hence, a final point of note in this case study is the potential
for future investigation of the reversal in trajectory should a return to conditions more
indicative of the early flow record eventuate.
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4. Preliminary analysis of the flow –
recruitment response of mountain
galaxias in the Marne River, South
Australia (case study 3)
4.1 Summary
Mountain galaxias (Galaxias olidus) are a species with a known requirement for flow to
complete their life cycle. Analysis of adult and young of the year (YOY) population monitoring
data from autumn and flow data from the Marne River from 2002 to 2010 suggests a positive
linear relationship between May to December streamflow statistics and total population
abundance and the abundance of YOY. Linear regression of mountain galaxias recruitment
and flow volume was used to model recruitment success over 32 years (1975–2006) for two
flow scenarios: the gauged record (representative of current development) and a modelled
no-development scenario.
Despite the linear nature of the model, changes to predicted populations under the two
scenarios were non-linear, suggesting impacts on flow resulting from development are not
consistent across years. The mean ratio of recruitment strength under current development to
no-development scenarios was 65 per cent, and the ratio varied from 21 to 122 per cent. The
greatest observed impacts from water resource development seemed to occur during low to
moderate flow years, rather than very low rainfall and runoff years, which tended to have poor
recruitment under both scenarios. Recruitment success classes based on observed
performance suggest the net effect of current development appears to be a four-fold increase
in the frequency of years where poor recruitment results, and a decrease in the number of
good years by around 50 per cent. Existing development also appears to create sequential
years of poor recruitment, not observed under the no-development scenario. Under the
current-development scenario modelling, consecutive failed seasons occurred on two
occasions.
There was evidence in the data of a non-linear relationship (power function) between total
population and flow which gave a superior fit to the data than the model adopted. However,
this was considered less defensible for extrapolation to conditions outside those observed, as
biological reality would suggest an upper threshold to the response would occur. Hence
although uncertainties exist around extrapolating the observed linear relationship, it is
perhaps more likely to be conservative, rather than an over-prediction, based on observed
data. Because of this, changes in recruitment patterns over the modelling period may be
greater than indicated. More sophisticated modelling techniques and the incorporation of
observation errors will be in future applied to this dataset, which continues to be collected.
For a highly flow-dependent, short-lived species (at this site few individuals appear to reach
three years of age) such as mountain galaxias, multiple failed years increases the probability
of local extinction. Of additional concern, more recent monitoring data (not able to be included
in this study) suggests the last three years have resulted in poor recruitment (Hammer,
unpublished data). Under current development, survivorship also appears to be poor at the
site, and this is likely due to a combination of the loss of low flows and factors such as the
infilling of refuge pools. The Marne River site provides a high-value refuge for Galaxias olidus,
as there exists a natural barrier to the upstream migration of introduced predators (in
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particular redfin and trout). Restoration of the low flows in the catchment would improve the
probability of the species persisting.
The correlation of autumn mountain galaxias fish census data and streamflow from May to
December of the previous year supports the reliance of the species on flow to successfully
breed and persist as a population. Population monitoring data suggests that while some
recruitment is occurring most years, the limited number of fish surviving to age classes
beyond 1+ restricts the abundance of the population. This clearly increases the level of risk
the populations is exposed to. Despite consistently low numbers of observed individuals
(even zero on some occasions), the population currently persists (Hammer, personal
communication).
4.2 Introduction
Galaxias olidus is a species with a known life-history requirement for flowing water (a
summary of the relevant literature is presented in McNeil & Hammer 2007). The species has
been observed in reproductive condition from autumn through to spring and is known to lay
eggs in riffles (McNeil & Hammer 2007), and as such can be considered to have an obligate
requirement for flowing water.
The species is well-suited as an indicator taxa for water resource management purposes,
being widespread (though many populations are isolated – Hammer 2007) and having a wellestablished causal dependence on flow. In the development of environmental water
requirements in the Mount Lofty Ranges, the species was one of two rheophilic fish species
used to assess the various potential allocation policies (Vanlaarhoven & van der Wielen
2009).
The Marne River catchment is located around 80 km north-east of Adelaide. The river rises in
the eastern Mount Lofty Ranges, discharging to the River Murray about 30 km downstream of
the township of Swan Reach. The catchment has been defined as having two broad zones,
referred to in the report of an Environmental Flows Technical Panel for the Marne River
(MREFTP 2003) as the Hills and Plains zones. The two zones are separated by the Marne
Gorge, where the streamflow-gauging station used in this report is located.
The impacts of surface water development in the form of farm dam storages in the higher
rainfall headwater in the Mount Lofty Ranges were assessed in 2002 (Savadamuthu 2002).
Key findings from this study were that impacts depended on the depth of rainfall received and
were highest in dry years, with the most significant impacts on zero-flow durations and low
flows (Savadamuthu 2002; MREFTP 2003).
This case study uses data collected by Dr Michael Hammer for the South Australian MDB
NRM Board, and streamflow data collected by the South Australian Government at
streamflow gauge A4260605 on the Marne River near the Marne River gorge.
4.3 Methods
4.3.1 Ecological and hydrological data
Fish length data from annual population monitoring conducted in autumn from 2002 to 2010
at three sites in the Marne River were obtained. Monitoring methods included fyke netting and
electro-fishing (for additional detail on sampling methods see Hammer 2007). Preliminary
population models have been developed for mountain galaxias in the eastern Mount Lofty
Ranges (MLR; e.g. Hammer 2007), allowing reasonable length–age transformations, from
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which counts of YOY could be estimated from population data. Counts of population and YOY
from the three sites were pooled to provide a catchment-wide estimate of the population and
the number of young surviving to autumn (assuming a spring emergence).
A number of flow statistics were generated using flow data from streamflow gauge A4260605
that incorporated both duration and magnitude of flows from 1 May to 31 December of the
previous year. Inspection of cross-section and long-section survey data during site installation
indicated a riffle flow at the nearby gravel bed upstream of the site should occur at mean daily
flows exceeding 0.052 m3s-1. This threshold was used to sum both the total number of days
during the period of interest with riffle wetting flows, and the longest duration of an individual
riffle inundating flow event. Magnitude-based metrics included the mean and median daily
flows, and non-zero daily flows for the study period.
4.3.2 Ecological and hydrological modelling
Exploratory data analysis indicated a linear relationship between a number of the flow
statistics and the fish census data, for YOY and for the total population abundance. Mean
daily streamflow provided a very good (exponential) fit to total fish abundance (data not
presented); however, while it provided a good statistical fit, the exponential model was
considered an unreliable method to estimate fish populations outside the observed range.
This, combined with low survivorship in the study reaches, led to the decision to adopt the
linear model of YOY (age class 0+) fish for this study.
Hence, YOY were modelled as a simple linear regression of streamflow using the statistical
application R (R Core Development Team 2011). Model diagnostics involved inspection of the
residuals and testing for normality. No evidence of temporal autocorrelation was evident in
model residuals, and these were not significantly different from normal (Shapiro-Wilk test W =
0.9, p = 0.45).
Gauged and modelled streamflow data were available from 1975 to 2006, and this was
selected as a period to investigate long-term recruitment variability and population
persistence. Gauged data constituted records from two sites: station A4260529 with flow data
from January 1975 to March 2006; and station A4260605 with flow data from August 2001 to
December 2010. (The latter station replaced the former a short distance downstream at a site
offering superior hydrometric potential.) As the sites were operated concurrently for a time,
the current gauged record could be extended back to the start of the modelled period
according to:
mean daily flow at A4260605 = mean daily flow at A4260529 * 1.1076 (R2 = 0.98)
Two other nearby streamflow gauges were used to infill periods where station A4260529 was
out of service. From 1989 to 1992, station A5040512 was selected as the optimal station for
infill owing to its close match to station A4260605 during cease-to-flow periods (adjustment
factor 3.40, R2 = 0.84). Several short (~30 day) periods of missing data from the earlier
gauging station were infilled using the same site, or closest match available, using the same
linear interpolation technique.
Modelled data included both current and no-development scenarios (the current model with
all farm dam storages removed; see Savadamuthu 2002 for details on model development).
The difference between the two modelled scenarios was obtained at a daily time step and the
difference was added to the gauged data record as a means to estimate the streamflow that
would have been observed if no development were present.
The relevant streamflow statistics were calculated on the current and no-development
scenarios time-series at an annual time step, and used to generate estimates of the YOY for
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mountain galaxias at the site. Differences between the two time-series of annual estimates of
YOY numbers in autumn were analysed to determine major differences.
4.4. Results
4.4.1 Fish census data as a function of observed flow
A summary of the ecohydrological data used in this analysis is presented in Table 10. YOY
and adult fish were observed every year except 2009, when no fish were observed (Table
10). Duration and magnitude statistics were positively correlated with fish population
abundance and recruitment, and fish and flow were intra-correlated.
A positive linear association was evident between YOY and the total number of fish sampled.
The YOY as a proportion of the observed total fish varies from 13 to 87 per cent, with a mean
of 40 per cent. Despite this strongly linear correlation within a given fish census, no
relationship was evident between consecutive year censes of adults (> 0+ fish) and the YOY
the following year, nor was there any apparent influence of flow on this relationship.
Table 10: Selected annual streamflow summary statistics (2002–08) and autumn fish census
data* (2002–10) for the Marne River catchment
Year
Total
YOY
Duration
max.dur
mean
nz.mean
nz.med
median
fish
2002
71
14
84
77
0.229
0.316
0.041
0.008
2003
15
2
16
13
0.012
0.025
0.016
0.000
2004
72
27
92
80
0.305
0.451
0.084
0.021
2005
166
54
70
70
0.394
0.677
0.077
0.004
2006
184
87
129
114
0.431
0.569
0.138
0.098
2007
23
12
4
4
0.004
0.016
0.004
0.000
2008
23
20
6
3
0.010
0.020
0.014
0.001
2009
0
0
-
-
-
-
-
-
2010
14
7
-
-
-
-
-
-
* Abbreviations denote the following:
duration = the duration in days of riffle flow (>=0.052 m3s-1) for the preceding flow season 1 May to 31 December
max.durr = the duration in days of the longest continuous period where riffle flow occurred
mean = the mean daily flow in m3s-1 1 May to 31 December in the preceding flow season
nz.mean = the mean daily flow in m3s-1 for non-zero-flow days
nz.median = the median daily flow from 1 May to 31 December calculated on days where flow occurred (i.e. nonzero-flow days)
median = the overall median flow from 1 May to 31 December
Flow statistics for both flow duration and magnitude were all more or less monotonic, if not
linear, predictors of autumn total population and YOY fish counts. Magnitude and durations
statistics were highly inter-correlated, with durations generally less reliable predictors of fish
populations (e.g. higher and heterogeneous variance) than magnitudes. YOY were modelled
as a linear function of the non-zero median flow during the previous year (Figure 28)
according to the equation:
YOY = nz flow * 567.5 (F = 67.3 on 6 d.f. r2 = 0.90, p < 0.001)
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80
60
40
0
20
Young of the year
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Non-zero median flow (cumecs)
Figure 28: Linear regression model of Marne River mountain galaxias YOY in autumn as a
function of non-zero median flow. The least squares fit to the data and 95 per cent confidence
intervals are shown.
4.4.2 Comparison of modelled flow regimes
Across the entire modelling period, flow occurred on around 61 and 48 per cent of days in the
no-development and current-development scenarios respectively. This equates to average
seasonal no-flow periods of around 150 days for the no-development scenario, and 200 days
under current conditions.
Figure 29 shows the mean daily non-zero streamflow over the entire modelled period, ranked
in order of decreasing flow volume (i.e. flow duration curve). The plotting positions assigned
to both time-series are the non-zero-flow percentiles for the no-development scenario to allow
comparison. The number of days on which flow occurred and the flow magnitude for given
exceedence percentile are decreased under current-development levels. Flows above the
10th exceedence percentile non-zero-flow exhibit no discernible difference when presented
this way.
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Figure 29: Modelled no-development and current development ranked daily flows for
exceedence percentile indicated (note percentiles calculated on no-development scenario
and hence are not applicable to the current flow).
4.2.3 Modelled YOY abundance
Figure 30 shows annual estimates of YOY abundances in autumn from 1975 to 2006 based
on the linear regression presented above. Predicted YOY numbers in all but three of the 32
years in the modelled record are higher under the no-development scenario than under
current-development levels. During the early part of the record, the two predictions are quite
similar, but consistently low predictions indicated in the current-development scenario from
1991 to 2002 are not apparent in the no-development scenario time-series.
Categories of recruitment success based on observed data are used in Figure 31 to compare
the number of good, moderate and poor years (>50; 10–50 and <10 YOY respectively). Under
the no-development scenario, it is estimated that good recruitment would occur in better than
50 per cent of years, and poor recruitment would occur in only 6 per cent of years (Figure 31).
Under modelled current development, poor recruitment is estimated to occur in 25 per cent of
years, with good recruitment in 34 per cent of years. Poor recruitment occurs in consecutive
years on two occasions in the current scenario, but does not occur in the no-development
scenario. Actual data indicate from 2002 to 2010 (n=9) three poor, and two good recruitment
years have been observed.
There is a general tendency for relatively high numbers of YOY to be found in both timeseries in Figure 31, while the number of low predictions is increased in the currentdevelopment scenario. In comparison with the no-development scenario, the frequency of
poor recruitment years is increased by a factor of four under the current scenario (Figure 31).
The frequency of years where moderate recruitment success occurs was similar for the two
scenarios, while the frequency of good recruitment years was reduced by 50 per cent. The
modelling suggests that good mountain galaxias recruitment as suggested by the typical
sampling effort currently employed in population monitoring would have occurred at a
frequency greater than 1 in 2 under a no-development scenario.
The ratio of the two YOY predictions for a given year provides a means to compare the
modelled differences due to development pressures. The average proportion of current
population predictions is 65 per cent of no-development predictions, and this value varies
from 21 to 122 per cent. Predictions for the current scenario are within ±20 per cent of the no-
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250
development scenario on eight occasions over the modelling period. There is considerable
variation over the record, and no consistent pattern is apparent in the ratio.
150
100
0
50
Predicted young of the year
200
no devt
current
1975
1980
1985
1990
1995
2000
2005
year
Modelled frequency
Figure 30: Predicted mountain galaxias YOY numbers in three sites for the Marne River under
current and no-development scenarios, 1975–2006.
20
18
16
14
12
10
8
6
4
2
0
poor ( < 10)
moderate (10 - 50)
good (> 50)
Number of juveniles in autumn
Current development
No development
Figure 31: Recruitment success of mountain galaxias for 32 years under scenarios of current
water resource development and modelled no-development.
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4.5 Discussion
The correlation between flow and the size of the mountain galaxias population in the
upstream reaches of the Marne River has been observed and reported previously, with
concerns raised about the effects of recent drought conditions coupled with the capture of
streamflow in farm dams for a number of years (Hammer 2007, 2009). A feature of the Marne
River population is the poor survivorship in upstream pool locations, where data in this study
were obtained from. Extant populations are present in only small numbers and over the
observation period (2002–11). It is apparent that while some recruitment appears to occur in
most years, good recruitment events boost population numbers to enable persistence of the
population over ensuring poor years (Hammer 2009). The incidence of good recruitment
years is of course of limited benefit if populations are unable to disperse to reliable refugia in
other areas of the catchment. Data not presented in this study suggests widespread dispersal
of mountain galaxias occurred only once during the fish population monitoring period.
Hammer (2009) indicates that refuge pools under current-development levels may no longer
be capable of supporting viable populations.
The extrapolation of the observed linear relationship produces varying, but often considerable
differences in predicted YOY abundance. Modelling undertaken in this case study suggests
major impacts on the frequency of poor recruitment years, which were predicted to increase
by a factor of four. Also of note in comparing the two modelled YOY abundance predictions is
the occurrence on two occasions in the current-development record of poor recruitment on
consecutive years. The number of years where moderate numbers of YOY are predicted do
not vary greatly between the two scenarios, but the effects of abstraction were also evident in
the frequency of good recruitment years, which are reduced in frequency by around 50 per
cent.
To determine the likely implications on population dynamics of these shifts in recruitment
success the longevity of the species requires consideration. Length data for mountain
galaxias from upper Marne River sites over recent years suggests poor survivorship, although
as with YOY fish, the proportion of fish surviving to higher age classes increases with flow
duration and volume. Numbers of fish surviving to age classes older than 1+ have been very
low since 2005. Data presented in MREFTP (2003) indicated that one site where data was
gathered for this study (‘Vigars’) featured only large individuals, suggesting that while
recruitment had not occurred in the most recent year, survivorship was reasonable (MREFTP
2003). More recent data indicating poor survivorship at the site suggests a decline in habitat
suitability has occurred from 2002 to 2010 at the upstream sampling sites. Moreover, the
negative effects of water abstraction on the mountain galaxias population over more recent
dry years (post-2006) are likely to have been worse than indicated in the modelling study.
Given the short lifespan of mountain galaxias and recent poor survivorship in the upper Marne
River, populations are now likely to rely on years with high recruitment to create some
resilience in the population to buffer poor survivorship across low recruitment periods, and to
support system-wide dispersal. An increase in the number of years with limited recruitment
increases risks to the population in the periods between good recruitment and dispersal
opportunities.
The modelling in this case study suggests the removal of low flows as a result of water
development may be particularly critical for mountain galaxias in the upper Marne River. The
hydrological impacts of farm dam development in the catchment have been well-reported
(Savadamuthu 2002’ MREFTP 2003). Across all years in the study conducted by
Savadamuthu (2002), the main impacts were on early-season runoff intercepted as farm
dams filled, and on late-season flows as irrigation pumping began and water diversion to
dams again interrupted streamflow. The net result of these impacts is a shortening of flow
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duration, with the extent of decrease greatest in drier years. The predicted impacts for fish
recruitment are evident in Figure 30, where the greatest deviation from the no-development
prediction occurs in the early 1990s, especially from 1994 to 1997 when annual rainfall at
Keyneton in the upper catchment averaged below 400 mm compared with a mean for the
modelling period of 530 mm. In terms of the biology of mountain galaxias, the results of this
alteration to hydrology are a shortened duration of riffle flow for recruitment, and reduced
water quality over the summer to autumn cease-to-flow period as freshening flows from storm
events are intercepted by farm dams.
Other than predation by trout and redfin (a major risk to the population regionally; McNeil &
Hammer 2009), loss of habitat from drought and water abstraction are the main risks to the
upper Marne River mountain galaxias population. Hydrological issues aside, the present
absence of predatory exotic species in the catchment means the site presents ideal refuge
habitat. Moreover the population has proven extremely resilient, given very poor survivorship
in recent times. If the flow regime could be improved through restoring low-flow volumes to
maintain refuge pools, there is a good probability the population will persist through drier
climatic conditions in future. The regularly low numbers of mature adult mountain galaxias
observed in population monitoring suggests the upper Marne River population is highly
dependent on the survival of a small number of mature fish, possibly at an unknown (thus
unsampled) refuge location. This warrants further investigation to determine the main factors
preventing improved survivorship. Monitoring flow and water quality variations at multiple
refugia within the catchment will provide important supporting information to determine spatial
factors that affect survivorship and population persistence.
While simplistic, the modelling approach employed in this study provides some objective
estimates of the level of impact of water development on mountain galaxias in the upper
Marne River. From a hydrological perspective, it is not conclusively known how runoff
patterns in the Marne River would be affected if no development was present. Nor is it known
exactly what streamflow regimes would have been experienced by biota over evolutionary
time-scales in the region before clearance. There is no clear solution to what constitutes an
optimal regime to manage for, but the restoration of low flow is clearly a critical factor in the
Marne River.
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5. Biological monitoring of South
Australian aquatic biota
5.1 Current programs
The monitoring of aquatic ecological biota in South Australia is currently undertaken for two
main purposes:
1. The EPA monitors aquatic macroinvertebrates to provide the necessary information to
assess the ecological condition of rivers at a regional scale.
2. NRM boards monitor biota to assess the effectiveness of water allocation and
management plans at key sites across their regions.
Environment Protection Authority macroinvertebrate
monitoring program
The EPA recently reviewed its Ambient Water Quality monitoring program that much of the
AusRivAS data used in the case studies was collected under. The review showed a number
of inadequacies in this approach to providing information necessary to assess the ecological
condition of rivers as required under state legislation. Following this review, the EPA has
implemented high-intensity regional-based programs, with a fixed site as well as random site
component, with the aim of characterising stream condition within a region. Regions are
sampled on a rolling basis with frequency of sampling dependent on perceived environmental
risk.
NRM board monitoring programs
Under state legislation, NRM boards are required to produce a number of management plans
identifying how their natural resources will be managed. In the water resources realm, the
highest level of management is assigned to resources at most risk and this is achieved by
prescribing a resource, after all extractive uses are licensed. The administration of the
prescribed resource is set out under a water allocation plan (WAP). For a given region the
WAP is developed by estimating the extent of the sustainable resource, determining the
existing level of use, and comparing these values. The size of the resource, existing use
pressure and the means by which individual shares of the resource will be allocated are set
out in the WAP.
Gaps in monitoring effort
There is currently, and has never been, a state-based data collection program for biological
responses to flow or groundwater level. This represents a significant gap, given the
importance of providing water for the environment in WAPs. The following sections discuss
issues relating to the monitoring of macroinvertebrate, fish and streamflow.
Macroinvertebrate sampling
Fortunately for contemporary natural resource managers, when site selection was made for
the Ambient Water Quality Program macroinvertebrate under AusRivAS sampling protocols,
sampling sites were often co-located with state streamflow gauges. Although this program
has now changed focus to better address agency core business (see above), the data
NATIONAL WATER COMMISSION — Low flows report series
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collected from 1994 to 2007 represents a considerable resource for investigations of the
current nature. This data was critical in verifying theorised hydro-ecological responses that
underpin draft WAPs in the Mount Lofty Ranges (see Vanlaarhoven & van der Wielen 2009).
The revised EPA macroinvertebrate sampling program will provide more detailed data for a
region, with the trade-off that time-series data will have gap years when no information will be
available. Clearly data of this nature is suited to answering different questions to time-series
data collected at only one site per region – it is certainly better suited to the reporting needs of
the EPA. However this should be recognised as not being focused on flow-related ecology
other than regional-scale state of the environment reporting. This gap needs targeted
investment if water allocation and future climate change adaptation plans are grounded in
good ecological understanding.
The value of the existing AusRivAS site time-series data has in no way been fully recognised
through this initial flow-response study. Much more detailed investigation of the data is
possible and warranted. The main gaps that seem to present in macroinvertebrate sampling
relate to:

determining the trajectories of change within systems across seasonal and supraseasonal drought and then through recovery phases where streamflow volumes
increase

patterns of meta-population dynamics that support higher value macroinvertebrate
communities – the latter question should in part be addressed by the new protocols.
The existing macroinvertebrate dataset has and will continue to be valuable if only because of
the longevity of the sampling and the co-location of sites with streamflow gauges. Ideally,
targeted high spatial intensity regional programs as well as long-term sites collecting data to
indicate trajectory over time would exist to help with analyses of changes such as those
observed in Mosquito Creek (case study 2). It could be possible that regional boards have
their own macroinvertebrate sampling programs achieving this. Some savings in
macroinvertebrate sorting can be made if targeted taxa are the focal point. There is potential
to develop suitable indices based on the ratios of a small number of flow-dependent traits with
existing data, and monitor these to demonstrate flow-based ecological trajectories.
Fish
The Marne River mountain galaxias data were invaluable in this study, and given the flow
dependence of this species, focusing on sites where populations are present (especially
where predatory species are absent) is clearly of value. Understanding the flow–ecology
relationships of rheophilic species from a quantitative perspective is only beginning, but has
much promise to create simple, flow-based rules for managers to implement.
The collection of annual fish population monitoring data for any species with a flow
dependence places managers in a much better position to assess flow-related impacts than in
the past. Length data that can be matched to an understanding of the length–age class
relationship (see Hammer 2009) are particularly valuable. As with any flow-related monitoring
strategy, good measures of flow at the site of interest and between key refugia are important
questions most suited to a research-based approach.
Streamflow
The issue of poor streamflow precision at low flows was apparent in preparing flow data for
analysis in this study. The question of when a cease-to-flow started and ended may seem
straightforward, but in reality is not so easy to achieve at a streamflow gauge – especially at
sites where ecological assets are located. This question is also complicated by groundwater
NATIONAL WATER COMMISSION — Low flows report series
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inflows, which are spatially unpredictable, with a reach a few hundred metres upstream or
downstream of a gauge possibly not reflecting the hydrological environment at that site,
especially during early and late season baseflow. Future monitoring effort for biological
sampling should look to ensure some measure of flow as close as possible to the site of
interest is undertaken, and ideally field verification of flow or no-flow conditions should be
undertaken as often as possible during the start and end of seasonal flows
NATIONAL WATER COMMISSION — Low flows report series
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Appendix 1: Table of
macroinvertebrate traits
Table 11: Traits and trait states used in analysis.
Trait*
Trait states abbreviation
resp.gill
Respiration
Duration of
terrestrial phase of
life-history
Electrical
conductivity (EC)
tolerance class
Voltinism
Time to maturity
Dispersal
Method of
reproduction
Description
Gills
resp.pneu
Pneumostome
resp.plast
Plastron, spiracle
terr.quart
Less than 3 months
terr.aqua
Obligate aquatic taxa
terr.month
Less than 4 weeks
terr.surf
Surface aquatic
terr.semi
Semi-terrestrial
terr.edge
Edge dwelling
ec.low
Low (< 7mS/cm)
ec.med.low
Moderate ( 7 – 20 mS/cm)
ec.med
Moderately high (20-50 mS/cm)
ec.high
Highly tolerant (>50mS/cm)
univoltine
Single generation per year
semivoltine
< one generation per year
multivoltine
> one generation per year
matur.month
< 4 weeks
matur.quart
4 – 12 weeks
matur.semi.ann
3 – 6 months
matur.ann
12 months
matur.sup.ann
> 12 months
disp.drift
Present in the drift
disp.flight
Has a flight dispersal life-history phase
reprod.terr
Reproduces via terrestrial eggs
reprod.aq
Aquatic eggs
reprod.ovo
Ovoviviparous
ffg.pred
Predatory
ffg.coll
Collector
Functional feeding
ffg.scrpr
Scraper
group*
ffg.shdr
Shredder
ffg.filt
Filterer
ffg.gath
Gatherer
* Functional feeding group assignments were provided by Peter Goonan of the South
Australian EPA. All other traits were obtained from Schafer et al. (2011).
NATIONAL WATER COMMISSION — Low flows report series
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Appendix 2: Trait group sample membership
Table 12: Assignment of macroinvertebrate samples to trait groups as discussed in Section 2
1
2
3
4
5
6
7
Bremer, S, 1997
Bremer, A, 1998
Bremer, S, 1998
Bremer, A, 2000
Bremer, A, 2005
Finniss, A, 1995
Finniss, S, 1999
Bremer, S, 1999
Cooper, A, 2004
Bremer, A, 2001
Bremer, S, 2003
First, A, 2000
Finniss, A, 2006
Hill, S, 1999
Bremer, S, 2000
First, A, 1994
Bremer, S, 2001
Bremer, A, 2004
Hindmarsh, A, 1995
Hindmarsh, S, 1995
Hill, S, 2001
Cooper, A, 1999
First, S, 1998
Bremer, A, 2002
Bremer, S, 2005
Hindmarsh, S, 1997
Hindmarsh, A, 1997
Light, A, 1994
Cooper, A, 2000
First, A, 1999
Bremer, A, 2003
Bremer, A, 2006
Hindmarsh, A, 1998
Hindmarsh, A, 2000
Light, S, 1994
Finniss, A, 1994
First, S, 2001
Cooper, A, 1995
First, S, 2005
Hindmarsh, A, 2001
Hindmarsh, A, 2002
Light, A, 1995
Finniss, S, 1994
First, A, 2003
Cooper, S, 1995
Hill, S, 1998
Hindmarsh, A, 2003
Hindmarsh, A, 2004
Light, A, 1997
Finniss, A, 2004
First, S, 2004
Cooper, A, 1998
Hill, S, 2000
Hindmarsh, A, 2007
Hindmarsh, A, 2005
Light, S, 1997
First, S, 1994
Hill, S, 2004
Cooper, S, 1998
Light, S, 1995
Rocky, A, 1995
Hindmarsh, S, 2005
Light, A, 1998
First, A, 1995
Hindmarsh, A, 1994
Cooper, S, 2000
Light, A, 2001
Rocky, A, 1997
Hindmarsh, A, 2006
Light, S, 1998
First, S, 1995
Hindmarsh, S, 1994
Cooper, A, 2001
Rocky, A, 1994
Rocky, A, 2000
Mosquito, A, 1997
Light, A, 1999
First, S, 1997
Torrens, S, 1994
Cooper, A, 2003
Rocky, S, 1995
Rocky, A, 2001
Mosquito, A, 1999
Light, S, 1999
First, S, 1999
Torrens, S, 1997
Cooper, A, 2006
Rocky, S, 1998
Rocky, A, 2002
Mosquito, A, 2000
Light, A, 2000
First, S, 2000
Torrens, S, 1998
Cooper, A, 2007
Rocky, S, 1999
Rocky, S, 2002
Mosquito, A, 2001
Light, S, 2001
First, A, 2001
Torrens, S, 1999
Hill, A, 2001
Rocky, A, 2003
Rocky, A, 2004
Myponga, A, 1998
Light, A, 2002
First, S, 2002
Torrens, S, 2000
Kanyaka, A, 1995
Rocky, S, 2003
Rocky, A, 2005
Myponga, S, 2001
Light, S, 2002
First, A, 2006
Torrens, A, 2001
Kanyaka, A, 2000
Rocky, S, 2004
-
Myponga, A, 2002
Light, A, 2003
First, A, 2007
Torrens, S, 2001
Rocky, S, 1997
Torrens, S, 2005
-
Myponga, A, 2004
Light, S, 2003
Hindmarsh, S, 1998
Torrens, S, 2002
Rocky, S, 2000
-
-
Myponga, S, 2004
Light, A, 2004
Hindmarsh, A, 1999
-
Rocky, S, 2001
-
-
Myponga, A, 2005
Light, S, 2004
Hindmarsh, S, 1999
-
Torrens, A, 1995
-
-
Myponga, S, 2005
Light, A, 2005
Hindmarsh, S, 2000
-
Torrens, A, 2000
-
-
Myponga, A, 2006
Light, S, 2005
Hindmarsh, S, 2001
-
-
-
-
-
Light, A, 2006
NATIONAL WATER COMMISSION — Low flows report series
65
Hindmarsh, S, 2002
-
-
-
-
-
Light, S, 2006
Hindmarsh, S, 2003
-
-
-
-
-
Light, A, 2007
Hindmarsh, S, 2004
-
-
-
-
-
-
Light, S, 2000
-
-
-
-
-
-
Mosquito, A, 1994
-
-
-
-
-
-
Mosquito, A, 1995
-
-
-
-
-
-
Mosquito, S, 1995
-
-
-
-
-
-
Mosquito, S, 1997
-
-
-
-
-
-
Mosquito, A, 1998
-
-
-
-
-
-
Mosquito, S, 1998
-
-
-
-
-
-
Mosquito, S, 1999
-
-
-
-
-
-
Mosquito, S, 2000
-
-
-
-
-
-
Rocky, S, 1994
-
-
-
-
-
-
Rocky, S, 2005
-
-
-
-
-
-
Torrens, A, 2002
-
-
-
-
-
-
Torrens, S, 2004
-
-
-
-
-
-
Samples are abbreviated as follows: ‘river name’, season, year of sampling’. For season, S=spring and A = autumn.
NATIONAL WATER COMMISSION — Low flows report series
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Appendix 3: Highly prevalent South
Australian macroinvertebrate families
in dataset
Table 13: Families observed in more than half of riffle samples collected at more than half of
the sites (total sites number = 11).
Family*
Mean # of samples recorded
Sites recorded
Chironominae*
91%
11
Orthocladiinae*
90%
11
Simuliidae
85%
11
Ceinidae
76%
11
Tanypodinae*
70%
11
Ceratopogonidae
65%
11
Naididae
54%
11
Hydroptilidae
51%
11
Hydropsychidae
61%
10
Hydrobiidae
81%
9
Caenidae
52%
9
Hygrobatidae
57%
8
Clavidae
52%
8
Gripopterygidae
66%
6
Leptophlebiidae
56%
6
Hydrobiosidae
51%
6
* Sub-families of the Chironomidae.
Table 14. As above, but for edge samples (total sites = 12)
Family
Mean prevalence
Occurrence
Chironominae*
98%
10
Ceinidae
80%
10
Orthocladiinae*
87%
9
Tanypodinae*
85%
9
Ceratopogonidae
74%
9
Corixidae
70%
9
Leptoceridae
65%
9
Dytiscidae
61%
9
Hydroptilidae
53%
9
Naididae
51%
9
Hydrobiidae
71%
8
Aytidae
54%
7
Coenagrionidae
52%
7
NB: Highly prevalent taxa recorded at higher taxonomic resolution also warrant mention for
both the riffle and edge list. Oligochaeta, Nematoda and Oribatida were all present in more
than half the samples at more than half of the sites.
1.
NATIONAL WATER COMMISSION — Low flows report series 67
Appendix 4: Relevant findings for the
Low Flow Ecological Response and
Recovery Project
Findings relevant to the aims of the Commission’s project are highlighted below, using the
four principals of low-flow ecological response presented in Marsh et al. (2012) to provide
context.
Principal 1: Low flows control the extent of physical aquatic habitat,
therefore influencing ecological patterns and processes.
Evidence presented in case study 1 generally suggests that cease-to-flow periods are more
critical in structuring macroinvertebrate communities than flow magnitude. Even very low
flows appear able to support key processes and it is the duration of flow and spatial extent of
(any) flowing habitat that apparently has the major influence on macroinvertebrate
communities in South Australian rivers. In this region, the duration of streamflow is typically
determined by the degree of any surface expression of groundwater.
Case study 2 demonstrates differential flow dependence between three functional groups of
macroinvertebrate families based on trait data, which links pattern (proportional
representation of functional groups) and process (streamflow). Positive, negative and neutral
responses are evident when proportional richness attributed to each functional group is
compared with antecedent streamflow.
Case study 3 identified a linear relationship between the number of YOY Galaxias olidus in
autumn and the magnitude and duration of streamflow in the previous flow season from May
to November. No flow-related threshold response was evident in the relationship; there was
instead a linear decline in recruitment associated with decreasing flow magnitude and
duration. Over observed conditions process uncertainty (variance) around the linear response
appears to be homogeneous.
Principal 2: Low flows mediate changes in habitat conditions, which in
turn drive changes in ecosystem patterns and processes.
Case study 1 found that riffle macroinvertebrate samples classified by trait prevalence divided
into seven groups. Antecedent low-flow conditions were important for two of these groups.
One group was predominantly associated with antecedent cease-to-flow periods whereas the
latter was from sites which had experienced low antecedent flow conditions without any flow
cessation. Both groups exhibited reduced functional dispersion of traits compared with all
other groupings, suggesting the impacts on trait diversity were similar although different
indicative traits were identified for each group. A third group exhibited high functional
dispersion, despite having the lowest median-ranked flow magnitude, but perennial flow
conditions. This evidence suggests that cease-to-flow periods are more influential on
community trait expression than flow magnitude.
In case study 2, a period of extreme drying over a decade at Mosquito Creek was assessed
for its impact on macroinvertebrate communities. During this time streamflow transitioned
from perennial to intermittent, with increasing annual periods of cease-to-flow. The magnitude
of flow decrease is demonstrated by the 40-fold decrease in median daily flow for periods
before and after 2001. The macroinvertebrate edge habitat samples demonstrate a step
change between autumn and spring sampling in 2005, concurrent with a marked increase in
EC that was almost certainly flow-mediated given a strong linear relationship between spring
NATIONAL WATER COMMISSION — Low flows report series
68
streamflow and conductivity (R2 = 0.78, p < 0.001). Spring samples demonstrated a larger
overall response to stream drying, and this may be attributable to the fauna being adapted to
more moderate conductivities and perennial flow conditions formerly experienced at the site.
Refugia are clearly highly important for persistence of the Galaxias olidus population in the
intermittent reaches of the upper Marne River. However, sites where survivorship was
previously recorded (MREFTP 2003) no longer appear suitable for supporting populations
with multiple age classes of reasonable abundance. The recently observed three consecutive
years of poor recruitment (<10 YOY recorded in autumn sampling) are without precedent in
modelled predictions from 1975 to 2006. It seems likely the poor recruitment and survivorship
in recent years has resulted from a reduction in low flows due to farm dam and groundwater
development, which have exacerbated the effects of drought conditions.
Principal 3: Low flows affect the sources and exchange in aquatic
ecosystems, resulting in altered ecosystem production and biotic
composition.
Case study 1 provides an example of indicative trophic trait states between riffle samples
collected following low-flow periods and those from perennial or long-flow-duration sites.
Predatory strategies were indicative of samples collected following periods of cease-to-flow,
while filter-feeding was associated with samples associated with perennial conditions (defined
here as continuous flow exceeding 365 days). A diverse range of feeding strategies were also
evident across more perennial rivers, where the least variable rivers were significantly
associated with a majority of the six functional feeding groups used in analysis. Highly
variable rivers did not have any indicative feeding strategies.
Principal 4: Low flows restrict dispersal, thereby increasing the
importance of refugia to sustain biota and multi-scale patterns in
biodiversity
Data were generally of insufficient spatial resolution to determine dispersal effects. Case
study 1 provides evidence that riffle sites where recent cease-to-flow events occurred
contained taxa able to disperse by flight and drift.
The length frequency data for Galaxias olidus in the upper Marne River from 2002 to 2010
indicate that survivorship is poor, with YOY numerically dominant in samples and very few
older individuals observed. Additional data not presented in this report suggest high
recruitment success seems to have facilitated dispersal between the three refugia on only one
observed occasion (2005–06). While recruitment appears broadly positive linear with respect
to flow based on the available data, dispersal appears to have a minimum flow threshold and
is apparently only supported by a moderate to high flow magnitudes and/or substantial
durations of flow. Periods where these flow conditions occur will also increase the probability
of refuge persistence, which is also a limiting factor over recent years. Since the 2005
recruitment event, G. olidus populations in the upper Marne River have been decreasing
(Hammer 2009), with few (or no recorded) fish or recruits observed in sampling since this
time.
1.
NATIONAL WATER COMMISSION — Low flows report series 69
Shortened forms
AusRivAS
Australian Rivers Assessment Scheme
DFW
South Australian Department for Water
EC
Electrical conductivity
EPA
Environment Protection Authority
ISA
Indicator Species Analysis
LAAA
List all abbreviations and acronyms
MLR
Mount Lofty Ranges
NMDS
Non-metric multidimensional scaling
NRMB
Natural resources management board
SAMDB
South Australian Murray-Darling Basin
WAP
Water allocation plan
YOY
Young of the year
NATIONAL WATER COMMISSION — Low flows report series
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Reports in the low flow series
Balcombe SR & Sternberg D 2012, Fish responses to low flows in dryland rivers of western
Queensland, National Water Commission, Canberra.
Barma Water Resources & Sinclair Knight Merz 2012, Low-flow hydrological monitoring and
modelling needs, report by for the National Water Commission, Canberra.
Barmah D & Varley I 2012a, Hydrologic modelling practices for estimating low flows –
stocktake, review and case studies, National Water Commission, Canberra
Barmah D & Varley I 2012b, Hydrologic modelling practices for estimating low flows –
guidelines, National Water Commission, Canberra
Bond N 2012, Fish responses to low-flows in lowland streams: a summary of findings from the
Granite Creeks system, Victoria, National Water Commission, Canberra.
Bond N, Thomson J & Reich P 2012, Macroinvertebrate responses to antecedent flow, longterm flow regime characteristics and landscape context in Victorian rivers, National
Water Commission, Canberra.
Chessman B et al 2012, Macroinvertebrate responses to low-flow conditions in New South
Wales rivers, National Water Commission, Canberra.
Deane D 2012, Macroinvertebrate and fish responses to low flows in South Australian rivers,
National Water Commission, Canberra.
Dostine PL & Humphrey CL 2012, Macroinvertebrate responses to reduced baseflow in a
stream in the monsoonal tropics of northern Australia, National Water Commission,
Canberra.
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Hardie, SA et al 2012, Macroinvertebrate and water quality responses to low flows in
Tasmanian rivers, National Water Commission, Canberra.
Kitsios A et al 2012, Fish and invertebrate responses to dry season and antecedent flow in
south-west Western Australian streams, National Water Commission, Canberra.
Leigh, C 2012, Macroinvertebrate responses to dry season and antecedent flow in highly
seasonal streams and rivers of the wet-dry tropics, Northern Territory, National Water
Commission, Canberra.
Mackay S et al; 2012, Low-flow hydrological classification of Australia, National Water
Commission, Canberra.
Marsh N et al 2012, Synthesis of case studies quantifying ecological responses to low flows,
National Water Commission, Canberra.
Marsh N et al 2012, Guidance on ecological responses and hydrological modelling for lowflow water planning, National Water Commission, Canberra.
Rolls R et al 2012, Review of literature quantifying ecological responses to low flows, National
Water Commission, Canberra.
Rolls R et al 2012, Macroinvertebrate responses to prolonged low flow in sub-tropical
Australia, National Water Commission, Canberra.
Sheldon F et al 2012, Early warning, compliance and diagnostic monitoring of ecological
responses to low flows, National Water Commission, Canberra.
Smythe-McGuiness Y et al 2012, Macroinvertebrate responses to altered low-flow hydrology
in Queensland rivers, National Water Commission, Canberra.
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