Compounding Effects of Agricultural Land Use and

Environmental Management (2018) 61:421–431 DOI 10.1007/s00267-017-0836-1

Compounding Effects of Agricultural Land Use and Water Use in Free-Flowing Rivers: Confounding Issues for Environmental Flows Scott A. Hardie1,2 Chris J. Bobbi1 ●

Received: 28 April 2016 / Accepted: 17 February 2017 / Published online: 3 March 2017 © Springer Science+Business Media New York 2017

Abstract Defining the ecological impacts of water extraction from free-flowing river systems in altered landscapes is challenging as multiple stressors (e.g., flow regime alteration, increased sedimentation) may have simultaneous effects and attributing causality is problematic. This multiple-stressor context has been acknowledged in environmental flows science, but is often neglected when it comes to examining flow-ecology relationships, and setting and implementing environmental flows. We examined the impacts of land and water use on rivers in the upper Ringarooma River catchment in Tasmania (south-east Australia), which contains intensively irrigated agriculture, to support implementation of a water management plan. Temporal and spatial and trends in river condition were assessed using benthic macroinvertebrates as bioindicators. Relationships between macroinvertebrate community structure and environmental variables were examined using univariate and multivariate analyses, focusing on the impacts of agricultural land use and water use. Structural changes in macroinvertebrate communities in rivers in the catchment indicated temporal and spatial declines in the ecological condition of some stretches of river associated with agricultural land and water use. Moreover, water

Electronic supplementary material The online version of this article (doi:10.1007/s00267-017-0836-1) contains supplementary material, which is available to authorized users. * Scott A. Hardie [email protected] 1

Water and Marine Resources, Department of Primary Industries, Parks, Water and Environment, New Town, Tasmania, Australia

2

School of Biological Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia

extraction appeared to exacerbate impairment associated with agricultural land use (e.g., reduced macroinvertebrate density, more flow-avoiding taxa). The findings of our catchment-specific bioassessments will underpin decisionmaking during the implementation of the Ringarooma water management plan, and highlight the need to consider compounding impacts of land and water use in environmental flows and water planning in agricultural landscapes. Keywords Water use Land use Macroinvertebrates Multiple stressors Environmental flows Adaptive management ●

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Introduction It is widely accepted that anthropogenic modification of landscapes impact the ecological integrity of rivers (Allan 2004; Hynes 1970). While water abstraction, via subsequent flow regime alteration, can adversely impact river condition, in agricultural landscapes multiple stressors (e.g., flow alteration, sedimentation, increased nutrients) may simultaneously affect riverine ecosystems (Matthaei et al. 2010; Wagenhoff et al. 2011) and disentangling causality can be difficult (Downes 2010). This multiple-stressor context has been acknowledged in environmental flows science (Arthington 2012; Davies et al. 2014; Poff et al. 2010), but is often neglected when it comes to examining flow-ecology relationships (Poff and Zimmerman 2010), and setting and implementing environmental flows (McGregor et al. 2016; Richter 2010). Clearly multiple stressors may present confounding issues for environmental flows and water planning, particularly in regard to setting realistic objectives,

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monitoring/evaluation of flow responses and adaptive management to balance the needs of water users and the environment. Free-flowing river systems (i.e., drainages without substantial instream dams) in agricultural catchments typically retain a closer resemblance to their natural flow regimes than dammed rivers (Acreman et al. 2014; Arthington 2012); however, water diversion or direct extraction can alter their hydrologic character. For example, in small-sized, free-flowing rivers in temperate zones, direct extraction from river channels can dramatically reduce low flows, increase sub-daily flow variability, and suppress freshes and moderate flow events. This is especially likely to occur during dry periods when baseflows are naturally low and extraction rates are typically greatest (Miller et al. 2007). The ecological consequences of such hydrologic alterations may be more subtle than those caused by large instream dams and their associated flow regulation (Chessman et al. 2010; Gillespie et al. 2015; Marchant and Hehir 2002), but understanding their effects is critical to effective water management in catchments with high levels of direct extraction. In addition, natural variation in geographic and environmental factors influence the ecological state of river systems (Marzin et al. 2013; Ward et al. 2002) and this needs to be accounted for when assessing impacts of water and land use. Tasmania is a small (~68,500 km2), temperate zone (40–43°S, 144–148°E) island state in the south-east of Australia that has approx. 157,000 km of rivers, many of which are relatively small (mean annual discharge (MAD) range = c. 20–650 GL), free-flowing, coast-draining systems with varied levels of catchment disturbance (CFEV 2005; DPIW 2007). Similar to regulatory frameworks in many other regions, water management plans (WMPs) are employed in catchments in Tasmania where competing demands for water resources are high (http://dpipwe.tas. gov.au/water/water-management-plans). In this paper, we present bioassessments that were undertaken in the upper Ringarooma River catchment, northern Tasmania to support the implementation of a WMP for the catchment (DPIPWE 2014b), which includes environmental flows (Bobbi et al. 2014; DPIW 2008). Using benthic macroinvertebrates as bioindicators, the aims of this study were to examine: (1) temporal trends in macroinvertebrate community composition at long-term monitoring sites in the catchment, and (2) spatial variation in benthic macroinvertebrates in the catchment during the 2012/13 irrigation season (which experienced dry climatic conditions), and relationships with environment variables, including land and water use. To address the first aim of the study, data from historical qualitative sampling of macroinvertebrates in the upper catchment were collated and linear models were used to explore temporal trends in metrics that represent community

Environmental Management (2018) 61:421–431

composition. To address the second aim, qualitative and quantitative macroinvertebrates samples were collected at the start (November 2012) and end (March 2013) of the irrigation season at 19 sites across the upper Ringarooma River catchment that had varied levels of upstream agricultural land use and water use. In addition, physicochemistry, and benthic algae, and organic detritus were measured at the study sites, and spatial geographic, hydrologic, geologic and land use data were collated for the study area. Univariate and multivariate analyses were used to examine the relationships between macroinvertebrate community structure and environmental variables, focusing on the impacts of agricultural land use and water use. The implications of the findings for water management, in the Ringarooma River catchment and more broadly, are discussed.

Materials and Methods Study Area The Ringarooma River catchment (area = 930 km2) has headwaters in the Ben Lomond Ranges (elevations ~1100 m a.s.l.) and drains north into Bass Strait (Fig. 1). Annual rainfall varies from ~1800 mm in the headwaters to ~600 mm on the coastal plains. This study focused on the middle and upper areas of the catchment, where agricultural activities (e.g., intensive cropping, dairy farming, cattle grazing) and plantation and production forestry occur. Soils in the upper catchment are predominantly derived from basalt, granite, and sand-stones and mud-stones, and rivers in this area have mostly rocky substrate (i.e., cobbledominated substrata); although, granitic fines are prevalent in some mid-catchment tributaries. Major tributaries of the Ringarooma River in the mid to upper catchment include the Cascade River, Dorset River, Federal Creek, Frome River, Legerwood Rivulet, and Maurice River, many of which enter the Ringarooma River upstream of the town of Branxholm (Fig. 1). The Ringarooma River has a perennial, predictable, and highly seasonal flow regime with high flows during winter–spring and low flows during summer–autumn (Bobbi et al. 2014). There are no dams on the main channel of the Ringarooma River (i.e., it is “free-flowing”); however, several agricultural water storages (farm dams) in the mid to upper catchment impound water which would have naturally flowed into the river via its tributaries. The Ringarooma River catchment has been influenced by water use practices associated with agriculture and forestry operations for over 100 years. However, since c. 1990 consumptive water use has increased markedly due to demands from irrigated agriculture, especially during summer–autumn


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Fig. 1 Location of ecological sampling sites (closed triangles) and the town of Branxholm (closed square) in the upper Ringarooma River catchment, Tasmania, Australia. See Table S1 for further information about the sites

(December–April) when direct extraction of water from the river system suppresses baseflows and increases diel flow variation in some reaches (see Fig. S1). In addition, the number of farm dams in the upper catchment has also increased during this period, and their capture of water in tributaries is also likely to suppress baseflows and freshes in the upper Ringarooma River.

Suppliers Inc.). Where the channel was < 15 m wide, fewer densiometer readings (minimum of four readings at three locations) were taken due to confinement of the river channel. Scour pad samples were pooled for each site and frozen in the dark prior to processing. Chlorophyll-a concentration (mg/m2) was calculated from analyses that followed standard methods (APHA 1992).

Physico-Chemical Measurements

Organic Carbon Analyses

Water temperature and electrical conductivity (LF 330, WTW, Weilheim, Germany), and dissolved oxygen (YSI 550DO, YSI Inc., Yellow Springs, OH, USA) were measured in situ on each sampling occasion at 19 ecological sampling sites (Table S1; Fig. 1) during November 2012 and March 2013.

The amount of organic carbon in detritus on areas of substrate that were sampled for macroinvertebrates was examined as this material can provide food resources and refuge for benthic invertebrates (Gooderham and Tsyrlin 2002; Lancaster and Downes 2013), and hence influence the composition of their communities. To do this, once macroinvertebrates had been removed from the seasonal quantitative sub-samples (see Methods for macroinvertebrate sampling), the remaining sediment and detritus was frozen, and the organic carbon content of the material was determined by the loss-on-ignition (LoI) method (Rayment and Lyons 2011). This involved a sub-sample (mean = 27.0 g) of each sample being dried at 105 °C for 24 h, weighed (±0.0002) and then combusted at 550 °C for 2 h. The remaining material was reweighed (±0.0002 g) and organic carbon content was calculated as: W 2 ð gÞ W 3 ð gÞ LoIðg=kgÞ ¼ 1000 W2 ðgÞ W1 ðgÞ

Benthic Algal Sampling At study sites on each sampling occasion (November 2012 and March 2013), benthic algal cover and riparian canopy cover was measured and benthic algae was sampled for chlorophyll-a analysis in riffle habitat along a transect that was perpendicular to the direction of flow (NRM 2009). At 15 locations across transects: algal cover was estimated using a 300 × 300 mm-metal grid with 64 cells; algal samples were collected from the surface of inundated cobble/boulders using 25-mm diameter scour pads (Eager Beaver®); and riparian canopy cover was estimated facing upstream, downstream, left bank, and right bank using a spherical convex densiometer (Model 43887 Forestry

where W1 = mass of crucible (g), W2 = mass of sample and crucible (g), and W3 = mass of ignited crucible and residue (g).

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Macroinvertebrate Sampling Qualitative sampling Historical qualitative (Australian River Assessment System; AusRivAS) (Krasnicki et al. 2002) benthic macroinvertebrate data for 13 sites in the upper Ringarooma River system that had been sampled on >2 occasions between autumn 1994 and spring 2009 (DPIPWE unpublished data) were collated with similar data collected at the 19 study sites during November 2012 and March 2013. Qualitative sampling was conducted over a 10-m2 area of substrate in riffle habitat using a kick net (250 × 365 mm, 250 µm mesh). All qualitative invertebrate samples were live picked in situ for 30 min, maximizing the number of taxa collected, with additional time allocated for picking if new taxa were detected within the final 10 min of the period. The samples were preserved in 90% ethanol. In addition, substrate composition (modified Wentworth scale), superficial silt cover (%) and detritus cover (%) were estimated at locations where qualitative macroinvertebrate samples were collected. The condition of riparian vegetation in a 100-m reach at the study sites was also scored and the width of the vegetated zone was estimated (Krasnicki et al. 2002). Macroinvertebrate samples were identified to family level in the laboratory using relevant taxonomic keys (e.g., Gooderham and Tsyrlin 2002) except in the following cases: Chironomidae (midges) were identified to sub-family level; Nematoda (nematodes), Oligochaeta (worms), Hirudinea (leeches), Acarina (mites), and Turbellaria (flatworms) were identified to order and class level. For simplicity, the terms “family” and “taxa” are hereafter used to describe identifications to these various taxonomic levels. Quantitative sampling Quantitative benthic macroinvertebrate samples were also collected during November 2012 and March 2013 from riffle habitat at the 19 study sites. Quantitative sampling involved the collection of ten surber samples (300 × 300 mm, 250 µm mesh) by hand disturbance of a 0.09-m2 area of substrate. The ten samples were pooled for sites and preserved (10% formalin) prior to sub-sampling (20%, box sub-sampler) in the laboratory. Taxa in the sub-samples were identified following the methods that were used for qualitative macroinvertebrate samples. Data Analyses For all qualitative macroinvertebrate samplings, observed/ expected taxa scores (O/E score) and O/E stream invertebrate grade number average level scores (“SIGNAL score”;


Chessman 1995) were calculated using Tasmanian singleseason AUSRIVAS bioassessment models for riffle habitats (Krasnicki et al. 2002). Total taxonomic richness (richness), percent of taxa belonging to the orders Ephemeroptera, Plecoptera, and Trichoptera (%EPT), and percent of taxa considered to be flow obligates (%Obligates), facultative users of flow (%Facultatives), and flow avoiders (%Avoiders) (Growns and Davis 1994; Warfe et al. 2014) were also calculated. Except for O/E and SIGNAL scores, the same metrics were also calculated for quantitative macroinvertebrate samples that were collected in 2012/13, along with total density (“density”, no./m2). Several environmental predictor variables were derived from field measurements and sampling for physico-chemistry, benthic algae and detritus, substrate composition and condition, and riparian vegetation condition (Table S2). In addition, geographic, geologic (Fig. S2), land use (Fig. S3), and hydrologic (Fig. S4) and other spatial datasets (e.g., CFEV 2005) were also used to derive numerous predictor variables for ecological study sites and their upstream catchments (see Table S2 and DPIPWE (2014a) for details). Relationships (co-linearity) between geographic, environmental, hydrologic, land use, and geologic predictor variables were examined using Pearson’s correlation coefficient (r). Similarly, relationships between macroinvertebrate response variables and predictor variables were examined using Spearman’s correlation coefficient (ρ). Many of the geographic, environmental, hydrologic, land use, and geologic predictor variables were highly correlated across the sites (DPIPWE 2014a); thus, there was a high degree of redundancy within predictor variables. According to land use and water use data, the study sites (and their upstream sub-catchments) formed three distinct groups in relation to agricultural land use and water use (hereafter referred to as site “treatments”): (1) low agricultural land use and low water use (LowAg), (2) high agricultural land use and low water use (HighAg), and (3) high agricultural land use and high water use (HighAg + HighWa) (Fig. S5). These treatments provided a logical and robust means of examining responses of macroinvertebrate communities to the effects of agricultural land use and consumptive water use, and were used in subsequent analyses. Linear regression was used to assess temporal trends in seasonal (spring and autumn) metrics that were derived from qualitative macroinvertebrate samples at long-term monitoring sites in the upper Ringarooma catchment. Differences between macroinvertebrate metrics in site treatments were explored using linear mixed-effects models due to unbalanced sample sizes within treatments (Zuur et al. 2009). Natural log or arcsin transformations of metrics were used where appropriate to satisfy model assumptions. For all models, season and treatment were fixed effects, and sites were treated as random effects. Fixed effects were


tested using likelihood ratio (LR) tests derived from comparing models with and without a season × treatment interaction (estimated using maximum likelihood (ML) methods) after their relative support had been assessed using Akaike’s information criterion values from generalized least squares fits based on restricted ML (REML). Once the minimum adequate model was found, it was refitted with REML to produce unbiased parameter estimates (Zuur et al. 2009). All models were fitted using the nlme package (v3.1–109) (Pinheiro et al. 2016) in R 3.0.1 (R Development Core Team 2014). Nonmetric multidimensional scaling (nMDS) was used to examine similarities in macroinvertebrate community composition within and between site treatments (vegan package (v2.0–8) (Oksanen et al. 2016) in R 3.0.1). For these analyses, ordinations were derived using Bray Curtis dissimilarity matrices of square-root-transformed taxonomic data. Correlations between 12 predictor variables (elevation, proportion of gravel substrate, detritus cover, silt cover, chlorophyll-a concentration in benthic algae, electrical conductivity, riparian vegetation cover, MAD corrected for sub-catchment area, proportion of basaltic and granitic soils, proportion of agricultural land use in sub-catchment, and percent of MAD allocated to water users; Table S2) and patterns in the nMDS ordinations were also examined. These predictor variables were found to be correlated with macroinvertebrate community structure (DPIPWE 2014a) and represented a range of potentially influential environmental conditions. The strength of correlations between the predictor variables and patterns of similarity in ordination space was tested using a vector fitting function in the vegan package. Permutational MANOVA (PERMANOVA; Anderson 2001; McArdle and Anderson 2001) and similarity percentage (SIMPER) analyses were used to examine differences in community composition between and within treatments (PRIMER, Version 6, PRIMER-E, Plymouth, UK). In addition, permutational analysis of multivariate dispersions (Anderson 2004) was used to determine if dispersion within the tested groups (treatments) may have contributed to significance levels. For treatment data, these tests were performed on a random subset (n = 4) of sites belonging to the LowAg and HighAg treatments to balance sample sizes in PERMANOVAs.

Results Temporal Trends in Macroinvertebrate Community Composition Typically 16–25 families of macroinvertebrates were recorded during samplings at the long-term monitoring site

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in the Ringarooma River at Branxholm (site R2) and no temporal trends were evident in taxonomic richness. However, significant linear, temporal trends (R2 range = 0.37–0.86, all P < 0.01; Table S3) were evident in several metrics relating to the composition and condition of macroinvertebrate communities (Fig. 2). Negative trends were evident in SIGNAL scores, %EPT taxa and %Obligate taxa in spring and autumn, O/E scores in spring, %Facultative taxa in autumn, while positive trends in %Avoider taxa occurred in spring and autumn. Over the same period, similar temporal trends (not shown) were also observed at other infrequently monitored sites in the upper Ringarooma catchment that had substantial levels of upstream agricultural land use and water use; whereas the composition of macroinvertebrate communities at unimpacted sites in the catchment was relatively stable. Univariate Relationships between Macroinvertebrate Communities and Land Use and Water Use All eight macroinvertebrate metrics had significant correlations with land use (Table S4) and hydrological variables (Table S5) in spring and autumn, with %Facultative and SIGNAL score being the least responsive metrics ( 0.490, P < 0.001). In contrast, in autumn LowAg and HighAg sites had similar community structure, but HighAg + HighWa sites had distinctive communities compared to the other treatments (both t > 1.780, P < 0.05; Fig. 4b) and chlorophyll-a concentration was strongly correlated with the structure of communities at these sites (R = 0.620, P = 0.006). LowAg sites were dominated by Leptoceridae, Leptophlebiidae, and Elmidae larvae in both seasons, while Simuliidae and Chironominae were abundant in spring and


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Fig. 3 Mean (±SE) of macroinvertebrate metrics for site treatments based on sampling during spring 2012 (black bars) and b autumn 2013 (gray bars). The following metrics are shown: a taxonomic richness b density, and percent c EPT taxa, d flow-avoiding taxa, e flow facultative taxa, and f flow obligate taxa

autumn, respectively (Table 1; Table S6). In both seasons, Orthocladiinae, Chironominae, and Leptoceridae were abundant at HighAg sites, while Scirtidae and Conoesucidae were dominant in autumn. HighAg + HighWa sites appeared to have a somewhat similar fauna to that of HighAg sites, but mayflies (except Baetidae in spring) were less characteristic of their communities. At HighAg + HighWa sites, Orthocladiinae, Chironominae, and Elmidae larvae were abundant across seasons, while Oligochaeta and Hydrachnidae were abundant in autumn.

Discussion

Fig. 4 NMDS ordinations of macroinvertebrate community composition based on quantitative sampling at sites in the upper Ringarooma River catchment in a spring 2012 and b autumn 2013. LowAg (open circles), HighAg (gray squares), and HighAg + HighWa (closed triangles) sites are enclosed in convex hulls

Structural changes in macroinvertebrate communities in rivers in the upper Ringarooma River catchment indicated temporal and spatial declines in the ecological condition of some stretches of river associated with agricultural land and water use. Moreover, water extraction appeared to exacerbate impairment associated with agricultural land use (e.g., reduced macroinvertebrate density, more flow-avoiding taxa). Despite the unregulated nature of the flow regime of the upper Ringarooma river system, these findings are unsurprising given the high levels of agricultural land and water use upstream of some sites (i.e., up to 30% of mean

428


Table 1 Results of SIMPER analysis of quantitative macroinvertebrate samples from spring and autumn for site treatments

Spring

Autumn

Ringarooma WMP (DPIPWE 2014b), and highlight the need to consider compounding impacts of land and water use in environmental flows and water planning in agricultural landscapes.

Leptoceridae

106

312

Relationships between Macroinvertebrate Communities and Land Use and Water Use

Simuliidae

222

–

Orthocladiinae

55

81

Leptophlebiidae

72

101

Taxa

Average abundance (no./m2)

LowAg

Elmidae larvae

38

107

Baetidae

70

–

Elmidae adults

32

–

Gripopterygidae

16

–

Hydrobiosidae

18

–

Scirtidae

–

93

Conoesucidae

–

69

Chironominae

–

167

Orthocladiinae

791

176

Chironominae

142

495

Simuliidae

126

38

Oligochaeta

51

–

Leptoceridae

77

178

Hydrobiosidae

34

43

HighAg

Elmidae larvae

48

–

Scirtidae

–

156

Conoesucidae

–

166

Orthocladiinae

178

31

Chironominae

144

9

Elmidae larvae

44

45

Simuliidae

52

4

Diptera pupae

20

–

Baetidae

26

–

Elmidae adults

12

15

Hydrachnidae

–

66

Conoesucidae

–

23

Oligochaeta

–

41

HighAg + HighWa

Only taxa contributing to ≥ 5% of the overall similarity within treatments are presented. Taxa are sorted by descending percent contribution to similarity in spring samples. See Table S6 for full SIMPER analysis results

annual summer discharge allocated and 90% of subcatchments used for agriculture) and the impacts of agricultural land use (e.g., Liess et al. 2012; Magierowski et al. 2012) and water use (e.g., Matthaei et al. 2010; Miller et al. 2007), which have been identified in previous studies. The findings of our catchment-specific bioassessments will underpin decision-making during the implementation of the

The long-term monitoring site in the Ringarooma River at Branxholm (Fig. 1) is strategically located downstream of the most intensively farmed areas of the upper catchment (Fig. S3) where the effects of land and water use on the Ringarooma River manifest. The structure of the macroinvertebrate community at this site changed substantially over the 19-year monitoring period (e.g., c. 15% reductions in proportions of EPT and flow obligate taxa, c. 20% increase in flow-avoiding taxa). This strongly suggests that since 1994 intensification of land and water use in the upper catchment has gradually impacted the river and that the impacts are continuing. Mechanisms associated with the decline were not examined, but the responses of flowrelated traits of macroinvertebrates suggest that reductions in summer–autumn baseflows in the upper Ringarooma River during this period (DPIPWE 2014a) are likely to have contributed to this change in the river’s benthic fauna. The upper Ringarooma River system contains perennial, free-stone (cobble-dominated), low-productivity streams. The benthic fauna in these streams is likely to be sensitive to flow regime alterations (especially reductions in flow; Miller et al. 2007; Walters and Post 2011), and increased sediment (Burdon et al. 2013; Jones et al. 2012) and algal (Liess et al. 2012) loads. Our sampling across the upper catchment in 2012/13 (an irrigation season with dry climatic conditions) suggests that the mechanisms associated with the changes in the structure of macroinvertebrate communities in reaches with high levels of upstream agricultural land and water use relate to excessive fine sediment (DPIPWE 2014a) and benthic algal loads. Furthermore, sampling of macroinvertebrates in edgewater and thalweg habitats during 2012/13 in the upper Ringarooma River (which experienced increased diel flow variability; see Fig. S1) and in a tributary with a stable baseflow regime indicate that unfavorable hydrologic/hydraulic conditions for taxa during summer–autumn periods in the upper Ringarooma River may have contributed to these changes (DPIPWE 2014a). Agricultural land use intensity and catchment vegetation cover are known to influence river health, including macroinvertebrate communities, in many regions (Carlson et al. 2013; Liess et al. 2012; Magierowski et al. 2012). Magierowski et al. (2012) found strong negative relationships between land use (especially stock grazing) and macroinvertebrate community structure in rivers across


Tasmania, while according to Warfe et al. (2014) macroinvertebrate communities in the state are highly responsive to flow regime alteration. The present study applied a similar analytical approach to Magierowski et al. (2012) at a local scale in the Ringarooma River catchment and found that while agricultural land use has adverse effects on macroinvertebrate communities, the combination of relatively high agricultural land use and high water use had stronger negative impacts. These impacts were most evident in autumn following a dry irrigation season and prolonged low flows, including a near cease-to-flow event in the Ringarooma River at Branxholm in mid-February 2013 (minimum of 0.003 m3/s; Fig. S1). These findings reflect the anthropogenic modification of the upper Ringarooma catchment and the substantial proportions of summer–autumn flows that are allocated in some stretches of the river system. Responses of macroinvertebrate communities to humaninduced flow reductions vary and include reductions in density (Walters and Post 2011), structural changes (Miller et al. 2007), and alterations to drift dynamics (James et al. 2009), while responses to the coupling of flow regime alteration with other stressors are likely to be complex and require detailed experimental research to identify causative mechanisms (e.g., Matthaei et al. 2010). Several researchers have advocated the use of biological and functional traits to explore mechanisms associated with responses macroinvertebrates to anthropogenic impacts (Brooks et al. 2011; Lange et al. 2014; Walters 2011). Across the upper Ringarooma catchment, high levels of water allocation were associated with reductions in the taxonomic richness and density of macroinvertebrate communities, while responses of flow obligate and avoiding taxa (negative and positive, respectively) at the sites with high land use and water use support the suggestion that changes in the flow regime were a driver of these changes. Spatial and temporal patterns of water extraction in freeflowing rivers (e.g., pumping timing and rates, distribution of exaction points, etc.) may influence the characteristics of hydrologic impacts and ecological responses. Heavy water extraction may produce hydrologic impacts that are similar to “hydropeaking” below hydroelectric dams, which typically increases sub-daily flow variability due to changes in energy demand and power station operation (Bevelhimer et al. 2015). The ecological effects of hydropeaking are well documented (e.g., Bruno et al. 2013; Finch et al. 2015; Miller and Judson 2014). However, to our knowledge this aspect of water use has rarely been considered in flow management strategies and underlying environmental flow assessments in river networks without major impoundments. During December–April periods, when baseflows are