Can biological traits of stream invertebrates help disentangle the effects of multiple stressors in an agricultural catchment?

Abstract

Summary Biological traits, which may give insights into the mechanisms driving the distribution of organisms along gradients of stressor intensities, have been proposed as a tool for disentangling the effects of multiple stressors acting simultaneously on scales ranging from climatic region to river basins, valleys, reaches and microhabitats. However, the combined effects of farming intensity and flow reduction on biological traits of stream invertebrates remain to be studied. We assessed the benthic invertebrate community and physicochemistry at 43 stream sites along gradients of farming intensity (0–95% of the catchment in intensively managed grassland) and water abstraction (0–92% streamflow reduction). Using general linear models and an information‐theoretic approach, we studied individual and combined effects of agricultural stressors on invertebrate traits and community composition. Traits often followed predictable patterns along stressor gradients, and non‐additive interactions between paired stressors were common. Farming intensity was more frequently related to life‐history, resistance and resilience traits, whereas water abstraction was correlated more often with general biological traits such as feeding habits, dietary preference and respiration. Further, traits and traditional measures of community structure, such as taxon relative abundances and community indices, offered a similar level of distinction along the gradients of stressor intensities. Our findings indicate that invertebrate traits can differentiate the effects of multiple stressors and provide insights into potential mechanisms. At the landscape scale, farming intensity exerted stronger effects via invertebrate habitat quality and water abstraction via food availability. At the reach scale, both fine sediment and nutrients affected habitat quality, whereas nutrients showed more marked effects via food availability. Finally, we propose a suite of traits that may provide the strongest differentiation of stressor intensities.

Effects of flow events and nutrient addition on stream periphyton and macroinvertebrates: an experimental study using flumes

“…Elbrecht et al (2016) did a similar study in Europe, and the results supported Matthaei et al (2010). Also Lange et al (2014) studied the effect of farming intensity and water abstraction on macroinvertebrate communities in 43 stream sites. However, farming intensity not only affected nutrient concentrations, but also fine sediment levels on the stream bed, as well as temperature and light conditions.…”

Functional diversity: a review of methodology and current knowledge in freshwater macroinvertebrate research

“…In 2014, almost all reviewed papers used Rao's quadratic entropy for measuring functional diversity of freshwater macroinvertebrates (e.g. Boersma et al, 2014;Feld et al, 2014;Kovalenko et al, 2014;Lange et al, 2014;Vaz et al, 2014).…”

Putting Flow–Ecology Relationships into Practice: A Decision-Support System to Assess Fish Community Response to Water-Management Scenarios

“…Correlations among ecologically relevant components of the flow regime may exist, with potential to bias predictions of ecological response; in which case modeling approaches robust to multicollinearity (e.g., machine learning techniques) could be explored. Furthermore, the interactive effects of streamflow, water quality, and physical habitat characteristics on ecological responses are widely recognized [72][73][74] but rarely incorporated into flow-ecology relationships [28,75]. Data on other limiting factors in fluvial ecosystems (e.g., concentrations of nutrients, dissolved oxygen, and suspended sediment) are typically not available at the same temporal extent and resolution as streamflow data (i.e., daily observations spanning decades), however a variety of existing modeling approaches (e.g., [76,77]) may prove useful to incorporate estimates for these variables into predictive models of stream ecology.…”