“…Water abstraction can increase the frequency and duration of low flows, enhance the effects of natural droughts and generate artificial droughts (Finn, Boulton & Chessman, 2009;Brooks, Chessman & Haeusler, 2011). see Richards et al, 1997;Dol edec et al, 2006;Mellado-D ıaz, Su arez Alonso & Vidal-Abarca Guti errez, 2008), but the effects of water abstraction on trait composition have been studied much less often (Brooks et al, 2011) than the effects of natural flow variations and droughts (Williams, 1996;Bêche et al, 2006;Bêche & Resh, 2007;Bonada, Rieradevall & Prat, 2007). see Richards et al, 1997;Dol edec et al, 2006;Mellado-D ıaz, Su arez Alonso & Vidal-Abarca Guti errez, 2008), but the effects of water abstraction on trait composition have been studied much less often (Brooks et al, 2011) than the effects of natural flow variations and droughts (Williams, 1996;Bêche et al, 2006;Bêche & Resh, 2007;Bonada, Rieradevall & Prat, 2007).…”
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.
“…Water abstraction can increase the frequency and duration of low flows, enhance the effects of natural droughts and generate artificial droughts (Finn, Boulton & Chessman, 2009;Brooks, Chessman & Haeusler, 2011). see Richards et al, 1997;Dol edec et al, 2006;Mellado-D ıaz, Su arez Alonso & Vidal-Abarca Guti errez, 2008), but the effects of water abstraction on trait composition have been studied much less often (Brooks et al, 2011) than the effects of natural flow variations and droughts (Williams, 1996;Bêche et al, 2006;Bêche & Resh, 2007;Bonada, Rieradevall & Prat, 2007). see Richards et al, 1997;Dol edec et al, 2006;Mellado-D ıaz, Su arez Alonso & Vidal-Abarca Guti errez, 2008), but the effects of water abstraction on trait composition have been studied much less often (Brooks et al, 2011) than the effects of natural flow variations and droughts (Williams, 1996;Bêche et al, 2006;Bêche & Resh, 2007;Bonada, Rieradevall & Prat, 2007).…”
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.
“…The accuracy of the LEDA models for the ACT, GL, and Y T data sets, measured as the average BC similarity between observed and modeled assemblages for unimpacted validation samples, was mostly within or above the range of 0.4 to 0.6 achieved in previous LEDA modeling for various biotic groups (Chessman et al 2008a, Brooks et al 2011. For all 3 data sets, accuracy rose significantly and substantially as the SP increased.…”
Section: Model Performancementioning
confidence: 54%
“…LEDA modeling (Chessman et al 2008a, Brooks et al 2011 creates biological reference data for comparison with observed data for any sample of interest (hereafter a target sample) as a simple average of reference-site samples that are environmentally matched to the target sample. Reference samples that are not matched to the target sample make no contribution to the prediction for that sample, although they may contribute to the prediction for other samples.…”
“…For instance, Brooks et al (2011) used traits of macroinvertebrate families with ordinal-scale states (sensu Poff et al 2006), such as swimming ability and occurrence in drift, to characterize river communities. They coded these states by integer values (e.g., swimming ability: none [code = 1], weak [2], and strong [3]) and then 'standardized' these values by division using the maximum value for the given trait (here 3).…”
Section: Difficulties In Handling Ordinal-scale Datamentioning
confidence: 99%
“…For the same reason, the Euclidean distance measure is incompatible with ordinal-scale data (Podani 2000, Podani andSchmera 2006). The solutions to this problem are: 1) the use of analytical methods developed for ordinal scale (Podani 2000(Podani , 2005, 2) the reduction of ordinal variables to nominal ones (as made by Poff et al 2006), or 3) the expansion of the ordinal scale to an interval (or ratio) scale (as done in some analyses by Brooks et al 2011). However, the expansion of the data scale should be justified and explained clearly to avoid mathematically nonsensical statements (i.e., standardization of an ordinal scale variable by its maximum).…”
Section: Difficulties In Handling Ordinal-scale Datamentioning
Traits-based community analyses are receiving increasing attention. However, consistent interpretation of empirical results and ecological understanding in stream ecology are limited by ambiguous terminology. Furthermore, the measurement scales used to analyze trait data, especially ordinal-scale data, are often inappropriately applied. We identify and discuss these shortcomings and offer a solution for an operative and algebraically correct treatment of traits and a unified nomenclature that facilitates direct comparison among traits-based studies. A unified terminology allows for logical translation among existing, alternative trait nomenclatures and should facilitate communication of research findings among stream ecologists and more directly connect stream traits-based research with general ecology.
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