The primary cause of urban stream degradation could be altered flow regime, or the range of stressors associated with urban stormwater runoff or with urban density. We used mixed linear models to assess whether various hydrologic indicators better predicted a macroinvertebrate assemblage composition index (SIGNAL score) than did attenuated imperviousness (AI; a landscape measure of urban stormwater effects) or total imperviousness (TI; a landscape measure of urban density). We found that SIGNAL score was best predicted by AI, with AI strongly preferred over the hydrologic indicators and TI. Predictors in the most plausible hydrologic model characterised the magnitude of low flow antecedent events, overfall flow variability, and antecedent flow flashiness. Our results suggest that while there are aspects of the flow regime that degrade urban streams, AI is a better predictor because it integrates both altered flow regime and other stressors such as reduced in-stream water quality.


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