Generic habitat suitability criteria (HC) are often developed from spatially and temporally
variable hydroecological datasets to increase generality, cost-effectiveness, and time-efficiency of
habitat models. For benthic macroinvertebrates (BMIs), however, there is no prior knowledge on
the spatiotemporal variation in their habitat preferences and how this may be reflected in the final
environmental flow (e-flow) predictions. In this study, we used a large, spatiotemporally variable
BMI-hydroecological dataset and developed generic, local, and season-specific subsets of HC for
three seasons and two river types within various data pre-treatment options. Each subset was used
to train a fuzzy habitat model, predict the habitat suitability in two hydrodynamically-simulated
river reaches, and develop/compare model-based e-flow scenarios. We found that BMIs shift their
habitat preferences among seasons and river types; consequently, spatiotemporally variable e-flow
predictions were developed, with the seasonal variation being greater than the typological one. Within
this variation, however, we found that with proper data pre-treatment, the minimum-acceptable
e-flows from the generic models mostly (65–90%) lay within the acceptable e-flows predicted by
the local and season-specific models. We conclude that, within specific limitations, generic BMI-HC
can be used for geographically extended, cost-effective e-flow assessments, compensating for the
within-limits loss of predictive accuracy.
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Generic habitat suitability criteria (HC) are often developed from spatially and temporally
variable hydroecological datasets to increase generality, cost-effectiveness, and time-efficiency of
habitat models. For benthic macroinvertebrates (BMIs), however, there is no prior knowledge on
the spatiotemporal variation in their habitat preferences and how this may be reflected in the final
environmental flow (e-flow) predictions. In this study, we used a large, spatiotemporally variable
BMI-hyd...
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