Reduced Complexity Models for Aquatic Habitat Suitability at the Regional Scale

Methods that accurately identify suitable aquatic habitat with minimal complexity are need to inform resource management. Habitat suitability models intersect environmental variables to predict habitat quality, but previous approaches are spatially and ecologically limited, and are rarely validated. This study estimated aquatic habitat at large spatial scales with publicly-available national datasets. We evaluated 15 habitat suitability models using unique combinations of percent mean annual discharge (MAD), velocity, gradient, and stream temperature to predict monthly habitat suitability for Bonneville Cutthroat Trout and Bluehead Sucker in Utah. Environmental variables were validated with observed instream conditions and species presence observations verified habitat suitability estimates. Results indicated that simple models using few environmental variables best predict habitat suitability. Stream temperature best predicted Bonneville Cutthroat Trout presence, and gradient and percent MAD best predicted Bluehead Sucker presence. Additional environmental variables improved habitat suitability accuracy in specific months, but reduced overall accuracy.