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.亟需开发复杂度较低且可精准识别适宜水生栖息地的方法，以支撑自然资源管理决策。栖息地适宜性模型通过整合环境变量来预测栖息地质量，但既往研究方法在空间与生态维度均存在局限，且极少得到验证。本研究借助公开可得的国家级数据集，开展大空间尺度下的水生栖息地评估工作。本研究以犹他州邦纳维尔割喉鳟（Bonneville Cutthroat Trout）与蓝头吸口鱼（Bluehead Sucker）为研究对象，利用年平均流量百分比（percent mean annual discharge, MAD）、流速、坡度与溪流温度的独有组合方案，对15种栖息地适宜性模型展开评估，以预测二者的月度栖息地适宜性。研究通过实测河道内条件对环境变量进行验证，并借助物种出现观测数据对栖息地适宜性评估结果开展校验。结果显示，使用少量环境变量的简易模型可最优预测栖息地适宜性。其中，溪流温度可最优预测邦纳维尔割喉鳟的出现情况，而坡度与年平均流量百分比则可最优预测蓝头吸口鱼的出现情况。额外添加的环境变量虽可在特定月份提升栖息地适宜性预测精度，但会降低整体预测准确性。