Abstract
The patterns of biodiversity in freshwater systems are shaped by biogeography, environmental gradients, and human-induced factors. In this study, we developed empirical models to explain fish species richness in subbasins of the Arkansas–White–Red River basin as a function of discharge, elevation, climate, land cover, water quality, dams, and longitudinal position. We used information-theoretic criteria to compare generalized linear mixed models and identified well-supported models. Subbasin attributes that were retained as predictors included discharge, elevation, number of downstream dams, percent forest, percent shrubland, nitrate, total phosphorus, and sediment. The random component of our models, which assumed a negative binomial distribution, included spatial correlation within larger river basins and overdispersed residual variance. This study differs from previous biodiversity modeling efforts in several ways. First, obtaining likelihoods for negative binomial mixed models, and thereby avoiding reliance on quasi-likelihoods, has only recently become practical. We found the ranking of models based on these likelihood estimates to be more believable than that produced using quasi-likelihoods. Second, because we had access to a regional-scale watershed model for this river basin, we were able to include model-estimated water quality attributes as predictors. Thus, the resulting models have potential value as tools with which to evaluate the benefits of water quality improvements to fish.
