Asymmetric dispersal in freshwater species distribution models varies depending on the spatial pattern of fish species

Abstract: Aim We estimated the relative contribution of environmental and dispersal predictors in distribution models of freshwater species with different distribution ranges (29 fish species). Specifically, we tested whether model performances vary depending on the fish species' range and distribution across sub-basins and whether the relationship with those predictors is stronger depending on the type of variables (environmental or asymmetrical dispersal) used for modeling. Methods The study area used for modeling was the Tocantins-Araguaia River basin, encompassing the entire hydrographic network. We applied six niche modeling methods to project the geographic distributions of 29 fish species within the Tocantins-Araguaia River basin, using environmental and asymmetrical dispersal predictors. Results Generally, the models built using dispersal predictors generated more accurate predictions than those using environmental variables. However, although we found no significant difference in the accuracy among models built using different variables, their accuracy metrics were correlated with the species range and distribution across sub-basins. Also, more restricted species (i.e., lower range and distribution limited to one sub-basin) showed a greater difference in model accuracy between models built using dispersal and environmental predictors, with more accurate models being generated for restricted species when modelled using dispersal-related predictors only. Conclusions the use of asymmetric dispersal predictors in SDM, besides generating accurate models, avoids/reduces model overpredictions to geographically close and climatically suitable areas, especially for restricted species, by predicting the species’ distribution based on their dispersal routes through the actual directional gradient of the basin’s hydrographic network.