Hydropower Plants as Dispersal Barriers in Freshwater Species Distribution Models: Using Restrictions through Asymmetrical Dispersal Predictors

Hydropower plants represent one of the greatest threats for freshwater fish by fragmenting the habitat and avoiding the species dispersal. This type of dispersal barrier is often disregarded when predicting freshwater species distribution due to the complexity in inserting the species dispersal routes, and thus the barriers, into the models. Here, we evaluate the impact of including hydroelectric dams into species distribution models through asymmetrical dispersal predictors on the predicted geographic distribution of freshwater fish species. For this, we used asymmetrical dispersal (i.e., AEM) as predictors for modeling the distribution of 29 native fish species of Tocantins-Araguaia River basin. After that, we included the hydropower power plant (HPP) location into the asymmetrical binary matrix for the AEM construction by removing the connections where the HPP is located, representing the downstream disconnection a dam causes in the fish species dispersal route. Besides having higher predicted accuracy, the models using the HPP information generated more realistic predictions, avoiding overpredictions to areas suitable but limited to the species dispersal due to an anthropic barrier. Furthermore, the predictions including HPPs showed higher loss of species richness and nestedness (i.e., loss of species instead of replacement), especially for the southeastern area which concentrates most planned and built HPPs. Therefore, using dispersal constraints in species distribution models increases the reliability of the predictions by avoiding overpredictions based on premise of complete access by the species to any area that is climatically suitable regardless of dispersal barriers or capacity. In conclusion, in this study, we use a novel method of including dispersal constraints into distribution models through a priori insertion of their location within the asymmetrical dispersal predictors, avoiding a posteriori adjustment of the predicted distribution.