Use of ecological niche modelling to predict distributions of freshwater fish species in Kansas

An essential innovation in aquatic biodiversity research would be a robust approach to accurately predict species' potential distributions. In this paper, I conduct an analysis to test the efficacy of ecological niche modelling for predicting fish species' potential distributions using an artificial-intelligence algorithm, the Genetic Algorithm for Rule-Set Prediction (GARP). Models of species' ecological niches are developed using GARP, and projected onto geography to predict species distributions. To test the validity of this approach, I used freshwater fish distribution data for twelve fish species occurring in Kansas. These taxa were chosen to represent phylogenetic, distribution, and habit diversity. I subset these data by omitting half of the counties from model building, and test models using the omitted counties. Models were tested using Receiver Operating Characteristic (ROC) analyses. Of the species tested, all were statistically significant with the models showing excellent predictive ability. Omission errors across taxa ranged from 0 to 17%. This inferential capacity opens doors to many synthetic analyses based on primary point-occurrence data.