Smallmouth bass (Micropterus dolomieu) is a warm-water fish species that is native to central and eastern North America. Climate change scenarios predict further extension northward of suitable habitat for smallmouth bass, which may negatively affect native fish species. We developed and compared predictive models of the distribution of bass in North America using four statistical approaches: logistic regression, classification tree, discriminant analysis, and artificial neural networks. We collected 4181 geo-referenced records of smallmouth bass occurrence and matched them with climate data. Artificial neural networks performed the best with the highest sensitivity (correctly predicting species presence) and specificity (correctly predicting absence), followed by discriminant analysis. Artificial neural networks indicated that winter air temperatures were the most important predictors of smallmouth bass occurrence, whereas the other approaches indicated that summer air temperatures were the best predictors of bass occurrence. Logistic regression and classification tree exhibited very low sensitivity, but very high specificity as a result of the large proportion of absences within the data set. Business-as-usual climate change scenarios suggest that smallmouth bass are expected to have suitable thermal habitat throughout most of Canada and the continental United States by 2100.
Classification Tree Extraction from Trained Artificial Neural Networks
