Predictive Models of Fish Species Distributions: A Note on Proper Validation and Chance Predictions

Abstract The prediction of species distributions is a primary goal in the study, conservation, and management of fisheries resources. Statistical models relating patterns of species presence or absence to multiscale habitat variables play an important role in this regard. Researchers, however, have paid little attention to how improper model validation and chance predictions can result in unfounded confidence in the performance and utility of such models. Using simulated and empirical data for 40 lake and stream fish species, we demonstrate that the commonly employed resubstitution approach to model validation (in which the same data are used for both model construction and prediction) produces highly biased estimates of correct classification rates and consequently an inaccurate perception of true model performance. In contrast, a jackknife approach to validation resulted in relatively unbiased estimates of model performance. The estimated rates of model correct classification are also shown to be substa...

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