Assessing the accuracy of species distribution models more thoroughly

Abstract: Species distribution models (SDMs) are empirical models relating species occurrence to environmental variables based on statistical or other response surfaces. SDMs can be used as a tool to solve some theoretical and applied ecological and environmental problems. The success of their applications depends on the accuracy of the models. In this study we propose an approach to thoroughly assess the accuracy of species distribution models. This includes three aspects: First is to use several accuracy indices that not only measure model discrimination capability, but also those that measure model reliability. The former is the power of the model that differentiates presences from absences; and the latter refers to the capability of the predicted probabilities to reflect the true probabilities that species occurs in individual locations. Previous studies have shown that some accuracy measures are sensitive to the prevalence of the test dataset, and that others are not. While all the reliability measures display this sensitivity to prevalence, only do some discriminatory measures fall into the latter group. Many researchers recommend the use of prevalence-insensitive measures in model accuracy assessment. However, using this approach the calibration power of the models cannot be assessed. We argue that calibration measures should also be provided in model accuracy assessments