The ghost of unbalanced species distribution data in geographical model predictions

Unbalanced samples are considered a drawback in predictive modelling of species' potential habitats, and a prevalence of 0.5 has been extensively recommended. We argue that unbalanced species distribution data are not such a problem from a statistical point of view, and that good models can be obtained provided that the right predictors and cut-off to convert probabilities into presence/absence are chosen. The effects of unbalanced prevalence should not be confused with those of low-quality data affected by false absences, low sample size, or unrepresentativeness of the environmental and spatial gradient. Finally, we point out the necessity of greater research effort aimed at improving both the quality of training data sets, and the processes of validating and testing of models.