Modeling Species Distribution using MAXENT

We used for modeling the software MAXENT [30] , a machine learning algorithm that applies the principle of maximum entropy to predict the potential distribution of species from presence-only data and environmental variables [26] . Currently, this widely used method is particularly efficient to handle complex interactions between response and predictor variables [15] , [28] , and is little sensitive to small sample sizes [29] . All models were computed using the version 3.3.3k of MAXENT (http://www.cs.princeton.edu/~schapire/maxent/). Runs were conducted with the default variable responses settings, and a logistic output format which results in a map of habitat suitability of the species ranging from 0 to 1 per grid cell, wherein the average observation should be close to 0.5 [15] . The models were evaluated by the area under the ROC curve (AUC), and three measures of overlap with the unbiased model (see below section “Model evaluation and statistical analyses”).