A maximum entropy model for predicting wild boar distribution in Spain

Abstract

Wild boar (Sus scrofa) populations in many areas of the Palearctic including the Iberian Peninsula have growncontinuously over the last century. This increase has led to numerous different types of conflicts due to the damagethese mammals can cause to agriculture, the problems they create in the conservation of natural areas, and the threatthey pose to animal health. In the context of both wildlife management and the design of health programs for diseasecontrol, it is essential to know how wild boar are distributed on a large spatial scale. Given that the quantifying of thedistribution of wild species using census techniques is virtually impossible in the case of large-scale studies, modelingtechniques have thus to be used instead to estimate animals’ distributions, densities, and abundances. In this study,the potential distribution of wild boar in Spain was predicted by integrating data of presence and environmental variablesinto a MaxEnt approach. We built and tested models using 100 bootstrapped replicates. For each replicate or simulation,presence data was divided into two subsets that were used for model fitting (60% of the data) and cross-validation(40% of the data). The final model was found to be accurate with an area under the receiver operating characteristiccurve (AUC) value of 0.79. Six explanatory variables for predicting wild boar distribution were identified on the basisof the percentage of their contribution to the model. The model exhibited a high degree of predictive accuracy, whichhas been confirmed by its agreement with satellite images and field surveys.Additional key words:Sus scrofa; environmental suitability; MaxEnt; spatial distribution; wildlife management;geographic information.