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 grown continuously over the last century. This increase has led to numerous different types of conflicts due to the damage these mammals can cause to agriculture, the problems they create in the conservation of natural areas, and the threat they pose to animal health. In the context of both wildlife management and the design of health programs for disease control, it is essential to know how wild boar are distributed on a large spatial scale. Given that the quantifying of the distribution of wild species using census techniques is virtually impossible in the case of large-scale studies, modeling techniques 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 variables into 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 characteristic curve (AUC) value of 0.79. Six explanatory variables for predicting wild boar distribution were identified on the basis of the percentage of their contribution to the model. The model exhibited a high degree of predictive accuracy, which has 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. This work has one supplementary table and three supplementary figures that do not appear in the printed article but that accompany the paper online.Abbreviations used: AUC (area under the receiver operating characteristic curve); GAM (generalised additive models); GBIF (global biodiversity information facility); GIMMS (global inventory modeling and mapping studies); GLM (generalized linear model); GRASS (geographic resources analysis support system); MaxEnt (maximum entropy); NDVI (normalized difference vegetation index); ROC (receiver operating characteristic); SDM (species distribution model); SRTM (shuttle radar topography mission); VCF (vegetation continuous fields); VIF (variance inflated factor).

Assessing the Risk of African Swine Fever Introduction into the European Union by Wild Boar

“…For example, the risk estimator of wild boar‐suitable habitat at the European border could be estimated only for EU target countries, as data on vegetation coverage were not available for the non‐EU countries of origin. It may be possible to improve estimates of wild boar‐suitable habitat by using maximum entropy models based on ecological parameters (Bosch et al., ), which can capture biological variability and uncertainty (Elith et al., ). Incorporating recent FAO data on wild boars obtained with hunting bugs (Khomenko et al., ) may also improve models of suitable habitat.…”

Wild boar in focus: Review of existing models on spatial distribution and density of wild boar and proposal for next steps

“…If applied, large volumes of occurrence data could be used to model the current distribution of boar, providing absence or non-zero data, replacing dependence on the more qualitative expert estimates from the IUCN. This study builds on a method of expert classification outlined in similar publications by the same author (Bosch et al, 2012;Bosch et al, 2014a). Based on the GLOBCOVER, a global map of land cover with 0.09 km 2 resolution, 10 experts assigned each of the 23 classes a value corresponding to habitat quality; 0 for absence, 0.1 for unsuitable, 1 suitable food or shelter, 1.5 suitable food and shelter but predominately only one, 2 suitable food and shelter.…”

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High probability areas for ASF infection in China along the Russian and Korean borders

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