Predicting wild boar damages to croplands in a mosaic of agricultural and natural areas. Issue 2 (1st April 2014)

• Record Type: Journal Article
• Authors: Ficetola, Gentile Francesco; Bonardi, Anna; Mairota, Paola; Leronni, Vincenzo; Padoa-Schioppa, Emilio
• Is Part Of: Current zoology. Volume 60:Issue 2(2014)
• Page Start: 170
• Page End: 179
• Publication Date: 2014-04-01
• Subjects: Environmental heterogeneity -- Human-wildlife conflict -- Landscape composition -- Predictive distribution model -- Protected areas -- Spatial scale -- Sus scrofa.; Zoology -- Periodicals; Zoology -- China -- Periodicals; 590.5
• DOI: 10.1093/czoolo/60.2.170
• Languages: English
• ISSNs: 1674-5507

Abstract

Abstract: Crop damages by wildlife is a frequent form of human-wildlife conflict. Identifying areas where the risk of crop damages is highest is pivotal to set up preventive measures and reduce conflict. Species distribution models are routinely used to predict species distribution in response of environmental changes. The aim of this paper was assessing whether species distribution models can allow to identify the areas most at risk of crop damages, helping to set up management strategies aimed at the mitigation of human-wildlife conflicts. We obtained data on wild boar Sus scrofa damages to crops in the Alta Murgia National Park, Southern Italy, and related them to landscape features, to identify areas where the risk of wild boar damages is highest. We used MaxEnt to build species distribution models. We identified the spatial scale at which landscape mostly affects the distribution damages, and optimized the regularization parameter of models, through an information-theoretic approach based on AIC. Wild boar damages quickly increased in the period 2007-2011; cereals and legumes were the crops more affected. Large areas of the park have a high risk of wild boar damages. The risk of damages was related to low cover of urban areas or olive grows, intermediate values of forest cover, and high values of shrubland cover within a 2-km radius. Temporally independent validation data demonstrated that models can successfully predict damages in the future. Species distribution models can accurately identify the areas most at risk of wildlife damages, as models calibrated on data collected during only a subset of years correctly predicted damages in the subsequent year.