Landscape predictors of wolf attacks on bear-hunting dogs in Wisconsin, USA

摘要: Context. In Europe and the United States, wolf–human conflict has increased as wolf populations have recovered recolonised human-dominated ecosystems. These conflicts may lead to negative attitudes towards wolves often complicate management. Wolf attacks on bear-hunting hounds (hereafter, hounds) are second-most common type of depredation domestic animals in Wisconsin, USA, and, typically, most costly terms compensation per individual animal. Understanding geospatial patterns which these depredations occur could promote alternative hunting practices or management strategies that reduce number conflicts.Aims. We compared variables differentiating between non-hounds (e.g., pets), we constructed a spatial, predictive model hounds, explored how landscape risk changed over time.Methods. characterised features hound using logistic regression. applied spatial geographic information system (GIS) display predict areas for attack.Key results. Our correctly classified 84% sites past depredations, 1999–2008, 78% nearby random-unaffected sites. The predicted 82% recent (2009–11) not used construction, thereby validating its power. Risk attack with percentage area public-access land nearby, size nearest pack, proximity decreased human development. National county forest lands had significantly (P < 0.001) more than did other land-ownership types, whereas private fewer.Conclusions. distinctive temporal signatures, peak occurring during black bear training seasons closer centre pack territories, larger packs public access less developed land.Implications. analysis can help hunters avoid high-risk areas, wildlife managers protect recreational use lands, costs predator recovery. present risk-adjusted equation. If choose, required, provide attacked by wolves, while suggest consider adjusting payments basis relative risk.