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Context In Europe and the United States, wolf–human conflict has increased as wolf populations have recovered and recolonised human-dominated ecosystems. These conflicts may lead to negative attitudes towards wolves and often complicate wolf management. Wolf attacks on bear-hunting hounds (hereafter, hounds) are the second-most common type of depredation on domestic animals in Wisconsin, USA, and, typically, the most costly in terms of compensation per individual animal. Understanding the geospatial patterns in which these depredations occur could promote alternative hunting practices or management strategies that could reduce the number of wolf–human conflicts. Aims We compared variables differentiating between wolf attacks on hounds and non-hounds (e.g., pets), we constructed a spatial, predictive model of wolf attacks on hounds, and we explored how the landscape of risk changed over time. Methods We characterised landscape features of hound depredations using logistic regression. We applied the spatial model to a geographic information system (GIS) to display spatial patterns and to predict areas of risk for wolf attack. Key results Our model correctly classified 84% of sites of past depredations, 1999–2008, and 78% of nearby random-unaffected sites. The model correctly predicted 82% of recent (2009–11) depredation sites not used in model construction, thereby validating its predictive power. Risk of wolf attack on hounds increased with percentage area of public-access land nearby, size of the nearest wolf pack, proximity of the nearest wolf pack, and decreased with percentage of human development. National and county forest lands had significantly (P
