Landscape predictors of wolf attacks on bear-hunting dogs in Wisconsin, USA
Erik R. Olson A B D , Adrian Treves B , Adrian P. Wydeven C and Stephen J. Ventura B
+ Author Affiliations
- Author Affiliations
A Northland College, Department of Natural Resources, 1411 Ellis Avenue, Ashland, WI 54806, USA.
B University of Wisconsin – Madison, Nelson Institute for Environmental Studies, 550 N Park Street, 70 Science Hall, Madison, WI 53706, USA.
C 25350 South Garden Avenue, Cable, WI 54821, USA.
D Corresponding author. Email: eolson@northland.edu
Wildlife Research 41(7) 584-597 https://doi.org/10.1071/WR14043
Submitted: 1 March 2014 Accepted: 19 December 2014 Published: 20 March 2015
Abstract
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 < 0.001) more hound depredations than did other land-ownership types, whereas private lands had significantly fewer.
Conclusions: Risk of wolf attacks on hounds had distinctive temporal and spatial signatures, with peak risk occurring during the black bear hound training and hunting seasons and in areas closer to the centre of wolf pack territories, with larger wolf packs and more public access land and less developed land.
Implications: Our analysis can help bear hunters avoid high-risk areas, and help wildlife managers protect wildlife and recreational use of public lands, and reduce public costs of predator recovery. We present a risk-adjusted compensation equation. If wildlife managers choose, or are required, to provide compensation for hounds attacked by wolves, while hunting on public lands, we suggest that managers consider adjusting compensation payments on the basis of the relative landscape of risk.
Additional keywords: black bear, Canis lupus, carnivore coexistence, depredation, hound, human–wildlife conflict, hunting dogs, modelling, risk mapping.
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