Predicting Human‐Carnivore Conflict: a Spatial Model Derived from 25 Years of Data on Wolf Predation on Livestock
Abstract: Many carnivore populations escaped extinction during the twentieth century as a result of legal protections, habitat restoration, and changes in public attitudes. However, encounters between carnivores, livestock, and humans are increasing in some areas, raising concerns about the costs of...
| Published in: | Conservation Biology |
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| Main Authors: | , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2004
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1111/j.1523-1739.2004.00189.x https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1523-1739.2004.00189.x http://onlinelibrary.wiley.com/wol1/doi/10.1111/j.1523-1739.2004.00189.x/fullpdf |
| Summary: | Abstract: Many carnivore populations escaped extinction during the twentieth century as a result of legal protections, habitat restoration, and changes in public attitudes. However, encounters between carnivores, livestock, and humans are increasing in some areas, raising concerns about the costs of carnivore conservation. We present a method to predict sites of human‐carnivore conflicts regionally, using as an example the mixed forest‐agriculture landscapes of Wisconsin and Minnesota (U.S.A.). We used a matched‐pair analysis of 17 landscape variables in a geographic information system to discriminate affected areas from unaffected areas at two spatial scales (townships and farms). Wolves ( Canis lupus ) selectively preyed on livestock in townships with high proportions of pasture and high densities of deer ( Odocoileus virginianus ) combined with low proportions of crop lands, coniferous forest, herbaceous wetlands, and open water. These variables plus road density and farm size also appeared to predict risk for individual farms when we considered Minnesota alone. In Wisconsin only, farm size, crop lands, and road density were associated with the risk of wolf attack on livestock. At the level of townships, we generated two state‐wide maps to predict the extent and location of future predation on livestock. Our approach can be applied wherever spatial data are available on sites of conflict between wildlife and humans. |
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