Open AccessAdapting a predictive spatial model for wolf Canis spp. predation on livestock in the Upper Peninsula, Michigan, USA
Author(s) -
Edge Justin L.,
Beyer Dean E.,
Belant Jerrold L.,
Jordan Mark J.,
Roell Brian J.
Publication year - 2011
Publication title -
wildlife biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.566
H-Index - 52
eISSN - 1903-220X
pISSN - 0909-6396
DOI - 10.2981/10-043
Subject(s) - livestock , canis , geography , peninsula , predation , pasture , ecology , limiting , forestry , biology , archaeology , engineering , mechanical engineering
Abstract Wolves Canis spp. in the Great Lakes region have expanded into rural areas where livestock production occurs, resulting in an increase of conflicts. We applied a predictive spatial model for livestock predations by wolves developed by Treves et al. (2004; hereafter the ‘2004 model’) to the Upper Peninsula (UP) of Michigan. The 2004 model did not satisfactorily discriminate between townships with (57.1%) and without (65.7%) wolf predations (61.4% overall) that occurred during 15 April 1996 ‐ 14 April 2009. Consequently, we adapted the 2004 model based on deer density and spatial data derived from the UP to maximize the model's predictive ability in the UP. We used matched pair analysis of six landscape variables significant in the 2004 model. Our adapted model improved on the 2004 model, and overall discriminated 70% of townships in our sample (N = 70). Affected townships (i.e. townships with predations) in the UP displayed a consistent set of landscape variables, including relatively higher proportions of pasture/hayfield and crops, and relatively lower proportions of coniferous forest. We extrapolated from the 35 affected townships to the entire UP to generate two maps, available to managers for assistance in predicting townships at higher risk of livestock predation by wolves. As wolves continue to recover in the Great Lakes region, predicting livestock predations by wolves can assist managers in limiting the number of conflicts, as well as costs of control and compensation.