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1 February 2009 Utility of Expert-Based Knowledge for Predicting Wildlife–Vehicle Collisions
Michael V. Hurley, Eric K. Rapaport, Chris J. Johnson
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Wildlife–vehicle collisions have important ecological, economic, and social effects. In North America and across northern Europe, moose (Alces alces) are one of the largest ungulates hit by motor vehicles. The force and increasing frequency of these collisions has resulted in a commitment by wildlife and transportation agencies to limit or reduce causal factors. In an effort to improve these mitigation strategies, we used the most readily available source of knowledge of collision factors, expert opinion, to develop a series of models that explained and predicted location of moose–vehicle collisions (MVC). We developed expert-based models using the Analytical Hierarchy Process (AHP) and we used a structured survey approach where experts could assess criteria relevancy, weight criteria, and review weights for consistency. We hypothesized that collisions were the product of habitat- or driver-related factors and we formulated the survey accordingly. We used the receiver operating characteristic to validate the resulting models and the Kappa index of agreement to quantify differences among spatial predictions originating from the experts. Local and nonlocal experts weighted the moose habitat classification as the most important criterion for identifying MVC. Among driver-related criteria, speed limit was weighted as the most important factor. Overall, habitat-based models were more proficient than driver-based models in predicting MVC within Mount Revelstoke and Glacier National Parks, Canada. Both local and nonlocal expert models were excellent predictors of MVC, with local experts slightly outperforming nonlocal experts. Considering that habitat-related criteria were more powerful for predicting MVC, and that habitat can vary considerably across study areas, we suggest that local experts be used when possible. The AHP is a valuable tool for wildlife, highway, and park managers to better understand why and where wildlife–vehicle collisions occur. Adopting this process, our data suggested that MVC were most strongly correlated with highway attractants associated with habitat. Vegetation management or alternative routing could minimize spatial juxtaposition of moose and motor vehicles.

Michael V. Hurley, Eric K. Rapaport, and Chris J. Johnson "Utility of Expert-Based Knowledge for Predicting Wildlife–Vehicle Collisions," Journal of Wildlife Management 73(2), 278-286, (1 February 2009).
Published: 1 February 2009

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