| Summary: | Moose (Alces alces)-vehicle collisions (MVC) can be costly by ecologically affecting population numbers, economically by vehicle damage, and socially through human injury or mortality. The purpose of this paper is to identify factors related to moose ecology, driver behaviour, and road design that are useful for predicting the spatial location of MVC on the Trans Canada Highway dissecting Mount Revelstoke and Glacier National Parks. We identified 6 subsets of logistic regression models and used Akaike's Information Criteria (AIC) to determine the most parsimonious model within each subset. In addition to this study being the first to examine collisions within these 2 parks, each of these 6 modelling procedures is unique in predicting MVC. Five of the six subsets modelled local-scale/field-based hypotheses of driver visibility, moose evidence, highway design, roadside vegetation, and mosoe habitat, while the sixth subset examined landscape-scale hypotheses through the use of a Geographic Information System (GIS). The Receiver Operator Characteristic (ROC) discriminated between MVC and random inference sites in order to validate the best fit model from each of the 6 subsets. A MVC probability map along the highway was created using the GIS model, providing a powerful and relatively efficient and inexpensive planning tool. The moose evidence model correctly classified the most MVC among the local-scale models. In relation to the spatial analysis, highway planning to reduce MVC risk within the parks should begin by assessing landscape-scale variables with emphasis on distance to wetland and landscape slope. This landscape-scale analysis should be followed by field-based modelling using moose evidence and habitat-related modelling with important predictors of moose tracks, game trails, and coniferous forest habitat. If highway planning cannot be effective in decreasing MVC, mitigation measures should include a public awareness program, speed reduction, and consideration of an alternative intercept foraging plan.
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