The prediction of moose-vehicle collisions in Mount Revelstoke and Glacier National Parks, Canada

Moose (Alces alces)-vehicle collisions (MVC) can result in large ecological and socio-economic costs. The increasing number of MVC across Canada are resulting in population-level effects for moose, greater numbers of human injury and mortality, and increased costs to motorists and insurance companies. In my thesis I developed a set of predictive models to better understand MVC and the locations where they might occur on the Trans Canada Highway bisecting Mount Revelstoke and Glacier National Parks in British Columbia, Canada.;The logistic regression model based on GIS data was the most successful predictor of MVC. Among the local-scale logistic regression models, the moose evidence model correctly classified the most MVC. For this model, variables moose tracks and game trails were statistically significant predictors of MVC. Of the expert-based models, habitat-related criteria were more effective at predicting MVC than driver-related criteria. Most experts weighted moose habitat as the most important factor influencing MVC. Local experts provided weightings that best represented MVC.;The SRC and KIA suggested that habitat-based expert models were more closely associated with the logistic regression model using GIS data than were driver-based expert models. The logistic regression model was only a slightly better predictor of MVC when compared to the expert-based models. In many cases, empirical data may not be available for constructing logistic regression models, thus expert-based modeling can be used as a substitute for developing effective predictive models. Highway planning to reduce MVC risk within Mount Revelstoke and Glacier National Parks should begin by assessing landscape-scale models using both logistic regression and expert-based modeling. Furthermore, finer-scale models using logistic regression moose evidence and habitat models should be completed at high risk MVC locations previously identified by the landscape-scale analysis.;I used logistic regression and expert-based approaches to develop MVC predictive models. Logistic regression models represented local-scale/field-based hypotheses of driver visibility, moose evidence, highway design, roadside vegetation, and moose habitat, as well as landscape-scale hypotheses based on Geographical Information System (GIS) data. I used the Analytical Hierarchy Process (AHP) to develop the expert-based models. Experts were classified as either local or non-local depending on whether their career-related experience was within the 2 National Parks. Experts weighted variables that were within either habitat or driver models. I used the Receiving Operator Characteristic (ROC) to measure the predictive accuracy of the logistic regression and expert-based models. I used the Kappa Index of Agreement (KIA) to compare maps of predicted MVC susceptibility generated using logistic regression and expert-based models.