Summary: | Fine-scale responses of wildlife to crossing roads and interacting with vehicles can be translated to coarse-scale behaviors measured by GPS radiotelemetry. Telemetry facilitates evaluating and predicting the relationship of wildlife to roads when direct observations of wildlife-road interactions are not available. We predicted wolverine response to roads and traffic using radiotelemetry. Our findings show how researchers can acquire a more holistic understanding of habitat suitability by quantifying both habitat selection and movement simultaneously. The fine-scale behavior of wildlife when crossing roads and interacting with traffic is likely to mirror natural responses to predation risk including not responding, pausing, avoiding, or increasing speed during crossing. We generated coarse-scale behavioral predictions based on these expectations that could be assessed with GPS radiotelemetry. We evaluated our predictions using an integrated step-selection analysis of wolverine (Gulo gulo luscus) space use in relation to spatially and temporally dynamic vehicle traffic on industrial roads in northern Alberta. We compared support for alternative models of road avoidance, increased speed near roads, and road avoidance and increased speed near roads. We predicted that wolverines would avoid roads and increase their speed near roads and that these behaviors would increase with traffic volume. We found that vehicle traffic was relatively low (0–30 vehicles/12 h) but important for explaining wolverine space use. Top winter and summer models indicated that wolverines avoided and increased speed near roads. Wolverine movement, but not avoidance, increased with traffic volume. We suggest that movement is a fine-scaled response that is more responsive to vehicle traffic than habitat selection. We show that roads, regardless of traffic volume, reduce the quality of wolverine habitats and that higher-traffic roads might be most deleterious. We suggest that wildlife behavior near roads should be viewed as a continuum and ...
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