Movement-integrated habitat selection reveals wolves balance ease of travel with human avoidance in a risk-reward trade-off

We captured and collared 46 wolves across 12 packs in eastern Manitoba between 2014-2019. Each wolf was fit with a GPS telemetry collar programmed to collect a GPS relocation every two hours across all seasons.

To produce the dataset, GPS locations were used to generate steps (linear connection between consecutive locations) using the integrated Step Selection Function (iSSA) framework. For a given used step, we randomly generated ten steps based on observed distributions of individual-level movement behaviour. We then extracted habitat covariates for each start and end point of a step, including proportion of forest, distance to linear features, and time of day.