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

Anthropogenic linear features often alter wildlife behaviour and movement. Landscape features, such as habitat, can have important mediating effects on wildlife response to disturbance and yet are rarely explicitly considered in how habitat and disturbance interact. We tested the movement and space-use responses of GPS-collared wolves to linear features with respect to adjacent habitat variation. We simultaneously modeled wolf movement and selection within a conditional logistic regression framework (integrated Step Selection Analysis). We explicitly considered how adjacent habitat alters these responses through putative effects such as movement friction. Classifying linear features based on the selection and movement response of wolves revealed that pairing transmission lines and primary roads increased the avoidance response to be greater than either feature on their own and provided evidence of a semi-permeable barrier to movement. In contrast, features with reduced human a..., 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. , , # Movement-integrated habitat selection reveals wolves balance ease of travel with human avoidance in a risk-reward trade-off

[https://doi.org/10.5061/dryad.ksn02v7g1](https://doi.org/10.5061/dryad.ksn02v7g1)

## Description of the data and file structure

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. 

GPS locations have been removed for conservation purposes. Please co...