1. Space-use behaviour reflects trade-offs in meeting ecological needs and can have consequences for individual survival and population demographics. The mechanisms underlying space-use can be understood by simultaneously evaluating habitat selection and movement patterns, and fine-resolution locational data are increasing our ability to do so. 2. We use high-resolution location data and an integrated step-selection analysis to evaluate caribou, moose, bear, and wolf habitat selection and movement behavior in response to anthropogenic habitat modification, though caribou data were limited. Space-use response to anthropogenic linear features (LFs) by predators and prey are hypothesized to increase predator hunting efficiency and are thus believed to be a leading factor in woodland caribou declines in western Canada. 3. We found that all species moved faster while on LFs. Wolves and bears were also attracted towards LFs, whereas prey species avoided them. Predators and prey responded less strongly and consistently to natural features such as streams, rivers and lakeshores. These findings are consistent with the hypothesis that LFs facilitate predator movement and increase hunting efficiency, while prey perceive such features as risky. 4. Understanding the behavioural mechanisms underlying space-use patterns is important in understanding how future land-use may impact predator-prey interactions. Explicitly linking behaviour to fitness and demography will be important to fully understand the implications of management strategies.

Adapted from Methods: The probability of selection was modelled as a function of landcover, distance to anthropogenic LFs, polygonal disturbances and riparian areas. The natural logarithm of step length, cosine of turning angle, and their interaction were included as modifiers of the observed movement parameters (used to generate random steps; Avgar et al. 2016). To allow the selection-free displacement rate to vary with feature type, the interaction between the natural logarithm transformed step length and each disturbance type of interest and riparian habitat were also included. We used inverse-variance weighted linear modelling to obtain population-level averages for each species (Murtaugh, 2007). The iSSA coefficient values for each variable were used as the response variable in a linear regression, with the variable’s availability (to account for potential functional responses; Mysterud & Ims, 1998), and the values of other iSSA coefficients that might be correlated with it as predictors, and the inverse of the estimated variance of the coefficient value as weights (using base R; R Core Team, 2014). The resulting intercept from each model can be interpreted as the average response to the feature, accounting for its availability and the uncertainty in each individual`s response.

Additional co-authors:
Scott McNay, Wildlife Infometrics

Glenn Sutherland, Wildlife Infometrics