A non-Euclidean movement model to quantify landscape connectivity with open spatial capture-recapture models

Open Population Spatial Capture-Recapture (OPSCR) models provide a unifying framework to simultaneously model demography and movement while accounting for imperfect detection of individuals. In OPSCR models, movements of individuals between primary occasions usually follow a random walk process that neglects the role of the landscape.

Here, we developed a non-Euclidean OPSCR model to explicitly estimate the extent to which individual movements are impeded or facilitated by spatial descriptors of the landscape, also referred to as landscape connectivity. 

We used simulations to validate the robustness of the model and then applied it to a 5-year, non-invasive genetic monitoring dataset of brown bears (Ursus arctos) in the Pyrenees mountain range (France, Spain and Andorra). We found smaller interannual movement of bears that were close to roads. The estimated resistance of the distance to roads was negative (-1.86 [-2.91, -0.37] and -1.77 [-2.85, -0.60] for females and males, respectively), meaning that the cost of moving increased close to roads.  

Our new OPSCR model provides a data-driven tool to assess the impact of landscape fragmentation on population connectivity using non-invasive spatial capture-recapture data.