Examining the occupancy-density relationship for a low density carnivore

The challenges associated with monitoring low-density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Noninvasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, arguably the most valuable wildlife monitoring tool. For some species, individual identification comes at no cost when unique attributes (e.g., pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive lab processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions. Here, we used a large-scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We used a combination of remote cameras and baited hair snares during 2013–2015 to sample across a 70,096 km2 region of western New York, USA. We fit occupancy and Royle-Nichols models to species detection-nondetection data collected by cameras, and spatial capture-recapture models to individual encounter data obtained by genotyped hair samples. We found a close relationship between grid-cell estimates of fisher state variables from the models using detection-nondetection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Spatially-explicit management recommendations for fisher were similar across models. We discuss design-based approaches to occupancy studies that can improve approximations to density.