Integrating Resource Selection Information with Spatial   Capture-Recapture
  Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSF) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture-recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. We develop a statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch-traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models.   Our method leads to increases in precision for estimating population density and parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of resource selection functions and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. Finally, we find that SCR models using standard symmetric and stationary encounter probability models produce biased estimates of density when animal space usage is related to a landscape covariate. Therefore, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture-recapture methods. 
J. Andrew Royle and Richard B. Chandler
q-bio.QM stat.AP