Forecasting wildlife movement with spatial capture-recapture

Wildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture-recapture modeling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals’ movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time. We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased, and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simul..., , , # Forecasting Wildlife Movement with Spatial Capture-Recapture [https://doi.org/10.5061/dryad.xpnvx0kn2](https://doi.org/10.5061/dryad.xpnvx0kn2) The file Right\_Whale\_SCR\_Data.RData contains the data used for the analysis of right whale movements and density. ## Description of the data and file structure Data contained in Right\_Whale\_SCR\_Data.RData are formatted in a list, named \"data\", and are loaded into R and analyzed by the Right\_whale\_scr\_models.R script. The list data includes the following objects. y: The right whale encounter history. A matrix with the element in row *i*, column *j* equal to 1 if individual *i* was detected on sampling occasion *j* and 0 otherwise. detDists: A matrix of detection distances. If individual *i* was detected on occasion *j*, row *i*, column *j* holds the distance between where it was detected and the closest line transect survey effort. y.pix: A matrix of sub-regions/pixels in which individuals were detected. If individual *i* was de...