Estimating spatially variable and density-dependent survival using open-population spatial capture–recapturemodels

Abstract

Open-population spatial capture–recapture (OPSCR) models use the spatialinformation contained in individual detections collected over multiple consec-utive occasions to estimate not only occasion-specific density, but alsodemographic parameters. OPSCR models can also estimate spatial variation invital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatialvariation in survival using spatial covariates but also estimates localdensity-dependent effects on survival within a unified framework. Using simu-lations, we show that OPSCR models provide sound inferences on the effect ofspatial covariates on survival, including multiple competing sources of mortal-ity, each with potentially different spatial determinants. Estimation of localdensity-dependent survival was possible but required more data due to thegreater complexity of the model. Not accounting for spatial heterogeneity insurvival led to up to 10% positive bias in abundance estimates. We provide anempirical demonstration of the model by estimating the effect of country anddensity on cause-specific mortality of female wolverines (Gulo gulo) in centralSweden and Norway. The ability to make population-level inferences onspatial variation in survival is an essential step toward a fully spatially explicitOPSCR model capable of disentangling the role of multiple spatial drivers ofpopulation dynamics. Smortality, nimbleSCR, population dynamics, population-level inferences, wolverines(Gulo gulo)

Estimating spatially variable and density-dependent survival using open-population spatial capture–recapturemodels