🤖 AI Summary
This study addresses a key limitation in traditional spatial capture–recapture models, which assume that animal activity centers follow a Poisson process and thereby ignore spatial dependencies arising from social aggregation, territorial behavior, or unobserved habitat preferences, leading to inaccurate density estimates. To overcome this, the authors propose a novel framework based on penalized regression splines, employing Laplace-approximated penalized marginal maximum likelihood to fit a log-Gaussian Cox process. This approach flexibly captures nonlinear effects of covariates on animal density and, for the first time, integrates penalized splines with log-Gaussian Cox processes into spatial capture–recapture modeling, thereby relaxing the restrictive conditional independence assumption of Poisson processes. Simulations and two empirical case studies demonstrate that the method substantially improves the accuracy of spatial density estimation while maintaining robustness in total population abundance inference.
📝 Abstract
Spatial capture-recapture models are routinely used to estimate the abundance and distribution of wild animal populations and involve a latent spatial point process of animal activity centres that describes the spatial distribution of individuals. While traditional spatial capture-recapture models use a Poisson process, the assumption of conditional independence between points is often violated in practice due to factors not included in the point process, such as social clustering, territoriality, or preferential selection of habitat due to unobserved covariates. Log-Gaussian Cox processes are commonly used in spatial statistics to overcome weaknesses of Poisson processes, but methods to fit them within spatial capture-recapture do not currently exist. Here, we present a spatial capture-recapture framework that allows for the use of penalized regression splines to describe the activity centre distribution, with model fitting via a Laplace-approximate penalized marginal maximum likelihood approach. Our method approximates using a log-Gaussian Cox process for activity centres, and allows flexible modelling of nonlinear effect of covariates on density. We illustrate the use of our method with a simulation study and two case-studies. We demonstrate that, while population size estimates of traditional approaches are robust to density model misspecification, our approach substantially improves the estimation of spatial animal distributions.