Explicit modeling of density dependence in spatial capture-recapture models

TL;DR

This paper develops a Bayesian spatial capture-recapture model that links habitat use to individual survival and recruitment through density-dependent processes, highlighting challenges in estimating unobserved individual locations.

Contribution

The study introduces a novel SCR model incorporating density dependence at the individual level, advancing understanding of habitat use and population dynamics.

Findings

Model accurately informs habitat use

Tends to underestimate density dependence effects

Difficulty in locating unobserved individuals affects estimates

Abstract

Density dependence occurs at the individual level and thus is greatly influenced by spatial local heterogeneity in habitat conditions. However, density dependence is often evaluated at the population level, leading to difficulties or even controversies in detecting such a process. Bayesian individual-based models such as spatial capture-recapture (SCR) models provide opportunities to study density dependence at the individual level, but such an approach remains to be developed and evaluated. In this study, we developed a SCR model that links habitat use to apparent survival and recruitment through density dependent processes at the individual level. Using simulations, we found that the model can properly inform habitat use, but tends to underestimate the effect of density dependence on apparent survival and recruitment. The reason for such underestimations is likely due to the difficulties of the current model in identifying the locations of unobserved individuals without using environmental covariates to inform these locations. How to accurately estimate the locations of unobserved individuals, and thus density dependence, remains a challenging topic in spatial statistics and statistical ecology.