A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data.

ABSTRACT: Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatiall

SUBMITTER: Zhang W 

PROVIDER: S-EPMC10078592 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature