Full Open Population Capture-Recapture Models with Individual Covariates

TL;DR

This paper introduces a flexible approach using complete data likelihoods for capture-recapture models with individual covariates, enabling easier modeling of population dynamics and demographic processes with missing data.

Contribution

It demonstrates how to fit complex open population capture-recapture models with individual covariates using standard software like JAGS/BUGS, without custom likelihoods.

Findings

Models can incorporate uncertain covariates without custom likelihoods.

The approach handles censored and partially observed covariates effectively.

Applications include modeling population size, birth rates, and multi-state processes.

Abstract

Traditional analyses of capture-recapture data are based on likelihood functions that explicitly integrate out all missing data. We use a complete data likelihood (CDL) to show how a wide range of capture-recapture models can be easily fitted using readily available software JAGS/BUGS even when there are individual-specific time-varying covariates. The models we describe extend those that condition on first capture to include abundance parameters, or parameters related to abundance, such as population size, birth rates or lifetime. The use of a CDL means that any missing data, including uncertain individual covariates, can be included in models without the need for customized likelihood functions. This approach also facilitates modeling processes of demographic interest rather than the complexities caused by non-ignorable missing data. We illustrate using two examples, (i) open population modeling in the presence of a censored time-varying individual covariate in a full robust-design, and (ii) full open population multi-state modeling in the presence of a partially observed categorical variable.