Bayesian mark–recapture–resight–recovery models: increasing user flexibility in the BUGS language

Estimating demographic parameters of interest is a critical component of applied conservation biology and evolutionary ecology, where demographic models and demographic data have become increasingly complex over the last several decades. These advances have been spurred by the development and use of information theoretic approaches, programs such as MARK and SURGE, and Bayesian inference. The use of Bayesian analyses has also become increasingly popular, where WinBUGS, JAGS, Stan, and NIMBLE provide increased user flexibility. Despite recent advances in Bayesian demographic modeling, some capture–recapture models that have been implemented in Program MARK remain unavailable to quantitative ecologists that wish to use Bayesian modeling approaches. We provide novel parameterizations of capture–mark–recapture–resight–recovery models implemented in Program MARK that have not yet been implemented in the BUGS language. Simulations show that the models described herein provide accurate parameter estimates. Our parameterizations of these models can easily be extended to estimate additional parameters such as entry probability, additional live states, or cause-specific mortality rates. Additionally, implementing these models in a Bayesian framework allows users to readily estimate parameters as mixtures, incorporate random individual or temporal variation, and use informative priors to assist with parameter estimation.

Keywords: Bayesian; demography; fitness; mark–resight; robust design; unobservable state.