Hierarchical modelling of population growth rate from individual capture–recapture data

Abstract

Summary Estimating rates of population change is essential to achieving theoretical and applied goals in population ecology, and the Pradel (1996, Biometrics, 52: 703.) temporal symmetry method permits direct estimation and modelling of the growth rate of open populations, using capture–recapture data from marked animals. We present a Bayesian formulation of the Pradel approach that permits a hierarchical modelling of the biological and sampling processes. Two parametrizations for the temporal symmetry likelihood are presented and implemented into a general purpose software in BUGS language. We first consider a set of simulated scenarios to evaluate performance of a Bayesian variable selection approach to test the temporal linear trend on survival and seniority probability, population growth rate and detectability. We then provide an example application on individual detection information of three species of burrowing nesting seabirds, whose populations cannot be directly counted. For each species, we assess the strength of evidence for temporal random variation and the temporal linear trend on survival probability, population growth rate and detectability. The Bayesian formulation provides more flexibility, by easily allowing the extension of the original fixed time effects structure to random time effects, an option that is still impractical in a frequentist framework.