Empirical Bayes estimation of the size of a closed population using photo-identification data

Abstract

Photo-ID data are broadly used for estimating animal abundance using capture-recapture models. Animals that do not possess either natural or acquired marks sufficient to allow re-identification are called unmarked, and when a substantial part of the population is composed of such individuals, the classical models described in the literature do not apply. In this paper an Empirical Bayes capture-recapture analysis for dealing with the estimation of the capture probabilities and the estimation of abundance N for populations that include unmarked individuals is presented. Using a Gibbs sampling approach, Monte Carlo estimates for the posterior distribution of N were obtained. The Empirical Bayes approach was found to improve precision in the estimation of N, compared to the results obtained using other Bayesian methods. Additionally, when the population included a very large number of unmarked individuals, inferences for N obtained using the Empirical Bayes approach were definitely superior to those obtained using any of the vague beta priors tested. The methodology was applied to bowhead whale data for the 1985 and 1986 photo-ID surveys.