Bayesian estimation of survival and capture probabilities using logit link and photoidentification data

Abstract

We develop a series of Bayesian statistical models for estimating survival of wild populations monitored using capture–recapture experiments and photoidentification data. The proposed methodology is based on Cormack–Jolly–Seber model [Cormack, R.M., 1964. Estimates of survival from the sighting of marked animals. Biometrika 51, 429–438; Jolly, G.M., 1965. Explicit estimates from capture–recapture data with both death and immigration—stochastic model. Biometrika 52, 225–247; Seber, G.A.F., 1965. A note on the multiple recapture census. Biometrika 52, 249–259]. Besides time effects in capture probabilities, the proposed models allow taking into account heterogeneity in capture probability caused by the existence of different groups of individuals in the population. For that purpose, the capture probabilities are fitted using a logistic model. Additionally, it is also possible to estimate group-specific survival rates. Goodness of fit is evaluated using Bayes factor methodology. The models are applied to an 11-year photoidentification capture–recapture experiment for bowhead whales, Balaena mysticetus. The best model provides an estimate close to the one obtained by Zeh et al. [2002. Survival of bowhead whales, Balaena mysticetus, estimated from 1981–1998 photoidentification data. Biometrics 58, 832–840] using the Jolly–Seber model, but accounting for heterogeneity in capture probabilities improves precision.