A Bayesian capture-recapture population model with simultaneous estimation of heterogeneity

We develop a Bayesian capture-recapture model that provides estimates of abundance as well as time-varying and heterogeneous survival and capture probability distributions. The model uses a state-space approach by incorporating an underlying population model and an observation model, and, here is applied to photo-identification data to estimate trends in the abundance and survival of a population of bottlenose dolphins (Tursiops truncatus) in northeast Scotland. Novel features of the model include simultaneous estimation of time-varying survival and capture probability distributions, estimation of heterogeneity effect for survival and capture, use of separate data to inflate the number of identified animals to the total abundance, and integration of separate observations of the same animals from right and left side photograph. A Bayesian approach using Markov chain Monte Carlo methods allows for uncertainty in measurement and parameters, and simulations confirm the model's validity. KEY WORDS: Abundance; Logits; Photo-identification; Survival; Trends.