Modelling latent individual heterogeneity in mark-recapture data with Dirichlet process priors

The natural subgroups often seen in mark-recapture studies and the complexity of real mark-recapture data means that parametric and discrete style models can be insufficient. Non-parametric models avoid these often restrictive assumptions. We consider the non-parametric Dirichlet process for modelling latent individual heterogeneity in probability of observation and the probability of remaining in or out of a marine sanctuary. Simulation studies demonstrated accurate estimation of multiple groups of latent individual heterogeneity. Simulations were also used to identify the limits of the Dirichlet process. The ability of the Dirichlet process to pick up unimodal heterogeneity was explored in order to avoid potential spurious multimodality. In application to a subset of the data from the North Atlantic humpback whales we were able to estimate annual population-level variation in usage of the marine sanctuary and three measures of individual-level variation. With the Dirichlet process prior we were able to detect multimodality in each parameter.