Improved inference on capture recapture models with behavioural effects

In the context of capture-recapture modeling for estimating the un- known size of a finite population it is often required a flexible framework for dealing with a behavioural response to trapping. Many alternative settings have been proposed in the literature to account for the variation of capture probability at each occasion depending on the previous capture history. Inference is typically carried out relying on the so-called condi- tional likelihood approach. We highlight that such approach may, with positive probability, lead to inferential pathologies such as unbounded es- timates for the finite size of the population. The occurrence of such likeli- hood failures is characterized within a very general class of behavioural ef- fect models. It is also pointed out that a fully Bayesian analysis overcomes the likelihood failure phenomenon. The overall improved performance of alternative Bayesian estimators is investigated under different non- informative prior distributions verifying their comparative merits with both simulated and real data.