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Wildlife monitoring for open populations can be performed using a number ofdifferent survey methods. Each survey method gives rise to a type of data and,in the last five decades, a large number of associated statistical models havebeen developed for analysing these data. Although these models have beenparameterised and fitted using different approaches, they have all beendesigned to model the pattern with which individuals enter and exit thepopulation and to estimate the population size. However, existing approachesrely on a predefined model structure and complexity, either by assuming thatparameters are specific to sampling occasions, or by employing parametriccurves. Instead, we propose a novel Bayesian nonparametric framework formodelling entry and exit patterns based on the Polya Tree (PT) prior fordensities. Our Bayesian non-parametric approach avoids overfitting wheninferring entry and exit patterns while simultaneously allowing moreflexibility than is possible using parametric curves. We apply our newframework to capture-recapture, count and ring-recovery data and we introducethe replicated PT prior for defining classes of models for these data.Additionally, we define the Hierarchical Logistic PT prior for jointlymodelling related data and we consider the Optional PT prior for modelling longtime series of data. We demonstrate our new approach using five different casestudies on birds, amphibians and insects.

A general modelling framework for open wildlife populations based on the Polya Tree prior