Bayesian non-parametric detection heterogeneity in ecological models

Detection heterogeneity is inherent to ecological data, arising from factors such as varied terrain or weather conditions, inconsistent sampling effort, of individuals themselves. Incorporating additional covariates into a statistical model one approach for addressing heterogeneity, but no guarantee that any set measurable will adequately address the and presence unmodelled has been shown produce biases in resulting inferences. Other approaches include use random effects, finite mixtures homogeneous subgroups. Here, we present non-parametric modelling detection Bayesian hierarchical framework. We employ Dirichlet process mixture which allows flexible number population subgroups without need pre-specify this mixture. describe approach, then consider its two common motifs: capture-recapture occupancy modelling. For each, model, models, approach. compare these using simulation studies, observe most reliable method varying degrees heterogeneity. also real-data examples, inferences each Analyses are carried out \texttt{nimble} package \texttt{R}, provides facilities models.