Bayesian Non-Parametric Detection Heterogeneity in Ecological Models

TL;DR

This paper introduces a Bayesian non-parametric method using Dirichlet process mixtures to model detection heterogeneity in ecological data, offering more flexible and reliable inference than traditional finite mixture or homogeneous models.

Contribution

It presents a novel non-parametric Bayesian approach for detection heterogeneity in ecological models, avoiding the need to predefine the number of subgroups and improving inference reliability.

Findings

Non-parametric approach outperforms finite mixture models in simulations.

Method provides more reliable detection heterogeneity modeling.

Real-data examples demonstrate practical advantages.

Abstract

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