Bayesian binomial mixture models for estimating abundance in ecological monitoring studies

TL;DR

This paper introduces Bayesian hierarchical binomial mixture models, including Binomial Conway-Maxwell Poisson variants, to accurately estimate species abundance and account for overdispersion in ecological monitoring data.

Contribution

It develops novel Bayesian models that handle spatial dispersion and variable covariates, improving abundance estimation in ecological studies.

Findings

Effective estimation of American Robin abundance in Baltimore.

Models successfully account for overdispersion and spatial variability.

Variable selection enhances model interpretability.

Abstract

Investigation of species abundance has become a vital component of many ecological monitoring studies. The primary objective of these studies is to understand how specific species are distributed across the study domain, as well as quantification of the sampling efficiency for detecting these species. To achieve these goals, preselected locations are sampled during scheduled visits, in which the number of species observed at each location is recorded. This results in spatially referenced replicated count data that are often unbalanced in structure and exhibit overdispersion. Motivated by the Baltimore Ecosystem Study, we propose Bayesian hierarchical binomial mixture models, including Binomial Conway-Maxwell Poisson (Bin-CMP) mixture models, that formally account for varying levels of spatial dispersion. Our proposed models also allow for variable selection of model covariates and grouping of dispersion parameters through the implementation of reversible jump Markov chain Monte Carlo methodology. Finally, using demographic covariates from the American Community Survey, we demonstrate the effectiveness of our approach through estimation of abundance for the American Robin (Turdus migratorius) in the Baltimore Ecosystem Study.