The Annals of Applied Statistics

Bayesian binomial mixture models for estimating abundance in ecological monitoring studies

Guohui Wu, Scott H. Holan, Charles H. Nilon, and Christopher K. Wikle

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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.

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Ann. Appl. Stat., Volume 9, Number 1 (2015), 1-26.

First available in Project Euclid: 28 April 2015

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American Community Survey American Robin Conway–Maxwell Poisson negative binomial overdispersion parallel computing unbalanced data underdispersion


Wu, Guohui; Holan, Scott H.; Nilon, Charles H.; Wikle, Christopher K. Bayesian binomial mixture models for estimating abundance in ecological monitoring studies. Ann. Appl. Stat. 9 (2015), no. 1, 1--26. doi:10.1214/14-AOAS801.

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Supplemental materials

  • Supplement to “Bayesian binomial mixture models for estimating abundance in ecological monitoring studies”.: The supplementary material contains the MCMC sampling algorithm, details regarding computation times for the models implemented, and additional figures.