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March 2015 Bayesian binomial mixture models for estimating abundance in ecological monitoring studies
Guohui Wu, Scott H. Holan, Charles H. Nilon, Christopher K. Wikle
Ann. Appl. Stat. 9(1): 1-26 (March 2015). DOI: 10.1214/14-AOAS801


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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Guohui Wu. Scott H. Holan. Charles H. Nilon. Christopher K. Wikle. "Bayesian binomial mixture models for estimating abundance in ecological monitoring studies." Ann. Appl. Stat. 9 (1) 1 - 26, March 2015.


Published: March 2015
First available in Project Euclid: 28 April 2015

zbMATH: 06446558
MathSciNet: MR3341105
Digital Object Identifier: 10.1214/14-AOAS801

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

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 1 • March 2015
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