  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.
