Journal
ENVIRONMENTAL AND ECOLOGICAL STATISTICS
Volume 13, Issue 4, Pages 409-426Publisher
SPRINGER
DOI: 10.1007/s10651-006-0020-x
Keywords
Bayesian hierarchical spatial Model; MCMC algorithm; spatial probit model
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Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which the species range is determined by a spatial probit model, including physical variables as covariates. Within that range, species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference for this model is illustrated using data on oak seedling counts.
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