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Hierarchical models of animal abundance and occurrence

June 16, 2010

Much of animal ecology is devoted to studies of abundance and occurrence of species, based on surveys of spatially referenced sample units. These surveys frequently yield sparse counts that are contaminated by imperfect detection, making direct inference about abundance or occurrence based on observational data infeasible. This article describes a flexible hierarchical modeling framework for estimation and inference about animal abundance and occurrence from survey data that are subject to imperfect detection. Within this framework, we specify models of abundance and detectability of animals at the level of the local populations defined by the sample units. Information at the level of the local population is aggregated by specifying models that describe variation in abundance and detection among sites. We describe likelihood-based and Bayesian methods for estimation and inference under the resulting hierarchical model. We provide two examples of the application of hierarchical models to animal survey data, the first based on removal counts of stream fish and the second based on avian quadrat counts. For both examples, we provide a Bayesian analysis of the models using the software WinBUGS.

Citation Information

Publication Year 2006
Title Hierarchical models of animal abundance and occurrence
DOI
Authors J. Andrew Royle, R.M. Dorazio
Publication Type Article
Publication Subtype Journal Article
Series Title Journal of Agricultural, Biological, and Environmental Statistics
Series Number
Index ID 5224683
Record Source USGS Publications Warehouse
USGS Organization Patuxent Wildlife Research Center