Hierarchical models of animal abundance and occurrence |
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Authors: | J Andrew Royle Robert M Dorazio |
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Institution: | 1. U.S. Geological Survey Patuxent Wildlife Research Center, 12100 Beech Forest Road, 20708, Laurel, MD 2. U.S. Geological Survey, Florida Integrated Science Center, Department of Statistics, University of Florida, P.O. Box 110339, 32611, Gainesville, FL
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Abstract: | 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. |
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