Abstract #302082

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JSM 2003 Abstract #302082
Activity Number: 81
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics & the Environment
Abstract - #302082
Title: A Hierarchical Bayesian Spatial Model for Count Data: Modeling Counts of Coho in the State of Oregon
Author(s): Ruben A. Smith*+ and Breda Munoz-Hernandez and Don L. Stevens
Companies: Oregon State University and Oregon State University/Universidad De Costa Rica and Oregon State University
Address: Kidder Hall 44, Corvallis, OR, 97331,
Keywords: environmental surveys ; MCMC ; hierarchical Bayesian models ; spatial modeling ; spatial prediction ; generalized linear mixed models
Abstract:

In an effort to monitor the salmon and aquatic habitat in the state of Oregon, the Oregon Department of Fish and Wildlife conducts three large-scale projects. To integrate the data from these three projects, an integrated monitoring design was implemented in 1998. Under this monitoring program the samples are selected under the EMAP protocol. The data collected may be used to estimate salmon abundance and create yearly maps of relative abundance. We propose a hierarchical Bayesian approach to model the counts of Coho salmon. Our model incorporates two random components, one that captures the spatial variability and a measurement error component. We specify priors for the parameters involved and implement the model in a Markov chain Monte Carlo framework.

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