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Activity Number: 471
Type: Invited
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #314447
Title: Habitat Management of Benthic Fishes in the Missouri River Using Hierarchical Bayesian Modeling
Author(s): Ali Arab* and Mark L. Wildhaber
Companies: Georgetown University and U.S. Geological Survey
Keywords: Hierarchical models ; fisheries ; zero-inflated models ; semiparametric ; Bayesian statistics ; Bivariate Poisson
Abstract:

Hierarchical Bayesian modeling approaches provide a flexible and effective tool for modeling problems related to habitat management in fisheries. Using this modeling framework, one is able to account for uncertainties in data and model parameters. Often, intensive management of rivers such as impoundments, flow regulations, and channelization of the river for purposes of navigation, flood control, and power generation results in dramatic physical changes to the river corridor, eliminating many acres of habitat for native fish. Scientists conduct studies evaluate the impact of such alterations and modifications of the river basin on the recruitment, growth, and relative abundance of selected benthic fish species. In this work, the analysis of catch data obtained by multiple gears using a hierarchical Bayesian zero-inflated Poisson model is discussed. Also, we discuss the extension of this approach to the analysis of multi-species catch data obtained by multiple gears using a semiparametric hierarchical Bayesian multivariate zero-inflated Poisson model.


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