Hierarchical Modelling for the Environmental Sciences: Statistical methods and applications

Paperback | May 12, 2006

EditorJames S. Clark, Alan E. Gelfand

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New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods foranalysis provide consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now arequirement for a clear exposition of the methodology through to application for a range of environmental challenges.

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New statistical tools are changing the ways in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods foranalysis provide consistent framework for infe...

Jim Clark is the Blomquist professor at Duke University, where his research focuses on how global change affects forests and grasslands. He received a B.S. from the North Carolina State University in Entomology (1979), a M.S. from the University of Massachusetts in Forestry and Wildlife (1984), and a Ph.D. from the University of Minn...

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Format:PaperbackDimensions:216 pages, 9.69 × 7.44 × 0.46 inPublished:May 12, 2006Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:019856967X

ISBN - 13:9780198569671

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Table of Contents

PrefacePart I. Introduction to hierarchical modeling1. Bradley P. Carlin, James S. Clark and Alan E. Gelfand: Elements of hierarchical Bayesian influence2. Kent Holsinger: Bayesian hierarchical models in geographical geneticsPart II. Hierarchical models in experimental settings3. James S. Clark and Shannon LaDeau: Synthesizing ecological experiments and observational data with hierarchical Bayes4. Janneke Hille Ris Lambers, Brian Aukema, Jeff Diez, Margaret Evans and Andrew Latimer: Effects of global change on inflorescence production: a Bayesian hierarchical analysisPart III. Spatial modeling5. Alan E. Gelfand, Andrew Latimer, Shanshan Wu and John A. Silander, Jr.: Building statistical models to analyse species distributions6. Kiona Ogle, Maria Uriarte, Jill Thompson, Jill Johnstone, Andy Jones, Yiching Lin, Eliot J. B. McIntire and Jess K. Zimmmerman: Implications of vulnerability to hurricane damage for long-term survival of tropical tree species: a Bayesian hierarchical analysisPart IV. Spatio-temporal modeling7. Li Chen, Montserrat Fuentes and Jerry M. Davis: Spatial temporal statistical modeling and prediction of environmental processes8. Christopher K. Wikle and Melvin B. Hooten: Hierarchical Bayesian spatio-temporal models for population spread9. Eric Gilleland, Douglas Nychka and Uli Schneider: Spatial models for the distribution of extremesReferencesIndex

Editorial Reviews

.,."if you are already quite well acquainted with Bayesian concepts and terminology then this book should provide an excellent guide to the application of these advanced statistical techniques within ecology." --Bulletin of the British Ecological Society "This book provides rare examples of how to apply HB in the environmental sciences and will be a key component of the analytical toolbox for environmental scientists using or learning to use this approach." --The Quarterly Review of Biology