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Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications


Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications Cover


Synopses & Reviews

Publisher Comments:

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 for anyalsis 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 a requirement for a clear exposition of the methodology through to application for a range of environmental challenges.

About the Author

James 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 Minnesota in Ecology (1988). At Duke University, Clark teaches Community Ecology and Ecological Models and Data. He has served as the Director of Graduate Studies for the University Program in Ecology and as Director of the Center on Global Change. Alan E. Gelfand is the J B Duke Professor of Statistics and Decision Sciences at Duke University. An early contributor to the development of computational machinery for fitting hierarchical Bayesian models, his current research focuses on the analysis of spatial and spatio-temporal data. His primary areas of application are to problems in environmental science, ecology, and climatology. He received a B.S. from the City College of New York and an M.S. and Ph.D. from Stanford University. After many years at the University of Connecticut, he joined the faculty at Duke University in August 2002.

Table of Contents


Part I. Introduction to hierarchical modeling

1. Elements of hierarchical Bayesian influence, Bradley P. Carlin, James S. Clark and Alan E. Gelfand

2. Bayesian hierarchical models in geographical genetics, Kent Holsinger

Part II. Hierarchical models in experimental settings

3. Synthesizing ecological experiments and observational data with hierarchical Bayes, James S. Clark and Shannon LaDeau

4. Effects of global change on inflorescence production: a Bayesian hierarchical analysis, Janneke Hille Ris Lambers, Brian Aukean, Jeff Diez, Margaret Evans and Andrew Latimer

Part III. Spatial modeling

5. Building statistical models to analyse species distributions, Alan E. Gelfand, Andrew Latimer, Shanshan Wu and John A. Silander, Jr.

6. Implications of vulnerability to hurricane damage for long-term survival of tropical tree species: a Bayesian hierarchical analysis, Kiona Ogle, Maria Uriarte, Jill Thompson, Jill Johnstone, Andy Jones, Yiching Lin, Eliot J. B. McIntire and Jess K. Zimmmerman

Part IV. Spatio-temporal modeling

7. Spatial temporal statistical modeling and prediction of environmental processes, Li Chen, Montserrat Fuentes and Jerry M. Davis

8. Hierarchical Bayesian spatio-temporal models for population spread, Christopher K. Wikle and Melvin B. Hooten

9. Spatial models for the distribution of extremes, Eric Gilleland, Douglas Nychka and Uli Schneider



Product Details

Clark, James S.
Oxford University Press, USA
Clark, James Samuel
Gelfand, Alan E.
Gelfand, Alan
null, Alan
Gelfand, Alan
null, James S.
Mathematical statistics
Environmental sciences
Life Sciences - Ecology
Bayesian statistical decision theory
Mathematical statistics -- Data processing.
Environmental Studies-General
Edition Description:
Oxford Biology
Publication Date:
Grade Level:
Professional and scholarly
73 line illus., tables
7.4 x 9.6 x 0.3 in 1.094 lb

Related Subjects

Science and Mathematics » Environmental Studies » Environment
Science and Mathematics » Environmental Studies » General
Science and Mathematics » Mathematics » Applied

Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications New Trade Paper
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Product details 216 pages Oxford University Press, USA - English 9780198569671 Reviews:
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