Hierarchical Modelling for the Environmental Sciences

Statistical Methods and Applications

Edited by James S. Clark and Alan Gelfand

Jointly edited by a leading ecologist and statistician, with contributions from recognized experts in the field
Introduces environmental scientists to modern statistical computation techniques
Provides a non-technical overview of hierarchical Bayes and Markov Chain Monte Carlo methods for analysis of environmental data
Includes chapters demonstrating the application of methods to a range of environmental challenges

Hierarchical Modelling for the Environmental Sciences

Statistical Methods and Applications

Edited by James S. Clark and Alan Gelfand

Description

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.

Hierarchical Modelling for the Environmental Sciences

Statistical Methods and Applications

Edited by James S. Clark and Alan Gelfand

Preface
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
References
Index

Hierarchical Modelling for the Environmental Sciences

Statistical Methods and Applications

Edited by James S. Clark and Alan Gelfand

Author Information

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 & 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.

Contributors:

Brian Aukema, Bradley P. Carlin; Li Chen; James S. Clark; Jerry M. Davis; Jeff Diez; Margaret Evans; Montserrat Fuentes; Alan E. Gelfand; Eric Gilleland; Janneke Hille Ris Lambers; Kent Holsinger; Melvin B. Hooten; Jill Johnstone; Andy Jones; Shannon LaDeau; Andrew Latimer; Yiching Lin; Eliot J. B. McIntire; Douglas Nychka; Kiona Ogle; Uli Schneider; John A. Silander, Jr.; Jill Thompson; Maria Uriarte; Christopher K. Wikle; Shanshan Wu; Jess K. Zimmmerman.

Hierarchical Modelling for the Environmental Sciences

Statistical Methods and Applications

Edited by James S. Clark and Alan Gelfand

Reviews and Awards

"...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