Hierarchical Modelling for the Environmental Sciences Statistical methods and applications

New Statistical tools are changing the wau 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 analysis provide constant 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 requirment for a clear exposition of the methodology through to application for a range of environmental challenges.

Preface; Part I. Introduction to hierarchical modeling; 1: Bradley P. Carlin, James S. Clark and Alan E. Gelfand: Elements of hierarchical Bayesian influence; 2: Kent Holsinger: Bayesian hierarchical models in geographical genetics; Part II. Hierarchical models in experimental settings; 3: James S. Clark and Shannon LaDeau: Synthesizing ecological experiments and observational data with hierarchical Bayes; 4: Janneke Hille Ris Lambers, Brian Aukema, Jeff Diez, Margaret Evans and Andrew Latimer: Effects of global change on inflorescence production: a Bayesian hierarchical analysis; Part III. Spatial modeling; 5: Alan E. Gelfand, Andrew Latimer, Shanshan Wu and John A. Silander, Jr.: Building statistical models to analyse species distributions; 6: 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 analysis; Part IV. Spatio-temporal modeling; 7: Li Chen, Montserrat Fuentes and Jerry M. Davis: Spatial temporal statistical modeling and prediction of environmental processes; 8: Christopher K. Wikle and Melvin B. Hooten: Hierarchical Bayesian spatio-temporal models for population spread; 9: Eric Gilleland, Douglas Nychka and Uli Schneider: Spatial models for the distribution of extremes; References; Index