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Hierarchical Modelling for the Environmental Sciences

Statistical Methods and Applications
EDITED BY
James S. Clark
Duke University, USA
AND
Alan E. Gelfand
Duke University, USA

Preface v
Contributors ix
Part I Introduction to hierarchical modeling 1
1 Elements of hierarchical Bayesian inference 3
Bradley P. Carlin, James S. Clark, and Alan E. Gelfand
2 Bayesian hierarchical models in geographical genetics 25
Kent E. Holsinger
Part II Hierarchical models in experimental settings 39
3 Synthesizing ecological experiments and observational data with
hierarchical Bayes 41
James S. Clark and Shannon LaDeau
4 Effects of global change on inflorescence production: a Bayesian
hierarchical analysis 59
Janneke Hille Ris Lambers, Brian Aukema, Jeff Diez, Margaret Evans, and
Andrew Latimer
Part III Spatial modeling 75
5 Building statistical models to analyze species distributions 77
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 98
Kiona Ogle, María Uriarte, Jill Thompson, Jill Johnstone, Andy Jones, Yiching Lin,
Eliot J. B. McIntire, and Jess K. Zimmerman
Part IV Spatio-temporal modeling 119
7 Spatial–temporal statistical modeling and prediction of environmental
processes 121
Li Chen, Montserrat Fuentes, and Jerry M. Davis

8 Hierarchical Bayesian spatio–temporal models for population spread 145
Christopher K. Wikle and Mevin B. Hooten
9 Spatial models for the distribution of extremes 170
Eric Gilleland, Douglas Nychka and Uli Schneider
References 185
Index 197

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