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This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers. 34 chapters covering methodology, ecological processes, environmental exposure, and statistical methods in climate science. About the Editors: Alan E. Gelfand is the James B. Duke Professor of Statistical Science at Duke University. He is a leader in Bayesian spatial modeling and analysis including a successful book in this area with Banerjee and Carlin. Montse Fuentes is the Dean of the Virginia Commonwealth University College of Humanities and Sciences and a Professor of Statistics. She leads a broad research program in statistical methods for spatial large scale environmental health studies. Jennifer A. Hoeting is Professor of Statistics at Colorado State University. Her research is focused on Bayesian, computational, and spatial statistics applied to address challenging problems in ecology. Richard L. Smith is the Mark L. Reed III Distinguished Professor of Statistics and Professor of Biostatistics at the University of North Carolina. His research covers theoretical and applied aspects of environmental statistics including extreme value theory, spatial statistics and applications to climate change, air pollution and health.