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.
- 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.
Table of Contents
1. Introduction (The editors)
Section 1: Methodology for Statistical Analysis of Environmental Processes
2. Fundamentals of modeling for environmental processes (A. Gelfand)
3. Time series methodology (P. Craigmile)
4. Dynamic models (A. Schmidt/H. Lopes)
5. Geostatistical modeling for environmental processes (S. Banerjee)
6. Spatial and Spatio-temporal Point Processes in Ecological Applications (J. Illian)
7. Data fusion (V. Berrocal)
8. Analysis of Extremes (D. Cooley/B. Hunter/R. Smith)
9. Environmental sampling methods (D. Zimmerman/S. Buckland)
10. The problem of zeros (J. Clark/A. Gelfand)
11. Gradient Analysis of Ecological Communities (Ordination) (M. Palmer)
Section 2: Topics in Ecological Processes
12. Species distribution models (O. Ovaskainen)
13. Capture-recapture and distance sampling to estimate population sizes (R. Barker)
14. Animal Movement Models (M. Hooten/D. Johnson)
15. Population Demography for Ecology (K. Newman)
16. Trait Modeling Methods (M. Lammens-Aiello/J. Silander)
17. Statistical Models of Vegetation Fires (J.M.C. Pereira/K. F. Turkman)
18. Spatial Statistical Models for Stream Networks (J. Ver Hoef/E. Peterson/D. Isaak)
Section 3: Topics in Environmental Exposure
19. Statistical Methods for Exposure Assessment (B. Reich/M. Fuentes/Y-N Huang)
20. Alternative Models for Estimating Air Pollution Exposure (M. Bell/J. Warren)
21. Preferential sampling with regard to exposure levels (P. Diggle/E. Giorgi)
22. Network Design (J. Zidek,/D. Zimmerman)
23. Dynamic source apportionment (J. Krall/H. Chang)
24. Dynamics of environmental epidemiology (F. Dominici/A. Wilson)
25. Connecting exposure to outcomes (A. Szpiro)
26. Experimental design for environmental epidemiology – cohort methods, case-crossover methods (L. Sheppard)
Section 4: Topics in Climatology
27. Modeling and Assessing Climate Trends (P. Craigmile/P. Guttorp)
28. Climate models (D. Stephenson)
29. Spatial Analysis for Climatology (D. Nychka/C. Wikle)
30. Data assimilation; inference for linking physical and probabilistic models for complex nonlinear dynamic systems (C. Jones/A. Budhiraja)
31. Spatial extremes with application to climate and environmental exposure (A. Davison, R. Huser, E. Thibaud )
32. Statistics in Oceanography (C. Wikle)
33. Paleoclimate and paleoecology (P. Craigmile/M. Haran/B. Li/E. Mannshardt/ B. Rajaratnam/M. Tingley)
34. Detection and attribution (D
35. Health risks of climate variability and change (K. Ebi, D. Hondula, P. Kinney, A. Monaghan, C. Morin, N. Ogden, M. Springmann)
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.
Montserrat (Montse) Fuentes, Ph.D., became dean of the Virginia Commonwealth University College of Humanities and Sciences on July 1, 2016. She came to VCU from North Carolina State University, where she served as the head of the Department of Statistics and James M. Goodnight Distinguished Professor of Statistics. She also served as center director for the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences, a research collaborative funded by the National Science Foundation. She received a dual bachelor’s degree in mathematics and music (piano) from the University of Valladolid in Spain and a Ph.D. in statistics from the University of Chicago.
Jennifer A. Hoeting is a Professor of Statistics at Colorado State University, where she has worked since 1994. She received her PhD from the University of Washington.
Richard L. Smith is Mark L. Reed III Distinguished Professor of Statistics and Professor of Biostatistics in the University of North Carolina, Chapel Hill. From 2010-2017 he was also Director of the Statistical and Applied Mathematical Sciences Institute, a Mathematical Sciences Institute supported by the National Science Foundation, and he will continue (through June 2018) as Associate Director of SAMSI. He obtained his PhD from Cornell University and previously held academic positions at Imperial College (London), the University of Surrey (Guildford, England) and Cambridge University. His main research interest is environmental statistics and associated areas of methodological research such as spatial statistics, time series analysis and extreme value theory. He is particularly interested in statistical aspects of climate change research, and in air pollution including its health effects. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, an Elected Member of the International Statistical Institute, and has won the Guy Medal in Silver of the Royal Statistical Society, and the Distinguished Achievement Medal of the Section on Statistics and the Environment, American Statistical Association. In 2004 he was the J. Stuart Hunter Lecturer of The International Environmetrics Society (TIES). He is also a Chartered Statistician of the Royal Statistical Society.