Ecological statistics: contemporary theory and application Edited by Gordon A. Fox, Simoneta Negrete-Yankelevich, and Vinicio J. Sosa
Table of contents: Vinicio J. Sosa, Simoneta Negrete-Yankelevich, and Gordon A. Fox: Introduction 1: Michael A. McCarthy: Approaches to Statistical Inference 2: Earl D. McCoy: Having the Right Stuff: the Effects of Data Constraints on Ecological Data Analysis 3: Shane A. Richards: Likelihood and Model Selection 4: Shinichi Nakagawa: Missing Data: Mechanisms, Methods and Messages 5: Gordon A. Fox: What You Don't Know Can Hurt You: Censored and Truncated Data in Ecological Research 6: Yvonne M. Buckley: Generalized Linear Models 7: Bruce E. Kendall: A Statistical Symphony: Instrumental Variables Reveal Causality and Control Measurement Error 8: James B. Grace, Samuel M. Scheiner, and Donald R. Schoolmaster, Jr.: Structural Equation Modeling: Building and Evaluating Causal Models 9: Jessica Gurevitch and Shinichi Nakagawa: Research Synthesis Methods in Ecology 10: Simoneta Negrete-Yankelevich and Gordon A. Fox: Spatial Variation and Linear Modeling of Ecological Data 11: Marc J. Lajeunesse and Gordon A. Fox: Statistical Approaches to the Problem of Phylogenetically Correlated Data 12: Jonathan R. Rhodes: Mixture Models for Overdispersed Data 13: Benjamin M. Bolker: Linear and Generalized Linear Mixed Models Appendix This novel book synthesizes a number of developments and changes in both our understanding and practice of ecological statistics, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language. More information about the book can be found at http://www.oup.com/us, http://Amazon.com, http://bn.com, or your local bookstore.
