442 pages, 93 b/w illus, 66 b/w tabs
By gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities and deal with missing data, modern Bayesian methods have become important in this area of statistical inference and forecasting.
Emphasising model choice and model averaging, this book presents up-to-date methods for analysing complex ecological data. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book's website.
The book is divided into three parts. ! Part 1 contains a wealth of material on aspects of such data, models analysis as well as the [historical] evolution of the subject. Part 2 is a good, self-contained introduction to Bayesian analysis ! Part 3 is a collection of interesting special topics in ecological applications. ! The authors write very well and illustrate with good examples. Both the technical and nontechnical discussions are good. --International Statistical Review (2011), 79, 1 ! the book under review will be of value for quantitative ecologists. The authors offer good practical advice on the implementation of MCMC and model selection, using data types familiar to wildlife ecologists. The text includes exercises at the end of each chapter in Sections 1 and 2; these and the primers on programs R and WinBUGS are attractive features. The authors have had a leading role promoting Reversible Jump MCMC as a tool for multimodel inference in wildlife and ecological applications, and their book continues this work. --The American Statistician, February 2011, Vol. 65, No. 1 ! a solid introduction to Bayesian modeling. ! The authors have produced a text that is not only of good use to those who are analyzing population ecological data, but to anyone desiring a good overview of Bayesian modeling in general. The examples are interesting and do not hinder those not in the discipline of population ecology from understanding the explanation of the statistical principles being discussed. I recommend the book for a graduate-level course on Bayesian modeling, as well as any course related to the Bayesian modeling of population ecological data. The reader is not expected to have a prior knowledge of Bayesian modeling, nor is there an assumption that readers are familiar with R or WinBUGS. ! --Journal of Statistical Software, August 2010, Volume 36
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