This new exciting book published by CRC Press is at the forefront of research in statistical ecology. Real ecological examples are considered throughout the book, which provides a thorough description and explanation of the statistical ideas and tools associated with Bayesian analyses. Example WinBUGS and R codes are also provided for many of the examples within the text and which are freely available from this website.
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, Bayesian Analysis for Population Ecology 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 below.
The first part of the book focuses on models and their corresponding likelihood functions. The authors examine classical methods of inference for estimating model parameters, including maximum-likelihood estimates of parameters using numerical optimisation algorithms. After building this foundation, the authors develop the Bayesian approach for fitting models to data. They also compare Bayesian and traditional approaches to model fitting and inference.
Exploring challenging problems in population ecology, this book shows how to use the latest Bayesian methods to analyse data. It enables readers to apply the methods to their own problems with confidence.
Table of Contents
Codes Relating to the Book
The following WinBUGS and R codes are example codes with an explanation of the form of the data and the example data set that is contained within the code. Note that this is not (yet) a complete list of codes relating to the book, and more will be added in due course. If you use any of these codes for your own work, please reference the book and associated website as the source of the code.
The below WinBUGS example codes can be downloaded by clicking on the relevant file names.
|radiotagged.odc||Radio tagging data with constant survival probability for a single cohort. Example data relates to Canvasback ducks.|
|caprecapCC.odc||Capture-recapture data for model C/C (constant recapture and survival probabilities). Example data relates to dippers.|
|caprecapCC2.odc||Capture-recapture data for the dippers data under model C/C2.|
|caprecapCT.odc||Capture-recapture data for the dippers data under model C/T.|
|caprecapTT.odc||Capture-recapture data for the dippers data under model T/T.|
|caprecapCC-statespace.odc||Capture-recapture data for model C/C (constant recapture and survival probabilities). Example data relates to dippers. This model specification uses a state- space framework.|
|caprecap-cov.odc||Capture-recapture data with survival regressed on a single covariate. Example data relates to white storks.|
|caprecap-covjump.odc||Capture-recapture data with survival regressed on covariates in the presence of model uncertainty. Example data relates to white storks with 10 possible covariates.|
The below R example codes can be downloaded by clicking on the relevant file names.
|radiotagged.R||Radio tagging data with constant survival probability for a single cohort. Example data relates to Canvasback ducks.|
|caprecapCC.R||Capture-recapture data allowing for models C/C. Example data relates to dippers.|
|caprecapCC2.R||Capture-recapture data for the dippers data under model C/C2.|
|caprecapRJ.R||Capture-recapture data for the dippers data using RJ over models C/C and C/C2.|
|caprecap-cov.R||Capture-recapture data with survival regressed on a single covariate. Example data relates to white storks.|
|caprecap-covjump.R||Capture-recapture data with survival regressed on covariates in the presence of model uncertainty. Example data relates to white storks with 10 possible covariates.|
Review by Joseph Hilbe - appeared in Journal of Statistical Software (2010) 36 Book Review 1.
National Centre for Statistical Ecology
Bayesian Analysis of Population Ecology Workshop 7th-10th September 2009.