Novel Statistical Tools for Conserving and Managing Populations 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 on the book's website. 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.

Novel Statistical Tools for Conserving and Managing Populations

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 on the book's website.

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.

The genesis of this volume was in a one-day meeting arranged under the auspices of the Nathematical Ecology Group, jointly of the British Region of the Biometric Society and the British Ecological Society, and held in the Natural History Museum in London on the 4th May 1982. The object of the meeting was to bring together individuals from different dis ciplines but with a common interest in ornithology. In this volume we have tried to preserve the flavour of the meeting so that all but two of the papers read or pre sented as posters can be found here. The two papers that have not been included have since been published elsewhere: see Birkhead and Nettleship (1983) and Cav~ (1983). Further papers have been added to the volume from contributors who were unable to attend the London meeting, or were unable to present a paper there. All of the papers were refereed by ourselves. A volume which contains papers by both statisticians and non-statisticians is inevitably going to be variable with regard to the depth and range of statistical techniques used. Thus non-statisticians are likely to find some of the papers written by statisticians difficult at times, and conversely statisticians n2Y find that they would have treated some problems differently from non-statisticians. It is hoped, however, that this volun~ will increase awareness of the interests and problems (including solutions), in the general area of ornithology, and stimulate cross-fertilisation of ideas.