King / Morgan / Gimenez Bayesian Analysis for Population Ecology

1. Auflage 2010
ISBN: 978-1-4398-1188-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 456 Seiten

Reihe: Chapman & Hall/CRC Interdisciplinary Statistics

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.

Zielgruppe

Ecologists, statisticians, and graduate students in statistical ecology.

Autoren/Hrsg.

King, Ruth

Morgan, Byron

Gimenez, Olivier

Brooks, Steve

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften Ökologie

Inhaltsverzeichnis

INTRODUCTION TO STATISTICAL ANALYSIS OF ECOLOGICAL DATA
Introduction
Population Ecology
Conservation and Management
Data and Models
Bayesian and Classical Statistical Inference
Senescence
Data, Models and Likelihoods
Introduction
Population Data
Modelling Survival
Multi-Site, Multi-State and Movement Data
Covariates and Large Data Sets; Senescence
Combining Information
Modelling Productivity
Parameter Redundancy
Classical Inference Based on the Likelihood
Introduction
Simple Likelihoods
Model Selection
Maximising Log-Likelihoods
Confidence Regions
Computer Packages
BAYESIAN TECHNIQUES AND TOOLS
Bayesian Inference
Introduction
Prior Selection and Elicitation
Prior Sensitivity Analyses
Summarising Posterior Distributions
Directed Acyclic Graphs
Markov Chain Monte Carlo
Monte Carlo Integration
Markov Chains
Markov Chain Monte Carlo (MCMC)
Implementing MCMC
Model Discrimination
Introduction
Bayesian Model Discrimination
Estimating Posterior Model Probabilities
Prior Sensitivity
Model Averaging
Marginal Posterior Distributions
Assessing Temporal/Age Dependence
Improving and Checking Performance
Additional Computational Techniques
MCMC and RJMCMC Computer Programs
R Code (MCMC) for Dipper Data
WinBUGS Code (MCMC) for Dipper Data
MCMC within the Computer Package MARK
R code (RJMCMC) for Model Uncertainty
WinBUGS Code (RJMCMC) for Model Uncertainty
ECOLOGICAL APPLICATIONS
Covariates, Missing Values and Random Effects
Introduction
Covariates
Missing Values
Assessing Covariate Dependence
Random Effects
Prediction
Splines
Multi-State Models
Introduction
Missing Covariate/Auxiliary Variable Approach
Model Discrimination and Averaging
State-Space Modelling
Introduction
Leslie Matrix-Based Models
Non-Leslie-Based Models
Capture-Recapture Data
Closed Populations
Introduction
Models and Notation
Model Fitting
Model Discrimination and Averaging
Line Transects
Appendix A: Common Distributions
Discrete Distributions
Continuous Distributions
Appendix B: Programming in R
Getting Started in R
Useful R Commands
Writing (RJ)MCMC Functions
R Code for Model C/C
R Code for White Stork Covariate Analysis
Appendix C: Programming in WinBUGS
WinBUGS
Calling WinBUGS from R
References
Index
A Summary, Further Reading, and Exercises appear at the end of most chapters.

Über Autor(innen)

Ruth King is a reader in statistics at the University of St. Andrews and a former EPSRC post-doctoral Research Fellow.
Byron J.T. Morgan is a professor of applied statistics at the University of Kent and co-director of the EPSRC National Centre for Statistical Ecology.
Olivier Gimenez is a research scientist in biostatistics at CNRS and a former Marie Curie research fellow.
Stephen P. Brooks is director of research at ATASS Ltd and a former professor of statistics at the University of Cambridge and EPSRC Advanced Fellow.