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.