Bayesian Analysis for Population Ecology

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

ISBN: 9781439811870 | 1439811873
Cover: Hardcover
Copyright: 10/30/2009

Rent

(Recommended)

$71.92

Term

Due

Price

Semester

Dec 13

$75.71

Quarter

Aug 16

$71.92

Short Term

Jul 3

$68.13

*This item is part of an exclusive publisher rental program and requires an additional convenience fee. This fee will be reflected in the shopping cart.
Buy New

Usually Ships in 3-5 Business Days

$103.44
eBook

Available Instantly

Online: 180 Days

Downloadable: 180 Days

$34.01

Modern Bayesian methods have an important role to play in population ecology. Statistical methods for the analysis of mark-recapture-recovery data on wild animals continue to develop in response to the availability of long-term data sets and advances in animal marking and tracking techniques. Bringing together top experts in the field, this book presents up-to-date Bayesian procedures in an accessible manner, illustrated by a wide range of real examples and complemented by accessible computer programs. The authors include WinBUGS and R code for all of the analyses that are performed in the text.

Acknowledgments	p. xi
Preface	p. xiii
Introduction to Statistical Analysis of Ecological Data	p. 1
Introduction	p. 3
Population Ecology	p. 3
Conservation and Management	p. 3
Data and Models	p. 4
Bayesian and Classical Statistical Inference	p. 6
Senescence	p. 9
Summary	p. 11
Further Reading	p. 11
Exercises	p. 12
Data, Models and Likelihoods	p. 15
Introduction	p. 15
Population Data
Modelling Survival	p. 21
Multi-Site, Multi-State and Movement Data	p. 28
Covariates and Large Data Sets; Senescence	p. 30
Combining Information	p. 33
Modelling Productivity	p. 35
Parameter Redundancy	p. 36
Summary	p. 40
Further Reading	p. 40
Exercises	p. 42
Classical Inference Based on Likelihood	p. 49
Introduction	p. 49
Simple Likelihoods	p. 49
Model Selection	p. 51
Maximising Log-Likelihoods	p. 56
Confidence Regions	p. 57
Computer Packages	p. 58
Summary	p. 61
Further Reading	p. 62
Exercises	p. 62
Bayesian Techniques and Tools	p. 67
Bayesian Inference	p. 69
Introduction	p. 69
Prior Selection and Elicitation	p. 75
Prior Sensitivity Analyses	p. 80
Summarising Posterior Distributions	p. 85
Directed Acyclic Graphs	p. 89
Summary	p. 93
Further Reading	p. 95
Exercises	p. 96
Markov Chain Monte Carlo	p. 99
Monte Carlo Integration	p. 99
Markov Chains	p. 101
Markov Chain Monte Carlo	p. 101
Implementing MCMC	p. 124
Summary	p. 141
Further Reading	p. 141
Exercises	p. 142
Model Discrimination	p. 147
Introduction	p. 147
Bayesian Model Discrimination	p. 148
Estimating Posterior Model Probabilities	p. 152
Prior Sensitivity	p. 170
Model Averaging	p. 172
Marginal Posterior Distributions	p. 176
Assessing Temporal/Age Dependence	p. 178
Improving and Checking Performance	p. 189
Additional Computational Techniques	p. 192
Summary	p. 193
	p. 194
Exercises	p. 195
MCMC and RJMCMC Computer Programs	p. 199
R Code (MCMC) for Dipper Data	p. 199
WinBUGS Code (MCMC) for Dipper Data	p. 205
MCMC within the Computer Package MARK	p. 209
R code (RJMCMC) for Model Uncertainty	p. 213
WinBUGS Code (RJMCMC) for Model Uncertainty	p. 226
Summary	p. 231
Further Reading	p. 232
Exercises	p. 233
Ecological Applications	p. 239
Covariates, Missing Values and Random Effects	p. 241
Introduction	p. 241
Covariates	p. 242
Missing Values	p. 251
Assessing Covariate Dependence	p. 259
Random Effects	p. 264
Prediction	p. 270
Splines	p. 270
Summary	p. 273
Further Reading
Multi-State Models	p. 277
Introduction	p. 277
Missing Covariate/Auxiliary Variable Approach	p. 277
Model Discrimination and Averaging	p. 286
Summary	p. 303
Further Reading	p. 304
State-Space Modelling	p. 307
Introduction	p. 307
Leslie Matrix-Based Models	p. 314
Non-Leslie-Based Models	p. 332
Capture-Recapture Data	p. 339
Summary	p. 342
Further Reading	p. 342
Closed Populations	p. 345
Introduction	p. 345
Models and Notation	p. 346
Model Fitting	p. 347
Model Discrimination and Averaging	p. 354
Line Transects	p. 356
Summary	p. 359
Further Reading	p. 360
Appendices	p. 363
Common Distributions	p. 365
Discrete Distributions	p. 365
Continuous Distributions	p. 368
Programming in R	p. 375
Getting Started in R	p. 375
Useful R Commands	p. 376
Writing (RJ)MCMC Functions	p. 378
R Code for Model C/C	p. 379
R Code for White Stork Covariate Analysis	p. 387
Summary	p. 398
Programming in WinBUGS	p. 401
WinBUGS	p. 401
Calling WinBUGS from R	p. 410
Summary	p. 415
References	p. 417
Index	p. 435
Table of Contents provided by Ingram. All Rights Reserved.

Please wait while the item is added to your bag...

FREE SHIPPING

Amazon no longer offers textbook rentals. We do!

Bayesian Analysis for Population Ecology

Summary

Table of Contents

Supplemental Materials