Bayesian Population Analysis Using Winbugs

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ISBN: 9780123870209 | 0123870208
Cover: Paperback
Copyright: 9/28/2011

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Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS and its open-source sister OpenBugs is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Bayesian Population Analysis Using WinBUGS goes right to the heart of the matter by providing ecologists with a comprehensive, yet concise, guide to applying WinBUGS to the types of models that they use most often: linear (LM), generalized linear (GLM), linear mixed (LMM) and generalized linear mixed models (GLMM). Comprehensive and richly-commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist. All WinBUGS/OpenBUGS analyses are completely integrated in software R. Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R.

Foreword	p. xi
Preface	p. xiii
Acknowledgments	p. xvii
Introduction
Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change	p. 1
Genesis of Ecological Observations	p. 6
The Binomial Distribution as a Canonical Description of the Observation Process	p. 9
Structure and Overview of the Contents of this Book	p. 13
Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision	p. 16
Summary and Outlook	p. 20
Exercises	p. 21
Brief Introduction to Bayesian Statistical Modeling
Introduction	p. 23
Role of Models in Science	p. 24
Statistical Models	p. 27
Frequentist and Bayesian Analysis of Statistical Models	p. 28
Bayesian Computation	p. 38
WinBUGS	p. 38
Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling	p. 41
Hierarchical Models	p. 43
Summary and Outlook	p. 44
Introduction to the Generalized Linear Model: The Simplest Model for Count Data
Introduction	p. 48
Statistical Models: Response = Signal + Noise	p. 48
Poisson GLM in R and WinBUGS for Modeling Time Series of Counts	p. 55
Poisson GLM for Modeling Fecundity	p. 66
Binomial GLM for Modeling Bounded Counts or Proportions	p. 67
Summary and Outlook	p. 71
Exercises	p. 72
Introduction to Random Effects: Conventional Poisson GLMM for Count Data
Introduction	p. 73
Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS	p. 82
Mixed Models with Random Effects for Variability among Groups (Site and Year Effects)	p. 90
Summary and Outlook	p. 110
Exercises	p. 112
State-Space Models for Population Counts
Introduction	p. 115
A Simple Model	p. 118
Systematic Bias in the Observation Process	p. 121
Real Example: House Martin Population Counts in the Village of Magden	p. 126
Summary and Outlook	p. 131
Exercises	p. 131
Estimation of the Size of a Closed Population from Capture-Recapture Data
Introduction	p. 134
Generation and Analysis of Simulated Data with Data Augmentation	p. 139
Analysis of a Real Data Set: Model M_tbh for Species Richness Estimation	p. 157
Capture-Recapture Models with Individual Covariates: Model M_t+X	p. 162
Summary and Outlook	p. 169
Exercises	p. 170
Estimation of Survival from Capture-Recapture Data Using the Cormack-Jolly-Seber Model
Introduction	p. 172
The CJS Model as a State-Space Model	p. 175
Models with Constant Parameters	p. 177
Models with Time-Variation	p. 183
Models with Individual Variation	p. 192
Models with Time and Group Effects	p. 199
Models with Age Effects	p. 208
Immediate Trap Response in Recapture Probability	p. 212
Parameter Identifiability	p. 216
Fitting the CJS to Data in the M-Array Format: The Multinomial Likelihood	p. 220
Analysis of a Real Data Set: Survival of Female Leisler's Bats	p. 231
Summary and Outlook	p. 237
Exercises	p. 238
Estimation of Survival Using Mark-Recovery Data
Introduction	p. 241
The Mark-Recovery Model as a State-Space Model	p. 243
The Mark-Recovery Model Fitted with the Multinomial Likelihood	p. 248
Real-Data Example: Age-Dependent Survival in Swiss Red Kites	p. 255
Summary and Outlook	p. 261
Exercises	p. 261
Estimation of Survival and Movement from Capture-Recapture Data Using Multistate Models
Introduction	p. 264
Estimation of Movement between Two Sites	p. 268
Accounting for Temporary Emigration	p. 281
Estimation of Age-Specific Probability of First Breeding	p. 288
Joint Analysis of Capture-Recapture and Mark-Recovery Data	p. 295
Estimation of Movement among Three Sites	p. 300
Real-Data Example: The Showy Lady's Slipper	p. 307
Summary and Outlook	p. 311
Exercises	p. 312
Estimation of Survival, Recruitment, and Population Size from Capture-Recapture Data Using the Jolly-Seber Model
Introduction	p. 316
The JS Model as a State-Space Model	p. 317
Fitting the JS Model with Data Augmentation	p. 319
Models with Constant Survival and Time-Dependent Entry	p. 328
Models with Individual Capture Heterogeneity	p. 335
Connections between Parameters, Further Quantities and Some Remarks on Identifiability	p. 339
Analysis of a Real Data Set: Survival, Recruitment and Population Size of Leisler's Bats	p. 341
Summary and Outlook	p. 345
Exercises	p. 346
Estimation of Demographic Rates, Population Size, and Projection Matrices from Multiple Data Types Using Integrated Population Models
Introduction	p. 348
Developing an Integrated Population Model (IPM)	p. 350
Example of a Simple IPM (Counts, Capture-Recapture, Reproduction)	p. 357
Another Example of an IPM: Estimating Productivity without Explicit Productivity Data	p. 363
IPMs for Population Viability Analysis	p. 366
Real Data Example: Hoopoe Population Dynamics	p. 371
Summary and Outlook	p. 379
Exercises	p. 380
Estimation of Abundance from Counts in Metapopulation Designs Using the Binomial Mixture Model
Introduction	p. 383
Generation and Analysis of Simulated Data	p. 388
Analysis of Real Data: Open-Population Binomial Mixture Models	p. 396
Summary and Outlook	p. 409
Exercises	p. 411
Estimation of Occupancy and Species Distributions from Detection/Nondetection Data in Metapopulation Designs Using Site-Occupancy Models
Introduction	p. 414
What Happens When p 1 and Constant and p is Not Accounted for in a Species Distribution Model?	p. 419
Generation and Analysis of Simulated Data for Single-Season Occupancy	p. 420
Analysis of Real Data Set: Single-Season Occupancy Model	p. 427
Dynamic (Multiseason) Site-Occupancy Models	p. 436
Multistate Occupancy Models	p. 450
Summary and Outlook	p. 459
Exercises	p. 460
Concluding Remarks
The Power and Beauty of Hierarchical Models	p. 464
The Importance of the Observation Process	p. 472
Where Will We Go?	p. 474
The Importance of Population Analysis for Conservation and Management	p. 476
A List of WinBUGS Tricks	p. 479
Two Further Useful Multistate Capture-Recapture Models	p. 487
References	p. 497
Index	p. 515
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