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Bayesian Population Analysis Using WinBUGS A Hierarchical Perspective

ISBN-10: 0123870208

ISBN-13: 9780123870209

Edition: 2012

Authors: Marc Kery, Michael Schaub

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Book details

Copyright year: 2012
Publisher: Elsevier Science & Technology Books
Publication date: 10/11/2011
Binding: Paperback
Pages: 554
Size: 6.00" wide x 9.00" long x 1.00" tall
Weight: 2.244

Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change
Genesis of Ecological Observations
The Binomial Distribution as a Canonical Description of the Observation Process
Structure and Overview of the Contents of this Book
Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision
Summary and Outlook
Brief Introduction to Bayesian Statistical Modeling
Role of Models in Science
Statistical Models
Frequentist and Bayesian Analysis of Statistical Models
Bayesian Computation
Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling
Hierarchical Models
Summary and Outlook
Introduction to the Generalized Linear Model: The Simplest Model for Count Data
Statistical Models: Response = Signal + Noise
Poisson GLM in R and WinBUGS for Modeling Time Series of Counts
Poisson GLM for Modeling Fecundity
Binomial GLM for Modeling Bounded Counts or Proportions
Summary and Outlook
Introduction to Random Effects: Conventional Poisson GLMM for Count Data
Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS
Mixed Models with Random Effects for Variability among Groups (Site and Year Effects)
Summary and Outlook
State-Space Models for Population Counts
A Simple Model
Systematic Bias in the Observation Process
Real Example: House Martin Population Counts in the Village of Magden
Summary and Outlook
Estimation of the Size of a Closed Population from Capture-Recapture Data
Generation and Analysis of Simulated Data with Data Augmentation
Analysis of a Real Data Set: Model M<sub>tbh</sub> for Species Richness Estimation
Capture-Recapture Models with Individual Covariates: Model M<sub>t+X</sub>
Summary and Outlook
Estimation of Survival from Capture-Recapture Data Using the Cormack-Jolly-Seber Model
The CJS Model as a State-Space Model
Models with Constant Parameters
Models with Time-Variation
Models with Individual Variation
Models with Time and Group Effects
Models with Age Effects
Immediate Trap Response in Recapture Probability
Parameter Identifiability
Fitting the CJS to Data in the M-Array Format: The Multinomial Likelihood
Analysis of a Real Data Set: Survival of Female Leisler's Bats
Summary and Outlook
Estimation of Survival Using Mark-Recovery Data
The Mark-Recovery Model as a State-Space Model
The Mark-Recovery Model Fitted with the Multinomial Likelihood
Real-Data Example: Age-Dependent Survival in Swiss Red Kites
Summary and Outlook
Estimation of Survival and Movement from Capture-Recapture Data Using Multistate Models
Estimation of Movement between Two Sites
Accounting for Temporary Emigration
Estimation of Age-Specific Probability of First Breeding
Joint Analysis of Capture-Recapture and Mark-Recovery Data
Estimation of Movement among Three Sites
Real-Data Example: The Showy Lady's Slipper
Summary and Outlook
Estimation of Survival, Recruitment, and Population Size from Capture-Recapture Data Using the Jolly-Seber Model
The JS Model as a State-Space Model
Fitting the JS Model with Data Augmentation
Models with Constant Survival and Time-Dependent Entry
Models with Individual Capture Heterogeneity
Connections between Parameters, Further Quantities and Some Remarks on Identifiability
Analysis of a Real Data Set: Survival, Recruitment and Population Size of Leisler's Bats
Summary and Outlook
Estimation of Demographic Rates, Population Size, and Projection Matrices from Multiple Data Types Using Integrated Population Models
Developing an Integrated Population Model (IPM)
Example of a Simple IPM (Counts, Capture-Recapture, Reproduction)
Another Example of an IPM: Estimating Productivity without Explicit Productivity Data
IPMs for Population Viability Analysis
Real Data Example: Hoopoe Population Dynamics
Summary and Outlook
Estimation of Abundance from Counts in Metapopulation Designs Using the Binomial Mixture Model
Generation and Analysis of Simulated Data
Analysis of Real Data: Open-Population Binomial Mixture Models
Summary and Outlook
Estimation of Occupancy and Species Distributions from Detection/Nondetection Data in Metapopulation Designs Using Site-Occupancy Models
What Happens When p 1 and Constant and p is Not Accounted for in a Species Distribution Model?
Generation and Analysis of Simulated Data for Single-Season Occupancy
Analysis of Real Data Set: Single-Season Occupancy Model
Dynamic (Multiseason) Site-Occupancy Models
Multistate Occupancy Models
Summary and Outlook
Concluding Remarks
The Power and Beauty of Hierarchical Models
The Importance of the Observation Process
Where Will We Go?
The Importance of Population Analysis for Conservation and Management
A List of WinBUGS Tricks
Two Further Useful Multistate Capture-Recapture Models