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Bayesian population analysis using WinBUGS : a hierarchical perspective / Marc Kéry and Michael Schaub.

By: Kéry, Marc.
Contributor(s): Schaub, Michael.
Material type: materialTypeLabelBookPublisher: Boston : Academic Press, 2012Edition: 1st ed.Description: xvii, 535 pages : illustrations (some colour) ; 23 cm.ISBN: 9780123870209; 0123870208.Subject(s): WinBUGS | POPULATION BIOLOGY | DATA PROCESSINGHoldings: GRETA POINT: 574.3:519.226 KER Other classification: ST 250 | WC 7000
Contents:
Foreword -- Preface -- Acknowledgements -- 1. INTRODUCTION -- 1.1 Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change -- 1.2 Genesis of Ecological Observations -- 1.3 The Binomial Distribution as a Canonical Description of the Observation Process -- 1.4 Structure and Overview of the Contents of this Book -- 1.5 Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision -- 1.6 Summary and Outlook -- 1.7 Exercises -- 2. BRIEF INTRODUCTION TO BAYESIAN STATISTICAL MODELING -- 2.1 Introduction -- 2.2 Role of Models in Science -- 2.3 Statistical Models -- 2.4 Frequentist and Bayesian Analysis of Statistical Models -- 2.5 Bayesian Computation -- 2.6 WinBUGS -- 2.7 Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling -- 2.8 Hierarchical Models -- 2.9 Summary and Outlook -- 3. INTRODUCTION TO THE GENERALIZED LINEAR MODEL: THE SIMPLEST MODEL FOR COUNT DATA -- 3.1 Introduction -- 3.2 Statistical Models: Response = Signal + Noise -- 3.3 Poisson GLM in R and WinBUGS for Modeling Time Series of Counts -- 3.4 Poisson GLM for Modeling Fecundity -- 3.5 Binomial GLM for Modeling Bounded Counts or Proportions -- 3.6 Summary and Outlook -- 3.7 Exercises -- 4. INTRODUCTION TO RANDOM EFFECTS: CONVENTIONAL POISSON GLMM FOR COUNT DATA -- 4.1 Introduction -- 4.2 Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS -- 4.3 Mixed Models with Random Effects for Variability among Groups (Site and Year Effects) -- 4.4 Summary and Outlook -- 4.5 Exercises -- 5. STATE-SPACE MODELS FOR POPULATION COUNTS -- 5.1 Introduction -- 5.2 A Simple Model -- 5.3 Systematic Bias in the Observation Process -- 5.4 Real Example: House Martin Population Counts in the Village of Magden -- 5.5 Summary and Outlook -- 5.6 Exercises -- 6. ESTIMATION OF THE SIZE OF A CLOSED POPULATION FROM CAPTURE-RECAPTURE DATA -- 6.1 Introduction -- 6.2 Generation and Analysis of Simulated Data with Data Augmentation -- 6.3 Analysis of a Real Data Set: Model Mtbh for Species Richness Estimation -- 6.4 Capture-Recapture Models with Individual Covariates: Model Mt+x -- 6.5 Summary and Outlook -- 6.6 Exercises -- 7. ESTIMATION OF SURVIVAL FROM CAPTURE-RECAPTURE DATA USING THE CORMACK-JOLLY-SEBER MODEL -- 7.1 Introduction -- 7.2 The CJS Model as a State-Space Model -- 7.3 Models with Constant Parameters -- 7.4 Models with Time-Variation -- 7.5 Models with Individual Variation -- 7.6 Models with Time and Group Effects -- 7.7 Models with Age Effects -- 7.8 Immediate Trap Response in Recapture Probability -- 7.9 Parameter Identifiability -- 7.10 Fitting the CJS to Data in the M-Array Format: The Multinomial Likelihood -- 7.11 Analysis of a Real Data Set: Survival of Female Leisler's Bats -- 7.12 Summary and Outlook -- 7.13 Exercises -- 8. ESTIMATION OF SURVIVAL USING MARK-RECOVERY DATA -- 8.1 Introduction -- 8.2 The Mark-Recovery Model as a State-Space Model -- 8.3 The Mark-Recovery Model Fitted with the Multinomial Likelihood -- 8.4 Real-Data Example: Age-Dependent Survival in Swiss Red Kites -- 8.5 Summary and Outlook -- 8.6 Exercises -- 9. ESTIMATION OF SURVIVAL AND MOVEMENT FROM CAPTURE-RECAPTURE DATA USING MULTISTATE MODELS -- 9.1 Introduction -- 9.2 Estimation of Movement Between Two Sites -- 9.3 Accounting for Temporary Emigration -- 9.4 Estimation of Age-Specific Probability of First Breeding -- 9.5 Joint Analysis of Capture-Recapture and Mark-Recovery Data -- 9.6 Estimation of Movement Among Three Sites -- 9.7 Real-Data Example: The Showy Lady's Slipper -- 9.8 Summary and Outlook -- 9.9 Exercises -- 10. ESTIMATION OF SURVIVAL, RECRUITMENT, AND POPULATION SIZE FROM CAPTURE-RECAPTURE DATA USING THE JOLLY-SEBER MODEL -- 10.1 Introduction -- 10.2 The JS Model as a State-Space Model -- 10.3 Fitting the JS Model with Data Augmentation -- 10.4 Models with Constant Survival and Time-Dependent Entry -- 10.5 Models with Individual Capture Heterogeneity -- 10.6 Connections Between Parameters, Further Quantities and Some Remarks on Identifiability -- 10.7 Analysis of a Real Data Set: Survival, Recruitment and Population Size of Leisler's Bats -- 10.8 Summary and Outlook -- 10.9 Exercises -- 11. ESTIMATION OF DEMOGRAPHIC RATES, POPULATION SIZE, AND PROJECTION MATRICES FROM MULTIPLE DATA TYPES USING INTEGRATED POPULATION MODELS -- 11.1 Introduction -- 11.2 Developing an Integrated Population Model (IPM) -- 11.3 Example of a Simple IPM (Counts, Capture-Recapture, Reproduction) -- 11.4 Another Example of an IPM: Estimating Productivity Without Explicit Productivity Data -- 11.5 IPMs for Population Viability Analysis -- 11.6 Real Data Example: Hoopoe Population Dynamics -- 11.7 Summary and Outlook -- 11.8 Exercises -- 12. ESTIMATION OF ABUNDANCE FROM COUNTS IN METAPOPULATION DESIGNS USING THE BINOMIAL MIXTURE MODEL -- 12.1 Introduction -- 12.2 Generation and Analysis of Simulated Data -- 12.3 Analysis of Real Data: Open-Population Binomial Mixture Models -- 12.4 Summary and Outlook -- 12.5 Exercises -- 13. ESTIMATION OF OCCUPANCY AND SPECIES DISTRIBUTIONS FROM DETECTION/NONDETECTION DATA IN METAPOPULATION DESIGNS USING SITE-OCCUPANCY MODELS -- 13.1 Introduction -- 13.2 What Happens When p < 1 and Constant and p is Not Accounted for in a Species Distribution Model? -- 13.3 Generation and Analysis of Simulated Data for Single-Season Occupancy -- 13.4 Analysis of Real Data Set: Single-Season Occupancy Model -- 13.5 Dynamic (Multiseason) Site-Occupancy Models -- 13.6 Multistate Occupancy Models -- 13.7 Summary and Outlook -- 13.8 Exercises -- 14. CONCLUDING REMARKS -- 14.1 The Power and Beauty of Hierarchical Models -- 14.2 The Importance of the Observation Process -- 14.3 Where Will We Go? -- 14.4 The Importance of Population Analysis for Conservation and Management -- APPENDIX 1: A List of WinBUGS Tricks -- APPENDIX 2: Two Further Useful Multistate Capture–Recapture Models -- References -- Index.
Scope and content: 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. Key Features • 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
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574.3:519.226 KER 1 Issued 05/05/2020 B021126

Includes bibliographical references and index.

Foreword -- Preface -- Acknowledgements -- 1. INTRODUCTION -- 1.1 Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change -- 1.2 Genesis of Ecological Observations -- 1.3 The Binomial Distribution as a Canonical Description of the Observation Process -- 1.4 Structure and Overview of the Contents of this Book -- 1.5 Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision -- 1.6 Summary and Outlook -- 1.7 Exercises -- 2. BRIEF INTRODUCTION TO BAYESIAN STATISTICAL MODELING -- 2.1 Introduction -- 2.2 Role of Models in Science -- 2.3 Statistical Models -- 2.4 Frequentist and Bayesian Analysis of Statistical Models -- 2.5 Bayesian Computation -- 2.6 WinBUGS -- 2.7 Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling -- 2.8 Hierarchical Models -- 2.9 Summary and Outlook -- 3. INTRODUCTION TO THE GENERALIZED LINEAR MODEL: THE SIMPLEST MODEL FOR COUNT DATA -- 3.1 Introduction -- 3.2 Statistical Models: Response = Signal + Noise -- 3.3 Poisson GLM in R and WinBUGS for Modeling Time Series of Counts -- 3.4 Poisson GLM for Modeling Fecundity -- 3.5 Binomial GLM for Modeling Bounded Counts or Proportions -- 3.6 Summary and Outlook -- 3.7 Exercises -- 4. INTRODUCTION TO RANDOM EFFECTS: CONVENTIONAL POISSON GLMM FOR COUNT DATA -- 4.1 Introduction -- 4.2 Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS -- 4.3 Mixed Models with Random Effects for Variability among Groups (Site and Year Effects) -- 4.4 Summary and Outlook -- 4.5 Exercises -- 5. STATE-SPACE MODELS FOR POPULATION COUNTS -- 5.1 Introduction -- 5.2 A Simple Model -- 5.3 Systematic Bias in the Observation Process -- 5.4 Real Example: House Martin Population Counts in the Village of Magden -- 5.5 Summary and Outlook -- 5.6 Exercises -- 6. ESTIMATION OF THE SIZE OF A CLOSED POPULATION FROM CAPTURE-RECAPTURE DATA -- 6.1 Introduction -- 6.2 Generation and Analysis of Simulated Data with Data Augmentation -- 6.3 Analysis of a Real Data Set: Model Mtbh for Species Richness Estimation -- 6.4 Capture-Recapture Models with Individual Covariates: Model Mt+x -- 6.5 Summary and Outlook -- 6.6 Exercises -- 7. ESTIMATION OF SURVIVAL FROM CAPTURE-RECAPTURE DATA USING THE CORMACK-JOLLY-SEBER MODEL -- 7.1 Introduction -- 7.2 The CJS Model as a State-Space Model -- 7.3 Models with Constant Parameters -- 7.4 Models with Time-Variation -- 7.5 Models with Individual Variation -- 7.6 Models with Time and Group Effects -- 7.7 Models with Age Effects -- 7.8 Immediate Trap Response in Recapture Probability -- 7.9 Parameter Identifiability -- 7.10 Fitting the CJS to Data in the M-Array Format: The Multinomial Likelihood -- 7.11 Analysis of a Real Data Set: Survival of Female Leisler's Bats -- 7.12 Summary and Outlook -- 7.13 Exercises -- 8. ESTIMATION OF SURVIVAL USING MARK-RECOVERY DATA -- 8.1 Introduction -- 8.2 The Mark-Recovery Model as a State-Space Model -- 8.3 The Mark-Recovery Model Fitted with the Multinomial Likelihood -- 8.4 Real-Data Example: Age-Dependent Survival in Swiss Red Kites -- 8.5 Summary and Outlook -- 8.6 Exercises -- 9. ESTIMATION OF SURVIVAL AND MOVEMENT FROM CAPTURE-RECAPTURE DATA USING MULTISTATE MODELS -- 9.1 Introduction -- 9.2 Estimation of Movement Between Two Sites -- 9.3 Accounting for Temporary Emigration -- 9.4 Estimation of Age-Specific Probability of First Breeding -- 9.5 Joint Analysis of Capture-Recapture and Mark-Recovery Data -- 9.6 Estimation of Movement Among Three Sites -- 9.7 Real-Data Example: The Showy Lady's Slipper -- 9.8 Summary and Outlook -- 9.9 Exercises -- 10. ESTIMATION OF SURVIVAL, RECRUITMENT, AND POPULATION SIZE FROM CAPTURE-RECAPTURE DATA USING THE JOLLY-SEBER MODEL -- 10.1 Introduction -- 10.2 The JS Model as a State-Space Model -- 10.3 Fitting the JS Model with Data Augmentation -- 10.4 Models with Constant Survival and Time-Dependent Entry -- 10.5 Models with Individual Capture Heterogeneity -- 10.6 Connections Between Parameters, Further Quantities and Some Remarks on Identifiability -- 10.7 Analysis of a Real Data Set: Survival, Recruitment and Population Size of Leisler's Bats -- 10.8 Summary and Outlook -- 10.9 Exercises -- 11. ESTIMATION OF DEMOGRAPHIC RATES, POPULATION SIZE, AND PROJECTION MATRICES FROM MULTIPLE DATA TYPES USING INTEGRATED POPULATION MODELS -- 11.1 Introduction -- 11.2 Developing an Integrated Population Model (IPM) -- 11.3 Example of a Simple IPM (Counts, Capture-Recapture, Reproduction) -- 11.4 Another Example of an IPM: Estimating Productivity Without Explicit Productivity Data -- 11.5 IPMs for Population Viability Analysis -- 11.6 Real Data Example: Hoopoe Population Dynamics -- 11.7 Summary and Outlook -- 11.8 Exercises -- 12. ESTIMATION OF ABUNDANCE FROM COUNTS IN METAPOPULATION DESIGNS USING THE BINOMIAL MIXTURE MODEL -- 12.1 Introduction -- 12.2 Generation and Analysis of Simulated Data -- 12.3 Analysis of Real Data: Open-Population Binomial Mixture Models -- 12.4 Summary and Outlook -- 12.5 Exercises -- 13. ESTIMATION OF OCCUPANCY AND SPECIES DISTRIBUTIONS FROM DETECTION/NONDETECTION DATA IN METAPOPULATION DESIGNS USING SITE-OCCUPANCY MODELS -- 13.1 Introduction -- 13.2 What Happens When p < 1 and Constant and p is Not Accounted for in a Species Distribution Model? -- 13.3 Generation and Analysis of Simulated Data for Single-Season Occupancy -- 13.4 Analysis of Real Data Set: Single-Season Occupancy Model -- 13.5 Dynamic (Multiseason) Site-Occupancy Models -- 13.6 Multistate Occupancy Models -- 13.7 Summary and Outlook -- 13.8 Exercises -- 14. CONCLUDING REMARKS -- 14.1 The Power and Beauty of Hierarchical Models -- 14.2 The Importance of the Observation Process -- 14.3 Where Will We Go? -- 14.4 The Importance of Population Analysis for Conservation and Management -- APPENDIX 1: A List of WinBUGS Tricks -- APPENDIX 2: Two Further Useful Multistate Capture–Recapture Models -- References -- Index.

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.

Key Features

• 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

GRETA POINT: 574.3:519.226 KER

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