Bayesian population analysis using WinBUGS: a hierarchical perspective
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Boston ; Heidelberg
Academic Press
2012
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xvii, 535 Seiten Illustrationen, Diagramme |
| ISBN: | 9780123870209 |
Internformat
MARC
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| 100 | 1 | |a Kéry, Manuel Marton Marc |d 1968- |e Verfasser |0 (DE-588)173499465 |4 aut | |
| 245 | 1 | 0 | |a Bayesian population analysis using WinBUGS |b a hierarchical perspective |c Marc Kéry and Michael Schaub (Swiss Ornithological Institute, 6204 Sempach, Switzerland) ; foreword by Steven R. Beissinger |
| 250 | |a First edition | ||
| 264 | 1 | |a Amsterdam ; Boston ; Heidelberg |b Academic Press |c 2012 | |
| 300 | |a xvii, 535 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 630 | 0 | 4 | |a WinBUGS |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Population biology |x Data processing | |
| 700 | 1 | |a Schaub, Michael |d 1967- |e Verfasser |0 (DE-588)173939554 |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804148564557824000 |
|---|---|
| adam_text | Contents
Foreword
xi
Preface
xiii
Acknowledgments
xvii
1.
Introduction
1.1
Ecology: The Study of Distribution and Abundance and of the Mechanisms
Driving Their Change
1
1.2
Genesis of Ecological Observations
6
1.3
The Binomial Distribution as a Canonical Description of the Observation
Process
9
1.4
Structure and Overview of the Contents of this Book
13
1.5
Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision
16
1.6
Summary and Outlook
20
1.7
Exercises
21
2.
Brief Introduction to Bayesian Statistical Modeling
2.1
Introduction
23
2.2
Role of Models in Science
24
2.3
Statistical Models
27
2.4
Frequentist and Bayesian Analysis of Statistical Models
28
2.5
Bayesian Computation
38
2.6
WinBUGS
38
2.7
Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling
41
2.8
Hierarchical Models
43
2.9
Summary and Outlook
44
3.
Introduction to the Generalized Linear Model:
The Simplest Model for Count Data
3.1
Introduction
48
3.2
Statistical Models: Response
=
Signal
+
Noise
48
3.3
Poisson
GLM in
R
and WinBUGS for Modeling Time Series of Counts
55
3.4
Poisson GLM
for Modeling Fecundity
66
3.5
Binomial GLM for Modeling Bounded Counts or Proportions
67
3.6
Summary and Outlook
71
3.7
Exercises
72
VII
Vlil
CONTENTS
4.
Introduction to Random Effects: Conventional
Poisson
GLMM for Count Data
4.1
Introduction
73
4.2
Accounting for Overdispersion by Random Effects-Modeling
in
R
and WinBUGS
82
4.3
Mixed Models with Random Effects for Variability among
Groups (Site and Year Effects)
90
4.4
Summary and Outlook
110
4.5
Exercises
112
5.
State-Space Models for Population Counts
5.1
Introduction
115
5.2
A Simple Model
118
5.3
Systematic Bias in the Observation Process
121
5.4
Real Example: House Martin Population Counts in the Village of Magden
126
5.5
Summary and Outlook
131
5.6
Exercises
131
6.
Estimation of the Size of a Closed Population from
Capture-Recapture Data
6.1
Introduction
134
6.2
Generation and Analysis of Simulated Data with Data Augmentation
139
6.3
Analysis of a Real Data Set: Model Mrbh for Species Richness Estimation
157
6.4
Capture-Recapture Models with Individual Covariates: Model Mt+X
162
6.5
Summary and Outlook
169
6.6
Exercises
170
7.
Estimation of Survival from Capture-Recapture Data Using
the Cormack-Jolly-Seber Model
7.1
Introduction
172
7.2
The CJS Model as a State-Space Model
175
7.3
Models with Constant Parameters
177
7.4
Models with Time-Variation
183
7.5
Models with Individual Variation
192
7.6
Models with Time and Group Effects
199
7.7
Models with Age Effects
208
7.8
Immediate Trap Response in Recapture Probability
212
7.9
Parameter Identifiability
216
7.10
Fitting the CJS to Data in the M-Array Format: The Multinomial Likelihood
220
7.11
Analysis of a Real Data Set: Survival of Female Leisler s Bats
231
7.12
Summary and Outlook
237
7.13
Exercises
238
CONTENTS
IX
8.
Estimation
of Survival Using Mark-Recovery Data
8.1
Introduction
241
8.2
The Mark-Recovery Model as a State-Space Model
243
8.3
The Mark-Recovery Model Fitted with the Multinomial Likelihood
248
8.4
Real-Data Example: Age-Dependent Survival in Swiss Red Kites
255
8.5
Summary and Outlook
261
8.6
Exercises
261
9.
Estimation of Survival and Movement from Capture—Recapture
Data Using Multistate Models
9.1
Introduction
264
9.2
Estimation of Movement between Two Sites
268
9.3
Accounting for Temporary Emigration
281
9.4
Estimation of Age-Specific Probability of First Breeding
288
9.5
Joint Analysis of Capture-Recapture and Mark-Recovery Data
295
9.6
Estimation of Movement among Three Sites
300
9.7
Real-Data Example: The Showy Lady s Slipper
307
9.8
Summary and Outlook
311
9.9
Exercises
312
10.
Estimation of Survival, Recruitment, and Population Size from
Capture-Recapture Data Using the Jolly—
Seber
Model
10.1
Introduction
316
10.2
The JS Model as a State-Space Model
317
10.3
Fitting the JS Model with Data Augmentation
319
10.4
Models with Constant Survival and Time-Dependent Entry
328
10.5
Models with Individual Capture Heterogeneity
335
10.6
Connections between Parameters, Further Quantities and Some Remarks
on Identifiability
339
10.7
Analysis of a Real Data Set: Survival, Recruitment and Population Size of
Leisler s Bats
341
10.8
Summary and Outlook
345
10.9
Exercises
346
11.
Estimation of Demographic Rates, Population Size,
and Projection Matrices from Multiple Data Types
Using Integrated Population Models
11.1
Introduction
348
11.2
Developing an Integrated Population Model (IPM)
350
11.3
Example of a Simple IPM (Counts, Capture-Recapture, Reproduction)
357
11.4
Another Example of an IPM: Estimating Productivity without Explicit
Productivity Data
363
χ
CONTENTS
11.5
IPMs for Population
Viability Analysis
366
11.6
Real Data Example: Hoopoe Population Dynamics
371
11.7
Summary and Outlook
379
11.8
Exercises
380
12.
Estimation of Abundance from Counts in Metapopulation
Designs Using the Binomial Mixture Model
12.1
Introduction
383
12.2
Generation and Analysis of Simulated Data
388
12.3
Analysis of Real Data: Open-Population Binomial Mixture Models
396
12-4
Summary and Outlook
409
12.5
Exercises
411
13.
Estimation of Occupancy and Species Distributions from
Detection/Nondetection Data in Metapopulation Designs
Using Site-Occupancy Models
13.1
Introduction
414
Π.2
What Happens When
ρ
< 1
and Constant and
p
is Not Accounted for in a Species
Distribution Model?
419
13.3
Generation and Analysis of Simulated Data for Single-Season Occupancy
420
1 3.4
Analysis of Real Data Set: Single-Season Occupancy Model
427
13.5
Dynamic (Multiseason) Site-Occupancy Models
436
13.6
Multistate Occupancy Models
450
13.7
Summary and Outlook
459
13.8
Exercises
460
14·
Concluding Remarks
14-1
The Power and Beauty of Hierarchical Models
464
14.2
The Importance of the Observation Process
472
14.3
Where Will We
Goľ
474
14Λ
The Importance of Population Analysis for Conservation and Management
476
Appendix
1:
A List of WinBUGS Tricks
479
Appendix
2:
Two Further Useful Multistate Capture-Recapture
Models
487
References
497
Index
515
Marc Kéry
· Michael Schaub
BAYESIAN POPULATION
ANALYSIS USING
WinBUGS
A hierarchical perspective
Bayesian Population Analysis using WinBUGS is an introduction to the analysis
of distribution, abundance, and population dynamics of animals and plants using
hierarchical models implemented in the leading Bayesian software WinBUGS. It will
be of interest to quantitative scientists working in the fields of population ecology,
conservation biology, evolutionary biology, population management, disease
ecology, fisheries or wildlife biology. The accessible text is ideal for self-study and
advanced graduate-level courses.
This book:
•
Is a statistical modeling book written by ecologists for ecologists
•
Contains analyses of simulated data, along with fully commented
R
code for
the generation of these data sets, as well as analyses of real data
•
Fully integrates with program R—all analyses are conducted by calling
WinBUGS from
R
•
Illustrates the tremendous modeling freedom given to ecologists when using
the simple and flexible BUGS language
Marc Kéry
and Michael Schaub are population ecologists with the Swiss
Ornithological Institute. Together, they have authored over
120
peer-reviewed
journal articles on a wide range of topics, including the analysis of large-scale
monitoring programs, demographic population analyses, experimental design for
animal and plant surveys, and the population ecology of rare species, as well as
Introduction to WinBUGS for Ecologists (Academic Press,
2010).
Cover photograph:
Peacock butterfly
ŕnac/Vs
ю.
Switzerland,
2001,
T. Marent
ISBN
978-0-12-387020-9
ELSEVIER
ACADEMIC PRESS
An imprint of
Elsevier
elsevierdirect.com
9 780
870 2
|
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| indexdate | 2024-07-10T00:09:08Z |
| institution | BVB |
| isbn | 9780123870209 |
| language | English |
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| spelling | Kéry, Manuel Marton Marc 1968- Verfasser (DE-588)173499465 aut Bayesian population analysis using WinBUGS a hierarchical perspective Marc Kéry and Michael Schaub (Swiss Ornithological Institute, 6204 Sempach, Switzerland) ; foreword by Steven R. Beissinger First edition Amsterdam ; Boston ; Heidelberg Academic Press 2012 xvii, 535 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier WinBUGS Datenverarbeitung Population biology Data processing Schaub, Michael 1967- Verfasser (DE-588)173939554 aut Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024542805&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024542805&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Kéry, Manuel Marton Marc 1968- Schaub, Michael 1967- Bayesian population analysis using WinBUGS a hierarchical perspective WinBUGS Datenverarbeitung Population biology Data processing |
| title | Bayesian population analysis using WinBUGS a hierarchical perspective |
| title_auth | Bayesian population analysis using WinBUGS a hierarchical perspective |
| title_exact_search | Bayesian population analysis using WinBUGS a hierarchical perspective |
| title_full | Bayesian population analysis using WinBUGS a hierarchical perspective Marc Kéry and Michael Schaub (Swiss Ornithological Institute, 6204 Sempach, Switzerland) ; foreword by Steven R. Beissinger |
| title_fullStr | Bayesian population analysis using WinBUGS a hierarchical perspective Marc Kéry and Michael Schaub (Swiss Ornithological Institute, 6204 Sempach, Switzerland) ; foreword by Steven R. Beissinger |
| title_full_unstemmed | Bayesian population analysis using WinBUGS a hierarchical perspective Marc Kéry and Michael Schaub (Swiss Ornithological Institute, 6204 Sempach, Switzerland) ; foreword by Steven R. Beissinger |
| title_short | Bayesian population analysis using WinBUGS |
| title_sort | bayesian population analysis using winbugs a hierarchical perspective |
| title_sub | a hierarchical perspective |
| topic | WinBUGS Datenverarbeitung Population biology Data processing |
| topic_facet | WinBUGS Datenverarbeitung Population biology Data processing |
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