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Analysis of Capture-Recapture Data [Hardback]

(University of Kent, Canterbury, UK), (University of Kent, Canterbury, UK)
  • Formāts: Hardback, 314 pages, height x width: 234x156 mm, weight: 602 g, 73 Tables, black and white; 18 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Interdisciplinary Statistics
  • Izdošanas datums: 01-Aug-2014
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1439836590
  • ISBN-13: 9781439836590
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Analysis of Capture-Recapture DataPalielināt
  • Formāts: Hardback, 314 pages, height x width: 234x156 mm, weight: 602 g, 73 Tables, black and white; 18 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Interdisciplinary Statistics
  • Izdošanas datums: 01-Aug-2014
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1439836590
  • ISBN-13: 9781439836590
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781439836590.html
For students already comfortable with probability and statistics, this graduate textbook explains how to model data from closed populations of animals and from open populations of wild animals where most of the data comes from the capture, recapture, and death of sample individuals. A single dataset on Great cormorants illustrates the estimation of key demographic parameters of survival probability from data collected on individually identifiable animals, and the fitting of a two-capture mixture model to single site breeding data. A companion website provides datasets, computer programs, and photographs. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

An important first step in studying the demography of wild animals is to identify the animals uniquely through applying markings, such as rings, tags, and bands. Once the animals are encountered again, researchers can study different forms of capture-recapture data to estimate features, such as the mortality and size of the populations. Capture-recapture methods are also used in other areas, including epidemiology and sociology.

With an emphasis on ecology, Analysis of Capture-Recapture Data covers many modern developments of capture-recapture and related models and methods and places them in the historical context of research from the past 100 years. The book presents both classical and Bayesian methods.

A range of real data sets motivates and illustrates the material and many examples illustrate biometry and applied statistics at work. In particular, the authors demonstrate several of the modeling approaches using one substantial data set from a population of great cormorants. The book also discusses which computer programs to use for implementing the models and contains 130 exercises that extend the main material. The data sets, computer programs, and other ancillaries are available at www.capturerecapture.co.uk.

The book is accessible to advanced undergraduate and higher-level students, quantitative ecologists, and statisticians. It helps readers understand model formulation and applications, including the technicalities of model diagnostics and checking.

Recenzijas

"...does a great job of concisely pulling together and categorizing relevant models from historic to very recent. In addition to describing the models, the book also provides the interested reader with related readings, software, and exercises at the end of each chapter...The sheer number and diversity of modeling approaches and examples covered in the book is quite impressive. Overall, this book provides a good survey of the models available in capturerecapture analysis, starting with those around 100 years old and moving into the very recent." Journal of the American Statistical Association, May 2016

" a very detailed monograph covering both classical and modern-day statistical methodology used for analyzing capturerecapture data. very clear accessible to most statisticians and quantitative ecologists. I think Analysis of CaptureRecapture Data does a great job of detailing the nuts and bolts of capturerecapture models commonly used in practice. In particular, for the more sophisticated or specialized capturerecapture models, this book certainly points the reader in the right direction for further details. I highly recommend it for those who havent encountered or heard of capturerecapture modeling before." Australian & New Zealand Journal of Statistics, 2016

"This book presents an excellent and compact overview of the existing methodological approaches to what is commonly called the capture-recapture area. Various approaches have been developed over at least 100 years, and it is a great achievement of the authors to bring these together in a very digestible overview." Biometrical Journal, 2015

" an excellent, easy-to-read monograph about capturerecapture models. it is well organized and the writing is clear and concise. I would recommend this book as a reference for the quantitative ecologist or statistician interested in knowing whats out there. And Im glad to have it on my bookshelf. a really great synthesis of much of the current capturerecapture and related population modeling literature " J. Andrew Royle, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 20, No. 2, 2015

"This book hits its target audience perfectly. an excellent basis for an advanced undergraduate course on capture-recapture methods, or by selecting sections of the book, part of a course on wildlife assessment and management methods impressive in its scope and breadth an excellent reference book for quantitative ecologists and statisticians The book comes with an attractive and well-organised website containing resources that are a real bonus for anyone wanting to develop teaching material on capture-recapture or take advantage of the educational material there for their own understanding of the topics covered. I highly recommend the book to anyone interested in capture-recapture methods, particularly as they relate to ecological problems." David Borchers, University of St Andrews, Scotland

"This volume will be useful as both a textbook and reference, introducing readers to the most recent methodological developments in drawing inferences about animal population dynamics from the study of marked individuals. In a rapidly changing discipline, this book does a good job of surveying the current art of the possible." Jim Nichols, Patuxent Wildlife Research Center, U.S. Geological Survey

"Analysis of Capture-Recapture Data is an invaluable companion to the modern theory and practice of capture-recapture modelling. It is a text with multifaceted appeal, ranging in coverage from traditional models to cutting-edge developments, and flowing effortlessly from practical model-fitting advice to advanced technical topics such as parameter redundancy. It is presented throughout in a concise, accessible style that strikes an impeccable balance between illumination of concepts and succinct mathematical detail. This book is a must-have for all statisticians working with ecological data and is also suitable for ecologists with a mild quantitative bent or as a course companion for students from senior undergraduate years onwards. The text can be used either as a dip-in reference or as a cover-to-cover read. Anyone who completes the latter can feel confident that they are up to date with everything that matters in this vibrant and expanding field." Rachel Fewster, Associate Professor, University of Auckland, New Zealand

Acknowledgements xv
Preface xvii
Notation xix
1 Introduction 1(10)
1.1 History and motivation
1(1)
1.2 Marking
2(1)
1.3 Introduction to the Cormorant data set
3(3)
1.3.1 Capture-recapture-recovery data
4(2)
1.3.2 Nest counts and productivity
6(1)
1.4 Modelling population dynamics
6(1)
1.5 Summary
7(1)
1.6 Further reading
8(3)
1.6.1 History and aspects of bird ringing
8(1)
1.6.2 Reviews, books and websites
8(1)
1.6.3 Of mice and men
9(1)
1.6.4 Design
9(2)
2 Model fitting, averaging and comparison 11(16)
2.1 Introduction
11(2)
2.1.1 Forming likelihoods
11(1)
2.1.2 Bayes theorem and methods
12(1)
2.1.3 Iterative methods and parameterisation
12(1)
2.2 Classical inference
13(6)
2.2.1 Point and interval estimation using maximum likelihood
13(2)
2.2.2 Flat likelihoods
15(1)
2.2.3 The EM algorithm
15(1)
2.2.4 Model comparison
15(3)
2.2.5 Model averaging
18(1)
2.2.6 Goodness-of-fit
19(1)
2.3 Bayesian inference
19(4)
2.3.1 Introduction
19(1)
2.3.2 Metropolis Hastings
20(1)
2.3.3 Gibbs sampling
21(1)
2.3.4 Using MCMC simulations
21(1)
2.3.5 Model probabilities and model averaging
22(1)
2.3.6 Reversible jump Markov chain Monte Carlo
22(1)
2.3.7 Hierarchical models
22(1)
2.3.8 Goodness-of-fit: Bayesian p-values and calibrated simulation
23(1)
2.4 Computing
23(1)
2.5 Summary
24(1)
2.6 Further reading
24(3)
3 Estimating the size of closed populations 27(30)
3.1 Introduction
27(1)
3.1.1 Background
27(1)
3.1.2 Assumptions
27(1)
3.2 The Schnabel census
28(2)
3.2.1 General notation
28(2)
3.3 Analysis of Schnabel census data
30(5)
3.3.1 Likelihoods based on the multinomial distribution
30(1)
3.3.2 Modelling based on the Poisson distribution, and relationship to the multinomial
30(1)
3.3.3 Chao's lower-bound estimator
31(2)
3.3.4 Conditional analysis
33(1)
3.3.5 Relationship between conditional and unconditional Poisson and multinomial analyses
34(1)
3.4 Model classes
35(3)
3.4.1 Model Mo
35(1)
3.4.2 Time-dependent capture probability: model class, Mt
36(1)
3.4.3 Behavioural capture probability: model class, Mb
37(1)
3.5 Accounting for unobserved heterogeneity
38(5)
3.5.1 Mixture models
38(2)
3.5.2 Horvitz-Thompson estimator
40(2)
3.5.3 Horvitz-Thompson-like estimator
42(1)
3.5.4 Other approaches to modelling heterogeneity
43(1)
3.6 Logistic-linear models
43(2)
3.7 Spuriously large estimates
45(1)
3.8 Bayesian modelling
45(1)
3.9 Medical and social applications
46(1)
3.9.1 Log-linear models
47(1)
3.10 Testing for closure
47(1)
3.11 N-mixture estimators
47(1)
3.12 Spatial capture-recapture models
48(3)
3.12.1 Likelihood formation for a spatial capture-recapture model
48(1)
3.12.2 Modelling animal locations
49(1)
3.12.3 Modelling the capture probability
50(1)
3.12.4 Alternative estimation methods
50(1)
3.12.5 Applications of spatial capture-recapture models
51(1)
3.13 Computing
51(1)
3.14 Summary
52(1)
3.15 Further reading
52(2)
3.16 Exercises
54(3)
4 Survival modelling: single-site models 57(30)
4.1 Introduction
57(2)
4.2 Mark-recovery models
59(10)
4.2.1 Time-dependence
60(1)
4.2.2 Incorporating age
61(2)
4.2.3 Full age-dependence in survival: the Cormack-Seber model
63(1)
4.2.4 Model selection for mark-recovery models
63(5)
4.2.5 Extensions of recovery models
68(1)
4.3 Mark-recapture models
69(5)
4.3.1 Cormack-Jolly-Seber model
70(1)
4.3.2 Explicit maximum-likelihood estimates for the CJS model
70(2)
4.3.3 Bayesian estimation for CJS model
72(1)
4.3.4 Incorporating heterogeneity in the CJS model
73(1)
4.3.5 Age-dependent models in capture recapture
73(1)
4.4 Combining separate mark-recapture and recovery data sets
74(1)
4.5 Joint recapture-recovery models
75(6)
4.5.1 Extensions of joint recapture-recovery models
80(1)
4.6 Computing
81(1)
4.7 Summary
82(1)
4.8 Further reading
82(1)
4.9 Exercises
83(4)
5 Survival modelling: multisite models 87(20)
5.1 Introduction
87(1)
5.2 Matrix representation
88(1)
5.3 Multisite joint recapture-recovery models
89(3)
5.4 Multistate models as a unified framework
92(2)
5.5 Extensions to multistate models
94(3)
5.5.1 Models with unobservable states
94(2)
5.5.2 Memory models
96(1)
5.6 Model selection for multisite models
97(2)
5.7 Multievent models
99(3)
5.7.1 A unified framework
100(2)
5.7.2 Applications of multievent models
102(1)
5.8 Computing
102(1)
5.9 Summary
103(1)
5.10 Further reading
103(1)
5.11 Exercises
104(3)
6 Occupancy modelling 107(14)
6.1 Introduction
107(2)
6.2 The two-parameter occupancy model
109(2)
6.2.1 Survey design
110(1)
6.3 Extensions
111(1)
6.3.1 Multiple seasons
111(1)
6.3.2 Multiple species
111(1)
6.3.3 Citizen science and presence-only data
111(1)
6.4 Moving from species to individual: abundance-induced hetero- geneity
112(1)
6.5 Accounting for spatial information
112(3)
6.5.1 Poisson process model
113(1)
6.5.2 Markov modulated Poisson process
114(1)
6.6 Accounting for spatial information
115(2)
6.6.1 Abundance-induced heterogeneity along a transect
116(1)
6.6.2 Incorporating clustering
116(1)
6.7 Computing
117(1)
6.8 Summary
117(1)
6.9 Further reading
118(1)
6.10 Exercises
118(3)
7 Covariates and random effects 121(28)
7.1 Introduction
121(1)
7.2 External covariates
122(2)
7.2.1 Regressions
122(2)
7.3 Threshold models
124(2)
7.4 Individual covariates
126(6)
7.4.1 Estimating N: observed heterogeneity
127(1)
7.4.2 Dealing with missing information: a classical approach to dealing with time-varying individual covariates in open populations
128(4)
7.4.3 Dealing with missing information: a Bayesian approach and comparison
132(1)
7.5 Random effects
132(4)
7.6 Measurement error
136(1)
7.7 Use of P-splines
136(2)
7.8 Senescence
138(1)
7.9 Variable selection
138(3)
7.9.1 Lasso
139(1)
7.9.2 The Bayesian approach to model selection
140(1)
7.10 Spatial covariates
141(2)
7.11 Computing
143(1)
7.12 Summary
144(1)
7.13 Further reading
144(1)
7.14 Exercises
145(4)
8 Simultaneous estimation of survival and abundance 149(16)
8.1 Introduction
149(1)
8.2 Estimating abundance in open populations
149(4)
8.2.1 The original Jolly-Seber model
150(1)
8.2.2 The Schwarz and Arnason formulation
151(1)
8.2.3 Alternative Jolly-Seber formulations
152(1)
8.3 Batch marking
153(1)
8.4 Robust design
154(4)
8.4.1 Closed robust design model
154(3)
8.4.2 Open robust design model
157(1)
8.5 Stopover models
158(3)
8.6 Computing
161(1)
8.7 Summary
161(1)
8.8 Further reading
161(1)
8.9 Exercises
162(3)
9 Goodness-of-fit assessment 165(22)
9.1 Introduction
165(1)
9.2 Diagnostic goodness-of-fit tests
165(15)
9.2.1 Contingency table tests of homogeneity
165(1)
9.2.2 Single-site goodness-of-fit tests
166(8)
9.2.3 Equivalence of score tests
174(1)
9.2.4 Multisite goodness-of-fit tests
175(4)
9.2.5 Joint recapture and recovery goodness-of-fit tests
179(1)
9.3 Absolute goodness-of-fit tests
180(3)
9.3.1 Sufficient statistic goodness-of-fit tests
180(3)
9.3.2 Bayesian p-values and calibrated simulation
183(1)
9.4 Computing
183(1)
9.5 Summary
183(1)
9.6 Further reading
184(1)
9.7 Exercises
184(3)
10 Parameter redundancy 187(20)
10.1 Introduction
187(3)
10.2 Using symbolic computation
190(2)
10.2.1 Determining the deficiency of a model
190(1)
10.2.2 Determining estimable parameters for parameter- redundant models
191(1)
10.3 Parameter redundancy and identifiability
192(1)
10.4 Decomposing the derivative matrix of full rank models
192(1)
10.5 Extension
193(1)
10.6 The moderating effect of data
194(1)
10.6.1 Missing data
195(1)
10.6.2 Near singularity
195(1)
10.7 Covariates
195(2)
10.8 Exhaustive summaries and model taxonomies
197(1)
10.9 Bayesian methods
198(4)
10.9.1 A Bayesian fit for a parameter-redundant model
198(2)
10.9.2 Weakly identifiable parameters
200(2)
10.10 Computing
202(1)
10.11 Summary
202(1)
10.12 Further reading
202(1)
10.13 Exercises
203(4)
11 State-space models 207(20)
11.1 Introduction
207(1)
11.2 Definitions
207(5)
11.2.1 Constructing Leslie matrices: the order of biological processes
211(1)
11.3 Fitting linear Gaussian models
212(3)
11.3.1 The stochastic Gompertz model
212(1)
11.3.2 Linear Gaussian models
213(1)
11.3.3 The Kalman filter
214(1)
11.3.4 Initialising the filter
215(1)
11.4 Models which are not linear Gaussian
215(2)
11.4.1 Conditionally Gaussian models
216(1)
11.5 Bayesian methods for state-space models
217(1)
11.6 Formulation of capture-reencounter models
218(3)
11.6.1 Capture-recapture data
219(1)
11.6.2 Ring-recovery data
220(1)
11.6.3 Multistate capture-recapture models
221(1)
11.7 Formulation of occupancy models
221(1)
11.8 Computing
222(1)
11.9 Summary
222(1)
11.10 Further reading
223(1)
11.11 Exercises
224(3)
12 Integrated population modelling 227(14)
12.1 Introduction
227(2)
12.1.1 Multiplying likelihoods
229(1)
12.2 Normal approximations of component likelihoods
229(2)
12.2.1 Multivariate normal approximation to capture re-encounter likelihoods
230(1)
12.3 Model selection
231(1)
12.4 Goodness-of-fit for integrated population modelling; calibrated simulation
231(1)
12.5 Previous applications
231(4)
12.6 Hierarchical modelling
235(1)
12.7 Computing
236(1)
12.8 Summary
237(1)
12.9 Further reading
237(1)
12.10 Exercises
238(3)
A Distributions Reference 241(2)
References 243(36)
Index 279
Rachel S. McCrea is a NERC research fellow in the National Centre for Statistical Ecology at the University of Kent.

Byron J.T. Morgan is an Emeritus Professor and honorary professorial research fellow in the School of Mathematics, Statistics and Actuarial Science at the University of Kent. He is also the co-director of the National Centre for Statistical Ecology.