Analysis of Capture-Recapture Data  book cover
1st Edition

Analysis of Capture-Recapture Data

ISBN 9781439836590
Published August 1, 2014 by Chapman and Hall/CRC
314 Pages 18 B/W Illustrations

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Book Description

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

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.

Table of Contents

History and motivation
Introduction to the Cormorant data set
Modelling population dynamics

Model fitting, averaging, and comparison
Classical inference
Bayesian inference

Estimating the size of closed populations
The Schnabel census
Analysis of Schnabel census data
Model classes
Accounting for unobserved heterogeneity
Logistic-linear models
Spuriously large estimates, penalized likelihood and elicited priors
Bayesian modeling
Medical and social applications
Testing for closure-mixture estimators
Spatial capture-recapture models

Survival modeling: single-site models
Mark-recovery models
Mark-recapture models
Combining separate mark-recapture and recovery data sets
Joint recapture-recovery models

Survival modeling: multi-site models
Matrix representation
Multi-site joint recapture-recovery models
Multi-state models as a unified framework
Extensions to multi-state models
Model selection for multi-site models
Multi-event models

Occupancy modelling
The two-parameter occupancy model
Moving from species to individual: abundance-induced heterogeneity
Accounting for spatial information

Covariates and random effects
External covariates
Threshold models
Individual covariates
Random effects
Measurement error
Use of P-splines
Variable selection
Spatial covariates

Simultaneous estimation of survival and abundance
Estimating abundance in open populations
Batch marking
Robust design
Stopover models

Goodness-of-fit assessment
Diagnostic goodness-of-fit tests
Absolute goodness-of-fit tests

Parameter redundancy
Using symbolic computation
Parameter redundancy and identifiability
Decomposing the derivative matrix of full rank models
The moderating effect of data
Exhaustive summaries and model taxonomies
Bayesian methods

State-space models
Fitting linear Gaussian models
Models which are not linear Gaussian
Bayesian methods for state-space models
Formulation of capture-re-encounter models
Formulation of occupancy models

Integrated population modeling
Normal approximations of component likelihoods
Model selection
Goodness of fit for integrated population modelling: calibrated simulation
Previous applications
Hierarchical modelling to allow for dependence of data sets

Appendix: Distributions reference

Summary, Further reading, and Exercises appear at the end of each chapter.

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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.


"...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 capture–recapture 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 capture–recapture data. … very clear … accessible to most statisticians and quantitative ecologists. … I think Analysis of Capture–Recapture Data does a great job of detailing the nuts and bolts of capture–recapture models commonly used in practice. In particular, for the more sophisticated or specialized capture–recapture models, this book certainly points the reader in the right direction for further details. I highly recommend it for those who haven’t encountered or heard of capture–recapture 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 capture–recapture 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 what’s out there. And I’m glad to have it on my bookshelf. … a really great synthesis of much of the current capture–recapture 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