314 Pages 18 B/W Illustrations
    by Chapman & Hall

    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.

    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.


    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