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Analysis of Capture-Recapture Data

By Rachel S. McCrea, Byron J. T. Morgan

Chapman and Hall/CRC – 2014 – 314 pages

Series: Chapman & Hall/CRC Interdisciplinary Statistics

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    July 31st 2014


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.


"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



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.

Author Bio

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.

Name: Analysis of Capture-Recapture Data (eBook)Chapman and Hall/CRC 
Description: By Rachel S. McCrea, Byron J. T. Morgan. 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...
Categories: Statistical Theory & Methods, Statistics for the Biological Sciences, Biodiversity