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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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    978-1-43-983659-0
    July 31st 2014

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 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.

Reviews

"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

Contents

Introduction

History and motivation

Marking

Introduction to the Cormorant data set

Modelling population dynamics

Model fitting, averaging, and comparison

Introduction

Classical inference

Bayesian inference

Computing

Estimating the size of closed populations

Introduction

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

Computing

Survival modeling: single-site models

Introduction

Mark-recovery models

Mark-recapture models

Combining separate mark-recapture and recovery data sets

Joint recapture-recovery models

Computing

Survival modeling: multi-site models

Introduction

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

Computing

Occupancy modelling

Introduction

The two-parameter occupancy model

Extensions

Moving from species to individual: abundance-induced heterogeneity

Accounting for spatial information

Computing

Covariates and random effects

Introduction

External covariates

Threshold models

Individual covariates

Random effects

Measurement error

Use of P-splines

Senescence

Variable selection

Spatial covariates

Computing

Simultaneous estimation of survival and abundance

Introduction

Estimating abundance in open populations

Batch marking

Robust design

Stopover models

Computing

Goodness-of-fit assessment

Introduction

Diagnostic goodness-of-fit tests

Absolute goodness-of-fit tests

Computing

Parameter redundancy

Introduction

Using symbolic computation

Parameter redundancy and identifiability

Decomposing the derivative matrix of full rank models

Extension

The moderating effect of data

Covariates

Exhaustive summaries and model taxonomies

Bayesian methods

Computing

State-space models

Introduction

Definitions

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

Computing

Integrated population modeling

Introduction

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

Computing

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 (Hardback)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