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.
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.
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.