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Cited by 324

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Handbook of Capture-Recapture Analysis

·2010·Princeton University Press eBooks

Abstract

Every day, biologists in parkas, raincoats, and rubber boots go into the field to capture and mark a variety of animal species. Back in the office, statisticians create analytical models for the field biologists' data. But many times, representatives of the two professions do not fully understand one another's roles. This book bridges this gap by helping biologists understand state-of-the-art statistical methods for analyzing capture-recapture data. In so doing, statisticians will also become more familiar with the design of field studies and with the real-life issues facing biologists. Reliable outcomes of capture-recapture studies are vital to answering key ecological questions. Is the population increasing or decreasing? Do more or fewer animals have a particular characteristic? In answering these questions, biologists cannot hope to capture and mark entire populations. And frequently, the populations change unpredictably during a study. Thus, increasingly sophisticated models have been employed to convert data into answers to ecological questions. This book, by experts in capture-recapture analysis, introduces the most up-to-date methods for data analysis while explaining the theory behind those methods. Thorough, concise, and portable, it will be immensely useful to biologists, biometricians, and statisticians, students in both fields, and anyone else engaged in the capture-recapture process.

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Metadata

Venue
Princeton University Press eBooks
Published
2010-12-31
OpenAlex
W34633382
Cited by
324
References
2
FWCI
10.04
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Handbook of Capture-Recapture Analysis. https://app.cochat.ai/papers/10.1515/9781400837717
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Handbook of Capture-Recapture Analysis. (2010). Princeton University Press eBooks. https://doi.org/10.1515/9781400837717

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.