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Capture-Recapture Models

Authors: Kenneth H. Pollock;

Capture-Recapture Models

Abstract

Here I briefly review capture-recapture models as they apply to estimation of demographic parameters (e.g., population size, survival, recruitment, emigration, and immigration) for wild animal populations. These models are now also widely used in a variety of other applications, such as the census undercount, incidence of disease, criminality, homelessness, and computer bugs (see Pollock 1991 for many references). Although they have their historical roots in the sixteenth century, capture-recapture models are basically a twentieth century phenomenon. The papers by Petersen and Lincoln (Seber 1982) from late last century and early this century represent early attempts by biologists to use capture-recapture methods. Later, as statistical inference took its modern form and provided powerful tools such as maximum likelihood methods, biometricians became involved. There has been an explosion of research that still seems to be accelerating at the century’s end. Fortunately, most of the research is still rooted in the need to solve biological questions. Section 2 reviews closed models; Section 3, open models; and Section 4, combined models. I conclude the article with my views on fruitful current and future research thrusts and how the pace of change is affecting them.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
77
Top 10%
Top 1%
Top 10%
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