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Maximum likelihood estimation for model Mt,α for capture-recapture data with misidentification.

R T R Vale1, R M Fewster, E L Carroll

  • 1IRD, Asteron Centre, 55 Featherston Street, Wellington, New Zealand.

Biometrics
|June 20, 2014
PubMed
Summary
This summary is machine-generated.

The Mt,α model for capture-recapture studies can estimate animal abundance with identification errors. However, it requires rich data and high capture probabilities for accurate results, otherwise, ignoring errors may be better.

Keywords:
Genetic capture-recaptureLatent multinomialMark recaptureMisidentificationNatural tagsPhoto-identification

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Area of Science:

  • Ecology
  • Population Biology
  • Statistical Modeling

Background:

  • Capture-recapture studies are vital for estimating population abundance.
  • Natural marks like DNA or photos are used for individual identification.
  • Classical models assume perfect identification, which is often unrealistic.

Purpose of the Study:

  • To investigate the Mt,α model for abundance estimation in closed populations with identification errors.
  • To derive and efficiently compute a closed-form likelihood for the Mt,α model.
  • To assess the statistical properties of maximum likelihood estimates under this model.

Main Methods:

  • Developed an exact closed-form expression for the likelihood of the Mt,α model.
  • Employed efficient computation of the likelihood.
  • Analyzed statistical properties (precision, bias) of abundance estimates.

Main Results:

  • The Mt,α model's indirect error estimation requires substantial data richness.
  • High capture probabilities or many capture occasions are needed for precise and unbiased estimates.
  • Under data limitations, ignoring misidentification errors can yield better results than the Mt,α model.

Conclusions:

  • The Mt,α model should be used cautiously due to its data demands.
  • Alternative strategies for handling misidentification errors should be explored.
  • Illustrative application to southern right whale (Eubalaena australis) population surveys.