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Estimating the size of an open population using sparse capture-recapture data.

Richard Huggins1, Jakub Stoklosa2, Cameron Roach3

  • 1School of Mathematics and Statistics, The University of Melbourne, Victoria, Australia.

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|June 21, 2017
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Summary

Analyzing sparse capture-recapture data in open populations is challenging. This study extends the Chao sparse estimator using regression and extrapolation for more accurate population size estimation.

Keywords:
ExtrapolationFrequentist inferenceMark-capture-recapturePopulation size estimationSparse recaptures

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Area of Science:

  • Ecology
  • Population Dynamics
  • Statistical Modeling

Background:

  • Sparse capture-recapture data from open populations present significant analytical challenges for existing frequentist methods.
  • The Chao sparse estimator is effective for closed populations with limited recaptures.

Purpose of the Study:

  • To adapt the Chao sparse estimator for application to open population settings.
  • To develop novel statistical methods for analyzing sparse capture-recapture data.

Main Methods:

  • Extension of the Chao (1989) closed population size estimator.
  • Application of linear regression and extrapolation techniques.
  • Conducting simulation studies and applying models to real-world sparse capture-recapture datasets.

Main Results:

  • Successful adaptation of the Chao estimator for open populations.
  • Demonstrated utility of regression and extrapolation in sparse data analysis.
  • Validation through simulation and empirical data application.

Conclusions:

  • The proposed method offers a viable approach for estimating population sizes from sparse open-population capture-recapture data.
  • This extension enhances the analytical toolkit for ecological and wildlife management studies dealing with limited data.