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Bayesian estimation of community size and overlap from random subsamples.

Erik K Johnson1, Daniel B Larremore2,3

  • 1Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, United States of America.

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|September 19, 2022
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Summary

Estimating community overlap and size is challenging with partial samples. This study introduces a Bayesian joint model for unknown community sizes, improving estimation accuracy and uncertainty quantification for ecological and genetic diversity measures.

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Area of Science:

  • Ecology
  • Bioinformatics
  • Statistics

Background:

  • Quantifying overlap between communities is crucial for ecological and genetic studies.
  • Partial sampling complicates accurate estimation of shared species, items, or genes.
  • Existing methods often assume known total community sizes, limiting their applicability.

Purpose of the Study:

  • To develop a statistical framework for estimating community overlap and size from partial samples.
  • To improve the accuracy of normalized beta-diversity indices (e.g., Jaccard, Sorenson-Dice) when community sizes are unknown.
  • To provide a robust method for quantifying uncertainty in these estimations.

Main Methods:

  • Developed a Bayesian joint model for simultaneously estimating community size and overlap.
  • Utilized species, item, or gene count data within the model.
  • Compared performance against methods assuming known community sizes.

Main Results:

  • The proposed Bayesian joint model yields systematically improved estimates of community overlap.
  • The model enhances the accuracy of central estimates and uncertainty quantification.
  • Refined estimates of community size are achieved using count data.

Conclusions:

  • This novel Bayesian approach effectively addresses the challenge of unknown community sizes in overlap estimation.
  • The method offers more reliable beta-diversity metrics in ecological and genetic research.
  • The joint modeling of size and overlap provides a powerful tool for comparative community analyses.