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Extending GroupStruct2: a Bayesian and machine-learning framework for testing taxonomic hypotheses using morphometric

Kin Onn Chan1, L Lee Grismer2,3,4

  • 1Department of Integrative Biology, MSU Museum, Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI 48824, USA Department of Herpetology, San Diego Natural History Museum San Diego United States of America https://ror.org/00kmpab62.

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Summary
This summary is machine-generated.

This study introduces GroupStruct2, an R application for robust taxonomic delimitation using morphometric data. It integrates Bayesian Gaussian Mixture Models and machine learning, offering a more nuanced approach than traditional statistics.

Keywords:
ANOVABorutaDAPCGroupStruct2MFAMorphologyPCAt-test

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Area of Science:

  • Ecology and Evolutionary Biology
  • Computational Biology
  • Statistical Modeling

Background:

  • Traditional taxonomic delimitation relies on basic statistics, limiting nuanced interpretation of morphometric data.
  • Existing methods often depend on visual inspection or p-value thresholds, lacking a probabilistic framework.

Purpose of the Study:

  • To adapt and integrate advanced statistical methods for enhanced morphometric taxonomic delimitation.
  • To develop a user-friendly application (GroupStruct2) combining classical and modern analytical approaches.
  • To provide a more robust and comprehensive framework for analyzing morphological variation.

Main Methods:

  • Utilized Bayesian model-testing with Gaussian Mixture Models (GMM) for inferring morphological clusters.
  • Implemented a Random Forest classification algorithm for identifying diagnostic characters.
  • Integrated these methods into GroupStruct2, a Shiny R-based application with a graphical user interface.

Main Results:

  • Demonstrated that GroupStruct2 provides a more nuanced perspective on morphological variation compared to traditional methods.
  • Showcased the ability to infer clusters with or without prior OTU groupings.
  • Validated the identification of diagnostic characters using machine learning.

Conclusions:

  • GroupStruct2 offers an integrative workflow combining frequentist and Bayesian methods for rigorous taxonomic studies.
  • The application democratizes access to advanced statistical analyses for researchers.
  • Promotes quantitative rigor in taxonomic research through accessible, robust statistical tools.