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CLADES: A classification-based machine learning method for species delimitation from population genetic data.

Jingwen Pei1, Chong Chu1,2, Xin Li1

  • 1Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut.

Molecular Ecology Resources
|April 19, 2018
PubMed
Summary

Machine learning, using the CLADES method, offers a novel approach to species delimitation. This technique accurately identifies species using genomic data, even with short divergence times and gene flow.

Keywords:
classificationmachine learningpopulation geneticsspecies delimitation

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Area of Science:

  • Ecology
  • Evolutionary Biology
  • Genomics

Background:

  • Species delimitation is fundamental to ecological and evolutionary research.
  • Multilocus genomic data are increasingly vital for understanding species boundaries.
  • Traditional methods face challenges with closely related species or gene flow.

Purpose of the Study:

  • To introduce CLADES, a machine learning-based method for species delimitation.
  • To demonstrate the efficacy of machine learning in classifying species using genomic data.
  • To provide a tool effective in challenging delimitation scenarios.

Main Methods:

  • Species delimitation framed as a classification problem using machine learning.
  • Support Vector Machine (SVM) trained on summary statistics from simulated genomic data.
  • CLADES classifies population pairs and maximizes species assignment likelihood.

Main Results:

  • CLADES demonstrates high accuracy and efficiency in species delimitation through simulations.
  • The method performs well compared to existing species delimitation techniques.
  • Effectiveness shown in scenarios with short species divergence and gene flow.

Conclusions:

  • Machine learning, via CLADES, provides a powerful and accurate tool for species delimitation.
  • CLADES offers a valuable alternative, particularly for difficult-to-delimit species.
  • The method advances the application of genomic data in evolutionary and ecological studies.