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Related Experiment Video

Updated: Jun 9, 2026

Quantifying Abdominal Pigmentation in Drosophila melanogaster
08:41

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Published on: June 1, 2017

Deep learning on butterfly phenotypes tests evolution's oldest mathematical model.

Jennifer F Hoyal Cuthill1,2,3, Nicholas Guttenberg1, Sophie Ledger4

  • 1Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo 152-8550, Japan.

Science Advances
|August 28, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning quantifies butterfly similarity, validating Müllerian mimicry theory. This evolutionary biology model shows species converge, driving mimicry complex diversity.

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Area of Science:

  • Evolutionary Biology
  • Bioinformatics
  • Computer Vision

Background:

  • Traditional phenomic analysis is limited.
  • Müllerian mimicry theory predicts species convergence.
  • Understanding mimicry complex evolution is crucial.

Purpose of the Study:

  • Quantify phenotypic similarity in Heliconius butterflies using deep learning.
  • Test key predictions of Müllerian mimicry theory.
  • Explore the role of mutual convergence in mimicry diversity.

Main Methods:

  • Applied deep convolutional triplet networks to 2468 butterfly photographs.
  • Calculated Euclidean phenotypic distances.
  • Constructed phenotypic neighbor-joining trees.

Main Results:

  • Demonstrated significant convergence between interspecies co-mimics.
  • Validated Müllerian mimicry theory's prediction of convergence.
  • Showed phenotypic trees correlate with gene phylogenies.
  • Indicated frequency-dependent mutual convergence and coevolutionary exchange.

Conclusions:

  • Deep learning enables quantitative phenomic analysis.
  • Supports reciprocal coevolution in mimicry complexes.
  • Reveals mutual convergence as a driver of mimicry diversity.