Unsupervised machine learning reveals mimicry complexes in bumblebees occur along a perceptual continuum

Briana Ezray, Drew C. Wham, Carrie E. Hill, Heather M. Hines

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Müllerian mimicry theory states that frequency-dependent selection should favour geographical convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes, each containing a predominant phenotype. Outside a few examples in butterflies, the location of transition zones between mimicry complexes and the factors driving mimicry zones has rarely been examined. To infer the patterns and processes of Müllerian mimicry, we integrate large-scale data on the geographical distribution of colour patterns of social bumblebees across the contiguous United States and use these to quantify colour pattern mimicry using an innovative, unsupervised machine-learning approach based on computer vision. Our data suggest that bumblebees exhibit geographically clustered, but sometimes imperfect colour patterns, and that mimicry patterns gradually transition spatially rather than exhibit discrete boundaries. Additionally, examination of colour pattern transition zones of three comimicking, polymorphic species, where active selection is driving phenotype frequencies, revealed that their transition zones differ in location within a broad region of poor mimicry. Potential factors influencing mimicry transition zone dynamics are discussed.

Original languageEnglish (US)
Article number20191501
JournalProceedings of the Royal Society B: Biological Sciences
Volume286
Issue number1910
DOIs
StatePublished - Sep 11 2019

Fingerprint

mimicry
artificial intelligence
Bombus
Learning systems
Color
color
transition zone
Geographical distribution
Phenotype
phenotype
Butterflies
computer vision
butterflies
Computer vision
geographical distribution
machine learning
Unsupervised Machine Learning
butterfly

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

Cite this

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title = "Unsupervised machine learning reveals mimicry complexes in bumblebees occur along a perceptual continuum",
abstract = "M{\"u}llerian mimicry theory states that frequency-dependent selection should favour geographical convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes, each containing a predominant phenotype. Outside a few examples in butterflies, the location of transition zones between mimicry complexes and the factors driving mimicry zones has rarely been examined. To infer the patterns and processes of M{\"u}llerian mimicry, we integrate large-scale data on the geographical distribution of colour patterns of social bumblebees across the contiguous United States and use these to quantify colour pattern mimicry using an innovative, unsupervised machine-learning approach based on computer vision. Our data suggest that bumblebees exhibit geographically clustered, but sometimes imperfect colour patterns, and that mimicry patterns gradually transition spatially rather than exhibit discrete boundaries. Additionally, examination of colour pattern transition zones of three comimicking, polymorphic species, where active selection is driving phenotype frequencies, revealed that their transition zones differ in location within a broad region of poor mimicry. Potential factors influencing mimicry transition zone dynamics are discussed.",
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Unsupervised machine learning reveals mimicry complexes in bumblebees occur along a perceptual continuum. / Ezray, Briana; Wham, Drew C.; Hill, Carrie E.; Hines, Heather M.

In: Proceedings of the Royal Society B: Biological Sciences, Vol. 286, No. 1910, 20191501, 11.09.2019.

Research output: Contribution to journalArticle

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AU - Wham, Drew C.

AU - Hill, Carrie E.

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