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Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model

Hoyal Cuthill, Jennifer F and Guttenberg, Nicholas and Ledger, Sophie and Crowther, Robyn and Huertas, Blanca (2019) 'Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model.' Science Advances, 5 (8). eaaw4967-. ISSN 2375-2548

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Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total phenotypic similarity across 2468 butterfly photographs, covering 38 subspecies from the polymorphic mimicry complex of Heliconius erato and Heliconius melpomene. Euclidean phenotypic distances, calculated using a deep convolutional triplet network, demonstrate significant convergence between interspecies co-mimics. This quantitatively validates a key prediction of Müllerian mimicry theory, evolutionary biology’s oldest mathematical model. Phenotypic neighbor-joining trees are significantly correlated with wing pattern gene phylogenies, demonstrating objective, phylogenetically informative phenome capture. Comparative analyses indicate frequency-dependent mutual convergence with coevolutionary exchange of wing pattern features. Therefore, phenotypic analysis supports reciprocal coevolution, predicted by classical mimicry theory but since disputed, and reveals mutual convergence as an intrinsic generator for the unexpected diversity of Müllerian mimicry. This demonstrates that deep learning can generate phenomic spatial embeddings, which enable quantitative tests of evolutionary hypotheses previously only testable subjectively.

Item Type: Article
Additional Information: Manuscript and combined supplementary information
Uncontrolled Keywords: Animals; Butterflies; Models, Theoretical; Wings, Animal; Biological Mimicry; Biological Coevolution; Deep Learning
Divisions: Faculty of Science and Health
Faculty of Science and Health > Life Sciences, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 19 Nov 2019 11:22
Last Modified: 06 Jan 2022 14:04

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