PT - JOURNAL ARTICLE
AU - Hoyal Cuthill, Jennifer F.
AU - Guttenberg, Nicholas
AU - Ledger, Sophie
AU - Crowther, Robyn
AU - Huertas, Blanca
TI - Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model
AID - 10.1126/sciadv.aaw4967
DP - 2019 Aug 01
TA - Science Advances
PG - eaaw4967
VI - 5
IP - 8
4099 - http://advances.sciencemag.org/content/5/8/eaaw4967.short
4100 - http://advances.sciencemag.org/content/5/8/eaaw4967.full
SO - Sci Adv2019 Aug 01; 5
AB - 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.