RT Journal Article
SR Electronic
T1 Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP eaaw4967
DO 10.1126/sciadv.aaw4967
VO 5
IS 8
A1 Hoyal Cuthill, Jennifer F.
A1 Guttenberg, Nicholas
A1 Ledger, Sophie
A1 Crowther, Robyn
A1 Huertas, Blanca
YR 2019
UL http://advances.sciencemag.org/content/5/8/eaaw4967.abstract
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.