DOI: 10.5061/dryad.2hp1978
title: Data from: Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model

description: 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 surprising
 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.
description: Butterfly photographs
 from Hoyal Cuthill et al 2019 Science Advances2468 photographs of
 butterfly specimens from subspecies of the species Heliconius erato and
 Heliconius melpomene. Butterfly specimens are held in the collections of
 the Natural History Museum, London (NHM). File names correspond to NHM
 specimen numbers. Additional joined data including subspecies identity is
 shown in Table S2 (Hoyal Cuthill et al 2019). A dorsal (upper, labelled D)
 and ventral (lower, labelled V) photograph is included for each butterfly
 specimen. Please cite the associated journal paper (Hoyal Cuthill et al
 2019) in any reference to these images. Photographers: Robyn Crowther and
 Sophie Ledger, Natural History Museum, London.ButterflyPhotographsHoyalCuthilletal2019ScienceAdvances.zipTable S2 from Hoyal Cuthill et al 2019 Science AdvancesTable S2. Taxonomic and locality data recorded for historical specimens of Heliconius erato and Heliconius melpomene held in the collections of the Natural History Museum (NHM), London. Where approximate latitude and longitude of the capture locality were not recoverable from the recorded locality data (6% of specimens), longitude and latitude are marked NaN. Hybrid status is recorded based on additional taxonomic information recorded on specimen labels, NHM records and the taxonomic checklist of Lamas, 2004 and www.buttefliesofamerica.com (bold).TableS2HoyalCuthilletal2019ScienceAdvances.xlsxSupplementary Computer Code ipynb formatSupplementary Computer Code from Hoyal Cuthill et al 2019 Science Advances containing the Python scripts used for deep learning in ipynb file format. Please cite this associated journal paper in all references to this code.SupplementaryComputerCode.ipynbSupplementary Computer Code pdf formatSupplementary Computer Code from Hoyal Cuthill et al 2019 Science Advances containing the Python scripts used for deep learning, pdf of ipynb file. Please cite this associated journal paper in all references to this code.SupplementaryComputerCode.pdf