2019/08/02 by Jennifer F. Hoyal Cuthill, Nicholas Guttenberg, Sophie Ledger +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Neuroscience · #Biomimetic flight and propulsion mechanisms #Butterfly #Convergence (economics) #Convergent evolution #Deep learning #Mimicry #Neural Networks and Reservoir Computing #Neurobiology and Insect Physiology Research #Phenotypic trait #Phylogenetics #Similarity (geometry) #cs.LG #q-bio.PE #stat.ML
paper · pdf · doi:10.1126/sciadv.aaw4967
published as Sci Adv 5, eaaw4967 (2019) · Manuscript and combined supplementary information
openalex publication_date 2019/08/02 · arxiv created 2019/08/15 · arxiv updated 2019/08/16 · openalex created_date 2019/08/22 · openalex updated_date 2026/08/05
. 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.