Deep learning on butterfly phenotypes tests evolution's oldest mathematical model
This work addresses the challenge of objectively testing evolutionary hypotheses in biology, providing a quantitative method for phenomic analysis that supports classical mimicry theory, though it is incremental in applying existing deep learning techniques to new biological data.
The researchers tackled the problem of quantifying phenotypic similarity in butterflies to test evolutionary theory, using deep learning on 2468 photographs of 38 subspecies to validate key predictions of Müllerian mimicry, showing significant convergence between co-mimics and correlation with gene phylogenies.
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 $\textit{Heliconius erato}$ and $\textit{Heliconius melpomene}$. 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.