Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model

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 re...

传统解剖学分析仅能捕获真实表型组（phenomic）信息的极小一部分。本研究采用深度学习技术，对2468张蝴蝶照片的整体表型相似性开展量化分析，样本涵盖38个亚种，均来自多态性拟态复合体，分属红带袖蝶（Heliconius erato）与诗神袖蝶（Heliconius melpomene）。通过深度卷积三元组网络计算得到的欧氏表型距离，证实跨物种共拟态者间存在显著的趋同演化现象。这一结果从量化层面验证了缪勒拟态理论的核心预测——该理论亦是进化生物学领域最古老的数学模型。表型邻接（neighbor-joining）树与翅斑图案基因的系统发育树呈显著相关，证明本研究实现了客观且具备系统发育信息价值的表型组捕获。比较分析结果显示，翅斑图案特征的共演化交换呈现频率依赖性的双向趋同特征。因此，本研究的表型分析结果支持经典拟态理论所预测的双向共演化过程——尽管该理论曾一度引发争议，且