对蝴蝶表型的深度学习测试了进化中最古老的数学模型
Deep learning on butterfly phenotypes tests evolution's oldest mathematical model
摘要：
传统的解剖学分析只能捕获一小部分真实的表型信息。在这里，我们应用深度学习来量化2468张蝴蝶照片的总表型相似性，涵盖了$\textit{Heliconius erato}$和$\textit{Heliconius melpomene}$多态拟态复合体的38个亚种。使用深度卷积三重网络计算的欧几里得表型距离证明了物种间共模拟物之间的显着收敛。这在数量上验证了进化生物学最古老的数学模型穆勒拟态理论的一个关键预测。表型相邻连接树显着相关的翅型基因的遗传，表现出客观的，遗传学信息的表型捕获。比较分析表明，频率依赖性，相互收敛与共同进化的翅膀图案功能的交流。因此，表型分析支持互惠共同进化，经典的模仿理论预测，但自有争议，并揭示了相互收敛作为一个内在的发电机，令人惊讶的多样性穆勒模仿。这表明，深度学习可以生成表型空间嵌入，从而能够对以前只能主观测试的进化假设进行定量测试。
英文摘要：
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.
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