Dorsoventral comparison of intraspecific variation in the butterfly wing pattern using a convolutional neural network.

Butterfly wing patterns exhibit notable differences between the dorsal and ventral surfaces, and morphological analyses of them have provided insights into the ecological and behavioural characteristics of wing patterns. Conventional methods for dorsoventral comparisons are constrained by the need for homologous patches or shared features between two surfaces, limiting their applicability across species. We used a convolutional neural network (CNN)-based analysis, which can compare images of the two surfaces without focusing on homologous patches or features, to detect dorsoventral bias in two types of intraspecific variation: sexual dimorphism and mimetic polymorphism. Using specimen images of 29 species, we first showed that the level of sexual dimorphism calculated by CNN-based analysis corresponded well with traditional assessments of sexual dissimilarity, demonstrating the validity of the method. Dorsal biases were widely detected in sexual dimorphism, suggesting that the conventional hypothesis of dorsally biased sexual selection can be supported in a broader range of species. In contrast, mimetic polymorphism showed no such bias, indicating the importance of both surfaces in mimicry. Our study demonstrates the potential versatility of CNN in comparing wing patterns between the two surfaces, while elucidating the relationship between dorsoventrally different selections and dorsoventral biases in intraspecific variations.