Identification of hybrids between the Japanese giant salamander (Andrias japonicus) and Chinese giant salamander (Andrias cf. davidianus) using deep learning and smartphone images

Abstract

Human-mediated hybridization between native and non-native species is causing biodiversity loss worldwide. Hybridization has contributed to the extinction of many species through direct and indirect processes such as loss of reproductive opportunity and genetic introgression. Therefore, it is essential to manage hybrids to conserve biodiversity. However, specialized knowledge is required to identify the target species based on visual characteristics when two species have similar features. Although image recognition technology can be a powerful tool for identifying hybrids, studies have yet to utilize deep learning approaches. Hence, this study aimed to identify hybrids between the native Japanese giant salamander (Andrias japonicus) and the non-native Chinese giant salamander (Andrias cf. davidianus) using EfficientNetV2 and smartphone images. We used smartphone images of 11 individuals of native A. japonicus (five training and six test images) and 20 individuals of hybrids between A. japonicus and A. cf. davidianus (five training and 15 test images). In our experimental environment, an AI model constructed with EfficientNetV2 exhibited 100% accuracy in identifying hybrids. In addition, gradient-weighted class activation mapping revealed that the AI model was able to classify A. japonicus and hybrids between A. japonicus and A. cf. davidianus on the basis of the dorsal head spot patterning. Our approach thus enables the identification of hybrids against A. japonicus, which was previously considered difficult by non-experts. Furthermore, since this study achieved reliable identification using smartphone images, it is expected to be applied to a wide range of citizen science projects.