Discussion
In this study, we identified hybrids between native Japanese giant salamanders and non-native Chinese giant salamanders from images using deep learning. Historically, visual screening by experts and DNA analysis were applied to identify hybrids. However, the scarcity of experts and the time and cost of DNA analysis were barriers to effective screening. Therefore, we have proposed a novel approach to identifying hybrids using an image recognition technique. A total of 6 native and 15 hybrid individuals were used, and all were correctly classified by the AI model with an accuracy of 100% in our experimental setting. Furthermore, highlighted regions that affect the AI model’s prediction suggested that the model distinguished between native and hybrid species based on spot patterns. Although deep learning has already been applied to species and individual identification, this is the first study we know that identifies hybrid species.
EfficientNet-V2 demonstrated that head spot patterns could be used to identify native and hybrid species. One reason for successfully classifying all individuals is the quality of the training and test images. In this study, photographs were taken from a short distance; thus, the high accuracy can be attributed to the clear spot patterns in the images. Another reason is that the salamanders’ heads were photographed from a similar angle (Supplementary Figure 3). For example, previous studies have shown that different photo angles reduce identification accuracy (Arzoumanian et al. 2005). In this research, we photographed all individuals from directly above. These images used for training and testing facilitated the comparison of spot patterns and ensured highly accurate results. Training and test images obtained on the same day could also have influenced the high performance. In the future, our approach performance should be carefully evaluated in a varied environment, using images from different dates and locations, before implementing this technology in the field.
Visualized distribution of the heat maps was different for native and hybrid species. For the native species, the model focused on distinctive and large black spots, while for the hybrids, it focused on the pale and ambiguous wide region. These results suggest that the differing spot patterns between native and hybrid species can be utilized for classification. In general, native species have distinctive and large black spots (Supplementary Figure 1), whereas the spots of hybrids are more indistinct than those of native species (Supplementary Figure 2). Experts use these spot pattern differences as one of the criteria to identify hybrid individuals. The results of this study revealed that deep learning would distinguish between native and hybrid species using the same pattern recognition as experts. The heat map could be used as an instruction guide for the general public on hybrid identification because the highlighted graphical figures are visually comprehensible.
Although our approach has achieved high accuracy in identifying native species and hybrids in this study, several challenges still exist. Firstly, we did not consider the hybridization degree, which affects the spot pattern in hybrids. The hybrid captured in Hiroshima used in this study was found in the river recently, which suggests that the generation is less advanced. Since hybrid individuals between Japanese and Chinese giant salamanders are fertile, the spot pattern varied depending on several factors, such as generation. Future work should examine the relationship between the degree of genetic introgression and identification accuracy. Secondly, combining this method with DNA analysis is essential because deep learning-based identification has limitations. For example, due to hybridization, some hybrids have previously been observed with spots indistinguishable from those of Japanese giant salamanders. DNA analysis is the only method to determine the species in such cases. Therefore, our technology could be applied for the early detection of suspected hybrids through citizen science and quick identification by computer vision. In addition, advanced research might allow the identification of backcrossed hybrids that are difficult to distinguish even for experts because spots are extremely close to native species. Finally, this study was conducted in the daytime in uniform photographic conditions. Giant salamanders must be photographed under lights in field-based surveys because they are nocturnal. In the future, it is necessary to determine whether images obtained under various light conditions could be used to identify hybrids.
Hybridization between native and invasive species is one of the major causes of biodiversity loss (Bourret et al. 2022). However, detecting hybrids was challenging when the hybrids were similar to the native species. Deep learning image recognition techniques can be a valuable tool to support the visual identification of hybrids. We proposed a new approach for classifying native species and hybrids using smartphone images that could be utilized in citizen science. Hybrid identification based on spot patterns has previously been difficult and thus limited to experts; however, artificial intelligence analysis allows the public to detect hybrids easily. In particular, the distribution of hybrids is expanding, meaning that managing hybrids is a priority task for conserving Japanese giant salamanders. The findings of this study can potentially prevent the future spread of hybrids by providing a method for the efficient discovery of these individuals.