Introduction
Biodiversity is essential for human health, well-being, and a stable environment. Although significant efforts have been devoted toward conservation, biodiversity loss remains a global challenge (Johnson et al. 2017). Anthropogenic activities such as urbanization, agricultural intensification, and species exploitation reduce biodiversity, and evidence indicates that species extinction rates are progressing much faster than in the past (Ceballos et al. 2015). In addition, globalization has led to the introduction of various organisms from their native habitats into new environments and the establishment of non-native populations in new areas. These invasive species cause ecosystem impacts such as predation, niche displacement, and introduction of diseases (Doherty et al. 2016; Haubrock et al. 2021; Kortz and Magurran 2019; Scheele et al. 2019). Furthermore, non-native species are recognized as a driver of recent extinctions (Bellard et al. 2016). The impact of non-native species on biodiversity and ecosystems is accelerating, and this trend is expected to continue (Pyšek et al. 2020). Therefore, the mitigation of biological invasions is essential for biodiversity conservation.
When non-native species are introduced into a new habitat, newcomers sometimes encounter close relatives. In such cases, hybridization occurs due to incomplete reproductive isolation from closely related species. Hybridization in non-native species is frequently observed and considered an evolutionary mechanism that determines invasion success (Bock et al. 2021). For example, hybrid fitness is occasionally superior to the parental species; (i.e., hybrid vigor). In addition, hybrids also have intermediate traits or different traits from the parent species, and some traits may determine the establishment success of invasive species (Coulter et al. 2020). For instance, a meta-analysis of plants, animals, and fungi demonstrated that invasive hybrids have a larger body size and are more fecund than their parent species (Hovick and Whitney 2014). Furthermore, early invasive populations are affected by density-dependent processes such as the Allee effect. However, hybridization provides mating partners for invasive species, which could reduce the Allee effect and promote invasions (Yamaguchi et al. 2019).
Hybrids of similar species pose a threat to genetic diversity because introduced alleles may eventually replace the native alleles (Fitzpatrick and Shaffer 2007). Therefore, controlling hybrid species is necessary to conserve biodiversity. However, difficulties in distinguishing between native and hybrid species is a critical issue when trapping hybrids. Hybrids were detected using morphological characteristics until the mid-1960s (Allendorf et al. 2001). This approach assumes that hybrids exhibit intermediate characteristics of their parent species; however, this assumption does not generalize to all cases because they often show a mosaic of parental phenotypes. In addition, morphological characteristics cannot be determined whether an individual is a first-generation or a backcross-generation hybrid. Misidentification of species can also cause conservation problems. For example, inadequate identification of target species could negatively impact native species. In fact, native frogs have been killed in Australia due to misjudgments while removing the non-native cane toad (Rhinella marina ) (Somaweera et al. 2010).
The development of molecular genetic techniques, such as allozyme electrophoresis and PCR, has overcome these challenges (Allendorf et al. 2001). DNA analysis allows accurate species identification and can reveal individuals’ degree of hybridization, which would be difficult to determine using morphological traits. However, these analyses are time-consuming and costly, limiting the quick identification of hybrids and large-scale surveys.
In recent years, deep learning image recognition technology, a novel group of artificial intelligence approaches, has begun to be utilized in both species and individual identification in ecology. Identifying and counting animal species in images provides basic but essential information (Tuia et al. 2022). Many previous studies have combined camera traps and deep learning to identify species. For instance, Norouzzadeh et al. (2018) used 3.2 million images from camera traps in the Serengeti National Park to successfully identify 48 species. In addition, these techniques have been applied to individual identification, such as green turtles (Carter et al. 2014), chimpanzees (Schofield et al. 2019), and brown bears (Clapham et al. 2020). Furthermore, this technology has already been used to detect non-native species (Ashqar and Abu-Naser 2019; Guo et al. 2022; Takaya et al. 2022). Although a similar approach may provide a new method for identifying hybrids in the field, studies have yet to apply deep learning models to identify hybrids.
The Japanese giant salamander (Andrias japonicus ) is an amphibian endemic to Japan and is threatened with extinction, as its population has declined due to habitat degradation and fragmentation (Tochimoto et al. 2007; Taguchi and Natuhara 2009; Yamasaki et al. 2013). In the 2022 IUCN Red List, the conservation status rank of this species was changed from Near Threatened to Vulnerable (IUCN 2022). One reason for this change is its hybridization with the non-native Chinese giant salamander (Andrias davidianus ), which is the same genus as A. japonicus . The Chinese giant salamander is also threatened with extinction in their original habitat, but individuals introduced to Japan in the early 1970s have become wild, and hybridization with Japanese giant salamanders is an issue. For example, the Kyoto City government survey revealed that only 4 (2%) out of 244 captured individuals were native species, and the remaining were 240 (98%) hybrids and non-native species in the Kamo river basin in Kyoto, requiring rapid action (The Kyoto City Government 2015). However, the number of areas where hybrids were caught is increasing and has already been confirmed in six prefectures in western Japan (Kyoto, Mie, Nara, Shiga, Okayama, and Hiroshima). Since hybrid species have a spot pattern that inherits the characteristics of both native and non-native species, individuals with the potential for hybridization are captured by visual screening and DNA analysis is also applied for accurate identification. While this approach is reliable, identifying hybrids by their spot patterns requires specialized knowledge, and DNA analysis is time-consuming and expensive. If identifying hybrid salamanders from images could work well, it does not need time and cost as DNA analysis. It also facilitates early detection and effective capture of suspected hybrid individuals via citizen science, thereby contributing to the effective conservation of native Japanese giant salamanders.
The first objective of this study is to identify hybrids between Japanese giant salamanders and Chinese giant salamanders from images based on deep learning. Our approach allows the public to photograph and detect hybrid individuals without specialized knowledge. In recent years, citizen science has been adopted to manage invasive species (Larson et al. 2020), and a similar method could be applied to hybrids. The second objective is to clarify which features the AI model uses as criteria to determine hybrids. Spot patterns are difficult to quantify compared to measurable morphological features. However, techniques such as Grad-CAM allow visualization of the important region for the AI model’s prediction. If specific essential areas in identifying hybrids can be clarified, that information is valuable for the general public to identify hybrids. Although there is a proposal to divide the Chinese giant salamander into three species (Turvey et al. 2019), our study usesAndrias davidianus instead of making this distinction.