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\begin{document}

\title{Identification of hybrids between the Japanese giant salamander (Andrias
japonicus) and Chinese giant salamander (Andrias davidianus) using deep
learning and smartphone images}



\author[1]{Kosuke Takaya}%
\author[2]{Takeshi Ise}%
\author[3]{Yuki Taguchi}%
\affil[1]{Kyoto University Graduate School of Agriculture Faculty of Agriculture}%
\affil[2]{Kyoto University Field Science Education and Research Center}%
\affil[3]{Asa Zoological Park}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Biological invasions are recognized as one of the factors causing
biodiversity loss. Incomplete reproductive isolation with a closely
related species can result in hybridization when a non-native species is
introduced into a new habitat. Management of hybrids is essential for
biodiversity conservation; however, the distinction between the two
species becomes a challenge in cases of hybrids with similar
characteristics to native species. 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 native Japanese giant salamanders (Andrias japonicus)
and non-native Chinese giant salamanders (Andrias davidianus) using
EfficientNet and smartphone images. We used smartphone images of 11
native individuals (with 5 training and 6 test images) and 20 hybrid
individuals (with 5 training and 15 test images). In our experimental
environment, an AI model constructed with efficientNet-V2 showed 100\%
accuracy in identifying hybrids. In addition, highlighting the regions
that influenced the AI model's predictions using Grad-CAM revealed that
salamander head spots are responsible for correctly classifying native
and hybrid species. The results of this study revealed that our approach
is one of the methods that enable the identification of hybrids, which
was previously considered difficult without identification by the
experts. Furthermore, since this study achieved high-performance
identification using smartphone images, it is expected to be applied to
a wide range of low-cost identification using citizen science.%
\end{abstract}%



\sloppy


\textbf{Identification of hybrids between the Japanese giant salamander
(\emph{Andrias japonicus}) and Chinese giant salamander (\emph{Andrias
davidianus}) using deep learning and smartphone images}

Kosuke Takaya \textsuperscript{*a)}, Yuki Taguchi
\textsuperscript{b)}and Takeshi Ise \textsuperscript{c)}

\textsuperscript{a)} Graduate School of Agriculture, Kyoto University,
Kyoto, Japan

\textsuperscript{b)} Hiroshima City Asa Zoological Park, Hiroshima,
Japan

\textsuperscript{c)} Field Science Education and Research Center, Kyoto
University, Kyoto, Japan

*Corresponding author: Kosuke Takaya, Graduate School of Agriculture,
Kyoto University, Oiwake, Kitashirakawa Sakyo-ku, Kyoto 606-8502, Japan;
takaya.kosuke.25a@st.kyoto-u.ac.jp; TEL: 075-753-6425; FAX: 075-753-6451

\textbf{Abstract}

Biological invasions are recognized as one of the factors causing
biodiversity loss. Incomplete reproductive isolation with a closely
related species can result in hybridization when a non-native species is
introduced into a new habitat. Management of hybrids is essential for
biodiversity conservation; however, the distinction between the two
species becomes a challenge in cases of hybrids with similar
characteristics to native species. 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 native Japanese giant salamanders (\emph{Andrias
japonicus} ) and non-native Chinese giant salamanders (\emph{Andrias
davidianus} ) using EfficientNet and smartphone images. We used
smartphone images of 11 native individuals (with 5 training and 6 test
images) and 20 hybrid individuals (with 5 training and 15 test images).
In our experimental environment, an AI model constructed with
efficientNet-V2 showed 100\% accuracy in identifying hybrids. In
addition, highlighting the regions that influenced the AI model's
predictions using Grad-CAM revealed that salamander head spots are
responsible for correctly classifying native and hybrid species. The
results of this study revealed that our approach is one of the methods
that enable the identification of hybrids, which was previously
considered difficult without identification by the experts. Furthermore,
since this study achieved high-performance identification using
smartphone images, it is expected to be applied to a wide range of
low-cost identification using citizen science.

\textbf{Keywords}

\emph{Andrias japonicus}\textbf{, giant salamander, efficientNet-V2,
gradient-weighted class activation mapping, hybrid}

\textbf{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 (Py\selectlanguage{polish}š\selectlanguage{english}ek 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
(\emph{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 (\emph{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 (\emph{Andrias davidianus} ), which is the same genus
as \emph{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
uses\emph{Andrias davidianus} instead of making this distinction.

\textbf{Materials and Methods}

\textbf{Image acquisition}

In this study, 11 native individuals and 20 hybrid individuals were used
(Supplementary Figures 1 and 2). The Hiroshima City Asa Zoological Park,
where these individuals are kept, has been researching and trying to
breed Japanese giant salamanders since 1971 and is one of the leading
facilities in Japan for research and conservation on this species.
Native individuals were already kept at the Japanese giant salamander
conservation and breeding facility. Hybrids were captured in the wild in
Hiroshima Prefecture in May 2022 and were kept in the same facility.

11 native individuals were photographed on August 20, 2022, at 11:00 AM.
Each individual was recorded for approximately 30 seconds using an
iPhone 11 with a 12-megapixel camera. We photographed the Japanese giant
salamanders in the water from above (Supplementary Figure 3). The videos
were converted to ten still JPEG images (1920 \selectlanguage{ngerman}× 1080) per second using
the Free Video to JPG Converter.

20 hybrid individuals were photographed on November 19, 2022, at 2:00 PM
using an iPhone SE equipped with a 12-megapixel camera. The method of
image collection was the same as for the native individuals.

\textbf{Ethics declarations}

The Law protects Japanese giant salamanders for the Protection of
Cultural Properties as a ''National Natural Monument.'' Therefore, this
study was approved by the Hiroshima City Asa Zoological Park, which has
permission from the Agency for Cultural Affairs and is categorized as a
non-invasive study.

\textbf{Framework}

The framework of this study is shown in Figure 1. The salamander heads
in the images were automatically detected using YOLOv5 (Redmon et al.
2016) and used as either training or test images. The training data used
5 native and 5 hybrid individuals selected randomly from the two groups
(Table 1; Supplementary Figures 1 and 2). The 6 remaining native
individuals and 15 remaining hybrid individuals not used in training
were selected as test images. These images were resized to 224 x 224
pixels to ensure consistency in size. Additionally, augmentation
processing (rotation, crop, brightness, gaussian noise, color jitter,
and saturation) was added to the training data to prevent over-fitting.
After augmentation, 70\% of the images for training and 30\% for
validation were randomly separated for analysis.

\textbf{Visualization using Grad-CAM}

Deep learning based on convolutional neural networks has achieved high
accuracy in different areas of image recognition, such as image
classification, object detection, and image segmentation. This high
performance is obtained from processing substantial amounts of data with
deep neural networks. However, the multilayer, nonlinear structure of
deep learning causes difficulty in interpreting the model. This lack of
interpretability is a major disadvantage of deep learning, and this
technique is sometimes considered a ``black box'' method. In fact, in
fields such as clinical medicine, the lack of interpretability in models
is a barrier to the practical application of deep learning (Petch and
Nelson 2022). Recently, several approaches have been developed to
overcome these challenges. For example, Class Activation Mapping (CAM)
provides a heatmap visualization of the regions that influenced the
model's predictions, which is valuable information for human
interpretation of the results (Zhou et al. 2016). However, this method
is not applicable when the Global Average Pooling (GAP) layer is absent,
which means it depends on the network structure. Gradient-weighted class
activation mapping (Grad-CAM), a generalized version of CAM
unconstrained by model structure, has improved this problem (Selvaraju
et al. 2017). The algorithm uses class-specific gradient information in
the final convolutional layer of a CNN to visualize important regions.
This study used Grad-CAM to show visual evidence in classifying native
and hybrid species.

\textbf{EfficientNet-V2}

This study trained EfficientNet-V2 to classify images. EfficientNet is a
convolutional neural network that scales down the number of layers while
scaling down the model (Tan and Le 2019). EfficientNet-V2 is an improved
version of EfficientNet with increased training speed and parameter
efficiency relative to the previous EfficientNet (Tan and Le 2021). The
EfficientNet-V2 model employs neural architecture search (NAS) to
optimize model accuracy, size, and training speed. In this study, the
EfficientNetV2-B0 model was used as the network, and fine-tuning was
performed using a model that had been pre-trained with the Imagenet21k
data set. The number of epochs was set to 50, and the batch size was set
to 32 for training. Adam was used as the optimization algorithm
(optimizer), and dropout was set to 0.3. We employed early stopping to
prevent overfitting. Automatic termination was performed when validation
loss did not improve more than 0.001 for five consecutive epochs, and we
used the weights when validation loss was the best. These analyses were
performed using the NVIDIA DGX Station A100. Finally, overall accuracy
was used for evaluation.



\textbf{Results}

\textbf{Classification of native and hybrid species}

The 6 native individuals and 15 hybrid individuals used in the test
(1,881 images) were all correctly classified, with an accuracy of 100\%
in our experimental environment. The classification results are
presented in the confusion matrix (Figure 2). The vertical axis is the
ground truth, and the horizontal axis is the model's prediction. The
number in each cell indicates the number of images classified as native
or hybrid, and the color of each cell indicates the percentage of images
per ground truth. For example, pale blue indicates a ratio of 0.0,
indicating that no image is classified for that cell. In contrast, dark
blue indicates a ratio of 1.0, indicating that all images were
classified for that cell.

\textbf{Visualization using Grad-CAM}

The original images of the native and hybrid species, and the activation
maps visualized by Grad-CAM, are shown in Figure 3 and Figure 4. Other
individuals used in the analysis are shown in Supplementary Figures 4
and 5. The red indicates the regions the model considers when outputting
the prediction results. The results show that the head spots of
salamanders are a key area for the classification of native and hybrid
species. For native species, the visualized figures indicated that the
network learns to recognize relatively distinctive and large black
spots. On the other hand, for hybrids, the visualization indicated that
the network learned to focus on pale and ambiguous wide regions rather
than distinctive and large black spots. However, because the spot
pattern differs from individual to individual, the results of Grad-CAM
visualization also differ from individual to individual (Supplementary
Figures 4 and 5).

\textbf{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.

\textbf{Conclusion}

In this study, we applied deep learning to identify hybrids between
Japanese and Chinese giant salamanders. Our results show that the head
of giant salamanders is effective for classification. The use of
Grad-CAM also revealed that the spot pattern is important for
identifying the two species. Visual identification of hybrids has
historically been restricted to specialists, but our approach could give
a possibility for the public to identify hybrids. These results support
the identification of hybrids, especially within the context of citizen
science.

\textbf{Acknowledgements}

We are very grateful to the Hiroshima City Asa Zoological Park for their
cooperation in our research on Japanese giant salamanders. Under
permission from the Agency for Cultural Affairs, the Asa Zoological Park
is researching and breeding the Japanese giant salamander, a nationally
protected species. This work was supported by the Sasakawa Scientific
Research Grant from The Japan Science Society.

\textbf{Author contributions}

Kosuke Takaya: Conceptualization, data analysis, interpretation, and
preparation of the first original manuscript

Yuki Taguchi: Conceptualization, data collection, interpretation, and
suggestions for the original manuscript

Takeshi Ise: Guided all steps of the analysis and manuscript
preparation.

\textbf{Competing interests}

The authors declare no conflicts of interest associated with this
manuscript.

\textbf{Data availability statement}

The code and models are both also archived at Zenodo.

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