Next Article in Journal
How Food Consumption Trends Change the Direction of Sheep Breeding in China
Previous Article in Journal
General Epidemiology of Animal Viruses
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Animals
Volume 14
Issue 21
10.3390/ani14213046
animals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China

by
Lei Han
1,
Minghang Zhou
2,
Ting Zhang
1,
Wenge Zhao
1 and
Peng Liu
1,3,*
1
College of Life Science and Technology, Harbin Normal University, Harbin 150025, China
2
School of Mathematical Sciences, Harbin Normal University, Harbin 150025, China
3
Key Laboratory of Biodiversity of Aquatic Organisms, Harbin Normal University, Harbin 150025, China
*
Author to whom correspondence should be addressed.
Animals 2024, 14(21), 3046; https://doi.org/10.3390/ani14213046
Submission received: 12 August 2024 / Revised: 2 October 2024 / Accepted: 16 October 2024 / Published: 22 October 2024
(This article belongs to the Section Herpetology)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

In the context of future global climate change, investigating the potential suitable habitats for species is essential for animal conservation, particularly for the most environmentally sensitive amphibians. The Northeastern China Salamander (Hynobius leechii), a member of the Hynobiidae family, is endemic to Northeastern China and is classified as a national Class II protected species. This study utilized distribution records of the Northeastern China Salamander to predict its suitable habitat distribution under both current and future climate conditions. The results indicated that several climate variables significantly influence habitat suitability, including precipitation during the warmest season, precipitation in the driest quarter, the seasonal variation coefficient of temperature, average temperature in the wettest season, and mean diurnal range. Currently, the Northeastern China Salamander is mainly distributed in the Liaodong Peninsula of Liaoning Province, the Changbai Mountain area of Jilin Province and a few areas of Heilongjiang Province. Under projected future climate scenarios, the area of suitable habitat is expected to gradually increase, with a continued expansion toward higher latitudes. The findings of this manuscript provide a valuable reference for amphibian conservation efforts in Northeastern China.

Abstract

The Northeastern China Salamander (Hynobius leechii) is classified as a rare, nationally protected Class II wild animal in China. Its population is declining, and its habitat is deteriorating. This study aimed to predict the distribution of suitable habitats for the Northeastern China Salamander under both current and future climate scenarios, utilizing the MaxEnt model optimized through ENMeval parameters. Species distribution data were collected from field surveys, existing literature, amphibian records in China, and the Global Biodiversity Information Network. A total of 97 records were compiled, with duplicate records within the ENMTools grid unit removed, ensuring that only one record existed within every 5 km. Ultimately, 58 distinct distribution points for the Northeastern China Salamander were identified. The R software package ‘ENMeval 2.0’ was employed to optimize the feature complexity (FC) and regularization multiplier (RM), and the optimized model was applied to assess the suitable distribution regions for the Northeastern China Salamander under present and future climate conditions. The findings indicated that rainfall and temperature are the primary environmental factors influencing Hynobius. Currently, the suitable habitat for the Northeastern China Salamander constitutes 6.6% of the total area of Northeastern China. Projections for the periods of 2050 and 2070 suggest that suitable habitats for the Northeastern China Salamander will continue to expand towards higher latitudes across three climate scenarios. While this study focuses solely on climate change factors and acknowledges certain limitations, it serves as a reliable reference and provides essential information for the distribution and conservation of the Northeastern China Salamander.

1. Introduction

In recent years, global climate change has brought new challenges to the survival and protection of wild animals [1]. Relevant studies indicate that over 80% of existing species worldwide will be impacted by rising temperatures and other environmental factors [2,3]. These changes are expected to alter the geographical distribution patterns, species richness, and genetic diversity of wild animals. Amphibians, in comparison to other animals, are ectothermic and exhibit heightened sensitivity to climate change, making them valuable indicator species for monitoring environmental alterations. This increased sensitivity is primarily attributed to their limited mobility and heightened responsiveness to fluctuations in temperature and humidity [4,5,6]. Climate change influences amphibians’ ability to adapt to their habitats, subsequently affecting their geographical distribution patterns and species richness. Notably, among the 93 amphibian species classified as Class I and Class II protected wildlife in China, the Hynobiidae family constitutes 31% [7]. Given these two viewpoints, investigating the geographic distribution patterns and habitat adaptability of Hynobiidae species is essential for the effective conservation of amphibians. Within the context of global climate change, there are two prevailing perspectives regarding the alteration of suitable habitats for amphibians. The first perspective, which is widely accepted, posits that the suitable habitats for amphibians are continuously diminishing. Studies indicate a decline in the number of 662 amphibian species globally [8], with projections suggesting that by 2080, more than half of the world’s amphibian species will experience a loss of over 50% of their existing habitats [9]. In China, it is estimated that amphibian species will lose approximately 20% of their current habitats [10]. Domestic assessments reveal that 43.1% of amphibian species are projected to be threatened in the future [11]. Furthermore, international research on the diversity and habitat selection of amphibians and reptiles supports this perspective [12]. The second perspective posits that rising environmental temperatures will create favorable climate conditions for certain amphibian species, thereby increasing the volume of suitable habitats available to them. Domestic research on the distribution patterns of amphibians in the southwest karst geomorphic region, along with studies on the habitat preferences of the Chinese Warty Newt (Paramesotriton chinensis) and international research on salamander distributions, supports this viewpoint [13,14,15].
Species distribution modeling is a research methodology that establishes the relationship between species occurrence and environmental variables, enabling the prediction of regional distribution patterns for target species. This approach has been extensively applied in various fields, including the planning of nature protection areas, regional predictions of alien species encroachment, and the identification of potential distribution areas for key species in the context of ecological restoration [16]. Current studies indicate that the MaxEnt model offers several advantages, including superior fitting of prediction results to actual conditions, the largest predictable area, the most stable outputs, and the capability to effectively validate prediction outcomes [17,18]. Notably, this model demonstrates improved predictive performance even with limited species distribution data [19]. Despite its shortcomings, including low computational efficiency, a risk of overfitting, and dependence on environmental variables [20,21], the maximum entropy model has gained widespread acceptance in habitat suitability analyses and in predicting potential habitats for rare flora and fauna [22,23,24,25], particularly in forecasting the potential distribution of amphibians [26,27,28].
Currently, foreign scholars have utilized the maximum entropy model to generate comprehensive predictions regarding amphibians in various regions, including the Northeastern China Salamander [29]. Research on habitat adaptability and distribution prediction of amphibians utilizing the maximum entropy model in China has primarily concentrated on species such as Chinese giant salamander (Andrias davidianus), Wushan Salamander (Liua shihi), Jinfo Salamander (Pseudohynobius jinfo) and Wenxian Knobby Salamander (Tylototriton wenxianensis) [30,31,32]. Additionally, studies have investigated the suitable habitat distribution areas for Hainan Stream Treefrog (Buergeria oxycephala), Hanlui Brown Frog (Rana hanluica), and Hainan Odorous Frog (Odorrana hainanensi5) within the order Anura [33,34,35]. However, there remains a paucity of research concerning the habitat adaptability and distribution predictions of Hynobiidae species in China, particularly regarding the habitat adaptability of amphibians in the unique environmental and climatic conditions of Northeastern China.
The Northeastern China Salamander, belonging to the genus Hynobius within the amphibian family Hynobiidae, predominantly inhabits hilly regions at altitudes ranging from 100 to 800 m [36]. This species enters a period of dormancy from early April until late September or early October each year. Its preferred habitat consists of mountain gullies characterized by dense tree coverage, abundant vegetation, and a consistent supply of high-quality water throughout the year, the Northeastern China Salamander is widely distributed across Northeastern China and the Korean Peninsula and is classified as a national Class II protected species. In 2023, it was designated as a vulnerable (VU) species in the Vertebrates Section of China’s Red List of Biodiversity [7,33]. Its conservation is threatened by several factors, including habitat loss, climate change, ecological competition, the impact of pathogens, the invasion of alien species, and pollution resulting from human activities. Notably, it is the only representative of the genus Hynobius within the Hynobiidae family found in Northeastern China, highlighting its ecological significance [37].
The Northeastern China Salamander, a rare amphibian, holds significant ecological and social value, warranting focused study and conservation efforts. First, it is a vital component of biodiversity; conserving this species is essential for maintaining the diversity and stability of its ecosystem. Second, due to its sensitivity to climatic and environmental changes, the Northeastern China Salamander serves as an indicator species, with fluctuations in its population reflecting the overall health of the ecological environment. Third, with an evolutionary history spanning approximately 230 million years, this salamander is crucial for research in biological evolution, phylogeny, and species adaptation. Fourth, amphibians play a critical role in ecosystems by controlling pest populations and facilitating material circulation; thus, protecting the Northeastern China Salamander is integral to sustaining these ecosystem services [37,38]. Fifth, as a unique species, it harbors distinctive genetic resources, which may hold potential value for future biotechnological applications. Sixth, research on the Northeastern China Salamander encompasses several critical fields, including ecology, climate change, habitat management, and disease monitoring. Given the effects of climate change and human activities, the urgency for conservation efforts is growing. Through various research initiatives and collaborative efforts, we are dedicated to providing a scientific foundation and practical guidance for the protection of the Northeastern China Salamander, ensuring its continued vital role within the ecosystem. Lastly, conservation initiatives for the Northeastern China Salamander often encompass habitat preservation, benefiting many other wildlife species and indirectly safeguarding broader biomes. In light of global climate change, studying the habitat suitability of the Northeastern China Salamander in its native region is crucial for guiding the conservation of uropods and other ectothermic animals in Northeastern China, thereby establishing a theoretical foundation for ecological protection and sustainable development in the area.

2. Materials and Methods

2.1. Study Area

Northeastern China comprises the provinces of Heilongjiang, Jilin, Liaoning, and the eastern part of Inner Mongolia, covering an area of 1.46 million km2, which represents 15.2% of the total landmass of China. This area is positioned on the eastern fringe of Eurasia, bordered to the east by Korea, to the south by the Bohai Sea, to the west by Mongolia, and to the north by Russia. The region is vast and features a diverse and intricate natural and geographical landscape, including mountains, hills, plains, and wetlands [39]. The eastern limits are defined by the Yalu and Tumen Rivers, while the southern boundaries are outlined by the Yellow Sea and the Bohai Sea. The western border is established by land frontiers, and the northern limit is marked by the Ussuri River and the Heilongjiang River. Within this area, one can observe the high, middle, and low elevations of the Greater Khingan Mountains, Lesser Khingan Mountains, and Changbai Mountains. At its core lies the extensive Songliao Great Plain and the Bohai Depression. Northeastern China extends across the middle temperate and cold temperate zones from south to north, displaying a temperate monsoon climate with four distinctive seasons. Average annual precipitation ranges from about 1000 mm in the southeast to below 300 mm in the northwest. This region supports a wide variety of animal and plant species, making the conservation and sustainable management of these resources essential for promoting local economic growth and preserving ecological equilibrium [40].

2.2. Distribution Data Collection and Processing

This research made use of information gathered from the second National Survey of Terrestrial Wildlife Resources, which was carried out by the National Forestry and Grassland Administration, along with a special survey focused on biodiversity conservation conducted by the Ministry of Environmental Protection. The study included maps and distribution data for amphibians in China sourced from Amphibia China and the Global Biodiversity Information Network (http://www.gbif.org/). Only those distribution records with accurate latitude and longitude coordinates were chosen for the analysis. Initially, 97 distribution locations for the Northeastern China Salamander were gathered. The field investigation will be conducted from 2014 to 2022, primarily employing the transect method. This study will encompass a total of 19 sampling points located in Heilongjiang Province and the Changbai Mountain region. To improve the model’s predictive accuracy, these sites were optimized according to the following criteria: (1) discarding inaccurate and duplicated geographical coordinates; (2) excluding duplicate entries within the highest resolution (2.5 arcmin, approximately 5 km) from the World Climate Database (http://www.worldclim.org/), ensuring that each 5 km area contains only one record of distribution. In the end, 58 unique distribution sites for the Northeastern China Salamander were selected for additional analysis (Figure 1).

2.3. Environmental Variables

In this study, we selected 19 climate factors and 3 topographic factors. The current climate data sources span from 1950 to 2000 (available at http://www.worldclim.org/), while future climate data were obtained from the Sixth International Coupled Model Comparison Programme (CMIP6), generated by the BCC-CSM2-MR climate system model developed by the National Climate Center in Beijing. We considered three climate scenarios: SSP 126 (low-level GHG emissions), SSP 245 (medium-level GHG emissions), and SSP 580 (high-level GHG emissions). The topographic factors include a Digital Elevation Model (DEM) with a resolution of 500 m, sourced from the Resources and Environmental Sciences Data Center at the Chinese Academy of Sciences (http://www.Resdc.cn/). Additionally, Elevation (elev), Slope (slo), and Aspect (asp) data were calculated based on the DEM data [38,41]. In species distribution modeling, multicollinearity among environmental variables can lead to model overfitting, thereby reducing accuracy. Therefore, correlation analysis among environmental variables is essential before applying the ecological niche model. To mitigate multicollinearity, ArcGIS 10.8 software was employed to process all environmental factor data, resulting in ASCII format data with a consistent range and a resolution of 2.5 arcminutes. The environmental and distribution data were then input into R v4.0.5, utilizing the ENMTools.pl (https://github.com/danlwarren/ENMTools, accessed on 15 October 2024) to calculate the Pearson correlation coefficient (r) for all environmental factors [42,43]. If the correlation between any two environmental variables exceeded ±0.8, we selected the variable with greater biological significance for inclusion in the model (Figure 2) [23,44]. Adjust the geographical coordinate system of the environmental factors to WGS 1984 UTM Zone 48 N. Subsequently, extract the Northeast China region from the environmental factor’s raster data layer. Finally, utilize the ArcToolbox tool to convert the environmental factors raster data and save it in the file format required by MaxEnt. Based on the results from the test runs of all MaxEnt variables, we excluded environmental variables that contributed minimally to the model, ultimately identifying 10 climate variables for modeling (Table 1).

2.4. Parameter Optimization and Model Construction

The R software package ENMeval is employed to optimize the MaxEnt model, which incorporates five parameters: linear feature (L), quadratic feature (Q), fragmentation feature (H), product feature (P), and threshold feature (T). The feature combinations analyzed include “L, LQ, H, LQH, LQHP, LQHTP”. The frequency doubling control setting is established within an interval of 0.5 to 6, with increments of 0.5. The optimal model is identified when the deltaAICc value reaches its minimum. Both distribution data (in CSV format) and environmental variables (in ASCII format) are imported into MaxEnt v3.4.1 software. The testing dataset comprises a random selection of 30% of the distribution data, while the training dataset consists of the remaining 70%. The analysis involves checking the jackknife option, setting the number of replicates to 10, selecting Bootstrap for the replicated run type, and retaining the default settings for other parameters. This model employs the knife-cutting method to assess the contribution rates and importance of environmental variables in relation to the distribution of dominant species [45].
In this research, the receiver operating characteristic curve (ROC curve) is utilized to evaluate the model’s accuracy [46]. Currently, ROC curve analysis is viewed as the premier method for assessing viable regions. The area beneath the curve, referred to as the Area Under the Curve (AUC), is determined by the space enclosed by the curve and the X-axis, with typical values falling between 0.5 and 1. An AUC value surpassing 0.8 indicates that the model possesses a high level of precision and is considered to have a notable reference value. In contrast, an AUC value exceeding 0.9 implies that the model demonstrates very high accuracy and holds considerable reference significance. Moreover, AUCdiff, which indicates the deviation between the training AUC and the test AUC, is used to assess the extent of overfitting within the model. AUCdiff values that approach 0 denote a lesser degree of overfitting and suggest enhanced predictive performance [47]. In addition, the contribution rates of climate factors obtained from the MaxEnt model, the importance of substitutions, and results from the knife-cutting technique were thoroughly analyzed to determine the key climate factors affecting the distribution of the Northeastern China Salamander. Based on the response curves of the main climatic variables, the suitable ranges for each climate factor impacting the Northeastern China Salamander were established.

2.5. Analysis of the Change in Potential Suitable Growth Area

To investigate the changes in spatial patterns of the potential habitat for the Northeastern China Salamander in the future, we utilized ArcGIS 10.8 software to reclassify all predicted results, converting them into a binary matrix file consisting of ‘0’ and ‘1’ based on a Cloglog threshold of ≥0.4 [23]. Specifically, areas categorized as medium and high suitability were identified as potential habitats for the Northeastern China Salamander [41]. Using the contemporary potential habitat area as a baseline, we calculated the future changes in potential habitat areas. The results of this comparative analysis were subsequently processed in ArcGIS 10.8 to visually represent the anticipated alterations in the spatial patterns of potential habitats for the Northeastern China Salamander. All classifications of suitable areas and regional variations were computed using ArcGIS 10.8. The simulated results were categorized according to the habitat suitability index value in *.asc format, with values ranging from 0 to 1, indicating that suitability increases with higher values [45]. Employing the natural break point classification method in ArcGIS 10.8, we divided the predicted suitable habitats for the Northeastern China Salamander into four categories: unsuitable area (0–0.2), low-suitable area (0.2–0.4), moderately suitable area (0.4–0.6), and highly suitable area (0.6–1) [48]. The moderate and highly suitable areas were designated as suitable habitats for the Northeastern China Salamander, allowing us to determine the predicted range of suitable habitats in Northeastern China. Further analyses and forecasts of the trends in habitat changes for the Northeastern China Salamander will be conducted in the future.

3. Results

3.1. Model Optimization and Accuracy Evaluation

Utilizing data from 58 distribution sites and 10 climate factors, the MaxENT model, optimized through Enmeval data software, was employed to predict the potential distribution areas of Hynobius both currently and in the future. When the feature class (FC) is set to LQHP and the regularization multiplier (RM) is 4.5 and the delta AICc value is 0, indicating that the model is optimal (Figure 3). Under these parameters, the prediction results from MaxEnt revealed an AUC value of 0.955 ± 0.008 for the receiver operating characteristic curve (Figure 4), signifying excellent predictive performance.

3.2. The Importance of Environmental Variables

As illustrated in Table 1, the MaxEnt model predicts that the cumulative contribution of the top four climate factors accounts for 95.6% of the model’s explanatory power. These factors include Precipitation of Warmest Quarter (bio18, 52%), Precipitation of Driest Quarter (bio17, 26.6%), Temperature Seasonality (bio4, 9.6%), and Mean Temperature of Wettest Quarter (bio8, 7.4%). Furthermore, the proportion of these top four climate factors in the displacement importance value—indicative of the model’s dependence on these variables—reaches 99.3%. The values for these four factors are as follows: Precipitation of Warmest Quarter (bio18, 48.5%), Precipitation of Driest Quarter (bio17, 21.4%), Temperature Seasonality (bio4, 21.4%), and Mean Temperature of Wettest Quarter (bio8, 14.6%). According to the knife test analysis, the top four climate factors ranked by their training gain scores are Precipitation of Warmest Quarter (bio18), Precipitation of Driest Quarter (bio17), Temperature Seasonality (bio4), and Mean Diurnal Range (bio2), as depicted in (Figure 5). A comprehensive analysis of the test results from the three methods (the Percentage Contribution, the Permutation Importance, the jackknife test analysis) indicates that the primary climatic factors influencing the potentially suitable distribution of the Northeastern China Salamander are the Precipitation of Warmest Quarter (bio18), Precipitation of Driest Quarter (bio17), Temperature Seasonality (bio4), Mean Temperature of Wettest Quarter (bio8), and Mean Diurnal Range (bio2). These findings suggest that the distribution of the Northeastern China Salamander is significantly influenced by temperature and precipitation. Based on the response curves of the major climate factors (Figure 6), the optimal ranges for the Precipitation of Warmest Quarter (bio18), Precipitation of Driest Quarter (bio17), Temperature Seasonality (bio4), Mean Temperature of Wettest Quarter (bio8), and Mean Diurnal Range (bio2) for the Northeastern China Salamander are as follows: >494.09 mm, >27.22 mm, <1240.8, >22.04 °C, <10.99 °C.

3.3. Distribution and Area of Suitable Habitat for Northeastern China Salamander in Northeastern China Under Modern Climate Conditions

The results of the MaxEnt model (Figure 7) indicate that the suitable habitats for the Northeastern China Salamander are primarily located in the southeastern region of Northeastern China, specifically within the Liaodong Peninsula of Liaoning Province and the Changbai Mountain area of Jilin Province. Under current climate conditions, the total suitable distribution area for Hynobius leechii is approximately 96,500 km2, which represents 6.6% of the total area of Northeastern China. The limited habitat area presents significant challenges for the conservation of the Northeastern China Salamander. Among this, the high suitability area covers about 47,500 km2, predominantly found in the coastal regions of Liaoning Province and near the Yalu River in Jilin Province. The medium suitability area, which encompasses the eastern part of Liaoning Province and the Changbai Mountain region in southeastern Jilin Province, is distributed in a ring-like pattern, covering 48,900 km2, or 50.67% of the total suitable habitat. The low suitability area surrounds both the medium and high suitability regions and also includes a scattered distribution in the eastern part of Heilongjiang Province, totaling 73,300 km2, which accounts for 5.02% of the total area of Northeastern China. Conversely, the area deemed unsuitable for habitation spans 1,290,000 km2, representing 88.3% of the total area of Northeastern China.

3.4. Potential Distribution and Changes in Northeastern China Salamander in Northeastern China Under Future Climate Scenarios

The concentration of greenhouse gas emissions significantly influences the distribution of suitable habitats for the Northeastern China Salamander. Between 2050 and 2070, under three distinct climatic scenarios, the potential suitable distribution area for this species is projected to gradually increase, while the unsuitable distribution area is expected to decrease (Figure 8). Notably, the suitable habitat is anticipated to expand towards higher latitudes, with the primary growth concentrated in the eastern regions of Northeastern China. As illustrated in the accompanying table, the total potential distribution area for the Northeastern China Salamander is expected to rise under the three climate scenarios when compared to current conditions in 2050. Specifically, under the SSP126 climate scenario, the area designated as most suitable and sub-suitable is projected to increase by 177,670 km2 and 182,730 km2, respectively. Under the SSP245 climate scenario, the increases are estimated at 239,180 km2 for the most suitable area and 177,080 km2 for the sub-suitable area. Finally, under the SSP585 climate scenario, the increases are projected to be 283,380 km2 for the most suitable area and 202,690 km2 for the sub-suitable area (Table 2).
As illustrated in the table, the total potential distribution area for the Northeastern China Salamander is projected to increase under three climate scenarios by the year 2070, compared to current climate conditions, while the unsuitable area is expected to decrease. Specifically, under the SSP126 climate scenario, the area designated as most suitable and sub-suitable is projected to increase by 205.92 × 104 km2 and 206.92 × 104 km2, respectively. Under the SSP245 climate scenario, these areas are expected to expand by 350.03 × 104 km2 and 145.48 × 104 km2, respectively. Lastly, under the SSP585 climate scenario, the most suitable area is anticipated to increase by 382.10 × 104 km2, while the sub-suitable area is projected to rise by 138.32 × 104 km2.
The area of suitable habitats is projected to increase across three climate scenarios in the upcoming two periods, although notable differences exist among them. In the SSP126 scenario, the total area increase is the lowest, whereas the SSP585 scenario exhibits the highest total area increase. By 2050, the areas of suitable habitats under the SSP245 and SSP585 scenarios increase by the same amount; however, low and medium suitability areas experience greater increases in 2050, while the most suitable areas see the largest increase by 2070. A comparison of the changes in suitable habitat areas between the three scenarios for 2050 and 2070 reveals that the area of the most suitable zone is consistently increasing. In the SSP126 climate scenario, the area of the sub-suitable zone increases, while it decreases in the other two scenarios. Additionally, the area of the low-suitable zone increases under the SSP126 scenario but decreases under the SSP245 scenario, with a slight decrease observed under the SSP585 scenario. The increase in suitable habitats may be attributed to the unique geographical location of Northeastern China. Concurrently, employing models to predict distribution areas can enhance our understanding of future trends in suitable habitats and support the long-term conservation of the Northeastern China Salamander.

4. Discussion

In this research, we utilized an enhanced MaxEnt model to forecast the distribution patterns of suitable habitats for the Northeastern China Salamander under both existing and anticipated climate scenarios. The model’s high AUC value demonstrates its strong predictive accuracy [43]. Conversely, numerous previous studies have utilized default parameters when configuring the MaxEnt model [49,50], which may result in overfitting and a significant omission rate, ultimately producing inaccurate predictions. The ENMeval software is commonly employed to refine the regularization multiplier (RM) and feature combination (FC) within the MaxEnt framework, thus improving the accuracy of predictions made by the model [37]. Recently, a growing number of researchers have contended that optimizing the MaxEnt model is essential before making predictions, given that default parameters can introduce particular biases [51]. Our results reinforce this viewpoint.
As poikilotherms, amphibians’ survival and distribution are significantly influenced by external environmental factors. Humidity, temperature, vegetation cover and disease factors in the environment will all affect the distribution of tailed amphibians, particularly changes in climatic conditions [52,53,54]. In this study, the Precipitation of the Warmest Quarter (bio18) and the Precipitation of the Driest Quarter (bio17) emerge as the two most critical climate variables determining the distribution of potential suitable habitats for the Northeastern China Salamander. The distribution of most amphibians is greatly affected by Elevation. Due to the relatively small altitude range in Northeastern China, the average altitude is about 200 m, and the highest altitude is only about 2650 m. However, the Northeastern China Salamander is mainly distributed in hills and mountains with an altitude of 100–800 m. Therefore, in this study, the altitude has little influence on the distribution of the Northeastern China Salamander. Humidity is essential for amphibians, as it significantly impacts their survival, reproduction, and overall health. This finding aligns with the broader understanding that, like many amphibians, the Northeastern China Salamander’s habitat distribution is closely linked to water availability, corroborating previous studies that state, “species richness of amphibians is more strongly correlated with water” [55,56]. From a physiological standpoint, this correlation is understandable, as amphibians possess permeable skin and depend on moist environments for survival. Juveniles typically inhabit aquatic environments, while adults primarily reside on land, with some species remaining in water; however, adults generally prefer wet and cool habitats. Thus, it is evident that the distribution pattern of these amphibians is predominantly driven by their biological needs, rather than indirect factors, reinforcing the notion that water is the primary determinant affecting amphibian distribution [13]. Surface water resources in Northeastern China are limited, and water scarcity significantly impacts the survival and reproduction of amphibians. The warmest season in this region occurs from June to August, during which heavy rainfall can create many temporary pools. These pools can maintain stable water levels for a certain period, providing a more suitable environment for Hynobius tadpoles. This helps prevent tadpole mortality due to high temperatures and insufficient water. In Northeastern China, the dry season primarily occurs in winter, and an increase in precipitation during the driest quarter can lead to greater water accumulation. This accumulation ensures adequate humidity for the hibernation of the Northeastern China Salamander and offers more habitat options post-hibernation, aligning with research findings on the habitat environments of amphibians in southwest karst regions [13]. Furthermore, the seasonal variation coefficient of air temperature (bio4), the average temperature of the wettest quarter (bio8), and the Mean Diurnal Range (bio2) influence the distribution of the Northeastern China Salamander. For amphibians, environmental temperature plays a crucial role in their behavior, as their body temperature is largely dependent on external conditions. Fluctuations in temperature can affect behaviors such as evading predators, foraging, and mating [57]. In summary, the primary climatic factors influencing the distribution of Hynobius are related to water and temperature, corroborating the conclusion that these two factors are critical in determining the distribution of amphibians. Our research findings indicate that the most suitable habitats for the Northeastern China Salamander are primarily located in the coastal regions of Liaoning Province and the border region of Jilin Province. This suggests that the species may have specific ecological preferences in these locales [58]. Analyzing the key climatic factors that influence the distribution of the Northeastern China Salamander, we conclude that these areas are in close proximity to bodies of water, such as the sea or rivers, which contribute to higher humidity levels conducive to the species’ survival. However, this concentrated distribution also renders the salamander more susceptible to threats posed by climate change and regional natural disasters, thereby increasing the risk of extinction for the entire population [59].
In the future, the suitable habitat area for the Northeastern China Salamander is projected to continue expanding. By the years 2050 and 2070, the largest suitable habitat area is expected under the SSP585 scenario, followed by SSP456, with SSP126 showing the smallest area. This finding aligns with the perspective of Wang and colleagues, who suggest that higher concentrations of greenhouse gas emissions generally result in a broader range of climatic factors, such as precipitation and temperature, thereby leading to a greater change in habitat area [60]. Under scenarios characterized by high emissions and carbon usage, the suitable habitat area for the northeast small salamander is anticipated to expand further. This conclusion is consistent with studies on other salamander species, including the Blue-spotted Salamander (Ambystoma laterale), Red-Back Salamander (Plethodon cinereus), and Wushanba Salamander (Hynobius yiwuensis), which also indicate that suitable activity ranges increase with rising temperatures and greenhouse gas concentrations [32,61]. Domestic research has demonstrated that both tailed and tailless amphibians are likely to expand their suitable habitats in the future. For instance, a study in the Hengduanshan area found that Batrachuperus pinchonii, a member of the family Liliidae, exhibits the most significant increase in suitable habitat area across three emission scenarios [62]. The future niche for amphibians in karst and non-karst landforms is projected to increase by 24.1% and 14.2%, respectively [13]. Similarly, foreign studies have demonstrated that the suitable habitat area for certain amphibian species is expected to expand significantly in the future. In the Mexican rainforest, for example, the suitable habitat area for 22% of tailless amphibians is projected to increase by more than 50% [63]. The suitable habitats for salamanders in the western United States are expanding [15]. The changes in the suitable habitat area for the northeast small salamander align with these findings. We hypothesize that these results are linked to the northeast small salamander’s origin in the colder regions of the northeast. This species is now finding suitable habitats in the southeastern part of Northeastern China, characterized by higher temperatures and humidity, and this trend is expected to continue as temperatures and rainfall increase in future climate scenarios. Consequently, the temperature and humidity levels in other colder areas of the northeast have reached conditions conducive to the northeast small salamander’s survival, leading to a continued northward expansion of its suitable living area. The data indicates that the northeast small salamander is persistently spreading northward into higher altitudes. These findings are consistent with the distribution patterns of amphibians in the Hengduan Mountains and salamanders in Canada and the northeastern United States. This supports the conclusion that amphibians generally tend to migrate towards higher elevations and latitudes [62,64]. Additionally, the research corroborates the observations made by Yao and colleagues regarding the significant northward and eastward distribution trends of Lepidopodidae species, with little evidence of southward movement [65]. The results of this study provide a comprehensive prediction of the future diffusion trends of suitable habitats for the northeast small salamander and offer valuable insights for its research and conservation.
Methods that leverage climate data and potential changes in key factors influencing species survival to assess species distributions may exhibit certain limitations [15]. In some cases, alternative approaches may surpass the performance of the MaxEnt model [66]. Although the MaxEnt model has been effectively utilized to predict the distribution of both tailed and tailless amphibians [67,68], it is evident that the model is inadequate in quantifying species locomotor capabilities, neglects evolutionary potential, and overlooks biological impacts. This shortcoming leads to the identification of certain existing distribution areas as unsuitable for habitation [23,50]. Consistent with other studies, this research posits that climate is the primary driver of species distribution [69,70] while failing to account for additional factors such as the limited mobility of amphibians, geographical conditions, human interference, infectious diseases, and variations in water availability. While other habitat increases may occur, climate change is widely regarded as a significant threat to global amphibian conservation. Research indicates that amphibian populations worldwide are likely to continue their decline as new threats associated with climate change emerge [71]. Nonetheless, our findings indicate that the MaxEnt model remains a valuable tool for predicting future suitable habitats [72,73]. Collectively, while these factors can enhance our understanding of species’ ecological niches and distributions, their integration poses significant challenges. Modeling that incorporates a broad array of variables and their interactions requires more specific data and may risk overparameterization. Therefore, the development of distribution models is crucial for exploring species distribution and improving our understanding of the effects of individual environmental factors.

5. Conclusions

This research utilized the MaxEnt model to assess the potential habitats suitable for Hynobius in both current and future contexts. The results revealed that various climatic factors have a substantial effect on habitat suitability, such as precipitation during the warmest period (bio18), precipitation in the driest quarter (bio17), the seasonal variation coefficient of temperature (bio4), average temperature in the wettest season (bio8), and the mean diurnal temperature range (bio2). At present, optimal habitats are mainly found in the Liaodong Peninsula of Liaoning Province and in the bordering area of Jilin Province in China. Predictions for future climate scenarios indicate a potential increase in suitable regions, with new habitats likely to arise in higher latitudes and suitable distributions also expected in the southeastern section of Heilongjiang Province. These findings contribute to a deeper comprehension of the distribution of the Northeastern China Salamander and offer essential insights for pinpointing its appropriate distribution zones in Northeastern China. This presents a new direction for future research on amphibians in Northeastern China, contributing to the understanding of biodiversity and the conservation of organisms in the region.

Author Contributions

Conceptualization, W.Z. and P.L.; Software, L.H. and M.Z.; Validation, T.Z.; Formal analysis, L.H. and T.Z.; Investigation, L.H.; Data curation, L.H. and M.Z.; Writing—original draft, L.H.; Writing—review & editing, W.Z. and P.L.; Supervision, W.Z. and P.L.; Project administration, P.L.; Funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Heilongjiang Provincial Natural Science Foundation Joint Guidance Project (LH2022C049).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Panetta, A.M.; Stanton, M.L.; Harte, J. Climate warming drives local extinction: Evidence from observation and experimentation. Sci. Adv. 2018, 4, eaaq1819. [Google Scholar] [CrossRef] [PubMed]
  2. Wu, J.; Lv, J.; Ai, L. The impacts of climate change on the biodiversity: Vulnerability and adaptation. Ecol. Environ. Sci. 2009, 18, 693–703, (In Chinese with English Abstract). [Google Scholar]
  3. Thomas, C.D.; Cameron, A.; Green, R.E.; Bakkenes, M.; Beaumont, L.J.; Collingham, Y.C.; Erasmus, F.B.N.; De Siqueira, M.F.; Grainger, A.; Hannah, L.; et al. Extinction risk from climate change. Nature 2004, 427, 145–148. [Google Scholar] [CrossRef] [PubMed]
  4. Urban, M.C. Accelerating extinction risk from climate change. Science 2015, 348, 571–573. [Google Scholar] [CrossRef]
  5. Han, X.; Sun, B.; Zhang, Q.; Teng, L.; Zhang, F.; Liu, Z. Metabolic regulation reduces the oxidative damage of arid lizards in response to moderate heat events. Integr. Zool. 2023. [Google Scholar] [CrossRef]
  6. Jiang, S.; Zhang, C.; Pan, X.; Storey, K.B.; Zhang, W. Distinct metabolic responses to thermal stress between invasive freshwater turtle Trachemys scripta elegans and native freshwater turtles in China. Integr. Zool. 2024. [Google Scholar] [CrossRef]
  7. National Forestry and Grassland Administration Ministry of Agriculture and Rural Affairs. List of National Key Protected Wild Animals. Chin. J. Wildl. 2021, 42, 605–640. (In Chinese) [Google Scholar]
  8. Vidal, M.A.; Henríquez, N.; Torres-Díaz, C.; Collado, G.; Acuña-Rodríguez, I.S. Identifying strategies for effective biodiversity preservation and species status of Chilean amphibians. Biology 2024, 13, 169. [Google Scholar] [CrossRef]
  9. Warren, R.; VanDerWal, J.; Price, J.; Welbergen, J.A.; Atkinson, I.; Ramirez-Villegas, J.; Osborn, T.J.; Jarvis, A.; Shoo, L.P.; Williams, S.E.; et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Chang. 2013, 3, 678–682. [Google Scholar] [CrossRef]
  10. Duan, R.; Kong, X.; Huang, M.; Varela, S.; Ji, X. The potential effects of climate change on amphibian distribution, range fragmentation and turnover in China. PeerJ 2016, 4, e2185. [Google Scholar] [CrossRef]
  11. Shi, N.; Guo, N.; Liu, G.; Wang, Q.; Han, Y.; Xiao, N. Spatial distribution pattern and influencing factors of amphibians and reptiles in Beijing. Acta. Ecol. Sin. 2022, 42, 3806–3821, (In Chinese with English Abstract). [Google Scholar]
  12. Ali, W.; Javid, A.; Hussain, A.; Bukhari, S.M. Diversity and habitat preferences of amphibians and reptiles in Pakistan: A review. J. Asia-Pac. Biodivers. 2018, 11, 173–187. [Google Scholar] [CrossRef]
  13. Wang, B. Geographical Distribution Pattern and Climate Change Impact on Amphibians in the Karst Aeras of Southwest China. Ph.D. Thesis, Central South University of Forestry and Technology, Changsha, China, 2020. (In Chinese). [Google Scholar]
  14. Yan, S. Spatial Distribution Pattern for Salamander (Paramesotriton) Species in the Mountains of Southern China. Master’s Thesis, Guizhou Normal University, Guiyang, China, 2023. (In Chinese). [Google Scholar]
  15. Nottingham, S.; Pelletier, T.A. The impact of climate change on western Plethodon salamanders’ distribution. Ecol. Evol. 2021, 11, 9370–9384. [Google Scholar] [CrossRef] [PubMed]
  16. Xu, Z.; Peng, H.; Peng, S. The development and evaluation of species distribution models. Acta. Ecol. Sin. 2015, 35, 557–567, (In Chinese with English Abstract). [Google Scholar]
  17. Thapa, A.; Wu, R.; Hu, Y.; Nie, Y.; Singh, P.B.; Khatiwada, J.R.; Yan, L.; Gu, X.; Wei, F. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 2018, 8, 10542–10554. [Google Scholar] [CrossRef]
  18. Alfaya, P.; Casanovas, J.G.; Lobón-Rovira, J.; Matallanas, B.; Cruz, A.; Arana, P.; Alonso, G. Using MaxEnt algorithm to assess habitat suitability of a potential Iberian lynx population in central Iberian Peninsula. Community. Ecol. 2019, 20, 266–276. [Google Scholar] [CrossRef]
  19. Costa, G.C.; Nogueira, C.; Machado, R.B.; Colli, G.R. Sampling bias and the use of ecological niche modeling in conservation planning: A field evaluation in a biodiversity hotspot. Biodivers. Conserv. 2010, 19, 883–899. [Google Scholar] [CrossRef]
  20. Zhang, B.; Wang, H. Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: A comparative study of four methods. GIScience Remote Sens. 2022, 59, 71–95. [Google Scholar] [CrossRef]
  21. Lissovsky, A.A.; Dudov, S.V. Species-Distribution Modeling: Advantages and Limitations of Its Application. 2. MaxEnt. Biol. Bull. Rev. 2021, 11, 265–275. [Google Scholar] [CrossRef]
  22. Qian, T.; Qin, S.; Wu, Z.; Xi, C.; Wang, J. Impacts of human interference on the potential distribution of Yunnan snub-nosed monkeys by MaxEnt model. Acta Theriol. Sin. 2022, 42, 349–361, (In Chinese with English Abstract). [Google Scholar]
  23. Tang, Y.; Pi, J.; Liu, X.; Xiang, J.; Zeng, C.; Li, D. Predicting potential distribution of Corbicula fluminea under climate change scenarios using MaxEnt model. Acta Ecol. Sin. 2023, 43, 4250–4259, (In Chinese with English Abstract). [Google Scholar]
  24. Luo, M.; Wang, H.; Lyu, Z. Evaluating the performance of species distribution models Biomod2 and MaxEnt using the giant panda distribution data. Chin. J. Appl. Ecol. 2017, 28, 4001–4006, (In Chinese with English Abstract). [Google Scholar]
  25. Ying, B.; Tian, K.; Guo, H.; Yang, X.; Li, W.; Li, Q.; Luo, Y.; Zhang, X. Predicting potential suitable habitats of Kandelia obovata in China under future climatic scenarios based on MaxEnt model. Acta Ecol. Sin. 2024, 44, 224–234, (In Chinese with English Abstract). [Google Scholar]
  26. Groff, L.A.; Marks, S.B.; Hayes, M.P. Using ecological niche models to direct rare amphibian surveys: A case study using the Oregon Spotted Frog (Rana pretiosa). Herpetol. Conserv. Biol. 2014, 9, 354–368. [Google Scholar]
  27. Pourhallaji, M.; Dargahi, M.D.; Kami, H.G.; Pouyani, E.R.; Abed, M.H. Species distribution modeling and environmental suitability of the Southern crested newt, Triturus karelinii (Strauch, 1870) (Amphibia: Caudata) in Iran. J. Wildl. Biodivers 2021, 5, 44–52. [Google Scholar]
  28. Kim, H.W.; Adhikari, P.; Chang, M.H.; Seo, C. Potential distribution of amphibians with different habitat characteristics in response to climate change in South Korea. Animals 2021, 11, 2185. [Google Scholar] [CrossRef]
  29. Borzée, A.; Litvinchuk, S.N.; Ri, K.; Andersen, D.; Nam, T.Y.; Jon, G.H.; Man, H.S.; Choe, J.S.; Kwon, S.; Othman, S.N.; et al. Update on Distribution and Conservation Status of Amphibians in the Democratic People’s Republic of Korea: Conclusions Based on Field Surveys, Environmental Modelling, Molecular Analyses and Call Properties. Animals 2021, 11, 2057. [Google Scholar] [CrossRef]
  30. Zhang, P.; Grenouillet, G.; Dong, X.; Zheng, Y.; Lek, S.; Chang, J. Capturing response differences of species distribution to climate and human pressures by incorporating local adaptation: Implications for the conservation of a critically endangered species. J. Environ. Manag. 2021, 284, 111998. [Google Scholar] [CrossRef]
  31. Ma, Q.; Wan, L.; Shi, S.; Wang, Z. Impact of climate change on the distribution of three rare salamanders (Liua shihi, Pseudohynobius jinfo, and Tylototriton wenxianensis) in Chongqing, China, and their conservation implications. Animals 2024, 14, 672. [Google Scholar] [CrossRef]
  32. Tao, J.; Hu, Y.; Jiang, J.; Yang, W.; Zhao, T.; Su, S. Prediction of potential suitable distribution areas for an endangered salamander in China. Animals 2024, 14, 1390. [Google Scholar] [CrossRef]
  33. Mu, C.; Qi, X.; Xie, L.; Qian, T.; Zhou, Z.; Lu, Y.; Mo, Y.; Li, P. MaxEnt-Based prediction on the geographical distribution of Hainan steam treefrog (Buergeria oxycephala) in Hainan island. Chin. J. Wildl. 2021, 42, 809–816, (In Chinese with English Abstract). [Google Scholar]
  34. Xia, X.; Li, Y.; Yang, D.D.; Pi, Y.Y. Potential geographical distribution of Rana hanluica in China under climate change. Chin. J. Appl. Ecol. 2021, 32, 4307–4314, (In Chinese with English Abstract). [Google Scholar]
  35. Huang, Y.J.; Lu, J.B.; Wang, F.T.; Lin, Y.H.; Liu, L.; Mi, H.X.; Mo, F.Q.; Fang, J.; Li, J.L. Predicting the potential geographic distribution of Hainan Odorous Frog (Odorrana hainanensis) in Hainan province by MaxEnt. Chin. J. Zool. 2017, 52, 30–41, (In Chinese with English Abstract). [Google Scholar]
  36. Zhao, W. The Amphibia and Reptilia Fauna of Heilongjiang, 1st ed.; Science Press: Beijing, China, 2008; pp. 24–27. (In Chinese) [Google Scholar]
  37. Fei, L.; Ye, C.; Jiang, J. Colorful Illustrations of Amphibians in China, 1st ed.; Sichuan Publishing Group Sichuan Science and Technology Press: Chengdu, China, 2010; pp. 38–39. (In Chinese) [Google Scholar]
  38. Jiang, C.; Zhang, X.; Xie, W.; Wang, R.; Feng, C.; Ma, L.; Li, Q.; Yang, Q.; Wang, H. Predicting the potential distribution of the fall armyworm Spodoptera frugiperda (J.E. Smith) under climate change in China. Glob. Ecol. Conserv. 2022, 33, e01994. [Google Scholar] [CrossRef]
  39. Yuan, C.C.; Qin, N.; Li, Y.N.; Zhang, H.T.; Zhang, Q.P.; Zhang, D.X. The Characteristics of Spatial and Temporal Changes of Black Soil Resources Occupied by Urban Expansion in Northeast China and its Influencing Factors from 1986–2018. Chin. J. Soil Sci. 2024, 55, 610–621. (In Chinese) [Google Scholar]
  40. Chen, X.X. The Impact of Snowpack on Soil Hydrothermal in Typical Cropland Regions of Northeast China. Ph.D. Thesis, University of Chinese Academy of Sciences, Beijing, China, 2023. [Google Scholar]
  41. Ye, X.; Zhang, M.; Lai, W.; Yang, M.; Fan, H.; Zhang, G.; Chen, S.; Liu, B. Prediction of potential suitable distribution of Phoebe bournei based on MaxEnt optimization model. Acta. Ecol. Sin. 2021, 41, 8135–8144, (In Chinese with English Abstract). [Google Scholar]
  42. Sillero, N. What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol. Model. 2011, 222, 1343–1346. [Google Scholar] [CrossRef]
  43. Mota, J.F.; Pérez-García, F.J.; Jiménez, M.L.; Amate, J.J.; Peñas, J. Phytogeographical relationships among high mountain areas in the Baetic Ranges (South Spain). Global. Ecol. Biogeogr. 2002, 11, 497–504. [Google Scholar] [CrossRef]
  44. Yang, X.Q.; Kushwaha, S.P.S.; Saran, S.; Xu, J.; Roy, P.S. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda, L. in Lesser Himalayan foothills. Ecol. Eng. 2013, 51, 83–87. [Google Scholar] [CrossRef]
  45. Li, Y.; Lu, L.; Tian, X.; Duan, Y. Prediction of the suitable habitat distribution of Oriental White Stork in Shandong Province. Acta. Ecol. Sin. 2023, 43, 2194–2201, (In Chinese with English Abstract). [Google Scholar]
  46. Phillips, J.S.; Anderson, P.R.; Schapire, E.R. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  47. John, E.; Bunting, P.; Hardy, A.; Roberts, O.; Giliba, R.; Silayo, D.S. Modelling the impact of climate change on Tanzanian forests. Divers. Distrib. 2020, 26, 1663–1686. [Google Scholar] [CrossRef]
  48. Zhang, K.L.; Yao, L.J.; Meng, J.S.; Tao, J. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci. Total Environ. 2018, 634, 1326–1334. [Google Scholar] [CrossRef] [PubMed]
  49. Stickley, S.F.; Fraterrigo, J.M. Microclimate species distribution models estimate lower levels of climate-related habitat loss for salamanders. Nat. Conserv. 2023, 72, 126333. [Google Scholar] [CrossRef]
  50. Wan, C.; Fang, K.; Wu, J.; Mu, X.; Dong, F.; Zhang, H. Prediction of potential distribution of Gambusia affinis in China based on MaxEnt model. Freshw. Fish. 2024, 54, 3–10, (In Chinese with English Abstract). [Google Scholar]
  51. Karuppaiah, V.; Maruthadurai, R.; Das, B.; Soumia, P.S.; Gadge, A.S.; Thangasamy, A.; Ramesh, S.V.; Shirsat, D.V.; Mahajan, V.; Krishna, H.; et al. Predicting the potential geographical distribution of onion thrips, Thrips tabaci in India based on climate change projections using MaxEnt. Sci. Rep. 2023, 13, 7934. [Google Scholar] [CrossRef]
  52. Wu, J. The hazard and unsureness of reducing habitat ranges in response to climate warming for 91 amphibian species in China. Acta Oecol. 2020, 108, 103640. [Google Scholar] [CrossRef]
  53. Daniel, E.; Axel, H. Vegetation cover and occurrence of salamanders in the western Mediterranean. Integr. Zool. 2021, 17, 456–467. [Google Scholar]
  54. Wan, B.; Chen, G.; Poon ES, K.; Fung, H.S.; Lau, A.; Sin, S.Y.W. Environmental factors and host sex influence the skin microbiota structure of Hong Kong newt (Paramesotriton hongkongensis) in a coldspot of chytridiomycosis in subtropical East Asia. Integr. Zool. 2024. [Google Scholar] [CrossRef]
  55. Qian, H.; Wang, X.; Wang, S.; Li, Y. Environmental determinants of amphibian and reptile species richness in China. Ecography 2007, 30, 471–482. [Google Scholar] [CrossRef]
  56. Zheng, Z.; Gong, D.; Sun, C. Elevational pattern of species richness and species range size of herpetofauna in Baishuijiang Nature Reserve: A test of Rapoport’s rule. J. Ecol. 2014, 33, 537–546, (In Chinese with English Abstract). [Google Scholar]
  57. Xu, X.; Zhao, W.; Liu, P. Effect of environmental temperature on body temperature during reproductive period and embryonic development in different geographic populations of Rana dybowskii. Acta. Ecol. Sin. 2018, 38, 2965–2973, (In Chinese with English Abstract). [Google Scholar]
  58. Siemens, A.L.; Bogart, J.P.; Linton, J.E.; Norris, D.R. Predicting the occurrence of an endangered salamander in a highly urbanized landscape. Endang. Species. Res. 2023, 52, 81–95. [Google Scholar] [CrossRef]
  59. Bakare, A.G.; Kour, G.; Akter, M.; Iji, P.A. Impact of climate change on sustainable livestock production and existence of wildlife and marine species in the South Pacific island countries: A review. Int. J. Biometeorol. 2020, 64, 1409–1421. [Google Scholar] [CrossRef] [PubMed]
  60. Wang, M.; Zhang, C.; Mi, C.; Han, L.; Li, M.; Xu, W.; Yang, W. Potential impacts of climate change on suitable habitats of Marco Polo sheep in China. Chin. J. Appl. Ecol. 2021, 32, 3127–3135, (In Chinese with English Abstract). [Google Scholar]
  61. Widmer, B.W.; Gehring, T.M.; Heumann, B.W.; Nicholson, K.E. Climate change and range restriction of common salamanders in eastern Canada and the United States. J. Wildl. Manag. 2022, 86, e22235. [Google Scholar] [CrossRef]
  62. Zhao, Z. Prediction of Future Changes in the Suitable Distribution Area of Rare and Endangered Amphibian Species in the Southern Hengduan Mountains and Their Optimization Conservation. Master’s Thesis, Lanzhou University, Lanzhou, China, 2022. [Google Scholar]
  63. Ballesteros-Barrera, C.; Tapia-Pérez, O.; Zárate-Hernández, R.; Leyte-Manrique, A.; Martínez-Bernal, A.; Vargas-Miranda, B.; Martínez-Coronel, M.; Ortiz-Burgos, S. The potential effect of climate change on the distribution of endemic Anurans from Mexico’s tropical dry forest. Diversity 2022, 14, 650. [Google Scholar] [CrossRef]
  64. Chen, I.C.; Hill, J.K.; Ohlemüller, R.; Roy, D.B.; Thomas, C.D. Rapid range shifts of species associated with high levels of climate warming. Science 2011, 333, 1024–1026. [Google Scholar] [CrossRef]
  65. Yao, M.; Wei, M.; Nie, H. Geographic distribution pattern and dispersal route of Urodela in China. Chin. J. Zool. 2018, 53, 1–16, (In Chinese with English Abstract). [Google Scholar]
  66. Vasilakos, C.; Kavroudakis, D.; Georganta, A. Machine learning classification ensemble of multitemporal sentinel-2 images: The case of a mixed mediterranean ecosystem. Remote Sens. 2020, 12, 2005. [Google Scholar] [CrossRef]
  67. Zellmer, A.J.; Slezak, P.; Katz, T.S. Clearing up the crystal ball: Understanding uncertainty in future climate suitability projections for amphibians. Herpetologica 2020, 76, 108–120. [Google Scholar] [CrossRef]
  68. Zhao, Z.; Xiao, N.; Liu, G.; Li, J. Prediction of the potential geographical distribution of five species of Scutiger in the south of Hengduan Mountains Biodiversity Conservation Priority Zone. Acta. Ecol. Sin. 2022, 42, 2636–2647, (In Chinese with English Abstract). [Google Scholar]
  69. Saupe, E.E.; Barve, V.; Myers, C.E.; Soberón, J.; Barve, N.; Hensz, C.M.; Peterson, A.T.; Owens, H.L.; Lira-Noriega, A. Variation in niche and distribution model performance: The need for a priori assessment of key causal factors. Ecol. Model. 2012, 237–238, 11–22. [Google Scholar] [CrossRef]
  70. Xie, J.; Fei, X.; Qiu, L.; Lou, X.; Huang, J.; Zheng, R. Effects of climate change on habitat suitability of Hynobius yiwuensis in Yiwu city. J. Zhejiang Norm. Univ. Nat. Sci. 2023, 46, 66–73, (In Chinese with English Abstract). [Google Scholar]
  71. Luedtke, J.A.; Chanson, J.; Neam, K.; Hobin, L.; Maciel, A.O.; Catenazzi, A.; Borzée, A.; Hamidy, A.; Aowphol, A.; Jean, A.; et al. Ongoing declines for the world’s amphibians in the face of emerging threats. Nature 2023, 622, 308–314. [Google Scholar] [CrossRef]
  72. Zhang, Z.; Mammola, S.; Xian, W.; Zhang, H. Modelling the potential impacts of climate change on the distribution of ichthyoplankton in the Yangtze Estuary, China. Divers. Distrib. 2019, 26, 126–137. [Google Scholar] [CrossRef]
  73. Rohman, M.; Prasetyo, L.B.; Kusrini, M.D. Predicting spatial distribution of Asian Horned Frog (Megophrys montana Kuhl & Van Hasselt 1882) in Java Island using citizen science’s data. IOP Conf. Ser. Earth. Environ. Sci. 2021, 771, 012027. [Google Scholar]
Animals 14 03046 g001
Figure 1. Distribution points of Northeastern China Salamander.
Figure 1. Distribution points of Northeastern China Salamander.
Animals 14 03046 g001
Animals 14 03046 g002
Figure 2. Correlation matrix between environmental variables of Northeastern China Salamander.
Figure 2. Correlation matrix between environmental variables of Northeastern China Salamander.
Animals 14 03046 g002
Animals 14 03046 g003
Figure 3. Delta AICc of the MaxEnt models under different regularization multipliers (RMs) and feature combinations (FCs).
Figure 3. Delta AICc of the MaxEnt models under different regularization multipliers (RMs) and feature combinations (FCs).
Animals 14 03046 g003
Animals 14 03046 g004
Figure 4. Receiver operating characteristic (ROC) curve and AUC value.
Figure 4. Receiver operating characteristic (ROC) curve and AUC value.
Animals 14 03046 g004
Animals 14 03046 g005
Figure 5. Jackknife test gain for environmental variables in Northeastern China Salamander.
Figure 5. Jackknife test gain for environmental variables in Northeastern China Salamander.
Animals 14 03046 g005
Animals 14 03046 g006
Figure 6. The response curves of major climate factors (Red is the response curve, blue is the standard error).
Figure 6. The response curves of major climate factors (Red is the response curve, blue is the standard error).
Animals 14 03046 g006
Animals 14 03046 g007
Figure 7. Potential suitable habitats of the Northeastern China Salamander in Northeastern China under current climatic conditions.
Figure 7. Potential suitable habitats of the Northeastern China Salamander in Northeastern China under current climatic conditions.
Animals 14 03046 g007
Animals 14 03046 g008
Figure 8. Potentially suitable climatic distribution of Northeastern China Salamander under different climate change scenarios: (a) SSP126 in 2050; (b) SSP126 in 2070; (c) SSP245 in 2050; (d) SSP245 in 2070; (e) SSP585 in 2050; (f) SSP585 in 2070.
Figure 8. Potentially suitable climatic distribution of Northeastern China Salamander under different climate change scenarios: (a) SSP126 in 2050; (b) SSP126 in 2070; (c) SSP245 in 2050; (d) SSP245 in 2070; (e) SSP585 in 2050; (f) SSP585 in 2070.
Animals 14 03046 g008
Table 1. Contribution and permutation importance of environmental variables in MaxEnt models.
Table 1. Contribution and permutation importance of environmental variables in MaxEnt models.
CodeEnvironmental VariablePercentage Contribution (%)Permutation Importance (%)
bio18Precipitation of Warmest Quarter5248.5
bio17Precipitation of Driest Quarter26.621.4
bio4Temperature Seasonality9.614.8
bio8Mean Temperature of Wettest Quarter7.414.6
bio15Precipitation Seasonality2.40.1
bio3Mean Diurnal Range1.20
bio2Mean Diurnal Range0.50
AspAspect0.10.4
EleElevation0.10
SloSlope00.1
Table 2. The area of suitable habitats across different periods, with values in parentheses indicating variations compared to the current period (×10⁴ km2).
Table 2. The area of suitable habitats across different periods, with values in parentheses indicating variations compared to the current period (×10⁴ km2).
GradeCurrent20502070
SSP126SSP245SSP585SSP126SSP245SSP585
Low7.32520.01116.81334.12414.08331.82930.468
(12.686)(9.488)(26.799)(6.758)(24.504)(23.143)
Moderate4.90023.17322.60825.16925.59219.44818.732
(18.273)(17.708)(20.269)(20.692)(14.548)(13.832)
High4.75322.52028.67133.09125.34539.75642.963
(17.767)(23.918)(28.338)(20.592)(35.003)(38.210)
Total16.97865.70468.09292.38465.02091.03392.163
(48.726)(51.114)(75.406)(48.042)(74.055)(75.185)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Han, L.; Zhou, M.; Zhang, T.; Zhao, W.; Liu, P. Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China. Animals 2024, 14, 3046. https://doi.org/10.3390/ani14213046

AMA Style

Han L, Zhou M, Zhang T, Zhao W, Liu P. Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China. Animals. 2024; 14(21):3046. https://doi.org/10.3390/ani14213046

Chicago/Turabian Style

Han, Lei, Minghang Zhou, Ting Zhang, Wenge Zhao, and Peng Liu. 2024. "Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China" Animals 14, no. 21: 3046. https://doi.org/10.3390/ani14213046

APA Style

Han, L., Zhou, M., Zhang, T., Zhao, W., & Liu, P. (2024). Prediction of Potential Suitable Distribution Areas for Northeastern China Salamander (Hynobius leechii) in Northeastern China. Animals, 14(21), 3046. https://doi.org/10.3390/ani14213046

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop