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Article

Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling

by
Huihui Zhang
1,2,
Xiao Sun
1,
Guoshuai Zhang
1,
Xinke Zhang
1,
Yujing Miao
1,
Min Zhang
1,
Zhan Feng
1,2,
Rui Zeng
3,
Jin Pei
4,* and
Linfang Huang
1,*
1
Key Laboratory of Chinese Medicine Resources Conservation, State Administration of Traditional Chinese Medicine of China, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China
2
School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang 330000, China
3
College of Pharmacy, Southwest Minzu University, Chengdu 610041, China
4
School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(1), 631; https://doi.org/10.3390/su15010631
Submission received: 26 September 2022 / Revised: 7 December 2022 / Accepted: 23 December 2022 / Published: 30 December 2022
(This article belongs to the Section Sustainability, Biodiversity and Conservation)
Browse Figures
Versions Notes

Abstract

:
Continued global climate and environmental changes have led to habitat narrowing or migration of medicinal plants. Gentiana rhodantha Franch. ex Hemsl. is a medicinal plant for ethnic minorities in China, and it has a remarkable curative effect in the treatment of lung-heat cough. However, its habitat is gradually decreasing, and the species has been listed as an endangered ethnic medicine due to excessive harvesting. Here, based on CMIP6 bioclimatic data and 117 species occurrence records, the maximum entropy model (MaxEnt), combined with ArcGIS technology, was applied to predict the potentially suitable habitats for G. rhodantha under different climate scenarios. The results showed that the most critical bioclimatic variables affecting G. rhodantha are the precipitation of the warmest quarter (Bio18) and the mean temperature of the coldest quarter (Bio11). The highly suitable habitats of G. rhodantha are mainly concentrated in Belt and Road (“B&R”) countries, including China, Bhutan, and Vietnam. However, under different climate change scenarios, the fragmentation extent of suitable habitats in China will generally increase, the suitable area will show a decreasing trend as a whole, the distribution center will shift to the northeast, and the distance will increase with time. Notably, the shrinkage of the high suitability area was the most obvious for the 2081–2100 SSP585 scenario, with a total of 358,385.2 km2. These findings contribute to the understanding of the geo-ecological characteristics of this species, and provide guidelines for the conservation, management, monitoring, and cultivation of G. rhodantha.

1. Introduction

Ecological adaptations, and interactions between biotic and abiotic species, affect the distribution of plant species [1]. Among them, the impact of climate change on ecological adaptation on regional and global scales is particularly obvious, including the living habits, morphological characteristics, and spatial distribution of species [2,3,4]. Thus, understanding the relationship between the geographical distribution of species and the specific environmental factors is an essential process for maintaining ecological diversity [5]. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) indicated that unless there are deep reductions in global greenhouse gas emissions, the goal of limiting warming well below 2 °C and close to 1.5 °C will be out of reach [6]. In addition, because of the link between climate warming and extreme weather, more attention should be paid to the ecological impacts of long-term warming (the period beyond 2050), and decarbonization to achieve net-zero CO2 emissions should be the foremost goal [7], which may be more conducive to the development of global biodiversity and the protection of the ecological environment. Meanwhile, more than 80% of the world’s terrestrial areas are experiencing extreme heat due to human-induced greenhouse gas forcing [6,8], which could cause changes in the growth habits of medicinal plants, including significant shrinkage or migration of habitats [9]. It could place the exploitation and utilization of resources of endangered medicinal plants in a more dangerous situation. Overall, the geographical distribution of many species may have undergone some changes due to climate influences, which, in part, reflect how species experience climate change across a wide variety of changing landscapes [10]. Therefore, to help managers better address various issues related to species distribution and range change, further assessment of the influence of climate change on the spatial distribution of individual species in the landscape is essential [11].
Species distribution models (SDMs), an important tool for assessing the ecological demands of species, predict the possible distribution areas of species in terms of regional ecology and biogeography, in the context of limited distribution data [12]. Mathematical relationships are established based on known speciation records and corresponding environmental variables to predict suitable conditions for maintaining species populations, thereby estimating the suitable spatial distribution of the species throughout the study area [13,14]. The maximum entropy (MaxEnt) model showed higher accuracy and reproducibility [15], shorter running time, and simpler operation than many previously reported ecological models [4,16]. It could solve the characteristics of small sample data volume compared with other models. It also displays the distribution area of species in different periods. Therefore, in the present study, MaxEnt was chosen to predict the distribution of G. rhodantha planting areas and the interaction between species distribution with environmental variables.
G. rhodantha (Gentianaceae) is an endemic Chinese perennial herb with high medicinal, economic, and ornamental values. It blooms with brilliant purple flowers and normally grows well in alpine scrub, grassland, and understory. It is mainly distributed in the Southwest region, including Yunnan, Sichuan, Guizhou, Gansu, Shaanxi, Henan, Hubei, and Guangxi. The dried whole plant of G. rhodantha has been listed in Chinese Pharmacopoeia 2020 for its beneficial effects in clearing heat and detoxifying medicine [17]. The main component of G. rhodantha is the flavonoid mangiferin, which has the effects of inhibiting the central nervous system, relieving cough and inhibiting bacteria. As a traditional ethnic medicine, the aerial parts of G. rhodantha are wildly used by Tibetan and Miao nationalities for the treatment of hepatitis, jaundice, phthisis, and dysentery [18]. At present, many compound formulations, such as Feilike Mixture, Kangfuling Tablets, and Lianlong Capsules, contain this plant. Thus, its market demand increases year by year, which accelerates the endangerment speed of G. rhodantha wild medicinal resources. However, most researchers to date have mainly focused on the rich pharmacological activities (such as anti-inflammatory, hepatoprotective, and antibacterial) and phylogenetic relationships [19,20,21,22]. Few studies have analyzed the habitat distribution of G. rhodantha from a macro perspective and how the ecological environment affects the sustainability of this species. Therefore, exploring the relationship between G. rhodantha and various environmental factors and determining which factors influence the distribution of this species for its sustainability is of great significance.
This research aimed to (1) determine the main environmental factors that affect the distribution of G. rhodantha; (2) determine the spatial distribution range of suitable habitats for this species and guide its introduction and cultivation; (3) estimate the pattern change of G. rhodantha and understand its migration trajectory, and (4) identify potential regions for resource extinction of G. rhodantha and propose conservation measures.

2. Materials and Methods

2.1. Samples and Species Occurrence Records

This study was undertaken on a global scale, and reference occurrence data of G. rhodantha Franch. were downloaded from the Global Biodiversity Information Facility database (GBIF, http://www.tropicos.org/ accessed on 20 March 2022), the China Virtual Herbarium (http://v5.cvh.org.cn/ accessed on 20 March 2022), the National Specimen Information Infrastructure (http://www.nsii.org.cn/ accessed on 20 March 2022), and published literature. By removing the distribution points that had no geographic coordinate information, the preliminary species occurrence records were obtained. Then, duplicate, fuzzy, and neighboring records were removed using the “Trim duplicate occurrences” tool on ENMTools version 1.4.4, which is a Prel script with a graphical user interface written with the Tk package to filter the distribution data in the raster of the environment layer [23,24,25]. Only one occurrence record for each grid was selected to reduce sampling deviation. Finally, 117 valid records were retained for constructing the models, and the data were saved in .csv format (Table S1). The 1:400,000-scale Chinese administrative division vector diagram [ID: GS (2019)1822], downloaded from the National Basic Geographic Information System (http://bzdt.ch.mnr.gov.cn/index.html accessed on 20 March 2022), was used as the analysis base map, and ArcGIS10.7 was used to generate the sample site map (Figure 1).

2.2. Environmental Variables

Climate and environmental conditions determine the distribution of a species. SDM could be used to determine the habitat suitability of a species. A total of 19 bioclimatic variables were downloaded from the WorldClim database (version 2.1) (http://www.worldclim.org/ accessed on 5 April 2022), with a resolution of 2.5 arcminutes or approximately 5 km2 per pixel [26]. Sixteen soil data were obtained from the Harmonized World Soil Database version 1.2 (http://www.fao.org/ accessed on 5 April 2022). Three terrain data were downloaded from the WorldClim database, and elevation data were calculated in ArcGIS software to obtain other terrain variables (slope and aspect) [27]. Land cover and vegetation data were collected from the Global Map database (https://github.com/ accessed on 5 April 2022). The spatial resolution of all environmental data was resampled to 2.5 arcminutes by ArcGIS10.7 software [28].
The multicollinearity of variables may affect prediction accuracy [29] Pearson’s correlation (r) analysis of ENMTools1.4.3 was used by eliminating one variable per pair with correlations of (r > 0.80) to eliminate the overfitting of environmental factors [27], as shown in Figure 2. Among the 40 original environmental variables, 12 highly correlated bioclimatic variables and 11 autocorrelative soil factors were eliminated. Finally, only 17 variables were ultimately selected for modeling, including seven bioclimatic variables, five soil factors, three topographical variables, and one land cover, along with one vegetation data (Table 1).
The Coupled Model Intercomparison Project for the sixth phase (CMIP6) is more sensitive and helpful for predicting future climate change than the CMIP5 [30,31], and it was used as the basis for the IPCC to issue a climate assessment report in 2021 [32]. Therefore, the future climate data were selected from one global climate model (BCC-CSM2-MR, the Beijing Climate Center Climate System Model) of CMIP6 [33] (Wu et al., 2019), because researchers in China have often used BCC-CSM2-MR alone to assess species distributions. In previous studies, researchers simulated different shared socioeconomic pathways (SSPs), which explain different levels of future climate change [34,35,36]. For example, SSP126 represents the most optimistic scenarios of future greenhouse gas emissions, i.e., limiting global warming to below 2 °C; SSP245 indicates a future increase in global temperatures of approximately 3 °C; SSP370 represents the mid-range of baseline outcomes produced by the energy system model; and SSP585 represent the worst scenario of future greenhouse gas emissions by 2100. In the present study, three shared socioeconomic pathways (SSP126, SSP245, and SSP585) and three periods were chosen to predict the potential distribution of G. rhodantha in the future: 2050s (average of predictions for 2041–2060), 2070s (average of predictions for 2061–2080), and 2090s (average of predictions for 2081–2100). Moreover, on the basis of the condition that soil and terrain factors remain static over the next few decades, only climatic factors were used for future periods in this study, while soil, land cover, vegetation, and terrain factors were used for the current period [37].

2.3. Model Calibration

The kuenm package in R software was used to optimize the MaxEnt model; it allows robust processes of model calibration, facilitating the creation of final models on the basis of model significance, performance, and simplicity. The models are filtered first to detect statistically significant models; the omission rate criterion is applied to this reduced set of models; and finally, among the significant and low-omission candidate models, those with values of delta Akaike information criterion (AICc) lower than 2 are selected [38]. AICc values indicate how well models fit the data while penalizing complexity to favor simple models [39]. Therefore, for G. rhodantha, 1160 candidate models were created using the kuenm package to test candidate solutions for each of the environmental variables, all possible combinations of the feature types (linear = l, quadratic = q, product = p, threshold = t, and hinge = h), and regularization multiplier settings (0.1–4.0 at intervals of 0.1) [40]. Finally, using the kuenm and AICc values (the class with the lowest delta AICc was preferred), the best set of candidate solutions that feature combination (FC), including L, Q, H, and the regularization multiplier, set to 3.9, was chosen.

2.4. Species Distribution Modeling Process

MaxEnt 3.4.3 software (The model was created by Phillips’ team in 2004, based on the principle of maximum entropy. Download from: https://biodiversityinformatics.amnh.org/ accessed on 20 March 2022) and ArcGIS 10.7 software (ArcGIS was developed by Environment System Research Institute in the USA. Download from: https://www.esri.com/ accessed on 20 March 2022) was used to analyze and evaluate the distribution of suitable habitats for G. rhodantha at different periods. For evaluation of the predictive performance of the model, 75% of the data was used for training (10 replications), and the remaining 25% was used to evaluate the performance of the model. The maximum iteration mode was to set select “Bootstrap,” and the maximum number of repetitions was set to 10,000 [41,42,43]. A maximum of 1000 iterations was allowed for each model to run, to ensure sufficient time for model convergence. Ecological habitat suitability areas could be divided in many ways. The model estimates a probability distribution of species on the basis of its current presence points and associated environmental variables, and provides a spatial representation of habitat suitability varying from 0 (not suitability) to 1 (highest suitability). Among them, the ecological habitat suitability areas of G. rhodantha were divided into four by using the equal spacing method: low (25–50% probability of occurrence), medium (50–75% probability of occurrence), high (75% probability of occurrence), and values below 25% were omitted as unsuitable habitat, based on logical thresholds [43,44].
Previous studies [45,46] have demonstrated that the receiver operating characteristic (ROC) curve of the knife-cut method is an analytical method to evaluate the reliability of the model. Therefore, in this study the area under the ROC (AUC) was used to measure the accuracy of the model predictions. AUC values (0–1) between 0.9 and 1 were considered excellent and highly accurate [47]. Moreover, to determine which variables matter most for the species being modeled, a jackknife test was implemented to analyze the percent contribution and permutation importance of each environmental variable, to identify the dominant environmental factors [27,47,48]. When the presence probability is >0.5, the value of the corresponding environmental factor is generally considered to be suitable for species growth [27,49].

2.5. Changes in the Core Distribution

The trends of suitable habitat for G. rhodantha in different periods and the changes in core (the geometric center location of suitable habitat) and area were compared [10,50] and analyzed in the SDM toolbox (GIS toolkit operating based on Python) of ArcGIS software [50]. The species distributions were then reduced to one individual centroid point [47,51]. Next, for those time-dependent estimated variations, their magnitudes and directions were created, and the movements of the centroids of various SDMs were tracked to examine the shifts in distribution.

2.6. Ecological Niche Calculation

Ecological niches are quantitative expressions of the status and role of species in a community, and they could better explain the environmental adaptations and resource utilization capacity of populations [52]. Niche breadth reflects the state of adaptation of a species or population to its environment, or the extent to which it uses resources [53,54]. Niche overlap reflects the degree of similarity in the spatial and temporal dimensions of resource use by different species [55]. In the present study, ENMTools version 1.4.4 was used to calculate the Schoener’D (D), Hellinger’s-based I (I) values, and Levins’s-based B values of the species, to compare the current and future trends in the niche overlap, range overlap, and niche breadth of G. rhodantha [23]. Niche overlap ranges from 0 (no habitat features used in common) to 1 (complete overlap).

3. Results

3.1. Model Evaluation and Potential Suitable Habitat for Gentiana rhodantha under Current Climate

In the ROC analysis, based on the training data, the AUC was 0.992, which indicated favorable model performance (Figure S1). The current highly suitable habitats for G. rhodantha were mainly distributed in China, Bhutan, Vietnam, India, Myanmar, Colombia, Nepal, and South Korea (Figure 3). The area of highly suitable areas is 556,032 km2, accounting for 17.77% of the total suitable area (Table S5).

3.2. Evaluation of Important Climatic Variables

The Jackknife test was used to determine the key environmental factors contributing to the modeling process (Table 1 and Figure S1). Among the 17 environmental variables, bioclimatic variables contributed the most to the growth of G. rhodantha. Their accumulated contribution rate reached as much as 91.31% and the permutation importance was as high as 96.16%, indicating that those factors exerted a decisive part in the distribution. In particular, the precipitation of the warmest quarter (Bio18) was the highest contributing factor, accounting for 51.71% of all environmental factors. Meanwhile, the mean temperature of the coldest quarter (Bio11) had the highest permutation importance, accounting for 73.20% of overall environmental factors. When only single variables were used, factors (e.g., Bio18, Bio15, Bio11, and Bio4) related to temperature and precipitation had higher weights. Topographical factors accounted for 6.75% of the overall factors, indicating that these factors play a role in the growth distribution of G. rhodantha. However, soil factors, land cover, and vegetation only had a small effect on the growth, with a total contribution of less than 3%.
The relationships between 17 environmental variables and G. rhodantha suitability are shown in Figure S2. The response curve results indicated that G. rhodantha was able to grow in regions with precipitation of warmest quarter (Bio18) of 449–1154 mm, coefficient of variation of precipitation seasonality (Bio15) ranging from 61.06 to 93.10 mm, mean temperature of the coldest quarter (Bio11) from 0.78 °C to 9.93 °C, standard deviation of temperature (Bio4) of 432.94–822.03 °C, mean diurnal air temperature range (Bio2) from 6.39 °C to 9.36 °C, and alt of 682.34–3637.92 m.

3.3. Potential Ecologically Suitable Distribution of G. rhodantha in Global under Future Climate Conditions

The analysis and prediction of the potential suitable distribution of G. rhodantha in the world, under the three climate scenarios in 2050s, 2070s, and 2090s are shown in Figure 4. Under all greenhouse gas emission scenarios, the ecologically suitable distribution areas for G. rhodantha were mainly concentrated in Asia, including China, Bhutan, Vietnam, India, Myanmar, Colombia, Nepal, South Korea, and North Korea.
Class II showed that the distribution of the high-suitability habitats was overall decreased, compared with the present. From the temporal dimension, except for the SSP126 scenario, where a slight increase could be found in the high-suitability habitats of G. rhodantha, the SSP245 and SSP585 scenarios showed a substantial decrease in the area of the high-suitability region over time. In particular, in the period 2080–2100, the SSP585 scenario showed a reduction in area change by more than half of the current one. In addition, MaxEnt analysis found that the most suitable regions in the world for G. rhodantha, as a unique plant in China, are countries along the “B&R”, including South Korea, North Korea, Bhutan, Myanmar, Vietnam, and Nepal. Thus, G. rhodantha could be used as a potential medicinal plant for foreign exchanges and cooperation (Table S5 and Figure 5).
Class I showed that the distribution of the medium-suitability area of G. rhodantha is generally expanding compared with the current one. In particular, the SSP245 scenario conditions in the period 2081–2100 are the most widely distributed. Apart from China, countries such as Myanmar, Nepal, South Korea, North Korea, and Russia are expected to become the priority countries for the cultivation of G. rhodantha. The overall expansion trend of the area change showed a trend of an increase, followed by a decrease, with an increase in the SSP126 and SSP245 scenarios and a gradual decrease in the area of the moderately suitable zone over time, in the SSP585 scenario (Figure 5). The future global temperature increasing by 3 °C may not lead to the reduction in the medium suitability region. Beyond this range, the growth and development may face a dangerous state.

3.4. Predicting the Potential Future Distribution of G. rhodantha in China

Figure 6 and Figure 7 and Table S6 show the future changes in suitable habitats for G. rhodantha in China. Among the 15 key provinces with highly suitable habitats surveyed, except for Liaoning, Tianjin, Shandong, Gansu, and Jiangsu, which showed slight expansion, other cities showed a general trend of decline under different scenarios of climate change. In particular, the provinces located in the Yunnan-Kweichow Plateau region exhibited the most significant decrease (Figure 8). Furthermore, a few highly suitable habitats can be found in Hunan, under the current climatic condition. However, G. rhodantha is on the verge of extinction in the region with time and climate scenario changes. Three cases were considered to be more explicit about the trends of different future climate scenarios in medium-suitability habitats: decreasing, relatively stable, and increasing. Significant area decreases were observed in four provinces, including Guangxi, Hunan, Hubei, and Yunnan; the overall performance of the regional distribution in Shandong, Shaanxi, Jiangsu, Tibet, Tianjin, Hubei, and Gansu was relatively stable; and an intermittent growth trend could be found in Sichuan, Chongqing, Guizhou, and Liaoning (Figure S4).
Figure 9 and Figure S3 and Table S7 show the distribution of loss and expanding area of suitable habitats (ecological similarity of 50–100%) under different climate scenarios. Figure S3A shows that the expansion range from the most optimistic scenario to the most pessimistic model is decreasing, and the area contraction range is increasing with the emission scenario. Area expansion was significant in the SSP245 scenario for the period 2061–2080 (amplification of 366,825.3 km2). Area shrinkage was most pronounced at the SSP585 scenario for the period 2081–2100 (contraction of 358,385.2 km2), and the area contraction of the suitable habitats during this period far exceeded its growth (difference of 171,488 km2), with an overall negative trend (Figure S3B). This finding indicated that the resource distribution of G. rhodantha in China could be massively reduced in that period, and the survival crisis could gradually expand with the aggravation of harsh environment, which may accelerate its endangerment.

3.5. Shift of Appropriate Habitat Distribution Center

At present, the distribution center for G. rhodantha was predicted to be in Jiangjin District, Chongqing (28.65° N, 106.33° E; Figure 10 and Table S2). In the SSP126 climate scenario, the core of suitable habitats were predicted to shift to 106.77° E, 29.47° N in Banan District, to 107.08° E, 29.54° N in Chongqing (migration distance of 101.29 km) by the 2050s (migration distance of 122.76 km), and 107.13° E, 29.61° N (migration distance of 132.05 km) in the southern part of Fuling District, Chongqing, by the 2070s and 2090s. Similarly, under the SSP245 scenario, the core of the suitable habitat was predicted to move to the north of Fuling District, Chongqing (107.53° E, 29.77° N; migration distance of 170.15 km) and (107.49° E, 29.94° N; migration distance 182.59 km) by the 2050s and 2070s, respectively, and to the northeast of Zhong County, Chongqing (108.05° E, 30.35° N; migration distance of 251.27 km) by 2090. More importantly, the distribution center was estimated to shift to the south of the central part of Lishui County, Sichuan Province (106.87° E, 30.18° N; migration distance of 178.07 km), and to the south of Qianfeng District, Sichuan Province (106.87° E, 30.48° N; migration distance of 209.52 km) under SSP585 by the 2070s and 2090s, respectively. In brief, the distribution center of suitable habitat could shift to the northeast, with increasing distances over time.

3.6. Climatic Niche Overlap

Niche overlap shows the similarity of the use of different environmental resources by the same species. Here, ecological niche changes in G. rhodantha from different future scenarios were compared. The size of the niche overlap reflects the similarity of plant utilization resources; a large niche overlap demonstrates that their ecological requirements are close, the utilization of resources is similar, and the biological characteristics are similar under certain environmental conditions. The values of D and I presented consistent trends of niche overlap by each period (Table S8). Among them, SSP585-2090s had the lowest niche overlap (D = 0.8056, I = 0.5547), whereas SSP126-2050s had the highest niche overlap (D = 0.9157, I = 0.7069). The overall level of ecological niches decreased with the progressive severity of CO2 emissions, under different climate scenarios, compared with the current period, indicating that the similarity of habitat ecological resources was decreasing, therefore changing the environment greatly. This result is similar to the distribution change trend of the suitable habitats shown in the previous section.
Niche width reflects how well a specialization or generalization has adapted to its environment or utilized its resources; the wider the ecotone of a species, the less specialized the species is, i.e., it tends to be a generalized species. Conversely, the narrower the ecological niche of a species, the stronger the degree of specialization of that species, i.e., it tends to be more of a specialized species. In short, if the niche width is wider, it is more adaptable to the environment, the fuller the use of resources, and it could survive in harsh habitats, conducive to a wide distribution. If the niche breadth is narrower, it is at a disadvantage in the competition for resources. In Table S4, the values of the inverse concentration showed an increasing trend with time and poor ecological conditions. It also reflects the fact that as a representative species of the Gentiana narrow group, G. rhodantha may be in a more dangerous position in the future competition for the distribution of available resources, especially in areas where its high suitability areas are greatly reduced and more worthy of attention.

4. Discussion

4.1. Factors Controlling the Distribution of Gentiana rhodantha

In this study, the key environmental factors driving the growth and distribution of G. rhodantha were found to be the precipitation of the warmest quarter and the mean temperature of the coldest quarter. Environmental factors play a key role in the biological processes that drive species growth [56]. Identifying specific environmental factors that have shaped and maintained the geographic distribution of species in evolutionary and ecological terms is indispensable [57]. Several researchers have demonstrated that among all the factors affecting vegetation, temperature and precipitation are the most direct and critical [58]. The present study found that the distribution of G. rhodantha was mainly controlled by precipitation-related (Bio 18 and Bio 15) and temperature-related (Bio 11 and Bio 4) bioclimatic variables. Changes in precipitation patterns could affect plant physiological and ecological processes at different scales [59,60], including the plasticity response of vegetative growth [61] and reproductive growth. They could also affect soil moisture and nutrient availability [62,63], thus regulating plant growth and development. Consequently, the Bio18 changes in China during different periods were compared (Figures S5 and S6). An overall decreasing trend was found from the SSP126 to SSP585 precipitation of the warmest quarter, consistent with the trend of the distribution area in the previous section. According to “Flora of China”, G. rhodantha is a typical alpine plant that likes a cool climate, has strong cold hardiness, and grows well in more moist soil. Thus, the overall decrease in precipitation in the warmest month may also be a critical factor in the future reduction in high-suitability habitats.
In addition, temperature has a wide range of effects on plants by directly influencing plant growth and distribution, by maintaining plant morphology, physiology, chemistry, and biochemical activities, such as photosynthesis, respiration, and material transfer [40,64]. Some researchers have demonstrated that temperature changes could directly influence the photosynthetic capacity and growth rate of plants [65]. It also affects soil moisture content and plant nutrient uptake and utilization [66]. Previous studies [67]) have shown that the overall temperature of the Yunnan-Guizhou Plateau showed a significant upward trend, and the rate of change in warming showed a trend of high in the west and low in the east, which may be one of the reasons why the center of mass shifted to the northeast.
Furthermore, soil diversity creates biodiversity; soil and its organisms contribute to the development of agriculture and forestry and promote plant growth [68,69]. Therefore, understanding the role being played by soil factors in plant growth and development is an important process in the analysis of habitat change, due to it being integral to human nutrition and wellbeing and the economy. Karst limestones are sedimentary rock outcrops mainly composed of calcium carbonate and generally characterized by poor soil development, low soil water content, periodic water deficiency, and heat stress [70,71]. In the present work, MaxEnt was used to predict the important soil factors (e.g., CaCO3, CaSO4, gravel, and clay) in karst landscapes. However, their overall contribution was less than 3%, which could only indicate that the influence of the soil environment on the suitable distribution of G. rodantha is not very obvious in the current data range. Subsequent inter-root soil collection from karst landscapes could be considered to determine microorganisms. Then, the association between soil factors and the main chemical components of G. rodantha could be analyzed to further explore the effect of soil factors on its growth and development.
The MaxEnt modeling provided strong statistical validation and robust maps of the potential distribution of G. rodantha, based on existing data. However, biotic interactions, artificial interference, ultraviolet irradiation, and plateau meteorology were not included in this study because of a lack of accurate data on these variables. Limitations in spatial data, and the assumption that species could migrate to climate-friendly areas under climate change, have led to uncertainty in species distribution projections [72,73]. In fact, the actual direction of distribution of species in a changing climate is regulated by many factors, such as dispersal ability, age of the species [74,75], ecological niche amplitude and biodiversity, plant seed sizes, and generation lengths [76]. Despite the limitations of predictions via SDM, they have long been used as an important source of data to predict the future ecological suitability of species resources. SDM assesses scientific adaptation strategies at the community and ecosystem levels and makes relevant predictions that aim to help offset future warming impacts on biota [77,78]. To date, many studies have used SDM analysis to predict the future distribution trends of rare and endangered species (e.g., Meconopsis punicea, Coptis deltoidea, Cervus elaphus maral, and Manis crassicaudata), which also makes an important contribution to their timely conservation and sustainable exploitation [43,79,80,81].

4.2. Impacts of Climate Change on the Area Migration of G. rhodantha

This study is the first to report the impacts of global climate change on the geographical distribution of G. rhodantha, using MaxEnt modeling. Previous studies were mostly limited to Southwest China or the Yunnan-Kweichow Plateau, and no systematic analysis of their global changes in different scenarios has been reported [82]. In the present study, the global distribution of G. rhodantha was examined, and the variability of its suitable habitats in China was specifically analyzed. The suitable habitats were distributed in “B&R” countries. The “B&R” initiative in the new era has provided new coordinates, a new mission, and values to the internationalization of the traditional Chinese medicine (TCM) resource industry. Taking the localization of TCM resources as the entry point, the international division of labor in countries and regions along the “B&R”, and adoption of a balanced value chain governance approach, could help establish a symbiotic economic model and friendly cooperation among countries to achieve mutual benefits. G. rhodantha is endemic to China, and its resource distribution could be spread in suitable areas in Myanmar, Nepal, Korea, Russia, and other regions in the future, indicating that its original plant is suitable for foreign export and seed introduction, which could contribute to the internationalization of TCM. Previously, many Chinese herbs have been flowing internationally through the Silk Road, such as Rheum palmatum L., Cinnamomum cassia Presl, Zanthoxylum bungeanum Maxim, Crocus sativus L., and Liquidambar orientalis Mill. The international circulation of these herbal medicines with unique curative effects also reflects that natural medicine is receiving increasing attention and importance worldwide, under the trend of advocating green and returning to nature. The sustainable utilization of Chinese medicine resources is not only the basis and prerequisite for the sustainable development of the socio-economic and TCM industry but also an important part of the international development of herbs.
Under the current climatic conditions, the high-suitability habitats were mainly distributed in Southwest China, which includes the south and west margins of the Sichuan Basin, the mountain area of Southwestern Yunnan-Kweichow Plateau, Ta-pa Mountains, and the southern part of the Hengduan Mountains. This result may be due to mountains being frequently found to contain biodiversity hotspots and centers of endemism [83,84,85], such as the west and south distribution areas adjoined to “Indo-Burma”, and the Northwest distribution areas belonging to “mountains of Southwest China” [86].
Future climate change may affect species distribution and ecosystem vulnerability in these areas. Temperature rise may be harmful to Paeonia delavayi, which is distributed in the mountainous regions of Southwest China, and its suitable habitat range could decrease [10]. It may also further damage the suitable growth areas of fir species [87]. Similarly, the results of the present study showed that climate change may influence the distribution size of G. rhodantha in Southwest China, with a gradual reduction of its highly suitable regions. If bioclimatic factors are not reasonably controlled, the reduction in genuine production regions in high-suitability habitats after the 2050s may be detrimental to the development and utilization of medicinal qualities of the ethnic medicine G. rhodantha and the maintenance of species diversity. Remarkably, in the 2090s-SSP585 scenario, a part of the current highly suitable habitats turned into medium- or low-suitability habitats, which may affect the quality of the herbal medicine and lead to a reduction in the content of its active medicinal components. Unfortunately, G. rhodantha, as a representative of Miao medicine, could be on the verge of extinction in the Wenshan Zhuang and Miao Autonomous Prefecture, Qiannan Buyi and Miao Autonomous Prefecture, Qiandongnan Miao and Dong Autonomous Prefecture, and Xiangxi Tujia and Miao Autonomous Prefecture in the future. Thus, the protection of suitable areas is an urgent matter, particularly in ethnic medicine production areas. Otherwise, it could accelerate the reduction in the number of genuine production regions of medicinal materials, the scarcity of wild resources, and the obstruction of the cultural transmission of the use of ethnic medicines.
The prediction results of the MaxEnt model showed that the centroid of the currently suitable area is located in the Jiangjin District, Chongqing China, but the distribution of highly suitable areas of G. rhodantha could shift from Central Northeast Chongqing to the territory of Sichuan, with the increase in greenhouse gas emissions. The possible reason is that mountains are one of the centers of plant biodiversity, with mountain ranges mainly located in the east or northeast of this region. The Ridge and Valley Province of Chuandong is located at the junction of Sichuan and Chongqing, and it is one of the most distinctive fold mountain belts in the world. It includes the growing core regions of G. rhodantha, such as Jiangjin District, Banan District, and Sichuan Guang’an. The overall terrain is high in the north and low in the south, high in the west and low in the east, which is the most neatly combined area of northeast-southwest mountain ranges in China, including rich animal and plant resources. The northeast part consists of Ta-pa Mountains and tributaries, such as Guanmian Mountains and Wushan Mountains, and the central Mingyue Mountain is the boundary mountain between Chongqing and Sichuan. In addition, the zone of G. rhodantha in Southwest Shaanxi is gradually expanding, possibly because the Qinling-Daba Mountains are the center of biodiversity for many ancient endemic plants [16]. Mountainous areas with high topographic diversity have many microhabitats that could form refuge by buffering disturbances. Simultaneously, precipitous mountains are divided by river valleys and deep gorges, shaping the highly dissected topography, which could contribute to speciation and growth by vicariance [84,85,88,89]. Consequently, further research is needed to obtain supplementary knowledge about the potential impacts of future mountain climate and habitat fragmentation on the survival and migration of the wild population of G. rhodantha.

4.3. Implications for Conservation Planning

This research indicated that the future suitable habitat for G. rhodantha has an overall shrinking trend, with the deterioration of the bioclimatic environment. Ecological climate change could lead to negative impacts on the sustainable utilization of the high-quality herbal medicine of G. rhodantha. It is one example of the many Gentiana medicinal plants that may be threatened by climate change and anthropogenic factors [90]. According to the “Red List of Chinese Species (2004)”, many Gentiana species have been listed as endangered or critically endangered, such as Gentiana duclouxii, Gentiana omeiensis, Gentiana curviphylla, and Gentiana yunnanensis. These plants are also characteristic of Miao medicine and Tibetan medicine. However, no comprehensive related research could be found on the distribution prediction and ecological planning of Gentian ethnomedicine resources. Therefore, they shall be explored in the next work by the authors. Thee aerial and underground parts of G. rhodantha are rich in medicinal components [91]. The demand for G. rhodantha and some other alpine medicinal plants is likely to outstrip the supply because their roots, rhizomes, bulbs, or whole plants are destructively harvested [92]. This phenomenon may lead to overharvesting and accelerate the extinction of local G. rhodantha. In addition, the mountainous areas of the Ridge and Valley Province of Chuandong are mostly tourist resorts in the southwest, where Tibetans, Miao, Hui, Han, and other ethnic groups live. Human disturbances in G. rhodantha habitat come not only from the local community but also from high tourism flows at high environmental costs. These chronic human disturbances, combined with climate change, could lead to severe destruction of habitats for endemic mountain plant species. Promoting the principles of environmental protection, harmony between man and nature, and sustainable management plans for G. rhodantha and other Gentiana medicinal species are indispensable to address the issues.
In general, the survival of endemic species is affected by three aspects: loss of living environment, vulnerability to human disturbance, and endangerment. Appropriate conservation strategies based on a socioecological framework of landscape planning are currently needed to solve these problems [93]. Given that rainfall is one of the main factors affecting the distribution of G. rhodantha, integrated programs aimed at soil and water conservation could increase the production of G. rhodantha. Moreover, habitat suitability plays a key role in maintaining ecosystem functions in the research area, as it may keep the ecological balance of G. rhodantha and several other important endemic species. Creating nature reserves is another effective in-situ strategy for the protection of biodiversity and ecosystem services. Future adaptive management strategies should take into account the effects of climate change on the distribution of G. rhodantha (especially on alpine plants and in scarce ethnomedicinal flora) and ecosystem-based solutions, to preserve the biodiversity balance of the special landforms of Kasite and the Yunnan-Guizhou Plateau, because this region is one of the areas where the Gentiana genus exhibits the highest genetic diversity in the world [94,95]. In these special environmental areas, the principles of “preparing for the worst” could be applied in the decision-making process [96]. Meanwhile, governments and academicians should take more actions to empower local medicinal plant conservation awareness and prevent unsustainable land use. Commercial cultivation is an effective strategy to solve the gap between the supply and demand of medicinal plants and reduce harsh mining [97,98]. Ensuring the high quality of medicinal materials is also an important prerequisite for medicinal plant cultivation. Before making a planting plan, the suitability of the habitat should be considered. The assessment of the current to future habitat suitability of G. rhodantha in the present study could provide valuable information to the search for authentic regional planting plans. The planting of medicinal herbs should first consider highly suitable areas, among which, most areas of the Yunnan-Kweichow Plateau, the Ridge and Valley Province of Chuandong, the south of Qinling-Daba Mountains, and the Hengduan Mountains to the southeast of Himalayas Mountains could be given priority. In addition, formulating ex-situ and in-situ wild protection strategies for G. rhodantha and establishing a corresponding information network and a database to monitor population dynamics systematically and continuously are urgent. Importantly, a medicinal plant cultivation model should also be constructed to integrate and develop the ecological, economic, and social benefits of the mountainous areas, thereby promoting the development of TCM [9,97,99].

5. Conclusions

This study was the first to use the optimized MaxEnt model to evaluate and predict the suitable habitat distribution of G. rhodantha, under current and future climate scenarios, which is of great significance for the conservation of this species. The results indicated that climate change could lead to decreased availability of highly suitable habitats of G. rhodantha, especially in the Miao region of Southwest China. Under the current climate model, the most important variables that affect the suitable ecological environment of G. rhodantha are the precipitation of the warmest quarter (Bio18 contribution rate of 51.71%; range of 499.50–1154.41 mm) and the mean temperature of the coldest quarter (Bio11 permutation importance of 73.20%; range of 0.78 °C–9.93 °C). Under future climate change scenarios, the suitable habitats were predicted to shift to high-latitude areas in Central-Eastern Chongqing, and eventually reach Southern Sichuan. Meanwhile, considering the introduction and cultivation of original plants of G. rhodantha in “B&R” countries (Myanmar, Nepal, South Korea, and Russia) could possibly help promote the cultural exchange of TCM. In conclusion, the results of this study could provide a theoretical reference for the planting area selection and production zoning of endangered Gentiana species, such as G. rhodantha, and guide its rational management and designation of conservation strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15010631/s1, Supplementary Tables: Table S1. Sampling locations. Table S2. Migration distances of cores for suitable habitats under different climate scenarios. Table S3. Niche overlap and range overlap of potential distribution areas of G. rhodantha. Table S4. The niche breath of potential distribution areas of G. rhodantha. Table S5. The distribution area of G. rhodantha in the middle and highly suitable habitats ranked in the top 10 countries in the world. Table S6. The distribution area of G. rhodantha in the middle and highly suitable growth areas ranked in the top 13 Provinces in China. Table S7. Future area changes of G. rhodantha in China. Table S8. Niche overlap of potential distribution areas of G. rhodantha. Supplementary Figures: Figure S1. (A) Represents the results of the jackknife test of variables’ contribution in modeling G. rhodantha habitat suitability distribution. (B) ROC curve and AUC value under the current period (10 replicated runs). The current period was from 1950 to 2000. Figure S2. Response curves of 17 environmental variables in the G. rhodantha habitat distribution model. Figure S3. (A) Future area contraction and amplification of G. rhodantha in China; (B) Future trend of G. rhodantha in China (Expansion-Loss). Figure S4. Trends of G. rhodantha in medium suitable habitats under different scenarios in the top 15 provinces of China. Figure S5. Changes in Bio18 of G. rhodantha in China under future climate scenarios. (A) Under SSP126 scenario in the 2050s, 2070s, and 2090s; (B) Under SSP245 scenario in the 2050s, 2070s and 2090s; and (C) Under SSP585 scenario in the 2050s, 2070s and 2090s. Figure S6. Changes in Bio18 over different periods.

Author Contributions

Methodology, G.Z., X.Z., Y.M., M.Z. and Z.F.; Software, X.S. and Y.M.; Formal analysis, H.Z. and X.S.; Resources, X.Z. and J.P.; Data curation, H.Z. and M.Z.; Writing – review & editing, H.Z., J.P. and L.H.; Visualization, R.Z.; Supervision, L.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (U1812403-1, 82073960 and 82274045), the Open Fund of State Key Laboratory of Southwestern Chinese Medicine Resources (SKLTCM2022015), Beijing Natural Scientific Foundation (7202135), CAMS Innovation Fund for Medical Sciences (CIFMS, 2022-I2M-1-017) and National Science & Technology Fundamental Resources Investigation Program of China (2018FY100701), which are gratefully acknowledged.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Distribution records of G. rhodantha were used for the MaxEnt model. The 1:400,000-scale Chinese administrative division vector diagram [ID: GS(2019)1822].
Figure 1. Distribution records of G. rhodantha were used for the MaxEnt model. The 1:400,000-scale Chinese administrative division vector diagram [ID: GS(2019)1822].
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Figure 2. (a) Pearson correlation analysis of bioclimate factors. (b) Field photo of G. rhodantha (taken in Guizhou). (c) Pearson correlation analysis of soil factors.
Figure 2. (a) Pearson correlation analysis of bioclimate factors. (b) Field photo of G. rhodantha (taken in Guizhou). (c) Pearson correlation analysis of soil factors.
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Figure 3. Predicted current planting distribution of G. rhodantha in the world.
Figure 3. Predicted current planting distribution of G. rhodantha in the world.
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Figure 4. Potentially suitable habitats of G. rhodantha in global under future climate scenarios. (a) Under SSP126 scenario in 2050s, 2070s and 2090s; (b) Under SSP245 scenario in 2050s, 2070s and 2090s; (c) Under SSP585 scenario in 2050s, 2070s and 2090s.
Figure 4. Potentially suitable habitats of G. rhodantha in global under future climate scenarios. (a) Under SSP126 scenario in 2050s, 2070s and 2090s; (b) Under SSP245 scenario in 2050s, 2070s and 2090s; (c) Under SSP585 scenario in 2050s, 2070s and 2090s.
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Figure 5. Changes in the top 10 countries of the global area distribution of G. rhodantha from current to future periods. Note: Class1: Ecological similarity is 50–75% (medium suitability); Class2: Ecological similarity is 75–100% (High suitability).
Figure 5. Changes in the top 10 countries of the global area distribution of G. rhodantha from current to future periods. Note: Class1: Ecological similarity is 50–75% (medium suitability); Class2: Ecological similarity is 75–100% (High suitability).
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Figure 6. Area (km2) of suitable habitats for G. rhodantha in the top 13 provinces of China in various periods. Note: Class I: Ecological similarity is 50–75% (medium suitability); Class II: Ecological similarity is 75–100% (High suitability).
Figure 6. Area (km2) of suitable habitats for G. rhodantha in the top 13 provinces of China in various periods. Note: Class I: Ecological similarity is 50–75% (medium suitability); Class II: Ecological similarity is 75–100% (High suitability).
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Figure 7. Potentially suitable habitats of G. rhodantha in China under future climate scenarios. (a) Under SSP126 scenario in 2050s, 2070s and 2090s; (b) Under SSP245 scenario in 2050s, 2070s and 2090s; (c) Under SSP585 scenario in 2050s, 2070s and 2090s. The 1:400,000-scale Chinese administrative division vector diagram [ID: GS(2019)1822].
Figure 7. Potentially suitable habitats of G. rhodantha in China under future climate scenarios. (a) Under SSP126 scenario in 2050s, 2070s and 2090s; (b) Under SSP245 scenario in 2050s, 2070s and 2090s; (c) Under SSP585 scenario in 2050s, 2070s and 2090s. The 1:400,000-scale Chinese administrative division vector diagram [ID: GS(2019)1822].
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Figure 8. Trends of G. rhodantha in high suitable habitats under different scenarios in the top 15 provinces of China. The green line represents the change in area of G. rhodantha under different scenarios. The dotted line represents the trend of change.
Figure 8. Trends of G. rhodantha in high suitable habitats under different scenarios in the top 15 provinces of China. The green line represents the change in area of G. rhodantha under different scenarios. The dotted line represents the trend of change.
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Figure 9. Unchanged, contracted and expanded suitable habitats for G. rhodantha under different future climate scenarios. (a) Under SSP126 scenario in 2050s, 2070s and 2090s; (b) Under SSP245 scenario in 2050s, 2070s and 2090s; (c) Under SSP585 scenario in 2050s, 2070s and 2090s. The 1:400,000-scale Chinese administrative division vector diagram [ID: GS(2019)1822].
Figure 9. Unchanged, contracted and expanded suitable habitats for G. rhodantha under different future climate scenarios. (a) Under SSP126 scenario in 2050s, 2070s and 2090s; (b) Under SSP245 scenario in 2050s, 2070s and 2090s; (c) Under SSP585 scenario in 2050s, 2070s and 2090s. The 1:400,000-scale Chinese administrative division vector diagram [ID: GS(2019)1822].
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Figure 10. Migration routes of cores for suitable habitats under different climate scenarios. The yellow circle represents the central location of suitable habitat for the current period. The green line shows the migration trajectory of the location of suitable habitat centers under different scenarios for the period 2041–2060. The blue line shows the migration trajectory of the location of the center of suitable habitat for the different scenarios for the period 2061–2080. The red line shows the migration trajectory of the location of the center of suitable habitat for the different scenarios for the period 2081–2100.
Figure 10. Migration routes of cores for suitable habitats under different climate scenarios. The yellow circle represents the central location of suitable habitat for the current period. The green line shows the migration trajectory of the location of suitable habitat centers under different scenarios for the period 2041–2060. The blue line shows the migration trajectory of the location of the center of suitable habitat for the different scenarios for the period 2061–2080. The red line shows the migration trajectory of the location of the center of suitable habitat for the different scenarios for the period 2081–2100.
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Table
Table 1. The contribution of each environmental variable in Maxent modeling.
Table 1. The contribution of each environmental variable in Maxent modeling.
NameRelative Contribution
Rate (%)		Permutation Importance (%)RangeDescription
Bio_1851.70898.1968499.50–1154.41Precipitation of Warmest Quarter (mm)
Bio_1118.365673.20410.78–9.93Mean Temperature of Coldest Quarter (℃)
Bio_48.57920.0961432.94–822.03Temperature Seasonality (standard deviation × 100)
Bio_155.93140.541261.06–93.10Precipitation Seasonality (Coefficient of Variation)
Alt4.36342.3656682.34–3637.92Elevation (m)
Bio_24.25639.75926.39–9.36Mean Diurnal Range (Mean of monthly (max temp–min temp))
Slope2.18620.2789≥75.34Slope (°)
Bio_192.09161.31229.72–47.55Precipitation of Coldest Quarter
Ve0.69530.773810.87–112.06Vegetation cover factor
Lc0.56590.14471.38–7.90Land type
Bio_80.37513.051718.75–24.44Mean Temperature of Wettest Quarter
T_CEC_CLAY0.2290.088836.83–57.85Cation exchange capacity of cohesive soil (dS/m)
T_TEB0.20810.034512.08–36.13Top layer exchangeable base (cmol/kg)
Aspect0.20430.0797≥20.95Aspect (°)
T_CACO30.13510.0015≥0.04Top layer carbonate or lime content (% wt)
T_GRAVEL0.10450.07143.48–12.85Top gravel volume percentage (% wt)
T_CASO400≤−0.01Top layer sulfate content (% wt)
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Zhang, H.; Sun, X.; Zhang, G.; Zhang, X.; Miao, Y.; Zhang, M.; Feng, Z.; Zeng, R.; Pei, J.; Huang, L. Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling. Sustainability 2023, 15, 631. https://doi.org/10.3390/su15010631

AMA Style

Zhang H, Sun X, Zhang G, Zhang X, Miao Y, Zhang M, Feng Z, Zeng R, Pei J, Huang L. Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling. Sustainability. 2023; 15(1):631. https://doi.org/10.3390/su15010631

Chicago/Turabian Style

Zhang, Huihui, Xiao Sun, Guoshuai Zhang, Xinke Zhang, Yujing Miao, Min Zhang, Zhan Feng, Rui Zeng, Jin Pei, and Linfang Huang. 2023. "Potential Global Distribution of the Habitat of Endangered Gentiana rhodantha Franch: Predictions Based on MaxEnt Ecological Niche Modeling" Sustainability 15, no. 1: 631. https://doi.org/10.3390/su15010631

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