Next Article in Journal
Transcription Factor Protein (TFP)-Trait Relationships During Sugarcane Internode Development
Next Article in Special Issue
Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy
Previous Article in Journal
Impact of Treated Swine Wastewater on Elemental Distribution in the Growth of Habanero Pepper Seedlings
Previous Article in Special Issue
Development, Design, and Improvement of an Intelligent Harvesting System for Aquatic Vegetable Brasenia schreberi
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 6
10.3390/agronomy15061474
agronomy-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

Evaluation of Suitable Cultivation Regions in China for Siraitia grosvenorii Using a MaxEnt Model and Inductively Coupled Plasma Mass Spectrometry

by
Fei Dong
1,2,†,
Xiaojie Yan
2,†,
Jingru Song
2,
Xiyang Huang
2,
Chuanming Fu
2,
Fenglai Lu
2,* and
Dianpeng Li
2,3,*
1
School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
2
Key Laboratory of Plant Functional Phytochemicals and Sustainable Utilization, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region, Chinese Academy of Sciences, Guilin 541006, China
3
Engineering Research Center of Innovative Traditional Chinese, Zhuang and Yao Materia Medica, Ministry of Education, Guangxi University of Chinese Medicine, Nanning 530200, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(6), 1474; https://doi.org/10.3390/agronomy15061474
Submission received: 12 May 2025 / Revised: 11 June 2025 / Accepted: 12 June 2025 / Published: 17 June 2025
(This article belongs to the Special Issue Application of Machine Learning and Modelling in Food Crops)
Browse Figures
Versions Notes

Abstract

Global climate change is reshaping the habitat suitability of medicinal plants, potentially compromising their phytochemical integrity and therapeutic efficacy. Siraitia grosvenorii, an edible medicinal plant in China, has expanded its cultivation area into non-native habitats. Therefore, this study analyzed the suitable cultivation region under different periods in China based on the MaxEnt model, and 59 samples were investigated to explore the interrelationships between chemical constituents and climatic variables through multivariate statistical analysis, which will contribute to meeting the sustainable supply of high-quality S. grosvenorii. We discovered that appropriate habitats cover an area of 58.76 × 104 km2, mainly in the southern parts of China. Under future climate conditions, suitable habitats decrease and shift to the northeast along the current habitats. The precipitation levels of the driest month, precipitation seasonality, and temperature seasonality were crucial for its distribution. Furthermore, 11 elements were identified to distinguish samples from different suitable areas through orthogonal partial least squares discriminant analysis. Correlation analysis revealed a strong association between chemical constituents and various climatic factors. This study offers valuable insights into potential S. grosvenorii cultivation areas in China and provides reference indicators for quality evaluation.

1. Introduction

The connection between plant distribution patterns and climate change has become a key focus in global climate change and biogeography research [1,2]. Plant growth is a complex system process influenced by the combined effects of factors such as soil, topography, climate, and management. Among them, climate plays a pivotal role in determining the distribution and chemical composition of medicine plants. Alterations in climate conditions change the quality and therapeutic effectiveness of medicine plants [3,4]. The abrupt warming events detected in the last century indicated that the global mean temperature is projected to rise sharply in the future, primarily resulting from the interaction between intrinsic climate cycles/shifts and anthropogenic greenhouse gas emissions, which may lead to negative impacts on economies and societies [5]. Due to global warming, the original habitats of many medicinal plant species no longer provide suitable conditions for their growth [6,7]. Therefore, it is imperative to determine the impact of climate change on the regional distribution of medicinal plants to inform strategies for their cultivation, conservation, and sustainable use.
Siraitia grosvenorii (Swingle) C. Jeffrey ex Lu et Z. Y. Zhang belongs to the Cucurbitaceae family. This plant is renowned for its long-standing application in Traditional Chinese Medicine (TCM) for treating bronchial conditions such as coughs and asthma, and it is also valued as a natural, low-calorie sweetener [8,9,10]. S. grosvenorii is native to Guilin, and its cultivation area has been significantly expanded in recent years because of the increasingly widespread application of mogrosides from it as a promising new-generation sweetener. By 2022, the total planting area had reached 2 × 104 hectares. The current disorganized and scattered cultivation layout presents notable challenges to the sustainable development of the S. grosvenorii industry, especially in relation to the planting activities of farmers. Previous research has found that the proportions of components are strongly correlated with the geographical region of growth, and some chemical constituents can be used as a key indicator when evaluating the geo-authenticity of TCMs [11]. Recent studies have concentrated on isolating and investigating the pharmacological activities of the chemical constituents of S. grosvenorii. It is unclear whether there are elemental differences in S. grosvenorii in different suitable areas and what effects climatic conditions have on these differences. The adaptability of S. grosvenorii to the climate in which it is grown must be taken into consideration before its introduction to the area and subsequent cultivation. Therefore, developing effective analytical methods and determining the suitability of different regions are crucial for ensuring the quality of S. grosvenorii.
To address the challenges posed by climate change, numerous studies have used climate data to build species distribution models (SDM) [12,13]. It is a powerful approach to analyze species distribution patterns by integrating known species data with environmental factors to simulate geographical ranges and predict responses to climate change [14,15,16]. Among them, the MaxEnt model is the most widely used and has been proven to be reliable in predicting species distributions across multiple studies due to its characteristics of fast computation, flexible operation, high accuracy and stability, which can be intuitively visualized through Geographic Information Systems (GIS) and in combination with advanced analytical techniques such as chemometric methods and metabonomics [17,18,19]. For instance, Zhan et al. [20] demonstrated that temperature and precipitation are key factors influencing the distribution of Panax notoginseng. Their analysis predicted that future suitable habitats for this species would decrease and shift toward the central-eastern region of Yunnan. They also found that the saponin content is relatively lower when it is grown in the most suitable area. Wan et al. [21] used chemical composition analysis and a MaxEnt model to assess the suitable area of Tussilago farfara L. Their model predicted that suitable habitats will initially increase with climate change but then decrease. The more high-quality habitats for T. farfara are mainly found in the Dingxi District. Although Wei et al. [22] utilized the Geographic Information System for Global Medicinal Plants and MaxEnt to analyze suitable production areas worldwide of S. grosvenorii, their approach has certain limitations, such as data over-fitting and insufficient sampling points, which hinder its ability to reflect the actual planting conditions. Elements not only serve as unique geographical markers for tracing the origin of products but also influence secondary metabolic processes, thereby affecting the content of effective chemical components in plants from different geographical habitats [23,24]. We believe that integrating multielement data with information about climatic variables will offer a comprehensive means of evaluating and predicting the geographic distribution of S. grosvenorii.
In this study, inductively coupled plasma mass spectrometry (ICP-MS) was used to identify the elements in S. grosvenorii samples from different geographic locations. Using this information in combination with a MaxEnt model, we aimed to investigate changes in suitable habitats for S. grosvenorii under various climate scenarios, to identify the key climatic variables influencing its distribution, and to elucidate the relationship between these climatic factors and the accumulation of chemical constituents within the plant. The findings of this study will provide valuable scientific insights that may facilitate the selection of optimal areas for artificial cultivation and the establishment of quality control standards for S. grosvenorii.

2. Materials and Methods

2.1. Distribution Points of S. grosvenorii and Environmental Variables

Geographical distribution data for S. grosvenorii were obtained through field surveys by collecting a total of 153 distribution records from July 2021 to October 2022. To avoid overfitting caused by high spatial autocorrelation, a buffer radius of 1 km was set, and only one distribution point in any 2 km range was retained. These 153 valid records comprised 80 sampling points (Figure A1a). Due to the unique growing environment of S. grosvenorii and factors such as local climate conditions and cultivation practices, this study included samples from sites near the survey locations. However, sampling was limited in certain areas, and only 59 samples were collected in total (Figure A1b).
The World Climate Database (https://worldclim.org, accessed on 7 July 2023) provides 19 environmental variables with a 2.5 arc/min spatial resolution to create the current climate (from 1970 to 2000), from which the altitude reference geospatial data cloud Shuttle Radar Topography Mission elevation data was obtained. This was converted to American Standard Code for Information Interchange format using ArcGIS (version 10.8.1; ESRI, Redlands, CA, USA) mapping software [16]. We used the BCC-CSM2-MR (Beijing Climate Center Climate System Model) Shared Socio-Economic Pathways model of the Sixth International Coupled Model Intercomparison Project (CMIP6) as the basis for our estimations of future climatic data (for the periods 2041–2060 and 2061–2080). This approach offers a more precise and scientific representation of potential future climate change scenarios [25]. The model includes four emission scenarios outlined in the IPCC Sixth Assessment Report. For our analysis, we selected three scenarios: sustainable development (SSP126), local development (SSP370), and normal conventional development conditions (SSP585). To construct our MaxEnt model, we initially used 19 climate variables (Table A1).

2.2. Species Distribution Modeling

The MaxEnt model, short for Maximum Entropy, is a widely used statistical method in ecological modeling for predicting species distributions. It employs five distinct environmental constraints-linear, product, hinge, quadratic, and threshold features to estimate the potential geographic distribution of species. Additionally, MaxEnt highlights the importance of environmental variables, offering insights into factors influencing species distribution [26]. The accuracy of our MaxEnt model could potentially be affected by the multicollinearity of variables [27,28]. First, we conducted a preliminary simulation experiment in MaxEnt 3.4.1 using the S. grosvenorii distribution data and 19 climate variables. Secondly, a Pearson correlation analysis of the environmental factors was conducted by IBM SPSS 25.0. Using the results of this simulation and correlation analysis (Figure 1), we removed those ecological variables with 0 contribution rates and correlation coefficients of >0.8 [29]. Thirdly, we selected environmental variables closely associated with the growth of S. grosvenorii by reviewing relevant literature and considering actual cultivation conditions [30]. Finally, we selected 10 key climatic variables for the simulations using MaxEnt 3.4.1.
The distribution of suitable habitats for S. grosvenorii at various times was analyzed and evaluated using MaxEnt and ArcGIS. For this analysis, 75% of the distribution data for training and 25% for testing. The calculations were run 10 times with default settings for all other parameters. Model accuracy was assessed using the receiver operating characteristic (ROC) area under the curve (AUC), which provides model performance results from logistic regressions and is independent of any specific threshold. A higher AUC value, approaching 1, indicates greater predictive accuracy of the model [31,32,33].

2.3. Potentially Suitable Area Partitions

Based on the MaxEnt model outcomes, the ASC files from MaxEnt were imported into GIS and converted to grid data. These grids were then overlaid on China’s administrative map for visual analysis. Using the natural breaks classification method in ArcGIS [34,35], the potential habitats of S. grosvenorii in China were categorized into four suitability levels: unsuitable (0–0.1), low-suitability (0.1–0.3), moderate-suitability (0.3–0.5), and high-suitability (0.5–1). A habitat suitability distribution map for S. grosvenorii in China was generated, and the area of each suitability zone was calculated using ArcGIS.

2.4. Analysis of Suitable Area Change and Centroid Transfer in Different Periods

Utilizing ArcMap 10.8.1, the “Quick Reclassify to Binary” tool was employed to redefine the suitable area range with a suitability threshold of 0.1, thereby converting raster layers into binary format. Subsequently, the “Overlay Analysis” tool was used to compute changes in the suitable area size across different periods relative to the current period. Additionally, the “Centroid Changes (Lines)” tool was applied to determine the direction and distance of centroid shifts between different periods and the current period.

2.5. Chemical Information

To better assess the environmental impact on the quality of S. grosvenorii, we investigated the relationship between its chemical composition and climatic factors, focusing on four identified chemical compounds and various elements. We used our previous research results without needing to filter and repeat the records [30].
The dried samples (0.3 g) were mixed with 65% concentrated nitric acid (6 mL) and digested using a microwave digestion system (Multiwave PRO, Anton Paar, Austria), according to the following heating program: (i) 10 min ramp to 120 °C, maintained for 5 min; (ii) 20 min ramp to 180 °C, maintained for 20 min; and (iii) 40 min for cooling the digestion vessels. ICP-MS operating parameters were as follows: RF power, 1548 W; auxiliary gas and cooling gas flow rates, 0.79 L/min and 13.9 L/min, respectively; temperature of atomization chamber, 2 °C; carrier gas flow rate, 1.16 L/min. They were then placed in acid at 160 °C until nearly dry. The digested samples were diluted to 100 mL with ultrapure water. The standard solution used for determining mineral elements is multi-element mixed solutions (GSB 04-1767-2004, Ca, Mg, Na, Cu, Mn, Zn, B, Al, Ba, Sr, Ti, Cd, Ni, Tl, Co, As, Li, Be, Cr, Ga, Sn, Sb, Pb, Bi, GSB 04–1751–2004, Se) and the internal standard solutions (GSB 04–2827–2011, Cs, Rh, In, Sc solutions) with a concentration of 1000 μg/mL, both were purchased from Guobiao (Beijing, China) Testing & Certification Co., Ltd. with a concentration of 100 μg/mL. These were determined using ICP-MS (Thermo ICAP QC, Thermo Inc., Bremen, Germany). K was used for flame photometry (FP6431, Jingke Industrial Co., Ltd., Shanghai, China). The spike recoveries of the standard solutions were used to assess the efficiency and reproducibility of the elemental composition analysis (labeled concentrations were 500 μg/mL for Ca and Mg, 0.1 μg/mL for Se, and 100 μg/mL for other elements).

2.6. Statistical Analysis

To evaluate variations in chemical information between samples from suitable habitats, significant differences (p < 0.05) were analyzed using multiple comparison tests with IBM SPSS 25.0. SIMCA 14.1 was used for the orthogonal partial least squares discriminant analysis (OPLS-DA) of S. grosvenorii samples and obtaining variable importance in projection (VIP > 1) predictive values for the 26 elements. Each sample was analyzed in triplicate to further enhance the precision of the dataset. The recovery rates ranged from 83 to 115%, demonstrating the reliability of the analytical method. The precision method validation revealed relative standard deviation (RSD) values of 2.3–4.6% for precision, indicating that the instrument has good precision. Correlation analysis was performed, and the results were visualized using a heatmap generated by ChiPlot (https://www.chiplot.online).

3. Results

3.1. MaxEnt Model Accuracy Evaluation

The potential distribution model of S. grosvenorii based on 10 environmental variables and field-surveyed data achieved AUC values of 0.994 (exceeding 0.90) (Figure 2), indicating excellent predictive accuracy and confirming that it can be effectively used to predict the suitable habitat distribution of S. grosvenorii. Due to the elevation, slope, and soil type also being able to influence the quality of S. grosvenorii, future research will integrate actual cultivation conditions to enhance prediction accuracy.

3.2. Evaluation of the Dominant Environmental Variables Affecting the Growth and Composition of S. grosvenorii

To assess these variables more specifically, we calculated regularized training gains using a jackknife test (Figure A2). The results showed that bio_14, bio_7, bio_2, and bio_9 ranked highest in terms of the regularized training gain, which indicated that these climate variables provided more relevant information and had a greater impact on the distribution of S. grosvenorii compared to other climate factors.
As presented in Table 1, the cumulative contributions of precipitation and temperature were 86% and 14%, respectively, indicating that these are the key variables affecting S. grosvenorii distribution. The results of comprehensive jackknife testing and percent contribution analysis showed that the precipitation of driest month (bio_14), precipitation seasonality (bio_15), temperature seasonality (bio_4), Isothermality (bio_3), annual temperature (bio_7), and precipitation of warmest quarter (bio_18) were the most influential factors, providing the most effective information and having the greatest impact on S. grosvenorii distribution.
To further elucidate the climatic characteristics of the predicted suitable regions for S. grosvenorii under current climatic conditions, detailed analyses were conducted on the response curves of climate factors that substantially influence its geographical distribution (Figure 3). Using the range of suitable habitats (p > 0.5) as the standard, we found the optimal range for bio_14 to be 41.19–61.26 mm, with the highest distribution occurring at 47.58 mm (Figure 3a). For bio_15, the optimal range is 55.80–66.28 mm, peaking at 59.99 mm (Figure 3b). Bio_3 ranges from 25.9 to 28.7 °C, with the highest probability at 27.43 °C (Figure 3d). Bio_04 and bio_07 are 688–782 °C and 25–29 °C, respectively (Figure 3c,e). For bio_18, the optimal range is 508–712 mm (Figure 3f). These results align with the natural growth conditions of S. grosvenorii, highlighting that adequate precipitation and stable temperature variations are the primary environmental variables that affect its survival.

3.3. Suitable Regions in China for S. grosvenorii Cultivation in Current Climate Conditions

Using the prediction results from our optimized MaxEnt model, ArcGIS was used to divide the distribution map into segments, enabling a clearer visualization of the potentially suitable areas for S. grosvenorii under current climate conditions, as illustrated in Figure 4. According to the criteria for the classification of suitable zones, unsuitable habitat covers an area of 90.1 × 105 km2, representing 93.89% of the total area of China. The high-, medium-, and low-suitability zones cover areas of 7.56 × 104 km2, 11.19 × 104 km2, and 40.01 × 104 km2, respectively, accounting for 6.11% of the total area of China. The highly suitable areas are mainly concentrated at the junction of northeastern Guangxi and Hunan, central and western Jiangxi, and northwestern Fujian near the border of Jiangxi. These highly suitable areas cover 0.78% of the total area of China. The medium- and low-suitability areas are distributed along the periphery of the highly suitable regions and account for 5.33% of China’s total area. The distribution range predicted by the model aligns with existing distribution points of S. grosvenorii, indicating a high concordance between the predicted and actual suitable habitats. This highlights the reliability of the MaxEnt model’s predictions.

3.4. Potentially Suitable Areas for S. grosvenorii in Future Climate Conditions

3.4.1. Future Changes in Suitable Habitats for S. grosvenorii

As depicted in Figure 5, the future distribution pattern of suitable habitats is similar from the current pattern, with slight variations under different scenarios. In the three future climate scenarios of SSP126–2050s, SSP126–2070s, SSP370–2070s, SSP585–2050s, and SSP585–2070s, projected contractions of the amount of suitable habitat from the amount in the current climate were reductions of 11.53%, 1.19%, 30.20%, 8.49%, and 6.05%, respectively (Table 2). In the SSP370–2050s scenario, the suitable area was projected to increase by 1.88% to 59.75 × 104 km2. The smallest area of suitable habitat, 40.97 × 104 km2, was found in the SSP370–2070s scenario. Overall, the highly suitable habitats in central and northern Guangxi remained unchanged. However, the majority of the main production areas in the regions surrounding Guilin were predicted to change from moderate to low suitability.

3.4.2. Changes in the Distribution Core of S. grosvenorii

In the future climate change scenarios evaluated, the areas of suitable habitat for S. grosvenorii were predicted to shrink and expand in distinct regions around the current suitable areas. The expansion areas show minimal variation across different periods, while the contraction areas are generally much larger than the expansion areas (Figure 6). The expanded regions were primarily located northeast of the current moderately suitable areas, including the prefectures of Shaoyang, Huaihua, Fuzhou, and Qiandongnan. Conversely, the contracted regions were primarily located in the Shangrao and Hechi prefectures, northeast of the current moderately suitable areas. Among the various future climate scenarios, the expansion area reached its peak of 15.08 × 104 km2 under the SSP370 scenario in the 2050s, exceeding the current highly suitable habitats area of 7.56 × 104 km2 (Table 3).
Under current climate conditions, the central suitable habitat area is located in Ji’an, Jiangxi province (114.43° E, 26.67° N), bordering Hunan province. In future climate scenarios, most distribution centroids remain in Jiangxi but have shifted northeast. Exceptions include SSP126 (112.49° E, 26.65° N) and SSP585 (111.97° E, 26.17° N) during the 2050s, which were projected to experience a southwestern shift into Hunan. The centroid of SSP585 in the 2070s experienced a displacement of 2.32 km compared to the current centroid, representing the greatest shift in distance (Figure 7).

3.5. Chemical Composition of S. grosvenorii

3.5.1. Element Contents of S. grosvenorii

ICP-MS was used to simultaneously determine the concentrations of 26 elements in the fruit samples of 59 S. grosvenorii populations across habitats with varying suitability. These include 25 samples from high-suitable habitats (Guilin, Liuzhou, Hunan), 21 from medium-suitability (Guizhou), and 13 from low-suitability habitats (Jiangxi). As shown in Table 4, K exhibited the highest content (16,555.05 μg/g), suggesting efficient absorption mechanisms, followed by Mg and Ca with mean contents of 718.73 and 421.58 μg/g, respectively. Significant differences in element content were observed across habitats, with higher Mg, Mn, Se, B, Co, and Cr contents in high-suitability areas compared to low-suitability regions. These findings highlight the influence of suitable habitats on the compositional properties of S. grosvenorii.

3.5.2. OPLS-DA

A supervised discriminant analysis was performed using the OPLS-DA model. As shown in Figure 8a, samples in different suitable regions were successfully classified. The model parameters R2X, R2Y, and Q2 were 0.608, 0.885, and 0.788, respectively, surpassing the basic parameter standards for model reliability. After 200 permutation tests, the intercepts of R2 (0.184) and Q2 (−0.371) were found to be below 0.3 and −0.05, respectively, indicating that the model was robust and not overfitting (Figure 8b). The VIP values obtained from the model indicated the contribution of each element. Variables with VIP > 1, including Be, Se, Ga, K, Tl, Sb, Bi, B, Ti, Co, and Cr, were identified as potential biomarkers for distinguishing S. grosvenorii samples from different suitable regions (Figure 8c).

3.5.3. Correlation Analysis

To explore the relationship between the quality of S. grosvenorii and climate factors in different suitable areas, we selected elements and previously identified biomarkers such as saponins (mogroside V and 11-oxo-mogroside V) and flavonoids (grosvenorine I, grosvenorine II) to conduct a correlation analysis. The correlation between climatic factors and chemical composition was described in detail in Figure 8d and Table A2, demonstrating that climatic factors significantly influence chemical content. Specifically, bios_4, 7, 12, 14, and 17 were positively correlated with K, B, Be, Ti, Co, and Ga but negatively correlated with Cr and Bi. Bios_3, 15, and 18 were positively correlated with Se, Cr, Sb, and Bi but negatively correlated with K, B, Be, Ti, Co, and Ga. Be, Tl, mogroside V,11-oxo-mogroside V, grosvenorine I, grosvenorine II, and Bi displayed strong positive correlations with bio_2, while B, K, Co, Sb, and Se were significantly negatively correlated with bio_2. Cr, Sb, and Se were significantly positive with bio_18, while Ti and Be showed negative correlations with it. Although the correlation coefficients for these four components with climatic factors are relatively low, they have shown significant correlations. Overall, based on the climate influence on suitable areas, bio_14, bio_15, bio_4, and bio_3 significantly influenced the chemical constituents and suitable distribution areas of S. grosvenorii.

4. Discussion

4.1. The Primary Environmental Factors Influencing the Distribution of S. grosvenorii

Global warming impacts the adaptability of plants, causing shifts in the suitable habitat ranges of various plant species [2,36,37]. Among the environmental factors studied, our research revealed that precipitation and temperature play a significant role in shaping its range. Similar patterns have been observed in other plant species [38,39]. For instance, precipitation of the driest period and the annual temperature range were the primary climatic factors influencing the distribution of Camellia sinensis [40]. Likewise, the distribution of sugarcane was predominantly affected by accumulated temperatures and annual precipitation [41]. The study investigations demonstrated that S. grosvenorii thrives in humid, foggy, and cool conditions with significant diurnal temperature variation. It requires 7–8 h of daily sunlight exposure for optimal growth, thriving best in temperatures between 18 and 32 °C, with an annual rainfall of 1900–2600 mm. Additionally, April to November is the growth cycle of S. grosvenorii, and the temperature should remain stable above 15 °C in April to promote the plant growth; the average temperatures should stay below 28 °C, with short-term maximum temperatures not exceeding 38 °C during its flowering and fruiting period from June to August. During its fruit expansion period, adequate water should be supplied to maintain soil moisture levels between 60% and 80% to meet fruit swelling requirements. From September to November, the temperatures in October should not drop below 15 °C [42]. Overall, if there is continuous high temperature and drought, water should be sprayed on the vines and the ground stumps in the late afternoon to retain moisture and reduce temperature, thereby promoting fruit enlargement. If there is too much rain, the inter-ridge paths in S. grosvenorii fields should be dug out to improve drainage. In this study, the response ranges to temperature and annual precipitation of the suitable area of S. grosvenorii were 25–29 °C and 1773–1886 mm, which is consistent with the climate of the actual suitable area. Huang et al. [43] found that temperature and precipitation have a great influence on the distribution of tropical plant species in tropical and subtropical regions. The subtropical plant S. grosvenorii is a short-day with heat-sensitive species. The warm, wet climate with low mountainous terrain, characteristic of southern China, produces favorable conditions for its growth [44]. However, its distributions and chemical component accumulation may be negatively impacted by global warming. It is crucial to implement preventive measures to address this issue.

4.2. Correlation of Climatic Variables and Chemical Composition

Plant secondary metabolite synthesis and accumulation are significantly influenced by various climate factors [45]. Studies have found that environmental factors such as temperature and precipitation influence the element absorption of various plants [46]. In this study, the K, B, Co, Ga, Be, and Ti in S. grosvenorii were positively correlated with temperature (bios_4, 7, and 9; 25–32 °C) and precipitation (bios_12, 14, and 17; 1198–1754 mm), while negatively correlated with bios_3 and 15. Previous research has demonstrated that K plays a critical role in a plant’s adaptability to drought by regulating photosynthesis and biochemical metabolic pathways [47]. Additionally, studies have revealed that the levels of K, Ca, Mg, and Fe in this plant are notably higher, indicating their potential contribution to metabolic processes, muscle health, and cardiovascular well-being [24,48]. The precipitation being negatively correlated with the Mg and B content were found in grapes and Rheum tanguticum [11,49]. In addition, we found that the four chemical compounds had a positive interaction with bio_2, 3, and 7, but negative correlations with bio_14, 17, and 18. These results indicated that the climate factors have varying degrees of impact on the accumulation of S. grosvenorii’s chemical composition. Therefore, the regulation of secondary metabolism in S. grosvenorii could be explored with the changing climatic conditions. However, the specific mechanisms governing element accumulation in S. grosvenorii remain unclear and necessitate in-depth exploration in subsequent research.

4.3. Changes in S. grosvenorii Distribution in the Future

The suitable zones for S. grosvenorii in the current period are primarily located in southern China and are climatically concentrated in the subtropical region. This aligns well with the actual coordinates of S. grosvenorii collected, indicating strong model simulation performance. Under SSP126, the area of potential high-suitability zones shows a decreasing trend, while the total suitability area exhibits a pattern of initial expansion followed by contraction. Similarly, Lai Baiwen ’s [50] study found that the area of high-suitability zones is slightly higher than in the current period, but the total suitability area tends to decrease. This discrepancy may stem from differences in the selection of climate factors during model prediction. Under SSP585, the suitable regions exhibit a notable decline, primarily characterized by low-suitability zones becoming unsuitable for cultivation. The increasing frequency of extreme climatic events exacerbates the contraction of S. grosvenorii’s suitable zones. The findings on centroid migration indicated that the centroids consistently stay within the subtropical region across all time frames. Under SSP126, the centroid migrates northeast–southwest–northeast, while under SSP585, it follows a northwest–southwest–northeast trajectory, primarily shifting toward the northeast. Recent studies have highlighted significant shifts in species distributions due to environmental changes. Prevéy et al. [51] project that Vaccinium vitisidaea is expected to face a reduction in its presence in southern low-altitude regions and expand into higher altitude and higher latitude areas, which aligns with our findings. By employing Maxent modeling and remote sensing technologies, we identified suitable cultivation areas for S. grosvenorii and analyzed elemental–environmental relationships. These insights assist in optimizing cultivation and processing, fostering sustainable agriculture and food security, which contributes to the achievement of the SDG-UN 2030 agenda, particularly SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action) [52].

5. Conclusions

In this study, we utilized ICP-MS technology and the MaxEnt model to investigate the relationship between climate factors and chemical constituents of S. grosvenorii. We systematically evaluated the potential distributions and their shift in suitable areas under future climate. Under current climate conditions, the most suitable habitats for S. grosvenorii were found to be in Guilin and its surrounding areas. Future climate projections indicated a northeastward shift in suitable areas, while the highly suitable areas are expected to remain relatively unchanged. Significant differences in chemical constituents were observed among samples from different suitable habitats. And bio_14, bio_15, bio_4, and bio_3 were found to significantly influence the suitable distribution areas and chemical constituents of S. grosvenorii. The findings provide valuable insights into the factors affecting S. grosvenorii composition and identify suitable areas in China for S. grosvenorii cultivation. In response to the current global climate warming, environmental degradation, and the issue of insufficient protection measures or awareness among farmers, S. grosvenorii has been listed as Near Threatened on the IUCN Red List. To address this, we propose designating regions with rich resources, such as Yongfu and Longsheng in Guilin, as core nature reserves and establishing a germplasm resource conservation center, with the aim of promoting the species’ sustainable development.

Author Contributions

Conceptualization, F.L.; methodology, F.D., X.Y. and J.S.; software, F.D. and C.F.; validation, F.L. and J.S.; formal analysis, C.F.; investigation, F.D., X.Y., J.S., X.H. and C.F.; resources, X.Y., J.S., X.H. and C.F.; data curation, F.D.; writing—original draft preparation, F.D.; writing—review and editing, J.S., C.F. and F.L.; visualization, X.Y. and F.D.; supervision, F.L. and D.L.; project administration, X.H. and D.L.; funding acquisition, F.L. and D.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Key Research and Development Program of China, grant number 2022YFD1600302, National Natural Science Foundation of China, grant number U20A2004, Guangxi Science and Technology Major Project, grant number GuikeAA AA23023035, Natural Science Foundation of Guangxi, grant number 2023GXNSFDA026053, and Guilin Innovation Platform and Talent Plan, grant number 20220120-3.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Potential environmental factors affecting the distribution of S. grosvenorii.
Table A1. Potential environmental factors affecting the distribution of S. grosvenorii.
Environment
Variable		Interpretation
bio_1Annual mean temperature (°C)
bio_2Mean diurnal range (Mean of monthly (max temp–min temp)) (°C)
bio_3Isothermality (bio_2/bio_7) (×100)
bio_4Temperature seasonality (standard deviation ×100)
bio_5Max temperature of warmest month (°C)
bio_6Min temperature of coldest month (°C)
bio_7Temperature annual range (bio_5/bio_6) (°C)
bio_8Mean temperature of wettest quarter (°C)
bio_9Mean temperature of driest quarter (°C)
bio_10Mean temperature of warmest quarter (°C)
bio_11Mean temperature of coldest quarter (°C)
bio_12Annual precipitation (mm)
bio_13Precipitation of wettest month (mm)
bio_14Precipitation of driest month (mm)
bio_15Precipitation seasonality (Coefficient of variation) (mm)
bio_16Precipitation of wettest quarter (mm)
bio_17Precipitation of driest quarter (mm)
bio_18Precipitation of warmest quarter (mm)
bio_19Precipitation of coldest quarter (mm)
Table A2. Correlation analysis between chemical constituents and climate factors.
Table A2. Correlation analysis between chemical constituents and climate factors.
Chemical Constituentsbio_2bio_3bio_4bio_7bio_9bio_12bio_14bio_15bio_17bio_18
Be0.436 **−0.461 **0.778 **0.813 **−0.2190.1010.233−0.628 **0.344 **−0.486 **
Se−0.436 **0.068−0.377 **−0.454 **0.390 **0.292 *0.1620.372 **0.0570.517 **
Ga0.154−0.416 **0.532 **0.530 **0.1700.456 **0.442 **−0.370 **0.493 **−0.026
K−0.327 *−0.633 **0.406 **0.294 *0.1110.526 **0.611 **−0.506 **0.644 **−0.058
Tl0.380 **−0.0330.307 *0.380 **−0.126−0.027−0.059−0.1880.022−0.191
Sb−0.309 *0.165−0.378 **−0.418 **0.538 **0.338 **0.1350.467 **0.0260.633 **
Bi0.294 *0.477 **−0.273 *−0.175−0.024−0.372 **−0.496 **0.437 **−0.531 **0.098
B−0.438 **−0.501 **0.1850.0680.2040.447 **0.528 **−0.361 **0.510 **0.015
Ti0.101−0.563 **0.633 **0.599 **−0.0040.286 *0.451 **−0.603 **0.505 **−0.339 **
Co−0.193−0.439 **0.311 *0.2440.377 **0.614 **0.572 **−0.2440.554 **0.211
Cr−0.0520.526 **−0.571 **−0.529 **0.078−0.274 *−0.428 **0.632 **−0.515 **0.345 **
mogroside V0.261 *0.199−0.0130.046−0.065−0.213−0.2480.164−0.254−0.017
11-oxo-mogroside V0.351 **0.0190.2240.296 *−0.057−0.025−0.066−0.083−0.034−0.119
grosvenorine I0.367 **0.1330.1070.189−0.078−0.284 *−0.2380.010−0.234−0.260 *
grosvenorine II0.291 *−0.0100.2120.264 *−0.262 *−0.199−0.148−0.122−0.115−0.275 *
Note: Asterisks indicate significant differences: *, p < 0.5; **, p < 0.1.

Appendix B

Agronomy 15 01474 g0a1
Figure A1. (a) Geographical distribution points of S. grosvenorii for MaxEnt; (b) distribution points of S. grosvenorii for ICP-MS.
Figure A1. (a) Geographical distribution points of S. grosvenorii for MaxEnt; (b) distribution points of S. grosvenorii for ICP-MS.
Agronomy 15 01474 g0a1
Agronomy 15 01474 g0a2
Figure A2. The jackknife test result for the environmental factors (1, S. grosvenorii).
Figure A2. The jackknife test result for the environmental factors (1, S. grosvenorii).
Agronomy 15 01474 g0a2

References

  1. Céline, B.; Cleo, B.; Paul, L.; Wilfried, T.; Franck, C. Impacts of climate change on the future of biodiversity. Ecol. Lett. 2012, 15, 365–377. [Google Scholar] [CrossRef]
  2. Shi, J.Y.; Xia, M.X.; He, G.Q.; Gonzalez, N.C.T.; Zhou, S.; Lan, K.; Ouyang, L.; Shen, X.B.; Jiang, X.L.; Cao, F.L.; et al. Predicting Quercus gilva distribution dynamics and its response to climate change induced by GHGs emission through MaxEnt modeling. J. Environ. Manag. 2024, 357, 120841. [Google Scholar] [CrossRef] [PubMed]
  3. Applequist, W.L.; Brinckmann, J.A.; Cunningham, A.B.; Hart, R.E.; Heinrich, M.; Katerere, D.R.; van Andel, T. Scientists’ warning on climate change and medicinal plants. Planta Med. 2020, 86, 10–18. [Google Scholar] [CrossRef]
  4. Lu, X.Y.; Jin, Y.Y.; Wang, Y.Z.; Chen, Y.L.; Fan, X.H. Multimodal integrated strategy for the discovery and identification of quality markers in traditional Chinese medicine. J. Pharm. Anal. 2022, 12, 701–710. [Google Scholar] [CrossRef]
  5. Varotsos, C.A.; Franzke, C.L.E.; Efstathiou, M.N.; Degermendzhi, A.G. Evidence for two abrupt warming events of SST in the last century. Theor. Appl. Climatol. 2014, 116, 51–60. [Google Scholar] [CrossRef]
  6. Yang, Y.; He, J.; Liu, Y.X.; Zeng, J.X.; Zeng, L.Q.; He, R.L.; Guiang, M.M.; Li, Y.Q.; Wu, H. Assessment of Chinese suitable habitats of Zanthoxylum nitidum in different climatic conditions by Maxent model, HPLC, and chemometric methods. Ind. Crops Prod. 2023, 196, 116515. [Google Scholar] [CrossRef]
  7. Zhao, Q.; Mi, Z.Y.; Lu, C.; Zhang, X.F.; Chen, L.J.; Wang, S.Q.; Niu, J.F.; Wang, Z.Z. Predicting potential distribution of Ziziphus spinosa (Bunge) H.H. Hu ex F.H. Chen in China under climate change scenarios. Ecol. Evol. 2022, 12, e8629. [Google Scholar] [CrossRef]
  8. Gong, P.; Guo, Y.X.; Chen, X.F.; Cui, D.D.; Wang, M.G.; Yang, W.J.; Chen, F.X. Structural characteristics, antioxidant and hypoglycemic activities of polysaccharide from Siraitia grosvenorii. Molecules 2022, 27, 4192. [Google Scholar] [CrossRef]
  9. Li, H.; Li, R.L.; Jiang, W.; Zhou, L.M. Research progress of pharmacological effects of Siraitia grosvenorii extract. J. Pharm. Pharmacol. 2022, 74, 953–960. [Google Scholar] [CrossRef]
  10. Liu, H.; Du, Y.; Liu, L.L.; Liu, Q.S.; Mao, H.H.; Cheng, Y. Anti-depression-like effect of Mogroside V is related to the inhibition of inflammatory and oxidative stress pathways. Eur. J. Pharmacol. 2023, 955, 175828. [Google Scholar] [CrossRef]
  11. Zhao, S.; Xiong, F.; Wang, L.; Wang, B.; Chen, K.; Chen, C.; Zhou, G. Study on the quality characteristics and geographical origin authentication of wild Rheum tanguticum in three authentic regions. J. Food Compos. Anal. 2023, 123, 105463. [Google Scholar] [CrossRef]
  12. Soilhi, Z.; Sayari, N.; Benalouache, N.; Mekki, M. Predicting current and future distributions of Mentha pulegium L. in Tunisia under climate change conditions, using the MaxEnt model. Ecol. Inform. 2022, 68, 101533. [Google Scholar] [CrossRef]
  13. Bai, J.Y.; Wang, H.C.; Hu, Y.K. Prediction of potential suitable distribution of Liriodendron chinense (Hemsl.) Sarg. in China based on future climate change using the optimized MaxEnt model. Forests 2024, 15, 988. [Google Scholar] [CrossRef]
  14. Liu, X.D.; Zhang, Y.; Wu, M.H.; Ma, Z.G.; Huang, Z.H.; Tian, F.; Dong, S.H.; Luo, S.M.; Zhou, Y.; Zhang, J.J.; et al. The scientific elucidation of Daodi medicinal materials. Chin. Med. 2020, 15, 86. [Google Scholar] [CrossRef] [PubMed]
  15. Fletcher, R.J.; Hefley, T.J.; Robertson, E.P.; Zuckerberg, B.; McCleery, R.A.; Dorazio, R.M. A practical guide for combining data to model species distributions. Ecology 2019, 100, e02710. [Google Scholar] [CrossRef]
  16. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  17. Hu, W.J.; Wang, Y.Y.; Dong, P.; Zhang, D.; Yu, W.W.; Ma, Z.Y.; Chen, G.C.; Liu, Z.H.; Du, J.G.; Chen, B.; et al. Predicting potential mangrove distributions at the global northern distribution margin using an ecological niche model: Determining conservation and reforestation involvement. For. Ecol. Manag. 2020, 478, 118517. [Google Scholar] [CrossRef]
  18. Moreno, R.; Zamora, R.; Molina, J.R.; Vasquez, A.; Herrera, M.Á. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Ecol. Inform. 2011, 6, 364–370. [Google Scholar] [CrossRef]
  19. Li, Z.; Hu, Z.; Zhao, Y.; Yang, L.; Tang, H.; Li, J. Geographical variations in Polygonatum polysaccharides and saponins biosynthesis in Polygonatum species. Ind. Crops Prod. 2025, 227, 120814. [Google Scholar] [CrossRef]
  20. Zhan, P.; Wang, F.Y.; Xia, P.G.; Zhao, G.H.; Wei, M.T.; Wei, F.G.; Han, R.L. Assessment of suitable cultivation region for Panax notoginseng under different climatic conditions using MaxEnt model and high-performance liquid chromatography in China. Ind. Crops Prod. 2022, 176, 114416. [Google Scholar] [CrossRef]
  21. Wan, G.Z.; Guo, Z.H.; Xi, S.Y.; Jin, L.; Chen, J. Spatial variability and climate response characteristics of chemical components of Tussilago farfara L. Ind. Crops Prod. 2023, 204, 117352. [Google Scholar] [CrossRef]
  22. Wei, X.Q.; Lei, X.; He, W.J. Study on ecological suitability of Siraitia grosvenorii in producing areas in the world. Guizhou Agric. Sci. 2020, 48, 119–124. (In Chinese) [Google Scholar]
  23. Wang, S.; Chen, P.; Liu, Y.; Chen, C.; Tian, J.; Liu, Z.; Li, B.; Mei, X.; Chen, Y.; Zhang, Y.; et al. Geographical origin traceability of sweet cherry (Prunus avium (L.) Moench) in China using stable isotope and multi-element analysis with multivariate modeling. Food Chem. X. 2024, 23, 101477. [Google Scholar] [CrossRef] [PubMed]
  24. Huang, X.P.; Lei, L.; Lei, S.X.; Zhu, W.W.; Yan, J. Traceability of the geographical origin of Siraitia grosvenorii based on multielement contents coupled with chemometric techniques. Sci. Rep. 2021, 11, 21150. [Google Scholar] [CrossRef]
  25. Shi, X.D.; Wang, J.W.; Zhang, L.; Chen, S.X.; Zhao, A.L.; Ning, X.D.; Fan, G.R.; Wu, N.S.; Zhang, L.; Wang, Z.D. Prediction of the potentially suitable areas of Litsea cubeba in China based on future climate change using the optimized MaxEnt model. Ecol. Indic. 2023, 148, 110093. [Google Scholar] [CrossRef]
  26. Yang, Z.W.; Yang, X.J.; Huang, Y.H.; Zhang, Y.L.; Guo, Y.; Feng, M.C.; Qin, M.X.; Jin, N.; Amjad, M.; Wang, C.; et al. Potential impacts of climate change on the spatial distribution pattern of Naked oats in China. Agronomy 2025, 15, 362. [Google Scholar] [CrossRef]
  27. Jarnevich, C.S.; Stohlgren, T.J.; Kumar, S.; Morisette, J.T.; Holcombe, T.R. Caveats for correlative species distribution modeling. Ecol. Inform. 2015, 29, 6–15. [Google Scholar] [CrossRef]
  28. Li, J.; Fan, G.; He, Y. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci. Total Environ. 2020, 698, 134141. [Google Scholar] [CrossRef]
  29. Zhao, X.F.; Lei, M.; Wei, C.H.; Guo, X.X. Assessing the suitable regions and the key factors for three Cd-accumulating plants (Sedum alfredii, Phytolacca americana, and Hylotelephium spectabile) in China using MaxEnt model. Sci. Total Environ. 2022, 852, 158202. [Google Scholar] [CrossRef]
  30. Dong, F.; Song, J.R.; Fu, C.M.; Huang, X.Y.; Yang, X.R.; Lu, F.L.; Li, D.P. Spatial variability and climate response characteristics of the chemical components in Siraitia grosvenorii (Luo Han Guo). Ind. Crops Prod. 2025, 228, 120897. [Google Scholar] [CrossRef]
  31. Abolmaali, S.M.R.; Tarkesh, M.; Bashari, H. MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran. Ecol. Inform. 2018, 43, 116–123. [Google Scholar] [CrossRef]
  32. Qiu, H.H.; Han, H.R.; Cheng, X.Q.; Kang, F.F. Understanding sustainability of woody species suitability zones on the Loess Plateau for optimal creation zone selection in response to future climate change. J. Environ. Manag. 2025, 375, 124239. [Google Scholar] [CrossRef] [PubMed]
  33. Zhao, Y.; Deng, X.W.; Xiang, W.H.; Chen, L.; Ouyang, S. Predicting potential suitable habitats of Chinese fir under current and future climatic scenarios based on Maxent model. Ecol. Inform. 2021, 64, 101393. [Google Scholar] [CrossRef]
  34. Cao, B.; Bai, C.K.; Wu, K.Y.; Xue, Y.; Yang, J.J.; Gao, P.F.; Liang, H.; Li, G.S. Concentrated conservation and utilization: Four medicinal crops for diabetes treatment showed similar habitat distribution patterns in China. Ind. Crops Prod. 2020, 152, 112478. [Google Scholar] [CrossRef]
  35. Li, Y.X.; Shao, W.H.; Jiang, J.M. Predicting the potential global distribution of Sapindus mukorossi under climate change based on MaxEnt modelling. Environ. Sci. Pollut. Res. Int. 2022, 29, 21751–21768. [Google Scholar] [CrossRef] [PubMed]
  36. He, Y.L.; Ma, J.M.; Chen, G.S. Potential geographical distribution and its multi-factor analysis of Pinus massoniana in China based on the maxent model. Ecol. Indic. 2023, 154, 110790. [Google Scholar] [CrossRef]
  37. Zhang, Q.; Shen, X.B.; Jiang, X.L.; Fan, T.T.; Liang, X.C.; Yan, W.D. MaxEnt modeling for predicting suitable habitat for endangered tree Keteleeria davidiana (Pinaceae) in China. Forests 2023, 14, 394. [Google Scholar] [CrossRef]
  38. Xi, S.Y.; Guo, X.D.; Ma, X.H.; Jin, L. Impacts of climate change on the suitable habitat of Angelica sinensis and analysis of its drivers in China. Sci. Rep. 2025, 15, 3508. [Google Scholar] [CrossRef]
  39. Zhu, Q.Y.; Wang, F.M.; Yi, Q.X.; Zhang, X.Y.; Chen, S.T.; Zheng, J.Y.; Li, J.L.; Xu, T.Y.; Peng, D.L. Modeling soybean cultivation suitability in China and its future trends in climate change scenarios. J. Environ. Manag. 2023, 345, 118934. [Google Scholar] [CrossRef]
  40. Tesfay, G.; Zhao, Y.; Zhao, M.; Li, K.; Demelash, T.; Xu, Y. Assessing the Impacts of Climate Change on Geographical Distribution of Tea (Camellia sinensis L.) in Kenya with Maximum Entropy Model. Agronomy 2024, 14, 2080. [Google Scholar] [CrossRef]
  41. Guga, S.; Xu, J.; Riao, D.; Li, K.; Han, A.; Zhang, J. Combining MaxEnt model and landscape pattern theory for analyzing interdecadal variation of sugarcane climate suitability in Guangxi, China. Ecol. Indic. 2021, 131, 108152. [Google Scholar] [CrossRef]
  42. Chen, N.; Cao, W.C.; Yuan, Y.Y.; Wang, Y.H.; Zhang, X.J.; Chen, Y.J.; Yiasmin, M.N.; Tristanto, N.A.; Hua, X. Recent advancements in mogrosides: A review on biological activities, synthetic biology, and applications in the food industry. Food Chem. 2024, 449, 139277. [Google Scholar] [CrossRef] [PubMed]
  43. Huang, Z.; Xie, L.; Wang, H.; Zhong, J.; Li, Y.; Liu, J.; Ou, Z.; Liang, X.; Li, Y.; Huang, H.; et al. Geographic distribution and impacts of climate change on the suitable habitats of Zingiber species in China. Ind. Crops Prod. 2019, 138, 111429. [Google Scholar] [CrossRef]
  44. Shivani; Thakur, B.K.; Mallikarjun, C.P.; Mahajan, M.; Kapoor, P.; Malhotra, J.; Dhiman, R.; Kumar, D.; Kumar Pal, P.; Kumar, S. Introduction, adaptation and characterization of monk fruit (Siraitia grosvenorii): A non-caloric new natural sweetener. Sci. Rep. 2021, 11, 6250. [Google Scholar] [CrossRef]
  45. Zheng, T.; Sun, J.; Shi, X.; Liu, D.; Sun, B.; Deng, Y.; Zhang, D.; Liu, S. Evaluation of climate factors affecting the quality of red huajiao (Zanthoxylum bungeanum maxim.) Based on UPLC-MS/MS and MaxEnt model. Food Chem. X 2022, 16, 100522. [Google Scholar] [CrossRef]
  46. Rajkumar, M.; Prasad, M.N.V.; Swaminathan, S.; Freitas, H. Climate change driven plant–metal–microbe interactions. Environ. Int. 2013, 53, 74–86. [Google Scholar] [CrossRef]
  47. Cooper, R.B.; Blaser, R.E.; Brow, R.H. Potassium Nutrition effects on net photosynthesis and morphology of alfalfa. Soil Sci. Soc. Am. J. 1967, 31, 231–235. [Google Scholar] [CrossRef]
  48. Liu, Y.Q.; Yu, W.Q.; Bi, M.F.; Zhang, Y.T.; Guan, Y.; Jiang, T.M. Comprehensive nutrient profiling and untargeted metabolomic assessment of Siraitia grosvenorii from different regions and varying degrees of processing. Appl. Sci. 2025, 15, 1020. [Google Scholar] [CrossRef]
  49. Richardson, J.B.; Chase, J.K. Transfer of macronutrients, micronutrients, and toxic elements from soil to grapes to white wines in uncontaminated vineyards. Int. J. Environ. Res. Public Health 2021, 18, 13271. [Google Scholar] [CrossRef]
  50. Lai, B.W. Genetic Structure and Demographic History of Siraitia grosvenorii Based on Whole-Genome Resequencing. Master’s Thesis, Guangxi Normal University, Guilin, China, 2023. [Google Scholar]
  51. Prevéy, J.S.; Parker, L.E.; Harrington, C.A.; Lamb, C.T.; Proctor, M.F. Climate change shifts in habitat suitability and phenology of huckleberry (Vaccinium membranaceum). Agric. For. Meteorol. 2020, 280, 107803. [Google Scholar] [CrossRef]
  52. Varotsos, C.A.; Cracknell, A.P. Remote sensing letters contribution to the success of the sustainable development goals-UN 2030 agenda. Remote Sens. Lett. 2020, 11, 715–719. [Google Scholar] [CrossRef]
Agronomy 15 01474 g001
Figure 1. Correlation of environmental variables.
Figure 1. Correlation of environmental variables.
Agronomy 15 01474 g001
Agronomy 15 01474 g002
Figure 2. ROC test of the MaxEnt species distribution model for S. grosvenorii (1, S. grosvenorii).
Figure 2. ROC test of the MaxEnt species distribution model for S. grosvenorii (1, S. grosvenorii).
Agronomy 15 01474 g002
Agronomy 15 01474 g003
Figure 3. Response curves for the main environmental factors affecting S. grosvenorii growth and composition. (a) Precipitation of driest month (bio_14); (b) precipitation seasonality (bio_15); (c) temperature seasonality (bio_4); (d) isothermality (bio_3); (e) temperature annual range (bio_7) (f) precipitation of warmest quarter (bio_18).
Figure 3. Response curves for the main environmental factors affecting S. grosvenorii growth and composition. (a) Precipitation of driest month (bio_14); (b) precipitation seasonality (bio_15); (c) temperature seasonality (bio_4); (d) isothermality (bio_3); (e) temperature annual range (bio_7) (f) precipitation of warmest quarter (bio_18).
Agronomy 15 01474 g003
Agronomy 15 01474 g004
Figure 4. Prediction of suitable areas for S. grosvenorii in modern climate conditions.
Figure 4. Prediction of suitable areas for S. grosvenorii in modern climate conditions.
Agronomy 15 01474 g004
Agronomy 15 01474 g005
Figure 5. Predicted suitable habitats for S. grosvenorii in different future climate scenarios (in 2050s and 2070s). (A–C). 2050s, SSP126, SSP370, and SSP585; (D–F). 2070s, SSP126, SSP370, and SSP585.
Figure 5. Predicted suitable habitats for S. grosvenorii in different future climate scenarios (in 2050s and 2070s). (A–C). 2050s, SSP126, SSP370, and SSP585; (D–F). 2070s, SSP126, SSP370, and SSP585.
Agronomy 15 01474 g005
Agronomy 15 01474 g006
Figure 6. Unchanged, contracted, and expanded suitable habitats for S. grosvenorii in different future climate scenarios. (A–C). 2050s, SSP126, SSP370, and SSP585; (D–F). 2070s, SSP126, SSP370, and SSP585.
Figure 6. Unchanged, contracted, and expanded suitable habitats for S. grosvenorii in different future climate scenarios. (A–C). 2050s, SSP126, SSP370, and SSP585; (D–F). 2070s, SSP126, SSP370, and SSP585.
Agronomy 15 01474 g006
Agronomy 15 01474 g007
Figure 7. Migration routes of core suitable habitats for S. grosvenorii in different climate scenarios. The yellow, purple, and blue lines represent SSP126, SSP370, and SSP585 from 2050s to 2070s, respectively.
Figure 7. Migration routes of core suitable habitats for S. grosvenorii in different climate scenarios. The yellow, purple, and blue lines represent SSP126, SSP370, and SSP585 from 2050s to 2070s, respectively.
Agronomy 15 01474 g007
Agronomy 15 01474 g008
Figure 8. (a) OPLS-DA score plot for S. grosvenorii samples (1, highly suitable habitats; 2, moderately suitable habitats; 3, low-suitability habitats); (b) R2 and Q2 intercept values from 200 OPLS-DA permutations; (c) VIP values of 26 elements; (d) correlation analysis between climate factors and chemical constituents of S. grosvenorii.
Figure 8. (a) OPLS-DA score plot for S. grosvenorii samples (1, highly suitable habitats; 2, moderately suitable habitats; 3, low-suitability habitats); (b) R2 and Q2 intercept values from 200 OPLS-DA permutations; (c) VIP values of 26 elements; (d) correlation analysis between climate factors and chemical constituents of S. grosvenorii.
Agronomy 15 01474 g008
Table 1. Percentage contribution and permutation importance of environmental variables.
Table 1. Percentage contribution and permutation importance of environmental variables.
VariablesPercent Contribution %Permutation Importance (%)
bio_1475.34.5
bio_155.16.3
bio_44.50.8
bio_33.06.1
bio_72.58.8
bio_182.40.5
bio_92.318
bio_122.07.9
bio_21.723.5
bio_171.323.6
Table 2. The predicted area and variation in potentially suitable areas of S. grosvenorii under different future climate scenarios.
Table 2. The predicted area and variation in potentially suitable areas of S. grosvenorii under different future climate scenarios.
Climate ScenarioSSP126SSP370SSP585
Area (×104 km2)Percent (%)Area (×104 km2)Percent (%)Area (×104 km2)Percent (%)
2050s51.93−11.52%59.791.88%53.71−8.49%
2070s57.99−1.19%40.97−30.20%55.14−6.05%
Table 3. Unchanged, contracted, and expanded area (km2) of suitable habitat for S. grosvenorii under different future climate scenarios.
Table 3. Unchanged, contracted, and expanded area (km2) of suitable habitat for S. grosvenorii under different future climate scenarios.
ItemArea (×104 km2)Area Proportion/%
UnchangedContractionExpansionTotalUnchangedContractionExpansionTotal
SSP1262050s45.7113.166.95−6.2188.03%25.34%13.38%−11.96%
2070s51.287.599.371.7788.42%13.09%16.15%3.06%
SSP3702050s45.4913.3915.081.6976.07%22.39%25.22%2.83%
2070s36.2522.634.89−17.7488.47%55.23%11.93%−43.31%
SSP5852050s45.8213.059.24−3.8185.32%24.31%17.20%−7.10%
2070s42.6016.2812.25−4.0377.25%29.52%22.21%−7.31%
Table 4. Element contents of S. grosvenorii grown in suitable cultivation areas (μg/g).
Table 4. Element contents of S. grosvenorii grown in suitable cultivation areas (μg/g).
ElementsHigh-Suitable Habitats (n = 25)Moderate Suitable Habitats (n = 21)Low Suitable Habitats (n = 13)Average
K17,978.21 ± 6075.25 b12,487.16 ± 733.93 c20,389.41 ± 2372.64 a16,555.05 ± 5175.21
Ca424.04 ± 97.50 a367.44 ± 136.6 a418.58 ± 144.36 a421.58 ± 127.65
Mg708.48 ± 126.35 ab785.04 ± 113.79 a662.67 ± 49.38 b718.73 ± 117.76
Cu11.39 ± 3.55 a8.76 ± 2.55 b12.48 ± 2.42 a10.87 ± 3.31
Fe35.70 ± 20.05 a26.6 ± 5.66 a37.89 ± 16.07 a33.40 ± 15.94
Mn15.58 ± 5.10 a13.48 ± 4.62 a13.75 ± 4.14 a14.27 ± 4.89
Se1.47 ± 0.50 a1.05 ± 0.39 bc0.78 ± 0.19 c1.10 ± 0.49
Zn16.17 ± 9.07 a13.25 ± 4.58 a14.42 ± 8.72 a14.61 ± 7.45
B22.93± 15.42 a8.26 ± 5.81 b20.15 ± 10.54 a17.11 ± 13.14
Al16.41 ± 8.97 a8.79 ± 7.63 b18.94 ± 9.07 a14.71 ± 9.38
Ba2.94 ± 1.80 b2.03 ± 1.31 b5.14 ± 4.30 a3.37 ± 2.67
Sr3.12 ± 1.82 b2.68 ± 1.26 b4.68 ± 1.17 a3.49 ± 1.67
Ti5.16 ± 1.02 b4.13 ± 1.44 b8.08 ± 3.24 a5.79 ± 2.35
Cd0.02 ± 0.02 a0.02 ± 0.02 a0.02 ± 0.02 a0.02 ± 0.02
Ni1.56 ± 0.95 a0.84 ± 0.62 b1.80 ± 0.67 a1.40 ± 0.87
Tl0.005 ± 0.04 b0.01 ± 0.01 b0.02 ± 0.01 a0.01 ± 0.01
Co0.16 ± 0.10 ab0.05 ± 0.03 c0.22 ± 0.11 a0.14 ± 0.10
As0.05 ± 0.04 a0.05 ± 0.03 a0.02 ± 0.02 ab0.04 ± 0.03
Li0.08 ± 0.09 ab0.08 ± 0.07 ab0.10 ± 0.15 ab0.09 ± 0.09
Be0.001 ± 0.001 b0.001 ± 0.001 b0.006 ± 0.001 a0.003 ± 0.002
Cr0.69 ± 0.51 ab0.97 ± 0.37 a0.15 ± 0.07 c0.60 ± 0.49
Ga0.007 ± 0.003 b0.007 ± 0.002 b0.01 ± 0.003 a0.008 ± 0.004
Sn0.11 ± 0.79 a0.09 ± 0.03 ab0.06 ± 0.04 b0.09 ± 0.06
Sb0.70 ± 0.64 a0.26 ± 0.22 b0.08 ± 0.04 b0.35 ± 0.30
Pb1.11 ± 0.10 ab0.54 ± 0.22 b1.49 ± 1.24 a1.05 ± 0.93
Bi0.02 ± 0.02 b0.04 ± 0.01 a0.02 ± 0.01 b0.03 ± 0.01
Note: The compounds were extracted and analyzed in triplicate, and the results were presented as mean ± standard deviation. Different letters (a–c) in the same row indicate statistically significant differences (p < 0.05).
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

Dong, F.; Yan, X.; Song, J.; Huang, X.; Fu, C.; Lu, F.; Li, D. Evaluation of Suitable Cultivation Regions in China for Siraitia grosvenorii Using a MaxEnt Model and Inductively Coupled Plasma Mass Spectrometry. Agronomy 2025, 15, 1474. https://doi.org/10.3390/agronomy15061474

AMA Style

Dong F, Yan X, Song J, Huang X, Fu C, Lu F, Li D. Evaluation of Suitable Cultivation Regions in China for Siraitia grosvenorii Using a MaxEnt Model and Inductively Coupled Plasma Mass Spectrometry. Agronomy. 2025; 15(6):1474. https://doi.org/10.3390/agronomy15061474

Chicago/Turabian Style

Dong, Fei, Xiaojie Yan, Jingru Song, Xiyang Huang, Chuanming Fu, Fenglai Lu, and Dianpeng Li. 2025. "Evaluation of Suitable Cultivation Regions in China for Siraitia grosvenorii Using a MaxEnt Model and Inductively Coupled Plasma Mass Spectrometry" Agronomy 15, no. 6: 1474. https://doi.org/10.3390/agronomy15061474

APA Style

Dong, F., Yan, X., Song, J., Huang, X., Fu, C., Lu, F., & Li, D. (2025). Evaluation of Suitable Cultivation Regions in China for Siraitia grosvenorii Using a MaxEnt Model and Inductively Coupled Plasma Mass Spectrometry. Agronomy, 15(6), 1474. https://doi.org/10.3390/agronomy15061474

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