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Article

Distribution of Trachelospermum jasminoides Under the Influence of Different Environmental Factors

by
Huan Yu
†,
Zhihang Zhuo
†,
Zhipeng He
,
Quanwei Liu
,
Xinqi Deng
and
Danping Xu
*
College of Life Science, China West Normal University, Nanchong 637002, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work and should be regarded as co-first authors.
Agriculture 2025, 15(3), 285; https://doi.org/10.3390/agriculture15030285
Submission received: 11 December 2024 / Revised: 17 January 2025 / Accepted: 27 January 2025 / Published: 28 January 2025
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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Abstract

:
Trachelospermum jasminoides (Lindl.) Lem. is a well-known herb with important medicinal and economic values. It is widely used in the treatment of inflammations in China. As global climate change intensifies, the ecological niche of plants has correspondingly shifted. Therefore, understanding the distribution of suitable habitats for T. jasminoides under different climate conditions is of great significance for its cultivation, introduction, and conservation. This research utilizes the MaxEnt model in combination with the Geographic Information System (ArcGIS) to analyze the present and future potential habitat distributions of T. jasminoides. Based on 227 documented occurrence points and 15 ecological variables, the results emphasize that the key environmental limitations influencing the optimal habitats of T. jasminoides are the precipitation during the coldest quarter, the mean temperature of the driest quarter, precipitation in the warmest quarter, temperature seasonality (standard deviation × 100), and the human impact index. At present, the combined area of suitable and highly suitable habitats for T. jasminoides amounts to 15.76 × 104 km2, with the highly suitable habitats predominantly situated in East and Central China. Based on climate scenario forecasts, within the SSP1-2.6 climate scenario, the total suitable habitat area for T. jasminoides is projected to increase relative to the current situation. Nevertheless, in the SSP2-4.5 and SSP5-8.5 climate scenarios, the suitable habitat area is anticipated to initially rise and then decline. The distribution center is mainly concentrated in the provinces of Hunan and Jiangxi, with the centroid shifting southeastward compared to the current situation. The findings of this research offer valuable insights for the effective cultivation, preservation, and sustainable use of T. jasminoides resources.

1. Introduction

Trachelospermum jasminoides (Lindl.) Lem., a climbing plant belonging to the Apocynaceae family, is a well-known medicinal herb recorded in the Chinese Pharmacopoeia for its use in treating inflammation [1]. It grows in mountainous areas, stream banks, roadsides, forest edges, or mixed forests, often climbing on trees or clinging to walls and rock surfaces. This plant prefers warm and humid environments, with moderate tolerance to both cold and heat; however, extremely harsh cold conditions are detrimental to its growth [2]. The main bioactive compounds in T. jasminoides are lignans, triterpenoids, and flavonoids, which exhibit anti-inflammatory, antimicrobial, and antiviral activities [3]. It is used to treat conditions such as arthritis, musculoskeletal pain, rheumatism, and pharyngitis, and may also have anxiolytic and antidepressant effects [4,5,6]. In addition to its medicinal value, T. jasminoides has ornamental value and can help reduce air pollution, improving air quality [7,8]. Furthermore, it can be used to extract natural cellulose fibers for applications in textiles and composite materials [9]. Studies indicate that in the last two decades, the abundance of climbing plants has been on the rise in most forest ecosystems. The proliferation and expansion of these plants could potentially influence the structure of forest communities and the cycling of nutrients [10,11]. As such, climbing plants constitute significant structural and functional elements of numerous forest ecosystems, exerting a substantial impact on forest carbon cycles. Current research on T. jasminoides primarily focuses on its chemical composition, medicinal properties, and ecological functions, but there is a lack of studies on its geographical distribution and the factors influencing it. This becomes especially crucial amidst global climate warming, rendering it essential to forecast shifts in species’ suitable habitat distribution ahead of time.
Climate change, as one of the most pressing issues of the 21st century, has had widespread impacts across various fields, including forestry, agriculture, biodiversity conservation, ecosystem stability, and energy supply [12]. Throughout the 1900s, there was an observed 0.8 °C uptick in the Earth’s surface temperature. Forecasts suggest that this figure could escalate to anywhere between 1.4 and 5.8 °C over the course of the 2000s [13]. As temperatures continue to climb, shifts in global hydrological patterns are expected, along with a higher frequency of extreme weather events. The habitats of numerous medicinal plants are changing and deteriorating as a result of the growing effects of global climate change and human interference [14]. Currently, approximately one-fifth of plant species are facing the threat of extinction due to global warming [15]. In the future, plants are expected to experience significant climatic changes within their geographic distribution ranges. Plants may respond and adapt to these changes by exhibiting niche flexibility, or some plant taxa may show climate fidelity [16], tracking their preferred climatic conditions by shifting their geographical distributions [17]. Therefore, it is crucial to enhance our comprehension of climate change and utilize species distribution models to forecast species’ distributions and movements. This is essential for preserving species diversity and ensuring the sustainable utilization of resources [18].
GARP, MaxEnt, CLIMEX, and BIOCLIM are the species potential distribution prediction models that are frequently utilized [19]. GARP is a model based on a background genetic algorithm. Through an iterative process of resampling and replacing input data during training and testing, the potential geographic distribution of species is simulated by GARP [20]. CLIMEX is a semi-mechanistic model employed to investigate the connections between climate, species distribution, and growth behaviors [21]. Bioclim is an R package that combines climatic and soil characteristics to classify regions based on their suitability for plant growth and nutritional activities [22]. MaxEnt is extensively used for its outstanding predictive accuracy, robustness, and user-friendly design [23]. The MaxEnt model, crafted by Phillips, serves as a geographic-scale spatial distribution instrument, underpinned by the principle of maximum entropy. Leveraging known species distribution records and external condition data, it forecasts the species’ geographic dispersion in specific temporal and spatial settings [24]. MaxEnt not only helps identify the dominant environmental factors influencing target species [25], but it is also utilized to assess shifts in species’ suitable habitats under varying climate change scenarios. As a result, it is frequently employed for species distribution prediction [26], conservation of endangered plants and animals [27], and management of invasive species spread [28]. The model represents species’ habitat preferences as probabilities, enabling precise predictions of suitable habitats for plants [29]. Typically, MaxEnt is integrated with the ArcGIS system to forecast species distribution under varying climate conditions. This integration facilitates the formulation of effective strategies to alleviate the impact of climate change on species and safeguard biodiversity.
In light of worldwide climate shifts, this study employs the MaxEnt model in conjunction with ArcGIS software to forecast the potential geographic distribution of T. jasminoides under current and three distinct future climate conditions. The research aims to examine the distribution of areas with varying suitability levels for T. jasminoides under existing climate conditions. Additionally, the study will identify the key external conditions influencing its geographic distribution and predict the potential evolution of its distribution under different future climate scenarios. The study will also track the migration of its distribution center across various climate contexts. The insights gained from these analyses are intended to guide strategies for the introduction and cultivation of T. jasminoides and enhance efforts to conserve species diversity in the face of climate change.

2. Materials and Methods

2.1. Collection of Distribution Data for T. jasminoides

In this study, the distribution data for T. jasminoides in China were initially sourced from the China Virtual Herbarium (CVH, https://www.cvh.ac.cn/; accessed on 22 September 2024), the Global Biodiversity Information Facility (GBIF, https://doi.org/10.15468/dl.ktz84t; accessed on 24 September 2024), and published literature on T. jasminoides. A total of 708 distribution points were gathered. The data were processed using ENM Tools version 1.4.4, during which duplicate, unclear, and adjacent records were excluded. The cleaned data were then exported as a .csv file and subsequently imported into ArcGIS 10.3 for further analysis. To avoid spatial autocorrelation, only one point was randomly retained for distribution points with a distance of less than 2.5 km between them. Finally, 227 valid distribution points for T. jasminoides in China were used for modeling (Figure 1).

2.2. Selection and Processing of Environmental Variables

The ecological niche of a species is influenced by environmental factors such as human activities, soil conditions, topographic factors, and bioclimatic factors [30]. Considering the comprehensiveness and complexity of these environmental variables, 19 bioclimatic factors, 6 soil factors, 1 topographic factor, and 27 human activity-related variables were initially selected. The data for the 19 climate variables were obtained from the WorldClim database (WorldClim v2.1, https://www.worldclim.org/; accessed on 20 September 2024), covering three periods: the current period (1970s–2000s), the future 2050s (2040s–2060s), and the future 2090s (2080s–2100s), all with a uniform spatial resolution of 2.50. Future climate data were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the medium-resolution Climate System Model (BCC-CSM2-MR), using three selected socioeconomic pathways: SSP1-2.6, SSP2-4.5, and SSP5-8.5. SSP1-2.6 corresponds to a low greenhouse gas emission scenario aimed at a sustainable future, SSP2-4.5 presents a moderate emission scenario with socioeconomic development following the current trajectory, while SSP5-8.5 describes a rapidly developing society with extremely high greenhouse gas emissions [31]. The six soil factors were sourced from the World Soil Information Service, Version 1.2 (http://www.fao.org/; accessed 20 September 2024), while the topographic data were retrieved from the WorldClim database. Elevation data were processed in ArcGIS to calculate additional topographic variables, including slope and aspect [14]. Human footprint (hf) data were sourced from the third edition of the Global Human Footprint dataset (http://www.ciesin.columbia.edu/wild_areas; accessed on 20 September 2024). The hf, which ranges from 0 to 50, offers a combined value that signifies the effects of population density, land conversion, accessibility, and power infrastructure on global land use [32]. If a higher value represents greater human intervention.
The combined effect of multiple external factors may lead to model overfitting, thereby reducing its predictive performance [33]. Correlation analysis of environmental variables can effectively avoid this situation. First, the “Extract Values to Points” tool in ArcGIS v10.3 was used to obtain the environmental values for each data point. Then, correlation analysis was performed using the “corrplot” package in R. Due to the limited number of soil and topographic factors, no correlation analysis was performed for them. However, since there are more climate factors (19 in total), Pearson correlation analysis was conducted, resulting in a correlation matrix. A correlation heatmap was then generated based on this matrix (Figure 2). Environmental variables whose absolute correlation coefficients exceeded 0.8 being removed [34]. In the end, 7 climate factors, 6 soil factors, 1 terrain factor, and 15 environmental variables, including human activities (Table 1), were selected and imported into MaxEnt. Subsequently, the Jackknife test was utilized to identify the primary environmental factors affecting the distribution of T. jasminoides.

2.3. Model Construction and Evaluation

The 227 occurrence points of T. jasminoides and the 15 selected environmental factors were entered into MaxEnt 3.4.4 for modeling. In the process, 75% of the data were randomly assigned to the training set, and the remaining 25% were used as the test set to assess the model’s prediction accuracy. The model was configured for “cross-validation” with a maximum of 500 iterations, and the output format was set to logistic validation. The relative contributions of each environmental factor were evaluated using jackknife analysis, and the main limiting external conditions were identified based on their contribution percentage and importance values. To assess the accuracy of the model’s predictions, the receiver operating characteristic (ROC) curve was used, with the area under the curve (AUC) serving as the accuracy metric [35]. The AUC value evaluation criteria are as follows: 0.50–0.60 indicates poor model performance, 0.60–0.70 indicates fair performance, 0.70–0.80 indicates good performance, 0.80–0.90 indicates very good performance, and 0.90–1.00 indicates excellent performance [36].

2.4. Data Processing

The average results of the MaxEnt simulations for each period were imported into ArcGIS 10.4.1 software in “.asc” format. The prediction results, originally in ASCII format, were then converted to raster format and reclassified for the visualization of suitable habitats for T. jasminoides. The MaxSSS method [37] was employed to assess the habitat suitability index of T. jasminoides, determining the potential distribution probability (p) on a scale ranging from 0 to 1. A threshold of 0.47 was applied to exclude areas with probabilities lower than this value. Based on the suitability index, the suitable areas were categorized into five levels: highly suitable habitat (p ≥ 0.7), moderately suitable habitat (0.5 ≤ p < 0.7), low suitable habitat (0.3 ≤ p < 0.5), and unsuitable habitat (0.1 ≤ p < 0.3). The centroid indicates the overall spatial location of suitable habitats for T. jasminoides, with its movement reflecting shifts in these habitats. The potential dominant distribution areas for each period were identified using the spatial analysis tools in ArcGIS software. Additionally, with the aid of the SD-MTool package in the R environment, the centroid positions of the overall suitable survival areas for the current and future periods were determined. The changes in the centroid positions reflect the overall movement direction of the suitable distribution areas. Furthermore, the geosphere package in R (https://cran.r-project.org/web/packages/geosphere/index.html, 24 September 2024) was used to calculate the centroid displacement distances under different climate change scenarios [38].

3. Results

3.1. Model Accuracy Evaluation and Analysis of Key Environmental Factors

The average AUC (area under the curve) value for the MaxEnt model is 0.967 under the current scenario and the three greenhouse gas emission scenarios for the 2050s and 2090s (Figure 3). Based on the assessment standards, this suggests that the MaxEnt model boasts outstanding predictive capabilities and can be efficiently utilized to forecast the suitable habitats of T. jasminoides.
This study used the MaxEnt model to assess the contribution rates of various external factors to the prediction of dominant distribution areas (Table 2). The key climate variables include precipitation of coldest quarter (Bio19, 55.3%), human footprint index (hf, 10.5%), and temperature seasonality (Bio4, 9.8%), with the top three environmental variables contributing a total of 75.6%. Environmental factors are ranked according to their importance for the MaxEnt model; precipitation of coldest quarter (Bio19, 63.3%) and elevation (alt, 18.9%) were found to have the highest importance, with a combined contribution of 82.2%. Through Jackknife validation (Figure 4), the correlation analysis of 15 selected environmental variables revealed that the environmental variable with the highest gain when modeled individually was precipitation of coldest quarter (Bio19), followed by mean temperature of driest quarter (Bio9), precipitation of warmest quarter (Bio18), temperature seasonality (Bio4), and human footprint index (hf). To sum up, rainfall, temperature, and human actions are the main environmental elements affecting the distribution of ideal habitats for T. jasminoides.
If the species threshold surpasses 0.5, it signifies that the environmental conditions are conducive to the species’ survival. The influence curves of pivotal external conditions on the distribution of ideal habitats for T. jasminoides (Figure 5) reveal these trends: T. jasminoides can survive when precipitation in the coldest quarter is over 95.95 mm, and the occurrence probability grows with increasing precipitation. Once precipitation of coldest quarter reaches 441.04 mm, the occurrence probability reaches 0.989, and after this point, the probability no longer increases with further precipitation. The occurrence probability of T. jasminoides increases with mean temperature of driest quarter within a certain range, and when mean temperature of driest quarter exceeds 10.62 °C, the probability exceeds 0.5. The occurrence probability peaks at around 0.992 when the mean temperature of driest quarter reaches 20.52 °C, and then begins to decline, but the conditions remain suitable for the species’ survival. The survival probability exceeds 0.5 when the precipitation during the warmest quarter exceeds 775.49 mm. The probability reaches 0.989 within the range of 1203.9 to 1892.62 mm, indicating the most favorable conditions for the species’ survival. For temperature seasonality (standard deviation × 100), suitable conditions for T. jasminoides occur when temperature seasonality (standard deviation × 100) is between 189.02 and 529.27, and the probability of occurrence is maximized at 397.9. Temperature seasonality measures the seasonal temperature fluctuation throughout the year by calculating the standard deviation of temperature. A value between 189.02 and 529.27 indicates moderate temperature variation, neither too extreme nor too stable. The human footprint index shows a positive correlation with occurrence probability within a certain range. When human footprint index exceeds 19.75, the occurrence probability exceeds 0.5, and the highest occurrence probability for T. jasminoides is observed when human footprint index is between 33.125 and 42.552.

3.2. Potential Suitable Distribution Areas of T. jasminoides Under Current Climate Conditions

The MaxEnt model’s depiction of the present potential favorable habitats for T. jasminoides in China (Figure 6) reveals that these habitats are chiefly located in the central, eastern, and southern regions of the country. Areas with high favorability are predominantly scattered across central and eastern China, with denser concentrations in the provinces of Taiwan, Zhejiang, Jiangsu, Jiangxi, and Hunan. Under existing circumstances, the area of habitats with low favorability for T. jasminoides spans 119.30 × 104 km2, that with moderate favorability covers 11.32 × 104 km2, and that with high favorability amounts to 4.45 × 104 km2, culminating in a total suitable habitat expanse of 135.07 × 104 km2.

3.3. Future Climate Potential Suitable Distribution Areas of T. jasminoides

The present research utilized the MaxEnt model to predict the potential optimal distribution areas under three different climate hypotheses for the 2050s and 2090s, respectively (Figure 7). The findings indicate that, in contrast to the current situation, the distribution of habitats with moderate and high favorability in southern China is expected to further expand in the future. However, the primary distribution of these habitats will still be concentrated in central and eastern China, including Taiwan, Jiangxi, Zhejiang, Jiangsu, and Fujian provinces. A contrast of the shifts in the ideal habitat area from present to future climate conditions (Table 3) reveals that under the SSP1-2.6 scenario, the ideal habitat area keeps growing, with the highly suitable habitat area rising by 6.97% in the 2050s and 7.87% in the 2090s. Under the SSP2-4.5 and SSP5-8.5 climate hypotheses, the area of optimal distribution zones shows a trend of initially increasing and then decreasing. Specifically, under the SSP2-4.5 scenario, by the 2050s, the areas of moderate and high advantageous habitats for T. jasminoides will be 11.97 × 104 km2 and 4.8 × 104 km2, respectively, showing an increase of 5.74% and 7.87% compared to the present. By the 2090s, the areas of moderate and high-advantage distribution will be 11.88 × 104 km2 and 4.23 × 104 km2, with increases of 4.95% for moderate suitability habitats, but a 4.84% decrease in high suitability habitats. Under the SSP5-8.5 hypothesis, it is projected that by the 2050s, the total suitable distribution area will reach 187.02 × 104 km2, with moderate and high suitability areas expanding to 30.35 × 104 km2 and 5.95 × 104 km2, reflecting increases of 168.11% and 33.71%, respectively, compared to current levels. By the 2090s, the overall suitable distribution area is anticipated to reduce to 135.62 × 104 km2, but it will still exceed the current suitable area, with the moderate and high suitability habitat areas increasing by 9.54% and 13.03%, respectively. In general, when compared to the current situation, the extent of habitats with moderate suitability is expected to expand under every climate scenario. Under the SSP5-8.5 emission hypothesis, the area of excellent habitat regions is expected to see the most significant increase.

3.4. Migration Patterns of T. jasminoides Centroid Under Various Climate Scenarios

Under different climate hypotheses, the movement of the centroid of suitable survival areas for T. jasminoides varies. In general, under the high-emission SSP5-8.5 hypothesis, the change in the suitable survival center of this species is more pronounced. However, in the medium-emission SSP2-4.5 and low-emission SSP1-2.6 hypotheses, the movement distance of the centroid is relatively small (Figure 8). At present, the centroid of jasmine is located in Liuyang Town, Liuyang City, Hunan Province (113.40° E, 28.17° N). Under the SSP1-2.6 emission hypothesis, from now to the 2050s, the centroid will move southeast by 50.45 km to Wenjia Town, Liuyang City (113.90° E, 28.07° N). From the 2050s to the 2090s, the centroid will move further southwest to Zhentou Town, Liuyang City (113.38° E, 28.03° N), a shift of 51.67 km. In the SSP2-4.5 scenario, the centroid will move 37.83 km southeast by the 2050s, with a further 17.60 km shift southeast from the 2050s to the 2090s. In the SSP5-8.5 emission hypothesis, by the 2050s, the centroid of the suitable survival area will move 89.77 km to the northwest, to Baimaqiao Street, Ningxiang City, Hunan (112.50° E, 28.25° N). From the 2050s to the 2090s, the centroid will shift 147.09 km southeast to Dongyuan Township, Shangli County, Jiangxi Province (113.90° E, 27.81° N).

4. Discussion

T. jasminoides is not only an important medicinal herb with significant value, but it also plays a role in improving air quality and reducing pollution, offering certain ecological benefits [8]. Currently, research on the suitable distribution of T. jasminoides remains limited. To address this, this research combines the MaxEnt model and ArcGIS. Based on the current niche hypothesis of Trachelospermum jasminoides, it predicts its potential favorable survival areas under current climate conditions and the changes in suitable survival areas under different future climates. Additionally, this research pinpoints the crucial environmental factors that restrict its optimal growth. These findings provide a scientific basis for the cultivation, conservation, and resource utilization of the species amidst climate change. The MaxEnt model, a popular ecological niche model, is renowned for its precision in predicting species distributions. Studies have indicated that, given known conditions, the higher the entropy of a species, the more accurate the prediction is [24]. The high AUC value of the MaxEnt model attests to its accuracy and reliability. The AUC value of 0.967 achieved in this research suggests that the MaxEnt model offers an extremely reliable and precise prediction of the distribution of T. jasminoides.
This study analyzed 19 climate variables and selected those with an absolute correlation coefficient above 0.8 (Figure 2). Additionally, we estimated the relative contributions of these climate variables to the Maxent model (Table 4). After a comprehensive analysis, seven climate variables were ultimately selected for modeling. Although some variables were excluded due to high collinearity, they may still have ecological significance. For instance, the precipitation of wettest month (Bio13) and precipitation of warmest quarter (Bio18) both have a strong correlation to precipitation, with an absolute correlation coefficient over 0.8. Variables with higher contributions to the MaxEnt model were retained based on their estimated relative impact. This study found that precipitation of coldest quarter (441.04–760.56 mm) is the most critical environmental variable limiting the distribution of T. jasminoides. In addition, precipitation of warmest quarter (1203.9–1892.6 mm), mean temperature of driest quarter (20.52–27.03 °C), temperature seasonality (standard deviation × 100) (189.02–529.27 mm), and the human footprint index (33.125–42.552) are also key factors that influence the distribution of T. jasminoides. The results suggest that precipitation, temperature, and human activities are the key determinants influencing the zones that constitute favorable habitats for T. jasminoides. This aligns with the current study’s conclusion that “precipitation and temperature are the most direct and vital factors affecting plant growth” [39]. Herbaceous plants often lengthen their growth period in warmer and more humid environments, likely because high temperatures and precipitation together accelerate the germination phase, with elevated CO2 further enhancing this effect. Moreover, higher temperatures and increased precipitation also promote the onset of flowering [40]. Previous studies have shown that water scarcity limits the height and diameter at breast height (DBH) of Davidia involucrata Baill seedlings, thereby restricting their growth [41]. Moreover, cooler temperatures lower the chlorophyll levels in the leaves of Davidia involucrata and hinder stomatal growth. This results in a decline in both the net photosynthetic rate and the transpiration rate [42]. Our study suggests that T. jasminoides prefers warm and humid environments, consistent with the findings of these previous studies. Apart from climatic factors, the human footprint index (hf) also influences the distribution of T. jasminoides. Human activities can indirectly or directly affect species distribution by altering land use patterns, influencing climate conditions, and impacting biodiversity [43]. In this research, within a certain scope, as human activities grow, the likelihood of T. jasminoides being present also rises. This phenomenon may be attributed to multiple factors. For instance, the intensification of human activities has exacerbated global warming, enhancing the atmosphere’s water-holding capacity, which is reflected in the increase in total precipitation and the intensification of extreme precipitation events. Furthermore, monsoon rainfall intensity has significantly increased, particularly in monsoon regions of Asia and Africa. Driven by thermal changes in the climate system, the tropical rain belt may shift toward the poles, potentially leading to further expansion of monsoon climate zones [44]. Notably, the suitable habitats for T. jasminoides are primarily distributed in subtropical monsoon regions, which aligns with the observed increase in suitable habitat areas under medium- and high-emission scenarios in this study. Additionally, human activities may indirectly benefit T. jasminoides by clearing or managing competing plant species, thereby providing it with more resources and space. For example, certain sensitive plants are more vulnerable to environmental disturbances, whereas T. jasminoides, as a highly adaptable climbing plant, is better able to occupy ecological niches, further enhancing its survival potential. Moreover, human activities, such as urbanization, agriculture, and road construction, have disrupted native habitats while creating new ecological edge environments. These edge environments may be more favorable for the growth of T. jasminoides. For instance, it can take advantage of open spaces or newly constructed vertical structures (such as buildings and fences) for climbing and spreading, thereby facilitating its distribution [45]. Together, these variables influence the ecological niche and geographic distribution of T. jasminoides.
The migration of the distribution centroid and spatial pattern changes of T. jasminoides represent significant ecological responses to climate change. In the SSP1-2.6 emission hypothesis, the suitable survival areas for T. jasminoides continue to expand. Nevertheless, in the SSP2-4.5 and SSP5-8.5 emission hypotheses, this area first grows and then shrinks. In contrast to the current situation, the distribution of areas with moderate and high suitability for survival of T. jasminoides further expands in southern China, with a trend of contraction towards the southeast. However, the main distribution of medium and highly suitable habitats remains concentrated in central and eastern China, including Taiwan, Jiangxi, Zhejiang, Jiangsu, and Fujian provinces. From a spatial perspective, the climate types suitable and highly suitable for the distribution of T. jasminoides are mainly subtropical monsoon climate and tropical monsoon climate. Global warming has enhanced the atmospheric water-holding capacity, which is manifested in an increase in total precipitation and intensification of precipitation extremes. The intensity of monsoon precipitation has significantly increased, particularly in the monsoon regions of Asia and Africa. Due to changes driven by the thermal dynamics of the climate system, the tropical rain belt may shift towards the poles, potentially expanding the monsoon climate zone, which would lead to an expansion in the areas deemed moderately and highly suitable for habitation. Furthermore, the suitable habitat range for T. jasminoides typically diminishes from the southeastern coastal zones to the northwestern inland areas, a pattern that aligns with the general distribution of annual rainfall across China. The level of suitability also tends to decline from the southeastern coasts moving towards the northwestern interior, reflecting the country’s overall annual precipitation distribution. The terrain of China transitions through three altitude gradients stretching from the eastern coasts to the western areas, with the optimal survival areas for T. jasminoides situated predominantly in the southern section of the third elevation gradient. Among diverse environmental elements, elevation is deemed to notably impact the distribution of rainfall [46]. Higher altitudes generally experience less rainfall due to lower air density, which reduces moisture content and, consequently, precipitation. This research, the high-suitability distribution of T. jasminoides are predominantly inhabits plains, hills, and other low-altitude areas, where abundant precipitation is more favorable for its growth. Due to the monsoon effect, the southeastern coastal areas of China experience abundant rainfall, particularly Taiwan, which region is the far southeast of China. The annual rainfall here typically exceeds 1600 mm [47], matching the model’s forecasted optimal precipitation range for T. jasminoides. In the 2050s, under the SSP5-8.5, the expanse of highly suitable habitats is the greatest, primarily concentrated in the mid and lower sections of the Yangtze River Basin, and the total area of suitable habitats shows the greatest increase. In this study, it is noted that the projected wetting trend in the Yangtze River Basin under the SSP5-8.5 scenario (22.47 mm/10a) is significantly higher than the wetting trend under the SSP1-2.6 scenario (14.76 mm/10a) (“mm/10a” represents the amount of precipitation increase in millimeters per decade, indicating the rate of change in precipitation over a ten-year period). Furthermore, from the near-term to long-term future, the wetting trend in the Yangtze River Basin will slow under the SSP1-2.6 scenario, while it will accelerate under the SSP5-8.5 scenario, especially in the southeastern part of the Yangtze River Basin where winter precipitation is projected to be wetter [48]. This aligns with the study’s conclusion that precipitation of coldest quarter is the key constraint affecting the spread of favorable living spaces for T. jasminoides, suggesting that the habitat predictions for T. jasminoides in this study are credible. Under the SSP1-2.6 and SSP2-4.5 emission hypotheses, the fluctuations in the optimal survival areas of T. jasminoides are relatively small, indicating that the fluctuations in these areas are minor. This stability is conducive to the establishment of long-term production bases for T. jasminoides, which can promote local economic development while also preserving species diversity.
Investigating the shifts in species’ geographical distribution amidst climate change holds great significance in furnishing scientific data [49]. In this particular study, the MaxEnt model was utilized to forecast the potential suitable habitat distribution of T. jasminoides under both present and future climate scenarios. The results indicate that Taiwan, Jiangxi, Fujian, Jiangsu, and Zhejiang provinces are suitable for the long-term cultivation and development of T. jasminoides. These regions can serve both for economic cultivation, such as medicinal use, and for ornamental purposes in landscaping and greening. However, this study has certain limitations. The predicted suitable habitat distribution of T. jasminoides is based on the current ecological niche, mapped through the Maxent model to represent the ideal distribution. However, some originally suitable habitats may have been occupied by human infrastructure such as buildings or campsites. In such cases, these areas can only be considered suitable habitats if restoration plans are implemented. Therefore, the predicted suitable habitat area may be larger than the actual suitable habitat area. In addition, the research findings indicate that the human footprint index (hf) is the predominant external factor limiting its suitable survival distribution of T. jasminoides. Future studies could compare the spatial distribution under natural conditions (without human activity) with the spatial distribution under human-influenced conditions [50].

5. Conclusions

This research utilized the MaxEnt model and the ArcGIS system to map the potential suitable survival distributions of T. jasminoides under different emission hypotheses for the 2050s and 2090s based on its current niche. The results indicate that among the 15 selected environmental factors in the model, the precipitation of the coldest quarter (Bio19) is the most critical environmental factor limiting the distribution of T. jasminoides. Additionally, other variables such as the mean temperature of the driest quarter (Bio18), precipitation of the warmest quarter (Bio9), ocean temperature (standard deviation × 100) (Bio4), and the human footprint index (hf) are the main factors affecting the suitable habitats of this species. In the future, the high-suitability survival areas of T. jasminoides will continue to be concentrated in the southeastern regions of China, spanning provinces such as Taiwan, Jiangxi, Fujian, Jiangsu, and Zhejiang, shifting southeastward compared to the current situation. Under the SSP1-2.6 climate hypothesis, the suitable survival area continues to increase; however, under the SSP2-4.5 and SSP5-8.5 climate hypotheses, the suitable survival area first expands and then contracts. In the 2050s of the 21st century, under the SSP5-8.5 climate hypothesis, the area of high-suitability survival reaches its peak, with the most significant expansion of the overall suitable survival area. Nevertheless, human activities may cause the actual suitable survival area to be smaller than predicted. The current distribution center of T. jasminoides is at 113.40° E, 28.17° N. These findings support the introduction and cultivation strategies for T. jasminoides, contributing to local economic growth and biodiversity conservation in the face of future climate change.

Author Contributions

Conceptualization, Z.Z.; data curation, H.Y., D.X. and Z.Z.; formal analysis, Z.H.; funding acquisition, D.X. and Z.Z.; investigation, H.Y.; methodology, H.Y. and Q.L.; resources, D.X.; software, D.X. and X.D.; visualization, Z.H.; writing—original draft, H.Y. and Z.Z.; writing—review and editing D.X. and Z.Z.; supervision, D.X. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the China West Normal University Support Program (20A007, 20E051, 21E040, and 22kA011).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data supporting the results are available in a public repository at: https://figshare.com/s/675ca076f29c1cba8317 (accessed on 26 November 2024) and https://doi.org/10.15468/dl.ktz84t (accessed on 24 September 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution points of T. jasminoides in China during the current period (green solid circles indicate regions where the species is currently present).
Figure 1. Distribution points of T. jasminoides in China during the current period (green solid circles indicate regions where the species is currently present).
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Figure 2. Correlation heatmap of bioclimatic factors (dark blue and dark red indicate that the absolute value of the correlation coefficient is greater than 0.8. The colors gradually transition from the sides towards the center, with the absolute value of the correlation coefficient also decreasing accordingly).
Figure 2. Correlation heatmap of bioclimatic factors (dark blue and dark red indicate that the absolute value of the correlation coefficient is greater than 0.8. The colors gradually transition from the sides towards the center, with the absolute value of the correlation coefficient also decreasing accordingly).
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Figure 3. ROC curve and AUC value (the AUC value represents the area under the ROC curve; a larger value indicates higher accuracy in the model’s predictions).
Figure 3. ROC curve and AUC value (the AUC value represents the area under the ROC curve; a larger value indicates higher accuracy in the model’s predictions).
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Figure 4. Variable importance determined via the folding Jackknife.
Figure 4. Variable importance determined via the folding Jackknife.
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Figure 5. Response curves of dominant environmental factors ((a) denotes the response curve of the presence probability of T. jasminoides to the precipitation of coldest quarter; (b) denotes the response curve of the presence probability of T. jasminoides to the mean temperature of driest quarter; (c) denotes the response curve of the presence probability of T. jasminoides to the precipitation of warmest quarter; (d) denotes the response curve of the presence probability of T. jasminoides to the temperature seasonality (standard deviation × 100); (e) denotes the response curve of the presence probability of T. jasminoides to the human footprint index).
Figure 5. Response curves of dominant environmental factors ((a) denotes the response curve of the presence probability of T. jasminoides to the precipitation of coldest quarter; (b) denotes the response curve of the presence probability of T. jasminoides to the mean temperature of driest quarter; (c) denotes the response curve of the presence probability of T. jasminoides to the precipitation of warmest quarter; (d) denotes the response curve of the presence probability of T. jasminoides to the temperature seasonality (standard deviation × 100); (e) denotes the response curve of the presence probability of T. jasminoides to the human footprint index).
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Figure 6. Optimal survival areas for T. jasminoides under current climate conditions (yellow represents low suitable habitat distribution, red represents moderate suitable habitat distribution, and purple represents high suitable habitat distribution).
Figure 6. Optimal survival areas for T. jasminoides under current climate conditions (yellow represents low suitable habitat distribution, red represents moderate suitable habitat distribution, and purple represents high suitable habitat distribution).
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Figure 7. Optimal survival areas for T. jasminoides under future climate conditions: (a) optimal survival area distribution of T. jasminoides under the SSP1-2.6 scenario in the 2050s; (b) optimal survival area distribution of T. jasminoides under the SSP1-2.6 scenario in the 2090s; (c) optimal survival area distribution of T. jasminoides under the SSP2-4.5 scenario in the 2050s; (d) optimal survival area distribution of T. jasminoides under the SSP2-4.5 scenario in the 2090s; (e) optimal survival area distribution of T. jasminoides under the SSP5-8.5 scenario in the 2050s; (f) optimal survival area distribution of T. jasminoides under the SSP5-8.5 scenario in the 2090s. Yellow indicates areas of low suitability, red indicates areas of moderate suitability, and purple indicates areas of high suitability.
Figure 7. Optimal survival areas for T. jasminoides under future climate conditions: (a) optimal survival area distribution of T. jasminoides under the SSP1-2.6 scenario in the 2050s; (b) optimal survival area distribution of T. jasminoides under the SSP1-2.6 scenario in the 2090s; (c) optimal survival area distribution of T. jasminoides under the SSP2-4.5 scenario in the 2050s; (d) optimal survival area distribution of T. jasminoides under the SSP2-4.5 scenario in the 2090s; (e) optimal survival area distribution of T. jasminoides under the SSP5-8.5 scenario in the 2050s; (f) optimal survival area distribution of T. jasminoides under the SSP5-8.5 scenario in the 2090s. Yellow indicates areas of low suitability, red indicates areas of moderate suitability, and purple indicates areas of high suitability.
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Figure 8. Migration of the T. jasminoides Centroid Under Future Climate Conditions (the blue arrows indicate the movement direction of the centroid in different years under the SSP5-8.5 emission hypothesis; the red arrows show the centroid movement direction in different years under the SSP2-4.5 emission hypothesis; the green arrows represent the movement direction of the centroid in different years under the SSP1-2.6 emission hypothesis; the solid black dot marks the current position of the centroid).
Figure 8. Migration of the T. jasminoides Centroid Under Future Climate Conditions (the blue arrows indicate the movement direction of the centroid in different years under the SSP5-8.5 emission hypothesis; the red arrows show the centroid movement direction in different years under the SSP2-4.5 emission hypothesis; the green arrows represent the movement direction of the centroid in different years under the SSP1-2.6 emission hypothesis; the solid black dot marks the current position of the centroid).
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Table 1. The fifteen environmental variables used for modeling.
Table 1. The fifteen environmental variables used for modeling.
AbbreviationEnvironmental VariablesUnit
Bio03Isothermality (BIO2/BIO7) (×100)-
Bio04Temperature seasonality (standard deviation × 100)-
Bio09Mean temperature of driest quarter℃
Bio10Mean temperature of warmest quarter℃
Bio15Precipitation seasonality (Coefficient of variation)-
Bio18Precipitation of warmest quartermm
Bio19Precipitation of coldest quartermm
Ref_depthReference depthcm
altAltitude (elevation above sea level) (m)m
USDAUnited States Department of Agriculture-
PHSoil potential of hydrogen-
OCOrganic Carbong/kg
t-standTemperature standardized-
UV-B3Ultraviolet B radiation (280–315 nm)W/m2
hfHuman footprint index-
Table 2. Contribution and permutation importance estimation of environmental variables in the MaxEnt model of Trachelospermum jasminoides.
Table 2. Contribution and permutation importance estimation of environmental variables in the MaxEnt model of Trachelospermum jasminoides.
VariableFull Name of the VariablePercent Contribution (%)Permutation Importance (%)
Bio19Precipitation of coldest quarter55.363.3
hfHuman footprint index10.53.3
Bio04Temperature seasonality (standard deviation × 100)9.84.6
Bio03Isothermality (bio2/bio7) (× 100)5.50.2
Bio18Precipitation of warmest quarter4.82.7
Bio10Mean temperature of warmest quarter3.60.6
UV-B3Ultraviolet B radiation (280–315 nm)3.10.3
Ref_depthReference depth2.90.4
Bio15Precipitation seasonality (Coefficient of variation)1.55.5
Bio09Mean temperature of driest quarter1.40
altAltitude (elevation above sea level) (m)118.9
OCOrganic Carbon0.20.1
USDAUnited States Department of Agriculture0.20.1
PHSoil potential of hydrogen0.20.1
t-standTemperature standardized0.20.1
Table 3. Prediction of survival areas for Trachelospermum jasminoides under current and future climatic conditions.
Table 3. Prediction of survival areas for Trachelospermum jasminoides under current and future climatic conditions.
ScenariosDecadeTotal Suitable RegionsRegions of Low Habitat SuitabilityRegions of Medium Habitat SuitabilityRegions of High Habitat Suitability
Area
(104 km2)		Area
Change (%)		Area
(104 km2)		Area
Change (%)		Area
(104 km2)		Area
Change (%)		Area
(104 km2)		Area
Change (%)
-Current135.07-119.30-11.32-4.45-
SSP1-2.62050s139.553.32%122.692.84%12.106.45%4.766.97%
2090s148.619.57%130.739.58%13.2717.23%4.613.60%
SSP2-4.52050s139.593.35%122.822.95%11.975.74%4.807.87%
2090s134.58−0.36%118.47−0.70%11.884.95%4.23−4.94%
SSP5-8.52050s187.0238.46%150.7226.34%30.35168.11%5.9533.71%
2090s135.620.41%118.19−0.93%12.409.54%5.0313.03%
Table 4. Contribution and permutation importance estimation of climate variables in the MaxEnt model of Trachelospermum jasminoides.
Table 4. Contribution and permutation importance estimation of climate variables in the MaxEnt model of Trachelospermum jasminoides.
AbbreviationClimate VariablesPercent Contribution (%)Permutation Importance (%)Unit
Bio01Annual mean temperature1.30.1℃
Bio02Mean diurnal temperature range11℃
Bio03Isothermality2.40-
Bio04Temperature seasonality (standard deviation × 100)2.20.8-
Bio05Max temperature of warmest month1.40℃
Bio06Min temperature of coldest month0.10.2℃
Bio07Temperature annual range1.12.3℃
Bio08Mean temperature of wettest quarter1.87.7℃
Bio09Mean temperature of driest quarter3.60℃
Bio10Mean temperature of warmest quarter4.70.4℃
Bio11Mean temperature of coldest quarter1.40℃
Bio12Annual precipitation0.41.5mm
Bio13Precipitation of wettest month3.62.2mm
Bio14Precipitation of driest month1.31mm
Bio15Precipitation seasonality27.7-
Bio16Precipitation of wettest quarter2.62.4mm
Bio17Precipitation of driest quarter1.11.3mm
Bio18Precipitation of warmest quarter14.10.6mm
Bio19Precipitation of coldest quarter53.970.6mm
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Yu, H.; Zhuo, Z.; He, Z.; Liu, Q.; Deng, X.; Xu, D. Distribution of Trachelospermum jasminoides Under the Influence of Different Environmental Factors. Agriculture 2025, 15, 285. https://doi.org/10.3390/agriculture15030285

AMA Style

Yu H, Zhuo Z, He Z, Liu Q, Deng X, Xu D. Distribution of Trachelospermum jasminoides Under the Influence of Different Environmental Factors. Agriculture. 2025; 15(3):285. https://doi.org/10.3390/agriculture15030285

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Yu, Huan, Zhihang Zhuo, Zhipeng He, Quanwei Liu, Xinqi Deng, and Danping Xu. 2025. "Distribution of Trachelospermum jasminoides Under the Influence of Different Environmental Factors" Agriculture 15, no. 3: 285. https://doi.org/10.3390/agriculture15030285

APA Style

Yu, H., Zhuo, Z., He, Z., Liu, Q., Deng, X., & Xu, D. (2025). Distribution of Trachelospermum jasminoides Under the Influence of Different Environmental Factors. Agriculture, 15(3), 285. https://doi.org/10.3390/agriculture15030285

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