Prediction of the Potential Distribution of Tirpitzia sinensis in China Based on the MaxEnt Model

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This study utilized 174 valid distribution records of Tirpitzia. sinensis in China, along with 10 environmental variables, to predict its current and future distribution patterns. Using the MaxEnt model and ArcGIS software, the study also identified the climatic factors limiting its distribution. The results are as follows: (1) The MaxEnt model demonstrated extremely high predictive accuracy, with an Area Under the Curve (AUC) value of 0.985. Currently, the total suitable area for Tirpitzia sinensis covers 1,869,438 km², accounting for 19.47% of China's land area. This area is mainly concentrated in western Guangxi Province, southeastern Yunnan Province, southern Guizhou Province, southeastern Sichuan Province, and southwestern Chongqing Municipality. (2) The primary environmental factors influencing the potential distribution of Tirpitzia sinensis include precipitation during the warmest quarter (bio18), minimum temperature of the coldest month (bio06), temperature seasonality (bio04), and mean temperature of the driest quarter (bio09). (3) Under future climate change scenarios, the potential distribution area of Tirpitzia sinensis is expected to expand compared to the current distribution, with an overall trend of northward migration. Specifically, under the SSP126 climate scenario, the distribution center is projected to shift from Guizhou Province to Hunan Province. Under the SSP585 climate scenario, this shift extends further towards Hunan and Hubei Provinces. Tirpitzia sinensis Maximum Entropy Model Potential Distribution Area Climate Change Environmental Factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction In the field of global change and biogeography research, the response of vegetation to climate change has always been a core focus (Bellard et al., 2012 ). Climate, as a key environmental factor influencing species and vegetation distribution at both regional and global scales, has profound and far-reaching effects on biodiversity and species range (Hamann & Wang, 2006 ). Future climate change may lead to changes in the distribution and abundance of species (Ehrlén & Morris, 2015 ), the extinction of species populations (Bestion et al., 2015 ), alterations in distribution ranges (Chen et al., 2011 ), phenological changes (Merilä & Hendry, 2014 ), as well as changes in physiological characteristics (Dillon et al., 2010 ). Exploring the trends in potential geographic distribution of species under future climate change scenarios and analyzing their response mechanisms are of irreplaceable importance for developing scientifically sound biodiversity conservation strategies. Furthermore, a comprehensive assessment of the impacts of climate change on species distribution can provide strong support for policymakers, enabling them to effectively address challenges arising from shifts in species ranges. This, in turn, helps maintain the stability of ecosystems and ensures the sustainable development of biodiversity (Zhang et al., 2018 ). Climate is one of the main determinants delimiting geographical distribution of plant species on large scale. There is a considerable amount of research declaring climate change lead to the range expansion or retraction in plant species ranges. To assess the vulnerability of plant species under a rapidly changing climate, we can use species distribution modelling (SDM) to predict species climate niches and project their potential future range shifts (Thuiller et al., 2005 ). Currently, various SDMs are widely applied, including the Maximum Entropy Model (MaxEnt), Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Models (GLM), and CLIMEX (CL) (Duan et al., 2014 ). Among them, the MaxEnt model, based on the principle of maximum entropy, has gained significant attention from researchers due to its superior accuracy, reproducibility, ease of operation, and low computational resource requirements. The MaxEnt model is a widely used species distribution modeling method in the fields of ecology and biogeography. By analyzing the relationship between known species distribution points and environmental variables, it effectively predicts the potential distribution areas of species. This is highly significant for assessing species' suitable habitats and ecological niches. Additionally, it identifies key environmental factors influencing species distribution, providing insights into their ecological requirements and adaptive mechanisms (Cao et al., 2022 ). In recent years, the MaxEnt model has been extensively applied in predicting the potential distribution of flora and fauna, as well as in habitat conservation. For instance, Chen Haojie et al. (Chen et al., 2021 ) used this model to analyze the potential distribution of Populus euphratica in China under future climate change scenarios; Liu Hua et al. (Liu et al., 2023 ) studied the potential distribution of Liriodendron chinense ; and Gu Yuanyang et al. (Gu et al., 2020 ) predicted the potential habitat of the Panthera tigris , integrating the results with cost-distance methods to refine natural reserve planning within its habitat range. Tirpitzia sinensis , belonging to the Linaceae family and the genus Tirpitzia , is a heterostylous plant that typically grows as a shrub or small tree. It thrives at elevations of 300–2400 meters, often on slopes and limestone substrates. The species is primarily distributed in the southwestern regions of China, including Guangxi, Guizhou, southeastern Yunnan, Hunan, and Chongqing, with occasional records in Vietnam. As a medicinal plant, Tirpitzia sinensis is valued for its stems and leaves, which possess properties for promoting blood circulation, reducing swelling, relieving pain, and facilitating bone healing (Du et al., 2014 ). Gu Ronghui et al. (Gu, 2015 ) conducted research on the chemical composition of Tirpitzia sinensis and discovered adenosine (5, Adenosine, TS-28), isolated from the plant for the first time. Adenosine is an extracellular signaling molecule that plays a critical role in human physiological activities. Its functions include inducing vasodilation, regulating blood pressure and heart rate, modulating the sympathetic nervous system, and exhibiting antithrombotic activity. By utilizing DS for reverse target screening of new compounds, the study speculated that Compound 2 is related to wound healing, Compound 3 is associated with inflammatory responses and cancer, and Compound 4 may have relevance to inflammation, aging processes, and wound healing. This study, based on specimen distribution records and environmental data for Tirpitzia sinensis in China, simulated the plant's potential distribution under current climatic conditions. It conducted an in-depth analysis of the key environmental factors influencing plant distribution and identified the environmental range suitable for plant growth. Additionally, the study selected two climate scenarios, SSP126 and SSP585, for the time periods 2021–2040, 2041–2060, 2061–2080 and 2081–2100, to analyze changes in the geographic distribution of Tirpitzia sinensis and the shift in its geometric center under future climatic conditions. By clarifying the impact of climate change on the potential distribution of Tirpitzia sinensis , this study provides a scientific basis for the conservation of its population resources, the selection of artificial cultivation areas, and ecosystem management. Furthermore, given the significant medicinal value of Tirpitzia sinensis , the findings also hold vital importance for ensuring the sustainable use of its germplasm resources. In the future, as climate change intensifies, similar predictive studies will offer valuable references for the conservation and management of other important species. Materials and methods Collection and screening of distribution data The distribution data of Tirpitzia sinensis were obtained from three main sources: Field sampling: Latitude and longitude coordinates were recorded using GPS during field surveys. Literature review: Relevant publications were reviewed to extract provided latitude and longitude data, or geographic coordinates were identified based on known place names (Huang et al., 2019 ). Online platforms: Distribution data, including latitude and longitude, were retrieved from various platforms such as the Chinese Virtual Herbarium (CVH, https://www.cvh.ac.cn/ ) , the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/ ) , and the Plant Photo Bank of China (PPBC, http://ppbc.iplant.cn/ ). For records or specimens that lacked direct latitude and longitude information but included specific location details, coordinates were determined using Baidu Maps' coordinate picker tool. To ensure the accuracy of the predictive model and avoid clustering of multiple data points within the same area, distribution data were filtered using ENMtools at a spatial resolution of 2.5′ (approximately 5 km) from the WorldClim Global Climate Database ( http://www.worldclim.org ) (Warren et al., 2010 ).Retain only one point per grid cell to reduce sampling bias in species distribution data. Ultimately, a total of 174 distribution records for Tirpitzia sinensis were obtained (Fig. 1 ). Acquisition and processing of climate data The current climate data (1970–2000) and future climate data (2021–2040, 2041–2060, 2061–2080, 2081–2100) used in this study were downloaded from the WorldClim Global Climate Database ( https://www.worldclim.org/ ). These data include 19 climate variables related to temperature and precipitation, as well as elevation data, all with a spatial resolution of 2.5′ (Fritz et al., 2017 ). Future climate data were selected from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) and utilized the Beijing Climate Center Climate System Model 2 Medium Resolution (BCC-CSM2-MR), which is suitable for China's geographic environment. Two Shared Socioeconomic Pathways (SSPs) were considered: SSP126 and SSP585. SSP126 represents a sustainable development pathway aimed at limiting global warming to below 2°C, while SSP585 represents a business-as-usual pathway with projected warming of 3.3–5.7°C (Riahi et al., 2017 ). Slope and aspect data used in the study were extracted from DEM (Digital Elevation Model) elevation data, which were obtained from the Computer Network Information Center of the Chinese Academy of Sciences and the International Scientific Data Service Platform ( http://www.gscloud.cn/ ) (Bi et al., 2005 ). The downloaded climate raster data (.tif) were converted into the (.asc) format compatible with the MaxEnt model using ArcGIS software. Through pre-experimental runs of the MaxEnt model, the contribution percentage and permutation importance of the environmental factors were obtained, and factors with a contribution rate of 0 were excluded. Subsequently, Spearman correlation analysis was performed on 22 environmental variables using the ENMTools tool. Due to collinearity among variables, which could lead to overfitting of the distribution prediction model, variables with a correlation coefficient |r| ≥ 0.8 were prioritized for exclusion, giving preference to removing those with lower contribution rates (Gao et al., 2023 ). Based on the contribution rate results from the MaxEnt model and the correlation analysis (Fig. 2 ), a total of 10 key environmental factors were ultimately selected (Table.1). Table.1 The environmental variables used for the MaxEnt Model Variable code Variable type Unit bio01 Annual mean temperature ℃ bio04 Temperature seasonality ℃ bio06 Min temperature of coldest month ℃ bio09 Mean temperature of driest quarter ℃ biol1 Mean temperature of coldest quarter ℃ bio14 Precipitation of driest month mm bio18 Precipitation of warmest quarter mm aspect Aspect ° slope Slope ° elev Elevation m Construction and Parameter Optimization of the MaxEnt Model In this study, the Kuenm R package was used to optimize two parameters, namely Feature Combination (FC) and Regularization Multiplier (RM). By setting the range of the RM value from 0 to 4 with an interval of 0.5 and performing cross-combinations with five types of feature combinations, including Linear (L), Quadratic (Q), Product (P), Threshold (T), and Hinge (H), a total of 238 candidate models can be evaluated. Finally, the parameter combination with an omission rate of less than 5% and the minimum natural logarithm value of AICc was selected. That is, when the delta AICc value is 0, the corresponding FC and RM combination is the optimal one (Cobos et al., 2019 ). Import 174 distribution data points and the selected 10 environmental factors into the MaxEnt 3.4.4 software to construct a species distribution prediction model. The model parameters are set as follows: First, the data is randomly divided into a training set and a testing set, with 75% of the distribution points used to train the model and 25% used to test the model's accuracy. To ensure the model's stability and result reproducibility, the analysis is run 10 times with repetitions, while other parameters are kept at the default settings of the MaxEnt software. The average value of the results is taken as the final output, and the output format is set to logistic for logical output representation (Zhao et al., 2021 ). To evaluate the accuracy of the model's prediction results, the Receiver Operating Characteristic Curve (ROC) was employed, with the Area Under the ROC Curve (AUC) used as the evaluation metric (Cao, 2010 ). The AUC value reflects the reliability of the model's predictions and ranges from 0 to 1. Generally, the closer the AUC value is to 1, the better the model's predictive performance. Specifically, if the AUC value is less than 0.7, the model's predictive performance is poor; when the AUC value is between 0.7 and 0.8, the prediction results are at an average level; an AUC value between 0.8 and 0.9 indicates good predictive ability; and when the AUC value exceeds 0.9, the model's predictions are excellent and highly reliable (Li et al., 2023 ). Suitability zone classification Import the ASC format result files output by the MaxEnt model into ArcMap and use the Spatial Analyst toolbox for further analysis. Apply the Reclassify tool and use the natural breaks classification method in ArcMap to divide the potential suitable zones of Tirpitzia sinensis into four levels: non-suitable zone, low suitability zone, medium suitability zone, and high suitability zone. The suitability index levels are classified as follows: 0–0.1 (non-suitable zone), 0.1–0.3 (low suitability zone), 0.3–0.5 (medium suitability zone), and 0.5–1 (high suitability zone) (Zheng et al., 2024 ). Subsequently, calculate the proportion of each suitability level and determine the area size of each suitability level. Changes in suitability zone patterns and centroid shifts Using ArcMap software, the suitable habitat zones of Tirpitzia sinensis were divided into suitable and non-suitable zones based on the suitability index classification from the previous step. Using the SDMToolbox toolkit in ArcMap, the data from the earlier period was set as the current data, and the data from the later period was set as the future data. The changes in suitable zones between consecutive periods were categorized into non-suitable zones, expansion zones, contraction zones, and stable zones (Li et al., 2021 ). In ArcMap, the suitable zone maps under contemporary and future climate scenarios (SSP126 and SSP585) were overlaid, and the SDMToolbox toolkit was applied to calculate the vector centroids of the suitable zones (Jia et al., 2024 ). By analyzing the shifts in vector centroids under different climate conditions, the distribution trends of suitable zones for Tirpitzia sinensis were revealed, providing insights into the spatial pattern changes of its suitable distribution areas under contemporary and future climate scenarios. Results and analysis Evaluation of MaxEnt model accuracy After running the MaxEnt model ten times, both the training and test set AUC values reached 0.985 (Fig. 3 ). This result clearly demonstrates the model's exceptionally high accuracy, meeting an excellent standard. Therefore, it can be reliably used to predict the suitable zones for Tirpitzia sinensis. Major environmental factors influencing the geographical distribution of Tirpitzia sinensis This study identified the major environmental factors influencing the geographical distribution of Tirpitzia sinensis based on the regularized training gain of 10 environmental variables in the MaxEnt model, as well as their percent contribution and permutation importance. Table 2 shows that the precipitation of the warmest quarter (bio18), minimum temperature of the coldest month (bio06), temperature seasonality (bio04), and elevation (elev) are key environmental factors. Among them, bio18 had the highest percent contribution, reaching 57.8%, followed by bio06 (18.9%), bio4 (10%), and elevation (3.4%). In terms of permutation importance, bio06 had the greatest impact at 71.3%, while bio18 was 3.3%, and bio04 and elevation contributed relatively less, at 1.7% and 1.5%, respectively. Figure 4 reveals that different environmental factors have varying degrees of influence on the distribution of Tirpitzia sinensis . The mean temperature of the driest quarter (bio09) showed the highest regularized training gain (2.37), followed by bio18, bio11, bio01, bio06 and bio04, with training gains of 1.96, 1.88, 1.83, 1.72, and 1.49, respectively. By combining the percent contribution and jackknife test results, the main environmental factors influencing the geographical distribution of Tirpitzia sinensis are bio18, bio06, bio04, and bio09. Table.2 Percent contribution and permutation importance of environmental variables Variable code Variable type Percent contribution(%) Permutation importance(%) bio18 Precipitation of warmest quarter 57.8 3.3 bio06 Min temperature of coldest month 18.9 71.3 bio04 Temperature seasonality 10 1.7 elev Elevation 3.4 1.5 biol1 Mean temperature of coldest quarter 3.3 2.3 bio14 Precipitation of driest month 2.5 0.7 bio01 Annual mean temperature 1.4 0.7 bio09 Mean temperature of driest quarter 1.2 17.9 aspect Aspect 0.9 0.4 slope Slope 0.6 0.4 Response curves of environmental factors A detailed analysis was conducted on the four main environmental factors influencing the distribution of Tirpitzia sinensis . The response curves of these factors to the presence probability of Tirpitzia sinensis are shown in Fig. 5 .It is generally considered that when the presence probability exceeds 0.5, the corresponding range of environmental factors is suitable for the growth of Tirpitzia sinensis . As shown in Fig. 5 , the presence probability of Tirpitzia sinensis exhibits an increasing and then decreasing trend with the increase in the four environmental factors: bio18, bio06, bio04 and bio09. bio18: The suitable range for growth is 582.4–907.1 mm. The presence probability reaches its maximum value of 0.67 when the precipitation is 656.7 mm, indicating that Tirpitzia sinensis prefers relatively drier areas. bio06: The suitable range is 2.6–8.1°C. The presence probability peaks at 0.70 when the temperature is 5.4°C. bio04: The suitable range is 432.9–712.8, with the presence probability reaching a maximum value of 0.66 when the standard deviation is 513.3. bio09: The suitable range is 7.7–14.1°C. The presence probability is highest at 0.73 when the temperature is 11.7°C. Changes in suitability levels of Tirpitzia sinensis Using MaxEnt modeling, the distribution of Tirpitzia sinensis was divided into four suitability levels: non-suitable, low suitability, medium suitability, and high suitability zones. Under current climatic conditions, the total suitable area for Tirpitzia sinensis is 1,869,438 km², accounting for 19.47% of China's total area. Among these: The high suitability zone covers 355,195 km², making up 3.70% of China's total area. These regions are mainly concentrated in the western part of Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, southern Guizhou Province, southeastern Sichuan Province, and southwestern Chongqing Municipality. The medium suitability zone spans 633,774 km², representing 6.60% of China's total area. The low suitability zone extends over 880,469 km², accounting for 9.17% of China's total area. Under the SSP126 climate scenario, from 2021 to 2040, the total suitable area for Tirpitzia sinensis is 2,027,673 km², accounting for 21.12% of China's total area. Among these, the high suitability zone covers 451,974 km² (4.71%), mainly concentrated in western Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, southeastern Sichuan Province, Chongqing Municipality, and northwestern Hunan Province; the medium suitability zone spans 755,489 km² (7.87%); and the low suitability zone covers 820,211 km² (8.54%). Between 2041 and 2060, the total suitable area increases to 2,052,349 km², accounting for 21.38% of China's total area. The high suitability zone expands to 533,661 km² (5.56%), distributed in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, southeastern Sichuan Province, Chongqing Municipality, and western Hunan Province. The medium suitability zone is 728,764 km² (7.59%), and the low suitability zone is 789,925 km² (8.23%). From 2061 to 2080, the total suitable area slightly decreases to 1,986,812 km², accounting for 20.70% of China's total area. The high suitability zone increases to 592,687 km² (6.17%), mainly located in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, southwestern Hubei Province, and northern Guangdong Province. The medium suitability zone shrinks to 633,973 km² (6.92%), and the low suitability zone is 730,153 km² (7.61%). Between 2081 and 2100, the total suitable area decreases further to 1,965,383 km², accounting for 20.47% of China's total area. The high suitability zone reduces to 492,783 km² (5.13%), mainly distributed in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, and southwestern Hubei Province. The medium suitability zone shrinks to 600,519 km² (6.26%), and the low suitability zone increases to 872,082 km² (9.08%). Under the SSP585 climate scenario, from 2021 to 2040, the total suitable area for Tirpitzia sinensis is 1,979,623 km², accounting for 20.62% of China's total area. Among these, the high suitability zone covers 416,739 km² (4.34%), mainly concentrated in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, and western Chongqing Municipality. The medium suitability zone spans 630,040 km² (6.56%), while the low suitability zone covers 932,844 km² (9.72%). From 2041 to 2060, the total suitable area increases to 2,114,849 km², accounting for 22.03% of China's total area. The high suitability zone expands to 495,648 km² (5.16%), mainly distributed in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, and southwestern Hubei Province. The medium suitability zone is 653,050 km² (6.80%), and the low suitability zone is 966,151 km² (10.06%). From 2061 to 2080, the total suitable area increases further to 2,344,282 km², accounting for 24.42% of China's total area. The high suitability zone reaches 679,341 km² (7.08%), mainly located in northwestern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, and southwestern Hubei Province. The medium suitability zone is 672,221 km² (7.00%), and the low suitability zone covers 992,720 km² (10.34%). From 2081 to 2100, the total suitable area decreases slightly to 2,201,694 km², accounting for 29.93% of China's total area. The high suitability zone reduces to 542,744 km² (5.65%), mainly distributed in Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, western Hubei Province, and southern Shaanxi Province. The medium suitability zone shrinks to 535,103 km² (5.57%), while the low suitability zone expands to 1,123,848 km² (11.71%) (Figs. 6 , 7 and 8 ). Changes in suitable areas and centroid shifts of Tirpitzia sinensis Based on changes in the distribution area of Tirpitzia sinensis , the contraction and expansion of suitable areas during different periods can be compared. Under the SSP126 climate scenario, from 2021 to 2040 compared to the current period, the suitable area of Tirpitzia sinensis expanded by 187,547 km² and contracted by 18,922 km². The main expansion regions were southern Shaanxi Province, southern Henan Province, and southern Anhui Province, while the primary contraction region was southwestern Yunnan Province. From 2041 to 2060 compared to 2021–2040, the suitable area expanded by 58,226 km² and contracted by 32,556 km². The main expansion regions were southern Shaanxi Province and southern Gansu Province, while the primary contraction regions were southern Henan Province and central Jiangxi Province. From 2061 to 2080 compared to 2041–2060, the suitable area expanded by 38,864 km² and contracted by 114,736 km². The main expansion region was southern Henan Province, while the primary contraction regions were central Jiangxi Province and southern Guangdong Province. From 2081 to 2100 compared to 2061–2080, the suitable area expanded by 60,730 km² and contracted by 83,455 km². The main expansion regions were central Sichuan Province and southern Gansu Province, while the primary contraction regions were central Henan Province, central Anhui Province, and southwestern Yunnan Province (Figs. 9 and 10 ). Under the SSP585 climate scenario, from 2021 to 2040 compared to the current period, the suitable area of Tirpitzia sinensis expanded by 178,944 km² and contracted by 63,675 km². The main expansion zones were southern Henan Province and southern Anhui Province, while the primary contraction zones were southwestern Yunnan Province, central Guangxi Zhuang Autonomous Region, and central Jiangxi Province. From 2041 to 2060 compared to 2021–2040, the suitable area expanded by 185,483 km² and contracted by 77,008 km². The main expansion zones were central Shaanxi Province, central Henan Province, northern Anhui Province, and central Jiangsu Province, while the primary contraction zones were eastern Jiangxi Province, western Fujian Province, and northern Guangdong Province. From 2061 to 2080 compared to 2041–2060, the suitable area expanded by 390,816 km² and contracted by 165,333 km². The main expansion zones were central Shaanxi Province, southern Shanxi Province, northern Henan Province, and southern Shandong Province, while the primary contraction regions were southwestern Yunnan Province, southern Guangdong Province, and central Jiangxi Province. From 2081 to 2100 compared to 2061–2080, the suitable area expanded by 286,700 km² and contracted by 473,088 km². The main expansion zones were southern Gansu Province, northern Shaanxi Province, central Shanxi Province, southwestern Hebei Province, and central-northern Shandong Province, while the primary contraction zones were southwestern Yunnan Province, northern Guangdong Province, eastern Guangxi Zhuang Autonomous Region, eastern Hunan Province, and northwestern Jiangxi Province (Figs. 9 and 10 ). Under the current climatic conditions, the distribution center of Tirpitzia sinensis is located in Tianzhu County, Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou Province. Under the SSP126 climate scenario, during the periods 2021–2040, 2041–2060, 2061–2080, and 2081–2100, the distribution center is located in Zhijiang Dong Autonomous County, Huaihua City, Hunan Province, with a northwestward shift within Zhijiang Dong Autonomous County. Under the SSP585 climate scenario, the distribution center is located in Zhijiang Dong Autonomous County, Huaihua City, Hunan Province during 2021–2040. In 2041–2060, the center shifts to Mayang Miao Autonomous County, Huaihua City, Hunan Province; in 2061–2080, it moves to Yongshun County, Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province; and in 2081–2100, it shifts further to Enshi City, Enshi Tujia and Miao Autonomous Prefecture, Hubei Province (Fig. 11 ). Under the SSP126 climate scenario, the distribution center of Tirpitzia sinensis migrates from Guizhou Province to Hunan Province. Under the SSP585 scenario, it migrates from Guizhou Province to Hunan Province and further to Hubei Province. Overall, the distribution center shows a trend of northward migration in the future. Compared to SSP126, the SSP585 climate scenario results in a larger magnitude of distribution center migration. Discussion Tirpitzia sinensis , as an important traditional medicinal plant in China, possesses significant medicinal value in its stems and leaves, including promoting blood circulation to remove stasis, reducing swelling and alleviating pain, as well as joining bones and tendons. This study employed the MaxEnt model to predict the potential geographical distribution patterns of this species under current and future climate change scenarios, and identified key environmental factors influencing its distribution, providing a theoretical basis for the scientific conservation and management of this important medicinal plant resource. Model construction and evaluation This study systematically integrated 174 distribution point data, covering the core distribution areas in southwestern China and marginal regions in Vietnam. Spatial filtering was performed using ENMtools to effectively reduce data clustering bias. Based on contribution rates and Spearman correlation analysis, 10 key variables (such as bio18 and bio06) were selected from the initial environmental variables, significantly mitigating the risk of overfitting caused by variable collinearity. The Kuenm R package was used to optimize feature combinations (FC) and regularization multipliers (RM), determining the optimal model parameter combination. Model evaluation results showed that after 10 repeated runs, the AUC values for both the training and test sets reached 0.985, indicating excellent predictive performance of the model. However, it should be noted that this study still has the following methodological limitations: First, the issue of spatial autocorrelation has not been fully addressed (Naimi et al., 2011 ), which may affect the reliability of model parameter estimates; second, independent validation sets or temporally stratified sampling methods were not used for model validation (Hijmans, 2012 ). These factors may have somewhat impacted the robustness of the model results. Mechanisms by which key environmental factors influence the distribution of Tirpitzia sinensis The modeling results indicate that the potential geographical distribution of Tirpitzia sinensis is primarily influenced by four environmental variables: precipitation of the warmest quarter (bio18), minimum temperature of the coldest month (bio06), standard deviation of temperature seasonality (bio04), and mean temperature of the driest quarter (bio09). These factors collectively constrain the species’ distribution limits by affecting water availability, thermal tolerance, and climatic stability, reflecting a multifactorial ecological adaptation strategy. Among these, bio18 emerged as the most influential variable, with a contribution rate of 57.8%. This variable reflects not only moisture availability during the growing season but also aligns closely with the reproductive phenology of the species. Previous studies have shown that Tirpitzia sinensis flowers primarily between May and August (Hu et al., 2021 ), during which precipitation levels critically influence reproductive success. Excess rainfall during this period can lead to flower drop, thereby reducing fruit set. This sensitivity to summer precipitation partially explains its preference for relatively dry habitats. Such ecological preferences are consistent with its predominant occurrence in limestone karst regions, which are characterized by nutrient-poor soils and rapid water drainage. The minimum temperature of the coldest month (bio06) determines the species’ overwintering survival capacity and poses a major constraint on its poleward or altitudinal expansion. Cold stress has been shown to inhibit cell division and compromise membrane integrity, leading to reduced germination and slower growth (Sakai & Larcher, 1987 ). The suitable temperature range for overwintering in Tirpitzia sinensis is estimated to be 2.6–8.1°C, suggesting a limited tolerance to cold environments. Likewise, bio09 plays an important role in regulating the species’ physiological activity under seasonal drought. High temperatures during the dry season increase evapotranspiration, and when soil moisture is insufficient, water deficits can result in early wilting and growth suppression (Chaves et al., 2003 ). Temperature seasonality (bio04), which measures annual temperature fluctuations, also significantly contributes to the model. Tirpitzia sinensis tends to inhabit subtropical monsoon regions with relatively stable temperatures, favoring environments with moderate seasonal variability. This likely reflects its need for stable metabolic functioning. Studies have reported that excessive temperature variability can negatively affect seed germination and seedling establishment in shrub species (Jump & Peuelas, 2005 ), particularly those lacking dormancy mechanisms. Moreover, the species exhibits morphological adaptations such as thickened leaves and low specific leaf area (SLA), which enhance drought tolerance and water storage capacity. These traits represent a typical resource-conservative strategy, allowing Tirpitzia sinensis to survive under water-limited and thermally stressful conditions (Xiong et al., 2022 ). The synergy between climatic filtering and functional trait expression explains the species' high sensitivity to both temperature and moisture variables, and defines the ecological boundaries of its realized niche. Impact of future climate change on habitat adaptability of Tirpitzia sinensis Under future climate scenarios, the potential suitable habitat of Tirpitzia sinensis is projected to exhibit a general trend of northward expansion. Particularly under the high-emission SSP585 scenario, the total suitable area is expected to reach a peak of 2.344 million km² during the period 2061–2080, with the distribution centroid shifting from Guizhou to parts of Hunan and Hubei. This indicates that Tirpitzia sinensis possesses strong climatic niche tracking capacity, enabling it to cope with global warming to some extent through geographic migration toward regions with hot and dry climates. However, the actual migration process may be constrained by several ecological factors. First, Tirpitzia sinensis may exhibit niche conservatism, relying heavily on its native dry-hot environmental conditions and specific soil properties, which could limit its ability to establish in newly suitable regions (Wiens & Graham, 2005 ). Second, the species primarily relies on insect pollination and short-distance seed dispersal. In the fragmented karst landscape, these dispersal limitations are likely to result in a lagged migration response, with actual expansion rates falling behind the rate of climate-driven habitat shifts (Corlett & Westcott, 2013 ). In addition, Tirpitzia sinensis may face biological interaction challenges in newly suitable areas. Changes in precipitation during the flowering season could interfere with pollinator activity, while native drought-tolerant shrubs may outcompete its seedlings and inhibit population establishment. These factors, which are not accounted for in the current species distribution model, underscore the importance of incorporating ecological processes into future model development for more realistic projections. It is also worth noting that this study employed a single General Circulation Model (BCC-CSM2-MR) for climate input and did not integrate the uncertainty range derived from multiple climate models (Buisson et al., 2010 ). Future research should consider CMIP6 multi-model ensemble approaches and dispersal simulations to improve the ecological realism and predictive reliability of habitat shift assessments. In summary, although Tirpitzia sinensis demonstrates certain adaptive potential under climate change, the actual shifts in its distribution will be jointly governed by climatic suitability, ecological niche characteristics, species interactions, and dispersal constraints. These insights provide a theoretical basis for climate-resilient conservation and assisted migration strategies for this ecologically specialized medicinal shrub. Conclusion This study employed the MaxEnt model with 174 validated distribution records and 10 environmental variables to predict the potential distribution of Tirpitzia sinensis in China under current and future climate scenarios. Under current climatic conditions, Tirpitzia sinensis is primarily distributed in western Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, southern Guizhou Province, southeastern Sichuan Province, and southwestern Chongqing Municipality. Under future climate change scenarios, the suitable zones for Tirpitzia sinensis are expected to expand and exhibit a northward migration trend. In the SSP126 climate scenario, the distribution center of Tirpitzia sinensis shifts from Guizhou Province to Hunan Province, while in the SSP585 scenario, it moves further from Guizhou Province to Hunan and Hubei provinces. The main environmental factors influencing the probability of Tirpitzia sinensis presence include bio18, bio06, bio04, and bio09. The suitable ranges for these factors are as follows: bio18 (582.4–907.1 mm), bio06 (2.6–8.1°C), bio04 (432.9–712.8), and bio09 (7.7–14.1°C). These findings indicate that Tirpitzia sinensis is well-suited to grow in warm and relatively dry regions with adequate precipitation. Declarations All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Conflict of Interest The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (32360330, 32360262), China Scholarship Council (202308520101), Guizhou Provincial Program on Commercialization of Scientific and Technological Achievements (QianKeHeChengGuo [2022]010), Water-Fertilizer Coupling and Biodiversity Restoration in Karst Rocky Desertification (QianJiaoJi[2023]004), The Joint Fund of the National Natural Science Foundation of China and the Karst Science Research Center of Guizhou Province (U1812401). Author Contribution T.-X.X.and M.-Y.L.conceived and designed the study; M.-Y.L., Z.C., and Z.-R.D. conducted field surveys to obtain partial latitude and longitude data of Tirpitzia sinensis; M.-Y.L. collected the data; T.-X.X. and M.-Y.L. analyzed the data and drafted the manuscript; T.-X.X., M.-Y.L., and S.W. revised various drafts of the manuscript. 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10:00:05","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":108940,"visible":true,"origin":"","legend":"\u003cp\u003eProportions of habitat suitability ranks for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e in China across different time periods\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6863150/v1/5bb4d9afb2ff4ad3c59fc0a1.jpeg"},{"id":85387884,"identity":"8c44c831-23ba-4f14-b6e1-b2e342b5da91","added_by":"auto","created_at":"2025-06-25 10:08:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":60586,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in habitat area for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e in China across different time 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area of \u003cem\u003eTirpitzia sinensis \u003c/em\u003eunder two future climate scenarios\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6863150/v1/6a4bbeab779990d5cca4b648.png"},{"id":85387888,"identity":"8a56c2d7-9411-442e-af30-f1f3f37e6433","added_by":"auto","created_at":"2025-06-25 10:08:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":96330,"visible":true,"origin":"","legend":"\u003cp\u003eMigration routes of the distribution centroid of\u003cem\u003e Tirpitzia sinensis\u003c/em\u003e in China under two future climate scenarios\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-6863150/v1/d7b33ecf731103866b2f52e4.png"},{"id":91890063,"identity":"02a634d3-1b68-4f08-b216-4c503a80ec6d","added_by":"auto","created_at":"2025-09-22 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Climate, as a key environmental factor influencing species and vegetation distribution at both regional and global scales, has profound and far-reaching effects on biodiversity and species range (Hamann \u0026amp; Wang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Future climate change may lead to changes in the distribution and abundance of species (Ehrl\u0026eacute;n \u0026amp; Morris, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the extinction of species populations (Bestion et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), alterations in distribution ranges (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), phenological changes (Meril\u0026auml; \u0026amp; Hendry, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), as well as changes in physiological characteristics (Dillon et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Exploring the trends in potential geographic distribution of species under future climate change scenarios and analyzing their response mechanisms are of irreplaceable importance for developing scientifically sound biodiversity conservation strategies. Furthermore, a comprehensive assessment of the impacts of climate change on species distribution can provide strong support for policymakers, enabling them to effectively address challenges arising from shifts in species ranges. This, in turn, helps maintain the stability of ecosystems and ensures the sustainable development of biodiversity (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate is one of the main determinants delimiting geographical distribution of plant species on large scale. There is a considerable amount of research declaring climate change lead to the range expansion or retraction in plant species ranges. To assess the vulnerability of plant species under a rapidly changing climate, we can use species distribution modelling (SDM) to predict species climate niches and project their potential future range shifts (Thuiller et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Currently, various SDMs are widely applied, including the Maximum Entropy Model (MaxEnt), Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Models (GLM), and CLIMEX (CL) (Duan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Among them, the MaxEnt model, based on the principle of maximum entropy, has gained significant attention from researchers due to its superior accuracy, reproducibility, ease of operation, and low computational resource requirements. The MaxEnt model is a widely used species distribution modeling method in the fields of ecology and biogeography. By analyzing the relationship between known species distribution points and environmental variables, it effectively predicts the potential distribution areas of species. This is highly significant for assessing species' suitable habitats and ecological niches. Additionally, it identifies key environmental factors influencing species distribution, providing insights into their ecological requirements and adaptive mechanisms (Cao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In recent years, the MaxEnt model has been extensively applied in predicting the potential distribution of flora and fauna, as well as in habitat conservation. For instance, Chen Haojie et al. (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used this model to analyze the potential distribution of \u003cem\u003ePopulus euphratica\u003c/em\u003e in China under future climate change scenarios; Liu Hua et al. (Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studied the potential distribution of \u003cem\u003eLiriodendron chinense\u003c/em\u003e; and Gu Yuanyang et al. (Gu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) predicted the potential habitat of the \u003cem\u003ePanthera tigris\u003c/em\u003e, integrating the results with cost-distance methods to refine natural reserve planning within its habitat range.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTirpitzia sinensis\u003c/em\u003e, belonging to the Linaceae family and the genus \u003cem\u003eTirpitzia\u003c/em\u003e, is a heterostylous plant that typically grows as a shrub or small tree. It thrives at elevations of 300\u0026ndash;2400 meters, often on slopes and limestone substrates. The species is primarily distributed in the southwestern regions of China, including Guangxi, Guizhou, southeastern Yunnan, Hunan, and Chongqing, with occasional records in Vietnam. As a medicinal plant, \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is valued for its stems and leaves, which possess properties for promoting blood circulation, reducing swelling, relieving pain, and facilitating bone healing (Du et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Gu Ronghui et al. (Gu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) conducted research on the chemical composition of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e and discovered adenosine (5, Adenosine, TS-28), isolated from the plant for the first time. Adenosine is an extracellular signaling molecule that plays a critical role in human physiological activities. Its functions include inducing vasodilation, regulating blood pressure and heart rate, modulating the sympathetic nervous system, and exhibiting antithrombotic activity. By utilizing DS for reverse target screening of new compounds, the study speculated that Compound 2 is related to wound healing, Compound 3 is associated with inflammatory responses and cancer, and Compound 4 may have relevance to inflammation, aging processes, and wound healing.\u003c/p\u003e \u003cp\u003eThis study, based on specimen distribution records and environmental data for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e in China, simulated the plant's potential distribution under current climatic conditions. It conducted an in-depth analysis of the key environmental factors influencing plant distribution and identified the environmental range suitable for plant growth. Additionally, the study selected two climate scenarios, SSP126 and SSP585, for the time periods 2021\u0026ndash;2040, 2041\u0026ndash;2060, 2061\u0026ndash;2080 and 2081\u0026ndash;2100, to analyze changes in the geographic distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e and the shift in its geometric center under future climatic conditions. By clarifying the impact of climate change on the potential distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e, this study provides a scientific basis for the conservation of its population resources, the selection of artificial cultivation areas, and ecosystem management. Furthermore, given the significant medicinal value of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e, the findings also hold vital importance for ensuring the sustainable use of its germplasm resources. In the future, as climate change intensifies, similar predictive studies will offer valuable references for the conservation and management of other important species.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eCollection and screening of distribution data\u003c/p\u003e \u003cp\u003eThe distribution data of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e were obtained from three main sources: Field sampling: Latitude and longitude coordinates were recorded using GPS during field surveys. Literature review: Relevant publications were reviewed to extract provided latitude and longitude data, or geographic coordinates were identified based on known place names (Huang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Online platforms: Distribution data, including latitude and longitude, were retrieved from various platforms such as the Chinese Virtual Herbarium (CVH, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cvh.ac.cn/\u003c/span\u003e\u003cspan address=\"https://www.cvh.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, the Global Biodiversity Information Facility (GBIF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and the Plant Photo Bank of China (PPBC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ppbc.iplant.cn/\u003c/span\u003e\u003cspan address=\"http://ppbc.iplant.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e For records or specimens that lacked direct latitude and longitude information but included specific location details, coordinates were determined using Baidu Maps' coordinate picker tool. To ensure the accuracy of the predictive model and avoid clustering of multiple data points within the same area, distribution data were filtered using ENMtools at a spatial resolution of 2.5′ (approximately 5 km) from the WorldClim Global Climate Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Warren et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).Retain only one point per grid cell to reduce sampling bias in species distribution data. Ultimately, a total of 174 distribution records for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcquisition and processing of climate data\u003c/p\u003e \u003cp\u003eThe current climate data (1970–2000) and future climate data (2021–2040, 2041–2060, 2061–2080, 2081–2100) used in this study were downloaded from the WorldClim Global Climate Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e These data include 19 climate variables related to temperature and precipitation, as well as elevation data, all with a spatial resolution of 2.5′ (Fritz et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Future climate data were selected from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) and utilized the Beijing Climate Center Climate System Model 2 Medium Resolution (BCC-CSM2-MR), which is suitable for China's geographic environment. Two Shared Socioeconomic Pathways (SSPs) were considered: SSP126 and SSP585. SSP126 represents a sustainable development pathway aimed at limiting global warming to below 2°C, while SSP585 represents a business-as-usual pathway with projected warming of 3.3–5.7°C (Riahi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Slope and aspect data used in the study were extracted from DEM (Digital Elevation Model) elevation data, which were obtained from the Computer Network Information Center of the Chinese Academy of Sciences and the International Scientific Data Service Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gscloud.cn/\u003c/span\u003e\u003cspan address=\"http://www.gscloud.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Bi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The downloaded climate raster data (.tif) were converted into the (.asc) format compatible with the MaxEnt model using ArcGIS software.\u003c/p\u003e \u003cp\u003eThrough pre-experimental runs of the MaxEnt model, the contribution percentage and permutation importance of the environmental factors were obtained, and factors with a contribution rate of 0 were excluded. Subsequently, Spearman correlation analysis was performed on 22 environmental variables using the ENMTools tool. Due to collinearity among variables, which could lead to overfitting of the distribution prediction model, variables with a correlation coefficient |r| ≥ 0.8 were prioritized for exclusion, giving preference to removing those with lower contribution rates (Gao et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on the contribution rate results from the MaxEnt model and the correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ), a total of 10 key environmental factors were ultimately selected (Table.1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1\u003c/b\u003e The environmental variables used for the MaxEnt Model\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable code\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable type\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual mean temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature seasonality\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin temperature of coldest month\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of driest quarter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebiol1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of coldest quarter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of driest month\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of warmest quarter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003easpect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e°\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e°\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelev\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConstruction and Parameter Optimization of the MaxEnt Model\u003c/p\u003e \u003cp\u003eIn this study, the Kuenm R package was used to optimize two parameters, namely Feature Combination (FC) and Regularization Multiplier (RM). By setting the range of the RM value from 0 to 4 with an interval of 0.5 and performing cross-combinations with five types of feature combinations, including Linear (L), Quadratic (Q), Product (P), Threshold (T), and Hinge (H), a total of 238 candidate models can be evaluated. Finally, the parameter combination with an omission rate of less than 5% and the minimum natural logarithm value of AICc was selected. That is, when the delta AICc value is 0, the corresponding FC and RM combination is the optimal one (Cobos et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImport 174 distribution data points and the selected 10 environmental factors into the MaxEnt 3.4.4 software to construct a species distribution prediction model. The model parameters are set as follows: First, the data is randomly divided into a training set and a testing set, with 75% of the distribution points used to train the model and 25% used to test the model's accuracy. To ensure the model's stability and result reproducibility, the analysis is run 10 times with repetitions, while other parameters are kept at the default settings of the MaxEnt software. The average value of the results is taken as the final output, and the output format is set to logistic for logical output representation (Zhao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo evaluate the accuracy of the model's prediction results, the Receiver Operating Characteristic Curve (ROC) was employed, with the Area Under the ROC Curve (AUC) used as the evaluation metric (Cao, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The AUC value reflects the reliability of the model's predictions and ranges from 0 to 1. Generally, the closer the AUC value is to 1, the better the model's predictive performance. Specifically, if the AUC value is less than 0.7, the model's predictive performance is poor; when the AUC value is between 0.7 and 0.8, the prediction results are at an average level; an AUC value between 0.8 and 0.9 indicates good predictive ability; and when the AUC value exceeds 0.9, the model's predictions are excellent and highly reliable (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSuitability zone classification\u003c/p\u003e \u003cp\u003eImport the ASC format result files output by the MaxEnt model into ArcMap and use the Spatial Analyst toolbox for further analysis. Apply the Reclassify tool and use the natural breaks classification method in ArcMap to divide the potential suitable zones of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e into four levels: non-suitable zone, low suitability zone, medium suitability zone, and high suitability zone. The suitability index levels are classified as follows: 0–0.1 (non-suitable zone), 0.1–0.3 (low suitability zone), 0.3–0.5 (medium suitability zone), and 0.5–1 (high suitability zone) (Zheng et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Subsequently, calculate the proportion of each suitability level and determine the area size of each suitability level.\u003c/p\u003e \u003cp\u003eChanges in suitability zone patterns and centroid shifts\u003c/p\u003e \u003cp\u003eUsing ArcMap software, the suitable habitat zones of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e were divided into suitable and non-suitable zones based on the suitability index classification from the previous step. Using the SDMToolbox toolkit in ArcMap, the data from the earlier period was set as the current data, and the data from the later period was set as the future data. The changes in suitable zones between consecutive periods were categorized into non-suitable zones, expansion zones, contraction zones, and stable zones (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In ArcMap, the suitable zone maps under contemporary and future climate scenarios (SSP126 and SSP585) were overlaid, and the SDMToolbox toolkit was applied to calculate the vector centroids of the suitable zones (Jia et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By analyzing the shifts in vector centroids under different climate conditions, the distribution trends of suitable zones for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e were revealed, providing insights into the spatial pattern changes of its suitable distribution areas under contemporary and future climate scenarios.\u003c/p\u003e "},{"header":"Results and analysis","content":"\u003cp\u003eEvaluation of MaxEnt model accuracy\u003c/p\u003e\u003cp\u003eAfter running the MaxEnt model ten times, both the training and test set AUC values reached 0.985 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This result clearly demonstrates the model's exceptionally high accuracy, meeting an excellent standard. Therefore, it can be reliably used to predict the suitable zones for \u003cem\u003eTirpitzia sinensis.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMajor environmental factors influencing the geographical distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study identified the major environmental factors influencing the geographical distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e based on the regularized training gain of 10 environmental variables in the MaxEnt model, as well as their percent contribution and permutation importance. Table\u0026nbsp;2 shows that the precipitation of the warmest quarter (bio18), minimum temperature of the coldest month (bio06), temperature seasonality (bio04), and elevation (elev) are key environmental factors. Among them, bio18 had the highest percent contribution, reaching 57.8%, followed by bio06 (18.9%), bio4 (10%), and elevation (3.4%). In terms of permutation importance, bio06 had the greatest impact at 71.3%, while bio18 was 3.3%, and bio04 and elevation contributed relatively less, at 1.7% and 1.5%, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals that different environmental factors have varying degrees of influence on the distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e. The mean temperature of the driest quarter (bio09) showed the highest regularized training gain (2.37), followed by bio18, bio11, bio01, bio06 and bio04, with training gains of 1.96, 1.88, 1.83, 1.72, and 1.49, respectively. By combining the percent contribution and jackknife test results, the main environmental factors influencing the geographical distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e are bio18, bio06, bio04, and bio09.\u003c/p\u003e\u003cp\u003e \u003cb\u003eTable.2\u003c/b\u003e Percent contribution and permutation importance of environmental variables\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable code\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable type\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent contribution(%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePermutation importance(%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of warmest quarter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin temperature of coldest month\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature seasonality\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelev\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebiol1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of coldest quarter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of driest month\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual mean temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of driest quarter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003easpect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eResponse curves of environmental factors\u003c/p\u003e\u003cp\u003eA detailed analysis was conducted on the four main environmental factors influencing the distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e. The response curves of these factors to the presence probability of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.It is generally considered that when the presence probability exceeds 0.5, the corresponding range of environmental factors is suitable for the growth of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the presence probability of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e exhibits an increasing and then decreasing trend with the increase in the four environmental factors: bio18, bio06, bio04 and bio09. bio18: The suitable range for growth is 582.4–907.1 mm. The presence probability reaches its maximum value of 0.67 when the precipitation is 656.7 mm, indicating that \u003cem\u003eTirpitzia sinensis\u003c/em\u003e prefers relatively drier areas. bio06: The suitable range is 2.6–8.1°C. The presence probability peaks at 0.70 when the temperature is 5.4°C. bio04: The suitable range is 432.9–712.8, with the presence probability reaching a maximum value of 0.66 when the standard deviation is 513.3. bio09: The suitable range is 7.7–14.1°C. The presence probability is highest at 0.73 when the temperature is 11.7°C.\u003c/p\u003e\u003cp\u003eChanges in suitability levels of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eUsing MaxEnt modeling, the distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e was divided into four suitability levels: non-suitable, low suitability, medium suitability, and high suitability zones. Under current climatic conditions, the total suitable area for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is 1,869,438 km², accounting for 19.47% of China's total area. Among these: The high suitability zone covers 355,195 km², making up 3.70% of China's total area. These regions are mainly concentrated in the western part of Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, southern Guizhou Province, southeastern Sichuan Province, and southwestern Chongqing Municipality. The medium suitability zone spans 633,774 km², representing 6.60% of China's total area. The low suitability zone extends over 880,469 km², accounting for 9.17% of China's total area.\u003c/p\u003e\u003cp\u003eUnder the SSP126 climate scenario, from 2021 to 2040, the total suitable area for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is 2,027,673 km², accounting for 21.12% of China's total area. Among these, the high suitability zone covers 451,974 km² (4.71%), mainly concentrated in western Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, southeastern Sichuan Province, Chongqing Municipality, and northwestern Hunan Province; the medium suitability zone spans 755,489 km² (7.87%); and the low suitability zone covers 820,211 km² (8.54%). Between 2041 and 2060, the total suitable area increases to 2,052,349 km², accounting for 21.38% of China's total area. The high suitability zone expands to 533,661 km² (5.56%), distributed in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, southeastern Sichuan Province, Chongqing Municipality, and western Hunan Province. The medium suitability zone is 728,764 km² (7.59%), and the low suitability zone is 789,925 km² (8.23%). From 2061 to 2080, the total suitable area slightly decreases to 1,986,812 km², accounting for 20.70% of China's total area. The high suitability zone increases to 592,687 km² (6.17%), mainly located in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, southwestern Hubei Province, and northern Guangdong Province. The medium suitability zone shrinks to 633,973 km² (6.92%), and the low suitability zone is 730,153 km² (7.61%). Between 2081 and 2100, the total suitable area decreases further to 1,965,383 km², accounting for 20.47% of China's total area. The high suitability zone reduces to 492,783 km² (5.13%), mainly distributed in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, and southwestern Hubei Province. The medium suitability zone shrinks to 600,519 km² (6.26%), and the low suitability zone increases to 872,082 km² (9.08%).\u003c/p\u003e\u003cp\u003eUnder the SSP585 climate scenario, from 2021 to 2040, the total suitable area for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is 1,979,623 km², accounting for 20.62% of China's total area. Among these, the high suitability zone covers 416,739 km² (4.34%), mainly concentrated in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, and western Chongqing Municipality. The medium suitability zone spans 630,040 km² (6.56%), while the low suitability zone covers 932,844 km² (9.72%). From 2041 to 2060, the total suitable area increases to 2,114,849 km², accounting for 22.03% of China's total area. The high suitability zone expands to 495,648 km² (5.16%), mainly distributed in northern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, and southwestern Hubei Province. The medium suitability zone is 653,050 km² (6.80%), and the low suitability zone is 966,151 km² (10.06%). From 2061 to 2080, the total suitable area increases further to 2,344,282 km², accounting for 24.42% of China's total area. The high suitability zone reaches 679,341 km² (7.08%), mainly located in northwestern Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, and southwestern Hubei Province. The medium suitability zone is 672,221 km² (7.00%), and the low suitability zone covers 992,720 km² (10.34%). From 2081 to 2100, the total suitable area decreases slightly to 2,201,694 km², accounting for 29.93% of China's total area. The high suitability zone reduces to 542,744 km² (5.65%), mainly distributed in Guizhou Province, eastern Sichuan Province, Chongqing Municipality, northwestern Hunan Province, western Hubei Province, and southern Shaanxi Province. The medium suitability zone shrinks to 535,103 km² (5.57%), while the low suitability zone expands to 1,123,848 km² (11.71%) (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChanges in suitable areas and centroid shifts of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBased on changes in the distribution area of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e, the contraction and expansion of suitable areas during different periods can be compared. Under the SSP126 climate scenario, from 2021 to 2040 compared to the current period, the suitable area of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e expanded by 187,547 km² and contracted by 18,922 km². The main expansion regions were southern Shaanxi Province, southern Henan Province, and southern Anhui Province, while the primary contraction region was southwestern Yunnan Province. From 2041 to 2060 compared to 2021–2040, the suitable area expanded by 58,226 km² and contracted by 32,556 km². The main expansion regions were southern Shaanxi Province and southern Gansu Province, while the primary contraction regions were southern Henan Province and central Jiangxi Province. From 2061 to 2080 compared to 2041–2060, the suitable area expanded by 38,864 km² and contracted by 114,736 km². The main expansion region was southern Henan Province, while the primary contraction regions were central Jiangxi Province and southern Guangdong Province. From 2081 to 2100 compared to 2061–2080, the suitable area expanded by 60,730 km² and contracted by 83,455 km². The main expansion regions were central Sichuan Province and southern Gansu Province, while the primary contraction regions were central Henan Province, central Anhui Province, and southwestern Yunnan Province (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnder the SSP585 climate scenario, from 2021 to 2040 compared to the current period, the suitable area of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e expanded by 178,944 km² and contracted by 63,675 km². The main expansion zones were southern Henan Province and southern Anhui Province, while the primary contraction zones were southwestern Yunnan Province, central Guangxi Zhuang Autonomous Region, and central Jiangxi Province. From 2041 to 2060 compared to 2021–2040, the suitable area expanded by 185,483 km² and contracted by 77,008 km². The main expansion zones were central Shaanxi Province, central Henan Province, northern Anhui Province, and central Jiangsu Province, while the primary contraction zones were eastern Jiangxi Province, western Fujian Province, and northern Guangdong Province. From 2061 to 2080 compared to 2041–2060, the suitable area expanded by 390,816 km² and contracted by 165,333 km². The main expansion zones were central Shaanxi Province, southern Shanxi Province, northern Henan Province, and southern Shandong Province, while the primary contraction regions were southwestern Yunnan Province, southern Guangdong Province, and central Jiangxi Province. From 2081 to 2100 compared to 2061–2080, the suitable area expanded by 286,700 km² and contracted by 473,088 km². The main expansion zones were southern Gansu Province, northern Shaanxi Province, central Shanxi Province, southwestern Hebei Province, and central-northern Shandong Province, while the primary contraction zones were southwestern Yunnan Province, northern Guangdong Province, eastern Guangxi Zhuang Autonomous Region, eastern Hunan Province, and northwestern Jiangxi Province (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnder the current climatic conditions, the distribution center of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is located in Tianzhu County, Qiandongnan Miao and Dong Autonomous Prefecture, Guizhou Province. Under the SSP126 climate scenario, during the periods 2021–2040, 2041–2060, 2061–2080, and 2081–2100, the distribution center is located in Zhijiang Dong Autonomous County, Huaihua City, Hunan Province, with a northwestward shift within Zhijiang Dong Autonomous County. Under the SSP585 climate scenario, the distribution center is located in Zhijiang Dong Autonomous County, Huaihua City, Hunan Province during 2021–2040. In 2041–2060, the center shifts to Mayang Miao Autonomous County, Huaihua City, Hunan Province; in 2061–2080, it moves to Yongshun County, Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province; and in 2081–2100, it shifts further to Enshi City, Enshi Tujia and Miao Autonomous Prefecture, Hubei Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Under the SSP126 climate scenario, the distribution center of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e migrates from Guizhou Province to Hunan Province. Under the SSP585 scenario, it migrates from Guizhou Province to Hunan Province and further to Hubei Province. Overall, the distribution center shows a trend of northward migration in the future. Compared to SSP126, the SSP585 climate scenario results in a larger magnitude of distribution center migration.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eTirpitzia sinensis\u003c/em\u003e, as an important traditional medicinal plant in China, possesses significant medicinal value in its stems and leaves, including promoting blood circulation to remove stasis, reducing swelling and alleviating pain, as well as joining bones and tendons. This study employed the MaxEnt model to predict the potential geographical distribution patterns of this species under current and future climate change scenarios, and identified key environmental factors influencing its distribution, providing a theoretical basis for the scientific conservation and management of this important medicinal plant resource.\u003c/p\u003e \u003cp\u003eModel construction and evaluation\u003c/p\u003e \u003cp\u003eThis study systematically integrated 174 distribution point data, covering the core distribution areas in southwestern China and marginal regions in Vietnam. Spatial filtering was performed using ENMtools to effectively reduce data clustering bias. Based on contribution rates and Spearman correlation analysis, 10 key variables (such as bio18 and bio06) were selected from the initial environmental variables, significantly mitigating the risk of overfitting caused by variable collinearity. The Kuenm R package was used to optimize feature combinations (FC) and regularization multipliers (RM), determining the optimal model parameter combination. Model evaluation results showed that after 10 repeated runs, the AUC values for both the training and test sets reached 0.985, indicating excellent predictive performance of the model. However, it should be noted that this study still has the following methodological limitations: First, the issue of spatial autocorrelation has not been fully addressed (Naimi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which may affect the reliability of model parameter estimates; second, independent validation sets or temporally stratified sampling methods were not used for model validation (Hijmans, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These factors may have somewhat impacted the robustness of the model results.\u003c/p\u003e \u003cp\u003eMechanisms by which key environmental factors influence the distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe modeling results indicate that the potential geographical distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is primarily influenced by four environmental variables: precipitation of the warmest quarter (bio18), minimum temperature of the coldest month (bio06), standard deviation of temperature seasonality (bio04), and mean temperature of the driest quarter (bio09). These factors collectively constrain the species\u0026rsquo; distribution limits by affecting water availability, thermal tolerance, and climatic stability, reflecting a multifactorial ecological adaptation strategy.\u003c/p\u003e \u003cp\u003eAmong these, bio18 emerged as the most influential variable, with a contribution rate of 57.8%. This variable reflects not only moisture availability during the growing season but also aligns closely with the reproductive phenology of the species. Previous studies have shown that \u003cem\u003eTirpitzia sinensis\u003c/em\u003e flowers primarily between May and August (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), during which precipitation levels critically influence reproductive success. Excess rainfall during this period can lead to flower drop, thereby reducing fruit set. This sensitivity to summer precipitation partially explains its preference for relatively dry habitats. Such ecological preferences are consistent with its predominant occurrence in limestone karst regions, which are characterized by nutrient-poor soils and rapid water drainage. The minimum temperature of the coldest month (bio06) determines the species\u0026rsquo; overwintering survival capacity and poses a major constraint on its poleward or altitudinal expansion. Cold stress has been shown to inhibit cell division and compromise membrane integrity, leading to reduced germination and slower growth (Sakai \u0026amp; Larcher, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). The suitable temperature range for overwintering in \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is estimated to be 2.6\u0026ndash;8.1\u0026deg;C, suggesting a limited tolerance to cold environments. Likewise, bio09 plays an important role in regulating the species\u0026rsquo; physiological activity under seasonal drought. High temperatures during the dry season increase evapotranspiration, and when soil moisture is insufficient, water deficits can result in early wilting and growth suppression (Chaves et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Temperature seasonality (bio04), which measures annual temperature fluctuations, also significantly contributes to the model. \u003cem\u003eTirpitzia sinensis\u003c/em\u003e tends to inhabit subtropical monsoon regions with relatively stable temperatures, favoring environments with moderate seasonal variability. This likely reflects its need for stable metabolic functioning. Studies have reported that excessive temperature variability can negatively affect seed germination and seedling establishment in shrub species (Jump \u0026amp; Peuelas, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), particularly those lacking dormancy mechanisms.\u003c/p\u003e \u003cp\u003eMoreover, the species exhibits morphological adaptations such as thickened leaves and low specific leaf area (SLA), which enhance drought tolerance and water storage capacity. These traits represent a typical resource-conservative strategy, allowing \u003cem\u003eTirpitzia sinensis\u003c/em\u003e to survive under water-limited and thermally stressful conditions (Xiong et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The synergy between climatic filtering and functional trait expression explains the species' high sensitivity to both temperature and moisture variables, and defines the ecological boundaries of its realized niche.\u003c/p\u003e \u003cp\u003eImpact of future climate change on habitat adaptability of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003eUnder future climate scenarios, the potential suitable habitat of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is projected to exhibit a general trend of northward expansion. Particularly under the high-emission SSP585 scenario, the total suitable area is expected to reach a peak of 2.344\u0026nbsp;million km\u0026sup2; during the period 2061\u0026ndash;2080, with the distribution centroid shifting from Guizhou to parts of Hunan and Hubei. This indicates that \u003cem\u003eTirpitzia sinensis\u003c/em\u003e possesses strong climatic niche tracking capacity, enabling it to cope with global warming to some extent through geographic migration toward regions with hot and dry climates.\u003c/p\u003e \u003cp\u003eHowever, the actual migration process may be constrained by several ecological factors. First, \u003cem\u003eTirpitzia sinensis\u003c/em\u003e may exhibit niche conservatism, relying heavily on its native dry-hot environmental conditions and specific soil properties, which could limit its ability to establish in newly suitable regions (Wiens \u0026amp; Graham, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Second, the species primarily relies on insect pollination and short-distance seed dispersal. In the fragmented karst landscape, these dispersal limitations are likely to result in a lagged migration response, with actual expansion rates falling behind the rate of climate-driven habitat shifts (Corlett \u0026amp; Westcott, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, \u003cem\u003eTirpitzia sinensis\u003c/em\u003e may face biological interaction challenges in newly suitable areas. Changes in precipitation during the flowering season could interfere with pollinator activity, while native drought-tolerant shrubs may outcompete its seedlings and inhibit population establishment. These factors, which are not accounted for in the current species distribution model, underscore the importance of incorporating ecological processes into future model development for more realistic projections.\u003c/p\u003e \u003cp\u003eIt is also worth noting that this study employed a single General Circulation Model (BCC-CSM2-MR) for climate input and did not integrate the uncertainty range derived from multiple climate models (Buisson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Future research should consider CMIP6 multi-model ensemble approaches and dispersal simulations to improve the ecological realism and predictive reliability of habitat shift assessments. In summary, although \u003cem\u003eTirpitzia sinensis\u003c/em\u003e demonstrates certain adaptive potential under climate change, the actual shifts in its distribution will be jointly governed by climatic suitability, ecological niche characteristics, species interactions, and dispersal constraints. These insights provide a theoretical basis for climate-resilient conservation and assisted migration strategies for this ecologically specialized medicinal shrub.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed the MaxEnt model with 174 validated distribution records and 10 environmental variables to predict the potential distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e in China under current and future climate scenarios. Under current climatic conditions, \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is primarily distributed in western Guangxi Zhuang Autonomous Region, southeastern Yunnan Province, southern Guizhou Province, southeastern Sichuan Province, and southwestern Chongqing Municipality. Under future climate change scenarios, the suitable zones for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e are expected to expand and exhibit a northward migration trend. In the SSP126 climate scenario, the distribution center of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e shifts from Guizhou Province to Hunan Province, while in the SSP585 scenario, it moves further from Guizhou Province to Hunan and Hubei provinces. The main environmental factors influencing the probability of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e presence include bio18, bio06, bio04, and bio09. The suitable ranges for these factors are as follows: bio18 (582.4\u0026ndash;907.1 mm), bio06 (2.6\u0026ndash;8.1\u0026deg;C), bio04 (432.9\u0026ndash;712.8), and bio09 (7.7\u0026ndash;14.1\u0026deg;C). These findings indicate that \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is well-suited to grow in warm and relatively dry regions with adequate precipitation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest\u003c/strong\u003e \u003cp\u003eThe research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (32360330, 32360262), China Scholarship Council (202308520101), Guizhou Provincial Program on Commercialization of Scientific and Technological Achievements (QianKeHeChengGuo [2022]010), Water-Fertilizer Coupling and Biodiversity Restoration in Karst Rocky Desertification (QianJiaoJi[2023]004), The Joint Fund of the National Natural Science Foundation of China and the Karst Science Research Center of Guizhou Province (U1812401).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.-X.X.and M.-Y.L.conceived and designed the study; M.-Y.L., Z.C., and Z.-R.D. conducted field surveys to obtain partial latitude and longitude data of Tirpitzia sinensis; M.-Y.L. collected the data; T.-X.X. and M.-Y.L. analyzed the data and drafted the manuscript; T.-X.X., M.-Y.L., and S.W. revised various drafts of the manuscript. All authors have read and agreed to the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to express their sincere gratitude to the WorldClim database for providing the valuable climate data used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., \u0026amp; Courchamp, F. (2012). Impacts of climate change on the future of biodiversity. \u003cem\u003eEcol Lett, 15\u003c/em\u003e(4), 365-377. https://doi.org\u003cstrong\u003e/\u003c/strong\u003e10.1111/j.1461-0248.2011.01736.x\u003c/li\u003e\n\u003cli\u003eBestion, E., Teyssier, A., Richard, M., Clobert, J., \u0026amp; Cote, J. (2015). 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Analysis of Potential Geographic Distribution of Solanum rostratum Based on Optimized MaxEnt Model in Agro-pastoral Ecotone of Northern China. . \u003cem\u003eActa Agrestia Sinica\u003c/em\u003e, 1-14. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Tirpitzia sinensis, Maximum Entropy Model, Potential Distribution Area, Climate Change, Environmental Factors","lastPublishedDoi":"10.21203/rs.3.rs-6863150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6863150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eTirpitzia sinensis\u003c/em\u003e, a traditional medicinal plant, is known for its ability to promote blood circulation, reduce swelling, alleviate pain, and assist in bone healing through its stems and leaves. This study utilized 174 valid distribution records of \u003cem\u003eTirpitzia. sinensis\u003c/em\u003e in China, along with 10 environmental variables, to predict its current and future distribution patterns. Using the MaxEnt model and ArcGIS software, the study also identified the climatic factors limiting its distribution. The results are as follows: (1) The MaxEnt model demonstrated extremely high predictive accuracy, with an Area Under the Curve (AUC) value of 0.985. Currently, the total suitable area for \u003cem\u003eTirpitzia sinensis\u003c/em\u003e covers 1,869,438 km\u0026sup2;, accounting for 19.47% of China's land area. This area is mainly concentrated in western Guangxi Province, southeastern Yunnan Province, southern Guizhou Province, southeastern Sichuan Province, and southwestern Chongqing Municipality. (2) The primary environmental factors influencing the potential distribution of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e include precipitation during the warmest quarter (bio18), minimum temperature of the coldest month (bio06), temperature seasonality (bio04), and mean temperature of the driest quarter (bio09). (3) Under future climate change scenarios, the potential distribution area of \u003cem\u003eTirpitzia sinensis\u003c/em\u003e is expected to expand compared to the current distribution, with an overall trend of northward migration. Specifically, under the SSP126 climate scenario, the distribution center is projected to shift from Guizhou Province to Hunan Province. Under the SSP585 climate scenario, this shift extends further towards Hunan and Hubei Provinces.\u003c/p\u003e","manuscriptTitle":"Prediction of the Potential Distribution of Tirpitzia sinensis in China Based on the MaxEnt Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 10:00:00","doi":"10.21203/rs.3.rs-6863150/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-02T08:14:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T07:17:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T18:37:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256276622778962761749340213687444194423","date":"2025-07-22T22:10:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63150460723047641201859594627706323558","date":"2025-07-21T01:17:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-20T18:09:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318266231189164530354179055434779715908","date":"2025-06-25T07:18:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T13:21:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-15T22:16:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-15T22:15:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-06-10T12:31:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d5971d5b-00a9-4d7e-bd66-fdcd3237b21d","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T16:03:54+00:00","versionOfRecord":{"articleIdentity":"rs-6863150","link":"https://doi.org/10.1007/s10661-025-14604-3","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-09-15 15:57:42","publishedOnDateReadable":"September 15th, 2025"},"versionCreatedAt":"2025-06-25 10:00:00","video":"","vorDoi":"10.1007/s10661-025-14604-3","vorDoiUrl":"https://doi.org/10.1007/s10661-025-14604-3","workflowStages":[]},"version":"v1","identity":"rs-6863150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6863150","identity":"rs-6863150","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}