Species distribution data for LJF were collected through field surveys conducted in major growing areas in China from 2023. These sample areas included Gansu, Shanxi, Sichuan, Hunan, Hubei, Hebei, Henan, Shandong, Guangdong, Jiangxi, and Yunnan. The selection of these sample sites aimed to comprehensively cover the primary LJF-producing regions. These samples were identified by Professor Chen Yuan of Gansu Agricultural University. Additionally, we complemented by resource survey reports and literature reports, as well as various online databases, including the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/), the Chinese Virtual Herbarium database (CVH, http://www.cvh.ac.cn/), Flora of China, and regional flora databases, to obtain distribution points of LJF. In cases where specific geo-coordinates were lacking in certain records, Google Earth 7.0 was utilized to estimate latitude and longitude based on the described geographical locations. Subsequently, a geographic distribution map was created using ArcGIS 10.8. with map data sourced from the National Basic Geographic Information Center (http://www.ngcc.cn/ngcc/). In total, 298 specimen record points for LJF were ultimately identified and screened ( Figure 2 ).

We utilized a total of 36 environmental factors, comprising 19 bioclimatic variables, 3 soil variables, elevation, awc (AWC Range), and solar radiation variables ( Table 1 ). The 19 bioclimatic variables, solar radiation, and elevation data were presented in grid format with a spatial resolution of 2.5 arcmin, approximately 1 km², obtained from the WorldClim database (https://www.worldclim.org) (Fick and Hijmans, 2017). Soil data were sourced from the Harmonized World Soil Database (HWSD, http://www.fao.org/soils-portal/) (Sanchez et al., 2010). For predicting future distributions, three shared socioeconomic pathways (SSP126, SSP370, and SSP585) were downloaded (BCC-CSM2-MR), representing radiative forcing levels in 2100 (Reddy and Saravanan, 2023). SSP126 (radiation intensity of 2.6 W/m²) reflects a low-emission scenario, SSP370 (radiation intensity of 7.0 W/m²) represents a medium-emission scenario, and SSP 585 (radiation intensity of 8.5 W/m²) corresponds to a high-emission scenario (Zheng et al., 2022b). We utilized the 2050s and 2090s to predict the future potential distribution of LJF. High correlation among environmental variables can lead to overfitting of the model. To avoid high correlation among variables, a correlation analysis was performed on the initial set of 36 environmental variables. Variables with a correlation coefficient |r| ≥ 0.8 were considered, and among them, those with significant contribution values were selected (Dong et al., 2022; He et al., 2021a; Herrando-Moraira et al., 2019). Ultimately, seven factors were identified and retained: bio4, bio6, bio12, eleven, s-oc, srad2, and srad4 ( Figure 3 ).

The MaxEnt 3.4.1 software integrates environmental variables and distribution data for predicting species distribution and habitat (Tang et al., 2021). Prior to model establishment, the TIFF (tag image file format) format of each variable is converted to the ASC (action script communication) format. The training set is set to 75%, and the test set is set to 25% in the model (Zhang et al., 2021). We employed 100 repetitions in “Bootstrap” mode with the output format set to “Logistic,” maintaining default settings for other parameters (500 iterations, 0.00001 convergence threshold, and 10,000 background points) (Shen et al., 2022). The weights of each factor in the suitable area of LJF were assessed using the knife-cutting method, identifying key limiting factors influencing the distribution of LJF. Model accuracy was evaluated using the ROC (receiver operating characteristic curve) and the area under the curve (AUC) value. The AUC value, ranging from 0.5 to 1.0, is widely used to assess the accuracy of prediction models, with higher values indicating greater accuracy (Vanagas, 2004; Wang et al., 2019). Based on the MaxEnt model results, the suitability distribution range of LJF in China was extracted using ArcGIS software.

The suitability was divided into four grades by Jenks’ natural breaks, namely, no suitability (0–0.2), low suitability (0.2–0.4), medium suitability (0.4–0.6), and high suitability (0.6–1), to obtain the potential distribution area of LJF (Zheng et al., 2022b; Shen et al., 2022).

The SDM toolbox, integrated into ArcGIS, is a software package designed to calculate changes in the suitable area for species. It helps determine the migration direction and distance of species over time. In this study, the core changes in LJF distributions were identified by converting predictions of species’ suitable distributions into binary vectors, where the species suitability probability p ≥ 0.04 is considered to be the total suitable region, while p < 0.04 is considered to be unsuitable. Then, using the Spatial Analysis Tools, the centroid coordinates of the LJF were precisely located based on different climate predictions. Finally, changes in the distribution were examined by tracking the shifts in the centroid across various SDMs

Weigh 1.0 g of LJF powder and place it in a stoppered conical flask. Add 50 mL of 50% methanol, record the mass, and perform ultrasonic extraction for 30 min. After cooling, compensate for the weight loss with 50% methanol, shake the mixture, and filter it. Then, filter the sample vial with a 0.22-μm microporous membrane and store it in the sampling bottle for HPLC analysis.

To establish HPLC fingerprints, the chromatographic conditions were optimized and the established method was verified. (1) Method Establishment: On the HPLC detection platform, the analytical conditions were set as follows: (a) Column, Symmetry C18 (5 µm, 4.6 mm * 250 mm); (b) Mobile phase: Acetonitrile (Merck) as mobile phase A, ultrapure water with 0.3% methanoic acid (Aladdin) as mobile phase B; (c) The gradient elution program is shown in Table 2 ; (d) Wavelength, column temperature, flow rate, and injection volume: 245 nm, 38°C, 1 mL min⁻¹, and 10 μL, respectively. (2) Method Validation: Evaluate precision by injecting the same sample solution six times in a row, and assess repeatability by analyzing six different sample solutions prepared in parallel with the same procedure and batch of LJF. Stability tests of the sample solutions are conducted by analyzing them at 0-, 2-, 4-, 8-, 16-, and 24-h intervals (Jin et al., 2009; Wan et al., 2021).

For the sake of studying the effect of habitat suitability on the accumulation of ecological factors, we extracted the ecological factor values of the sampling points by ArcGIS based on the longitude and latitude information of LJF. Then, the stepwise regression method was performed using R 4.3.2 software to conduct a correlation analysis of the chemical composition of medicinal materials, including Hyperoside (Hyp), Rutin (Rt), Chlorogenic acid (Ch.a), Cynaroside (Cyn), Isochlorogenic acid A (Isa A), and ecological factors. Finally, a correlation analysis was constructed to assess the relationship between the chemical composition and ecological factors. This analysis was superimposed with the habitat suitability distribution map of LJF to create a medicinal quality zoning map.

The AUC value (area under the ROC curve), being unaffected by the threshold, is widely used to assess the accuracy of prediction models (Zheng et al., 2022b). An AUC between 0.9 and 1 signifies an “excellent” model, while an AUC between 0.8 and 0.9 indicates a “good” model (Swets, 1988). In the context of ecological factors, the ROC curve generated by the MaxEnt model illustrates an AUC value of 0.892 for the LJF distribution model based on 36 environmental variables ( Figure 4A ). This value surpasses 0.8, indicating the model’s good accuracy, and the prediction results can be used for studying the division of LJF suitable areas.

Supplementary Table S1 provides an overview of the contribution and significance of Maxent model factor variables in shaping the distribution of LJF. In the current period, the seven variables that contribute the most to LJF are bio6, bio12, bio4, srad2, srad4, elev, and s_oc, with contribution rates of 44.6%, 40.3%, 8.5%, 3.2%, 2.4%, 0.8%, and 0.1%, respectively. The permutation importance rates for these variables are 58.1%, 25.5%, 4.6%, 0.5%, 7.7%, 2.2%, and 0.4%, respectively. Jackknife test simulations for the current period demonstrated that when simulations were conducted with a single variable, bio6 had the highest regularization training gain, indicating its provision of more effective model simulation information ( Figure 4B ). When the bio6 variable was removed from the simulation, the variable with the greatest decrease in regularization training gain was also bio6, suggesting that this variable had information about the other variables. Utilizing response curves ( Figure 5 ), we determined thresholds (presence probability p > 0.55) for key biotic factor parameters. The identified thresholds for these parameters are as follows: Min temperature of the coldest month (bio6) ranges from −4 to 4°C, temperature seasonality (bio4) spans 700 to 900, annual precipitation (bio12) falls between 500 and 1,000 mm, solar radiation in February (srad2) varies from 7,500 to 9,000 kJ·m⁻²·day⁻¹, solar radiation in April (srad4) ranges from 13,500 to 14,500 kJ·m⁻²·day⁻¹, subsoil organic carbon (s_oc) at 11,000, and elevation (elev) from 0 to 2,500 m. The results showed that temperature, precipitation, elevation, and soil characteristics are the primary ecological factors influencing the growth of LJF. LJF thrives in temperatures approximately 20°C (Zheng et al., 2022a). Extreme temperature will damage the photosynthetic activity of plants, affect the stability of proteins, accumulate excessive reactive oxygen species (ROS), and change the production and signal transduction of plant hormones, thus having a significant harmful effect on plant growth, development, quality, and so on (Ohama et al., 2017; Li et al., 2018; Huang et al., 2023). LJF typically thrives in precipitation ranging from 600 to 1,000 mm. Moreover, water availability significantly impacts seedling emergence and plant growth (Khaeim et al., 2022). Long-term drought will restrict the growth and development of plants, and the photosynthesis of plant leaves will also begin to decline, and the accumulation of secondary metabolites will be weakened (Chaves et al., 2009; He et al., 2021b; Yang et al., 2007). Altitude emerges as the dominant ecological factor influencing the pharmacodynamic components of LJF. The content of components increases with the elevation of the planting area, ranging from 4 to 2,100 m (Seyis et al., 2020). LJF, being a vine or woody plant, exhibits a preference for deep soil. Research indicates that the ideal soil type for LJF is neutral or slightly alkaline, characterized by loose, deep, fertile sandy soil, and a higher soil exchange capacity (Yang et al., 2017).

Initially predicted using the MaxEnt model, the distribution of LJF under current ecological factors is illustrated in Figure 4C . The identified distribution areas in northern Yunnan and Guangdong, central Sichuan and Hebei, central and eastern Shandong, and southern Liaoning align with relevant records (Sun et al., 2023). Subsequently, ArcGIS software was employed to reclassify the LJF distribution, leading to the determination of the potential suitable distribution area under the current ecological factors, as depicted in Figure 4D . The results indicate that the potential suitable distribution area of LJF covers 3,376,198 km², constituting 35.17% of the national land area. The highly suitable habitat area covered an expanse of 574,879 km², spanning the central and western parts of Sichuan (Chengdu and Ziyang), the southern part of Shandong (Linyi), the northern part of Henan (Xinxiang and Anyang), the southern and central eastern parts of Hebei (Xingtai and Chengde), the central and western parts of Guangdong (Qingyuan and Shaoguan), the southeastern part of Shanxi (Jincheng), the southern part of Liaoning (Dalian), Yunnan, and Taiwan. Moderately suitable areas are primarily distributed in the Taihang Mountains, Hengduan Mountains, Wuyi Mountains, Loess Plateau, and the middle and lower reaches of the Yangtze River, covering an area of 1,460,417 km². This indicates that the modern potential distribution suitability areas simulated by MaxEnt largely align with contemporary distribution records. In summary, LJF exhibits high adaptability in the Huaihe River Basin, Qinling, and North China Plain. The warm and humid climate, along with sufficient rainfall in most areas of Sichuan, Yunnan, and Guangdong in the southwest and southern regions, creates a conducive ecological environment for the growth of LJF. These findings further emphasize LJF’s adaptability to diverse ecological conditions.

The changes in the distribution of LJF under different future climate scenarios are depicted in Figure 6 and Supplementary Table S2 . In comparison to the present conditions, the total suitable area is projected to increase in the future, although the area of high suitable habitat is expected to decrease. For the period from 2041 to 2060, the highly suitable habitat areas under the SSP126, SSP370, and SSP585 climate scenarios are 288,333 km², 302,170 km², and 311,684 km², respectively. These figures represent a reduction of 49.84%, 47.44%, and 45.78%, respectively, compared to the current climate scenarios. The moderately suitable habitat areas under these scenarios increased by 22.38%, decreased by 1.58%, and increased by 14.06%, respectively. For the period from 2081 to 2100, the highly suitable habitat area under SSP126, SSP370, and SSP585 was 106,024 km², 120,035 km², and 379,427 km², respectively. All these values significantly decrease compared to current conditions. In contrast, the area of moderately suitable habitat increased. This is believed to be attributed to the gradual increase in greenhouse gas emissions, where elevated temperatures will limit plant growth, leading to the gradual transformation of highly suitable habitat areas into moderately suitable ones (Wan et al., 2023). In the future, LJF high suitability areas are expected to expand in southern Gansu, southeastern Tibet, and southern Liaoning ( Supplementary Figure S1 ). Compared with current climate conditions and future emission scenarios, the LJF high suitability area expands to higher-altitude areas. In future emission scenarios, rising temperatures associated with increased concentrations of CO₂ released can lead to species migration towards higher-altitude or -latitude areas (Bertrand et al., 2011; Pernicová et al., 2024).

The SDM_Toolbox within ArcGIS 10.8 was utilized to calculate the centroid distribution of LJF suitable areas under various environmental scenarios and time periods, thereby deriving the centroid migration trajectory ( Figure 7 ). Currently, the center of gravity of the suitable habitat for LJF is in Fancheng District, Fanyang City, Hubei Province (111.881004 E, 32.083149 N). Under the 2050s-SSP126 climate scenario, the center of gravity for highly suitable areas will shift eastward to Xixian County, Xinyang City, Henan Province (114.613104 E, 32.35066 N), with a migration distance of 258.73 km. Under the 2090s-SSP126 climate scenario, the center of gravity of the suitable habitat will migrate to the Duodao District in Jingmen City, Hubei Province (112.161545 E, 30.829002 N), with a migration distance of 141.97093 km. In the scenario of 2050s-SSP370, the center of gravity of the highly suitable area will migrate westward to Shennongjia District of Hubei Province (110.682713 E, 31.722339 N), covering a migration distance of 120.02 km. In the 2090s-SSP370 scenario, the gravity center moves southwest to Changyang Tujia Autonomous County, Yichang City (110.507134 E, 30.702936 N), covering a distance of 201.38847 km. Under the 2050s-SSP585 scenario, the center of gravity of suitable habitat will migrate eastward to Songxian County, Luoyang City, Henan Province (110.682713 E, 31.722339 N), with a migration distance of 210.94 km. Under the 2090s-SSP585 scenario, the center of gravity of suitable habitat moves to Yuancheng District, Nanyang City, Henan Province (112.624916 E, 32.90641 N). Simulation of future distributions indicates that under three different climate scenarios (SSP126, SSP370, and SSP585), the suitable distribution areas for LJF are decreasing. This phenomenon is also observed in other plants such as Leonurus japonicus and Leucanthemum vulgare (Wang et al., 2023b; Ahmad et al., 2019). In the high emissions scenario for 2050, temperatures are projected to rise by approximately 1.3°C and by over 2.79°C in the 2090s (Ford et al., 2012; Brouziyne et al., 2021). Consequently, the population of LJF is expected to generally decline, and habitat fragmentation is anticipated to become more severe than it is currently. Under the emission scenarios for the 2050s and 2090s, the predicted suitable areas are projected to shift towards southern regions at higher elevations. Studies have shown that the altitudinal distribution of species is largely driven by temperature gradients (Lenoir et al., 2008; Ashraf et al., 2016; Kumar et al., 2022). However, besides the impact of temperature changes, the suitable distribution of species may also involve biological characteristics, geological factors, or other disturbance factors (Dong et al., 2022). LJF thrives in moist, warm climates. Therefore, there is a tendency to migrate to the south.

Chromatographic fingerprinting enables the qualitative and quantitative assessment of TCM quality by analyzing chemical information and depicting the peak distribution of specific components. At a detection wavelength of 245 nm, there is high separation between chromatographic peaks, elevated response, symmetrical peak shapes, and a stable baseline, facilitating fingerprint establishment. The results of method validation showed that the relative standard deviation (RSD) for the precision of Rt, Ch.a, Hyp, Cyn, and Isa A were 2.3%, 2.2%, 3.6%, 3.5%, and 1.9%, respectively. The RSD for reproducibility were 2.0%, 3.7%, 3.2%, 3.1%, and 2.7%, respectively, and the RSD for stability were 3.0%, 3.2%, 2.8%, 3.1%, and 2.5%, respectively. These results indicate that the established method is suitable for the development of HPLC fingerprints of LJF. The Chromatographic Fingerprint is imported into the Similarity Evaluation System for Chromatographic Fingerprint of TCM (version 2012). Subsequently, the reference spectrum and time width are set, peak areas are normalized, multi-point correction is conducted, peaks are automatically matched, and the standard fingerprint is generated. The standard fingerprints of LJF in 11 different regions are presented in Figure 8A . Based on chromatographic peak matching information, 21 common peaks were selected. The similarity values ranged between 0.817 and 0.989, indicating that the chemical composition of LJF from different locations varied to some extent ( Table 3 ). Five compounds, Ch.a, Rt, Hyp, Cyn, and Isa A, were identified through the control ( Figure 8B ). Subsequently, a quantitative analysis of the five components in LJF from different origins was conducted ( Supplementary Figure S2 ). The results revealed that the average contents of Ch.a, Hyp, and Isa A were higher in medium and high suitable areas than in low suitable areas. The contents gradually decreased from high suitable areas to low suitable areas, whereas the contents of Rt and Cyn in low suitable areas were higher than those in medium suitable areas. This indicates that the suitable growth area is not necessarily conducive to the accumulation of all secondary metabolites. Research indicates that certain species, under the influence of environmental stress, produce specific secondary metabolites to enhance adaptability to adversity and facilitate the formation of authentic medicinal materials (Li et al., 2020).

The five identified components were correlated with the ecological factors identified by the MaxEnt model ( Figure 9 ), indicating a significant impact of ecological factors on these components. In LJF, Hyp and Isa A show negative correlations with srad3, srad4, srad5, and srad6, while Rt, Ch.a, and Cyn exhibit a positive correlation (p < 0.01). Additionally, Rt, Ch.a, and Cyn display negative correlations with bio9, bio19, bio18, bio17, bio16, bio11, bio12, bio13, and bio14, whereas they are positively correlated with bio8. Furthermore, bio8 shows a negative correlation with Hyp (p < 0.05) and a positive correlation with Ch.a (p < 0.05). The correlation between the same ecological factor and different components varies, as does the correlation between different ecological factors and the same component. To address this issue, the spatial analysis function and correlation model of ArcGIS software were employed to create spatial distribution maps for the content of four common peaks (the analysis was not conducted for the correlation between Cyn and factors due to its lack of obvious correlation). Figure 10 illustrates the spatial distribution of the contents of Hyp and Ch.a. Ultimately, the content spatial distribution maps of LJF and the habitat suitability map were superimposed to generate the quality zoning map of LJF in the distribution area, as depicted in Figure 11 . In Figure 11 , high-quality LJF is predominantly distributed in central and southern Hebei Province, northern Henan Province, central Shandong Province, central Sichuan Province, southern Guangdong Province, and the Taiwan Peninsula. Poor-quality LJF is distributed in northern Gansu, western Sichuan, northeastern Liaoning, and northwestern Shanxi. By comparing the zoning map with the habitat suitability distribution map, it is evident that the low-quality area is not a suitable area. This implies that ecological factors unfavorable to the growth of LJF also hinder the accumulation of LJF pharmacodynamic components. However, suitable areas are not necessarily high-quality areas. Optimal conditions for the growth and development of certain herbs and the accumulation of secondary metabolites do not necessarily align. This discrepancy can be attributed to the complexity involved in the synthesis and accumulation of secondary metabolites in medicinal plants. Research indicates that active ingredients (secondary metabolites) are produced and accumulate under stressful (adverse) conditions. Since the active substances in medicinal plants are predominantly secondary metabolites, this is largely considered a result of plant adaptation to adversity (Al-Khayri et al., 2023). In this study, by considering the spatial correlation between habitat suitability and quality suitability, an LJF quality zoning map was obtained, facilitating LJF production zoning.