Predicting the potential habitats of two Lycium species and the quality suitability of Lycium Chinese Mill. Cortex under climate change

Predicting the potential habitats of two Lycium species and the quality suitability of Lycium Chinese Mill. Cortex under climate change | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting the potential habitats of two Lycium species and the quality suitability of Lycium Chinese Mill. Cortex under climate change Yuting Liu, Zikang Lu, Xiangrui Fu, Chaohui Wang, Chao Feng, Yongxing Song, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5490896/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jul, 2025 Read the published version in BMC Ecology and Evolution → Version 1 posted 9 You are reading this latest preprint version Abstract Lycii Cortex is a frequently utilized traditional Chinese medicine with notable therapeutic properties. The impact of climate change on its distribution and quality of Lycii Cortex is a significant concern. In this study, it investigated the geographic distribution of two sources of plants for Lycii Cortex and collected data on the distribution of samples from different origins via an online survey. HPLC was employed to ascertain the concentrations of kukoamine B and kukoamine A in the samples. Subsequently, the integrated ecological factor data were employed to forecast the prospective expansion areas of Lycium Chinese Mill. and Lycium barbarum L. under future climatic conditions, the migration trajectory of suitable habitat centers of mass, and the potential impact of climatic factors on the quality of Lycii Cortex at varying times using Maxent and ArcGIS. The current climate scenario indicates that suitable habitats for L. barbarum are primarily distributed in the northern, northwestern, and southwestern regions of China, while L. Chinese is predominantly distributed in the central, southern, and southeastern regions of China. In the RCP4.5 from 2050s to 2070s, the total area deemed suitable for both two Lycii Cortex species is significantly reduced. The mean distribution center of L. barbarum shifted towards higher latitudes, while that of L. Chinese shifted towards lower latitudes. It was predicted that in the future, the area of suitable quality of Lycii Cortex would appear to decrease. The results of this study can provide a reference for the determination of the suitable cultivation area of Lycii Cortex in China and the sustainable development of two Lycium species resources. Suitability area Lycii Cortex Maxent Climate factors HPLC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Climatic factors significantly influence the geographical distribution of species. The growth of the population has been demonstrated to result in increased resource demand and energy consumption, which has been shown to lead to elevated carbon emissions. These changes will lead to an increase in adverse impacts on future climate and a continuous rise in global temperatures [ 1 ]. Global warming and the increased frequency of extreme weather events are expected to alter the geographical distribution of numerous plant species [ 2 – 4 ]. It is predicted that under future climate scenarios, the distribution area of Acacia mearnsii , a harmful weed, will expand in Asia. Furthermore, global climate change is likely to increase the risk of invasion of this plant in Asia [ 5 ]. Vishnu posited that climate fluctuations may result in a reduction of suitable habitats for the Indigenthus virus (Roxb.) in India and Sri Lanka, both currently and in the future [ 6 ]. Moreover, the effects of climate change on the distribution and bioactive components of medicinal plants warrant further investigation [ 7 ]. For instance, Uranishi et al studied the significant changes in the content of chlorogenic acid and dicaffeoylquinic acid isomers in the leaves of Chrysanthemum indicum L. under the influence of environmental factors. And climatic change was found to have the most significant impact on the suitability of its habitat [ 8 ]. It can be reasonably deduced that the suitability of a habitat has a considerable influence on the accumulation of secondary metabolites in medicinal plants [ 9 – 11 ]. Lycii Cortex , commonly referred to as wolfberry root, is derived from the dried root bark of the Solanaceae plant Lycium chinense Mill. or Lycium barbarum L. [ 12 ]. Lycii Cortex is widely used in proprietary Chinese medicines and Chinese herbal formulas [ 13 ], and can effectively treat vomiting blood, hemoptysis, and lung-heat cough. Recent studies have demonstrated that Lycii Cortex has the capacity to reduce blood sugar, blood pressure, blood lipids, and anti-osteoporotic activity [ 14 , 15 ]. A considerable number of active ingredients are present in Lycii Cortex [ 16 ]. However, there is currently no established standard for determining the content of indicator ingredients in Lycii Cortex as outlined in the Chinese Pharmacopoeia [ 12 ]. In recent years, there have been many reports of alkaloids such as kukoamine B and kukoamine A in Lycii Cortex [ 17 , 18 ]. Li et al. mention that kukoanes B may regulate nuclear transcription factors to reduce inflammation [ 19 ]. Kukoanes A and B have been demonstrated to treat Alzheimer's disease (AD) and type 2 diabetes (T2D) by inhibiting amyloid aggregation [ 20 ]. Studies indicate that Lycii Cortex requires 4–5 years of growth to accumulate adequate active ingredients [ 21 ]. It is plausible that climate factors exert a considerable influence on its growth. Furthermore, previous historical research on the origin of Lycii Cortex indicates that climate change may also affect the changes in the origin of two sources of plants for Lycii Cortex [ 22 ]. Climate change and environmental overexploitation have significantly diminished the wild resources of Lycii Cortex [ 23 ]. Nevertheless, the majority of studies on the composition and pharmacological activity of Lycii Cortex have concentrated on its components [ 24 , 25 ]. In contrast, studies examining the overall distribution and quality evaluation of this plant are scarce, with a greater focus on the study of Chinese wolfberry [ 26 – 28 ]. To evaluate the influence of climate change on species distribution, this study utilizes representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) for the 2050s and 2070s, which model potential greenhouse gas emissions [ 29 – 31 ]. The focal point of this study is the direct impact of natural environmental variables on species distribution, precluding the involvement of socio-economic driving factors such as urbanization and agricultural expansion. Consequently, the Representative Concentration Pathways (RCPs) provide clear climate forcing pathways, thereby circumventing uncertainty introduced by complex socio-economic parameters. Consequently, the Shared Socioeconomic Pathways (SSP) pathway, which incorporates a more comprehensive range of socio-economic factors, was not selected. The Maximum Entropy Model (Maxent) is a statistical model that correlates species occurrence with environmental variables. Its objective is to predict potential spatial shifts, identify influencing factors and determine the likely direction of migration of species distribution [ 32 ]. Maxent is widely utilized for its simplicity, rapid modeling capabilities [ 33 ], and effectiveness in predicting species distribution, particularly with small sample sizes and limited geographic ranges [ 34 ]. It is also recognized for its accuracy and reliability [ 35 ]. Maxent has been extensively applied to predict suitable regional and geographic distributions of various medicinal plant species, including simulations of climate change effects on the distribution of C. arabica in Ethiopia [ 36 ]. Hosseini et al. utilized the Maxent model to predict the distribution area of two Thymus species in Iran under climate change [ 37 ]. Furthermore, there have been studies employing the Maxent model to investigate the impact of climate change on the future distribution of three Ferulago species in Iran [ 38 ]. Similarly, Waheed et al. employed the Maxent model to forecast the potential geographic range of invasive species, such as X. strumarium , in Pakistan under the influence of climate change [ 39 ]. Furthermore, researchers studying Lonicera japonica Flos. integrated chemical content analysis with Maxent to assess the influence of ecological factors on quality, predict suitable habitats under various scenarios, and generate high-resolution habitat maps [ 40 ]. Various chemical indicators have been employed in these studies to evaluate how habitat suitability affects medicinal quality [ 41 ]. HPLC is used to determine the content of chemical components, which has high sensitivity and can quickly and efficiently separate and detect the type and content of compounds in samples. It is instrumental in assessing differences in secondary metabolites of medicinal plants across various regions [ 42 ]. Alkaloids represent the primary active ingredients within the secondary metabolites of Lycii Cortex . Among these, kukoamine B and kukoamine A are the predominant alkaloid components within Lycii Cortex . Accordingly, two alkaloidal components, kukoamine B and kukoamine A, were selected for this study as indicators for the determination of the content of active ingredients of Lycii Cortex . The objective of this study is to evaluate the habitat suitability of two sources of plants for Lycii Cortex , L. barbarum and L. Chinese , derived from disparate sources, in the context of both the present and future climates. To this end, the study will integrate data on their geographical distribution, information on the quality of their effective ingredients, and climate factors. Explore the relationship between climate factors and two bioactive alkaloids in Lycii Cortex , conduct quality suitability analysis, predict the quality suitability areas, and provide scientific basis for the sustainable development of two Lycium species and environmental protection of suitable planting areas. Materials and methods Species occurrence records and sample collection Data from L. barbarum and L. Chinese sources across China were obtained from the China Virtual Herbarium (CVH) ( https://www.cvh.ac.cn/ ) and the Global Biodiversity Information Facility (GBIF) ( https://www.gbif.org/ ). L. barbarum and L. Chinese samples were found throughout China. L. barbarum was predominantly distributed in Ningxia, Xinjiang, and Inner Mongolia, aligning with recent studies on its distribution [ 43 ]. In contrast, L. Chinese was mainly found in central and southern China, consistent with Tang et al.'s observations [ 44 ]. Additionally, 14 records of Lycii Cortex were collected from various provinces: 4 from Henan, 2 from Shanxi, 2 from Jiangsu, and 1 each from Shaanxi, Hebei, Hubei, Anhui, Guangdong, and Jilin. The 154 occurrences for L. barbarum and 740 occurrences for L. Chinese were collected in this study. To reduce sampling bias, we remove duplicate samples and obvious geocoding errors or blurred data as much as possible. At the same time, considering the large scale of the study, we refer to other people’s data screening methods and the rationality and accuracy of the model operation [ 45 ]. To minimize sampling bias and eliminate the risk of counting the same points more than once, we consider non-overlapping observations obtained using a fishing net [ 46 ], and samples were screened within 16 km × 16 km geographic grids based on established data screening methods [ 47 , 48 ]. For grids with multiple Lycii Cortex samples, the sample nearest to the grid center was chosen as the representative sample. After applying the fishing net screening in ArcMap, 70 L. barbarum occurrences and 216 L. Chinese occurrences were identified (Fig. 1 ). The occurrence records for two sources of plants for Lycii Cortex were compiled into a '.csv' file for model construction using Maxent software. Model construction of the species distribution The 19 bioclimatic variables were identified as critical factors in developing niche models for the distribution of two species. Data from the Geospatial Data Cloud ( https://www.gscloud.cn/ ) and the Global Climate Database ( http://www.worldclim.org/ ) were utilized, covering the current period (1970–2000) and future projections for the 2050s (2041–2060) and 2070s (2061–2080). Bioclimatic variables (Bio1 ~ Bio19) had a spatial resolution of 1 km × 1 km and were analyzed under three greenhouse gas emission scenarios: RCP2.6, RCP4.5, and RCP8.5, derived from the Fifth Assessment Report of IPCC (Table S1 ). The 19 climate variables were screened by using Pearson correlation analysis, minimizing the risk of overfitting due to multicollinearity [ 49 ]. Climatic factors with high collinearity (r > 0.8) which deemed significant for two Lycium species growth were retained, and therefore were retained for analysis. The jackknife test method was employed to assess the relative importance of each environmental variable [ 50 ]. Subsequently, on the basis of the actual growth conditions of two Lycium species and the extant literature on the environmental response of Lycium plants and closely related taxa, a subset variable was retained for further analysis [ 43 , 44 ]. We iteratively run the Maxent model, removing variables with 0 contribution at each step, and repeat this process ten times to obtain the final set of participating climate factors, with all other parameters set to default values. Based on previous researchers’ methods, correlation analysis and Jackknife experiments (Fig. S1 ) identified 9 climatic factors for L. barbarum and 11 for L. Chinese , which were used for Maxent modeling (Table 1 ) [ 45 ]. This model was used to predict the potential distributions of L. barbarum and L. Chinese under current and future scenarios. The model was constructed using Maxent software (version 3.3.3k, Steven Phillips et al., New York, NY, USA). The software prevents sampling from non-occurrence habitats of the target species or compensates for survey bias using coordinate data [ 51 ]. In this study, the maximum number of background points was set to 1 × 10 4 , while the maximum number of iterations was set to 1 × 10 6 during data processing. The Maxent model utilizes an ASCII-coded file containing geographical distribution and environmental factor information for two sources of plants for Lycii Cortex . The 75% percent of the randomly selected distribution data was used to train the model, while the remaining 25% was reserved for testing and validation. The model's predictive power was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC) [ 52 ]. The AUC value indicates the model's performance, ranging from 0 to 1, with values closer to 1 representing stronger predictive accuracy. The AUC > 0.9 signifies optimal modeling results, AUC values between 0.7 and 0.9 are considered good, while the AUC < 0.7 generally indicates moderate model performance [ 53 , 54 ]. Using the reclassification tool in ArcMap10.8 and referring to the method of Li et al., it was used to categorize the occurrence probability of the two Lycii Cortex species into four levels: unsuitable area (0-0.2), poorly suitable area (0.2–0.4), moderately suitable area (0.4–0.6), and highly suitable area (0.6-1) [ 45 ]. Next, the center of suitability in the ecologically suitable area was analyzed using centroid migration. The spatial statistics tool was used to determine the average center of the ecologically suitable area, while local analysis merged periods under the same greenhouse gas emission scenario. The centroid migration trajectory was last derived using the point set transfer line function. HPLC chemical composition analysis Store the collected Lycii Cortex samples in a cool, dry, and well-ventilated area. Add 0.5 g of sample powder to 5 mL of a methanol-0.5% acetic acid aqueous solution, followed by ultrasonication for 35 min and centrifugation for 20 min. The supernatant is then filtered through a 0.22 µm membrane to prepare the test solution. The chemical composition was analyzed using HPLC equipped with a Zorbax SB-AQ C 18 column (250 mm × 4.6 mm, 5 µm). The mobile phase consisted of acetonitrile (A) and 0.1% trifluoroacetic acid in water (B). The gradient elution protocol was as follows: 0–20 min, 90–85% B; 20–30 min, 85–82% B; 30–35 min, 82–78% B; 35–50 min, 78–55% B; 50–60 min, 55–5% B; 60–65 min, 5% B. The total run time was 65 min, with a flow rate of 1.0 mL∙min⁻¹, detection wavelength of 280 nm, injection volume of 10 µL, and column temperature maintained at 40°C. Concurrently, the content of kukoamine B and kukoamine A in 14 Lycii Cortex samples from different habitats were measured, with the total content calculated as the sum of these two components. Lycium Chinese Mill. Cortex quality suitability analysis The concentrations of kukoamine B and kukoamine A were measured in Lycium Chinese Mill. Cortex samples from various production regions. The levels of these two compounds were employed as the primary criteria for evaluating quality in this study. 14 collection sites, identified as fitting the L. Chinese suitability distribution, were analyzed for their quality suitability. Regression analysis of the two components in the samples was performed using SPSS 23.0 (IBM, Shanghai, China), developing a model that links their concentrations to climatic factors. Although yearly variations in the quality of Lycii Cortex at the same location were noted, they were less significant than the effect of environmental factors on quality across different locations. To ensure the accuracy of environmental data at the collection sites, this study referenced previous studies [ 55 ] and employed environmental parameters from a 1 km × 1 km grid to predict the quality suitability area. The grid calculator and fuzzy overlay function in Arcmap 10.8 were applied to integrate spatial distribution maps of the two chemical components with ecological suitability maps, producing current and future integrated quality suitability maps [ 56 ]. Lastly, the mask extraction function was used to generate an integrated quality area map for the two alkaloid components of Lycii Cortex . Results Prediction and model evaluation of the distribution of two Lycium species Verification of modeling results In this study, the AUC values of the training set for both L. barbarum and L. Chinese exceeded 0.900 (Table 2 ). The results indicated that the Maxent model effectively predicted the suitable habitat distribution of two sources of plants for Lycii Cortex , demonstrating high confidence and accuracy in the model's calculations of habitat suitability. Environmental variables analysis The values presented in the variable contributions table of the Maxent analysis represent the average percentage contribution derived from 10 replicate runs. The results indicated that Bio2 (31.8%), Bio6 (16.4%), and Bio17 (15.5%) were the three primary environmental factors influencing L. barbarum distribution, with a cumulative contribution of 63.7%. However, based on Permutation Importance (PI), the three most impactful environmental factors were Bio12 (23.7%), Bio13 (22.4%), and Bio1 (15.5%). Consequently, the Maxent model was integrated with jackknife test analysis, revealing that Bio6, Bio1, and Bio13 are the most critical factors for L. barbarum habitat distribution, indicating that these factors provide the most valuable and unique information for determining L. barbarum distribution. The response curves of the primary ecological factors revealed that climatic influences on L. barbarum generally increased with rising factor values, with distribution probability initially rising before declining. Among these factors, the suitable range for the minimum temperature of the coldest month (Bio6) is -18 to -6.2°C, with an optimal temperature of -11°C, as shown in Fig. 2 A. In Fig. 2 B, the suitable range for annual average temperature (Bio1) is 5 to 12.5°C, with an optimal temperature of approximately 9°C. In Fig. 2 C, the suitable range for precipitation in the wettest month (Bio13) is 2.5 to 13 mm, with optimal precipitation around 6 mm. The three most important environmental variables with the greatest impact on L. Chinese were Bio1 (44.6%), Bio13 (33.6%) and Bio4 (5.4%), with a cumulative contribution rate of 83.6%. However, Bio13 (43.7%), Bio11 (9.6%), and Bio4 (9.4%) have greater PI. According to the results of jackknife, the three most important factors affecting the distribution of L. Chinese are Bio1, Bio13, and Bio12. The response curves of the primary ecological factors indicate that the suitable range for annual average temperature (Bio1) is 13.2 to 23°C, with an optimal temperature of 18°C, as shown in Fig. 2 D. As shown in Fig. 2 E, the suitable range for the wettest monthly precipitation (Bio13) is 16 to 56 mm, with an optimal precipitation of 25 mm. In Fig. 2 F, the suitable range for average annual precipitation (Bio12) is 75 to 180 mm, with an optimal precipitation of 162.5 mm, as depicted in Fig. 2 D-F. Predicted distribution under different scenarios Predicting the distribution of L. barbarum Figure 3 illustrates the potential distribution of two sources of plants for Lycii Cortex , as estimated by the Maxent model under current climate conditions. Based on the available data, the optimal growth regions for L. barbarum (Fig. 3 A) in current are predominantly concentrated in northern and northwestern China. The total suitable area reaches 2.6919 × 10 6 km², representing 25.75% of the total research area. The highly suitability area is primarily concentrated in Northwest China, central and southwestern Shanxi, western Hebei, central and eastern Tibet, and western Sichuan, collectively accounting for 4.26% of the total research area (Table 3 ). According to the predictions for the future period (Fig. 3 B-G), L. barbarum showed a decrease in the total suitable area of three suitable habitats compared to current scenarios, except for the RCP2.6 scenario in 2070s where the total suitable area increased. Specifically, in the RCP2.6-2050s and RCP2.6-2070s, the total suitable area of L. barbarum will decrease by 5.84% and increase by 5.83%, respectively, compared with the current period. The total suitable area for L. barbarum will decrease by 13.71% and 12.20% respectively by RCP4.5-2050s and RCP4.5-2070s. For the emission scenario of RCP8.5, the total suitable area for L. barbarum will decrease by 8.32% and 1.33% respectively by 2050s and 2070s. Additionally, the highly suitability area for L. barbarum exhibited varying degrees of decline under different emission scenarios, while the areas of moderately and poorly suitability showed both increases and decreases. The highly suitability area for L. barbarum is projected to decrease by 7.67% in the 2050s and by 11.38% in the 2070s under the RCP2.6 scenario; for the RCP4.5 scenario, it is expected to decrease by 8.93% and 7.48% in the 2050s and 2070s; it is anticipated to decrease by 13.38% in the RCP8.5-2050s and by 12.25% in the RCP8.5-2070s. The moderately suitable area is projected to increase by 0.32% and 1.41% in the RCP2.6-2050s and RCP2.6-2070s; under the RCP4.5 scenario, it is expected to decrease by 12.56% in the 2050s and by 5.08% in the 2070s; it is anticipated to decrease by 19.04% in the RCP8.5-2050s but increase by 11.84% in the RCP8.5-2070s. The poorly suitability area is projected to decrease by 8.09%, 15.61%, and 2.04% under different emission scenarios by the 2050s; under the RCP2.6-2070s, it is expected to increase by 12.77%, while under the RCP4.5-2070s and RCP8.5-2070s, it is anticipated to decrease by 16.77% and 4.12%, respectively. Predicting the distribution of L. Chinese The areas deemed suitable for cultivation of L. Chinese (Fig. 3 H) in current are primarily concentrated in North China, Central China, South China, East China, and Southwest China, with some distribution in Northeast and Northwest China. The total area deemed suitable is 3.3516 × 10 6 km², representing 32.07% of the total research area. The highly suitability area is primarily situated in central China, central Hebei, central Shaanxi, central Zhejiang, northern Guangdong, north-eastern Guangxi, Chongqing, eastern Sichuan, and other regions, collectively accounting for 2.53% of the total research area (Table 4 ). For L. Chinese , the total suitable area in future projections (Fig. 3 I-N) is not expected to change significantly, although a general reduction in area is anticipated (Table 4 ). The total suitable area for L. Chinese is projected to decrease by 4.26% in the RCP2.6-2050s and by 4.55% in the RCP2.6-2070s. The total suitable area for L. Chinese is anticipated to decrease by 2.67% in the RCP4.5-2050s and by 2.48% in the RCP4.5-2070s. In the RCP8.5-2050s and RCP8.5-2070s, the total suitable area for L. Chinese is expected to decrease by 4.20% and 3.35%. The areas of highly and moderately suitability for L. Chinese have significantly decreased under various scenarios, while the area of poorly suitability has increased in all scenarios except for the RCP8.5-2070s. Under the RCP2.6 scenario, highly and moderately suitability areas are projected to decrease by 10.60% in the 2050s and by 27.62% in the 2070s; they are expected to decrease by 10.40% in RCP4.5-2050s and by 22.37% in RCP4.5-2070s; they are anticipated to decrease by 12.92% and 19.56% in the RCP8.5-2050s and RCP8.5-2070s. The moderately suitability area is projected to decrease by 11.04% in RCP2.6-2050s and by 5.38% in RCP2.6-2070s; it is expected to decrease by 6.39% in RCP4.5-2050s and by 6.24% in RCP4.5-2070s; for the RCP8.5 scenario, it is anticipated to decrease by 16.57% and 2.33% in the 2050s and 2070s. The poorly suitability area is projected to increase by 6.36% in the 2050s and by 1.28% in the 2070s under the RCP2.6 scenario; it is expected to increase by 6.41% in RCP4.5-2050s and by 6.73% in RCP4.5-2070s; under the RCP8.5 scenario, it is anticipated to increase by 14.08% in the 2050s but decrease by 1.45% in the 2070s. The migration trend of geometric centers of suitable habitats for different periods of L. barbarum and L. Chinese under climate change In this study, the geometric center represents the central spatial location of the potentially the total suitable area for two sources of plants for Lycii Cortex under three emission scenarios, spanning from the present to the 2050s and 2070s. At present, the location of the most suitable habitat for the potential geometric center of L. barbarum is identified as Gande County Qinghai Province (100.663555°E, 34.224862°N). The geometric center of L. barbarum in the RCP2.6 scenario exhibited a migration of 92.76127 km from Gande County Qinghai Province, to Zeku County Qinghai Province, and subsequently, an additional 81.13688 km to Tongde County Qinghai Province. Under the RCP4.5 scenario, the geometric center initially migrated 85.52054 km within Gande County Qinghai Province, and subsequently migrated 88.34171 km to Maqin County Qinghai Province. Under the RCP8.5 scenario, the geometric center exhibits a migration of 129.36854 km from Gande County Qinghai Province to Zeku County Qinghai Province, followed by a further migration of 62.16803 km to Tongde County Qinghai Province (Fig. 4 A). In the current scenario, the most suitable habitat for the geometric center of L. Chinese is located in Shizhu Tujia Autonomous County, Chongqing (108.165436°E, 29.867593°N). In the RCP2.6 scenario, the predicted geometric center of the suitable habitat for L. Chinese will migrate 82.9126 km from Shizhu Tujia Autonomous County Chongqing city to Qianjiang District Chongqing city, and then 44.0186 km to Pengshui Miao Tujia Autonomous County Chongqing city from 2050s to 2070s. For the RCP4.5 scenario, the predicted migration distance is 27.8479 km from Shizhu Tujia Autonomous County Chongqing city to Pengshui Miao and Tujia Autonomous County Chongqing city, followed by a further 33.88568 km to Shizhu Tujia Autonomous County Chongqing city. In the RCP8.5 scenario, the predicted migration distance is 86.2666 km from Shizhu Tujia Autonomous County in Chongqing city to Xianfeng County in Hubei Province, followed by an additional 115.61227 km to Xiushan Tujia and Miao Autonomous County in Chongqing city (Fig. 4 B). Quality suitability analysis The content of alkaloid active ingredients in L . Chinese Cortex The contents of two alkaloids, kukoamine B and kukoamine A, were measured in the collected L. Chinese Cortex samples (Table 5 ). Among these samples, S1 was collected from highly suitable area, S2 to S11 were collected from moderately suitable area, and S12 to S14 were collected from poorly suitable area. The average contents of the two alkaloids in the samples from each suitability category were calculated, considering the unequal number of samples in each area. The results indicated that the average total alkaloid content in samples from highly and moderately suitable areas was higher than that in poorly suitable area. Correlation between the content of environmental variable components This study developed models to describe the relationship between the content of kukoamine B and kukoamine A and various ecological factors. The model describing the relationship between kukoamine B content and ecological factors is: Y 1 =-575.744-0.291X 1 -4.662X 2 + 21.452X 3 + 2.932X 4 -0.186X 5 + 0.319X 6 -10.116X 7 + 3.747X 8 ( P < 0.05, Y 1 represents the kukoamine B content in Lycii Cortex , X 1 = Bio1, X 2 = Bio2, X 3 = Bio3, X 4 = Bio5, X 5 = Bio12, X 6 = Bio13, X 7 = Bio14, X 8 = Bio19). The regression analysis results indicated that within specific ranges, Bio1, Bio2, Bio12, and Bio14 limit kukoamine B accumulation, while Bio3, Bio5, Bio13, and Bio19 promote its accumulation. The model describing the relationship between kukoamine A content and ecological factors is: Y 2 = 0.196X 1 -0.006X 2 -0.526X 3 + 0.178X 4 ( P < 0.05, Y 2 represents kukoamine A content, X 1 = Bio3, X 2 = Bio12, X 3 = Bio14, X 4 = Bio19). The analysis revealed that Bio3 and Bio19 promote kukoamine A accumulation within specific ranges, whereas Bio12 and Bio14 restrict its accumulation within similar ranges. Different periods quality suitability analysis Table 6 shows the current quality suitable areas and the proportion of area. The current spatial distribution and suitable area of kukoamine B content in L. Chinese are presented in Fig. 5 A (Table S2). The poorly suitability area for kukoamine B decreases in the 2070s under the RCP8.5 scenario but increases during other periods. The decrease in moderately suitable area is most pronounced in the 2050s under the RCP8.5 scenario, while the most significant decrease in highly suitable area occurs in the 2070s under the RCP2.6 scenario. The total suitable area is smaller than the current suitable area (Fig. 5 B-G). The current spatial distribution and suitable area of kukoamine A content in L. Chinese are presented in Fig. 5 H (Table S3). For kukoamine A, the poorly suitability area increases in the 2050s under the RCP4.5 and RCP8.5 scenarios but decreases during other periods. The moderately suitable area decreases in all periods except for the 2070s under the RCP8.5 scenario, while the highly suitable area increases in the 2050s under the RCP8.5 scenario but decreases in other periods (Fig. 5 I-N). The future predictions indicate that under all three emission scenarios, the areas of highly and moderately suitability for the two alkaloids in L. Chinese generally show a decreasing trend compared to the current period. The areas of poorly suitability area, with some increases and decreases. Overall, changes in the quality-suitable areas for Lycii Cortex align closely with the ecologically suitable areas, both showing a general decline. Comprehensive quality suitability analysis Based on the previous research, the spatial distribution maps of the two alkaloid components were overlaid to conduct a comprehensive analysis of L. Chinese ’s quality area, identifying the optimal areas for Lycii Cortex growth and component accumulation (Table 7 ). The distribution map is presented in Fig. 6 A. The results indicate that the comprehensive quality-suitable areas are primarily located in southern Beijing, central and southern Hebei, southern Henan, central and southern Shaanxi, northern Sichuan, as well as in Hunan, Hubei, Anhui, Jiangxi, and Zhejiang. Furthermore, some high-quality areas are identified in central Ningxia, southern Gansu, Tibet, northern Yunnan, and northern Guangxi. The highly suitable area encompasses 0.1754 × 10⁶ km², representing 6.68% of the study area. The total suitable area constitutes 27.36% of the total area, equivalent to 2.6264 × 10⁶ km². The quality area of L. Chinese (Fig. 6 B-G), derived from the combined analysis of the two components, decreases to varying extents under different emission scenarios. The changes in different quality-suitable areas were similar to those of kukoamine A, with the most pronounced decrease occurring in the 2050s under the RCP2.6 scenario. Discussions Lycii Cortex is a widely used therapeutic agent in clinical practice, with a significant evidence base supporting its efficacy. In recent years, it has been the subject of close scrutiny by scholars from a range of countries. Given that the growth and accumulation of active ingredients in Lycii Cortex necessitates a period of four to five years or longer, this study predicts the potential influence of climate change on two sources of plants for Lycii Cortex used ArcGIS and Maxent. Environmental variables affecting the distribution of Lycium spp. In order to minimize the risk of overfitting caused by multicollinearity, climate variables were screened through correlation analysis combined with jackknife in the study [ 57 ], and factors with importance of 0 were removed from Maxent. The remaining factors were then used for Maxent modelling. While the contribution of certain variables may be less substantial, it is our contention that these factors are nevertheless significant for the growth, distribution or component accumulation of Lycii Cortex . Consequently, they remain an integral component of the model construction. Our research identifies the key climatic variables influencing the suitable distribution of L. barbarum as the minimum temperature of coldest month (Bio6), annual average temperature (Bio1), and precipitation of wettest month (Bio13). The suitable range of Bio1 is 5–12.5°C, which is similar with the 8–9°C range of L. barbarum studied by Wang [ 43 ]. And L. barbarum are mainly distributed within the suitable range of winter average temperature − 10°C to 10°C [ 43 ]. As the temperature falls below − 15°C, the germination rate of dormant branches is known to undergo a significant decrease [ 58 ]. Furthermore, L. barbarum can also survive during the winter dormancy period when the lowest temperature reaches − 41.5°C [ 59 ]. Consequently, we consider that L. barbarum can thrive within the optimal range of -18 to -6.2°C for Bio6. The primary climatic factors determining the suitable distribution of L. Chinese are annual average temperature (Bio1), precipitation of wettest month (Bio13), and annual precipitation (Bio12). Tang et al. also concluded that precipitation of wettest month (Bio13) and minimum temperature of coldest month (Bio6) were the primary climatic variables for L. Chinese . Although L. Chinese demonstrates a degree of drought tolerance, it still requires a certain amount of precipitation for its survival [ 44 ]. The cold resistance and water avoidance characteristics of two Lycium species may be closely related to the ecological environment of their origin and geographical distribution areas [ 44 ]. This finding is consistent with the research results that have previously been obtained. Predicted distribution potential of Lycium spp. It is anticipated that climate change will have an impact on the geographical distribution of certain species, resulting in a reduction in growth and range [ 60 ]. The present study predicted changes in the distribution of suitable areas for L. barbarum and L. Chinese . The highly suitable areas for L. barbarum are currently located in provinces such as Hebei, Inner Mongolia, Shaanxi, Shanxi, Gansu, Ningxia, Qinghai, and Xinjiang, similar to the research by Wang et al. [ 61 , 62 ]. In the current period, L. Chinese is mainly distributed in the southern, southwestern, central, and eastern regions, as well as provinces such as northeastern China, Hebei, Shanxi, Shaanxi, and Gansu. This distribution is similar to the research results of Tang et al. [ 44 ]. Despite the strong adaptability exhibited by L. Chinese and its distribution across numerous regions of China, the species remains sensitive to external environmental changes and requires favorable conditions for sustained growth. Consequently, the suitability index is suboptimal in the majority of regions. Two Lycium species demonstrate a preference for light conditions, exhibit drought tolerance, and evade waterlogging, thereby exhibiting a certain degree of adaptability to temperature and precipitation [ 44 ]. During the early stage of growth and development, Lycium species requires less precipitation. However, as the fruit approaches maturity, greater precipitation is required to ensure the stability of the root system and the completion of the maturation process. The soil moisture content is typically found to be within the range of 16% and 22%. Excessive water or waterlogging has been demonstrated to result in diminished plant growth [ 63 ]. Such conditions have been shown to cause root rot and death. Furthermore, the distribution of two Lycium species is predominantly associated with subtropical monsoon climate and temperate monsoon climate, characterized by hot summers and cold winters. In the context of global climate change, there has been an observed increase in extreme precipitation events [ 64 ]. This is not conducive to the expansion of the distribution area of two Lycium species. This finding is consistent with the conclusion drawn in the present study that the total suitable area of L. barbarum and L. Chinese will generally decline in the future. Prediction of centroid migration of two Lycium species Due to ongoing global warming, many species may shift to more favorable habitats, with their distribution centers moving toward colder, wetter regions [ 65 ] or shifting to higher latitudes or altitudes [ 66 ]. This may explain why the suitable distribution centers for L. barbarum are projected to migrate from southeast to northwest, aligning with findings from Wang et al. [ 43 ] and resembling Dong et al.'s prediction of the centroid migration of Astrolus membranaceus var. mongholicus [ 41 ]. However, as the climate continues to warm, the accelerated water cycle leads to increased total precipitation, making global humid regions even wetter [ 67 ]. This study speculated that the current distribution center of L. Chinese , located in the mid-latitude region, will shift toward areas with decreasing precipitation, moving from northwest to southeast. The survival prospects of two Lycium species are anticipated to improve. In conclusion, precipitation and temperature significantly influence the distribution of ecologically suitable areas for Lycii Cortex . Predicted quality suitable area of Lycii Cortex The quality of traditional Chinese medicine (TCM) can be evaluated based on the secondary metabolites of medicinal plants. Climate change and rising global carbon dioxide levels can provide carbon substrates for secondary metabolism in medicinal plants. The synthesis and accumulation of these substances are strongly influenced by the growth environment of TCM, leading to a significant increase in alkaloid content [ 68 ]. To maintain the medicinal value of Lycii Cortex , it is crucial to understand the influence of environmental variables on its growth. Lu et al. demonstrated that the accumulation of most chemical components in wolfberry is influenced by precipitation and temperature factors [ 26 ]. Research has indicated that the alkaloid components of kukoamine B and kukoamine A in Lycii Cortex can be utilized as quality markers to evaluate the quality of Lycii Cortex [ 69 ]. As Li et al. demonstrate in their research, the total alkaloid content of Lycii Cortex is closely related to its quality and growth environment [ 70 ]. This study was conducted for the purpose of determining and calculating the content of two alkaloids in Lycium Chinese Mill. Cortex , with a view to conducting a quality suitability analysis. The results showed that the quality of Lycii Cortex in highly and moderately suitable areas was superior to that in poorly suitable area. These high-quality areas are characterized by four distinct seasons, moderate temperatures, sufficient precipitation, and abundant sunshine [ 70 ]. It has been demonstrated that the content of alkaloids may be subject to an increase in response to environmental stress conditions, such as drought [ 71 ]. However, according to the characteristics of Lycium species, a soil moisture content of 16–22% is conducive to growth [ 63 ]. The increase in precipitation probability may lead to soil moisture levels exceeding this range, thus breaking drought stress and potentially causing root waterlogging and plant death. This phenomenon is not conducive to the accumulation of alkaloids in Lycii Cortex , and also impacts the distribution of areas conducive to the quality of Lycii Cortex , thereby reducing the Lycii Cortex quality suitable area. It has been shown that annual precipitation (Bio12) and precipitation of driest period (Bio14) have a restrictive effect on the accumulation of alkaloids in Lycii Cortex . Research conducted by Rong et al. has indicated that Bio12 demonstrates a negative correlation with the accumulation of alkaloid content. This finding also aligns with the results obtained in this study [ 57 ]. In summary, the quality suitable area of Lycii Cortex will be affected, resulting in a decrease in the quality suitable area. Research limitations Protecting their habitats based on distribution advantages is essential to minimize the potential impact of future environmental fluctuations on ecosystems and to ensure the sustainable use of resources. In addition, it is imperative that urgent protective measures be implemented in order to mitigate the risk of extinction of these two Lycium species in the wild. In regions where wild Lycium species are present, in situ conservation measures should be reinforced and the harvesting of Lycii Cortex should be prohibited to prevent excessive extraction. Furthermore, it is imperative that policies and measures related to protection are promulgated in order to encourage reasonable planting and resource conservation in pre-determined potential suitable habitat areas. Currently, the limited number of data obtained from literature reviews and collecting pose samples constraints on the quality suitability analysis. Therefore, more extensive sampling is needed for in-depth future research. Although the Maxent model used in this study is appropriate, future studies should incorporate ensemble modeling to predict the potential distribution and quality suitability of Lycii Cortex , which may enhance predictive accuracy under future climatic conditions. Conclusion This study used the Maxent model and ArcGIS, combined with HPLC, to predict the potential distributions of two Lycium species under climatic influences, along with Lycium Chinese Mill. Cortex quality suitability analysis. In reference to the findings of the present study, it is posited that a scientific reference has been furnished for the purpose of predicting the future distribution of two sources of plants for Lycii Cortex in China. Furthermore, it is suggested that guidance has been provided for the selection of optimal cultivation sites for Lycii Cortex . Declarations Ethics approval and consent to participate Not applicable (animal or human trials are not addressed in this paper). Consent for publication Not Applicable. Availability of data and materials The authors confirm that data supporting the results of this study are available in the article and its supplementary material. Competing interests The authors declare that they have no conflict of interest. Funding This research was supported financially by the Innovation Team of Hebei Province Modern Agricultural Industry Technology System (No. HBCT2023080201), the Scientific and Technological Project of Shijiazhuang City of Hebei Province (No. 241200013A), the Scientific research project of Hebei Administration of Traditional Chinese Medicine (No. 2025069), and the ability establishment of sustainable use for Chinese medicine resources (No. 202400262157-3). Authors’ contributions LYT: Data curation, methodology, formal analysis, visualization, and writing-original draft preparation; LZK: Data curation, formal analysis, writing—review and editing; FXR: Data curation, reviewing and editing; WCH reviewing and editing; FC: formal analysis, methodology; SYX: visualization, reviewing and editing; GX: reviewing and editing; PL: funding acquisition, formal analysis; CTC: reviewing and editing; MDL: Project administration, funding acquisition, resources, methodology, and writing-reviewing and editing. Acknowledgements Authors would like to acknowledge their universities for supporting the research. References Tan Z, Yuan Y, Huang S, Ma Y, Hong Z, Wang Y, et al. Geographical distribution and predict potential distribution of Angelica L. genus. Environ Sci Pollut Res. 2023;30:46562–73. https://doi.org/10.1007/s11356-023-25490-y . Ostad-Ali-Askari K, Ghorbanizadeh KH, Shayannejad M, Zareian MJ. 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Alami MM, Guo S, Mei Z, Yang G, Wang X. Environmental factors on secondary metabolism in medicinal plants: exploring accelerating factors. Med Plant Biol. 2024;3:e016. https://doi.org/10.48130/mpb-0024-0016 . Tables Table 1 Percentage contribution of key environment variables used for modeling. Abbreviation Description contribution (%) Permutation importance (%) Lycium barbarum L. Bio 2 mean diurnal range/℃ 31.8 11.3 Bio 6 minimum temperature of coldest month/℃ 16.4 8.1 Bio 17 precipitation of driest quarter/mm 15.5 9.1 Bio 10 mean·temperature of warmest quarter/℃ 14.4 0.9 Bio 1 annual mean temperature/℃ 10.4 15.5 Bio 13 precipitation of wettest period/mm 5.1 22.4 Bio 12 annual precipitation/mm 5.1 23.7 Bio 3 isothermally·[(Bio2/Bio7) *100] 0.6 7.0 Bio 19 precipitation of coldest quarter /mm 0.5 1.9 Lycium Chinense Mill. Bio 1 annual mean temperature/℃ 44.6 9.0 Bio 13 precipitation of wettest period/mm 33.6 43.7 Bio 4 temperature seasonality 5.4 9.4 Bio 12 annual precipitation/mm 4.8 2.3 Bio 7 temperature annual range (Bio5~Bio6)/℃ 2.5 3.9 Bio 10 mean·temperature of warmest quarter/℃ 2.5 5.0 Bio 11 mean temperature of coldest quarter/℃ 2.2 9.6 Bio 17 precipitation of driest quarter/mm 1.3 5.1 Bio 2 mean diumnal range/℃ 1.3 4.2 Bio 14 precipitation of driest period/mm 1.1 5.3 Bio 18 precipitation of warmest quarter/mm 0.7 2.5 Table 2 Use the Maxent model to calculate the AUC values of two Lycium species at different periods. Origin NO. Various periods AUC Lycium barbarum L. 1 Current 0.956 2 RCP2.6-2050s 0.911 3 RCP2.6-2070s 0.917 4 RCP4.5-2050s 0.917 5 RCP4.5-2070s 0.918 6 RCP8.5-2050s 0.917 7 RCP8.5-2070s 0.909 Lycium Chinense Mill. 1 Current 0.961 2 RCP2.6-2050s 0.919 3 RCP2.6-2070s 0.916 4 RCP4.5-2050s 0.924 5 RCP4.5-2070s 0.919 6 RCP8.5-2050s 0.921 7 RCP8.5-2070s 0.918 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2025 Read the published version in BMC Ecology and Evolution → Version 1 posted Editorial decision: Revision requested 17 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 27 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers invited by journal 28 Apr, 2025 Submission checks completed at journal 27 Apr, 2025 First submitted to journal 27 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zikang","middleName":"","lastName":"Lu","suffix":""},{"id":449171492,"identity":"43009665-e362-4b7a-a05f-78efdf9b62ef","order_by":2,"name":"Xiangrui Fu","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiangrui","middleName":"","lastName":"Fu","suffix":""},{"id":449171493,"identity":"bec91996-7a01-4fa5-ba46-6af2d969f35d","order_by":3,"name":"Chaohui Wang","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chaohui","middleName":"","lastName":"Wang","suffix":""},{"id":449171494,"identity":"1d76cf98-ab11-434c-846c-fbe73db5c7b5","order_by":4,"name":"Chao Feng","email":"","orcid":"","institution":"Shunyi Hospital, Beijing Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Feng","suffix":""},{"id":449171495,"identity":"ad9afa96-c9df-4dbf-8e64-0b7c259964f1","order_by":5,"name":"Yongxing Song","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongxing","middleName":"","lastName":"Song","suffix":""},{"id":449171496,"identity":"e028db36-69c0-41ee-9156-d914fe39e142","order_by":6,"name":"Xian Gu","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Gu","suffix":""},{"id":449171497,"identity":"a943f2d6-ac91-4a99-b08c-7cea343a4998","order_by":7,"name":"Tianchuan Chai","email":"","orcid":"","institution":"Hebei Provincial Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianchuan","middleName":"","lastName":"Chai","suffix":""},{"id":449171498,"identity":"6b978df7-0e9f-4157-aa85-cc5889fed714","order_by":8,"name":"Lin Pei","email":"","orcid":"","institution":"Hebei Province Traditional Chinese Medicine Processing Technology Innovation Center","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Pei","suffix":""},{"id":449171499,"identity":"49efd4c3-d724-49e6-9ccc-d0ac2dfa4283","order_by":9,"name":"Donglai Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFCC5AZmBoYDDGzsDYwHEojTkgjVwnOAgUQtDBIJIJIIoNsO1FJQcUeOT/LxgwMP2w7nMbAfProBnxazMw8bmGeceWbMJp1mcCCx7XAxA09a2g28Wm4AbeFtO5zYJp0A1pLYIMFjRoSWf4fr2ySPfyBFS8PhBDYJHmJtAfmF59gzwzaenIIDCefSE9sI+uV48gFmnpo78vLtxzc+/FFmndjPfvgYXi1AwP4DzmRkY2BgI6AcHfwhUf0oGAWjYBSMCAAAEWZUIojMW+cAAAAASUVORK5CYII=","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Donglai","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-11-20 12:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5490896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5490896/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12862-025-02413-8","type":"published","date":"2025-07-30T16:12:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81652623,"identity":"a558017a-990d-45b2-89a2-10100ee3bb04","added_by":"auto","created_at":"2025-04-29 16:48:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253782,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution records of two \u003cem\u003eLycii Cortex\u003c/em\u003e species samples and Collected samples (\u003cstrong\u003eA\u003c/strong\u003e), habitat of \u003cem\u003eLycii Cortex\u003c/em\u003e (\u003cstrong\u003eB\u003c/strong\u003e) and single plant of \u003cem\u003eLycii Cortex\u003c/em\u003e (\u003cstrong\u003eC\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/8609f4b0a0e6d6d5fc3e3bb9.jpg"},{"id":81652377,"identity":"7d587144-aa17-49af-a66c-7070ff61b844","added_by":"auto","created_at":"2025-04-29 16:40:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":794998,"visible":true,"origin":"","legend":"\u003cp\u003eThe response curves of the current existence probability of \u003cem\u003eLycium barbarum\u003c/em\u003e L. (\u003cstrong\u003eA\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) and \u003cem\u003eLycium Chinense\u003c/em\u003e Mill. (\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eF\u003c/strong\u003e) to the top three environmental variables.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/517dad94ddc244be9162b1ea.jpg"},{"id":81652378,"identity":"416fc4a3-4f8b-4344-add6-1d2d4f755ac8","added_by":"auto","created_at":"2025-04-29 16:40:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6603125,"visible":true,"origin":"","legend":"\u003cp\u003eThe habitat distribution of \u003cem\u003eLycium barbarum \u003c/em\u003eL. (\u003cstrong\u003eA\u003c/strong\u003e) and \u003cem\u003eLycium Chinense \u003c/em\u003eMill. (\u003cstrong\u003eH\u003c/strong\u003e) under current climate conditions, as well as the suitable habitat changes of \u003cem\u003eLycium barbarum \u003c/em\u003eL. (\u003cstrong\u003eB-G\u003c/strong\u003e) and \u003cem\u003eLycium Chinense \u003c/em\u003eMill. (\u003cstrong\u003eI-N\u003c/strong\u003e) under three climate scenarios in the future (2050s, 2070s).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/eb54fe5c3ae18f9f80049a89.jpg"},{"id":81652379,"identity":"24643f4f-bc83-4465-8589-a6f3854a4288","added_by":"auto","created_at":"2025-04-29 16:40:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":315544,"visible":true,"origin":"","legend":"\u003cp\u003eCentroid migration map of suitable habitat of L. \u003cem\u003ebarbarum\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e) and L.\u003cem\u003e Chinese\u003c/em\u003e(\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/805423abd2b566ff97273960.jpg"},{"id":81652382,"identity":"9167ee92-a548-4cb1-aaab-30f31eb2da74","added_by":"auto","created_at":"2025-04-29 16:40:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":224230,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of suitable quality areas for kukoamine B (\u003cstrong\u003eA\u003c/strong\u003e) and kukoamine A (\u003cstrong\u003eH\u003c/strong\u003e) under current climate conditions, as well as the changes in the distribution of suitable quality areas for kukoamine B (\u003cstrong\u003eB-G\u003c/strong\u003e) and kukoamine A (\u003cstrong\u003eI-N\u003c/strong\u003e) under three climate scenarios in the future (2050s and 2070s).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/3fd5216f4dd80a045af0c41c.jpg"},{"id":81652381,"identity":"a1693562-0879-4033-a628-04dd245641cf","added_by":"auto","created_at":"2025-04-29 16:40:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":170565,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of L.\u003cem\u003eChinese\u003c/em\u003e comprehensive suitable quality areas under current climate conditions (\u003cstrong\u003eA\u003c/strong\u003e), as well as the changes in the distribution of suitable quality areas under three climate scenarios in the 2050s and 2070s in the future (\u003cstrong\u003eB-G\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/4e2af54ad1651df5fb28fdd4.jpg"},{"id":88268200,"identity":"70a2d740-7bf0-4c38-b19d-103c5285ee7e","added_by":"auto","created_at":"2025-08-04 16:50:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9658843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/c6a6dee8-2c77-4c64-a67e-4229e3f38c6d.pdf"},{"id":81653035,"identity":"acfa0607-4c4a-4858-8209-f90dadf34f34","added_by":"auto","created_at":"2025-04-29 16:56:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":159258,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5490896/v1/392687405ff00e3bb4ae52f7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting the potential habitats of two Lycium species and the quality suitability of Lycium Chinese Mill. Cortex under climate change","fulltext":[{"header":"Background","content":"\u003cp\u003eClimatic factors significantly influence the geographical distribution of species. The growth of the population has been demonstrated to result in increased resource demand and energy consumption, which has been shown to lead to elevated carbon emissions. These changes will lead to an increase in adverse impacts on future climate and a continuous rise in global temperatures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Global warming and the increased frequency of extreme weather events are expected to alter the geographical distribution of numerous plant species [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is predicted that under future climate scenarios, the distribution area of \u003cem\u003eAcacia mearnsii\u003c/em\u003e, a harmful weed, will expand in Asia. Furthermore, global climate change is likely to increase the risk of invasion of this plant in Asia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Vishnu posited that climate fluctuations may result in a reduction of suitable habitats for the \u003cem\u003eIndigenthus virus\u003c/em\u003e (Roxb.) in India and Sri Lanka, both currently and in the future [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, the effects of climate change on the distribution and bioactive components of medicinal plants warrant further investigation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For instance, Uranishi et al studied the significant changes in the content of chlorogenic acid and dicaffeoylquinic acid isomers in the leaves of \u003cem\u003eChrysanthemum indicum\u003c/em\u003e L. under the influence of environmental factors. And climatic change was found to have the most significant impact on the suitability of its habitat [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It can be reasonably deduced that the suitability of a habitat has a considerable influence on the accumulation of secondary metabolites in medicinal plants [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eLycii Cortex\u003c/em\u003e, commonly referred to as wolfberry root, is derived from the dried root bark of the Solanaceae plant \u003cem\u003eLycium chinense\u003c/em\u003e Mill. or \u003cem\u003eLycium barbarum\u003c/em\u003e L. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \u003cem\u003eLycii Cortex\u003c/em\u003e is widely used in proprietary Chinese medicines and Chinese herbal formulas [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and can effectively treat vomiting blood, hemoptysis, and lung-heat cough. Recent studies have demonstrated that \u003cem\u003eLycii Cortex\u003c/em\u003e has the capacity to reduce blood sugar, blood pressure, blood lipids, and anti-osteoporotic activity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A considerable number of active ingredients are present in \u003cem\u003eLycii Cortex\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, there is currently no established standard for determining the content of indicator ingredients in \u003cem\u003eLycii Cortex\u003c/em\u003e as outlined in the Chinese Pharmacopoeia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In recent years, there have been many reports of alkaloids such as kukoamine B and kukoamine A in \u003cem\u003eLycii Cortex\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Li et al. mention that kukoanes B may regulate nuclear transcription factors to reduce inflammation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Kukoanes A and B have been demonstrated to treat Alzheimer's disease (AD) and type 2 diabetes (T2D) by inhibiting amyloid aggregation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies indicate that \u003cem\u003eLycii Cortex\u003c/em\u003e requires 4\u0026ndash;5 years of growth to accumulate adequate active ingredients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It is plausible that climate factors exert a considerable influence on its growth. Furthermore, previous historical research on the origin of \u003cem\u003eLycii Cortex\u003c/em\u003e indicates that climate change may also affect the changes in the origin of two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Climate change and environmental overexploitation have significantly diminished the wild resources of \u003cem\u003eLycii Cortex\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nevertheless, the majority of studies on the composition and pharmacological activity of \u003cem\u003eLycii Cortex\u003c/em\u003e have concentrated on its components [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, studies examining the overall distribution and quality evaluation of this plant are scarce, with a greater focus on the study of Chinese wolfberry [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo evaluate the influence of climate change on species distribution, this study utilizes representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) for the 2050s and 2070s, which model potential greenhouse gas emissions [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The focal point of this study is the direct impact of natural environmental variables on species distribution, precluding the involvement of socio-economic driving factors such as urbanization and agricultural expansion. Consequently, the Representative Concentration Pathways (RCPs) provide clear climate forcing pathways, thereby circumventing uncertainty introduced by complex socio-economic parameters. Consequently, the Shared Socioeconomic Pathways (SSP) pathway, which incorporates a more comprehensive range of socio-economic factors, was not selected. The Maximum Entropy Model (Maxent) is a statistical model that correlates species occurrence with environmental variables. Its objective is to predict potential spatial shifts, identify influencing factors and determine the likely direction of migration of species distribution [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Maxent is widely utilized for its simplicity, rapid modeling capabilities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and effectiveness in predicting species distribution, particularly with small sample sizes and limited geographic ranges [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is also recognized for its accuracy and reliability [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Maxent has been extensively applied to predict suitable regional and geographic distributions of various medicinal plant species, including simulations of climate change effects on the distribution of C. \u003cem\u003earabica\u003c/em\u003e in Ethiopia [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Hosseini et al. utilized the Maxent model to predict the distribution area of two \u003cem\u003eThymus\u003c/em\u003e species in Iran under climate change [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, there have been studies employing the Maxent model to investigate the impact of climate change on the future distribution of three \u003cem\u003eFerulago\u003c/em\u003e species in Iran [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, Waheed et al. employed the Maxent model to forecast the potential geographic range of invasive species, such as \u003cem\u003eX. strumarium\u003c/em\u003e, in Pakistan under the influence of climate change [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, researchers studying \u003cem\u003eLonicera japonica\u003c/em\u003e Flos. integrated chemical content analysis with Maxent to assess the influence of ecological factors on quality, predict suitable habitats under various scenarios, and generate high-resolution habitat maps [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Various chemical indicators have been employed in these studies to evaluate how habitat suitability affects medicinal quality [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. HPLC is used to determine the content of chemical components, which has high sensitivity and can quickly and efficiently separate and detect the type and content of compounds in samples. It is instrumental in assessing differences in secondary metabolites of medicinal plants across various regions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Alkaloids represent the primary active ingredients within the secondary metabolites of \u003cem\u003eLycii Cortex\u003c/em\u003e. Among these, kukoamine B and kukoamine A are the predominant alkaloid components within \u003cem\u003eLycii Cortex\u003c/em\u003e. Accordingly, two alkaloidal components, kukoamine B and kukoamine A, were selected for this study as indicators for the determination of the content of active ingredients of \u003cem\u003eLycii Cortex\u003c/em\u003e. The objective of this study is to evaluate the habitat suitability of two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e, L. \u003cem\u003ebarbarum\u003c/em\u003e and L. \u003cem\u003eChinese\u003c/em\u003e, derived from disparate sources, in the context of both the present and future climates.\u003c/p\u003e \u003cp\u003eTo this end, the study will integrate data on their geographical distribution, information on the quality of their effective ingredients, and climate factors. Explore the relationship between climate factors and two bioactive alkaloids in \u003cem\u003eLycii Cortex\u003c/em\u003e, conduct quality suitability analysis, predict the quality suitability areas, and provide scientific basis for the sustainable development of two \u003cem\u003eLycium\u003c/em\u003e species and environmental protection of suitable planting areas.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSpecies occurrence records and sample collection\u003c/h2\u003e \u003cp\u003eData from L. \u003cem\u003ebarbarum\u003c/em\u003e and L. \u003cem\u003eChinese\u003c/em\u003e sources across China were obtained from the China 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) and 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). L. \u003cem\u003ebarbarum\u003c/em\u003e and L. \u003cem\u003eChinese\u003c/em\u003e samples were found throughout China. L. \u003cem\u003ebarbarum\u003c/em\u003e was predominantly distributed in Ningxia, Xinjiang, and Inner Mongolia, aligning with recent studies on its distribution [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In contrast, L. \u003cem\u003eChinese\u003c/em\u003e was mainly found in central and southern China, consistent with Tang et al.'s observations [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Additionally, 14 records of \u003cem\u003eLycii Cortex\u003c/em\u003e were collected from various provinces: 4 from Henan, 2 from Shanxi, 2 from Jiangsu, and 1 each from Shaanxi, Hebei, Hubei, Anhui, Guangdong, and Jilin.\u003c/p\u003e \u003cp\u003eThe 154 occurrences for L. \u003cem\u003ebarbarum\u003c/em\u003e and 740 occurrences for L. \u003cem\u003eChinese\u003c/em\u003e were collected in this study. To reduce sampling bias, we remove duplicate samples and obvious geocoding errors or blurred data as much as possible. At the same time, considering the large scale of the study, we refer to other people\u0026rsquo;s data screening methods and the rationality and accuracy of the model operation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To minimize sampling bias and eliminate the risk of counting the same points more than once, we consider non-overlapping observations obtained using a fishing net [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and samples were screened within 16 km \u0026times; 16 km geographic grids based on established data screening methods [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For grids with multiple \u003cem\u003eLycii Cortex\u003c/em\u003e samples, the sample nearest to the grid center was chosen as the representative sample. After applying the fishing net screening in ArcMap, 70 L. \u003cem\u003ebarbarum\u003c/em\u003e occurrences and 216 L. \u003cem\u003eChinese\u003c/em\u003e occurrences were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The occurrence records for two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e were compiled into a '.csv' file for model construction using Maxent software.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel construction of the species distribution\u003c/h3\u003e\n\u003cp\u003eThe 19 bioclimatic variables were identified as critical factors in developing niche models for the distribution of two species. Data from the Geospatial Data Cloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gscloud.cn/\u003c/span\u003e\u003cspan address=\"https://www.gscloud.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the 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) were utilized, covering the current period (1970\u0026ndash;2000) and future projections for the 2050s (2041\u0026ndash;2060) and 2070s (2061\u0026ndash;2080). Bioclimatic variables (Bio1\u0026thinsp;~\u0026thinsp;Bio19) had a spatial resolution of 1 km \u0026times; 1 km and were analyzed under three greenhouse gas emission scenarios: RCP2.6, RCP4.5, and RCP8.5, derived from the Fifth Assessment Report of IPCC (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 19 climate variables were screened by using Pearson correlation analysis, minimizing the risk of overfitting due to multicollinearity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Climatic factors with high collinearity (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8) which deemed significant for two \u003cem\u003eLycium\u003c/em\u003e species growth were retained, and therefore were retained for analysis. The jackknife test method was employed to assess the relative importance of each environmental variable [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Subsequently, on the basis of the actual growth conditions of two \u003cem\u003eLycium\u003c/em\u003e species and the extant literature on the environmental response of \u003cem\u003eLycium\u003c/em\u003e plants and closely related taxa, a subset variable was retained for further analysis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. We iteratively run the Maxent model, removing variables with 0 contribution at each step, and repeat this process ten times to obtain the final set of participating climate factors, with all other parameters set to default values. Based on previous researchers\u0026rsquo; methods, correlation analysis and Jackknife experiments (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) identified 9 climatic factors for L. \u003cem\u003ebarbarum\u003c/em\u003e and 11 for L. \u003cem\u003eChinese\u003c/em\u003e, which were used for Maxent modeling (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This model was used to predict the potential distributions of L. \u003cem\u003ebarbarum\u003c/em\u003e and L. \u003cem\u003eChinese\u003c/em\u003e under current and future scenarios. The model was constructed using Maxent software (version 3.3.3k, Steven Phillips et al., New York, NY, USA). The software prevents sampling from non-occurrence habitats of the target species or compensates for survey bias using coordinate data [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the maximum number of background points was set to 1 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e, while the maximum number of iterations was set to 1 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e during data processing. The Maxent model utilizes an ASCII-coded file containing geographical distribution and environmental factor information for two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e. The 75% percent of the randomly selected distribution data was used to train the model, while the remaining 25% was reserved for testing and validation. The model's predictive power was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The AUC value indicates the model's performance, ranging from 0 to 1, with values closer to 1 representing stronger predictive accuracy. The AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9 signifies optimal modeling results, AUC values between 0.7 and 0.9 are considered good, while the AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.7 generally indicates moderate model performance [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Using the reclassification tool in ArcMap10.8 and referring to the method of Li et al., it was used to categorize the occurrence probability of the two \u003cem\u003eLycii Cortex\u003c/em\u003e species into four levels: unsuitable area (0-0.2), poorly suitable area (0.2\u0026ndash;0.4), moderately suitable area (0.4\u0026ndash;0.6), and highly suitable area (0.6-1) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Next, the center of suitability in the ecologically suitable area was analyzed using centroid migration. The spatial statistics tool was used to determine the average center of the ecologically suitable area, while local analysis merged periods under the same greenhouse gas emission scenario. The centroid migration trajectory was last derived using the point set transfer line function.\u003c/p\u003e\n\u003ch3\u003eHPLC chemical composition analysis\u003c/h3\u003e\n\u003cp\u003eStore the collected \u003cem\u003eLycii Cortex\u003c/em\u003e samples in a cool, dry, and well-ventilated area. Add 0.5 g of sample powder to 5 mL of a methanol-0.5% acetic acid aqueous solution, followed by ultrasonication for 35 min and centrifugation for 20 min. The supernatant is then filtered through a 0.22 \u0026micro;m membrane to prepare the test solution. The chemical composition was analyzed using HPLC equipped with a Zorbax SB-AQ C\u003csub\u003e18\u003c/sub\u003e column (250 mm \u0026times; 4.6 mm, 5 \u0026micro;m). The mobile phase consisted of acetonitrile (A) and 0.1% trifluoroacetic acid in water (B). The gradient elution protocol was as follows: 0\u0026ndash;20 min, 90\u0026ndash;85% B; 20\u0026ndash;30 min, 85\u0026ndash;82% B; 30\u0026ndash;35 min, 82\u0026ndash;78% B; 35\u0026ndash;50 min, 78\u0026ndash;55% B; 50\u0026ndash;60 min, 55\u0026ndash;5% B; 60\u0026ndash;65 min, 5% B. The total run time was 65 min, with a flow rate of 1.0 mL∙min⁻\u0026sup1;, detection wavelength of 280 nm, injection volume of 10 \u0026micro;L, and column temperature maintained at 40\u0026deg;C. Concurrently, the content of kukoamine B and kukoamine A in 14 \u003cem\u003eLycii Cortex\u003c/em\u003e samples from different habitats were measured, with the total content calculated as the sum of these two components.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLycium Chinese\u003c/b\u003e \u003cb\u003eMill.\u003c/b\u003e \u003cb\u003eCortex\u003c/b\u003e \u003cb\u003equality suitability analysis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe concentrations of kukoamine B and kukoamine A were measured in \u003cem\u003eLycium Chinese\u003c/em\u003e Mill. \u003cem\u003eCortex\u003c/em\u003e samples from various production regions. The levels of these two compounds were employed as the primary criteria for evaluating quality in this study. 14 collection sites, identified as fitting the L. \u003cem\u003eChinese\u003c/em\u003e suitability distribution, were analyzed for their quality suitability. Regression analysis of the two components in the samples was performed using SPSS 23.0 (IBM, Shanghai, China), developing a model that links their concentrations to climatic factors. Although yearly variations in the quality of \u003cem\u003eLycii Cortex\u003c/em\u003e at the same location were noted, they were less significant than the effect of environmental factors on quality across different locations. To ensure the accuracy of environmental data at the collection sites, this study referenced previous studies [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and employed environmental parameters from a 1 km \u0026times; 1 km grid to predict the quality suitability area. The grid calculator and fuzzy overlay function in Arcmap 10.8 were applied to integrate spatial distribution maps of the two chemical components with ecological suitability maps, producing current and future integrated quality suitability maps [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Lastly, the mask extraction function was used to generate an integrated quality area map for the two alkaloid components of \u003cem\u003eLycii Cortex\u003c/em\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ePrediction and model evaluation of the distribution of two\u003c/b\u003e \u003cb\u003eLycium\u003c/b\u003e \u003cb\u003especies\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eVerification of modeling results\u003c/h3\u003e\n\u003cp\u003eIn this study, the AUC values of the training set for both L. \u003cem\u003ebarbarum\u003c/em\u003e and L. \u003cem\u003eChinese\u003c/em\u003e exceeded 0.900 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results indicated that the Maxent model effectively predicted the suitable habitat distribution of two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e, demonstrating high confidence and accuracy in the model's calculations of habitat suitability.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental variables analysis\u003c/h2\u003e \u003cp\u003eThe values presented in the variable contributions table of the Maxent analysis represent the average percentage contribution derived from 10 replicate runs. The results indicated that Bio2 (31.8%), Bio6 (16.4%), and Bio17 (15.5%) were the three primary environmental factors influencing L. \u003cem\u003ebarbarum\u003c/em\u003e distribution, with a cumulative contribution of 63.7%. However, based on Permutation Importance (PI), the three most impactful environmental factors were Bio12 (23.7%), Bio13 (22.4%), and Bio1 (15.5%). Consequently, the Maxent model was integrated with jackknife test analysis, revealing that Bio6, Bio1, and Bio13 are the most critical factors for L. \u003cem\u003ebarbarum\u003c/em\u003e habitat distribution, indicating that these factors provide the most valuable and unique information for determining L. \u003cem\u003ebarbarum\u003c/em\u003e distribution. The response curves of the primary ecological factors revealed that climatic influences on L. \u003cem\u003ebarbarum\u003c/em\u003e generally increased with rising factor values, with distribution probability initially rising before declining. Among these factors, the suitable range for the minimum temperature of the coldest month (Bio6) is -18 to -6.2\u0026deg;C, with an optimal temperature of -11\u0026deg;C, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, the suitable range for annual average temperature (Bio1) is 5 to 12.5\u0026deg;C, with an optimal temperature of approximately 9\u0026deg;C. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, the suitable range for precipitation in the wettest month (Bio13) is 2.5 to 13 mm, with optimal precipitation around 6 mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe three most important environmental variables with the greatest impact on L. \u003cem\u003eChinese\u003c/em\u003e were Bio1 (44.6%), Bio13 (33.6%) and Bio4 (5.4%), with a cumulative contribution rate of 83.6%. However, Bio13 (43.7%), Bio11 (9.6%), and Bio4 (9.4%) have greater PI. According to the results of jackknife, the three most important factors affecting the distribution of L. \u003cem\u003eChinese\u003c/em\u003e are Bio1, Bio13, and Bio12. The response curves of the primary ecological factors indicate that the suitable range for annual average temperature (Bio1) is 13.2 to 23\u0026deg;C, with an optimal temperature of 18\u0026deg;C, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, the suitable range for the wettest monthly precipitation (Bio13) is 16 to 56 mm, with an optimal precipitation of 25 mm. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, the suitable range for average annual precipitation (Bio12) is 75 to 180 mm, with an optimal precipitation of 162.5 mm, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredicted distribution under different scenarios\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003ePredicting the distribution of L.\u003c/b\u003e \u003cb\u003ebarbarum\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the potential distribution of two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e, as estimated by the Maxent model under current climate conditions. Based on the available data, the optimal growth regions for L. \u003cem\u003ebarbarum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) in current are predominantly concentrated in northern and northwestern China. The total suitable area reaches 2.6919 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e km\u0026sup2;, representing 25.75% of the total research area. The highly suitability area is primarily concentrated in Northwest China, central and southwestern Shanxi, western Hebei, central and eastern Tibet, and western Sichuan, collectively accounting for 4.26% of the total research area (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the predictions for the future period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-G), L. \u003cem\u003ebarbarum\u003c/em\u003e showed a decrease in the total suitable area of three suitable habitats compared to current scenarios, except for the RCP2.6 scenario in 2070s where the total suitable area increased. Specifically, in the RCP2.6-2050s and RCP2.6-2070s, the total suitable area of L. \u003cem\u003ebarbarum\u003c/em\u003e will decrease by 5.84% and increase by 5.83%, respectively, compared with the current period. The total suitable area for L. \u003cem\u003ebarbarum\u003c/em\u003e will decrease by 13.71% and 12.20% respectively by RCP4.5-2050s and RCP4.5-2070s. For the emission scenario of RCP8.5, the total suitable area for L. \u003cem\u003ebarbarum\u003c/em\u003e will decrease by 8.32% and 1.33% respectively by 2050s and 2070s.\u003c/p\u003e \u003cp\u003eAdditionally, the highly suitability area for L. \u003cem\u003ebarbarum\u003c/em\u003e exhibited varying degrees of decline under different emission scenarios, while the areas of moderately and poorly suitability showed both increases and decreases. The highly suitability area for L. \u003cem\u003ebarbarum\u003c/em\u003e is projected to decrease by 7.67% in the 2050s and by 11.38% in the 2070s under the RCP2.6 scenario; for the RCP4.5 scenario, it is expected to decrease by 8.93% and 7.48% in the 2050s and 2070s; it is anticipated to decrease by 13.38% in the RCP8.5-2050s and by 12.25% in the RCP8.5-2070s. The moderately suitable area is projected to increase by 0.32% and 1.41% in the RCP2.6-2050s and RCP2.6-2070s; under the RCP4.5 scenario, it is expected to decrease by 12.56% in the 2050s and by 5.08% in the 2070s; it is anticipated to decrease by 19.04% in the RCP8.5-2050s but increase by 11.84% in the RCP8.5-2070s. The poorly suitability area is projected to decrease by 8.09%, 15.61%, and 2.04% under different emission scenarios by the 2050s; under the RCP2.6-2070s, it is expected to increase by 12.77%, while under the RCP4.5-2070s and RCP8.5-2070s, it is anticipated to decrease by 16.77% and 4.12%, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePredicting the distribution of L.\u003c/b\u003e \u003cb\u003eChinese\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe areas deemed suitable for cultivation of L. \u003cem\u003eChinese\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH) in current are primarily concentrated in North China, Central China, South China, East China, and Southwest China, with some distribution in Northeast and Northwest China. The total area deemed suitable is 3.3516 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e km\u0026sup2;, representing 32.07% of the total research area. The highly suitability area is primarily situated in central China, central Hebei, central Shaanxi, central Zhejiang, northern Guangdong, north-eastern Guangxi, Chongqing, eastern Sichuan, and other regions, collectively accounting for 2.53% of the total research area (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor L. \u003cem\u003eChinese\u003c/em\u003e, the total suitable area in future projections (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI-N) is not expected to change significantly, although a general reduction in area is anticipated (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The total suitable area for L. \u003cem\u003eChinese\u003c/em\u003e is projected to decrease by 4.26% in the RCP2.6-2050s and by 4.55% in the RCP2.6-2070s. The total suitable area for L. \u003cem\u003eChinese\u003c/em\u003e is anticipated to decrease by 2.67% in the RCP4.5-2050s and by 2.48% in the RCP4.5-2070s. In the RCP8.5-2050s and RCP8.5-2070s, the total suitable area for L. \u003cem\u003eChinese\u003c/em\u003e is expected to decrease by 4.20% and 3.35%.\u003c/p\u003e \u003cp\u003eThe areas of highly and moderately suitability for L. \u003cem\u003eChinese\u003c/em\u003e have significantly decreased under various scenarios, while the area of poorly suitability has increased in all scenarios except for the RCP8.5-2070s. Under the RCP2.6 scenario, highly and moderately suitability areas are projected to decrease by 10.60% in the 2050s and by 27.62% in the 2070s; they are expected to decrease by 10.40% in RCP4.5-2050s and by 22.37% in RCP4.5-2070s; they are anticipated to decrease by 12.92% and 19.56% in the RCP8.5-2050s and RCP8.5-2070s. The moderately suitability area is projected to decrease by 11.04% in RCP2.6-2050s and by 5.38% in RCP2.6-2070s; it is expected to decrease by 6.39% in RCP4.5-2050s and by 6.24% in RCP4.5-2070s; for the RCP8.5 scenario, it is anticipated to decrease by 16.57% and 2.33% in the 2050s and 2070s. The poorly suitability area is projected to increase by 6.36% in the 2050s and by 1.28% in the 2070s under the RCP2.6 scenario; it is expected to increase by 6.41% in RCP4.5-2050s and by 6.73% in RCP4.5-2070s; under the RCP8.5 scenario, it is anticipated to increase by 14.08% in the 2050s but decrease by 1.45% in the 2070s.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe migration trend of geometric centers of suitable habitats for different periods of L.\u003c/b\u003e \u003cb\u003ebarbarum\u003c/b\u003e \u003cb\u003eand L.\u003c/b\u003e \u003cb\u003eChinese\u003c/b\u003e \u003cb\u003eunder climate change\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, the geometric center represents the central spatial location of the potentially the total suitable area for two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e under three emission scenarios, spanning from the present to the 2050s and 2070s. At present, the location of the most suitable habitat for the potential geometric center of L. \u003cem\u003ebarbarum\u003c/em\u003e is identified as Gande County Qinghai Province (100.663555\u0026deg;E, 34.224862\u0026deg;N). The geometric center of L. \u003cem\u003ebarbarum\u003c/em\u003e in the RCP2.6 scenario exhibited a migration of 92.76127 km from Gande County Qinghai Province, to Zeku County Qinghai Province, and subsequently, an additional 81.13688 km to Tongde County Qinghai Province. Under the RCP4.5 scenario, the geometric center initially migrated 85.52054 km within Gande County Qinghai Province, and subsequently migrated 88.34171 km to Maqin County Qinghai Province. Under the RCP8.5 scenario, the geometric center exhibits a migration of 129.36854 km from Gande County Qinghai Province to Zeku County Qinghai Province, followed by a further migration of 62.16803 km to Tongde County Qinghai Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the current scenario, the most suitable habitat for the geometric center of L. \u003cem\u003eChinese\u003c/em\u003e is located in Shizhu Tujia Autonomous County, Chongqing (108.165436\u0026deg;E, 29.867593\u0026deg;N). In the RCP2.6 scenario, the predicted geometric center of the suitable habitat for L. \u003cem\u003eChinese\u003c/em\u003e will migrate 82.9126 km from Shizhu Tujia Autonomous County Chongqing city to Qianjiang District Chongqing city, and then 44.0186 km to Pengshui Miao Tujia Autonomous County Chongqing city from 2050s to 2070s. For the RCP4.5 scenario, the predicted migration distance is 27.8479 km from Shizhu Tujia Autonomous County Chongqing city to Pengshui Miao and Tujia Autonomous County Chongqing city, followed by a further 33.88568 km to Shizhu Tujia Autonomous County Chongqing city. In the RCP8.5 scenario, the predicted migration distance is 86.2666 km from Shizhu Tujia Autonomous County in Chongqing city to Xianfeng County in Hubei Province, followed by an additional 115.61227 km to Xiushan Tujia and Miao Autonomous County in Chongqing city (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003eQuality suitability analysis\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eThe content of alkaloid active ingredients in L\u003c/b\u003e. \u003cb\u003eChinese Cortex\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe contents of two alkaloids, kukoamine B and kukoamine A, were measured in the collected L. \u003cem\u003eChinese Cortex\u003c/em\u003e samples (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among these samples, S1 was collected from highly suitable area, S2 to S11 were collected from moderately suitable area, and S12 to S14 were collected from poorly suitable area. The average contents of the two alkaloids in the samples from each suitability category were calculated, considering the unequal number of samples in each area. The results indicated that the average total alkaloid content in samples from highly and moderately suitable areas was higher than that in poorly suitable area.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between the content of environmental variable components\u003c/h2\u003e \u003cp\u003eThis study developed models to describe the relationship between the content of kukoamine B and kukoamine A and various ecological factors. The model describing the relationship between kukoamine B content and ecological factors is: Y\u003csub\u003e1\u003c/sub\u003e=-575.744-0.291X\u003csub\u003e1\u003c/sub\u003e-4.662X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;21.452X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;2.932X\u003csub\u003e4\u003c/sub\u003e-0.186X\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.319X\u003csub\u003e6\u003c/sub\u003e-10.116X\u003csub\u003e7\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;3.747X\u003csub\u003e8\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Y\u003csub\u003e1\u003c/sub\u003e represents the kukoamine B content in \u003cem\u003eLycii Cortex\u003c/em\u003e, X\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio1, X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio2, X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio3, X\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio5, X\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio12, X\u003csub\u003e6\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio13, X\u003csub\u003e7\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio14, X\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio19). The regression analysis results indicated that within specific ranges, Bio1, Bio2, Bio12, and Bio14 limit kukoamine B accumulation, while Bio3, Bio5, Bio13, and Bio19 promote its accumulation. The model describing the relationship between kukoamine A content and ecological factors is: Y\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.196X\u003csub\u003e1\u003c/sub\u003e-0.006X\u003csub\u003e2\u003c/sub\u003e-0.526X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.178X\u003csub\u003e4\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Y\u003csub\u003e2\u003c/sub\u003e represents kukoamine A content, X\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio3, X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio12, X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio14, X\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Bio19). The analysis revealed that Bio3 and Bio19 promote kukoamine A accumulation within specific ranges, whereas Bio12 and Bio14 restrict its accumulation within similar ranges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferent periods quality suitability analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the current quality suitable areas and the proportion of area. The current spatial distribution and suitable area of kukoamine B content in L. \u003cem\u003eChinese\u003c/em\u003e are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA (Table S2). The poorly suitability area for kukoamine B decreases in the 2070s under the RCP8.5 scenario but increases during other periods. The decrease in moderately suitable area is most pronounced in the 2050s under the RCP8.5 scenario, while the most significant decrease in highly suitable area occurs in the 2070s under the RCP2.6 scenario. The total suitable area is smaller than the current suitable area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-G). The current spatial distribution and suitable area of kukoamine A content in L. \u003cem\u003eChinese\u003c/em\u003e are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH (Table S3). For kukoamine A, the poorly suitability area increases in the 2050s under the RCP4.5 and RCP8.5 scenarios but decreases during other periods. The moderately suitable area decreases in all periods except for the 2070s under the RCP8.5 scenario, while the highly suitable area increases in the 2050s under the RCP8.5 scenario but decreases in other periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI-N). The future predictions indicate that under all three emission scenarios, the areas of highly and moderately suitability for the two alkaloids in L. \u003cem\u003eChinese\u003c/em\u003e generally show a decreasing trend compared to the current period. The areas of poorly suitability area, with some increases and decreases. Overall, changes in the quality-suitable areas for \u003cem\u003eLycii Cortex\u003c/em\u003e align closely with the ecologically suitable areas, both showing a general decline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive quality suitability analysis\u003c/h2\u003e \u003cp\u003eBased on the previous research, the spatial distribution maps of the two alkaloid components were overlaid to conduct a comprehensive analysis of L. \u003cem\u003eChinese\u003c/em\u003e\u0026rsquo;s quality area, identifying the optimal areas for \u003cem\u003eLycii Cortex\u003c/em\u003e growth and component accumulation (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The distribution map is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA. The results indicate that the comprehensive quality-suitable areas are primarily located in southern Beijing, central and southern Hebei, southern Henan, central and southern Shaanxi, northern Sichuan, as well as in Hunan, Hubei, Anhui, Jiangxi, and Zhejiang. Furthermore, some high-quality areas are identified in central Ningxia, southern Gansu, Tibet, northern Yunnan, and northern Guangxi. The highly suitable area encompasses 0.1754 \u0026times; 10⁶ km\u0026sup2;, representing 6.68% of the study area. The total suitable area constitutes 27.36% of the total area, equivalent to 2.6264 \u0026times; 10⁶ km\u0026sup2;. The quality area of L. \u003cem\u003eChinese\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-G), derived from the combined analysis of the two components, decreases to varying extents under different emission scenarios. The changes in different quality-suitable areas were similar to those of kukoamine A, with the most pronounced decrease occurring in the 2050s under the RCP2.6 scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003e \u003cem\u003eLycii Cortex\u003c/em\u003e is a widely used therapeutic agent in clinical practice, with a significant evidence base supporting its efficacy. In recent years, it has been the subject of close scrutiny by scholars from a range of countries. Given that the growth and accumulation of active ingredients in \u003cem\u003eLycii Cortex\u003c/em\u003e necessitates a period of four to five years or longer, this study predicts the potential influence of climate change on two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e used ArcGIS and Maxent.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnvironmental variables affecting the distribution of\u003c/b\u003e \u003cb\u003eLycium spp.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn order to minimize the risk of overfitting caused by multicollinearity, climate variables were screened through correlation analysis combined with jackknife in the study [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], and factors with importance of 0 were removed from Maxent. The remaining factors were then used for Maxent modelling. While the contribution of certain variables may be less substantial, it is our contention that these factors are nevertheless significant for the growth, distribution or component accumulation of \u003cem\u003eLycii Cortex\u003c/em\u003e. Consequently, they remain an integral component of the model construction. Our research identifies the key climatic variables influencing the suitable distribution of L. \u003cem\u003ebarbarum\u003c/em\u003e as the minimum temperature of coldest month (Bio6), annual average temperature (Bio1), and precipitation of wettest month (Bio13). The suitable range of Bio1 is 5\u0026ndash;12.5\u0026deg;C, which is similar with the 8\u0026ndash;9\u0026deg;C range of L. \u003cem\u003ebarbarum\u003c/em\u003e studied by Wang [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. And L. \u003cem\u003ebarbarum\u003c/em\u003e are mainly distributed within the suitable range of winter average temperature \u0026minus;\u0026thinsp;10\u0026deg;C to 10\u0026deg;C [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. As the temperature falls below \u0026minus;\u0026thinsp;15\u0026deg;C, the germination rate of dormant branches is known to undergo a significant decrease [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Furthermore, L. \u003cem\u003ebarbarum\u003c/em\u003e can also survive during the winter dormancy period when the lowest temperature reaches \u0026minus;\u0026thinsp;41.5\u0026deg;C [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Consequently, we consider that L. \u003cem\u003ebarbarum\u003c/em\u003e can thrive within the optimal range of -18 to -6.2\u0026deg;C for Bio6. The primary climatic factors determining the suitable distribution of L. \u003cem\u003eChinese\u003c/em\u003e are annual average temperature (Bio1), precipitation of wettest month (Bio13), and annual precipitation (Bio12). Tang et al. also concluded that precipitation of wettest month (Bio13) and minimum temperature of coldest month (Bio6) were the primary climatic variables for L. \u003cem\u003eChinese\u003c/em\u003e. Although L. \u003cem\u003eChinese\u003c/em\u003e demonstrates a degree of drought tolerance, it still requires a certain amount of precipitation for its survival [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The cold resistance and water avoidance characteristics of two \u003cem\u003eLycium\u003c/em\u003e species may be closely related to the ecological environment of their origin and geographical distribution areas [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This finding is consistent with the research results that have previously been obtained.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePredicted distribution potential of\u003c/b\u003e \u003cb\u003eLycium spp.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIt is anticipated that climate change will have an impact on the geographical distribution of certain species, resulting in a reduction in growth and range [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The present study predicted changes in the distribution of suitable areas for L. \u003cem\u003ebarbarum\u003c/em\u003e and L. \u003cem\u003eChinese\u003c/em\u003e. The highly suitable areas for L. \u003cem\u003ebarbarum\u003c/em\u003e are currently located in provinces such as Hebei, Inner Mongolia, Shaanxi, Shanxi, Gansu, Ningxia, Qinghai, and Xinjiang, similar to the research by Wang et al. [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In the current period, L. \u003cem\u003eChinese\u003c/em\u003e is mainly distributed in the southern, southwestern, central, and eastern regions, as well as provinces such as northeastern China, Hebei, Shanxi, Shaanxi, and Gansu. This distribution is similar to the research results of Tang et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Despite the strong adaptability exhibited by L. \u003cem\u003eChinese\u003c/em\u003e and its distribution across numerous regions of China, the species remains sensitive to external environmental changes and requires favorable conditions for sustained growth. Consequently, the suitability index is suboptimal in the majority of regions. Two \u003cem\u003eLycium\u003c/em\u003e species demonstrate a preference for light conditions, exhibit drought tolerance, and evade waterlogging, thereby exhibiting a certain degree of adaptability to temperature and precipitation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. During the early stage of growth and development, \u003cem\u003eLycium\u003c/em\u003e species requires less precipitation. However, as the fruit approaches maturity, greater precipitation is required to ensure the stability of the root system and the completion of the maturation process. The soil moisture content is typically found to be within the range of 16% and 22%. Excessive water or waterlogging has been demonstrated to result in diminished plant growth [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Such conditions have been shown to cause root rot and death. Furthermore, the distribution of two \u003cem\u003eLycium\u003c/em\u003e species is predominantly associated with subtropical monsoon climate and temperate monsoon climate, characterized by hot summers and cold winters. In the context of global climate change, there has been an observed increase in extreme precipitation events [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This is not conducive to the expansion of the distribution area of two \u003cem\u003eLycium\u003c/em\u003e species. This finding is consistent with the conclusion drawn in the present study that the total suitable area of L. \u003cem\u003ebarbarum\u003c/em\u003e and L. \u003cem\u003eChinese\u003c/em\u003e will generally decline in the future.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrediction of centroid migration of two\u003c/b\u003e \u003cb\u003eLycium\u003c/b\u003e \u003cb\u003especies\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDue to ongoing global warming, many species may shift to more favorable habitats, with their distribution centers moving toward colder, wetter regions [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] or shifting to higher latitudes or altitudes [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This may explain why the suitable distribution centers for L. \u003cem\u003ebarbarum\u003c/em\u003e are projected to migrate from southeast to northwest, aligning with findings from Wang et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and resembling Dong et al.'s prediction of the centroid migration of \u003cem\u003eAstrolus membranaceus\u003c/em\u003e var. \u003cem\u003emongholicus\u003c/em\u003e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, as the climate continues to warm, the accelerated water cycle leads to increased total precipitation, making global humid regions even wetter [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This study speculated that the current distribution center of L. \u003cem\u003eChinese\u003c/em\u003e, located in the mid-latitude region, will shift toward areas with decreasing precipitation, moving from northwest to southeast. The survival prospects of two \u003cem\u003eLycium\u003c/em\u003e species are anticipated to improve. In conclusion, precipitation and temperature significantly influence the distribution of ecologically suitable areas for \u003cem\u003eLycii Cortex\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePredicted quality suitable area of\u003c/b\u003e \u003cb\u003eLycii Cortex\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe quality of traditional Chinese medicine (TCM) can be evaluated based on the secondary metabolites of medicinal plants. Climate change and rising global carbon dioxide levels can provide carbon substrates for secondary metabolism in medicinal plants. The synthesis and accumulation of these substances are strongly influenced by the growth environment of TCM, leading to a significant increase in alkaloid content [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. To maintain the medicinal value of \u003cem\u003eLycii Cortex\u003c/em\u003e, it is crucial to understand the influence of environmental variables on its growth. Lu et al. demonstrated that the accumulation of most chemical components in wolfberry is influenced by precipitation and temperature factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Research has indicated that the alkaloid components of kukoamine B and kukoamine A in \u003cem\u003eLycii Cortex\u003c/em\u003e can be utilized as quality markers to evaluate the quality of \u003cem\u003eLycii Cortex\u003c/em\u003e [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. As Li et al. demonstrate in their research, the total alkaloid content of \u003cem\u003eLycii Cortex\u003c/em\u003e is closely related to its quality and growth environment [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. This study was conducted for the purpose of determining and calculating the content of two alkaloids in \u003cem\u003eLycium Chinese\u003c/em\u003e Mill. \u003cem\u003eCortex\u003c/em\u003e, with a view to conducting a quality suitability analysis. The results showed that the quality of \u003cem\u003eLycii Cortex\u003c/em\u003e in highly and moderately suitable areas was superior to that in poorly suitable area. These high-quality areas are characterized by four distinct seasons, moderate temperatures, sufficient precipitation, and abundant sunshine [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. It has been demonstrated that the content of alkaloids may be subject to an increase in response to environmental stress conditions, such as drought [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. However, according to the characteristics of \u003cem\u003eLycium\u003c/em\u003e species, a soil moisture content of 16\u0026ndash;22% is conducive to growth [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The increase in precipitation probability may lead to soil moisture levels exceeding this range, thus breaking drought stress and potentially causing root waterlogging and plant death. This phenomenon is not conducive to the accumulation of alkaloids in \u003cem\u003eLycii Cortex\u003c/em\u003e, and also impacts the distribution of areas conducive to the quality of \u003cem\u003eLycii Cortex\u003c/em\u003e, thereby reducing the \u003cem\u003eLycii Cortex\u003c/em\u003e quality suitable area. It has been shown that annual precipitation (Bio12) and precipitation of driest period (Bio14) have a restrictive effect on the accumulation of alkaloids in \u003cem\u003eLycii Cortex\u003c/em\u003e. Research conducted by Rong et al. has indicated that Bio12 demonstrates a negative correlation with the accumulation of alkaloid content. This finding also aligns with the results obtained in this study [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In summary, the quality suitable area of \u003cem\u003eLycii Cortex\u003c/em\u003e will be affected, resulting in a decrease in the quality suitable area.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eResearch limitations\u003c/h2\u003e \u003cp\u003eProtecting their habitats based on distribution advantages is essential to minimize the potential impact of future environmental fluctuations on ecosystems and to ensure the sustainable use of resources. In addition, it is imperative that urgent protective measures be implemented in order to mitigate the risk of extinction of these two \u003cem\u003eLycium\u003c/em\u003e species in the wild. In regions where wild \u003cem\u003eLycium\u003c/em\u003e species are present, in situ conservation measures should be reinforced and the harvesting of \u003cem\u003eLycii Cortex\u003c/em\u003e should be prohibited to prevent excessive extraction. Furthermore, it is imperative that policies and measures related to protection are promulgated in order to encourage reasonable planting and resource conservation in pre-determined potential suitable habitat areas. Currently, the limited number of data obtained from literature reviews and collecting pose samples constraints on the quality suitability analysis. Therefore, more extensive sampling is needed for in-depth future research. Although the Maxent model used in this study is appropriate, future studies should incorporate ensemble modeling to predict the potential distribution and quality suitability of \u003cem\u003eLycii Cortex\u003c/em\u003e, which may enhance predictive accuracy under future climatic conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study used the Maxent model and ArcGIS, combined with HPLC, to predict the potential distributions of two \u003cem\u003eLycium\u003c/em\u003e species under climatic influences, along with \u003cem\u003eLycium Chinese\u003c/em\u003e Mill. \u003cem\u003eCortex\u003c/em\u003e quality suitability analysis. In reference to the findings of the present study, it is posited that a scientific reference has been furnished for the purpose of predicting the future distribution of two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e in China. Furthermore, it is suggested that guidance has been provided for the selection of optimal cultivation sites for \u003cem\u003eLycii Cortex\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (animal or human trials are not addressed in this paper).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that data supporting the results of this study are available in the article and its supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported financially by the Innovation Team of Hebei Province Modern Agricultural Industry Technology System (No. HBCT2023080201), the Scientific and Technological Project of Shijiazhuang City of Hebei Province (No. 241200013A), the Scientific research project of Hebei Administration of Traditional Chinese Medicine (No. 2025069), and the ability establishment of sustainable use for Chinese medicine resources (No. 202400262157-3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLYT: Data curation, methodology, formal analysis, visualization, and writing-original draft preparation; LZK: Data curation, formal analysis, writing—review and editing; FXR: Data curation, reviewing and editing; WCH reviewing and editing; FC: formal analysis, methodology; SYX: visualization, reviewing and editing; GX: reviewing and editing; PL: funding acquisition, formal analysis; CTC: reviewing and editing; MDL: Project administration, funding acquisition, resources, methodology, and writing-reviewing and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors would like to acknowledge their universities for supporting the research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTan Z, Yuan Y, Huang S, Ma Y, Hong Z, Wang Y, et al. 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Med Plant Biol. 2024;3:e016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48130/mpb-0024-0016\u003c/span\u003e\u003cspan address=\"10.48130/mpb-0024-0016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Percentage contribution of key environment variables used for modeling.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003econtribution (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003ePermutation importance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eLycium barbarum\u0026nbsp;\u003c/em\u003eL.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003emean diurnal\u0026nbsp;range/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eminimum temperature of coldest month/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eprecipitation of driest quarter/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003emean\u0026middot;temperature of warmest quarter/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eannual mean temperature/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eprecipitation of wettest period/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eannual precipitation/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eisothermally\u0026middot;[(Bio2/Bio7) *100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eprecipitation of coldest quarter\u0026nbsp;/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eLycium Chinense\u0026nbsp;\u003c/em\u003eMill.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eannual mean temperature/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e44.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eprecipitation of wettest period/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003etemperature seasonality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eannual precipitation/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003etemperature annual range (Bio5~Bio6)/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003emean\u0026middot;temperature of warmest quarter/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003emean temperature of coldest quarter/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eprecipitation of driest quarter/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003emean diumnal range/℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eprecipitation of driest period/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eBio 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eprecipitation of warmest quarter/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Use the Maxent model to calculate the AUC values of two \u003cem\u003eLycium\u0026nbsp;\u003c/em\u003especies at different\u0026nbsp;periods.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eOrigin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eNO.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eVarious periods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLycium barbarum\u0026nbsp;\u003c/em\u003eL.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP2.6-2050s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP2.6-2070s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP4.5-2050s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP4.5-2070s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP8.5-2050s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP8.5-2070s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLycium Chinense\u0026nbsp;\u003c/em\u003eMill.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP2.6-2050s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP2.6-2070s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP4.5-2050s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP4.5-2070s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP8.5-2050s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eRCP8.5-2070s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"bmc-ecology-and-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evob","sideBox":"Learn more about [BMC Ecology and Evolution](http://bmcevolbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/evob/default.aspx","title":"BMC Ecology and Evolution","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Suitability area, Lycii Cortex, Maxent, Climate factors, HPLC","lastPublishedDoi":"10.21203/rs.3.rs-5490896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5490896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eLycii Cortex\u003c/em\u003e is a frequently utilized traditional Chinese medicine with notable therapeutic properties. The impact of climate change on its distribution and quality of \u003cem\u003eLycii Cortex\u003c/em\u003e is a significant concern. In this study, it investigated the geographic distribution of two sources of plants for \u003cem\u003eLycii Cortex\u003c/em\u003e and collected data on the distribution of samples from different origins via an online survey. HPLC was employed to ascertain the concentrations of kukoamine B and kukoamine A in the samples. Subsequently, the integrated ecological factor data were employed to forecast the prospective expansion areas of \u003cem\u003eLycium Chinese\u003c/em\u003e Mill. and \u003cem\u003eLycium barbarum\u003c/em\u003e L. under future climatic conditions, the migration trajectory of suitable habitat centers of mass, and the potential impact of climatic factors on the quality of \u003cem\u003eLycii Cortex\u003c/em\u003e at varying times using Maxent and ArcGIS. The current climate scenario indicates that suitable habitats for L. \u003cem\u003ebarbarum\u003c/em\u003e are primarily distributed in the northern, northwestern, and southwestern regions of China, while L. \u003cem\u003eChinese\u003c/em\u003e is predominantly distributed in the central, southern, and southeastern regions of China. In the RCP4.5 from 2050s to 2070s, the total area deemed suitable for both two \u003cem\u003eLycii Cortex\u003c/em\u003e species is significantly reduced. The mean distribution center of L. \u003cem\u003ebarbarum\u003c/em\u003e shifted towards higher latitudes, while that of L. \u003cem\u003eChinese\u003c/em\u003e shifted towards lower latitudes. It was predicted that in the future, the area of suitable quality of \u003cem\u003eLycii Cortex\u003c/em\u003e would appear to decrease. The results of this study can provide a reference for the determination of the suitable cultivation area of \u003cem\u003eLycii Cortex\u003c/em\u003e in China and the sustainable development of two \u003cem\u003eLycium\u003c/em\u003e species resources.\u003c/p\u003e","manuscriptTitle":"Predicting the potential habitats of two Lycium species and the quality suitability of Lycium Chinese Mill. Cortex under climate change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 16:40:08","doi":"10.21203/rs.3.rs-5490896/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-17T06:50:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-15T10:42:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7849822929570293073701924395668942874","date":"2025-05-27T06:21:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32916013746882740627100707749309876412","date":"2025-05-14T07:22:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T12:50:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19204253474714548146953397977828712936","date":"2025-05-06T03:42:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T14:17:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-28T00:13:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Ecology and Evolution","date":"2025-04-27T04:02:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-ecology-and-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evob","sideBox":"Learn more about [BMC Ecology and Evolution](http://bmcevolbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/evob/default.aspx","title":"BMC Ecology and Evolution","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"391e5256-38be-4c02-87ae-d722fd8df7b1","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:40:09+00:00","versionOfRecord":{"articleIdentity":"rs-5490896","link":"https://doi.org/10.1186/s12862-025-02413-8","journal":{"identity":"bmc-ecology-and-evolution","isVorOnly":false,"title":"BMC Ecology and Evolution"},"publishedOn":"2025-07-30 16:12:58","publishedOnDateReadable":"July 30th, 2025"},"versionCreatedAt":"2025-04-29 16:40:08","video":"","vorDoi":"10.1186/s12862-025-02413-8","vorDoiUrl":"https://doi.org/10.1186/s12862-025-02413-8","workflowStages":[]},"version":"v1","identity":"rs-5490896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5490896","identity":"rs-5490896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}