Optimized MaxEnt Modeling for Habitat Suitability of Clematis tenuifolia on the Qinghai-Tibet Plateau: Implications for Conservation Under Climate Change

Optimized MaxEnt Modeling for Habitat Suitability of Clematis tenuifolia on the Qinghai-Tibet Plateau: Implications for Conservation 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 Article Optimized MaxEnt Modeling for Habitat Suitability of Clematis tenuifolia on the Qinghai-Tibet Plateau: Implications for Conservation Under Climate Change Yanhui Ye, Wenqiang Huang, Lingchen Tong, Wu Zheng, Aohang Jing, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7231124/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study predicts the current and future potential distribution of Clematis tenuifolia in China using the MaxEnt model, optimized via the ENMeval package. Environmental predictors included climate, soil characteristics, and anthropogenic influences. The model demonstrated high predictive performance (AUC = 0.982). Currently, suitable habitats (~ 103.97 × 10⁴ km²) are primarily distributed across Tibet (Shigatse, Shannan, Lhasa, Nyingchi, Chamdo), western Sichuan, northwestern Yunnan, and Qinghai. Highly suitable areas (13.14%) are concentrated in southern Tibet and southwestern Sichuan. Under future climate scenarios (SSP126 to SSP585), the total suitable area is projected to decline by 29.78–44.26 × 10⁴ km² by the 2070s, with highly suitable areas shrinking by 5.10–9.08 × 10⁴ km². The SSP585 scenario predicts the most substantial losses. Habitat contraction is expected mainly in Ngari (Tibet), northern Yunnan, southern Qinghai, and northwestern Sichuan, with limited and fragmented expansions in southeastern Tibet. The habitat centroid is projected to shift southeastward by 35–77 km, stabilizing near the Bomi–Motuo region in Nyingchi. The findings highlight the species’ pronounced sensitivity to diurnal temperature range and seasonal fluctuations, suggesting that climate warming could pose a serious threat to its long-term survival. These results offer critical insights for biodiversity conservation and the strategic management of germplasm resources within Tibetan medicinal flora under future climate change. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Clematis tenuifolia Royle MaxEnt model habitat prediction ENMeval Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction The genus Clematis , belonging to the family Ranunculaceae, is widely distributed and morphologically diverse, comprising approximately 300 species. These species are primarily found in temperate and subtropical regions across the globe, with the greatest richness occurring in the Northern Hemisphere. China is recognized as one of the main centers of Clematis diversity, harboring around 108 species as recorded in the Flora of China 1 .The medicinal value of Clematis species has attracted increasing attention in recent years. Modern pharmacological research has confirmed that many species possess notable therapeutic potential. Historical use of Clematis in traditional Chinese medicine can be traced back to the Tang Dynasty 2 . For instance, Wei Ling Xian (Clematis chinensis ) was documented in the Xin Xiu Ben Cao (Newly Revised Materia Medica) as a treatment for various wind-related disorders, as well as a remedy for tonifying the five internal organs, relieving abdominal cold stagnation, and promoting lightness of the body with long-term use 3 .With the continuous advancement of Chinese medicine, research into the pharmacological properties of Clematis has deepened. Notably, species such as Clematis mandshurica and Clematis heracleifolia have shown medicinal efficacy comparable to Tripterygium wilfordii (Thunder God Vine), indicating their significant potential for therapeutic applications 4,5 . Clematis tenuifolia (Tibetan Clematis) is a species within the genus Clematis , primarily distributed in Tibet and western Sichuan, and has a long history of use in traditional Tibetan medicine. In the classical Tibetan medical text Jingzhu Materia Medica, the plant is referred to as “zhegonba,” with its roots and rhizomes described as pungent, bitter, warm, and toxic in nature. It is associated with the liver, spleen, and kidney meridians 6 , and is traditionally used to dispel wind and dampness, unblock meridians, relieve pain, and dissolve masses and nodules. Despite its historical and ethnopharmacological significance, modern medical research on C. tenuifolia remains limited, particularly regarding its ecological characteristics and suitable habitat range 7 . To address this gap, the present study employs the MaxEnt (maximum entropy) model to predict the current potential distribution of C. tenuifolia , as well as future shifts in its suitable habitat under projected climate change scenarios. The MaxEnt (Maximum Entropy) model, a widely used species distribution modeling approach grounded in ecological niche theory, applies the principle of maximum entropy to estimate the most uniform probability distribution of a species’ potential range. By incorporating known species occurrence data and environmental variables, the model infers potential distribution patterns under given environmental constraints, while maintaining the highest level of uncertainty consistent with the known data 8 . MaxEnt does not require complete background data, performs well with small sample sizes, and allows for intuitive visualization of the relationships between species occurrence and environmental predictors. Owing to these advantages, it has been extensively applied in species distribution modeling and ecological forecasting 9,10 . However, the conventional MaxEnt model exhibits strong subjectivity in parameter configuration and pronounced overfitting tendencies in practical applications, potentially leading to biased predictions 11 . To address these limitations, we employed the ENMeval package for MaxEnt optimization 12 . This approach leverages information-theoretic criteria (e.g., AICc, BIC) to systematically evaluate model performance across parameter combinations via *k*-fold cross-validation. It automatically identifies optimal regularization multipliers and feature classes, thereby mitigating overfitting risks while enhancing predictive accuracy and stability 13,14 . This refined framework has demonstrated robust advantages in biodiversity conservation and distribution modeling of rare/endangered species 15 . In this study, the ENMeval-optimized MaxEnt model was applied to predict current and future habitats of C. tenuifolia on the Tibetan Plateau, providing critical baseline data for its medicinal research and informing science-driven strategies for germplasm conservation and sustainable utilization 16 . Materials and methods Species Occurrence records: acquisition and preprocessing Occurrence records of C. tenuifolia were primarily sourced from: (1) Field surveys, (2) The Global Biodiversity Information Facility (GBIF; https://www.gbif.org/), (3) The Chinese Virtual Herbarium (https://www.cvh.ac.cn/), and (4) Peer-reviewed literature. Initial compilation yielded 123 occurrence points. To mitigate spatial sampling bias and model overfitting, these points were processed in ENMTools v1.4.4 using spatial thinning 17 . Subsequent spatial filtering in ArcGIS 10.8 (ESRI, Redlands, CA) involved: Constructing a 5-km grid across the study area, Retaining only one randomly selected point per grid cell, Removing records with aberrant coordinates (e.g., ocean points, coordinate mismatches) 18 . This protocol generated 46 validated occurrence points (Fig. 1) for distribution modeling. environment variables preprocessing A total of 59 environmental variables were analyzed, comprising 19 bioclimatic factors, elevation, aspect, soil properties, and human activity indicators. Data sources included:Bioclimatic variables and topography: WorldClim database (v2.1; 30s resolution; https://www.worldclim.org/),Soil properties: China National Soil Information Platform (http://soilcredit.mee.gov.cn),Human footprint index: Global Human Modification dataset (derived from WorldClim 2.0). To mitigate multicollinearity effects that compromise model accuracy 19 , we implemented a two-step variable selection protocol: 1.Pearson correlation filtering: Variables with |r| >0.8 were excluded using R v4.1.2 2.Variance inflation factor (VIF) screening: Covariant predictors (VIF > 10) were iteratively removed A total of 10 candidate environmental predictors were retained following multicollinearity filtering. Their ecological relevance was further assessed using permutation importance analysis in MaxEnt based on species occurrence data. Five variables with contribution rates exceeding 1% were identified as key drivers of Clematis tenuifolia habitat suitability: isothermality (BIO3); ratio of diurnal to annual temperature range), mean temperature of the wettest quarter (BIO8), mean temperature of the driest quarter (BIO9), base saturation in topsoil (T_bs), and the Human Footprint Index (Hfp) 20,21 . To assess future climate change impacts on C. tenuifolia habitat suitability, we utilized CMIP6 climate projections from the CMCC-ESM2 model under four Shared Socioeconomic Pathways (SSPs): SSP126 (sustainable development) SSP245(intermediate development) SSP370 (regional rivalry) SSP585 (fossil-fueled development) 22 . The analysis compared the baseline period (1970–2000) with two future horizons: mid-century (2041–2060) and late-century (2061–2080). All environmental layers were preprocessed in ArcGIS 10.8 (ESRI, Redlands, CA) to ensure: 1.Coordinate system uniformity (WGS84) 2.Consistent 30-arcsecond spatial resolution 3.Identical geographic extent (70°–105°E, 25°–40°N) Data were converted to ASCII format via the SDMtoolbox v2.5 plugin for MaxEnt compatibility, with archival copies retained for reproducibility. Table 1 Contribution and importance of environmental variables Code Variable Unit Contribution (%) Permutation importance (%) bio3 Isothermality (diurnal/annual temperature range ratio) - 53.4 29.6 bio8 Mean temperature of wettest quarter °C 15.6 44.6 Bio9 Mean temperature of driest quarter °C 15.4 20.2 t_bs Base saturation (topsoil 0–30 cm) % 12.1 3.6 hfp Human Footprint Index % 3.5 2.0 MaxEnt Model Calibration Default MaxEnt parameters (e.g., regularization multiplier = 1, feature classes = LQHPT) frequently induce overfitting to training data, compromising transferability to novel environments 23 . Such non-optimized settings may fail to capture true ecological niche relationships due to species-specific and environmental context dependencies. To enhance predictive accuracy, ecological interpretability, and methodological standardization, we implemented ENMeval-guided parameter optimization with spatial validation and variable screening 24,25 . Key complexity regulators—regularization multipliers (RMs) and feature classes (FCs)—were systematically evaluated 26 . Our protocol: 1.Tested RMs from 0.5 to 4 (0.5 increments) 2.Assessed four FCs: L (Linear), LQ (Linear + Quadratic), LQH (LQ + Hinge), LQP (LQ + Product) 3.Evaluated all 32 combinations via corrected Akaike Information Criterion (AICc) The optimal configuration (RM = 3, FC = LQ) yielded ΔAICc = 0 (indicating maximal parsimony and predictive power) 27,28 . Results and analyses Construction and accuracy evaluation of MaxEnt model The five selected environmental predictors and 46 validated occurrence records were formatted according to MaxEnt requirements and imported into MaxEnt v3.4.4 for modeling. Optimized settings (LQ feature class, regularization multiplier = 3) were applied during model construction. The dataset was partitioned by randomly assigning 25% of the occurrence points as the test set and the remaining 75% as the training set 18 . Model fitting was conducted using the "Subsample" replication method (10 iterations) to ensure robustness. Key configurations included: Maximum iterations: 5,000 Jackknife analysis for variable importance quantification Response curve generation for marginal effect visualization Output format: Logistic probability Other parameters: Default values 29,30 Model performance was evaluated using: 1. Omission rate (threshold = 10%) 2. Area Under the Receiver Operating Characteristic Curve (AUC) 31 AUC interpretation followed established benchmarks 32 : < 0.6: Prediction failure 0.6–0.7: Poor 0.7–0.8: Moderate 0.8–0.9: Good ≥ 0.9: Exceptional discriminative capacity Consequently, the model achieved an exceptional AUC of 0.982 (Fig. 3) with omission rates below 5%, demonstrating robust predictive reliability. The dominant environmental factors affecting the distribution of C. Tenuifolia As illustrated in Fig. 4, the five most influential environmental variables determining the potential distribution of C. tenuifolia on the Tibetan Plateau were, in descending order of importance: isothermality (i.e., the ratio of diurnal to annual temperature range), mean temperature of the wettest quarter, mean temperature of the driest quarter, topsoil base saturation, and the Human Footprint Index. Among these, the three bioclimatic factors—BIO3 (isothermality), BIO8 (mean temperature of the wettest quarter), and BIO9 (mean temperature of the driest quarter)—accounted for a combined contribution of 84.4%, indicating that temperature-related variables are the primary determinants of habitat suitability for this species. Response curve analysis (Fig. 5) revealed that the probability of C. tenuifolia occurrence increased steadily with rising isothermality values. Similarly, habitat suitability improved as the mean temperature of the wettest quarter increased, peaking at approximately 7℃; beyond this threshold, the probability of occurrence declined. A comparable trend was observed for the mean temperature of the driest quarter, with the highest suitability at − 1℃, followed by a marked decrease as temperatures continued to rise 33 . Distribution of C. tenuifolia in China under the current climate conditions The processed future climate variables (in ASCII format) and occurrence records were imported into MaxEnt for projection under future scenarios. The model generated ensemble predictions averaged across 10 replicate runs, with output values expressed as cloglog-transformed habitat suitability indices ranging continuously from 0 to 1. Higher values indicate greater environmental suitability for C. tenuifolia. Based on established species distribution modeling protocols 34,35 , habitat suitability thresholds were defined using: 1.Lowest Presence Threshold (LPT): Minimum suitability value across all training occurrence records 36 2.Standardized breakpoints 37 : 0.4, 0.6, and 1.0 Continuous cloglog-transformed outputs were discretized in ArcGIS 10.8 by applying these thresholds. Habitat suitability was subsequently categorized into four classes: Unsuitable: 0 < HSI ≤ LPT Low suitability: LPT < HSI ≤ 0.4 Moderate suitability: 0.4 < HSI ≤ 0.6 High suitability: 0.6 < HSI ≤ 1.0 Under current climatic conditions,as illustrated in Fig. 6,the potential habitat of C. tenuifolia is primarily distributed across Shigatse, Shannan, Lhasa, Nyingchi, and Chamdo in Tibet, as well as in Ya’an, Ganzi Tibetan Autonomous Prefecture, and Liangshan Yi Autonomous Prefecture in western Sichuan, and Diqing Tibetan Autonomous Prefecture in northwestern Yunnan. Additionally, smaller suitable areas are found in Huangnan Tibetan Autonomous Prefecture and Xining City in Qinghai Province. The total suitable habitat area is estimated at 103.97 × 10⁴ km², accounting for approximately 0.024% of China’s total land area. Highly suitable habitats (13.12 × 10⁴ km², 13.14% of the total suitable area) are mainly concentrated in southern Shigatse, southern Lhasa, and eastern parts of Shannan and Chamdo, as well as in southwestern Ganzi and northwestern Liangshan (Sichuan). Scattered patches are also present in northwestern Nyingchi, Ya’an (Sichuan), and Diqing (Yunnan). Moderately suitable areas cover 11.75 × 10⁴ km² (11.77% of the total suitable area) and generally surround the highly suitable zones. These areas are mainly located in Pulan and Quner counties (Ngari), Saga and Angren counties (Shigatse), Gongjue and Jiangda counties (Chamdo), Lang and Gongbu Jiangda counties (Nyingchi), Huo and Daofu counties (Sichuan), Deqin and Fugong counties (Yunnan), as well as in Xining and Haidong (Qinghai). Low-suitability habitats occupy 79.10 × 10⁴ km², representing 79.25% of the total suitable area. These areas extend outward from the moderately suitable zones into Zanda and Geji counties (Ngari), Shenzha and Bangor counties (Nagqu), Aba Tibetan and Qiang Autonomous Prefecture and Mianyang City (Sichuan), Longnan City (Gansu), and Mangya and Delingha (Qinghai), forming a general spatial trend of expansion from the southwest toward the northeast. Potential habitat changes of C. tenuifolia in China under future climate scenarios Given that the 2050s and 2070s represent the medium- and long-term climate change assessment windows, respectively, they are commonly used in ecological projection studies to avoid short-term natural variability (e.g., 2030s) and the high uncertainty associated with long-term forecasts (e.g., 2090s). This time-frame selection aligns with IPCC assessment report protocols for evaluating ecological impacts 38 . Accordingly, two future time periods (2050s and 2070s) under four Shared Socioeconomic Pathways (SSPs) were selected to simulate the potential distribution of C. tenuifolia in Tibet. Compared with the current climate baseline, all future scenarios projected a contraction in the total suitable habitat area as well as in low-suitability zones (Fig. 7, Table 2). The greatest reduction in total suitable area was observed under the SSP585 scenario in the 2050s. In contrast, the largest decline in low-suitability area occurred under the SSP370 scenario by the 2070s, amounting to a decrease of 30.12 × 10⁴ km². From a temporal perspective, habitat suitability generally declined in both future periods (2050s and 2070s) compared to current conditions. Notably, under SSP585, a slight decrease in suitable area was observed between the 2050s and 2070s. The SSP245scenario showed the most pronounced decline between the two periods, with a reduction of 8.56 × 10⁴ km², indicating that C. tenuifolia is highly sensitive to climatic changes. As future climate conditions continue to deviate from the species’ optimal niche, the contraction of suitable habitat suggests a continued population decline is likely 39 . Table 2 Area of habitat suitability classes for Clematis tenuifolia under different time periods (×10⁴ km²) Suitable habitats Comparison indicator Current SSP126 2050s SSP126 2070s SSP245 2050s SSP245 2070s SSP370 2050s SSP370 2070s SSP585 2050s SSP585 2070s High-suitable habitats Area/km 2 13.12 7.25 7.97 7.84 5.89 8.02 7.12 4.04 5.28 Reduction in area 5.87 5.15 5.28 7.23 5.10 6.00 9.08 7.84 Middle-suitable habitats Area/km 2 11.75 8.79 8.90 8.97 7.77 9.89 9.17 6.44 8.16 Reduction in area 2.96 2.85 2.78 3.98 1.86 2.58 5.31 3.15 Low-suitable habitats Area/km 2 79.10 56.52 54.29 57.37 51.96 54.57 48.98 49.23 48.99 Reduction in area 22.58 24.81 21.73 27.14 24.53 30.12 29.87 30.11 Total-suitable habitats Area/km 2 103.97 72.56 71.17 74.19 65.63 72.48 65.27 59.71 62.42 Reduction in area 31.41 32.80 29.78 38.34 31.49 38.70 44.26 41.55 Changes in the spatial pattern of the habitat of C. tenuifolia under climate change scenarios The spatial dynamics of C. tenuifolia habitat suitability under future climate scenarios are summarized in Table 3 and illustrated in Fig. 8,Fig. 9. Currently, highly suitable areas are concentrated in Yadong, Kangma, Gangba, Dingjie, Tingri, and Nyalam counties (Shigatse); Lhazi and Mozhugongka counties (Lhasa); Naidong, Qusong, and Gacha counties (Shannan); Lang and Gongbo’gyamda counties and Milin City (Nyingchi); Karuo, Chaya, and Mangkang counties (Chamdo); as well as in Daocheng, Jiulong counties and Muli Tibetan Autonomous County (Garzê Tibetan Autonomous Prefecture, Sichuan). Compared to present conditions, projections for the 2050s under all four SSP scenarios (SSP126, SSP2-4.5, SSP370, SSP585) show a clear trend of overall habitat contraction with only minor areas of expansion. For instance, under SSP585 in the 2050s, expansion is minimal (0.62 × 10⁴ km², or just 0.62%), whereas contraction reaches 21.93 × 10⁴ km² (44.08% loss). The most significant contractions occur in Zanda, Burang, and Ger counties (Ngari, Tibet), Lijiang and northern Kunming (Yunnan), most of Huangnan Tibetan Autonomous Prefecture (Qinghai), and northern Garzê (Sichuan). By the 2070s, this contraction intensifies under SSP126, SSP2-4.5, and SSP370, with major losses in northern Shigatse (Tibet), central and northern Kunming, Qujing, and Chuxiong Yi Autonomous Prefecture, as well as central Nujiang Lisu Autonomous Prefecture (Yunnan), and northern Garzê (Sichuan). Notably, under SSP585 in the 2070s, expansion slightly exceeds contraction, particularly in southeastern Tibet, eastern Hainan Tibetan Autonomous Prefecture (Qinghai), and northern Nujiang (Yunnan). In summary, key regions of habitat change for C. tenuifolia include northern Shigatse and western Ngari (Tibet), northern Garzê (Sichuan), northern Nujiang (Yunnan), and eastern Hainan Prefecture (Qinghai), which are projected to experience the most pronounced shifts in habitat suitability under climate change. Unit: ×10 4 km 2 Table 3 Changes in the potential suitable area of distribution of C. tenuifolia under future climate scenarios compared with the current situation Period Future climate models Expansion of suitable habitat area The area of the suitable zone remains unchanged Shrinking area of suitable zone 2050s SSP126 1.21 33.97 15.15 SSP245 1.21 34.93 14.26 SSP370 2.51 35.01 14.11 SSP585 0.62 27.20 21.93 2070s SSP126 2.37 32.60 2.58 SSP245 1.80 30.08 6.08 SSP370 2.63 32.87 4.66 SSP585 6.73 25.41 2.48 Shifting the centre of gravity of suitable habitats for C. tenuifolia under climate change scenarios As shown in Fig. 10, projected shifts in the distribution centroid of C. tenuifolia under four carbon emission scenarios (SSP126, SSP2-4.5, SSP370, and SSP585) are predominantly concentrated within Bomi County, Nyingchi City, Tibet Autonomous Region, with minor deviations toward the Bomi–Motuo boundary. Two distinct temporal patterns of centroid migration are observed. From the present to the 2050s, the centroid consistently shifts southeastward. However, from the 2050s to the 2070s, the trajectories diverge: the centroid continues southwestward under SSP126 and SSP2-4.5, but reverses northwestward under SSP370 and SSP585. The current distribution centroid is located in Bomi County (95°57′36″E, 29°49′43″N). Under the SSP126 scenario, the centroid migrates 35.15 km southeastward to Motuo County by the 2050s, and an additional 8.87 km southwestward by the 2070s. Under SSP2-4.5, the centroid shifts 64.55 km southeast to Kangyu Township (Bomi County) by the 2050s, followed by a 53.69 km southwestward shift to Duoji Township (Bomi County) by the 2070s. In contrast, under SSP370 and SSP585, the centroid initially shifts southeastward in the 2050s—76.50 km to Songzong Township (SSP370) and 45.77 km to Zamu Township (SSP585)—but then reverses direction, migrating northwestward by 31.82 km and 44.99 km, respectively, reaching Motuo County by the 2070s. All migration distances were calculated as great-circle distances using the WGS84 ellipsoid. These results reveal a transitional pattern of “initial southeastward shift followed by divergent migration,” with all centroids remaining within Nyingchi City. This indicates that the southeastern Tibetan Plateau will likely remain the ecological core zone for C. tenuifolia under future climate change. Discussion Model Optimisation and Predictive Reliability In this study, MaxEnt model parameters—regularization multiplier (RM = 3) and feature class (FC = LQ)—were optimized using the ENMeval package, which significantly reduced the risk of overfitting and yielded a high predictive accuracy (AUC = 0.982) 40 . Compared to the default MaxEnt configuration, the optimized model more precisely captured the ecological niche requirements of Clematis tenuifolia, particularly its sensitivity to temperature-related variables, with BIO3, BIO8, and BIO9 collectively contributing 84.4% to model performance. These findings are consistent with recent studies on endangered species such as Rhodiola crenulata , where optimized MaxEnt models demonstrated improved predictive reliability. This reinforces the importance of parameter tuning when modeling species with limited occurrence records 41 . Ecological significance of key environmental factors The habitat suitability of C. tenuifolia increased with rising isothermality (BIO3), supporting its ecological adaptation to pronounced diurnal temperature fluctuations and intense solar radiation in alpine environments. The peak response at 7°C for the mean temperature of the wettest quarter (BIO8), and the threshold at − 1°C for the mean temperature of the driest quarter (BIO9), suggest that mild, moist winters and cool summers are critical for the species’ survival—conditions that align closely with the monsoon-influenced climate of the southeastern Qinghai–Tibetan Plateau 42 . Topsoil base saturation (T_bs) was also an important driver, likely reflecting its role in regulating root-level mineral uptake essential for physiological function in nutrient-poor mountain soils 43 . In contrast, the human footprint index (HFP) showed a relatively low contribution (3.5%), indicating limited current anthropogenic disturbance within the species’ distribution range. However, future urban expansion and infrastructure development may exacerbate habitat fragmentation, particularly in marginal areas 44 . Mechanisms of Climate Change Threats to Suitable Areas The substantial contraction of suitable habitats under all future climate scenarios (ranging from 29.78 × 10 4 km²to 44.26 × 10 4 km²by the 2070s) is primarily attributed to warming-induced disruption of temperature thresholds. In particular, the SSP585 scenario projects winter temperatures (BIO9) exceeding the species tolerance limit (–1℃) during the driest quarter, resulting in rapid habitat loss across fringe zones in northwestern Sichuan and northern Yunnan—accounting for a contraction of up to 44.08%. While localized expansions are projected in southeastern Tibet due to increased warming and humidification, such gains are outweighed by widespread thermal stress and intensified drought elsewhere. For example, mean temperatures in the wettest quarter (BIO8) are projected to surpass 7℃ in the Ali region, a threshold beyond which regeneration is inhibited. The species distribution centroid is projected to shift southeastward by 35–77 km, stabilizing near Bomi and Motuo—areas characterized by consistent humidity and moderated temperatures due to Indian Ocean monsoon influence. These findings underscore the potential of southeastern Tibet to serve as a future climatic refuge for C. tenuifolia under ongoing climate change 45 , 46 . tenuifolia habitat selection and protection strategies To mitigate this risk, a series of integrated conservation strategies are urgently needed alongside continued pharmacological research. First, the projected centroid of future habitat suitability—centered around the Bomi-Motuo region in Nyingchi—should be designated as an ecological redline zone, and a dedicated C. tenuifolia nature reserve should be established to restrict anthropogenic disturbances. Second, monitoring of extant populations in southern Tibet (e.g., southern Shigatse and Shannan) and southwestern Sichuan (e.g., southwestern Garzê Prefecture) should be intensified and incorporated into the national park system. A cold-storage germplasm bank is recommended in Lhasa or Nyingchi to preserve seeds and live specimens from populations undergoing habitat loss (e.g., Ali, northern Yunnan, and southern Qinghai). In partnership with Tibetan medicine research institutions, artificial cultivation trials should be initiated to screen for heat-tolerant genotypes and promote pilot-scale bionic cultivation in climatically favorable zones of southeastern Tibet. Furthermore, vertical ecological corridors should be established along the Yarlung Tsangpo River Basin to connect current high-suitability areas in southern Tibet with emerging zones in the southeast, thereby facilitating natural population migration. In areas experiencing severe contraction (e.g., Pulan County in Ali and northern Garzê), local extinction may be delayed by implementing microclimate interventions such as artificial humidification and shading. Conclusion Under current climatic conditions, the core habitat of C. tenuifolia (13.12×10 4 km²) is primarily located in southern Tibet (including Shigatse and Shannan) and southwestern Sichuan. This distribution is strongly shaped by climatic variables such as isothermality (BIO3), mean temperature of the wettest quarter (BIO8), and mean temperature of the driest quarter (BIO9), which together contribute over 84% to the habitat suitability model—highlighting the species’ strong ecological dependence on the alpine monsoon climate. Under future climate scenarios projected to the 2070s, the total suitable habitat area is expected to contract by 30–44%, with highly suitable areas declining by 39–69%, particularly under the SSP5–8.5 scenario. Major contraction hotspots include the Ali Plateau and the ecotonal regions spanning Sichuan, Yunnan, and Tibet, whereas minor expansions in southeastern Tibet are insufficient to offset the overall decline. Collectively, these findings suggest that C. tenuifolia populations are likely to undergo significant reduction in response to ongoing global warming.Consequently, while advancing modern pharmacological research on this species, equal emphasis must be placed on conserving its natural resources and ensuring sustainable utilization. Declarations Funding: This research was funded by the Forestry Doctoral Program (Phase I) at Xizang Agriculture and Animal Husbandry University (Grant No.: 533325001); Special Funds for Development and Reform of Local Universities Supported by the Central Government in 2024: Construction and Enhancement of Comprehensive Service Capabilities of Science and Technology Courtyards for Agriculture and Animal Husbandry with Plateau Characteristics (XK2024-04, XK2024-01, XY2024-03); Special Funds for Development and Reform of Local Universities Supported by the Central Government in 2025: Construction and Enhancement of Comprehensive Service Capabilities of Science and Technology Courtyards for Agriculture and Animal Husbandry with Plateau Characteristics (YJSXK2025-22, YJSXY2025-05); National Natural Science Foundation of China (Grant Nos.: 31860141; 31360119); Graduate Education Innovation Program of Xizang Agriculture and Animal Husbandry University (YJS2024-26; YJS2024-28; YJS2024-31); Major Science and Technology Project of Xizang Autonomous Region (XZ202201ZD0005G02); Key Laboratory of Forestry Ecological Engineering on the Xizang Plateau The Seventh Batch of Flexible Talent Project at Xizang Agriculture and Animal Husbandry University (53013001804) Author Contribution Y.Y. and W.H. contributed equally to the conception, design, data analysis, and writing of this manuscript. 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Identifying climate refugia for high-elevation Alpine birds under current climate warming predictions. Glob. Change Biol. 28 , 4276–4291 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":148288,"visible":true,"origin":"","legend":"\u003cp\u003ePoint records of Clematis tenuifolia in China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend：The map was created using ArcMap 10.8 (ESRI, Redlands, CA, USA; https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview ),Administrative boundaries were sourced from the National Geomatics Center of China (\u003c/strong\u003e\u003ca href=\"http://www.ngcc.cn/\" target=\"https://chat.deepseek.com/a/chat/s/_blank\"\u003e\u003cstrong\u003ehttp://www.ngcc.cn\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e) and projected in WGS84 coordinate system.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/035d612021506cc3cc44db79.jpg"},{"id":92898794,"identity":"88184db5-5fe1-4733-9451-75aa4a0741aa","added_by":"auto","created_at":"2025-10-06 20:34:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214279,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of 59 environmental factors\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/e801651280182b0cc79e93e2.jpg"},{"id":92898119,"identity":"6566dae6-a6b2-4dec-a459-a7d3181d460f","added_by":"auto","created_at":"2025-10-06 20:18:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69875,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of model performance for C. tenuifolia habitat suitability on the Tibetan Plateau.\u003c/p\u003e\n\u003cp\u003e(A) Omission rate curve at 10% training presence threshold (black dashed line). The predicted omission rate (red line) closely aligns with the expected rate (grey line), indicating minimal model overfitting.\u003c/p\u003e\n\u003cp\u003e*(B) Receiver operating characteristic (ROC) curve for test data. The area under the curve (AUC = 0.982 ± 0.012) demonstrates exceptional predictive discrimination, significantly exceeding random prediction (grey dashed line).*\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/125722311609a8a6fd96cb47.jpg"},{"id":92898116,"identity":"a3de2298-ff76-4b38-96a3-ba435091641c","added_by":"auto","created_at":"2025-10-06 20:18:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81135,"visible":true,"origin":"","legend":"\u003cp\u003eJackknife test scores of the potential distribution area model of \u003cem\u003eC. tenuifolia\u003c/em\u003e under different environmental factors\u003c/p\u003e\n\u003cp\u003eFigure notes: bio3, ratio of diurnal to annual temperature difference; bio8, mean temperature of the wettest quarter; bio9, mean temperature of the driest quarter; tbs, basic saturation of upper soil; hfp, human footprint\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/4d15571ec72282b4c7a56430.jpg"},{"id":92898795,"identity":"17da925e-55fd-412e-b4d5-6e3e29c821e8","added_by":"auto","created_at":"2025-10-06 20:34:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45465,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves of important environmental factors affecting the potential distribution of \u003cem\u003eC. tenuifolia\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/d1463eef25a50982fcda146f.jpg"},{"id":92898125,"identity":"69b4d820-2365-4848-ac0d-93757fbf8165","added_by":"auto","created_at":"2025-10-06 20:18:37","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":145303,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Clematis tenuifolia in China under current climatic conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend：The map was created using ArcMap 10.8 (ESRI, Redlands, CA, USA; https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview ),Administrative boundaries were sourced from the National Geomatics Center of China (\u003c/strong\u003e\u003ca href=\"http://www.ngcc.cn/\" target=\"https://chat.deepseek.com/a/chat/s/_blank\"\u003e\u003cstrong\u003ehttp://www.ngcc.cn\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e) and projected in WGS84 coordinate system.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/11293d1522fb51f80b27d022.jpg"},{"id":92898798,"identity":"4e8f45c6-02d6-4551-bbe3-29bf66e27566","added_by":"auto","created_at":"2025-10-06 20:34:37","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":255145,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the potential habitat of Clematis tenuifolia in China under future climate scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend：The map was created using ArcMap 10.8 (ESRI, Redlands, CA, USA; https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview ),Administrative boundaries were sourced from the National Geomatics Center of China (\u003c/strong\u003e\u003ca href=\"http://www.ngcc.cn/\" target=\"https://chat.deepseek.com/a/chat/s/_blank\"\u003e\u003cstrong\u003ehttp://www.ngcc.cn\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e) and projected in WGS84 coordinate system.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/42d647d0bee868cb2a6c0e59.jpg"},{"id":92898797,"identity":"65c398d4-55e5-4e70-8069-4f70bcd6d0bc","added_by":"auto","created_at":"2025-10-06 20:34:37","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":76786,"visible":true,"origin":"","legend":"\u003cp\u003eThe projected distribution habitat area in two periods(2050s，2070s)showing expansion，\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/b00c72ba3b64e9d7aa3cdd29.jpg"},{"id":92898800,"identity":"d1b398bf-4509-49ec-aba2-9c2567ca35ea","added_by":"auto","created_at":"2025-10-06 20:34:38","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":265601,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the distribution of potential suitable areas for\u003cem\u003e C. tenuifolia\u003c/em\u003e in China under climate change scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend：The map was created using ArcMap 10.8 (ESRI, Redlands, CA, USA; https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview ),Administrative boundaries were sourced from the National Geomatics Center of China (\u003c/strong\u003e\u003ca href=\"http://www.ngcc.cn/\" target=\"https://chat.deepseek.com/a/chat/s/_blank\"\u003e\u003cstrong\u003ehttp://www.ngcc.cn\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e) and projected in WGS84 coordinate system.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/e67a10fa9842c5eb867546a0.jpg"},{"id":92898122,"identity":"f521e616-b49b-447e-bca0-0b47f079beeb","added_by":"auto","created_at":"2025-10-06 20:18:37","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":51053,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the centroid of suitable habitats for \u003cem\u003eC. tenuifolia\u003c/em\u003e under environmental change scenarios\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend：The map was created using ArcMap 10.8 (ESRI, Redlands, CA, USA; https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview ),Administrative boundaries were sourced from the National Geomatics Center of China (\u003c/strong\u003e\u003ca href=\"http://www.ngcc.cn/\" target=\"https://chat.deepseek.com/a/chat/s/_blank\"\u003e\u003cstrong\u003ehttp://www.ngcc.cn\u003c/strong\u003e\u003c/a\u003e\u003cstrong\u003e) and projected in WGS84 coordinate system.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/da23be772e73dcbf0ec02c66.jpg"},{"id":96710276,"identity":"b477cde4-de85-4730-a58f-1991c7ed4d5f","added_by":"auto","created_at":"2025-11-25 10:10:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3051391,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7231124/v1/5d6c796e-1dbd-40b8-bce8-5011ed27713b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimized MaxEnt Modeling for Habitat Suitability of Clematis tenuifolia on the Qinghai-Tibet Plateau: Implications for Conservation Under Climate Change","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe \u003cem\u003egenus Clematis\u003c/em\u003e, belonging to the family Ranunculaceae, is widely distributed and morphologically diverse, comprising approximately 300 species. These species are primarily found in temperate and subtropical regions across the globe, with the greatest richness occurring in the Northern Hemisphere. China is recognized as one of the main centers of \u003cem\u003eClematis\u003c/em\u003e diversity, harboring around 108 species as recorded in the Flora of China\u003csup\u003e1\u003c/sup\u003e.The medicinal value of \u003cem\u003eClematis\u003c/em\u003e species has attracted increasing attention in recent years. Modern pharmacological research has confirmed that many species possess notable therapeutic potential. Historical use of Clematis in traditional Chinese medicine can be traced back to the Tang Dynasty\u003csup\u003e2\u003c/sup\u003e. For instance, Wei Ling Xian \u003cem\u003e(Clematis chinensis\u003c/em\u003e) was documented in the Xin Xiu Ben Cao (Newly Revised Materia Medica) as a treatment for various wind-related disorders, as well as a remedy for tonifying the five internal organs, relieving abdominal cold stagnation, and promoting lightness of the body with long-term use \u003csup\u003e3\u003c/sup\u003e.With the continuous advancement of Chinese medicine, research into the pharmacological properties of \u003cem\u003eClematis\u003c/em\u003e has deepened. Notably, species such as\u003cem\u003e\u0026nbsp;Clematis mandshurica\u003c/em\u003e and \u003cem\u003eClematis heracleifolia\u003c/em\u003e have shown medicinal efficacy comparable to Tripterygium wilfordii (Thunder God Vine), indicating their significant potential for therapeutic applications\u003csup\u003e4,5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClematis tenuifolia\u003c/em\u003e (Tibetan Clematis) is a species within the \u003cem\u003egenus Clematis\u003c/em\u003e, primarily distributed in Tibet and western Sichuan, and has a long history of use in traditional Tibetan medicine. In the classical Tibetan medical text Jingzhu Materia Medica, the plant is referred to as “zhegonba,” with its roots and rhizomes described as pungent, bitter, warm, and toxic in nature. It is associated with the liver, spleen, and kidney meridians\u003csup\u003e6\u003c/sup\u003e, and is traditionally used to dispel wind and dampness, unblock meridians, relieve pain, and dissolve masses and nodules.\u003c/p\u003e\n\u003cp\u003eDespite its historical and ethnopharmacological significance, modern medical research on \u003cem\u003eC. tenuifolia\u003c/em\u003e remains limited, particularly regarding its ecological characteristics and suitable habitat range\u003csup\u003e7\u003c/sup\u003e. To address this gap, the present study employs the MaxEnt (maximum entropy) model to predict the current potential distribution of \u003cem\u003eC. tenuifolia\u003c/em\u003e, as well as future shifts in its suitable habitat under projected climate change scenarios.\u003c/p\u003e\n\u003cp\u003eThe MaxEnt (Maximum Entropy) model, a widely used species distribution modeling approach grounded in ecological niche theory, applies the principle of maximum entropy to estimate the most uniform probability distribution of a species’ potential range. By incorporating known species occurrence data and environmental variables, the model infers potential distribution patterns under given environmental constraints, while maintaining the highest level of uncertainty consistent with the known data\u003csup\u003e8\u003c/sup\u003e. MaxEnt does not require complete background data, performs well with small sample sizes, and allows for intuitive visualization of the relationships between species occurrence and environmental predictors. Owing to these advantages, it has been extensively applied in species distribution modeling and ecological forecasting \u003csup\u003e9,10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, the conventional MaxEnt model exhibits\u0026nbsp;strong subjectivity in parameter configuration\u0026nbsp;and\u0026nbsp;pronounced overfitting tendencies\u0026nbsp;in practical applications, potentially leading to biased predictions\u003csup\u003e11\u003c/sup\u003e. To address these limitations, we employed the\u0026nbsp;ENMeval package for MaxEnt optimization\u003csup\u003e12\u003c/sup\u003e. This approach leverages\u0026nbsp;information-theoretic criteria\u0026nbsp;(e.g., AICc, BIC) to systematically evaluate model performance across parameter combinations via\u0026nbsp;*k*-fold cross-validation. It automatically identifies optimal\u0026nbsp;regularization multipliers\u0026nbsp;and\u0026nbsp;feature classes, thereby\u0026nbsp;mitigating overfitting risks\u0026nbsp;while enhancing predictive accuracy and stability\u003csup\u003e13,14\u003c/sup\u003e. This refined framework has demonstrated\u0026nbsp;robust advantages\u0026nbsp;in biodiversity conservation and distribution modeling of rare/endangered species\u003csup\u003e15\u003c/sup\u003e. In this study, the ENMeval-optimized MaxEnt model was applied to predict current and future habitats of \u003cem\u003eC. tenuifolia\u0026nbsp;\u003c/em\u003eon the Tibetan Plateau, providing\u0026nbsp;critical baseline data\u0026nbsp;for its medicinal research and informing\u0026nbsp;science-driven strategies\u0026nbsp;for germplasm conservation and sustainable utilization\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eSpecies Occurrence records: acquisition and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOccurrence records of \u003cem\u003eC. tenuifolia\u003c/em\u003e were primarily sourced from:\u003c/p\u003e\n\u003cp\u003e(1) Field surveys,\u003c/p\u003e\n\u003cp\u003e(2) The Global Biodiversity Information Facility (GBIF; https://www.gbif.org/),\u003c/p\u003e\n\u003cp\u003e(3) The Chinese Virtual Herbarium (https://www.cvh.ac.cn/), and\u003c/p\u003e\n\u003cp\u003e(4) Peer-reviewed literature.\u003c/p\u003e\n\u003cp\u003eInitial compilation yielded 123 occurrence points. To mitigate spatial sampling bias and model overfitting, these points were processed in ENMTools v1.4.4 using spatial thinning\u003csup\u003e17\u003c/sup\u003e. Subsequent spatial filtering in ArcGIS 10.8 (ESRI, Redlands, CA) involved:\u003c/p\u003e\n\u003cp\u003eConstructing a 5-km grid across the study area,\u003c/p\u003e\n\u003cp\u003eRetaining only one randomly selected point per grid cell,\u003c/p\u003e\n\u003cp\u003eRemoving records with aberrant coordinates (e.g., ocean points, coordinate mismatches)\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis protocol generated 46 validated occurrence points (Fig.\u0026nbsp;1) for distribution modeling.\u003c/p\u003e\n\u003ch3\u003eenvironment variables preprocessing\u003c/h3\u003e\n\u003cp\u003eA total of 59 environmental variables were analyzed, comprising 19 bioclimatic factors, elevation, aspect, soil properties, and human activity indicators. Data sources included:Bioclimatic variables and topography: WorldClim database (v2.1; 30s resolution; https://www.worldclim.org/),Soil properties: China National Soil Information Platform (http://soilcredit.mee.gov.cn),Human footprint index: Global Human Modification dataset (derived from WorldClim 2.0).\u003c/p\u003e\n\u003cp\u003eTo mitigate multicollinearity effects that compromise model accuracy\u003csup\u003e19\u003c/sup\u003e, we implemented a two-step variable selection protocol:\u003c/p\u003e\n\u003cp\u003e1.Pearson correlation filtering: Variables with |r| \u0026gt;0.8 were excluded using R v4.1.2\u003c/p\u003e\n\u003cp\u003e2.Variance inflation factor (VIF) screening: Covariant predictors (VIF\u0026thinsp;\u0026gt;\u0026thinsp;10) were iteratively removed\u003c/p\u003e\n\u003cp\u003eA total of 10 candidate environmental predictors were retained following multicollinearity filtering. Their ecological relevance was further assessed using permutation importance analysis in MaxEnt based on species occurrence data. Five variables with contribution rates exceeding 1% were identified as key drivers of Clematis tenuifolia habitat suitability: isothermality (BIO3); ratio of diurnal to annual temperature range), mean temperature of the wettest quarter (BIO8), mean temperature of the driest quarter (BIO9), base saturation in topsoil (T_bs), and the Human Footprint Index (Hfp)\u003csup\u003e20,21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo assess future climate change impacts on \u003cem\u003eC. tenuifolia\u003c/em\u003e habitat suitability, we utilized CMIP6 climate projections from the CMCC-ESM2 model under four Shared Socioeconomic Pathways (SSPs):\u003c/p\u003e\n\u003cp\u003eSSP126 (sustainable development)\u003c/p\u003e\n\u003cp\u003eSSP245(intermediate development)\u003c/p\u003e\n\u003cp\u003eSSP370 (regional rivalry)\u003c/p\u003e\n\u003cp\u003eSSP585 (fossil-fueled development)\u003csup\u003e22\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003eThe analysis compared the baseline period (1970\u0026ndash;2000) with two future horizons: mid-century (2041\u0026ndash;2060) and late-century (2061\u0026ndash;2080). All environmental layers were preprocessed in ArcGIS 10.8 (ESRI, Redlands, CA) to ensure:\u003c/p\u003e\n\u003cp\u003e1.Coordinate system uniformity (WGS84)\u003c/p\u003e\n\u003cp\u003e2.Consistent 30-arcsecond spatial resolution\u003c/p\u003e\n\u003cp\u003e3.Identical geographic extent (70\u0026deg;\u0026ndash;105\u0026deg;E, 25\u0026deg;\u0026ndash;40\u0026deg;N)\u003c/p\u003e\n\u003cp\u003eData were converted to ASCII format via the SDMtoolbox v2.5 plugin for MaxEnt compatibility, with archival copies retained for reproducibility.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eContribution and importance of environmental variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eContribution (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePermutation importance (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsothermality (diurnal/annual temperature range ratio)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean temperature of wettest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBio9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean temperature of driest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et_bs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase saturation (topsoil 0\u0026ndash;30 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehfp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Footprint Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eMaxEnt Model Calibration\u003c/h2\u003e\n \u003cp\u003eDefault MaxEnt parameters (e.g., regularization multiplier\u0026thinsp;=\u0026thinsp;1, feature classes\u0026thinsp;=\u0026thinsp;LQHPT) frequently induce overfitting to training data, compromising transferability to novel environments\u003csup\u003e23\u003c/sup\u003e. Such non-optimized settings may fail to capture true ecological niche relationships due to species-specific and environmental context dependencies.\u003c/p\u003e\n \u003cp\u003eTo enhance predictive accuracy, ecological interpretability, and methodological standardization, we implemented ENMeval-guided parameter optimization with spatial validation and variable screening \u003csup\u003e24,25\u003c/sup\u003e. Key complexity regulators\u0026mdash;regularization multipliers (RMs) and feature classes (FCs)\u0026mdash;were systematically evaluated\u003csup\u003e26\u003c/sup\u003e. Our protocol:\u003c/p\u003e\n \u003cp\u003e1.Tested RMs from 0.5 to 4 (0.5 increments)\u003c/p\u003e\n \u003cp\u003e2.Assessed four FCs: L (Linear), LQ (Linear\u0026thinsp;+\u0026thinsp;Quadratic), LQH (LQ\u0026thinsp;+\u0026thinsp;Hinge), LQP (LQ\u0026thinsp;+\u0026thinsp;Product)\u003c/p\u003e\n \u003cp\u003e3.Evaluated all 32 combinations via corrected Akaike Information Criterion (AICc)\u003c/p\u003e\n \u003cp\u003eThe optimal configuration (RM\u0026thinsp;=\u0026thinsp;3, FC\u0026thinsp;=\u0026thinsp;LQ) yielded \u0026Delta;AICc\u0026thinsp;=\u0026thinsp;0 (indicating maximal parsimony and predictive power)\u003csup\u003e27,28\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and analyses","content":"\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eConstruction and accuracy evaluation of MaxEnt model\u003c/h2\u003e\n \u003cp\u003eThe five selected environmental predictors and 46 validated occurrence records were formatted according to MaxEnt requirements and imported into MaxEnt v3.4.4 for modeling. Optimized settings (LQ feature class, regularization multiplier\u0026thinsp;=\u0026thinsp;3) were applied during model construction. The dataset was partitioned by randomly assigning 25% of the occurrence points as the test set and the remaining 75% as the training set \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eModel fitting was conducted using the \u0026quot;Subsample\u0026quot; replication method (10 iterations) to ensure robustness. Key configurations included:\u003c/p\u003e\n \u003cp\u003eMaximum iterations: 5,000\u003c/p\u003e\n \u003cp\u003eJackknife analysis for variable importance quantification\u003c/p\u003e\n \u003cp\u003eResponse curve generation for marginal effect visualization\u003c/p\u003e\n \u003cp\u003eOutput format: Logistic probability\u003c/p\u003e\n \u003cp\u003eOther parameters: Default values\u003csup\u003e29,30\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eModel performance was evaluated using:\u003c/p\u003e\n \u003cp\u003e1. Omission rate (threshold\u0026thinsp;=\u0026thinsp;10%)\u003c/p\u003e\n \u003cp\u003e2. Area Under the Receiver Operating Characteristic Curve (AUC)\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eAUC interpretation followed established benchmarks\u003csup\u003e32\u003c/sup\u003e:\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.6: Prediction failure\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e0.6\u0026ndash;0.7: Poor\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e0.7\u0026ndash;0.8: Moderate\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e0.8\u0026ndash;0.9: Good\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;0.9: Exceptional discriminative capacity\u003c/p\u003e\n \u003cp\u003eConsequently, the model achieved an exceptional AUC of 0.982 (Fig.\u0026nbsp;3) with omission rates below 5%, demonstrating robust predictive reliability.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe dominant environmental factors affecting the distribution of\u003c/strong\u003e \u003cstrong\u003eC.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eTenuifolia\u003c/h3\u003e\n\u003cp\u003eAs illustrated in Fig. 4, the five most influential environmental variables determining the potential distribution of \u003cem\u003eC. tenuifolia\u003c/em\u003e on the Tibetan Plateau were, in descending order of importance: isothermality (i.e., the ratio of diurnal to annual temperature range), mean temperature of the wettest quarter, mean temperature of the driest quarter, topsoil base saturation, and the Human Footprint Index. Among these, the three bioclimatic factors\u0026mdash;BIO3 (isothermality), BIO8 (mean temperature of the wettest quarter), and BIO9 (mean temperature of the driest quarter)\u0026mdash;accounted for a combined contribution of 84.4%, indicating that temperature-related variables are the primary determinants of habitat suitability for this species.\u003c/p\u003e\n\u003cp\u003eResponse curve analysis (Fig. 5) revealed that the probability of \u003cem\u003eC. tenuifolia\u003c/em\u003e occurrence increased steadily with rising isothermality values. Similarly, habitat suitability improved as the mean temperature of the wettest quarter increased, peaking at approximately 7℃; beyond this threshold, the probability of occurrence declined. A comparable trend was observed for the mean temperature of the driest quarter, with the highest suitability at \u0026minus;\u0026thinsp;1℃, followed by a marked decrease as temperatures continued to rise\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of\u003c/strong\u003e \u003cstrong\u003eC. tenuifolia\u003c/strong\u003e \u003cstrong\u003ein China under the current climate conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe processed future climate variables (in ASCII format) and occurrence records were imported into MaxEnt for projection under future scenarios. The model generated ensemble predictions averaged across 10 replicate runs, with output values expressed as cloglog-transformed habitat suitability indices ranging continuously from 0 to 1. Higher values indicate greater environmental suitability for C. tenuifolia.\u003c/p\u003e\n\u003cp\u003eBased on established species distribution modeling protocols\u003csup\u003e34,35\u003c/sup\u003e, habitat suitability thresholds were defined using:\u003c/p\u003e\n\u003cp\u003e1.Lowest Presence Threshold (LPT): Minimum suitability value across all training occurrence records\u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e2.Standardized breakpoints\u003csup\u003e37\u003c/sup\u003e: 0.4, 0.6, and 1.0\u003c/p\u003e\n\u003cp\u003eContinuous cloglog-transformed outputs were discretized in ArcGIS 10.8 by applying these thresholds. Habitat suitability was subsequently categorized into four classes:\u003c/p\u003e\n\u003cp\u003eUnsuitable: 0\u0026thinsp;\u0026lt;\u0026thinsp;HSI\u0026thinsp;\u0026le;\u0026thinsp;LPT\u003c/p\u003e\n\u003cp\u003eLow suitability: LPT\u0026thinsp;\u0026lt;\u0026thinsp;HSI\u0026thinsp;\u0026le;\u0026thinsp;0.4\u003c/p\u003e\n\u003cp\u003eModerate suitability: 0.4\u0026thinsp;\u0026lt;\u0026thinsp;HSI\u0026thinsp;\u0026le;\u0026thinsp;0.6\u003c/p\u003e\n\u003cp\u003eHigh suitability: 0.6\u0026thinsp;\u0026lt;\u0026thinsp;HSI\u0026thinsp;\u0026le;\u0026thinsp;1.0\u003c/p\u003e\n\u003cp\u003eUnder current climatic conditions,as illustrated in Fig. 6,the potential habitat of \u003cem\u003eC. tenuifolia\u003c/em\u003e is primarily distributed across Shigatse, Shannan, Lhasa, Nyingchi, and Chamdo in Tibet, as well as in Ya\u0026rsquo;an, Ganzi Tibetan Autonomous Prefecture, and Liangshan Yi Autonomous Prefecture in western Sichuan, and Diqing Tibetan Autonomous Prefecture in northwestern Yunnan. Additionally, smaller suitable areas are found in Huangnan Tibetan Autonomous Prefecture and Xining City in Qinghai Province. The total suitable habitat area is estimated at 103.97 \u0026times; 10⁴ km\u0026sup2;, accounting for approximately 0.024% of China\u0026rsquo;s total land area.\u003c/p\u003e\n\u003cp\u003eHighly suitable habitats (13.12 \u0026times; 10⁴ km\u0026sup2;, 13.14% of the total suitable area) are mainly concentrated in southern Shigatse, southern Lhasa, and eastern parts of Shannan and Chamdo, as well as in southwestern Ganzi and northwestern Liangshan (Sichuan). Scattered patches are also present in northwestern Nyingchi, Ya\u0026rsquo;an (Sichuan), and Diqing (Yunnan).\u003c/p\u003e\n\u003cp\u003eModerately suitable areas cover 11.75 \u0026times; 10⁴ km\u0026sup2; (11.77% of the total suitable area) and generally surround the highly suitable zones. These areas are mainly located in Pulan and Quner counties (Ngari), Saga and Angren counties (Shigatse), Gongjue and Jiangda counties (Chamdo), Lang and Gongbu Jiangda counties (Nyingchi), Huo and Daofu counties (Sichuan), Deqin and Fugong counties (Yunnan), as well as in Xining and Haidong (Qinghai).\u003c/p\u003e\n\u003cp\u003eLow-suitability habitats occupy 79.10 \u0026times; 10⁴ km\u0026sup2;, representing 79.25% of the total suitable area. These areas extend outward from the moderately suitable zones into Zanda and Geji counties (Ngari), Shenzha and Bangor counties (Nagqu), Aba Tibetan and Qiang Autonomous Prefecture and Mianyang City (Sichuan), Longnan City (Gansu), and Mangya and Delingha (Qinghai), forming a general spatial trend of expansion from the southwest toward the northeast.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential habitat changes of\u003c/strong\u003e \u003cstrong\u003eC. tenuifolia\u003c/strong\u003e \u003cstrong\u003ein China under future climate scenarios\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that the 2050s and 2070s represent the medium- and long-term climate change assessment windows, respectively, they are commonly used in ecological projection studies to avoid short-term natural variability (e.g., 2030s) and the high uncertainty associated with long-term forecasts (e.g., 2090s). This time-frame selection aligns with IPCC assessment report protocols for evaluating ecological impacts\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAccordingly, two future time periods (2050s and 2070s) under four Shared Socioeconomic Pathways (SSPs) were selected to simulate the potential distribution of \u003cem\u003eC. tenuifolia\u003c/em\u003e in Tibet. Compared with the current climate baseline, all future scenarios projected a contraction in the total suitable habitat area as well as in low-suitability zones (Fig. 7, Table 2). The greatest reduction in total suitable area was observed under the SSP585 scenario in the 2050s. In contrast, the largest decline in low-suitability area occurred under the SSP370 scenario by the 2070s, amounting to a decrease of 30.12 \u0026times; 10⁴ km\u0026sup2;.\u003c/p\u003e\n\u003cp\u003eFrom a temporal perspective, habitat suitability generally declined in both future periods (2050s and 2070s) compared to current conditions. Notably, under SSP585, a slight decrease in suitable area was observed between the 2050s and 2070s. The SSP245scenario showed the most pronounced decline between the two periods, with a reduction of 8.56 \u0026times; 10⁴ km\u0026sup2;, indicating that \u003cem\u003eC. tenuifolia\u003c/em\u003e is highly sensitive to climatic changes. As future climate conditions continue to deviate from the species\u0026rsquo; optimal niche, the contraction of suitable habitat suggests a continued population decline is likely\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eArea of habitat suitability classes for Clematis tenuifolia under different time periods (\u0026times;10⁴ km\u0026sup2;)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSuitable habitats\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP126\u003c/p\u003e\n \u003cp\u003e2050s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP126\u003c/p\u003e\n \u003cp\u003e2070s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP245\u003c/p\u003e\n \u003cp\u003e2050s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP245\u003c/p\u003e\n \u003cp\u003e2070s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP370\u003c/p\u003e\n \u003cp\u003e2050s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP370\u003c/p\u003e\n \u003cp\u003e2070s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP585\u003c/p\u003e\n \u003cp\u003e2050s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSSP585\u003c/p\u003e\n \u003cp\u003e2070s\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHigh-suitable habitats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduction in area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMiddle-suitable habitats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduction in area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLow-suitable habitats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduction in area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal-suitable habitats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduction in area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in the spatial pattern of the habitat of\u003c/strong\u003e \u003cstrong\u003eC. tenuifolia\u003c/strong\u003e \u003cstrong\u003eunder climate change scenarios\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial dynamics of \u003cem\u003eC. tenuifolia\u003c/em\u003e habitat suitability under future climate scenarios are summarized in Table 3 and illustrated in Fig. 8,Fig. 9. Currently, highly suitable areas are concentrated in Yadong, Kangma, Gangba, Dingjie, Tingri, and Nyalam counties (Shigatse); Lhazi and Mozhugongka counties (Lhasa); Naidong, Qusong, and Gacha counties (Shannan); Lang and Gongbo\u0026rsquo;gyamda counties and Milin City (Nyingchi); Karuo, Chaya, and Mangkang counties (Chamdo); as well as in Daocheng, Jiulong counties and Muli Tibetan Autonomous County (Garz\u0026ecirc; Tibetan Autonomous Prefecture, Sichuan).\u003c/p\u003e\n\u003cp\u003eCompared to present conditions, projections for the 2050s under all four SSP scenarios (SSP126, SSP2-4.5, SSP370, SSP585) show a clear trend of overall habitat contraction with only minor areas of expansion. For instance, under SSP585 in the 2050s, expansion is minimal (0.62 \u0026times; 10⁴ km\u0026sup2;, or just 0.62%), whereas contraction reaches 21.93 \u0026times; 10⁴ km\u0026sup2; (44.08% loss). The most significant contractions occur in Zanda, Burang, and Ger counties (Ngari, Tibet), Lijiang and northern Kunming (Yunnan), most of Huangnan Tibetan Autonomous Prefecture (Qinghai), and northern Garz\u0026ecirc; (Sichuan).\u003c/p\u003e\n\u003cp\u003eBy the 2070s, this contraction intensifies under SSP126, SSP2-4.5, and SSP370, with major losses in northern Shigatse (Tibet), central and northern Kunming, Qujing, and Chuxiong Yi Autonomous Prefecture, as well as central Nujiang Lisu Autonomous Prefecture (Yunnan), and northern Garz\u0026ecirc; (Sichuan). Notably, under SSP585 in the 2070s, expansion slightly exceeds contraction, particularly in southeastern Tibet, eastern Hainan Tibetan Autonomous Prefecture (Qinghai), and northern Nujiang (Yunnan).\u003c/p\u003e\n\u003cp\u003eIn summary, key regions of habitat change for \u003cem\u003eC. tenuifolia\u003c/em\u003e include northern Shigatse and western Ngari (Tibet), northern Garz\u0026ecirc; (Sichuan), northern Nujiang (Yunnan), and eastern Hainan Prefecture (Qinghai), which are projected to experience the most pronounced shifts in habitat suitability under climate change.\u003c/p\u003e\n\u003cp\u003eUnit: \u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eChanges in the potential suitable area of distribution of \u003cem\u003eC. tenuifolia\u003c/em\u003e under future climate scenarios compared with the current situation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFuture climate models\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpansion of suitable habitat area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThe area of the suitable zone remains unchanged\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShrinking area of suitable zone\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e2050s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e2070s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eShifting the centre of gravity of suitable habitats for\u003c/strong\u003e \u003cstrong\u003eC. tenuifolia\u003c/strong\u003e \u003cstrong\u003eunder climate change scenarios\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 10, projected shifts in the distribution centroid of \u003cem\u003eC. tenuifolia\u003c/em\u003e under four carbon emission scenarios (SSP126, SSP2-4.5, SSP370, and SSP585) are predominantly concentrated within Bomi County, Nyingchi City, Tibet Autonomous Region, with minor deviations toward the Bomi\u0026ndash;Motuo boundary. Two distinct temporal patterns of centroid migration are observed. From the present to the 2050s, the centroid consistently shifts southeastward. However, from the 2050s to the 2070s, the trajectories diverge: the centroid continues southwestward under SSP126 and SSP2-4.5, but reverses northwestward under SSP370 and SSP585.\u003c/p\u003e\n\u003cp\u003eThe current distribution centroid is located in Bomi County (95\u0026deg;57\u0026prime;36\u0026Prime;E, 29\u0026deg;49\u0026prime;43\u0026Prime;N). Under the SSP126 scenario, the centroid migrates 35.15 km southeastward to Motuo County by the 2050s, and an additional 8.87 km southwestward by the 2070s. Under SSP2-4.5, the centroid shifts 64.55 km southeast to Kangyu Township (Bomi County) by the 2050s, followed by a 53.69 km southwestward shift to Duoji Township (Bomi County) by the 2070s.\u003c/p\u003e\n\u003cp\u003eIn contrast, under SSP370 and SSP585, the centroid initially shifts southeastward in the 2050s\u0026mdash;76.50 km to Songzong Township (SSP370) and 45.77 km to Zamu Township (SSP585)\u0026mdash;but then reverses direction, migrating northwestward by 31.82 km and 44.99 km, respectively, reaching Motuo County by the 2070s.\u003c/p\u003e\n\u003cp\u003eAll migration distances were calculated as great-circle distances using the WGS84 ellipsoid. These results reveal a transitional pattern of \u0026ldquo;initial southeastward shift followed by divergent migration,\u0026rdquo; with all centroids remaining within Nyingchi City. This indicates that the southeastern Tibetan Plateau will likely remain the ecological core zone for \u003cem\u003eC. tenuifolia\u003c/em\u003e under future climate change.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eModel Optimisation and Predictive Reliability\u003c/h2\u003e\u003cp\u003eIn this study, MaxEnt model parameters\u0026mdash;regularization multiplier (RM\u0026thinsp;=\u0026thinsp;3) and feature class (FC\u0026thinsp;=\u0026thinsp;LQ)\u0026mdash;were optimized using the ENMeval package, which significantly reduced the risk of overfitting and yielded a high predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.982)\u003csup\u003e40\u003c/sup\u003e. Compared to the default MaxEnt configuration, the optimized model more precisely captured the ecological niche requirements of Clematis tenuifolia, particularly its sensitivity to temperature-related variables, with BIO3, BIO8, and BIO9 collectively contributing 84.4% to model performance. These findings are consistent with recent studies on endangered species such as \u003cem\u003eRhodiola crenulata\u003c/em\u003e, where optimized MaxEnt models demonstrated improved predictive reliability. This reinforces the\u003c/p\u003e\u003cp\u003eimportance of parameter tuning when modeling species with limited occurrence records\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEcological significance of key environmental factors\u003c/h3\u003e\n\u003cp\u003eThe habitat suitability of \u003cem\u003eC. tenuifolia\u003c/em\u003e increased with rising isothermality (BIO3), supporting its ecological adaptation to pronounced diurnal temperature fluctuations and intense solar radiation in alpine environments. The peak response at 7\u0026deg;C for the mean temperature of the wettest quarter (BIO8), and the threshold at \u0026minus;\u0026thinsp;1\u0026deg;C for the mean temperature of the driest quarter (BIO9), suggest that mild, moist winters and cool summers are critical for the species\u0026rsquo; survival\u0026mdash;conditions that align closely with the monsoon-influenced climate of the southeastern Qinghai\u0026ndash;Tibetan Plateau\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Topsoil base saturation (T_bs) was also an important driver, likely reflecting its role in regulating root-level mineral uptake essential for physiological function in nutrient-poor mountain soils\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In contrast, the human footprint index (HFP) showed a relatively low contribution (3.5%), indicating limited current anthropogenic disturbance within the species\u0026rsquo; distribution range. However, future urban expansion and infrastructure development may exacerbate habitat fragmentation, particularly in marginal areas\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eMechanisms of Climate Change Threats to Suitable Areas\u003c/h3\u003e\n\u003cp\u003eThe substantial contraction of suitable habitats under all future climate scenarios (ranging from 29.78 \u0026times; 10\u003csup\u003e4\u003c/sup\u003ekm\u0026sup2;to 44.26 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e km\u0026sup2;by the 2070s) is primarily attributed to warming-induced disruption of temperature thresholds. In particular, the SSP585 scenario projects winter temperatures (BIO9) exceeding the species tolerance limit (\u0026ndash;1℃) during the driest quarter, resulting in rapid habitat loss across fringe zones in northwestern Sichuan and northern Yunnan\u0026mdash;accounting for a contraction of up to 44.08%. While localized expansions are projected in southeastern Tibet due to increased warming and humidification, such gains are outweighed by widespread thermal stress and intensified drought elsewhere. For example, mean temperatures in the wettest quarter (BIO8) are projected to surpass 7℃ in the Ali region, a threshold beyond which regeneration is inhibited. The species distribution centroid is projected to shift southeastward by 35\u0026ndash;77 km, stabilizing near Bomi and Motuo\u0026mdash;areas characterized by consistent humidity and moderated temperatures due to Indian Ocean monsoon influence. These findings underscore the potential of southeastern Tibet to serve as a future climatic refuge for \u003cem\u003eC. tenuifolia\u003c/em\u003e under ongoing climate change\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003etenuifolia\u003c/b\u003e \u003cb\u003ehabitat selection and protection strategies\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTo mitigate this risk, a series of integrated conservation strategies are urgently needed alongside continued pharmacological research. First, the projected centroid of future habitat suitability\u0026mdash;centered around the Bomi-Motuo region in Nyingchi\u0026mdash;should be designated as an ecological redline zone, and a dedicated \u003cem\u003eC. tenuifolia\u003c/em\u003e nature reserve should be established to restrict anthropogenic disturbances. Second, monitoring of extant populations in southern Tibet (e.g., southern Shigatse and Shannan) and southwestern Sichuan (e.g., southwestern Garz\u0026ecirc; Prefecture) should be intensified and incorporated into the national park system.\u003c/p\u003e\u003cp\u003eA cold-storage germplasm bank is recommended in Lhasa or Nyingchi to preserve seeds and live specimens from populations undergoing habitat loss (e.g., Ali, northern Yunnan, and southern Qinghai). In partnership with Tibetan medicine research institutions, artificial cultivation trials should be initiated to screen for heat-tolerant genotypes and promote pilot-scale bionic cultivation in climatically favorable zones of southeastern Tibet. Furthermore, vertical ecological corridors should be established along the Yarlung Tsangpo River Basin to connect current high-suitability areas in southern Tibet with emerging zones in the southeast, thereby facilitating natural population migration. In areas experiencing severe contraction (e.g., Pulan County in Ali and northern Garz\u0026ecirc;), local extinction may be delayed by implementing microclimate interventions such as artificial humidification and shading.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUnder current climatic conditions, the core habitat of \u003cem\u003eC. tenuifolia\u003c/em\u003e (13.12\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u0026sup2;) is primarily located in southern Tibet (including Shigatse and Shannan) and southwestern Sichuan. This distribution is strongly shaped by climatic variables such as isothermality (BIO3), mean temperature of the wettest quarter (BIO8), and mean temperature of the driest quarter (BIO9), which together contribute over 84% to the habitat suitability model\u0026mdash;highlighting the species\u0026rsquo; strong ecological dependence on the alpine monsoon climate.\u003c/p\u003e\u003cp\u003eUnder future climate scenarios projected to the 2070s, the total suitable habitat area is expected to contract by 30\u0026ndash;44%, with highly suitable areas declining by 39\u0026ndash;69%, particularly under the SSP5\u0026ndash;8.5 scenario. Major contraction hotspots include the Ali Plateau and the ecotonal regions spanning Sichuan, Yunnan, and Tibet, whereas minor expansions in southeastern Tibet are insufficient to offset the overall decline. Collectively, these findings suggest that \u003cem\u003eC. tenuifolia\u003c/em\u003e populations are likely to undergo significant reduction in response to ongoing global warming.Consequently, while advancing modern pharmacological research on this species, equal emphasis must be placed on conserving its natural resources and ensuring sustainable utilization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was funded by the Forestry Doctoral Program (Phase I) at Xizang Agriculture and Animal Husbandry University (Grant No.: 533325001); Special Funds for Development and Reform of Local Universities Supported by the Central Government in 2024: Construction and Enhancement of Comprehensive Service Capabilities of Science and Technology Courtyards for Agriculture and Animal Husbandry with Plateau Characteristics (XK2024-04, XK2024-01, XY2024-03); Special Funds for Development and Reform of Local Universities Supported by the Central Government in 2025: Construction and Enhancement of Comprehensive Service Capabilities of Science and Technology Courtyards for Agriculture and Animal Husbandry with Plateau Characteristics (YJSXK2025-22, YJSXY2025-05); National Natural Science Foundation of China (Grant Nos.: 31860141; 31360119); Graduate Education Innovation Program of Xizang Agriculture and Animal Husbandry University (YJS2024-26; YJS2024-28; YJS2024-31); Major Science and Technology Project of Xizang Autonomous Region (XZ202201ZD0005G02); Key Laboratory of Forestry Ecological Engineering on the Xizang Plateau The Seventh Batch of Flexible Talent Project at Xizang Agriculture and Animal Husbandry University (53013001804)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.Y. and W.H. contributed equally to the conception, design, data analysis, and writing of this manuscript. L.T.: Methodol-ogy, Project administration. Z.W.: Data curation, Visualization, Supervision. A.J.: Writing\u0026mdash;review and editing. Y.H.:Conceptualization, Funding acquisition, Supervision.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availabilityAll data used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEditorial Committee of Flora of China, C. A. o. S. Flora of China, vol. 28, Ranunculaceae. (1990).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Y. \u0026amp; Snap, L. c. p. Progress in the study of chemical composition and pharmacological effects of Mongolian medicinal herbs of the genus Clematis. \u003cem\u003eChinese Journal of Ethnic Medicine\u003c/em\u003e, 65\u0026ndash;68 (2011). 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Change Biol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 4276\u0026ndash;4291 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clematis tenuifolia Royle, MaxEnt model, habitat prediction, ENMeval","lastPublishedDoi":"10.21203/rs.3.rs-7231124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7231124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study predicts the current and future potential distribution of \u003cem\u003eClematis tenuifolia\u003c/em\u003e in China using the MaxEnt model, optimized via the ENMeval package. Environmental predictors included climate, soil characteristics, and anthropogenic influences. The model demonstrated high predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.982). Currently, suitable habitats (~\u0026thinsp;103.97 \u0026times; 10⁴ km\u0026sup2;) are primarily distributed across Tibet (Shigatse, Shannan, Lhasa, Nyingchi, Chamdo), western Sichuan, northwestern Yunnan, and Qinghai. Highly suitable areas (13.14%) are concentrated in southern Tibet and southwestern Sichuan. Under future climate scenarios (SSP126 to SSP585), the total suitable area is projected to decline by 29.78\u0026ndash;44.26 \u0026times; 10⁴ km\u0026sup2; by the 2070s, with highly suitable areas shrinking by 5.10\u0026ndash;9.08 \u0026times; 10⁴ km\u0026sup2;. The SSP585 scenario predicts the most substantial losses. Habitat contraction is expected mainly in Ngari (Tibet), northern Yunnan, southern Qinghai, and northwestern Sichuan, with limited and fragmented expansions in southeastern Tibet. The habitat centroid is projected to shift southeastward by 35\u0026ndash;77 km, stabilizing near the Bomi\u0026ndash;Motuo region in Nyingchi. The findings highlight the species\u0026rsquo; pronounced sensitivity to diurnal temperature range and seasonal fluctuations, suggesting that climate warming could pose a serious threat to its long-term survival. These results offer critical insights for biodiversity conservation and the strategic management of germplasm resources within Tibetan medicinal flora under future climate change.\u003c/p\u003e","manuscriptTitle":"Optimized MaxEnt Modeling for Habitat Suitability of Clematis tenuifolia on the Qinghai-Tibet Plateau: Implications for Conservation Under Climate Change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 20:18:32","doi":"10.21203/rs.3.rs-7231124/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61593877-a0d8-42bf-a74d-23d00cae7ae5","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55792855,"name":"Earth and environmental sciences/Climate sciences"},{"id":55792856,"name":"Biological sciences/Ecology"},{"id":55792857,"name":"Earth and environmental sciences/Ecology"},{"id":55792858,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-11-24T17:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 20:18:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7231124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7231124","identity":"rs-7231124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}