{"paper_id":"36a6c4e9-6f24-49a3-9373-7e9a10cf3bec","body_text":"The Impact of Climate Change on the Suitable Habitats of Three Wild Peonies in Mountain-Plain Intersection Zone of the Yellow River Basin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Climate Change on the Suitable Habitats of Three Wild Peonies in Mountain-Plain Intersection Zone of the Yellow River Basin Haotian Guo, Yuyang He, Peixia Ye, Jihui Xia, Shanshan Jin, Mengli Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5260001/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Plant Ecology → Version 1 posted 8 You are reading this latest preprint version Abstract Climate change has caused habitat fragmentation and niche shifts in ecosystems, affecting reproduction patterns. Wild peonies, key to peony breeding, offer insights into climate adaptation for conservation and sustainable use. This study uses the Biomod2 ensemble model to predict habitats for Paeonia jishanensis , Paeonia ostii , and Paeonia rockii in Mountain-Plain Intersection Zone of the Yellow River Basin, and pinpoints key environmental variables. The results indicate that precipitation is the primary environmental variable affecting the distribution of the three wild peonies. During the baseline period, peony conservation areas are concentrated in the Funiu and Xiong'er Mountains of the Yi-Luo River Basin. Future climate scenarios predict an expansion of these areas, with the SSP370 scenario showing the most significant increase. This suggests that mild warming may benefit peony distribution, with Xiaoqinling becoming a crucial new conservation area. Climate change may shift conservation areas northward, although within a limited range. Furthermore, protected areas during the baseline period cover only 23.2% of the key conservation areas, with the rate of conservation gaps ranging from 44.4–87.5% under various climate scenarios, and these gaps are largely concentrated in the southern part of the Yi-Luo River Basin. This research provides a robust scientific foundation for the development of conservation strategies and the sustainable utilization of wild peonies resources in Mountain-Plain Intersection Zone of the Yellow River Basin. Climate change Species distribution modeling Wild peonies Migration trends Conservation gaps Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Paeonia is a perennial deciduous shrub belonging to the genus Paeonia, comprising nine wild species and one cultivated species. Based on the differences in cultivation regions and wild progenitors, the varieties of peonies can be divided into four groups: Central Plains, Northwest, Jiangnan, and Southwest(Han et al. 2021 ). Among them, the wild progenitors of the Central Plains group include the Paeonia jishanensis , Paeonia ostii and Paeonia rockii (Li et al. 2018 ), primarily distributed in Mountain-Plain Intersection Zone of the Yellow River Basin (Liu et al. 2022 ). These wild peonies are not only key to the genetic resources and genetic diversity of peonies but also play a crucial role in the natural evolution and artificial cultivation process of peonies (Zhang et al. 2017 ; Han et al. 2021 ). However, with the intensification of global climate change, most wild species are facing the risk of endangerment (Zhang et al. 2018 ; Liu et al. 2021b ; Dong et al. 2022 ). According to the AR6 Synthesis Report: Climate Change 2023 , continued greenhouse gas emissions will lead to further global temperature rise(Change 2023), which may pose a serious threat to plant species, causing habitat loss, ecological niche shifts, and adjustments in reproductive cycles, thereby affecting the distribution range of species(Beaumont et al. 2016 ; Dyderski et al. 2018 ; Rubenstein et al. 2023 ). To protect this precious plant resource, scientists have taken various measures, including using molecular marker technology to assess genetic diversity and establish a peony gene bank(Yuan et al. 2014 ; Yang et al. 2020 ; Yuan et al. 2022 ), studying the ecological adaptability of peonies(Zhang et al. 2018 ; Dong et al. 2022 ; Lan et al. 2023 ), and preserving genetic resources under non-native conditions through ex-situ conservation strategies(Wang 2020 ). These comprehensive research and conservation efforts are essential for ensuring the long-term survival of wild peonies genetic resources. To predict and respond to potential changes, scientists have developed various Species Distribution Models (SDMs) to predict the potential distribution of species under different environmental conditions, and SDMs are widely applied across terrestrial, freshwater, and marine ecosystems(Guisan & Thuiller 2005 ; Elith & Leathwick 2009 ). These models not only help identify the sensitivity and vulnerability of species but also provide a scientific basis for the conservation of biodiversity and ecosystem services. Commonly used species distribution models include the Generalized Additive Model (GAM), Generalized Boosted Model (GBM), Generalized Linear Model (GLM), Random Forest (RF), and Maximum Entropy Models (MAXENT), each with its advantages and disadvantages(Hao et al. 2019 ). The Biomod2 package (version 4.2-5-2, released in May 2024) offers up to 12 species distribution modeling methods, including Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Flexible Discriminant Analysis (FDA), GAM, GBM, GLM, RF, SRE (One Rectilinear Envelope similar to BIOCLIM), MAXENT, MAXNET (Maximum Picking Neural Net Model), Multivariate Adaptive Regression Splines (MARS), and eXtreme Gradient Boosting (XGBOOST)(Wilfried Thuiller et al. 2024). Biomod2 can construct both individual models and ensemble models (EM) based on single models, thereby improving the accuracy and reliability of the prediction results(Hao et al. 2020 ; Wang et al. 2024 ). This model has been widely applied in various fields, including endangered species conservation and invasive species research(Gama et al. 2016 ; Erfanian et al. 2021 ; Wani et al. 2022 ; Zhu et al. 2023 ; Wang et al. 2024 ; Wen et al. 2024 ). The Mountain-Plain Intersection Zone of the Yellow River Basin, mainly situated in Henan Province, has unique geographical and climatic conditions(Niu et al. 2022a ), nurturing a rich variety of biological species and ecosystems, providing a suitable environment for the growth of plants such as wild peonies. Recent studies have shown that biodiversity in this region is threatened by multiple factors, especially human activities and climate change. For example, urbanization, agricultural expansion, and water resource development are increasing the vulnerability of ecosystems, putting many endemic species at risk of habitat loss(Yang et al. 2021 ; Zhang et al. 2024b ; Zhang et al. 2024c ). Henan Province has made efforts to protect key national protected wild animals, plants, and typical ecosystems by establishing various types of nature reserves(Wang 2022 ). However, despite the effectiveness of existing conservation measures, there are still gaps in the protection of specific species such as wild peonies. The current conservation network has not fully covered the distribution areas of these species, especially under the background of climate change, the effectiveness of these protected areas faces new challenges. To address these issues, recent research has begun to focus on the impact of climate change on plant species and the optimization of conservation strategies. For instance, some scholars have used SDMs to assess potential distribution changes of plant species under different climate scenarios, revealing the profound impact of climate change on biodiversity, especially in specific ecosystems of the Yellow River basin(Yin et al. 2020 ; Ren et al. 2022 ; Duncanson et al. 2023 ). Concurrently, an increasing number of studies are focusing on conservation gaps, emphasizing the assessment and optimization of existing nature reserves to ensure their adaptability to the ongoing environmental changes(Schwager & Berg 2021 ). However, previous studies have often analyzed the distribution of single species(Zhang et al. 2018 ; Dong et al. 2022 ), while comprehensive research on the shared habitats of multiple species is relatively scarce. Implementing conservation strategies in these areas can effectively protect a variety of species and enhance the efficiency of conservation efforts. Assuming that with global warming, the key conservation areas for the three wild peonies will gradually increase in area and migrate northward. Under the current and future climate change context, how will the key conservation areas for the three wild peonies in Mountain-Plain Intersection Zone of the Yellow River Basin change, and what do these changes imply for the conservation of wild peonies? Therefore, this study aims to use the Biomod2 ensemble model to predict the location and area changes of the key conservation areas for the three wild peonies under different climate scenarios and identify the main environmental variables affecting their distribution. The research results will guide the formulation of more targeted conservation strategies, help fill the current conservation gaps, and promote ecological protection and sustainable development in the Yellow River basin. At the same time, by deeply understanding the challenges brought by climate change, practical suggestions will be provided for future conservation measures to ensure the long-term survival of wild peonies and their habitats. Methods Study Area The Mountain-Plain Intersection Zone of the Yellow River Basin, located in the central-northern part in Henan Province (33.65°-36.12° N, 110.35°-116.10° E), constitutes the main body of the middle and lower reaches of the Yellow River (Fig. 1). Spanning an area of about 36,000 square kilometers, this region experiences a climate that bridges the warm temperate and northern subtropical zones, marked by pronounced seasonal changes. The area's annual mean temperature oscillates between 5°C and 15°C, while precipitation varies from 600 to 1000 millimeters(Niu et al. 2022a). The landscape of this zone transitions from the western highlands of loess plateaus and low mountains to the eastern mountainous plains and alluvial plains, creating a topographical gradient that descends from west to east(Niu et al. 2022b). This varied terrain and geomorphology, combined with the distinctive climate, support a diverse array of habitats for wildlife. Furthermore, the ecological communities within this zone are notably responsive to climatic fluctuations. Data Collection and Screening This study focuses on the wild progenitors of the Central Plains peony variety group, which are the national key protected wild plants: P. jishanensis (a second-class national key protected wild plant), P. ostii (a second-class national key protected wild plant), and P. rockii (a first-class national key protected wild plant). Distribution data were obtained from the Chinese Virtual Herbarium (http://www.cvh.ac.cn/), the Chinese Field Herbarium (https://www.cfh.ac.cn/), the Chinese Plant Image Library (http://ppbc.iplant.cn/), and relevant literature(Hong et al. 2017; Zhang et al. 2018; Liu et al. 2021b). During the data collection process, strict screening was conducted to exclude duplicate, blank, and artificially cultivated distribution records, and only one valid distribution point was retained within a 1-kilometer range. This process yielded 17 valid points for P. jishanensis , 16 for P. ostii , and 10 for P. rockii . In the model construction, 31 environmental variables were selected, including 19 climate variables and 12 non-climate variables (with 3 topographic variables, 5 soil variables, and 4 habitat variables). The climate variables (bio1-bio11 for temperature-related variables, bio12-bio19 for precipitation-related variables) were all sourced from the WorldClim database (https://www.worldclim.org/), with a spatial resolution of 30″, covering the baseline period (1970-2000), the 2050s (2041-2060), and the 2090s (2081-2100). The climate data for the 2050s and 2090s were provided by the Beijing Climate Center's Climate System Model (BCC-CEM2-MR), adopting three Shared Socioeconomic Pathways (SSP) scenarios: SSP126 (low global warming), SSP370 (moderate global warming), and SSP585 (high global warming). Topographic data were sourced from the Geospatial Data Cloud (http://www.gscloud.cn/), including altitude (alt), slope (slo), and aspect (asp). Soil variables encompass available water content class (awc_class), topsoil organic carbon (t_oc), topsoil pH (t_pH), subsoil organic carbon (s_oc), and subsoil pH (s_pH), with data sourced from the World Soil Database (https://www.fao.org/). Habitat variables include vegetation cover (fvc), land use/cover change (lucc), normalized difference vegetation index (ndvi), and vegetation index (veg), with data sourced from the Tibetan Plateau Data Center (http://data.tpdc.ac.cn), the Resource and Environmental Science Data Center (http://www.resdc.cn), the National Science and Technology Infrastructure Platform (http://www.nesdc.org.cn), and the National Cryosphere Data Center (http://www.ncdc.ac.cn), all websites were accessed on June 15, 2024. When predicting the suitable habitat areas for species under future climate change, it is assumed that non-climate variables remain constant. During the model construction process, to avoid overfitting in the prediction results caused by high collinearity among climate variables, it is necessary to select climate variables. Preliminary models were constructed using MaxEnt 3.4.4 software to assess the contribution rate of each climate variable, and Pearson correlation analysis of climate variables was performed using ENMTools software to obtain the correlation coefficients ( r ) between different climate variables. During the selection process, climate variables with an absolute correlation coefficient r not exceeding 0.8 were retained; if the correlation coefficient exceeded 0.8, the variable with the higher contribution rate was retained. Ultimately, the selected climate variables were combined with non-climate variables to form the environmental variable set for the Biomod2 model. Construction and Evaluation of SDMs This study employs the Biomod2 software package (version 4.2-5) to construct SDMs, utilizing 12 individual models including ANN, CTA, FDA, GAM, GBM, GLM, MARS, MAXENT, MAXNET, RF, SRE, and XGBOOST to predict the suitable habitat areas for the three wild peonies. To ensure the accuracy of the models, 75% of the distribution data is used as the training set, with the remaining 25% as the test set. Additionally, 1000 pseudo-absence points are randomly generated and the process is repeated twice, with the model run 10 times to equalize the weighted sum of presence points and pseudo-absence points (prevalence = 0.5), ultimately yielding 240 results. Model accuracy is assessed using the True Skill Statistic (TSS) and the Receiver Operating Characteristic (ROC) curve, with TSS > 0.8 and ROC > 0.9 set as the criteria for good or excellent predictive performance(Dutra Silva et al. 2019; Zhao et al. 2021; Huang et al. 2023). Based on the accuracy assessment results of the individual models, those with TSS ≥ 0.8 are selected to construct ensemble models using three methods: EMmean (Mean of probabilities over the selected models), EMmedian (Median of probabilities over the selected models), and EMwmean (Probabilities from the selected models are weighted according to their evaluation scores obtained). The model with the highest accuracy is chosen for subsequent research. The predicted results of the ensemble model are visualized in ArcGIS 10.8, and the suitable habitat areas are categorized into non-suitable ( p < 0.3) and suitable ( p ≥ 0.3) based on the distribution probability. Identification of Key Protection Areas and Protection Gaps The Yellow River Basin in Henan is home to 24 natural protected areas of various types, including 8 national forest parks, 5 national wetland parks, 6 provincial nature reserves, and 5 national nature reserves, covering a total area of 3,282.3 km². The relevant data is sourced from the Protected Area Platform (http://www.zrbhq.cn/), the Ministry of Ecology and Environment of the People's Republic of China (https://www.mee.gov.cn/), the National Forestry and Grassland Science Data Center (https://www.forestdata.cn/), the Henan Provincial Forestry Bureau (https://lyj.henan.gov.cn/), and other management department resources. The overlapping areas of the suitable habitat zones for P. jishanensis , P. ostii , and P. rockii , are defined as key conservation areas, and their dynamic changes under different climate scenarios for the baseline period, the 2050s, and the 2090s are analyzed. Using ArcGIS 10.8, the geometric center (centroid) of the key conservation areas is extracted, and the migration direction and distance of the centroid under different climate scenarios are calculated. By overlaying the key conservation areas under different climate scenarios with the existing natural protected areas, the areas of the key conservation areas that fall outside the existing protected areas are identified, which are referred to as the conservation gap areas. Results Selection of Environmental Variables and Ensemble Models By analyzing the correlation between climate variables and assessing the contribution rate of the MaxEnt model, climate variables with correlation coefficients lower than 0.8 and higher contribution rates to the model were selected and combined with non-climate variables for the construction of the Biomod2 model (Table 1). Then, ensemble models were constructed using three methods: EMmean, EMmedian, and EMwmean, and their predictive accuracies were evaluated (Table 2). The results show that all ensemble models have ROC values higher than 0.9 and TSS values above 0.8, indicating high predictive accuracy. Particularly, the ensemble model constructed using EMwmean showed the best performance, with the ROC and TSS values for P. jishanensis , P. ostii , and P. rockii being 0.979, 0.954, 0.987 and 0.929, 0.867, 0.954, respectively. These models are suitable for further modeling and predictive analysis. Table 1. Environmental variables for constructing the Biomod2 model Species Environmental variables Climate variables Non-climate variables P. jishanensis bio2、bio3、bio5、bio6、bio12、bio13、bio14、bio15、bio16 alt、asp、slo、awc_class、t_oc、t_pH、s_oc、s_pH、fvc、lucc、ndvi、veg P. ostii bio3、bio7、bio11、bio12、bio14、bio15、bio18 P. rockii bio2、bio3、bio7、bio11、bio12、bio13、bio14、bio15 Note：Mean Diurnal Range (bio2), Isothermality (bio3), Max Temperature of Warmest Month (bio5), Min Temperature of Coldest Month (bio6), Temperature Annual Range (bio7), Mean Temperature of Coldest Quarter (bio11), Annual Precipitation (bio12), Precipitation of Wettest Month (bio13), Precipitation of Driest Month (bio14), Precipitation Seasonality (bio15), Precipitation of Wettest Quarter (bio16), Precipitation of Warmest Quarter (bio18). Table 2. Accuracy assessment of ensemble models constructed by different methods Species ROC TSS EMmean EMmedian EMwmean EMmean EMmedian EMwmean P. jishanensis 0.979 0.963 0.980 0.929 0.863 0.935 P. ostii 0.954 0.949 0.954 0.867 0.855 0.870 P. rockii 0.987 0.977 0.987 0.954 0.904 0.957 Assessment of the Importance of Environmental Variables on the Distribution of Three Wild Peonies The EMwmean model was utilized to evaluate the impact of various environmental factors on the geographic spread of wild peonies, with findings depicted in Fig. 2. Fig. 2 reveals that the key environmental drivers for the distribution of P. jishanensis , P. ostii , and P. rockii differ. Among them, the distribution of P. jishanensis is most significantly affected by Precipitation of Wettest Quarter (bio16), Precipitation of Wettest Month (bio13), Isothermality (bio3), and the Max Temperature of Warmest Month (bio5); the distribution of P. ostii is mainly influenced by altitude (alt), Annual Precipitation (bio12), Temperature Annual Range (bio7), and Precipitation Seasonality (bio15); the distribution of P. rockii is most significantly affected by slope (slo), Precipitation of Driest Month (bio14), Precipitation of Wettest Month (bio13), Annual Precipitation (bio12), and topsoil pH (t_pH). Among all modeled environmental variables, precipitation variables have the most significant impact on the geographical distribution of the three wild peonies, with importance values of 40.8%, 30.4%, and 44.2%, respectively. This indicates that precipitation is the main driving factor affecting the distribution of P. jishanensis , P. ostii , and P. rockii . Distribution Prediction of Key Protected Areas under Different Climate Scenarios Based on the ensemble model constructed using the EMwmean method, predictions of the suitable habitat areas for P. jishanensis , P. ostii , and P. rockii were made, and the key conservation areas were identified by overlaying the suitable habitat areas of these three wild peonies (Table 3). In the baseline period (Fig. 3), the total suitable habitat area for P. jishanensis is 5881.9 km², primarily distributed in the southern foothills of the Taihang Mountain and the middle and upper reaches of the main Yellow River tributary, the Yi-Luo River Basin, such as the northern slopes of the Funiu Mountain and the western slopes of the Xiaoqinling. The suitable habitat areas for P. ostii and P. rockii are located in the northern foothills of the Funiu Mountain to the central Xiong’er Mountain region in the southwestern part of the Mountain-Plain intersection zone of the Yellow River Basin. This region features complex and diverse terrain, and a relatively low altitude. The suitable habitat areas are 6047.2 km² and 4543.1 km², respectively. The total area of the key conservation areas, formed by the suitable habitat areas of the three wild peonies, is 1130.6 km². These areas are mainly concentrated in the Funiu and Xiong'er Mountain regions within the Yi-Luo River Basin, the main Yellow River tributary. This result provides important data support for the further development of effective conservation strategies. Table 3. Suitable habitat and key conservation area sizes for three wild peonies under different climate scenarios Climate scenario Area (km²) P. jishanensis P. ostii P. rockii Key protected area Baseline period 5881.9 6047.2 4543.1 1130.6 2050s-SSP126 3499.3 10632.6 4662.5 1213.9 2090s-SSP126 7314.6 6400.0 5522.9 1616.0 2050s-SSP370 7472.2 9435.4 4626.4 2631.3 2090s-SSP370 8213.9 10875.7 6098.6 3771.5 2050s-SSP585 4106.2 10480.6 5475.0 1659.0 2090s-SSP585 6945.1 10822.2 4514.6 2133.3 Under different climate scenarios in the 2050s and 2090s, the distribution of key protected areas remains relatively consistent with the baseline period (Fig. 4), but their areas all show an increasing trend (Table 4). In comparison to the baseline period, the SSP126 scenario exhibits the least significant expansion of key protected areas, with protection rates of 7.4% for the 2050s and 42.9% for the 2090s. Conversely, this scenario also shows the most considerable contraction, with rates of -49.2% for the 2050s and -39.7% for the 2090s. The main contraction areas are located in the central reaches of the Yi-Luo River Basins, on the edge of the Xiong'er and Funiu Mountain, where industrial and agricultural activities and human activities are frequent, causing certain impacts on the ecological environment. Under the SSP370 scenario, both the total area and the expansion area of key protected areas increase significantly, with expansion areas of 1688.2 km² and 2766.7 km², respectively, mainly concentrated in the high-altitude areas in the southwest of the southwest Mountain-Plain intersection zone of the Yellow River Basin, especially between the Xiaoqinling and Funiu Mountain. In comparison, under the SSP585 scenario, the expansion and contraction of key protected areas are both better than the SSP126 scenario, with the expansion and contraction areas being roughly the same as under the SSP585 scenario. In general, regardless of climate change, the range and area of key protected areas for P. jishanensis , P. ostii , and P. rockii are all larger than the baseline period. In future climate scenarios, key protected areas are mainly concentrated in the southwest and central parts of the Mountain-Plain intersection zone of the Yellow River Basin, especially from Xiong'er mountain in the center to Funiu mountain in the south, which matches the current distribution range. Additionally, due to the higher altitude, the Xiaoqinling area in the Mountain-Plain intersection zone has a widespread distribution of wild peonies and is also recognized as a key protected area for the three species of wild peonies, except under the 2090s-SSP126 climate scenario. Table 4. Area and change of key protected areas of wild peonies in the future climate Period Total area of key protected areas Unchanged area (km²) Loss area (km²) Expanded area (km²) Change rate (%) Loss rate (%) Expansion rate (%) Baseline period 1130.6 — — — — — — 2050s-SSP126 1213.9 574.3 -556.3 639.6 7.4 -49.2 56.6 2090s-SSP126 1616.0 681.3 -449.3 934.7 42.9 -39.7 82.7 2050s-SSP370 2631.3 943.1 -187.5 1688.2 132.7 -16.6 149.3 2090s-SSP370 3771.5 1004.9 -125.7 2766.7 233.6 -11.1 244.7 2050s-SSP585 1659.0 636.8 -493.8 1022.2 46.7 -43.7 90.4 2090s-SSP585 2133.3 728.5 -402.1 1404.9 88.7 -35.6 124.3 Centroid Migration and Conservation Gap Analysis of Key Protected Areas Using ArcGIS 10.8 software, the centroids of key protected areas for P. jishanensis, P. ostii , and P. rockii , under different climate scenarios were extracted, and their migration directions and distances were analyzed (Fig. 5). The results of the centroid migration analysis indicate that under future climate variables, the changes in the key protected areas for the three wild peonies are not significant and remain mainly concentrated in the Xiong'er Mountain area of the Yi-Luo River Basin, a major tributary of the Yellow River. During the baseline period, the centroid of the key protected area was located in the southern part of Xiong'er Mountain, with coordinates of (111.47°E, 34.03°N) and an altitude of 1016 m. Under the SSP126 scenario, the centroid migration was the most pronounced, first moving 18.9 km to the northwest and then 28.2 km to the southeast. Under the SSP370 scenario, the centroids all moved to the northwest, with two migration distances of 13.2 km and 2.6 km, respectively. Under the SSP585 scenario, the geometric center of the key protected area first moved 13.7 km to the northeast and then 6.5 km to the southwest. Overall, except for the 2090s-SSP126 scenario, the centroids in other periods and climates show a trend of moving northward, indicating a trend towards migration to higher latitude areas, but the migration distances are relatively small. By overlaying the distribution of key protected areas under various climate scenarios with the baseline period's distribution of nature reserves, the protection gap areas under different climate scenarios were identified (Table 5). According to Table 5, the current nature reserves only cover 23.2% of the key protected areas, and there is still a protection gap area of 867.8 km², which accounts for 76.8% of the total area of the key protected areas. This gap area is mainly located on the southern side of Xiong'er Mountain and the northern side of Funiu Mountain in the Yi-Luo River Basin, an important tributary of the Yellow River. Under different future climate scenarios, the protection rate of the key protected areas ranges from 12.5% to 55.6%. Especially under the SSP370 scenario, the protection rate is the highest, with protection rates of 44.2% and 55.6% for the 2050s and 2090s. The protection gap areas are mainly concentrated in the narrow areas between Xiong'er and Funiu Mountain in the Yi-Luo River Basin. These results highlight the necessity to strengthen protection measures under the background of future climate change to reduce protection gaps and improve the protection efficiency of key protected areas. Table 5. Area and location of conservation gap areas Period Area of key protected areas within nature reserves (km²) Protection rate (%) Gap rate (%) Geographic range of protection gap River basin Baseline period 262.7 23.2% 76.8% North to Xiong'er Mountain, South to Funiu Mountain Yi-Luo River Basin 2050s-SSP126 442.0 36.4% 63.6% North to Xiaoqinling, South to Funiu Mountain Yi-Luo River Basin 2090s-SSP126 330.2 12.5% 87.5% North to Xiong'er Mountain, South to Funiu Mountain Yi-Luo River Basin 2050s-SSP370 733.4 44.2% 55.8% North to Xiaoqinling, South to Funiu Mountain Yi-Luo River Basin 2090s-SSP370 898.1 55.6% 44.4% North to Xiaoqinling, South to Funiu Mountain Yi-Luo River Basin 2050s-SSP585 667.1 17.7% 82.3% North to Xiaoqinling, South to Funiu Mountain Yi-Luo River Basin 2090s-SSP585 540.5 25.3% 74.7% North to Xiaoqinling and Xiong'er Mountain, South to Funiu Mountain Yi-Luo River Basin Discussion Environmental Variables Affecting the Geographic Distribution of Wild Peonies The geographical distribution of species is typically influenced by a variety of environmental variables, including climate, topography, soil, human footprint, and ultraviolet radiation intensity, among others(Zhang et al. 2018; Liu et al. 2021b; Schwager & Berg 2021; Dong et al. 2022; Zhang et al. 2024a). The results of this study indicate that precipitation is the primary environmental variable affecting the geographic distribution of the three wild peonies, with specific indicators such as Annual Precipitation, Precipitation of Wettest Month, Precipitation of Driest Month, Precipitation Seasonality, and Precipitation of Driest Quarter. Relevant studies(Zhang et al. 2018; Liu et al. 2021b; Dong et al. 2022; Lan et al. 2023) have found that Annual Precipitation plays a key role in the distribution of P. jishanensis and P .rockii , the research by Peng L(Peng et al. 2019) further indicates that the Precipitation of the Wettest Month and Annual Precipitation also play a significant role in the cultivation suitability of P. ostii , which is largely consistent with the findings of this study. Adequate precipitation not only provides the necessary moisture for wild peonies but also promotes photosynthesis and the absorption of nutrients(Zhang et al. 2018; Qi et al. 2020). Particularly in spring, moderate precipitation aids in the sprouting and growth of wild peonies(Zhang et al. 2021). Precipitation influences wild peonies growth by regulating soil temperature, pH, and structure, and enhancing soil oxygen, which affects root respiration and nutrient uptake. However, extreme precipitation events may have adverse effects on wild peonies. Excessive precipitation can lead to overly high soil moisture, causing root hypoxia(Wang et al. 2018), and increasing the risk of waterlogging, which may lead to root rot and the development of root rot diseases(Liu et al. 2021a; Zhang et al. 2021; Yang et al. 2023). Thus, moderate precipitation is essential for the health of wild peonies. In addition to precipitation, other environmental variables also impact the geographic distribution of wild peonies. The intensity of ultraviolet radiation can significantly affect plant photosynthesis, stomatal opening and closing, and the absorption of nutrients, especially at higher altitudes where the intensity of ultraviolet radiation is higher, and its impact is more pronounced(Roro et al. 2016; Chen et al. 2022). Changes in land use types directly affect the quality of habitats for wild peonies, and variables such as soil conditions, microclimate, and vegetation structure under different land use types can all influence the adaptability and growth conditions of wild peonies(Wang et al. 2021; Li et al. 2022). Therefore, although studies have shown that precipitation is a key factor affecting the distribution of the three wild peonies, other environmental variables should not be overlooked. Future conservation and management measures should take into account these complex environmental variables to devise effective strategies for protecting and maintaining the living environment of these rare plants. Moreover, preventive measures against extreme climate events should also be included in future ecological protection plans to reduce potential ecological risks. Expansion and Migration of Suitable Habitats for Wild Peonies Conducting research on the expansion and migration of species' suitable habitats is instrumental in gaining a deeper understanding of the impact of climate change on species distribution, particularly the changes in their suitable environments. It also allows for the assessment of species' adaptive capacity and resilience. The study identified that the main protected areas for P. jishanensis , P. ostii , and P. rockii are currently centered in a small area at the mountain-plain transition in the Yellow River Basin, specifically south of Xiong'er Mountain and north of Funiu Mountain. In future climate scenarios, the protected areas for these species will expand significantly, particularly around Xiong'er and Funiu Mountain. The expansion will be greatest under moderate warming (SSP370), followed by high (SSP585) and low (SSP126) warming scenarios. This is likely due to the favorable topography and microclimate of the mountains, offering an ideal environment for peony growth(Mamantov et al. 2021). Habitat diversity(Rahbek et al. 2019), precipitation patterns(Du et al. 2021) and the potential ecological corridor effects in mountainous regions all create favorable conditions for the migration and diffusion of wild peonies. Global warming affects the geographical distribution of plants in various ways, mainly manifested in the growth cycle, reproductive capacity, phenological phases, and competitive relationships with other species of plants, leading to an increase in the area of key protected areas. Moderate temperature increases may extend the growing season, providing a longer growth period for wild peonies and promoting their growth and reproduction(Liu et al. 2018; Fang et al. 2024). With moderate temperature increases, high-altitude areas in high-latitude regions may gradually become suitable for the growth of wild peonies, thereby facilitating the expansion of their distribution range(Zhang et al. 2018). Temperature changes may also affect the competitive relationships between different species in plant communities, creating conditions for wild peonies to gain a competitive advantage in new environments(Tang et al. 2022). This study found that under different future climate conditions, the centroid of key protected areas for the three species generally shows a trend of moving northward, indicating a shift from relatively low-latitude areas in the Mountain-Plain intersection zone to relatively high-latitude areas, but still surrounding the Xiong'er Mountain area, which is in higher latitudes and higher altitudes. This finding is consistent with related research results, such as an increase in the suitable area for P. ostii in high-latitude regions(Peng et al. 2019), and studies by Lan R(Lan et al. 2023) and Dong P(Dong et al. 2022) also show a trend of the centroid of P. rockii migrating towards higher latitudes. Therefore, in the process of protecting and developing the utilization of P. jishanensis , P. ostii , and P. rockii , it is necessary to fully consider the possibility of their migration to higher latitudes under the background of climate change. This involves not only the designation of protected areas but also the optimization of management measures and the construction of related ecological corridors to promote species migration and gene exchange, ensuring that wild peonies can adapt well to new ecological environments and maintain population stability under the background of climate change. Conservation of Wild Peonies under Climate Change The increase in the suitable habitat area for species due to climate change provides both opportunities for the expansion of biodiversity and potential ecological risks. Moderate temperature increases may promote the growth of certain species, allowing them to expand into new habitats, thereby enriching biodiversity to a certain extent(Zhang et al. 2018; Tang et al. 2022). For instance, with the improvement of climatic conditions, wild peonies may establish stable populations in new areas, leading to more diverse ecosystems. However, such changes may also disrupt the original ecological balance, leading to the reorganization of species competition(Åkesson et al. 2021), and even triggering ecological invasions(Yamamoto & Jones 2024). When new species enter, they may pose a threat to native species, disrupt the ecological chain, and cause a decline or disappearance of certain native species. Moreover, the uncertainty of climate change and its long-term effects may pose threats to the survival and reproduction of species, with habitat fragmentation and degradation being particularly significant(Banks-Leite et al. 2020). Therefore, when formulating conservation measures, it is important to focus on current climate change trends while also deeply understanding its long-term impacts on ecosystem functions and biodiversity conservation to ensure actions promote the health and sustainability of ecosystems. To address the ecological risks brought by climate change and ensure the survival of wild peonies, the following conservation strategies are recommended: 1) Establish and improve a climate change monitoring and early warning system to respond to climate change in a timely manner; 2) Strengthen protection efforts in the southern part of the Yi-Luo River Basin, especially in the areas between Xiong'er and Funiu Mountain, to reduce the negative impact of climate change on the distribution of wild peonies; 3) Enhance the connectivity of ecosystems to ensure species migration and gene flow; 4) Promote ecological restoration and adaptive management to increase the resilience of ecosystems to climate change; 5) Strengthen scientific research support to provide a scientific basis for formulating and adjusting conservation measures. The implementation of these strategies can effectively protect and restore the suitable habitats of wild peonies, reduce the adverse effects of climate change on their survival, and ensure the long-term reproduction and ecological balance of this precious species. Conclusion This study, based on the Biomod2 ensemble model constructed, combined with selected environmental variables such as climate, topography, soil, and habitat, deeply analyzes the impact of climate change on the distribution of three important wild peonies— P. jishanensis , P. ostii , and P. rockii , in Mountain-Plain intersection zone of the Yellow River Basin. The results show that the main environmental variable affecting the distribution of these three wild peonies is precipitation, which is also influenced by temperature, topography, soil, and habitat variables. Under the background of future climate change, the suitable distribution areas of the three wild peonies show a trend of moving northward. The existing nature reserves do not effectively cover the key protected areas for wild peonies, and the protection gap areas are mainly located in the middle and upper reaches of the Yi-Luo River, an important tributary of the Yellow River, in a narrow strip between Xiong'er and Funiu Mountain. This study reveals the profound impact of climate change on the living environment of P. jishanensis , P. ostii , and P. rockii , providing a scientific basis for formulating effective protection strategies, which is of great significance for better protecting and utilizing this precious plant resource and guiding the protection and management work of wild peonies. Future research and protection work should focus on exploring the physiological and ecological adaptation mechanisms of wild peonies under different climate scenarios, restoring and protecting important suitable habitats and ecological corridors, and increasing the connectivity between populations. At the same time, it is necessary to regularly monitor the impact of climate change on the growth and distribution of wild peonies and adjust protection strategies in a timely manner to enhance their adaptability and resilience to climate change. Declarations Acknowledgements This work was supported by funding from Henan Province, China (222102110418 awarded to DY). We thank YL, EZ, SL, XH, XW, PX for technical and field assistance. We are grateful for the contributions of WorldClim in providing climate data (https://worldclim.org/) and the Food and Agriculture Organization of the United Nations (FAO) for their World Soil Database (https://www.fao.org/), both of which were instrumental in the generation of data used in this publication. Author Contributions HG and DY conceived and designed the study; HG, YH, PY, and JX performed the experiments and analyzed the data; HG, SJ, MZ, and DY wrote and edited the manuscript. Funding This work was supported by funding from Henan Province, China (222102110418 awarded to DY). Data availability The data that support the findings of this study are available from the authors upon reasonable request. Competing Interests The authors declare no competing interests. References Åkesson A, Curtsdotter A, Eklöf A, Ebenman B, Norberg J, Barabás G (2021) The importance of species interactions in eco-evolutionary community dynamics under climate change. Nature Communications 12:4759. http://doi.org/10.1038/s41467-021-24977-x Banks-Leite C, Ewers RM, Folkard-Tapp H, Fraser A (2020) Countering the effects of habitat loss, fragmentation, and degradation through habitat restoration. 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09:38:29\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5260001/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5260001/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s11258-024-01485-8\",\"type\":\"published\",\"date\":\"2025-01-07T15:57:46+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":71663113,\"identity\":\"0b90ef44-2952-4754-ad70-862199d8f54c\",\"added_by\":\"auto\",\"created_at\":\"2024-12-17 14:02:06\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":669348,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe distribution in Mountain-Plain Intersection Zone of the Yellow River Basin\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5260001/v1/1e42fd40b63e324126994c48.png\"},{\"id\":71663118,\"identity\":\"a76074a1-e2a4-4d4f-9363-049752cd8446\",\"added_by\":\"auto\",\"created_at\":\"2024-12-17 14:02:06\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":19312,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eImportance of environmental variables (a: \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, b: \\u003cem\\u003eP. ostii\\u003c/em\\u003e, c: \\u003cem\\u003eP. rockii\\u003c/em\\u003e)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5260001/v1/c31bb47fd5f6088e16e113e7.png\"},{\"id\":71663117,\"identity\":\"b80234a0-d2be-4297-b0f8-8b423910622b\",\"added_by\":\"auto\",\"created_at\":\"2024-12-17 14:02:06\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":310306,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution of suitable habitats and key conservation areas for three wild peonies during the baseline period (a: \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, b: \\u003cem\\u003eP. ostii\\u003c/em\\u003e, c: \\u003cem\\u003eP. rockii\\u003c/em\\u003e, d: Key protected area)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5260001/v1/1ab8e66bfbcf2870addfe9e4.png\"},{\"id\":71663115,\"identity\":\"f2139237-6643-475a-bd93-11d11c62d229\",\"added_by\":\"auto\",\"created_at\":\"2024-12-17 14:02:06\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":189126,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDistribution map of key protected areas of wild peonies in the future climate ( a: 2050s-SSP126, b:2090s-SSP126, c: 2050s-SSP370, d:2090s-SSP370, e: 2050s-SSP585, f: 2090s-SSP585 )\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5260001/v1/0bd7eb6c7f00b045ee7ecbd5.png\"},{\"id\":71664822,\"identity\":\"e936cdf5-0aa3-46e7-bdb6-29acdc2d1f52\",\"added_by\":\"auto\",\"created_at\":\"2024-12-17 14:10:06\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":30196,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCentroid migration changes of key protected areas for wild peonies under different climate scenarios\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5260001/v1/f0dc548cfd139c1f7425cec3.png\"},{\"id\":73693943,\"identity\":\"786ae78b-562f-479a-9e85-b9d6729a1244\",\"added_by\":\"auto\",\"created_at\":\"2025-01-13 16:09:46\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1826392,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5260001/v1/0374c072-5dfa-4754-a65f-08858a6ca2fa.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The Impact of Climate Change on the Suitable Habitats of Three Wild Peonies in Mountain-Plain Intersection Zone of the Yellow River Basin\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003ePaeonia\\u003c/em\\u003e is a perennial deciduous shrub belonging to the genus Paeonia, comprising nine wild species and one cultivated species. Based on the differences in cultivation regions and wild progenitors, the varieties of peonies can be divided into four groups: Central Plains, Northwest, Jiangnan, and Southwest(Han et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Among them, the wild progenitors of the Central Plains group include the \\u003cem\\u003ePaeonia jishanensis\\u003c/em\\u003e, \\u003cem\\u003ePaeonia ostii\\u003c/em\\u003e and \\u003cem\\u003ePaeonia rockii\\u003c/em\\u003e(Li et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), primarily distributed in Mountain-Plain Intersection Zone of the Yellow River Basin (Liu et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These wild peonies are not only key to the genetic resources and genetic diversity of peonies but also play a crucial role in the natural evolution and artificial cultivation process of peonies (Zhang et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Han et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, with the intensification of global climate change, most wild species are facing the risk of endangerment (Zhang et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Liu et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2021b\\u003c/span\\u003e; Dong et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). According to the \\u003cem\\u003eAR6 Synthesis Report: Climate Change 2023\\u003c/em\\u003e, continued greenhouse gas emissions will lead to further global temperature rise(Change 2023), which may pose a serious threat to plant species, causing habitat loss, ecological niche shifts, and adjustments in reproductive cycles, thereby affecting the distribution range of species(Beaumont et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Dyderski et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Rubenstein et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). To protect this precious plant resource, scientists have taken various measures, including using molecular marker technology to assess genetic diversity and establish a peony gene bank(Yuan et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Yang et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Yuan et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), studying the ecological adaptability of peonies(Zhang et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Dong et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Lan et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), and preserving genetic resources under non-native conditions through ex-situ conservation strategies(Wang \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). These comprehensive research and conservation efforts are essential for ensuring the long-term survival of wild peonies genetic resources.\\u003c/p\\u003e \\u003cp\\u003eTo predict and respond to potential changes, scientists have developed various Species Distribution Models (SDMs) to predict the potential distribution of species under different environmental conditions, and SDMs are widely applied across terrestrial, freshwater, and marine ecosystems(Guisan \\u0026amp; Thuiller \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Elith \\u0026amp; Leathwick \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). These models not only help identify the sensitivity and vulnerability of species but also provide a scientific basis for the conservation of biodiversity and ecosystem services. Commonly used species distribution models include the Generalized Additive Model (GAM), Generalized Boosted Model (GBM), Generalized Linear Model (GLM), Random Forest (RF), and Maximum Entropy Models (MAXENT), each with its advantages and disadvantages(Hao et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The Biomod2 package (version 4.2-5-2, released in May 2024) offers up to 12 species distribution modeling methods, including Artificial Neural Network (ANN), Classification Tree Analysis (CTA), Flexible Discriminant Analysis (FDA), GAM, GBM, GLM, RF, SRE (One Rectilinear Envelope similar to BIOCLIM), MAXENT, MAXNET (Maximum Picking Neural Net Model), Multivariate Adaptive Regression Splines (MARS), and eXtreme Gradient Boosting (XGBOOST)(Wilfried Thuiller et al. 2024). Biomod2 can construct both individual models and ensemble models (EM) based on single models, thereby improving the accuracy and reliability of the prediction results(Hao et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This model has been widely applied in various fields, including endangered species conservation and invasive species research(Gama et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Erfanian et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Wani et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Zhu et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Wen et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe Mountain-Plain Intersection Zone of the Yellow River Basin, mainly situated in Henan Province, has unique geographical and climatic conditions(Niu et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2022a\\u003c/span\\u003e), nurturing a rich variety of biological species and ecosystems, providing a suitable environment for the growth of plants such as wild peonies. Recent studies have shown that biodiversity in this region is threatened by multiple factors, especially human activities and climate change. For example, urbanization, agricultural expansion, and water resource development are increasing the vulnerability of ecosystems, putting many endemic species at risk of habitat loss(Yang et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2024b\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2024c\\u003c/span\\u003e). Henan Province has made efforts to protect key national protected wild animals, plants, and typical ecosystems by establishing various types of nature reserves(Wang \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). However, despite the effectiveness of existing conservation measures, there are still gaps in the protection of specific species such as wild peonies. The current conservation network has not fully covered the distribution areas of these species, especially under the background of climate change, the effectiveness of these protected areas faces new challenges. To address these issues, recent research has begun to focus on the impact of climate change on plant species and the optimization of conservation strategies. For instance, some scholars have used SDMs to assess potential distribution changes of plant species under different climate scenarios, revealing the profound impact of climate change on biodiversity, especially in specific ecosystems of the Yellow River basin(Yin et al. \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Ren et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Duncanson et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Concurrently, an increasing number of studies are focusing on conservation gaps, emphasizing the assessment and optimization of existing nature reserves to ensure their adaptability to the ongoing environmental changes(Schwager \\u0026amp; Berg \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, previous studies have often analyzed the distribution of single species(Zhang et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Dong et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), while comprehensive research on the shared habitats of multiple species is relatively scarce. Implementing conservation strategies in these areas can effectively protect a variety of species and enhance the efficiency of conservation efforts.\\u003c/p\\u003e \\u003cp\\u003eAssuming that with global warming, the key conservation areas for the three wild peonies will gradually increase in area and migrate northward. Under the current and future climate change context, how will the key conservation areas for the three wild peonies in Mountain-Plain Intersection Zone of the Yellow River Basin change, and what do these changes imply for the conservation of wild peonies? Therefore, this study aims to use the Biomod2 ensemble model to predict the location and area changes of the key conservation areas for the three wild peonies under different climate scenarios and identify the main environmental variables affecting their distribution. The research results will guide the formulation of more targeted conservation strategies, help fill the current conservation gaps, and promote ecological protection and sustainable development in the Yellow River basin. At the same time, by deeply understanding the challenges brought by climate change, practical suggestions will be provided for future conservation measures to ensure the long-term survival of wild peonies and their habitats.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy Area\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Mountain-Plain Intersection Zone of the Yellow River Basin, located in the central-northern part in Henan Province (33.65\\u0026deg;-36.12\\u0026deg; N, 110.35\\u0026deg;-116.10\\u0026deg; E), constitutes the main body of the middle and lower reaches of the Yellow River (Fig. 1). Spanning an area of about 36,000 square kilometers, this region experiences a climate that bridges the warm temperate and northern subtropical zones, marked by pronounced seasonal changes. The area\\u0026apos;s annual mean temperature oscillates between 5\\u0026deg;C and 15\\u0026deg;C, while precipitation varies from 600 to 1000 millimeters(Niu et al. 2022a). The landscape of this zone transitions from the western highlands of loess plateaus and low mountains to the eastern mountainous plains and alluvial plains, creating a topographical gradient that descends from west to east(Niu et al. 2022b). This varied terrain and geomorphology, combined with the distinctive climate, support a diverse array of habitats for wildlife. Furthermore, the ecological communities within this zone are notably responsive to climatic fluctuations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Collection and Screening\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study focuses on the wild progenitors of the Central Plains peony variety group, which are the national key protected wild plants: \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e (a second-class national key protected wild plant), \\u003cem\\u003eP. ostii\\u003c/em\\u003e (a second-class national key protected wild plant), and \\u003cem\\u003eP. rockii\\u003c/em\\u003e (a first-class national key protected wild plant). Distribution data were obtained from the Chinese Virtual Herbarium (http://www.cvh.ac.cn/), the Chinese Field Herbarium (https://www.cfh.ac.cn/), the Chinese Plant Image Library (http://ppbc.iplant.cn/), and relevant literature(Hong et al. 2017; Zhang et al. 2018; Liu et al. 2021b). During the data collection process, strict screening was conducted to exclude duplicate, blank, and artificially cultivated distribution records, and only one valid distribution point was retained within a 1-kilometer range. This process yielded 17 valid points for \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, 16 for \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and 10 for \\u003cem\\u003eP. rockii\\u003c/em\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the model construction, 31 environmental variables were selected, including 19 climate variables and 12 non-climate variables (with 3 topographic variables, 5 soil variables, and 4 habitat variables). The climate variables (bio1-bio11 for temperature-related variables, bio12-bio19 for precipitation-related variables) were all sourced from the WorldClim database (https://www.worldclim.org/), with a spatial resolution of 30\\u0026Prime;, covering the baseline period (1970-2000), the 2050s (2041-2060), and the 2090s (2081-2100). The climate data for the 2050s and 2090s were provided by the Beijing Climate Center\\u0026apos;s Climate System Model (BCC-CEM2-MR), adopting three Shared Socioeconomic Pathways (SSP) scenarios: SSP126 (low global warming), SSP370 (moderate global warming), and SSP585 (high global warming). Topographic data were sourced from the Geospatial Data Cloud (http://www.gscloud.cn/), including altitude (alt), slope (slo), and aspect (asp). Soil variables encompass available water content class (awc_class), topsoil organic carbon (t_oc), topsoil pH (t_pH), subsoil organic carbon (s_oc), and subsoil pH (s_pH), with data sourced from the World Soil Database (https://www.fao.org/). Habitat variables include vegetation cover (fvc), land use/cover change (lucc), normalized difference vegetation index (ndvi), and vegetation index (veg), with data sourced from the Tibetan Plateau Data Center (http://data.tpdc.ac.cn), the Resource and Environmental Science Data Center (http://www.resdc.cn), the National Science and Technology Infrastructure Platform (http://www.nesdc.org.cn), and the National Cryosphere Data Center (http://www.ncdc.ac.cn), all websites were accessed on June 15, 2024. When predicting the suitable habitat areas for species under future climate change, it is assumed that non-climate variables remain constant.\\u003c/p\\u003e\\n\\u003cp\\u003eDuring the model construction process, to avoid overfitting in the prediction results caused by high collinearity among climate variables, it is necessary to select climate variables. Preliminary models were constructed using MaxEnt 3.4.4 software to assess the contribution rate of each climate variable, and Pearson correlation analysis of climate variables was performed using ENMTools software to obtain the correlation coefficients (\\u003cem\\u003er\\u003c/em\\u003e) between different climate variables. During the selection process, climate variables with an absolute correlation coefficient r not exceeding 0.8 were retained; if the correlation coefficient exceeded 0.8, the variable with the higher contribution rate was retained. Ultimately, the selected climate variables were combined with non-climate variables to form the environmental variable set for the Biomod2 model.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConstruction and Evaluation of SDMs\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study employs the Biomod2 software package (version 4.2-5) to construct SDMs, utilizing 12 individual models including ANN, CTA, FDA, GAM, GBM, GLM, MARS, MAXENT, MAXNET, RF, SRE, and XGBOOST to predict the suitable habitat areas for the three wild peonies. To ensure the accuracy of the models, 75% of the distribution data is used as the training set, with the remaining 25% as the test set. Additionally, 1000 pseudo-absence points are randomly generated and the process is repeated twice, with the model run 10 times to equalize the weighted sum of presence points and pseudo-absence points (prevalence = 0.5), ultimately yielding 240 results. Model accuracy is assessed using the True Skill Statistic (TSS) and the Receiver Operating Characteristic (ROC) curve, with TSS \\u0026gt; 0.8 and ROC \\u0026gt; 0.9 set as the criteria for good or excellent predictive performance(Dutra Silva et al. 2019; Zhao et al. 2021; Huang et al. 2023).\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the accuracy assessment results of the individual models, those with TSS \\u0026ge; 0.8 are selected to construct ensemble models using three methods: EMmean (Mean of probabilities over the selected models), EMmedian (Median of probabilities over the selected models), and EMwmean (Probabilities from the selected models are weighted according to their evaluation scores obtained). The model with the highest accuracy is chosen for subsequent research. The predicted results of the ensemble model are visualized in ArcGIS 10.8, and the suitable habitat areas are categorized into non-suitable (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.3) and suitable (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026ge; 0.3) based on the distribution probability.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIdentification of Key Protection Areas and Protection Gaps\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Yellow River Basin in Henan is home to 24 natural protected areas of various types, including 8 national forest parks, 5 national wetland parks, 6 provincial nature reserves, and 5 national nature reserves, covering a total area of 3,282.3 km\\u0026sup2;. The relevant data is sourced from the Protected Area Platform (http://www.zrbhq.cn/), the Ministry of Ecology and Environment of the People\\u0026apos;s Republic of China (https://www.mee.gov.cn/), the National Forestry and Grassland Science Data Center (https://www.forestdata.cn/), the Henan Provincial Forestry Bureau (https://lyj.henan.gov.cn/), and other management department resources. The overlapping areas of the suitable habitat zones for \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e, are defined as key conservation areas, and their dynamic changes under different climate scenarios for the baseline period, the 2050s, and the 2090s are analyzed.\\u003c/p\\u003e\\n\\u003cp\\u003eUsing ArcGIS 10.8, the geometric center (centroid) of the key conservation areas is extracted, and the migration direction and distance of the centroid under different climate scenarios are calculated. By overlaying the key conservation areas under different climate scenarios with the existing natural protected areas, the areas of the key conservation areas that fall outside the existing protected areas are identified, which are referred to as the conservation gap areas.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSelection of Environmental Variables and Ensemble Models\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBy analyzing the correlation between climate variables and assessing the contribution rate of the MaxEnt model, climate variables with correlation coefficients lower than 0.8 and higher contribution rates to the model were selected and combined with non-climate variables for the construction of the Biomod2 model (Table 1). Then, ensemble models were constructed using three methods: EMmean, EMmedian, and EMwmean, and their predictive accuracies were evaluated (Table 2). The results show that all ensemble models have ROC values higher than 0.9 and TSS values above 0.8, indicating high predictive accuracy. Particularly, the ensemble model constructed using EMwmean showed the best performance, with the ROC and TSS values for \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e being 0.979, 0.954, 0.987 and 0.929, 0.867, 0.954, respectively. These models are suitable for further modeling and predictive analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1. Environmental variables for constructing the Biomod2 model\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"103%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003eSpecies\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003eEnvironmental variables\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 44px;\\\"\\u003e\\n \\u003cp\\u003eClimate variables\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eNon-climate variables\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. jishanensis\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 44px;\\\"\\u003e\\n \\u003cp\\u003ebio2、bio3、bio5、bio6、bio12、bio13、bio14、bio15、bio16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003ealt、asp、slo、awc_class、t_oc、t_pH、s_oc、s_pH、fvc、lucc、ndvi、veg\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. ostii\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 44px;\\\"\\u003e\\n \\u003cp\\u003ebio3、bio7、bio11、bio12、bio14、bio15、bio18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. rockii\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 44px;\\\"\\u003e\\n \\u003cp\\u003ebio2、bio3、bio7、bio11、bio12、bio13、bio14、bio15\\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\\u003eNote：Mean Diurnal Range (bio2), Isothermality (bio3), Max Temperature of Warmest Month (bio5), Min Temperature of Coldest Month (bio6), Temperature Annual Range (bio7), Mean Temperature of Coldest Quarter (bio11), Annual Precipitation (bio12), Precipitation of Wettest Month (bio13), Precipitation of Driest Month (bio14), Precipitation Seasonality (bio15), Precipitation of Wettest Quarter (bio16), Precipitation of Warmest Quarter (bio18).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2. Accuracy assessment of ensemble models constructed by different methods\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"595\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSpecies\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eROC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eTSS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eEMmean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eEMmedian\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eEMwmean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eEMmean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eEMmedian\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eEMwmean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. jishanensis\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.979\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.963\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.980\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.929\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.863\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.935\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. ostii\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.954\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.949\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.954\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.867\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.855\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\"\\u003e\\n \\u003cp\\u003e0.870\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. rockii\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.987\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.977\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.987\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.954\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.904\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.957\\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\\u003eAssessment of the Importance of Environmental Variables on the Distribution of Three Wild Peonies\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe EMwmean model was utilized to evaluate the impact of various environmental factors on the geographic spread of wild peonies, with findings depicted in Fig. 2. Fig. 2 reveals that the key environmental drivers for the distribution of \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e differ. Among them, the distribution of \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e is most significantly affected by Precipitation of Wettest Quarter (bio16), Precipitation of Wettest Month (bio13), Isothermality (bio3), and the Max Temperature of Warmest Month (bio5); the distribution of \\u003cem\\u003eP. ostii\\u0026nbsp;\\u003c/em\\u003eis mainly influenced by altitude (alt), Annual Precipitation (bio12), Temperature Annual Range (bio7), and Precipitation Seasonality (bio15); the distribution of \\u003cem\\u003eP. rockii\\u003c/em\\u003e is most significantly affected by slope (slo), Precipitation of Driest Month (bio14), Precipitation of Wettest Month (bio13), Annual Precipitation (bio12), and topsoil pH (t_pH). Among all modeled environmental variables, precipitation variables have the most significant impact on the geographical distribution of the three wild peonies, with importance values of 40.8%, 30.4%, and 44.2%, respectively. This indicates that precipitation is the main driving factor affecting the distribution of \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDistribution Prediction of Key Protected Areas under Different Climate Scenarios\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the ensemble model constructed using the EMwmean method, predictions of the suitable habitat areas for \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e were made, and the key conservation areas were identified by overlaying the suitable habitat areas of these three wild peonies (Table 3). In the baseline period (Fig. 3), the total suitable habitat area for \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e is 5881.9 km\\u0026sup2;, primarily distributed in the southern foothills of the Taihang Mountain and the middle and upper reaches of the main Yellow River tributary, the Yi-Luo River Basin, such as the northern slopes of the Funiu Mountain and the western slopes of the Xiaoqinling. The suitable habitat areas for \\u003cem\\u003eP. ostii\\u003c/em\\u003e and \\u003cem\\u003eP. rockii\\u003c/em\\u003e are located in the northern foothills of the Funiu Mountain to the central Xiong\\u0026rsquo;er Mountain region in the southwestern part of the Mountain-Plain intersection zone of the Yellow River Basin. This region features complex and diverse terrain, and a relatively low altitude. The suitable habitat areas are 6047.2 km\\u0026sup2; and 4543.1 km\\u0026sup2;, respectively. The total area of the key conservation areas, formed by the suitable habitat areas of the three wild peonies, is 1130.6 km\\u0026sup2;. These areas are mainly concentrated in the Funiu and Xiong\\u0026apos;er Mountain regions within the Yi-Luo River Basin, the main Yellow River tributary. This result provides important data support for the further development of effective conservation strategies.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 3. Suitable habitat and key conservation area sizes for three wild peonies under different climate scenarios\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003eClimate scenario\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"4\\\" style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003eArea (km\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. jishanensis\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. ostii\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP. rockii\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003eKey protected area\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003eBaseline period\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e5881.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e6047.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e4543.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003e1130.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003e2050s-SSP126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e3499.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e10632.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e4662.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003e1213.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003e2090s-SSP126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e7314.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e6400.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e5522.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003e1616.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003e2050s-SSP370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e7472.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e9435.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e4626.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003e2631.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003e2090s-SSP370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e8213.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e10875.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e6098.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003e3771.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003e2050s-SSP585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e4106.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e10480.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e5475.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003e1659.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003e2090s-SSP585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e6945.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e10822.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e4514.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 28px;\\\"\\u003e\\n \\u003cp\\u003e2133.3\\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\\u003eUnder different climate scenarios in the 2050s and 2090s, the distribution of key protected areas remains relatively consistent with the baseline period (Fig. 4), but their areas all show an increasing trend (Table 4). In comparison to the baseline period, the SSP126 scenario exhibits the least significant expansion of key protected areas, with protection rates of 7.4% for the 2050s and 42.9% for the 2090s. Conversely, this scenario also shows the most considerable contraction, with rates of -49.2% for the 2050s and -39.7% for the 2090s. The main contraction areas are located in the central reaches of the Yi-Luo River Basins, on the edge of the Xiong\\u0026apos;er and Funiu Mountain, where industrial and agricultural activities and human activities are frequent, causing certain impacts on the ecological environment. Under the SSP370 scenario, both the total area and the expansion area of key protected areas increase significantly, with expansion areas of 1688.2 km\\u0026sup2; and 2766.7 km\\u0026sup2;, respectively, mainly concentrated in the high-altitude areas in the southwest of the southwest Mountain-Plain intersection zone of the Yellow River Basin, especially between the Xiaoqinling and Funiu Mountain. In comparison, under the SSP585 scenario, the expansion and contraction of key protected areas are both better than the SSP126 scenario, with the expansion and contraction areas being roughly the same as under the SSP585 scenario.\\u003c/p\\u003e\\n\\u003cp\\u003eIn general, regardless of climate change, the range and area of key protected areas for \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e are all larger than the baseline period. In future climate scenarios, key protected areas are mainly concentrated in the southwest and central parts of the Mountain-Plain intersection zone of the Yellow River Basin, especially from Xiong\\u0026apos;er mountain in the center to Funiu mountain in the south, which matches the current distribution range. Additionally, due to the higher altitude, the Xiaoqinling area in the Mountain-Plain intersection zone has a widespread distribution of wild peonies and is also recognized as a key protected area for the three species of wild peonies, except under the 2090s-SSP126 climate scenario.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 4. Area and change of key protected areas of wild peonies in the future climate\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePeriod\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eTotal area of key protected areas\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eUnchanged area (km\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLoss area (km\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eExpanded area (km\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eChange rate (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLoss rate (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eExpansion rate (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBaseline period\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1130.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2050s-SSP126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1213.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e574.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-556.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e639.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e7.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-49.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e56.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2090s-SSP126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1616.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e681.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-449.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e934.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e42.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-39.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e82.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2050s-SSP370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2631.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e943.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-187.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1688.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e132.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-16.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e149.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2090s-SSP370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e3771.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1004.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-125.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2766.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e233.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-11.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e244.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2050s-SSP585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1659.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e636.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-493.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1022.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e46.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-43.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e90.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2090s-SSP585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2133.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e728.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-402.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1404.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e88.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e-35.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e124.3\\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\\u003eCentroid Migration and Conservation Gap Analysis of Key Protected Areas\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing ArcGIS 10.8 software, the centroids of key protected areas for \\u003cem\\u003eP. jishanensis,\\u003c/em\\u003e \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e, under different climate scenarios were extracted, and their migration directions and distances were analyzed (Fig. 5). The results of the centroid migration analysis indicate that under future climate variables, the changes in the key protected areas for the three wild peonies are not significant and remain mainly concentrated in the Xiong\\u0026apos;er Mountain area of the Yi-Luo River Basin, a major tributary of the Yellow River. During the baseline period, the centroid of the key protected area was located in the southern part of Xiong\\u0026apos;er Mountain, with coordinates of (111.47\\u0026deg;E, 34.03\\u0026deg;N) and an altitude of 1016 m. Under the SSP126 scenario, the centroid migration was the most pronounced, first moving 18.9 km to the northwest and then 28.2 km to the southeast. Under the SSP370 scenario, the centroids all moved to the northwest, with two migration distances of 13.2 km and 2.6 km, respectively. Under the SSP585 scenario, the geometric center of the key protected area first moved 13.7 km to the northeast and then 6.5 km to the southwest. Overall, except for the 2090s-SSP126 scenario, the centroids in other periods and climates show a trend of moving northward, indicating a trend towards migration to higher latitude areas, but the migration distances are relatively small.\\u003c/p\\u003e\\n\\u003cp\\u003eBy overlaying the distribution of key protected areas under various climate scenarios with the baseline period\\u0026apos;s distribution of nature reserves, the protection gap areas under different climate scenarios were identified (Table 5). According to Table 5, the current nature reserves only cover 23.2% of the key protected areas, and there is still a protection gap area of 867.8 km\\u0026sup2;, which accounts for 76.8% of the total area of the key protected areas. This gap area is mainly located on the southern side of Xiong\\u0026apos;er Mountain and the northern side of Funiu Mountain in the Yi-Luo River Basin, an important tributary of the Yellow River. Under different future climate scenarios, the protection rate of the key protected areas ranges from 12.5% to 55.6%. Especially under the SSP370 scenario, the protection rate is the highest, with protection rates of 44.2% and 55.6% for the 2050s and 2090s. The protection gap areas are mainly concentrated in the narrow areas between Xiong\\u0026apos;er and Funiu Mountain in the Yi-Luo River Basin. These results highlight the necessity to strengthen protection measures under the background of future climate change to reduce protection gaps and improve the protection efficiency of key protected areas.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 5. Area and location of conservation gap areas\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003ePeriod\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eArea of key protected areas within nature reserves (km\\u0026sup2;)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eProtection rate (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGap rate (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGeographic range of protection gap\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eRiver basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eBaseline period\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e262.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e23.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e76.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNorth to Xiong\\u0026apos;er Mountain, South to Funiu Mountain\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYi-Luo River Basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2050s-SSP126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e442.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e36.4%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e63.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNorth to Xiaoqinling, South to Funiu Mountain\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYi-Luo River Basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2090s-SSP126\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e330.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e12.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e87.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNorth to Xiong\\u0026apos;er Mountain, South to Funiu Mountain\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYi-Luo River Basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2050s-SSP370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e733.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e44.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e55.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNorth to Xiaoqinling, South to Funiu Mountain\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYi-Luo River Basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2090s-SSP370\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e898.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e55.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e44.4%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNorth to Xiaoqinling, South to Funiu Mountain\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYi-Luo River Basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2050s-SSP585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e667.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e17.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e82.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNorth to Xiaoqinling, South to Funiu Mountain\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYi-Luo River Basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2090s-SSP585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e540.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e25.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e74.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNorth to Xiaoqinling and Xiong\\u0026apos;er Mountain, South to Funiu Mountain\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eYi-Luo River Basin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEnvironmental Variables Affecting the Geographic Distribution of Wild Peonies\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe geographical distribution of species is typically influenced by a variety of environmental variables, including climate, topography, soil, human footprint, and ultraviolet radiation intensity, among others(Zhang et al. 2018; Liu et al. 2021b; Schwager \\u0026amp; Berg 2021; Dong et al. 2022; Zhang et al. 2024a). The results of this study indicate that precipitation is the primary environmental variable affecting the geographic distribution of the three wild peonies, with specific indicators such as Annual Precipitation, Precipitation of Wettest Month, Precipitation of Driest Month, Precipitation Seasonality, and Precipitation of Driest Quarter. Relevant studies(Zhang et al. 2018; Liu et al. 2021b; Dong et al. 2022; Lan et al. 2023) have found that Annual Precipitation plays a key role in the distribution of \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e and \\u003cem\\u003eP .rockii\\u003c/em\\u003e, the research by Peng L(Peng et al. 2019) further indicates that the Precipitation of the Wettest Month and Annual Precipitation also play a significant role in the cultivation suitability of \\u003cem\\u003eP. ostii\\u003c/em\\u003e, which is largely consistent with the findings of this study. Adequate precipitation not only provides the necessary moisture for wild peonies but also promotes photosynthesis and the absorption of nutrients(Zhang et al. 2018; Qi et al. 2020). Particularly in spring, moderate precipitation aids in the sprouting and growth of wild peonies(Zhang et al. 2021). Precipitation influences wild peonies growth by regulating soil temperature, pH, and structure, and enhancing soil oxygen, which affects root respiration and nutrient uptake. However, extreme precipitation events may have adverse effects on wild peonies. Excessive precipitation can lead to overly high soil moisture, causing root hypoxia(Wang et al. 2018), and increasing the risk of waterlogging, which may lead to root rot and the development of root rot diseases(Liu et al. 2021a; Zhang et al. 2021; Yang et al. 2023). Thus, moderate precipitation is essential for the health of wild peonies.\\u003c/p\\u003e\\n\\u003cp\\u003eIn addition to precipitation, other environmental variables also impact the geographic distribution of wild peonies. The intensity of ultraviolet radiation can significantly affect plant photosynthesis, stomatal opening and closing, and the absorption of nutrients, especially at higher altitudes where the intensity of ultraviolet radiation is higher, and its impact is more pronounced(Roro et al. 2016; Chen et al. 2022). Changes in land use types directly affect the quality of habitats for wild peonies, and variables such as soil conditions, microclimate, and vegetation structure under different land use types can all influence the adaptability and growth conditions of wild peonies(Wang et al. 2021; Li et al. 2022). Therefore, although studies have shown that precipitation is a key factor affecting the distribution of the three wild peonies, other environmental variables should not be overlooked. Future conservation and management measures should take into account these complex environmental variables to devise effective strategies for protecting and maintaining the living environment of these rare plants. Moreover, preventive measures against extreme climate events should also be included in future ecological protection plans to reduce potential ecological risks.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eExpansion and Migration of Suitable Habitats for Wild Peonies\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConducting research on the expansion and migration of species\\u0026apos; suitable habitats is instrumental in gaining a deeper understanding of the impact of climate change on species distribution, particularly the changes in their suitable environments. It also allows for the assessment of species\\u0026apos; adaptive capacity and resilience. The study identified that the main protected areas for \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e are currently centered in a small area at the mountain-plain transition in the Yellow River Basin, specifically south of Xiong\\u0026apos;er Mountain and north of Funiu Mountain. In future climate scenarios, the protected areas for these species will expand significantly, particularly around Xiong\\u0026apos;er and Funiu Mountain. The expansion will be greatest under moderate warming (SSP370), followed by high (SSP585) and low (SSP126) warming scenarios. This is likely due to the favorable topography and microclimate of the mountains, offering an ideal environment for peony growth(Mamantov et al. 2021). Habitat diversity(Rahbek et al. 2019), precipitation patterns(Du et al. 2021) and the potential ecological corridor effects in mountainous regions all create favorable conditions for the migration and diffusion of wild peonies.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eGlobal warming affects the geographical distribution of plants in various ways, mainly manifested in the growth cycle, reproductive capacity, phenological phases, and competitive relationships with other species of plants, leading to an increase in the area of key protected areas. Moderate temperature increases may extend the growing season, providing a longer growth period for wild peonies and promoting their growth and reproduction(Liu et al. 2018; Fang et al. 2024). With moderate temperature increases, high-altitude areas in high-latitude regions may gradually become suitable for the growth of wild peonies, thereby facilitating the expansion of their distribution range(Zhang et al. 2018). Temperature changes may also affect the competitive relationships between different species in plant communities, creating conditions for wild peonies to gain a competitive advantage in new environments(Tang et al. 2022). This study found that under different future climate conditions, the centroid of key protected areas for the three species generally shows a trend of moving northward, indicating a shift from relatively low-latitude areas in the Mountain-Plain intersection zone to relatively high-latitude areas, but still surrounding the Xiong\\u0026apos;er Mountain area, which is in higher latitudes and higher altitudes. This finding is consistent with related research results, such as an increase in the suitable area for \\u003cem\\u003eP. ostii\\u003c/em\\u003e in high-latitude regions(Peng et al. 2019), and studies by Lan R(Lan et al. 2023) and Dong P(Dong et al. 2022) also show a trend of the centroid of \\u003cem\\u003eP. rockii\\u003c/em\\u003e migrating towards higher latitudes. Therefore, in the process of protecting and developing the utilization of \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e, it is necessary to fully consider the possibility of their migration to higher latitudes under the background of climate change. This involves not only the designation of protected areas but also the optimization of management measures and the construction of related ecological corridors to promote species migration and gene exchange, ensuring that wild peonies can adapt well to new ecological environments and maintain population stability under the background of climate change.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConservation of Wild Peonies under Climate Change\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe increase in the suitable habitat area for species due to climate change provides both opportunities for the expansion of biodiversity and potential ecological risks. Moderate temperature increases may promote the growth of certain species, allowing them to expand into new habitats, thereby enriching biodiversity to a certain extent(Zhang et al. 2018; Tang et al. 2022). For instance, with the improvement of climatic conditions, wild peonies may establish stable populations in new areas, leading to more diverse ecosystems. However, such changes may also disrupt the original ecological balance, leading to the reorganization of species competition(Åkesson et al. 2021), and even triggering ecological invasions(Yamamoto \\u0026amp; Jones 2024). When new species enter, they may pose a threat to native species, disrupt the ecological chain, and cause a decline or disappearance of certain native species. Moreover, the uncertainty of climate change and its long-term effects may pose threats to the survival and reproduction of species, with habitat fragmentation and degradation being particularly significant(Banks-Leite et al. 2020).\\u003c/p\\u003e\\n\\u003cp\\u003eTherefore, when formulating conservation measures, it is important to focus on current climate change trends while also deeply understanding its long-term impacts on ecosystem functions and biodiversity conservation to ensure actions promote the health and sustainability of ecosystems. To address the ecological risks brought by climate change and ensure the survival of wild peonies, the following conservation strategies are recommended: 1) Establish and improve a climate change monitoring and early warning system to respond to climate change in a timely manner; 2) Strengthen protection efforts in the southern part of the Yi-Luo River Basin, especially in the areas between Xiong\\u0026apos;er and Funiu Mountain, to reduce the negative impact of climate change on the distribution of wild peonies; 3) Enhance the connectivity of ecosystems to ensure species migration and gene flow; 4) Promote ecological restoration and adaptive management to increase the resilience of ecosystems to climate change; 5) Strengthen scientific research support to provide a scientific basis for formulating and adjusting conservation measures. The implementation of these strategies can effectively protect and restore the suitable habitats of wild peonies, reduce the adverse effects of climate change on their survival, and ensure the long-term reproduction and ecological balance of this precious species.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study, based on the Biomod2 ensemble model constructed, combined with selected environmental variables such as climate, topography, soil, and habitat, deeply analyzes the impact of climate change on the distribution of three important wild peonies\\u0026mdash;\\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e, in Mountain-Plain intersection zone of the Yellow River Basin. The results show that the main environmental variable affecting the distribution of these three wild peonies is precipitation, which is also influenced by temperature, topography, soil, and habitat variables. Under the background of future climate change, the suitable distribution areas of the three wild peonies show a trend of moving northward. The existing nature reserves do not effectively cover the key protected areas for wild peonies, and the protection gap areas are mainly located in the middle and upper reaches of the Yi-Luo River, an important tributary of the Yellow River, in a narrow strip between Xiong'er and Funiu Mountain. This study reveals the profound impact of climate change on the living environment of \\u003cem\\u003eP. jishanensis\\u003c/em\\u003e, \\u003cem\\u003eP. ostii\\u003c/em\\u003e, and \\u003cem\\u003eP. rockii\\u003c/em\\u003e, providing a scientific basis for formulating effective protection strategies, which is of great significance for better protecting and utilizing this precious plant resource and guiding the protection and management work of wild peonies. Future research and protection work should focus on exploring the physiological and ecological adaptation mechanisms of wild peonies under different climate scenarios, restoring and protecting important suitable habitats and ecological corridors, and increasing the connectivity between populations. At the same time, it is necessary to regularly monitor the impact of climate change on the growth and distribution of wild peonies and adjust protection strategies in a timely manner to enhance their adaptability and resilience to climate change.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u0026nbsp;\\u003c/strong\\u003eThis work was supported by funding from Henan Province, China (222102110418 awarded to DY). We thank YL, EZ, SL, XH, XW, PX for technical and field assistance.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWe are grateful for the contributions of WorldClim in providing climate data (https://worldclim.org/) and the Food and Agriculture Organization of the United Nations (FAO) for their World Soil Database (https://www.fao.org/), both of which were instrumental in the generation of data used in this publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e HG and DY conceived and designed the study; HG, YH, PY, and JX performed the experiments and analyzed the data; HG, SJ, MZ, and DY wrote and edited the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003eThis work was supported by funding from Henan Province, China (222102110418 awarded to DY).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e The data that support the findings of this study are available from the authors upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e The authors declare no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eÅkesson A, Curtsdotter A, Ekl\\u0026ouml;f A, Ebenman B, Norberg J, Barab\\u0026aacute;s G (2021) The importance of species interactions in eco-evolutionary community dynamics under climate change. 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Ecological Indicators 132:108256. http://doi.org/10.1016/j.ecolind.2021.108256\\u003c/li\\u003e\\n\\u003cli\\u003eZhu Y, Xu X, Xi Z, Liu J (2023) Conservation priorities for endangered trees facing multiple threats around the world. Conservation Biology 37:e14142. http://doi.org/10.1111/cobi.14142\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"plant-ecology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"vege\",\"sideBox\":\"Learn more about [Plant Ecology](https://www.springer.com/journal/11258)\",\"snPcode\":\"11258\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11258/3\",\"title\":\"Plant Ecology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Climate change, Species distribution modeling, Wild peonies, Migration trends, Conservation gaps\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5260001/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5260001/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eClimate change has caused habitat fragmentation and niche shifts in ecosystems, affecting reproduction patterns. Wild peonies, key to peony breeding, offer insights into climate adaptation for conservation and sustainable use. This study uses the Biomod2 ensemble model to predict habitats for \\u003cem\\u003ePaeonia jishanensis\\u003c/em\\u003e, \\u003cem\\u003ePaeonia ostii\\u003c/em\\u003e, and \\u003cem\\u003ePaeonia rockii\\u003c/em\\u003e in Mountain-Plain Intersection Zone of the Yellow River Basin, and pinpoints key environmental variables. The results indicate that precipitation is the primary environmental variable affecting the distribution of the three wild peonies. During the baseline period, peony conservation areas are concentrated in the Funiu and Xiong'er Mountains of the Yi-Luo River Basin. Future climate scenarios predict an expansion of these areas, with the SSP370 scenario showing the most significant increase. This suggests that mild warming may benefit peony distribution, with Xiaoqinling becoming a crucial new conservation area. Climate change may shift conservation areas northward, although within a limited range. Furthermore, protected areas during the baseline period cover only 23.2% of the key conservation areas, with the rate of conservation gaps ranging from 44.4\\u0026ndash;87.5% under various climate scenarios, and these gaps are largely concentrated in the southern part of the Yi-Luo River Basin. This research provides a robust scientific foundation for the development of conservation strategies and the sustainable utilization of wild peonies resources in Mountain-Plain Intersection Zone of the Yellow River Basin.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Impact of Climate Change on the Suitable Habitats of Three Wild Peonies in Mountain-Plain Intersection Zone of the Yellow River Basin\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-12-17 14:02:01\",\"doi\":\"10.21203/rs.3.rs-5260001/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-12-03T16:05:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-11-01T10:59:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"181045139445269096959626500469811959198\",\"date\":\"2024-10-30T11:59:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"263730906718421524819493942679912860232\",\"date\":\"2024-10-24T04:21:42+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-10-16T16:02:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-10-16T08:34:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-10-16T08:02:48+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Plant Ecology\",\"date\":\"2024-10-14T09:25:59+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"plant-ecology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"vege\",\"sideBox\":\"Learn more about [Plant Ecology](https://www.springer.com/journal/11258)\",\"snPcode\":\"11258\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11258/3\",\"title\":\"Plant Ecology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"8ad858fc-3279-414e-9d75-0dfd63a221ce\",\"owner\":[],\"postedDate\":\"December 17th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-01-13T16:03:34+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5260001\",\"link\":\"https://doi.org/10.1007/s11258-024-01485-8\",\"journal\":{\"identity\":\"plant-ecology\",\"isVorOnly\":false,\"title\":\"Plant Ecology\"},\"publishedOn\":\"2025-01-07 15:57:46\",\"publishedOnDateReadable\":\"January 7th, 2025\"},\"versionCreatedAt\":\"2024-12-17 14:02:01\",\"video\":\"\",\"vorDoi\":\"10.1007/s11258-024-01485-8\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11258-024-01485-8\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5260001\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5260001\",\"identity\":\"rs-5260001\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}