Species richness prediction and priority conservation planning for rare Michelia species in China

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Using the optimized MaxEnt and Marxan models, we investigated the relationship between species richness and various factors by predicting the species richness of rare Michelia species based on distribution data and natural ecological factors in China. Additionally, national nature reserves and parks were overlaid with priority conservation zones having irreplaceability values ranging from 80 to 100 to identify conservation gaps. The findings indicate that rare Michelia species are found in southern Yunnan Province, which exhibits the highest concentration. The high richness zones are expected to shrink to 0.62×10 4 km 2 under future climate scenarios. Northern high latitudes and higher altitudes are expected to offer better habitats for the majority of rare Michelia species. With the intensification of climate change, it is anticipated that this migration will exceed 150 km. Priority conservation zones for rare Michelia species are primarily located in the southeastern part of the Tibet Autonomous Region, the south-central part of Yunnan Province, the central part of Sichuan Province, the western part of Chongqing Municipality, the southern part of Guizhou Province, the northern part of Guangxi Zhuang Autonomous Region, the southern part of Hunan Province, the northern part of Guangdong Province, the eastern and southern parts of Jiangxi Province, the northwestern part of Fujian Province, the southern part of Zhejiang Province, the central part of Taiwan Province, and the southwestern part of Hainan Province. These priority conservation zones account for only 0.86% of the land area of China, with 6.6×10 4 km 2 of prioritized conservation zones not yet designated as nature reserves or parks. To effectively embody the principle that 'green mountains are golden mountains,' we recommend expanding conservation zones for rare Michelia species within designated priority zones and enhancing habitat conservation measures. Earth and environmental sciences/Ecology/Biodiversity Earth and environmental sciences/Ecology/Climate change ecology Earth and environmental sciences/Ecology/Forestry Global warming Rare Michelia species Species richness Priority conservation zones Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Biodiversity, a fundamental aspect of nature, serves as the core and foundation for all life on Earth (Pörtner H O et al., 2023). The global average land surface air temperature has shown a sustained warming trend over the past 100 years, with a more pronounced increase in the last 50 years (Naidoo S et al., 2022). Climate change affects the functioning and stability of ecosystems, and extreme climate events may reshape ecological structure, ecosystem diversity, and ecosystem functions (Hong P et al., 2022 ). Rare Michelia species is a relatively primitive genus within the Magnoliaceae family and is endemic to southeastern Asia. Comprising 41 species, it is most prevalent in China, where it accounts for over 80% of all Michelia species worldwide. In the realm of science, the rare Michelia species is crucial for understanding the origins and evolution of the Magnoliaceae family, as well as for elucidating the natural hierarchy within this family (Liang C B et al., 1993 ) Due to the interaction of several factors, including large distances for gene exchange, ecological damage, and global climate change, the spatial distribution of rare and narrowly distributed Michelia species has become increasingly complex, with most of these species being classified as having extremely small populations (Tang C Q et al., 2011). Thus, predicting trends in species richness and conserving rare Michelia species are critical for preserving biodiversity. Ecological niche modeling is a relatively recent field within ecological niche theory. It has been extensively used for assessing the risk of invasive species, conserving rare species, evaluating the effects of climate change on ecosystems, and addressing other related issues ( Sandhya Kiran G et al., 2024 ). A variety of ecological niche models have been developed, including the BIOCLIM model, the GARP model, the ENFA model, the RF model, and the MaxEnt model (Aldiansyah S et al,2024). Compared with the MaxEnt model, other ecological niche models, despite their theoretically high prediction accuracy, have limited practical application due to the difficulty of obtaining data on non-existent geographic distribution points. As a result, the prediction outcomes may not accurately reflect real world conditions. In contrast, the MaxEnt model demonstrates superior reliability and practicality in these contexts ( Wang P et al., 2024 ). The MaxEnt model does not require data on non-existent geographic distribution points and can still produce reliable results with fewer distribution points. However, most research relies on the default parameters of the MaxEnt model for simulation predictions. Early studies by researchers and developers, using data from 266 animal and plant species across widely separated geographical locations, established the default parameter configurations for the model (Li X et al., 2023 ). In recent years, researchers have explored model optimization to enhance prediction accuracy, and the optimized MaxEnt model has been utilized to predict potential suitability zones for species such as Pterocarpus marsupium (Ghosh B G et al., 2021), Acacia caven (Velasco N et al., 2023 ) and Ginkgo biloba (Zhang X et al., 2023 ). As nature conservation efforts advance, identifying priority biodiversity conservation zones has become a standard practice for preserving biodiversity and serves as a critical foundation for establishing nature reserves and developing conservation strategies amidst limited resources (Miu I V et al., 2020). Common methods for identifying priority conservation zones for species include scientific assessments, the Marxan model, the Zonation model and the C-Plan model(Ma L et al., 2024). Janßen utilized a variety of conservation planning models and drew on the research and expertise of previous scholars, ultimately evaluating the Marxan model as having the greatest relative reliability and results (Janßen H et al., 2019 ). Bernasconi's study on biodiversity conservation using the Marxan model found that this model outperforms traditional anthropogenic delineation planning in terms of cost effectiveness and efficiency, and does so more rapidly (Bernasconi P et al. 2016 ). Originally developed for marine conservation planning, the Marxan model has been optimized and refined by its developers and is now widely used for terrestrial conservation planning. Based on theoretical principles of biodiversity, the Marxan model defines the scope of nature reserves in a scientific and rational manner. It utilizes the core principle of the simulated annealing algorithm to iteratively optimize reserve boundaries by randomly selecting subsets of planning zones, thereby minimizing costs and achieving the biological conservation goals set by decision-makers ( Silvestro D et al., 2011). In this study, data on natural ecological factors and geographic distribution points of rare Michelia species were used, along with the MaxEnt model optimized with R language software, to simulate the spatial distribution of species richness of rare Michelia in China under various climatic conditions. Based on this, the human interference factor was incorporated as a cost parameter in the Marxan model to identify priority conservation zones with irreplaceability values ranging from 80 to 100 for rare Michelia species in China. The model results were then overlaid with data from nature reserves and parks to identify zones with conservation gaps. 2. Method and materials 2.1 Species identification and distribution point data collection A total of 28 rare Michelia species were identified in China based on reputable sources, including the China Biodiversity Red List (Version 2013), the Threatened Species List of China's Higher Plants (Version 2017), and the National Key Protected Wild Plants (Version 2021). The primary sources of information regarding the geographical distribution of rare Michelia species in China include the following: Chinese Field Herbarium (CFH, https://www.cfh.ac.cn/), Plant Photo Bank of China (PPBC, http://www.ppbc.iplant.cn/), National Plant Specimen Resource Center (NPSRC, https://www.cvh.ac.cn/), Global Biodiversity Information Facility (GBIF, https://www.gbif.org/), National Specimen Information Infrastructure (NSII, https://www.nsii.org.cn/), A literature search was conducted on journal websites using the Latin and Chinese names of rare Michelia species to gather information on their geographic distribution as reported in the literature. For distribution points with names but lacking coordinate information, we obtained and recorded the corresponding latitude and longitude using the Baidu Coordinate Pickup System (https://api.map.baidu.com/) to complete their geographic distribution data (Li H et al., 2024). In addition, latitude and longitude information for the sampling points was extracted from digitized atlases of species distribution in Chinese and english literature, yearbooks, and news sources using ArcGIS software (Wang C et al., 2021). To ensure the accuracy of MaxEnt model predictions, we integrated the ecological characteristics of each species and addressed anomalies, errors, and duplicates. Ultimately, a total of 2,088 distribution data points for rare Michelia species in China were collected and compiled (Fig. 1). 2.2 Factors collection and screening In this study, 38 natural ecological factors, including ecoclimatic, soil, topography, and distance from water systems, were considered in the construction of the MaxEnt model. Among these, Ecoclimatic factors were sourced from the World Climate Database (https://worldclim.org/) with a spatial resolution of 1 km. Soil factors were obtained from the National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn/) with a spatial resolution of 1 km. Topographic factors were provided by a digital elevation model from Geospatial Data Cloud (https://www.gscloud.cn/), and elevation, slope, and slope direction data were extracted using ArcGIS software with a spatial resolution of 90m. Elements such as rivers, lakes, reservoirs, and other water bodies were downloaded from the Geo-database (https://www.openstreetmap.org/), and the ArcGIS spatial analysis tool was then used to generate raster data representing the distribution of distances from water systems through Euclidean distance calculation. To ensure the smooth operation of the model, all the aforementioned natural ecological factors were standardized into ASCII raster files with a 1 km resolution using ArcGIS software and subsequently imported into the MaxEnt model (Zhang X et al., 2023). In China, land is state-owned, and private land sales are prohibited, which complicates the acquisition of land price data (Zhang J et al., 2020). In the Marxan model run, planning unit costs for each sub-basin were considered. In this study, the human tnterference factor (HIF) raster data, which integrates land use, night lights, gross domestic product, population density, road density, and human activity intensity, was used as a proxy for the cost parameter. Among these, land use data were downloaded from Globeland (http://www.globallandcover.com/) with a spatial resolution of 1 km, and the Con and Setnull functions of the raster calculator were used to extract land use types affected by human intervention, including paddy fields, drylands, urban zones, rural settlements, and other construction lands. Night lights data and gross domestic product data were downloaded from the National Earth System Science Data Center (https://www.geodata.cn/) with a spatial resolution of 1 km. Population density data were downloaded from the Center for Socioeconomic Data and Applications (https://sedac.ciesin.columbia.edu/) with a spatial resolution of 1 km. Road Density data were downloaded from the GRIP Global Road Database (https://www.globio.info/) with a spatial resolution of 8 km. Human activity intensity data, developed by the Urban Environmental Monitoring and Modeling Team of the School of Land Science and Technology, China Agricultural University (https://www.x-mol.com/groups/li_xuecao/news/48145/), with a spatial resolution of 1km. Since the Marxan model can only use one cost layer in the analysis, we used Matlab software to determine the weights of factors. Specifically, we employed the Analytic Hierarchy Process (AHP) to establish subjective weights and the Entropy Weight Method (EWM) to derive objective weights. Subsequently, we calculated the combined factor weights using the Oriented Distance Function (ODF) to integrate the human interference factor raster data (HIF) (Zhang Y et al., 2024). Data on China's nature reserves and parks were obtained from the China Nature Reserve Specimen Resource Platform (http://www.papc.cn). Due to the lack of data on nature reserves and parks in Taiwan Province, additional information was sourced from the World Database on Protected Areas (http://www.unep-wcmc.org/wdpa/). Given that the conservation levels of nature reserves and parks in Taiwan Province are not specified, all nature reserves and parks data from Taiwan Province, classified as national conservation level, were integrated with the data for mainland China to analyze conserve gaps. 2.3 MaxEnt model building and simulation When performing MaxEnt model simulations, high correlations between natural ecological factors can lead to overfitting. Therefore, it is necessary to screen the factors (Ghosh B G et al., 2021; Velasco N et al., 2023; Zhang X et al.,2023). First, species geographic distribution data and various natural ecological factors were input into MaxEnt model for preliminary simulation, and natural ecological factors with a percent contribution greater than 1% were selected. Second, the extracted values of these natural ecological factors were analyzed for correlation using Matlab software. If the absolute value of the correlation coefficient between similar natural ecological factors exceeded 0.8, only the factor with the higher contribution rate was retained. Ultimately, 11 natural ecological factors were identified for MaxEnt modeling (Fig. 2) (Lu S et al., 2021). The MaxEnt model operates on the principle of identifying the potential geographic distribution of species by maximizing entropy, subject to constraints imposed by the species' known geographic distribution and relevant ecological factors (Sandhya Kiran G et al., 2024). To avoid unreliable predictions, the MaxEnt model was not run with default parameters during simulation due to its susceptibility to sampling bias and overfitting issues (Quiroga M P et al., 2022). In this study, the MaxEnt model was optimized using the kuenm package for R language software. The study balanced the marginal range of MaxEnt modeling data error by setting the Regulation Multiplier (RM) from 0.5 to 4, with increments of 0.5, resulting in a total of 8 RM values. Additionally, the MaxEnt model provides five types of Feature Combinations (FC): Linear features (L), Quadratic features (Q), Hinge features (H), Product features (P), and Threshold features (T), resulting in a total of 31 possible FC parameter combinations. The MaxEnt model is run with various combinations of feature types to control model complexity and enhance prediction performance (Velasco N et al., 2023; Quiroga M P et al., 2022). The akaike information criterion (ΔAICc) was used to assess model complexity and goodness of fit, with the best model being identified as the one with the smallest ΔAICc value (ΔAICc = 0). The next step involved evaluating the extent of model overfitting to species distribution points by analyzing models with a 10% training omission rate (OR10%) of less than 5% (Zhang X et al.,2023). In addition, this study also evaluated the model prediction performance based on the AUC ratio (AUC ratio), and when the AUC ratio is greater than 1, it indicates that the model prediction is better relative to the stochastic model prediction (Peterson A T et al., 2008). The geographic distribution data for rare Michelia species, along with relevant natural ecological factors, were incorporated into the optimized MaxEnt model. In this process, 75% of the geographic distribution data were used for training and accuracy assessment, while 25% were reserved for testing. This procedure was repeated over 10 simulations (Ghosh B G et al., 2021; Velasco N et al., 2023; Zhang X et al.,2023). The performance of the MaxEnt model is evaluated using the Area Under the Curve (AUC), which assesses both model fitting and prediction accuracy. The AUC value ranges from 0.5 to 1, with values closer to 1 indicating higher prediction accuracy (Zhang X et al.,2023; Quiroga M P et al., 2022). According to the Maximum training sensitivity plus specificity threshold (MTSS), the probability of species presence (P) is defined as a no suitability zones when it is less than the threshold (MTSS) (Peterson A T et al., 2008). On this basis, with reference to the IPCC criteria for evaluating the probability of species existence, potential suitability zones were categorized into three levels: P P ≥ MTSS for low suitability zones, and P ≥ 0.66 for high suitability zones (Lu S et al., 2021). Subsequently, the SDMtoolbox function in ArcGIS software was used to calculate the migration distance and direction from the center of the suitability zone for each rare Michelia species under future climate scenarios, and the potential suitability zones for 28 rare Michelia species were overlaid to estimate species richness patterns ( Tang C Q et al., 2018). In order to facilitate the visualization analysis, species richness (S) of of rare Michelia species was classified into five levels: S = 0 for no richness, 0 < S ≤ 5 for low richness, 5 < S ≤ 10 for medium richness, 10 ≤ S < 15 for higher richness, and 15 ≤ S for high richness (Lu S et al., 2021). 2.4 Marxan model building and simulation The Marxan model is an advanced conservation planning tool that employs Simulated Annealing (SA) to identify optimal configurations of planning units. Through iterative computations and repeated evaluations, it utilizes sophisticated mathematical algorithms and computational technologies to achieve optimal conservation outcomes at minimal cost. This model is particularly effective in addressing real-world planning challenges, such as the establishment of nature reserves (Fajardo J et al., 2014). The equation is as follows: In Eq. 2–2, Pus refers to the sub-basin planning units. Cost denotes the expenditure associated with these planning units. Boundary length defined as the sum of the selected planning cell boundary lengths adjusted by the Boundary Length Modifier (BLM), implies that a smaller total boundary length corresponds to a more compact design. Penalty represents a value assigned when the species conservation goals are not met and is calibrated using the Species Protection Factor (SPF). Digital elevation model data was segmented into 92,736 sub-basin planning units using the hydrological analysis tool in ArcGIS software. Conservation objectives for rare Michelia species were established based on the species' IUCN status, endemism in China, and the distribution of its high suitability zones (Fajardo J et al.,2014). The selected conservation targets were based on the distribution of high suitability zones for rare Michelia species, as conserved by the MaxEnt model (Wang Y et al., 2023). The conservation cost of each planning unit was assigned based on the human interference factor, which was calculated using the AHP-EWM assignment method. The sensitivity analysis of BLM values was conducted using Zonae Cogito software, with the initial BLM value set at 0.0241. This value was calculated as the ratio of the maximum cost value of the planning cell domain to the longest boundary lengths. Based on this, the remaining values were incremented according to a function of 2 n , and the optimal BLM value was determined iteratively. Additionally, the SPF Calibration function in the QGIS software toolbox was utilized, selecting the 'As group' mode with a gradient value of 0.1 to identify the optimal SPF value. This approach aimed to balance the conservation of rare Michelia species with considerations of boundary compactness and cost-effectiveness (Nguyen N T H et al., 2022). During the execution of the Marxan model, the number of iterations was set to 100. Each planning unit was assigned a value within the ranging from 80 to 100, which represents the number of times it was selected in the optimal planning scheme. A higher value indicates greater irreplaceability, reflecting a higher frequency of selection across the 100 iterations (Zhang L et al., 2020). In this study, planning units with an irreplaceability value range of 80 to 100 were designated as priority conservation zones. This designation was subsequently combined with data from Chinese nature reserves and parks for an overlay analysis to identify gaps in conservation. 3. Results and discussion 3.1 MaxEnt model parameter optimization results As detailed in Table 1 , after optimizing the modulation multiplicity (RM) and feature combination (FC) of the MaxEnt model using the Kuenm package in R, the average value of the akaike information criterion (ΔAICc) was 0. The 10% omission rate (10% OR) consistently remained below 5%, and the AUC ratios (AUCratio) were all greater than 1.7, meeting the evaluation criteria for an optimal model. When using the optimized MaxEnt model to simulate suitability zones for rare Michelia species, the mean AUC for each species exceeded 0.9, with standard errors ranging from 0.007 to 0.0129. Michelia guangxiensis had the smallest mean AUC at 0.9497, while Michelia pubinervia had the largest mean AUC at 0.9986. These results indicate that the optimized MaxEnt model significantly outperforms the default parameter model in predictive accuracy and transferability, providing a more precise prediction of the distribution of rare Michelia species under both baseline (1970–2000) and future (2061–2080) climate scenarios. Table 1 Optimization parameters and evaluation values of MaxEnt model Species RM FC ΔAICc OR10% AUCratio AUC ± SD MTSS Michelia guangdongensis 1 LQ 0 0 1.9496 0.9849 ± 0.0038 0.1601 Michelia velutina 2 LQ 0 0 1.9376 0.9840 ± 0.0025 0.1301 Michelia angustioblonga 4 LQH 0 0.125 1.9839 0.9953 ± 0.0011 0.4864 Michelia cavaleriei 3 QH 0 0.0512 1.7484 0.9620 ± 0.0023 0.2324 Michelia kisopa 2 LQH 0 0.1428 1.9293 0.9873 ± 0.0037 0.2998 Michelia guangxiensis 2.5 L 0 0 1.8678 0.9497 ± 0.0129 0.4752 Michelia opipara 1.5 Q 0 0 1.9778 0.9940 ± 0.0030 0.3534 Michelia shiluensis 4 LT 0 0 1.8876 0.9775 ± 0.0106 0.2678 Michelia iteophylla 3 L 0 0 1.9601 0.9883 ± 0.0034 0.5065 Michelia gioi 4 LQH 0 0.25 1.9193 0.9809 ± 0.0052 0.2966 Michelia elegans 4 H 0 0 1.9659 0.9857 ± 0.0028 0.5523 Michelia lacei 1.5 Q 0 0 1.9846 0.9939 ± 0.0019 0.434 Michelia crassipes 1.5 PT 0 0.0161 1.8555 0.9751 ± 0.0015 0.2623 Michelia fulva 1.5 Q 0 0 1.9846 0.9933 ± 0.0025 0.153 Michelia baillonii 2.5 LQ 0 0.2 1.9770 0.9850 ± 0.0126 0.0881 Michelia xanthantha 2 L 0 0.2 1.8223 0.9863 ± 0.0053 0.2316 Michelia sphaerantha 2.5 LQT 0 0.3333 1.9425 0.9825 ± 0.0037 0.295 Michelia champaca 0.5 LH 0 0.0556 1.8894 0.9810 ± 0.0046 0.1223 Michelia pubinervia 1 LP 0 0.3333 1.9670 0.9986 ± 0.0007 0.4581 Michelia odora 0.5 QP 0 0.0833 1.8560 0.9707 ± 0.0012 0.1625 Michelia wilsonii 1 QP 0 0 1.7701 0.9679 ± 0.0043 0.0841 Michelia szechuanica 2 LQT 0 0 1.8983 0.9735 ± 0.0059 0.2651 Michelia coriacea 3 T 0 0 1.9728 0.9863 ± 0.0054 0.56 Michelia flaviflora 1.5 PT 0 0.25 1.9821 0.9787 ± 0.0043 0.3674 Michelia fujianensis 2 LPT 0 0 1.9679 0.9919 ± 0.0009 0.2176 Michelia masticata 4 L 0 0 1.9438 0.9881 ± 0.0088 0.4139 Michelia martinii 0.5 LQ 0 0.0285 1.7582 0.9696 ± 0.0013 0.1534 Michelia chapensis 2 LPTH 0 0.0459 1.7761 0.9517 ± 0.0033 0.2036 3.2 Species richness fitting and Centroid migration in suitability zones As shown in Fig. 3 , during the baseline period (1970–2000), the species richness of rare Michelia species was broadly distributed, encompassing approximately 25.35% of the land area of China. In northern China, only eastern Shandong Province, northern Gansu Province, and southern Shanxi Province exhibit limited distribution, while the majority of the remaining zones are predominantly located south of the Qinling Mountains-Huaihe River, a region characterized by a typical monsoon climate. Low richness is primarily distributed in southern-central Tibet, eastern-central Sichuan, southern Gansu, southern Shanxi, eastern-central Hubei, northern Hunan, northern Jiangxi, southern Anhui, eastern Shandong, southern-central Jiangsu, Shanghai, and northern Zhejiang Provinces, covering an area of approximately 110.65×10 4 km 2 , which accounts for about 11.53% of the land area of China. the species richness of rare Michelia species is primarily distributed in southeastern Tibet, northern Yunnan, southern-central Sichuan Province, Chongqing Province, southern-central Guizhou, western Hubei Province, western and southern Hunan Province, Jiangxi Province, southern Zhejiang Province, Fujian Province, Taiwan Province, Guangdong Province, Guangxi Province, and Hainan Province, covering an area of approximately 105.13×10 4 km 2 , which accounts for about 10.96% of the land area of China. Higher richness is primarily distributed in southern-central Yunnan Province, central Guangxi Province, central Guangdong Province, central Fujian Province, central Taiwan Province, and southern-central Hainan Province, covering an area of approximately 26.66×10 4 km 2 , which accounts for about 2.78% of the land area of China. High richness is primarily distributed in southern-central Yunnan Province, covering an area of approximately 0.82×10 4 km 2 , which accounts for about 0.08% of the land area of China. In the future (2061–2080), under the two climate scenarios, low richness is projected to spread to the central Tibet Autonomous Region, northwestern Sichuan Province, southern Gansu Province, central Shanxi Province, Hubei Province, Henan Province, southwestern Shanxi Province, and Shandong Province, with richness expected to be maintained at 11.47–16.84% of the land area of China. Medium richness is expected to spread to zones such as eastern Sichuan Province, northern Guizhou Province, northern Hunan Province, northern Jiangxi Province, and southern Anhui Province, with richness expanding to 1.17–2.73% of the land area of China. Higher richness is projected to gradually disappear in the northern Guangxi Zhuang Autonomous Region, Fujian Province, and Taiwan Province. High richness will be maintained in southern Yunnan Province; however, it is anticipated to decrease by 0.02% under the SSP585 climate scenario. Figure 4 and Fig. 5 show that under future climate scenarios, the potential suitability distribution zones for most rare Michelia species will migrate northwest and southwest within China, while a few will shift to the northeast and southeast. Furthermore, with the increase in CO 2 emission concentrations, the potential suitability zones are projected to migrate even farther. Among them, the migration distances of species such as M. gioi , M. guangdongensis , M. velutina , M. guangxiensis , M. iteophylla , M. martinii , M. odora , M. fujianensis , M. wilsonii , M. coriacea , M. masticata , and M. szechuanica are projected to exceed 150 km under the SSP585 climate scenario. 3.4 Human Interference Factor calculation As shown in Fig. 6 , the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) were used, respectively, to calculate the weights of the various socioeconomic factors. An optimization decision matrix is then constructed using the Oriented Distance Function (ODF) to calculate the combined weights for each factor. Subsequently, a raster calculator for superposition analysis is employed to obtain the human interference factor raster, taking into account various socioeconomic factors based on their combined weights. As shown in Fig. 7 , the intensity of anthropogenic interference trends gradually increases from west to east. Zones with higher levels of anthropogenic interference are primarily located east of the Heihe-Tengchong line in China, with significant interference activities concentrated around major urban agglomerations, such as the Beijing-Tianjin-Hebei, Yangtze River Delta, Central Plains, and Chengdu-Chongqing urban agglomerations. Overall, the zoning of anthropogenic disturbance intensity in China is more consistent with the anthropogenic disturbance factor data generated from each economic factor. The conservation cost, determined by allocating costs to each sub-basin planning unit, was derived from the data on anthropogenic disturbance factors. This conservation cost was then used as the input file for the Marxan modeling procedure. 3.5 Marxan model parameter optimization results To conduct a sensitivity analysis, BLM values were varied within a range of 0 to 100 using a specific formula, and the planning results exhibited substantial variation across the 14 distinct BLM values employed. As illustrated in Fig. 8 , BLM values ranging from 0 to 0.064 show a sharp decrease in boundary length as conservation costs increase. However, as conservation costs continue to rise, the rate of change in boundary length progressively diminishes. A distinct inflection point is observed at a BLM value of 0.064, where both the conservation cost and boundary length are relatively low. This BLM value is considered relatively optimal for the study. The optimized BLM values were used as a reference, and subsequently, the SPF values were adjusted based on the sensitivity analysis. These adjusted SPF values predominantly fell within the range of 0.1 to 0.3, all of which are lower than the default SPF values used in the Marxan model. 3.6 Analysis of Priority conservation zones After conducting 100 iterative runs of the Marxan model, planning units with irreplaceability values ranging from 80 to 100 were identified, totaling 539 units. This resulted in a prioritized conservation zones of 8.27×10 4 km 2 , primarily concentrated in the southeastern Tibet Autonomous Region, south-central Yunnan Province, central Sichuan Province, western Chongqing Province, southern Guizhou Province, northern Guangxi Zhuang Autonomous Region, and southern Hunan Province. Additional zones include northern Guangdong Province, eastern and southern Jiangxi Province, northwestern Fujian Province, southern Zhejiang Province, central Taiwan Province, and southwestern Hainan Province. This conserved zone constitutes only 0.86% of of the land area of China. As shown in Fig. 9 , the Priority conservation zones for rare Michelia species covers an area of approximately 0.56×10 4 km 2 within the nature reserves. Among them, the zones conserved by national nature reserves primarily include the Yarlung Tsangpo Grand Canyon Nature Reserve in the Tibet Autonomous Region, the Xishuangbanna National Nature Reserve in Yunnan Province, the Maolan National Nature Reserve in Guizhou Province, the Wuyi Mountain National Nature Reserve in Jiangxi Province, the Longqishan National Nature Reserve in Fujian Province, the Matou Mountain National Nature Reserve in Jiangxi Province, and the Shaoguan Danxia Mountain National Nature Reserve in Guangdong Province, among others. Those zones primarily conserved by provincial and municipal nature reserves include the Guilin Ocean Mountain Autonomous Nature Reserve in the Guangxi Autonomous Region, the Qiandongnan Moon Mountain Nature Reserve in Guizhou Province, the Hezhou Xiling Mountain Nature Reserve in Guangxi Autonomous Region, the Jiangjin Sifang Mountain Nature Reserve in Chongqing Municipality, the Tongbiguan Nature Reserve in Yunnan Province, and the Qiandongnan Libo Jialiang Sanlian Cave Nature Reserve in Guizhou Province, among others. As shown in Fig. 10 , Priority Conservation zones for rare Michelia species covers an area of approximately 1.11×10 4 km 2 within the parks. Among them, the zones conserved by national parks primarily include the Tropical Rainforest National Park in Hainan Province, the Danda Wildlife Important Habitat in Taiwan Province, the Wuyi Mountain National Park in Taiwan Province, the Yushan National Park in Taiwan Province, the Dongjiang Lake National Wetland Park in Hunan Province, and the Giant Panda National Park in Sichuan Province. The largest zones conserved by provincial and municipal parks primarily include the Heyuan Wanlvhu Forest Nature Park in Guangdong Province, the Mengla Yiwu Forest Nature Park in Yunnan Province, the Shaoguan Nanxiong Zhugui Meiguan Forest Nature Park in Guangdong Province, the Qingyuan Tianhu Forest Nature Park in Guangdong Province, the Shaoguan Renhua Forest Nature Park in Guangdong Province, and the Ningde Mindong Grand Canyon Forest Nature Park in Fujian Province. 4. Discussion 4.1 Impact of climate change on the geographical distribution of species Understanding the spatial distribution of plant diversity and the mechanisms influencing it has been a central focus for ecologists and biogeographers for an extended period (Maestre F T et al., 2021). Species distributions are shaped by various factors and result from long-term interactions between species and their environment. Global climate change alters temperature and precipitation patterns, prompting species to migrate to new climatic conditions that are better suited for their survival and reproduction (A. Lee-Yaw J et al., 2022). In this study, an optimized MaxEnt model was employed to predict the potential suitability zones for 28 rare Michelia species under both baseline and future scenarios. The results indicated that the species richness of rare Michelia is predominantly concentrated in subtropical and tropical zones, with only a few species found in highland climates. Additionally, as latitude increases, the species richness of rare Michelia gradually declines. This is consistent with Zhou's study on the species diversity pattern of typical evergreen broadleaf forests in China, which found that species richness decreases gradually with increasing latitude (Zhou R et al., 2023 ). In the future, as climate scenarios worsen, the high richness in southern Yunnan Province is projected to contract, with potential suitability zones for most rare Michelia species shifting to northern high-latitude and high-elevation zones. In more severe climate scenarios, the centroid migration of potential suitability zones for most species has significantly exceeded that of the SSP126 scenario, with migration distances surpassing 150 km. This finding aligns with previous research, which suggests that as global temperatures rise and external pressures increase, the original habitats of many species will fall below their survival thresholds (Pepin N C et al.,2022). Consequently, most species are expected to migrate to higher-altitude mountains or higher-latitude zones in search of more potential suitability zones. 4.2 Analysis of Priority conservation zones The Marxan model is a widely used conservation planning software that excels in prioritizing conservation zones (Fajardo J et al.,2014; Wang Y et al., 2023 ; Nguyen N T H et al., 2022; Zhang L et al., 2022). Planning units with high irreplaceability values represent the best habitats for conserved species and can be defined as prioritized biodiversity conservation zones characterized by the lowest conservation costs, minimal human disturbance, and relatively high spatial concentration (Silvestro D et al., 2022 ). The model also considers more socioeconomic factors than the MaxEnt model, as it identifies priority zones to mitigate human-ecological conflicts and minimize human impacts on species (Daigle R M,et al., 2020). In contrast, the MaxEnt model places greater emphasis on simulating species distribution suitability based on natural ecological factors (e.g., climate, soil, and topography). Building on this, the Marxan model proposes a method for identifying optimal habitats for species conservation, aiming to minimize management costs (He P et al., 2021 ). In this study, we established the distribution of suitability habitats for rare Michelia species in China and used a conservation planning model to identify priority conservation zones with high irreplaceability values. We analyzed these zones in conjunction with vector data from nature reserves and parks in China, revealing significant conservation gaps, with approximately 66,000 km² of priority conservation zones remaining unconserved. Consequently, long-term dynamic monitoring and the establishment of conservation gene banks are necessary to prevent the loss of genetic resources. 4.3 Analysis of research limitations In this study, the MaxEnt model was optimized using the Kuenm package within R software, which constrained the complexity of the model parameters to a certain extent and improved the predictive accuracy of the model. However, there are still shortcomings, the MaxEnt model is based on the core of ecological niche theory, which assumes that a species exists without competing resources. In reality, the distribution of a species is influenced not only by climate, soil, topography, and human interference, but also by factors such as interspecific and extraspecific competition, threats from pests and diseases, and genetic variation (Wang P et al., 2024 ). In addition, the sample information that can be obtained is primarily concentrated in zones such as roads and rivers. This concentration may introduce spatial bias in the collected data and result in varying collection strengths, leading to certain errors in the simulation results (Mahatara D et al., 2021 ). To more accurately predict potential shifts in the geographic distribution of rare Michelia species, future efforts will incorporate the aforementioned biological factors, expand surveys of these species, and enhance the collection of data on their distribution points. The Marxan model serves as an ideal decision support tool; however, the practical implementation of specific plans must be tailored to the local context to optimize the approach and ensure its feasibility. While employing human interference factor to represent conservation costs effectively highlights differences between planning units, it does not directly address conservation management costs. To better align with the realities of China's land system, future plans should incorporate land ownership as an additional indicator for assessing conservation costs (Zhou N, et al., 1998). 5. Conclusion In this study, we utilized the optimized MaxEnt model to predict the species richness of rare Michelia species as well as analyzed the relationship between species richness and ecological factors. Based on this analysis, we combined ecological with socioeconomic factors using the Marxan model to identify priority conservation zones for rare Michelia species. With an irreplaceability value range of 80 out of 100, we identified the conserve gaps in priority conservation zones across different ecological conservation zones based on this analysis. The species richness pattern of the rare Michelia species is primarily concentrated in southern China, with the highest richness observed in southern Yunnan. Under varying future climate conditions, the area of high richness is projected to range from 0.62×10 4 km 2 to 2.27×10 4 km 2 . In the future, under various climate scenarios, most potential suitability zones for rare Michelia species are projected to shift westward to higher latitudes and altitudes, with the relocation distance potentially reaching 150 km as climate scenarios intensify. Priority conservation zones for rare Michelia species are primarily located in southern China, covering an area of approximately 8.27×10 4 km 2 . Priority conservation zones within nature reserves and parks cover an area of 1.67×10 4 km 2 , yet 79.8% of these priority conservation zones remain unconserved by nature reserves and parks. This study provides a theoretical basis for the conservation and biogeography of rare Michelia species, offering new insights into the formation of their distribution patterns and evolutionary trends. Declarations Funding This work was supported by the national natural science foundation of China (31700467), Major scientific and technological projects of Yunnan Province(202202AD080010), provincial fund for basic research in Yunnan Province (202401AT070294), young talent trogram of Yunnan Province “Xing Dian Ying Talent Support Program”(XDYC-QNRC-2022-0251). CRediT authorship contribution statement Jimin Tang: Writing – review & editing, Writing – original draft,Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Zhi Chen: Writing –review & editing, Writing – original draft, Visualization, Methodology, Formal analysis. Xiaojie Yin: Writing – review & editing, Writing –original draft, Visualization, Validation, Supervision, Software, Re­sources, Project administration, Methodology, Investigation, Fundingacquisition, Formal analysis, Data curation, Conceptualization. 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factor\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5583021/v1/a6b3bd234f94bdda6598a521.png"},{"id":72076267,"identity":"679b5fa2-ee8f-482f-9574-8fc45dd071ce","added_by":"auto","created_at":"2024-12-21 15:50:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":567178,"visible":true,"origin":"","legend":"\u003cp\u003eCombined human disturbance factors\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5583021/v1/3430566474eee7491a4118d8.png"},{"id":72076244,"identity":"b21db798-8f57-4b58-9598-fea0abaa0f07","added_by":"auto","created_at":"2024-12-21 15:50:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":58655,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of cost and boundary length for different BLM Values\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-5583021/v1/36c2b60a6e62d59a3e7cf848.png"},{"id":72076281,"identity":"2f0b393d-fc63-42b7-a335-bd45bafcb50e","added_by":"auto","created_at":"2024-12-21 15:51:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":684696,"visible":true,"origin":"","legend":"\u003cp\u003ePriority conservation zones within nature reserves\u003c/p\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-5583021/v1/1c201526160591dfc69459b2.png"},{"id":72076260,"identity":"19545614-1224-4a09-b9c3-d7b733fa2ce3","added_by":"auto","created_at":"2024-12-21 15:50:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":649493,"visible":true,"origin":"","legend":"\u003cp\u003ePriority conservation zones within parks\u003c/p\u003e","description":"","filename":"fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-5583021/v1/4f42b7458a064e41eb7bb5d7.png"},{"id":87757193,"identity":"81874298-be9c-4d68-b4d9-c3ce6919eb99","added_by":"auto","created_at":"2025-07-28 16:10:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8315351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5583021/v1/ec0afcfe-ff86-4d52-982a-9b41d11eeb71.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Species richness prediction and priority conservation planning for rare Michelia species in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBiodiversity, a fundamental aspect of nature, serves as the core and foundation for all life on Earth (P\u0026ouml;rtner H O et al., 2023). The global average land surface air temperature has shown a sustained warming trend over the past 100 years, with a more pronounced increase in the last 50 years (Naidoo S et al., 2022). Climate change affects the functioning and stability of ecosystems, and extreme climate events may reshape ecological structure, ecosystem diversity, and ecosystem functions (Hong P et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rare \u003cem\u003eMichelia\u003c/em\u003e species is a relatively primitive genus within the \u003cem\u003eMagnoliaceae\u003c/em\u003e family and is endemic to southeastern Asia. Comprising 41 species, it is most prevalent in China, where it accounts for over 80% of all \u003cem\u003eMichelia\u003c/em\u003e species worldwide. In the realm of science, the rare \u003cem\u003eMichelia\u003c/em\u003e species is crucial for understanding the origins and evolution of the \u003cem\u003eMagnoliaceae\u003c/em\u003e family, as well as for elucidating the natural hierarchy within this family (Liang C B et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) Due to the interaction of several factors, including large distances for gene exchange, ecological damage, and global climate change, the spatial distribution of rare and narrowly distributed \u003cem\u003eMichelia\u003c/em\u003e species has become increasingly complex, with most of these species being classified as having extremely small populations (Tang C Q et al., 2011). Thus, predicting trends in species richness and conserving rare \u003cem\u003eMichelia\u003c/em\u003e species are critical for preserving biodiversity.\u003c/p\u003e \u003cp\u003eEcological niche modeling is a relatively recent field within ecological niche theory. It has been extensively used for assessing the risk of invasive species, conserving rare species, evaluating the effects of climate change on ecosystems, and addressing other related issues ( Sandhya Kiran G et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A variety of ecological niche models have been developed, including the BIOCLIM model, the GARP model, the ENFA model, the RF model, and the MaxEnt model (Aldiansyah S et al,2024). Compared with the MaxEnt model, other ecological niche models, despite their theoretically high prediction accuracy, have limited practical application due to the difficulty of obtaining data on non-existent geographic distribution points. As a result, the prediction outcomes may not accurately reflect real world conditions. In contrast, the MaxEnt model demonstrates superior reliability and practicality in these contexts ( Wang P et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The MaxEnt model does not require data on non-existent geographic distribution points and can still produce reliable results with fewer distribution points. However, most research relies on the default parameters of the MaxEnt model for simulation predictions. Early studies by researchers and developers, using data from 266 animal and plant species across widely separated geographical locations, established the default parameter configurations for the model (Li X et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In recent years, researchers have explored model optimization to enhance prediction accuracy, and the optimized MaxEnt model has been utilized to predict potential suitability zones for species such as \u003cem\u003ePterocarpus marsupium\u003c/em\u003e (Ghosh B G et al., 2021), \u003cem\u003eAcacia caven\u003c/em\u003e (Velasco N et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and \u003cem\u003eGinkgo biloba\u003c/em\u003e (Zhang X et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs nature conservation efforts advance, identifying priority biodiversity conservation zones has become a standard practice for preserving biodiversity and serves as a critical foundation for establishing nature reserves and developing conservation strategies amidst limited resources (Miu I V et al., 2020). Common methods for identifying priority conservation zones for species include scientific assessments, the Marxan model, the Zonation model and the C-Plan model(Ma L et al., 2024). Jan\u0026szlig;en utilized a variety of conservation planning models and drew on the research and expertise of previous scholars, ultimately evaluating the Marxan model as having the greatest relative reliability and results (Jan\u0026szlig;en H et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Bernasconi's study on biodiversity conservation using the Marxan model found that this model outperforms traditional anthropogenic delineation planning in terms of cost effectiveness and efficiency, and does so more rapidly (Bernasconi P et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Originally developed for marine conservation planning, the Marxan model has been optimized and refined by its developers and is now widely used for terrestrial conservation planning. Based on theoretical principles of biodiversity, the Marxan model defines the scope of nature reserves in a scientific and rational manner. It utilizes the core principle of the simulated annealing algorithm to iteratively optimize reserve boundaries by randomly selecting subsets of planning zones, thereby minimizing costs and achieving the biological conservation goals set by decision-makers ( Silvestro D et al., 2011).\u003c/p\u003e \u003cp\u003eIn this study, data on natural ecological factors and geographic distribution points of rare \u003cem\u003eMichelia\u003c/em\u003e species were used, along with the MaxEnt model optimized with R language software, to simulate the spatial distribution of species richness of rare \u003cem\u003eMichelia\u003c/em\u003e in China under various climatic conditions. Based on this, the human interference factor was incorporated as a cost parameter in the Marxan model to identify priority conservation zones with irreplaceability values ranging from 80 to 100 for rare \u003cem\u003eMichelia\u003c/em\u003e species in China. The model results were then overlaid with data from nature reserves and parks to identify zones with conservation gaps.\u003c/p\u003e"},{"header":"2. Method and materials","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Species identification and distribution point data collection\u003c/h2\u003e\n \u003cp\u003eA total of 28 rare \u003cem\u003eMichelia\u003c/em\u003e species were identified in China based on reputable sources, including the China Biodiversity Red List (Version 2013), the Threatened Species List of China\u0026apos;s Higher Plants (Version 2017), and the National Key Protected Wild Plants (Version 2021). The primary sources of information regarding the geographical distribution of rare \u003cem\u003eMichelia\u003c/em\u003e species in China include the following: Chinese Field Herbarium (CFH, https://www.cfh.ac.cn/), Plant Photo Bank of China (PPBC, http://www.ppbc.iplant.cn/), National Plant Specimen Resource Center (NPSRC, https://www.cvh.ac.cn/), Global Biodiversity Information Facility (GBIF, https://www.gbif.org/), National Specimen Information Infrastructure (NSII, https://www.nsii.org.cn/), A literature search was conducted on journal websites using the Latin and Chinese names of rare \u003cem\u003eMichelia\u003c/em\u003e species to gather information on their geographic distribution as reported in the literature. For distribution points with names but lacking coordinate information, we obtained and recorded the corresponding latitude and longitude using the Baidu Coordinate Pickup System (https://api.map.baidu.com/) to complete their geographic distribution data (Li H et al., 2024). In addition, latitude and longitude information for the sampling points was extracted from digitized atlases of species distribution in Chinese and english literature, yearbooks, and news sources using ArcGIS software (Wang C et al., 2021). To ensure the accuracy of MaxEnt model predictions, we integrated the ecological characteristics of each species and addressed anomalies, errors, and duplicates. Ultimately, a total of 2,088 distribution data points for rare \u003cem\u003eMichelia\u003c/em\u003e species in China were collected and compiled (Fig. 1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Factors collection and screening\u003c/h2\u003e\n \u003cp\u003eIn this study, 38 natural ecological factors, including ecoclimatic, soil, topography, and distance from water systems, were considered in the construction of the MaxEnt model. Among these, Ecoclimatic factors were sourced from the World Climate Database (https://worldclim.org/) with a spatial resolution of 1 km. Soil factors were obtained from the National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn/) with a spatial resolution of 1 km. Topographic factors were provided by a digital elevation model from Geospatial Data Cloud (https://www.gscloud.cn/), and elevation, slope, and slope direction data were extracted using ArcGIS software with a spatial resolution of 90m. Elements such as rivers, lakes, reservoirs, and other water bodies were downloaded from the Geo-database (https://www.openstreetmap.org/), and the ArcGIS spatial analysis tool was then used to generate raster data representing the distribution of distances from water systems through Euclidean distance calculation. To ensure the smooth operation of the model, all the aforementioned natural ecological factors were standardized into ASCII raster files with a 1 km resolution using ArcGIS software and subsequently imported into the MaxEnt model (Zhang X et al., 2023).\u003c/p\u003e\n \u003cp\u003eIn China, land is state-owned, and private land sales are prohibited, which complicates the acquisition of land price data (Zhang J et al., 2020). In the Marxan model run, planning unit costs for each sub-basin were considered. In this study, the human tnterference factor (HIF) raster data, which integrates land use, night lights, gross domestic product, population density, road density, and human activity intensity, was used as a proxy for the cost parameter. Among these, land use data were downloaded from Globeland (http://www.globallandcover.com/) with a spatial resolution of 1 km, and the Con and Setnull functions of the raster calculator were used to extract land use types affected by human intervention, including paddy fields, drylands, urban zones, rural settlements, and other construction lands. Night lights data and gross domestic product data were downloaded from the National Earth System Science Data Center (https://www.geodata.cn/) with a spatial resolution of 1 km. Population density data were downloaded from the Center for Socioeconomic Data and Applications (https://sedac.ciesin.columbia.edu/) with a spatial resolution of 1 km. Road Density data were downloaded from the GRIP Global Road Database (https://www.globio.info/) with a spatial resolution of 8 km. Human activity intensity data, developed by the Urban Environmental Monitoring and Modeling Team of the School of Land Science and Technology, China Agricultural University (https://www.x-mol.com/groups/li_xuecao/news/48145/), with a spatial resolution of 1km.\u003c/p\u003e\n \u003cp\u003eSince the Marxan model can only use one cost layer in the analysis, we used Matlab software to determine the weights of factors. Specifically, we employed the Analytic Hierarchy Process (AHP) to establish subjective weights and the Entropy Weight Method (EWM) to derive objective weights. Subsequently, we calculated the combined factor weights using the Oriented Distance Function (ODF) to integrate the human interference factor raster data (HIF) (Zhang Y et al., 2024).\u003c/p\u003e\n \u003cp\u003eData on China\u0026apos;s nature reserves and parks were obtained from the China Nature Reserve Specimen Resource Platform (http://www.papc.cn). Due to the lack of data on nature reserves and parks in Taiwan Province, additional information was sourced from the World Database on Protected Areas (http://www.unep-wcmc.org/wdpa/). Given that the conservation levels of nature reserves and parks in Taiwan Province are not specified, all nature reserves and parks data from Taiwan Province, classified as national conservation level, were integrated with the data for mainland China to analyze conserve gaps.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 MaxEnt model building and simulation\u003c/h2\u003e\n \u003cp\u003eWhen performing MaxEnt model simulations, high correlations between natural ecological factors can lead to overfitting. Therefore, it is necessary to screen the factors (Ghosh B G et al., 2021; Velasco N et al., 2023; Zhang X et al.,2023). First, species geographic distribution data and various natural ecological factors were input into MaxEnt model for preliminary simulation, and natural ecological factors with a percent contribution greater than 1% were selected. Second, the extracted values of these natural ecological factors were analyzed for correlation using Matlab software. If the absolute value of the correlation coefficient between similar natural ecological factors exceeded 0.8, only the factor with the higher contribution rate was retained. Ultimately, 11 natural ecological factors were identified for MaxEnt modeling (Fig.\u0026nbsp;2) (Lu S et al., 2021).\u003c/p\u003e\n \u003cp\u003eThe MaxEnt model operates on the principle of identifying the potential geographic distribution of species by maximizing entropy, subject to constraints imposed by the species\u0026apos; known geographic distribution and relevant ecological factors (Sandhya Kiran G et al., 2024). To avoid unreliable predictions, the MaxEnt model was not run with default parameters during simulation due to its susceptibility to sampling bias and overfitting issues (Quiroga M P et al., 2022). In this study, the MaxEnt model was optimized using the kuenm package for R language software. The study balanced the marginal range of MaxEnt modeling data error by setting the Regulation Multiplier (RM) from 0.5 to 4, with increments of 0.5, resulting in a total of 8 RM values. Additionally, the MaxEnt model provides five types of Feature Combinations (FC): Linear features (L), Quadratic features (Q), Hinge features (H), Product features (P), and Threshold features (T), resulting in a total of 31 possible FC parameter combinations. The MaxEnt model is run with various combinations of feature types to control model complexity and enhance prediction performance (Velasco N et al., 2023; Quiroga M P et al., 2022). The akaike information criterion (\u0026Delta;AICc) was used to assess model complexity and goodness of fit, with the best model being identified as the one with the smallest \u0026Delta;AICc value (\u0026Delta;AICc\u0026thinsp;=\u0026thinsp;0). The next step involved evaluating the extent of model overfitting to species distribution points by analyzing models with a 10% training omission rate (OR10%) of less than 5% (Zhang X et al.,2023). In addition, this study also evaluated the model prediction performance based on the AUC ratio (AUC ratio), and when the AUC ratio is greater than 1, it indicates that the model prediction is better relative to the stochastic model prediction (Peterson A T et al., 2008).\u003c/p\u003e\n \u003cp\u003eThe geographic distribution data for rare \u003cem\u003eMichelia\u003c/em\u003e species, along with relevant natural ecological factors, were incorporated into the optimized MaxEnt model. In this process, 75% of the geographic distribution data were used for training and accuracy assessment, while 25% were reserved for testing. This procedure was repeated over 10 simulations (Ghosh B G et al., 2021; Velasco N et al., 2023; Zhang X et al.,2023). The performance of the MaxEnt model is evaluated using the Area Under the Curve (AUC), which assesses both model fitting and prediction accuracy. The AUC value ranges from 0.5 to 1, with values closer to 1 indicating higher prediction accuracy (Zhang X et al.,2023; Quiroga M P et al., 2022).\u003c/p\u003e\n \u003cp\u003eAccording to the Maximum training sensitivity plus specificity threshold (MTSS), the probability of species presence (P) is defined as a no suitability zones when it is less than the threshold (MTSS) (Peterson A T et al., 2008). On this basis, with reference to the IPCC criteria for evaluating the probability of species existence, potential suitability zones were categorized into three levels: P\u0026thinsp;\u0026lt;\u0026thinsp;MTSS for no suitability zones, 0.66\u0026thinsp;\u0026gt;\u0026thinsp;P\u0026thinsp;\u0026ge;\u0026thinsp;MTSS for low suitability zones, and P\u0026thinsp;\u0026ge;\u0026thinsp;0.66 for high suitability zones (Lu S et al., 2021). Subsequently, the SDMtoolbox function in ArcGIS software was used to calculate the migration distance and direction from the center of the suitability zone for each rare \u003cem\u003eMichelia\u003c/em\u003e species under future climate scenarios, and the potential suitability zones for 28 rare \u003cem\u003eMichelia\u003c/em\u003e species were overlaid to estimate species richness patterns ( Tang C Q et al., 2018). In order to facilitate the visualization analysis, species richness (S) of of rare \u003cem\u003eMichelia\u003c/em\u003e species was classified into five levels: S\u0026thinsp;=\u0026thinsp;0 for no richness, 0\u0026thinsp;\u0026lt;\u0026thinsp;S\u0026thinsp;\u0026le;\u0026thinsp;5 for low richness, 5\u0026thinsp;\u0026lt;\u0026thinsp;S \u0026le;\u0026thinsp;10 for medium richness, 10\u0026thinsp;\u0026le;\u0026thinsp;S\u0026thinsp;\u0026lt;\u0026thinsp;15 for higher richness, and 15\u0026thinsp;\u0026le;\u0026thinsp;S for high richness (Lu S et al., 2021).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Marxan model building and simulation\u003c/h2\u003e\n \u003cp\u003eThe Marxan model is an advanced conservation planning tool that employs Simulated Annealing (SA) to identify optimal configurations of planning units. Through iterative computations and repeated evaluations, it utilizes sophisticated mathematical algorithms and computational technologies to achieve optimal conservation outcomes at minimal cost. This model is particularly effective in addressing real-world planning challenges, such as the establishment of nature reserves (Fajardo J et al., 2014). The equation is as follows:\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"https://myfiles.space/user_files/132203_cef980177e9a226b/132203_custom_files/img1734795888.png\" width=\"539\" height=\"47\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Eq.\u0026nbsp;2\u0026ndash;2, Pus refers to the sub-basin planning units. Cost denotes the expenditure associated with these planning units. Boundary length defined as the sum of the selected planning cell boundary lengths adjusted by the Boundary Length Modifier (BLM), implies that a smaller total boundary length corresponds to a more compact design. Penalty represents a value assigned when the species conservation goals are not met and is calibrated using the Species Protection Factor (SPF).\u003c/p\u003e\n \u003cp\u003eDigital elevation model data was segmented into 92,736 sub-basin planning units using the hydrological analysis tool in ArcGIS software. Conservation objectives for rare \u003cem\u003eMichelia\u003c/em\u003e species were established based on the species\u0026apos; IUCN status, endemism in China, and the distribution of its high suitability zones (Fajardo J et al.,2014). The selected conservation targets were based on the distribution of high suitability zones for rare \u003cem\u003eMichelia\u003c/em\u003e species, as conserved by the MaxEnt model (Wang Y et al., 2023). The conservation cost of each planning unit was assigned based on the human interference factor, which was calculated using the AHP-EWM assignment method. The sensitivity analysis of BLM values was conducted using Zonae Cogito software, with the initial BLM value set at 0.0241. This value was calculated as the ratio of the maximum cost value of the planning cell domain to the longest boundary lengths. Based on this, the remaining values were incremented according to a function of 2\u003csup\u003en\u003c/sup\u003e, and the optimal BLM value was determined iteratively. Additionally, the SPF Calibration function in the QGIS software toolbox was utilized, selecting the \u0026apos;As group\u0026apos; mode with a gradient value of 0.1 to identify the optimal SPF value. This approach aimed to balance the conservation of rare \u003cem\u003eMichelia\u003c/em\u003e species with considerations of boundary compactness and cost-effectiveness (Nguyen N T H et al., 2022).\u003c/p\u003e\n \u003cp\u003eDuring the execution of the Marxan model, the number of iterations was set to 100. Each planning unit was assigned a value within the ranging from 80 to 100, which represents the number of times it was selected in the optimal planning scheme. A higher value indicates greater irreplaceability, reflecting a higher frequency of selection across the 100 iterations (Zhang L et al., 2020). In this study, planning units with an irreplaceability value range of 80 to 100 were designated as priority conservation zones. This designation was subsequently combined with data from Chinese nature reserves and parks for an overlay analysis to identify gaps in conservation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 MaxEnt model parameter optimization results\u003c/h2\u003e \u003cp\u003eAs detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, after optimizing the modulation multiplicity (RM) and feature combination (FC) of the MaxEnt model using the Kuenm package in R, the average value of the akaike information criterion (ΔAICc) was 0. The 10% omission rate (10% OR) consistently remained below 5%, and the AUC ratios (AUCratio) were all greater than 1.7, meeting the evaluation criteria for an optimal model. When using the optimized MaxEnt model to simulate suitability zones for rare \u003cem\u003eMichelia\u003c/em\u003e species, the mean AUC for each species exceeded 0.9, with standard errors ranging from 0.007 to 0.0129. \u003cem\u003eMichelia guangxiensis\u003c/em\u003e had the smallest mean AUC at 0.9497, while \u003cem\u003eMichelia pubinervia\u003c/em\u003e had the largest mean AUC at 0.9986. These results indicate that the optimized MaxEnt model significantly outperforms the default parameter model in predictive accuracy and transferability, providing a more precise prediction of the distribution of rare \u003cem\u003eMichelia\u003c/em\u003e species under both baseline (1970\u0026ndash;2000) and future (2061\u0026ndash;2080) climate scenarios.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimization parameters and evaluation values of MaxEnt model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔAICc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR10%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUCratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAUC\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMTSS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia guangdongensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9849\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia velutina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9840\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia angustioblonga\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9953\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia cavaleriei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9620\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia kisopa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9873\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia guangxiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9497\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia opipara\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9940\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia shiluensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9775\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMichelia iteophylla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9883\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia gioi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9809\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia elegans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9857\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia lacei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9939\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia crassipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9751\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia fulva\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9933\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia baillonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9850\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia xanthantha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9863\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia sphaerantha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9825\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia champaca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9810\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia pubinervia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9986\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia odora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9707\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia wilsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9679\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia szechuanica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9735\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia coriacea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9863\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia flaviflora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9787\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia fujianensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9919\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia masticata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9881\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia martinii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9696\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMichelia chapensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLPTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.9517\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Species richness fitting and Centroid migration in suitability zones\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, during the baseline period (1970\u0026ndash;2000), the species richness of rare \u003cem\u003eMichelia\u003c/em\u003e species was broadly distributed, encompassing approximately 25.35% of the land area of China. In northern China, only eastern Shandong Province, northern Gansu Province, and southern Shanxi Province exhibit limited distribution, while the majority of the remaining zones are predominantly located south of the Qinling Mountains-Huaihe River, a region characterized by a typical monsoon climate. Low richness is primarily distributed in southern-central Tibet, eastern-central Sichuan, southern Gansu, southern Shanxi, eastern-central Hubei, northern Hunan, northern Jiangxi, southern Anhui, eastern Shandong, southern-central Jiangsu, Shanghai, and northern Zhejiang Provinces, covering an area of approximately 110.65\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e, which accounts for about 11.53% of the land area of China. the species richness of rare \u003cem\u003eMichelia\u003c/em\u003e species is primarily distributed in southeastern Tibet, northern Yunnan, southern-central Sichuan Province, Chongqing Province, southern-central Guizhou, western Hubei Province, western and southern Hunan Province, Jiangxi Province, southern Zhejiang Province, Fujian Province, Taiwan Province, Guangdong Province, Guangxi Province, and Hainan Province, covering an area of approximately 105.13\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e, which accounts for about 10.96% of the land area of China. Higher richness is primarily distributed in southern-central Yunnan Province, central Guangxi Province, central Guangdong Province, central Fujian Province, central Taiwan Province, and southern-central Hainan Province, covering an area of approximately 26.66\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e, which accounts for about 2.78% of the land area of China. High richness is primarily distributed in southern-central Yunnan Province, covering an area of approximately 0.82\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e, which accounts for about 0.08% of the land area of China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the future (2061\u0026ndash;2080), under the two climate scenarios, low richness is projected to spread to the central Tibet Autonomous Region, northwestern Sichuan Province, southern Gansu Province, central Shanxi Province, Hubei Province, Henan Province, southwestern Shanxi Province, and Shandong Province, with richness expected to be maintained at 11.47\u0026ndash;16.84% of the land area of China. Medium richness is expected to spread to zones such as eastern Sichuan Province, northern Guizhou Province, northern Hunan Province, northern Jiangxi Province, and southern Anhui Province, with richness expanding to 1.17\u0026ndash;2.73% of the land area of China. Higher richness is projected to gradually disappear in the northern Guangxi Zhuang Autonomous Region, Fujian Province, and Taiwan Province. High richness will be maintained in southern Yunnan Province; however, it is anticipated to decrease by 0.02% under the SSP585 climate scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that under future climate scenarios, the potential suitability distribution zones for most rare \u003cem\u003eMichelia\u003c/em\u003e species will migrate northwest and southwest within China, while a few will shift to the northeast and southeast. Furthermore, with the increase in CO\u003csub\u003e2\u003c/sub\u003e emission concentrations, the potential suitability zones are projected to migrate even farther. Among them, the migration distances of species such as \u003cem\u003eM. gioi\u003c/em\u003e, \u003cem\u003eM. guangdongensis\u003c/em\u003e, \u003cem\u003eM. velutina\u003c/em\u003e, \u003cem\u003eM. guangxiensis\u003c/em\u003e, \u003cem\u003eM. iteophylla\u003c/em\u003e, \u003cem\u003eM. martinii\u003c/em\u003e, \u003cem\u003eM. odora\u003c/em\u003e, \u003cem\u003eM. fujianensis\u003c/em\u003e, \u003cem\u003eM. wilsonii\u003c/em\u003e, \u003cem\u003eM. coriacea\u003c/em\u003e, \u003cem\u003eM. masticata\u003c/em\u003e, and \u003cem\u003eM. szechuanica\u003c/em\u003e are projected to exceed 150 km under the SSP585 climate scenario.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Human Interference Factor calculation\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) were used, respectively, to calculate the weights of the various socioeconomic factors. An optimization decision matrix is then constructed using the Oriented Distance Function (ODF) to calculate the combined weights for each factor. Subsequently, a raster calculator for superposition analysis is employed to obtain the human interference factor raster, taking into account various socioeconomic factors based on their combined weights.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the intensity of anthropogenic interference trends gradually increases from west to east. Zones with higher levels of anthropogenic interference are primarily located east of the Heihe-Tengchong line in China, with significant interference activities concentrated around major urban agglomerations, such as the Beijing-Tianjin-Hebei, Yangtze River Delta, Central Plains, and Chengdu-Chongqing urban agglomerations. Overall, the zoning of anthropogenic disturbance intensity in China is more consistent with the anthropogenic disturbance factor data generated from each economic factor. The conservation cost, determined by allocating costs to each sub-basin planning unit, was derived from the data on anthropogenic disturbance factors. This conservation cost was then used as the input file for the Marxan modeling procedure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Marxan model parameter optimization results\u003c/h2\u003e \u003cp\u003eTo conduct a sensitivity analysis, BLM values were varied within a range of 0 to 100 using a specific formula, and the planning results exhibited substantial variation across the 14 distinct BLM values employed. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, BLM values ranging from 0 to 0.064 show a sharp decrease in boundary length as conservation costs increase. However, as conservation costs continue to rise, the rate of change in boundary length progressively diminishes. A distinct inflection point is observed at a BLM value of 0.064, where both the conservation cost and boundary length are relatively low. This BLM value is considered relatively optimal for the study. The optimized BLM values were used as a reference, and subsequently, the SPF values were adjusted based on the sensitivity analysis. These adjusted SPF values predominantly fell within the range of 0.1 to 0.3, all of which are lower than the default SPF values used in the Marxan model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Analysis of Priority conservation zones\u003c/h2\u003e \u003cp\u003eAfter conducting 100 iterative runs of the Marxan model, planning units with irreplaceability values ranging from 80 to 100 were identified, totaling 539 units. This resulted in a prioritized conservation zones of 8.27\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e, primarily concentrated in the southeastern Tibet Autonomous Region, south-central Yunnan Province, central Sichuan Province, western Chongqing Province, southern Guizhou Province, northern Guangxi Zhuang Autonomous Region, and southern Hunan Province. Additional zones include northern Guangdong Province, eastern and southern Jiangxi Province, northwestern Fujian Province, southern Zhejiang Province, central Taiwan Province, and southwestern Hainan Province. This conserved zone constitutes only 0.86% of of the land area of China.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the Priority conservation zones for rare \u003cem\u003eMichelia\u003c/em\u003e species covers an area of approximately 0.56\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e within the nature reserves. Among them, the zones conserved by national nature reserves primarily include the Yarlung Tsangpo Grand Canyon Nature Reserve in the Tibet Autonomous Region, the Xishuangbanna National Nature Reserve in Yunnan Province, the Maolan National Nature Reserve in Guizhou Province, the Wuyi Mountain National Nature Reserve in Jiangxi Province, the Longqishan National Nature Reserve in Fujian Province, the Matou Mountain National Nature Reserve in Jiangxi Province, and the Shaoguan Danxia Mountain National Nature Reserve in Guangdong Province, among others. Those zones primarily conserved by provincial and municipal nature reserves include the Guilin Ocean Mountain Autonomous Nature Reserve in the Guangxi Autonomous Region, the Qiandongnan Moon Mountain Nature Reserve in Guizhou Province, the Hezhou Xiling Mountain Nature Reserve in Guangxi Autonomous Region, the Jiangjin Sifang Mountain Nature Reserve in Chongqing Municipality, the Tongbiguan Nature Reserve in Yunnan Province, and the Qiandongnan Libo Jialiang Sanlian Cave Nature Reserve in Guizhou Province, among others.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, Priority Conservation zones for rare \u003cem\u003eMichelia\u003c/em\u003e species covers an area of approximately 1.11\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e within the parks. Among them, the zones conserved by national parks primarily include the Tropical Rainforest National Park in Hainan Province, the Danda Wildlife Important Habitat in Taiwan Province, the Wuyi Mountain National Park in Taiwan Province, the Yushan National Park in Taiwan Province, the Dongjiang Lake National Wetland Park in Hunan Province, and the Giant Panda National Park in Sichuan Province. The largest zones conserved by provincial and municipal parks primarily include the Heyuan Wanlvhu Forest Nature Park in Guangdong Province, the Mengla Yiwu Forest Nature Park in Yunnan Province, the Shaoguan Nanxiong Zhugui Meiguan Forest Nature Park in Guangdong Province, the Qingyuan Tianhu Forest Nature Park in Guangdong Province, the Shaoguan Renhua Forest Nature Park in Guangdong Province, and the Ningde Mindong Grand Canyon Forest Nature Park in Fujian Province.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Impact of climate change on the geographical distribution of species\u003c/h2\u003e \u003cp\u003eUnderstanding the spatial distribution of plant diversity and the mechanisms influencing it has been a central focus for ecologists and biogeographers for an extended period (Maestre F T et al., 2021). Species distributions are shaped by various factors and result from long-term interactions between species and their environment. Global climate change alters temperature and precipitation patterns, prompting species to migrate to new climatic conditions that are better suited for their survival and reproduction (A. Lee-Yaw J et al., 2022). In this study, an optimized MaxEnt model was employed to predict the potential suitability zones for 28 rare \u003cem\u003eMichelia\u003c/em\u003e species under both baseline and future scenarios. The results indicated that the species richness of rare \u003cem\u003eMichelia\u003c/em\u003e is predominantly concentrated in subtropical and tropical zones, with only a few species found in highland climates. Additionally, as latitude increases, the species richness of rare \u003cem\u003eMichelia\u003c/em\u003e gradually declines. This is consistent with Zhou's study on the species diversity pattern of typical evergreen broadleaf forests in China, which found that species richness decreases gradually with increasing latitude (Zhou R et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the future, as climate scenarios worsen, the high richness in southern Yunnan Province is projected to contract, with potential suitability zones for most rare \u003cem\u003eMichelia\u003c/em\u003e species shifting to northern high-latitude and high-elevation zones. In more severe climate scenarios, the centroid migration of potential suitability zones for most species has significantly exceeded that of the SSP126 scenario, with migration distances surpassing 150 km. This finding aligns with previous research, which suggests that as global temperatures rise and external pressures increase, the original habitats of many species will fall below their survival thresholds (Pepin N C et al.,2022). Consequently, most species are expected to migrate to higher-altitude mountains or higher-latitude zones in search of more potential suitability zones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Analysis of Priority conservation zones\u003c/h2\u003e \u003cp\u003eThe Marxan model is a widely used conservation planning software that excels in prioritizing conservation zones (Fajardo J et al.,2014; Wang Y et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nguyen N T H et al., 2022; Zhang L et al., 2022). Planning units with high irreplaceability values represent the best habitats for conserved species and can be defined as prioritized biodiversity conservation zones characterized by the lowest conservation costs, minimal human disturbance, and relatively high spatial concentration (Silvestro D et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The model also considers more socioeconomic factors than the MaxEnt model, as it identifies priority zones to mitigate human-ecological conflicts and minimize human impacts on species (Daigle R M,et al., 2020). In contrast, the MaxEnt model places greater emphasis on simulating species distribution suitability based on natural ecological factors (e.g., climate, soil, and topography). Building on this, the Marxan model proposes a method for identifying optimal habitats for species conservation, aiming to minimize management costs (He P et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, we established the distribution of suitability habitats for rare \u003cem\u003eMichelia\u003c/em\u003e species in China and used a conservation planning model to identify priority conservation zones with high irreplaceability values. We analyzed these zones in conjunction with vector data from nature reserves and parks in China, revealing significant conservation gaps, with approximately 66,000 km\u0026sup2; of priority conservation zones remaining unconserved. Consequently, long-term dynamic monitoring and the establishment of conservation gene banks are necessary to prevent the loss of genetic resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Analysis of research limitations\u003c/h2\u003e \u003cp\u003eIn this study, the MaxEnt model was optimized using the Kuenm package within R software, which constrained the complexity of the model parameters to a certain extent and improved the predictive accuracy of the model. However, there are still shortcomings, the MaxEnt model is based on the core of ecological niche theory, which assumes that a species exists without competing resources. In reality, the distribution of a species is influenced not only by climate, soil, topography, and human interference, but also by factors such as interspecific and extraspecific competition, threats from pests and diseases, and genetic variation (Wang P et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, the sample information that can be obtained is primarily concentrated in zones such as roads and rivers. This concentration may introduce spatial bias in the collected data and result in varying collection strengths, leading to certain errors in the simulation results (Mahatara D et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To more accurately predict potential shifts in the geographic distribution of rare \u003cem\u003eMichelia\u003c/em\u003e species, future efforts will incorporate the aforementioned biological factors, expand surveys of these species, and enhance the collection of data on their distribution points. The Marxan model serves as an ideal decision support tool; however, the practical implementation of specific plans must be tailored to the local context to optimize the approach and ensure its feasibility. While employing human interference factor to represent conservation costs effectively highlights differences between planning units, it does not directly address conservation management costs. To better align with the realities of China's land system, future plans should incorporate land ownership as an additional indicator for assessing conservation costs (Zhou N, et al., 1998).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we utilized the optimized MaxEnt model to predict the species richness of rare \u003cem\u003eMichelia\u003c/em\u003e species as well as analyzed the relationship between species richness and ecological factors. Based on this analysis, we combined ecological with socioeconomic factors using the Marxan model to identify priority conservation zones for rare \u003cem\u003eMichelia\u003c/em\u003e species. With an irreplaceability value range of 80 out of 100, we identified the conserve gaps in priority conservation zones across different ecological conservation zones based on this analysis.\u003c/p\u003e \u003cp\u003eThe species richness pattern of the rare \u003cem\u003eMichelia\u003c/em\u003e species is primarily concentrated in southern China, with the highest richness observed in southern Yunnan. Under varying future climate conditions, the area of high richness is projected to range from 0.62\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e to 2.27\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e. In the future, under various climate scenarios, most potential suitability zones for rare \u003cem\u003eMichelia\u003c/em\u003e species are projected to shift westward to higher latitudes and altitudes, with the relocation distance potentially reaching 150 km as climate scenarios intensify.\u003c/p\u003e \u003cp\u003ePriority conservation zones for rare \u003cem\u003eMichelia\u003c/em\u003e species are primarily located in southern China, covering an area of approximately 8.27\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e. Priority conservation zones within nature reserves and parks cover an area of 1.67\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e, yet 79.8% of these priority conservation zones remain unconserved by nature reserves and parks. This study provides a theoretical basis for the conservation and biogeography of rare \u003cem\u003eMichelia\u003c/em\u003e species, offering new insights into the formation of their distribution patterns and evolutionary trends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the national natural science foundation of China (31700467), Major scientific and technological projects of Yunnan Province(202202AD080010), provincial fund for basic research in Yunnan Province (202401AT070294), young talent trogram of Yunnan Province “Xing Dian Ying Talent Support Program”(XDYC-QNRC-2022-0251).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJimin Tang: Writing\u0026nbsp;–\u0026nbsp;review \u0026amp; editing, Writing\u0026nbsp;–\u0026nbsp;original draft,Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Zhi Chen: Writing\u0026nbsp;–review \u0026amp; editing, Writing\u0026nbsp;–\u0026nbsp;original draft, Visualization, Methodology, Formal analysis. Xiaojie Yin: Writing\u0026nbsp;–\u0026nbsp;review \u0026amp; editing, Writing\u0026nbsp;–original draft, Visualization, Validation, Supervision, Software, Re­sources, Project administration, Methodology, Investigation, Fundingacquisition, Formal analysis, Data curation, Conceptualization. Jiao Teng: Visualization,Methodology, Investigation, Data curation. Weijie Gao : Writing\u0026nbsp;–\u0026nbsp;original draft, Methodology, Inves­tigation, Formal analysis, Data curation. Yifei Liu: Writing\u0026nbsp;–\u0026nbsp;review \u0026amp; editing, Formal analysis, Re­sources. Xiuyu Li: Writing\u0026nbsp;–\u0026nbsp;review \u0026amp; editing, Formal analysis Formal analysis, Data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.If anyone wants to get data from it, contact Jimin Tang.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAldiansyah, S. \u0026amp; Wahid, K. A. Species Distribution Modelling Using Bioclimatic Variables on Critically Endangered Endemic Species (Macrocephalon Maleo) in Sulawesi[J]. \u003cem\u003eASEAN J. Sci. Technol. 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Use Policy\u003c/em\u003e. \u003cb\u003e117\u003c/b\u003e, 106126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landusepol.2022.106126\u003c/span\u003e\u003cspan address=\"10.1016/j.landusepol.2022.106126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Global warming, Rare Michelia species, Species richness, Priority conservation zones","lastPublishedDoi":"10.21203/rs.3.rs-5583021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5583021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNumerous species are at risk of extinction due to habitat degradation caused by human activity and global warming. Using the optimized MaxEnt and Marxan models, we investigated the relationship between species richness and various factors by predicting the species richness of rare \u003cem\u003eMichelia\u003c/em\u003e species based on distribution data and natural ecological factors in China. Additionally, national nature reserves and parks were overlaid with priority conservation zones having irreplaceability values ranging from 80 to 100 to identify conservation gaps. The findings indicate that rare \u003cem\u003eMichelia\u003c/em\u003e species are found in southern Yunnan Province, which exhibits the highest concentration. The high richness zones are expected to shrink to 0.62\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e under future climate scenarios. Northern high latitudes and higher altitudes are expected to offer better habitats for the majority of rare \u003cem\u003eMichelia\u003c/em\u003e species. With the intensification of climate change, it is anticipated that this migration will exceed 150 km. Priority conservation zones for rare \u003cem\u003eMichelia\u003c/em\u003e species are primarily located in the southeastern part of the Tibet Autonomous Region, the south-central part of Yunnan Province, the central part of Sichuan Province, the western part of Chongqing Municipality, the southern part of Guizhou Province, the northern part of Guangxi Zhuang Autonomous Region, the southern part of Hunan Province, the northern part of Guangdong Province, the eastern and southern parts of Jiangxi Province, the northwestern part of Fujian Province, the southern part of Zhejiang Province, the central part of Taiwan Province, and the southwestern part of Hainan Province. These priority conservation zones account for only 0.86% of the land area of China, with 6.6\u0026times;10\u003csup\u003e4\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e of prioritized conservation zones not yet designated as nature reserves or parks. To effectively embody the principle that 'green mountains are golden mountains,' we recommend expanding conservation zones for rare \u003cem\u003eMichelia\u003c/em\u003e species within designated priority zones and enhancing habitat conservation measures.\u003c/p\u003e","manuscriptTitle":"Species richness prediction and priority conservation planning for rare Michelia species in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-21 15:50:52","doi":"10.21203/rs.3.rs-5583021/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-24T06:53:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-13T12:55:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-12T14:31:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128004339028070583590750093838496237703","date":"2025-02-03T08:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323847655510382628305580518800939236407","date":"2025-02-01T03:53:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-05T13:16:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-05T13:13:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-12-19T16:53:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-18T14:27:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-12-05T01:53:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30fe0b08-ac35-46aa-bfbc-28f5a972aecc","owner":[],"postedDate":"December 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41841530,"name":"Earth and environmental sciences/Ecology/Biodiversity"},{"id":41841531,"name":"Earth and environmental sciences/Ecology/Climate change ecology"},{"id":41841532,"name":"Earth and environmental sciences/Ecology/Forestry"}],"tags":[],"updatedAt":"2025-07-28T16:08:04+00:00","versionOfRecord":{"articleIdentity":"rs-5583021","link":"https://doi.org/10.1038/s41598-025-11025-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-23 15:57:39","publishedOnDateReadable":"July 23rd, 2025"},"versionCreatedAt":"2024-12-21 15:50:52","video":"","vorDoi":"10.1038/s41598-025-11025-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-11025-7","workflowStages":[]},"version":"v1","identity":"rs-5583021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5583021","identity":"rs-5583021","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}