Phylogeography  of Allium macrostemon: south-north divergence reveals a natural geographic isolation boundary in the Qinling Mountains-Huaihe River Line in China

Phylogeography of Allium macrostemon: south-north divergence reveals a natural geographic isolation boundary in the Qinling Mountains-Huaihe River Line in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Phylogeography of Allium macrostemon: south-north divergence reveals a natural geographic isolation boundary in the Qinling Mountains-Huaihe River Line in China chunxue Jiang, tian Shi, zhongmei Mo, cai Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3933291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There are many physical and geographic boundaries in China, but there are few studies on the natural geographical isolation boundary of the Qinling Mountains-Huaihe River Line (QHL) using molecular ecological evidence. The purpose of this study was to explore the genetic diversity, genetic structure, and possible origins of Allium macrostemon and to verify whether the QHL played a role in the structure of A. macrostemon populations. Results Analysis of chloroplast DNA and nuclear ITS molecular markers showed a very high level of genetic differentiation among populations ( F ST > 0.25). ombined with chloroplast DNA and nuclear ITS data, A. macrostemon populations could be grouped into northern and southern flora, with the southern flora further divided into southwestern and central-southeastern flora. The results of niche simulation show that the distribution area of A. macrostemon will reach the maximum in the future. Conclusion The data points to a geographic barrier that has been maintaining the regional separation of A. macrostemon . The QHL, which has been found to be a north-south dividing line in phylogeography and population genetic structure and promotes physical geographic isolation, has played an important role in this process. This study can provide a scientific theoretical basis for the conservation, development, and utilization of A. macrostemon resources. Further, it can provide a reference for the systematic geographic pattern of large-scale spatial distribution of plants in China and enrich our understanding of the evolutionary history of plant species diversity in East Asia. A. macrostemon Genetic diversity Genetic variation Population structure Qinling Mountains-Huaihe River Line Glacial refugia Figures Figure 1 Figure 2 Figure 3 Introduction Geographic isolation due to uplift of mountain ranges and climate fluctuations associated with glacier oscillations can lead to dramatic changes in the morphologies and geographic distributions of many species [ 1 ]. Increasingly, biological populations have been found to coincide with known geographic boundaries, such as mountains, river systems, or straits, that act as physical barriers to past or contemporary dispersal, facilitating intraspecific systematic geographic subdivision and divergence. China has a number of geographic distribution zones, separated by biogeographic boundaries. From west to east, there are at least seven genealogical discontinuity locations in China which have been supported by molecular ecological data. These include: the Mekong-Salween Divide in the Himalaya-Hengduan Mountains region [ 2 ], the Tanaka-Kaiyong Line in the southwest region[ 3 ], the Sichuan Basin [ 4 ], the region near 105° E [ 5 ], the second and third step dividing line [ 6 ], the North China region [ 7 ], and the East China Sea/Tsushima–Korean straits [8, 9, 10]. However, very little is known regarding the impact of the Qinling Mountains-Huaihe River Line (QHL), a natural geographic isolation boundary, on the phylogeographical patterns and genetic structures of plants. Geological history and climatic oscillations are important drivers of evolution and genetic structure of plant species [11, 12]. Since the start of the Quaternary glacial period, global temperatures have generally decreased, though strong fluctuations between cold and warm climates, repeated interleavings of ice and interglacial periods, and geological events and monsoon fluctuations have greatly affected the geographic distributions, population dynamics, and genetic diversity patterns of species [ 13 , 14 , 15 ]. The strong uplift of the Tibetan Plateau was an important geological event that resulted in changes to the global climate and stimulated the East Asian Monsoon, making the meridional zonation of the Chinese flora increasingly obvious [ 16 , 17 , 18 ]. Thus, alternations of dry and wet climate have deeply influenced the distribution patterns and historical evolution of Chinese plants. Stable climate, mature plant communities across a vast landscape, high spatial heterogeneity, and a long evolutionary history provided the foundation for the formation of the rich and diverse Chinese flora. China is therefore an important region of species diversity in the Northern Hemisphere [ 19 ] and an important center for species conservation, speciation, and evolution [ 20 ]. Molecular phylogeographic studies in China have largely focused on woody plants. For example, Gong [ 21 ] used molecular data to predict the glacial survival of Ginkgo populations within two refugia in southwestern and eastern China, providing the first evidence of the existence of a refuge area in eastern China on the West Tianmu Mountains. Cercidiphyllum populations were genotyped using chloroplast and ribosomal DNA sequences along with microsatellite loci to assess molecular structure and diversity in relation to past (Last Glacial Maximum) and present distributions based on ecological niche modelling [ 22 ]. These results showed that the occurrence of the Asian monsoon and associated tectonic events, as well as Pleistocene climate changes, had a pervasive influence on the phylogeographic structure of plants in subtropical China, and they also showed how Late Neogene climatic/tectonic changes promoted speciation and lineage diversification in East Asia’s Tertiary relict flora [ 23 , 24 ]. Compared with long-lived woody plants, herbaceous plants undergo far more life cycles within a given time period and are therefore expected to respond more quickly to environmental changes. Thus, herbaceous plants may provide better opportunities to study the drivers of diversification and speciation [ 25 , 26 ]. However, to the best of our knowledge, only a few herbs from this region, such as Dysosma versipellis , Primula ovalifolia , and Oryza stavia , have been studied to date [ 25 , 27 , 28 , 29 ]. Moreover, phylogeographic research is still lacking for plants distributed over large-scale ranges and spanning different climate zones and multiple tropical biodiversity regions, especially in China. The Qinling-Huaihe River Line (QHL), which runs from the Qinling Mountains to the Huaihe River, is located at about 33° north latitude and includes the Qinling and Dabie Orogenic Belt (QDB), the Yangtze River Plain, and the southern mountains of Anhui [ 30 , 31 , 32 , 33 ] and is the natural geographic dividing line between North and South China [ 34 ]. Obvious differences in climatic conditions and geographical features exist between regions north and south of the line. For example, the southern region has a subtropical or tropical climate with more precipitation and higher temperatures than the northern region. The northern region has a temperate climate with four distinct seasons, and precipitation has obvious seasonal characteristics [ 35 ]. Previous research has shown that both northern (i.e., north of the QHL) and southern (i.e., south of the QHL) regions of China have retained glacial refugia for plants, and these sanctuaries are considered to be sites of extended range in the late interglacial or post-glacial period [ 27 , 36 , 37 ]. However, very little is known regarding the impact of the QHL on the phylogeographic patterns and genetic structures of plants. Allium macrostemon is a perennial herb belonging to the family Amaryllidaceae. It is an edible Chinese herb with a variety of health and healing properties [ 38 ]. Current studies on A. macrostemon mainly focus on its chemical composition, pharmacological action, and potential as a source of crude drugs [ 39 , 40 , 41 ]. It is widely distributed in the wild mountains and fields spanning the QHL in eastern and western China. A. macrostemon is a polyploid species with a polyploid series of 2n, 3n, 4n, 5n, 6n, and 7n. Populations with 5n ploidy have been found in Japan, and populations with 3n, 4n, 5n, and 7n ploidy have been found in China, with the remaining ploidies yet to be discovered in nature [ 42 , 43 , 44 ]. Analysis of the systematic taxonomic status of A. macrostemon using the nuclear DNA (nrDNA) ITS, chloroplast DNA (cpDNA), and trn L-F sequences have indicated that the species may represent a new group[ 45 ]. Previous studies in our laboratory have clearly distinguished A. macrostemon from other related Allium plants and have supported the use of A. macrostemon as an independent monophyletic group [ 46 ]. However, there are few reports on the systematic geography of A. macrostemon . As an herbaceous plant, A. macrostemon has a wide range, spanning different climate zones and multiple tropical biodiversity regions in China. It is sensitive to climate change and environmental fluctuations, and its DNA base substitution rate is higher than that of woody plants, which can thus provide more historical information on its evolution. In this study, A . macrostemon from both sides of the QHL and spanning east-west across China were sampled. Simple sequence repeats (SSRs), cpDNA genes ( psb A- trn H, rps 16, and trn L-F), and nrDNA (ITS) fragments were used to analyze the genetic structures and phylogeographic patterns of 50 large stem grass populations. Meanwhile, existing geographic distribution and climate data combined with the niche simulation MaxEnt software were used to predict A. macrostemon distributions in different time periods. In this study, we mainly aimed to address the following questions: (1) Do populations of A. macrostemon exhibit varying degrees of genetic diversity? (2) Is there a QHL pattern in the phylogeographic structure of A. macrostemon ? (3) What were the possible reasons for the genetic differentiation and distribution patterns of A. macrostemon ? These analyses could provide a scientific theoretical basis for the conservation, development, and utilization of A. macrostemon resources as well as an important theoretical basis for further discussion on plant evolution and species diversity in China. They will also further enrich our understanding of the molecular systematic evolution and biogeography of East Asian herbaceous plants. Materials and methods Sampling, experimental design, and DNA extraction Leaf samples of Allium macrostemon were collected during 2015–2020 from 50 natural populations across its entire geographic range in China. At the same time, the latitude, longitude, and altitude at each collection site were recorded using the global positioning system (GPS) (Table S1 ). Leaves for DNA extraction were collected in the field and rapidly dried with silica gel. Field collection follows the ethics and legality of the local government and is permitted by the government. The formal identification of the plant material was undertaken by Professor Zhao Cai, and voucher specimens were deposited at Guizhou University. A total of 288 A. macrostemon individuals from 24 of the 50 natural populations were used for simple sequence repeats (SSR) analysis. Chloroplast DNA (cpDNA) and nuclear ribosomal DNA (nrDNA) fragments were sequenced from all 50 natural populations (574 cpDNA individuals and 581 nrDNA individuals) for analyses of population structure and genetic diversity. Phylogenetic analysis of 13 Macrostemons from northern China (YCS, SHS, NMG, TSS and XAS populations), western China (JGX and YXS populations) and Central and eastern China (DPS, FXQ, SYS, GLS and XYS populations) was performed using SSR, cpDNA and nrDNA sequences. Existing geographic distribution and climate data and the niche simulation software MaxEnt were used to predict potential A. macrostemon distributions in different time periods. Total genomic DNA was isolated from dried leaves using the modified CTAB method [ 47 ]. SSR marker amplification and cpDNA and nrDNA sequencing Five pairs of polymorphic primers were identified by SSR analysis (Table S2) [ 48 , 49 ], SSR amplification was performed in 24 selected representative A. macrostemon populations (Table 1 ). Three chloroplast gene fragments (psbA-trnH, rps16 and trnL-F) were amplified and sequenced from 574 individuals, and the nuclear gene ITS was amplified and sequenced from 581 individuals. Primer design and PCR amplification were performed as described by Hamilton [ 50 ], Oxelman et al. [ 51 ], Taberlet [ 52 ] and Wendel et al. [ 53 ]. The sequencing was performed on Sangon Biotech's (Shanghai, China) ABI 3730 automatic sequencer. The primers for the sequencing reaction are the same as those used in the respective PCR. Simulation of past species distributions using MaxEnt MaxEnt software was used to predict the potential distributions of A. macrostemon during the Last Glacial Maximum (LGM), Mid-Holocene, current, and future periods. Geographic distribution data (197 distribution datapoints points from our sampled populations and other data consulted) and climatic environment factors (19 climatic factors) of A. macrostemon were analyzed using the MaxEnt model and ArcGIS. The main climatic factors affecting the geographical distribution of A. macrostemon were obtained, and potential suitable areas for different grades of A. macrostemon were characterized to reveal the influence of climate change on spatio-temporal patterns of A. macrostemon . Data analysis The expected heterozygosity (He), observed heterozygosity (Ho), effective number of alleles (Ne), observed number of alleles (Na), Shannon's information index (I), percentage of polymorphic loci (PPL), and other genetic parameters from each population were calculated using GenAlex 6.5 software [ 54 ]. The polymorphism information content (PIC) of each SSR locus was calculated using Cervus 3.0 [ 55 ]. Genetic variance analysis (AMOVA) was performed using GenAlex 6.5 [ 54 ] to detect the genetic variation and differentiation among and within populations, Nei's genetic distance, Mantel test, and principal coordinate analysis (PCoA) were performed. STRUCTURE 2.3.3 [ 56 ] was used to analyze the genetic structure of A. macrostemon . Sequence data was checked visually using Chromas 2.6 and manually edited and assembled using DNAStar 5.0 [ 57 ] Sequences were then aligned using ClustalW with MEGA 7.0 [ 58 ]. The evolutionary trees of all samples were obtained. The identification of DNA haplotypes in each population, as well as the estimations of haplotype diversity ( Hd ) and nucleotide diversity (π), were performed using DnaSP 5.0 [ 59 , 60 , 61 ]. Total genetic diversity ( H T ) and average within-population diversity ( H S ) were estimated using PERMUT 2.0 [ 62 ]. Two parameters for population differentiation ( G ST and N ST ) were calculated using PERMUT 2.0 with 1,000 permutations. These two parameters were then compared to test whether N ST was significantly larger than G ST , which would indicate the presence of phylogeographic structure. Based on the principle of simplicity, the haplotype network diagram was constructed using the median-joining method [ 63 ]. To estimate genetic variance components, the patterns of genetic variation within and between populations and groups were examined using hierarchical AMOVA in Arlequin 3.5, with significance tests based on 1,000 permutations [ 64 ]. Mismatch distribution analyses were conducted to test population expansion. Further, the Tajima’s D and Fu’s F S statistics were calculated to test for evidence of population expansion events using DnaSP 5.0 [ 59 , 60 , 61 ]. All expansion tests were implemented in Arlequin 3.5 with 10,000 permutations for the significance tests. The geographic distribution data and climatic environment data of A. macrostemon were analyzed using the MaxEnt and ArcGIS software. The climatic factors that mainly affected the geographic distribution of A. macrostemon were identified, and the potential suitable areas for each grade of A. macrostemon were characterized. Results Genetic diversity and structure based on SSRs SSRs were amplified at five loci in 288 A. macrostemon individuals from 24 different populations and used to estimate genetic diversity (Table S2). The mean expected heterozygosity (He), observed heterozygosity (Ho), effective number of alleles (Ne), observed number of alleles (Na), Shannon information index (I), mean polymorphism information content (PIC), and percentage of polymorphic loci (PPL) were 0.498, 0.808, 2.357, 3.008, 0.871, 0.834, and 80.8, respectively. These five SSRs all showed high polymorphism at the species level (Table 1 ). The highest genetic diversity was found at SSR ACE039, while the lowest genetic diversity was found at SSR ACM096 (Table S2). The DPS, SYS, THS, JAS, and QDS populations had high levels of genetic diversity, while the NXS, JGX, and GLS populations had low levels of genetic diversity (Table 1 ). Table 1 Genetic diversity of 24 A. macrostemon populations based on SSR markers population H e H o N e N a I PPL(%) BXS 0.576 0.800 3.417 5.000 1.204 80.00 CZS 0.459 0.800 1.928 2.800 0.762 80.00 DPS 0.551 1.000 2.272 2.600 0.846 100.00 FZS 0.493 0.800 2.148 3.000 0.858 80.00 GLS 0.378 0.600 1.725 2.200 0.660 60.00 HDS 0.339 0.600 1.398 1.600 0.530 60.00 HSS 0.534 0.800 2.822 3.600 1.000 80.00 JAS 0.716 1.000 3.598 4.800 1.389 100.00 JGX 0.358 0.600 1.548 2.000 0.602 60.00 JLQ 0.494 0.800 2.244 3.000 0.871 80.00 NJS 0.549 1.000 2.389 2.600 0.846 100.00 NMG 0.533 0.800 2.672 4.000 1.022 80.00 NXS 0.200 0.400 0.800 0.800 0.277 40.00 QDS 0.566 1.000 2.325 3.200 0.916 100.00 SHS 0.536 0.800 2.850 3.600 1.013 80.00 SMX 0.507 0.800 2.719 3.200 0.920 80.00 SYS 0.695 1.000 4.052 6.000 1.422 100.00 THS 0.604 1.000 2.811 3.000 1.003 100.00 TSS 0.367 0.600 1.630 2.000 0.623 60.00 WSS 0.522 0.800 2.546 3.400 0.955 80.00 XAS 0.520 0.800 2.625 3.000 0.930 80.00 XWX 0.569 1.000 2.375 2.800 0.910 100.00 YCS 0.463 0.800 1.992 2.200 0.738 80.00 ZTS 0.415 0.800 1.670 1.800 0.602 80.00 Mean 0.498 0.808 2.357 3.008 0.871 80.83 Note: Pop: population name; H e: expected heterozygosity; H o: observed heterozygosity; N e: effective number of allele; N a: observed allele number. I : Shannon information index; PPL: percentage of polymorphic loci Population molecular analysis of variance (AMOVA) based on SSR markers showed that the genetic variation of A. macrostemon was mainly within populations, accounting for 76% of the total variation within the larger population (Table 2 ). The Mantel test showed that there was a significant correlation between geographic distance and Nei's genetic distance (r = 0.226, p = 0.03 < 0.05, Fig. S1 ), indicating that there was significant geographic isolation among populations of A. macrostemon . Principal coordinate analysis showed that individuals from the same population clustered together, while only a few individuals from the ZTS, SHS, QDS, and SYS populations had crossover with individuals from other populations (Fig. S2). After genetic structure analysis using STRUCTURE, no obvious inflection point for the logarithm L(K) value of the corresponding posterior probability was detected (Fig. 1 a). The maximum delta K value was found when K = 3 (Fig. 1 b). Therefore, the 24 A. macrostemon populations were divided into three groups. Some individuals in the 24 A. macrostemon populations were mixed to different degrees, which indicated that there was some gene exchange between the populations (Fig. 1 c). Based on Nei's genetic distance and geographic distribution, the 24 A. macrostemon populations could be divided into three groups: north, southwest, and central-southeast (Fig. 3 a and 3 b). Groupings based on neighbor-joining cluster analysis were consistent with the results based on structure analysis. Genetic diversity and genetic structure based on cpDNA and nrDNA sequences By concatenating alignments from three cpDNA sequences ( psb A- trn H, 539bp; rps 16, 739 bp; trn L-F, 652 bp), 1930 bp of total cpDNA sequence was obtained from 574 individuals, containing 66 variant sites and G + C content of 32.99%. A total of 42 chloroplast haplotypes (H1-H42) were identified (Fig. 2 a and 2 b; Table S3). Haplotype H1 was the most common, appearing in 144 individuals, had the widest distribution, appearing in 14 populations, and was the oldest haplotype (Table S3, Fig. S3a). In addition, multiple chloroplast haplotypes were found in 12 populations. The remaining 38 populations were monomorphic populations. The species showed high haplotype diversity and nucleotide diversity ( Hd = 0.904, π = 2.08×10 − 3) at the species level. The total genetic diversity ( H T ) of chloroplast segments of A. macrostemon was 0.860, and the average genetic diversity ( H S ) of the population was 0.121. The haplotype diversity ( Hd ) of the SHS population was the highest (0.758), and the nucleotide diversity (π) of the JHS population was the highest (1.700×10 − 3). Haplotype and nucleotide diversity were higher in eastern regions (DPS and JHS), northeastern regions (HCS, BXS, SHS, and HLJ), and central regions (SMX and XYS). Other regional differences were not statistically significant (Table S3). The 633 bp nrDNA ITS sequence of A. macrostemon was obtained from 581 individuals, containing 391 variant sites and 50.43% G + C content. These polymorphic sites revealed a total of 65 karyotypes (H1-H65)(Fig. 2 c and 2 d; Table S3). Among these, the H7 haplotype had the highest frequency, found in 96 individuals, and the widest distribution range. The core karyotype type of the ITS network center was H7, which was presumed to be the oldest haplotype (Table S3, Fig. S3b). Similar to the results of cpDNA analysis, there was no karyotype sharing between different regions (north, southwest, and central-southeast), only between populations in the same region (Table S3, Fig. S3b). These findings suggest that different populations in the same area often experienced genetic exchange at their cpDNA and ITS loci. Seven populations (TSS, XAS, YCS, SMX, NXS, HCS, and SYS) contained more than three karyotypes, and 34 populations had only one karyotype. Compared with chloroplast gene sequences, ribosome gene sequences showed higher haplotype diversity and nucleotide diversity at the species level ( Hd = 0.957, π = 9.162×10 − 2) (Table S3). Different from cpDNA results, populations in southwestern China showed high genetic diversity. Molecular analysis of variance (AMOVA) based on cpDNA and ITS sequence data further revealed the genetic structure of A. macrostemon . For cpDNA sequences, the inter-population genetic variation (93.45%) was significantly higher than the intra-population genetic variation (6.55%), and the F ST value was 0.93445, which was also significant. The results using ITS sequences were similar to those of cpDNA sequences, with the variation mainly coming between populations and an F ST value of 0.94058 (Table 2 ). The Nst genetic differentiation coefficients were not significantly greater than Gst (cpDNA: Nst = 0.930, Gst = 0.859, p > 0.05; nrDNA: Nst = 0.937, Gst = 0.808, p > 0.05), indicating that A. macrostemon had no significant systematic geographic structure. Table 2 AMOVA analysis of A. macrostemon populations based on SSR markers、cpDNA and nrDNA sequences Source of variation d.f. Sum of squares Variance components Percentage of variation (%) Fixation index ( F st ) ssr Among populations 23 311.696 13.552 24 Within populations 552 880.667 1.595 76 0.238 Total 575 1192.363 15.147 100 cpDNA Among populations 49 1463.228 2.58579 93.45 Within populations 525 95.044 0.18138 6.55 0.93445 Total 574 1558.272 2.76717 100 nrDNA Among populations 49 16389.947 28.63321 94.06 0.94058 Within populations 532 960.464 1.80878 5.94 Total 581 17350.411 30.442 100 Inference of demographic history Based on the mismatch distribution analysis of cpDNA and ITS sequences, the Tajima’s D values for the overall population were negative and nonsignificant, with a Tajima’s D of -1.42056 ( p > 0.10) for the chloroplast sequences and − 0.71303 ( p > 0.10) for the nuclear sequences. The Fu 's F s value was − 6.394 for the chloroplast sequences and 37.290 for the nuclear sequences. The mismatch distribution analysis produced multimodal curves, and the observed values were contrary to the expected values (Fig. S4). This violated the population expansion model, indicating that A . macrostemon did not experience significant population expansion, but rather was in dynamic equilibrium. Analysis of suitable establishment areas for A. macrostemon MaxEnt software is used to forecast the potential distribution of A. macrostemon in China. The predicted value is very high (AUC = 0.983), which can be used to characterize the migration route and distribution changes in the Quaternary glacial period. The results show that during LGM stage, global climate cooling leads to obvious contraction and southward migration of A. macrostemon high suitable area. The Middle Holocene climate is warm and humid, similar to the modern climate, and the distribution of A. macrostemon in this period is obviously expanded, and the predicted distribution range in this period is similar to the modern climate. It is predicted that the distribution area of A. macrostemon will expand slightly in the future to reach the largest distribution area (Fig. 3 ). The highest contribution rate of each ecological factor is the warm season precipitation (39.8%). The variation coefficient of precipitation (15.5%) and mean temperature in the coldest season (12.8%) also contributed greatly, indicating that temperature, precipitation and season have a great influence on the distribution of A. macrostemon (Table S4). The suitable area of A. macrostemon showed a trend of decreasing first and then increasing at different periods. In the future, the total suitable area of A. macrostemon will reach its maximum, and the distribution center may move northward. Discussion Population genetic diversity In this study, estimates of genetic variation using both cpDNA and ITS sequence data were higher than the average genetic diversity of angiosperms (cpDNA: 0.67; nrDNA: 0.137) [ 65 , 52 ]. These results were further supported using SSR molecular markers, indicating that the genetic diversity of A . macrostemon as estimated in this study was higher. Haplotype diversity and total genetic diversity of A. macrostemon based on chloroplast gene analysis (cpDNA: Hd = 0.904, H T = 0.868) was slightly lower than estimates using nuclear genes (nrDNA: Hd = 0.957, H T = 0.890). The genetic diversity of A. macrostemon estimated using chloroplast genes was higher than other herbaceous plants such as Allium mongolica ( H T = 0.693) [ 66 ] and Fritillaria pallidifora ( H T = 0.670) [ 67 ]. The genetic diversity of the population is caused by a variety of factors, which are influenced by the geographic distribution, biological characteristics, population size, and breeding system of the species [ 65 ]. At present, many studies have used various molecular markers to explore the genetic diversity of herbs, such as Typha domingensis and Typha latifolia [ 68 ]. Estimates of genetic diversity in these species were low, which may be related to their high self-crossing rates and strong vegetative reproduction abilities [ 69 ]. The genetic and nucleotide diversity of nuclear genes in A. macrostemon were higher than those using chloroplast genes, which may be related to the relative conservation of chloroplast genes and their maternal inheritance patterns [ 70 ]. In addition, the genetic diversity of narrowly distributed species is lower than that of wide-spread species. A. macrostemon is a wide-spread species with a large population and strong adaptability within the group. Thus, A. macrostemon has maintained a relatively rich genetic diversity [ 71 ]. The influence of multiple forces on contemporary genetic structure Geographic structure is common in plants with continuous distributions, which is usually due to distance isolation or environmental isolation [ 72 , 73 ]. In this study, SSR data indicated that genetic variation mainly occurred between populations. However, low levels of gene flow may lead to population adaptation to the local ecological environment, thus accelerating genetic differentiation among populations, and genetic drift can be a major factor affecting population genetic structure [ 74 , 75 , 76 ]. In addition, F ST analysis of both cpDNA and nrDNA data indicated that the proportion of inter-population genetic differentiation in the total genetic diversity was about 0.93445 and 0.94058, respectively (Table 2 ). Wright [ 76 ] believed that the level of inter-population genetic differentiation was extremely high ( F ST > 0.25). It is clear that genetic distance and geographic distance between populations are positively correlated (r = 0.226, p = 0.03 < 0.05) (Figure S1 ), so topography may be one of the most important factors leading to differentiation. The isolation between populations is caused by physical obstacles such as the complex terrain and mountains in China. In addition, the mismatch distributions of cpDNA and ITS showed a multimodal curve, and the observed values did not match the expected values, violating the population expansion model and indicating that the population of A. macrostemon had not undergone a significant expansion. This further indicated that the population of A. macrostemon had structure influenced by geography. Due to the special geographic environment and north-south changes in climate, there are obvious differences in natural conditions and geographic characteristics on both sides of the QHL, which is the geographic north-south dividing line of China [ 34 ]. The QHL serves as the dividing line between subtropical and temperate monsoon climate zones, separating the warm and humid southeast from the cold and dry northwest. The relatively mild Pleistocene climate in the lower elevations can provide a relatively stable microclimate environment for a certain range of habitats [ 77 ]. In addition, the complex geological and environmental conditions of the QHL may affect the formation of the geographic structure of the A. macrostemon lineage. A phylogenetic tree was constructed based on cpDNA and ITS sequence data results were consistent, A. macrostemon from different regions were divided into southern and northern flora, with the southern flora further divided into southwestern and central-southeastern flora (Fig. 2 b and 2 d). The possible factors for the formation of this genetic structure include geological events, the geographic environment, and climate change. Ecological factors such as temperature, rainfall, and other climatic conditions have significant effects on interspecific and intraspecific variation [ 78 , 79 ]. As a widely distributed species, A. macrostemon has three reproduction modes consisting of bud, bulb, and seed and has strong ecological adaptability and reproductive ability. Therefore, we conclude that the geographic structure of A. macrostemon is mainly affected by the geographic barrier, which prevents gene exchange among different populations. Therefore, we speculate that the formation of the existing geographic structure of A. macrostemon may be the result of allopatric differentiation caused by its long-term adaptation to different geological events, climatic conditions, and elevation differences in the distribution areas of Qinling-Huaihe and Wushan-Xuefeng. In fact, previous studies have shown that the QHL currently plays a key role in shaping plant dispersal and that it is an important boundary in China that separates ecologically distinct habitats [ 80 , 81 ]. Natural adaptations and physical barriers can explain the differences between the two subpopulations. In summary, our results support the hypothesis that the QHL contributes to the intraspecific differentiation pattern in A. macrostemon . Having high cpDNA haplotype and nucleotide diversity is a characteristic of ice age sanctuaries [ 21 ], and these stable and diverse environments are conducive to the maintenance of species richness. The haplotype and nucleotide diversity of A. macrostemon were high in select populations both north (TSS, SMX, HCS, and SHS) and south (DPS and SNX) of the QHL. Thus, these northern and southern populations were potential habitats for the plant despite their great north-south geographical distance. Consistent with this, areas on either side have been shown to be glacial refugia for other species [ 36 , 37 ]. Niche simulations indicated that the distribution of A. macrostemon during the LGM period was significantly reduced compared to the current distribution, which may be related to the global climate cooling during the LGM period. During the LGM period, the highly suitable areas of A. macrostemon tended to shrink to the south and north. Therefore, we speculate that the A. macrostemon population may have taken refuge in place during the ice age and migrated to high altitude or low altitude areas to find a suitable living environment. This is consistent with the results of SSR, cpDNA and nrDNA, indicating that the species may have split into at least two glacial refugia across the QHL, one in the south and one in the north. MaxEnt simulations suggested that A. macrostemon expanded in the middle of the Holocene, migrating south or to lower altitudes in warmer climates and thus making it better suited to growth in high temperatures and relatively humid environments. It is predicted to continue to shrink in the future, and the center of its distribution will likely move northward. A. macrostemon is a widely distributed species, and the past and future changes in its distribution may be a signal for future changes in other populations with similar ecological habits. Conclusions In this study, individuals from a total of 50 A. macrostemon populations were collected, and the genetic structure and geographic distribution patterns of this species were analyzed by combining cpDNA, ITS and SSR. The QHL, which has been found to be a north-south dividing line in phylogeography and population genetic structure and promotes physical geographic isolation, has played an important role in this process. The genetic diversity of A. macrostemon was found to be high, and this genetic variation mainly existed among the populations. The formation of the present systematic geographic pattern and genetic structure is influenced by climate fluctuations and environmental heterogeneity. Populations of A. macrostemon can be divided into two branches in the north and south, with evidence of at least two glacial sanctuaries both north and south of the QHL during the Quaternary glacial period. This study can provide scientific theoretical basis for the conservation, development, and utilization of A. macrostemon resources. Further, it can provide a reference for the systematic geographic pattern of large-scale spatial distribution of plants in China and enrich our understanding of the evolutionary history of plant species diversity in East Asia. Abbreviations cpDNA: chloroplast DNA nrDNA: Ribosomal DNA π : nucleotide diversity G ST : the level of population differentiation at the species level N ST : an estimate of population subdivisions for phylogenetically ordered alleles LGM: last glacial maximum MID: mid Holocene AUC: the area under the curve N m : gene flow Pop ： population name He: expected heterozygosity Ho: observed heterozygosity Ne: effective number of allele Na: observed allele number I: Shannon information index PPL: percentage of polymorphic loci He: expected heterozygosity Ho: observed heterozygosity PIC: polymorphic information content Pi: Nucleotide diversity H d : Diversity of haplotypes; H: Number of haploty AMOVA: analyses of molecular variance F ST : Genetic differentiation among populations Declarations Acknowledgements This work was supported by the projects of Construction Program of Biology First-class Discipline in Guizhou (GNYL [2017]009), this project received funding from the National Natural Science Foundation of China (NSFC, No. 32260252). Key Laboratory Opening Project of Education Department of Guizhou Province ministry of Education (Guizhou Education Cooperation KY [2019]033), Guizhou Science and Technology Support Plan Project (Guizhou Science and Technology Cooperation Support ([2019] 2451-2), Open Research Fund of Guizhou Provincial Key Laboratory for Biodiversity Conservation and Utilization in the Fanjing Mountain Region ([2020]2003) and Construction of modern industrial technology system for Chinese medicinal materials in Guizhou Province (No. GZCYTX-02). Thanks very much for those above founders. Funding information Guizhou Province Biology First-class Discipline Construction Project (GNYL[2017]009), the National Natural Science Foundation of China (NSFC, No. 32260252). Key Laboratory Opening Project of Education Department of Guizhou Province ministry of Education (Guizhou Education Cooperation KY [2019]033), Guizhou Science and Technology Support Plan Project (Guizhou Science and Technology Cooperation Support ([2019] 2451-2), Open Research Fund of Guizhou Provincial Key Laboratory for Biodiversity Conservation and Utilization in the Fanjing Mountain Region ([2020]2003) and Construction of modern industrial technology system for Chinese medicinal materials in Guizhou Province (No. GZCYTX-02). Competing interests The authors declare that there are no competing interests. Data availability statement Genetic data for all unique haplotypes are available on GenBank (Accession numbers OM102763-OM102953) Ethics approval and consent to participate Not applicable Consent for publication Not applicable. SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3933291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273148011,"identity":"c6e3f6d3-e4aa-46c6-99c0-25cca03de662","order_by":0,"name":"chunxue Jiang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"chunxue","middleName":"","lastName":"Jiang","suffix":""},{"id":273148012,"identity":"43d828c6-9c98-4413-8218-fc2be43d1fc4","order_by":1,"name":"tian Shi","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"tian","middleName":"","lastName":"Shi","suffix":""},{"id":273148013,"identity":"04de46a2-9648-4034-8695-9d7526aeddb5","order_by":2,"name":"zhongmei Mo","email":"","orcid":"","institution":"Tongren University，Tongren","correspondingAuthor":false,"prefix":"","firstName":"zhongmei","middleName":"","lastName":"Mo","suffix":""},{"id":273148014,"identity":"65a795ad-ab75-4352-932d-9090132bebe7","order_by":3,"name":"cai Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDACCSjJxsx84MCHH6Ro4WNnSzw4s4d4LQwMcvw8xoc52IjQIT+7+eGDHzUWQIfxfDjMwMMgzy92AL8WxjnHjA17joH8wrvhcIEFg+HM2Qn4tTBLJJhJM7BBtczgYUgwuE1AC5tE+jdphn8gLTwPDvOwEaGFRyLHTJqxDayFgTgtEhI5xYa9fSAtbAbAQJYg7Bf5GekbH/z4Vscg33/48YcPP2zk+aUJaIGB+gaorcQpHwWjYBSMglGAHwAAKRk4RZpQlzYAAAAASUVORK5CYII=","orcid":"","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"cai","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-02-06 08:30:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3933291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3933291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51229728,"identity":"4d415ede-fbac-458c-94be-867ba0da2742","added_by":"auto","created_at":"2024-02-16 13:41:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211811,"visible":true,"origin":"","legend":"\u003cp\u003ea. L(K) values for K from 3 to 16; b. Delta K values for K from 3 to 15; c. The 288 \u003cem\u003eA. macrostemon \u003c/em\u003eplants were divided into 3 groups (K=3). Each \u003cem\u003eA. macrostemon\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eindividual is represented by a vertical bar and colored according to the assigned group.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3933291/v1/fd830d0fa60cfa0a98f81338.png"},{"id":51229582,"identity":"164b388f-c9a2-4abc-a219-89acd563e974","added_by":"auto","created_at":"2024-02-16 13:33:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":656037,"visible":true,"origin":"","legend":"\u003cp\u003ea. The geographic distribution of haplotypes based on cpDNA sequences \u0026nbsp;according to phylogenetic grouping; b. The geographic distribution of all cpDNA haplotypes. c. The geographic distribution of haplotypes based on nrDNA ITS sequences according to phylogenetic grouping; d. The geographic distribution of all nrDNA haplotypes. Population numbers 1-50 are same population numbers as listed in Table S1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3933291/v1/6e8089acb2567b4835215b77.png"},{"id":51229580,"identity":"3387b709-e049-4a00-be5d-badedc3ac9b2","added_by":"auto","created_at":"2024-02-16 13:33:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1438869,"visible":true,"origin":"","legend":"\u003cp\u003ea. Last Glacial Maximum (LGM); b. Mid-Holocene; c. Current.; d. Future in 2050.\u003c/p\u003e\n\u003cp\u003eThe suitable area of \u003cem\u003eA. macrostemon\u003c/em\u003e showed a trend of decreasing first and then increasing at different periods. In the future, the total suitable area of \u003cem\u003eA. macrostemon\u003c/em\u003ewill reach its maximum, and the distribution center may move northward.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3933291/v1/99dd71c6f609d328188d5c93.png"},{"id":52063893,"identity":"da47ae7d-4242-4a0d-b90a-8024f6b8e8c5","added_by":"auto","created_at":"2024-03-06 06:16:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1896181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3933291/v1/9e7aae14-fdfa-4be1-ad18-076765712ecb.pdf"},{"id":51229583,"identity":"8ed73d9a-7ff0-4f0d-b3b5-1197c93de36f","added_by":"auto","created_at":"2024-02-16 13:33:58","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3189760,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-3933291/v1/7c2ace7f5b6d6f9d069baccc.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phylogeography of Allium macrostemon: south-north divergence reveals a natural geographic isolation boundary in the Qinling Mountains-Huaihe River Line in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGeographic isolation due to uplift of mountain ranges and climate fluctuations associated with glacier oscillations can lead to dramatic changes in the morphologies and geographic distributions of many species [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Increasingly, biological populations have been found to coincide with known geographic boundaries, such as mountains, river systems, or straits, that act as physical barriers to past or contemporary dispersal, facilitating intraspecific systematic geographic subdivision and divergence. China has a number of geographic distribution zones, separated by biogeographic boundaries. From west to east, there are at least seven genealogical discontinuity locations in China which have been supported by molecular ecological data. These include: the Mekong-Salween Divide in the Himalaya-Hengduan Mountains region [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], the Tanaka-Kaiyong Line in the southwest region[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the Sichuan Basin [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the region near 105\u0026deg; E [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the second and third step dividing line [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the North China region [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the East China Sea/Tsushima\u0026ndash;Korean straits [8, 9, 10]. However, very little is known regarding the impact of the Qinling Mountains-Huaihe River Line (QHL), a natural geographic isolation boundary, on the phylogeographical patterns and genetic structures of plants.\u003c/p\u003e \u003cp\u003eGeological history and climatic oscillations are important drivers of evolution and genetic structure of plant species [11, 12]. Since the start of the Quaternary glacial period, global temperatures have generally decreased, though strong fluctuations between cold and warm climates, repeated interleavings of ice and interglacial periods, and geological events and monsoon fluctuations have greatly affected the geographic distributions, population dynamics, and genetic diversity patterns of species [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The strong uplift of the Tibetan Plateau was an important geological event that resulted in changes to the global climate and stimulated the East Asian Monsoon, making the meridional zonation of the Chinese flora increasingly obvious [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, alternations of dry and wet climate have deeply influenced the distribution patterns and historical evolution of Chinese plants. Stable climate, mature plant communities across a vast landscape, high spatial heterogeneity, and a long evolutionary history provided the foundation for the formation of the rich and diverse Chinese flora. China is therefore an important region of species diversity in the Northern Hemisphere [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and an important center for species conservation, speciation, and evolution [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular phylogeographic studies in China have largely focused on woody plants. For example, Gong [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] used molecular data to predict the glacial survival of \u003cem\u003eGinkgo\u003c/em\u003e populations within two refugia in southwestern and eastern China, providing the first evidence of the existence of a refuge area in eastern China on the West Tianmu Mountains. \u003cem\u003eCercidiphyllum\u003c/em\u003e populations were genotyped using chloroplast and ribosomal DNA sequences along with microsatellite loci to assess molecular structure and diversity in relation to past (Last Glacial Maximum) and present distributions based on ecological niche modelling [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These results showed that the occurrence of the Asian monsoon and associated tectonic events, as well as Pleistocene climate changes, had a pervasive influence on the phylogeographic structure of plants in subtropical China, and they also showed how Late Neogene climatic/tectonic changes promoted speciation and lineage diversification in East Asia\u0026rsquo;s Tertiary relict flora [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Compared with long-lived woody plants, herbaceous plants undergo far more life cycles within a given time period and are therefore expected to respond more quickly to environmental changes. Thus, herbaceous plants may provide better opportunities to study the drivers of diversification and speciation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, to the best of our knowledge, only a few herbs from this region, such as \u003cem\u003eDysosma versipellis\u003c/em\u003e, \u003cem\u003ePrimula ovalifolia\u003c/em\u003e, and \u003cem\u003eOryza stavia\u003c/em\u003e, have been studied to date [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, phylogeographic research is still lacking for plants distributed over large-scale ranges and spanning different climate zones and multiple tropical biodiversity regions, especially in China.\u003c/p\u003e \u003cp\u003eThe Qinling-Huaihe River Line (QHL), which runs from the Qinling Mountains to the Huaihe River, is located at about 33\u0026deg; north latitude and includes the Qinling and Dabie Orogenic Belt (QDB), the Yangtze River Plain, and the southern mountains of Anhui [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and is the natural geographic dividing line between North and South China [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Obvious differences in climatic conditions and geographical features exist between regions north and south of the line. For example, the southern region has a subtropical or tropical climate with more precipitation and higher temperatures than the northern region. The northern region has a temperate climate with four distinct seasons, and precipitation has obvious seasonal characteristics [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous research has shown that both northern (i.e., north of the QHL) and southern (i.e., south of the QHL) regions of China have retained glacial refugia for plants, and these sanctuaries are considered to be sites of extended range in the late interglacial or post-glacial period [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, very little is known regarding the impact of the QHL on the phylogeographic patterns and genetic structures of plants.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAllium macrostemon\u003c/em\u003e is a perennial herb belonging to the family Amaryllidaceae. It is an edible Chinese herb with a variety of health and healing properties [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Current studies on \u003cem\u003eA. macrostemon\u003c/em\u003e mainly focus on its chemical composition, pharmacological action, and potential as a source of crude drugs [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It is widely distributed in the wild mountains and fields spanning the QHL in eastern and western China. \u003cem\u003eA. macrostemon\u003c/em\u003e is a polyploid species with a polyploid series of 2n, 3n, 4n, 5n, 6n, and 7n. Populations with 5n ploidy have been found in Japan, and populations with 3n, 4n, 5n, and 7n ploidy have been found in China, with the remaining ploidies yet to be discovered in nature [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Analysis of the systematic taxonomic status of \u003cem\u003eA. macrostemon\u003c/em\u003e using the nuclear DNA (nrDNA) ITS, chloroplast DNA (cpDNA), and \u003cem\u003etrn\u003c/em\u003eL-F sequences have indicated that the species may represent a new group[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies in our laboratory have clearly distinguished \u003cem\u003eA. macrostemon\u003c/em\u003e from other related \u003cem\u003eAllium\u003c/em\u003e plants and have supported the use of \u003cem\u003eA. macrostemon\u003c/em\u003e as an independent monophyletic group [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, there are few reports on the systematic geography of \u003cem\u003eA. macrostemon\u003c/em\u003e. As an herbaceous plant, \u003cem\u003eA. macrostemon\u003c/em\u003e has a wide range, spanning different climate zones and multiple tropical biodiversity regions in China. It is sensitive to climate change and environmental fluctuations, and its DNA base substitution rate is higher than that of woody plants, which can thus provide more historical information on its evolution. In this study, \u003cem\u003eA\u003c/em\u003e. \u003cem\u003emacrostemon\u003c/em\u003e from both sides of the QHL and spanning east-west across China were sampled. Simple sequence repeats (SSRs), cpDNA genes (\u003cem\u003epsb\u003c/em\u003eA-\u003cem\u003etrn\u003c/em\u003eH, \u003cem\u003erps\u003c/em\u003e16, and \u003cem\u003etrn\u003c/em\u003eL-F), and nrDNA (ITS) fragments were used to analyze the genetic structures and phylogeographic patterns of 50 large stem grass populations. Meanwhile, existing geographic distribution and climate data combined with the niche simulation MaxEnt software were used to predict \u003cem\u003eA. macrostemon\u003c/em\u003e distributions in different time periods. In this study, we mainly aimed to address the following questions: (1) Do populations of \u003cem\u003eA. macrostemon\u003c/em\u003e exhibit varying degrees of genetic diversity? (2) Is there a QHL pattern in the phylogeographic structure of \u003cem\u003eA. macrostemon\u003c/em\u003e? (3) What were the possible reasons for the genetic differentiation and distribution patterns of \u003cem\u003eA. macrostemon\u003c/em\u003e? These analyses could provide a scientific theoretical basis for the conservation, development, and utilization of \u003cem\u003eA. macrostemon\u003c/em\u003e resources as well as an important theoretical basis for further discussion on plant evolution and species diversity in China. They will also further enrich our understanding of the molecular systematic evolution and biogeography of East Asian herbaceous plants.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling, experimental design, and DNA extraction\u003c/h2\u003e \u003cp\u003eLeaf samples of \u003cem\u003eAllium macrostemon\u003c/em\u003e were collected during 2015\u0026ndash;2020 from 50 natural populations across its entire geographic range in China. At the same time, the latitude, longitude, and altitude at each collection site were recorded using the global positioning system (GPS) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Leaves for DNA extraction were collected in the field and rapidly dried with silica gel. Field collection follows the ethics and legality of the local government and is permitted by the government. The formal identification of the plant material was undertaken by Professor Zhao Cai, and voucher specimens were deposited at Guizhou University. A total of 288 \u003cem\u003eA. macrostemon\u003c/em\u003e individuals from 24 of the 50 natural populations were used for simple sequence repeats (SSR) analysis. Chloroplast DNA (cpDNA) and nuclear ribosomal DNA (nrDNA) fragments were sequenced from all 50 natural populations (574 cpDNA individuals and 581 nrDNA individuals) for analyses of population structure and genetic diversity. Phylogenetic analysis of 13 Macrostemons from northern China (YCS, SHS, NMG, TSS and XAS populations), western China (JGX and YXS populations) and Central and eastern China (DPS, FXQ, SYS, GLS and XYS populations) was performed using SSR, cpDNA and nrDNA sequences. Existing geographic distribution and climate data and the niche simulation software MaxEnt were used to predict potential \u003cem\u003eA. macrostemon\u003c/em\u003e distributions in different time periods. Total genomic DNA was isolated from dried leaves using the modified CTAB method [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSSR marker amplification and cpDNA and nrDNA sequencing\u003c/h2\u003e \u003cp\u003eFive pairs of polymorphic primers were identified by SSR analysis (Table S2) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], SSR amplification was performed in 24 selected representative \u003cem\u003eA. macrostemon\u003c/em\u003e populations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three chloroplast gene fragments (psbA-trnH, rps16 and trnL-F) were amplified and sequenced from 574 individuals, and the nuclear gene ITS was amplified and sequenced from 581 individuals. Primer design and PCR amplification were performed as described by Hamilton [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], Oxelman et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], Taberlet [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and Wendel et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The sequencing was performed on Sangon Biotech's (Shanghai, China) ABI 3730 automatic sequencer. The primers for the sequencing reaction are the same as those used in the respective PCR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSimulation of past species distributions using MaxEnt\u003c/h2\u003e \u003cp\u003eMaxEnt software was used to predict the potential distributions of \u003cem\u003eA. macrostemon\u003c/em\u003e during the Last Glacial Maximum (LGM), Mid-Holocene, current, and future periods. Geographic distribution data (197 distribution datapoints points from our sampled populations and other data consulted) and climatic environment factors (19 climatic factors) of \u003cem\u003eA. macrostemon\u003c/em\u003e were analyzed using the MaxEnt model and ArcGIS. The main climatic factors affecting the geographical distribution of \u003cem\u003eA. macrostemon\u003c/em\u003e were obtained, and potential suitable areas for different grades of \u003cem\u003eA. macrostemon\u003c/em\u003e were characterized to reveal the influence of climate change on spatio-temporal patterns of \u003cem\u003eA. macrostemon\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe expected heterozygosity (He), observed heterozygosity (Ho), effective number of alleles (Ne), observed number of alleles (Na), Shannon's information index (I), percentage of polymorphic loci (PPL), and other genetic parameters from each population were calculated using GenAlex 6.5 software [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The polymorphism information content (PIC) of each SSR locus was calculated using Cervus 3.0 [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Genetic variance analysis (AMOVA) was performed using GenAlex 6.5 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] to detect the genetic variation and differentiation among and within populations, Nei's genetic distance, Mantel test, and principal coordinate analysis (PCoA) were performed. STRUCTURE 2.3.3 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] was used to analyze the genetic structure of \u003cem\u003eA. macrostemon\u003c/em\u003e. Sequence data was checked visually using Chromas 2.6 and manually edited and assembled using DNAStar 5.0 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] Sequences were then aligned using ClustalW with MEGA 7.0 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The evolutionary trees of all samples were obtained.\u003c/p\u003e \u003cp\u003eThe identification of DNA haplotypes in each population, as well as the estimations of haplotype diversity (\u003cem\u003eHd\u003c/em\u003e) and nucleotide diversity (π), were performed using DnaSP 5.0 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Total genetic diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e) and average within-population diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eS\u003c/em\u003e\u003c/sub\u003e) were estimated using PERMUT 2.0 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Two parameters for population differentiation (\u003cem\u003eG\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e) were calculated using PERMUT 2.0 with 1,000 permutations. These two parameters were then compared to test whether \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e was significantly larger than \u003cem\u003eG\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e, which would indicate the presence of phylogeographic structure. Based on the principle of simplicity, the haplotype network diagram was constructed using the median-joining method [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. To estimate genetic variance components, the patterns of genetic variation within and between populations and groups were examined using hierarchical AMOVA in Arlequin 3.5, with significance tests based on 1,000 permutations [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Mismatch distribution analyses were conducted to test population expansion. Further, the Tajima\u0026rsquo;s D and Fu\u0026rsquo;s F\u003csub\u003eS\u003c/sub\u003e statistics were calculated to test for evidence of population expansion events using DnaSP 5.0 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. All expansion tests were implemented in Arlequin 3.5 with 10,000 permutations for the significance tests.\u003c/p\u003e \u003cp\u003eThe geographic distribution data and climatic environment data of \u003cem\u003eA. macrostemon\u003c/em\u003e were analyzed using the MaxEnt and ArcGIS software. The climatic factors that mainly affected the geographic distribution of \u003cem\u003eA. macrostemon\u003c/em\u003e were identified, and the potential suitable areas for each grade of \u003cem\u003eA. macrostemon\u003c/em\u003e were characterized.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity and structure based on SSRs\u003c/h2\u003e \u003cp\u003eSSRs were amplified at five loci in 288 \u003cem\u003eA. macrostemon\u003c/em\u003e individuals from 24 different populations and used to estimate genetic diversity (Table S2). The mean expected heterozygosity (He), observed heterozygosity (Ho), effective number of alleles (Ne), observed number of alleles (Na), Shannon information index (I), mean polymorphism information content (PIC), and percentage of polymorphic loci (PPL) were 0.498, 0.808, 2.357, 3.008, 0.871, 0.834, and 80.8, respectively. These five SSRs all showed high polymorphism at the species level (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The highest genetic diversity was found at SSR ACE039, while the lowest genetic diversity was found at SSR ACM096 (Table S2). The DPS, SYS, THS, JAS, and QDS populations had high levels of genetic diversity, while the NXS, JGX, and GLS populations had low levels of genetic diversity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eGenetic diversity of 24 \u003cem\u003eA. macrostemon\u003c/em\u003e populations based on SSR markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003epopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003ee\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003eo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003ee\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPL(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBXS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCZS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFZS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJGX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJLQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNJS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNMG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNXS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXWX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Pop: population name; \u003cem\u003eH\u003c/em\u003ee: expected heterozygosity; \u003cem\u003eH\u003c/em\u003eo: observed heterozygosity; \u003cem\u003eN\u003c/em\u003ee: effective number of allele; \u003cem\u003eN\u003c/em\u003ea: observed allele number. \u003cem\u003eI\u003c/em\u003e: Shannon information index; PPL: percentage of polymorphic loci\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePopulation molecular analysis of variance (AMOVA) based on SSR markers showed that the genetic variation of \u003cem\u003eA. macrostemon\u003c/em\u003e was mainly within populations, accounting for 76% of the total variation within the larger population (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Mantel test showed that there was a significant correlation between geographic distance and Nei's genetic distance (r\u0026thinsp;=\u0026thinsp;0.226, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.03\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), indicating that there was significant geographic isolation among populations of \u003cem\u003eA. macrostemon\u003c/em\u003e. Principal coordinate analysis showed that individuals from the same population clustered together, while only a few individuals from the ZTS, SHS, QDS, and SYS populations had crossover with individuals from other populations (Fig. S2).\u003c/p\u003e \u003cp\u003eAfter genetic structure analysis using STRUCTURE, no obvious inflection point for the logarithm L(K) value of the corresponding posterior probability was detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The maximum delta K value was found when K\u0026thinsp;=\u0026thinsp;3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Therefore, the 24 \u003cem\u003eA. macrostemon\u003c/em\u003e populations were divided into three groups. Some individuals in the 24 \u003cem\u003eA. macrostemon\u003c/em\u003e populations were mixed to different degrees, which indicated that there was some gene exchange between the populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Based on Nei's genetic distance and geographic distribution, the 24 \u003cem\u003eA. macrostemon\u003c/em\u003e populations could be divided into three groups: north, southwest, and central-southeast (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Groupings based on neighbor-joining cluster analysis were consistent with the results based on structure analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic diversity and genetic structure based on cpDNA and nrDNA sequences\u003c/h3\u003e\n\u003cp\u003eBy concatenating alignments from three cpDNA sequences (\u003cem\u003epsb\u003c/em\u003eA-\u003cem\u003etrn\u003c/em\u003eH, 539bp; \u003cem\u003erps\u003c/em\u003e16, 739 bp; \u003cem\u003etrn\u003c/em\u003eL-F, 652 bp), 1930 bp of total cpDNA sequence was obtained from 574 individuals, containing 66 variant sites and G\u0026thinsp;+\u0026thinsp;C content of 32.99%. A total of 42 chloroplast haplotypes (H1-H42) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; Table S3). Haplotype H1 was the most common, appearing in 144 individuals, had the widest distribution, appearing in 14 populations, and was the oldest haplotype (Table S3, Fig. S3a). In addition, multiple chloroplast haplotypes were found in 12 populations. The remaining 38 populations were monomorphic populations. The species showed high haplotype diversity and nucleotide diversity (\u003cem\u003eHd\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.904, π\u0026thinsp;=\u0026thinsp;2.08\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;3) at the species level. The total genetic diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sub\u003e) of chloroplast segments of \u003cem\u003eA. macrostemon\u003c/em\u003e was 0.860, and the average genetic diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e) of the population was 0.121. The haplotype diversity (\u003cem\u003eHd\u003c/em\u003e) of the SHS population was the highest (0.758), and the nucleotide diversity (π) of the JHS population was the highest (1.700\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;3). Haplotype and nucleotide diversity were higher in eastern regions (DPS and JHS), northeastern regions (HCS, BXS, SHS, and HLJ), and central regions (SMX and XYS). Other regional differences were not statistically significant (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 633 bp nrDNA ITS sequence of \u003cem\u003eA. macrostemon\u003c/em\u003e was obtained from 581 individuals, containing 391 variant sites and 50.43% G\u0026thinsp;+\u0026thinsp;C content. These polymorphic sites revealed a total of 65 karyotypes (H1-H65)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed; Table S3). Among these, the H7 haplotype had the highest frequency, found in 96 individuals, and the widest distribution range. The core karyotype type of the ITS network center was H7, which was presumed to be the oldest haplotype (Table S3, Fig. S3b). Similar to the results of cpDNA analysis, there was no karyotype sharing between different regions (north, southwest, and central-southeast), only between populations in the same region (Table S3, Fig. S3b). These findings suggest that different populations in the same area often experienced genetic exchange at their cpDNA and ITS loci. Seven populations (TSS, XAS, YCS, SMX, NXS, HCS, and SYS) contained more than three karyotypes, and 34 populations had only one karyotype. Compared with chloroplast gene sequences, ribosome gene sequences showed higher haplotype diversity and nucleotide diversity at the species level (\u003cem\u003eHd\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.957, π\u0026thinsp;=\u0026thinsp;9.162\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2) (Table S3). Different from cpDNA results, populations in southwestern China showed high genetic diversity.\u003c/p\u003e \u003cp\u003eMolecular analysis of variance (AMOVA) based on cpDNA and ITS sequence data further revealed the genetic structure of \u003cem\u003eA. macrostemon\u003c/em\u003e. For cpDNA sequences, the inter-population genetic variation (93.45%) was significantly higher than the intra-population genetic variation (6.55%), and the \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e value was 0.93445, which was also significant. The results using ITS sequences were similar to those of cpDNA sequences, with the variation mainly coming between populations and an \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e value of 0.94058 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Nst genetic differentiation coefficients were not significantly greater than Gst (cpDNA: Nst\u0026thinsp;=\u0026thinsp;0.930, Gst\u0026thinsp;=\u0026thinsp;0.859, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; nrDNA: Nst\u0026thinsp;=\u0026thinsp;0.937, Gst\u0026thinsp;=\u0026thinsp;0.808, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that \u003cem\u003eA. macrostemon\u003c/em\u003e had no significant systematic geographic structure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAMOVA analysis of \u003cem\u003eA. macrostemon\u003c/em\u003e populations based on SSR markers、cpDNA and nrDNA sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ed.f.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariance components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of variation (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003est\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e880.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1192.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecpDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1463.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.58579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1558.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.76717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enrDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16389.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.63321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e960.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.80878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17350.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eInference of demographic history\u003c/h2\u003e \u003cp\u003eBased on the mismatch distribution analysis of cpDNA and ITS sequences, the Tajima\u0026rsquo;s D values for the overall population were negative and nonsignificant, with a Tajima\u0026rsquo;s D of -1.42056 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.10) for the chloroplast sequences and \u0026minus;\u0026thinsp;0.71303 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.10) for the nuclear sequences. The \u003cem\u003eFu\u003c/em\u003e's \u003cem\u003eF\u003c/em\u003es value was \u0026minus;\u0026thinsp;6.394 for the chloroplast sequences and 37.290 for the nuclear sequences. The mismatch distribution analysis produced multimodal curves, and the observed values were contrary to the expected values (Fig. S4). This violated the population expansion model, indicating that \u003cem\u003eA\u003c/em\u003e. \u003cem\u003emacrostemon\u003c/em\u003e did not experience significant population expansion, but rather was in dynamic equilibrium.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of suitable establishment areas for\u003c/b\u003e \u003cb\u003eA. macrostemon\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMaxEnt software is used to forecast the potential distribution of \u003cem\u003eA. macrostemon\u003c/em\u003e in China. The predicted value is very high (AUC\u0026thinsp;=\u0026thinsp;0.983), which can be used to characterize the migration route and distribution changes in the Quaternary glacial period. The results show that during LGM stage, global climate cooling leads to obvious contraction and southward migration of \u003cem\u003eA. macrostemon\u003c/em\u003e high suitable area. The Middle Holocene climate is warm and humid, similar to the modern climate, and the distribution of \u003cem\u003eA. macrostemon\u003c/em\u003e in this period is obviously expanded, and the predicted distribution range in this period is similar to the modern climate. It is predicted that the distribution area of \u003cem\u003eA. macrostemon\u003c/em\u003e will expand slightly in the future to reach the largest distribution area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest contribution rate of each ecological factor is the warm season precipitation (39.8%). The variation coefficient of precipitation (15.5%) and mean temperature in the coldest season (12.8%) also contributed greatly, indicating that temperature, precipitation and season have a great influence on the distribution of \u003cem\u003eA. macrostemon\u003c/em\u003e (Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe suitable area of \u003cem\u003eA. macrostemon\u003c/em\u003e showed a trend of decreasing first and then increasing at different periods. In the future, the total suitable area of \u003cem\u003eA. macrostemon\u003c/em\u003e will reach its maximum, and the distribution center may move northward.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePopulation genetic diversity\u003c/h2\u003e \u003cp\u003eIn this study, estimates of genetic variation using both cpDNA and ITS sequence data were higher than the average genetic diversity of angiosperms (cpDNA: 0.67; nrDNA: 0.137) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These results were further supported using SSR molecular markers, indicating that the genetic diversity of \u003cem\u003eA\u003c/em\u003e. \u003cem\u003emacrostemon\u003c/em\u003e as estimated in this study was higher. Haplotype diversity and total genetic diversity of \u003cem\u003eA. macrostemon\u003c/em\u003e based on chloroplast gene analysis (cpDNA: \u003cem\u003eHd\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.904, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e = 0.868) was slightly lower than estimates using nuclear genes (nrDNA: \u003cem\u003eHd\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.957, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e = 0.890). The genetic diversity of \u003cem\u003eA. macrostemon\u003c/em\u003e estimated using chloroplast genes was higher than other herbaceous plants such as \u003cem\u003eAllium mongolica\u003c/em\u003e (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e = 0.693) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] and \u003cem\u003eFritillaria pallidifora\u003c/em\u003e (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e = 0.670) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The genetic diversity of the population is caused by a variety of factors, which are influenced by the geographic distribution, biological characteristics, population size, and breeding system of the species [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. At present, many studies have used various molecular markers to explore the genetic diversity of herbs, such as \u003cem\u003eTypha domingensis\u003c/em\u003e and \u003cem\u003eTypha latifolia\u003c/em\u003e [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Estimates of genetic diversity in these species were low, which may be related to their high self-crossing rates and strong vegetative reproduction abilities [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The genetic and nucleotide diversity of nuclear genes in \u003cem\u003eA. macrostemon\u003c/em\u003e were higher than those using chloroplast genes, which may be related to the relative conservation of chloroplast genes and their maternal inheritance patterns [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. In addition, the genetic diversity of narrowly distributed species is lower than that of wide-spread species. \u003cem\u003eA. macrostemon\u003c/em\u003e is a wide-spread species with a large population and strong adaptability within the group. Thus, \u003cem\u003eA. macrostemon\u003c/em\u003e has maintained a relatively rich genetic diversity [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe influence of multiple forces on contemporary genetic structure\u003c/h2\u003e \u003cp\u003eGeographic structure is common in plants with continuous distributions, which is usually due to distance isolation or environmental isolation [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. In this study, SSR data indicated that genetic variation mainly occurred between populations. However, low levels of gene flow may lead to population adaptation to the local ecological environment, thus accelerating genetic differentiation among populations, and genetic drift can be a major factor affecting population genetic structure [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. In addition, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e analysis of both cpDNA and nrDNA data indicated that the proportion of inter-population genetic differentiation in the total genetic diversity was about 0.93445 and 0.94058, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Wright [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] believed that the level of inter-population genetic differentiation was extremely high (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.25). It is clear that genetic distance and geographic distance between populations are positively correlated (r\u0026thinsp;=\u0026thinsp;0.226, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), so topography may be one of the most important factors leading to differentiation. The isolation between populations is caused by physical obstacles such as the complex terrain and mountains in China. In addition, the mismatch distributions of cpDNA and ITS showed a multimodal curve, and the observed values did not match the expected values, violating the population expansion model and indicating that the population of \u003cem\u003eA. macrostemon\u003c/em\u003e had not undergone a significant expansion. This further indicated that the population of \u003cem\u003eA. macrostemon\u003c/em\u003e had structure influenced by geography.\u003c/p\u003e \u003cp\u003eDue to the special geographic environment and north-south changes in climate, there are obvious differences in natural conditions and geographic characteristics on both sides of the QHL, which is the geographic north-south dividing line of China [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The QHL serves as the dividing line between subtropical and temperate monsoon climate zones, separating the warm and humid southeast from the cold and dry northwest. The relatively mild Pleistocene climate in the lower elevations can provide a relatively stable microclimate environment for a certain range of habitats [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. In addition, the complex geological and environmental conditions of the QHL may affect the formation of the geographic structure of the \u003cem\u003eA. macrostemon\u003c/em\u003e lineage. A phylogenetic tree was constructed based on cpDNA and ITS sequence data results were consistent, \u003cem\u003eA. macrostemon\u003c/em\u003e from different regions were divided into southern and northern flora, with the southern flora further divided into southwestern and central-southeastern flora (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The possible factors for the formation of this genetic structure include geological events, the geographic environment, and climate change. Ecological factors such as temperature, rainfall, and other climatic conditions have significant effects on interspecific and intraspecific variation [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. As a widely distributed species, \u003cem\u003eA. macrostemon\u003c/em\u003e has three reproduction modes consisting of bud, bulb, and seed and has strong ecological adaptability and reproductive ability. Therefore, we conclude that the geographic structure of \u003cem\u003eA. macrostemon\u003c/em\u003e is mainly affected by the geographic barrier, which prevents gene exchange among different populations. Therefore, we speculate that the formation of the existing geographic structure of \u003cem\u003eA. macrostemon\u003c/em\u003e may be the result of allopatric differentiation caused by its long-term adaptation to different geological events, climatic conditions, and elevation differences in the distribution areas of Qinling-Huaihe and Wushan-Xuefeng. In fact, previous studies have shown that the QHL currently plays a key role in shaping plant dispersal and that it is an important boundary in China that separates ecologically distinct habitats [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Natural adaptations and physical barriers can explain the differences between the two subpopulations. In summary, our results support the hypothesis that the QHL contributes to the intraspecific differentiation pattern in \u003cem\u003eA. macrostemon\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eHaving high cpDNA haplotype and nucleotide diversity is a characteristic of ice age sanctuaries [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and these stable and diverse environments are conducive to the maintenance of species richness. The haplotype and nucleotide diversity of \u003cem\u003eA. macrostemon\u003c/em\u003e were high in select populations both north (TSS, SMX, HCS, and SHS) and south (DPS and SNX) of the QHL. Thus, these northern and southern populations were potential habitats for the plant despite their great north-south geographical distance. Consistent with this, areas on either side have been shown to be glacial refugia for other species [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Niche simulations indicated that the distribution of \u003cem\u003eA. macrostemon\u003c/em\u003e during the LGM period was significantly reduced compared to the current distribution, which may be related to the global climate cooling during the LGM period. During the LGM period, the highly suitable areas of \u003cem\u003eA. macrostemon\u003c/em\u003e tended to shrink to the south and north. Therefore, we speculate that the \u003cem\u003eA. macrostemon\u003c/em\u003e population may have taken refuge in place during the ice age and migrated to high altitude or low altitude areas to find a suitable living environment. This is consistent with the results of SSR, cpDNA and nrDNA, indicating that the species may have split into at least two glacial refugia across the QHL, one in the south and one in the north. MaxEnt simulations suggested that \u003cem\u003eA. macrostemon\u003c/em\u003e expanded in the middle of the Holocene, migrating south or to lower altitudes in warmer climates and thus making it better suited to growth in high temperatures and relatively humid environments. It is predicted to continue to shrink in the future, and the center of its distribution will likely move northward. \u003cem\u003eA. macrostemon\u003c/em\u003e is a widely distributed species, and the past and future changes in its distribution may be a signal for future changes in other populations with similar ecological habits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, individuals from a total of 50 \u003cem\u003eA. macrostemon\u003c/em\u003e populations were collected, and the genetic structure and geographic distribution patterns of this species were analyzed by combining cpDNA, ITS and SSR. The QHL, which has been found to be a north-south dividing line in phylogeography and population genetic structure and promotes physical geographic isolation, has played an important role in this process. The genetic diversity of \u003cem\u003eA. macrostemon\u003c/em\u003e was found to be high, and this genetic variation mainly existed among the populations. The formation of the present systematic geographic pattern and genetic structure is influenced by climate fluctuations and environmental heterogeneity. Populations of \u003cem\u003eA. macrostemon\u003c/em\u003e can be divided into two branches in the north and south, with evidence of at least two glacial sanctuaries both north and south of the QHL during the Quaternary glacial period. This study can provide scientific theoretical basis for the conservation, development, and utilization of \u003cem\u003eA. macrostemon\u003c/em\u003e resources. Further, it can provide a reference for the systematic geographic pattern of large-scale spatial distribution of plants in China and enrich our understanding of the evolutionary history of plant species diversity in East Asia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ecpDNA:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003echloroplast DNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003enrDNA:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRibosomal DNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026pi;\u0026nbsp;:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enucleotide diversity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eG \u003csub\u003eST\u003c/sub\u003e :\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe level of population differentiation at the species level\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eN\u003csub\u003eST\u003c/sub\u003e:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ean estimate of population subdivisions for phylogenetically ordered alleles\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLGM:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003elast glacial maximum\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMID:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003emid Holocene\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAUC:\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe area under the curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eN \u003csub\u003em\u003c/sub\u003e :\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003egene flow\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePop\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003epopulation name\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHe:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eexpected heterozygosity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHo:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eobserved heterozygosity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNe:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eeffective number of allele\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNa:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eobserved allele number\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eI:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShannon information index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePPL:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003epercentage of polymorphic loci\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHe:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eexpected heterozygosity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHo:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eobserved heterozygosity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePIC:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003epolymorphic information content\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePi:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNucleotide diversity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003csub\u003ed\u003c/sub\u003e:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiversity of haplotypes;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumber of haploty\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAMOVA:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eanalyses of molecular variance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eF \u003csub\u003eST\u003c/sub\u003e :\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic differentiation among populations\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the projects of\u0026nbsp;Construction Program of Biology First-class Discipline in Guizhou\u0026nbsp;(GNYL [2017]009), this project received funding from the National Natural Science Foundation of China (NSFC, No. 32260252). Key Laboratory Opening Project of Education Department of Guizhou Province ministry of Education (Guizhou Education Cooperation KY [2019]033), Guizhou Science and Technology Support Plan Project (Guizhou Science and Technology Cooperation Support ([2019] 2451-2),\u0026nbsp;Open Research Fund of Guizhou Provincial Key Laboratory for Biodiversity Conservation and Utilization in the Fanjing Mountain Region ([2020]2003)\u0026nbsp;and Construction of modern industrial technology system for Chinese medicinal materials in Guizhou Province (No. GZCYTX-02). Thanks very much for those above founders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuizhou Province Biology First-class Discipline Construction Project (GNYL[2017]009), the National Natural Science Foundation of China (NSFC, No. 32260252). Key Laboratory Opening Project of Education Department of Guizhou Province ministry of Education (Guizhou Education Cooperation KY [2019]033), Guizhou Science and Technology Support Plan Project (Guizhou Science and Technology Cooperation Support ([2019] 2451-2), Open Research Fund of Guizhou Provincial Key Laboratory for Biodiversity Conservation and Utilization in the Fanjing Mountain Region ([2020]2003) and Construction of modern industrial technology system for Chinese medicinal materials in Guizhou Province (No. GZCYTX-02).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic data for all unique haplotypes are available on GenBank\u003c/p\u003e\n\u003cp\u003e(Accession numbers OM102763-OM102953)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSUPPORTING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional supporting information may be found online in the Supporting Information section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu, J., M\u0026ouml;ller, M., Provan, J., Gao, L. 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The divergence of two independent lineages of an endemic Chinese gecko, Gekko swinhonis, launched by the Qinling orogenic belt. \u003cem\u003eMolecular Ecology\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(12), 2490-2500. https://doi.org/10.1111/j.1365-294X.2010.04660.x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"A. macrostemon, Genetic diversity, Genetic variation, Population structure, Qinling Mountains-Huaihe River Line, Glacial refugia","lastPublishedDoi":"10.21203/rs.3.rs-3933291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3933291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere are many physical and geographic boundaries in China, but there are few studies on the natural geographical isolation boundary of the Qinling Mountains-Huaihe River Line (QHL) using molecular ecological evidence. The purpose of this study was to explore the genetic diversity, genetic structure, and possible origins of \u003cem\u003eAllium macrostemon\u003c/em\u003e and to verify whether the QHL played a role in the structure of \u003cem\u003eA. macrostemon\u003c/em\u003e populations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalysis of chloroplast DNA and nuclear ITS molecular markers showed a very high level of genetic differentiation among populations (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.25). ombined with chloroplast DNA and nuclear ITS data, \u003cem\u003eA. macrostemon\u003c/em\u003e populations could be grouped into northern and southern flora, with the southern flora further divided into southwestern and central-southeastern flora. The results of niche simulation show that the distribution area of \u003cem\u003eA. macrostemon\u003c/em\u003e will reach the maximum in the future.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe data points to a geographic barrier that has been maintaining the regional separation of \u003cem\u003eA. macrostemon\u003c/em\u003e. The QHL, which has been found to be a north-south dividing line in phylogeography and population genetic structure and promotes physical geographic isolation, has played an important role in this process. This study can provide a scientific theoretical basis for the conservation, development, and utilization of \u003cem\u003eA. macrostemon\u003c/em\u003e resources. Further, it can provide a reference for the systematic geographic pattern of large-scale spatial distribution of plants in China and enrich our understanding of the evolutionary history of plant species diversity in East Asia.\u003c/p\u003e","manuscriptTitle":"Phylogeography of Allium macrostemon: south-north divergence reveals a natural geographic isolation boundary in the Qinling Mountains-Huaihe River Line in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 13:33:53","doi":"10.21203/rs.3.rs-3933291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0552802c-80e0-441c-80d4-71a9dbc2b76e","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-06T06:16:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 13:33:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3933291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3933291","identity":"rs-3933291","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}