Effects of mountain uplift and climate change on phylogeography and species divergence of East Asia Morella

Effects of mountain uplift and climate change on phylogeography and species divergence of East Asia Morella | 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 Effects of mountain uplift and climate change on phylogeography and species divergence of East Asia Morella cai zhao, Yu Xia Lu, Shan Shan He, Chun Xue Jiang, Jian Feng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6558644/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Journal of Plant Research → Version 1 posted 4 You are reading this latest preprint version Abstract Mountain uplift and Quaternary climate oscillations have profoundly influenced plant species' distribution and diversification, yet their impacts on demographic history and biogeographic patterns remain unclear. This study investigates the effects of habitat fragmentation and climatic shifts on genetic diversity and phylogeographic distribution of four East Asian Morella species. Using chloroplast DNA (cpDNA) sequences and simple sequence repeats (SSR) were used to study the species divergence and genetic structure of Morella from 477 individuals of 63 populations. The whole-genome resequencing was also applied to ensure the accuracy of the estimation of species differentiation time and phylogenetic relationship. We identified species-specific haplotypes, only H2 haplotype was shared by M. rubra and M. adenophora , and H3 was shared by M. esculenta and M. rubra in cpDNA sequence. Phylogenetic analysis revealed a topology of M. esculenta + ( M. nana ( M. rubra + M. adenophora )), with significant gene flow among species. Its divergence occurring between 5.02 and 12.72 Ma was completed before the Quaternary period. Results suggest Late Miocene-Pliocene geological and climatic shifts drove speciation, while Quaternary climate fluctuations shaped their geographic distribution, with potential refugia maintaining genetic diversity. Our findings highlight the roles of orogeny and paleoclimate in speciation and range dynamics, providing insights into East Asia's history of lineage differentiation. Morella Genetic diversity Species differentiation Demographic dynamics Management and protection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction East Asia is known as the "museum" of plants, which is rich in temperate flora diversity in the world (López-Pujol et al. 2011 ; Wang et al. 2015 ; Lu et al. 2018 ). At the same time, the central and southern mountainous areas of China are the main centres of endemic plants, and the stability of Tertiary may provide favorable conditions for the long-term reproduction of residual plant lineages(López‐Pujol et al. 2011). The mountainous regions in southern China are complex and diverse, with vast territory and distinct seasons (Wu et al. 2023 ), which can provide a variety of habitats and maintain species diversity in a short distance, especially under unfavorable climatic conditions (Hoorn et al. 2013 ; Favre et al. 2016 ). What is important is that mountain uplift and climate change will greatly affect the geographical distribution and species divergence of plants (Liu et al. 2013 ; Hickerson et al. 2010 ; Zheng et al. 2021 ), and the systematic geographical pattern reflects the influence in the process of neutral evolution of species (Avise 2000 ; Hewitt 2004 ). The collision between the Indian plate and the Eurasian plate led to the uplift of the Qinghai-Tibet Plateau (QTP) (Zhao et al. 2025 ), which greatly affected the geological features and habitat structure of the region. The mountains, deep valleys and rivers formed with the uplift of the plateau may be obstacles to the migration and diffusion of plant species, which have profoundly affected the geographical distribution pattern and genetic structure of plant species in this area (Wen et al. 2014 ; Ickert‐Bond and Renner 2016). Adjacent to the eastern part of the QTP, the changes of climate and topography will be influenced by the Qinghai-Tibet Plateau to a greater or lesser extent. Moreover, the geographical environment in southwest China is extremely complex, which has always been the focus of global biodiversity research. Complex topography and local selection will also lead to species divergence and genetic differences (López‐Pujol et al. 2011; Wambulwa et al. 2022 ; Hu et al. 2022 ). In addition, the violent movement of the QTP created the unique topographical and environmental characteristics of the Yunnan-Guizhou Plateau (Xie et al. 2017 ). The strong orogeny in Yunnan-Guizhou Plateau resulted in the landform pattern of high in the west and low in the east, which separated it from the surrounding areas (Dai et al. 2013 ; Wang et al. 2014 ). Topographic heterogeneity and orogeny will form new habitats, promote the formation of species and affect the distribution pattern of species (Yin et al. 2021 ). The orogeny movement will lead to the regional transfer of species, which will lead to the fragmentation of a large area of habitats into smaller patches, that is, habitat fragmentation (Sun et al. 2003; Xie et al. 2017 ). Climate change usually leads to the expansion or contraction of the protected areas of plant habitat, which is an important factor affecting the geographical distribution pattern of species (Yang et al. 2022 ; Liu et al. 2023 ). The impact of climate change on each species is independent and not directly related. Species at the edge of geographical distribution are more sensitive to climate change (Taberlet et al. 1998 ; Matías et al. 2017 ). The origin and evolution of species are generally closely related to many historical events, such as the uplift of mountains and different evolutionary adaptations of species caused by climate change (Lowry et al. 2008 ; Liu et al. 2013 ). The uplift of the Qinghai-Tibet Plateau not only changed the topography of East Asia, but also led to great changes in the climate (Raymo and Ruddiman, 1992 ; Wang et al. 2010 ). It leads to the change of surrounding environment and affects the formation and divergence of species. Morella is a perennial evergreen shrub or tree of Myricaceae, which grows mostly in valleys and forests. Only four species are distributed in East Asia: Morella rubra Lour, Morella nana (A. Chev.) J. Herb, Morella adenophora (Hance) J. Herb. and Morella esculenta (Buch. - Ham. ex D. Don) I. M. Turner (Herbert,2005; Liu, 2016 ). The research of Herbert ( 2005 ) confirmed that four species distributed in East Asia have high bootstrap support in phylogenetic clades. M. esculenta is at the base of four species, and the other three species present a complex group relationship (Figs. S1). Chen's (2014) research on nuclear gene ITS and cpDNA ( psb A -trn H) has not resolved the relationship of the remaining three species. Therefore, in order to determine the relationship of four Morella species distributed in East Asia, it is necessary to study by various means. Liu ( 2016 ) resolved the relationship of four species, but did not resolve the issues of historical dynamics and genetic divergence. Morella rubra is widely distributed in East Asia, and is often cultivated for its edible value. At present, a large number of cultivated varieties have been cultivated in China, such as "Crystal", "Dongkui" and "Big Leaf and Fine Pedicel". M. esculenta is mainly distributed in subtropical and tropical areas from East Asia to Southeast Asia, and is found in many provinces of China. M. adenophora is mainly distributed in Guangdong, Guangxi and Hainan Island. M. nana is mainly distributed in the higher altitude areas of Yunnan-Guizhou Plateau. M. nana and M. adenophora are endemic to China (Chen, 2014 ; Liu, 2016 ). The young clades of M. esculenta and M. adenophora are easily distinguished by dense hairs, the short clades and petioles of M. nana and M. rubra are glabrous or sparsely hairy, and there are dense golden glands on the back of M. rubra (Zhong and Xie, 2010 ; Chen, 2014 ; Jia, 2016 ). The plants of this genus have high medicinal and edible value, and can be eaten fresh and made into fruit wine. They can resist bacteria and inflammation, treat rheumatic pain, and resist oxidation and glycosylation (Zhou and Yang, 2000 ; Sun et al. 2013 ; Yang et al. 2015 ; Bai et al. 2020 ). With the beneficial value of species being known and valued by people, as well as environment change and human intervention, the wild germplasm resources of Morella have been severely degraded, and the number of individuals in the population is low. M. adenophora has been classified as a threatened species or a vulnerable (VU) species. Wild species play a vital role in the breeding programme, because of their wide variability in morphological structure, resistance and quantitative traits (Laidò et al. 2013 ; Wang et al. 2017 ). Genetic diversity is the basis of ecosystem diversity, and it is also the basis of species to survive, adapt to the environment and evolve in the process of evolution (Liu et al. 2022 ). Understanding the geographical distribution and genetic information of species is very important for the protection of wild germplasm resources (Shahzad et al. 2017 ). However, the previous studies on Morella mostly focused on one species, for example, Liu et al. ( 2013 ) and Ju et al. ( 2023 ) studied only one species of M. rubra . However, there is little research on the population history and genetic differences of Morella . Although Liu ( 2016 ) studied the genetic relationship of four species based on some individuals, this study is to expand the sample size of species and comprehensively analyze the reasons that affect the lineage geography and species divergence of four Morella species in East Asia by combining population genetics. Here, this study uses SSR markers, cpDNA markers and whole-genome resequencing analysis to solve the following problems:1) to obtain the genetic structure and evolutionary history of four species;2) infer the history of gene flow and population statistics, and demonstrate the formation of the current distribution pattern;3) 3) to explore geological movement and climate change on the divergence of four Morella species; 4) to put forward some reasonable suggestions on the utilization of Morella resources and make protection strategies. Material and methods Sample collection In the present study, 404 individuals from 62 populations of 4 species were collected in the south of China for cpDNA analysis (Table S1 and Fig. 1 a), and Casuarina equisetifolia was taken as an outgroup. 477 individuals from 63 populations were collected for SSR analysis (Table S1 ). On the basis of cpDNA and SSR analysis, in order to further determine the phylogenetic relationship among the four species, On the basis of cpDNA and SSR analysis, in order to further determine the phylogenetic relationship among the four species, 12 individuals from 12 populations, including four species (each species contains three populations) (Table S1 ), were used for whole-genome resequencing analysis, with Rhoiptelea chiliantha , Pterocarya stenoptera , Quercus fabri , Betula luminifera and Casuarina equisetifolia as outgroups to further verify the phylogenetic relationship and divergence time. The collected materials cover the entire geographical distribution of four species. It was found that the wild population of Morella was small, so some natural populations and wild individuals that could be collected in the wild were relatively few, and M. adenophora was a threatened or vulnerable species (VU). Each population includes 2 ~ 13 individuals, and only GDTH has one individual. Each sample is spaced at least 20 meters apart, and healthy young leaves are dried with silica gel for DNA extraction. All specimens are kept in the herbarium of College of Life Sciences, Guizhou University (sample numbers: zhaocaiM1 ~ zhaocaiM63). The plants collected in this study were identified by Dr. Zhao Cai (associate professor of Guizhou University, research on plant systematics and evolution). Figure 1 a and Table S1 provide detailed information of latitude, longitude and altitude of all populations. It should be emphasized that although the collected samples are unbalanced among populations, it will not affect our phylogeographic research results. The samples of similar phylogeographic studies are also unbalanced. For instance, sample sizes for Tetrastigma hemsleyanum (individuals from 6 to 15), Notopterygium (individuals from 2 to 17) and Machilus thunbergii (individuals from 2 to 8) were unbalanced (Wang et al. 2015 ; Shahzad et al. 2017 ; Fan et al. 2022 ). However, they can present the entire geographical distribution range and representative sample the species and can cover the geographical distribution of representative populations. DNA extraction and sequencing Total DNA was extracted by plant DNA extraction kit (Tiangen, Beijing, China). Put the processed materials in a mortar, add a small amount of silicon dioxide, and liquid nitrogen, quickly grind them into powder, and transfer them into a 2 mL centrifuge tube. For the extraction method, refer to the kit steps. We used a 1.5% agar gel to detect the quality of extracted DNA, and the ideal sample was used in the follow-up experiment. In this study, five chloroplast gene primers ( psb A- trn H, trn D- psb M, trn L- trn F, ycf1 1205- ycf1 2402 and ycf1 3125- ycf1 4381) (Table S2 ) (Liu 2016 ), and five pairs of SSR primers (Table S3) (Zhang et al. 2009 ; Chen 2014 ; Liu 2016 ) were screened out by consulting relevant references and pre-testing (Firstly, some samples were randomly selected for PCR amplification, and the PCR products were detected by 1.5% agarose gel electrophoresis, and the primers with clear and single bands were preliminarily screened. Then, the primers were re-screened by polyacrylamide gel electrophoresis to determine whether the primers were stably amplified and polymorphic, and the qualified primers were screened for subsequent experiments.), which were used to amplify more than 400 samples of four Morella species. The sequencing reaction is carried out in the Beijing Tsingke Biotech Co, Ltd. The amplification conditions are: 2×Taq PCR Master Mix 12.5 uL, DNA template 1uL, upstream and downstream primers 1uL each, and the remaining volume was made up to 25 uL with ddH 2 O. PCR amplification program: pre-denaturation at 94 ℃ for 2 min, denaturation at 94 ℃ for 30 s, annealing at 52 ℃-60 ℃ for 30 s, extension at 72 ℃ for 30 s, and final extension at 72 ℃ for 7 min, the reaction was set to 35 cycles. Genetic diversity and genetic structure A number of cpDNA fragments were spliced together with PhyloSuite 1.2.2 (Zhang et al. 2020 ). The haplotype ( N ), haplotype diversity ( H d ) and nucleotide diversity index ( π ) of each population of the species were identified by DnaSP 6.0 (Rozas 2009 ). Genetic diversity (expected heterozygosity ( He ), observed heterozygosity ( Ho ), effective allele number ( Ne ), the mean number of alleles ( Na ), percentage of polymorphic loci ( PPL ) and Shannon's information index ( I ) of Morella population were analyzed based on SSR using GenAIex6.5 (Peakall and Smouse 2012 ). The polymorphism information content ( PIC ) of SSR loci was measured by CERVUS 3.0 (Kalinowski et al. 2007 ). The Analysis of molecular variance (AMOVA) analysis of cpDNA and SSR was carried out with Arlequin 3.5 (Excoffier and Lischer 2010 ) to evaluate the genetic variation and divergence among species. The Structure v2.3.4 (Pritchard et al. 2000 ) was used to analyze the genetic structure of SSR sequences by Bayesian clustering algorithm, aiming to identify genetic populations with similar allele frequencies. To determine the population number ( K value), we set the range of K from 1 to 10, and perform 10 independent operations for each K value, using a Model called the "Admixture Model". For calculation parameters, set Length of burnin peroid to 100,000 and Number of MCMC Reps after Burnin to 1,000,000. After the operation is completed, we obtain the logarithmic likelihood ( L(K )) rate of change corresponding to different K values. To determine the best K value, we follow the method proposed by Evanno et al. ( 2005 ) by calculating the Delta K value between successive K values. In addition, the PERMUT 2.0 software was used to calculate the mean in-population genetic diversity ( Hs ), total genetic diversity ( Ht ), and genetic divergence coefficients N ST and G ST based on chloroplast DNA data. The genetic distance of Nei's was calculated by GenAlex 6.5 software, and the Neighbor-Joining (NJ) tree was constructed by cluster analysis by MEGA 6.0 software. In addition, Mantel test was carried out by GenAlex 6.5 software to explore the correlation between Nei's genetic distance and geographical distribution and altitude among species. Based on the genetic distance, the principal coordinate analysis (PCoA) of all populations of four species was further carried out by GenAlex 6.5 software to reveal the genetic structure and potential geographical patterns among populations. Phylogenetic relationship and estimation of divergence time The cpDNA sequence was analyzed by MEGA v 6.0 software, and the maximum likelihood (ML) model was bootstrap 1000 times to obtain the phylogenetic clade. The cpDNA intermediate connection network was constructed using Network10.2 (Bandelt et al. 1999 ) genealogical relationship analysis. ArcGIS v 10.2 was used to map the haplotype geographical distribution of the population. The software package BEAST 1.10.4 (Suchard et al. 2018 ) was used to estimate the divergence time of Morella species. In order to correct the divergence time more accurately, a calibration point was introduced in BEAST analysis. Based on Herbert's (2005) in-depth analysis of systematics and biogeography of Myricaceae, it was determined that the divergence time between M. esculenta and three other Morella species M. nana , M. adenophora , and M. rubra occurred at about 12.72 Ma (SD = 1.7). In order to determine the best nucleotide substitution model, according to Akaike information criterion, ModelFinder was used to select the model in PhyloSuite 1.2.2 (Kalyaanamoorthy et al. 2017 ). In the analysis, we adopted the Relaxed Clock Unorrelated Lognormal Method, set the Markov chain to run for 10,000 generations, take samples every 1,500 generations, and set the burn-in value to 10%. In order to ensure the high reliability of the evolutionary tree, Tracer v.1.7.2 is used to test the convergence of the Markov chain. Finally, the Tree ANNOTATOR 1.10.4 (Suchard et al. 2018 ) is used to obtain the phylogenetic tree of Maximum Clade Credibility with the greatest reliability, and FigTree 1.4.2 is used to check the beautification tree. Finally, the differentiation time of Morella species was deduced. In order to verify and compare the reliability of divergence time of four species based on cpDNA data, on this basis, three individuals of each species and five outgroups were selected for whole-genome resequencing, and the sequencing reaction was completed in Shanghai Personal Biotechnology Co, Ltd, and the results of 17 individuals were extracted from the genome sequence based on GeneMiner software (Xie et al. 2024 ), the angiosperm 353 gene (AGS) set was extracted from the genome sequence to construct phylogenetic tree and estimate the divergence time. AGS is composed of 353 universal low-copy nuclear genes, which were identified by systematic comparative analysis of more than 600 angiosperms, these genes can be widely used in phylogenetic research and population genetics of various taxonomic scales (Zhang et al. 2022 ). We used GeneMiner software (Xie et al. 2024 ) to analyze the sequencing data, which can effectively mine phylogenetic molecular markers from transcriptomics, genomics and other NGS data. Gene mining is carried out by GeneMiner software. reads of the target gene are screened from the original data, and the candidate genes are generated by sequence assembly. After quality control, the low-quality data are eliminated. The low-copy nuclear genes of each sample were merged and trimmed, and only the genes with the maximum difference rate (Max. Diff) less than 10% were retained, and then merged and trimmed again. Maximum Likelihood (ML) method was used to construct the phylogenetic tree, and the Bootstrap value was set to 1000 to analyze the statistical support. Inference of Population history The mismatch distribution of cpDNA sequences of four Morella species was detected by DnaSP 6.0, and the population expansion was tested by Arlequin v 3.5 and Tajima's D (Tajima, 1989 ), Fu and Li’ s F* (Fu and Li, 1993 ) and Fu and Li’ s D* (Fu and Li, 1993 ). In addition, in order to evaluate the continuity of species population dynamic changes and avoid errors caused by transient occurrences, we used BEAST v 2.7.6 (Bouckaert et al. 2019 ) to conduct Bayesian skyline plots (BSPs) for analysis, to obtain the dynamic changes of population size over time. In this analysis, we use the average base mutation rates (2×10 − 9 substitutions/site/year) previously approximated by Wolfe et al. ( 1987 ) for angiosperm chloroplast DNA (1 ~ 3 ×10 − 9 substitutions/site/year), alongside a population coalescent Bayesian Skyride model for the prior tree and strict molecular clock. To ensure robust inference, we use random initial tree, linear model and setting the length of MCMC chain to make ESS ≥ 200 (five cpDNA haplotypes are 10,000,000 chains). To assess the reliability of the results, we conducted three separate analyses and combined their outcomes using TRACER 1.5, a tool designed for summarizing and visualizing the output of BEAST analyses. Ultimately, we leveraged the R programming language to analyze and visualize the BSPs, providing insights into the historical dynamics of the population size over time. We also used BARRIER 2.2 (Manni et al. 2004 ) to analyze whether there is an intergroup gene flow barrier in the four species distribution areas. In order to assess whether the four species suffer from recent Bottleneck effects, the BOTTLENECK software (Piry et al. 1999 ) is used for analysis. The software provides three different mutation models to calculate bottleneck effects: the infinite allelic mutation model (IAM), the stepped-mutation model (SMM), and the biphasic mutation model (TPM). If the test results are statistically significant, it indicates that the group may have experienced a bottleneck. Ecological niche modeling In order to verify the niche differences among target species, we used MAXENT (Phillips et al. 2006 ) based on field specimen collection, literature report, Global Biodiversity Information Facility (GBIF; https://www.gbif.org/ ) and Chinese Virtual Herbarium (CVH; http://www.cvh.ac.cn/ ), delete the repeated geographical records, and predict the niche model of four species. The bioclimatic environment was obtained by downloading from WordClim database ( http://www.worldclim.org/ ). At first, 19 climatic factors were screened to avoid the interaction among various factors affecting the prediction results. The variables were derived from monthly temperature and precipitation data, enabling the creation of more ecologically relevant parameters (Fava et al. 2020 ). These bioclimatic indicators capture yearly patterns (such as average yearly temperature and total yearly rainfall), seasonal variations (including yearly fluctuations in temperature and rainfall), and extreme or constraining environmental conditions (for example, minimum and maximum monthly temperatures) (Fava et al. 2020 ; Wu et al. 2024 ). Based on the relationship between the high Percent contribution and the high permeability, and Pearson correlation coefficient in SPSS, 10 climatic factors were screened out for niche model prediction, so as to improve the accuracy of prediction. The 10 climatic factors are as follows: M. esculenta (bio18, bio4, bio15, bio14, bio11, bio7, bio13, bio12, bio19 and bio16), M. adenophora (bio18, bio2, bio15, bio4, bio14, bio6, bio1, bio3, bio19 and bio8), M. rubra (bio18, bio4, bio15, bio2, bio6, bio14, bio1, bio11, bio9 and bio6), and M. nana please refer to our previous research (Wu et al. 2024 ). For MAXENT modeling, we use the default parameters for analysis. In order to evaluate the statistical performance of the model, we used the area under the “receiver operating characteristic (ROC) curve” (AUC; Fawcett, 2006 ). The model with AUC approximately 1 showed that the prediction ability was good. We use ArcMap v10.5 to draw a suitable distribution range. Results Genetic diversity analysis By correcting and splicing five cpDNA fragments ( psb A- trn H, trn D- psb M, trn L- trn F, ycf 11205- ycf 12402, and ycf 13125- ycf 14381) were used to analyze 404 individuals from 62 populations of the four Morella species. The total length of cpDNA fragment was 3157bp, of which psb A- trn H, trn D- psb M, ycf1 1205- ycf1 2402, ycf1 3125- ycf1 4381 and trn L- trn F fragments are 435, 587, 855, 887 and 393bp, respectively. Among the four species, 51 cpDNA haplotypes were detected (Fig. 1 ; Table 1 ), of which only H2 haplotype was shared by M. rubra and M. adenophora , and H3 was shared by M. esculenta and M. rubra . In addition, all haplotypes had species specificity. In M. rubra there were 14 haplotypes, and GXLG and GXSB haplotypes in Guangxi had the highest diversity. Among the 16 haplotypes of M. nana , the population YNPL had the highest haplotype diversity. Morella esculenta has 18 haplotypes. The Guizhou populations (GZZJ, GZQL and GZQX) also had the highest haplotype diversity. The M. adenophora had five haplotypes, of which H27, H48, H49 and H50 were unique to the population, and H2 was a shared haplotype (Table 1 ). Genetic diversity analysis indicated that the total genetic diversity ( Ht ) was 0.913, and the average genetic diversity within populations ( Hs ) was 0.205. The genetic diversity of the four species of Morella was relatively high. At the species level, the haplotype diversity of the four species was the highest in M. nana ( H d =0.819) and the lowest in M. adenophora ( H d =0.234). The highest nucleotide diversity was found in M. esculenta ( π = 0.00266) and the lowest in M. adenophora ( π = 0.0001) (Table 2 ). Genetic diversity analysis was conducted on 477 individuals from 63 populations of four species using five highly polymorphic SSR primers. The results showed that the average genetic diversity index of wild Morella species was at a relatively high level ( Na = 3.53, Ne = 2.896, I = 1.07, Ho = 0.924, He = 0.597, PIC = 0.844, PPL = 93.33%) (Table S4). A higher I value means a higher genetic diversity, indicating that the genetic diversity of YNLY is higher ( I = 1.671). Based on the above data analysis, the genetic diversity of the populations of YNLY, GXTE, GXTB and GXSS in this study is high, while the genetic diversity of FJCL and YNLC is low. The polymorphism information content ( PIC ) value was related to the degree of genetic variation at the locus, with PIC = 0.844, indicating that there might be variation within Morella populations (Table S4). The genetic diversity analysis of the five SSR microsatellite loci showed that SSR4 had the highest genetic diversity, while SSR2 showed a low level of genetic diversity. The genetic divergence coefficient ( Fst ) = 0.317, F = -0.591 (Table S4), suggesting that there might have been inbreeding or genetic drift among the four species. Table 1 Population haplotype distribution and genetic diversity parameters of the four Morella species with cpDNA. Species Population N Nh Hd π cpDNA Chlorotypes M. rubra GZDY 13 1 0.000 0.00000 H4(13) GXJA 4 1 0.000 0.00000 H4(4) AHGC 7 2 0.286 0.00036 H4(6),H28(1) GXLB 10 3 0.378 0.00026 H36(8),H37(1),H38(1) GXLG 8 4 0.821 0.00035 H36(3),H38(1),H39(2),H40(2) FJCL 5 1 0.000 0.00000 H43(5) GZDB 7 1 0.000 0.00000 H4(7) GZKY 9 1 0.000 0.00000 H4(9) FJXM 5 1 0.000 0.00000 H4(5) SCNJ 6 2 0.600 0.00038 H4(3),H44(3) ZJLS 5 2 0.600 0.00019 H4(3),H46(2) GDMX 3 1 0.000 0.00000 H4(3) AHFT 3 1 0.000 0.00000 H4(3) GXSB 2 2 1.000 0.00032 H2(1),H3(1) GXDC 6 2 0.333 0.00032 H2(5),H47(1) GDGN 3 1 0.000 0.00000 H2(3) GDTH 1 1 0.000 0.00000 H51(1) JXTH 3 1 0.000 0.00000 H4(3) ZJNL 5 1 0.000 0.00000 H4(5) M. nana GZQG 5 1 0.000 0.00000 H5(5) GZPZ 10 1 0.000 0.00000 H6(10) GZXR 4 1 0.000 0.00000 H6(4) YNPL 11 3 0.636 0.00058 H9(5),H10(1),H11(5) YNLQ 2 1 0.000 0.00000 H12(2) GZQX 2 2 1.000 0.00286 H5(1),H13(1) GZSC 4 1 0.000 0.00000 H5(4) SCYB 10 1 0.000 0.00000 H16(10) SCRH 12 2 0.409 0.00013 H17(9),H18(3) YNDL 9 2 0.222 0.00007 H19(8),H20(1) YNNJ 9 1 0.000 0.00000 H19(9) YNQB 8 2 0.250 0.00016 H6(7),H21(1) YNYL 12 1 0.000 0.00000 H5(12) GZZJ 2 2 1.000 0.00032 H5(1),H6(1) YNHZ 11 1 0.000 0.00000 H6(11) GZWN 8 1 0.000 0.00000 H5(8) YNZY 12 1 0.000 0.00000 H5(12) YNES 10 2 0.200 0.00013 H22(9),H23(1) YNLC 5 1 0.000 0.00000 H12(5) GZSY 9 1 0.000 0.00000 H5(9) GZLZ 7 2 0.286 0.00037 H6(6),H24(1) M. esculenta GXTA 7 3 0.524 0.00036 H8(5),H25(1),H26(1) GXTB 9 1 0.000 0.00000 H8(9) YNYA 4 2 0.500 0.00080 H29(1),H30(3) GZQL 9 5 0.806 0.00048 H31(1),H32(4),H33(2),H34(1),H35(1) YNYB 7 1 0.000 0.00000 H1(7) SCQA 11 1 0.000 0.00000 H15(11) YNLY 9 2 0.222 0.00028 H41(8),H42(1) GXTE 5 1 0.000 0.00000 H8(5) GXSS 6 2 0.333 0.00021 H8(5),H45(1) GZZF 5 2 0.400 0.00026 H7(1),H8(4) YNXC 4 2 0.500 0.00144 H14(3),H15(1) GXSW 5 2 0.400 0.00153 H3(1),H8(4) M. adenophora GXXN 9 1 0.000 0.00000 H2(9) GXHZ 10 2 0.356 0.00011 H2(8),H27(2) GXFA 3 1 0.000 0.00000 H2(3) GXFB 3 1 0.000 0.00000 H2(3) GXDA 5 1 0.000 0.00000 H2(5) GXDB 7 1 0.000 0.00000 H2(7) GXDD 2 1 0.000 0.00000 H2(2) GXQB 3 1 0.000 0.00000 H2(3) GXQA 11 4 0.673 0.00029 H2(6),H48(3),H49(1),H50(1) GXHP 3 1 0.000 0.00000 H2(3) N , number of samples; Nh , Number of haplotypes; H d , gene diversity; π, nucleotide diversity averaged across loci. Table 2 Genetic diversity parameters of the four Morella species with cpDNA. species M S Ps (θw) H d π M. rubra 105 17 0.005426 0.001038 0.611 0.00084 M. nana 162 26 0.008317 0.001469 0.819 0.00194 M. esculenta 81 39 0.012472 0.002512 0.807 0.00266 M. adenophora 56 4 0.001277 0.000278 0.234 0.0001 All 404 67 0.021433 0.003259 0.917 0.00407 M, sequence number; S, separation bit number; ps = S/m; θw = ps/α; H d , gene diversity; π, nucleotide diversity averaged across loci. Genetic divergence and Genetic structure analysis The AMOVA analysis based on cpDNA data indicated that genetic variation mainly occurred among groups (61.23%) (Table 3 ). The genetic divergence coefficient Nst was greater than Gst (Nst = 0.804, Gst = 0.775; P < 0.05), suggesting a clear phylogeographic structure among M. rubra and M. adenophora, M. esculenta and M. nana . A series of analyses using SSR markers further revealed the genetic structure of the four species of Morella . The AMOVA analysis of SSR indicated that genetic variation primarily originated from within populations, accounting for 85% of the total variation (Table 3 ). The results of principal component analysis (PCoA) indicated that although there was a certain degree of genetic overlap among species (Fig. 2 ), the populations of the four species maintained a relatively clear genetic divergence overall. NJ tree shows that 63 populations can be clearly divided into four main genetic groups (Fig. 3 c). Corresponding to the species, M. esculenta and M. nana form different clades, and some populations of M. rubra and M. adenophora are mixed on the phylogenetic tree surface. Meanwhile, Mantel tests demonstrated a positive correlation between genetic distance and geographic distance in Morella (R2 = 0.0332, P = 0.000) (Fig. S2 ), and also a positive correlation between genetic distance and altitude (R2 = 0.0293, P = 0.000) (Fig. S3). The results suggest that the IBD model supports the role of geographic distance and altitude in driving the genetic divergence of the four species. Bayesian clustering analysis based on SSR data determined the optimal K value, dividing the 63 populations of the four species of Morella into four groups: the first group was M. rubra , the second group was M. nana , the third group was M. esculenta , and the fourth group was M. adenophora (Fig. 3 a and b). Under this cluster number, the genetic composition of each population exhibits specific characteristics, and also reveals the gene exchange between populations. The result of the SSR genetic structure indicate that there were genetic similarities between different species, and the mixing of groups (marked green, red, blue, and yellow, respectively) shows widespread gene exchange, genetic diversity, and potential hybridization events between species (Fig. 3 b). Additionally, barrier analysis showed that there were genetic boundaries among the four species, and the boundaries a, b and c could divide the four species as a whole. But there was still gene exchange among some populations of different species, especially among the populations of M. rubra and M. adenophora (Fig. 4 ). Table 3 Analysis of molecular variance (AMOVA) for Morella species based on cpDNA and SSR Sign Source of variation Sum of squares Variance components Percentage of variation Fixation Index ( F st) cpDNA Among groups 2454.8 8.13776 Va 61.23 F SC =0.94 Among populations within groups 1829.99 4.86289 Vb 36.59 F ST =0.98 Within populations 99.142 0.28989 Vc 2.18 F CT =0.61 Total 4383.931 13.29053 SSR Among group 18.505 0.02321 Va 5.93 F SC =0.10 Among populations within groups 50.971 0.03551 Vb 9.07 F ST =0.15 Within populations 295.111 0.33271 Vc 85 F CT =0.06 Total 364.587 0.39143 Phylogenetic relationship and estimation of divergence time In order to determine the phylogenetic position of the four species, in the present study, cpDNA haplotype phylogenetic clade was constructed based on Maximum Likelihood (ML)and Maximum Parsimony (MP) using the genus Casuarina equisetifolia was used as outgroup(Fig. 1 e). The analysis results indicate that the phylogenetic tree can be divided into four groups, which clearly reveals the interspecific relationship among them. M. esculenta is located at the base of the phylogenetic tree, followed by M. nana , in which M. rubra and M. adenophora form a sister group. The results of whole-genome resequencing showed that M. esculenta was at the base of the clade, M. rubra and M. adenophora a form sister group, and then form a sister group with M. nana (Fig. 1 d). Among cpDNA haplotypes, H2, H4, H5, H6 and H8 with the highest distribution frequency are located at the central position of the network diagram and may be ancestral haplotypes. The remaining haplotypes with lower frequencies are located outside the network diagram, and we speculate that they may be young haplotypes that have diverged from ancestral haplotypes. Moreover, the haplotype sharing phenomenon between M. rubra and M. adenophora also reveals the close genetic relationship between them (Fig. 1 c). The results of BEAST analysis based on cpDNA data reveal a more detailed divergence timeline. The results showed that M. nana was differentiated from M. rubra and M. adenophora at about 10.45Ma (95%HDP), and began to intraspecific differentiate of M. esculenta at 8.38 Ma (95%HDP) in the late Miocene. In addition, further divergence within M. nana population began at 8.77 Ma (95%HDP) in the Late Miocene. the divergence of M. rubra and M. adenophora occurred at 5.02Ma (95%HDP) in the Pliocene, and the intraspecific divergence of M. rubra started at 3.8 Ma (95%HDP) in the early Pliocene, and the intraspecific divergence time of M. adenophora was the latest at 3.01Ma (95%HDP) in the late Pliocene (Fig. 1 e). The time accuracy and relationships among the four species were further estimated using whole-genome resequencing of AGS. As shown in the figure (Fig. 1 d), the four species diverged into two clades at 12.72 Ma in the late Miocene, and the M. esculenta diverged at the earliest, intraspecific divergence occurring around 6.65 Ma (95%HDP) in the late Miocene. The other three species form a complex group relationship, and M. rubra and M. adenophora form a sister group. The divergence of M. nana and the other two species began at about 10.12 Ma (95%HDP) in the late Miocene and intraspecific divergence occurred at 5.49 Ma (95%HDP) in the early Pliocene. The intraspecific divergence of M. rubra occurred at 4.19 Ma (95%HDP) in the Pliocene. The internal bifurcation of M. adenophora occurred in the Pliocene at about 4.17 Ma (95%HDP) (Fig. 1 d). It is consistent with the species divergence time analyzed by cpDNA data, which further confirms the validity and reliability of these gene sets in studying plant phylogeny and historical evolution. Inference of Population history Based on the cpDNA sequence, we conducted various analyses to determine the population history of four Morella species. The neutrality test (Table S6) provided that the Tajima's D statistical value of the species was not significantly positive (0.71428, P > 0.10), and the Fu and Li's D values (1.85822, P < 0.02) were positive at the overall level, suggesting that the population expansion may was not significant in history. For individual species, the statistical values of Tajima's D and Fu and Li's D for M. adenophora and M. rubra were not significantly negative (P > 0.10), indicating that the species may have undergone expansion during evolution. The statistical value of Tajima's D of M. esculenta was not significantly positive, but it cannot fully explain the trend of deviation from expansion in this region. Tajima's D of M. nana was not significantly positive (0.90941(P > 0.10)), the Fu and Li's D and Fu and Li's F were both negative and did not reach statistical significance (P > 0.10). These results further support that the species may follow the neutral evolution model and remain relatively stable on the scale of research time. The curve of mismatch distribution analysis (Fig. S4) shows a multimodal distribution pattern at the at the overall level and in an independent single species, which was inconsistent with the expected unimodal distribution, indicating that there was insufficient evidence to support that these species have experienced significant expansion. In summary, the results of neutrality test and mismatch distribution analysis revealed that the population size of the four species of Morella may remained relatively stable, and population may have expanded on a small scale and briefly in evolutionary history. Because of the neutrality test and mismatch distribution analysis detection are instantaneous, which can only explain the state of species in a period of time, but can‘t clearly indicate whether the species has expanded during the evolutionary process. Therefore, the BSPs curve obtained by combining cpDNA data is shown in the figure (Fig. 5 ), which shows that the effective population of Morella has been in the amplification mode since 1.2 Ma. Among them, the expansion trend of M. nana mainly occurred in 0.4 ~ 0.05 Ma, and the population size of M. esculenta mainly expanded in 0.7 ~ 0.1 Ma, and the population of M. rubra expanded since 0.15 Ma. However, the overall expansion of M. adenophora was not obvious, and it expanded only after 0.04 Ma (Fig. 5 ) . Bottleneck effect (Table S7) detection showed that under IAM, TPM and SMM models, Bottleneck effect may occur in some populations of 4 Morella species, which is of great importance for understanding the genetic history and dynamics of species. Present and past (Last Glacial Maximum) distribution In this study, MAXENT has a good prediction effect on the potential distribution of four plants in East Asia (AUV > 0.990), which shows that the model and species distribution information have a high degree of fitting, and the current distribution range covers the existing range of this species. Niche simulation results (Fig. 6 ) indicated that during the Last Glacial Maximum (LGM), the climate was cold and the survival environment was lost. To adapt to environmental changes, the species formed fragmented habitats, with their distribution ranges contracting to form refuges and presenting a patchy distribution pattern while migrating towards suitable areas, mainly in a southward direction. The results showed that M. esculenta was mainly distributed in Yunnan and Guangxi, M. rubra was mainly distributed in Guangxi, M. adenophora was mainly distributed in Guangxi, and M. nana was distributed in Yunnan and Guizhou. This suggests that potential refuges existed in these areas during the ice age. Subsequently, the suitable areas of the species slightly expanded from small fragments to their current distribution ranges. The expansion of Morella mainly occurred in the southwestern region of China, suggesting that the main suitable area for the Morella is the southwestern region. The species distribution model indicated that the distribution ranges of M. adenophora , M. esculenta , and M. rubra were restricted during the LGM period, but rapidly expanded from the LGM to the present. Interestingly, the distribution range of M. nana did not change greatly from the LGM to the present. The knife cut test analysis of the selected environmental variables indicated that warmest-season precipitation (bio18) was the most influential factor affecting Morella ’s distribution in both the LGM and present eras, underscoring its significance in determining the species' geographic spread. Discussions Genetic diversity and genetic structure Genetic diversity is the foundation and core of biodiversity, which reflects the adaptive changes made by species in response to environmental changes (Jia, 2016 ). Genetic diversity is a key factor for the survival and sustainable existence of species in their environment, and species in an unsuitable environment will lead to a decrease in genetic diversity (Wambulwa et al. 2022 ). Our research revealed that the genetic diversity of Morella was high (cpDNA: Hd = 0.917, π = 0.00407; SSR markers: Na = 3.53, Ne = 2.896, I = 1.07, Ho = 0.924, He = 0.597) (Table 1 ; Table S4). Morella is a perennial woody plant with a long-life span, and different generations can share their habitats, which may play an internal buffer role in preventing the loss of genetic diversity (Liu et al. 2023 ). At the species level, M. nana has the highest haplotype diversity, while M. adenophora has relatively low haplotype diversity. The average nucleotide diversity of M. esculenta is the highest, which further confirms the richness of its genetic diversity. The genetic diversity of four species in this study may be affected by many potential factors. Firstly, genetic diversity may be affected by the distribution range, and species with a wide distribution range may show high genetic diversity (Liu et al. 2022 ). In general, the widely distributed M. esculenta showed high genetic diversity, while that of M. adenophora with a narrow distribution, was lower. Secondly, the longer a species has evolved, the more genetic variation there was (Xu et al. 2021 ), This study indicated that the genetic variation of M. esculenta was high, it’s evolutionary history is longer, as evidenced by the origin and differentiation time for the four Morella species. Furthermore, climate change and man-made destruction will also affect the genetic diversity of species, for example, the distribution range of Morella may shrink during LGM period (Fig. 6 ), and the effective population size is greatly reduced, which leads to the decrease of genetic diversity (Keppel et al. 2012). In addition, M. rubra is trained as a fruit, and it tastes delicious, so the wild species may be damaged to a higher degree, thus affecting genetic diversity. Many factors work together on the genetic diversity of species, including but not limited to geographical distribution, gene flow and genetic communication among populations (Nybom, 2004 ). Four Morella species are mainly distributed in ecologically diverse areas such as Guangxi, Guizhou and Yunnan. The differences of climate and topography in different regions promote the generation of genetic variation to adapt to their respective niches and environmental pressures. The genetic diversity of M. esculenta may be related to its wide distribution in complex and changeable mountain habitats. M. esculenta was the first to differentiate, which may also create its rich genetic diversity. At the same time, the existence of shared haplotypes and the exchange of genetic species between species may also promote genetic diversity. In addition, bottleneck effect and population expansion events also play a great role in shaping genetic diversity (Yun et al. 2020 ). Our research shows that some populations of Morella have experienced bottleneck events, and their genetic diversity has also been affected. To sum up, the genetic diversity of Morella is the result of many factors, and the interaction of these factors shapes the level of genetic diversity of Morella . AMOVA analysis (Table 3 ) showed that the genetic variation of SSR markers mainly occurred in within populations, while that of cpDNA markers mainly occurred in among groups, which was consistent with previous analysis based on cpDNA sequences and SSR markers (Liu et al. 2022 ;Gao et al. 2022 ). There may be many reasons for the opposite results of AMOVA. Firstly, cpDNA is mainly maternal inheritance, with lower gene flow, low recombination and mutation rate, which makes the lineage inheritance of species clearer (Comes and Kadereit, 1998 ; Avise, 2000 ), whereas SSR is a parental inheritance, and the recombination and substitution rate of parental inheritance is high (Mort et al. 2007 ), so that the genetic differences between them are different. Moreover, the higher genetic variation between populations may be caused by the complex evolutionary processes that species have undergone over the course of history (Li et al. 2022 ). Thirdly, the phenomenon that genetic variation mainly occurs within populations mostly occurs in woody plants (Liu et al. 2013 ). Four Morella species are perennial woody plants, and the genetic variation is similar to other woody plants. The genetic differentiation coefficient Nst based on cpDNA is greater than Gst, which indicates that the four Morella species have obvious lineage geographical structure, haplotypes with similar genetic distance are more likely to appear in geographically adjacent regions (Pons and Petit, 1996 ). Barrier analysis (Fig. 4 ) showed that there were genetic communication barriers among the four species, and three geographical (a, b and c) boundaries could separate the four species. However, there were genetic communication between M. adenophora and some populations of M. rubra , which also proved that M. adenophora and M. rubra were sister group with close genetic relationship. The results of structure showed that Morella was divided into four groups when K = 4, and some populations among the groups had gene exchange. NJ tree based on Nei's genetic distance method supported the clustering results of structure. Each group contains individuals from different populations, which reveals that there is a certain degree of gene exchange between populations. This genetic mixing may reflect that the genetic differences between populations are not completely independent, but overlap. These findings further confirm the genetic diversity among species and the existence of hybridization events. Phylogenetic relationship of four Morella species Accurate species classification and identification plays an important role in conservation biology, ecology and genealogy geography, which directly affects the reliability of phylogenetic research, the accuracy of species evolution analysis and the scientific formulation of conservation measures (Dagnino et al. 2017 ). In this study, the phylogenetic relationship between four Morella species distributed in East Asia was analyzed based on the data of cpDNA and SSR sequencing (Fig. 1 e; Fig. 3 c). The results showed that the four species formed a monophyletic clade with high bootstrap support. The genetic structure based on SSR sequencing data shows that 63 Morella populations can be divided into four distinct genetic groups, which is consistent with their taxonomic classification. The populations of M. esculenta and M. nana showed obvious divergence on the phylogenetic tree, and the genetic divergence was significant. In contrast, the populations of M. rubra and M. adenophora showed some genetic mixing on the phylogenetic tree, suggesting that they may have a close phylogenetic relationship. Phylogenesis based on cpDNA data further revealed the phylogenetic relationship of the four species. M. rubra and M. adenophora share a haplotype H2, and they have a common clade. M. rubra and M. esculenta share the H3 haplotype, Morella is a dioecious plant (Fig. 1 e), which can be pollinated by wind, insects and animals (Pang, 2008 ). The geographical proximity may provide opportunities for the exchange of genetic materials between species. Phylogenetic tree divides four species into four groups, which clearly reveals the relationship between them. Phylogenetic analysis showed that M. esculenta differentiated the earliest, followed by M. nana and M. rubra . However, M. adenophora is classified into clades of M. nana , which is the closest to it in phylogenetic relationship. It may be because cpDNA is maternal inheritance, and the gene exchange of cpDNA marker is limited, so cpDNA marker is easier to infiltrate ( Whittemore et al. 1991; Du et al. 2009 ; Wan et al. 2017 ). The NJ tree based on SSR sequencing data shows that M. rubra and M. adenophora have communication, but they are in different clades (Fig. 1 e). The ML phylogenetic tree constructed by the results of whole-genome resequencing further clearly showed the phylogenetic relationship of four Morella species, and confirmed that M. esculenta was the earliest species to differentiate, and M. nana , M. rubra and M. adenophora formed a complex group relationship, in which M. rubra and M. adenophora formed a pair of closely related sister group (Fig. 1 d). In a word, the four Morella species distributed in East Asia are monophyletic clades of each other, and M. rubra and M. adenophora are closely related sister group because of gene exchange. Our results are consistent with those of Liu ( 2016 ). We increased the sampling amount, verified and analyzed the phylogenetic relationship of four Morella species distributed in East Asia by SSR, cpDNA and whole-genome resequencing, and further proved their genetic relationship and phylogenetic position. Species divergence and population history The formation and diversity of species may be affected by mountain barriers, and complex topography will form different environmental heterogeneity and niche changes (Fjeldså et al. 2012 ). Molecular clock analysis revealed that four Morella species began to differentiate in the late Miocene (Fig. 1 d and e), the first being M. esculenta , and then M. nana began to differentiate. The divergence between M. rubra and M. adenophora occurred in the Pliocene, suggesting that Morella had a long divergence process. The phylogenetic analysis based on whole-genome resequencing further demonstrates the reliability of the estimation of divergence time of four species. We believe that the divergence of four Morella species is related to the uplift of QTP. Previous studies and literature review have shown that the QTP uplift occurred in the middle Miocene (15 − 13 Mya), late Miocene (8 − 7 Mya) or Pliocene to Early Pleistocene (3.6–1.7 Mya), with the strongest uplift beginning at 3.6 Ma (Coleman and Hodges, 1995 ; Li and Fang, 1999 ; Shahzad et al. 2017 ). The divergence time of four Morella species in this study corresponds to the uplift time of the QTP, and the environmental differences caused by this long-term geological event may have caused species divergence. Terrain heterogeneity and climate fluctuation caused by the uplift of QTP have shaped the current species distribution pattern (Yu et al. 2019 ). This is consistent with previous research results (Lei et al. 2007 ; Lei et al. 2015 ; Shahzad et al. 2017 ). Due to the influence of geographical barriers, gene exchange is limited, and there are differences in different ecological environments, which will promote adaptive evolution of species, thus intensifying genetic divergence. Terrain heterogeneity has driven the formation and maintenance of genetic structures of four Morella species (Li et al. 2024 ). From late Miocene to Cenozoic, it is of great importance for the origin of biodiversity in East Asia (Sun et al. 2014 ). Significant climatic oscillations and geological events in the Late Miocene, such as continental drift, mountain range formation and climate change, especially the uplift of the Tibetan Plateau, led to great changes in the geomorphology and climate of neighboring areas. The interaction of these processes provides new habitats and niches for biodiversity in subtropical regions, and may also create favorable conditions for species divergence or the formation of new species. The climatic transition to a more suitable East Asian monsoon climate (Sun and Wang, 2005 ). It ensures the adaptation and evolution of species to the new environment and promotes the reconstruction of ecosystems and biological communities. For instance, the rate of speciation in Primulina was strongly linked on the East Asian monsoon climate (Kong et al. 2017 ). The East Asian monsoon climate leads to the increase of precipitation (Li et al. 2024 ), and the niche model shows that the Precipitation of Warmest Quarter has the greatest influence on species distribution, which further provides suitable habitats for M. esculenta and M. nana , and promotes species divergence. The unfavorable conditions in Pliocene led to the migration of species distribution and contraction into a low latitude refuge. Species divergence may be influenced by environmental changes and biological characteristics, and geographic isolation plays a role in promoting species formation (Givnish et al. 2011 ; Rabosky and Adams, 2012 ). These factors together shape the species formation and divergence of M. rubra and M. adenophora . Mantel test shows (Fig. S3) that population altitude has a certain driving effect on genetic divergence of four species of Morella , which indicates that four Morella species are sensitive to climate change, and environmental heterogeneity guides four Morella species to adapt to local environmental changes (Zhang et al. 2016 ). Long-term environmental heterogeneity has shaped the existing lineage geographical structure of species. Neutrality test and mismatch analysis proved that the effective population of four Morella species had been in a relatively stable equilibrium state on the scale of research time. Niche simulation showed that Morella had at least three shelters in LGM period (Fig. 6 ). In LGM period, the temperature was low, the species could not survive, and the effective population size might be greatly reduced. Then, the temperature rose, the species' suitable area expanded and the effective population size became larger. The BSPs curve showed that Morella expanded from about 1.2 Ma (Fig. 5 ), and four species expanded in different periods before this period. The results showed that complex climate change may be an important factor affecting the historical and current geographical distribution of Morella (Zheng et al. 1998; Wu et al. 2024 ). Based on the analysis of cpDNA data, H2, H4 and H5 haplotypes are considered as ancestral haplotypes. Niche simulation showed that M. esculenta was mainly distributed in Yunnan and Guangxi, M. rubra was mainly distributed in Guangxi, M. adenophora was mainly distributed in Guangxi, and M. nana was mainly distributed in Yunnan and Guizhou during LGM period. Combined with SSR and cpDNA genetic diversity analysis, it is speculated that these areas may be ice age shelters for four species. These shelters may provide an environment for species to survive and adapt during the ice age, thus shaping their genetic structure and existing geographical distribution model. Management and conservation strategies for Morella resource Biological diversity is an important part of maintaining ecological balance, and understanding genetic diversity of species is essential to understanding the evolution of biological diversity (Ren et al. 2024 ). With the loss of global biodiversity, especially the low genetic diversity of vulnerable species, it is essential to protect them (Tao et al. 2024 ). Habitat fragmentation is an important cause of species threat and endangerment (Yang et al. 2023 ). Genetic variation and habitat are a key factor in ensuring the continued presence of a species continued presence in the environment (Wambulwa et al. 2022 ). Only by ensuring sufficient genetic variation can we promote the long-term survival and evolution of species (Dai et al. 2015 ). In this study, the genetic diversity of 63 populations of 4 Morella species was analyzed based on cpDNA and SSR. The results showed that the genetic diversity of some populations was low, and our field investigation found that some populations were seriously damaged, which led to the gradual narrowing of the suitable area of species and the serious loss of wild resources. Therefore, it is urgent to effectively manage and protect these important wild resources of Morella . The M. rubra is distributed in many areas and has strong adaptability. However, due to its delicious edible fruit, it has caused serious human destruction, forced the disappearance of wild resources, and sharply decreased genetic diversity. M. adenophora has been listed as a vulnerable species (IUCN Red List of Threatened Species) and its distribution area is narrow. Because the M. nana is only distributed at high altitude area of Yunnan-Guizhou Plateau, the growth range is limited. M. esculenta is mainly distributed in tropical and subtropical regions from East Asia to Southeast Asia, and in subtropical regions of China, it has a wild range, and early origin, and is at the base of clade. Morella is a perennial deciduous woody plant with high value in medicine, food, ecology and economy (Bai et al. 2020 ). For this reason, it is urgent to protect this resource. Combined with the niche model, Guangxi, Yunnan and Guizhou are ice age shelters of Morella , which shows that Morella has a suitable living environment in these areas. The populations of YNLY, YNDL, GXTB and GZQL contain high genetic diversity or abundant haplotypes, which can correspond to shelters. We suggest that nature reserves should be established in such areas for in-situ protection. Maintain the sustainability of the living environment, avoid habitat fragmentation, improve the adaptability of the population, and ensure species richness. Secondly, ex-situ conservation, for the populations whose genetic diversity has been lost and whose habitats have been destroyed, moves to an environment suitable for species growth for protection, such as botanical gardens, nature reserves and other effective protected areas. These two protective measures are effective and desirable. In addition, science and technology can be used to assist the propagation of Morella , such as tissue culture and artificial seedling raising, which can solve the problems related to plant regeneration, breeding and cultivation (Cabrera et al. 2012). In a word, the adaptive habitat of Morella wild resources has been seriously damaged, and it is difficult for some populations to survive in the original habitat, so effective measures must be taken for management and protection. Conclusions In this study, the genetic structure and systematic geographical distribution of 63 populations of four Morella species were studied by molecular systematics. Our study suggests that geological movement and climate change can well explain the species divergence model and genetic structure well. In addition, genetic diversity plays an important role in the adaptation of species to environmental change, and ice age shelters provide a suitable living environment for species. Morella has remained relatively stable in the evolutionary scale of the research time, and it did not begin to expand until 1.2 Ma. The results of this study will enhance our understanding of the evolution, phylogenetic relationships and distribution of the four species of Morella in East Asia. Based on our research, we propose that these four species are closely related and that there is gene exchange among the species. Whole-genome resequencing and cpDNA support the topology of (( M. rubra and M. adenophora ) M. nana ) M. esculenta . In a word, our study made a clear phylogenetic relationship of the four Morella species, and existing geographical distribution pattern of species was the common result of climate change and geological movement, combined niche simulation prediction with genetic diversity, and generalized the protection strategy of wild resources of Morella . Declarations Author Contributions HSS came up with the original concept; HSS, LYX, ZC and JCX designed the study, selected species and conducted field investigations; FJ, ZLH, LY and CYT participate in field sample collection; HSS and LYX did the lab work, analyzed the genetic data and wrote the manuscript; ZC supervised the laboratory work and genetic analysis. All authors contributed to the final draft. Funding This work was supported by National Natural Science Foundation of China (32260252) and Guizhou science and technology support project ([2019]2451-2). Data availability The data generated or analyzed in our study were submitted to GenBank (https://www.ncbi.nlm.nih.gov/). The accession numbers are: PQ337779-PQ337829, PQ337830-PQ337880: PQ337881-PQ337931 and PQ337932-PQ337982. Ethics approval and consent to participate Our research did not involve any ethical issues, and our collection was in line with Chinese laws. 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Episodes Journal of International Geoscience 21: 152-158. https://doi.org/10.18814/epiiugs/1998/v21i3/003 Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Journal of Plant Research → Version 1 posted Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 16 May, 2025 Editor assigned by journal 06 May, 2025 First submitted to journal 03 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6558644","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457674135,"identity":"9d07a77d-cf10-4012-b551-4e75960f9e56","order_by":0,"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":""},{"id":457674136,"identity":"6e0c17e6-5844-413d-b95e-7958a39e8e13","order_by":1,"name":"Yu Xia Lu","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"Xia","lastName":"Lu","suffix":""},{"id":457674137,"identity":"1272b573-3b42-4ca0-be3c-c7554c2a9a69","order_by":2,"name":"Shan Shan He","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"Shan","lastName":"He","suffix":""},{"id":457674138,"identity":"ed769840-1072-44ee-8106-edf86a9a9e59","order_by":3,"name":"Chun Xue Jiang","email":"","orcid":"","institution":"Guizhou Agricultural College: Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"Xue","lastName":"Jiang","suffix":""},{"id":457674139,"identity":"15e851eb-a12b-4ef0-8acc-217636e7150e","order_by":4,"name":"Jian Feng","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Feng","suffix":""},{"id":457674140,"identity":"1800813c-96cb-4c6b-a68b-e7642ed2105b","order_by":5,"name":"Li Hong Zhao","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"Hong","lastName":"Zhao","suffix":""},{"id":457674141,"identity":"1e9aad4b-a78d-4df9-b473-f5e0d46d68df","order_by":6,"name":"Yue Li","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":457674142,"identity":"3f5af9a8-261f-4993-a5d8-7944900302f8","order_by":7,"name":"Yu Ting Chen","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"Ting","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-04-29 17:33:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6558644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6558644/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10265-025-01675-z","type":"published","date":"2025-11-17T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83153790,"identity":"d5fb9031-9db3-4991-8b6b-66865e9f2792","added_by":"auto","created_at":"2025-05-20 14:16:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186143,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies distribution, morphology, phylogeny, and interspecies relationships obtained from cpDNA. (a) Geographic distribution of cpDNA haplotypes for the four \u003cem\u003eMorella\u003c/em\u003e species. Each circle represents a population and each color represents each haplotype. The colored outlines of the circles distinguish the four species, where purple indicates \u003cem\u003eM. esculenta,\u003c/em\u003eblue indicates \u003cem\u003eM. nana\u003c/em\u003e, green indicates\u003cem\u003e M. adenophora\u003c/em\u003e, and red indicates \u003cem\u003eM. rubra\u003c/em\u003e; (b) Morphological characteristics of four species of \u003cem\u003eMorella\u003c/em\u003e; (c) cpDNA\u003cstrong\u003e \u003c/strong\u003ehaplotype intermediate connection network of four plants. Blue stands for \u003cem\u003eM. nana\u003c/em\u003e, purple stands for \u003cem\u003eM. esculenta\u003c/em\u003e, red stands for \u003cem\u003eM. rubra\u003c/em\u003e and green stands for \u003cem\u003eM. adenophora\u003c/em\u003e. The colors of the four species are consistent with the geographical distribution (Fig.1a) and morphological of haplotypes (Fig.1b). The size of the circle is proportional to the number of haplotypes; (d) Phylogenetic tree of 353 gene based on whole-genome resequencing and calibration time node, and the yellow dashed line represents the time boundary of the Quaternary period, the divergence time is at the bottom of the phylogenetic tree. The numerical values on the phylogenetic tree branches represent the bootstrap values (BS); (e) Phylogenetic tree of cpDNA haplotypes for the four \u003cem\u003eMorella\u003c/em\u003especies and the yellow dashed line represents the time boundary of the Quaternary period, the divergence time is at the bottom of the phylogenetic tree. The numerical values on the left and right of the phylogenetic tree branches represent the bootstrap values (BS) of the Maximum Likelihood (ML) tree and the Maximum Parsimony (MP) tree, respectively, indicating slight discrepancies between the two methods. The asterisk \"*”denotes branches with BS \u0026lt; 50%.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/1220c1f5d5b49968174234bd.jpg"},{"id":83153797,"identity":"77d6b302-4e4c-4368-a196-5ed06f479349","added_by":"auto","created_at":"2025-05-20 14:16:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62688,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Coordinate Analysis (PCoA) of four \u003cem\u003eMorella\u003c/em\u003e species based on SSR Sequencing Data. Each point represents an individual. Among them, blue represents \u003cem\u003eM. nana\u003c/em\u003e, red represents \u003cem\u003eM. esculenta\u003c/em\u003e, gray represents\u003cem\u003e M. adenophora\u003c/em\u003e and yellow represents \u003cem\u003eM. rubra\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/0d931ec8daaa7a5968729ca9.jpg"},{"id":83153794,"identity":"d4bc7f79-8ac9-4911-9b6d-0d598fb7b6ef","added_by":"auto","created_at":"2025-05-20 14:16:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":138870,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of genetic structure of 477 \u003cem\u003eMorella\u003c/em\u003e individuals based on SSR sequencing data. (a) Presenting the Delta K; (b) The STRUCTURE diagram of 477 samples at \u003cem\u003eK\u003c/em\u003e=4 shows that green represents \u003cem\u003eM. nana\u003c/em\u003e, red represents \u003cem\u003eM. rubra\u003c/em\u003e, blue represents \u003cem\u003eM. esculenta\u003c/em\u003e and yellow represents \u003cem\u003eM. adenophora\u003c/em\u003e, and showed the gene exchange among the four species; (c) Interpopulation NJ trees of \u003cem\u003eMorella\u003c/em\u003e based on Nei's genetic distance.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/f2e917847e30d5cbc8d6b24b.jpg"},{"id":83153791,"identity":"63ae97b6-eac8-4f10-980a-64909b3c17d3","added_by":"auto","created_at":"2025-05-20 14:16:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79223,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic barrier analysis of four \u003cem\u003eMorella\u003c/em\u003e species based on cpDNA data. Red represents the population location of\u003cem\u003e M. rubra\u003c/em\u003e, blue represents the population location of\u003cem\u003e M. nana\u003c/em\u003e, purple represents the population location of\u003cem\u003e M. esculenta\u003c/em\u003e, and green represents the population location of\u003cem\u003e M. adenophora\u003c/em\u003e. Blue lines indicate Delaunay triangulation, and red lines indicate Barrier robustness\u003cem\u003e \u003c/em\u003e(a, b and c represent three genetic boundaries).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/47dfae78cfbaa27a7a6d64b7.jpg"},{"id":83153793,"identity":"e86348c2-0d8b-4ee8-9e34-c0bd39a84752","added_by":"auto","created_at":"2025-05-20 14:16:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81240,"visible":true,"origin":"","legend":"\u003cp\u003eBSPs based on chloroplast DNA sequence. The y axis is the product between effective population size (\u003cem\u003eN\u003c/em\u003ee) and the generation time and the x axis is time in millions of years. The dotted line represents the median of the \u003cem\u003eN\u003c/em\u003ee. The upper and lower black lines represent the confidence interval of the highest 95% posterior density (HPD) interval of \u003cem\u003eN\u003c/em\u003ee. (a) BSPs for \u003cem\u003eMorella\u003c/em\u003e populations. (b) BSPs for \u003cem\u003eM. nana\u003c/em\u003epopulations. (c) BSPs for \u003cem\u003eM. esculenta\u003c/em\u003e populations. (d) BSPs for \u003cem\u003eM. rubra\u003c/em\u003e populations. (e) BSPs for \u003cem\u003eM. adenophora\u003c/em\u003e populations.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/6895abd48407893f89379d16.jpg"},{"id":83155243,"identity":"5ca84439-9bb6-4251-8062-37fe9d796d58","added_by":"auto","created_at":"2025-05-20 14:32:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":207649,"visible":true,"origin":"","legend":"\u003cp\u003ePotential distribution of \u003cem\u003eMorella\u003c/em\u003e based on ecological niche modeling using MAXENT. (a) Predicted distribution of \u003cem\u003eM. esculenta\u003c/em\u003e during the LGM. (b) Predicted distribution of \u003cem\u003eM. esculenta\u003c/em\u003eduring the current. (c) Predicted distribution of \u003cem\u003eM. rubra\u003c/em\u003e during the LGM. (d) Predicted distribution of \u003cem\u003eM. rubra\u003c/em\u003e during the current. (e) Predicted distribution of \u003cem\u003eM. adenophora\u003c/em\u003e during the LGM. (f) Predicted distribution of \u003cem\u003eM. adenophora\u003c/em\u003e during the current. (g) Predicted distribution of \u003cem\u003eM. nana\u003c/em\u003e during the LGM. (h) Predicted distribution of \u003cem\u003eM. nana\u003c/em\u003e during the current. g and h quote the published articles of our team (Wu et al. 2024).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/d11b524fa3560de57568aa5e.jpg"},{"id":96651064,"identity":"db02c797-beba-4cd7-b883-a73b625e699b","added_by":"auto","created_at":"2025-11-24 16:13:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2270605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/887adacd-b0bc-44d0-abe2-d059f47529ce.pdf"},{"id":83154730,"identity":"1b153900-3b18-45a6-90bd-3954fa712623","added_by":"auto","created_at":"2025-05-20 14:24:24","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3092582,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6558644/v1/9cb9989318f654ed361a1d92.docx"}],"financialInterests":"","formattedTitle":"Effects of mountain uplift and climate change on phylogeography and species divergence of East Asia Morella","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEast Asia is known as the \"museum\" of plants, which is rich in temperate flora diversity in the world (L\u0026oacute;pez-Pujol et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, the central and southern mountainous areas of China are the main centres of endemic plants, and the stability of Tertiary may provide favorable conditions for the long-term reproduction of residual plant lineages(L\u0026oacute;pez‐Pujol et al. 2011). The mountainous regions in southern China are complex and diverse, with vast territory and distinct seasons (Wu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which can provide a variety of habitats and maintain species diversity in a short distance, especially under unfavorable climatic conditions (Hoorn et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Favre et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). What is important is that mountain uplift and climate change will greatly affect the geographical distribution and species divergence of plants (Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hickerson et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the systematic geographical pattern reflects the influence in the process of neutral evolution of species (Avise \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hewitt \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The collision between the Indian plate and the Eurasian plate led to the uplift of the Qinghai-Tibet Plateau (QTP) (Zhao et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which greatly affected the geological features and habitat structure of the region. The mountains, deep valleys and rivers formed with the uplift of the plateau may be obstacles to the migration and diffusion of plant species, which have profoundly affected the geographical distribution pattern and genetic structure of plant species in this area (Wen et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ickert‐Bond and Renner 2016). Adjacent to the eastern part of the QTP, the changes of climate and topography will be influenced by the Qinghai-Tibet Plateau to a greater or lesser extent. Moreover, the geographical environment in southwest China is extremely complex, which has always been the focus of global biodiversity research. Complex topography and local selection will also lead to species divergence and genetic differences (L\u0026oacute;pez‐Pujol et al. 2011; Wambulwa et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, the violent movement of the QTP created the unique topographical and environmental characteristics of the Yunnan-Guizhou Plateau (Xie et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The strong orogeny in Yunnan-Guizhou Plateau resulted in the landform pattern of high in the west and low in the east, which separated it from the surrounding areas (Dai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTopographic heterogeneity and orogeny will form new habitats, promote the formation of species and affect the distribution pattern of species (Yin et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The orogeny movement will lead to the regional transfer of species, which will lead to the fragmentation of a large area of habitats into smaller patches, that is, habitat fragmentation (Sun et al. 2003; Xie et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Climate change usually leads to the expansion or contraction of the protected areas of plant habitat, which is an important factor affecting the geographical distribution pattern of species (Yang et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The impact of climate change on each species is independent and not directly related. Species at the edge of geographical distribution are more sensitive to climate change (Taberlet et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Mat\u0026iacute;as et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The origin and evolution of species are generally closely related to many historical events, such as the uplift of mountains and different evolutionary adaptations of species caused by climate change (Lowry et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The uplift of the Qinghai-Tibet Plateau not only changed the topography of East Asia, but also led to great changes in the climate (Raymo and Ruddiman, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It leads to the change of surrounding environment and affects the formation and divergence of species.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMorella\u003c/em\u003e is a perennial evergreen shrub or tree of Myricaceae, which grows mostly in valleys and forests. Only four species are distributed in East Asia: \u003cem\u003eMorella rubra\u003c/em\u003e Lour, \u003cem\u003eMorella nana\u003c/em\u003e (A. Chev.) J. Herb, \u003cem\u003eMorella adenophora\u003c/em\u003e (Hance) J. Herb. and \u003cem\u003eMorella esculenta\u003c/em\u003e (Buch. - Ham. ex D. Don) I. M. Turner (Herbert,2005; Liu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The research of Herbert (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) confirmed that four species distributed in East Asia have high bootstrap support in phylogenetic clades. \u003cem\u003eM. esculenta\u003c/em\u003e is at the base of four species, and the other three species present a complex group relationship (Figs. S1). Chen's (2014) research on nuclear gene ITS and cpDNA (\u003cem\u003epsb\u003c/em\u003eA\u003cem\u003e-trn\u003c/em\u003eH) has not resolved the relationship of the remaining three species. Therefore, in order to determine the relationship of four \u003cem\u003eMorella\u003c/em\u003e species distributed in East Asia, it is necessary to study by various means. Liu (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) resolved the relationship of four species, but did not resolve the issues of historical dynamics and genetic divergence.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMorella rubra\u003c/em\u003e is widely distributed in East Asia, and is often cultivated for its edible value. At present, a large number of cultivated varieties have been cultivated in China, such as \"Crystal\", \"Dongkui\" and \"Big Leaf and Fine Pedicel\". \u003cem\u003eM. esculenta\u003c/em\u003e is mainly distributed in subtropical and tropical areas from East Asia to Southeast Asia, and is found in many provinces of China. \u003cem\u003eM. adenophora\u003c/em\u003e is mainly distributed in Guangdong, Guangxi and Hainan Island. \u003cem\u003eM. nana\u003c/em\u003e is mainly distributed in the higher altitude areas of Yunnan-Guizhou Plateau. \u003cem\u003eM. nana\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e are endemic to China (Chen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The young clades of \u003cem\u003eM. esculenta\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e are easily distinguished by dense hairs, the short clades and petioles of \u003cem\u003eM. nana\u003c/em\u003e and \u003cem\u003eM. rubra\u003c/em\u003e are glabrous or sparsely hairy, and there are dense golden glands on the back of \u003cem\u003eM. rubra\u003c/em\u003e (Zhong and Xie, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jia, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The plants of this genus have high medicinal and edible value, and can be eaten fresh and made into fruit wine. They can resist bacteria and inflammation, treat rheumatic pain, and resist oxidation and glycosylation (Zhou and Yang, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bai et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the beneficial value of species being known and valued by people, as well as environment change and human intervention, the wild germplasm resources of \u003cem\u003eMorella\u003c/em\u003e have been severely degraded, and the number of individuals in the population is low. \u003cem\u003eM. adenophora\u003c/em\u003e has been classified as a threatened species or a vulnerable (VU) species. Wild species play a vital role in the breeding programme, because of their wide variability in morphological structure, resistance and quantitative traits (Laid\u0026ograve; et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Genetic diversity is the basis of ecosystem diversity, and it is also the basis of species to survive, adapt to the environment and evolve in the process of evolution (Liu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding the geographical distribution and genetic information of species is very important for the protection of wild germplasm resources (Shahzad et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the previous studies on \u003cem\u003eMorella\u003c/em\u003e mostly focused on one species, for example, Liu et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Ju et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studied only one species of \u003cem\u003eM. rubra\u003c/em\u003e. However, there is little research on the population history and genetic differences of \u003cem\u003eMorella\u003c/em\u003e. Although Liu (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) studied the genetic relationship of four species based on some individuals, this study is to expand the sample size of species and comprehensively analyze the reasons that affect the lineage geography and species divergence of four \u003cem\u003eMorella\u003c/em\u003e species in East Asia by combining population genetics.\u003c/p\u003e \u003cp\u003eHere, this study uses SSR markers, cpDNA markers and whole-genome resequencing analysis to solve the following problems:1) to obtain the genetic structure and evolutionary history of four species;2) infer the history of gene flow and population statistics, and demonstrate the formation of the current distribution pattern;3) 3) to explore geological movement and climate change on the divergence of four \u003cem\u003eMorella\u003c/em\u003e species; 4) to put forward some reasonable suggestions on the utilization of \u003cem\u003eMorella\u003c/em\u003e resources and make protection strategies.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eIn the present study, 404 individuals from 62 populations of 4 species were collected in the south of China for cpDNA analysis (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), and Casuarina equisetifolia was taken as an outgroup. 477 individuals from 63 populations were collected for SSR analysis (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). On the basis of cpDNA and SSR analysis, in order to further determine the phylogenetic relationship among the four species, On the basis of cpDNA and SSR analysis, in order to further determine the phylogenetic relationship among the four species, 12 individuals from 12 populations, including four species (each species contains three populations) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), were used for whole-genome resequencing analysis, with \u003cem\u003eRhoiptelea chiliantha\u003c/em\u003e, \u003cem\u003ePterocarya stenoptera\u003c/em\u003e, \u003cem\u003eQuercus fabri\u003c/em\u003e, \u003cem\u003eBetula luminifera\u003c/em\u003e and \u003cem\u003eCasuarina equisetifolia\u003c/em\u003e as outgroups to further verify the phylogenetic relationship and divergence time. The collected materials cover the entire geographical distribution of four species. It was found that the wild population of \u003cem\u003eMorella\u003c/em\u003e was small, so some natural populations and wild individuals that could be collected in the wild were relatively few, and \u003cem\u003eM. adenophora\u003c/em\u003e was a threatened or vulnerable species (VU). Each population includes 2\u0026thinsp;~\u0026thinsp;13 individuals, and only GDTH has one individual. Each sample is spaced at least 20 meters apart, and healthy young leaves are dried with silica gel for DNA extraction. All specimens are kept in the herbarium of College of Life Sciences, Guizhou University (sample numbers: zhaocaiM1\u0026thinsp;~\u0026thinsp;zhaocaiM63). The plants collected in this study were identified by Dr. Zhao Cai (associate professor of Guizhou University, research on plant systematics and evolution). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provide detailed information of latitude, longitude and altitude of all populations.\u003c/p\u003e \u003cp\u003eIt should be emphasized that although the collected samples are unbalanced among populations, it will not affect our phylogeographic research results. The samples of similar phylogeographic studies are also unbalanced. For instance, sample sizes for \u003cem\u003eTetrastigma hemsleyanum\u003c/em\u003e (individuals from 6 to 15), \u003cem\u003eNotopterygium\u003c/em\u003e (individuals from 2 to 17) and \u003cem\u003eMachilus thunbergii\u003c/em\u003e (individuals from 2 to 8) were unbalanced (Wang et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shahzad et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, they can present the entire geographical distribution range and representative sample the species and can cover the geographical distribution of representative populations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction and sequencing\u003c/h3\u003e\n\u003cp\u003eTotal DNA was extracted by plant DNA extraction kit (Tiangen, Beijing, China). Put the processed materials in a mortar, add a small amount of silicon dioxide, and liquid nitrogen, quickly grind them into powder, and transfer them into a 2 mL centrifuge tube. For the extraction method, refer to the kit steps. We used a 1.5% agar gel to detect the quality of extracted DNA, and the ideal sample was used in the follow-up experiment. In this study, five chloroplast gene primers (\u003cem\u003epsb\u003c/em\u003eA-\u003cem\u003etrn\u003c/em\u003eH, \u003cem\u003etrn\u003c/em\u003eD-\u003cem\u003epsb\u003c/em\u003eM, \u003cem\u003etrn\u003c/em\u003eL-\u003cem\u003etrn\u003c/em\u003eF, \u003cem\u003eycf1\u003c/em\u003e1205-\u003cem\u003eycf1\u003c/em\u003e2402 and \u003cem\u003eycf1\u003c/em\u003e3125-\u003cem\u003eycf1\u003c/em\u003e4381) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) (Liu \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and five pairs of SSR primers (Table S3) (Zhang et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chen \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) were screened out by consulting relevant references and pre-testing (Firstly, some samples were randomly selected for PCR amplification, and the PCR products were detected by 1.5% agarose gel electrophoresis, and the primers with clear and single bands were preliminarily screened. Then, the primers were re-screened by polyacrylamide gel electrophoresis to determine whether the primers were stably amplified and polymorphic, and the qualified primers were screened for subsequent experiments.), which were used to amplify more than 400 samples of four \u003cem\u003eMorella\u003c/em\u003e species. The sequencing reaction is carried out in the Beijing Tsingke Biotech Co, Ltd. The amplification conditions are: 2\u0026times;Taq PCR Master Mix 12.5 uL, DNA template 1uL, upstream and downstream primers 1uL each, and the remaining volume was made up to 25 uL with ddH\u003csub\u003e2\u003c/sub\u003eO. PCR amplification program: pre-denaturation at 94 ℃ for 2 min, denaturation at 94 ℃ for 30 s, annealing at 52 ℃-60 ℃ for 30 s, extension at 72 ℃ for 30 s, and final extension at 72 ℃ for 7 min, the reaction was set to 35 cycles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGenetic diversity and genetic structure\u003c/h3\u003e\n\u003cp\u003eA number of cpDNA fragments were spliced together with PhyloSuite 1.2.2 (Zhang et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The haplotype (\u003cem\u003eN\u003c/em\u003e), haplotype diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e) and nucleotide diversity index (\u003cem\u003eπ\u003c/em\u003e) of each population of the species were identified by DnaSP 6.0 (Rozas \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Genetic diversity (expected heterozygosity (\u003cem\u003eHe\u003c/em\u003e), observed heterozygosity (\u003cem\u003eHo\u003c/em\u003e), effective allele number (\u003cem\u003eNe\u003c/em\u003e), the mean number of alleles (\u003cem\u003eNa\u003c/em\u003e), percentage of polymorphic loci (\u003cem\u003ePPL\u003c/em\u003e) and Shannon's information index (\u003cem\u003eI\u003c/em\u003e) of \u003cem\u003eMorella\u003c/em\u003e population were analyzed based on SSR using GenAIex6.5 (Peakall and Smouse \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The polymorphism information content (\u003cem\u003ePIC\u003c/em\u003e) of SSR loci was measured by CERVUS 3.0 (Kalinowski et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The Analysis of molecular variance (AMOVA) analysis of cpDNA and SSR was carried out with Arlequin 3.5 (Excoffier and Lischer \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to evaluate the genetic variation and divergence among species.\u003c/p\u003e \u003cp\u003eThe Structure v2.3.4 (Pritchard et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) was used to analyze the genetic structure of SSR sequences by Bayesian clustering algorithm, aiming to identify genetic populations with similar allele frequencies. To determine the population number (\u003cem\u003eK\u003c/em\u003e value), we set the range of \u003cem\u003eK\u003c/em\u003e from 1 to 10, and perform 10 independent operations for each K value, using a Model called the \"Admixture Model\". For calculation parameters, set Length of burnin peroid to 100,000 and Number of MCMC Reps after Burnin to 1,000,000. After the operation is completed, we obtain the logarithmic likelihood (\u003cem\u003eL(K\u003c/em\u003e)) rate of change corresponding to different \u003cem\u003eK\u003c/em\u003e values. To determine the best \u003cem\u003eK\u003c/em\u003e value, we follow the method proposed by Evanno et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) by calculating the Delta K value between successive \u003cem\u003eK\u003c/em\u003e values. In addition, the PERMUT 2.0 software was used to calculate the mean in-population genetic diversity (\u003cem\u003eHs\u003c/em\u003e), total genetic diversity (\u003cem\u003eHt\u003c/em\u003e), and genetic divergence coefficients \u003cem\u003eN\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and \u003cem\u003eG\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e based on chloroplast DNA data.\u003c/p\u003e \u003cp\u003eThe genetic distance of Nei's was calculated by GenAlex 6.5 software, and the Neighbor-Joining (NJ) tree was constructed by cluster analysis by MEGA 6.0 software. In addition, Mantel test was carried out by GenAlex 6.5 software to explore the correlation between Nei's genetic distance and geographical distribution and altitude among species. Based on the genetic distance, the principal coordinate analysis (PCoA) of all populations of four species was further carried out by GenAlex 6.5 software to reveal the genetic structure and potential geographical patterns among populations.\u003c/p\u003e\n\u003ch3\u003ePhylogenetic relationship and estimation of divergence time\u003c/h3\u003e\n\u003cp\u003eThe cpDNA sequence was analyzed by MEGA v 6.0 software, and the maximum likelihood (ML) model was bootstrap 1000 times to obtain the phylogenetic clade. The cpDNA intermediate connection network was constructed using Network10.2 (Bandelt et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) genealogical relationship analysis. ArcGIS v 10.2 was used to map the haplotype geographical distribution of the population. The software package BEAST 1.10.4 (Suchard et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used to estimate the divergence time of \u003cem\u003eMorella\u003c/em\u003e species. In order to correct the divergence time more accurately, a calibration point was introduced in BEAST analysis. Based on Herbert's (2005) in-depth analysis of systematics and biogeography of Myricaceae, it was determined that the divergence time between \u003cem\u003eM. esculenta\u003c/em\u003e and three other \u003cem\u003eMorella\u003c/em\u003e species \u003cem\u003eM. nana\u003c/em\u003e, \u003cem\u003eM. adenophora\u003c/em\u003e, and \u003cem\u003eM. rubra\u003c/em\u003e occurred at about 12.72 Ma (SD\u0026thinsp;=\u0026thinsp;1.7). In order to determine the best nucleotide substitution model, according to Akaike information criterion, ModelFinder was used to select the model in PhyloSuite 1.2.2 (Kalyaanamoorthy et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the analysis, we adopted the Relaxed Clock Unorrelated Lognormal Method, set the Markov chain to run for 10,000 generations, take samples every 1,500 generations, and set the burn-in value to 10%. In order to ensure the high reliability of the evolutionary tree, Tracer v.1.7.2 is used to test the convergence of the Markov chain. Finally, the Tree ANNOTATOR 1.10.4 (Suchard et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) is used to obtain the phylogenetic tree of Maximum Clade Credibility with the greatest reliability, and FigTree 1.4.2 is used to \u003cb\u003echeck\u003c/b\u003e the \u003cb\u003ebeautification\u003c/b\u003e tree. Finally, the differentiation time of \u003cem\u003eMorella\u003c/em\u003e species was deduced.\u003c/p\u003e \u003cp\u003eIn order to verify and compare the reliability of divergence time of four species based on cpDNA data, on this basis, three individuals of each species and five outgroups were selected for whole-genome resequencing, and the sequencing reaction was completed in Shanghai Personal Biotechnology Co, Ltd, and the results of 17 individuals were extracted from the genome sequence based on GeneMiner software (Xie et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the angiosperm 353 gene (AGS) set was extracted from the genome sequence to construct phylogenetic tree and estimate the divergence time. AGS is composed of 353 universal low-copy nuclear genes, which were identified by systematic comparative analysis of more than 600 angiosperms, these genes can be widely used in phylogenetic research and population genetics of various taxonomic scales (Zhang et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We used GeneMiner software (Xie et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to analyze the sequencing data, which can effectively mine phylogenetic molecular markers from transcriptomics, genomics and other NGS data. Gene mining is carried out by GeneMiner software. reads of the target gene are screened from the original data, and the candidate genes are generated by sequence assembly. After quality control, the low-quality data are eliminated. The low-copy nuclear genes of each sample were merged and trimmed, and only the genes with the maximum difference rate (Max. Diff) less than 10% were retained, and then merged and trimmed again. Maximum Likelihood (ML) method was used to construct the phylogenetic tree, and the Bootstrap value was set to 1000 to analyze the statistical support.\u003c/p\u003e\n\u003ch3\u003eInference of Population history\u003c/h3\u003e\n\u003cp\u003eThe mismatch distribution of cpDNA sequences of four \u003cem\u003eMorella\u003c/em\u003e species was detected by DnaSP 6.0, and the population expansion was tested by Arlequin v 3.5 and Tajima's D (Tajima, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), Fu and Li\u0026rsquo; s F* (Fu and Li, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and Fu and Li\u0026rsquo; s D* (Fu and Li, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). In addition, in order to evaluate the continuity of species population dynamic changes and avoid errors caused by transient occurrences, we used BEAST v 2.7.6 (Bouckaert et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to conduct Bayesian skyline plots (BSPs) for analysis, to obtain the dynamic changes of population size over time. In this analysis, we use the average base mutation rates (2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e substitutions/site/year) previously approximated by Wolfe et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) for angiosperm chloroplast DNA (1\u0026thinsp;~\u0026thinsp;3 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e substitutions/site/year), alongside a population coalescent Bayesian Skyride model for the prior tree and strict molecular clock. To ensure robust inference, we use random initial tree, linear model and setting the length of MCMC chain to make ESS\u0026thinsp;\u0026ge;\u0026thinsp;200 (five cpDNA haplotypes are 10,000,000 chains). To assess the reliability of the results, we conducted three separate analyses and combined their outcomes using TRACER 1.5, a tool designed for summarizing and visualizing the output of BEAST analyses. Ultimately, we leveraged the R programming language to analyze and visualize the BSPs, providing insights into the historical dynamics of the population size over time. We also used BARRIER 2.2 (Manni et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to analyze whether there is an intergroup gene flow barrier in the four species distribution areas. In order to assess whether the four species suffer from recent Bottleneck effects, the BOTTLENECK software (Piry et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) is used for analysis. The software provides three different mutation models to calculate bottleneck effects: the infinite allelic mutation model (IAM), the stepped-mutation model (SMM), and the biphasic mutation model (TPM). If the test results are statistically significant, it indicates that the group may have experienced a bottleneck.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEcological niche modeling\u003c/h2\u003e \u003cp\u003eIn order to verify the niche differences among target species, we used MAXENT (Phillips et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) based on field specimen collection, literature report, Global Biodiversity Information Facility (GBIF; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Chinese Virtual Herbarium (CVH; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cvh.ac.cn/\u003c/span\u003e\u003cspan address=\"http://www.cvh.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), delete the repeated geographical records, and predict the niche model of four species. The bioclimatic environment was obtained by downloading from WordClim database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). At first, 19 climatic factors were screened to avoid the interaction among various factors affecting the prediction results. The variables were derived from monthly temperature and precipitation data, enabling the creation of more ecologically relevant parameters (Fava et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These bioclimatic indicators capture yearly patterns (such as average yearly temperature and total yearly rainfall), seasonal variations (including yearly fluctuations in temperature and rainfall), and extreme or constraining environmental conditions (for example, minimum and maximum monthly temperatures) (Fava et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on the relationship between the high Percent contribution and the high permeability, and Pearson correlation coefficient in SPSS, 10 climatic factors were screened out for niche model prediction, so as to improve the accuracy of prediction. The 10 climatic factors are as follows: \u003cem\u003eM. esculenta\u003c/em\u003e (bio18, bio4, bio15, bio14, bio11, bio7, bio13, bio12, bio19 and bio16), \u003cem\u003eM. adenophora\u003c/em\u003e (bio18, bio2, bio15, bio4, bio14, bio6, bio1, bio3, bio19 and bio8), \u003cem\u003eM. rubra\u003c/em\u003e (bio18, bio4, bio15, bio2, bio6, bio14, bio1, bio11, bio9 and bio6), and \u003cem\u003eM. nana\u003c/em\u003e please refer to our previous research (Wu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For MAXENT modeling, we use the default parameters for analysis. In order to evaluate the statistical performance of the model, we used the area under the \u0026ldquo;receiver operating characteristic (ROC) curve\u0026rdquo; (AUC; Fawcett, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The model with AUC approximately 1 showed that the prediction ability was good. We use ArcMap v10.5 to draw a suitable distribution range.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity analysis\u003c/h2\u003e \u003cp\u003eBy correcting and splicing five cpDNA fragments (\u003cem\u003epsb\u003c/em\u003eA-\u003cem\u003etrn\u003c/em\u003eH, \u003cem\u003etrn\u003c/em\u003eD-\u003cem\u003epsb\u003c/em\u003eM, \u003cem\u003etrn\u003c/em\u003eL-\u003cem\u003etrn\u003c/em\u003eF, \u003cem\u003eycf\u003c/em\u003e11205-\u003cem\u003eycf\u003c/em\u003e12402, and \u003cem\u003eycf\u003c/em\u003e13125-\u003cem\u003eycf\u003c/em\u003e14381) were used to analyze 404 individuals from 62 populations of the four \u003cem\u003eMorella\u003c/em\u003e species. The total length of cpDNA fragment was 3157bp, of which \u003cem\u003epsb\u003c/em\u003eA-\u003cem\u003etrn\u003c/em\u003eH, \u003cem\u003etrn\u003c/em\u003eD-\u003cem\u003epsb\u003c/em\u003eM, \u003cem\u003eycf1\u003c/em\u003e1205-\u003cem\u003eycf1\u003c/em\u003e2402, \u003cem\u003eycf1\u003c/em\u003e3125-\u003cem\u003eycf1\u003c/em\u003e4381 and \u003cem\u003etrn\u003c/em\u003eL-\u003cem\u003etrn\u003c/em\u003eF fragments are 435, 587, 855, 887 and 393bp, respectively. Among the four species, 51 cpDNA haplotypes were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), of which only H2 haplotype was shared by \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e, and H3 was shared by \u003cem\u003eM. esculenta\u003c/em\u003e and \u003cem\u003eM. rubra\u003c/em\u003e. In addition, all haplotypes had species specificity. In \u003cem\u003eM. rubra\u003c/em\u003e there were 14 haplotypes, and GXLG and GXSB haplotypes in Guangxi had the highest diversity. Among the 16 haplotypes of \u003cem\u003eM. nana\u003c/em\u003e, the population YNPL had the highest haplotype diversity. \u003cem\u003eMorella esculenta\u003c/em\u003e has 18 haplotypes. The Guizhou populations (GZZJ, GZQL and GZQX) also had the highest haplotype diversity. The \u003cem\u003eM. adenophora\u003c/em\u003e had five haplotypes, of which H27, H48, H49 and H50 were unique to the population, and H2 was a shared haplotype (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Genetic diversity analysis indicated that the total genetic diversity (\u003cem\u003eHt\u003c/em\u003e) was 0.913, and the average genetic diversity within populations (\u003cem\u003eHs\u003c/em\u003e) was 0.205. The genetic diversity of the four species of \u003cem\u003eMorella\u003c/em\u003e was relatively high. At the species level, the haplotype diversity of the four species was the highest in \u003cem\u003eM. nana\u003c/em\u003e (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e =0.819) and the lowest in \u003cem\u003eM. adenophora\u003c/em\u003e (\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e =0.234). The highest nucleotide diversity was found in \u003cem\u003eM. esculenta\u003c/em\u003e (\u003cem\u003eπ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00266) and the lowest in \u003cem\u003eM. adenophora\u003c/em\u003e (\u003cem\u003eπ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic diversity analysis was conducted on 477 individuals from 63 populations of four species using five highly polymorphic SSR primers. The results showed that the average genetic diversity index of wild \u003cem\u003eMorella\u003c/em\u003e species was at a relatively high level (\u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.53, \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.896, \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.07, \u003cem\u003eHo\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.924, \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.597, \u003cem\u003ePIC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.844, PPL\u0026thinsp;=\u0026thinsp;93.33%) (Table S4). A higher \u003cem\u003eI\u003c/em\u003e value means a higher genetic diversity, indicating that the genetic diversity of YNLY is higher (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.671). Based on the above data analysis, the genetic diversity of the populations of YNLY, GXTE, GXTB and GXSS in this study is high, while the genetic diversity of FJCL and YNLC is low. The polymorphism information content (\u003cem\u003ePIC\u003c/em\u003e) value was related to the degree of genetic variation at the locus, with \u003cem\u003ePIC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.844, indicating that there might be variation within \u003cem\u003eMorella\u003c/em\u003e populations (Table S4). The genetic diversity analysis of the five SSR microsatellite loci showed that SSR4 had the highest genetic diversity, while SSR2 showed a low level of genetic diversity. The genetic divergence coefficient (\u003cem\u003eFst\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;0.317, \u003cem\u003eF\u003c/em\u003e = -0.591 (Table S4), suggesting that there might have been inbreeding or genetic drift among the four species.\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\u003ePopulation haplotype distribution and genetic diversity parameters of the four \u003cem\u003eMorella\u003c/em\u003e species with cpDNA.\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNh\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHd\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eπ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecpDNA Chlorotypes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"18\" rowspan=\"19\"\u003e \u003cp\u003e\u003cem\u003eM. rubra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZDY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(6),H28(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH36(8),H37(1),H38(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH36(3),H38(1),H39(2),H40(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFJCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH43(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZKY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFJXM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(3),H44(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZJLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(3),H46(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(1),H3(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(5),H47(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDGN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH51(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJXTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZJNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH4(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"20\" rowspan=\"21\"\u003e \u003cp\u003e\u003cem\u003eM. nana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZQG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZPZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH6(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZXR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH6(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH9(5),H10(1),H11(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNLQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH12(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZQX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(1),H13(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCYB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH16(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH17(9),H18(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH19(8),H20(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH19(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNQB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH6(7),H21(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNYL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZZJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(1),H6(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNHZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH6(11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNZY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH22(9),H23(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH12(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH5(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZLZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH6(6),H24(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cem\u003eM. esculenta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH8(5),H25(1),H26(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH8(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNYA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH29(1),H30(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZQL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH31(1),H32(4),H33(2),H34(1),H35(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNYB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH1(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCQA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH15(11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH41(8),H42(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH8(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH8(5),H45(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGZZF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH7(1),H8(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYNXC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH14(3),H15(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH3(1),H8(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cem\u003eM. adenophora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXXN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXHZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(8),H27(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXFB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXQB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXQA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(6),H48(3),H49(1),H50(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGXHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH2(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eN\u003c/em\u003e, number of samples; \u003cem\u003eNh\u003c/em\u003e, Number of haplotypes; \u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e, gene diversity; π, nucleotide diversity averaged across loci.\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\u003eGenetic diversity parameters of the four \u003cem\u003eMorella\u003c/em\u003e species with cpDNA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003especies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(θw)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eπ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM. rubra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.005426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM. nana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.008317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM. esculenta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.012472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM. adenophora\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.001277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.021433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eM, sequence number; S, separation bit number; ps\u0026thinsp;=\u0026thinsp;S/m; θw\u0026thinsp;=\u0026thinsp;ps/α; \u003cem\u003eH\u003c/em\u003e\u003csub\u003ed\u003c/sub\u003e, gene diversity; π, nucleotide diversity averaged across loci.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic divergence and Genetic structure analysis\u003c/h2\u003e \u003cp\u003eThe AMOVA analysis based on cpDNA data indicated that genetic variation mainly occurred among groups (61.23%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The genetic divergence coefficient Nst was greater than Gst (Nst\u0026thinsp;=\u0026thinsp;0.804, Gst\u0026thinsp;=\u0026thinsp;0.775; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a clear phylogeographic structure among \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora, M. esculenta and M. nana\u003c/em\u003e. A series of analyses using SSR markers further revealed the genetic structure of the four species of \u003cem\u003eMorella\u003c/em\u003e. The AMOVA analysis of SSR indicated that genetic variation primarily originated from within populations, accounting for 85% of the total variation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results of principal component analysis (PCoA) indicated that although there was a certain degree of genetic overlap among species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the populations of the four species maintained a relatively clear genetic divergence overall. NJ tree shows that 63 populations can be clearly divided into four main genetic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Corresponding to the species, \u003cem\u003eM. esculenta\u003c/em\u003e and \u003cem\u003eM. nana\u003c/em\u003e form different clades, and some populations of \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e are mixed on the phylogenetic tree surface. Meanwhile, Mantel tests demonstrated a positive correlation between genetic distance and geographic distance in Morella (R2\u0026thinsp;=\u0026thinsp;0.0332, P\u0026thinsp;=\u0026thinsp;0.000) (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), and also a positive correlation between genetic distance and altitude (R2\u0026thinsp;=\u0026thinsp;0.0293, P\u0026thinsp;=\u0026thinsp;0.000) (Fig. S3). The results suggest that the IBD model supports the role of geographic distance and altitude in driving the genetic divergence of the four species.\u003c/p\u003e \u003cp\u003eBayesian clustering analysis based on SSR data determined the optimal \u003cem\u003eK\u003c/em\u003e value, dividing the 63 populations of the four species of \u003cem\u003eMorella\u003c/em\u003e into four groups: the first group was \u003cem\u003eM. rubra\u003c/em\u003e, the second group was \u003cem\u003eM. nana\u003c/em\u003e, the third group was \u003cem\u003eM. esculenta\u003c/em\u003e, and the fourth group was \u003cem\u003eM. adenophora\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b). Under this cluster number, the genetic composition of each population exhibits specific characteristics, and also reveals the gene exchange between populations. The result of the SSR genetic structure indicate that there were genetic similarities between different species, and the mixing of groups (marked green, red, blue, and yellow, respectively) shows widespread gene exchange, genetic diversity, and potential hybridization events between species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Additionally, barrier analysis showed that there were genetic boundaries among the four species, and the boundaries a, b and c could divide the four species as a whole. But there was still gene exchange among some populations of different species, especially among the populations of \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular variance (AMOVA) for \u003cem\u003eMorella\u003c/em\u003e species based on cpDNA and SSR\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource of variation\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\u003est)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ecpDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmong groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2454.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.13776 Va\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e=0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmong populations within groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1829.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.86289 Vb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e=0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28989 Vc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e=0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4383.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.29053\u003c/p\u003e \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\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmong group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02321 Va\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eSC\u003c/sub\u003e=0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmong populations within groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03551 Vb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e=0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin populations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33271 Vc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eCT\u003c/sub\u003e=0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39143\u003c/p\u003e \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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic relationship and estimation of divergence time\u003c/h2\u003e \u003cp\u003eIn order to determine the phylogenetic position of the four species, in the present study, cpDNA haplotype phylogenetic clade was constructed based on Maximum Likelihood (ML)and Maximum Parsimony (MP) using the genus \u003cem\u003eCasuarina equisetifolia\u003c/em\u003e was used as outgroup(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The analysis results indicate that the phylogenetic tree can be divided into four groups, which clearly reveals the interspecific relationship among them. \u003cem\u003eM. esculenta\u003c/em\u003e is located at the base of the phylogenetic tree, followed by \u003cem\u003eM. nana\u003c/em\u003e, in which \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e form a sister group. The results of whole-genome resequencing showed that \u003cem\u003eM. esculenta\u003c/em\u003e was at the base of the clade, \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e a form sister group, and then form a sister group with \u003cem\u003eM. nana\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Among cpDNA haplotypes, H2, H4, H5, H6 and H8 with the highest distribution frequency are located at the central position of the network diagram and may be ancestral haplotypes. The remaining haplotypes with lower frequencies are located outside the network diagram, and we speculate that they may be young haplotypes that have diverged from ancestral haplotypes. Moreover, the haplotype sharing phenomenon between \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e also reveals the close genetic relationship between them (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe results of BEAST analysis based on cpDNA data reveal a more detailed divergence timeline. The results showed that \u003cem\u003eM. nana\u003c/em\u003e was differentiated from \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e at about 10.45Ma (95%HDP), and began to intraspecific differentiate of \u003cem\u003eM. esculenta\u003c/em\u003e at 8.38 Ma (95%HDP) in the late Miocene. In addition, further divergence within \u003cem\u003eM. nana\u003c/em\u003e population began at 8.77 Ma (95%HDP) in the Late Miocene. the divergence of \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e occurred at 5.02Ma (95%HDP) in the Pliocene, and the intraspecific divergence of \u003cem\u003eM. rubra\u003c/em\u003e started at 3.8 Ma (95%HDP) in the early Pliocene, and the intraspecific divergence time of \u003cem\u003eM. adenophora\u003c/em\u003e was the latest at 3.01Ma (95%HDP) in the late Pliocene (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The time accuracy and relationships among the four species were further estimated using whole-genome resequencing of AGS. As shown in the figure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), the four species diverged into two clades at 12.72 Ma in the late Miocene, and the \u003cem\u003eM. esculenta\u003c/em\u003e diverged at the earliest, intraspecific divergence occurring around 6.65 Ma (95%HDP) in the late Miocene. The other three species form a complex group relationship, and \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e form a sister group. The divergence of \u003cem\u003eM. nana\u003c/em\u003e and the other two species began at about 10.12 Ma (95%HDP) in the late Miocene and intraspecific divergence occurred at 5.49 Ma (95%HDP) in the early Pliocene. The intraspecific divergence of \u003cem\u003eM. rubra\u003c/em\u003e occurred at 4.19 Ma (95%HDP) in the Pliocene. The internal bifurcation of \u003cem\u003eM. adenophora\u003c/em\u003e occurred in the Pliocene at about 4.17 Ma (95%HDP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). It is consistent with the species divergence time analyzed by cpDNA data, which further confirms the validity and reliability of these gene sets in studying plant phylogeny and historical evolution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInference of Population history\u003c/h2\u003e \u003cp\u003eBased on the cpDNA sequence, we conducted various analyses to determine the population history of four \u003cem\u003eMorella\u003c/em\u003e species. The neutrality test (Table S6) provided that the Tajima's D statistical value of the species was not significantly positive (0.71428, P\u0026thinsp;\u0026gt;\u0026thinsp;0.10), and the Fu and Li's D values (1.85822, P\u0026thinsp;\u0026lt;\u0026thinsp;0.02) were positive at the overall level, suggesting that the population expansion may was not significant in history. For individual species, the statistical values of Tajima's D and Fu and Li's D for \u003cem\u003eM. adenophora\u003c/em\u003e and \u003cem\u003eM. rubra\u003c/em\u003e were not significantly negative (P\u0026thinsp;\u0026gt;\u0026thinsp;0.10), indicating that the species may have undergone expansion during evolution. The statistical value of Tajima's D of \u003cem\u003eM. esculenta\u003c/em\u003e was not significantly positive, but it cannot fully explain the trend of deviation from expansion in this region. Tajima's D of \u003cem\u003eM. nana\u003c/em\u003e was not significantly positive (0.90941(P\u0026thinsp;\u0026gt;\u0026thinsp;0.10)), the Fu and Li's D and Fu and Li's F were both negative and did not reach statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.10). These results further support that the species may follow the neutral evolution model and remain relatively stable on the scale of research time. The curve of mismatch distribution analysis (Fig. S4) shows a multimodal distribution pattern at the at the overall level and in an independent single species, which was inconsistent with the expected unimodal distribution, indicating that there was insufficient evidence to support that these species have experienced significant expansion. In summary, the results of neutrality test and mismatch distribution analysis revealed that the population size of the four species of \u003cem\u003eMorella\u003c/em\u003e may remained relatively stable, and population may have expanded on a small scale and briefly in evolutionary history.\u003c/p\u003e \u003cp\u003eBecause of the neutrality test and mismatch distribution analysis detection are instantaneous, which can only explain the state of species in a period of time, but can\u0026lsquo;t clearly indicate whether the species has expanded during the evolutionary process. Therefore, the BSPs curve obtained by combining cpDNA data is shown in the figure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which shows that the effective population of \u003cem\u003eMorella\u003c/em\u003e has been in the amplification mode since 1.2 Ma. Among them, the expansion trend of \u003cem\u003eM. nana\u003c/em\u003e mainly occurred in 0.4\u0026thinsp;~\u0026thinsp;0.05 Ma, and the population size of \u003cem\u003eM. esculenta\u003c/em\u003e mainly expanded in 0.7\u0026thinsp;~\u0026thinsp;0.1 Ma, and the population of \u003cem\u003eM. rubra\u003c/em\u003e expanded since 0.15 Ma. However, the overall expansion of \u003cem\u003eM. adenophora\u003c/em\u003e was not obvious, and it expanded only after 0.04 Ma (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Bottleneck effect (Table S7) detection showed that under IAM, TPM and SMM models, Bottleneck effect may occur in some populations of 4 \u003cem\u003eMorella\u003c/em\u003e species, which is of great importance for understanding the genetic history and dynamics of species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePresent and past (Last Glacial Maximum) distribution\u003c/h2\u003e \u003cp\u003eIn this study, MAXENT has a good prediction effect on the potential distribution of four plants in East Asia (AUV\u0026thinsp;\u0026gt;\u0026thinsp;0.990), which shows that the model and species distribution information have a high degree of fitting, and the current distribution range covers the existing range of this species. Niche simulation results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) indicated that during the Last Glacial Maximum (LGM), the climate was cold and the survival environment was lost. To adapt to environmental changes, the species formed fragmented habitats, with their distribution ranges contracting to form refuges and presenting a patchy distribution pattern while migrating towards suitable areas, mainly in a southward direction. The results showed that \u003cem\u003eM. esculenta\u003c/em\u003e was mainly distributed in Yunnan and Guangxi, \u003cem\u003eM. rubra\u003c/em\u003e was mainly distributed in Guangxi, \u003cem\u003eM. adenophora\u003c/em\u003e was mainly distributed in Guangxi, and \u003cem\u003eM. nana\u003c/em\u003e was distributed in Yunnan and Guizhou. This suggests that potential refuges existed in these areas during the ice age. Subsequently, the suitable areas of the species slightly expanded from small fragments to their current distribution ranges. The expansion of \u003cem\u003eMorella\u003c/em\u003e mainly occurred in the southwestern region of China, suggesting that the main suitable area for the \u003cem\u003eMorella\u003c/em\u003e is the southwestern region. The species distribution model indicated that the distribution ranges of \u003cem\u003eM. adenophora\u003c/em\u003e, \u003cem\u003eM. esculenta\u003c/em\u003e, and \u003cem\u003eM. rubra\u003c/em\u003e were restricted during the LGM period, but rapidly expanded from the LGM to the present. Interestingly, the distribution range of \u003cem\u003eM. nana\u003c/em\u003e did not change greatly from the LGM to the present. The knife cut test analysis of the selected environmental variables indicated that warmest-season precipitation (bio18) was the most influential factor affecting \u003cem\u003eMorella\u003c/em\u003e\u0026rsquo;s distribution in both the LGM and present eras, underscoring its significance in determining the species' geographic spread.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity and genetic structure\u003c/h2\u003e \u003cp\u003eGenetic diversity is the foundation and core of biodiversity, which reflects the adaptive changes made by species in response to environmental changes (Jia, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Genetic diversity is a key factor for the survival and sustainable existence of species in their environment, and species in an unsuitable environment will lead to a decrease in genetic diversity (Wambulwa et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our research revealed that the genetic diversity of \u003cem\u003eMorella\u003c/em\u003e was high (cpDNA: Hd\u0026thinsp;=\u0026thinsp;0.917, π\u0026thinsp;=\u0026thinsp;0.00407; SSR markers: \u003cem\u003eNa\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.53, \u003cem\u003eNe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.896, \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.07, \u003cem\u003eHo\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.924, \u003cem\u003eHe\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.597) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S4). \u003cem\u003eMorella\u003c/em\u003e is a perennial woody plant with a long-life span, and different generations can share their habitats, which may play an internal buffer role in preventing the loss of genetic diversity (Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the species level, \u003cem\u003eM. nana\u003c/em\u003e has the highest haplotype diversity, while \u003cem\u003eM. adenophora\u003c/em\u003e has relatively low haplotype diversity. The average nucleotide diversity of \u003cem\u003eM. esculenta\u003c/em\u003e is the highest, which further confirms the richness of its genetic diversity. The genetic diversity of four species in this study may be affected by many potential factors. Firstly, genetic diversity may be affected by the distribution range, and species with a wide distribution range may show high genetic diversity (Liu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In general, the widely distributed \u003cem\u003eM. esculenta\u003c/em\u003e showed high genetic diversity, while that of \u003cem\u003eM. adenophora\u003c/em\u003e with a narrow distribution, was lower. Secondly, the longer a species has evolved, the more genetic variation there was (Xu et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), This study indicated that the genetic variation of \u003cem\u003eM. esculenta\u003c/em\u003e was high, it\u0026rsquo;s evolutionary history is longer, as evidenced by the origin and differentiation time for the four \u003cem\u003eMorella\u003c/em\u003e species. Furthermore, climate change and man-made destruction will also affect the genetic diversity of species, for example, the distribution range of \u003cem\u003eMorella\u003c/em\u003e may shrink during LGM period (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and the effective population size is greatly reduced, which leads to the decrease of genetic diversity (Keppel et al. 2012). In addition, \u003cem\u003eM. rubra\u003c/em\u003e is trained as a fruit, and it tastes delicious, so the wild species may be damaged to a higher degree, thus affecting genetic diversity.\u003c/p\u003e \u003cp\u003eMany factors work together on the genetic diversity of species, including but not limited to geographical distribution, gene flow and genetic communication among populations (Nybom, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Four \u003cem\u003eMorella\u003c/em\u003e species are mainly distributed in ecologically diverse areas such as Guangxi, Guizhou and Yunnan. The differences of climate and topography in different regions promote the generation of genetic variation to adapt to their respective niches and environmental pressures. The genetic diversity of \u003cem\u003eM. esculenta\u003c/em\u003e may be related to its wide distribution in complex and changeable mountain habitats. \u003cem\u003eM. esculenta\u003c/em\u003e was the first to differentiate, which may also create its rich genetic diversity. At the same time, the existence of shared haplotypes and the exchange of genetic species between species may also promote genetic diversity. In addition, bottleneck effect and population expansion events also play a great role in shaping genetic diversity (Yun et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our research shows that some populations of \u003cem\u003eMorella\u003c/em\u003e have experienced bottleneck events, and their genetic diversity has also been affected. To sum up, the genetic diversity of \u003cem\u003eMorella\u003c/em\u003e is the result of many factors, and the interaction of these factors shapes the level of genetic diversity of \u003cem\u003eMorella\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAMOVA analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that the genetic variation of SSR markers mainly occurred in within populations, while that of cpDNA markers mainly occurred in among groups, which was consistent with previous analysis based on cpDNA sequences and SSR markers (Liu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e;Gao et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There may be many reasons for the opposite results of AMOVA. Firstly, cpDNA is mainly maternal inheritance, with lower gene flow, low recombination and mutation rate, which makes the lineage inheritance of species clearer (Comes and Kadereit, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Avise, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), whereas SSR is a parental inheritance, and the recombination and substitution rate of parental inheritance is high (Mort et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), so that the genetic differences between them are different. Moreover, the higher genetic variation between populations may be caused by the complex evolutionary processes that species have undergone over the course of history (Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thirdly, the phenomenon that genetic variation mainly occurs within populations mostly occurs in woody plants (Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Four \u003cem\u003eMorella\u003c/em\u003e species are perennial woody plants, and the genetic variation is similar to other woody plants.\u003c/p\u003e \u003cp\u003eThe genetic differentiation coefficient Nst based on cpDNA is greater than Gst, which indicates that the four \u003cem\u003eMorella\u003c/em\u003e species have obvious lineage geographical structure, haplotypes with similar genetic distance are more likely to appear in geographically adjacent regions (Pons and Petit, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Barrier analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that there were genetic communication barriers among the four species, and three geographical (a, b and c) boundaries could separate the four species. However, there were genetic communication between \u003cem\u003eM. adenophora\u003c/em\u003e and some populations of \u003cem\u003eM. rubra\u003c/em\u003e, which also proved that \u003cem\u003eM. adenophora\u003c/em\u003e and \u003cem\u003eM. rubra\u003c/em\u003e were sister group with close genetic relationship. The results of structure showed that \u003cem\u003eMorella\u003c/em\u003e was divided into four groups when \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, and some populations among the groups had gene exchange. NJ tree based on Nei's genetic distance method supported the clustering results of structure. Each group contains individuals from different populations, which reveals that there is a certain degree of gene exchange between populations. This genetic mixing may reflect that the genetic differences between populations are not completely independent, but overlap. These findings further confirm the genetic diversity among species and the existence of hybridization events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic relationship of four Morella species\u003c/h2\u003e \u003cp\u003eAccurate species classification and identification plays an important role in conservation biology, ecology and genealogy geography, which directly affects the reliability of phylogenetic research, the accuracy of species evolution analysis and the scientific formulation of conservation measures (Dagnino et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, the phylogenetic relationship between four \u003cem\u003eMorella\u003c/em\u003e species distributed in East Asia was analyzed based on the data of cpDNA and SSR sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The results showed that the four species formed a monophyletic clade with high bootstrap support. The genetic structure based on SSR sequencing data shows that 63 \u003cem\u003eMorella\u003c/em\u003e populations can be divided into four distinct genetic groups, which is consistent with their taxonomic classification. The populations of \u003cem\u003eM. esculenta\u003c/em\u003e and \u003cem\u003eM. nana\u003c/em\u003e showed obvious divergence on the phylogenetic tree, and the genetic divergence was significant. In contrast, the populations of \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e showed some genetic mixing on the phylogenetic tree, suggesting that they may have a close phylogenetic relationship.\u003c/p\u003e \u003cp\u003ePhylogenesis based on cpDNA data further revealed the phylogenetic relationship of the four species. \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e share a haplotype H2, and they have a common clade. \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. esculenta\u003c/em\u003e share the H3 haplotype, \u003cem\u003eMorella\u003c/em\u003e is a dioecious plant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee), which can be pollinated by wind, insects and animals (Pang, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The geographical proximity may provide opportunities for the exchange of genetic materials between species. Phylogenetic tree divides four species into four groups, which clearly reveals the relationship between them. Phylogenetic analysis showed that \u003cem\u003eM. esculenta\u003c/em\u003e differentiated the earliest, followed by \u003cem\u003eM. nana\u003c/em\u003e and \u003cem\u003eM. rubra\u003c/em\u003e. However, \u003cem\u003eM. adenophora\u003c/em\u003e is classified into clades of \u003cem\u003eM. nana\u003c/em\u003e, which is the closest to it in phylogenetic relationship. It may be because cpDNA is maternal inheritance, and the gene exchange of cpDNA marker is limited, so cpDNA marker is easier to infiltrate \u003cb\u003e(\u003c/b\u003eWhittemore et al. 1991; Du et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wan et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The NJ tree based on SSR sequencing data shows that \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e have communication, but they are in different clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The ML phylogenetic tree constructed by the results of whole-genome resequencing further clearly showed the phylogenetic relationship of four \u003cem\u003eMorella\u003c/em\u003e species, and confirmed that \u003cem\u003eM. esculenta\u003c/em\u003e was the earliest species to differentiate, and \u003cem\u003eM. nana\u003c/em\u003e, \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e formed a complex group relationship, in which \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e formed a pair of closely related sister group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). In a word, the four \u003cem\u003eMorella\u003c/em\u003e species distributed in East Asia are monophyletic clades of each other, and \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e are closely related sister group because of gene exchange. Our results are consistent with those of Liu (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We increased the sampling amount, verified and analyzed the phylogenetic relationship of four \u003cem\u003eMorella\u003c/em\u003e species distributed in East Asia by SSR, cpDNA and whole-genome resequencing, and further proved their genetic relationship and phylogenetic position.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSpecies divergence and population history\u003c/h2\u003e \u003cp\u003eThe formation and diversity of species may be affected by mountain barriers, and complex topography will form different environmental heterogeneity and niche changes (Fjelds\u0026aring; et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Molecular clock analysis revealed that four \u003cem\u003eMorella\u003c/em\u003e species began to differentiate in the late Miocene (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed and e), the first being \u003cem\u003eM. esculenta\u003c/em\u003e, and then \u003cem\u003eM. nana\u003c/em\u003e began to differentiate. The divergence between \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e occurred in the Pliocene, suggesting that \u003cem\u003eMorella\u003c/em\u003e had a long divergence process. The phylogenetic analysis based on whole-genome resequencing further demonstrates the reliability of the estimation of divergence time of four species. We believe that the divergence of four \u003cem\u003eMorella\u003c/em\u003e species is related to the uplift of QTP. Previous studies and literature review have shown that the QTP uplift occurred in the middle Miocene (15\u0026thinsp;\u0026minus;\u0026thinsp;13 Mya), late Miocene (8\u0026thinsp;\u0026minus;\u0026thinsp;7 Mya) or Pliocene to Early Pleistocene (3.6\u0026ndash;1.7 Mya), with the strongest uplift beginning at 3.6 Ma (Coleman and Hodges, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Li and Fang, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Shahzad et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The divergence time of four \u003cem\u003eMorella\u003c/em\u003e species in this study corresponds to the uplift time of the QTP, and the environmental differences caused by this long-term geological event may have caused species divergence. Terrain heterogeneity and climate fluctuation caused by the uplift of QTP have shaped the current species distribution pattern (Yu et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is consistent with previous research results (Lei et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lei et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shahzad et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Due to the influence of geographical barriers, gene exchange is limited, and there are differences in different ecological environments, which will promote adaptive evolution of species, thus intensifying genetic divergence. Terrain heterogeneity has driven the formation and maintenance of genetic structures of four \u003cem\u003eMorella\u003c/em\u003e species (Li et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom late Miocene to Cenozoic, it is of great importance for the origin of biodiversity in East Asia (Sun et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Significant climatic oscillations and geological events in the Late Miocene, such as continental drift, mountain range formation and climate change, especially the uplift of the Tibetan Plateau, led to great changes in the geomorphology and climate of neighboring areas. The interaction of these processes provides new habitats and niches for biodiversity in subtropical regions, and may also create favorable conditions for species divergence or the formation of new species. The climatic transition to a more suitable East Asian monsoon climate (Sun and Wang, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It ensures the adaptation and evolution of species to the new environment and promotes the reconstruction of ecosystems and biological communities. For instance, the rate of speciation in \u003cem\u003ePrimulina\u003c/em\u003e was strongly linked on the East Asian monsoon climate (Kong et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The East Asian monsoon climate leads to the increase of precipitation (Li et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the niche model shows that the Precipitation of Warmest Quarter has the greatest influence on species distribution, which further provides suitable habitats for \u003cem\u003eM. esculenta\u003c/em\u003e and \u003cem\u003eM. nana\u003c/em\u003e, and promotes species divergence. The unfavorable conditions in Pliocene led to the migration of species distribution and contraction into a low latitude refuge. Species divergence may be influenced by environmental changes and biological characteristics, and geographic isolation plays a role in promoting species formation (Givnish et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rabosky and Adams, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These factors together shape the species formation and divergence of \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eMantel test shows (Fig. S3) that population altitude has a certain driving effect on genetic divergence of four species of \u003cem\u003eMorella\u003c/em\u003e, which indicates that four \u003cem\u003eMorella\u003c/em\u003e species are sensitive to climate change, and environmental heterogeneity guides four \u003cem\u003eMorella\u003c/em\u003e species to adapt to local environmental changes (Zhang et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Long-term environmental heterogeneity has shaped the existing lineage geographical structure of species. Neutrality test and mismatch analysis proved that the effective population of four \u003cem\u003eMorella\u003c/em\u003e species had been in a relatively stable equilibrium state on the scale of research time. Niche simulation showed that \u003cem\u003eMorella\u003c/em\u003e had at least three shelters in LGM period (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In LGM period, the temperature was low, the species could not survive, and the effective population size might be greatly reduced. Then, the temperature rose, the species' suitable area expanded and the effective population size became larger. The BSPs curve showed that \u003cem\u003eMorella\u003c/em\u003e expanded from about 1.2 Ma (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and four species expanded in different periods before this period. The results showed that complex climate change may be an important factor affecting the historical and current geographical distribution of \u003cem\u003eMorella\u003c/em\u003e (Zheng et al. 1998; Wu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on the analysis of cpDNA data, H2, H4 and H5 haplotypes are considered as ancestral haplotypes. Niche simulation showed that \u003cem\u003eM. esculenta\u003c/em\u003e was mainly distributed in Yunnan and Guangxi, \u003cem\u003eM. rubra\u003c/em\u003e was mainly distributed in Guangxi, \u003cem\u003eM. adenophora\u003c/em\u003e was mainly distributed in Guangxi, and \u003cem\u003eM. nana\u003c/em\u003e was mainly distributed in Yunnan and Guizhou during LGM period. Combined with SSR and cpDNA genetic diversity analysis, it is speculated that these areas may be ice age shelters for four species. These shelters may provide an environment for species to survive and adapt during the ice age, thus shaping their genetic structure and existing geographical distribution model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eManagement and conservation strategies for Morella resource\u003c/h2\u003e \u003cp\u003eBiological diversity is an important part of maintaining ecological balance, and understanding genetic diversity of species is essential to understanding the evolution of biological diversity (Ren et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With the loss of global biodiversity, especially the low genetic diversity of vulnerable species, it is essential to protect them (Tao et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Habitat fragmentation is an important cause of species threat and endangerment (Yang et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Genetic variation and habitat are a key factor in ensuring the continued presence of a species continued presence in the environment (Wambulwa et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Only by ensuring sufficient genetic variation can we promote the long-term survival and evolution of species (Dai et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this study, the genetic diversity of 63 populations of 4 \u003cem\u003eMorella\u003c/em\u003e species was analyzed based on cpDNA and SSR. The results showed that the genetic diversity of some populations was low, and our field investigation found that some populations were seriously damaged, which led to the gradual narrowing of the suitable area of species and the serious loss of wild resources. Therefore, it is urgent to effectively manage and protect these important wild resources of \u003cem\u003eMorella\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eM. rubra\u003c/em\u003e is distributed in many areas and has strong adaptability. However, due to its delicious edible fruit, it has caused serious human destruction, forced the disappearance of wild resources, and sharply decreased genetic diversity. \u003cem\u003eM. adenophora\u003c/em\u003e has been listed as a vulnerable species (IUCN Red List of Threatened Species) and its distribution area is narrow. Because the \u003cem\u003eM. nana\u003c/em\u003e is only distributed at high altitude area of Yunnan-Guizhou Plateau, the growth range is limited. \u003cem\u003eM. esculenta\u003c/em\u003e is mainly distributed in tropical and subtropical regions from East Asia to Southeast Asia, and in subtropical regions of China, it has a wild range, and early origin, and is at the base of clade. \u003cem\u003eMorella\u003c/em\u003e is a perennial deciduous woody plant with high value in medicine, food, ecology and economy (Bai et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For this reason, it is urgent to protect this resource. Combined with the niche model, Guangxi, Yunnan and Guizhou are ice age shelters of \u003cem\u003eMorella\u003c/em\u003e, which shows that \u003cem\u003eMorella\u003c/em\u003e has a suitable living environment in these areas. The populations of YNLY, YNDL, GXTB and GZQL contain high genetic diversity or abundant haplotypes, which can correspond to shelters. We suggest that nature reserves should be established in such areas for in-situ protection. Maintain the sustainability of the living environment, avoid habitat fragmentation, improve the adaptability of the population, and ensure species richness. Secondly, ex-situ conservation, for the populations whose genetic diversity has been lost and whose habitats have been destroyed, moves to an environment suitable for species growth for protection, such as botanical gardens, nature reserves and other effective protected areas. These two protective measures are effective and desirable. In addition, science and technology can be used to assist the propagation of \u003cem\u003eMorella\u003c/em\u003e, such as tissue culture and artificial seedling raising, which can solve the problems related to plant regeneration, breeding and cultivation (Cabrera et al. 2012). In a word, the adaptive habitat of \u003cem\u003eMorella\u003c/em\u003e wild resources has been seriously damaged, and it is difficult for some populations to survive in the original habitat, so effective measures must be taken for management and protection.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, the genetic structure and systematic geographical distribution of 63 populations of four \u003cem\u003eMorella\u003c/em\u003e species were studied by molecular systematics. Our study suggests that geological movement and climate change can well explain the species divergence model and genetic structure well. In addition, genetic diversity plays an important role in the adaptation of species to environmental change, and ice age shelters provide a suitable living environment for species. \u003cem\u003eMorella\u003c/em\u003e has remained relatively stable in the evolutionary scale of the research time, and it did not begin to expand until 1.2 Ma. The results of this study will enhance our understanding of the evolution, phylogenetic relationships and distribution of the four species of \u003cem\u003eMorella\u003c/em\u003e in East Asia. Based on our research, we propose that these four species are closely related and that there is gene exchange among the species. Whole-genome resequencing and cpDNA support the topology of ((\u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e) \u003cem\u003eM. nana\u003c/em\u003e) \u003cem\u003eM. esculenta\u003c/em\u003e. In a word, our study made a clear phylogenetic relationship of the four \u003cem\u003eMorella\u003c/em\u003e species, and existing geographical distribution pattern of species was the common result of climate change and geological movement, combined niche simulation prediction with genetic diversity, and generalized the protection strategy of wild resources of \u003cem\u003eMorella\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHSS came up with the original concept; HSS, LYX, ZC and JCX designed the study, selected species and conducted field investigations; FJ, ZLH, LY and CYT participate in field sample collection; HSS and LYX did the lab work, analyzed the genetic data and wrote the manuscript; ZC supervised the laboratory work and genetic analysis. All authors contributed to the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (32260252) and Guizhou science and technology support project ([2019]2451-2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated or analyzed in our study were submitted to GenBank (https://www.ncbi.nlm.nih.gov/). The accession numbers are: PQ337779-PQ337829, PQ337830-PQ337880: PQ337881-PQ337931 and PQ337932-PQ337982.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research did not involve any ethical issues, and our collection was in line with Chinese laws. The samples we collected were not privately owned, and we consulted the local forestry bureau and relevant laws and regulations when we collected them. We collected only a small number of leaves and did not endanger the individual plants, in accordance with the relevant management regulations for the protection of vulnerable plants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data are available at Supplementary material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAvise JC\u003cstrong\u003e (\u003c/strong\u003e2000) Phylogeography: the history and formation of species. 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Episodes Journal of International Geoscience 21: 152-158. https://doi.org/10.18814/epiiugs/1998/v21i3/003\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-plant-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpre","sideBox":"Learn more about [Journal of Plant Research](http://link.springer.com/journal/10265)","snPcode":"10265","submissionUrl":"https://www.editorialmanager.com/jpre/default2.aspx","title":"Journal of Plant Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Morella, Genetic diversity, Species differentiation, Demographic dynamics, Management and protection","lastPublishedDoi":"10.21203/rs.3.rs-6558644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6558644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMountain uplift and Quaternary climate oscillations have profoundly influenced plant species' distribution and diversification, yet their impacts on demographic history and biogeographic patterns remain unclear. This study investigates the effects of habitat fragmentation and climatic shifts on genetic diversity and phylogeographic distribution of four East Asian \u003cem\u003eMorella\u003c/em\u003e species. Using chloroplast DNA (cpDNA) sequences and simple sequence repeats (SSR) were used to study the species divergence and genetic structure of \u003cem\u003eMorella \u003c/em\u003efrom 477 individuals of 63 populations. The whole-genome resequencing was also applied to ensure the accuracy of the estimation of species differentiation time and phylogenetic relationship. We identified species-specific haplotypes, only H2 haplotype was shared by \u003cem\u003eM. rubra\u003c/em\u003e and \u003cem\u003eM. adenophora\u003c/em\u003e, and H3 was shared by \u003cem\u003eM. esculenta\u003c/em\u003e and \u003cem\u003eM. rubra\u003c/em\u003e in cpDNA sequence. Phylogenetic analysis revealed a topology of \u003cem\u003eM. esculenta\u003c/em\u003e + (\u003cem\u003eM. nana\u003c/em\u003e (\u003cem\u003eM. rubra \u003c/em\u003e+ \u003cem\u003eM. adenophora\u003c/em\u003e)), with significant gene flow among species. Its divergence occurring between 5.02 and 12.72 Ma was completed before the Quaternary period.\u003cstrong\u003e \u003c/strong\u003eResults suggest Late Miocene-Pliocene geological and climatic shifts drove speciation, while Quaternary climate fluctuations shaped their geographic distribution, with potential refugia maintaining genetic diversity. Our findings highlight the roles of orogeny and paleoclimate in speciation and range dynamics, providing insights into East Asia's history of lineage differentiation.\u003c/p\u003e","manuscriptTitle":"Effects of mountain uplift and climate change on phylogeography and species divergence of East Asia Morella","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 14:16:19","doi":"10.21203/rs.3.rs-6558644/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-16T15:14:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-16T14:07:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-06T05:43:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Plant Research","date":"2025-05-03T09:22:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-plant-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpre","sideBox":"Learn more about [Journal of Plant Research](http://link.springer.com/journal/10265)","snPcode":"10265","submissionUrl":"https://www.editorialmanager.com/jpre/default2.aspx","title":"Journal of Plant Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0552802c-80e0-441c-80d4-71a9dbc2b76e","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:11:24+00:00","versionOfRecord":{"articleIdentity":"rs-6558644","link":"https://doi.org/10.1007/s10265-025-01675-z","journal":{"identity":"journal-of-plant-research","isVorOnly":false,"title":"Journal of Plant Research"},"publishedOn":"2025-11-17 15:58:24","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-05-20 14:16:19","video":"","vorDoi":"10.1007/s10265-025-01675-z","vorDoiUrl":"https://doi.org/10.1007/s10265-025-01675-z","workflowStages":[]},"version":"v1","identity":"rs-6558644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6558644","identity":"rs-6558644","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}