Phylogeographical analysis on the alpine endemic plant Meconopsis punicea (Papaveraceae) based on chloroplast and nuclear genes

Phylogeographical analysis on the alpine endemic plant Meconopsis punicea (Papaveraceae) based on chloroplast and nuclear genes | 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 Phylogeographical analysis on the alpine endemic plant Meconopsis punicea (Papaveraceae) based on chloroplast and nuclear genes Zhi Ou, Jian Xiong, Yuanqi Jiang, Yongdong Dai, Yan Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1844011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Meconopsis punicea (Papaveraceae) is only distributed in the alpine areas of the Qinghai Plateau (QHP) and Hengduan Mountains (HDM) in China. As an important ornamental flower and traditional Tibetan medicine, in addition to habitat loss, its higher economic value drives over-exploitation of this resource, which has accelerated the reduction of its distribution. How to make its protection strategy is still unfounded as unclear genetic diversity characteristics of M. punicea . In this study, we analyzed the genetic diversity and phylogeographic structures of M. punicea representing 11 populations 106 individuals using 4-locus dataset ( matK , ndhF , rbcL and rpb2 , a total of 2576 bp). A total of 76 sequence types were identified (Hd = 0.987, Pi = 0.00447). Six phylogenetic clades were recognized, and 3 clades were restricted to unique geographic distribution (Clade A corresponded to population SD, B to LT, and C to BL). The zoige area was referred as the differentiation center and a glacial refuge of M. punicea . M. punicea most likely originated from the southern HDM in the early Miocene (~ 22 Myr). Two dispersal routes were suggested and the ecological and geographical difference were revealed as the main driving forces for species differentiation of M. punicea. The suitable distribution area was predicted about 158,000 km 2 mainly in Sichuan, Gansu and Qinghai. Our current results provide much clear and detailed understanding for the diversity and geographical spatial distribution of the endemic alpine plant M. punicea . It will also be able to provide theoretical guidance for formulating the urgently needed protection strategies of M. punicea . 4-locus phylogeny genetic structure molecular clock divergence time Origin suitable distribution prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Meconopsis punicea , a perennial herb in the family Papaveraceae, is one of the most distinctive members of the genus with its sumptuous satiny red pendent flower (Wu and Chuang, 1980 ; Ying et al. 2006 ). It has very high ornamental value, and is also a traditional Tibetan medicine, which has kinds of pharmacological effects such as heat-clearing, analgesic, hypotensive, and antibacterial (Kala 2003 ; Liu et al. 2012 ; Shang et al. 2015 ). M. punicea has a rather narrow distribution which mainly distribute on plateau hillsides or meadows at an altitude of about 3000–4500 m in northwestern Sichuan, southeastern Qinghai, and southwestern Gansu (Chuang 1981 ; Zhang and Grey-Wilson 2008 ). Thus, as driven by the benefits of the industrialized production of medicine, the limited wild resources of M. punicea are drastically reduced. And it has been listed as wild plant under State protection (category II) (China Plant Red Data Book 1992). Phylogeography could explore the entire geographical distribution pattern, and discuss the historical factors and evolutionary process of the speciation (Avise 2000 ). Phylogeography of two Meconopsis species (i.e. Meconopsis cambrica, Meconopsis integrifolia) explain the genetic differentiation pattern and the potential refugia, it is of great significance to deeply understand the speciation and evolution of species (Valtueña 2012; Yang 2012). In our current study, we conducted a range-wide sampling across the distribution of M. punicea and collected 11 populations with a total of 106 samples. Three chloroplast DNA sequences, i.e., matK (maturase K), ndhF (NADH dehydrogenase subunit F), rbcL (ribulose-15-bisphosphate carboxylase/oxygenase large subunit) and one nuclear gene rpb2 (the second largest subunit of RNA polymerase ІІ), were sequenced and analyzed for all samples. The purpose of this study was attempted to characterize the genetic diversity, genetic differentiation, and the genetic structure of M. punicea . Furthermore, we also aimed to evaluate the spatial pattern and the historical process of population demography, and explore the correlations between species differentiation and geological history of the Qinghai-Tibetan Plateau. Materials And Methods Sampling M. punicea is mainly distributed in the Qinghai Plateau (QHP) and Hengduan Mountains (HDM). In terms of administrative divisions, it contains Qinghai, Sichuan and Gansu provinces in China. Therefore, the samples used in this study were collected from the three provinces and cover the distribution range of M. punicea . A total of 11 populations were collected: five populations in Qinghai, four populations in Sichuan, and two populations in Gansu (Fig.1, Supplementary table 1). The samplings were distributed ranging from 30.97° N -35.58° N to 95.87° E -103.74° E, and the altitudes ranged from 3000 m to 4500 m. Ten individuals were collected from each population, with a total of 110 samples. The distance between sample of individuals in each population was at least 50 m apart. To ensure the quality of experimental materials, fresh leave s collected in the field were dried with silica gel desiccant and stored in -20 ℃. DNA extraction, gene sequence amplification and sequencing Genomic DNA was extracted from silica-dried leaf materials using TIANGEN Fast Plant Genome Kit. The extracted DNA were tested on a 1.2% agarose gel for gel electrophoresis, and the purity and concentration were measured using the Nano Drop 2000 ultraviolet spectrophotometer. Candidate molecular markers were screened based on the success rate of amplification and sequencing. Three chloroplast gene fragments matK (maturase K), ndhF (NADH dehydrogenase subunit F), rbcL (ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit), and one nuclear gene rpb2 (the second largest subunit sequences of RNA polymerase ІІ) were selected to conduct the further phylogeographic analysis of M. punicea . Partial chloroplast matK gene of M. punicea was amplified using the primers matK-F (5’-ACTGTATCGCACTATGTATCA-3’) and matK-R (5’- GAACTAGTC GGATGGAGTAG -3’). The ndhF gene sequences were amplified using the primers ndhF-F (5’-CTGTCTATTCAGCAAATAAAT-3’) and ndhF-R (5’-CGATTATAGG ACCAATCATATA-3’). The rbcL gene was amplified using the primers rbcL-F (5’-ATGTCACCACAAACAGARACTAAAGC-3’) and rbcL-R (5’-CTTTTAGTAAAA GATTGGGCCGAG-3’). The rpb2 gene was amplified with the primer pair Rpb2 -105F (5’-GATAGTATGTGGGACCA AGG-3’) and Rpb2 -1164R (5’-CCCTCTCGA ATGACTAT TGG-3’) (Xiao 2013). The polymerase chain reaction (PCR) mixture was performed following the manufacturer’s instructions (Tsingke Biotech Co. Ltd.) and the PCR procedure was performed according to our previous screening results (Xiong 2016; Jiang 2018; Qu et al. 2018). The PCR products were separated by electrophoresis in 1.5% agarose gels. The clear bands were cut and removed from the gels for purification using a Gel Band Purification Kit (BioTeke Co. Ltd, Beijing, China) and directly sequenced with an automatic sequence analyzer (ABI 7500, Sangon Biotech Co. Ltd). The sequences were assembled with SeqMan 7.1. Each locus was aligned using MAFFT 3.7 with the default settings (Katoh et al. 2002) and manual adjustments were made in MEGA 5 (Tamura et al. 2011). Population genetic diversity and genetic structure analyses Nei’s nucleotide diversity ( Pi ) and haplotype diversity ( Hd ) of the matK , ndhF , rbcL , and rpb2 DNA sequences were estimated by using DnaSP 6.12.01 (Librado & Rozas, 2009). To assess the genetic differentiation in M. punicea , we performed an analysis of molecular variance (AMOVA) on the populations of M. punicae using Arlequin 3.1 (Excoffier et al. 2007). Gene flow was estimated according to Wright’s principles Nm = (1-Fst)/4Fst. Geographic mapping was illustrated in Google Earth 7.1. Mantel tests were carried out to test the correlation between genetic distance and geographic distance using TFPGA 1.3 (Miller 1997). Mismatch distribution, neutrality test and historical demography A mismatch distribution for M. punicea was investigated to using DnaSP 6.12.01. A unimodal distribution indicates that populations have experienced recent demographic expansions, while multimodal distributions are consistent with stability (Fu 1997). In addition, historical demographic changes were examined with neutrality tests for the species and individual populations, including Tajima’s D, Fu and Li’s D*, Fu and Li’s F* (Excoffier 2004). Tajima’s D test detects the relationship between the number of separation sites and nucleotide diversity (Pi). Fu-Li’s D & F test measures the difference between total polymorphic sites and single-base mutations. The paraments of sum-of-squared deviations (SSD) and the Harpending's Raggedness index (Rag) were calculated with Arlequin 3.1. These parameters provide information about demographic histories, with significant negative values indicating evidence of demographic expansions, whereas the positive values indicating evidence of population bottlenecks. Phylogenetic relationships and geographic structure analysis The phylogenetic relationships of haplotypes of M. punicea were constructed by using the single and combined 3 chloroplast genes. Meanwhile, the relationship of alleles was conducted by using the nuclear gene rpb2 . Furthermore, DNA sequences of chloroplast genes and nuclear gene were combined to retrieve the evolutionary history of M. punicea. M. horridula and M. betonicifolia chosen as outgroups as their suitable phylogenetic relationship (Liu et al. 2014). The best model was selected in each dataset by using Modeltest 3.7 (Posada and Crandall 2001). Phylogenetic tree of the haplotypes and alleles of M. punicae were analyzed using the Maximum Likelihood (ML) method in Raxml 7.2 (Stamatakis 2014). Divergence time estimation The divergence time of M. punicea and its relatives was estimated with combined the 4 genes using a Bayesian method in BEAST 1.8.4 (Drummond and Rambaut, 2007). A total of 156 sequences were sampled within the subf. Papaveroideae to estimate the divergence time in M. punicea (Jork and Kadereit 1995; Yuan 2002; Xiao 2013; Liu et al. 2014) (Supplementary table 2).. Nandinadomestica and Hydrastis canadensis were chosen as the outgroups. The oldest known fossil of Papaveroideae, Palaeoaster sp. is assigned to the Latest Cretaceous (74.5–64.5 million years ago, Myr) (Smith et al. 2001). The calibration 74.5 Myr was settled according to the previous research (Valtueña et al. 2012). Accordingly, subf. Papaveroideae was defined as monophyletic, and its root was set with a normally distributed prior with a mean of 70 Myr and a standard deviation of 2 Myr. The uncorrelated relaxed clock was used as the clock model, and a birth death prior was set for branch lengths. Other priors were made in default settings, and the Markov Chain Monte Carlo (MCMC) was initiated on a random starting tree. Two MCMC analyses were run for 100 million generations with parameters sampled every 1000 generations. The first runs were used to examine the MCMC performance, and operators were adjusted as suggested by the output analysis. Finally, two BEAST runs were performed. Convergence of the runs was assessed using Tracer 1.6 (Rambaut and Drummond 2009), which indicated that most parameter values had effective sample sizes well above 150, then the two separate runs dataset were combined using LogCombiner 2.1.2 (Bouckaert et al. 2014). TreeAnnotator 2.1.2 was used to calculate node ages and the upper and lower bounds of the 95% highest posterior density interval (HPD) for divergence time from the combined tree file with the burn-in of 20% aged trees (Bouckaert et al. 2014). The chronogram was visualized using Figtree 1.6.2 (Drummond and Rambaut 2007). Prediction of suitable distribution area of M. punicea Species occurrence data was collected from ongoing field studies and published research papers. Bioclim variables were downloaded from the CliMond Archive (https://www.climond.org/) (Kriticos et al., 2012) A total of20 species occurrence data were used (Supplementary table 3). And a total of 35 typical climate variables at a grid resolution of 10' were obtained from CliMond the CRU CL2.0 dataset. (https://www.climond.org/). These factors contained the core set of 19 variables (temperature and precipitation), and an extended set of 16 additional variables (solar radiation and soil moisture) at a global extent (Supplementary table 4). All climate variables data collection sources were from 1961 - 1990 (30 years centered on 1975). Species distribution modelling were constructed based on species redundance with MaxEnt v3.4.1 (Phillips et al., 2006). randomly 25% of the data points were extracted as the test data, and “do jackknife to measure variable importance” was selected. The output grid format was set as “cloglog” The output result “.asc” was visualized with Global mapper17. Results The genetic diversity analysis A total of 106 samples of M. punicea were successfully amplified and sequenced. The aligned length of matK was 660 bp, containing 41 polymorphic sites (variation rates: 6.24%), with values of Hd (0.931) and Pi (0.00788), respectively. The length of ndhF was 691 bp, containing 21 polymorphic sites (variation rates: 3.04%), with lower values of Hd (0.438) and Pi (0.00135). The length of rbcL was 683 bp, containing 6 polymorphic sites (variation rates: 0.88%), with the significant low Hd (0.110) and Pi (0.00027). The length of the nuclear gene rpb2 was 542 bp, containing 69 polymorphic sites (variation rates: 12.78%), and its Hd and Pi were 0.987, 0.00447, respectively. The combinatorial dataset was contained 2576 bp after combined the 3 chloroplast DNA fragments and the nuclear gene rpb2 , and total polymorphic sites were 136, with mutation rate 5.28%. Sixty-nine parsimony informative sites were recognized, accounting for 2.65% of the total sequence. The matK and rpb2 gene sequences have relatively more information sites among the 4 genes. A total of 76 sequence types were identified from the concatenated sequences of 4 genes (Table 1). All 76 sequence types were submitted to GenBank ( matK : OM640468-OM640543; ndhF : OM640544-OM640619; rbcL :OM654956-OM655031; rpb2 :OM655032- OM655107). Table 1 Genetic diversity of M. punicea based on the 4 genes datasets Paraments matK ndhF rbcL 3 chloroplast genes rpb2 4 genes Length 660 691 683 2034 542 2576 Total number of mutations, Eta 45 21 6 72 83 155 Number of polymorphic sites 41 21 6 68 69 136 Parsimony informative sites 32 11 2 45 25 69 Sequence types 36 15 5 46 38 76 Hd 0.931 0.438 0.110 0.949 0.844 0.987 sd of Hd 0.011 0.06 0.042 0.010 0.031 0.005 Pi 0.00788 0.00135 0.00027 0.00310 0.00963 0.00447 sd of Pi 0.00065 0.00034 0.00011 0.00028 0.00154 0.00041 Theta (per site) from Eta 0.01308 0.0058 0.00168 0.00677 0.02936 0.01151 A high level of haplotype diversity ( Hd = 0.987) but a relatively low level of nucleotide diversity ( Pi = 0.00447) is detected among the populations of M. punicea , (Table 2). The populations GD and BM have the highest haplotype diversity ( Hd = 1.00). Among the 76 sequence types, types 2, 40, 44, 46 and 50 are distributed in two populations, the others were unique to only one population. Table 2 Genetic diversity of the 11 populations of M. punicea Population Samples num. Total S eq. types S eq. types num. Hd Pi Shared hap. Fst Nm REG 10 7 1-7 0.867 0.00400 2 0.43318 0.32713 SD 10 7 8-14 0.911 0.00128 0.49481 0.25524 HL 10 6 2, 15-19 0.778 0.00398 2 0.43375 0.32637 DLJ 10 7 20-26 0.911 0.00104 0.50012 0.24988 BL 6 4 27-30 0.800 0.00171 0.48811 0.26218 LT 10 8 31-38 0.933 0.00212 0.47452 0.27685 CD 10 8 39-46 0.956 0.00162 40,44,46 0.48722 0.26312 GD 10 10 47-56 1.000 0.00309 50 0.45498 0.29947 BM 10 10 57-66 1.000 0.00386 0.43811 0.32063 MQ 10 8 40,44,46,50, 67-70 0.956 0.00346 40,44,46,50 0.44683 0.30950 JZ 10 6 71-76 0.844 0.00106 0.49955 0.25045 Genetic structure and historical dynamics of M. punicea Mismatch analysis and neutrality tests were calculated based on the 4-locus sequences of the 11 populations. Mismatch analysis of M. punicea were calculated with two curves (expected and observed curves) to detect historical demographic changes. Neutrality tests for the whole and different populations were carried out using Tajima’s D, Fu and Li’s D*, and Fu and Li’s F* methods (Excoffier 2004), respectively. The results of neutrality tests show that, except population MQ, all the populations do not experience a significant expansion as their negative parameters of Tajima's D, Fu and Li's D* and Fu and Li’s F*. Although the Tajima's D, Fu and Li's D*, Fu and Li's F* of MQ are significant, SSD and Raggedness are not significant, and the mismatch curve shows obvious multimodal characteristics, the non-significant expansion with demographic equilibrium is also suggested. (Table 3, Fig. 2). Table 3 Neutrality tests of each population of M. punicea Population Fu and Li's D * Fu and Li’s F * Tajima’s D SSD Raggedness REG -1.75684 -1.95124 -1.64125 0.04110 0.06025 SD -1.78608 -1.94152 -1.53620 0.03983 0.07605 HL -1.32553 -1.54795 -1.50721 0.07663 0.09877 DLJ -1.00080 -1.04388 -0.70843 0.03977 0.14519 BL -0.96650 -1.05215 -0.99024 0.49859 ** 0.13778 LT -1.51599 -1.67817 -1.40434 0.04364 0.06765 CD -0.41245 -0.54857 -0.71356 0.23406 * 0.04938 GD -0.18999 -0.29439 -0.47492 0.01944 0.05531 BM -1.65303 -1.78240 -1.35554 0.04351 0.48000 MQ -2.08729 * -2.27426 * -1.79512 * 0.03376 0.09580 JZ -0.76777 -0.92940 -1.00504 0.04421 0.11309 Whole -4.53570 ** -4.13466 ** -2.02875 * 0.00280 0.00241 Phylogenetic relationships and phylogeographic patterns To clarify the phylogenetic relationships among M. punicea , the combined 3 chloroplast genes and the single rpb2 gene dataset were conducted and analyzed, respectively. Meanwhile, the combinatorial dataset of 4 genes were also analyzed as more informative sites obtained. Three chloroplast genes dataset acquired a total of 46 haplotypes, which could divide into 8 clades (Clade A-H) (Table 4, Fig. 3A, B), with each clade including 2 to 19 haplotypes. Among them, Clade D and E are distributed in QHP. Clade E contains 19 haplotypes of 5 populations. The haplotypes of Clade D are derived from 4 populations. In contrast, clade A, G and H are all from single population—LT, SD, and BL, respectively, which distribute in the HDM. In terms of population genetic differentiation, REG contains 4 phylogenetic clades, while SD, BL and MQ had only one phylogenetic clade. It reveals that there are significant differences in the genetic diversity of different populations. Rpb2 acquired a total of 38 alleles derived from 106 sequences data, and it forms 7 clades: Clade①-⑦ (Table 4, Fig. 3C, D). The numbers of alleles in each clade were between 1 and 11. Among them, Clade ① and ⑥ have very abundant alleles, containing 11 and 10 respectively. Clade ⑥ contained 10 populations (only without SD), and SD was belonged to Clade ④. When combined the 4 genes to form a multilocus dataset, 76 sequence types were obtained and 6 main clades could be divided (Clade A-F ) (Table4, Fig. 3E, F). The numbers of sequence types in each branch were between 4 and 27. Among them, clade F had the most 27 sequence types. The spatial genetic structure of 11 populations presented certain valuable phylogenetic structures. And their phylogeographic structures were described as followed. Clade A contained 8 sequence types, which were mainly from population SD. Clade B contained 7sequence types, which were only distributed in the LT population. There were 4 sequence types in Clade C , which are only distributed in the BL population. These 3 clades all had narrow distribution, suggesting that genetic isolation was formed in different geographic environments. Clade D had 18 sequence types, which were mainly from the northern HDM (populations of HL, REG and DLJ). Clade E contained 12 sequence types, which were distributed in 4 populations: JZ, GD, MQ and CD, all located in the QHP. Clade F contained 27 sequence types, which were from JZ, GD, MQ, BM, and CD, all in QHP. The wide distributions of Clade D-F indicated that these 3 lineages spread out around QHP and HDM, exhibiting wide distribution characteristics with low genetic isolation. These results also demonstrated that the genetic differentiation happened between QHP and HDM. Table 4 Phylogenetic structure of M. punicea based on 3 different datasets Pop. Sample 3 chloroplast genes nuclear gene r pb2 4 genes A B C D E F G H ① ② ③ ④ ⑤ ⑥ ⑦ A B C D E F REG 10 2 5 1 2 2 7 1 9 1 SD 10 10 10 10 HL 10 2 8 1 9 10 DLJ 10 10 9 1 10 BL 6 6 4 2 6 LT 10 9 1 1 1 4 4 1 9 CD 10 5 5 1 7 2 5 5 GD 10 4 6 3 5 2 3 7 BM 10 10 4 1 1 4 10 MQ 10 10 1 1 5 3 9 1 JZ 10 7 3 10 7 3 Mantel test and AMOVA analyses The mantel test was used to test the correlations between the genetic distance and geographic distance of M. punicea with the combined 4 genes. The results reveal that the correlations were not significant (y=0.0103x+0.3764, r=0.075, p=0.29). It does not show obvious isolation-by-distance characteristics. The AMOVA analyses uncover 46.75% variation among populations, and 53.25% genetic variation within populations. Genetic differentiation is not significant among all populations. While based on the haplotypes geographic mapping, the genetic difference was significantly observed between HDM and QHP, indicating that the difference of geographical environment between HDM and QHP had big influence on the species differentiation of M. punicea. The prediction of the suitable distribution area of M. punicea The prediction result of the suitable distribution area was obtained of M. punicea with the species distribution modeling method. The main suitable distribution areas appear in the north edge of Hengduan mountains and Qinghai plateau, containing Sichuan, Gansu and Qinghai, the total suitable distribution area was 158,000 km 2 (suitable index>0.7) (Fig. 5). this result revealed that the urgently needed protection strategies should be carried out as its quite small distribution area. Divergence time estimation and evolutionary routes revealing The divergence time were estimated with the combined 4 genes of main taxa of Papaveroideae, with emphasis on M. punicea by using fossil calibration, and the differentiation time of each node was obtained for M. punicea (Fig. 6A). The divergence of the crown group of M. punicea occurred approximately 22.24 Myr (95% HPD=13.34-29.63). The main clades of M. punicea differentiated from 12.10-18.26 Myr (Fig. 6B). This differentiation time just coincided with the later period of third geological uplift event of the QTP (Harrison 1995; Li and Fang 1998). The huge geological uplift changes have increased the species differentiation of M. punicea. While the stable stage after the third uplift of the QTP just provided opportunities for the migration and spread of species and gene exchange, which made M. punicea expand on the plateau and its ecological niche further expanded. The divergence time estimation indicates that the most recent common ancestor of M. punicea and its close relatives was dated to the early Miocene (18.26 Myr) (95% HPD=12.67-25.03) in the southern HDM (Fig. 6C). Two possible routes are suggested from south to north and northwest (Fig. 6C). It spread westward to SD and northward to HDM (HL, REG, LT and DLJ), two important secondary endemic centers are suggested: SD and LT populations. According to the presence of Clade E in the REG population, it is speculated that the distribution around QHP (MQ, GD, JZ and BM) resulted from the diffusion of REG. And then scattered to westward on the plateau (to CD population). Discussion In this study, the phylogeography of 106 samplings representing 11 populations of M. punicea were analyzed. The conjoint analysis with four genes ( matK , ndhF and rbcL and rpb2 ) uncovers the high genetic diversity and genetic differentiation characteristics of M. punicea . There are at least 6 main phylogenetic clades, three of which are endemic to local geographic populations. The geographical difference may play the important role in the genetic differentiation of this species. We clearly identified the ecogeographical variation patterns based on the 4 gene phylogeney. REG contains the most 4 phylogenetic clades, it is suggested that the population REG should be a glacial refuge, and might be an important genetic diversity center. According to molecular clock dating, M. punicea most likely originated from the southern HDM (BL population) in the early Miocene. The gene flow spread north to the high-altitude areas of the HHM, and then northward to HDM (HL, REG, LT and DLJ) along two dispersal routes. The differentiation time of main nodes of M. punicea (12.10-18.26 Myr) coincided with the later period of the third geological uplift event of the QTP (Qiu et al. 2011 ). It was suggested that ecological and geographical environment changes were the main driving forces for the species differentiation of M. punicea . The suitable distribution area was predicted about 158,000 km 2 . The small distribution range shows that this species has high requirement on the ecological environment. which revealed that the urgently needed protection strategies should be carried out in order to keep the population stable. Meanwhile, the sampling point CD is far from the predicted suitable distribution area, it implies that the possibility of diversity loss at this point, so protection efforts should be increased. Declarations Funding This project was supported by the National Natural Science Foundation of China (Grant No. 32160404, and No. 31460218), and supported by Scientific Research Fund of Yunnan Provincial Department of Education project (2020J0408), and the youth talent support program of Yunnan Ten Thousand Talent Program (YNWR-QNBJ-2019-211). Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author contributions All authors contributed to the study conception and design. Zhi Ou collected samples, analyzed the genetic diversity and phylogeographic structures. Jian Xiong collected samples, amplified and sequenced 4 genes (matK, ndhF and rbcL). Yuanqi Jiang collected samples, amplified and sequenced rpb2 genes. Yongdong Dai estimated the divergence time and predicted the suitable distribution area. Yan Qu wrote, and reviewed this paper. all authors commented on this version of the manuscript. All authors read and approved the final manuscript. Data Availability DNA sequences and other metadata have been submitted to GenBank. 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Mol Phylogenet Evol 59:225–244 Qu Y, Zhao WY, Ou Z, Leng QS, Xiong J (2018) Analysis of chloroplast gene ndhF and rbcL sequences of Tibetan medicine plants of Meconopsis . J Cent South Univ Forestry Technol 38(6):84–89 Rambaut A, Drummond A (2009) Tracer: MCMC Trace Analysis Tool, Version 1.5.0. Available at: http://tree.bio.ed.ac.uk/software/tracer /University of Oxford Ronquist F, Teslenko M, Mark PVD, Ayres DL, Darling A, Hohna S (2012) Mrbayes 3.2, efficient bayesian phylogenetic inference and model choice across a large model space. Syst Biol 61:539–542. https://doi.org/10.1093/sysbio/sys029 Shang XF, Wang DS, Miao XL, Wang Yu, Zhang JJ, Wang XZ, Zhang Y, Pan H (2015) Antinociceptive and anti-tussive activities of the ethanol extract of the flowers of Meconopsis punicea maxim. BMC Complem Altern M 15:154. https://doi.org/10.1186/s12906-015-0671-y Smith UR (2001) Revision of the Cretaceous fossil genus Palaeoaster (Papaveraceae) and clarification of pertinent species of Eriocaulon, Palaeoaster, and Sterculiocarpus. Novon 11:258–260 Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. https://doi.org/10.1093/bioinformatics/btu033 Valtueña FJ, Preston CD, Kadereit JW (2012) Phylogeography of a Tertiary relict plant, Meconopsis cambrica (Papaveraceae), implies the existence of northern refugia for a temperate herb. Mol Ecol 21(6):1423–1437. https://doi.org/10.1111/j.1365-294X.2012.05473.x Wu C, Chuang H (1980) A study on the taxonomic system of the genus Meconopsis . Acta Bot Yunnanica 2:371–381 Xiao W (2013) Molecular systematics of Meconopsis Vig. (Papaveraceae): taxonomy, polyploidy evolution, and historical biogeography from a phylogenetic insight. The University of Texas at Austin Xiong J (2016) Genetic diversity and pedigree geography of the endangered plant Meconopsis punicea . A Master Thesis of Southwest Forestry University Yang FS, Qin AL, Li YF, Wang XQ (2012) Great genetic differentiation among populations of Meconopsis integrifolia and its implication for plant speciation in the Qinghai-Tibetan plateau. PLoS ONE 7:e37196 Ying M, Xie H, Nie Z, Gu Z, Yang Y (2006) A karyomorphological study on four species of Meconopsis vig. (Papaveraceae) from the Hengduan Mountains, SW China. Caryologia 59:1 Yuan CC (2002) The phylogeny, systematics and biogeography of Meconopsis vig. (Papaveraceae) and Craigia W.W. Sm. & W.E. Evans (Tiliaceae). Dissertation (Zhong Shan University) Zhang ML, Grey-Wilson C (2008) Meconopsis Viguier. In: Wu ZY, Raven PH (eds) Flora of China, vol 7. Science Press/Missouri Botanical Garden Press, Beijing/St Louis, pp 262–278 Additional Declarations No competing interests reported. Supplementary Files Supplymentarytable1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1844011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":120445470,"identity":"60b72cfb-d2de-4144-92fc-f3bca3809f82","order_by":0,"name":"Zhi Ou","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Ou","suffix":""},{"id":120445471,"identity":"353d62d9-917a-453c-896b-0f0961694155","order_by":1,"name":"Jian Xiong","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Xiong","suffix":""},{"id":120445472,"identity":"2a7ac9c1-5a4a-49e1-be6a-86a960838ae5","order_by":2,"name":"Yuanqi Jiang","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yuanqi","middleName":"","lastName":"Jiang","suffix":""},{"id":120445473,"identity":"ffdafc71-fe24-4550-9134-7feb36689fa2","order_by":3,"name":"Yongdong Dai","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongdong","middleName":"","lastName":"Dai","suffix":""},{"id":120445474,"identity":"5cc6d748-3aba-475f-b384-7facc598b12e","order_by":4,"name":"Yan Qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYNCCCgYG9gYgzUO8ljNA1QdI0sLYRooWvhs5hp8L59Ul9kgkMD5428Ygb05Ii+SNHGPpmdsOg7QwG85tYzDc2UBAi8GNHANp3m0HcvdLJLBJ87YxJBgcIKzF+DfvnLpcoC3sv4nVYibN28AM0sLGTJQWyTPPyqx5jh2u7+F52Cw555yE4QZCWviOJ2++zVNTZ8zDnnzww5syG3mCtjAc4DCAshgbgIQEIfUgLewPiFA1CkbBKBgFIxoAAP+mPpLpsX9aAAAAAElFTkSuQmCC","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Qu","suffix":""}],"badges":[],"createdAt":"2022-07-10 15:14:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1844011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1844011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24110513,"identity":"fbf7c746-7833-4f81-b0a6-e74bec512688","added_by":"auto","created_at":"2022-07-20 20:12:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1390622,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of 11 populations of \u003cem\u003eMeconopsis punicea\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/e4c2fdf256f6b0aa98a5d66f.png"},{"id":24110507,"identity":"922186ef-19cc-4963-ba07-f0f53bd03ad3","added_by":"auto","created_at":"2022-07-20 20:12:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":202951,"visible":true,"origin":"","legend":"\u003cp\u003eMismatch distribution analyses of each population of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/d673a440584a8a266976e451.png"},{"id":24111519,"identity":"ea453db2-3afc-42ac-87fe-175fa24f6d62","added_by":"auto","created_at":"2022-07-20 20:17:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1601217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogeographic structure and geographic mapping of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA,B the phylogenetic tree and phylogeographic mapping based on three chloroplast genes;\u003c/p\u003e\u003cp\u003eC,D the phylogenetic tree and phylogeographic mapping based on\u003cem\u003e rpb2\u003c/em\u003e gene;\u003c/p\u003e\u003cp\u003eE,F the phylogenetic tree and phylogeographic mapping based on 4 genes combined three chloroplast genes and nucleic \u003cem\u003erpb2\u003c/em\u003e gene\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/b4ec2b9de4f43960c4ff9012.png"},{"id":24110511,"identity":"d21a0712-07ae-4297-9427-ed2e6bb036cb","added_by":"auto","created_at":"2022-07-20 20:12:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12316,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between geographic distance and genetic distance of \u003cem\u003eM. punicea\u003c/em\u003e by mantel test\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/281feab617d009790ac60411.png"},{"id":24110509,"identity":"c6b7d20d-67f5-4fb7-b38f-6f28ab127aa2","added_by":"auto","created_at":"2022-07-20 20:12:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":278468,"visible":true,"origin":"","legend":"\u003cp\u003eThe prediction of the suitable distribution area of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/fe1ed039018fe94c2867b86d.png"},{"id":24111520,"identity":"a604aa6e-1ce5-4f2d-a6d6-ad665c292e34","added_by":"auto","created_at":"2022-07-20 20:17:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1097819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe divergence time estimation and the possible dispersal routes of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA. The divergence time estimation of Papaveroideae; B. The divergence time estimation of \u003cem\u003eM. punicea\u003c/em\u003e; C. the possible dispersal routes of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/029ded56b4869387a35446a6.png"},{"id":25397566,"identity":"133ddbcb-52ea-4587-aa3a-117a469514ad","added_by":"auto","created_at":"2022-08-19 06:14:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5350361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/65238617-a1da-4b2e-83c4-9c26378b7646.pdf"},{"id":24111521,"identity":"f194d227-aecb-471b-9dce-3080601728d3","added_by":"auto","created_at":"2022-07-20 20:17:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22454,"visible":true,"origin":"","legend":"","description":"","filename":"Supplymentarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1844011/v1/6fb6cc564c111be0d1dde1ed.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phylogeographical analysis on the alpine endemic plant Meconopsis punicea (Papaveraceae) based on chloroplast and nuclear genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eMeconopsis punicea\u003c/em\u003e, a perennial herb in the family Papaveraceae, is one of the most distinctive members of the genus with its sumptuous satiny red pendent flower (Wu and Chuang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Ying et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It has very high ornamental value, and is also a traditional Tibetan medicine, which has kinds of pharmacological effects such as heat-clearing, analgesic, hypotensive, and antibacterial (Kala \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eM. punicea\u003c/em\u003e has a rather narrow distribution which mainly distribute on plateau hillsides or meadows at an altitude of about 3000\u0026ndash;4500 m in northwestern Sichuan, southeastern Qinghai, and southwestern Gansu (Chuang \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Zhang and Grey-Wilson \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Thus, as driven by the benefits of the industrialized production of medicine, the limited wild resources of \u003cem\u003eM. punicea\u003c/em\u003e are drastically reduced. And it has been listed as wild plant under State protection (category II) (China Plant Red Data Book 1992). Phylogeography could explore the entire geographical distribution pattern, and discuss the historical factors and evolutionary process of the speciation (Avise \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Phylogeography of two \u003cem\u003eMeconopsis\u003c/em\u003e species (i.e. \u003cem\u003eMeconopsis cambrica, Meconopsis integrifolia) explain the\u003c/em\u003e genetic differentiation pattern and the potential refugia, it is of great significance to deeply understand the speciation and evolution of species (Valtue\u0026ntilde;a 2012; Yang 2012). In our current study, we conducted a range-wide sampling across the distribution of \u003cem\u003eM. punicea\u003c/em\u003e and collected 11 populations with a total of 106 samples. Three chloroplast DNA sequences, i.e., \u003cem\u003ematK\u003c/em\u003e (maturase K), \u003cem\u003endhF\u003c/em\u003e (NADH dehydrogenase subunit F), \u003cem\u003erbcL\u003c/em\u003e (ribulose-15-bisphosphate carboxylase/oxygenase large subunit) and one nuclear gene \u003cem\u003erpb2\u003c/em\u003e (the second largest subunit of RNA polymerase ІІ), were sequenced and analyzed for all samples. The purpose of this study was attempted to characterize the genetic diversity, genetic differentiation, and the genetic structure of \u003cem\u003eM. punicea\u003c/em\u003e. Furthermore, we also aimed to evaluate the spatial pattern and the historical process of population demography, and explore the correlations between species differentiation and geological history of the Qinghai-Tibetan Plateau.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eSampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eM. punicea\u003c/em\u003e is mainly distributed in the Qinghai Plateau (QHP) and Hengduan Mountains (HDM). In terms of administrative divisions, it contains Qinghai, Sichuan and Gansu provinces in China. Therefore, the samples used in this study were collected from the three provinces and cover the distribution range of\u003cem\u003e\u0026nbsp;M. punicea\u003c/em\u003e. A total of 11 populations were collected: five populations in Qinghai, four populations in Sichuan, and two populations in Gansu (Fig.1, Supplementary table 1). The samplings were distributed ranging from 30.97\u0026deg;\u0026nbsp;N -35.58\u0026deg;\u0026nbsp;N to 95.87\u0026deg;\u0026nbsp;E -103.74\u0026deg;\u0026nbsp;E, and the altitudes ranged from 3000 m to 4500 m.\u003c/p\u003e\n\u003cp\u003eTen individuals were collected from each population, with a total of 110 samples. The distance between sample of individuals in each population was at least 50 m apart. To ensure the quality of experimental materials, fresh leave s collected in the field were dried with silica gel desiccant and stored in -20 ℃.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction, gene sequence amplification and sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from silica-dried leaf materials using\u0026nbsp;TIANGEN Fast Plant Genome Kit. The extracted DNA were tested on a 1.2% agarose gel for gel electrophoresis, and the purity and concentration were measured using the Nano Drop 2000 ultraviolet spectrophotometer.\u003c/p\u003e\n\u003cp\u003eCandidate molecular markers were screened based on the success rate of amplification and sequencing. Three chloroplast gene fragments \u003cem\u003ematK\u003c/em\u003e (maturase K), \u003cem\u003endhF\u003c/em\u003e (NADH dehydrogenase subunit F),\u003cem\u003e\u0026nbsp;rbcL\u003c/em\u003e (ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit), and one nuclear gene\u003cem\u003e\u0026nbsp;rpb2\u003c/em\u003e (the second largest subunit sequences of RNA polymerase ІІ) were selected to conduct the further phylogeographic analysis of \u003cem\u003eM. punicea\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ePartial chloroplast \u003cem\u003ematK\u0026nbsp;\u003c/em\u003egene of \u003cem\u003eM.\u003c/em\u003e \u003cem\u003epunicea\u003c/em\u003e was\u0026nbsp;amplified using the primers\u0026nbsp;matK-F (5\u0026rsquo;-ACTGTATCGCACTATGTATCA-3\u0026rsquo;) and matK-R (5\u0026rsquo;- GAACTAGTC GGATGGAGTAG -3\u0026rsquo;). The\u0026nbsp;\u003cem\u003endhF\u0026nbsp;\u003c/em\u003egene sequences\u0026nbsp;were\u0026nbsp;amplified using the primers\u0026nbsp;ndhF-F (5\u0026rsquo;-CTGTCTATTCAGCAAATAAAT-3\u0026rsquo;) and ndhF-R (5\u0026rsquo;-CGATTATAGG ACCAATCATATA-3\u0026rsquo;). The \u003cem\u003erbcL\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003egene\u003cem\u003e\u0026nbsp;\u003c/em\u003ewas\u0026nbsp;amplified using the primers\u0026nbsp;rbcL-F (5\u0026rsquo;-ATGTCACCACAAACAGARACTAAAGC-3\u0026rsquo;) and rbcL-R (5\u0026rsquo;-CTTTTAGTAAAA GATTGGGCCGAG-3\u0026rsquo;).\u0026nbsp;The\u0026nbsp;\u003cem\u003erpb2\u0026nbsp;\u003c/em\u003egene\u003cem\u003e\u0026nbsp;\u003c/em\u003ewas\u0026nbsp;amplified with the primer pair\u0026nbsp;\u003cem\u003eRpb2\u003c/em\u003e-105F (5\u0026rsquo;-GATAGTATGTGGGACCA AGG-3\u0026rsquo;) and \u003cem\u003eRpb2\u003c/em\u003e-1164R (5\u0026rsquo;-CCCTCTCGA ATGACTAT TGG-3\u0026rsquo;)\u0026nbsp;(Xiao 2013).\u0026nbsp;The polymerase chain reaction (PCR) mixture was performed following the manufacturer\u0026rsquo;s instructions (Tsingke Biotech Co. Ltd.) and the PCR procedure was performed according to our previous screening results (Xiong 2016; Jiang 2018; Qu et al. 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PCR products were separated by electrophoresis in 1.5% agarose gels. The clear bands were cut and removed from the gels for purification using a Gel Band Purification Kit (BioTeke Co. Ltd, Beijing, China) and directly sequenced with an automatic sequence analyzer (ABI 7500, Sangon Biotech Co. Ltd).\u003c/p\u003e\n\u003cp\u003eThe sequences were assembled with SeqMan 7.1. Each locus was aligned using MAFFT 3.7 with the default settings (Katoh et al. 2002) and manual adjustments were made in MEGA 5 (Tamura et al. 2011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation genetic diversity and genetic structure analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNei\u0026rsquo;s nucleotide diversity (\u003cstrong\u003e\u003cem\u003ePi\u003c/em\u003e\u003c/strong\u003e) and haplotype diversity (\u003cstrong\u003eHd\u003c/strong\u003e) of the \u003cem\u003ematK\u003c/em\u003e, \u003cem\u003endhF\u003c/em\u003e, \u003cem\u003erbcL\u003c/em\u003e, and \u003cem\u003erpb2\u003c/em\u003e DNA sequences were estimated by using\u0026nbsp;DnaSP 6.12.01\u0026nbsp;(Librado \u0026amp; Rozas, 2009). To assess the genetic differentiation in \u003cem\u003eM. punicea\u003c/em\u003e, we performed an analysis of molecular variance (AMOVA) on the populations of\u0026nbsp;\u003cem\u003eM. punicae\u0026nbsp;\u003c/em\u003eusing Arlequin 3.1 (Excoffier et al. 2007). Gene flow was estimated according to Wright\u0026rsquo;s principles Nm = (1-Fst)/4Fst. Geographic mapping was illustrated in Google Earth 7.1. Mantel tests were carried out to test the correlation between genetic distance and geographic distance using\u0026nbsp;TFPGA 1.3 (Miller 1997).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMismatch distribution, neutrality test and historical demography\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA mismatch distribution for \u003cem\u003eM. punicea\u003c/em\u003e was investigated to using DnaSP 6.12.01. A unimodal distribution indicates that populations have experienced recent demographic expansions, while multimodal distributions are consistent with stability (Fu 1997).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, historical demographic changes were examined with neutrality tests for the species and individual populations, including \u003cem\u003eTajima\u0026rsquo;s\u003c/em\u003e D, \u003cem\u003eFu and Li\u0026rsquo;s\u003c/em\u003e D*,\u003cem\u003e\u0026nbsp;Fu and Li\u0026rsquo;s\u003c/em\u003e F* (Excoffier 2004). \u003cem\u003eTajima\u0026rsquo;s\u0026nbsp;\u003c/em\u003eD test detects the relationship between the number of separation sites and nucleotide diversity (Pi). \u003cem\u003eFu-Li\u0026rsquo;s D\u0026nbsp;\u003c/em\u003e\u0026amp;\u003cem\u003e\u0026nbsp;F\u003c/em\u003e test measures the difference between total polymorphic sites and single-base mutations. The paraments of sum-of-squared deviations (SSD) and the Harpending\u0026apos;s Raggedness index (Rag) were calculated with Arlequin 3.1. These parameters provide information about demographic histories, with significant negative values indicating evidence of demographic expansions, whereas the positive values indicating evidence of population bottlenecks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic relationships and geographic structure analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phylogenetic relationships of haplotypes of \u003cem\u003eM. punicea\u003c/em\u003e were constructed by using the single and combined 3 chloroplast genes. Meanwhile, the relationship of alleles was conducted by using the nuclear gene \u003cem\u003erpb2\u003c/em\u003e. \u0026nbsp;Furthermore, DNA sequences of chloroplast genes and nuclear gene\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere combined to retrieve the evolutionary history of \u003cem\u003eM. punicea. M. horridula\u003c/em\u003e and \u003cem\u003eM. betonicifolia\u003c/em\u003e chosen as outgroups as their suitable phylogenetic relationship (Liu et al. 2014). The best model was selected in each dataset by using Modeltest 3.7 (Posada and Crandall 2001). Phylogenetic tree of the haplotypes and alleles of \u003cem\u003eM. punicae\u0026nbsp;\u003c/em\u003ewere analyzed using the Maximum Likelihood (ML) method in Raxml 7.2 (Stamatakis 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDivergence time estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe divergence time of \u003cem\u003eM. punicea\u003c/em\u003e and its relatives was estimated with combined the 4 genes using a Bayesian method in BEAST 1.8.4 (Drummond and Rambaut, 2007). A total of 156 sequences were sampled within the subf. Papaveroideae to estimate the divergence time in \u003cem\u003eM. punicea\u0026nbsp;\u003c/em\u003e(Jork and Kadereit 1995; Yuan 2002; Xiao 2013; Liu et al. 2014) (Supplementary table 2).. \u003cem\u003eNandinadomestica\u0026nbsp;\u003c/em\u003eand \u003cem\u003eHydrastis canadensis\u0026nbsp;\u003c/em\u003ewere chosen as the outgroups.\u003c/p\u003e\n\u003cp\u003eThe oldest known fossil of Papaveroideae,\u0026nbsp;\u003cem\u003ePalaeoaster\u003c/em\u003e sp.\u0026nbsp;is assigned to the Latest Cretaceous (74.5\u0026ndash;64.5 million years ago, Myr) (Smith et al. 2001). The calibration 74.5 Myr was settled according to the previous research (Valtue\u0026ntilde;a\u0026nbsp;et al. 2012). Accordingly, subf. Papaveroideae was defined as monophyletic, and its root was set with a normally distributed prior with a mean of 70 Myr and a standard deviation of 2 Myr. The uncorrelated relaxed clock was used as the clock model, and a birth death prior was set for branch lengths. Other priors were made in default settings, and the Markov Chain Monte Carlo (MCMC) was initiated on a random starting tree. Two MCMC analyses were run for 100 million generations with parameters sampled every 1000 generations. The first runs were used to examine the MCMC performance, and operators were adjusted as suggested by the output analysis. Finally, two BEAST runs were performed. Convergence of the runs was assessed using Tracer 1.6 (Rambaut and Drummond 2009), which indicated that most parameter values had effective sample sizes well above 150, then the two separate runs dataset were combined using LogCombiner 2.1.2 (Bouckaert et al. 2014). TreeAnnotator 2.1.2 was used to calculate node ages and the upper and lower bounds of the 95% highest posterior density interval (HPD) for divergence time from the combined tree file with the burn-in of 20% aged trees (Bouckaert et al. 2014). The chronogram was visualized using Figtree 1.6.2 (Drummond and Rambaut 2007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of suitable distribution area of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecies occurrence data was collected from ongoing field studies and published research papers. Bioclim variables were downloaded from the CliMond Archive (https://www.climond.org/) (Kriticos et al., 2012)\u003c/p\u003e\n\u003cp\u003eA total of20 species occurrence data were used (Supplementary table 3). And a total of 35 typical climate variables at a grid resolution of 10\u0026apos; were obtained from CliMond the CRU CL2.0 dataset. (https://www.climond.org/). These factors contained the core set of 19 variables (temperature and precipitation), and an extended set of 16 additional variables (solar radiation and soil moisture) at a global extent (Supplementary table 4). All climate variables data collection sources were from 1961 - 1990 (30 years centered on 1975).\u003c/p\u003e\n\u003cp\u003eSpecies distribution modelling were constructed based on species redundance with MaxEnt v3.4.1 (Phillips et al., 2006). randomly 25% of the data points were extracted as the test data, and \u0026ldquo;do jackknife to measure variable importance\u0026rdquo; was selected. The output grid format was set as \u0026ldquo;cloglog\u0026rdquo; The output result \u0026ldquo;.asc\u0026rdquo; was visualized with Global mapper17.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe genetic diversity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 106 samples of \u003cem\u003eM. punicea\u003c/em\u003e were successfully amplified and sequenced. The aligned length of \u003cem\u003ematK\u003c/em\u003e was 660 bp, containing 41 polymorphic sites (variation rates: 6.24%), with values of \u003cstrong\u003eHd\u003c/strong\u003e (0.931) and \u003cstrong\u003e\u003cem\u003ePi\u003c/em\u003e\u003c/strong\u003e (0.00788), respectively. The length of\u003cem\u003e\u0026nbsp;ndhF\u003c/em\u003e was 691 bp, containing 21 polymorphic sites (variation rates: 3.04%), with lower values of \u003cstrong\u003eHd\u003c/strong\u003e (0.438) and \u003cstrong\u003e\u003cem\u003ePi\u003c/em\u003e\u003c/strong\u003e (0.00135). The length of\u003cem\u003e\u0026nbsp;rbcL\u003c/em\u003e was 683 bp, containing 6 polymorphic sites (variation rates: 0.88%), with the significant low \u003cstrong\u003eHd\u003c/strong\u003e (0.110) and \u003cstrong\u003e\u003cem\u003ePi\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(0.00027). The length of the nuclear gene \u003cem\u003erpb2\u003c/em\u003e was 542 bp, containing 69 polymorphic sites (variation rates: 12.78%), and its\u003cstrong\u003e\u0026nbsp;Hd\u003c/strong\u003e and\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Pi\u003c/em\u003e\u003c/strong\u003e were 0.987, 0.00447, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe combinatorial dataset was contained 2576 bp after combined the 3 chloroplast DNA fragments and the nuclear gene \u003cem\u003erpb2\u003c/em\u003e, and total polymorphic sites were 136, with mutation rate 5.28%. Sixty-nine parsimony informative sites were recognized, accounting for 2.65% of the total sequence. The \u003cem\u003ematK\u003c/em\u003e and \u003cem\u003erpb2\u003c/em\u003e gene sequences have relatively more information sites among the 4 genes. A total of 76 sequence types were identified from the concatenated sequences of 4 genes (Table 1). All 76 sequence types were submitted to GenBank (\u003cem\u003ematK\u003c/em\u003e: OM640468-OM640543; \u003cem\u003endhF\u003c/em\u003e: OM640544-OM640619; \u003cem\u003erbcL\u003c/em\u003e:OM654956-OM655031; \u003cem\u003erpb2\u003c/em\u003e:OM655032- OM655107).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Genetic diversity of \u003cem\u003eM. punicea\u0026nbsp;\u003c/em\u003ebased on the 4 genes datasets\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003eParaments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e\u003cem\u003ematK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cem\u003endhF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cem\u003erbcL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.547703180212014%\"\u003e\n \u003cp\u003e3 chloroplast genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e\u003cem\u003erpb2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e2034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2576\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003eTotal number of mutations, Eta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e155\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003eNumber of polymorphic sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e136\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003eParsimony informative sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003eSequence types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.987\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003esd of Hd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePi\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e0.00788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.00135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.00027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e0.00310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e0.00963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00447\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003esd of Pi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e0.00065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.00034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.00011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e0.00028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e0.00154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.035335689045937%\"\u003e\n \u003cp\u003eTheta (per site) from Eta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.60070671378092%\"\u003e\n \u003cp\u003e0.01308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e0.00168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.547703180212014%\"\u003e\n \u003cp\u003e0.00677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.363957597173146%\"\u003e\n \u003cp\u003e0.02936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.484098939929329%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01151\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA high level of haplotype diversity (\u003cstrong\u003eHd\u003c/strong\u003e= 0.987) but a relatively low level of nucleotide diversity (\u003cstrong\u003e\u003cem\u003ePi\u003c/em\u003e\u003c/strong\u003e = 0.00447) is detected among the populations of\u003cem\u003e\u0026nbsp;M. punicea\u003c/em\u003e, (Table 2). The populations GD and BM have the highest haplotype diversity (\u003cstrong\u003eHd\u003c/strong\u003e= 1.00). Among the 76 sequence types, types 2, 40, 44, 46 and 50 are distributed in two populations, the others were unique to only one population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Genetic diversity of the 11 populations of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"96%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSamples num.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal S\u003c/strong\u003e\u003cstrong\u003eeq. types\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003cstrong\u003eeq. types num.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePi\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eShared hap.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNm\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.43318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.32713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e8-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.49481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.25524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e2, 15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.43375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.32637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eDLJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e20-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.50012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.24988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e27-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.48811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.26218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e31-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.47452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.27685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e39-46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e40,44,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.48722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.26312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e47-56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.45498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.29947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e57-66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.43811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.32063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eMQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e40,44,46,50, 67-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e40,44,46,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.44683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.30950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eJZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e71-76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.00106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.49955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.25045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic structure and historical dynamics of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMismatch analysis and neutrality tests were calculated based on the 4-locus sequences of the 11 populations. Mismatch analysis of \u003cem\u003eM. punicea\u003c/em\u003e were calculated with two curves (expected and observed curves) to detect historical demographic changes. Neutrality tests for the whole and different populations were carried out using \u003cem\u003eTajima\u0026rsquo;s\u003c/em\u003e D, \u003cem\u003eFu and Li\u0026rsquo;s\u003c/em\u003e D*, and \u003cem\u003eFu and Li\u0026rsquo;s\u003c/em\u003e F* methods (Excoffier 2004), respectively.\u003c/p\u003e\n\u003cp\u003eThe results of neutrality tests show that, except population MQ, all the populations do not experience a significant expansion as their negative parameters of \u003cem\u003eTajima\u0026apos;s\u0026nbsp;\u003c/em\u003eD, \u003cem\u003eFu and Li\u0026apos;s D*\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFu and Li\u0026rsquo;s\u003c/em\u003e F*. Although the \u003cem\u003eTajima\u0026apos;s\u0026nbsp;\u003c/em\u003eD, \u003cem\u003eFu and Li\u0026apos;s\u0026nbsp;\u003c/em\u003eD*, \u003cem\u003eFu and Li\u0026apos;s\u0026nbsp;\u003c/em\u003eF* of MQ are significant, SSD and Raggedness are not significant, and the mismatch curve shows obvious multimodal characteristics, the non-significant expansion with demographic equilibrium is also suggested. (Table 3, Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Neutrality tests of each population of \u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFu and Li\u0026apos;s\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eD\u003cem\u003e*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Fu and Li\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eF\u003cem\u003e*\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTajima\u0026rsquo;s\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003eSSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003eRaggedness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-1.75684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-1.95124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-1.64125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.04110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.06025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-1.78608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-1.94152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-1.53620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.03983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.07605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-1.32553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-1.54795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-1.50721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.07663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.09877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eDLJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-1.00080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-1.04388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-0.70843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.03977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.14519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-0.96650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-1.05215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-0.99024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.49859\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.13778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-1.51599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-1.67817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-1.40434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.04364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.06765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-0.41245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-0.54857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-0.71356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.23406\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.04938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-0.18999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-0.29439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-0.47492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.01944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.05531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-1.65303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-1.78240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-1.35554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.04351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.48000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eMQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-2.08729\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-2.27426\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-1.79512\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.03376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.09580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003eJZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-0.76777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-0.92940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-1.00504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.04421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.11309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.049586776859504%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhole\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.140495867768596%\"\u003e\n \u003cp\u003e-4.53570\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\"\u003e\n \u003cp\u003e-4.13466\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.702479338842975%\"\u003e\n \u003cp\u003e-2.02875\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.603305785123966%\"\u003e\n \u003cp\u003e0.00280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.87603305785124%\"\u003e\n \u003cp\u003e0.00241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic relationships\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and phylogeographic patterns\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo clarify the phylogenetic relationships among \u003cem\u003eM. punicea\u003c/em\u003e, the combined 3 chloroplast genes and the single \u003cem\u003erpb2\u003c/em\u003e gene dataset were conducted and analyzed, respectively. Meanwhile, the combinatorial dataset of 4 genes were also analyzed as more informative sites obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree chloroplast genes dataset acquired a total of 46 haplotypes, which could divide into 8 clades (Clade A-H) (Table 4, Fig. 3A, B), with each clade including 2 to 19 haplotypes. Among them, Clade D and E are distributed in QHP. Clade E contains 19 haplotypes of 5 populations. The haplotypes of Clade D are derived from 4 populations. In contrast, clade A, G and H are all from single population\u0026mdash;LT, SD, and BL, respectively, which distribute in the HDM. In terms of population genetic differentiation, REG contains 4 phylogenetic clades, while SD, BL and MQ had only one phylogenetic clade. It reveals that there are significant differences in the genetic diversity of different populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRpb2\u003c/em\u003e acquired a total of 38 alleles derived from 106 sequences data, and it forms 7 clades: Clade①-⑦ (Table 4, Fig. 3C, D). The numbers of alleles in each clade were between 1 and 11. Among them, Clade ① and ⑥ have very abundant alleles, containing 11 and 10 respectively. Clade ⑥ contained 10 populations (only without SD), and SD was belonged to Clade ④.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen combined the 4 genes to form a multilocus dataset, 76 sequence types were obtained and 6 main clades could be divided (Clade \u003cstrong\u003eA-F\u003c/strong\u003e) (Table4, Fig. 3E, F). The numbers of sequence types in each branch were between 4 and 27. Among them, clade \u003cstrong\u003eF\u003c/strong\u003e had the most 27 sequence types. The spatial genetic structure of 11 populations presented certain valuable phylogenetic structures. And their phylogeographic structures were described as followed.\u003c/p\u003e\n\u003cp\u003eClade \u003cstrong\u003eA\u0026nbsp;\u003c/strong\u003econtained 8 sequence types, which were mainly from population SD. Clade \u003cstrong\u003eB\u003c/strong\u003e contained 7sequence types, which were only distributed in the LT population. There were 4 sequence types in Clade\u003cstrong\u003e\u0026nbsp;C\u003c/strong\u003e, which are only distributed in the BL population. These 3 clades all had narrow distribution, suggesting that genetic isolation was formed in different geographic environments. Clade \u003cstrong\u003eD\u003c/strong\u003e had 18 sequence types, which were mainly from the northern HDM (populations of HL, REG and DLJ). Clade \u003cstrong\u003eE\u003c/strong\u003e contained 12 sequence types, which were distributed in 4 populations: JZ, GD, MQ and CD, all located in the QHP. Clade\u003cstrong\u003e\u0026nbsp;F\u0026nbsp;\u003c/strong\u003econtained 27 sequence types, which were from JZ, GD, MQ, BM, and CD, all in QHP. The wide distributions of Clade \u003cstrong\u003eD-F\u003c/strong\u003e indicated that these 3 lineages spread out around QHP and HDM, exhibiting wide distribution characteristics with low genetic isolation. These results also demonstrated that the genetic differentiation happened between QHP and HDM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Phylogenetic structure of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eM. punicea\u003c/em\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ebased on 3 different datasets\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"7.106598984771574%\"\u003e\n \u003cp\u003ePop.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"7.4450084602368864%\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" width=\"31.810490693739425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 chloroplast genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" width=\"28.934010152284262%\"\u003e\n \u003cp\u003e\u003cstrong\u003enuclear gene \u003cem\u003er\u003c/em\u003e\u003cem\u003epb2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" width=\"24.703891708967852%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3737574552683895%\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.964214711729622%\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.174950298210735%\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.970178926441352%\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.168986083499006%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.9761431411530817%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.572564612326044%\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.3737574552683895%\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\"\u003e\n \u003cp\u003e①\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\"\u003e\n \u003cp\u003e②\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\"\u003e\n \u003cp\u003e③\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\"\u003e\n \u003cp\u003e④\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\"\u003e\n \u003cp\u003e⑤\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\"\u003e\n \u003cp\u003e⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.7713717693836974%\"\u003e\n \u003cp\u003e⑦\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.572564612326044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.174950298210735%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.3737574552683895%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.572564612326044%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.174950298210735%\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.157057654075547%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.130730050933786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eREG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.470288624787776%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.093378607809847%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.244482173174872%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4142614601018675%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.395585738539898%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.112054329371817%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.130730050933786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.470288624787776%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.093378607809847%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.244482173174872%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4142614601018675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.395585738539898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.112054329371817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.130730050933786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.470288624787776%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.093378607809847%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.244482173174872%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4142614601018675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.395585738539898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.112054329371817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.130730050933786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDLJ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.470288624787776%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.093378607809847%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.244482173174872%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4142614601018675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.395585738539898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.112054329371817%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.130730050933786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.470288624787776%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.093378607809847%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.244482173174872%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4142614601018675%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.395585738539898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.112054329371817%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.130730050933786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.470288624787776%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.093378607809847%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.244482173174872%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4142614601018675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.395585738539898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.112054329371817%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.130730050933786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eJZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.470288624787776%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.093378607809847%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.244482173174872%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.4142614601018675%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.395585738539898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.074702886247878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.735144312393888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.904923599320883%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.565365025466893%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.112054329371817%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMantel test and AMOVA analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mantel test was used to test the correlations between the genetic distance and geographic distance of \u003cem\u003eM. punicea\u003c/em\u003e with the combined 4 genes. The results reveal that the correlations were not significant (y=0.0103x+0.3764, r=0.075, p=0.29). It does not show obvious isolation-by-distance characteristics.\u003c/p\u003e\n\u003cp\u003eThe AMOVA analyses uncover 46.75% variation among populations, and 53.25% genetic variation within populations. Genetic differentiation is not significant among all populations. While based on the haplotypes geographic mapping, the genetic difference was significantly observed between HDM and QHP, indicating that the difference of geographical environment between HDM and QHP had big influence on the species differentiation of \u003cem\u003eM. punicea.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe prediction of the suitable distribution area of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prediction result of the suitable distribution area was obtained of \u003cstrong\u003e\u003cem\u003eM. punicea\u003c/em\u003e\u003c/strong\u003e with the species distribution modeling method. The main suitable distribution areas appear in the north edge of Hengduan mountains and Qinghai plateau, containing Sichuan, Gansu and Qinghai, the total suitable distribution area was 158,000 km\u003csup\u003e2\u003c/sup\u003e (suitable index\u0026gt;0.7) (Fig. 5). this result revealed that the urgently needed protection strategies should be carried out as its quite small distribution area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDivergence time estimation and evolutionary routes revealing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe divergence time were estimated with the combined 4 genes of main taxa of Papaveroideae, with emphasis on \u003cem\u003eM. punicea\u003c/em\u003e by using fossil calibration, and the differentiation time of each node was obtained for \u003cem\u003eM. punicea\u003c/em\u003e (Fig. 6A). The divergence of the crown group of \u003cem\u003eM. punicea\u003c/em\u003e occurred approximately 22.24 Myr (95% HPD=13.34-29.63). The main clades of \u003cem\u003eM. punicea\u003c/em\u003e differentiated from 12.10-18.26 Myr (Fig. 6B). This differentiation time just coincided with the later period of third geological uplift event of the QTP (Harrison 1995; Li and Fang 1998). The huge geological uplift changes have increased the species differentiation of \u003cem\u003eM. punicea.\u003c/em\u003e While the stable stage after the third uplift of the QTP just provided opportunities for the migration and spread of species and gene exchange, which made \u003cem\u003eM. punicea\u003c/em\u003e expand on the plateau and its ecological niche further expanded.\u003c/p\u003e\n\u003cp\u003eThe divergence time estimation indicates that the most recent common ancestor of \u003cem\u003eM. punicea\u003c/em\u003e and its close relatives was dated to the early Miocene (18.26 Myr) (95% HPD=12.67-25.03) in the southern HDM (Fig. 6C). Two possible routes are suggested from south to north and northwest (Fig. 6C). It spread westward to SD and northward to HDM (HL, REG, LT and DLJ), two important secondary endemic centers are suggested: SD and LT populations. According to the presence of Clade E in the REG population, it is speculated that the distribution around QHP (MQ, GD, JZ and BM) resulted from the diffusion of REG. And then scattered to westward on the plateau (to CD population).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the phylogeography of 106 samplings representing 11 populations of \u003cem\u003eM. punicea\u003c/em\u003e were analyzed. The conjoint analysis with four genes (\u003cem\u003ematK\u003c/em\u003e, \u003cem\u003endhF\u003c/em\u003e and \u003cem\u003erbcL\u003c/em\u003e and \u003cem\u003erpb2\u003c/em\u003e) uncovers the high genetic diversity and genetic differentiation characteristics of \u003cem\u003eM. punicea\u003c/em\u003e. There are at least 6 main phylogenetic clades, three of which are endemic to local geographic populations. The geographical difference may play the important role in the genetic differentiation of this species.\u003c/p\u003e \u003cp\u003eWe clearly identified the ecogeographical variation patterns based on the 4 gene phylogeney. REG contains the most 4 phylogenetic clades, it is suggested that the population REG should be a glacial refuge, and might be an important genetic diversity center.\u003c/p\u003e \u003cp\u003eAccording to molecular clock dating, \u003cem\u003eM. punicea\u003c/em\u003e most likely originated from the southern HDM (BL population) in the early Miocene. The gene flow spread north to the high-altitude areas of the HHM, and then northward to HDM (HL, REG, LT and DLJ) along two dispersal routes. The differentiation time of main nodes of \u003cem\u003eM. punicea\u003c/em\u003e (12.10-18.26 Myr) coincided with the later period of the third geological uplift event of the QTP (Qiu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It was suggested that ecological and geographical environment changes were the main driving forces for the species differentiation of \u003cem\u003eM. punicea\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe suitable distribution area was predicted about 158,000 km\u003csup\u003e2\u003c/sup\u003e. The small distribution range shows that this species has high requirement on the ecological environment. which revealed that the urgently needed protection strategies should be carried out in order to keep the population stable. Meanwhile, the sampling point CD is far from the predicted suitable distribution area, it implies that the possibility of diversity loss at this point, so protection efforts should be increased.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the National Natural Science Foundation of China (Grant No. 32160404, and No. 31460218), and supported by Scientific Research Fund of Yunnan Provincial Department of Education project (2020J0408), and the youth talent support program of Yunnan Ten Thousand Talent Program (YNWR-QNBJ-2019-211).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll authors contributed to the study conception and design.\u0026nbsp;\u003c/em\u003eZhi Ou\u0026nbsp;collected samples, analyzed the genetic diversity and phylogeographic structures.\u0026nbsp;Jian Xiong collected samples, amplified and sequenced 4 genes (matK, ndhF and rbcL). Yuanqi Jiang collected samples, amplified and sequenced rpb2 genes.\u0026nbsp;Yongdong Dai\u0026nbsp;estimated the divergence time and\u0026nbsp;predicted the suitable distribution area.\u0026nbsp;Yan Qu\u0026nbsp;wrote, and reviewed this paper.\u0026nbsp;\u003cem\u003eall authors commented on this version of the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA sequences and other metadata have been submitted to GenBank. (matK: OM640468-OM640543; ndhF: OM640544-OM640619; rbcL:OM654956-OM655031; rpb2:OM655032- OM655107) [http://www.ncbi.nlm.nih.gov/]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAvise JC (2000) Phylogeography: The History and Formation of Species. Harvard University Press, Cambridge, Massachusetts\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouckaert R, Heled J, K\u0026uuml;hnert D et al (2014) Beast 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol 10:e1003537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pcbi.1003537\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1003537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChuang H (1981) The systematic evolution and the geographical distribution of \u003cem\u003eMeconopsis\u003c/em\u003e vig. Acta Bot Yunnanica 3:139\u0026ndash;146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. 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Science Press/Missouri Botanical Garden Press, Beijing/St Louis, pp 262\u0026ndash;278\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"4-locus phylogeny, genetic structure, molecular clock, divergence time, Origin, suitable distribution prediction","lastPublishedDoi":"10.21203/rs.3.rs-1844011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1844011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eMeconopsis punicea\u003c/em\u003e (Papaveraceae) is only distributed in the alpine areas of the Qinghai Plateau (QHP) and Hengduan Mountains (HDM) in China. As an important ornamental flower and traditional Tibetan medicine, in addition to habitat loss, its higher economic value drives over-exploitation of this resource, which has accelerated the reduction of its distribution. How to make its protection strategy is still unfounded as unclear genetic diversity characteristics of \u003cem\u003eM. punicea\u003c/em\u003e. In this study, we analyzed the genetic diversity and phylogeographic structures of \u003cem\u003eM. punicea\u003c/em\u003e representing 11 populations 106 individuals using 4-locus dataset (\u003cem\u003ematK\u003c/em\u003e, \u003cem\u003endhF\u003c/em\u003e, \u003cem\u003erbcL\u003c/em\u003e and \u003cem\u003erpb2\u003c/em\u003e, a total of 2576 bp). A total of 76 sequence types were identified (Hd\u0026thinsp;=\u0026thinsp;0.987, Pi\u0026thinsp;=\u0026thinsp;0.00447). Six phylogenetic clades were recognized, and 3 clades were restricted to unique geographic distribution (Clade \u003cb\u003eA\u003c/b\u003e corresponded to population SD, \u003cb\u003eB\u003c/b\u003e to LT, and \u003cb\u003eC\u003c/b\u003e to BL). The zoige area was referred as the differentiation center and a glacial refuge of \u003cem\u003eM. punicea\u003c/em\u003e. \u003cem\u003eM. punicea\u003c/em\u003e most likely originated from the southern HDM in the early Miocene (~\u0026thinsp;22 Myr). Two dispersal routes were suggested and the ecological and geographical difference were revealed as the main driving forces for species differentiation of \u003cem\u003eM. punicea.\u003c/em\u003e The suitable distribution area was predicted about 158,000 km\u003csup\u003e2\u003c/sup\u003e mainly in Sichuan, Gansu and Qinghai. Our current results provide much clear and detailed understanding for the diversity and geographical spatial distribution of the endemic alpine plant \u003cem\u003eM. punicea\u003c/em\u003e. It will also be able to provide theoretical guidance for formulating the urgently needed protection strategies of \u003cem\u003eM. punicea\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Phylogeographical analysis on the alpine endemic plant Meconopsis punicea (Papaveraceae) based on chloroplast and nuclear genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-20 20:12:22","doi":"10.21203/rs.3.rs-1844011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"927878aa-ca81-4156-8b21-e753beb68fda","owner":[],"postedDate":"July 20th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-08-19T06:14:22+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-20 20:12:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1844011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1844011","identity":"rs-1844011","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}