Rosa chinensis var. spontanea and R. lucidissima complex are the morphologically very similar key ancestors of modern roses with high importance in rose research and breeding. Although widely distributed in subtropical central and southwestern China, these two taxa are highly endangered. We sampled a total of 221 specimens and 330 DNA samples from 25 populations across the two taxa's whole range. Leaf morphological traits were compared. Two chloroplast DNA intergenic spacers  and ITS were used for population genetics and phylogenetic study to delimit the boundary between the two taxa, assess the genetic variation, uncover the possible evolutionary mechanism responsible for the differentiation within the complex, and make the conservation recommendations. The complex exhibited high levels of genetic variation  and high population differentiation even over small geographic distance. We suggest R. chinensis var. spontanea and R. lucidissma be treated as independent taxa, and the northern populations around and within the Sichuan Basin being R. chinensis var. spontanea, having broader leaflets and paler full-blooming flowers, while those in the middle and southern Yunnan-Guizhou Plateau and the adjacent regions being R. lucidissma, having narrower leaflets and darker full-blooming flowers. Transitional areas between the southeastern Sichuan Basin and northeastern Guizhou are the contact or the hybridization zone of the two taxa. Ancestral haplotypes of the complex (R. lucidissma) evolved at about 1.21–0.86 Mya in southeastern Yunnan-Guizhou Plateau and its adjacent regions and survived there during the Quaternary Oscillation. Ancestral haplotypes of R. chinensis var. spontanea deviated from R. lucidissma at about 0.022–0.031 Mya at the transitional areas  between the northeastern edge of Yunnan-Guizhou Plaeteau and the southeastern border of Sichuan Basin, where they survived the LGM. The evolution of the complex included spatial isolation and inter-species hybridization. The complex's endangered status might be the result of over-exploitation for its ornamental and medical value, or due to reforestation of some originally open habitats. We provide specific recommendations for the two taxa's in situ and ex situ conservation. Phylogeographical studies utilizing genetic markers having phylogenetic signals such as cpDNAs and nrITS and analyzing the spatial distribution of haplotypes across species or species complex ranges, allow for verifying taxonomy at various taxonomic levels. Besides, such studies can unravel historical processes that led to the formation of species or affected their distribution  and identify potential glacial refugia Avise, . RefugiaRosa L. (Rosaceae) comprises about 150–200 species widely distributed in the temperate and subtropical northern hemisphere, and about half of the rose species occur in Asia, especially in China  Sweet have been contributing characteristic of recurrent flowering, tea scent, and multiple floral colors to modern roses  Yü et Ku  are climbing shrubs. Plants flower from early March to early May with extraordinary petal color variation , both taxa are evaluated as Rank II. The high importance of the above two taxa in rose research and breeding, and conservation concerns necessitate understanding the extent and structure of their genetic diversity. In our study, we sampled R. chinensis var. spontanea and R. lucidissima across their whole range, and used two chloroplast DNA regions and ITS to (1) delimit the boundary between the two taxa and uncover the possible evolutionary mechanism responsible for the differentiation within the complex; (2) analyze the extent and structure of the genetic variation of the complex; (3) identify the possible refugia during the glaciation period and the population history of the complex; and (4) make the conservation recommendations.According to the specimen records, rovinces . Plants rovinces  and demarovinces  failed trovinces . Rosa chRosa rugosa (sect. Cinnamomeae), R. woodsii (sect. Carolinae), R. mairei (sect. Pimpinellifoliae), R. banksiae (sect. Banksianae), and R. bracteata (sect. Bracteatae) were used as outgroups in the phylogenetic analysis. Fresh leaves of these species were collected from the Yunnan Provincial Flower Gemplasm Resources Nursery in the Flower Research Institute, Yunnan Academy of Agricultural Sciences in Kunming, China.A total of 330 individuals (4 to 21 per population depending on the population size) were sampled from 25 populations, covering the whole geographical distribution of the complex . Fresh ypetL/psbE, trnL(UAG)/rpl32-F, trnH/psbA, trnDF/trnT, trnS/trnfM, rp120/rps12, cpDNA-c/cpDNA-f, F71/R1516, trnG (UUC)-trnS (GCU), trnS/psbC), we chose trnG (UUC)-trnS (GCU) and petL-psbE due to their relatively high variation and ease for amplification. The trnG (UUC)-trnS (GCU) (hereafter referred to as trnG-trnS) was amplified following the protocol of Shaw et al.  with some modification to decrease the negative effect of secondary metabolites and epicuticular waxes. From a total of 10 cpDNA regions , and top leaflets of each compound leaf were measured with a caliper.trnG-trnS, and petL-psbE were assembled with DNAStar , aligned with Clustal X version 1.81  were calculated with HAPLONST /GST , which should be distributed as χ2 with (N – 2) degrees of freedom (df), where N is the number of sequences in the tree  × 10−9 substitutions per site per year (s/s/y)  and Bayesian inference (BI). Prior to phylogenetic analyses, sequences were aligned using MAFFT .CT) and to total allelic differentiation (CrT). The data of cpDNA haplotypes and their distribution among populations were used as input data for this analysis. Negative values indicate that the diversity or the differentiation of a population is lower than the mean of the whole dataset  was 1,359 and 1,213 bp in length, respectively. A total of 25 polymorphic sites and 10 indels were detected in the concatenated dataset. In total, there were 22 haplotypes (H1-H22) and their GenBank accession numbers were shown  was present in 12 out of the 16 R. chinensis var. spontanea populations and in 2 out of the 9 R. lucidissima populations. All the 14 populations with H3 were on the boundary of the Sichuan Basin, such as WuS and YC, etc. east in the Three Gorges Mountain Region (TGMR), NJ and PC north in the southern Qingling-Dabashan Mountains, WX and MX northwest in the Longmenshan Mountains, MB and LB southwest in the Xiaoliangling Mountains. Although not detected in the south edge of Sichuan Basin, H3 existed more south to DJ and WA where the south edge of Sichuan Basin transited to the northeastern Yunnan-Guizhou Plateau. Haplotype H5 was only found in northern populations of R. chinensis var. spontanea . Except for H3 and H5, the other seven haplotypes were all “private” to a single population of R. chinensis var. spontanea. Among the 14 haplotypes detected in R. lucidissima, except for the shared haplotype H3, all others were private ones.The spatial distribution and relative frequencies of the haplotypes are shown in Three populations had three haplotypes. Most individuals in population HJ were haplotype H7, two individuals were haplotype H2 and the rest had haplotype H8. Besides the widespread haplotype H3, population LB also had two more haplotypes. Most individuals of population LZ in southwestern Guangxi were haplotype H12, and the other two individuals belonged to H13 and H14, respectively. Five populations had two haplotypes. Populations BD and FJ in the TGMR, and population NJ in the Qingling-Dabashan Mountains, had another private haplotype  besides H3. Most individuals in population AL had haplotype H1, while three individuals were H2. Most individuals in population NC were haplotype H9 and two individuals were H16. The other populations had only one haplotype.hT) of the complex was 0.768 ± 0.085, that of R. chinensis var. spontanea was 0.596 ± 0.12 and that of R. lucidissima was 0.972 ± 0.04. The genetic diversity within the population (hS) of the complex was 0.133 ± 0.037, that of chinensis var. spontanea was 0.122 ± 0.048 and that of R. lucidissima was 0.154 ± 0.061. The total haplotype diversity (Hd) and the nucleotide  of the R. chinensis var. spontanea - lucidissima complex and each taxa are shown in R. chinensis var. spontanea, the among population component was 89.54%, and in R. lucidissima, it was 92.44%. The Nm calculated for the complex was 0.053, and for R. chinensis var. spontanea and R. lucidissima it was 0.064 and 0.047, respectively, meaning extremely low gene flow among populations. The genetic diversity analysis revealed that genetic differentiation among populations of the complex , R. chinensis var. spontanea  and R. lucidissima  all was high. The comparison of NST and GST indicated a lack of phylogeographic structure either in the complex (p = 0.245), R. chinensis var. spontanea (p = 0.152), or in R. lucidissima (p = 0.999).The chloroplast genetic diversity  were identified and sequence variation for ribotypes can be seen in R. chinensis var. spontanea. Ribotype rH3 was present in almost all populations of R. chinensis var. spontanea and in two populations (PS and DJ) of R. lucidissima. These populations were around the Sichuan Basin. The second most popular ribotype was rH2, present in the majority of the populations of R. lucidissima, but in only one population of R. chinensis var. spontanea population (RH). Ribotype rH5 was found in populations of both taxa at the conjunction of Sichuan Basin and Yunnan-Guizhou Plateau, namely in populations NC, PS, YY, and DJ. Ribotype rH12 was found in R. chinensis var. spontanea populations of MB, RS, and PW on the western border of the Sichuan Basin. rH20 was in ZG and YC of R. chinensis var. spontanea, while rH13 was found in R. lucidissima populations PS and WA. The other ribotypes were all private. Among them, ribotypes rH4, rH10, and rH19 were represented by a single individual each.The ribotype rH3 (206 of 422 sequences) predominated in the complex, especially in hT) of the complex was 0.726 ± 0.075, that of R. chinensis var. spontanea was 0.464 ± 0.107 and that of R. lucidissima was 0.907 ± 0.031. The genetic diversity within the population (hS) of the complex was 0.344 ± 0.051, that of chinensis var. spontanea was 0.295 ± 0.07 and that of R. lucidissima was 0.431 ± 0.064. The total haplotype diversity (Hd) and the nucleotide  of the complex and each taxa based on ITS are shown in R. chinensis var. spontanea, the among population component was 55.72%, and in R. lucidissima, it was 40.22%. The Nm calculated for the complex was 0.225, and for R. chinensis var. spontanea and R. lucidissima it was 0.434 and 0.226, respectively. The genetic differentiation of the whole complex  and R. lucidissima  was higher than R. chinensis var. spontanea .The ITS genetic diversity . The majority of the haplotypes of R. lucidissima evolved much earlier than those of R. chinensis var. spontanea. Within R. lucidissima, haplotypes H9 and H13 are the most ancestral that diverged about 0.25–0.18 and 0.14–0.1 Mya . The group of haplotypes H7, H21, and H8 diverged the next at about 0.08–0.11 Mya (node G) followed by haplotype H14 at about 0.036–0.051 Mya (node H). An ancestor that evolved at that time (node H), gave rise to a large number of haplotypes of both R. lucidissim and R. chinensis var. spontanea. Within this clade, the ancestral haplotype H18 of R. chinensis var. spontanea diverged from R. lucidissim haplotypes about 0.022–0.031 Mya (node I), and a group of R. lucidissim haplotypes H1, H2, H17, H19, H20 diverged about 0.006–0.008 Mya (node L). In R. chinensis var. spontanea, except for the most ancestral haplotype H18, other haplotypes diverged very recently at about 0.0086–0.012 Mya (node K).GenBank accession numbers from OL774542 to OL774546 were for the topology . The 22 R. bracteata, R. banksiae, R rugosa, R. woodsii, and R. mairei, respectively. The ITS ribotypes identified in R. chinensis var. spontanea- R. lucidissima complex also formed with high support of a separate clade in the tree  from others, even though they had low genetic diversity (Cs), while NC and LB had both high genetic differentiation and high genetic diversity. Contributions of the population of R. lucidissima (except for WA and DJ having low contribution) to both genetic diversity and allelic diversity was similarly high.The results of the analysis of population contribution to genetic diversity by Contrib are shown in R. caninae complex , the ancestors of the southern haplotypes apparently survived on the Jiuwan Mountain in Huanjiang (HJ), where there exist many relic plant species of the Quaternary Glacials  there. So, the Daloushan and Wulingshan Mountains were the complex's refugia in LGM. With the ending of the Quaternary glacials and the coming of the postglacial period, the ancestral haplotypes of the haplotypes in the refugia dispersed northeast along the Wushan Mountains to the Three Gorges Mountains Region and dispersed north along the Ridge and Valleys of the east Sichuan to the Dabashan Mountains, from where the most widely distributed haplotypes (H3) originated and then widely distributed the mountains around the Basin. The southern haplotypes and northern ones within the complex kept being isolated by the environment (IBE) due to the combination of altitude, climate and soil  than in the other parts of the taxa range, supporting the above hypothesis. In R. chinensis var. spontanea, population BD, FJ, NJ, LB, and NC all had high genetic diversity, but only RH within Daloushan in the northeastern Yunnan-Guizhou Plateau adjacent to the Sichuan Basin harbored the ancient haplotype H18. Thus, these areas could be possible refugia for R. chinensis var. spontanea. The population LB in the southwestern edge of Sichuan Basin (Xiaoxiangling Mountains), and NC in southeastern Sichuan Basin having high diversity but young haplotypes, indicated that these areas might be the contact zone or areas of high immigration from diverse sources of R. chinensis var. spontanea- R. lucidissim complex because of their consistent and favorable local conditions.Refugial populations are expected to have higher genetic diversity and longer demographic history than populations that evolved later , comparable to that of R. praelucens, endemic to Shangrila of Yunnan Plateau . This extent and structure of genetic diversity appears to be a result of, on one hand, a wide geographical range, covering a mosaic of plateaus, mountains, basins, and gorges in Southwest China, and, on the other hand, numerous geographic barriers within this range limiting seed dispersal and promoting genetic drift. The comprising complex plants are long-living woody shrubs with hermaphroditic flowers pollinated mainly by moths, bees, and other small insects. The migration distance of these insects is limited to several kilometers  are dispersed mainly by the vertebrates not the birds, and thus within a rather limited distance. Also, because it is mainly distributed in the middle and lower parts of the mountains and along the valleys, the huge mountains act as the barriers to the pollen and seeds dispersal of R. chinensis var. spontanea and R. lucidissima. All these factors may limit inter-population gene flow and contribute to isolation and genetic differentiation of populations.Despite with a relatively short evolutionary history (1.21–0.86 Mya), Herrera, , R. arvea Ghosh, , and R. s) was relatively higher, and resulting in less percent of genetic variation existing among the populations and a higher gene flow among populations. These discordant between cpDNA and ITS may result from different dispersal vectors for seeds and pollen, as gene flows due to pollination might be easier and further than those due to seeds dispersal. And also, hybridization within the complex might decrease the genetic differentiation among populations.However, compared to that of cpDNA, genetic diversity based on nuclear DNA (ITS) within the populations  have high conservation value according to their genetic contribution and haplotype distribution.During our 2 years of outdoor investigation, we did not find any individuals in Yanhe and Xingren in Guizhou, Lingchuan, Fushui, and Cangwu in Guangxi, where there once had specimens records and local people used to dig the roots of in situ are plant micro-reserves  can be found in the article/HYJ conceived and designed the article and wrote the manuscript. LZ, HJY, XQQ, and TZ performed the experiment. HYJ, LZ, HZ, and QGW collected the plant materials. HYJ, CLM, and NNZ analyzed the data. All authors contributed to the article and approved the submitted version.This work was financially supported by the National Key R&D Program of China (2019YFD1000400) and the National Natural Science Foundation of China (31760087), the Major Program for Science and Technology of Yunnan Province (202102AE090052 and 202002AA100007).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genetic data can provide insights into population history, but first, we must understand the patterns that complex histories leave in genomes. Here, we consider the admixed human population of Cabo Verde to understand the patterns of genetic variation left by social and demographic processes. First settled in the late 1400s, Cabo Verdeans are admixed descendants of Portuguese colonizers and enslaved West African people. We consider Cabo Verde’s well-studied historical record alongside genome-wide SNP data from 563 individuals from 4 regions within the archipelago. We use genetic ancestry to test for patterns of nonrandom mating and sex-specific gene flow, and we examine the consequences of these processes for common demographic inference methods and genetic patterns. Notably, multiple population genetic tools that assume random mating underestimate the timing of admixture, but incorporating nonrandom mating produces estimates more consistent with historical records. We consider how admixture interrupts common summaries of genomic variation such as runs of homozygosity. While summaries of runs of homozygosity may be difficult to interpret in admixed populations, differentiating runs of homozygosity by length class shows that runs of homozygosity reflect historical differences between the islands in their contributions from the source populations and postadmixture population dynamics. Finally, we find higher African ancestry on the X chromosome than on the autosomes, consistent with an excess of European males and African females contributing to the gene pool. Considering these genomic insights into population history in the context of Cabo Verde’s historical record, we can identify how assumptions in genetic models impact inference of population history more broadly. Patterns of genetic variation are often used to infer human population histories, such as fluctuations in population size, the timing of gene flow, and changes in population structure  structure rather than potentially small changes in allele frequencies. Understanding the patterns generating the distribution of ancestry within an admixed population is also important as genomic data have shown how widespread admixture is throughout human evolution .In admixed populations, nonrandom mating can be driven by factors that correlate with genetic ancestry. For example, spouse pairs within multiple Latino populations are correlated in their genomic ancestry .The recently admixed human population of Cabo Verde presents a particularly relevant opportunity to examine how sex-biased admixture and nonrandom mating shape genetic patterns and influence demographic inference. Cabo Verde is an archipelago off the coast of Senegal, inhabited today by admixed descendants of Portuguese colonizers and enslaved West African people who settled the unpopulated islands beginning in the mid-1400s , Fogo (n = 129), Boa Vista (n = 26), and the 3 northwestern islands in aggregate , although it does not capture the fluctuations in migration rate over time that occurred within the islands , the evidence of ancestry-assortative mating in Cabo Verde . The low amounts of all length classes of ROH in Santiago are consistent with several historical attributes of the islands, including serial founding beginning with the settlement of Santiago. Serial founding resulting in bottlenecks is expected to increase shorter ROH by reducing the number of ancestral lineages. But because shorter ROH reflects background relatedness from the source populations, shorter ROH are expected to be more sensitive to the contributions of the source populations.Comparing the Cabo Verdean islands to each other, we observed the lowest levels of total ROH in all length classes of ROH in Santiago , while long ROH are not depleted . This observation that Santiago has significantly lower levels of shorter ROH compared to even the West African reference is perhaps surprising given the canonical view of African source populations as generically having the lowest ROH in worldwide samples. This is consistent with the idea that shorter ROH tend to come from older shared ancestors in the source populations, and that these tracts can be further interrupted with local ancestry from another source population upon admixture. In contrast, long ROH are more likely to be driven by postadmixture dynamics such as small population size and mating preferences, and long ROH tracts may span multiple local ancestries. Indeed, we observed that ROH often span multiple ancestries, with long ROH spanning more switches in local ancestry per megabase. Specifically, 1.21% of shorter ROH contained at least one ancestry switch and 18.99% of long ROH contained at least one ancestry switch. Dividing ancestry switches by tract length, shorter ROH contain an average of 0.072 ancestry switches/Mb (standard deviation = 0.89), while long ROH contain an average of 0.089 switches/Mb (standard deviation = 0.24) .Despite recent colonization bottlenecks, we observed that some admixed individuals in Cabo Verde present lower levels of ROH per genome than either of the source population proxies. Specifically, our classification of ROH into shorter and long ROH  revealedDiscussion.While ROH patterns in Cabo Verde are consistent with several historical observations, we again note that caution is warranted in applying methods and expectations built using nonadmixed populations. We also note that, though we used a model-based ROH detection approach that included steps to mitigate genotyping errors and biases in allele frequency estimates, sensitivity and specificity of ROH detection is generally lower for shorter ROH. Furthermore, records suggest that enslaved African individuals in Cabo Verde came from the Senegambian region of Africa, and lack of genomic data from this vast region makes it difficult to assess how the genetic diversity of contributing African peoples might contribute to low shorter ROH levels in Cabo Verde. These sources of uncertainty necessitate caution in inferring population history from ROH distributions. However, the observed differences in ROH among the islands suggest that ROH are sensitive to population genetic processes even on the short timescale since Cabo Verde’s settlement. Together, these observations and caveats underscore the need for future work testing the effects of admixture on ROH and on ROH detection, as we explore in the P < 1 × 10−8 for Santiago, Fogo, and the Northwest Cluster; P = 0.0013 for Boa Vista), consistent with sex-biased contributions of the founders. To quantitatively explore these differences in male vs female demographic history, we used a model-based approach to estimate differences between male and female contributions  and by our comparison of admixture timing estimates (above). Using the method of Goldberg and Rosenberg for a model of constant admixture, we compared observed and expected X-chromosomal and autosomal ancestry over a grid of possible parameter values. Following the implementation from To further understand population structure in Cabo Verde, we next consider how autosomal and X-chromosome ancestry and genetic variation patterns can be used to infer the sex-specific demographic history of the last ∼20 generations since founding. Some earlier analyses of postadmixture population structure using Y-chromosome diversity suggested sex-biased demographic processes . Specifiibutions . A modelGiven our observation that source populations make distinct contributions to ROH in Cabo Verde , we nextIn this study, we leveraged patterns of genetic variation in Cabo Verde to infer the demographic history of the past ∼20 generations. We found that distinct genetic patterns of 4 island regions within the archipelago reflect the colonization history of the islands, including island-specific settlement timing, admixture dynamics, mating patterns, and sex-biased demography. Together these results demonstrate how patterns of ancestry and genetic variation are shaped by social and demographic forces on short timescales. By better understanding how complex population histories generate genetic variation, we can improve the interpretation of inference from populations without historical records.The observed island-specific genetic patterns reflect the complex ecological and social factors that shaped the settlement of Cabo Verde. Historical evidence suggests that the islands were founded in a stepping-stone pattern, with settlement stages that were accompanied by important economic and sociocultural shifts . Early wAdmixture in Cabo Verde is consistent with a model of continuous gene flow from Europe and West Africa, with European males and African females contributing predominantly to the Cabo Verdean gene pool, as seen in other admixed populations that result from the trans-Atlantic slave trade , IBD Fi, and kinUsing a mechanistic model of sex-biased admixture , we usedn = 26). Nonetheless, we highlight differences in mating patterns among the islands as a topic that could be explored further in future studies. For example, our inferences suggest that assortative mating appears the strongest in Santiago. Santiago has many unique demographic features, such as being the oldest population and having the largest population size of the islands. As we learn more about ancestry-assortative mating, future investigations may shed light on how these demographic attributes and others contribute to mating patterns.Despite only a few hundred years of unique population histories among the islands, we also found that patterns of genetic variation reflect island-specific patterns in postadmixture dynamics. Observed patterns of IBD, kinship, and long ROH are all consistent with overall high levels of relatedness within Cabo Verde, particularly in Fogo and the northwestern islands. In other worldwide populations, similarly high IBD levels have been attributed to founder effects , or the neutrality of evolution. All of the timing methods we used placed admixture timing for the different islands closer together than historical dates of settlement, consistent with historical expectations that the initial admixture in the southern islands was significant, and that many individuals that occupied the northern and eastern islands during the second and third settlement stages of Cabo Verde were already admixed. For example, the serial founding of the islands may explain why estimates of admixture timing for Boa Vista were closer to historical records. While Boa Vista was founded most recently, it was founded by already admixed individuals. This observation, together with IBD and kinship patterns, support the serial founding of the groups of islands as the main model of settlement of Cabo Verde, as opposed to their independent settling as some historical data suggest . This tyWe found that Cabo Verde’s island-specific demographic history and admixture dynamics have important genomic consequences, as observed with ROH. Despite the relatively recent colonization of the islands, some individuals presented even lower overall levels of ROH than African reference populations. We found that low overall levels of ROH in Cabo Verde are driven by shorter-length ROH. This observation is consistent with the idea that shorter ROH can be attributed to older shared ancestors from the source populations, and these tracts can be interrupted with admixed ancestry upon admixture. However, many admixed individuals still present excess long ROH, likely reflecting postadmixture processes such as serial founding and ancestry-assortative mating. The observed island-specific distributions of ROH are consistent with the colonization history of the islands. For example, the cultural dynamics and higher rates of first-cousin marriages in Fogo , possiblIn sum, we provide insights into the population history of Cabo Verde and demonstrate how admixed populations can provide powerful test cases for understanding demographic processes and genomic consequences in recent human history. We show that patterns of shared ancestry between and within the islands (quantified with IBD and kinship estimates) reflect serial founder effects as well as settlement patterns such as postadmixture nonrandom mating. We find that accounting for nonrandom mating allows us to improve inference of admixture timing and better contextualize genomic consequences of admixture dynamics, such as ROH. We find that differences in ancestry on the X chromosome vs the autosomes reflect sex-biased demographic processes. Given the ubiquity of admixture throughout modern human population, these results provide important, generalizable considerations for the study of recent human evolution.jkac183_Supplementary_Fig_1Click here for additional data file.jkac183_Supplementary_Fig_2Click here for additional data file.jkac183_Supplementary_Fig_3Click here for additional data file.jkac183_Supplementary_Fig_4Click here for additional data file.jkac183_Supplementary_Fig_5Click here for additional data file.jkac183_Supplementary_Fig_6Click here for additional data file.jkac183_Supplementary_Fig_7Click here for additional data file.jkac183_Supplementary_Fig_8Click here for additional data file.jkac183_Supplementary_Fig_9Click here for additional data file.jkac183_Supplementary_Fig_10Click here for additional data file.jkac183_Supplementary_Fig_11Click here for additional data file.jkac183_Supplementary_Fig_12Click here for additional data file.jkac183_Supplementary_Table_1Click here for additional data file.jkac183_Supplementary_Table_2Click here for additional data file.jkac183_Supplementary_Table_3Click here for additional data file.


Blumeria graminis f. sp. tritici causes wheat powdery mildew disease. Here, we study its spread and evolution by analyzing a global sample of 172 mildew genomes. Our analyses show that B.g. tritici emerged in the Fertile Crescent during wheat domestication. After it spread throughout Eurasia, colonization brought it to America, where it hybridized with unknown grass mildew species. Recent trade brought USA strains to Japan, and European strains to China. In both places, they hybridized with local ancestral strains. Thus, although mildew spreads by wind regionally, our results indicate that humans drove its global spread throughout history and that mildew rapidly evolved through hybridization.The fungus  Blumeria graminis f. sp. tritici causes wheat powdery mildew disease. Here, Sotiropoulos et al. analyze a global sample of 172 mildew genomes, providing evidence that humans drove global spread of the pathogen throughout history and that mildew rapidly evolved through hybridization with local fungal strains.The fungus  Blumeria graminis is a fungal plant pathogen that causes powdery mildew, the eighth most economically damaging disease in wheat1. It can lead to estimated annual yield losses of 7.6–19.9%3. The species B. graminis contains multiple formae speciales, defined by their host specificity, e.g., Blumeria graminis forma specialis tritici (B.g. tritici) is specialized to infect hexaploid (bread) wheat, while B.g. dicocci grows mostly on tetraploid wheat. B. graminis has a sexual life cycle leading to chasmothecia formation to survive harsh conditions and an asexual (disease) cycle. The 166 Mb genome of wheat powdery mildew was recently sequenced and assembled into full chromosomes4.6. From there, it spread throughout Eurasia, playing a crucial role in the development of agriculture and civilization. In the Americas, wheat was introduced by Europeans during the 16th to 17th century7. Intensifying trade established trade routes and exchange of wheat varieties worldwide. During the last century, countries like the USA or Russia became major exporters of wheat8, while others such as Japan, where wheat was introduced ~2000 years ago, received large imports from the USA9.Wheat was domesticated in the Fertile Crescent ~10,500 years ago10. The complex breeding and trade history of wheat raises questions on how wheat diseases spread, adapted, and co-evolved with their hosts. Barley powdery mildew was shown to spread by wind up to ~100 km per year11. However, it is not known whether wind can transport mildew spores between continents, or whether humans are required as vectors, as was shown e.g. for chestnut blight, coffee rust, Dutch elm, wheat blast, wheat yellow rust, and Fusarium oxysporum disease fungi19.Human activities have long been associated with the spread of pathogens, as demonstrated by the COVID-19 pandemic20. For example, the introduction of the hybrid crop triticale was rapidly mirrored by the hybridization of mildews of its two parent species21. That study also proposed that wheat mildew itself is an ancient hybrid of mildews that grew on tetraploid or diploid wheat progenitors. Hybridization can even occur between barley and wheat mildew formae speciales, which diverged millions of years ago22, demonstrating that hybridization is possible between very distantly related lineages. For simplicity, we hereafter refer to recombination between mildew lineages as “hybridizations”, regardless of whether they occur between formae speciales or between lineages of the same forma specialis, which diverged hundreds or thousands of years ago.Crop breeding can also drive the emergence of new pathogen strainsCalonectria spp., Cryphonectria parasitica, Hemileia vastatrix, Hymenoscyphus fraxineus, Ophiostoma spp., Magnaporthe oryzae, Parastagonospora nodorum, Puccinia striiformis, Puccinia triticina, Rhynchosporium commune, and Zymoseptoria tritici37) explored different aspects of pathogen evolution. Some used reduced representation data such as SSRs and RADseq31, while others used whole-genome sequencing technologies38. Furthermore, multiple studies suggested the hybridization of different pathogen lineages as drivers of pathogen evolution36. Analyses of powdery mildew pathogens were so far limited to isolates representing relatively small geographical regions41, and there were, to our knowledge, only a few studies on worldwide samples of obligate plant biotrophs using whole-genome sequencing .Previous population genomics studies on global or regional samples of plant pathogenic fungi 21. In all geographical populations with at least eight individuals, both mating types were found not statistically deviating from the expected 50/50 ratio, suggesting sexual recombination within populations . To add robustness to the analysis, we also performed PCoA using Bray–Curtis and Nei’s genetic distances, which yielded very similar results  was between seven and eight, with the most probable K = 7 Fig. . The forThe ADMIXTURE plots were also suggestive of hybrid mildew strains. For example, an East Asian component predominates in most Chinese isolates . Interestingly, the Mantel test showed strong correlations between genetic and geographical distances between isolates when broad datasets were used, such as between continents or across Eurasia, indicating that wind dispersal over large distances is not a major driver of pathogen spread  effectively discriminate mildew populations.6, we hypothesized that B.g. tritici also emerged there. Thus, mildews from that region were expected to be most diverse. Indeed, the B.g. tritici mildew population from Israel had the highest sequence diversity π, the highest average differences within populations (ADW) using simple mismatch distances for LD-pruned and non-LD-pruned SNP datasets, as well as the highest Watterson’s Theta, closely followed by European mildews , barley (B.g. hordei) and wild grasses 44 were included as outgroups. Overall topologies of all networks were consistent, with the different formae speciales proximal to B.g. dicocci, but distant from B.g. tritici populations  that can be used as an approximation of the extent of population differentiation45. In our coalescent analysis, B.g. dicocci started with the highest effective population size (Ne), followed by an early decrease in Ne  values  originating in the Fertile Crescent and both formae speciales being potentially able to infect either host. This is in accordance with previous studies49. Nevertheless, F4 statistics indicated that there is gene flow between B.g. dicocci and B.g. tritici lineages  with coalescent methods MSMC2). Where available, we selected eight isolates per population that best reflected their diversity based on PCoA,  showed slower separation from each other over time, which is in contrast to their rapid separation from USA and ARG populations, and the near-complete separation from the B.g. dicocci population . Although molecular dating of such recent events has to be taken with caution, we propose that this could reflect a bottleneck created by modern breeding, starting in the middle of the 20th century, where monocultures of elite wheat cultivars replaced the older, much more diverse landraces. Widely used resistance genes, such as Pm850, and the use of modern fungicides and/or modern deep plowing of crop residues, may have contributed to selective pressure. After this decrease in Ne, most populations showed a rapid expansion. A similar recent decrease in Ne was also found in the fungal pathogen Rhynchosporium secalis51. This could be the result of adaptation to modern crop cultivars or reductions in crop rotations.A notable recent feature, of all Ne ~2000–5000 generations ago  recovered, Ne decreased again in both populations 200–500 generations ago , followed by alleles found in the whole population . All populations showed the typical U-shaped distribution for derived allele frequency with two peaks at the extremes, indicating no substructureforma specialis into the USA population . We found that the genomes of both the Japanese and CHNh isolates are comprised of large blocks that can be assigned to either of their putative parent populations . Indeed, we found the highest and most significant positive values when the USA or CHNa are used as pop3, indicating preferential allele sharing (Supplementary Table Z-score = 14.89). Japanese mildews being hybrids between USA and CHNa was further supported by the TreeMix analysis, indicating one recent migration from the USA/Australian clade to Japan  than e.g., rice blast36 and was less diverse than Dutch elm disease fungus19 and wheat leaf rust27. However, π was in the same order of magnitude in all these studies. The observed differences could, for example, be due to fungal lifestyles, genomic repeat content, sample bias, or evolutionary history . Additionally, most previous studies reported little geographical clustering of the populations. Instead, they showed that multiple different genetic lineages co-occur in the same geographical regions36. In contrast, we found a strong association between genetic and geographical distances between populations. Similar geographical clustering was found in Z. tritici and P. nodorum33.In recent years, multiple fungal plant pathogen population genomic studies have become available55  being a particularly good example of how only two haplotypes that were likely spread by trade founded a new European pathogen population34. In summary, our data show how humans were the main vector for the spread of mildew throughout history, and how mildew rapidly and routinely adapted to new hosts through hybridization.Our analyses showed that more recently, powdery mildew was brought from the USA to Japan Fig. , step 6,Blumeria graminis forma specialis tritici used for at least one of the analyses. The isolates were introduced into the collection by either reviving the chasmothecia following the methods of Müller and colleagues4, or by transporting and propagating conidia from leaves. Isolates that were acquired through chasmothecia were single-cell colony isolated to ensure that only one clonal individual was collected. Here, each isolate was propagated on 10-day-old freshly cut wheat leaves in Petri dishes. After four days, a single colony was picked with a toothpick and inoculated onto a new leaf. We repeated this twice to ensure one individual was isolated and then sub-cultured to obtain sufficient spores for sequencing. The dates of collection of the isolates vary between 1990 and 2019 and the isolates are listed in Supplementary Data Triticum aestivum and Triticum turgidum. A list of the isolates, the country, the coordinates of collection, the year of collection, and the wheat species/subspecies that they were collected from is shown in Supplementary Data formae speciales were used: B.g. hordei, B.g. avenae, B.g. poae, B.g. lolii, B.g. secalis, and B.g. dactylidis.A total of 224 sequenced isolates of 68. Illumina paired-end sequence reads of 150 bp read length and an insert size of ca. 500 bp were used to generate 1 to 6 Gb sequence data per isolate using either HiSeq 4000 or NovaSeq 6000 technologies. The remaining isolates were previously sequenced with the same DNA extraction protocol and their sequences were retrieved from the NCBI Short Read Archive (SRA). All Illumina sequence data is available from the SRA under project numbers shown in Supplementary Data In total, whole-genome sequencing was performed at different time points for 87 isolates. DNA extraction was based on chloroform- and CTAB based-protocol by Bourras and colleagues69 was used with the following settings: illuminaclip=TRuSeq3-PE.fa:2:30:10, leading = 10, trailing = 10, slidingwindow = 5:10, minlen = 50. The reference genome Blumeria graminis f. sp. tritici CHE_96224 version 3.16 was used to align the reads by using the short-read aligner bwa mem v0.7.17-r118870 with the following settings: -M. We used samtools v1.9 for sorting and removing duplicate reads71. In order to mark PCR duplicates in the alignment (bam) files, we used the MarkDuplicates module of Picard tools version 2.18.25-SNAPSHOT. The average genome-wide coverage  ranged between 10 and 60-fold for all the isolates with an approximate average of 45.46. Two isolates that had less than x20 average coverage were excluded from the analyses. We finally used “samtools index” to create indices for the bam files. The mapped reads were also further checked visually for their quality and GC% content, using the software qualimap2 and the multi-bamqc function72. GC content averaged 43.8% across isolates, with a standard deviation of around 0.9%  and removed the InDels. The number of SNPs retained was 2,051,444. Finally, we made another dataset, where we kept only biallelic SNPs using vcftools:–max-alleles 2 (version 0.1.5)76. It was used for the site frequency spectrum (SFS) and fastsimcoal2 analyses. No SNPs were considered from the chromosome Bgt_chr-Un.The mapped reads were used to make variant calling files using GATK v.4.1.2.077. Then, ncbi+ blast v2.7.1+ was used in order to blast specific genes 78. The mating type genes were either taken from the NCBI or identified by using mating type genes from closely related species. Then, the isolates were classified into two mating types. The Fisher’s exact test was used on the entire dataset and on individual populations with more than eight isolates to check if there was a significant deviation from the expected 50/50 ratio of the two mating types. A chi-squared test was used to test for deviation from 50/50 in larger datasets (Supplementary Table In order to compare specific genes and loci between the reference CHE_96224) and different isolates, we created de novo assemblies for all the isolates. The trimmed reads were assembled using SPAdes genome assembler v3.12.024 and di79. Nei’s genetic distance80 was calculated between individuals with the StAMPP package in R81.We used the SNPs from GATK to calculate the simple mismatch measure of dissimilarity, Bray–Curtis (Dice dissimilarity) distance, and individual Nei’s genetic distance between all 224 isolates. Simple mismatch and Bray–Curtis distances were calculated with the vegan (v2.5-7) R packageTo identify clonal isolates (clone correction), a simple mismatch distance was used. Since, for clone correction, we used only LD-pruned SNPs (of which most are singletons), we assumed a close to a random distribution of SNPs. The threshold of minimum genetic distance was defined by using two isolates (ISR_30p and ISR_30w) that we knew are clonal and were separated for ~260 generations of propagation. We then excluded one isolate each for pairs that had less than fifty times the simple mismatch distance between these two (which was about 0.004).Additionally, we identified three isolates in the de novo assemblies with both mating types, possibly due to contamination. These were also excluded. The remaining 172 isolates that passed filtering were used with the rest of the analyses. We used the Pearson correlation coefficient in R with the cor function to check for the correlation between the SNP quality (QUAL) and the alternative allele frequency (AF). They were highly correlated  pruned SNPs with a minor allele frequency (MAF) >0.05. In order to prune the LD sites, we used PLINK v1.9 with the parameters:–indep-pairwise 50 10 0.1. LD pruning and filtering resulted in 24,574 SNPs. The resulting vcf file was imported with the vcfR package (version 1.12.0) and transformed to a genlight object format in R. The PCoA analysis was performed using the glPca command from the adegenet R package version 2.1.3. We visualized the PCoA  with the ggplot2 package version 3.3.2. For the display, isolates were grouped according to country or region. We grouped the isolates from central Russia and Kazakhstan as middle Asia for this.Blumeria graminis belongs to a monophyletic group, the closest species with genomic sequences available are in the same family (Erysiphaceae) like Erysiphe spp. . However, because it is a monophyletic group, only a few thousand common SNPs were left after the identification. Thus, we used instead the raw Illumina reads of six powdery mildew isolates of the same species, but from different formae speciales . The pipeline that was used for these isolates was similar to the one for the B.g. tritici ones. We only performed the GATK SNP calling and filtered for a genotyping rate of >50%, due to the low number of isolates, along with retaining only SNPs with no intra-specific polymorphism. Furthermore, the QUAL that was used for filtering was less than 50. After filtering, alleles that were identical in at least two of the three different formae speciales, were assigned as ancestral. 2,367,351 SNPs were assigned as ancestral .We used different outgroups to identify ancestral alleles in the SNPs for the demography, haploblocks, and the signatures of selection analyses. As 83.The statistics on the genome-wide coverage, minor allele frequency, and INDELs percentage were calculated as follows: We used bedtools coverage to calculate the mean coverage through the whole genome. We used vcftools to acquire the minor allele frequency of SNPs and INDELs for all individuals, along with the singleton information for the whole dataset. The site frequency spectrum was calculated for the different populations, in order to look at the distribution of the SNPs, using adegenet in R. Geographical collections had to have at least eight isolates to be considered as populations, as in previous studies in plants and animalsB.g. tritici isolates where exact sampling coordinates were available, we performed the Mantel test using all three genetic distances . We used R and several dedicated packages84. The vcfR package version 1.12.0 was used to input the vcf files and change their format85. We then used the StAMPP package version 1.6.1 to perform Nei’s genetic distance analysis81. To calculate the other two distances, we used the R package vegan using the genlight formatted files as input from vcfR. A matrix with the geographic distances was created using the dist command of the stats package version 3.6.3. Then, we performed a Mantel test with a 100,000 repetitions using the mantel.randtest command from the ade4 package version 1.7-1586. This test provides information on whether two matrices are correlated or not and, in our case, if the three distance matrices are correlated with the geographical distance one. We also used the kde2d command from the MASS package version 7.3-53 to calculate the two-dimensional kernel density estimate for all the correlated points in the genetic/geographic matrix87. We did all this on different levels: worldwide, continental level in Eurasia (excluding the Japanese mildew isolates due to their admixture) and finally, within populations for some powdery mildew populations in Asia. As the Mantel test does not have a strong power to detect correlation71, we complemented it with the more sensitive distance-based Moran Eigenvector Maps (dbMEM) analyses on the same levels to try and find a correlation using the package memgene version 1.0.189.We tried to associate the genetic distance with the geographical distance between the isolates. Thus, for those 145 90. From the analysis, for ADMIXTURE the most probable number of ancestral populations was followed according to the lowest cross-validation error number, after testing for different number of Ks (1–15). Thus, the value of K = 7 was considered to be the most supported number of ancestral populations given the data. We tried the same analysis by subsampling the populations to check for consistency and the majority of the findings was similar to the previous ADMIXTURE analysis results  and one sexual generation during the year. The sequence contigs of the 2018 assembly were used for blastn searches (without low complexity filter) against the 2009 assembly. Because the assembly of highly repetitive regions was of poor quality in the 2009 assembly, only genes and 2000 bp flanking regions were considered.−7 and 5 × 10−7 mutations per base pair per generation. We are aware that this mutation rate might be an overestimate, as deleterious mutations may be selected over time in a natural environment, leading to an overall lower evolutionary substitution rate over time.The following steps were used to exclude sequencing errors, and false positives: (i) for sequence scaffolds with multiple near-identical hits (representing e.g. tandem duplications), only the top hit was used. (ii) SNPs that were closer than 100 bp to the end of sequences scaffolds were excluded because of generally lower sequence quality toward the end of contigs. Using these criteria, we estimated the mutation rate to be between 4 × 10formae speciales, such as wild grasses powdery mildew. 4fdg SNPs were identified with an in-house script, which is stored in Github . We kept 5039 4fdg SNP sites (excluding those on chromosome Unknown) to calculate genetic distances between isolates. We created a Kinship matrix according to these distances. For the calculation of the 4fdg distances, we used the Kimura-two-parameter (K2P) substitution model implemented in our in-house script . Concatenated SNPs in coding sequences of genes were aligned with clustalw  using a gap creation penalty of 10 and a gap extension penalty of 0.2. The different datasets and distance matrices were used to check for the consistency of results. The SplitsTree4 (v4.17.1) software92 was used to create the corresponding networks based on the three different datasets using the NeighborNet algorithm.For the construction of phylogenetic networks, we used simple mismatch individual distances, concatenated SNPs in coding sequences of genes, and a fourfold degenerate sites (4fdg) dataset. For this analysis we also included samples from other 93. We estimated the whole-genome nucleotide diversity (π), in non-overlapping windows of 10 kb, assuming windows to be independent, for the different populations with the formula of Nei, implemented in the PopGenome R package94, for all SNPs along with Watterson’s theta (θ). The same analysis was repeated for eight randomly chosen isolates per population , Watterson’s estimator (theta), and the number of singletons  were used to assess the overall genetic diversity of the different mildew populations, similarly to previous studies97 and SnpSift v4.3t98 to annotate and predict the effect of all filtered SNPs. Then, from the output, we calculated the π and θ for the SNPs that had a low/medium/high effect annotated by SnpEff. Furthermore, we looked at the singletons count of SNPs across the genome. As there is a bias towards the reference, we were expecting an increase in the singletons of the European population were the reference is included  with regard to the simple mismatch dissimilarity between individuals for the LD-pruned SNPset. Moreover, we calculated Dxy using the pixy (v1.2.5.beta1) softwareST) between mildew populations as an approximation of the extent of population differentiation, we used the R package PopGenome94. We calculated FST for each chromosome separately and then estimated the weighted average (taking into account the chromosome size) without cut-off for minor allele frequency (MAF).To calculate the fixation index . The F4 test can be used with four populations, when we have a non-admixed outgroup population, to test if there is any gene flow between the other two and a fourth population.B.g. poae (BGPOA) as the outgroup. We designed the F4 comparisons as  with pop1 and pop2 being any pairs from our population's dataset. Here, we are looking for positive F4, indicating gene flow between ISRdic and other populations (the BABA configuration). We also used F4 with the model  in order to check for the population with the most shared drift with the JPN mildew population. In F4 statistics, we considered an absolute value of Z-score >3 significant.We used the F3 to indicate possible hybridization for the JPN population with the format  and for the CHNh population with the format . The results of these two analyses are shown separately in Supplementary Tables 101. Coalescence analyses use a backward-in-time algorithm, starting from the current generation and randomly building back the lineage of genomes. We investigated the effective population size of most geographic populations with more than eight isolates. Firstly, we created a SNPmappability mask using the reference genome v3.16. Then, we used vcftools to create SNP files from the bam files and mask files to cover the vcf files. After that, we diploidized the haploid vcf files and used generate_multihetsep.py to make the correct input format file for MSMC2. Then, we ran both the demography and afterward the cross-coalescence with 100 iterations and used eight isolates per population for eight wheat powdery mildew populations , including only isolates without any recombination from the admixture analysis. In order to choose eight isolates, we created a SNP PCoA of each population and we manually chose eight samples with big distances from other samples 104 (version 2.6.0.3) to simulate complex evolutionary models and estimate which ones would be more likely to be true. We were interested in testing the order of split events, along with the possible re-introduction of a EUR population ancestry to the USA or a split event from EUR to the USA. All the priors for these models/scenarios can be found in Supplementary Data B.g. dicocci, the USA and the CHNa mildew populations. There were 25 scenarios tested, as seen in Supplementary Fig. We also used fastsimcoal2To support the hypothesis that mildews from Japan and China were recent hybrids, we aimed to assign SNPs in the genomes of the putative hybrid populations (JPN and CHNh) to either of their two parent populations . SNPs were determined to belong to either of the two putative parents using the following criteria: A SNP in the putative hybrid has to occur in at least 70% of all isolates of the parent 1 population, but has to be completely absent from the parent 2 population, and vice versa. To address the possibility that an SNP might be simply absent from one parent due to undersampling, we ran the analysis multiple times by swapping a single isolate between parent 1 and parent 2. This reduced the number of SNPs that could be assigned to either parental population by 90–97%, depending on which two isolates were swapped. Our resampling approach indicates that the false positive rate of assigning an SNP to either of the two parents is 10% or less. Multiple consecutive sequence variants coming from one or the other parent were here referred to as “haploblocks”.B.g. lolii, B.g. dactylis, B.g. avenae, B.g. poae, B.g. hordei, and B.g. dicocci), but are completely absent in the European mildew isolates.To identify putative introgressions from distant relatives in mildews from the USA and ARG, we identified SNPs that occur in at least 85%  of the USA and ARG isolates plus at least one of the ancestral lines  using these SNPs.105. We excluded centromeres from the calculations and we kept only the windows that lay above the 0.999 thresholds of probable selection. Then, we compared the statistics obtained for all the windows.We used RAiSD, a software that calculates the mu value to identify candidate windows of selectionFurther information on research design is available in the Supplementary_InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting SummaryPeer Review File


Sclerotinia sclerotiorum is one of the most destructive fungal pathogens infecting a wide array of plant species worldwide. Management of this pathogen relies on the coordinated use of fungicides and resistant host cultivars with other control measures, but the effectiveness of these methods requires knowledge of the genetic variability and structure of the fungal populations. To provide insight into the genetic diversity and structure of this pathogen in West Azarbaijan province of Iran, a total of 136 isolates were collected from symptomatic sunflower and cabbage plants within fields in three regions and analysed using inter-simple sequence repeat (ISSR) markers and intergenic spacer (IGS) region of the rRNA gene sequences. A total of 83 ISSR multilocus genotypes (MLGs) were identified, some of which were shared among at least two regional or host populations but in a low frequency. High genotypic diversity, low levels of clonal fraction, and random association of ISSR loci in a region indicated a low level of clonal reproduction, and possibly a high level of sexually recombining life cycle for the pathogen in the province. Marker analyses revealed that the pathogen was spatially homogeneous among fields, and thus similar control measures, such as the choice of resistant cultivars and fungicides, may effectively manage S. sclerotiorum within the region. Four IGS haplotypes (IGS1–IGS4) were detected within populations with IGS3 being the most prevalent haplotype. The low IGS haplotype diversity, the absence of spatial structure, and shared MLGs among populations may suggest a single introduction and subsequent dispersal of S. sclerotiorum within West Azarbaijan province. Sclerotinia sclerotiorum is a devastating fungal pathogen causing various diseases such as white mold, stem rot, basal stem rot, and fruit rot in a wide range of plant species worldwide1. Yield losses posed by this necrotrophic pathogen vary and can reach up to 100% under favorable conditions and severe infection3.S. sclerotiorum because they are the main surviving structures and provide asexual and sexual inoculum for infection through myceliogenic and carpogenic germination, respectively. In myceliogenic germination, sclerotia produce hyphae that infect the basal stem and roots of plants, whereas, in carpogenic germination, they produce apothecia and wind-blown ascospores that infect aerial plant parts3. Both reproduction modes can result in a clonal population structure because S. sclerotiorum is a homothallic species, and sexual reproduction leads to progenies that do not segregate in any marker and gene5. Due to the long-term persistence of sclerotia in soil and multiple infection routes, along with a broad host range and lack of highly resistant cultivars, the pathogen has been proven difficult to manage in the field. Nonetheless, coordinated use of fungicides and partially resistant cultivars with other control measures that reduce the pathogen inoculum and manipulate the environment to create unfavorable conditions for the pathogen development, can assist in managing the disease in the field3. However, the effectiveness of control measures, particularly chemical control and resistant cultivar employment, requires knowledge of the genetic diversity and structure of the fungal populations6.Sclerotia are considered the prime components in the epidemiology and disease cycle of 6. Genetic structure of S. sclerotiorum from different hosts and geographic regions has been studied using mycelial compatibility group (MCG)10 and various molecular markers such as inter-simple sequence repeat (ISSR)11, random amplified polymorphic DNA (RAPD)12, restriction fragment length polymorphism (RFLP)10, sequence-related amplified polymorphism (SRAP) technique13, simple sequence repeat (SSR)20, and universal rice primers (URP)21. Results of several population genetic analyses have been consistent with homothallic mating and sclerotia production of S. sclerotiorum and found a clonal genetic structure with low genotypic diversity, linkage disequilibrium at marker loci, and dominance of one or a few multilocus genotypes (MLGs) over time and space23. However, a number of studies have found high genotypic diversity within populations and random association of marker alleles and concluded that recombination (outcrossing) infrequently occurs in the life cycle of S. sclerotiorum and contributes to the population structure of the pathogen in the studied regions28.Genetic structure is the outcome of main evolutionary forces, including reproduction/ mating system, gene flow, random genetic drift, selection, and mutation. A thorough understanding of the role of each determinant on genetic structure can assist in predicting the potential risk of a fungal pathogen and improving control strategiesSclerotinia sclerotiorum is a common pathogen of these crops causing substantial yield losses in the region. Hence, a number of studies have been conducted to understand the MCG and aggressiveness diversity of isolates from sunflower30 and cabbage31 in the province. The genetic diversity of S. sclerotiorum isolates from four northern provinces of Iran have been studied using molecular markers32, but no information is available on genetic diversity and structure of the pathogen populations in West Azarbaijan or other regions of the country. Therefore, this study aimed to determine the genetic variation of S. sclerotiorum in sunflower and cabbage fields located in various regions of West Azarbaijan province. The specific objectives were: (a) to determine inter- and intrapopulation diversity using ISSR markers, (b) to infer the relative role of clonal reproduction and recombination in population biology of the pathogen, and (c) to identify haplotypes and to estimate diversity based on intergenic spacer (IGS) region of the rRNA gene sequences.West Azarbaijan, located in northwest Iran, is one of the leading agriculture production provinces of the country. This province is ranked first for the cultivation of oilseed and confectionary varieties of sunflower nationwide. Vegetables are also important crops that are grown in various regions of the province, and among which, cabbage is mainly cultivated in Urmia . The seven ISSR primers amplified 65 scorable and reproducible loci in 136 isolates, of which 55 (84.62%) were polymorphic across the dataset Table  and, thuThe number of ISSR loci amplified in geographic populations did not differ considerably and ranged from 64 for isolates from Khoy and Salmas to 65 for isolates from Urmia Table . HoweverS. sclerotiorum isolates  and H´ (1.52), while Khoy had the lowest genotypic diversity (G´ = 1.00 and H´ = 1.00). Clonal fraction also differed across geographic populations and varied from 0.00 in Khoy to 0.38 in Urmia. The mean gene diversity (Hexp) of the studied populations was 0.21. The highest Hexp (0.21) was estimated in Urmia and Salmas, while the Khoy had a lower (0.17) gene diversity (Table The number of observed MLGs in geographic populations varied from 14 in Khoy to 59 in Urmia (Table G´ (1.40) and H´ (1.13) as well as lower clonal fraction (0.30) compared to the population from cabbage . In contrast, Hexp in the cabbage population was found to be higher (0.24) compared to the sunflower population (0.20)  of genetic variation could be attributed to the differences among geographic populations and between the samples collected from the two hosts, respectively. Pairwise comparisons of geographic populations also indicated a nonsignificant genetic differentiation between them  higher than zero  had only one isolate, while 23 genotypes (27.38%) had two or more isolates indicating clonal individuals. Clones of three MLGs were sampled in a relatively higher frequency than the others, but only MLG3 was widely distributed and was present in isolates from the two hosts in the two regions. In addition, estimation of association in ISSR loci indicated significant gametic disequilibrium in the host populations and two geographic populations. These findings were consistent with previous reports on S. sclerotiorum from various hosts and regions35 and revealed the occurrence of clonal reproduction through sclerotia and/or self-fertilization in the pathogen populations in the province.The populations of S. sclerotiorum populations, the frequencies of the most prevalent MLGs were not high, and the overall proportion of distinct MLGs was 0.62. Molecular marker studies of S. sclerotiorum populations have revealed MLG proportions of 0.12 for various crops in New York36, 0.29 for canola in northern Iran27, 0.45 for dry bean in the USA, France, Australia, and Mexico37, 0.51 for common bean in Argentina21, 0.58 for rapeseed in China20, 0.59 for various crops and meadow buttercup in the UK16, 0.59 for common bean in Brazil18 and 0.61 for various crops in England, Wales, Scotland, Norway and Australia17. Furthermore, in the present study, proportions of MLGs ranged from 0.62 to 1 in geographic populations and were 0.70 and 0.63 in the samples collected from sunflower and cabbage, respectively. The maximum MLG proportion and the absence of clones in Khoy population could be due to the limited number of isolates collected from this region, nevertheless, the ranges of MLG proportions in the populations were generally larger than those found in northern provinces of Iran (0.21–0.45)27 and several other regions such as UK (0.24–0.72)16 and Australia (0.28–0.68)28. The different MLG proportions observed in West Azarbaijan and northern provinces of Iran27 may be attributable to the difference in sample sizes, sampling strategies, and spatial scales evaluated in the two studies; the present study investigated three geographic populations within the province, while in the previous study27, samples from each of the four provinces were considered as a single population. Altogether, a comparatively high genotypic diversity observed in this study, as well as the low levels of clonal fraction and presence of reticulations in Neighbor-Net, could indicate that S. sclerotiorum in West Azarbaijan has not been under strict clonal reproduction and recombination has played a role in the life cycle of the pathogen in the region. However, recombination rates appear to have varied in different populations so that ISSR alleles only in Salmas population were found to be in random association, and the other populations were in gametic disequilibrium.Despite the clonality in the studied S. sclerotiorum populations; however, AMOVA analyses indicated the absence of significant variation among the studied populations in West Azarbaijan. The fungal populations from the two host plants showed a very low but nonsignificant level of differentiation in AMOVA and were partly non-overlapped in DAPC. However, the occurrence of shared MLGs between the two populations and a very low inter-population variation (1%) suggest that host identity has not been a barrier to gene flow and MLGs have moved between crops. The observed loose structure might be the result of genetic drift or slight selection pressure exerted by the host plants, but further investigations are needed to precisely unravel processes underlying the very low level of differentiation between the two populations. Although STRUCTURE and DAPC detected various numbers of genetic clusters within populations, the analyses were inconclusive because very few MLGs were assigned to the inferred clusters38. It is documented that several factors such as uneven sampling across existing population structure and migration rates between groups can significantly affect the results of admixture methods (STRUCTURE and DAPC) and lead to misspecification of genetic groups. Therefore, it is recommended that the data should additionally be investigated with ordination-based or other methods in order to confirm the results of admixture methods and to determine genetic structuring within populations effectively41. In the present study, population structure analyses based on PCoA and Neighbor-Net yielded different results compared to the admixture methods, and revealed the absence of genetic structuring in the populations. These findings could indicate that S. sclerotiorum isolates likely represent a genetically homogeneous or a very loosely structured population; nevertheless, further study using additional isolates and other molecular markers is suggested to provide a more accurate inference on the population structure of this pathogen in the province.Parameters of within-population diversity provided some evidence for variation among the S. sclerotiorum isolates revealed the presence of four haplotypes with various frequencies in West Azarbaijan province. IGS3, IGS2, and IGS1  were the most frequent haplotypes and were widely distributed across most studied regions. Although the information on IGS haplotypes of this pathogen in Iran or other Asian countries is lacking, IGS analysis of S. sclerotiorum isolates in other continents indicated that IGS3 is common on various host plants in Europe, North America, and Australia and is the most frequent haplotype in England, New Zealand and the USA17. The two haplotypes, IGS1 and IGS2, have been found within the fungal populations in all studied countries except Australia and were the next most abundant haplotypes (after IGS3) in England17. IGS4 was also detected in a sunflower field in Urmia, which has previously been reported only from England17. Taken together, the results revealed that IGS haplotype composition and frequency in populations of S. sclerotiorum in West Azarbaijan is more similar to England populations than the other countries. This may suggest that both populations have originated from the same ancestral population, but confirmation of this hypothesis needs further investigation. Overall IGS haplotype diversity (0.553) in West Azarbaijan was found to be lower compared with those reported for Australia (0.663), England (0.659), and Scotland (0.632)17, suggesting that the studied populations likely descended from a founding population.Analysis of IGS sequences in S. sclerotiorum in West Azarbaijan province has a low IGS haplotype diversity. Furthermore, ISSR marker data revealed the absence of genetic structure based on geographic or host origins, high levels of admixture, and sharing some MLGs among the populations. These findings may suggest the introduction and subsequent dispersal of the pathogen within the province. Clonal reproduction was shown to occur in the region, but the presence of high genotypic diversity, low clonal fraction, and random association of ISSR alleles in a geographic population provided some evidence for the occurrence of recombination in various regions. Sclerotinia sclerotiorum is thus able to create new genotypes through recombination and to propagate well-adapted genotypes through clonal reproduction, which can facilitate the evolution of the pathogen against fungicides and resistant cultivars in the province. Since the pathogen is spatially homogeneous among fields within the province, similar control measures, including the choice of resistant cultivars and fungicides, may effectively manage S. sclerotiorum across the region.In conclusion, our results indicated that S. sclerotiorum isolates obtained from sclerotia collected from infected sunflower and cabbage plants in the fields located in West Azarbaijan, Iran, were used in this study , as described before42. Only one isolate was obtained from each of the sampled plants and used for subsequent studies. The isolates were grouped into three geographic  using the method of Dellaporta et al.43. A total of 40 ISSR primers were initially screened for their ability to amplify discernible and informative band patterns in a subset of isolates and, finally, seven primers  was constructed for each isolate by combining the data from seven ISSR primers, and the isolates with identical banding patterns were assigned to the same MLG (clone). To determine the minimum number of ISSR loci required in discriminating MLGs and accuracy of amplified loci in representing genetic diversity in the studied populations, a genotype accumulation curve was constructed by 1000 times randomly sampling of loci in the package 46. Nei's unbiased gene diversity (Hexp)47 was estimated in the package poppr v. 2.8.744.The number of amplified DNA fragments and percentage of polymorphic bands were calculated in GenAlEx v. 6.5n = 14) and host (n = 43) populations48 in the package poppr v. 2.8.744. Genotypic diversity indices G49 and H (Shannon and Weaver index)48 were also estimated in poppr v. 2.8.744, and were further divided by gmax (the number of eMLG) and ln(gmax), respectively, so that both indices could be compared for populations with different sample sizes48. Clonal fraction for populations was calculated using the formula: 1- (number of MLG/ number of isolates)50. All further analyses were conducted on clone-corrected dataset in which only one representative isolate per MLG was retained in the populations.To assess the diversity within populations, the number of MLGs and other parameters of MLG diversity were calculated for the full dataset and each of the geographic and host populations separately. As the populations had unequal sample sizes, their genotypic richness was compared by calculating the expected number of MLGs (eMLGs) using the rarefaction method corrected for the smallest sample sizes of geographic 52 that implements a model-based Bayesian clustering approach to identify the optimal number of genetic clusters (K) and assign individuals to the clusters based on genetic similarity. The data were analysed by running a series of simulations for any given K between 1 and 10, assuming an admixture model and correlated allele frequencies. For each K value, ten independent simulations were conducted with a run length of 200,000 Markov Chain Monte Carlo (MCMC) iterations after a burn-in of 50,000 iterations. STRUCTURE results were compiled with STRUCTURE HARVESTER53, and graphical representations of structural plots were constructed using STRUCTURE PLOT v. 2.054. To examine further the population structure, discriminant analysis of principal components (DAPC)55 was run in adegenet v. 2.1.0 package56 for R v. 3.6.3. DAPC is a hypothesis-free method that combines principal component analysis (PCA) and discriminant analysis (DA) to maximize variability between clusters and minimize variability within clusters, without presumptions of Hardy–Weinberg equilibrium or linkage disequilibrium. The optimal number of clusters was identified by find.clusters function, which implements successive K-means clustering with increasing values of K. The most probable number of clusters was inferred by identifying the lowest Bayesian Information Criterion (BIC)55. The optimal number of principal components (PCs) retained in the analyses was determined using xval.dapc function in adegenet v. 2.1.056. The genetic clustering inferred by Bayesian clustering and DAPC methods were compared to the results obtained from principal coordinate analysis (PCoA) in GenAlEx v. 6.546 and Neighbor-Net in SplitsTree v. 457. Both later analyses were carried out using Jaccard’s coefficient of similarity.To examine possible population structure and admixture of isolates, we used the software STRUCTURE v. 2.3IA) and unbiased estimate of multilocus linkage disequilibrium , and 0.25 µM each of the forward and reverse primers. Amplifications were carried out on a Corbett CG1-96 Palm-Cycler™ Thermal Cycler . The thermocycler programs for amplifications were 94 °C for 4 min, followed by 40 cycles of 94 °C for 30 S, 57 °C for 30 S, and 72 °C for 1 min, with a final extension at 72 °C for 10 min. PCR products were purified and sequenced by Beijing Genomics Institute (China).A portion of the IGS region in 59. The sequences of 26 IGS haplotypes17 were retrieved from GenBank and aligned with sequences generated in this study using MAFFT v. 7.45060. IGS haplotypes of our isolates were determined in DnaSP v. 561, and haplotype diversity of populations were calculated using the same software. To visualize the relationships among haplotypes from populations, median-joining networks were reconstructed in PopART v. 1.762.The IGS sequences were checked and edited with BioEdit v. 7.2.0Experimental research, including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation.Supplementary Information.


Elymus L., as the type species of genus Elymus L. The existing geographical distribution pattern and genetic spatial structure of E. sibiricus on Qinghai–Tibetan Plateau (QTP) are not yet clear. In this study, population genetic structure and demography history of 216 individuals from 44 E. sibiricus populations on QTP were studied used specific-locus amplified fragment sequencing (SLAF-seq). The result of genetic diversity showed that there was no single genetic diversity center was observed across all E. sibiricus populations. The results of genetic variation showed that 44 populations were clearly divided into the following three groups: Qinghai Plateau (Group I), South Tibet (Group II), and Hengduan Mountains (Group III). From the three analyses of AMOVA, Mantel test and Treemix, strong genetic differentiation across all populations and low genetic differentiation among populations within three groups. Molecular dating indicated that E. sibiricus diverged at 16.08 Ma (during the early Miocene) can be linked to the Himalayan Motion stage of QTP uplift. It is speculated that the reasons affecting the current phylogeographical pattern are as follows: (1) The environmental changes due to the uplift of the QTP; (2) The geographic distance between the populations ; (3) Geographical barriers (the Tanggula and Bayangela Mountains between Groups I and II). This study provides new evidence and historical perspective to the future exploration of the evolution and geographic distribution pattern of Elymus L.Elymus sibiricus L. is a perennial allotetraploid belonging to Triticeae of Poaceae,  Understanding the underlying phylogeographical processes of the species distribution in some areas may help guide prioritization in conservation. It also may facilitate forecasts on the ecosystem services under the future climate conditions. It is believed that geological events and climate fluctuations have profoundly affected the distribution of species and the history of population dynamics in mountainous areas . The glo2  and AFLP(Amplified Fragment Length Polymorphism)  . DespiteE. sibiricus on the QTP; (2) speculate the population historical dynamics of E. sibiricus, and explore the historical reasons for the formation of the existing distribution pattern and possible sanctuaries, to clarify the effects of geological events such as uplift and glacial cover of the QTP and human activities on the distribution pattern of plant species diversity in this area; (3) provide a historical perspective for the phylogeographical formation of alpine meadow population structure and dynamics; (4) discuss the conservation strategy for improving collection, evaluation, and breeding efficiency and pertinence of E. sibiricus germplasm resources and also important for genetic evolution theory of polyploid populations and biodiversity conservation on the QTP. Consequently, the phylogeographical study of the dominant community species in mountain systems has motivated growing scientific interest in this research field.In the recent years, many molecular markers have been used to infer the phylogeography and biogeography of species to understand their evolution patterns . TechnolE. sibiricus were collected from Gansu, Tibet, Sichuan, and Qinghai Provinces representing almost the entire distribution range of the species on QTP . The concentration and purity of the DNA were assessed with ultra-micro photometer. Then the extracted DNA was stored at –20°C for further use.E. sibiricus genome (has not been published yet) was selected as the reference genome for predicting restriction enzyme sites. Finally, the optimal enzyme RsaI and EcoRV-HF was selected. Then, all DNA samples were digested separately, and a 474–504 bp restriction enzyme fragment length was defined as a SLAF tag. Rice has the characteristics of small genome; easy manipulation; and dense molecular markers, and has commonalities with other grass crops in gene sequence; structure; sequence and function, so it is hailed as a model species for genome analysis by the international scientific community  was added to the 30°C end using dATP at 37°C, and then dual-index adapters were ligated to the A-tailed DNA fragments. A PCR amplification was subsequently performed using diluted restriction-ligation DNA as the template. The E.Z.N.A.® Cycle Pure Kit  was used to purify the PCR products. The purified PCR products were pooled and incubated at 37°C with RsaI, T4 DNA ligase, ATP, and Solexa adapter. Then, the products were purified with a quick spin column  and separated on a 2% agarose gel. All amplified fragments were diluted for the paired-end sequencing on an Illumina-HiSeq™ 2500 sequencing platform  at Beijing Biomarker Technologies Corporation .According to the genomic size and GC content, the ommunity . To evale10) and GC content were used to evaluate the single-base error rate of sequencing quality. Based on the sequence similarity, the high-quality paired-end reads were clustered using the BLAT software (E. sibiricus genome) by BWA software  between populations was also computed the genetic differentiation. Analysis of molecular variance and FST were calculated by Arlequin , Nei’s diversity index (H), observed allele number (Na), observed heterozygous number (Ho), Shannon’s wiener index (I), and polymorphism information content. These indexes can be calculated by POPGENE . The spaArlequin . The SNPThe Phylogenetic tree was constructed by MEGA X program  with ther2-value (Pearson’s squared correlation coefficient) by PLINK software .The linkage disequilibrium (LD) decay rate is crucial for maintenance of disequilibrium in a population . Becausesoftware  based onE. sibiricus; therefore, based on the data of Elymus species and Aegilops tauschii1, the MCMCtree program implemented in the PAML v4.8 package  in the birth–death process with species sampling were specified as 1, 1, and 0, respectively. A loose maximum bound for the root was set between 3 Ma and 9.4 Ma. The first 50,000 cycles in MCMCtree were discarded as burn-in, and every 50 cycles were sampled to obtain a total of 100,000 individuals.To infer the split and Admixture history among groups of 44 populations, the TreeMix  was used package  was used package . The indD , Last Glacial Maximum , Mid-Holocene , present (∼1950–2000), and projected future (2070)  from the National Geographic Center of China. The receiver operating characteristic (ROC) evaluated the accuracy of the model by the area under the curve (AUC)   and ArcGe (2070) . The spetal data  was downve (AUC) . The evave (AUC)  was liatve (AUC) . FinallyA total of 1,221.47 Mb reads were obtained from the Illumina HiSeq 2500 platform. The reads with a length of 125 bp were selected for the further evaluation and analysis to ensure the quality of the data. The average Q30 was 93.13% and the average GC content was 45.91%, which indicated that the reliability of the data. In addition, a total of 6,747,462 SLAF tags were explored, which 3,890,215 were polymorphic and the average depth was 5.10x. Finally, 30,159,737 SNPs were obtained in total .R2 = 0.314, p < 0.001) and latitude , and a significant ascent in Ho with increasing altitude . However, there were no significant differences in other genetic diversity indexes except Ho. The spatial distribution of genetic diversity of E. sibiricus across the whole sampling range was shown as that genetic diversity values were lower in northeastern region and western Hengduan Mountains of QTP and higher in the center and southeast of QTP  and expected heterozygous (He) were 0.4167 and 0.3750, respectively. The maximum and minimum values of Nei’s diversity index (H), Shannon–Wiener index (I), and polymorphism information content (PIC) appeared correspondingly in population QH09 and SC15, and these average values at species level were 0.4196, 0.5543, and 0.2971, respectively . Additiot of QTP . HoweverE. sibiricus populations were divided into three clusters based on the NJ tree as indicated with different colors in To explore the divergence among populations, in this study, 216 individuals of 44 E. sibiricus populations indicated that most of total variations mainly existed among groups and populations with the differentiation coefficient of 66.39 and 64.44%, respectively (FST) of the three groups were 0.12, 0.397, and 0.453, respectively. It indicated the notable local gene flow among the populations within each group. In addition, the FST value among three groups .Analyses of molecular variance of 44 ectively . The AMOe groups  showed Gm = 10, adding six migration events, E. sibiricus populations throughout the QTP. The TreeMix tree showed the gene flow mainly existed between Groups I and III. There were limited gene flow between Groups II and III, and no gene flow between Groups I and II. Additionally, the divergence time among 216 E. sibiricus individuals on the ML tree . Whereas the late divergence between Groups II and III happened 5.92 Ma ago. Thus, it was probably to infer that the northeast of QTP was the origin area of E. sibiricus population 16.08 Ma ago.From the unrooted maximum likelihood tree inferred by TreeMix  was observed, and the cluster result was consistent with PCA and phylogenetic relationship. The result indicated that E. sibiricus populations did not originate independently from one location on the QTP. All individuals with a low degree of Admixture were seen from all studied populations. From the dynamics of K = 2–10  was calculated for the three groups and the maximum r2 values of the three groups were 0.5584, 0.7174, and 0.5646, respectively . Populations exhibited expansion from the LGM to Mid-Holocene period. However, the highly suitable area had a slight contraction. Currently, potential suitable distribution further contracted in overall QTP range. Considering the changes of the potential distribution range in five periods, E. sibiricus had undergone the repeated historical process of contraction and expansion. Interestingly in the future, the species will potentially experience an obvious expansion. In addition, it was also found that the key environmental factors affecting the distribution of E. sibiricus were mean annual temperature (Bio1), mean temperature of coldest quarter (Bio11), annual precipitation (Bio12), and elevation (Elev)  .E. sibiricus populations in this study. The QTP is characteristic of complex geographical environment, which may be the cause of variation in within-population genetic diversity. Mountain barriers may play a key role in species formation and diversity as their topographic complexity may lead to ecological stratification and environmental heterogeneity  on the QTP. However, the results of our study were not completely consistent with their conclusion even in different Elymus species. The reasons for this difference were likely to be that the complex geological environment of a large-scale sampling area conceals the influence of altitude on genetic diversity, or the sampling sites higher than 3,800 m and lower than 3,000 m in this study are relatively scaring, and without covering the entire altitude range. Genetic structure analysis at the intra-specific level is important for future adaptive change or evolution , indicating that the isolation-by-distance model seems to describe the pattern of differentiation among the populations in this study. There was a significant correlation between genetic distance and geographical distance of E. sibiricus populations due to the main self-pollination breeding system of E. sibiricus. In general, selfing species commonly have lower levels of genetic diversity and higher differentiation between populations than outcrossing plants . Group II was distributed in the South-Tibetan river basin, which had almost no gene flow compared with the other two groups. The degree of Groups II and III was low with the cause of the river obstacle, and there were no gene flow among Groups I and II with the geographical isolation of Tanggula Mountains. Interestingly, the gene flow mainly existed between Groups I and III because two groups were not isolated by very huge physical barriers  The environment changes due to the uplift of the QTP; (2) The geographic distance between the populations ; (3) Geographical barriers (the Tanggula and Bayan Har Mountains between Groups I and II).The uplift of the mountain triggered the differentiation of species, changed the genetic structure of alpine plants, and affected the evolution of alpine plants . The reabarriers . In addiE. sibiricus populations showed Group I in the northeast of QTP first split compare with other groups, and the divergence time was 16.08 Ma. The divergence time between Group III and Group II was 5.92 Ma (late Miocene-early pliocene). The lasting climatic aridification and other events following the Himalayan Motion during 12∼6 Ma possibly played a direct role in the diversification of Group II and Group III. In addition, the abiotic event during 16∼5.92 Ma that led to the differentiation of these groups may be QTP uplift in 13∼7 Ma  might have an important role as an escape region.In this study, the divergence time of the 44  13∼7 Ma . During pulation . The LD pulation . Group IE. sibiricus also proved this historical process. The LGM is the period when the global temperature is the lowest, and it is also the coldest period recently. After the LGM, the temperature rose, the climate became warmer and humid, the suitable living area expanded, and the populations began to expand. These new climatic conditions may have been conducive to the expansion of populations of cold-resistant plants. During the glacial periods, most species experienced adverse climatic conditions in high-altitude mountain areas. Then contracted into refuges at low latitudes, and their ranges expanded again after the Ice Age, which leads to species divergence or secondary contact evolution  can be found at: National Center for Biotechnology Information (NCBI) BioProject database under accession number XY, YXG, and YZG designed the research. DL, CBZ, LY, LC, MY, WG, XL, JY, and SB provided individuals. MH and JZ performed the research and analyzed the data. MH wrote the manuscripts. XY, SS, and CJZ revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.