Leishmania transmission, little is known about the organization of sand fly populations in their environment. Here, we used 11 previously described microsatellite markers to investigate the population genetic structure of Phlebotomus ariasi, the main vector of Leishmania infantum in the region of Montpellier (South of France). From May to October 2011, we captured 1,253 Ph. ariasi specimens using sticky traps in 17 sites in the North of Montpellier along a 14-km transect, and recorded the relevant environmental data . Among the selected microsatellite markers, we removed five loci because of stutter artifacts, absence of polymorphism, or non-neutral evolution. Multiple regression analyses showed the influence of altitude and hillside , and the absence of influence of geographic distance on the genetic data. The observed significant isolation by elevation suggested a population structure of Ph. ariasi organized in altitudinal ecotypes with substantial rates of migration and positive assortative mating. This organization has implications on sand fly ecology and pathogen transmission. Indeed, this structure might favor the global temporal and spatial stability of sand fly populations and the spread and increase of L. infantum cases in France. Our results highlight the necessity to consider sand fly populations at small scales to study their ecology and their impact on pathogens they transmit.Despite their role in  Phlebotomus sand flies are vectors of medically important pathogens, such as Leishmania, the causative agent of leishmaniasis1, and arthropod-borne viruses 2. Phlebotomus ariasi Tonnoir, 1921 is the predominant sand fly species in the French Cevennes region3, and one of the two proven vectors, with Phlebotomus perniciosus Newstead, 1911, of leishmaniasis, which is caused by Leishmania infantum in the South of France4. Sand flies are abundant in suburban and rural environments and are often close to human and domestic animal populations5. Phlebotomus ariasi is found resting in houses, animal sheds, caves and weep holes in walls, near roads, and in villages. This species has a wide geographic distribution, including many countries of the Western Mediterranean region, such as Algeria, France, Italy, Morocco, Portugal, Spain, and Tunisia16.17 and phlebovirus infections18 has increased in France, probably linked to the recent extension of the vector distribution. However, the biology and ecology of Ph. ariasi, one of the main vectors of L. infantum, remain poorly known.During the last 10 years, the risk of emergence or re-emergence of leishmaniasisPh. ariasi populations (sylvatic and domestic) in the Cevennes region19. Studies based on isoenzyme data21, random amplified polymorphic DNA22, and mitochondrial cytochrome b (cytb) gene sequences11 showed differences among Ph. ariasi populations in known leishmaniasis foci in Europe. Finally, two microsatellite loci and other genetic markers were used to understand Ph. ariasi expansion in Europe during the Pleistocene glacial cycles23.Only few studies have been performed on this species. Analysis of cuticular hydrocarbons highlighted the presence of two Ph. ariasi populations at a local scale, and the impact of environmental factors  on their spatial organization. For this purpose, sampling was performed in a well-documented area , in which sand fly populations were already ecologically24 and morphometrically25 described, and where human and canine leishmaniasis caused by L. infantum are endemic26. The analysis was carried out using 11 previously described microsatellite loci for Ph. ariasi27.However, no population genetics study tackled the influence of geographic  together with environmental  factors in the distribution of sand flies. Therefore, the aims of this work were to study the structure of In total, 1,253 sand flies were genotyped using the 11 loci described in the Supplementary Table PAss = 0.8568. Twenty individuals were grouped in one strongly differentiated cluster (cluster 21 with PAss = 1) .The partition found by STRUCTURE and STRUCTURE harvester with K = 2 is probably meaningless.Discriminant analysis of principal components (DAPC) identified 40 clusters with a mean assignment probability Cytb fragment sequencing of seven individuals from cluster 21  and one of the two outliers  showed 99–100% similarity with Ph. ariasi  and F-statistics analyses, see below) or 10 loci (excluding Aria1).Except when specified otherwise, the 22 individuals of Group A were removed from the analyses to prevent Wahlund effects.Aria1 locus was almost monomorphic (HS = 0.06) and was removed from the data. Using regression approach we detected a marginally not significant Short Allele Dominance (SAD) for the Aria14 locus . As SAD results from a preferential amplification of the shortest allele in heterozygous individuals28, the Aria14 microsatellite profile of each homozygous individual was checked again following recommendation suggested in De Meeûs et al.29, corrected, and then analyzed using GENEMAPPER 4.0. After correction, SAD could not be detected on this locus any longer .The Aria2, Aria3, Aria10, Aria14, and Aria15). The unilateral exact binomial test indicated that the proportion of significant stuttering was not significantly higher than the expected proportion under the null hypothesis for Aria2 , Aria3  and Aria14 . Nevertheless, it was marginally not significant for Aria10  and highly significant for Aria15 . Therefore, alleles close in size were pooled, avoiding pooling together only rare alleles  for these two loci as recommended29. As the Aria10 locus became monomorphic after pooling, this locus was removed from the analysis. For Aria15, alleles 117, 119 and 121 were pooled with allele 123; allele 127 was pooled with allele 125; and allele 131 was pooled with allele 129. However, after correction, the Micro-Checker analysis and the unilateral exact binomial test  highlighted that stuttering still affected this locus. Therefore, Aria 15 was also removed from the analysis.The Micro-Checker analysis suggested stutter artifacts at five loci  remained significant after Benjamini and Yekutieli adjustment. Aria11 and Aria12 displayed outlier profiles with strongly negative FIS  analysis indicated that 7 locus pairs (out of 11) displayed a significant LD 19.4%). Three of these locus pairs , DAPC and BAPS provided no clear structure. This suggested that the distinction between group A (the 22 outliers) and group B  only relied on the Aria1 locus , the proportion of significant LD tests was 13.3% (two locus pairs), but none of these tests remained significant after Benjamini and Yekutieli adjustment. There was still a rather small but significant heterozygote deficit in subsamples . Variation across loci appeared pronounced . There was no relationship between the number of missing data at each locus and the FIS values . The jackknife method showed that the loci standard error of FIS was 0.055 and was 27 times bigger than that for FST (0.002). The Micro-Checker analysis suggested the presence of null alleles at four loci . According to the criteria described in De Meeûs30  was compared with the one obtained without it (Group B) , and the difference was significant . This translated into a Wahlund effect when Group A individuals were included in the data. These individuals were thus kept excluded.To check the result stability again, the https://www2.unil.ch/popgen/softwares/pcagen.htm). The first two axes were significant using the broken stick criterion but not by permutation testing  . For axis 2, the minimum model was: axis2 ~ hillside + altitude + latitude + longitude. Altitude and hillside played a significant role  and represented 51% and 15% of the total deviance, respectively  was undertaken with PCAGen  see “” section 2) see “” sectionAs showed above, latitude and longitude had a weak effect on the genetic structure. Geographic parameters were thus removed from the model and isolation by altitudinal distance was tested using the Mantel test for each hillside separately .p value = 0.00605) and marginally non-significant for the Southern hillside . When combined with the generalized binomial procedure31, computed with the MultiTest V132, isolation by altitude appeared highly significant .When individuals were grouped by altitude levels (see Table Ne) was 69 individuals (range: 39 to 98) across subsamples and methods . There was no effect of hillside . Nei's unbiased estimator of genetic diversities33 was HS = 0.52 and HT = 0.524 for the subsamples and total sample, respectively, with Meirmans and Hedrick's GST’’ = 0.01734, and then Nem = (1 − GST’’)/4 GST’’ = 14 (immigrants per generation in each subpopulation assuming an island model of migration). The correlation between Nei's GST and HS was strongly negative . Therefore, according to Wang’s criterion35, it is more accurate using FST. FST with the ENA correction for null alleles and 95% confidence intervals (95% CI) after 5,000 bootstraps over loci were computed with FreeNA36. This provided FST = 0.014 , with a corresponding Nem = 18 .The average effective population size  allowed identifying loci with technical problems and/or loci that may not be neutral concerning natural selection. Consequently, we removed five of the eleven loci for various reasons including the presence of incurable stutter artifacts (Aria10 and Aria15), absence of polymorphism (Aria1), or non-neutral evolution (Aria11 and Aria12). One locus with SAD could be corrected.In this study, using 11 microsatellite markers, we investigated the genetic structure of Aria2, Aria3, Aria4, Aria5, Aria13, and Aria14) confirmed the 22 outliers (Group A). These outliers were morphologically similar to Ph. ariasi and displayed 99–100% cytb sequence similarities. A previous study, based on chromatographic analysis of cuticular components, provided evidence that there are two distinct Ph. ariasi populations in our study area: one predominantly sylvatic, and the second one domestic19. However, in our study, there was no geographical distribution difference between individuals from Group A and Group B.The Bayesian analyses (DAPC and BAPS) for all remaining loci . However, the distinction between Group A and Group B mainly depends on a single genetic marker (Aria1), which is fixed for different alleles in each group, while the other markers display a rather weak, though significant, signal. This result is supported by the dendrogram as the same tree was obtained with 6 or 10 loci (excluding Aria1). Additional molecular and biological studies will be necessary to test the cryptic species hypothesis . As the taxonomic status of Group A individuals could not be elucidated, they were removed from the analyses.This finding could reflect the existence of cryptic species, as already suspected or reported for other sand fly species  be explained by (1) null alleles; (2) allelic dropout; or (3) positive assortative mating. In the last case, this would require a mutual attraction of sexual partners based on a sufficient proportion of the genome to allow the hitchhiking of microsatellite markers, which are theoretically non-coding DNA sequences.We observed an important and significant heterozygote deficit, instead of the heterozygote excess expected for dioecious populations with random matingPh. ariasi population.The glm approach showed a strong influence of altitude and hillside, and a weak (if any) influence of geographic distances on the genetic data. The proportion of deviance (67%) explained by altitude and hillside suggested the existence of ecotypes in this 43 (page 272), with the equation a ≈ 2FIS/(1 + FIS). With a FIS = 0.057, the assortative mating in our population would be ≈ 0.11 (≈ 11% of zygotes produced).The very strong migration rate estimated in our study (more than 25% of the effective subpopulation size) is hard to reconcile with the emergence of genetically distinct ecotypes. Different scenarios can explain ecotype structuring despite the strong migration rate: (1) the death of most immigrants before they can reproduce (unlikely scenario), and (2) significant assortative mating that can also explain the heterozygote deficit observed in our data (see above). The rate of codominant assortative mating based on genes homogeneously distributed in the genome can be approximated with the same equation as for selfing, as described in Hartl et al.25. It has been demonstrated that wing configuration is associated with wing beating frequency and mate recognition45. These features would lead to a preferential choice of partners that could explain assortative mating and ecotype structuring. More studies are necessary to investigate the link between phenotypic (wing configuration) and genetic (microsatellite) diversity.It is worth noting that previous research on the same species in the same study area highlighted differences in wing phenotypes according to altitude and hillside46. It also could explain the global but slow  geographical expansion of sand flies observed in France17.This structuring has undoubtedly implications on the ecology and evolution of sand flies. This structure might favor the global stability of their populations. Indeed, local environmental changes would have no or low effect on the overall population because the ecotypes would be affected independently. The high levels of migration rates associated with the well-known low capacity of flying of sand flies would help to colonize and recolonize at small-scale environmentsLeishmania transmission. Previous studies suggested the existence of different imbricated Leishmania transmission cycles at very small scales48. However, due to the low flying capacities of sand flies49, migrants are expected to disperse mainly over short distances. Therefore, the spread and increase of L. infantum cases in France might be mainly due to the movement of infected hosts.This particular population structure can also influence Ph. ariasi presents a genetic structure in ecotypes. These data highlight the necessity to consider sand fly populations at small and specific scales to determine their ecology and its impact on Leishmania transmission. This structure may explain the long-term stability of sand fly populations.This study demonstrates for the first time that 50, Kaeuffer et al.51, Frantz et al.52, Blair et al.53, Bohling et al.54, Manangwa et al.42.Not many papers compare available clustering algorithms to date and it is thus hard to really understand when and why such algorithms will converge or diverge in the best partition they offer. For some attempt comparisons see: Latch et al.This type of study needs to be extended to other sand fly species. Indeed, the large diversities of sand fly populations worldwide may correlate with different ecological vector capacities and sensitivity to control measures.55, and is characterized by the presence of plant species typical of scrubland habitats5.The field study was performed in the South of France, on the “massif de l’Oiselette” hill situated between the “Hérault”  and “Arre”  valleys. Sand flies were sampled along a 14 km transect from the “Saint Julien de la Nef” to “Le Vigan” villages, including “Roquedur-le-haut” (at 601 m above sea level) Fig. ; Table 225. Stations were selected based on the paper of Rioux et al.3, first to allow the comparison with the data obtained 30 years ago in terms of species distribution, density and abundance24, second because these stations were distributed along the 14 km transect and represented the altitude and hillside diversity necessary for our population genetics study. Station 7 and Station 8 were not present in Rioux et al.3. These two stations are transitional between the two hillsides, and were thus added in the present study to obtain information on the consequences of transitional ecosystems. The stations were grouped according to altitude and hillside  between May and September 2011. Seventeen localities (sampling stations) were sampled  and mounted in chloral gum59 described in Prudhomme et al.27. Extraction was performed at the UMR “Unité des Virus Emergents” . Each entire sand fly was ground using a Mixer Mill MM300  with one 3-mm tungsten bead in 200 μL Eagle’s Minimal Essential Medium at a frequency of 30 cycles s−1 for 3 min. A volume of 140 μL of each sample was then used for DNA purification by adding the Chelex resin suspension59 or by using the Eppendorf epMotion 5075 working station and the Macherey–Nagel NucleoSpin 96 Virus kit.Sand fly DNA was extracted using the Chelex method, 11 of the most polymorphic loci were selected among the previously described 16 microsatellite markers for Ph. ariasi27. Each 25 μL reaction mix included 1 pmol of forward  and reverse primers, 5 ng of sand fly DNA sample, 6 pmol of dNTP mix, 2.5 μL of 10X buffer, and 0.25 μL of Taq polymerase . DNA was amplified in a thermal cycler using the following conditions: initial denaturation step at 95 °C for 10 min, followed by 40 cycles at 95 °C for 30 s, the specific annealing temperature of each locus27 for 30 s, 72 °C for 1 min, and a final extension step at 72 °C for 10 min. For genotyping, 1 μL of PCR product was added into a standard loading mix  (both from APPLIED BIOSYSTEMS) and sequenced on an ABI Prism 3130 Genetic Analyzer (APPLIED BIOSYSTEMS) automated sequencer. Profiles were read and analyzed using GENEMAPPER 4.0 .Based on the microsatellite position in the sequence and the repeated pattern structurePh. ariasi samples was used to check the species identification (see “cytb gene fragment using the primers N1N-PDR [5′-CA(T/C) ATT CAA CC(A/T) GAA TGA TA-3′] and C3B-PDR [5′-GGT A(C/T)(A/T) TTG CCT CGA (T/A)TT CG(T/A) TAT GA-3′], according to a previously published protocol61 and the following conditions: initial denaturation at 94 °C of 3 min; 5 cycles of denaturation (94 °C for 30 s), annealing (40 °C for 60 s), and extension (68 °C for 60 s), followed by 40 cycles of denaturation (94 °C for 60 s), annealing (44 °C for 60 s) and extension (68 °C for 60 s), and a final extension (68 °C for 10 min). Direct sequencing in both directions was performed by Eurofins Genomics.DNA from a subset of ion see “” section62 that allowed their transformation into the formats needed for the different analyses. Samples with more than 50% of missing data were removed from the analyses.Microsatellite raw data were formatted for CREATEPh. ariasi populations, data were analyzed at different scales: hillside, altitude, and station. This analysis was performed with the loci selected after Linkage Disequilibrium (LD) and F-statistics analyses 64 was performed using the adegenet65 package for R66. This was followed by a Bayesian Analysis of Population Structure , and a STRUCTURE (version 2.3.4) analysis69 . STRUCTURE HARVESTER vA.270 was used to visualize the STRUCTURE analysis results, examine the ad hoc ΔK statistic, and determine the optimal number of clusters.As the clustering method accuracy can vary depending on the statistical properties of specific software programs and datasets71 from a Cavalli-Sforza and Edward's chord distance (DCSE)72 matrix as recommended73, between subsamples defined as combinations of the group obtained by Bayesian clustering and sampling stations. Because null alleles were suspected to occur, DCSE was computed with the INA correction with FreeNA36, after recoding missing data into null homozygotes following authors' recommendation.Finally, a dendrogram (NJTree) was built with MEGA 7G-based permutation test with 10,000 randomizations. This test was performed with FSTAT 2.9.4, an updated version of FSTAT 2.9.374 available at https://www.t-de-meeus.fr/ProgMeeusGB.html. This procedure is the most powerful for testing LD across different subsamples32. There are as many p values as locus pairs. Then, the False Discovery Rate (FDR) correction for multiple non-independent tests described by Benjamini et al.75 was applied with R 3.5.166.LD between each locus pair was tested with the F statistics76 were estimated with the Weir and Cockerham's unbiased estimators77. Significant departure from 0, for F statistics, was tested by randomizing alleles between individuals within subsamples  or individuals between subsamples within the total sample (population subdivision test). The p value corresponded to the number of times a statistic measured in randomized samples was as big as (or bigger than) the observed one . For local panmixia, the statistic used was f (Weir and Cockerham's FIS estimator). To test for subdivision, the G-based test78 was used. According to De Meeûs et al.32, the G-based test is the most powerful procedure when combining tests across loci.Wright's F-statistics were computed using the jackknife over populations method for each locus or 5,000 bootstraps over loci for the averages, as described in De Meeûs et al.79. Parameter estimates, testing, jackknife and bootstrap computations were done with FSTAT 2.9.4.The 95% confidence intervals (CI) of 30 and Manangwa et al.42 was used to discriminate demographic from technical causes of significant homozygote excess and LD. In the case of null alleles, FIS and FST artificially increase and a positive correlation is expected between the statistics, FIS standard error is at least twice that of FST, and a positive correlation is also expected between FIS and the number of missing data (putative null homozygotes). Correlations were tested with the unilateral Spearman’s rank correlation test in R 3.5.166. The frequency of null alleles was assessed with Micro-Checker v2.2.380 using Brookfield's second method81.The determination procedure described by De Meeûs80. A unilateral exact binomial test was used with R 3.5.166 to determine whether the observed proportion of significant stutter artifacts was greater than the expected 5% under the null hypothesis.The presence of stutter artifacts at each locus in each subsample was evaluated with Micro-Checker v2.2.342. The correlation between allele size and FIT was tested with the unilateral Spearman’s rank correlation test in R 3.5.166. In the case of SAD, a negative correlation is expected between allele size and FIT42.Short Allele Dominance (SAD) was assessed with the method described by Manangwa et al.29. Stuttering was addressed by pooling alleles close in size. To avoid a spurious increase of heterozygosity, each pooled group contained at least one frequent allele . SAD was addressed by going back to the chromatograms of homozygous individuals and trying to find a larger size micro-peak that could have been missed in the first reading.In the case of significant stutter artifacts or SAD, the incriminated loci were corrected using the method described by De Meeûs et al.FIS values were compared between subsample groups using the Wilcoxon signed rank test for paired data (the locus was the pairing unit).In some instances, https://www2.unil.ch/popgen/softwares/pcagen.htm). The significance of the first axes was tested using the broken stick criterion82 and 10,000 permutations of individuals across subsamples. The metric of each axis divided by the total genetic diversity corresponded to Weir & Cockerham's Theta (FST estimator) . The coordinates of subsamples for each significant axis were used as the response variable of generalized linear models (glm). General models were as follows: axis i ~ latitude + longitude + altitude + hillside + latitude: hillside + longitude:hillside + altitude:hillside . All glm's were done using R version 3.5.166, with the package rcmdr (R-commander)84. Model selection was performed using a forward stepwise model selection procedure and the Akaike Information Criterion85.A principal component analysis (PCA) was done with PCAGen 1.2.1  was tested by examining the differences between models  with analyses of variances. As the entry order of explanatory variables matters in R analyses, the mean partial 86 using the Euclidian distance computed with the Universal Transverse Mercator (UTM) coordinates 88 in FSTAT 2.9.4. As the Mantel test in FSTAT is bilateral by default, the p value was halved in the case of positive slope, or computed as: “1-/2” in the case of negative slope, to obtain unilateral p values for a positive slope.Geographic distances were computed with Genepop version 4.7.0es Table . Geneticp values obtained were then combined with the generalized binomial test31, with MultiTest32, taking into account all tests as recommended when the number of combined tests k < 489.The relationships between genetic and altitudinal distances were also tested in the conditions described above. To avoid any interaction, the analyses for the South and North hillsides were done separately. The two Ne). The results obtained with these methods were averaged and weighted by the number of times a usable value  was obtained. To obtain a range of possible Ne values, the same approach was performed for the minimum and maximum values obtained with each method.Four different methods were used to estimate the effective population sizes  to compute Ne. The average across the different values obtained with these frequencies was computed.First, NeEstimator v2Ne95 using Estim v2.296 (available at: https://www.t-demeeus.fr/ProgMeeusGB.html).The intra- and inter-loci correlation method was also used to compute 41 was used for each locus that displayed heterozygote excess, as follows: Ne = [− 1/(2 FIS)] −FIS/(1 + FIS).Finally, the heterozygote-excess method (expected in dioecious populations) described by BallouxGST" = n × (HT − HS)/[(n × HT − HS) × (1 − HS)]34, where HT and HS are Nei's unbiased estimates of genetic diversity in the total sample or within subsamples, respectively33, and n is the number of subsamples. Genetic diversities were estimated with FSTAT 2.9.4. Assuming an island model, this value was then used to obtain an approximation for the number of immigrants within subpopulations as Nem = (1 − GST")/(4 × GST"). However, according to Wang’s criterion35 if Nei’s GST33 is negatively correlated with HS, it is wiser to use FST for this computation.To determine the number of immigrants, the standardized differentiation index described by Meirmans and Hedrick was computed to correct for polymorphism excess: Supplementary informationSupplementary Table


Corydalis hendersonii Hemsl. (Papaveraceae). We sequenced four cpDNA fragments  and also the nuclear (ITS) region in 368 individuals from 30 populations across the species' range. The network and phylogenetic analysis based on cpDNA variations identified 15 chlorotypes that cluster into three distinct clades. However, our nuclear DNA results demonstrated that there were four genetic/geographical groups within C. hendersonii. Some common and highly divergent cpDNA and ITS haplotypes were distributed in the populations of central and northeastern Tibetan Plateau, and the highest nucleotide diversity and genetic differentiation were detected in the central region. Demographic tests further indicated that the populations of southwestern and western Tibet may have experienced recent range expansion, which most likely occurred during the last glacial maximum (LGM) and continued its expansion after the beginning of the Holocene. These two different groups of this species may have derived from potential refugia that existed in the central and/or northeastern regions of Tibet during recent interglacial periods. In addition, our AMOVA analyses detected high genetic differentiation along with the whole sampling range. Also, distinct phylogeographic structures were detected among populations of C. hendersonii based on both of cpDNA and ITS variation. These findings shed new light on the importance of climatic oscillations during Quaternary and complex local topography as causes of intraspecific diversification and demographic changes within cold-tolerant herbs in the Tibetan Plateau biodiversity hotspot.In response to past climatic changes, the species with different habits or adaptive traits likely have experienced very different evolutionary histories, especially for species that restricted to high mountain areas. In order to trace how Quaternary climatic oscillations affected range distributions and intraspecific divergence of such alpine plants on the Tibetan Plateau, here, we investigated maternally inherited chloroplast DNA (cpDNA) markers and biparentally inherited nuclear ribosomal internal transcribed spacer (ITS) DNA variations and aimed to explore the phylogeographic history of the endemic alpine species  Climate oscillations during the Quaternary glaciations have modified the geographical distributions of most species in the temperate zone of the northern hemisphere markedly, generating varying degrees of range expansion and contraction. Such demographic changes undoubtedly shaped the geographical patterns of genetic variation within and among populations . In the in situ glacial survival on the southern plateau platform linked to the complex topography where populations could retreat to the valley bottom before expanding to the upper slopes during interglacials , an alpine perennial herb endemic to the Tibetan Plateau, and is also a typical representative of the rare and endangered Tibetan medical plant. It currently grows at the river or rocky beach throughout the plateau, however, the distribution of this plant is limited to the high mountain areas at an altitude of 4,200–5,500 m  and nuclear ribosomal internal transcribed spacer (ITS) variations in the populations. Our major goals are: (1) to examine whether the interior Plateau serve as potential refugia for C. hendersonii during interglacial periods, (2) to reveal range dynamics of C. hendersonii in the history base on these two sets of genetic markers, and (3) to detect whether there is genetic divergence within the species largely due to the effects of climate oscillations during past glacial and interglacial periods.–5,500 m . Its ﬂow–5,500 m . The mat–5,500 m , which p–5,500 m . In the C. hendersonii were collected from 368 individuals in 30 natural populations, covering the entire distribution range of this species. Five populations on the northeastern, eight populations on the central, thirteen populations on the southwestern, and five populations on the western Tibet were sampled  and one nuclear ribosomal ITS fragments were amplified and sequenced following the suggested primers  or the modified CTAB procedure . Four cp primers . DetaileK that needs to be optimized, this method uses simulated annealing procedures to seek the best clustering option that can be defined between groups of populations among group genetic variation coefficients (FCT). We tested values for K in the range of 2–16, and the initial condition was set to 100 with 10,000 iterations. The conﬁguration with the largest associated FCT value after the 100 independent simulated annealing processes was retained as the best grouping of populations.In order to search for partitions of sampling sites that were genetically homogenous but maximally differentiated from each other, firstly, a spatial analysis of molecular variance (SAMOVA) was conducted with SAMOVA 2.0 , based oHE) and nucleotide diversity (π) were estimated by the software DnaSp version 5.0 (HS), total gene diversity (HT), and the coefficients of differentiation between GST and NST were estimated for four genetic/geographical groups  as well as the species overall based on cpDNA and ITS markers respectively, using the PERMUT software with 1,000 permutations . We performed 20 runs with a burn-in of 200,000 and 500,000 iterations, and used the program DISTRUCT version 1.1  as outgroups. The rapid-bootstrapping algorithm  with the thorough ML search option  was used to search for the best-scoring ML tree. A Bayesian approach was further used to estimate the relationship between haplotypes as implemented in the software MrBayes version 3.2.7 , using 1,000 parametric bootstrap replicates. When the hypothesis of rapid expansion was not rejected (see Results), we converted the parameter value τ into estimates of time since expansion (t) in years according to the formula t = τ/2u  substitution rates of 1.0 × 10-9 and 8.24 × 10-9 substitutions per site per year for cpDNA in angiosperms  method, a Bayesian approach for inferring past population size fluctuations from genetic data, as implemented in the software BEAST version 2.3.2 . BuildinC. hendersonii, we employed ecology niche models to predict the distributions of C. hendersonii at the present time, the Last Glacial Maximum  and the Last Inter-Glacial , respectively. In addition to the distribution records in this study , only two populations (populations 1 and 2) from the northeastern Tibet formed the last one group. However, this population grouping showed a weak geographical relationship between four genetic groups. In the SAMOVA analysis based on ITS dataset, FCT values also increased progressively as K was increased. For the first three FCT values, the FCT value was highest when K = 4. When K was between 5 and 16, at least one group consisted of only a single population. Therefore, in our dataset, C. hendersonii population could optimally be placed into four groups. These four groups included the northeastern group (populations 1 to 5), central group (populations 6 to 13), southwestern group (populations 14 to 26), and western group (populations 27 to 30)  and eight substitutions. The sequence length and variation for each chloroplast DNA fragment are shown in the C. hendersonii across the 30 surveyed populations (HE = 0.596 × 10-3) and nucleotide diversity (π = 0.38 × 10-3) at the species level. In each genetic group, the average diversity was highest in the central populations (π = 0.058 × 10-3) and lowest in western populations (π = 0.025 × 10-3), while populations from the eastern (π = 0.036 × 10-3) and southwestern (π = 0.037 × 10-3) Tibet showed intermediate levels  and the average gene diversity (HS) were the highest for the central populations , which also showed the highest between-population differentiation (GST = 0.894) , however, no obvious structure was detected within the four genetic groups because all comparisons failed to detect signiﬁcant larger NST values than GST ; 76.76% of the variation was explained if the C. hendersonii populations were grouped into four ITS SAMOVA groups  and southwestern  Tibet. FST values among populations within each geographic region were lower than that between the four geographic regions, with the lowest value having been detected within the southwestern populations .Among the four genetic groups of versity HT and theversity HT and theversity HT and theversity HT and theπ) overall populations ranged from 0.57 × 10-3 to 7.84 × 10-3. In each genetic group, the average diversity was highest in the central populations (π = 4.56 × 10-3) and lowest in eastern populations (π = 1.70 × 10-3), and populations from the southwestern (π = 3.43 × 10-3) and western (π = 3.24 × 10-3) Tibet showed intermediate levels  Table S9Two distinct groups were discerned in the 49 ITS haplotypes by the parsimony network  and the average gene diversity (HS) were the highest for the central populations . Signiﬁcant phylogeographic structures existed at the range-wide scale  as well as central  and western populations , however, no obvious structure was detected within the northeastern and southwestern populations because all comparisons failed to detect signiﬁcant larger NST values than GST  was detected between northeastern and western groups.Among the four genetic groups of versity HT and theversity HT and theversity HT and theversity HT and theC. hendersonii was identified by the Bayesian clustering algorithm tests using the ITS dataset, which revealed that the most likely number of clusters was K = 3 when we used △K statistics. These results suggested a hierarchical structure in the data  were mainly found in these populations  exhibited high levels of genetic diversity  together with the star-like phylogeny patterns radiating from one dominant haplotype C3 , and a distinct phylogeographic structure was detected among populations in C. hendersonii . Those results were consistent with previous studies on alpine taxa which suggested that high genetic differentiation among populations within a species was usually coupled with the obvious population structure  compared to that suggested by cpDNA dataset, a distinct phylogeographic structure was also detected among populations in C. hendersonii . Fst analysis also showed high intraspecific divergences between each pair of the four genetic groups  gene pool with those from other regions, especially from the populations of northeastern Tibet, showing a distinct signal of genetic admixture  and dispersing style (seeds vs. both pollen and seeds) among populations, as well as the fast substitution rate and the concerted evolution of the ITS sequences , the Natural Science Foundation of Qinghai Science & Technology Department (2019-ZJ-907), the State key laboratory construction project (2015DQ870717), the Open Project of State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University (2017-ZZ-03), the Natural Science Basic Research Plan in Shaanxi Province of China (2019JQ-051 and 2019SF-307), and the Fundamental Research Funds for the Central Universities (GK201703034).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.


Pinus strobiformis and P. flexilis. Using a multifaceted approach emphasising the spatial and environmental patterns of linkage disequilibrium and ancestry enrichment, we identified recently introgressed and background genetic variants to be driving adaptive evolution along different environmental gradients. Specifically, recently introgressed variants from P. flexilis were favoured along freeze-related environmental gradients, while background variants were favoured along water availability-related gradients. We posit that such mosaics of allelic variants within conifer hybrid zones will confer upon them greater resilience to ongoing and future environmental change and can be a key resource for conservation efforts.Extant conifer species may be susceptible to rapid environmental change owing to their long generation times, but could also be resilient due to high levels of standing genetic diversity. Hybridisation between closely related species can increase genetic diversity and generate novel allelic combinations capable of fuelling adaptive evolution. Our study unravelled the genetic architecture of adaptive evolution in a conifer hybrid zone formed between  Mitra Menon et al. study the hybrid zone of two conifer species in New Mexico, and report that adaptation to cooler environments is associated with introgressed variants, whereas drier environments are associated with background genetic variants. These results may indicate how mosaics of allelic variants could show greater resilience to ongoing and future environmental change. Given that conifers exhibit high levels of evolutionary conservation5, weak reproductive isolating barriers6, large effective population sizes (Ne)8 and high fecundities, hybridisation among conifers may less likely be maladaptive and could offer a mosaic of genomic variants that provide the raw material for rapid evolution10. Genomic mosaics in hybrid zones can be generated by: (1) introgressed variants that have recombined with the genomic background of the hybrid individuals and by (2) background genetic variants that arose denovo in the hybrid zone or are segregating across the range of either or both parental species. Most investigations of adaptive introgression have focussed on recently introgressed variants primarily due to their distinct and easily detectable molecular signatures, but see ref. 11. Studies in trees such as Populus and Quercus provide strong evidence for adaptive introgression and the generation of novel co-adapted allelic combinations through a buildup of association between recently introgressed variants and background genetic variants15. While we are rapidly accumulating data on the importance of introgression across several model and non-model organisms, information on species with much larger genomes, such as conifers, is rare17. Larger genome size is hypothesised to influence the genetic architecture of adaptive traits by minimising hard sweeps and limiting genic enrichment of adaptive loci19. Genetic architectures in species with larger genome sizes thus typically evolve via subtle and coordinated allele frequency shifts of a large number of loci (i.e. polygenic) rather than drastic allele frequency changes at a handful of loci. Across both model and non-model systems, the relative contribution of introgression to the evolution of polygenic trait architectures is still in its infancy. To this end, conifers present ideal systems to investigate how large genome sizes and their aforementioned life-history characteristics interact to influence the genetic architecture of adaptive introgression within natural hybrid zones.Despite growing evidence for hybridisation across The Tree of Life, the evolutionary consequences of hybridisation and introgression remain contentious due to the complex interplay between intrinsic and extrinsic selection pressures20, Pinus strobiformis and Pinus flexilis. These species have experienced recent ecological speciation with gene flow21. Pinus strobiformis is a key component of the montane mixed conifer ecosystems ranging from Jalisco in southern Mexico to southwestern Colorado in the USA22. Pinus flexilis also inhabits montane ecosystems but is often seen dominating subalpine and tree line habitats24. P. flexilis therefore is primarily differentiated from P. strobiformis by occurring in cooler environments26. Recent ecological niche divergence between the two species is also supported by strong among population differentiation at candidate genes associated with drought stress responses27 but weak overall level of genomic differentiation21. Populations at the northern range of P. strobiformis (as classified by ref. 28) primarily inhabit fragmented sky-islands and contain P. flexilis–P. strobiformis hybrids exhibiting a smooth clinal transition in genome-wide ancestry21 and at several morphological traits30. Despite its fragmented and disjunct distribution . Another key source of adaptive evolution in the hybrid zone that we focus on are variants segregating in the hybrid populations, hereon referred to as “background genetic variants”. The low overall genomic divergence across the species complex restricts us from discerning whether these background genetic variants arose denovo in the hybrid zone or were present in pure P. strobiformis with which the hybrid individuals share greater ancestry21. Thus, all variants not identified as “recently introgressed” are declared as “background genetic variants”. Our goal is to evaluate the relative contribution of these two classes of genomic variants towards adaptive evolution along an array of environmental gradients that are classified as most divergent and least divergent . Second, the hybrid zone populations occur in areas experiencing severe and sporadic drought events relative to that noted in the range of either parentals21. Thus, “background genetic variants” will have a higher contribution towards adaptive evolution along environmental gradients that are least divergent between the parentals and are associated with water availability, such as spring relative humidity (RH_sp). We addressed the above two hypotheses by using a multifaceted approach hinging on differences in expected genetic architectures of adaptive evolution from background genetic variants and recently introgressed variants along several environmental gradients  demonstrated a gradual latitudinal transition in genome-wide ancestry from P. strobiformis in the south to P. flexilis in the north  was 0.0057, while that among populations (FST) was 0.012. The groups here are defined as pure P. flexilis, pure P. strobiformis and the hybrid zone. Group-specific heterozygosities as well as estimates of population differentiation within a group displayed very few differences  and Spearman’s correlation coefficient (|ρ|) across three independent runs of Bayenv identified 500 unique SNPs as associated with 88 environmental gradients  as the reference database, 60% of the freeze-related contigs mapped to transposable elements, while 30% mapped to scaffolds containing abiotic stress tolerance encoding genes (Supplementary Data We utilised the genotype-environment association approach (GEA) implemented within Bayenv2 to identify loci displaying signatures of adaptive evolution within the hybrid zonementclass2pt{minimues Fig. . OverallFST for sets of outlier SNPs associated with 72 environmental gradients was significantly greater than the empirical null distribution of FST (p < 0.05) generated by bootstrapping putatively neutral SNPs per gradient . On average across sets of outlier SNPs, FST was three times larger than the global estimate and FCT was four times larger. Median LD (measured as r2) was significantly greater than the global average (p < 0.05) for outlier SNPs associated with 83 environmental gradients  with 72,889 SNPs biallelic across the hybrid zone to understand the contribution of environment, population structure, geography and ancestry towards the overall pattern of among population genetic variation. Since our main result was not influenced by pruning geography that was strongly correlated with other predictor matrices (see “Methods”), here we only report the findings from using all four predictor matrices. We were also not interested in identifying causal variants through RDA which is most problematic with using correlated predictors41. We show that estimates of ancestry were confounded with environment, as well as indirectly with other predictors through their relationship with environment in explaining the observed genotypic variance.Results from the GEA above corroborate previous studies demonstrating strong population differentiation for drought associated seedling traitsR2adj = 0.027, F19,78 = 1.14) yet significant (p < 0.001) amount of overall genotypic variance. Low R2adj could have resulted from weak population structure and the use of several putatively neutral genome-wide SNPs42. Of the series of partial models fitted within RDA, most were significant and the variance explained by the model conditioned on population structure (2.5%) closely followed the variance explained by the full model . The interaction effect model had an R2adj 1.6-fold larger than the model without it , indicating that outlier SNPs identified through Bayenv were likely confounded with spatial variation in ancestry due to hybridisation and ecological differentiation between the two parentals.The full model within RDA which included all four predictor matrices explained a small  and LD variance partitioning, we reveal that Bayenv outliers were a product of selection on recently introgressed and background genetic variants. Within LDna, we used 100 LD matrices, each containing 500 Bayenv outlier SNPs and 500 putatively neutral SNPs, to identify clusters of SNPs that are strongly associated even after adjusting for the overall elevated LD due to neutral introgression. Across these 100 replicate sets of LD matrices, we obtained a total of 464 outlier SNP clusters (OCs). To determine the dominant selective pressure generating these OCs, we evaluated the proportion of Bayenv outlier SNPs in each cluster and the environmental gradient they were associated with, as determined in Bayenv. Overall, SNPs associated with environmental gradients most divergent between the two species were overrepresented in the OCs as identified through LDna  and evaluated their association with environmental and geographical distances between populations. Adaptive evolution should increase DST for loci associated with the environmental gradients differentiating hybrid zone populations due to locally divergent selection pressures44. In contrast, adaptive evolution within the hybrid zone should increase DIS for environmentally associated loci that covary with the latitudinal axis of introgression and differentiate the two parental species45. Using the full multivariate regression model containing the total effect of environment and geography, we identified solar radiation and water availability-related gradients to be the strongest predictors of DST, while freeze-related gradients had the highest R2 for DIS (Table DIS) could arise in the absence of selection when populations experience recent and ongoing introgression46. To account for this, we implemented partial regression models that identify the primary components driving the elevated R2 values for DST and DIS along each environmental gradient. Environmental gradients listed below are described in Supplementary Data DST, the highest R2 was noted for SNPs associated with RAD_sm, whereas for DIS, winter degree days below zero (DD_0_wt) associated SNPs had the highest value. Second, for the pure effect of geography on DST, SNPs associated with CMD_at and CECSOL_s1 had the highest R2, while PAS_wt associated SNPs had the highest R2 when DIS was the response variable  latitudinal variation in ancestry and (3) environmental gradients related to freezing events covarying with latitude.In addition to the prevalence of Bayenv SNPs in the OCs identified above, the latitudinal gradient in hybrid ancestry and ecological niche divergence between the parentals in our study21 Fig.  could cale Table . Overallted Fig. . We alsonts Fig. . SpecifiP. flexilis. The latter set of SNPs were identified by estimating per tree parental genotype probability through genomic cline analyses. Our study highlights that recently introgressed variants from P. flexilis facilitated adaptive evolution along freeze-related environmental gradients, while background genetic variants drove adaptive evolution along water availability-related gradients. Of the 62,992 SNPs that were biallelic across the hybrid zone and biallelic across both parental populations, 28,763 were significantly introgressed from P. flexilis in at least 20% of the individuals. Since our assessment of adaptive introgression was not sensitive to the individual-based cutoff (Supplementary methods and results A), we present results only for SNPs that were significantly introgressed across at least 20% of the individuals. We note a higher than expected fold enrichment (FE) of P. flexilis ancestry for SNPs associated with freeze-related gradients and others that were strongly divergent between the two parental species . Similarly, for populations dominated by P. flexilis genomic background, P. strobiformis variants could be considered adaptively introgressed along environmental gradients that are similar to those in the range of pure P. strobiformis. We chose not to focus on this category of adaptively introgressed variants because (1) only 136 individuals across 26 populations in our study contained dominantly P. flexilis ancestry (>70%), underpowering any analysis of P. strobiformis adaptive introgression, (2) contemporary gene flow was absent between pure P. strobiformis and the hybrid zone21 and (3) old introgressed variants from P. strobiformis that have recombined into the hybrid genomic background following several generations of mixing would be difficult to identify in the absence of a linkage map, thereby increasing false positives. Overall, the interaction between genomic ancestry and degree of environmental similarity with either parental species was also supported by variance partitioning approaches implemented in RDA and matrix regression of Ohta’s D-statistics. Both approaches indicated a strong contribution of the confounding effect between environment and ancestry towards the granular spatial variation in genetic diversity across the hybrid zone  with environmental gradients all highlight the importance of covariance in allele frequencies facilitating adaptive evolution in species with large genome sizes and large Ne62. This polygenic architecture was prevalent across both recently introgressed and background genetic variants. The co-occurrence of freeze and water availability-related SNPs in some of the OCs identified through LDna echoes the role of hybridisation in generating novel, putatively adaptive gene complexes unique to hybrid zones64. These novel gene complexes likely resulted from ongoing asymmetric introgression from P. flexilis providing new allelic variants into a genomic background dominated by P. strobiformis and may confer a competitive advantage to hybrid populations under new selective regimes generated by rapidly changing climatic conditions. Together, the utilisation of random sets of SNPs within LDna and the permutation approach in enrichment analysis grants us confidence that despite the possibility of false positives due to strong confounding between ancestry and environmental selection gradients, several of the SNPs in the OCs are either physically linked to the true candidates or themselves the target of adaptive introgression. Further, none of the SNPs shared in an OC were from the same contig, indicating that they are less likely to be outliers due to physical linkage. The low R2 reported in our RDA and in LD variance partitioning could be concerning and we acknowledge that this could reflect several aspects of the genetic architecture that could not be characterised in this study. However, we would like to emphasise that low R2 could also be a result of weak population structure and an average rapid decay of LD that is common to most conifers65.Ongoing introgression causes localised increases in LD mimicking patterns expected under strong selection. Thus, in addition to the already expected polygenic architecture of local adaptation in species experiencing high gene flow66 or with the coupling of intrinsic and extrinsic barrier loci40. Purifying selection can generate weakly deleterious mutations, known to contribute significantly towards standing genetic diversity68. Relative to studies conducted in non-hybrid populations, those in hybrid zones may more likely to experience heterogeneity in the strength of purifying selection due to the interaction of introgressed alleles with different genomic backgrounds. Identifying the proportion of loci under spatially varying purifying selection (dependent or independent of environment) will have to await further development of population genomic resources in conifers (but see ref. 69). Coupling between intrinsic and extrinsic barriers is more likely to generate signatures that parallel local adaptation in tension zones formed by secondary contact40. In the absence of secondary contact in our study system21, such coupling might occur in patchy hybrid zones where certain populations are predominantly differentiated along environmental gradients that are involved in ecological speciation between P. strobiformis and P. flexilis. This could be driving elevated FST values of some SNPs associated with freezing temperatures and water availability  are likely to pick up longer term processes70 that could have identified some of the coupled loci mentioned above, if they exist. However, by combining genomic cline analyses with environmental associations we were able to capture the contribution of shorter term processes such as ongoing introgression71 towards adaptive evolution within the hybrid zone.Since our study was conducted in a hybrid zone, signatures of local adaptation and clinal change in allele frequencies detected here could be confounded with spatial variation in the strength of purifying selectionnts Fig. . Approac72. However, the uniform genome sampling provided through RADseq like approaches reduces ascertainment bias and false positives73. Adding to the influence of genome size on the architecture of adaptive evolution19 and regulatory regions being enriched for candidate loci18, the inverse relationship between Ne and the frequency of neutral mutations74 indicates that selection could be more prevalent in conifers due to their large Ne. Thus, compared to ddRADseq based association studies conducted in organisms with smaller genomes and lower Ne, those in conifers may more likely pick up true signals of adaptive evolution, even if they are only partially characterised due to low genomic coverage. As genomic resources develop further in conifer systems, combining coding and non-coding regions will provide a holistic landscape of the architecture underlying adaptive evolution in the P. strobiformis–P. flexilis hybrid zone. These will specifically be useful in ascertaining whether introgressed segments of the genome that are under positive selection in certain hybrid populations are in fact close to genes encoding for freeze tolerance. Further, identifying whether loci exhibiting both high environmental associations and strong among species divergence are located close to genes such as Leucine rich repeats or other candidate often involved in speciation75 will help us disentangle the degree to which some of the SNPs identified here were involved in maintaining species boundaries.Utilisation of ddRADseq datasets for detecting signals of local adaptation has been criticised due to low genomic coverage, specifically of genic regions, and high error rates64. Our work identifies the environmental gradients associated with adaptive evolution in a conifer hybrid zone and quantifies the relative importance of background genetic and recently introgressed variants. The combination of freeze and water availability-related SNPs in P. strobiformis–P. flexilis hybrid populations potentially make them an ideal seed source for conservation efforts focused on climate change mitigation. Preliminary results from ongoing common garden assays of P. strobiformis76 dovetail with our inference of higher drought and freeze tolerance in the hybrid zone populations likely generated through the presence of P. flexilis alleles in a genomic background dominated by P. strobiformis. Beyond the hybrid zone literature, we corroborate theoretical and empirical studies demonstrating that gene flow between ecologically differentiated populations or species can buffer population decline by increasing genetic diversity and providing novel allelic combinations.Hybridisation and introgression are pervasive across the Tree of Life. Several studies utilising genomic data have revealed a key role of introgressed variants in facilitating adaptation through the generation of novel co-adapted allelic combinations and by increasing standing genetic diversityPinus strobiformis, 12 populations (4–10 trees per population) from pure P. flexilis distributed from northern New Mexico to southern Wyoming and 98 populations (6–10 trees per population) from the P. strobiformis–P. flexilis hybrid zone . Classification of populations into pure parentals and hybrids was based on ref. 21 and further refined here using NGSAdmix78 with default parameter settings, minor allele frequency of 0.001 and K set as 2 to represent the two parentals. The reassessment done in this study is in lines with previous work indicating a gradual latitudinal transition in ancestry within the hybrid zone  from the Mexican range and parts of New Mexico containing pure one Fig. . To asseone Fig. . For bot81. Following size selection and isolation of pooled DNA from each library, we performed single-end sequencing of one library per lane (150 bp reads). All sequencing was conducted at Novogene using the Illumina HiSeq 4000 platform. The resulting FASTQ files, one per lane, were processed using dDocent82 and a series of custom post-filtering steps (Supplementary methods and results C). This process yielded a total of 73,243 SNPs, which were used as the starting dataset for all subsequent analyses.We extracted genomic DNA from 1122 trees sampled across 132 populations using the DNeasy Plant Kit (Qiagen). Multiplexed ddRADseq libraries were prepared by pooling 96 trees per library, following the procedure detailed in ref. 83 for the 1981–2010 normal. We also added ten 1-km resolution soil variables from SoilGrids v.0.5.384. Whereas the analyses listed below used all 88 environmental gradients  and their strength of association was given by the 87 to map the RADtags containing freeze associated SNPs to the fragmented genome assembly of Pinus lambertiana (v.1.0) using an e-value threshold of 1e−20. Multiple hits on the reference genome were resolved by retaining only the top hit for each RADtag.We pruned our genomic dataset to retain only 72,889 biallelic SNPs from across the hybrid zone. Prior to conducting environmental associations using Bayenv2FST, multilocus FCT and median LD. We determined whether the focal sets fell outside the 95th percentile of the bootstrapped distribution generated using equal numbers of putatively neutral SNPs per environmental gradient. Bootstrapped sets were matched in two-dimensional bins based on the observed values of minor allele frequencies and proportions of missing data for each observed focal set. LD was measured as the squared pairwise correlation coefficient (r2) obtained through the genetics package v.1.3.8.188 in R. We also utilised NGSAdmix with default settings and K = 2 to evaluate whether the overall pattern of population structure was influenced by the Bayenv outlier SNPs identified here.To further understand genetic architectures of these outlier SNPs, we estimated multilocus P. flexilis and hybrid zone populations21, it is likely that the focal SNP sets detected above were products of both neutral and adaptive introgression from P. flexilis. To test this expectation, we used RDA as implemented in the vegan package v.2.5.289 in R. Hellinger-transformed allele frequency estimates for each of the 98 populations were used as the response matrix, while the predictor matrices included an environmental matrix, ancestry matrix, population structure matrix and a geographical matrix. We reduced the dimensionality of the environmental data by performing principal components analysis on the scaled and centred environmental gradients and retaining only the top seven axes for the environmental matrix used in RDA. These seven axes explained >90% of variance in the dataset (Supplementary Data Q-score per population as estimated through NGSAdmix was used in the ancestry matrix. The first eigenvector of the covariance matrix obtained in Bayenv2 (see above) was used to account for population structure. For the geographical matrix, we used scaled and centred spatial transformations of latitude, longitude and elevation90. Overall, transformed SNP allele frequencies were modelled as a linear function of the predictor matrices and the significance of each fitted model was assessed using 9999 permutations. Even though we attempted to remove collinearity between environmental gradients by using PC axes, collinearity between the predictor matrices could still be of concern. To account for this, we estimated the variance inflation factor scores (VIFs) across all predictor matrices and pruned predictors that exceeded a VIF of ten to generate a reduced set of predictors91. All analyses that follow in this section were conducted both for the full set as well as for the reduced set of predictors. The varpart function in vegan was used to estimate proportions of variance in the genetic dataset explained by various combinations of the predictor matrices  were associated with geographical and environmental gradients to distinguish selection on background genetic variants from selection on recently introgressed variants. We utilised the 88 outlier sets  identified from Bayenv to partition LD among pairs of 98 populations using OhtaDstat v.2.095 package in R. For each environmental gradient and each pairwise comparison, we estimated median DIS and DST, which were then treated as the response matrices. Next, we obtained two predictor matrices: (1) pairwise geographical distances using the Vincenty ellipsoid formula implemented in geosphere v.1.5.796 package in R and (2) pairwise absolute differences along the respective environmental gradients. Overall, for each set of outliers  and both estimates of LD components (DST and DIS), we conducted three matrix regressions using the lgrMMRR function within PopGenReport v.3.0.497 package in R. These regressions included the total effect of environment and geography, the pure effect of environment and the pure effect of geography. The pure and confounded effects were calculated as in RDA. Using this approach, we were able to assess the contribution of environment, geography and the confounded effects towards the spatial partitioning of LD. Since the direction of introgression correlates with freezing temperatures, we expect the pure effect of environment on freeze-related Bayenv outliers to have a high predictive ability for DIS. For environmental gradients that did not differentiate P. strobiformis and P. flexilis  the fitted estimate for the P. flexilis-like genotype for a tree should lie outside the upper 95% confidence interval obtained from neutral simulations and (2) the SNP should be significantly introgressed across at least 20% of the trees. For step (2), we utilised a range of values to confirm that our assessment of adaptive introgression was not sensitive to the individual-based cutoff (Supplementary methods and results A).We used the genomic cline approach implemented in INTROGRESS v.1.2.3i) were overrepresented in the set of SNPs exhibiting significant introgression from P. flexilis using the following equation (following ref. 101):Bpf indicates the number of outliers identified through Bayenv that are also significantly introgressed from P. flexilis, B indicates the total number of Bayenv outliers, Spf is the number of SNPs that are significantly introgressed from P. flexilis and S is the total number of SNPs used in INTROGRESS. Statistical significance of the observed enrichment for each of the 88 outlier sets was determined by running 10,000 null permutations of association between a representative number of randomly sampled SNPs classified as Bayenv outliers and exceptionally introgressed. This approach helped avoid false signals of adaptive introgression due to the latitudinal gradient of ancestry covarying with several environmental gradients (Fig. To identify candidates for adaptive introgression, we conducted an enrichment analysis for each of the 88 outlier sets. Specifically, we asked whether the Bayenv outlier SNPs for each environmental gradient (nts Fig. .102, respectively. Statistical analyses were performed using a combination of publicly available software, packages available in R and several custom scripts designed to work well with R v.3.3.2 or python v.3. These packages and softwares are detailed within their respective Methods sections. For custom scripts, we have detailed the approach within the methods section and have made them publicly available via custom functions. Detailed outputs of all statistical analyses are provided in Supplementary Data 103 and ref. 104.Our study made use of 1122 individual trees sampled from 132 populations to obtain estimates of genomic ancestry and for evaluating genomic regions deviating from overall patterns of ancestry. This dataset was narrowed down to the 98 populations within the hybrid zone for Genotype-environment analyses and for evaluating patterns of LD among SNPs. The FASTQ files and environmental data obtained from climateNA for each of these populations is provided via NCBI SRA at PRJNA670193 and via Figshare at 10.6084/m9.figshare.c.5130104Further information on research design is available in the Peer Review FileSupplemental materialsDescription of Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting Summary


Guizhou province in southwest China has abundant genetic and cultural diversities, but the forensic features and genetic structure of Guizhou populations remain poorly understood due to the sparse sampling of present-day populations. Here, we present 30 insertion/deletion polymorphisms (InDels) data of 591 human individuals collected from four populations, Dong, Yi, Han, and Chuanqing residing in Guizhou. We calculated the forensic parameters of 30 InDel loci and found that this panel meets the efficiency of forensic personal identification based on the high combined power of discrimination, but it could only be used as a complementary tool in the parentage testing because of the lower combined probability of exclusion values. The studied populations are genetically closer related to geographically adjacent or linguistically related populations in southern China, such as the Tai-Kadai and Hmong-Mien speaking groups. The unrecognized ethnic Chuanqing people show an additional genetic affinity with Han Chinese, highlighting the role of possible military immigrations in their origin. Insertion/deletion polymorphisms (InDels) are biallelic length polymorphisms based on insertion or deletion of one or more nucleotides in the genome. InDels are widely distributed across the human genome, with the total number estimated at approximately 2 million . InDels ® DIPplex Kit)   to genotChuanqing people are an unrecognized ethnic group in Guizhou with a population at about 700,000. Most of the Chuanqing people live in Bijie Prefecture, Guizhou province, and speak a Sinitic language. There are two main hypotheses about the origin of Chuanqing in historical records: one suggests that they descended from Guizhou Turen (the indigenous people in Guizhou) and Han Chinese soldiers who were sent to Guizhou area in Ming dynasty, while the other suggests that Chuanqing are indigenous people in Guizhou without significant contribution from Han Chinese-related populations . But theThe Dong group, also known as Kam people, is the 10th largest ethnic minority group in China, accounting for a population of 2.9 million. Dong are native people of Guizhou and have the largest population at about 1.6 million in Tongren and Yuping Dong autonomous county in eastern Guizhou than in any other region of China. The language of Dong people belongs to the Tai-Kadai language family. The ancestor of southern Dong people may come from Guangxi Wuzhou and Guangdong Guangzhou. The population data of 30 autosomal InDels in Guangxi Dong group have been reported before , but the1. The Yi people have lived in southwest China, such as Sichuan, Yunnan, and Guizhou provinces as early as before the pre-Qin dynasty. The Yi people have different branches and various cultural diversities in different areas, for example, they have six dialects. We here genotyped the 30 InDel loci in Guizhou Yi individuals, constructed the phylogenetic trees with Yi groups from different areas, analyzed and verified whether there are any genetic differences among Yi people in different regions.The Yi ethnic group is the seventh largest ethnic minority group of China with about 8.7 million people according to the 2010 National Population CensusThe Han Chinese, with the world’s largest population at about 1.3 billion, make up over 92% of China’s vast population. The archeological and historical records suggest that Han Chinese can trace their ancestry to Huaxia tribes and possibly to the Neolithic Yan Huang tribes (Proto–Sino–Tibetan speakers) along the Upper and Middle Yellow River in northern China. After originating in the north , the HanThe Qiagen Investigator DIPplex Kit  of 30 autosomal InDel loci is the first mature commercial InDels kit for forensic identification. For the past few years, vast amounts of genetic data have been published based on this kit from worldwide populations, such as African , EuropeaA total of 591 unrelated individuals’ peripheral blood samples were gathered from four groups living in Guizhou province with informed consent, including 148 Dong, 152 Yi, 200 Han, and 91 Chuanqing individuals. A geographical map of the four ethnic groups is shown in ® PCR System 9700 Thermal Cycler . Amplification products were subsequently separated using capillary electrophoresis on an ABI 3500 Genetic Analyzer . The PCR products electropherograms were analyzed utilizing GeneMapper v3.2 software (Applied Biosystems) using DNA 9948 (Qiagen) and ddH2O as a positive control and negative control, respectively, accompanying every panel of DNA amplification and genotyping.We performed the amplification of 30 InDel loci with 12.5 μl volume reaction under the guidance of the manufacturer’s recommendations from Investigator DIPplex Kit . The multiplex PCR was carried out on the GeneAmp2, we calculated Nei’s genetic distances based on allele frequencies of the 30 InDels between studied groups and 93 worldwide reference populations. We used R Statistical Software v3.0.2 to plot the heatmaps. The principal component analyses (PCA) of two different datasets were conducted by STRAF  analysis was conducted based on raw genotype data by SNPAnalyzer Software v2.0  . Subsequby STRAF  and MVSPby STRAF  based onby STRAF  to calcuby STRAF  using geby STRAF . We set r2-values (data not listed) and p-values (r2 < 0.8 and p < 0.00167 after Bonferroni correction (0.05/30 = 0.00167). As shown in p < 0.05/30). The results indicated that we can estimate both the forensic characteristics of the single locus and the combined forensic efficiency indices in our following analysis using our newly genotyped InDel data of Dong, Yi, Han, and Chuanqing.We present the raw data of 30 InDel loci for our four newly genotyped populations of 591 individuals in Guizhou in p-values . There iThe allele frequencies and forensic parameters are presented in As we mentioned before, the allele frequency distributions of the 30 InDels are obviously different in different continental populations. We constructed a heatmap based on insertion allele frequencies of the 30 markers among the four studied populations and 93 worldwide populations, as shown in We first conducted a series of PCAs to expound the genetic background and population relationship between Dong, Yi, Han, and Chuanqing in Guizhou province and populations from different continents and language families.In the PCA plots based on the raw genotype data of 30 InDels of 6,561 individuals from 47 populations , we obseAt the population level, we carried out the PCA based on allele frequencies of 30 InDel loci of studied populations and other 47 reference populations. The first two PCs account for 73.65% of the variances among populations, as shown in To further investigate the genetic relationships between studied groups and more reference populations from different language families and geographical regions, we co-analyze Dong, Yi, Han, and Chuanqing data with a new allele frequency dataset composing of 15,180 individuals from 93 worldwide populations. We initially carried out the PCA among 97 populations at the population level. The first two PCs show a total of 73.65% variance . We idenK-values from 2 to 8. As shown in 3, the best K-value was observed at K = 4. When K > 4, we cannot find any further substructures in the 51 populations. At K = 2∼4, we observed that the groupings of 51 groups according to the proportions of various ancestry components are in accordance with geographic patterns. When K = 3, the Nigerians, European groups, American groups, and East Asian groups are distinguished because of the different ancestry compositions. At K = 4, the unique ancestry components of Uyghur, Kazakh, Yugur, Xinjiang Hui, and Tibetan groups are within the general East Asian pattern, and the other East Asian populations including our four studied groups  show similar ancestral proportions with each other. Dong, Yi, Chuanqing, and Han of Guizhou have the largest difference in ancestral components with Nigerians, followed by European groups, American groups, and then the Xinjiang Hui, Turkic-speaking, and Tibetan groups. Hence, InDel marks can be used as a forensic practice tool to infer geographic ancestral components.Furthermore, to further investigate the internal structure of ancestral components of 51 groups at the individual level, we conducted STRUCTURE analysis based on the raw genotype of 7,152 individuals and set the range of via an NJ algorithm based on the Fst genetic matrix. The identified three main branches of the NJ tree correspond well to the continental regions. As shown in We then calculated the pairwise Fst genetic distances between the studied populations and other 47 reference populations to confirm the observed genetic similarities. As shown in We calculated the Nei’s genetic distances based on the allele frequency dataset composing of 15,180 individuals from 93 worldwide populations and constructed an NJ tree to offer more detailed information for genetic similarities and dissimilarities. The values of Nei’s distances between studied populations and other reference groups are shown in Finally, to further understand the genetic relationships of four studied Guizhou populations with a larger dataset, the phylogenetic tree between studied populations and other 93 worldwide reference populations was reconstructed based on Nei’s genetic distance using the NJ method . Three mWe genotyped the 30 InDel loci in 591 unrelated individuals from four Guizhou groups, Chuanqing, Dong, Yi, and Han using the Investigator DIPplex Kit. We reported the first batch of population data and calculated the forensic parameters. We found that this panel meets the efficiency of forensic personal identification based on the high combined power of discrimination (>0.9999), but it could only be used as a complementary tool in the parentage testing because of the lower combined probability of exclusion values. We also observed the allele frequency differences between Asian, European, and American populations, which remind that those markers can be used in various forensic applications and designing suitable forensic kit for the corresponding populations. We note that a newly developed panel of genotyping 50 InDels (AGCU InDel 50 kit) has been designed for Chinese populations. Han Chinese are genetically substructured in a “north–south” cline inferred from autosomal genome-wide data with southern Han Chinese showing affinity with Tai–Kadai- and Hmong–Mien-speaking southern minorities in southern China . HoweverIn the PCA and genetic distance-based NJ tree analysis above, our newly studied Guizhou Dong clustered with Tai–Kadai-speaking populations, especially the populations from Guangxi, such as Guangxi Dong, Kelao, and Zhuang. This result seems to support the hypothesis that the ancestor of southern Dong people may come from Guangxi Wuzhou. We detected that there are genetic substructures within Yi ethnic groups. The Yi ethnic groups from different areas are far away from each other as shown in the NJ tree, which can also be seen from the Fst genetic distances of Sichuan Yi and Yunnan Yi with Guizhou Yi (Sichuan Yi 0.001955 and Yunnan Yi 0.007291). The result is consistent with previous surveys from ethnography that the Yi people have different branches and various cultural diversities in different areas.There are many hypotheses about the origin of Chuanqing people in Guizhou province. For example, one suggests that they descended from Guizhou Turen and Han Chinese soldiers who were sent to Guizhou area in Ming dynasty, while one suggests that Chuanqing are indigenous people in Guizhou. Based on the analysis of 30 InDels, we found that Chuanqing people have a closer genetic relationship with geographically adjacent Guangxi Kelao and Guizhou Han, which tends to support the role of possible military immigrations in their origin. However, we note that we could not be able to draw a clear conclusion about the origin of Chuanqing people based on 30 InDels. Further research based on genome-wide array genotyping or whole genome sequencing may shed more light on the origin of Chuanqing.In this study, we first investigated the forensic efficiency of the 30-InDel panel in Dong, Yi, Han, and Chuanqing residing in Guizhou, southeast China. We provided the first batch of forensic reference genotype data and forensic parameters of 30 autosomal InDel loci in 591 individuals from the above four populations. The results of the forensic statistical analysis demonstrate that this Investigator DIPplex Kit is sufficiently powerful for forensic individual identification and limited exclusive power for parental testing. We observed the genetic structure revealed by 30 InDels is generally consistent with geographical and linguistic classifications. In the future study, the whole-genome deep sequencing of represented populations with a large sample size should be carried out to provide a better understanding of the genetic structure and population history for Guizhou populations in southwest China.https://figshare.com/articles/30_InDel_loci_genotyped_from_Dong_Yi_Han_and_Chuanqing_in_Southwest_China/9937811.The datasets generated for this study can be found in the Figshare Our study was carried out according to the recommendations of the Guizhou Medical University Ethics Committee. The protocol was reviewed and approved by the Human and Ethics Committee of the Guizhou Medical University, China. All study participants provided written informed consent in accordance with the Declaration of Helsinki.JH and C-CW designed this study. HaZ and C-CW wrote the manuscript. YL and HaZ conducted the experiment. GH, ZR, HoZ, QW, JJ, MY, JG, XY, JS, JB, DP, RH, L-HW, C-CW, and JH analyzed the results. All authors reviewed the manuscript.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.


Spartina alterniflora, which were collected from Morehead City in North Carolina, Sapelo Island in Georgia, and Tampa Bay in Florida, were introduced to China in 1979. However, according to documented records, S. alterniflora from different origins were introduced to different areas when the species was first introduced to the coastal areas of China in the 1980s. In order to understand the relationship between the invasive S. alterniflora populations of China and the native S. alterniflora populations of the United States, and whether the genetic structure and genetic diversity of the invasive populations of China were affected by different introductions in the 1980s, molecular markers were used to determine the levels of gene flow and its effect on population differentiation. A total of 715 samples of S. alterniflora were collected from nine invasive populations in China and nine native populations from the United States. The genetic diversity and genetic structure of invasive and native populations were compared using microsatellite markers. The heterozygosity of Chinese invasive populations of S. alterniflora  were similar with those of native populations , which may attribute to its multiple introductions with the multisource populations from different geographic areas of the United States. However, the lower allelic diversities of Chinese invasive populations were detected, which may be due to the founder effect, or the bottleneck, which supports the theory that the allelic diversity is more sensitive to population bottlenecks than heterozygosity. The results of the STRUCTURE analysis among all sampling sites showed that the value of ΔK was largest when K = 2, which indicated that the invasive S. alterniflora populations in China had completed differentiated from the native populations of the United States. This may be because of admixture and hybridization of three non-overlapping original populations, or the postintroduction rapid evolution in China, and reproductive isolation under long-term geographic isolation. There was significant differentiation among invasive populations, which was mainly affected by different human-mediated introductions in 1980s. Significant genetic structure (K = 7) and high genetic differentiation (Fst = 0.30193) were detected in Chinese invasive populations, which may due to the low natural gene flow among populations. The genetic structure of the invasive populations in China was still affected by the human-mediated introductions in the 1980s, and the different initial introductions might promote differentiation among the invasive populations. In fact, the human-mediated long-distance dispersal should take the most of responsibility for the rapid spread of S. alterniflora along the coast of China. Multisource introductions of S. alterniflora are perhaps helpful for local adaptation but itself cannot cause rapid spread along the whole coast of China. Meanwhile, we suggest that the prevention of gene exchange among populations of S. alterniflora is the first and most important step in the control of the species on the coast of China, because admixture and hybridization of isolated populations might generate new heterosis and increase the difficulty of managing S. alterniflora in China.Hundreds of plants and half a kilogram of seeds of  The genotype, genetic diversity, and genetic structure of an invasive plant species always differ from those of native congeners. This may be due to rapid evolution after invasion, outcrossing, or genotypes with specific traits being better able to invade . IndividIn addition, when invasive species are establishing in the new range, they often suffer founder effects, bottlenecks, and eventually genetic drift as a result of finite number of individuals in the new colony, which can considerably decrease the genetic variation and change the allelic frequencies compared to the populations in the regions of origin . On the However, landscape factors and environmental conditions have a strong influence on gene flow and shape the spatial genetic variation accordingly. Both isolation by distance  and isolSpartina alterniflora is a perennial herb that is native to the lower intertidal salt marshes along the Atlantic and Gulf coasts of North America , Hebei (HB), Shandong (SD), Jiangsu (JS), Zhejiang (ZJ), Fujian (FJ), Guangdong (GD), Hongkong (XG), and Guangxi (GX) , Georgia (GA), and Florida (FL)  . In addiida (FL) . The samida (FL) , and we e states . To miniTotal genomic DNA was extracted from each of the silica gel–dried leaf samples (<10 mg) using a modified cetyltrimethylammonium bromide method .S. alterniflora (To investigate the characteristics of 35 microsatellite (SSR) loci isolated from rniflora , we randrniflora .S. alterniflora, we used a tailed primer method to perform polymerase chain reaction (PCR); that is, 5′ end of each forward primer was tailed with an M13 sequence (5′-GTTTTCCCAGTCACGAC-3′). Polymerase chain reaction amplification was carried out with a DNeasy Plant Mini Kit  in 30 μL reaction containing ≤100 ng of template genomic DNA, 1X GoTaq Colorless Master Mix and 0.2 μM of each of the three primers: a forward primer with a tail of the M13 sequence, a non-tailed reverse primer, and a 5′-labeled M13 primer .To amplify the SSR loci of Polymerase chain reaction cycling conditions were 5 min at 95°C, followed by 35 cycles of 15 s at 95°C, 30 s at 55°C, and 30 s at 72°C, with an extension of 10 min at 72°C in the final cycle. Polymerase chain reaction products were identified and analyzed with PTC200 .A), allelic richness (AR), the number of private alleles (PA), observed and expected heterozygosity (PA), and observed and expected heterozygosity (HO and HE), at each of the populations were estimated using GENALEX version 6.4 (FIS) were calculated using FSTAT version 2.9.3.2 (A), allelic richness (AR), and observed and expected heterozygosity (HO and HE) of the Chinese invasive group and the American native group were estimated and compared using GENALEX version 6.4 . We first excluded the populations with multiple introductions , and other populations with the same number of introduced sources were arranged from north to south. Then, we combined multiple introductions with multiple-source populations, and the populations in the same group were still listed from north to south.The mean number of alleles per locus  and geographic distance (in kilometers) of invasive and native populations were assessed using the Mantel test  within America with population assignments using GENALEX version 6.4 , allele frequencies (a), and inbreeding coefficients (f), in order to reach a swap acceptance rate between 20 and 60%, as suggested by m = 0.15, a = 0.4, and f = 0.5 were used. Six independent replicates with different random starting seeds were performed to check for convergence for each analysis with a burn-in 50,000,000 iterations followed by 5,000,000 MCMC iterations and a sampling interval of 2,000. Convergence of the MCMCs was checked by comparing the traces of each run using TRACER version 1.7 (To detect the direction and rates of contemporary migration among populations of on 3.0.4  was usedon 3.0.4 . Prelimision 1.7 . Among tA) and allelic richness (AR) in each sampling site ranged from 2.125 (LN) to 9.125 (GA2), and 2.014 (LN) to 7.855 (GA2), respectively. Observed and expected heterozygosity (HO and HE) in each sampling site ranged from 0.270 (LN) to 0.885 (GD), and 0.224 (LN) to 0.725 (GA2), respectively. Inbreeding coefficients (FIS) ranged from -0.957 (GD) to 0.296 (GA2) , and inbreeding coefficients were significant. From the data, the lowest and highest diversity populations were LN and GA2 respectively  were 9.750 and 13.125; allelic richness (AR) values were 9.666 and 13.007; observed heterozygosity (HO) values were 0.538 and 0.530, and expected heterozygosity (HE) values were 0.725 and 0.744 for the Chinese invasive and the American native groups, respectively (A) and allelic richness (AR) of the Chinese invasive group  were both lower than those of the American native group . However, observed and expected heterozygosity (HO and HE) of the Chinese invasive group  were similar with those of the American native group   .Fst = 0.30193; Fst = 0.10284; ARLEQUIN version 3.5 was used to detect considerable genetic differentiation, both among and within populations  within a given region. In the Chinese invasive group, the proportion of genetic variance was significantly partitioned among populations 30.19%; . In the FST values ranged from 0.04418 (ZJ and GX) to 0.56815 (LN and GD). FST was statistically significant between all population pairs  to 0.06871 (GA and FL), and FST was statistically significant between all population pairs  between the genetic differentiation index (FST) and geographic distance (in kilometers) among both the invasive and native populations  = -18,951.46, ΔK = 6,712.51; K = 2, the nine populations sampled from China were clustered as being genetically similar, and the nine populations sampled from America were clustered as being genetically similar, which was the same result shown by the population assignment and UPGMA  sampled from North Carolina clustered together, two populations (GA1 and GA2) sampled from Georgia clustered together, and three populations  from Florida clustered together  = -9,404.57, ΔK = 870.65; K = 3, although there was genetic admixture among the nine American populations, the results were basically consistent with those obtained using UPGMA.High degrees of genetic differentiation among the three populations  in the American native group were apparent in population assignment. The result of the UPGMA showed that NCtogether . The resK = 2, the value of ΔK was largest [mean Ln P(D) = -18,951.46, ΔK = 6,712.51; The results of the STRUCTURE analysis, based on Bayesian statistical model-based clustering, indicated the existence of obvious population genetic structure among the nine populations sampled from China. When c) ranged from 0.0051  to 0.2265 , and the average recent migration rates in pairwise comparisons of three American populations from North Carolina, Georgia, and Florida (ma), ranged from 0.0041  to 0.1230 . The lowest and highest values of contemporary gene flow in pairwise comparisons among the populations from China were both higher than those among the populations from the United States.The results estimated by the BAYESASS analysis showed the average recent migration rates in pairwise comparisons of the nine Chinese populations (mP < 0.05) to 0.0111  (P < 0.05) to 0.1230   . The low < 0.05) .S. alterniflora along the coastline of China and the United States, which were congruent with the direction of seasonal marine currents , almost 11 times larger than that in the opposite direction, which is congruent with the result of the STRUCTURE analysis that there is genetic mixture between the NC and GA populations ,  anglica . Spartinterparts . Spartincongener , 2007. Ocongener . All seecongener , so it wLespedeza cuneata are more competitive than either the ancestral or native genotypes. S. alterniflora plants were taller and denser and set up to four times more seed than US plants in both the field and common garden, and they considered that invasive plants evolved rapidly in the new range.The postintroduction rapid evolution also may enable invasive species to better adapt to novel environments in the introduced range . For exaA) and allelic richness (AR) of S. alterniflora from the coast of China was lower than those of the native S. alterniflora, which is the same finding as previous research by HO and HE) of the S. alterniflora from the Chinese invasive group was similar to those of the native S. alterniflora from the American native group  and geographic coordinates (latitudes) detected in the nine invasive populations on the coast of China  and geographic distance (in km) among the invasive populations  and the number of sources of each population on the coast of China in the historical reports when they were introduced in the 1980s. Three natural populations from the United States were thought to be divergent, and our study showed that there was significant differentiation among populations from North Carolina, Georgia, and Florida, except for the overlap of genotypes between the sampling sites of NC1 and sites of GA3  of S. alterniflora and the number of sources (AR and HE) of S. alterniflora and the number of sources were also positively correlated  in our previous research (HE) of S. alterniflora and the number of sources were also positively correlated  showed a weaker competitiveness than individuals of S. alterniflora in the northern population (the Tianjin and Shandong population) and the middle population . It is universally recognized that the evolutionary potential of a population is proportional to its amount of genetic diversity , causing a lower genetic diversity, thereby limiting the evolutionary potential  were significantly different, and the mean flowering date and the relative growth rate showed significant latitudinal variation. Spartina alterniflora from different populations maintained their morphological and physiological traits in the common garden experiment. Although Pairwise ntiation , 5. ZhanS. alterniflora on the Atlantic coast and the Gulf of North America through using RAPD marker analysis, cpDNA analysis, and microsatellites analysis. Spartina alterniflora is distributed from Freeport, Marine (43°N) to Hollywood, Florida (27°N) on the Atlantic coast  to Guangxi (20°N) on the coast of China  to a uniform distribution (genetic distance was positively correlated with geographic distance) in its invasive region (the coast of China) across a large geographic scale through regular observation of S. alterniflora.In addition, no matter how much we lose from invasive organisms, at least, they lead to unanticipated scenarios to understand the natural world better across large spatial and temporal scales, such as extinction, ecosystem function, and the response of species to climate change. They also provide opportunities to observe ecological and evolutionary processes in real-time and to quantify rate processes, such as genetic change, which is very difficult to observe with native species. We highlight the utility of using The datasets generated by this study are available on request to the corresponding author.LX set up most of the experimental plots, analyzed most of the data, ran the statistical models, created the graphics, and wrote the first draft of the manuscript. QG developed the research questions, designed the experiments, set up a part of the experimental plots, and analyzed a part of the data. All authors contributed to the manuscript revision, read 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.