Rumex alpinus L. , is a perennial plant native to the mountains of Central and Southern Europe. Currently, the distribution of R. alpinus has been partly affected by its utilization as a vegetable and a medicinal herb. In the mountains of the Czech Republic, it is considered an invasive plant, probably introduced into the Krkonoše Mountains by colonists from the Alps. This study's main aim was to verify whether R. alpinus was introduced into the Krkonoše Mountains by alpine colonists or whether it was anthropogenically introduced from the Carpathians. Furthermore, the genetic structure of native and introduced populations of R. alpinus was determined. For the evaluation of genetic structure, 417 samples of R. alpinus were collected from the Alps, Carpathians, Balkan, Pyrenees, and Czech Mountains. In total, 12 simple sequence repeat (SSR) markers were applied. The results of AMOVA showed a high 60% variation within populations, 27% variation among groups, and 13% among the population within groups. The overall unbiased gene diversity was high (^ĥ = 0.55). The higher level of genetic differentiation among populations  indicated restricted gene flow between populations. Compared to native populations, limited genetic variability was observed in the nonnative populations. It was concluded that local adaptation, low gene exchange, and genetic drift affected the genetic diversity of nonnative R. alpinus. The results support a genetic link between Alpine and Czech genotypes of R. alpinus, while the Carpathians genotypes corresponded to the Balkan genotype.Monk's rhubarb,  Rumex alpinus was introduced into the Krkonoše Mountains by alpine colonists or whether it was anthropogenically introduced from the Carpathians. Furthermore, the genetic structure of native and introduced populations of Rumex alpinus was determined.This study's main aim was to verify whether  Therefore, it is difficult to determine the origin of some species that may have been grown as medicinal or valuable plants in the past.Whether a plant is native or nonnative in a given area is often difficult to determine. The allochthonous origin of many archaeophytes, epoecophytes, and ephemerophytes, e.g., Kopecký, . The disKopecký, . HoweverKopecký, . AccordiKopecký, , this isR. alpinus is well‐established  from the Carpathians or from Austrian parts of Alp? (ii) Do differences in genetic diversity exist within native and nonnative habitats? (iii) Does population structure reflect geographic distances?(i) R. alpinus, which could provide new assessments of this species under a genetic context and produce valuable data for further control and management of plant invasions.This is the first study focusing on the genetic variability and population structure of the problematic weedy plant 22.1R. alpinus were collected from different locations in Europe  where the colonist's lived and R. alpinus probably occurred. Equally, individual plants of R. alpinus were collected in Tyrol and Styria in Austria. As control samples R. alpinus samples were collected from other mountain localities in the Alps (Lombardy and Graubünden) and the Pyrenees. Finally, to determine the true origin of R. alpinus were collected samples representing plant populations in the East and West Carpathians, and then in the mountainous regions of the Balkans. Populations from other mountainous regions of the Czech Republic (Jizera and Eagle Mountains) were collected for comparison and possible exclusion of other origins of R. alpinus.Between 2017 and 2020, plant samples of R. alpinus is a native species.In total, 417 individual leaf samples were collected, representing 31 populations with 9–20 individual plants per population. The 329 plant samples were collected in the European Mountains, Alps, West and East Carpathians, Pyrenees, and Balkan Peninsula, which represent areas where R. alpinus is considered a nonnative/invasive plant  Mountains where Kopecký, , overallKopecký, .2.2R. alpinus also reproduces vegetatively and to ensure a representative sample of the population. Samples were dried on silica gel and stored in a collection at the Faculty of Environmental Sciences Czech University of Life Sciences Prague.At each locality, leave samples were collected in plants separated approximately 200 m apart to avoid collecting the same plant because R. alpinus in two repetitions using the protocol of Doyle and Doyle . Electropherograms were analyzed and scored using GeneMarker ver. 1.8 (SoftGenetics).2.4R. alpinus populations within R program  with 1000 permutations was calculated in ARLEQUIN software ver. 3.5.2  were performed using the locprior model with admixture and correlated allele frequency  values among successive K runs to account for patterns of dispersal that are not homogeneous among populations .To identify potential bottleneck events in the populations under investigation, we employed BOTTLENECK 1.2.02 software , and the highest (I = 1.11) was in the population from Romania Muntele Mic , followed Pyrenees Mountains ; Balkan Mountains , Carpathian Mountains , and the highest level of genic diversity was found in populations from the Alps  , indicating that the population was not in Hardy–Weinberg equilibrium.Genetic variability was measured as the amount of observed or expected heterozygosity, presented in Table Based on the results, all Alpine, Balkan, and Czech Mountain populations had at least one monomorphic locus, meaning that every individual in the population had the same homozygous genotype at that locus. Specifically, in the Eagle Mountains population, loci 1, 2, 3, 4, and 10 were monomorphic. In contrast, loci 5, 7, 8, 9, and 12 exhibited an observed heterozygosity (HO) of 1000 and an expected heterozygosity (HE) of 0.514, suggesting that the population is highly inbred or clonal, as all individuals have the same genotype at the locus. On the other hand, the results for the Carpathian population Zakopane_Lejowa glade did not record any monomorphic locus. However, significant results on some loci suggest that the population might be experiencing some form of selection Table .The results of the AMOVA for all native and non‐native populations Table  indicateThe two first axes of the PCoA analysis Figure  of SSR dR. alpinus populations were quite high. The total value for the (FST) between populations was 0.40. The differences and degree of variability between the populations were clear, as shown in Figure Pair differences and the degree of variability between r = .65; p < .01) across all the localities. The linear regression model was identified as a representation of the relationship between geographical and genetic distances , K2 for Alps_Tyrol and Alps_Styria local populations , K3 for Styria local populations (89%), and K4 consists for Alps_Lombardy local populations (89%), genetic populations K5and K6 are predominant for Czech Mountain local populations . Cluster K8 consisted exclusively of Pyrenees populations (93%). The distribution of allele frequency ; data curation ; formal analysis ; methodology ; project administration ; resources ; writing – original draft . Vladimíra Müllerová Jurasová: Conceptualization ; formal analysis ; methodology ; resources ; validation ; writing – review and editing . Petra Hlasna Čepková: Data curation ; investigation ; methodology ; resources ; supervision ; writing – review and editing . Leona Leisova Svobodová: Data curation ; formal analysis ; software ; writing – review and editing . Pavel Svoboda: Data curation ; software ; writing – original draft . Michal Hejcman: Conceptualization ; investigation ; project administration ; writing – review and editing .None declared.This work received funding from the Internal Grant Agency (IGA) of the Czech University of Life Sciences Prague under grant agreement No 20184218 (M. Jungová). The authors are grateful for the financial support provided by the Czech Ministry of Agriculture RO0423.Appendix S1:Click here for additional data file.


Suberites diversicolor (n = 125) to test the relative importance of spatial scales (1–1400 km), local environmental conditions, and permeability of seascape barriers in shaping population genomic structure. With the SNP dataset, we show strong intralineage population structure, even at scales <10 km (average FST = 0.63), which was not detected previously using single markers. Most variation was explained by differentiation between populations (AMOVA: 48.8%) with signatures of population size declines and bottlenecks per lake. Although the populations were strongly structured, we did not detect significant effects of geographic distance, local environments, or degree of connection to the sea on population structure, suggesting mechanisms such as founder events with subsequent priority effects may be at play. We show that the inclusion of morphologically cryptic lineages that can be detected with the COI marker can reduce the obtained SNP set by around 90%. Future work on sponge genomics should confirm that only one lineage is included. Our results call for a reassessment of poorly dispersing benthic organisms that were previously assumed to be highly connected based on low‐resolution markers.The relative influence of geography, currents, and environment on gene flow within sessile marine species remains an open question. Detecting subtle genetic differentiation at small scales is challenging in benthic populations due to large effective population sizes, general lack of resolution in genetic markers, and because barriers to dispersal often remain elusive. Marine lakes can circumvent confounding factors by providing discrete and replicated ecosystems. Using high‐resolution double digest restriction‐site‐associated DNA sequencing , we genotyped populations of the sponge  Suberites diversicolor from marine lakes confirms genetic lineages, but provides new evidence for strong intralineage population structure. Geographic distance and local environments did not explain genetic differentiation, suggesting other mechanisms such as founder events with priority effects may be at play. Results indicate the potential need to reassess panmictic assumptions from poorly dispersing benthic organisms.High‐resolution genotyping of the marine sponge  Given the high genomic structuring observed in co‐distributed species from marine lakes  optimized laboratory and bioinformatic filtering methods for low‐coverage RAD‐generated data using old stock DNA extractions to assess effects of filtering, (b) compared phylogeographic and population genetic structure of COI and ITS markers from Becking et al. , with different environmental conditions, and along a gradient of connection to the surrounding sea, we assessed the relative influence of these drivers on genomic structure of g et al.  to RAD‐g22.1Suberites diversicolor is found in many marine lakes, but some of the sampled lakes had very low densities; therefore, sample sizes were smaller  were collected from 168 individuals of Suberites diversicolor  was approximated using Google Earth Pro (v. 7.3.2), maximum depth was measured using a handheld sonar system (Hawkeye), and water parameters temperature (°C) and salinity (ppt) were measured with a YSI Professional Plus multimeter at 10 locations per lake at 1 m intervals from the surface to 5 m depth. To define the connection to the surrounding sea, we measured maximum tidal amplitude simultaneously in the lake and the sea using Hobo water‐level loggers (Onset HOBO U20L). The degree of water exchange between the marine lakes and the adjacent sea was assessed by placing a water‐level logger inside the marine lake and one directly outside in the surrounding sea during a 48 h period. Pressure (Pa) was converted to depth (m) using the Hoboware Pro 3.7.16 software. The fraction of tidal amplitude of the lake compared with the sea was then calculated by (Lakemax)/(Seamax), where max stands for the maximum water level in either the lake or the sea. A maximum value of 1 would indicate limited (or no) obstruction to water flow in and out of the lake, and the minimum value of 0 would mean there is no water exchange at all. We categorized the level of connection to the surrounding sea as low (≤0.4), medium (0.5–0.7), and high (0.8–1). The sea locations were categorized as open.Lake area (m2.2DNA was extracted using the DNeasy Blood & Tissue kit (Qiagen), with the only modification from manufacturer instructions being an extended lysis time (overnight). DNA quality and quantity were assessed using 1.5% agarose gels and Qubit dsDNA HS assays. Next, double digest restriction site‐associated DNA (ddRAD) libraries were prepared following the protocol of Peterson et al. . The ada2.3http://repeatmasker.org/) was used to mask putative repetitive elements, low complexity regions, and short repeats using “Suberitidae” as a database . Raw fastq reads were demultiplexed using a maximum of one mismatch and removed if expected cut sites were not found. Resulting demultiplexed reads were trimmed of Illumina adapter contaminations and low‐quality reads using cutadapt v1.15 Martin,  and Trimgithub.com/tplinderoth/ngsQC/tree/master/snpCleaner; Bi et al., We screened for loci from putative microbes in different ways. First, potential bacterial, viral, and human sequence contamination were removed via Blasting to reference sequences from GenBank following Maas et al.  see the, a fast http://www.novocraft.com), and only uniquely mapping reads were retained. Picard (www.picard.sourceforge.net) was used to add read groups, SAMtools v1.9 . The most likely K was determined by running 10 replicate runs of each respective K, calculating the log‐likelihood value of each, and choosing the value of the real K after which the likelihood plateaus or increases only slightly  was calculated to eliminate the effect of within‐population diversity  based on a covariance matrix computed by ngsTools on genotype likelihoods  using GENODIVE, and overall heterozygosity and nucleotide diversity (π) using ANGSD ). Geographic distance was calculated as minimum pairwise distances in meters between lakes using lake coordinates as input for the geosphere package in R. Using averages of temperature (°C) and salinity (ppt) per lake, we performed a Principal Component Analysis (PCA) to calculate informative scores. The scores from all retained PCA axes were then used computing the environmental distance matrix using the function dist in R. Here, low distance values indicate lakes with a similar environment in terms of temperature and salinity, whereas high distance values indicate lakes with very dissimilar environments. Connection distance was calculated following the equations of Maas et al. .After sequencing and demultiplexing, we obtained 1,127,497,643 reads from 168 sponges. On average, we obtained 7,673,269 reads per individual. Individuals with less than 2,000,000 reads were removed from subsequent analyses. Based on the calculation table from Peterson et al. , an estiAfter filtering, we retained 125 sponges with 973,697,804 reads in total, with coverage ranging from 3.1–82.2× (average 24.0×). The number of genomic markers retained strongly varied depending on the inclusion or exclusion of the two major lineages. When including both lineages 541 SNPs were retained, yet when only including Lineage B the number of SNPs increased almost ninefold to 4826. For Lineage B, in total 23,742 SNPs were called over all tags, but after selecting one SNP per tag we retained the 4826 SNPs for subsequent analyses. Also for Lineage B, depending on the filtering options  the number of SNPs varied from 56 to 4826 Table .3.3A maximum likelihood phylogenetic tree based on 541 SNPs showed two divergent lineages for 125 individuals Figure . These lK , and Kalimantan2, which showed admixture from multiple groups. Furthermore, Sea Indonesia and Sea Australia also showed admixture from multiple groups.Admixture analyses showed Kalimantan1 to be consistently distinct from all other populations throughout the range of ancestral populations K Figure . The mos9 Figure , after wWhen analyzing the complete dataset, the AMOVA showed most variation to be explained by the two major lineages (A and B) Figure , 62%. Wi3.4We tested different filters to assess the influence on the genomic patterns. Within Lineage B, patterns remained highly similar for all filters  Figure . The fird = 0.153), indicating a tree‐like structure. The sea populations Sea Australia and Sea Indonesia showed higher reticulation than the marine lake populations, indicating potential introgression or hybridization events. All marine lake populations were diverged and showed no reticulation.Findings from the Splitstree network were consistent with patterns found for the PCA Figure . The netF'ST) showed high levels of genomic structuring (0.63 ± 0.13) . Striking was that the F'ST values among populations from highly connected lakes were equally strong as among highly and low connected lakes.Pairwise fixation indices  Figure . When in8 Figure . Within 32 Table . All paiThe migration network among lakes indicated the strongest relative bidirectional migration between marine lakes and the lagoon population in East‐Kalimantan Figure . Lagoon 3.5e) . Low genomic diversity was consistently observed in populations Papua1  and Papua27 . However, for populations Kalimantan2 and Papua5, there were differences in estimates on diversity depending on which method was chosen. When estimating heterozygosity from genotype likelihoods via ANGSD, we found high diversity for Kalimantan2 (0.1200) and low diversity for Papua5 (0.0188), yet when estimating expected heterozygosity via GENODIVE we found low diversity for Kalimantan2 (0.034) and high diversity for Papua5 (0.095). For Kalimantan2, this may be an artifact of low sample size (n = 2), as GENODIVE takes sample size into account. These were the only two lakes in which such a remarkable difference between estimates was observed.Population genomic diversity varied among lakes Table . The higWe assessed changes in effective population size for six locations using Stairway plots  was not influenced by lake area , degree of connection , salinity , or temperature  (Table Spearman correlation tests indicated that within‐population genomic diversity (nucleotide diversity π and heterozygosity H0) Table .r = 0.007, p = .50)  Figure . Finding4The existence of multiple independently derived populations in marine lakes provides an opportunity for fundamental research into the role of isolation in population divergence in marine taxa. By comparing sponge populations in the Indo‐Pacific from marine lakes and lagoons at different spatial scales, environmental conditions, and degree of connection to the sea, we detected high levels of genomic differentiation across the studied geographic area and provided new evidence of small scale structure for sessile species with a short dispersive larval stage. The structure of the populations could, however, not be explained by geographic distance, local environments or degree of connection to the sea alone. Our work exemplifies that a higher resolution of markers retained with reduced representation genome sequencing can elucidate small‐scale population genomic structure which are not visible with single marker studies. In general, we showed that old extractions yielding low‐coverage data from nonmodel organisms can readily be used in population genetic and genomic analyses to study small‐scale population differentiation. Below, we discuss our findings and potential implications for future phylogeographic and population genomic studies on sponges.4.1S. diversicolor, which had been split in Lineage A and B by Becking et al. (COI) and the Internal Transcribed Spacer 1&2 (ITS) nuclear markers. Investigation of morphological traits based on spicules and analyses of COI and ITS markers determined that Lineage B is likely one species, yet did not show any spatial intraspecific variation across populations , with admixture PCA and Splitstree analyses showing clear clustering by marine lake.The sponge populations show strong population structure and high levels of genomic differentiation across the study area. These results are in concordance with our hypothesis based on the life history traits of benthic sponges with a predominantly sessile life cycle and short larval durations . Previous studies using a low number of markers did find a pattern of isolation‐by‐distance for sponges in oceanic locations   and missing data included . Additionally, we tested the effects of using either genotype calls used by GENODIVE . As we observed population bottlenecks for all marine lake locations, regardless of their extent of connection to the sea, other factors such as strong founder events coupled with priority effects could be at play. A major objective of marine molecular ecology is to obtain accurate estimates of genomic structure, as it can inform efforts to identify units of management and design effective marine protected areas (MPAs) ; investigation ; methodology (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Stefan Prost: Formal analysis (supporting); methodology (supporting); software ; supervision (supporting); visualization (supporting); writing – review and editing (supporting). Christiaan A. de Leeuw: Data curation (supporting); investigation (supporting); methodology (supporting); writing – review and editing (supporting). Ke Bi: Formal analysis (supporting); methodology (supporting); resources ; software (lead); writing – review and editing (supporting). Lydia L. Smith: Formal analysis (supporting); methodology (supporting); supervision (supporting); writing – review and editing (supporting). Purwanto Purwanto: Resources (supporting); writing – review and editing (supporting). Ludi P. Aji: Resources (supporting); writing – review and editing (supporting). Ricardo F. Tapilatu: Resources (supporting); writing – review and editing (supporting). Rosemary G. Gillespie: Supervision (supporting); writing – review and editing (supporting). Leontine E. Becking: Conceptualization (lead); data curation (lead); formal analysis (supporting); funding acquisition (lead); investigation (lead); methodology (lead); supervision (lead); visualization (supporting); writing – original draft ; writing – review and editing .The following people provided help with logistics during fieldwork and/or labwork: . B. Alvarez, M. Ammer, Bahruddin, H. Breeuwer, L. Dong, M. Erdmann, B. Hoeksema, P. Kuperus, S. C. Lim, S. Menken, A. Miners, A. Oherenan, K. Peijnenburg, W. Renema, Suharsono, R. Tapilatu, Y. Tuti, B. Voetdijk, N. J. de Voogd, the staff of Nabucco Island Dive Resort, Derawan Dive Resort, Misool Eco resort, Papua Diving. We are grateful for useful discussions with Dr. P. Meirmans. This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH S10 Instrumentation grants S10RR029668 and S10RR027303. The Netherlands Organisation for Scientific Research provided funding to Leontine E. Becking through the grants RUBICON #825.12.007, VENI #863.14.020 and VI.Vidi.193.137. Fieldwork in Indonesia was made possible through additional financial support of National Geographic Society Waitt Grant, De Beukelaar‐van der Hucht Stichting, World Wildlife Fund, Netherlands‐INNO Fund, the Schure‐Beijerinck‐Popping Fund of the Royal Dutch Academy of Science (KNAW), Conservation International Ecosystem Based Management program (funded by the David and Lucile Packard Foundation), the Treub‐ Maatschappij Fund, the Leiden University Fund (LUF)/Slingelands, Singapore Airlines, the A.M. Buitendijk Fund and the J. J. ter Pelkwijk Fund . The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to the Indonesian Institute of Sciences (LIPI) and the Indonesian Ministry of Research and Technology (RISTEK) for providing the research permits .Nederlandse Organisatie voor Wetenschappelijk Onderzoek , NIH S10 Instrumentation grants , Leiden University Fund, National Geographic Society Waitt Grant, De Beukelaar‐van der Hucht Stichting, A.M. Buitendijk Fund, J.J. ter Pelkwijk Fund, WWF, Treub‐Maatschappij Fund, Singapore Airlines, Netherlands‐INNO Fund, David and Lucile Packard Foundation, Schure‐Beijerinck‐Popping Fund.The authors declare no conflict of interest.None.Figures S1–S7Tables S1–S7Click here for additional data file.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


Habenaria dentata has medicinal and ornamental value, but the number of wild populations is decreasing dramatically. Thus, conducting research on its genetic diversity and structure is necessary to provide a basis for its conservation. This study aimed to explore the genetic diversity of the wild plant H. dentata and protect and optimize its wild resources. The genetic diversity of 133 samples from six wild populations of H. dentata was analyzed using Inter Simple Sequence Repeat molecular markers to provide a scientific basis for the screening of improved germplasm resources. The results showed that the average number of alleles was 1.765, the average number of effective alleles was 1.424, the average Nei’s gene diversity index was 0.252, the average Shannon diversity index was 0.381, and the average percentage of polymorphic loci was 76.499%. The variation within the populations was 77.34%, and the variation between the populations was 22.66%. The gene flow was 1.705, which was greater than 1. The results of the cluster analysis showed that the six populations were mainly divided into four clusters and were not classified according to their geographical location. There was no significant correlation between the geographical location and genetic distance between the populations . The genetic diversity of H. dentata is high. Among the six wild populations, the genetic diversity of the Mulun population was the highest and this population can be used as a key protection unit. The study on the genetic diversity of H. dentata can not only reveal the reasons for the decrease in the number of individuals in the population to a certain extent, and put forward the protection strategy, but also provide a scientific basis for the breeding of excellent seed resources. H. dentata, which has high medicinal and ornamental value, is a terrestrial herb of the genus Habenaria, family Orchidaceae, which mainly grows in gullies or hillside woodlands at an altitude of 190–2300 m. The plant is widely distributed in China, such as Guangdong, Guangxi, Anhui, Fujian, Yunnan, Guizhou, and other areas in southwest China [H. dentata is widely distributed in China, the number of individuals in a single population in the field is small and the distribution is scattered. In addition, because of its medicinal and ornamental value, the number of wild populations is decreasing dramatically. Based on the above reasons, it is necessary to conduct research on this medicinal plant resource with great development potential to ensure its protection.As one of the largest families of angiosperms, Orchidaceae is a highly evolved group with a wide distribution in all the terrestrial ecosystems of the world except for the polar regions and deserts . Howeverst China . Additiost China  effects st China . It can H. dentata has mainly focused on resource investigation [Currently, the research on tigation , tissue tigation ,14, and tigation ,16,17, btigation . The gentigation . A studyH. dentata. Therefore, in order to reveal the reasons for the decline in the number of individuals and the genetic diversity of the population, this paper used ISSRs to analyze the genetic diversity and genetic relationship among 133 samples of six wild populations of the medicinal plant H. dentata. The findings can provide an important theoretical basis for the screening of excellent germplasm resources and the determination of key protection units at the molecular level.Molecular markers are one of the basic methods for the analysis of species’ genetic diversity. Molecular marker techniques, including inter-simple sequence repeats (ISSRs) and simple sequence repeats (SSRs), can identify the genetic differences between species and are not affected by environmental factors . ISSRs hH. dentata leaves that were free of pest and disease contamination were collected from 1 to 10 September 2021 from each field population according to the population size and number of individuals at the collection site. The collected fresh leaves were placed in molecular sample bags and quickly dried with varicolored silica gel. The samples were sealed in bags and brought back to the laboratory, where they were stored in a cool and dry place. The details of the sample collection sites and the number of individuals that were collected from each field population are shown in Mature, clean, and fresh H. dentata were placed in a sterile 2 mL centrifuge tube with the forceps, and then the appropriate amount of Polyvinylpyrrolidone (PVP) and 3–4 small steel balls were added. Then, the centrifuge tube was placed in a bead mill to crush the sample. Next, the total DNA was extracted from H. dentata using a modified CTAB method [Washed forceps and disposable small steel beads were placed in a washed mortar, and an appropriate amount of anhydrous ethanol was poured into it and ignited for sterilization. After the sterilized equipment was at room temperature, about 500 mg of the dried leaves of B method . The conH. dentata [The ISSR primers were synthesized based on the 100 universal primers that were published by Columbia College, Canada, and they were synthesized by Bioengineering Co., Ltd. . The genomic DNA of 30 samples  was used as a template, and 100 ISSR primers were used for the polymerase chain reaction (PCR) pre-amplification. The primers with clear bands and good polymorphism were screened out for the subsequent genetic diversity analysis of the six wild populations of  dentata .2+, and the remaining volume was made up with ddH2O. The PCR reaction procedure was as follows: pre-denaturation at 95 °C for 5 min, 40 cycles of 95 °C for 20 s, 52 °C for 20 s, and 72 °C for 2 min, which was followed by an extension at 72 °C for 20 min to obtain the PCR amplification products. The amplification products were placed on a 1.2% agarose gel (with 8 µL of 4S Green Plus non-toxic nucleic acid dye), electrophoresed for 40 min, and photographed in a UV gel imaging system.The PCR conditions were based on Lu et al.  and wereaN), effective number of alleles (Ne), Nei’s genetic diversity (He), Shannon diversity index (I), percentage of polymorphic loci (PPL), total genetic diversity (Ht), genetic diversity among populations (Hs), gene differentiation coefficient (Gst), and gene flow (Nm) [Using the electrophoresis spectrum of the DL 2000 marker as a reference, the bands were manually reviewed according to the electrophoretic mobility of each band. The clear bands were recorded as “1”, while those with missing or weak bands were recorded as “0”, and the original binary data matrix of the 0/1 format was established using Excel 2019 software . The “0/low (Nm) . The analow (Nm) . The “0/low (Nm) . Then, Glow (Nm) . The geolow (Nm) .Five samples were randomly selected from the six populations, and the quality of the DNA was detected. The results showed that the extracted DNA bands were clear and there was no obvious tailing phenomenon. The quality of the DNA basically ranged from 80 to 400 ng, and the OD260/280 ranged from 1.7 to 2.0. The results showed that the total DNA had few impurities, was of good quality, and could be used for subsequent genetic diversity tests.H. dentata were screened with several rounds of primers, and eight primers with high amplification success were selected. The details of the primers are shown in The samples of PPL in H. dentata was 100%, the I was 0.493, the He was 0.324, the Ne was 1.538, and the aN was 2.00. At the population level, six populations had no significant differences in the Na, Ne, He, I, or PPL (p > 0.05), and the genes of the six wild populations were evenly distributed across the group.The number of fragments that were obtained for each of the eight primers ranged between four and eight, the size of the fragments was basically 250–1000 bp, and the statistical results of the variation in the six populations are shown in PPL of the six wild populations ranged from 69.23 to 84.62%, with a mean value of 76.497%, and the highest number of polymorphic loci was found in the ML population and the lowest was found in the JFS and MQ populations. The aN ranged from 1.692 to 1.846, with a mean value of 1.765. The highest number of alleles was found in the ML population, while the lowest number of alleles was found in the JFS and MQ populations. The eN ranged from 1.353 to 1.506, with a mean value of 1.424, of which the ML population had the highest eN and the JFS population had the lowest. The I ranged from 0.321 to 0.436 with a mean value of 0.381, with the highest value for the ML population and the lowest for the JFS population. The highest genetic diversity was found in the ML population and the lowest was found in the JFS population.The eH of the populations of H. dentata is shown in Ht was 0.326 ± 0.019, the Hs was 0.252 ± 0.010, and the inter-population genetic diversity (Ht − Hs) was 0.074. Moreover, in the six wild populations of H. dentata, the intra-population variation (Hs/Ht) was 77.3% and the inter-population variation (Ht − Hs/Ht) was 22.7%, indicating that only 22.7% of the variation was among the populations and 77.3% was within the populations. The Nm was 1.706 (>1), indicating that the gene flow between the populations was more frequent and less prone to genetic differentiation. Furthermore, the results of the AMOVA analysis are presented in eH and the AMOVA analysis were in general agreement, indicating that the genetic variation in H. dentata mainly occurred within the populations and that there was little genetic differentiation between the populations.The As shown in H. dentata populations were mainly divided into four branches, with the ML and JFS populations clustering together, the LY and ZES populations clustering together, and these two populations clustering with the MQ and LJ populations. Further testing using the Mantel Test showed that there was no significant correlation between the geographical location and the genetic distance between the populations .As shown in H. dentata populations , which is a high level, supporting that H. dentata populations have more frequent gene exchange and are less likely to have genetic differentiation among the populations. Furthermore, the Gst was 0.227, indicating that only 22.7% of the variation was among the populations and 77.3% was within the populations. The AMOVA analysis also showed that only 27% of the genetic variation occurred among the populations and 73% of the genetic variation occurred within the populations. It has been shown that the genetic differentiation coefficient is 0.59 for self-incompatible taxa, 0.23 for heterozygous taxa, and 0.19 for mixed taxa, and, therefore, H. dentata is probably a heterozygous or mixed taxon plant [The genetic differentiation of populations can be divided into three levels according to the size of the ene flow ,37. In ton plant .Habenaria linearifolia and found that it is mainly located on a plateau that is surrounded by dense tall grasses. This growth environment limits seed dispersal and thus hinders gene exchange. Whereas, H. dentata is mainly distributed in hillsides or woodlands. Consequently, the seeds can be transmitted by the wind over a long distance, which increases the gene exchange between populations and is conducive to the accumulation of the population genetic diversity.The high level of gene flow among populations is closely related to the long-distance dispersal of seeds. As orchids produce numerous tiny seeds, once they are released into the air, they can be spread over a long distance with the wind ,40. HoweH. dentata also has a high reference value for the study of its population evolution history. It is mainly distributed in subtropical and warm temperate zones, and it is also rarely distributed in the Korean Peninsula. Researchers have found that the allozymes of the population of H. dentata on the Korean Peninsula are not diverse and the genetic diversity of the population is low. Thus, the distribution of H. dentata on the Korean Peninsula may have originated from a single ancestral population from southern China or southern Japan, which migrated to the Korean Peninsula from one or more refuges after the last glacial maximum [In this study, the genetic diversity of the ML and LY populations was the highest. Both populations were in nature reserves and were far away from human development. Because of fewer human activities, these populations have been better protected. However, the genetic diversity of the MQ population, which is also in protected areas and far from human development, was low. Therefore, it is speculated that there may be two reasons for this finding. (1) The MQ population is distributed in the mountain hilltop grass, and the dense tall grass hinders gene exchange between the populations. (2) The establishment time of this population was recent, and therefore, the genetic diversity may have not had time to accumulate. It has been suggested that populations with a short establishment time have usually not yet accumulated high genetic diversity through gene mutations or gene flow with other populations ,44. The  maximum .P. emersonii based on genetic distance, and the results showed that the six populations were not clustered strictly according to their geographical location and they were clustered according to individual differences. There was also no significant correlation between the genetic distance of the geographical location and the clusters . Therefore, this paper suggests that even though there is frequent genetic exchange between populations in close proximity, strong genetic mutations and selection can lead to genetic differentiation between populations.Currently, it is increasingly believed that genetic distances between different populations of the same species do not necessarily lead to genetic variation and that factors such as genetic mutations, selection, and gene flow are the main causes of genetic differentiation between populations . For exaH. dentata in Guangxi, China, was frequent, the genetic differentiation among the populations was high, and abundant genetic diversity was accumulated in the long-term evolutionary process. Therefore, it is suggested that the reason for the decline in the number of individuals in the population may not be due to a lack of genetic diversity, but rather due to extensive harvesting or the destruction of their natural habitats. Based on the results of this study, the following suggestions are proposed to strengthen the protection of the wild resources of H. dentata. (1) The seeds of different populations of H. dentata in different regions (provinces) should be collected as much as possible to establish a germplasm resource bank with mycorrhizal fungi to protect the genetic diversity. (2) For small-scale wild populations, it is recommended that measures should be taken to protect them in situ, and we suggest the establishment of natural protection zones to prevent the occurrence of poaching. (3) Then, individuals with high genetic diversity should be used for breeding improved varieties, realizing artificial sowing and planting, and increasing the number of populations. Although the findings of this study have provided a foundation for SSR or random amplified polymorphic DNA analysis methods, this study only used ISSRs to analyze the genetic diversity of this population, and the PCR amplification system has not been optimized. Furthermore, few polymorphic primers were screened out for genetic diversity analysis. To obtain more comprehensive genetic information, future studies should expand the sampling area, increase the sampling volume, and use a variety of molecular markers. This study combined the analysis and evaluation of the genetic diversity and genetic structure of the wild resources of H. dentata in China, and the findings provide a theoretical basis for the protection of the plant resources of H. dentata.The results of this study showed that the gene flow among the populations of


Pseudocercospora fijiensis, by testing the historical hypothesis against other plausible hypotheses. We analyzed the genetic structure of eight populations from six eastern and western African countries, using 20 microsatellite markers and tested competing scenarios of population foundation using the ABC‐RF methodology. We do find evidence for an invasion front consistent with the historical hypothesis, but also for the existence of another front never mentioned in historical records. We question the historical introduction point of the disease on the continent. Crucially, our results illustrate that even if ABC‐RF inferences may sometimes fail to infer a single, well‐supported scenario of invasion, they can be helpful in rejecting unlikely scenarios, which can prove much useful to shed light on disease dissemination routes.The reconstruction of geographic and demographic scenarios of dissemination for invasive pathogens of crops is a key step toward improving the management of emerging infectious diseases. Nowadays, the reconstruction of biological invasions typically uses the information of both genetic and historical information to test for different hypotheses of colonization. The Approximate Bayesian Computation framework and its recent Random Forest development (ABC‐RF) have been successfully used in evolutionary biology to decipher multiple histories of biological invasions. Yet, for some organisms, typically plant pathogens, historical data may not be reliable notably because of the difficulty to identify the organism and the delay between the introduction and the first mention. We investigated the history of the invasion of Africa by the fungal pathogen of banana  Pseudocercospora finjiensis. Using an ABC‐RF approach, we investigated the history of the invasion of Africa by P. fijiensis by testing the historical hypothesis against other plausible hypotheses. We do find evidence for an invasion front consistent with the historical hypothesis, but also for the existence of another front never mentioned in historical records and question the historical introduction point of the disease on the continent.In this study, we illustrate that, in some cases, accepting not to use available historical data can be a fruitful way of reconsidering invasion processes using the model of the worldwide emergence of the fungal banana pathogen  The textbook invasion, described as a propagule being introduced from a native area to an invaded area, is now considered as an exception. Multiplicity of native populations, multiple introductions, and admixture, as well as bridgehead populations , have almost systematically been detected , provides a suitable model to address this issue. BLSD is the most destructive banana leaf disease and is considered as among the most serious threats to global food security . Each P. fijiensis individual corresponded to a fungal culture strain derived from a single ascospore  isolated from necrotic infectious banana leaf tissue as described in Zapater et al. . The genetic relationships between African populations, based on the Cavalli‐Sforza chord distance, were represented graphically using an unrooted neighbor‐joining (NJ) tree as implemented in populations‐1.2.32 (http://bioinformatics.org/~tryphon/populations/#ancre_telechargement). Node supports were obtained by implementing 1000 bootstraps. Tree visualization was done using the R packages treeio  within the Gabonese samples was significantly lower than in the other samples using the test of comparison among groups of samples implemented in fstat2.9.3  was significantly greater when a Gabonese population was concerned using a Wilcoxon test implemented in R v.4.1.0  convergence and consistency between the 10 replicate runs were examined for each value of K. The optimal number of clusters was determined using the Delta(K) method ], implemented in structure harvester web version 0.6.94 . ABC methods involve the comparison of observed and simulated datasets on the basis of summary statistics. In this study, we computed the following summary statistics using arlsumstat v.3.5.2, a command line version of arlequin  using Principal Components Analyses (PCAs) to project the summary statistics obtained from simulated and observed data and by investigating for each summary statistic whether the observed value falls within the simulated values drawn from prior distributions. PCAs were performed and visualized using the R packages abc v.2.1 Csilléry et al., We simulated 40,000 datasets for each of the eight scenarios using 33.1Hs: 0.17, Ar: 1.6) than in South‐East Asia , with very few private alleles. UGA was the most diverse African population . Private allele richness was highest in the COG population (pAr: 0.15), which might stem from COG being sampled about 100 generations after the other populations  did not appear to be significantly lower in these samples . Similarly, pairwise FST did not appear to be significantly higher when the population pair included a Gabonese population .None of the pairs of loci presented significant linkage disequilibrium after FDR control. Average genetic diversity was much lower in Africa . Individuals from NGA and GA1 were mainly assigned to the same cluster (yellow). A dominance of the red cluster was observed in UGA, while individuals from GA2, CA5, and CA6 had totally mixed ancestries in multiple clusters. The DAPC analysis did not allow to clearly identify a number of clusters, but from K = 4, increasing the number of clusters did not improve significantly the fit , and a similar structuration to that identified using STRUCTURE was observed for K = 4 or 5: the populations COG and CIV were differentiated from the others, which were distributed between the other clusters without a clear separation among them (results not shown).Applying Evanno's ΔAppendix . When ind Figure . HoweverK was for K = 3 (Appendix When we removed the more recently sampled population (COG population), the best model identified using the Evanno's ΔAppendix . ConsideAppendix . The pop3.3M index of some African populations (GWsd_2). Three other summary statistics were often found at the right extremity of the distribution: the standard deviation over loci and populations of the heterozygosity (sd_H) and of the Garza‐Williamson's M index (sd_NGW), and the standard deviation over loci of some African populations  revealed that the simulations performed under the eight scenarios using the parameter values drawn from prior distributions were relatively close to the observed dataset. First, the representation of the PCA in the space of the summary statistics showed that the observed dataset fell well within the simulated ones Appendix . Second,Table At first glance, our analyses allowed us to reject confidently the scenarios that simulated multiple introductions from the same south‐east Asian source (scenarios MI_mig and MI_nomig), suggesting that all the African populations studied here originated from a single African bridgehead population, which itself originated from a native population from south‐east Asia. Regardless of the African population pairs considered, the MI_mig scenario consistently showed a low to very low percentage of votes .3.4The ABC‐RF analyses coupled with the results of the private allelic richness and pairwise genetic differentiation support the historical hypothesis of a propagation front starting in Gabon to the West of Africa but also indicate the presence of at least a second invasion front going through Western Africa and originating from Eastern Africa.3.4.1FST ≈ 0) could indicate that both populations came from the same invasion front.First, regarding the Gabonese population GA2, the results from the ABC‐RF analyses are most often not conclusive except for the analysis including the population from Uganda (UGA) Table : the 10 Hs and no private alleles.Second, regarding the analyses that included the Gabonese population GA1, the scenario describing GA1 as the source of the other African populations often had a low percentage of votes Table  and S11dRegarding the relationship between the two Gabonese populations GA1 and GA2, we could clearly reject the scenario stipulating that GA2 originated from GA1 as well as the hypothesis of recurrent migration between the two populations since their establishment, as all the scenarios that included migration received low support , suggesting that the population from Uganda acted as a bridgehead population for other African populations. The ABC tests including the population of Uganda (UGA) clearly favored the scenario of UGA being the source of the populations NGA from Nigeria, CA5 and CA6 from Cameroon, GA1 from Gabon and CIV from Ivory Coast, without subsequent migration between the source and the newly founded populations. All those tests showed an agreement among the 10 replicates with a relatively high percentage of votes for that scenario relatively to the others and a low to very low support for the scenario of the population from Uganda being the receiving population Table  and S11e3.4.3First, it is worth mentioning that for 8 pairs of populations, the scenarios with recurrent migration after founding consistently found very low support  whatever the historical scenario considered Table  and S11gThese results suggest that those west African populations may have been founded through rare long‐distance dissemination events rather than in the form of a continuous invasion front.Conversely, a scenario with migration between the African populations considered in the analysis was favored for the population group including the populations from Cameroon, CA5 and CA6, and the population GA2 from Gabon, as well as the populations from Ivory Coast (CIV) and from Congo (COG) although these last two did not exchange individuals with all of the other populations. For these pairs of populations, ABC scenario choice was inconclusive considering the historical scenario of founding (any of the two populations could have founded the other one), but scenarios with migration received more votes than scenarios without migration. The difficulty for those 3‐population tests to discriminate between the different scenarios might come from the presence of recurrent migrations between the two African populations since their establishment. Such a pattern is compatible with the assumption of a continuous invasion front from Gabon to Cameroon and Ivory Coast as described historically.Ar and a relatively high level of gene diversity suggesting older occurrence than the others populations.The population from Congo (COG), which could have been founded by the population from Uganda UGA, might also have played an important role in the establishment of other west African populations, such as the populations from Ivory Coast (CIV), from Nigeria (NGA) or from Gabon (GA1), as it could have served as a bridgehead population through stochastic founder events. Besides, scenarios including migration between the (COG) population from Congo and other west African populations from Cameroon (CA5 and CA6) and from Gabon (GA2) often had higher percentages of votes than the scenarios without migration, indicating recurrent migration between the population COG and the two Cameroonian populations and the Gabonese population GA2. The COG population also presented the highest level of private allelic richness 44.1P. fijiensis in Africa. We did find support for the existence of a front stemming from Gabon and going through Cameroon to Ivory Coast. Indeed, both between Gabon and Cameroon and between Cameroon and Ivory Coast scenarios including recurrent migration better represented the data than scenarios assuming pure divergence without migration. This suggests that populations from these countries were connected by recurrent gene flow in a stepping‐stone manner, which is consistent with the idea of a continuous front.According to the most commonly expressed historical hypothesis, the invasion of West Africa resulted from the introduction into Gabon of infected plant material brought from Asia, in 1978 , as well as two samples from neighboring countries (COG from Congo and NGA from Nigeria) were shown to originate from the population from Uganda (in Eastern Africa) rather than from the Western continuous propagation front. Between all five populations, scenarios without migration were preferred over scenarios assuming recurrent gene flow, suggesting a stochastic propagation associated with rare long‐distance migration events between these populations of distant countries.A recent study of BLSD invasion in an island context (Caribbean archipelago) has demonstrated that the two northern and southern fronts are mainly explained by both historical data and population genetic structure  and airborne sexual spores maintained during invasion as suggested in Carlier et al.  in its area of origin, before its introduction into Africa. The adaptive challenge faced in Africa was therefore probably mild enough for the invasion to succeed regardless of the degree of impoverishment of evolutionary potential during bottlenecks. Scenarios describing previous adaptations of invading populations to widespread human‐altered environments with homogeneous features are increasingly being identified in studies of damaging invasive species ; methodology ; writing – original draft . A. Rieux: Formal analysis ; methodology ; writing – review and editing . S. Robert: Conceptualization ; data curation ; formal analysis ; methodology ; writing – original draft . R. Vitalis: Formal analysis ; methodology ; writing – review and editing . M.‐F. Zapater: Data curation ; resources . C. Abadie: Data curation ; resources ; writing – review and editing . J. Carlier: Data curation ; resources ; writing – review and editing . V. Ravigné: Conceptualization ; formal analysis ; methodology ; supervision ; writing – original draft .This work was supported by the Région Languedoc‐Roussillon, the Agence Nationale de la Recherche (project EMERFUNDIS ANR 07‐BDIV‐003 and EMILE ANR 09‐BLAN‐0145‐01), and French Agropolis Fondation . VR was funded by the European Union , the Conseil Régional de La Réunion, and the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD).Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.Appendix S4Click here for additional data file.Appendix S5Click here for additional data file.Appendix S6Click here for additional data file.Appendix S7Click here for additional data file.Appendix S8Click here for additional data file.Appendix S9Click here for additional data file.Appendix S10Click here for additional data file.Appendix S11Click here for additional data file.


The Khmuic-speaking populations are believed to be the descendants of one of the earliest groups to settle in Mainland Southeast Asia. In Thailand, there are two agricultural Khmuic-speaking ethnic groups, the Khamu and Lua (Htin). These peoples primarily reside in scattered locations along the mountainous Thailand–Laos border in Nan province. In this study, we conducted genome-wide SNP analysis on 81 individuals from three Khamu and two Lua villages in northern Thailand. Our findings revealed that both the Khamu and Lua groups possess genetic structures that are distinct from other ethnicities in Southeast Asia, indicating a unique history of migration and settlement. Within the Khmuic group, the Khamu populations living in different locations exhibited similar genetic structures and displayed genetic affinities only with some hill-tribes and Tai-Kadai (Kra-Dai)-speaking groups in Thailand, suggesting potential intermixing or cultural exchange. Furthermore, the Lua people displayed a distinctive population structure, which could be attributed to the founder effect and endogamous marriage practices. Additionally, we discovered a relationship between the Khmuic-speaking populations in Thailand and a Neolithic ancient sample obtained from the Tham Pha Ling archaeological site in Laos. This study provides new insight into genetic substructure within the Khmuic-speaking people and their potential relationship to the indigenous inhabitants of Mainland Southeast Asia. With a population of more than 500,000, Khmuic-speaking group make up the second-largest ethnic minority in Laos, after the majority Lao ethnicity1. It is generally believed that these people previously lived in a much larger area than they do now, before being pushed or absorbed by succeeding Tai-Kadai (Kra-Dai) speakers who migrated into the lowlands of Southeast Asia sometime between 1000 and 2000 years ago2.Khmuic-speaking people refers to a collection of ethnic groups who speak language that belongs to the Khmuic branch of the Austroasiatic linguistic family. They are dispersed across Laos, Vietnam, China, and Thailand. Additionally, there is a small Khamu community that was established in the United State as refugees from the Vietnam War. Although there is little known about the early history of the Khmuic-speaking people, archaeological evidence suggests that they were among the earliest peoples to arrive in Mainland Southeast Asia3. The Khamu ethnic group primarily live in the northern region of Thailand, particularly in Chiang Rai, Nan, and Phayao provinces, with a population of roughly 10,198 people. The word Khamu, and its different derivation include Kmhmu, Kemu, Khmu, and Khammu, mean “real people” or “human being”. The Khamu are thought to be one of the oldest ethnic groups of Thailand who have a long history of coexisting peacefully with their natural environment. However, many of the Khamu people in Thailand are recent immigrants from Laos and Vietnam who fled the Vietnam War and the ensuing communist regimes. Since at least 200 years ago, they have migrated over the borders to regions in Thailand for searching new agricultural land3. The majority of Khamu settlements are remote, however in some places, the Khamu coexist with other local minority ethnic groups.In Thailand, there are two Khmuic-speaking ethnic groups, the Khamu and Lua , that are recognized by the government as hill tribes3. These people prefer their self-designation autonym as Lua, while the Thai government tends to call them as Htin which means local people. In some records, these people were erroneously identified as the Lawa, but they have to be distinguished from the unrelated Lawa ethnic group in northwestern Thailand. There are two subgroups of Lua people, the Mal and the Prai (or Pray), who are closely related, but have slightly difference in their spoken dialect. The majority of Lua people are dispersed throughout the mountainous region along the Thailand–Laos border, Nan province of Thailand and Sainyabuli province of Laos. There is substantial scholarly controversy over whether the Lua people had been in Thailand since prehistoric times or whether they moved from northern Laos at a later time. More likely, the Lua people began to settle in Thailand in the late 19th or early 20th century. However, some academics contend that the Lua were initially based in Nan province of Thailand before migrating to Laos and eventually returning along an old migration route to their original homeland1.The Lua is another hill-tribe in Thailand who speaks Khmuic language, with a population of approximately 48,025 individuals4. Further research found that these people shared recent common ancestry with the Mlabri, a group of nomadic hunter-gatherers of Thailand, and exhibited unique genetic structure that set them apart from the other ethnic groups in the country5.Marking one of the earliest genome-wide studies on the genetic diversity and structure of the Khmuic-speaking people, a Htin (Lua) population  from Nan province of Thailand were genotyped for roughly 50,000 autosomal SNPs along with a large geographic sample of Asian populations in 2009. The Htin (Lua) were discovered to have relatively low genetic diversity because of their small population size and isolation from other ethnic groups7. The same sample set of the Khmuic-speaking group, one Khamu, two Htin, and one Mlabri populations, was then reanalyzed using high resolution genome-wide SNP data. Researchers discovered that the Khmuic-speaking people shared a genetic heritage with Katuic-speaking people, another branch of the Austroasiatic language family. The study also discovered that the group of Khmuic/Katuic-speaking individuals shared their genotypes strongly within their own group but less frequently with other ethnic groups. However, the Khmuic and Katuic speaking people also showed some degree of intermarriage and cultural exchange with the Tai-Kadai-speaking groups in Laos and Northeastern Thailand that promoted their genetic affinity8.The genetic evidence that supports the ancestral affinity between the Khamu to the Htin (Lua) and Mlabri was first reported based on mitochondrial DNA variation, and then Y-chromosome sequencingDespite the fact that earlier genetic research on the Khmuic-speaking people had revealed their separate origin from other ethnic groups in the area, the small number of populations investigated prevented further insight of their diversity and ancestry. Therefore, using 81 Khmuic-speaking individuals from 5 villages in northern Thailand (three Khamu and two Lua populations), we produced an expanded genome-wide SNP data set. Allele and haplotype sharing within and between these two ethnic groups were analyzed and compared with surrounding Southeast Asian modern populations as well as ancient samples. Our findings reveal a fine-scale genetic substructure and a probable ancestry of the Khmuic-speaking people.13. Using a set of 149,384 SNP positions, we analyzed a total of 959 individuals, including various Asian samples, and plotted the results on a PCA plot  to measure genetic affinity between populations X and Y when separated from the outgroup (Mbuti population). Higher f3-statistics indicate closer genetic affinity between populations. Among various ethnicities in Thailand, the Thai and Mon populations had the lowest f3-statistics compared to other ethnic groups, including ancient DNA. Conversely, the Lua, Htin, and Hmong populations exhibited the highest f3-statistics. Notably, the Lua population displayed close genetic affinity with the Htin population, while the Khamu populations exhibited the greatest genetic diversity within the Khmuic language group , where W represents a selected Austroasiatic-speaking Soa ethnic group, X is another population in the Khmuic branch of the Austroasiatic family, and Y is a population outside the Austroasiatic family. Conventionally, a Z-score > 3 or <  − 3 indicates that Y shares significant excess ancestry with W or X, respectively. Nonsignificant Z-scores indicate that W and X form a clade and share equivalent amounts of ancestry with Y. Please take note that the Austroasiatic-speaking Bru population, which exhibits similar genetic structure to our chosen reference group, the Soa population, has not been investigated in this analysis. According to the f4-statistics, we found that the Khmuic-speaking populations closely aligned with the groups speaking Austroasiatic languages, particularly the Palaungic branch . When comparing the Khamu and Lua populations, the Khamu exhibited closer genetic affinity with the Tai-Kadai language group and some hill tribes of Thailand, such as Hmong, IuMien, and Lisu , we assessed if any ethnic group in Thailand displayed affinity with Southeast Asian ancient samples compared to the Han Chinese , Monic (Mon), and Katuic  Fig. . After s3. Through the analysis of high-resolution autosomal markers, we discovered that the genetic structure of the Khamu and Lua people differs significantly from other ethnic groups in Thailand and East Asia. The outgroup f3-statistics have indicated a significant degree of genetic drift shared among populations speaking the Khmuic language  ethnic group demonstrates reduced diversity and their effective population size decreasing around 2000–2500 years ago, especially subsequent to the migration of Tai-Kadai-speaking populations into Southeast Asia roughly 1000–2000 years ago15. This observation suggests that the genetic composition of contemporary Khmuic-speaking populations could have been notably influenced by a restricted number of initial settlers, resulting in decreased genetic diversity and heightened differentiation from other groups. However, a more comprehensive exploration involving various uniparentally inherited markers within the Khamu and Lua populations is required to elucidate the precise impact of the founder effect on these ethnic groups. Additionally, the mountainous northern region of Nan Province, where the Khamu and Lua villages are located . These groups have settled along the Thailand–Laos border, particularly in Nan provinceage Fig. . While o1. Some Khamu individuals settled in Thailand and interacted with lowland Tai-Kadai-speaking groups, resulting in cultural exchanges and possible intermarriage. For example, some Khamu people wear hand-woven textiles similar to those of the Tai Lue and are referred to as “Khamu Lue” reflecting the cultural influence of the Tai Lue in Khamu communities.Within the Khmuic-speaking group, a comparison of different Khamu populations living in various areas of Nan province, such as Mueang District, Tha Wang Pha District, and Song Khwae District Fig. , reveale1, where Tibeto-Burman-speaking people of the Sino-Tibetan language group are prevalent. The presence of Naxi-related ancestry in the Khamu of Thailand gene pool as seen in our result . Our genome-wide genotyping confirms that these two ethnic groups are, in fact, the same group. Despite the Lua people increasingly engage into lowland day-labor and their lifestyle gradually blend with other ethnic groups, our comprehensive genomic data still demonstrates that the genetic structure of the Lua people in Nan province remains distinct and different from that of other ethnic groups. This finding is consistent to a previous study based on mitochondrial DNA analysis, which identified unique haplogroups B6a, F1a1a, and M12a1a exclusive to the Lua (Htin) people and absent in any other ethnic groups in the northern region of Thailanders Fig. . Our admers Fig. .17. Currently, the Mlabri people are found only in two provinces of Thailand, namely Phrae and Nan, with a population of approximately 400 individuals18. Previous studies using genomic analysis have supported genetic affinities between the Mlabri and the HtinMal groups5. Furthermore, uniparental markers indicate paternal relationships among the Mlabri, HtinMal, HtinPray, and Khamu ethnic groups, while maternal genetic relationships are observed between the Mlabri and the Soa and Bru groups, who speak the Katuic language in northeastern Thailand7. Our present genomic data also confirm the close relationship between the Khmuic-speaking ethnic groups and the Mlabri, with the Lua people being closer to the Mlabri than the Khamu  of Khmuic language group share the same ancestry with the Mlabri, who is believed to be the descendants of indigenous inhabitants of Mainland Southeast Asia.There is a small group of hunting-gathering nomads living in northern Thailand, the Mlabri. The term “Mlabri” translates to “people in the forest,” combining “Mla” meaning people and “Bri” meaning forest. They have a distinct characterized by a nomadic lifestyle, constantly moving without permanent settlements. Some scholars propose that the Mlabri people are direct descendants of the Hoabinhian group, which was the original population in Southeast Asia9. The DNA was extracted from human remains aged approximately 2378–3071 years ago represents a link between the end of the Neolithic period and the Bronze Age. The genetic relationship between the Khmuic language group and the ancient DNA from Laos  predominantly reflects a first neolithic farmer-like source, a conclusion supported by comprehensive genome-wide SNP data. This Khmuic ancestry, alongside the Neolithic Fujianese-like source prevalent among Austronesian speakers, contributes to the preservation of genetic sub-structure within Southern China, a phenomenon established prior to the Holocene21. The presence of Khmuic ancestry across various Southeast Asian populations underscores the importance of investigating genetic information of these people. Our admixture graph results  living in northern Thailand. Our findings revealed that both the Khamu and Lua ethnic groups possess genetic structures that are distinct from other ethnicities in Thailand and Southeast Asia, indicating a unique history of migration and settlement. Upon examining within the Khmuic linguistic group, we observed that the Khamu populations living in different locations displayed similar genetic structures. Moreover, they exhibited genetic affinities with some hill-tribes and Tai-Kadai-speaking groups in Thailand, suggesting possible intermixing or cultural exchange over time. On the other hand, the Lua people displayed a distinct population structure, likely influenced by the founder effect and endogamous marriage practices.A significant finding of this study was the discovery of a genetic relationship between the Khmuic-speaking people in Thailand, particularly the Lua subgroup, and the Neolithic ancient DNA obtained from the archaeological site in Laos. This close genetic relationship between ancient DNA and the present-day ethnic group highlights the continuity of the population's development from ancient times to the present. This study provides new insight into genetic substructure within the Khmuic-speaking people and their potential relationship to the indigenous inhabitants of Mainland Southeast Asia.Ethical approval of this study was granted from the Human Experimentation Committee of the Research Institute for Health Sciences, Chiang Mai university, Thailand . During the research, we protect the rights of participants and their identity, and we confirm that all experiments were performed in accordance with relevant guidelines and regulations based on the experimental protocol on human subjects under the Declaration of Helsinki. Written informed consent from all volunteers was obtained prior to the interview and sample collection.A total of 95 unrelated subjects residing in five villages of Nan province, Thailand, were enrolled with written informed consent. Volunteers were healthy subjects who were over 20 years old, of Khmuic-speaking ethnicity and had no ancestors that were known to be from other recognized ethnic groups for at least three generations. We collected personal data using form-based oral interviews for self-reported unrelated lineages, linguistics, and migration histories. Following the manufacturer's instructions, we collected buccal or saliva samples and extracted DNA using the Gentra Puregene Buccal Cell Kit .10. Affymetrix Genotyping Console v4.2’s primary screening produced a total of 93 samples that were genotyped for 622,834 loci on the hg19 version of the human reference genome coordinates . We used PLINK version 1.90b5.224 to exclude loci and individuals with more than 5% missing data and also exclude mtDNA and sex chromosome from our analysis. We further excluded loci that did not pass the Hardy–Weinberg equilibrium test  or had more than 5% missing data, within any population. We used KING 2.325 to determine individual relatedness, and we removed one person from each pair of first degree kinship. After these quality control measures, there are 81 Khmuic-speaking people  less than 3.3×10–4 or were not in Hardy–Weinberg equilibrium with a significance level of P < 0.00005. As a result, 353,505 positions in a dataset consisting of 979 individuals from 90 populations  was carried out using smartpca from EIGENSOFT with the "lsqproject" and "autoshrink" options.In order to investigate the genetic structure and relationships of the analyzed sample, we used PLINK version 1.90b5.2 to perform pruning for linkage disequilibrium, excluding one variant from pairs with 14 was run from K = 2 to K = 10 with 100 replicates for each K and using random seeds with the -P option. For each K, the top 20 ADMIXTURE replicates with the highest likelihood for the major mode were displayed using PONG version 1.4.726. For these PCA and ADMIXTURE analyses, the ancient samples and highly drifted modern populations  were projected.To infer population structure, we employed 155,709 SNP positions derived from a sample set of 979 individuals, which encompassed both Asian samples and the outgroups represented by the Mbuti and French populations, for the ADMIXTURE analysis. The clustering tool ADMIXTURE version 1.3.027 from ADMIXTOOLS version 5.110 to calculate the f3 and f4-statistics, with assessed through block jackknife resampling across the genome and using Mbuti as the outgroup. A total of 353,505 SNPs from 979 samples were used in these analyses. Additional f4-statistics were computed when ancient samples were involved, using French as the outgroup to avoid deep attraction to Africans and only transversions  to avoid potential noise from ancient DNA damage patterns28. We used pheatmap package in R version 3.6.0 to visualize the heatmap of f3 and f4 profiles.To test admixture and excess ancestry sharing, we used admixr version 0.7.129 to phase the modern samples. We employed South Asian and East Asian populations as a reference panel (excluding the Kinh Vietnamese) and the recombination map from the 1000 Genomes Phase330 was also used. Our analysis specifically focused on modern population data, consisting of 359,539 SNPs. For the preparation of the reference panel, we extracted individuals of East and South Asian descent, as well as the overlapping sites with our data, for each chromosome from the 1000 Genomes Phase3 data using bcftools version 1.4. The phasing accuracy of SHAPEIT4 can be improved by increasing the number of conditioning neighbors in the Positional Burrows-Wheeler Transform (PBWT) on which haplotype estimation is based29. We conducted phasing with the option -pbwt-depth 8 for 8 conditioning neighbors, while keeping other parameters as default. Subsequently, we employed ChromoPainter version 231 on the phased dataset to initiate the investigation of haplotype sharing with sample sizes for each population were randomly down-sampled to 4 and 8. The former was used for 10 iterations of the EM (expectation maximization) process to estimate the switch rate and global mutation probability. The latter was employed for the chromosomal painting process with the estimated switch and global mutation rates. The output of this process was then used for downstream analyses. We then attempted to paint the chromosomes of each individual, with all the modern Asian samples serving as donors and recipients via the -a argument. The EM estimation yielded a switch rate of approximately 251.21 and a global mutation probability of approximately 0.00001, which were subsequently used as starting values for these parameters for all donors in the painting process. The heatmap results were generated using the pheatmap package in R.To examine the haplotype sharing between different groups, we used SHAPEIT version 4.1.3f4-statistics to choose representative ethnic groups that speak Austronesian, Tai-Kadai, Austroasiatic, Hmong-Mien, and Sino-Tibetan languages, which included Atayal, Dai, Cambodian, Miao, and Naxi, respectively. We employed the African Mbuti and North Indian populations  who speak Indo-European languages as outgroups. Our focus was on constructing the admixture graph for the Austroasiatic language family in Thailand. Thus, we categorized these populations according to their linguistic branches; Katuic (Bru and Soa), Monic (Mon), Palaungic , and Mlabri. Our interested Khmuic-speaking people were divided into the Khamu (consist of four Khamu populations) and Lua .To construct the admixture graph, our initial step involved selecting backbone populations from different language families in Southeast Asia. Specifically, we used 32. Initially, we computed pairwise f2 statistics between the groups using the “extract_f2” function with specific parameters; “maxmiss = 0” , “useallsnp: NO” , and “blg = 0.05” (SNP block size set in 0.05 morgans). Then, we extracted allele frequency products from the computed f2 blocks using “f2_from_precomp”. Next, for each scenario, we searched for the best-fitting admixture graph by running ten independent runs of “find_graphs”. From the 100 independent runs, we selected the one with the lowest score  using “random_admixturegraph”. To confirm the fitting graph, we tested the graph with the lowest score using “qpgraph” with parameters “numstart = 100, diag = 0.0001, return_fstats = TRUE”. This allowed us to check if the absolute value of the worst-fitting Z score was below 3. Starting with no migrations (numadmix = 0), we gradually added migrations until we found a fitting graph, which we considered as the best-fitting graph for that particular scenario.For modeling the admixture graph, we used a dataset of 359,539 SNPs from modern populations as the input for ADMIXTOOLS 2Supplementary Figures.Supplementary Tables.