Pinctada fucata population in the Mischief Reef of the South China Sea, as well as diversity and structure of the P. fucata populations. Population genomics data clearly revealed a closer genetic relationship between the Mischief Reef introduced population and the Lingshui population, indicating that Lingshui may be the potential geographical origin. Furthermore, several selected genomic regions and genes of the introduced population were identified, some of which may play important roles in the adaptation of temperature and salinity tolerance.In the present study, we employed population genetics analysis to investigate possible origins of the introduced Pinctada fucata population in the Mischief Reef of the South China Sea. The results of population genetic structure analysis showed a close relationship between the Mischief Reef introduced population and the Lingshui population, indicating that Lingshui may be the potential geographical origin. Furthermore, lower heterozygosity and nucleotide diversity were observed in the introduced population in Mischief Reef, indicating lower genetic diversity than in other native populations. We also identified some selected genomic regions and genes of the introduced population, including genes related to temperature and salinity tolerance. These genes may play important roles in the adaptation of the introduced population. Our study will improve our understanding of the invasion history of the P. fucata population. Furthermore, the results of the present study will also facilitate further control and prevention of invasion in Mischief Reef, South China Sea.Among the anthropogenic stresses that marine ecosystems face, biological invasions are one of the major threats. Recently, as a result of increasingly intense anthropogenic disturbance, numerous marine species have been introduced to their non-native ranges. However, many introduced species have uncertain original sources. This prevents the design and establishment of methods for controlling or preventing these introduced species. In the present study, genomic sequencing and population genetic analysis were performed to detect the geographic origin of the introduced  Marine ecosystems are threatened by a number of anthropogenic stresses, including overexploitation of living resources, pollution, climate change, biological invasion and others . NumerouPinctada  are widely distributed between the Indo-Pacific and Western Atlantic tropical and subtropical shallow-water areas, most of which are associated with reef environments [Pinctada fucata , also named P. martensii, is an economically important bivalve native to the coastal waters of the Tropic of Cancer and the Tropic of Capricorn in the Pacific Ocean [Pearl oysters belonging to the genus ronments . Pinctadic Ocean . It is pic Ocean ,11,12.Pinctada can be found in this area. Wang [Pinctada maculata was a unique species of the genus Pinctada in the Nansha Islands sea area. However, during a recent sampling at Mischief Reef, P. fucata was found in extreme abundance. According to the local fishermen, the occurrence of P. fucata in Mischief first occurred in 2016  sequencing and comparative genomics to detect the geographic origin of invasive P. fucata individuals were obtained from four geographic sites, including the native range  and non-native range   . All spehttps://github.com/lh3/bwa, accessed on 24 October 2017) was applied to align the clean reads (default parameters) to the P. fucata reference genome sequence [https://github.com/samtools/samtools/releases, accessed on 19 July 2018) [https://github.com/broadinstitute/gatk/releases, accessed on 24 April 2019) was utilized to identify and call high-quality SNPs, with refinement stages similar to those of Todesco et al. [BWA-mem  . GATK too et al. ,27. FinaP. fucata populations, wθ, πθ and Tajima’s D values were calculated for the assessment of genetic diversity using a sliding window approach and the step and window sizes were set as 5 kb and 10 kb, respectively [P. fucata populations pairwise, the fixation index (STF) values were also calculated in the same windows [P. fucata population from Mischief Reef and other populations. Principal component analysis was conducted via PLINK  and GCTA  [P. fucata population, PLINK (version 2.0) was also used to calculate the expected heterozygosity (eH) and observed heterozygosity (oH). Furthermore, admixture analysis was performed to detect population structure by using ADMIXTURE (version: 1.3.0) [For all ectively ,29. To m windows . We asseer 2019) . To esti: 1.3.0) . The numstG, mN and D [We also evaluated the genetic connectivity between the four populations by examining the gene flow between them. The analysis was performed based on the directional migration method by estimating the relative migration rates derived from Nm and D ,34,35. TNm and D .https://github.com/BGI-shenzhen/VCF2Dis, accessed on 25 July 2022) to calculate the pairwise genetic distances (p distance). Then, PHYLIPNEW  was utilized to construct an NJ tree (Neighbour-Joining tree). The squared Pearson’s correlation coefficient (r2) was calculated to evaluate the linkage disequilibrium (LD) in four P. fucata populations. The analysis was performed by using PopLDdecay version 3.40  [r2 values.Furthermore, we used VCF2Dis  . Then, wP. fucata, we screened the SNPs called across the introduced population and its potential original population (Lingshui population). Then, we estimated pairwise differentiation between the two populations and Tajima’s D, STF and ROD (1 − π-Mischiefθ/π-Lingshuiθ) were calculated by using a sliding window approach. The window size was 10 kb and the step size was 5 kb. In our study, an empirical threshold of 5% was set to identify outlier windows of STF (top 5%), Tajima’s D (bottom 5%) and ROD (top 5%) values. The outlier windows were considered candidate regions for selection with the following criteria: the top 5% of STF values or the top 5% of ROD values with the bottom 5% of negative Tajima’s D values.To identify selected genomic regions and genes of the introduced population of Pinctada martensii reference genome . The average mapping rate was 94.89% (range from 93.63% to 95.15%). In the present study, we selected the top 30 samples with the highest sequencing coverage for calibration. SNPs with a missing rate less than 50% and MAF greater than 0.01 were utilized for the downstream analysis. Then, we obtained 3,476,259 SNPs. Of these SNPs, 254,373 SNPs (7.32%) were aligned to exonic regions, 1,017,991 SNPs (29.28%) were aligned to intronic regions and 1,956,307 SNPs (56.28%) were aligned to intergenic regions. Among the 254,373 SNPs in exonic regions, 100,402 were nonsynonymous and 147,749 were synonymous.With the sequencing of 74 samples from four sites , we genewθ and πθ values of 0.005. Among the four populations, the wθ and πθ of the nonlocal population (Mischief Reef) were lowest, showing a lower gene diversity than in the other three populations (The genetic diversity of the four populations was assessed by estimating the nucleotide diversity. As shown in ulations A. The reP. fucata populations (The results of PCA showed a clear genetic relationship among the four ulations B. The toulations C. The toK = 2) unsuitable for two reasons. First, only Haiphong clearly formed an independent group and the introduced population in Mischief Reef was undistinguishable from the Lingshui and Zhanjiang populations. Furthermore, the CV error gap between K = 2 and K = 3 was not very significant and the second-order rate of change increased after K = 3. Thus, the clear genetic relationship when the ancestral cluster number was three provides a more biologically suitable argument than the more conservative number. The fixation index (STF) was then calculated to measure the genetic difference between the Mischief Reef population and other local populations  in a short time . The resulations . MeanwhiD for populations were significantly different. Among these populations, only the Tajima’s D of the Zhanjiang population was close to zero. For the Lingshui population and Mischief Reef population, the values were significantly lower than zero, showing potential selective sweeps.In the present study, the values of Tajima’s P. fucata. As shown in STF values, 436 genes were among the top 5% in terms of ROD values and 37 genes were among the top 5% of both STF values and ROD values. Among the 481 genes, 416 genes were annotated to the Nr, KEGG or GO database  of the P. fucata populations at the whole-genome level was 0.0034–0.0051, indicating significantly higher genetic diversity than in other studies [P. fucata populations in Lingshui and Zhanjiang showed expected heterozygosity values of 0.2636 and 0.2374, respectively. In our study, the expected heterozygosity of the four populations was 0.2377–0.2622. Thus, the genetic diversity of the four populations in our study was moderate. However, the Mischief Reef population showed lower nucleotide diversity than the other populations in the present study. Notably, our data clearly revealed lower genetic diversity in the introduced population than in the potential original population. This result is consistent with previous studies of introduced populations [In the present study, the nucleotide diversity ( studies . Huang e studies  estimateulations . It is wulations ,49,50.P. fucata populations. Furthermore, relatively rapid LD decay was observed in the Mischief Reef population. Previous studies indicated that a dramatic increase in LD was a common phenomenon in most crop domestication and some invasive organisms [As a critical parameter in population genetics, LD could provide deep insight into the complex population history, including selection, hybridization and mutation, of the introduced population during invasion . Unexpecrganisms ,52. We srganisms . Some rerganisms ,54. HencD represents the frequency of rare variants at a genomic locus. These parameters indicate whether the regions and genes are under selection. In our study, the Tajima’s D values of nonnative population and its potential origins were negative and significantly lower than those of other native populations. The results indicated an excess of rare nucleotide site variants compared to the expectation under a neutral model of evolution [The allele frequency and nucleotide diversity differentiation between populations provide important genetic evidence of selection processes . Meanwhivolution .P. fucata populations [Furthermore, we found some genes that were under selection in the introduced population, including the heat shock transcription factor 1 gene, an endoglucanase gene, a serine/threonine-protein phosphatase gene, a sodium channel protein and other genes. However, due to the limited number of selected genes, enrichment pathway analysis could not be performed. According to previous studies, salinity and sea surface temperature are the major factors affecting the distribution of ulations ,57. The P. fucata populations and an introduced nonlocal P. fucata population. Our results revealed a closer relationship between the Mischief Reef introduced population and the Lingshui population, indicating that Lingshui may be the potential geographical origin. Furthermore, our data revealed lower genetic diversity in the introduced population than in the potential original population. In the introduced population, we also identified some selected genomic regions and genes related to temperature and salinity tolerance. Our study will facilitate further aquatic conservation and protection in Mischief Reef, South China Sea. In the present study, we analysed the population genetic structure and diversity of three native


Quercus suber L. is a sclerophyllous tree species native to the western Mediterranean, a region that is considered highly vulnerable to increased temperatures and severe dry conditions due to environmental changes. Understanding the population structure and demographics of Q. suber is essential in order to anticipate whether populations at greater risk and the species as a whole have the genetic background and reproductive dynamics to enable rapid adaptation. The genetic diversity of Q. suber has been subject to different studies using both chloroplast and nuclear data, but population structure patterns remain unclear. Here, we perform genetic analyses on Q. suber using 13 nuclear microsatellite markers, and analysed 17 distinct locations across the entire range of the species. Structure analyses revealed that Q. suber may contain three major genetic clusters that likely result from isolation in refugia combined with posterior admixture and putative introgression from other Quercus species. Our results show a more complex structure scenario than previously inferred for Q. suber using nuclear markers and suggest that different southern populations contain high levels of genetic variation that may contribute to the resilience of Q. suber in a context of environmental change and adaptive pressure. The geographical distribution, demography and genetic diversity of European tree species have been shaped by environmental oscillations along the Quaternary period, with alternating expansions and contractions for both warm and cold adapted species . Understvia pollen is thought to enhance migration and adaptation to climatic changes in tree species and compensate for their long generation-time, particularly in species with continuous geographical distribution  is an evergreen sclerophyllous oak species of major ecological and economic importance in the western Mediterranean region, from the Iberian peninsula to southern Italy, and from Morocco to Tunisia. Where it is dominant, it forms a habitat that harbours important numbers of animal species . For the remaining sites , material was obtained from a cork oak provenance trial (FAIR I CT 95 0202) established in 1998 at Herdade Monte da Fava  as part of the European Forest Genetic Resources Programme  µM of each primer. Amplifications were performed under the following general conditions: an initial denaturation step at 94 °C for 5 min followed by 30 cycles consisting of denaturation at 94 °C for 30(60) s, annealing at 50(57) °C for 30 s, extension at 72 °C for 30(60)s and a final extension step at 72 °C for 10 min. PCR products were analysed by capillary electrophoresis using the ABI PRISM 310 automated sequencer. Genotypes were scored and visually controlled using GENEMAPPER v3.7  The software MICRO-CHECKER v2.2.3 (A set of 13 simple sequence repeat (SSR) loci was used for analyses. Of these, ten are anonymous nuclear microsatellites (nuSSR): MSQ13, MSQ4 ; QpZAG9,R v2.2.3  was usedo) and expected (He) heterozygosity, the W&C (IS ), fixation index (FST) and the number of observed alleles (Na) per locus were calculated using the GENEPOP package v 1.1.7  where one cluster contained Lazio, Sardinia, Sicily, a second cluster contained Corsica, Mekna, Guerbès, Apulia and the third cluster contained all other sites. The frequency of null allele (fna) per locus was calculated using PopGenReport  and allelic richness (Ar) per population were calculated with PopGenReport v 3.0.4. Pairwise FST across all populations was calculated for the 13 loci and the neutral loci data sets  with HIERFSTAT . Linkage disequilibrium (LD) across all pairs of loci and all 17 sampling sites was tested using GENEPOP with the default Monte Carlo based G-test for the null hypothesis of independence among genotypes at different loci, and the log likelihood ratio as test statistic .Deviations from Hardy–Weinberg Equilibrium (HWE) were estimated for the 13 loci and 17 sampling sites using GENEPOP, with the Monte Carlo based exact test for a null hypothesis of random union of gametes, and Quercus suber were inferred using the clustering method implemented in STRUCTURE v 2.3.4 , with the same parameters as the first analysis.Structure patterns among sampled populations of  v 2.3.4 . The pro v 1.2.2 . An anal v 1.2.2 . FinallyStructure_threader v 1.2.2  and correspondence analysis (CA) were performed, respectively, using the methods implemented in the R packages pcaMethods v 1.84  and ADEGhttps://github.com/CoBiG2/Qsuber_mssats.Data files and scripts generated for analyses can be found at e.g., K = 3 (see Results), with one cluster comprising the sites Sardinia, Lazio, Sicily, a second cluster comprising the sites Corsica, Apulia, Guerbès, Mekna, and a third comprising the remainder of the sites.Tests of non-neutrality were performed on the 13 SSR loci dataset using the program BAYESCAN v 2.1  which ase ranged between 0.187 (QpZag9) and 0.86 (QpZag110), while Ho ranged between 0.142 (QpZag9) and 0.777 (QpZag110). The average frequency of null alleles over all 13 loci, inferred with the method of ST per locus ranged between 0.013 (QpZag9) and 0.512 (MSQ13). The mean FIS  was always positive and ranged between 0.019 (MSQ13) and 0.263 (QmAJ1) when considering the populations separately and between 0.07 (QrZag7) and 0.27 (QmAJ1) when considering K = 3  and 0,183 (Lazio)  but differ from the remainder (FST = 0.1–0.24). Corsica also showed differentiation (FST = 0.1–0.21) from all other sites  . Pairwiser sites .p < 0.05) and in all loci except QrZag7 (p < 0.05) at eight out of 78 pairwise comparisons, when considering 17 sample sites (Exact tests of Hardy–Weinberg Equilibrium (HWE) showed an overall departure in all populations (t QrZag7 . Tests ole sites .K = 2) identified two candidate outlier loci, MSQ13 and QpZag110, for all prior odds  and with a false discovery rate of 5% (FDR = 0.05). The alpha parameter was positive for MSQ13  whereas for QpZag110 alpha was negative . The additional analysis considering three clusters (K = 3) confirmed the identification of the two candidate outlier loci for prior odds of 10 and 100, and FDR =0.05. Plots for the tests of non-neutrality for K = 2 and K = 3 with prior odds of 10 and FDR = 0.05 are shown in Tests of non-neutrality using BAYESCAN and considering two clusters , Lazio, Sardinia and Sicily form a separate cluster, whereas populations Apulia, Mekna Guerbès and Corsica show admixture in all individuals. The admixture in Corsica is balanced, with the smallest parental contribution observed in a single individual corresponding to 39.5% , the ΔK statistic indicated an optimal number of clusters of K = 2 , Fig. S2to 39.5% .K = 2  was observed in a small number of different gene pairs. These observed deviations from HWE are not surprising given the presence of null alleles. Furthermore, the species does not conform to the expectations of the Hardy–Weinberg principle, that assumes discrete generations. Like most oaks, expected . Error cexpected . HoweverST, had a positive value for the alpha parameter on the BAYESCAN analysis, which could indicate diversifying selection. Locus QpZag110, which had the highest observed heterozygosity and highest number of alleles, had a negative value of alpha, which suggests purifying selection. Outlier detection may result in a high false positive rate when the assumptions of the tests are not met  compared to earlier studies using SNPs, that typically sampled around six individuals per site  that roughly correspond to an East-West separation. This structural pattern is different to those of K = 3) shows a very distinct and homogeneous eastern cluster comprising the central italian population of Lazio and the island populations of Sicily and Sardinia, and a third cluster including Corsica and other sites with pronounced admixture . The placement of Corsica in a distinct third cluster was also evident in Analyses of the complete data set, with STRUCTURE and Maverick, and of the neutral data set, with STRUCTURE, show a separation of Quercus suber being structured in three, rather than two clusters, agrees with the pairwise FST calculations, which indicate that populations Lazio, Sicily and Sardinia have significant differentiation from all other populations but not among them, and that population Corsica is distinct from the remainder. Results from PCA and CA analyses also distinguish the group formed by Lazio, Sicily and Sardinia, and separate Corsica from the remainder, often showing a degree of affinity between the latter and populations Apulia, Guerbès and Mekna, which may be a reflection of the admixture pattern for these populations suggested by the Q-plot for K = 3. The identification of population Corsica as belonging to a separate cluster, distinct from the eastern and western clusters of Q. suber populations, was also hypothesized in the work of K = 2, a shared coancestry matrix assigned individuals from Corsica to a third cluster that is possibly a sub-cluster of the western group. This sub-cluster included populations from southern France (Landes), which are missing from our analyses.The hypothesis of K = 2, and strongly recommend against using ΔK alone  pairwise estimates for each sampling site. Estimates were obtained with the WC84 genetic distance  for the Click here for additional data file.10.7717/peerj.13565/supp-2Supplemental Information 2p-values obtained from a global test across samples using Fisher’s method. Values below p = 0.05 are considered statistically significant.Results of the Hardy–Weinberg exact tests for each population (a) and locus (b) showing the Chi- square value, degrees of freedom (Df) and Click here for additional data file.10.7717/peerj.13565/supp-3Supplemental Information 3p-values for the G-test with the null hypothesis of independence among genotypes. Values below p = 0.05 are considered statistically significant.Results of the linkage disequilibrium among pairs of loci and 17 populations, showing the Click here for additional data file.10.7717/peerj.13565/supp-4Supplemental Information 4K = 2 (a) and K = 3 (b) with neutrality prior odds of 10 and a false discovery rate of 5%. Locus 2: MSQ13; locus 8: QpZag110.Plots of the BAYESCAN tests of locus non-neutrality for Click here for additional data file.10.7717/peerj.13565/supp-5Supplemental Information 5K = 2 with 13 SSR (a), 10 nuSSR (b) and with 3 EST-SSR loci (c); Q-value plots from the MavericK analysis for K = 2 with 13 SSR (d), 10 nuSSR (e) and with 3 EST-SSR loci (f).Q-value plots from the STRUCTURE analysis for Click here for additional data file.10.7717/peerj.13565/supp-6Supplemental Information 6(A) Principal components analysis (PCA) showing axes 1 and 2; PC1 and PC2 explain 6.34% of the variance. (B) Correspondence analysis (CA) showing axes 1 and 2. Population labels as shown in Click here for additional data file.


NorthEast India, with its unique geographic location in the midst of the Himalayas and Bay of Bengal, has served as a passage for the movement of modern humans across the Indian subcontinent and East/Southeast Asia. In this study we look into the population genetics of a unique population called the Khasi, speaking a language  belonging to the Austroasiatic language family and residing amidst the Tibeto-Burman speakers as an isolated population. The Khasi language belongs to one of the three major broad classifications or phyla of the Austroasiatic language and the speakers of the three sub-groups are separated from each other by large geographical distances. The Khasi speakers are separated from their nearest Austroasiatic language-speaking sub-groups: the “Mundari” sub-family from East and peninsular India and the “Mon-Khmers” in Mainland Southeast Asia. We found the Khasi population to be genetically distinct from other Austroasiatic speakers, i.e. Mundaris and Mon-Khmers, but relatively similar to the geographically proximal Tibeto Burmans. The possible reasons for this genetic-linguistic discordance lie in the admixture history of different migration events that originated from East Asia and proceeded possibly towards Southeast Asia. We found at least two distinct migration events from East Asia. While the ancestors of today’s Tibeto-Burman speakers were affected by both, the ancestors of Khasis were insulated from the second migration event. Correlating the linguistic similarity of Tibeto-Burman and Sino-Tibetan languages of today’s East Asians, we infer that the second wave of migration resulted in a linguistic transition while the Khasis could preserve their linguistic identity. The Indian subcontinent is genetically one of the most diverse regions of the world harboring over 1.25 billion people (2011 census). The region has been part of the earliest waves of Anatomically Modern Human (AMH) migrations which peopled South and Southeast Asia, including Australia, beginning around 60,000 years ago . Over tiIn this study we look into the population genetics of the Khasi, residing amidst the Tibeto-Burman speakers as an isolated population. These Khasi speakers are separated by large physical distance from their nearest Austroasiatic language-speaking sub-groups: the Munda sub-family from East and peninsular India and the Mon-Khmer sub-family in Mainland Southeast Asia. Here, we dissect the genetic relationship of the Khasis with the other Austroasiatic subgroups and in an attempt to do so, reconstruct the population history of NorthEast India, and the neighboring East and Southeast Asia, in the context of the Khasi Austroasiatics.Despite a strategic location, most genetic studies on NEI populations have been done using either uniparental markers  or a smaThe Northeast Indian populations were clustered with populations of East and Southeast Asia than with mainland Indians . Other sThe Khasis are one of the few populations in the world that follow a matrilineal system of inheritance. Besides the linguistic similarity, anthropologists and archaeologists have established that the Khasis have cultural similarities with Mundaris and Mon-Khmer populations. It has been shown that they share similar stone tools and have similar death rituals of erecting memorial stones for the deceased . LinguisNevertheless, the presence of ancient Austroasiatics (AA) speakers across NEI still remains a possibility. Our previous study  on autosST distances calculated on Y-haplogroups of Austroasiatics and neighboring populations, they found the three Austroasiatic groups  to cluster together. They also found the Y haplogroup O-M95, restricted within the Austroasiatics and postulated to have originated in the Mundaris, is present in the Khasis at a frequency intermediate to that of Mundaris and Mon-Khmers. They suggested an initial presence of Austroasiatics in Central India with rapid migration to Southeast Asia via the Northeast corridor carrying the O-M95 haplogroup.There have been very few studies on the genetics of Khasi, so as to reach any plausible conclusions. One previous study on uniparental markers has proposed a genetic continuity between the Mundari Austroasiatics of Central India, Khasi-Khmuic, and Mon-Khmer . Using mThe cultural and linguistic similarities of the Khasis with other Austraoasiatic groups as mentioned earlier prompt us to investigate their genetic affinities. The geographic location of the Khasis also makes it imperative to investigate the impact of East Asian migrations on the genetic make-up of the Khasis.p < 10−6) in any of the 15 subpopulations. This combined dataset had 310110 SNPs.DNA samples from 22 individuals speaking the Khasi language were sequenced and merged with the genotype dataset of 1,451 individuals that were used in our previous study  using PLIn order to understand the overall population structure and the genetic affinities of the individuals in our dataset, we performed Principal Components Analyses (PCAs) using the smartpca program of the EIGENSOFT package . We perfA second PCA was run on a subset of populations used in the first PCA. This subset was chosen based on linguistic similarity and geographic proximity to the Khasis. Thus, we included the Austroasiatics from Central India (AACI), Austroasiatics of Malaysia (AAM), Khasi, and Tibeto Burmans (TBs).In order to understand how populations were related to each other through a common ancestor and the impact of genetic drift, we built ancestry graphs using TreeMix  version st between each subpopulation of AACI, AAM, TB, and the Khasi was estimated. These values were rounded to the third decimal place.Using PLINK  version f3 statistics measures the shared drift between two populations relative to an extremely diverged population outgroup. Using ADMIXTOOLS (v5.1)  where Mbuti Pygmy was the outgroup. Y was AACI, AAM, and TB subpopulations.Outgroup S (v5.1) , we calck), in each run of the analysis on a given dataset. ADMIXTURE estimates the proportion of each of the k ancestries in the genome of each individual of the dataset and also computes a cross-validation error (CVE) for that particular run. Standard error was estimated for the ancestry proportion estimates at the minimum CVE using the moving block bootstrap approach as implemented in ADMIXTURE.To infer the different ancestral components present in the admixed populations and the proportions of each such component in an individual’s genome, we performed unsupervised clustering as implemented in ADMIXTURE  (v1.3.0)k = 8. Ancestries for which tract lengths were estimated in Khasi included: “Southern EA-like”, “Jehai-like”, “MahMeri-like”, “Birhor-like (AACI-like)” and “ANI-like”. In addition to these ancestries, tract lengths corresponding to “Northern EA-like” ancestry were also estimated for Jamatia. It is to be noted here that the Northern EA-like ancestry was practically absent in the Khasis. We plotted the cumulative distribution of these tract lengths to compare the sizes of these tract lengths corresponding to the different ancestries in both Khasi and Jamatia.Dataset was phased using SHAPEIT v2.r790 . From thGene flow events between genetically distinct populations create linkage disequilibrium between all loci that are highly differentiated between the two ancestral populations. Segments resulting from admixture follow an exponential distribution, where as a result of recombination, this linkage disequilibrium pattern declines exponentially over time and from which the number of generations since admixture can be estimated . To dateTo detect 2-way admixture events in Jamatia and Khasi, we used Yakut as a surrogate donor of the “Northern EA-like” ancestry, Miazou for “Southern EA-like” ancestry and Birhor for “Austroasiatic-India or Mundari” ancestry. We chose Khasi and Jamatia as recipients (having ancestry from each of the source populations as a result of admixture) and estimated the time since the last admixture between the donors. We created co-ancestry curves for the surrogate donors (Miazou and Birhor) in both Khasi and Jamatia and another co-ancestry curve for donors Yakut and Birhor in Jamatia. The rate of decay in the curves was calculated which was equal to the number of generations since admixture took place.The first two Principal Components (PCs) of the PCA with all the mainland Indians  (On the same set of the population (as used in st  between = 0.019)  which waf3  we used the African Mbuti Pygmy as the outgroup. We measured the f3 values of Khasi with AAM, AACI, and TB  between Khasi and TB, indicative of an exclusive and recent shared genetic history. The mean f3 values were higher (mean = 0.277) for Khasi-AAM than between Khasi-AAI (mean = 0.265). The apparent discordance in the pattern of f3 and Fst values when Khasi are compared to AACI and AAM is largely due to the fact that Fst is affected by drift. It is to be noted here that we see the AAM populations be highly drifted in our Treemix analyses  but are very similar to that of the Tibeto Burmans, especially to the Jamatia and Tripuri. The major ancestry identified among the Khasi was also the predominant ancestry identified among the Southern-EA populations (like Dai). In the ADMIXTURE plot , it is dTo investigate the chronology of admixture events into Khasi, we performed local ancestry estimation, using RFMix . We idenWe then repeated the same analysis with the Jamatia (a subgroup of the TB) and with the same five ancestries i.e. Birhor-like, Jehai-like, MahMeri-like, ANI-like, and Southern EA-like ancestries. Furthermore another ancestry: the Northern EA-like was also included in the analyses because this was an additional ancestry present substantially in the TBs  but was absent among the Khasis. Similar to what was observed in the Khasis, tract lengths corresponding to the Birhor-like and Jehai-like ancestry are the smallest followed by MahMeri-like, ANI-like, Southern EA-like ancestries. This mimics the chronology of the admixture events that we see in the Khasis. However, the largest length of admixture tracts corresponded to the Northern EA-like ancestry . This inWe further dated these local admixture events using a method implemented in MOSAIC  that infThe Khasi are a relatively large population, subdivided into groups owing to geographical barriers, and show considerable heterogeneity as evident from an anthropometric study . We findDespite an overall genomic similarity of the TBs and Khasis, there is a distinct difference between the TB populations and the Khasis: unlike the TBs, the Khasis lack Northern East Asian ancestry. Results from our RFMix analysis suggest that there were at least two distinct waves of East Asian migration. The first wave brought the Southern East Asian ancestry that got incorporated in both the Khasis and the TBs and the second wave brought the Northern East Asian component. This migration started possibly from Northern EA and led to the introduction of the Northern EA-like genomic ancestry into the TB population but not in the Khasis. It is to be noted that the Northern EA component is absent from other Austroasiatic speakers as well (AACI and AAM), although some of these populations have substantial EA ancestry, i.e Southern EA ancestry. We, therefore, propose, in agreement with our previous study, that the ancestors of extant Austroasiatic speakers were widespread across Central India and Southeast Asia encompassing the present-day location of the TBs and Khasis. This is in agreement with other studies . It is hHigham has suggested that before Neolithic expansion, this region was inhabited by hunter gatherers . He alsoWe argue that the language of the extant TBs is a result of this linguistic shift, possibly evidence of elite dominance, which is a consequence of the migration and gene flow from Northern East Asia. When we look at the ancestral segments of Northern-EA ancestry, we find that they are among the longest ancestral segments in TB, preceded by segments of Southern EA-like ancestry. We postulate that the two migration events from East Asia were such that initially, populations bearing Southern EA-like ancestry arrived in NEI, and later came the populations of Northern EA-like ancestry. The Southern EA-like ancestral segments are also present in Khasis and AAM, the two Austroasiatic groups with substantial East Asian ancestry. In these populations, Southern EA-like ancestral segments are among the longest. The AACI however have negligible East Asian components in their genome. It is to be noted here that the language of the AACI, i.e. Mundari is much more distant from the other two branches of the AA family, namely Khasi-Khmuic and Mon-Khmer. The Khasi-Khmuic and Mon-Khmer are more similar to the Sino-Tibetan language. This is expected as our genetic data also confirms closer proximity and longer admixture of Khasi-Khmuic and Mon-Khmer speaking populations (Khasi and AAM) with Southern-EA populations. The admixture with populations of Northern EA-like ancestry is unique among the TB and their languages belong to Sino-Tibetan, a different language family altogether. In TBs this ancestry has been incorporated after the second migration wave. This leads us to conclude that the ancestral populations of TB have experienced a language shift, from a more proto-Khasi-Khmuic language to a language closer to that of the East Asians (the Tibeto Burman languages) and this has occurred due to the most recent admixture with populations with Northern EA-like ancestry.


ST revealed high genetic similarity between the different cultivated types, while the wild populations were divergent. Unlike wild populations, which exhibited genetic differentiation, there was no clear differentiation among all cultivated types. A principal coordinate analysis revealed that the first principal coordinate, distinguished most of the wild populations from the cultivated types, in agreement with the results obtained using a discriminant analysis of principal components and cluster analysis. Accessions of wild populations and landraces collected from southern and central Scandinavia showed a higher genetic similarity to Lantmännen accessios. It is therefore possible to link the diversity of the environments where wild populations were collected to the genetic diversity of the cultivated and wild gene pools. Additionally, least absolute shrinkage and selection operator (LASSO) models revealed associations between variation in temperature and precipitation and SNPs within genes controlling stomatal opening. Temperature was also related to kinase proteins, which are known to regulate plant response to temperature stress. Furthermore, the variation between wild populations and cultivars was correlated with SNPs within genes regulating root development. Overall, this study comprehensively investigated Nordic European red clover germplasm, and the results provide forage breeders with valuable information for further selection and development of red clover cultivars.Red clover is a highly valuable crop for the ruminant industry in the temperate regions worldwide. It also provides multiple environmental services, such as contribution to increased soil fertility and reduced soil erosion. This study used 661 single nucleotide polymorphism (SNP) markers via targeted sequencing using seqSNP, to describe genetic diversity and population structure in 382 red clover accessions. The accessions were selected from NordGen representing red clover germplasm from Norway, Sweden, Finland and Denmark as well as from Lantmännen, a Swedish seed company. Each accession was represented by 10 individuals, which was sequenced as a pool. The mean Nei’s standard genetic distance between the accessions and genetic variation within accessions were 0.032 and 0.18, respectively. The majority of the accessions had negative Tajima’s D, suggesting that they contain significant proportions of rare alleles. A pairwise F Rhizobium bacteria, red clover is more efficient at nitrogen fixation (n = 2x = 14); however, tetraploid (2n = 4x = 28) cultivars have been developed through chromosome doubling techniques , as described in For genotyping, SeqSNP was used with a set of 400 target SNPs developed by For determining the validity of converting the read counts of pool-seq into allele frequencies for data analysis, five randomly selected accessions were genotyped at both the individual and pool levels. Following this, the read counts of each pooled sample were converted to allele frequencies. A subsequent step involved converting the genotypic data of the five individuals of each accession into allele frequency data for that particular accession. This was followed by correlation analysis between the allele frequencies obtained from individual genotype sequencing and PoolSeq, which revealed a highly significant correlation  between them. Hence, SeqSNP is a highly reliable method to generate data for allele frequency-based data analyses.de novo SNP and INDEL calling generated 347 SNPs and 16 INDELs. Among the 347 novel SNPs, 91% were bi-allelic, 8% were tri-allelic, and 1% were tetra-allelic. Due to the complexity of analyzing pooled sequencing data and a mixture of diploid and tetraploid accessions, only polymorphic bi-allelic markers were used. Additionally, tetraploids were treated as diploids as described in de novo discovered bi-allelic SNPs , all with minor allele frequency of 5% or above, were used for data analyses in this study.Among the 400 target SNP loci genotyped, 5.5% were mono-allelic, 86% were bi-allelic, 7.5% were tri-allelic, and 0.75% were tetra-allelic across the 382 accessions studied. The remaining 0.25% were INDELs (insertion or deletion of a nucleotide). The S) was calculated using the adegenet package  was calculated using adegenet package. Additionally, Mantel’s randomized test comparing Nei’s standard genetic distance with the geographic coordinates of the germplasm collecting sites of the wild populations was performed to determine whether isolation by distance (IBD) has a significant effect on the genetic differences between the accessions.Tajima’s D was estimated using PoPoolation  based on package . Using t package  in R, paBoth principal component analysis (PCA) and principal coordinate analysis (PCoA) were used to determine the genetic relationships between the accessions. The PCA was conducted using the pcadapt package , and SNPThe Nei’s genetic distance based relationship between the accessions was further investigated through neighbor-joining (NJ) cluster analysis. The NJ tree was built using the ape package  and visuA discriminant analysis of principal components (DAPC) was performed using adegenet with the method described by 2) and imported via the raster  enrichment to find possible molecular functions differentiating the populations in the PCA clusters.The pcadapt package  was usedST values of all 661 SNPs between every pair of populations were calculated and the top 1% values of each pairwise calculation was selected. A final set of 430 SNPs with high FST values was selected for the LASSO models. The caret package (LOOCV) approach was used to train a regression model (glmnet) and the mean average error (MAE) was computed for model selection. The MAE and root mean square error (RMSE) were compared to the variance of each climate variable to validate the final model; and the error was smaller than the standard deviation of the input for all models. To validate the models further, a linear regression analysis was performed on the bioclimatic variables without including the SNP data. In cases where the LASSO model had a lower RMSE than the linear model, the SNPs were considered to have an effect.The least absolute shrinkage and selection operator (LASSO) regression model was used to connect environmental variables to the allele frequencies. As the number of SNPs exceeded the number of populations in this study, LASSO was considered an appropriate model due to the application of penalization and feature reduction. Among the accessions studied, only those representing wild populations were used for the LASSO models. This is because they could be considered to have adapted to the environments of the sampling sites. Considering the number of SNPs available for the data analysis was high (661), a method for selecting only the most relevant ones was devised to increase the biological aspect of model training. The goal of this analysis was to find the SNPs that genetically differentiated two populations. Thus, the F package  in R wasThe analysis of SNP effect on population differentiation in the PCA and LASSO models resulted in nine sets of SNPs, two from the PCA and seven from the LASSO models. A gene ontology (GO) enrichment analysis was carried out on each set of SNPs to find any biological function underlying the population differentiation. The GO analysis was performed with the workbench at dicots Plaza 5.0  where thde novo. Of the 317 de novo discovered bi-allelic SNPs, 292 were reported in de novo SNPs discovered in this study, compared to that of The SeqSNP-based sequencing of the 382 red clover accessions resulted in 661 bi-allelic SNP markers, which were then used for population genetics analysis of the accessions . AdditioFor data analysis, the accessions were grouped based on their origins and population types. The grouping results in nine origin-based groups  and eight population type-based groups . Because DLF, Local population, and Russia (among the origin-based groups) and Graminor and Unknown (among the population type-based groups) had only one accession each, they were excluded from some analyses.ST of each group  with any population structure. In the case of origin-based groups, the mean FST values were negative for Lantmännen, Graminor and Denmark, zero for Finland, and positive for Norway and Sweden. Apparently, the FST values of the different population type-based groups and origin-based groups were related due to the accessions they shared. The majority of the landrace accessions belong to Finland, and consequently the mean FST values for both groups were zero. Similarly, most of the accessions from Denmark were cultivars, and hence both Denmark (origin) and cultivars (population type) had a negative mean FST value. The mean FST for Sweden and Norway was higher, as they were mainly comprised of wild populations.The study revealed low genetic diversity and population structure considering the median and mean values of Nei’s standard genetic distance and Fch group Figure 1ST between groups showed high genetic similarity between the cultivated types , which accounted for 30.8% of the total variation distinguished most of the wild populations from the cultivated ones  smaller than the standard deviation (SD) indicates that there is a predictive effect of the feature (SNP). In other words, there is an effect of the selected markers on the predictive ability of the model. All LASSO models had RMSE smaller or about equal to their respective SD Table 2.Arabidopsis.The gene ontology (GO) enrichment analysis was used to validate the model results, in terms of biological functions. The reference gene-coding sequences, of the SNPs selected by the LASSO models were imported into the online tool Plaza workbench via BALST to find the corresponding genes. The models in which SNPs generated a GO enrichment were annual mean temperature, annual precipitation, and annual temperature range . FurtherFrom the PCA analysis, using the R software’s pcadapt package, the SNPs that significantly contributed to the clustering of the 382 accessions in the first two PCs were located within the coding regions of 22 and 38 genes, respectively  and uncharacterized gene sequences.This study revealed the genetic variation of 382 red clover accessions, including wild and cultivated types representing the red clover gene pool in the Nordic Region of Europe. Among the red clover accessions studied, 45 were known to be tetraploids. However, in order to facilitate the comparison with the diploid populations used in this study they were treated as diploids, following the explanation provided in The benefit of using pool-seq methods as opposed to individual sequencing methods is the number of populations that can be analyzed. With a pool of 10 individuals, pool-seq can analyze 10 times as many populations as individual genotype analysis for the same cost, assuming their sequencing depth is the same. The main challenge of pool-seq is the data analysis, as the representation of the sampled individuals within a pool can be uneven. The selection of reading depth relative to the number of individuals in each pool is very important. In the present study, using read counts from ten individuals at a sequencing depth of x501 was deemed appropriate, given the result of our in-house analysis that compared data generated through pool-seq and individual genotype sequencing.The genetic parameters estimated from ten individuals per pool were comparable with the results reported by The discovered genetic variation differentiated the groups of accessions to different extents based on their population type or origin. The major trend was a separation of the wild populations from the cultivars, with the landraces being represented within both groups. The genetic distance between the cultivated populations was low, and there was no clear separation between them based on their origins. This could be partly due to the strict outcrossing nature of the crop that facilitated a high rate of gene flow, resulting in high heterozygosity with reduced differences in allele frequency between the populations. A high level of heterozygosity has been previously reported in red clover populations analyzed at individual genotypes level, which was attributed to its strict outcrossing reproductive system . ContrarThis study was designed to identify informative SNPs from the perspective of red clover breeding and to generate knowledge regarding the extent to which the genetic material used for breeding reflects available genetic resources in red clover. These objectives are clearly reflected in the NJ tree Figure 7ST  indicates lower genetic differentiation in Northern European red clover. These results are not surprising since Europe and Asia cover a larger area. Furthermore, red clover was introduced to northern Europe relatively late compared to southern and central Europe  models to relate the SNP frequency across populations to a specific bioclimatic variable. Due to its ability to rank the importance of variables LASSO models are currently used in multiple fields where the number of samples is less than the number of variables. They are used in gene-based diagnostics , genome-Relating SNPs to bioclimatic variables with allele frequencies have previously been done via Bayesian models. However, for these models to converge, the data must fulfill the assumptions of the prior likelihood distribution. If the data does not fit the Bayesian model, a LASSO model could be used as an alternative as they rely on no prior information. LASSO models, however, are not adjusted based on population structure, unlike Bayesian models. As a result, additional steps are necessary in order to exclude possible artifacts due to population structure or statistical false discovery. A GO enrichment analysis was carried out for this purpose in the present study and satisfactory results were obtained.Four LASSO models showed enrichment for biological processes that can be regarded as plausible responses of plants that are growing in a particular environment Table 2.This study thoroughly described the genetic diversity and population structure of the Nordic red clover genetic resources, which include breeding populations, cultivars, landraces, and wild populations. As shown by this study, further genetic gains are possible by incorporating NordGen cultivars and landraces. Inclusion of selected landraces and wild populations based on the results exhibited in https://www.ncbi.nlm.nih.gov/; PRJNA765476.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: MG secured the funding with help from RO, CH, and other project participants. With inputs from CH and RO, MG designed the genotyping part of the study while JO planned the statistical analyses regarding bioclimatic variables. JO, CH, and MG conducted the greenhouse work, including sampling leaf tissue for DNA extraction. JO wrote the manuscript draft and revised it. MG, CH, and RO reviewed the different versions of the draft and assisted in the revision process. All authors contributed to the article and approved the submitted version.The study was fully funded by SLU Grogrund – Centre for Breeding of Food Crops, Swedish University of Agricultural Sciences.We would like to thank Linda Öhlund (Lantmännen) for providing us with seeds of red clover cultivars and breeding populations. We would also like to thank NordGen for supplying us with selected red clover germplasm from their collection. We are thankful to Mohammad El-Khalifeh (NordGen) for the additional help in selecting the accessions, and to the greenhouse management staff for keeping the plants healthy. We would like to thank David Parsons (SLU), Elisabet Nadeau (SLU) and Alf Ceplitis (Lantmännen) for constructive comments on the results of the study during the project meetings.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Oenothera primiveris. Across its range, this species exhibits a shift in its breeding system and floral traits from a self‐incompatible population with large flowers to self‐compatible populations with smaller flowers.To avoid inbreeding depression, plants have evolved diverse breeding systems to favor outcrossing, such as self‐incompatibility. However, changes in biotic and abiotic conditions can result in selective pressures that lead to a breakdown in self‐incompatibility. The shift to increased selfing is commonly associated with reduced floral features, lower attractiveness to pollinators, and increased inbreeding. We tested the hypothesis that the loss of self‐incompatibility, a shift to self‐fertilization (autogamy), and concomitant evolution of the selfing syndrome  will lead to increased inbreeding and population differentiation in We conducted a breeding system assessment, evaluated floral traits in the field and under controlled conditions, and measured population genetic parameters using RADseq data.O. primiveris. This shift includes variation in the breeding system and the mating system, a reduction in floral traits , a shift to greater autogamy, reduced genetic diversity, and increased inbreeding.Our results reveal a bimodal transition to the selfing syndrome from the west to the east of the range of The observed variation highlights the importance of range‐wide studies to understand breeding system variation and the evolution of the selfing syndrome within populations and species. The cost of sexual reproduction is a well‐established tenet of evolutionary biology  W.L. Wagner & Hoch, a shift in floral morphology, gradual shift in the mating system from outcrossing to selfing, and an increase in autogamy were not associated with significant increases in the inbreeding coefficient or evidence for loss of inbreeding depression, suggesting that mixed mating is maintained . This species exhibits a population‐level, longitudinal shift in variation in floral traits and breeding system suggestive of an evolutionary transition from SI to SC and an increase in selfing. Specifically, large SI flowers occur in the western portion of the range, whereas eastern populations have reduced, SC flowers  and the transition from mainly outcrossing to increased selfing, leading to the evolution of the selfing syndrome in the desert evening primrose,  Wagner, . It is kC Klein, . The speOenothera primiveris, the only member of Oenothera sect. Eremia, is an herbaceous annual or short‐lived perennial that occurs in sand dune habitats in the Mojave, Sonoran, and Chihuahuan Deserts of the United States and Mexico  pots using a 3:1 ratio of regular potting soil and perlite. We aimed for 15 maternal lines for each of the seven populations for which we had seeds, in order to evaluate breeding system, autogamy, and morphological traits, although the limited numbers of fruits collected from populations 6 and 7, poor germination, and limited number of flowers produced by all populations reduced the final number of individuals and maternal lines used for each experiment  at the Chicago Botanic Garden  for seven of the eight populations visited. Plants were grown from field‐collected fruits that were dried to 20% relative humidity before cleaning. Multiple fruits from each maternal line were collected in each population; seeds from each maternal line were cleaned and pooled. Cleaned seeds were maintained at 4°C before germination. Seeds were surface sterilized by submerging for 5 min in a 20% bleach solution and rinsed in deionized water before being placed on Petri dishes with 1.5% agar with 1 mL of Plant Preservative Mixture  on newly opened flowers to ensure maximum pollen viability and stigma receptivity. Flowers used as pollen recipients for both treatments were emasculated (anthers removed) with forceps prior to hand‐pollination to reduce cross‐contamination. Anthers were removed from pollen donors using forceps, and then all anthers were used to saturate the clean stigmatic surface of the recipient flower with pollen. In the case of within‐population outcrosses, anthers were placed in clean Petri dishes before performing the controlled cross, while in the self‐pollinations, the anthers were removed and rubbed onto the stigma of the same flower immediately. Forceps were cleaned with a 70% ethanol solution between crosses to prevent unintentional pollen transfer. A subset of plants was also used to assess seed set via autogamy, because variation in mating system and reductions in floral traits may contribute to variation in autogamous self‐pollination among populations. Jeweler's tags were used to record maternal and paternal information and cross‐type, and once flowers had senesced, a colored wire was tied around the top of the capsule to prevent the release of seeds as the fruit matured. Fruits were collected when mature, and seed number was recorded. Crosses were considered incompatible if no seeds were produced.To assess SI at the population level, an average self‐compatibility index (SCI) was calculated for each maternal line included in the study and then for each population Appendix . This inFloral trait data were collected to determine whether the changes associated with the selfing syndrome had evolved in populations showing SC and higher autogamy. Floral traits were measured in the field in March and April 2015 and March 2016 (Table 2 and added 5 μL of 0.03% toluene in hexane (= 23 ng) as an internal standard. One µL aliquot of each sample was injected into a Shimadzu GC‐17A gas chromatograph equipped with a Shimadzu QP5000 quadrupole, electron ionization (EI) mass spectrometer  as a detector. All analyses were made using splitless injections on a polar GC column , using ultra‐high‐purity (99.999%) helium as a mobile phase . The GC temperature and pressure parameters  were optimized to resolve floral volatiles common to Oenothera species with a total run time of 19 min/sample, allowing us to efficiently process high sample replicates. EI mass spectra (70 eV) were collected from m/z 40–350  at a detector voltage of 70 eV, with a scan speed of 1000 and a scan interval of 0.29 s. Compounds were tentatively identified using computerized mass spectral libraries  and Adams . For the digestion, the genome was cut with ApeKI  and then ligated with oligonucleotides, which included specific Illumina primers and a unique barcode. Unique 96 barcodes were obtained according to the specification of the protocol from Integrated DNA Technology . These barcodes ensured that only fragments containing the specific primers were amplified. Each PCR was carried out independently for all samples, and each library was then quantified using High sensitivity QubitTM kit  and then pooled in the final step before sequencing to ensure that an equivalent amount of each sample was present in the final genomic library. Sequencing was performed using llumina HiSeq Sequencing (150 bp paired‐end reads) at the Center for Genetic Medicine at Northwestern University.Total genomic DNA was extracted from leaf tissue collected in the field following a modified cetyltrimethylammonium (CTAB) protocol developed by Doyle and Doyle . Restricm = 3), maximum distance to create stacks (M = 3), and number of mismatches allowed while creating the catalog (n = 2). We used three individuals with the highest number of reads from each population to build the catalog. Finally, we used populations to identify SNPs, restricting the data to only the first locus per read. Only SNPs that were present in 70% of the individuals were considered in the final data set. Genepop and Structure outputs were obtained from Stacks and used for population genetic analysis.To build loci and detect haplotypes for each individual, we used the denovo.map.pl pipeline of Stacks version 1.28 , floral flare (field only), herkogamy, floral tube length, and total scent emission rates. Differences in overall scent composition and morphology between populations were visualized using nonmetric multidimensional scaling (NMDS) of Bray‐Curtis distance metrics, and statistical differences were determined using nonparametric analysis of similarities , number of effective alleles (NA), observed and expected heterozygosity (HO and HE), and differentiation index between populations (FST). Inbreeding coefficients (FIS) were obtained through Genepop  were obtained in NeEstimator version 2 , and a Tukey post hoc test showed that significant differences were found only between Pop 1 and Pops 4, 7, and 8, but not among the remaining comparisons  and the highest incidences of SC individuals in the easternmost populations . The westernmost population (Pop 1) produced almost no autogamous seeds, and easternmost populations  produced the most seeds via autogamy. In the west, Pops 2 and 3 produced intermediate numbers of autogamous seeds, while Pop 4, despite being in the west, was most similar to eastern populations , while populations with reduced flower size  produced more autogamous seeds than populations with large flower size .Autogamy differed significantly between populations  had nearly twice as large floral diameters  and three in the east . Three of the western populations  had flowers that were on average larger, with a nearly two‐fold difference in flower diameters .O. primiveris growing in the field in 2015 and 2016 emitted 37 volatile organic compounds (VOCs), including aliphatic, aromatic, monoterpene, sesquiterpene, and nitrogenous volatiles derived from amino acids , with Pops 1, 2, and 3 emitting more compounds (16.8 VOCs), on average, than those of Pop 4 (west) and Pop 5 (east) ‐β‐ocimene, ‐α‐farnesene, and 3‐methylbutyl aldoxime ‐α‐farnesene in their total emissions , where there was a decline from west to east, with lowest diversity in most eastern populations  were variable and found across all populations and were used to generate the data sets for further genetic analysis. A comparison of percentage polymorphic SNPs (%P) identified in each population found a large split across the range, with three populations in the west  having two‐ to three‐fold more polymorphic SNPs (ranging from 46% to 60%) than the remaining west population and the two sampled populations in the east . This pattern was replicated for all genetic diversity parameters , and with herkogamy . Similarly, %P, N, NA, and HE showed a significant negative correlation with SCI . Even though all populations evaluated showed low values of NE, populations with large flowers  had higher values of NE than populations with small flowers  had, on average, higher genetic diversity measured as %P, N, , and 3 hFIS) varied from −0.15 to 0.58 across the range and showed an east‐west split. The westernmost population (Pop 1) showed reduced inbreeding, and the remaining western populations had moderate inbreeding (Pops 2 and 3) or negative inbreeding (Pop 4), which may indicate assortative mating , nor with herkogamy  or SCI value . This pattern was mirrored in the estimates of selfing rate, where the four western populations showed moderate levels of selfing and low to moderate levels of inbreeding, while the two eastern population had higher selfing rates, associated with increased autogamy . The pairwise distances between populations with large flowers were lowest , compared to pairwise distances between populations with small flowers , whereas comparisons between populations with large and small flower sizes showed the greatest level of differentiation  (Table Genetic differentiation across the 1200 km range sampled Appendix  was mode1) Table , as supp1) Table , where tO. primiveris is consistent with the hypothesis that populations capable of self‐fertilization have evolved reduced floral traits, increased autogamy, higher inbreeding, increased selfing rate, and greater population differentiation. Our study of geographic variation in breeding system across the distribution of O. primiveris was consistent with the findings of Wagner  had the lowest number of SC individuals, larger flowers with higher scent emission rates, traits important to pollinator attraction, and low levels of genetic differentiation, suggestive of a selective advantage to outcrossing in these locations. By contrast, the eastern populations showed almost a complete shift to SC individuals, with smaller floral diameters, reduced or no herkogamy, and lower scent emission rates, that were capable of producing high numbers of seeds through autogamy. Although the smaller flowers of O. primiveris remain capable of attracting hawkmoth pollinators, as evidenced by the presence of hawkmoth scales on stigmas in small‐flowered Arizona populations , many of which are reduced in populations in the east. Reductions in floral size, scent emission rates, and chemical complexity have been documented for other plant species that show variation in selfing rates, including moth‐pollinated species such as Gregory, , and havO. primiveris would provide optimal conditions for a gradual increased expression of the genetic load, which would facilitate purging to eliminate the cost of inbreeding depression  for each population and information about maternal lines evaluated.Click here for additional data file.Appendix S2. Number of seeds produced through autogamous pollination for each population, mean flower diameter and mean herkogamy values for the population.Click here for additional data file.Appendix S3. Pollinator visitation rates and assessments of hawkmoth visitation in natural populations.Click here for additional data file.Appendix S4. Correlation between number of seeds produced through autogamous pollination and mean diameter (mm) for each population.Click here for additional data file.Appendix S5. Summary of floral scent composition and emission rates from natural populations.Click here for additional data file.Appendix S6. Summary of floral scent composition and emission rates from common greenhouse study, 2001.Click here for additional data file.Appendix S7. Comparison of pairwise genetic and spatial distance among six populations.Click here for additional data file.Appendix S8. Visual evaluation of pollen and moth scales deposition in stigmas of natural populations during 2001 flowering season.Click here for additional data file.


Many studies have described the diversity of Austronesian-speaking Taiwanese people to shed more light on their origin and their connection with the “Out of Taiwan” migrations. However, the genetic relationship between the non-Austronesian-speaking groups of Taiwan and the populations of continental Asia is still unclear. Here, we studied the diversity of mtDNA in 767 non-Austronesian speakers from 16 locations in Taiwan using partial sequencing obtained from the hypervariable segment I (HVS-I) and coding regions 8,001-9,000 and 9.801–10,900 and 85 complete mtDNA genome sequences. Bayesian analysis of population structure was used to examine their relationship with over 3662 individuals representing indigenous groups of Taiwan, continental East Asia, Japan, and Island Southeast Asia. The whole analysis identified 278 haplotypes. Complete genomes revealed 62 novel subhaplogroups, of which 31 were exclusive to Taiwan. Estimates of coalescence times of all subhaplogroups showed peaks of diversification greater than 5.0 kya, likely characterizing gene flow from continental East Asian groups but not excluding in situ Taiwanese ancestry. Furthermore, a significant number of clades exclusive to non-Austronesian speakers of Taiwan (NAN_Tw) showed coalescence peaks between 1.0 and 2.6 kya, suggesting possible late Neolithic to early metal age settlements of NAN_Tw and local expansion in Taiwan. The first comprehensive analysis of mitochondrial DNA in non-Austronesian-speaking people from Taiwan points to an early settlement of the island by Mainland Southeast Asians in the late Neolithic period, followed by periods of isolation and genetic expansion. Marie Lin and Jean Trejaut from Mackay Memorial Hospital, Taipei, Taiwan, characterized mitochondrial DNA segments from 672 speakers of Sinitic or so-called ‘Chinese languages’ living in Taiwanese cities and lowland rural regions. The researchers also fully sequenced the entire mitochondrial genomes of 85 of these individuals. They identified numerous different genetic lineages, including many unique to Taiwan, that dated back millennia, perhaps as far as around 5000 years ago. The results point to prehistoric settlements and periods of local expansion among the ancestors of today’s non-Austronesian speakers long before groups arrived from China in modern times. While Mandarin is the official language, over 16 languages are spoken, principally belonging to two linguistic families: Austronesian (AN) and Sino-Tibetan . During the last glacial maximum (LGM) over 20,000 years ago (20 kya), ice sheets covered much of the Northern Hemisphere, the sea level was low, and Taiwan still connected the East Asian continent. The discovery of Paleolithic artifacts of a tool industry at the “Changbin culture” site in Taitung and ancient human remains (bones and teeth of the Tso-Chen Man) in present-day southeast Tainan allowed archaeologists to show that Taiwan entered the Paleolithic era approximately 28 kya4.Taiwan is ethnically diverse5.The rising temperature toward the late Paleolithic era resulted in the rapid receding of ice and rising sea level to approximately 100 meters. Most locations in western Taiwan, a lowland region with diverse plains and hilly landscapes 6 and settled separately all over the island. Isolation by distance or geographic landscape caused cultures and languages to drift apart. Today, linguists recognize nine distinct local branches of PAN. These are still actively used by the indigenous peoples of Taiwan , who represent only 2.3% of the Island population7. A tenth branch of the Austronesian language family, the Malayo Polynesian7, developed in the Batanes Islands (Ivatan) and Luzon in the Philippines (2200 BC)8. It rapidly divided further into subbranches found nearby in Malaysia, North Vietnam, Indonesia, the Bismarck Archipelago, and the Pacific Ocean, then reached New Zealand less than a thousand years ago, and finally Madagascar in the Indian Ocean8.The Neolithic era of Taiwan  saw the first settlement of sedentary groups8.This linguistic diaspora was most likely also the result of trading networks. It started as early as 2800–2200 BC with the nearby Penghu Islands (Pescadores Islands) in the Taiwan Strait because of the need of the late Tabenkeng (or TPK) people in Taiwan to obtain material from the Penghu Island quarries to build their basalt adzes11. The improved food production contributed to population density. Ultimately, from 500 BC to 500 AD, these trading networks included the Philippines, mainland Southeast Asia, India, and even widespread trades within Taiwan. Imports, such as metal technology, all came from mainland Southeast Asia (MSEA). Fujian, Guangdong and Taiwan were not yet part of the Han, and no contact with “Han-China” existed yet12. A millennium later, Taiwan became part of an even more active East and Southeast Asia maritime network involving Fujian merchants, pirates, and Japanese traders and spread further in Southeast Asia (SEA) and MSEA13. Along with these traders, fishers from the China Sea and the Taiwan Strait often visited Matsu, Penghu Island, and the west coast of Taiwan, where some settled13.Along with these trading networks, new arrivals soon introduced millet and wet rice agriculture14. From 1895 to 1945, the Japanese instituted fifty years of colonial rule. Interestingly, the elements of Japanese culture are still part of Taiwan’s everyday life.The end of six millennia of a uniquely Austronesian-speaking place and the beginning of Taiwan’s recorded history started with the Dutch in 1,624. To expand their deerskin trade, they established a colonial regime among more than 130 lowland villages of Indigenous peoples. The Spanish later established a base in northern Taiwan15. These studies allowed scientists to use the polymorphism of East Asian populations to investigate a possible relationship between genetics and Austronesian language families in Southeast Asia16. Then, geneticists used the maternal mitochondrial DNA (mtDNA) and paternal Y chromosomal (Y-SNP and Y-STR) gene systems. These uni-parental gene systems never recombine; they are exclusively inherited maternally or paternally. In addition to the linguistic relationship with genetics, many studies have used these gene systems to characterize gene flow, mixture, and the origin of many autochthonous groups18 and have more recently used DNA from ancient human remains to retrace the origin of archaic humans in East Asia20.In the past few decades, molecular geneticists have characterized the genetic diversity of most Asian populations by first analyzing the highly polymorphic autosomal HLA system23. The three research groups23 showed that Taiwanese Han had a low level of admixture with autochthonous Taiwanese and were descendants of several lines of ancestry spatially distributed throughout continental Asia. Their finding was generally consistent with the self-information given by the participants. Importantly, they produced documentation about known disease risk variants potentially affecting specific groups21.To provide more insights into the distribution and evolutionary history of East Asian populations, several research groups compared high-coverage whole-genome sequencing and genome-wide SNP autosomal data of East Asians, Taiwanese Han, and indigenous people of Taiwan and Island Southeast Asia19. Rapid advances in complete genome sequencing also allow sequencing and analysis of large numbers of individuals. We used these methods to obtain 672 partial mitochondrial genomes of unrelated NAN_Tw individuals from main urban centers (Ur_Tw) and lowland locations all over the island (LL_Tw). Of these, 84 were used for complete mitochondrial genome sequencing. This study proposes to analyze the human demography, genetic diversity, and matrilineal ancestry of NAN_Tw (Ur_Tw and LL_Tw) individuals to characterize possible late Neolithic settlements and local expansion of non-Austronesian speakers in Taiwan. In doing so, this project is the first comprehensive mitochondrial DNA analysis of non-Austronesian-speaking Taiwanese individuals.However, only a few complete mtDNA sequences from non-Austronesian-speaking Taiwanese (NAN_Tw) people are available in the literature19. Individuals from the Matsu archipelago (n = 50)24 and the Fujian province (n = 148)26 represented East Coast China. Complete mitochondrial genomes from the literature included Vietnam (n = 609), Thailand (n = 560), Japan (n = 664), Northeast China (n = 257), Southeast China (n = 65), Malaysia (n = 87), and 260 individuals from the Philippines32  healthy, unrelated NAN_Tw individuals  from this study and samples from the literature were concatenated and aligned. Assignation of mtDNA to haplogroups of the samples was conducted using HaploGrep 2.0 software along with PhyloTree build 1734 was used to determine intrapopulation haplotype frequencies, pairwise fixation indices (Fst), gene diversity (H), nucleotide diversity (π), and the gene flow exchanged between groups (M = Nem) (Table The program Arlequin 3.5m) Table .35  as well as the unshared gene portion in the hybrid populations were inferred using the analysis of shared haplogroups between populations37 with p values generated using 1,000 coalescent simulations under a model of selective neutrality. Multidimensional scaling analysis (MDS) of the groups was performed with SPSS software version 1738 using pairwise fixation indices Fst (Arlequin 3.5)34 34 Fig. . To explhttps://sites.google.com/site/mtphyl/) was used to construct a phylogenetic tree comprising 77 unique complete mtDNA sequences from NAN_Tw. Coalescence time estimates of all clades comprising more than three subbranches, including literature data  was used when dating the 2550 concatenated nucleotides  used to construct median joining networks42. It should be noted that this rate was inferred from the combined rate of the HVS-I control region 41, and the substitution rate of the 2121 nps of coding region . The last was inferred from haplogroup E1 of the 8000-year-old human remains found in Liangdao41.mtPhyl software  between observed and expected mismatches  greater than 200 for all estimated parameters; on average, runs with Markov-Chain-Monte-Carlo (MCMC) chain lengths were greater than 1 × 107, and trees were sampled every 1000 generations. The effective population size for the posterior distribution of the estimated parameter values was determined using Skyline plot analysis with TRACER version 1.7.144.Determination of spatial range expansion and stationary population history were tested using mismatch distribution with Arlequin 3.545. To accurately detect the uppermost hierarchical level of structure for the BAPS analysis, we used the Evanno statistic46 based on the rate of change in the log marginal probability of data between successive K values. Mixture analysis with K = 2 to 22 was first carried out to determine the Delta K All participants gave written informed consent before biosample collection for subsequent population analysis. The study was authorized by the ethical committee of the Institutional Review Board (IRB) of the Mackay Memorial Hospital in Taipei, Taiwan (# 11MMHIS180).19  and an urban Taiwanese group (Ur_Tw) composed of Minnan and Hakka residents from Kaohsiung City, Pingtung County (thereby named Minnan_Ko and Hakka_Ko), and Minnan and Hakka from Taipeiups Fig. 47.Table n = 12, Heping Island = 10, and Xiaoliuqiu n = 11), the nucleotide diversity indices (π) were evenly distributed throughout Taiwan and showed no distinct differences among groups in continental Asia (π = 0.004 to 0.006)  groups than in Austronesian-speaking groups, the Philippines, Indonesia, Malaysia, and AN_Tw (Table  groups t6) Table . SimilarM = Nem) exchanged between groups , shown with AN_Tw indigenous groups (M = 14.09), contrasts with the high to very high gene flow (M = 145.55 to 10781) shown with NAN_Tw (Table M = 27.94 to 45.53) suggests that these groups had low Ne and likely remained relatively homogeneous, isolated and had a low migration rate (low m). We see a similar pattern for the Philippines and Malaysia . A significant negative Tajima’s D value (p < 0.01) was seen in all NAN_Tw (LL_Tw and Ur_Tw) groups with a sample size greater than 20 (Table p < 0.01) along with a nonsignificant sum of square difference test 43, as shown in mismatch distribution analysis  as a function of time (x-axis). All groups show a higher effective population size (Ne) presently than in the past. Although many groups do not show expansion in the last few 1000 years, they show expansion until the last two to three thousand years BP. For example, while Fujian, Taipei, Changhua and Kaohsiung show an increase in Ne 30 times greater at present than in the past, smaller groups such as Green Island and the Matsu archipelago show population expansion to a much lower level (8 times). Similarly, except for small island groups such as Matsu, Heping, and Green Island showing a very low effective population size (Ne = 250), all other groups showed an Ne ranging between 1700 and 2500. Interestingly, Xiaoliuqiu shows a stationary effective population size. This is not surprising, as this island is very small (2 km wide). Its population often changed and never reached more than a few hundred individuals. However, caution should be taken when analyzing groups with a sample size of less than 20 individuals .The gene flow  and kept all exclusive haplogroups  , self-declared individuals from the Fujian province who moved to Taiwan, and Matsu individualslot Fig.  shows th23, LL_Tw individuals show a significant ancestral relationship with mainland China (black dots). On the other hand, the Philippines (orange dots) and AN_Tw (red dots) groups show less mixture, suggesting a low amount of mixture between the NAN_Tw and AN_Tw groups and supporting results from Lo et al. 202023. These results are further supported by Supplementary Table S50. As shown in Fig. 51, the inertia ellipse representing individuals from China (black) overlaps with Japan (gray in the upper left). Similarly, the lower end of the ellipse (Black) overlaps with mainland Southeast Asia, likely the result of the southward expansion of the Han under the previous Qin Dynasty52.DAPC Fig.  places N53. We did not use tests for linguistic affiliation, as all NAN_Tw belong to the Sinitic family of languages. However, we applied the analysis to test for geographically associated clusters.We used a visualization tool of Bayesian Analysis of Population Structure (BAPS) to analyze the genetic diversity, mixture, and relative average of the maternal ancestry between NAN_Tw and groups of East Asia46, the BAPS analysis characterized the most significant cluster variation at K = 7 and K = 19  includes all NAN_Tw _Tw Fig.  and Chin 19 Fig.  but stilK = 7 , the urban Taiwanese group shows a level of gene source within a group of 82%, likely representing the diversity from all NAN_Tw target groups or resulting from recent demographic movements of lowland individuals toward central and attractive urban centers such as Taipei or Kaohsiung.BAPS was further used to estimate the extent of gene flow and the level of relatedness between groups of more than 20 individuals. At = 7 Fig. . Malaysi 19 Fig. , clusterK = 30 to construct a heatmap of pairwise gene flow between all source and target groups , the general mixture distribution between AN_Tw and NAN_Tw was less than 5% 20 used 2.3 million genotypes from 118 ethnic groups in the Philippines to show that modern humans in ISEA interbred with archaic Denisovans. Genome-wide data analysis facilitates our understanding of the evolutionary history of human populations, gene flows, and their genetic diversity. However, more is still required to characterize subtle parental fingerprints within groups in more restricted geographical areas.Recent studies23. Three groups of relationships were determined  from the Hanben archaeological site in northeastern Taiwan55. Its most recent dating could be from the early iron age (1.5 to 0.4 kya). Among their genetic dataset, three ancient remains carried the Y haplogroup O3a2b*-M7, and six carried O3a2c*-P164 . These Y-chromosome haplogroups are scarce among Austronesian speakers of insular Asia and are mainly associated with NAN individuals from mainland Asia. In agreement with our observation (exclusive mtDNA haplogroups with an age range of 1.0 to 2.6 kya), these findings corroborate late Neolithic era to early metal age (1.6 kya) settlements of non-Austronesian-speaking individuals in Taiwan54. Our previous study on archaeological human remains of the Neolithic Ling-Ding site II near Hualien in Taiwan . Similarly, these haplogroups, commonly seen in modern urban Taiwanese and continental Asia, support the findings of our study and the possibility of early settlements in Taiwan of non-Austronesian speakers from continental Asia. The BAPS analyses  produced sufficient diversity to represent a statistically significant population structure of the NAN-speaking Taiwanese individuals and their relationships with other groups in East Asia. We identified 271 haplotypes in Taiwan, and complete genome sequencing characterized 62 novel mtDNA haplogroups  seen in Fig. The substantial distribution of mtDNA diversity found among non-Austronesian speakers of Taiwan and their relationship with neighboring Asian groups offers a better understanding of the matrilineal structure of Taiwan. This is likely the result of repeated cultural influences from various non-Austronesian human settlements from mainland Asia over prehistoric and historic periods. To a lesser extent, it is also the result of Malayo–Polynesian interactions through trade between Western Indonesia and mainland Southeast Asia. Rapid progress in molecular genetics will reduce the stochastic effect from the analysis of uni-parental systems. However, this study contributes to a better genetic characterization of NAN-speaking Taiwanese individuals. It exposes the importance of using other gene systems and analyzing other ethnic groups on the island before pertinent information needed to unravel their genetic heritage becomes unreachable.Haplogroup diversity and distributionPhylogenetic tree of novel sub-haplogroups found among AN_TwMismatch distribution and Bayesian skyline plotsHeat map of gene flow (p = 0.01) for K=30Network of Haplogroups B4, F1, M8, N9, R9Network of Haplogroups D4a, D5, M7b, and M7c1aAncestry Mixture  K2-K30Delta KHaplogroup Frequency using this study and literature datasetsExtant Mixture with two Putative Parent Populations (Han and Austronesian-speaking groups)Dataset 1Supplementary Material


Freshwater endemism is thought to have been formed through the vicariance of connected water systems or the process by which ancestral populations colonized specific areas. The Korean Peninsula is well recognized for its high level of freshwater endemism with about 40% of freshwater fish species being endemic.Coreoleuciscus species, which is endemic in the Korean Peninsula.In this study, we attempted to reconstruct the process of speciation and phylogenetic dispersal of Coreoleuciscus based on mitochondrial cytochrome c oxidate subunit I (COI) sequences (1551 bp).We used fossil-calibrated divergence time estimation and ancestral distributional reconstruction to infer phylogeographic reconstruction of Coreoleuciscus species have originated from the independent colonization of different lineages in the ancestral populations, probably during the Late Plio-Pleistocene. The full-scale expansion of Coreoleuciscus populations appears to have taken place after major river structures were completed on the Korean Peninsula. We also provided evidence that the common ancestors of Coreoleuciscus was distributed in Eastern Eurasian continent and subsequently dispersed into the tip of East Asia. High genetic diversity was mainly concentrated in large drainage populations, while small populations showed an monomorphism, which could give important implications for planning the conservation and management of Coreoleuciscus.Our phylogeographic analysis based on a total of 626 individuals revealed that the two Coreoleuciscus species can be explained by the colonizer hypothesis that the endemic freshwater fish originated from the common ancestor in continental region.The phylogenetic background of the rheophilic The online version contains supplementary material available at 10.1007/s13258-022-01243-y. Coreoleuciscus species  is a small freshwater fish endemic to the Korean Peninsula , the Oligocene-Eocene, when the Baekdudaegan Mountain Range (BMR) was formed, which caused spatial separation between subdistricts of the Korean freshwater system  analysis to determine the ancestral ranges of major phylogenetic clades. Through these analyses, we expected to confirm whether the speciation of Coreoleuciscus occurred with post-dispersion isolation of ancestral populations that colonized during the Pleistocene, similar to the pathway suggested in the studies of bitterlings  sequences obtained from 626 ngs Jeon . More spC. splendidus and C. aeruginos individuals were collected from 31 locations in South Korea . All purified PCR products were sent for commercial sequencing at Macrogen Inc.  using the ABI PRISM BigDye™ Terminators v3.1 Cycle Sequencing Ready Reaction Kit , the ABI 3730xl DNA analyzer (Applied Biosystems) and the primers used in the PCR reaction. The Chromatogram for each sequence was checked using Sequencher 4.1.4 (Gene Codes Corporation). No deletions or insertions were found from all obtained COI sequences consisting of 1551 bp.The Cytochrome c oxidase subunit I (COI) gene was amplified in a 20 μl reaction volume including AccuPower PCR Premix , 100 ng genomic DNA and 10 pmol of forward  and reverse primers  , nucleotide diversity (π), number of haplotypes (h) and pairwise FST were estimated using DnaSP. A Median-joining haplotype network was constructed using PopART , Pungtungia (from Japan) Pseudopungtungia (from Korea) and Gnathopogon (from Korea and China) that were used as outgroups. The outgroup species were selected based on both the phylogenetic relationships and the geographic proximity. We used a relaxed uncorrelated lognormal clock model for the variation of substitution rates along each branch and a Yule prior for the relative node times in the tree. The best-fit substitution model was determined using jModelTest 2.1  analysis that was implemented in RASP  are distributed. We then assigned the maximum number of areas for each node to be 4. The analysis was run under the default setting for other parameters.To infer the ancestral geographic range of the Coreoleuciscus populations  than C. aeruginos   , which was lower than that of the Han River populations in C. splendidus, though the Nakdong River is a fairly large river similar in size to the Han River. Populations from small-sized drainages, including Ungcheon, Mangyeong, Samhwa and Yeongdeokosip, have lower haplotype diversity  than the populations from major river basins . Even in the same species, there were few haplotypes shared between drainages with high levels of pairwise-FST values (mean FST between Han and Geum = 0.83: mean FST between Nakdong and Seomjin = 0.95) (Table S1).A total of 73 complete COI haplotypes (1551 bp) were obtained from 31 ns Table . The meaCoreoleuciscus species in the haplotype network ] , which corresponds to the Late Plio-Pleistocene  and SKS (Nakdong River) of the Korean Peninsula, respectively . First, there may have been connections between watersheds of the Han and Nakdong River due to ‘river capture’ associated with geological fluctuations, albeit temporarily, which may have resulted in the formation of a population C. aeruginos in a drainage flowing to the other coastal side. This scenario can be supported by cases with similar distributional characteristics to Coreoleuciscus. For example, Koreocobitis naktongensis, an endemic species to the Nakdong River is a very close sister species of K. rotundicaudata, an species endemic to the Han River. Second, it is worth considering ‘estuary coalescence’, though the distance between the estuaries of the two rivers are quite large compared to the distance between the watersheds. Under this scenario, there was a huge confluence extending from the paleo-Yellow water system with the sea level fluctuation during the Pleistocene, through which the individuals  would have serially extended their distributions to the east of the Korean Peninsula.Considering the history of colonization, the path of C. splendidus had a strong genetic affinity with the Han River populations. Even for this reason, these east coastal populations of C. splendidus have been considered as an artificially introduced populations from the Han River  and the Bonghwa (C. aeruginos) of the Nakdong River are the examples. It is possible that these populations have historically had very little interaction with other within-drainage populations for some reason. Since populations that have undergone fragmentation are more likely to have lost genetic diversity  is likely to be very susceptible to anthropogenic disturbances because the population inhabit the spatially very small basin. Surprisingly, only three haplotypes were found in C. aeruginos of the Seomjin River, though it is a large drainage basin. The C. splendidus populations in the Mangyeong and Ungcheon rivers, which are small in size, also showed monomorphism in COI haplotypes. The populations of these streams require special management, and a systematic conservation plan should be established to avoid local extinction.There are several populations that are completely spatially isolated with very low genetic diversity. These small populations can be highly responsive to even minute environmental changes, which can lead to local extinction Below is the link to the electronic supplementary material.