Poa badensis, a rare steppe plant of calcareous rock/sand vegetation. To explore potential differences in reproductive success and genetic composition of peripheral populations, we analysed the absolute (north)westernmost occurrences in Western Germany and populations at the western margin (Eastern Austria) and the centre  of the Pannonicum, representing a part of the continuous range. Specifically, we discuss the genetic and reproductive constitution of the (north)westernmost exclave and draw conclusions on the species’ biogeographical and conservation history in this region. Therefore, we used two independent molecular marker systems  and a set of performance parameters. Overall, lowest regional genetic diversity was found in Western Germany, which is mainly a result of the specific history of two populations. However, this low genetic diversity was not accompanied by reduced reproductive success. The Eastern Austrian populations showed reduced genetic diversity and predominantly reduced performance, interpreted as a consequence of small population sizes. Central Hungarian populations showed the overall highest genetic diversity and comparatively high performance values. We observed high admixture and haplotype sharing between Austrian and Hungarian populations, indicating gene flow among these regions. In contrast, we interpreted the increased population differentiation within, and the clear distinctiveness of the German exclave as a long-term isolation of these (north)westernmost occurrences. Our results, overall, prove the good constitution of these populations and, together with their particular biogeographical history, highlight their conservation value.Many steppe species reach their (north)westernmost distribution limit in western Central Europe. This also applies to The online version of this article (10.1007/s10531-019-01722-x) contains supplementary material, which is available to authorized users. Investigating the constitution of (rare) species at their absolute distributional periphery, we can test commonly and controversially discussed biogeographical hypotheses, e.g. the ‘abundant centre’ distribution . At its (north)westernmost distribution limit the species grows at isolated sites in Central and Western Germany  and concentrated mostly at a regional scale westernmost distribution of this thermophilous (rock) steppe species  and chloroplast (cpDNA) sequence data. We also tested the reproductive success of this rare species across the same populations by estimating different performance parameters in a germination experiment. Based on these datasets, we evaluated the predicted general characteristics of peripheral populations. Specifically, we aimed at answering the following questions and finally draw conclusions on the species’ history in the German exclave: (1) Can we detect different patterns of genetic composition and reproductive success along our study transect, following predictions on centrality-marginality? Or do the patterns rather reflect a particular history of the three study regions, indicating long-term  refugia at the edge of the species’ distribution? (2) Do genetic and performance parameters vary in parallel: are populations of the (north)westernmost exclave less viable, i.e. showing decreased genetic diversity, highly  isolated populations and reduced reproductive success? Finally (3) we conclude what can be deduced from our results with respect to the species’ biogeographical and conservation history in the (north)westernmost exclave in Central Europe.In this study on Poa badensis as a diagnostic species of the order Stipo-Festucetalia pallentis  and the software FlowMax  likely connected occurrences, i.e. a distinct exclave, outside its continuous Pannonian distribution range.Focusing on the Central European distribution of P. badensis were later collected from the same populations  by taking one, randomly chosen, mature panicle per fruiting plant.To obtain an equal sampling across our study transect, we chose four populations per study region, i.e. twelve populations in total. In addition to this main dataset, we sampled material from the fifth population known in the Western German study region, the Mainzer Sand (G5). This population was not included in our main dataset due to the low number of individuals (i.e. four) found there, in our sampling year 2010). For molecular analyses, we collected leaf material from randomly chosen individuals across the respective population. Additionally, the number of flowering individuals was estimated at the same time to obtain population-size categories for each sampled population  and the number of seeds per panicle were counted. The seeds were distinguished as mature and undeveloped seeds; total seed mass was then determined for all mature seeds per individual panicle. For germination, usually ten seeds per individual mother plant were used, whereby undeveloped, shrunken seeds were excluded. The seeds were placed in petri dishes on a double-layered filter paper following a definite scheme, which allowed tracking each seed, individually. Petri dishes were placed in a climate chamber for 41 days. The settings for light and temperature mimic a day/night cycle with 12 h and 25 °C (with light) and 12 h and 15 °C (dark). The germination process was documented by differentiating four states for each seedling: ‘not germinated’, ‘radicle’ (visible), ‘cotyledon’ (visible), ‘dead’. Individuals, which successfully germinated (i.e. showing breakthrough of the radicle), were transplanted in multi-pot-plates filled with a standardised mixture of plant soil and quartz sand at a ratio of 2 to 1. After 2 months, seedling survival was determined by recording the plants survived in the multi-pot-plates.To isolate total genomic DNA from silica dried leaf material, ca. 20 mg per sample were ground with sterilized glass pellets and processed using the DNeasy™ Plant Mini Extraction Kit  according to the manufacturer’s protocol.Amplified Fragment Length Polymorphisms (AFLPs) profiles were generated for 7–23 individuals per population according to Vos et al. . These combinations have been applied successfully in previous studies  and were selected due to their potential for higher intraspecific variation. The amplification was carried out under standard PCR conditions as described in Plenk et al. . The parameter number of seeds only includes the mature seeds counted in the respective panicle. However, as we must assume that shed spikelets contained mostly mature seeds, this parameter might be underestimated. To assess the magnitude of this possible influence on our results, we calculated the proportion of shed spikelets compared to the total number of spikelets, which was only 17.6%. The number of seeds was also used to calculate a mean weight per seed (i.e. seed mass) from the total seed mass of all mature seeds within a single panicle. Fruit set was calculated as the proportion of mature seeds from the number of flowers whereas the germination rate and seedling survival rate were determined as the proportion of germinated seeds/survived seedlings related to the number of seeds applied in the germination experiment. Since our data does not meet the assumptions of parametric analyses, we performed a non-parametric Kruskal–Wallis rank sum test , fruit set (%), germination rate (%), and seedling survival rate (%). The K) by means of stochastic optimisation. Clustering of individuals was performed with a maximum of K = 13 clusters and nine replicates for each K. We performed the analyses with and without geographical coordinates of populations as an informative prior. A Bayesian admixture analysis . To reduce potential unbalances caused by the unequal sample size of our AFLP data , which are expected at each locus, for this adjusted sample size. Further, the percentage of polymorphic loci (PLP) was also calculated with this standardised sample size, whereby a locus is considered as polymorphic when Br ranged from 1.05 to 1.95 (5% level) or 1.01 to 1.99 . The mean seed mass per seed showed the highest values in the Hungarian (mean 0.3490 mg ± 0.4337) and the lowest in the Eastern Austrian region (mean 0.2899 mg ± 0.1990), demonstrating highly significant differences between regions (p < 0.001). In detail, seed mass was significantly different between the German and the Austrian, as well as the German and the Hungarian study region.In total, 1874 seeds from 223 mother plants were included in the germination experiment. On a regional scale, the number of mature seeds per panicle was highest in Central Hungary  and lowest in Eastern Austria (mean 22.63 ± 31.05), differing significantly among regions  due to significant differences between the Austrian and Hungarian and the German and Hungarian region. Detailed results of all performance parameters are summarised in Table Overall, the level of germination rate after 41 days was high: i.e. 80.8% ± 24.1). The values varied only slightly between 87.9%  and 71.1% . On regional level, germination was highest in the German exclave (mean 82.8% ± 22.4) and lowest in Central Hungary (mean 77.3% ± 26.8); albeit without statistical significance. A similar pattern was obtained for seedling survival with the regionally highest survival rate in Western Germany (mean 64.3% ± 24.5). The lowest survival rate was found in the Hungarian region (mean 47.5% ± 25.9); here, differences between regions were highly significant , indicating gradually higher survival rates towards the (north)westernmost exclave. However, none of the other performance parameters was significantly correlated with this estimate of peripherality.None of the performance parameters was significantly correlated with population size, although there was a slight trend to interdependence between fruit set, number of seeds and number of flowers. A significant positive correlation was shown for the seedling survival rate and geographical longitude  showed a heterogeneous composition (cf. Online Resource 2).For each study region about 80 individuals i.e. 241 in total) were included in the  in totalAMOVA) without regional structure, we found variation of 21.8% among and 78.2% within all populations (Online Resource 3). However, using a hierarchical approach based on the geographical origin (i.e. the three study regions), 11.0% of variation originated from the differentiation among regions, whereby 13.1% represented population differentiation within regions. The partitioning of data into two groups, representing the (north)westernmost exclave versus the Pannonian region (i.e. Austrian and Hungarian populations), revealed 12.3% of the variation among these groups and 14.1% among populations within groups. To test patterns of within-region population differentiation, we calculated two additional AMOVA analyses, i.e. one for the (north)westernmost exclave and one for the Pannonian region, resulting in 23.5% and 12.3% population differentiation, respectively (cf. Online Resource 3). The linear regression of pairwise transformed FST-values and geographical distances between populations (i.e. km) revealed a significant isolation-by-distance pattern . Within subsets we found significant isolation by distance in the Pannonian region , but not in the (north)westernmost exclave .Testing the molecular variance . German individuals were differentiated into the two other groups, one containing the populations Eckelsheim (G1) and Weilersberg  and the other one Heidesheim (G3) and Eierfels .admixture analysis based on spatial clustering of individuals  were generated for 94 individuals, resulting in an alignment of 1507 base pairs length and seven haplotypes. We found two main haplotype groups, separated by one specific mutation at bp 883 (atpI-H): one German group and one Pannonian group . Within the German group individuals from the population Eckelsheim (G1) formed one specific haplotype, whereas the second haplotype in this group contained all other German populations . In contrast, the Pannonian group is characterised by further structuring (five haplotypes), but most of the individuals  formed one major haplotype . The four individuals analysed were allocated to both haplotypes detected within the German exclave  of our studied populations with values compiled by Nybom  and highest  diversity values were found in the populations Eckelsheim  and Pilisszentkereszt , respectively. Interestingly, the population Weilersberg (G2), showed the second lowest genetic diversity , although being a large population (PSC: 4). After standardisation of AFLP band richness to the minimum populational sample size . The estimates after rarefaction were slightly higher in some of the populations . However, the highest and lowest values were again found in the populations Pilisszentkereszt (H3) and Eckelsheim (G1), respectively.Different genetic diversity estimates based on our ts Table . Compariby Nybom  for longHE, I) were highest in Central Hungary  and lowest in Western Germany . Genetic diversity of Eastern Austrian populations was only slightly higher  than in the (north)westernmost exclave. However, the distribution of genetic diversity across regions varied slightly for PLPs: here, the lowest regional diversity was found in the Eastern Austrian study region and this pattern retained also after rarefaction.On regional average, genetic diversity values  was monomorphic , reproductive success and genetic composition along our study transect. Regarding the demographic characterisation of our studied populations, Stipa capillata westernmost populations  and an increasing genetic differentiation among the (north)westernmost populations, as expected for a central-marginal gradient . The populations Heidesheim  and Eierfels  did not show reduced levels of genetic diversity; the values observed there are comparable to those of the continuous distribution in Central Hungary , a possible explanation for the high performance parameters observed in the (north)westernmost exclave might be the occurrence of one (or more) polyploidization events, likely occurring during warm  stages. This may have led to a stronger reproductive potential, probably as an evolutionary strategy to overcome the more unfavorable conditions at the absolute range limit. Moreover, polyploid populations may also show generally higher levels of genetic diversity  may have aided to retain a certain level of genetic diversity, as observed in the populations Heidesheim (G3) and Eierfels (G4). On the contrary, the populations Eckelsheim (G1) and Weilersberg (G2) are likely less viable and show low genetic diversity, which is driven by their histories. Although population size did not correlate with the genetic diversity in general, at least one of these two populations is characterised by small population size . Nevertheless, low genetic diversity of populations does not necessarily imply that they have to be less viable. Habel and Schmitt , 2018 arPoa badensis among other steppe species as a relict, surviving on sandy/rocky grassland sites in Central Europe since the last glacial. However, they also remarked that most of the populations occurring today are likely younger westernmost exclave as genetically impoverished. However, there are two populations, Eierfels (G4) and Heidesheim (G3), showing comparatively high levels of genetic diversity. As their history is very different, we will go into more detail. The population Eierfels (G4) is situated on a flat ridge of basic conglomerate in the Lower Nahe region which is protected as natural monument and harbours a unique xerothermic vegetation Korneck . The popP. badensis had been introduced to this site by taking (seed) material from the closely located population Heidesheim (G3). However, in our AFLP data we found the population Weilersberg grouping together with Eckelsheim and not with the assumed source of the seed transfer  is again located on a rocky outcrop belonging to the Waderner Schichten as part of the Rhinehessian Plateau , the population Weilersberg G2) is characterised by a different and considerably younger history. This area was originally used as a sandpit and later designated as a nature reserve. To our knowledge,  is charaeau Fig. ; 2018 miOur AFLP data revealed admixture between the populations Heidesheim (G3) and Eierfels  in situ survival, probably since early postglacial times, which we especially hypothesise for the populations Eckelsheim (G1), Eierfels (G4), and Mainzer Sand (G5). We moreover conclude, that the populations of the Waderner Schichten  may originate from the probably slightly earlier populated occurrences along the northern edge of the Rhinehessian plateau (e.g. Mainzer Sand and other previously existing ones) and spread there along the river systems of Nahe and subsidiary streams to the west and south  event. In this case, we would expect low genetic diversity and less genetic differentiation of these founder populations from a potential source in more central/eastern areas  adapted to low levels of genetic diversity Supplementary material 2 (PDF 2609 kb)Supplementary material 3 (PDF 96 kb)Supplementary material 4 (PDF 778 kb)Below is the link to the electronic supplementary material.


The benefits of ancestry informative SNP (AISNP) panels can best accrue and be properly evaluated only as sufficient reference population data become readily accessible. Ideally the set of reference populations should approximate the genetic diversity of human populations worldwide. The Kidd and Seldin AISNP sets are two panels that have separately accumulated thus far the largest and most diverse collections of data on human reference populations from the major continental regions. A recent tally in the ALFRED allele frequency database finds 164 reference populations available for all the 55 Kidd AISNPs and 132 reference populations for all the 128 Seldin AISNPs. Although much more of the genetic diversity in human populations around the world still needs to be documented, 81 populations have genotype data available for all 170 AISNPs in the union of the Kidd and Seldin panels. In this report we examine admixture and principal component analyses on these 81 worldwide populations and some regional subsets of these reference populations to determine how well the combined panel illuminates population relationships. Analyses of this dataset that focused on Native American populations revealed very strong cluster patterns associated with many of the individual populations studied. More panels have been published in the intervening years and the minimal overlap in SNPs in the various panels remains a problem. Many of the studies in the literature involve a unique set of SNPs evaluated on a unique set of population samples. The 1000 Genomes data have been analyzed for several sets of AIMs but those populations do not represent an ideal forensic sample of human diversity around the globe because they do not include the full range of genetic diversity. Reviews dealing with aspects of AIMs such as human identification, ancestry inference, mixture deconvolution, and panels for predicting individual phenotypes for eye, hair, and skin color have appeared7. The benefits of AISNPs have not been realized as quickly as they might have for various reasons. Multiple factors have combined to make it difficult to evaluate and compare the general utility of most of the proposed AISNP sets. The benefits of particular AISNP and other AIM panels can only accrue and be properly evaluated as the data for them accumulate and become readily accessible on a large number of reference populations approximating the genetic diversity of human populations worldwide.DNA-based polymorphisms that differ substantially in frequency among human populations can be employed to infer ancestry. The potential utility of ancestry informative markers (AIMs), especially ancestry informative SNPs (AISNPS), for forensic, anthropological, and medical applications has been reflected in part by a large number of research reports. Soundararajan ALlele FREquency Database  and the Forensic Reference-Resource on Genetics knowledge base  shows that complete frequency data have accumulated on 132 reference populations for the 128 Seldin AISNPs and on 164 reference populations for the 55 Kidd AISNPs. The union of the two panels encompasses 170 different autosomal AISNPs; the panels have 13 SNPs in common. Subsets of these 170 SNPs in the combined panels have also been published or made publicly available on additional populations and the allele frequencies are stored in the ALFRED database. Noteworthy are consortium datasets and commercial kits providing information for a large subset of the 170 Kidd and Seldin AISNPs. The 26 population samples from the Thousand Genomes consortium (Phase 3)8 have complete genotypes and allele frequencies for 169 of the 170 AISNPs. The one SNP (rs10954737) out of 170 not available from the Thousand Genomes dataset is especially helpful in distinguishing Native Americans as a group from other regions of the world and to a lesser extent distinguishing Native American groups from each other. Because of that systematic missing SNP data, we have not included 25 of the 26 Thousand Genomes populations in these analyses (see below).A recent tally from the 10 and the 55 SNP panel from the Kidd group14. We also note that a commercial kit, the ThermoFisher Precision ID Ancestry panel, is based on the union of these two panels and includes 165 of the full 170 AISNPs. In recent years an increasing number of studies have also appeared reporting SNP frequencies and/or genotypes for this panel (see e.g. our recent paper14 citing six of these studies). Because of these developments we have assembled and analyzed data on all 170 SNPs in 81 distinct population samples. Only populations with data for all 170 SNPs are included here.Here we report selected analyses not previously presented on 81 population samples that now have genotypes and allele frequencies available for 170 markers. This represents the union of SNPs in two of the AISNP panels that are among the few AIM panels that have been studied on a large number of diverse human populations. These consist of the 128 SNPs from the Seldin group11) and the Seldin Lab (128 SNPs9). Table 16. As documented in previous studies, all samples were collected with informed consent for population genetics studies such as this.Analyses presented include 81 population samples representing the major continental regions of the world that have genotypes available on all 170 of the ancestry informative SNPs in the combined panels from the Kidd Lab (55 SNPs18.Table http://www.xlstat.com/en/about-us/company.html).The SNP allele frequencies were obtained by gene counting assuming each locus was a two-allele codominant system. Hardy-Weinberg ratios were tested for all SNP-population combinations. Principal Component Analyses (PCA) used the XLSTAT 2018 software  iterations.We employed version 2.3.4 of the STRUCTURE softwareIndividuals were omitted if they were missing data for one third or more of the genotypes for any one SNP. The 81 populations analyzed on the combined set of 170 ancestry SNPs, included 3933 individuals. Out of the 668610 possible genotypes  in the dataset there were 12504 or 1.87% missing genotypes. The allele frequencies for the 170 AISNPs in each of the 81 populations are in Table 14 but concluded that the Kidd 55 provided better classification of populations than the Seldin 128. We have now undertaken a proper comparison by conducting STRUCTURE analyses on each SNP panel for exactly the same set of 81 populations. Figure Previous studies of the individual subsets of the full 170 SNPs involved different sets of populationsThe best structure runs for the two subsets, the 55 Kidd and 128 Seldin SNP panels, are in supplemental Figs. Since the model used in STRUCTURE assumes that ancestry of individuals comes from one or more of the K distinct “ancestral” sources, logically it follows that a “better” result has a higher percentage of individuals arising primarily from one of the K genetically distinct source populations. Therefore, to quantify the quality of the clusters we have used the level of assignment of individuals to any one of the individual K clusters. We calculated the percentage of individuals that have at least a given percentage of assignment to any cluster. The data are summarized in Table 13 identified nine optimal clusters in a dataset of 139 population samples analyzing only the 55 Kidd AISNPs. This previous pattern is very similar to the population clustering seen in Fig. The STRUCTURE results in our earlier reportOther than this new “Pygmy” cluster, the other clusters are easy to see in Fig. The first transition in Fig. 14, a more refined and visually clinal picture is seen within SW Asia. Whatever the more detailed pattern of genetic variation in Europe and Southwest Asia, the pattern for these populations with these loci is one of disproportionate assignment of individuals to two (or three) clusters comparing Southwest Asia and Southern Europe with Northwest and Northern Europe summarized in the population averages shown graphically . Then a relatively clean cluster visually  appears with the Druze and Samaritans from SW Asia. Sardinians, Roman Jews, and Turkish Cypriots then represent a transition to Southern Europe. The Southern Europeans display partial assignment to the cluster that is essentially a Northern Europe cluster. The Hungarians through the Komi Zyriane form a clear visual cluster for Northern Europe. While the visually clinal pattern from Southwest Asia to Northeast Europe can be seen at K = 10, the clinal pattern is completely obvious at K = 12. When additional Southwest Asia populations have been evaluated, albeit for only the 55 Kidd subset of the 170 SNPsly Figs.  and S3. 24. Many of those populations with strong values for that cluster in the earlier study13 could not be included in the current report because genotypes were not available for most of the Seldin Lab AISNPs.A clear break in the clustering pattern occurs in Fig. The Central Asian cluster has a transition that is clinal in appearance including the Outer and then Inner Mongolian population samples followed by the Southwestern China populations. Finally, the East Asians through Southeast Asian populations form a very “clean” cluster at K = 10. There is another clinal transition including the Micronesians to the two Melanesian populations that form a clear cluster.The Native Americans form a quite clean cluster at K = 10. Small amounts of allocation to other clusters amount to little more than noise at this level. Many Native American populations are significantly admixed, probably including some individuals in these population samples. However, when these samples were collected efforts were made to sample individuals that had no known close ancestors of European or African origin.At K = 12 in Fig. Figure Additional STRUCTURE analyses were also carried out for subsets of the 81 populations based on various groupings by geographical regions. The 8 principal groupings analyzed include: Sub-Saharan Africa (13 populations), North and Sub-Saharan Africa (21 populations), Europe-Southwest Asia-North Africa (29 populations), Europe-Southwest Asia (21 populations), South Central Asia , South Central Asia-East and SE Asia-Pacific (28 populations), Core East and SE Asia (18 populations), and Americas (9 populations with and without 3 outlier populations from other regions). The population subsets were explored with the full set of 170 AISNPs to determine whether better differentiation within these regions might be observed without the distraction of genetic diversity from populations outside of those regions. In most regional groupings analyzed no noticeable improvement was observed for the geographical groupings of populations analyzed compared to the clustering of populations observed in Fig. When the Sub-Saharan and Northeast African populations were analyzed as a group, the two Pygmy populations showed a distinct cluster as did the Northeast Africans, the Ethiopians and Somali. There was partial assignment of the three East Africans, the Chagga, Masai, and Sandawe to the Northeast Africa cluster but the Zaramo remained similar to the West Africans.Analyses of the North Africans, Southwest Asians, and Europeans provided little new except the Samaritans, a population that has undergone considerable genetic drift, separates by K = 4 onto a separate group. By K = 6 the Chuvash and the two Russian groups are primarily assigned to a group that has only partial similarity to other Northern European populations.In a Structure analysis focused on just the Central, NE, East, and SE Asian populations some more detailed grouping of populations is evident at K = 6 Fig. . The KhaIn significant contrast to most other regions, considerable differentiation was found among the nine Native American populations when other world regions were excluded. STRUCTURE analyses (not shown) of the nine populations resulted by K = 8 in most populations having assignment to a unique cluster except for the Maya, Guihiba, and Quechua being indistinguishable and the Ticuna subdividing into two clusters. All of the clusters showed noticeable partial assignment to other clusters. STRUCTURE analyses were then performed with three outlier groups and the nine Native American populations. Three outliers–Yoruba, European Americans, and Outer Mongolians–were chosen to help evaluate both the extent to which admixture from West African and European populations in recent generations contributes to the results as well as how the Mongolians would affect clustering. Results were generated for K = 2 to 13 with 20 runs at each K level. Selected runs are illustrated in Figs. In general, the results with the three outlier populations present were very similar to those found when the nine Native American populations were analyzed without the Mongolian outlier. The inclusion of the Yoruba and European American outliers shows low levels of allocation to these biogeographic areas in the Native American groups: from Europeans (0–6%) and to a lesser degree from Africans (0–1%) depending on individual population and K level examined.Our study illustrates both the value of extensive SNP data on a large number of populations and the difficulty in assembling such a dataset involving a large number of populations and individuals genotyped for the same set of SNPs. In order to add more populations we would have had to use fewer loci. We chose to opt for more comparably typed loci at this time and excluded the 1000 Genomes populations even though the missing SNP in the 1000 Genomes Project will make little if any difference in analyses. We have seen small and intermediate populations show partial assignment to two or more clusters. Usually in the literature this is often considered an indication of admixture but our analyses have shown that it is most conservative, barring historical evidence to the contrary, to consider these populations as simply intermediate for whatever reason. The dependence of the clustering pattern on the selection of populations is shown by the assignment of essentially all individuals in the Outer Mongolian sample to two groups, Europeans and Native Americans, until a sufficient number of clusters are allowed for the Outer Mongolians to be assigned to their own cluster. Similarly, in other analyses of regional subsets of populations, populations with partial assignment to two clusters become a clear cluster. We note the Northeast African populations as another example.There are biomedical implications from our findings. For example, the North African populations analyzed are clearly genetically distinguishable from most Middle East (Southwest Asian) populations and from Europeans. Northern Europeans are genetically distinguishable from Southern Europeans. Perhaps the most significant result is that “Asians”, as used in much biomedical literature, comprises four clearly distinguishable groups–South Asians, Central Asians, East Asians, and Southeast Asians–with many populations intermediate among the more extreme of those four clusters. It would not be surprising if even more discernible population groupings in Asia and elsewhere will emerge as the population sampling of human genetic diversity continues to improve and better AISNP panels are developed.Supplementary InformationSupplementary Dataset


Despite being the fourth largest island in the Mediterranean basin, the genetic variation of Corsica has not been explored as exhaustively as Sardinia, which is situated only 11 km South. However, it is likely that the populations of the two islands shared, at least in part, similar demographic histories. Moreover, the relative small size of the Corsica may have caused genetic isolation, which, in turn, might be relevant under medical and translational perspectives. Here we analysed genome wide data of 16 Corsicans, and integrated with newly  and previously generated samples from West Eurasia and North Africa. Allele frequency, haplotype-based, and ancient genome analyses suggest that although Sardinia and Corsica may have witnessed similar isolation and migration events, the latter is genetically closer to populations from continental Europe, such as Northern and Central Italians. Nowadays, approximately ~339,000 people inhabit the island1.Corsica, located south of the shore of Côte d’Azur (France), and west of Tuscany , is separated from Sardinia to its south by the Strait of Bonifacio. It is the fourth largest Mediterranean island 4. Archaeological and genetic data suggest episodic and discontinuous settlements during Mesolithic and transitional phases between Mesolithic and Neolithic periods6. The permanent human presence in Corsica is attested in the Neolithic period since the 6th millennium BC and possible interactions with mainland and other islands are suggested by the wider appearance of non-local lithic resources and similar ceramic traditions over a larger Western Mediterranean region7.The understanding of the peopling of Corsica has remained incomprehensive. From a geological perspective, during the last glaciation, Corsica and Sardinia formed a single landmass and its distance to Italy was reduced, possibly increasing connections with mainlandth century, followed by Roman Papacy from the 8th century onwards. From the end of the 11th century Corsica was under the government of Pisa. Then, with the intermittent period dominated by the kings of Aragon, Corsica was under the rule of Genoa from the 13th to the 18th century, when it was incorporated into France in 17688.In the last three millennia, the Corsican population witnessed several dramatic demographic changes due to conquests, epidemics outbreaks and economic crises. The Greeks established the city of Alalia (today Aleria) in 565 BC, which is also the first mention of Corsica in historical records. Subsequently, it witnessed numerous intrusions and conquests by different populations. Carthaginians and Etruscans dominated the island until the Roman occupation in the third century BC. Successive invasions by the Vandals, Ostrogoths and Saracens, took place until the beginning of the Byzantine influence in the 612, such as a marked North-South differentiation, confirmed by surname and linguistic studies13, and mirroring the geographic features of the island.Previous genetic surveys on Corsican population have shown regional differentiation16, while conversely, some have suggested genetic distinction18. Furthermore, the extent of Corsica’s genetic relationships with mainland populations is controversial. Close genetic connections have been observed between Corsica and continental Mediterranean populations20, whereas other studies have reported limited genetic influences from mainland12. Furthermore, it has been previously suggested that different regions in Corsica have received genetic influences from distinct sources, showing diverse affinities with surrounding populations12.Despite their geographic proximity, different investigations based on unilinear and autosomal markers have produced contradicting results in characterizing the relationship between Sardinia and Corsica. Some studies have found genetic affinity between the two islands22. Sardinia is a well-known example (see for instance23 and references therein). Preliminary studies have suggested Corsican potential in this context: an analysis on chromosome X microsatellite markers demonstrated an overall LD decrease in the innermost part of Corsica24 and some medical and association studies have been conducted on Corsican population27.Regardless of its genetic affinity, it is unclear to what extent the Corsican population shows a signature of demographic bottleneck or population decrease, as expected in a long term isolation scenario. Previous studies have demonstrated that isolated populations are valuable resources in genome-wide association studies. In geographically and/or culturally isolated conditions evolutionary forces and population dynamics can lead to the high level of homozygosity, reduced genetic diversity and increase in peculiar allele frequencies, which makes easier to trace genetic variants affecting medical or phenotypic traitsDespite its anthropological and epidemiological relevance, a genome-wide characterisation of Corsican population is not available so far. In order to evaluate the within-population genetic variation, and the affinity with other modern and ancient European populations, here we genotyped 16 Corsican samples collected from different locations of the island , togethe29. Corsicans show an excess of RoHs indicated by the high median values of total number and total length of RoHs, next to Sardinians and French Basques . RoH are uninterrupted segments of homozygous genotypes present in individuals. The number and extension of RoH stretches are correlated to the level of identity by descent and are largely affected by population’s demographic historyues Fig. . This ho31 implemented in the program ADMIXTURE32. At the level of lowest cross-validation index (K = 6), Corsicans and geographically related populations are composed mainly by three ancestry components - “Sardinian”, “Northern and Eastern Europe”, “Caucasus and Middle East”  than the latter. However, the large standard errors associated to the f3 statistics suggest that the differences in affinity of Corsica with Sardinian/Basque or mainland populations are negligible or non-significant. The tests of admixture based on the f3 statistics revealed an overall complexity in the admixture history of Corsica. Out of 2,652 tests performed, 18 were statistically significant (|Z| > 3), although only 8 remained significant using a more conservative threshold (|Z| > 4)  . Regardlj as a mosaic of genomic fragments inherited by n donor samples34, both in terms of total number (chunkcounts) or length of fragments . The resulting j x n coancestry chunkcount matrix was then employed to identify and characterise homogenous groups of individuals, in the form of a dendrogram. fineSTRUCTURE clustering results together with the ChromoPainter coancestry chunkcount matrix were subsequently used to identify, date and describe admixture events with GLOBETROTTER35.We inferred the fine-scale population structure harnessing the haplotype-sharing patterns among individuals. In detail, using ChromoPainter we reconstructed each analysed individual The complete fineSTRUCTURE dendrogram is represented in At a finer inspection, out of the 16 Corsican samples analysed, 12 formed a population specific cluster (Corsicans2) in the Southern European clade, related to North and Central Italian group. Three samples fell into three different clusters composed by North and Central Italians , French (French) and Iberians (Iberia2), respectively Fig. , possiblIn order to explore the relationships between populations, we performed a Principal Component Analysis (PCA) based on the chunkcount coancestry matrix. The first two PCs explain a large proportion of the total variance , similarly to Italian, Spanish and Balkan populations  and Iranian Neolithic (Iran_N) were considered in the same analysis . Compared to Corsicans, French and Spanish samples were characterised by a smaller proportion of Iranian Neolithic , and a slightly higher contribution from Western Hunter Gatherers (14% and 17%).In order to better characterize the “ancestral” composition of Corsican and European populations, we inferred their genetic relationship with a set of ancient individuals using qpAdmons Fig. . In addi41, which is situated only 11 km South. However, it is likely that the two populations have shared at least part of their demographic history, given their geographic proximity and similarities in the archaeological record. In addition, the relatively small size of Corsica may have contributed to create isolation conditions affecting the genetic variability of the autochthonous population24.Despite being the fourth largest island in the Mediterranean basin, the genetic variation of Corsica has not been explored as exhaustively as SardiniaOur analysis revealed that Corsican population shares several genomic features with Sardinia and North-Central Italy, creating a unique blend of genomic ancestries Fig. –3.30, relatively high homozygosity found in the current study confirms Corsican population as a potentially valuable resource for association studies, as also suggested by some previous surveys of autosomal markers27 and chromosome X microsatellite LD24 variation in Corsican population.The Corsican population shows a relatively high homozygosity characterised by high variance, suggesting that it witnessed both an isolation and migration phase. A similar pattern has been observed for Sardinians Fig. . As isolAllele frequency-based assignment algorithm and genetic distance methods showed that Corsica is more closely related to mainland populations from France, Italy, Spain and Greece rather than Sardinia Fig. . In contTo better characterise the genetic relationship of Corsica with other Mediterranean populations we have harnessed the information embedded in haplotype configurations. The analyses of the haplotype sharing patterns performed using ChromoPainter and fineSTRUCTURE provided substantial evidence of higher affinity between Corsica and Central or Northern Italian populations rather than Sardinian, French and Iberian populations Fig. . In fact36.When we investigated admixture evidence of Corsican population through f3 analysis, Corsicans could be described as a combination of allele frequencies from Sardinia, Northern Europe, Caucasus, North Africa and Arabian Peninsula . A simil42. In fact, we have found a similar proportion of Steppe Bronze Age (~19%) and Iranian Neolithic (~14%) in Corsicans.Lastly, we have evaluated the genetic relationship of Corsica and other Mediterranean populations with ancient Eurasian individuals through PCA and qpAdm Fig. . CorsicaIn conclusion, the genetic characterisation of Corsica is consistent with a closer genetic affinity with Northern and Central Italian populations rather than Sardinians, although sharing with the latter a noteworthy proportion of ancestry and similar demographic and isolation processes. The analysis of larger sample sizes from different regions of the island and genetic material from ancient specimen may help to further evaluate the genetic structure in the island and its demographic history.12 in order to maximize geographic coverage  were controlled with software KING v1.457 and two samples (one Yemen and one North Kannadi) were randomly removed from detected relative pairs. For some analyses SNPs in strong linkage disequilibrium (pairwise genotypic correlation R2 > 0.4) in a window of 1,000 SNPs, sliding the window by step of 25 SNPs, were excluded. Exact numbers of individuals, populations and markers used in each analysis are specified in DNA was extracted from blood/saliva samples and genotyping was carried out on Illumina 660 K platform (Human660W-Quad BeadChip). New samples were combined with data from previous studiesanalysed . The mer56, with sliding window of 50 SNPs , allowing for one heterozygous and five missing calls per window. RoH were defined as regions of at least 50 consecutive homozygous SNPs spanning at least 1,500 kb, with a gap of less than 1,000 kb between adjacent regions. The required minimum density was set at 50 kb/SNP58.Runs of homozygosity (RoH) were inferred using PLINK v1.932 was used to infer putative ancestral components in the Corsican population in a worldwide context. Clustering was performed 100 times at K = 2 to K = 15  individuals were randomly selected from each population. The inferred parameters were finally used in ChromoPainter specifying all the available samples as donors and recipients. This resulted in two different matrices, the “chunkcount” and the “chunklength”; the former summarises, for each recipient, the number of fragments inherited from each donor, while in the latter the same statistics is expressed in total genomic (in centimorgan) length. The coancestry matrices based on the length (chunklength) and number (chunkcount) of fragments were then obtained by combining the different chromosomal outputs with ChromoCombine. The obtained chunkcount coancestry matrix was harnessed to identify homogeneous groups of individuals using fineSTRUCTURE. In detail, two different runs of 4,000,000 iterations were performed, discarding the first 1 million as burn-in and using a thin interval of 10,000. For each of the clustering approaches, a hierarchical tree was inferred by taking advantage of the “Tree” method and the “maximum concordance state” approach, performing 1 million iterations. In order to assess the robustness of the clustering process, the pairwise coincidence statistics among individuals was evaluated  was carried out using the coancestry matrix of coping vectors created with ChromoPainter. On the whole, 624 samples were used, including all the samples from Europe, the Caucasus and Anatolia and the sub-set of Near Easterners, that on fineSTRUCTURE tree , formed 35. In detail, the painting profile obtained by ChromoPainter was harnessed by testing for any evidence of admixture using the options null.ind = 1 prop.ind = 0, and performing 100 bootstrap iterations. For each of the inferred admixture events, only those characterised by bootstrap values for time of admixture between 1 and 400 were considered. Subsequently, the time of admixture was estimated by repeating the same steps with options null.ind = 0 and prop.ind = 1. For the “non-local” analysis the same procedure has been repeated excluding clusters from the Southern European group as possible sources of the target. We considered a generation time of 30 years38.The time of admixture and mixture profile were estimated for all the previously inferred clusters (targets) using GLOBETROTTERet al. 201763 and Olalde et al. 201864 were used, from which non relevant individuals and all the samples with more than 70% of missingness were removed. After the final filtering 222 samples were retained  the tested sources were significantly different and b) the target may be reconstructed using a specific combination of sources. A p-value threshold of 0.01 was used, as well as the following set of sources (The admixture profile of all the analysed populations was reconstructed with qpADM sources :Anatolia_BA, Anatolia_N, Steppe_EMBA, WHG, EHG, Iran_N, Minoan_Lasithi, Mycenaean, Yorubas, Levant_N, CHG, Europe_EN, Europe_LNBA, Europe_MNChLand the following set of Outgroups:AfontovaGora3, EHG, ElMiron, GoyetQ116-1, Iran_N, Kostenki14, Levant_N, MA1, Mota, Natufian, Ust_Ishim, Vestonice16, CHG.All the tests for which the fitted model was supported were shown in Supplementary Figures and TablesSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5


Çatalhöyük is one of the most widely recognized and extensively researched Neolithic settlements. The site has been used to discuss a wide range of aspects associated with the spread of the Neolithic lifestyle and the social organization of Neolithic societies. Here, we address both topics using newly generated mitochondrial genomes, obtained by direct sequencing and capture-based enrichment of genomic libraries, for a group of individuals buried under a cluster of neighboring houses from the classical layer of the site’s occupation. Our data suggests a lack of maternal kinship between individuals interred under the floors of Çatalhöyük buildings. The findings could potentially be explained either by a high variability of maternal lineages within a larger kin group, or alternatively, an intentional selection of individuals for burial based on factors other than biological kinship. Our population analyses shows that Neolithic Central Anatolian groups, including Çatalhöyük, share the closest affinity with the population from the Marmara Region and are, in contrast, set further apart from the Levantine populations. Our findings support the hypothesis about the emergence and the direction of spread of the Neolithic within Anatolian Peninsula and beyond, emphasizing a significant role of Central Anatolia in this process. Neolithic Çatalhöyük (7100–5950 BC) is a world-renowned Neolithic settlement. Its size, remarkable preservation, presence of numerous works of Neolithic art, and large amounts of archeological data obtained through meticulous excavation have consolidated its unquestioned importance in the identification of a wide range of constituent elements of the Neolithic . All houInitially it was proposed that Çatalhöyük individuals buried together in the same building were biologically related, and groupings of houses and constituting neighborhoods were defined by biological affinity . Then, aThe emergence and expansion of the Neolithic within and outside of Anatolia is another issue that could be addressed with ancient DNA data from Çatalhöyük. This process is thought to be a sum of several waves and trajectories of migration ,14,15. TÇatalhöyük was undoubtedly a part of a large, far-reaching exchange network ,24 and cIn this work we address both the question of maternal kinship relations of individuals from Çatalhöyük, and the genetic affinities of Central Anatolian populations and what follows their potential relation to the westward spread of the Neolithic. We present ten new complete mitochondrial genomes from Çatalhöyük individuals buried under the floors of adjacent buildings dated to classic levels (Mellaart Phase VI A) of its occupation.Four adjacent, roughly contemporary buildings from Çatalhöyük South Area, dated to Mellaart Phase VI A , were sDNA was extracted from teeth and petrous parts of temporal bones in laboratories dedicated to working with human aDNA. The surface of the samples was decontaminated with the use of 2% bleach and UV light, and only the inner part of both the teeth and petrous bones were drilled for extraction. DNA isolation was performed both at the Middle East Technical University in Ankara (METU), Turkey, and at the Faculty of Biology, Adam Mickiewicz University in Poznan (AMU), Poland. In the METU laboratory, the DNA was extracted using a silica-powder-based method and modified binding buffer, as described by Allentoft et al. . In the The genomic libraries underwent Illumina sequencing  at the Science for Life Laboratory (SciLifeLab) facility in Stockholm (NGI Stockholm), Sweden. All preliminary pipeline computations of the sequencing data were undertaken on resources provided by the Swedish National Infrastructure for Computing (SNIC) through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) .The RNA baits for capture enrichment of complete mitochondrial genomes were prepared following the protocols described in Juras et al. . Two rouDNA sequencing data from both shotgun screening and mtDNA capture were processed with the use of a customizable analytical pipeline, described in . The adaThe mtDNA capture binary alignment map (bam) files were merged with shotgun screening data, using the merge tool from SAMtools package v1.8 . MisincoST) are described in detail in For comparative analyses, complete ancient mtDNA genomes were obtained from the literature and the ancient human mitochondrial genomes database (AmtDB) . Where aFor the purpose of this study, due to the limited number of samples available for Iran and Turkmenistan Neolithic and Chalcolithic, the samples were grouped together into Neolithic Middle East group (NME). Similarly, Pre-Pottery Neolithic samples from Jordan were grouped together with epipaleolithic Natufian samples from the same region into the Natufian and Neolithic Levant group (NNL). Furthermore, the Bronze Age samples from Turkey were grouped together as Bronze Age Near East group (BNE) with Jordan samples from the same period, and Neolithic, Chalcolithic, and Early Bronze Age samples from Armenia were merged into the Neolithic to Bronze Age Caucasus group (NBC).The Çatalhöyük samples were analyzed as part of the Central Anatolia Neolithic group containing additionally three individuals from Boncuklu Höyük and three from Tepecik-Çiftlik sites. It has been shown that the European gene pool was shaped by three major ancestral populations, including autochthonous hunter-gatherers, and two migrant groups from Near East in the Early Neolithic and Eurasian steppe in the Late Neolithic period ,55,56. HThe map in ST values were computed in Arlequin 3.5 [p-values. To visualize FST values, multidimensional scaling (MDS) analysis was employed, using the Python Scikit-learn 0.18.1 package [A centroid-based clustering approach was used to examine the PCA results to search for logical clusters within our data. The k-means method was used, as implemented in Scikit-learn v. 0.18.1 Python package , to the quin 3.5  on the squin 3.5  and 10,0 package .We found the overall DNA preservation on the site to be rather poor, as a majority of the samples had less than 1% of endogenous human DNA, with notable exceptions of sk. 21981 . Since oThe mitochondrial genomes were obtained for four individuals buried under the floors of building 96, two individuals from building 97, and two from buildings 89 and 80 C. All inAdditionally, to the individuals excluded from PCA and t-SNE analyzes based on previous reported genetic kinship, two more were found to belong to pairs of first-degree relatives based on the READ analysis . Both paThe results of PCA and t-SNE analyses show that the Neolithic Central Anatolian population falls within variations of other Neolithic and Chalcolithic populations from both the Middle and Near East, and Neolithic and Chalcolithic populations from Europe. In contrast, all those groups are set apart from both steppe and hunter-gatherer populations, to the point where the highest average silhouette for the k-means clustering of frequency-based PCA results for all the populations is for k value of 2, forming two clusters: steppe plus hunter-gatherer and other populations .With the outliers excluded, the Neolithic Central Anatolian (NCA) population is always shown to be closely related to the Neolithic Population from the Marmara region (NMR) belonging to the same cluster in both PCA A and t-SAdditionally, those two groups, NCA and NMR, cluster together with Neolithic Greece and Macedonia (NGM) and Neolithic Iberian (NIB) samples in k-means clustering of t-SNE results, when analyzed with both the narrowed and extended datasets . The NCA is also closely, but not statistically significantly, related to the population from the Marmara Region  and the Neolithic to Early Bronze Age samples from Caucasus (NBC) .When looking at the FT values  based onT values A, the NeMaternal kinship and social structure of ÇatalhöyükOur results show that all ten obtained genomes belong to different mitochondrial haplotypes. The case of the individuals buried within building 96 is especially interesting, as four different mitochondrial haplotypes suggest that at least four different maternal lineages were present in the group of individuals interred within this particular house. Such a high variability of mitochondrial haplogroups in a kin group, especially among children and females, could be explained by patrilocality. Assuming the house, as a typical Çatalhöyük structure, was occupied for 3–4 generations  and inhaHowever, the neighborhood was also proposed as a basic unit of both spatial and social organization of Çatalhöyük ,6. In thOn the other hand, direct kinship suggested by mitochondrial haplotypes and confirmed in the course of this study with the use of the READ method , was fouAlthough only mitochondrial genomes were obtained, when compared with available morphological  and spatThe genetic affinities of Central Anatolian Neolithic populationsThe genetic affinities of the Çatalhöyük inhabitants, pooled together with other Central Anatolia Neolithic individuals, unsurprisingly shows that this group was closely related to Near and Middle Eastern Neolithic and Chalcolithic populations, especially the Neolithic population from the Marmara region in north-western Turkey. These results support the generally accepted direction of migrations, associated with the spread of the Neolithic. The idea that the spread of the Neolithic was connected with the direct migration of populations from the Near East through Anatolia and then through the Balkans and Mediterranean coastlines has been the dominant hypothesis among archaeologists ,68 even Generally, the Central Anatolian Neolithic is thought to be unique and clearly distinguished from the Neolithic core zone in the Levant and northern Mesopotamia . Our resDespite the fact that the Neolithic in Central Anatolia might have been adopted by local hunter-gatherers ,17 with Our data suggests a lack of maternal kinship among ten analyzed individuals buried under the floors of selected adjacent Çatalhöyük buildings. This result can be interpreted as a sign that those burials were representing either large kin group with multiple maternal lineages, or a group of individuals selected for burial based on foundations other than genetic affinity. This fits well with previous research based on morphological traits of the human remains from the site. Population analyses show that the Central Anatolian Population, including Çatalhöyük, falls within the genomic variability of Near and Middle East Neolithic, having the closest affinity to the population from the Marmara Region. This result supports the hypothesis about the direction of the spread of the Neolithics within the Anatolian Peninsula and beyond and emphasizes the significant role of Central Anatolia in this process.


Bonasa umbellus, is broadly distributed across North America and displays considerable taxonomic diversity. Except for a genetic study of some western populations of ruffed grouse, nothing is known about genetic variation in other regions of Canada and the United States. Our objective is to examine patterns of mitochondrial DNA (mtDNA) variation in the ruffed grouse across western, central, and eastern parts of its distribution. We compare patterns of mtDNA variation to those characterized by morphology and ecology. Additionally, we evaluate the demographic history of the species based on mitochondrial haplotype diversity.The ruffed grouse, Patterns of mtDNA variation revealed geographic subdivision, with populations of ruffed grouse subdivided into 3 to 4 genetically distinct groups. This subdivision partially coincided with the ranges of described subspecies. Behavioral traits prohibiting long-distance movement and barriers to dispersal in response to physiography and unsuitable habitat help explain these patterns of subdivision. Historically, the ruffed grouse probably experienced a population expansion, possibly in response to changes during the Pleistocene. Bonasa umbellus) is the most broadly distributed species of grouse in North America, occupying early-succession deciduous and coniferous forests in the United States and Canada [The ruffed grouse  determine whether patterns of genetic variation are congruent with current taxonomic designations of several subspecies and (2) test hypotheses related to the demographic history of the species.In this study, we used nucleotide sequence data from a fragment of the mitochondrial cytochrome B. u. yukonesis , B. u. sabini , B. u. incana , B. u. monticola , B. u. togata . Canada: B. u. umbelloides , B. u. togata , B. u. labradorensis .Tissue biopsies  were collected earlier by wildlife game agents during hunting season. We examined 100 individuals, representing seven subspecies as follows: United States: b gene was amplified using the polymerase chain reaction (PCR) with primers H15295 [2, 4 μl dNTP mix, 0.2 μl of Takara Ex Taq polymerase, and 1–2 μl of DNA template. PCR reaction conditions included: (1) preliminary denaturation 1 cycle, 4 min at 94 °C; (2) 35 cycles with denaturation , annealing , extension  4 min extension at 72 °C.DNA was extracted with a DNeasy Blood and Tissue Kit . A 515 bp fragment of the mitochondrial cytochrome s H15295  and L148s H15295 . PCR wasPolymerase chain reaction products were purified with a QIAquick PCR Purification Kit (Qiagen), and sequence reactions were performed using a Big Dye Terminator Cycle sequencing kit v1.1  and an ABI 377 automated sequencer. Excess dye was removed using a Sephadex G-50 spin column . Both strands were sequenced, and contigs were created using Sequencher 4.0 .h), the average number of substitutions per site, and Nei’s [Hd) and nucleotide diversity (π). A mitochondrial haplotype network was constructed using the TCS 1.21 [DnaSP v6  was usednd Nei’s  estimateTCS 1.21 .Bonasa bonasia (GenBank AF230165), was used as an outgroup [Pairwise estimates of nucleotide differences between populations, estimated in DnaSP, were used in a neighbor-joining analysis in PAUP* v 4.0b10 . The hazoutgroup . An analoutgroup  was usedr [Fs [Several methods were used to test for historical changes in demography. We used the distribution of pairwise nucleotide differences or mismatch distribution to determine whether populations have remained constant or experienced demographic expansion . The degr . Confider [Fs  was usedHd) = 0.862. Five haplotypes  had the highest frequency (79%), with haplotype 2 occurring in 24% of individuals followed by 17% for haplotypes 11 and 14, 15% for haplotype 1, and 5% for haplotype 6  = 0.00552 and haplotype diversity . Second, an analysis of the four groups defined above resulted in an among group variation of 56.29% and 43.71% within populations . Finally, populations within each group were compared. No significant structure among localities within Group 1 was observed . Significant genetic structure was observed for Group 2  and Group 3 . The Wisconsin population accounted for the majority of among population variation in Group 2. After removal of the Wisconsin population, no evidence of genetic structure was observed for the remaining populations in Group 2 . Vermont and Pennsylvania accounted for most of the among population differences in Group 3, and with their removal no genetic structure was observed (p = 0.227). Wisconsin, Vermont, and Pennsylvania shared haplotype 11 in high frequency, and when these three localities were combined, no significant genetic differentiation was observed (p = 1.668).Several different configurations of AMOVA were performed. First, samples were grouped by individual states and provinces to test for overall geographic variation, yielding percentage of variation among populations of 32.58% and within populations of 67.42%  non-significant based on DnaSP. The generalized least-square approach in Arlequin was used to test for a population expansion, resulting in non-significance with SSD p = 0.544 and p = 0.671 for the raggedness index. For the standard neutral model examined in DnaSP Fu’s Fs= − 5.51511 (p = 0.035). Arlequin produced a similar value of Fu’s Fs (p = 0.038).The mismatch distribution was unimodal and deviated from that expected under a neutral model or constant population size, with the raggedness index of 0.0248  that shows relationships among the 19 haplotypes.Additional file 2. Nucleotide Sequences Used for Analyses. This spreadsheet contains the complete sequences of the 100 individual ruffed grouse plus the outgroup taxon used in this study. Individual subspecies are cross-referenced, and individual sequences representing the 19 haplotypes used for Table


Cervus elaphus) is a widespread wild ungulate in Europe that has suffered strong anthropogenic impacts over their distribution during the last centuries, but also at the present time, due its economic importance as a game species. Here we focus on the evolutionary history of the red deer in Iberia, one of the three main southern refugial areas for temperate species in Europe, and addressed the hypothesis of a cryptic refugia at higher latitudes during the Last Glacial Maximum (LGM). A total of 911 individuals were sampled, genotyped for 34 microsatellites specifically developed for red deer and sequenced for a fragment of 670 bp of the mitochondrial (mtDNA) D-loop. The results were combined with published mtDNA sequences, and integrated with species distribution models and historical European paleo-distribution data, in order to further examine the alternative glacial refugial models and the influence of cryptic refugia on European postglacial colonization history. Clear genetic differentiation between Iberian and European contemporary populations was observed at nuclear and mtDNA levels, despite the mtDNA haplotypes central to the phylogenetic network are present across western Europe (including Iberia) suggesting a panmictic population in the past. Species distribution models, fossil records and genetic data support a timing of divergence between Iberian and European populations that overlap with the LGM. A notable population structure was also found within the Iberian Peninsula, although several populations displayed high levels of admixture as a consequence of recent red deer translocations. Five D-loop sub-lineages were found in Iberia that belong to the Western European mtDNA lineage, while there were four main clusters based on analysis of nuclear markers. Regarding glacial refugial models, our findings provide detailed support for the hypothesis that red deer may have persisted in cryptic northern refugia in western Europe during the LGM, most likely in southern France, southern Ireland, or in a region between them , and these regions were the source of individuals during the European re-colonization. This evidence heightens the importance of conserving the high mitochondrial and nuclear diversity currently observed in Iberian populations.The red deer ( Understanding demographic and evolutionary history is fundamental to explain current species distributions and to forecast future trends under global climate change ,2. It haCervus elaphus) is a good model species to address the hypothesis of cryptic refugia and its influence on the European postglacial colonization history, owing to its current and past widespread natural distribution across Europe 95], respThe model was then hindcasted in order to integrate distributional data with genetic and paleontological evidence. The species’ climatic requirements were assumed to have remained stable over time to infer suitable areas for red deer in the past. This was carried out through replacing the bioclimatic variables selected in the final model by those estimated for the past scenarios. However, the environmental domain of the model in the past should be assessed before transference, since the model is only able to work in the environmental domain in which the model was parameterized. To this end, the methodology proposed by  was applbiomod2 using default specifications for each technique , generalized boosting model (GBM), classification tree analysis (CTA), artificial neural network (ANN), BIOCLIM and flexible discriminant analysis (FDA), and an ensemble of their forecasts] to assess the consistency of the predictions obtained from GLM. Analyses were carried out with ails see ). Resultails see  and theiails see  limit thails see . All staails see .https://c14.arch.ox.ac.uk/oxcal/OxCal.html) using the calibration curve Int-Cal13 15], to ant-Cal13  and the Table 1).An overall south-north pattern of decreasing nuclear diversity was observed among European populations, although a wide range of values was obtained, including in the Iberian Peninsula where there were signs of genetic bottlenecks and inbreeding for several populations  and when the levels of genetic differentiation among populations measured as FST were assessed and represented by neighbor-joining trees . However, other individuals in different Iberian populations depicted a membership proportion (qi) to European populations higher than 5% in STRUCTURE analysis. Although these individuals (n = 24) were allocated to the Iberian cluster in FCA, they were further excluded from STRUCTURE analysis at the Iberian level, together with one individual from BER and the specimens that harbored haplotype 11 , although the highest likelihood was achieved for rees see . TherefoK = 11, the maximized ΔK value was reached for K = 3 . While individuals from the CFR cluster were clearly separated from the remaining populations, the sub-structuring of populations from the MTR and SMR clusters was not so evident due to great admixture observed. Additionally, FCA distinguished the PNB population from the SMR cluster. Among all the Iberian populations a moderate genetic differentiation was apparent, with a global FST of 7.7% (95% confidence limits: 6.9–8.5%). The maximum pairwise FST value observed was 23% (PND-CFR), with a positive significant correlation between autosomal genetic distances (FST/(1-FST)) and geographic distances (log km) in the Mantel test   see .K = 8, although very similar ΔK values (maximized) were achieved for K = 3 and K = 8  population that segregates into two clusters and the Italian (IT) population that showed an admixture pattern between central and northern European populations. FCA analysis showed a consistent genetic structure relative to that obtained in the STRUCTURE analysis for K = 3 with EN and FR populations separated from the remaining populations .Concerning the central and northern European populations, in the STRUCTURE analysis the highest likelihood was reached for  = 8 see  Figs. WhTable 1). Signs of population expansion or selection were observed for the EN and HU populations (Table 1). Three main European mtDNA lineages can clearly be identified in the network using the 329 bp D-loop fragment . The regions with the highest number of haplotypes were the Iberian Peninsula and the British Isles. Nevertheless, most haplotypes detected in these two regions were not shared, with the exception of the central haplotypes and the haplotype 11 (see In the Western European lineage (haplogroup A), three mtDNA haplotypes central to the phylogenetic network were identified , from which various haplotypes and distinct evolutionary sub-lineages emerged. Apart from Iberia, these central haplotypes were also found in the British Isles , Norway (10 and 20), Hungary and Italy (10)  show some geographical structure: i) the green sub-lineage is present mainly in the central region (Montes de Toledo) and northern parts of Iberia; ii) the blue sub-lineage is observed in the southern region (Sierra Morena) of Iberia. The other sub-lineages were restricted to certain areas in Iberia . On the other hand, the central haplotypes  are distributed mostly in the central and north-eastern populations.The analysis of the 670 bp of the mtDNA D-loop identified 28 haplotypes among all the Iberian populations, which were maintained at 650 bp fragment size, but led to loss of two haplotypes (H06’ and H27’) when reduced to 329 bp , the one of simultaneous divergence of populations at t3 (Scenario 1) had the highest posterior probability at both European and Iberian levels (Table 2). Analyses to estimate the accuracy in scenario choice indicate that Type I and Type II errors for the best-supported scenario were moderately low for both analyses. Principal component analyses showed that summary statistics calculated for the posterior simulated datasets for the best-supported scenario explained the observed data well. Relative medians of absolute error values were moderately low for all parameters with the exception of t1 in Iberia analyses, indicating that estimates of posterior parameters are reliable (see Among the tested scenarios of population divergence using DIYABC (able see . AssuminTable 3). In addition, the TMRCA was estimated from all the Iberian haplotypes, varying between 12.1 and 38.4 ky BP, with an average of 24.2 ky BP (Table 3). Within the Iberian Peninsula, the putative historical demographic changes of red deer populations were also inferred using the Bayesian Skyline Plot approach. This analysis predicts an increasing population size over the last 12 ky for the Iberian populations . When considering the populations from British Isles, one putative cryptic refugium for the species, the Bayesian Skyline Plot hindcasted a huge increase of population size approximately starting 7–8 ky BP  was inferred from BEAST for the three main European lineages  and varies between 81.5 and 187.2 ky BP, with an average of 131.1 ky BP .GLM achieved a reasonable predictive performance when it was evaluated on the validation dataset, both in terms of discrimination (ROC = 0.73) and reliability  species distribution model, reinforcing the accuracy of spatial distribution modelling. From the archaeological data it is evident that red deer had a limited geographical distribution in central-northern Europe (CN-EU) and the British Isles (BI) during the LGM . However, in addition to the Mediterranean peninsulas , this species was also recorded in south-west France (SW-FR) and south-east France (SE-FR) during the LGM . In our study the estimated divergence (average) among lineages was 131.1 ky BP (CI95% 81.5–187.2), corresponding to a specific geological event–the Saalian Glacial Period . The divergence of the Western European lineage may be related to a partial discontinuity in red deer habitat suitability evident at 120 ky BP between western and eastern Europe , i.e. during the Eemian interglacial age (127–117 ky BP)  and geographic distance (log km) between the Iberian red deer populations quantified for both microsatellites and mitochondrial datasets (isolation-by-distance).Plots showing the relationship between genetic distance [pairwise (DOCX)Click here for additional data file.S4 FigK values obtained following Evanno et al. [K = 3 and K = 8. Population codes are described as in Fig 1 of the main manuscript.Bayesian clustering analyses performed in STRUCTURE  on microo et al.  procedur(DOCX)Click here for additional data file.S5 FigFig 6.Median joining network showing the evolutionary relationship of D-loop haplotypes found in this study (670 bp fragment size) for the red deer in Europe. Numbers within circles correspond to haplotype name described in the main manuscript for the 329 bp haplotypes (see details in (DOCX)Click here for additional data file.S6 FigCalibration plot showing the relationship between the predicted probability for red deer occurrence according to its climatic niche and the observed frequency of the species in the validation dataset. Open symbols indicate bins with < 15 localities, in which the frequency observed should be considered with caution .(DOCX)Click here for additional data file.S7 FigCervus elaphus in western Europe and North Africa during the Mid-Holocene (6 ky BP), Last Glacial Maximum  and interglacial period (120 ky BP) represented for the generalized boosting model (GBM), classification tree analysis (CTA) and the ensemble of their forecasts, according to the statistical model shown in Climatic suitability for the occurrence of (DOCX)Click here for additional data file.S8 FigFig 9) and the climatic suitability for occurrence of red deer at 22 kyBP , predicted according to the climatic niche for the species determined by a generalized linear model (GLM) model.Map showing the geographic distribution of the red deer fossil records dated within the period of Last Glacial Maximum ((DOCX)Click here for additional data file.


Miscanthus lutarioriparius is a native perennial Miscanthus species of China, which is currently used as raw material of papermaking and bioenergy crop. It also has been considered as a promising eco-bioindustrial plant, which can offer raw material and gene for the biomass industry. However, lack of germplasm resources and genetic diversity information of M. lutarioriparius have become the bottleneck that prevents the stable and further development of the biomass industry. In the present study, genetic diversity of 153 M. lutarioriparius individuals nine populations was studied using 27 Start Codon Targeted (SCoT) markers. High polymorphic bands (97.67%), polymorphic information content (0.26) and allele number (1.88) showed SCoT as a reliable marker system for genetic analysis in M. lutarioriparius. At the species, the percentage of polymorphic loci [PPL] was 97.2%, Nei’s gene diversity [H] was 0.36, Shannon index [I] was 0.54 and Expected Heterozygosity [He] was 0.56. Genetic variation within populations (84.91%) was higher than among populations (15.09%) based on analysis of molecular variance (AMOVA). Moderate level of genetic differentiation was found in M. lutarioriparius populations (Fst = 0.15), which is further confirmed by STRUCTURE, principal coordinates analysis (PCoA) and an unweighted pair group method with arithmetic mean (UPGMA) analysis that could reveal a clear separation between groups of the north and south of Yangtze River. The gene flow of the populations within the respective south and north of Yangtze River area was higher, but lower between the areas. There was no obvious correlation between genetic distance and geographic distance. The breeding systems, geographical isolation and fragmented habitat of M. lutarioriparius may be due to the high level of genetic diversity, moderate genetic differentiation, and the population, structure. The study further suggests some measure for conservation of genetic resources and provides the genetic basis for improving the efficiency of breeding based on the results of diversity analysis. Miscanthus spp. is a perennial herbaceous grass belonging to the Poaceae [Miscanthus resources, and Miscanthus lutarioriparius is native to China among all Miscanthus species [Miscanthus has a strong ability to adapt to different habitats owing to rich morphological and genetic diversity [Miscanthus can also provide raw materials for biorefineries to produce various chemicals, fuel, and biomaterials [Miscanthus varieties with high biomass yield and good energy-related quality is desirable. Poaceae  with ori species –3. Miscaiversity –5. It isiversity  high andiversity ,8, fast iversity , high waiversity ,11, highiversity , and higiversity . Additioaterials –17 and iaterials  and incraterials . TherefoM. × giganteus is the most commonly researched and utilized miscanthus plant in both Europe and America, and is a natural triploid hybrid found from northeast Asia [Miscanthus sinensis is the most widespread species across the world, and it has elicited great interest over its genetic diversity [M. floridulus and M. sacchariflora. In China, M. lutarioriparius is the only commercially used miscanthus species, which is grown for papermaking industry since 1950s [M. lutarioriparius biomass is produced from 1.2×105 ha wildly grown M. lutarioriparius in the coastal area of Dongting Lake. Therefore, its biomass yield and quality have been a historical concern. Miscanthus lutarioriparius is mainly distributed in the tidal flats of lakes and rivers alongside the middle and lower reaches of the Yangtze River in China [At present, ast Asia , 20–22. iversity –25, ecoliversity , biomassiversity , conversiversity , 29, striversity , 31, effiversity  and morece 1950s . Almost in China ,34.M. lutarioriparius is uncertain. Fortunately, Yan et al. have found that M. lutarioriparius grow well in marginal lands of semi-arid and arid areas [M. lutarioriparius. There are many types and large area of marginal land (769.37×104 ha) available for producing miscanthus in China [However, due to the downturn in the paper industry, the future of id areas  and can id areas , which wland 769.×104 ha aM. lutarioriparius. Therefore, it is difficult to draw any conclusion about its genetic diversity, resource protection, hybridization breeding, or screening for excellent germplasm. However, some data on genetic diversity and population structure of other Miscanthus species using molecular markers have been reported. Simple Sequence Repeats (SSR) [M. sinensis. In other studies, the genetic diversity and population structure of M. saccharilorus was assessed based on SSR [M. × giganteus using Diversity Arrays Technology (DArT). Therefore, molecular markers can serve an important role in unveiling the genetic diversity and population structure of M. lutarioriparius.Limited information exists regarding genetic analysis of ts (SSR) , Sequencts (SSR) , Amplifits (SSR) , Inter-Sts (SSR)  and Singts (SSR)  have beed on SSR , and SNPd on SSR  markers.d on SSR  estimateIn recent years, a novel marker system termed start codon targeted (SCoT) markers was developed by Collard and Mackill  based onM. lutarioriparius, which were collected from the wild populations. The main objectives of this study were to assess the genetic diversity and genetic relationship of M. lutarioriparius in China. These results could benefit M. lutarioriparius germplasm collection, conservation and future breeding.The present study is the first attempt to use SCoT markers to assess the level of genetic diversity of M. lutarioriparius were gathered from areas, which are not in any nature reserve or private land. Therefore, collection of the material did not require an additional approval of the ethics committee or other specific permission.This research did not involve the rare and endangered plants. Samples of M. lutarioriparius accessions were used in this study. The accessions were collected from 153 sites which covered the major distribution areas in China 2SO4, 100 mM KCl, 1% Triton X-100, pH 8.8), 2.5 mM Mg2+, 0.4 mM dNTPs, 0.5 μM primer, and 1 U Taq DNA polymerase . The PCR amplifications were performed using a professional thermocycler (Biometra Germany) with the following program: 94°C for 5 min, followed by 35 cycles of 94°C for 50 s, 55.3–64.1°C for 60 s, and 72°C for 90 s, and a final extension at 72°C for 7 min. PCR products were separated on 1.5% agarose gels and stained with ethidium bromide and photographed under UV light. The amplification products generated by SCoT primers were scored as present (1) or absent (0). Only clear and repeatable bands were considered.A total of 36 SCoT primers developed by Collard and Mackill , which wNa), effective number of alleles (Ne), Nei’s gene diversity (H) Shannon’s information index of Diversity (I), and the percentage of polymorphic loci (PPL). Nei's expected heterozygosity (He) was calculated using GenAlEx v.6.1[Excel 2013 was used to calculate the total number of bands (TNB), the number of polymorphic bands (NPB), and the percentage of polymorphic bands (PPB). The polymorphism information content (PIC) of SCoT primers was determined using POWERMARKER v3.25 . The soflEx v.6.1.M.lutarioriparius was analyzed in the software STRUCTURE v2.3.4 [K) were used to identify the optimum number of subpopulations following Evanno’s methods [The population structure of the 153 E v2.3.4  using adE v2.3.4 . The val methods . The str methods Principal coordinate analysis (PCoA) was performed using GenALEx v6.1 software to detect the genetic relationships among populations. Cluster analysis was performed using SAHN from NTSYS-pc version 2.10 with the unweighted pair group method with arithmetic mean (UPGMA) algorithm. SPSS 19.0 was used to calculate the correlation between the genetic distance and geographic distance matrices. The latitude and longitude of each population was replaced by the latitude and longitude of the midpoint of the population.Fst) using “pegas” [Nm) among populations  / 4 Fst), Nei’s genetic distance, and the genetic similarity were calculated using the software POWERMARKER v3.25 [Analysis of molecular variance (AMOVA) was performed to analyze the genetic differentiation  and Nei’s gene diversity (H) were important parameters for measuring the level of genetic diversity [H value ranged from 0.24to 0.36, with an average value of 0.32. PPL ranged between 75.29% and 93.47%, with an average value of 87.01%. Population 9 (Pop 9) showed the highest level of genetic diversity among all populations , while the lowest genetic diversity parameter values were found in Pop 6 . The total genetic diversity of M. lutarioriparius was high across China as indicated by the indexes of H = 0.36, I = 0.54, PPL = 97.20%, He = 0.56.The percentage of polymorphic loci  algorithm and cluster analysis, and the result is shown in M. lutarioriparius revealed that these populations divided into 2 groups  for 9 populations of 2 groups . All theM. lutarioriparius were separated into two clusters in the UPGMA dendrogram  was found among the populations, and the variance within populations was 85% (M. lutarioriparius was both significant (P<0.001). The results showed that variation was more abundant within populations than among populations, and the genetic variation within populations was the main source of total variation.The analysis of molecular variance (AMOVA) among populations showed that 15% of the genetic differentiation ( was 85% . The genNm) within populations from in the north Yangtze River area ranged from 1.32 to 24.75. The Nm within populations from the south of Yangtze River group ranged from 1.42 to 22.75. The results show that there was stronger connection within populations from the north area of Yangtze River, which is also true for the south group of Yangtze River. The low level of gene flow was found between populations from the north and the south of Yangtze River (ranging from 0.31 to 1.12), suggesting that the Yangtze River can server as a barrier to gene flow between populations on the both sides of the river.Results of M. lutarioriparius germplasm resources and detected the relationship among them. In the present investigation of M. lutarioriparius, SCoT makers displayed a high percentage of polymorphism (97.67%) and moderate PIC values of 0.26. This result indicates that SCoT markers were highly polymorphic and informative. To date, there are no studies reporting the genetic diversity of M. lutarioriparius. However, the values of PPB and PIC of SCoT markers were consistent with other molecular makers used in M. sinensis [M. sacchariflorus and M. × giganteus [M. lutarioriparius were low compared with Hemarthria [Elymus sibiricus [M. lutarioriparius, laying the foundation for identifying M. lutarioriparius germplasm resources, genetic map construction, gene mapping, and cloning.SCoT markers are novel molecular markers that target the translation initiation site and preferentially bind to genes that are actively transcribed. These primers have been shown to exhibit relatively high levels of polymorphism . It was sinensis , M. sacciganteus . The PICmarthria , Elymus ibiricus , and othMiscanthus lutarioriparius is not only important as a fine raw material for papermaking, but also as a biofuel crop and ecological improvement plant. Therefore, it is important to understand the genetic diversity of M. lutarioriparius populations and the potential for genetic improvement. In this study, the mean of genetic diversity within each population was 0.32, which was similar to Chinese and U.S. M. sinensis although different markers were used in these study [He = 0.162) and monocotyledons (He = 0.181) [M. lutarioriparius was similar to Panicum virgatum [Maytenus emarginata [Ziziphus mauritiana [se study , 24, 64,= 0.181) , 65. At virgatum , Maytenuarginata  and Ziziuritiana , which wuritiana . This maMiscanthus, which are cross-pollinated plants that have self-incompatibility. Miscanthus lutarioriparius can propagate through subterraneous stems under its natural state, as well as through sexual propagation through seeds. These reproductive characteristics have vital significance for maintaining the genetic diversity in populations [M. lutarioriparius has a relatively large distribution area from 111° to 120° E and 26° to 33° N according to our field research. Generally, the larger the distribution area, the greater the genetic diversity [Miscanthus lutarioriparius is a perennial herb, and its perennial long-life habits could provide more opportunity to accumulate mutations or special microstructure in different populations due to biotic processes. Third, since the 1950s, M. lutarioriparius has attracted attention as a paper-making material, especially in Huanan and Hubei provinces, where large-scale introduction and variety screening have taken place. Therefore, these types of perennial plants preserve variants between generations, thus increasing the genetic diversity of populations [Genetic diversity of populations is influenced by many factors, including breeding systems, population size, genetic drift, natural selection, mutation rate, and gene flow , 70. Firulations , likely iversity . Miscantulations .M. lutarioriparius were small, with only a few individuals found, due to predatory collection and habitat destruction. Population size correlates significantly with genetic diversity [M. lutarioriparius from these areas. In the natural environment, a low seed germination rate and vegetative reproduction through the rhizomes lead to low genetic diversity within populations in small population.In generally, the genetic diversity of populations in downstream of river was higher than that of populations in the upper and middle reaches of a river. The genetic diversity of Pop9 was the highest, it located in the downstream of the Yangtze River. However, the populations from the middle reaches of the Yangtze River  showed higher genetic diversity than those in the downstream Yangtze River . Pop3 and Pop4 located in the Dongting Lake area, where contains the largest, continuous distribution of this species in China. However, in Jiangsu, He’nan, and Anhui, however, the population size of iversity , 75, andMiscanthus lutarioriparius exhibited moderate differentiation among populations. Our results were not consistent with previous studies of Yan [M. lutarioriparius populations.The genetic structures of populations are extremely important for the evolution and adaptation of a species . Signifis of Yan . This coM. sinensis within populations was substantially greater than between populations [M. sinensis in Southwest China showed the similar result [M. lutarioriparius were similar to M. sinensis. The genetic variations in M. lutarioriparius populations was mainly derived from the genetic variation within populations.The results of AMOVA analysis showed that 15% of the variance was attributed to the differences among populations (P < 0.001), and 85% was attributed to the differences among individuals. The greater similarity of populations among rather than within individuals indicated that these populations were probably founded by many individuals and that there were high levels of gene flow among populations. Cross-pollinated plants often show huge variation within populations and a lower level of variation between populations , 25, 63.ulations . M. sineNm was high within M. lutarioriparius populations from the north group and south group of Yangtze River. When Nm is higher than 1, gene flow could prevent genetic differentiation between populations caused by genetic drift [Miscanthus lutarioriparius is wind-pollinated, and its gene flow rate is consistent with findings that cross-pollinated plants have high gene flow [Miscanthus lutarioriparius mainly grows along rivers and lakes, and seasonal floods can transfer seeds and subterraneous stems between different regions, further promoting genetic exchange between M. lutarioriparius populations. In addition, since the 1950s, M. lutarioriparius has been a vital raw material for paper making, and this plant was introduced widely to the area; therefore, humans have also contributed to the increased gene flow between different M. lutarioriparius populations.The ic drift . Factorsene flow . MiscantM. lutarioriparius populations from the north group and south group of Yangtze River. The River and mountains were usually the main reasons for limited gene communication. M. lutarioriparius was mainly distributed at elevations below 300 m  by field investigation. For example, the easternmost individuals of Pop 8 are very close to the Pop 7, but the two places were isolated by the Yangtze River, Dabie Mountain and Luoxiao Mountain. The gene flow (0.62) between the two populations was very low. Therefore, the geographic isolation could have prevented gene flow between the north group and south group of Yangtze River.Analysis indicated that gene flow was low between M. lutarioriparius could theoretically enter an infinite growth cycle. Under such a scenario, it would be difficult to determine the extent of each genotype by the range of plant growth on the ground. In this research, only scattered populations were found out of the large-scale concentrated range at in some distribution areas beside the coastal area of DongTing Lake and TaiHu Lake. In these areas, the number of individuals in each population is limited and populations are separated by mountains and rivers. There was no obvious correlation between genetic distance and geographical distance. This indicated that our increased sample scale and genetic diversity at the population level does not have a significant relationship. Additionally, it demonstrated that our sample scale is reasonable and that sampled individuals are included in populations.In population genetics research, sampling strategies have a strong relationship with the reliability of research results because individual localities will affect genetic diversity of a population and the population’s genetic structure . DetermiM. lutarioriparius was divided into two groups: those distributed to the north and to the south of the Yangtze River. To further distinguish the origins of the M. lutarioriparius populations, we used PCoA and UPGMA to study the distributions of M lutarioriparius, resulting again in classification into two types with the Yangtze River as a natural boundary between them. Judging by certain historical records, M. lutarioriparius was considered as a member of Phragmites in the past, being introduced across China for the last 50 years as the papermaking industry has expanded. Therefore, rather than relying on pollen and seed diffusion, human activities have had a more positive influence on the distribution of M. lutarioriparius.The genetic structure of a population is not directly correlated with its geographical distribution. According to the analysis of structure, Here, populations with smaller geographical distances were not clustered together, while the populations on either side of the Yangtze River were clustered together. This may be a result of population isolation along the Yangtze River, causing the populations (Pop1 and Pop2) with smaller geographical distances to generate certain differences. Similar findings have been observed in other species .Miscanthus lutarioriparius is endangered in some regions, this is likely not due to a loss of genetic diversity. According to our field investigation of M. lutarioriparius over many years, human activities have severely damaged the habitats of M. lutarioriparius. For example, there was large-scale M. lutarioriparius distribution in He’nan, Jiangsu, and Hubei from the 1950s to the 1970s, but now only small populations of this species are found in this area. In Hunan, after the recession of the papermaking industry, the habitat of M. lutarioriparius was used to plant poplar, leading to the decline of M. lutarioriparius populations. Therefore, M. lutarioriparius must be protected. We applied Andrew’s method to estimate the sampling size needed to protect these populations [M. lutarioriparius population in the Dongting Lake area; (2) increasing the sampling of other populations with high genetic diversities , and collecting subterraneous stems to encourage reproduction during sampling.ulations . The resMiscanthus lutarioriparius has adapted to various conditions and plays an important role in the wetland ecological environment. Simultaneously, M. lutarioriparius is considered to have potential as a second-generation cellulosic energy plant and biomaterial. For adapting to the requirements of developing energy plants in China, breeding of M. lutarioriparius germplasm suitable for growing in marginal land is necessary; therefore, understanding the genetic diversity level of M. lutarioriparius germplasm resources is the foundation for hybrid breeding. Using the results from this study, we can select distantly related materials as parents that give rise to offspring with higher genetic variation, providing better resources for selective breeding. For example, the materials in Hubei and Zhejiang populations with the greatest genetic distance could be used as parents.M. lutarioriparius. The results revealed that high genetic diversity and gene flow were detected at species level, which were attributed to its breeding system, large distribution area and anthropogenic movement of plant material. In addition, moderate genetic differentiation was found among populations, which was supported by habitat fragmentation and geographical isolation. Nine populations can be divided into two main groups by STRUCTURE analysis, PCoA and UPGMA, with the Yangtze River as a natural boundary between the two groups. All M. lutarioriparius accessions could be divided into two groups, with 92 accessions in Cluster A and 61 accessions in Cluster B. Lastly, we offered scientific measures for M. lutarioriparius protection. Thus, these results should help for selecting parents in hybridized breeding to exploiting new Miscanthus species and for further utilization in biomass energy and conservation.In conclusion, the SCoT marker system provides a highly efficient, reproducible and powerful tool for studying the genetic diversity and population structure of S1 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.