Festuca rubra s.l., which fitness can be highly dependent on its symbiotic fungus Epichloë festucae, to evaluate genetic variation and population structure across the European range. The 27 studied populations have previously been found to differ in frequencies of occurrence of the symbiotic fungus E. festucae and ploidy levels. As predicted, we found decreased genetic diversity in previously glaciated areas in comparison with nonglaciated regions and discovered three major maternal genetic groups: southern, northeastern, and northwestern Europe. Interestingly, host populations from Greenland were genetically similar to those from the Faroe Islands and Iceland, suggesting gene flow also between those areas. The level of variation among populations within regions is evidently highly dependent on the postglacial colonization history, in particular on the number of independent long‐distance seed colonization events. Yet, also anthropogenic effects may have affected the population structure in F. rubra. We did not observe higher fungal infection rates in grass populations with lower levels of genetic variability. In fact, the fungal infection rates of E. festucae in relation to genetic variability of the host populations varied widely among geographical areas, which indicate differences in population histories due to colonization events and possible costs of systemic fungi in harsh environmental conditions. We found that the plants of different ploidy levels are genetically closely related within geographic areas indicating independent formation of polyploids in different maternal lineages.We conducted a large‐scale population genetic survey of genetic diversity of the host grass  PopulatRapid range expansion from the refugia has often been found to be associated with a founder effect and, consequently, decreased genetic diversity and genetic drift . Many studies have demonstrated that a grass host benefits from Epichloë due to, for example, increased resistance to drought, herbivores, and pathogens  How is genetic diversity distributed among populations and regions? What is the level of geographic population genetic structuring and how has the postglacial colonization shaped the geographic distribution of haplotypes? (b) Is there association between E. festucae infection rates and the levels of genetic diversity in the host plant populations? (c) Does genetic structure within ploidy levels differ among geographic areas?In this study, our main two objectives are to gain further knowledge of genetic diversity patterns and seed flow in 22.1Festuca rubra L., s.l.  as a model for estimating genetic variability and population structure across the Europe range to evaluate the genetic resources to create novel plant gene combinations for use of crop varieties. F. rubra is an important turf grass that is widely cultivated in temperate regions  and pseudoviviparous propagules  has regularly been observed in Spain  Govor. (synonym F. richardsonii Hook.) and their hybrid (rubra x arctica) in northern Europe, and F. rothmaleri (Litard.) Markgr.‐Dann.  in Spain . Hereafter, we refer to the studied taxa as F. rubra.A total of 603 d Figure . The samE. festucae detection (infected E+/uninfected E−) has been described in Dirihan et al. (N = 566). The percentages of the endophyte infections varied from 0% to 82.5% among regions and from 0% to 95.8% among populations , hexaploids (2n = 6x = 42), and octoploids (2n = 8x = 56) were 12.7, 82.2, and 5.1%, respectively, in our data set . A total of thirteen highly polymorphic chloroplast microsatellite markers (cpSSRs) developed for F. rubra by von Cräutlein, Korpelainen, Helander, Väre, and Saikkonen (N = 603). Chloroplast DNA markers were chosen for the study because they are haploid and maternally inherited in angiosperms, which allows us a direct interpretation of seed‐mediated gene flow . The number of multilocus haplotypes  and the number of expected haplotypes  were computed with R 3.3.2  and number of multilocus haplotypes  were calculated over the entire sample set, for each region (n = 8) and population (n = 27), for each of the six clusters identified by BAPS , and for both endophyte infections statuses . We also calculated these statistics separately for plants with different ploidy levels . The percentage of polymorphic loci (%P), the mean number of observed alleles over loci (Na), the mean effective number of alleles over loci (Ne), the mean number of unique alleles over loci (Np), and the unbiased haploid genetic diversity over loci (uh) were computed using GenAlEx 6.5 software  of the loci  between regions, BAPS clusters, and endophyte infection status groups (infected E+/uninfected E−) in four regions  with sufficient number of E+ plants , the genetic mixture analysis at the individual level was conducted by performing 100 iterations of K (from 2–35). Under default settings, BAPS identified that the optimal partition of populations into clusters would be obtained with 20 clusters . A UPGMA tree was constructed based on the Kullback–Leibler divergence matrix, provided as an output of the BAPS analysis. However, as observed by, for example, Rodriquez et al.  in the GenAlEx v6.5 software; principal coordinates determined for the pairwise individual‐by‐individual haploid genetic distance matrix to reveal genetic similarities among individuals in different regions.The genetic relationships of individuals were assessed using a model‐based approach using the Bayesian Analysis of Population Structure (BAPS) software, version 6.0  obtained by BAPS program by using Arlequin software version 3.5  was used to estimate the degree of genetic differentiation among regions, among populations, and among the six clusters (33.1N = 588) based on the 13 combined cpDNA microsatellite loci. Among regions, the different haplotype numbers in relation to sample sizes (eMLG) and allelic diversity estimates (Ne and uh) were clearly highest in Spain and lowest in Greenland, but relatively high genetic diversities were also observed in the Faroe Islands . The highest mean numbers of private alleles over all loci (Np) were observed in the Faroe Islands, Iceland, and Spain (1.2–1.5), and lowest (0.1–0.4) in Greenland and southern Fennoscandia in Hanko and Norway  were identified in the sample set (Ne: 2.1–2.8 and uh: 0.456–0.608) and number of haplotypes (eMLG: 10–12/population) were detected in the Spanish populations, in FAS2 and FAS5 from the Faroe Islands and in HA2 from southern Fennoscandia. Lowest allelic diversity (Ne: 1–1.1 and uh: 0.040–0.106) and numbers of different haplotypes in relation to sample sizes (eMLG = 3.2–5/population) were found in three populations in northern Fennoscandia , in both Greenlandic populations, and in Iceland (IC1). The highest mean numbers of private alleles (Np = 0.3–0.5) were observed in southern populations in Spain  and northwestern populations in the Faroe Islands , Iceland  and also in one population (RBS1) in northern Fennoscandia.Among populations, haplotype numbers, allelic diversity estimates, and mean numbers of private alleles over loci varied largely within regions, except in Spain and Greenland  and two small clusters  included mainly northern Atlantic individuals from the Faroe Islands, Iceland, and Greenland. Cluster F  was mainly characterized by Fennoscandian individuals from southern and northern Finland and Norway. Cluster S1  included individuals from Spain (53.7%), as well as some from southern Fennoscandia and northern Atlantic. Cluster S2  included individuals mostly from Spain (71.4%), but also from the northern Atlantic and southern Fennoscandia. The remaining individuals from northern Fennoscandia and Atlantic area were included in the smallest clusters F–A and A–F.Based on the fixed K model produced by BAPS at df = 5, v.c = 1.56, p < 0.001; df = 1,200, v.c 1.51, p < 0.001, respectively), which indicates a very high degree of genetic differentiation among clusters. Based on pairwise FST values between clusters, all cluster pairs were significantly differentiated from each other, but differentiation was highest between Atlantic‐group (A) and the other clusters, and Fennoscandia‐group (F) and the other clusters, and lowest between Spain1‐ and Spain2‐groups, which co‐occurred abundantly in the same populations  among the six clusters  are summarized in Figure Fennoscandia‐group and cluster F–A with 66.7% of a Fennoscandian gene pool showed no ancestral gene flow from the other clusters. Atlantic‐group had 0.4% to 0.08% of admixed effect other clusters. Spain1‐group had some ancestral gene flow from the Atlantic‐group (3.3%). On the contrary, Spain2‐group had its main source of gene flow from Fennoscandia‐group (2.0%).The results of the admixture analysis shown as a gene flow network of six clusters  within populations , but no differences between SPGD and SPPOR, and SPLV and SPPOR populations suggesting effective gene flow between most Spanish populations  and the majority 94%) of the variation lies within populations Table . Pairwis% of the s Figure .FST = 0.125, p < 0.001), while no differentiation was detected among populations in Iceland  or Greenland  and Hanko , and greater among populations in Kilpisjärvi .In Fennoscandia, no genetic differentiation was found among four regions , but clearly more genetic differentiation was located among populations within regions. This indicates that the Fennoscandian regions are more differentiated from each other than regions in the northern Atlantic area Table . Moderat3.4Ne, uh) were significantly greater among infected than among uninfected plants , while more genetic diversity was found among uninfected plants in Iceland and Kevo . In contrast, allelic diversity estimates did not differ between infected and uninfected plants in Spain .The associations of the infection rates and allelic diversity estimates of populations differ based on the geographic locations. In the Faroe Islands, the allelic diversity estimates , but no differentiation between the plant groups in the Faroe Islands, Iceland, and Spain .The pairwise K = 6, Supporting information Appendix Spain1‐ and 2‐groups and lowest in Atlantic‐group.Infection frequencies varied from 35.9% to 73.9% among the BAPS clusters (3.5K = 6), all three ploidy levels  occurred in Atlantic‐, Fennoscandia‐, and Spain1‐groups and also in the small cluster A‐F  give an indication of small founder populations, which have encountered severe population bottlenecks and genetic drift. This may also be due to the predominance of vegetative reproduction in those populations  observed in a transplantation experiment in Kevo that infected plants originating from the Faroe Islands had a lower fitness compared to uninfected plants. This result suggests that long‐distance seed dispersers of F. rubra may lose infections during the establishment process, especially in harsh conditions. The genetic mismatches between the plant and the fungus genotypes may play a role in infection losses of new plant genotypes during the establishment process  in northern Fennoscandia. Based on the hypothesis by Gundel et al.  occur among Atlantic‐, Fennoscandia‐, and Spain1‐ and Spain2‐groups, which shows that plants of different ploidy levels are closely related which suggests independent formation of polyploids in different maternal lineages in different geographic areas. Similarly in Dupontia (Poaceae), several different ploidy levels were detected in three diverged genetic groups in a circumarctic area (Brysting, Fay, Leitch, & Aiken, In  Huyghe,  have pro Huyghe, . In F. r5F. rubra differing in the infection rate of the symbiotic fungus E. festucae and ploidy level. It shows that the level of genetic variation in different geographic regions is evidently highly dependent on the postglacial colonization history. The relationship between the host and the symbiotic fungus in the sense of genetic variability is not consistent but appears to differ among geographic regions. Diverse genetic structure among regions in F. rubra gives valuable background information for the development of F. rubra cultivars that would possess suitable combinations of the host and systemic fungus genotypes for agricultural use affected by abiotic stresses like drought, salinity, and high temperature. The present study is part of a larger ongoing project investigating local adaptation and phenotypic variation in European F. rubra and its symbiotic fungus E. festucae.This is the first comprehensive study that illustrates the genetic structure and geographic differentiation over a wide area in natural populations of None declared.H.V., M.H., and K.S. designed the collection of the data and performed sampling. M.v.C. and H.K. designed the genetic study. M.v.C. genotyped the samples. M.v.C. and P.L. analyzed the data. M.v.C. wrote the manuscript with contributions by all authors. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


LTα), located in the Major Histocompatibility Complex (MHC) class III region on chromosome 6, encodes a cytotoxic protein that mediates a variety of antiviral responses among other biological functions. Furthermore, several genotypes at this gene have been implicated in the onset of a number of complex diseases, including myocardial infarction, autoimmunity, and various types of cancer. However, little is known about levels of nucleotide variation and linkage disequilibrium (LD) in and near LTα, which could also influence phenotypic variance. To address this gap in knowledge, we examined sequence variation across ~ 10 kilobases (kbs), encompassing LTα and the upstream region, in 2039 individuals from the 1000 Genomes Project originating from 21 global populations.Lymphotoxin-α , in global populations. While these results are generally consistent with a model of balancing selection, we also uncovered a signature of positive selection in the form of long-range LD on chromosomes with derived alleles primarily in Eurasian populations. To reconcile these findings, which appear to support different models of selection, we argue that selective sweeps  of multiple derived alleles in and/or near Overall, this study identified candidate functional alleles in a biologically-relevant genomic region, and offers new insights into the evolutionary origins of these loci in modern human populations. LTα) in the MHC class III region encodes a potent cytotoxic polypeptide that plays a key role in regulating a number of biological processes, including lipid metabolism, coagulation, neurotransmission, and immunological response ) and the adjacent 5′ non-coding region (7411 bps) on chromosome 6, in 21 global populations from the 1000 Genomes Project. Of the 183 polymorphisms, 52 SNPs were located within LTα, while the remaining 131 SNPs were found in the 5′ region  occurred at relatively high frequency in all populations. More specifically, the minor allele frequency (MAF) at rs2229094  and rs1041981  ranged from 27.3 to 45.8% and from 20.2 to 33.8%, respectively, in South Asian populations. In Europeans, the rs2229094 minor C-allele and rs1041981 minor A-allele varied from 22.9 to 38.3% and from 24.8 to 30.9%, respectively, while the MAF at these loci ranged from 14.4 to 22.8% and from 38.4 to 54.8%, respectively, in East Asian populations. Likewise, the minor C-allele at rs2229094 and the minor A-allele at rs1041981 varied from 22.4 to 30.8% and from 36.3 to 61.5%, respectively, in populations of African descent . Further comparative analysis also showed that allelic variation at rs2229094 was present in the genomes of two closely related archaic species to modern humans—Neanderthals and Denisovans  across ~ 10 kbs of sequence, encompassing rs538402044, rs562333039, rs538877791, rs566451995 occurred at < 2.5% frequency in non-Africans, while these alleles were absent in populations of African descent. Furthermore, the minor C-allele at rs2229092 was common in Europeans varying from 5.1 to 7.0%, while it occurred at lower frequency in South Asians (from 1.7 to 5.5%) and in East Asians (from < 1 to 2.4%). In contrast, the rs2229092 C-allele was absent in indigenous Africans, but was observed at relatively low frequency (~ 1–2.5%) in African American and African Caribbean populations. Additionally, we found that synonymous variants were either absent or occurred at very low frequency (< 1.0%) in all populations  occurred at relatively high frequency in global populations  have previously been implicated in the onset of complex diseases  Africa includes 99 Esan , 113 Mandinka , 54 Mende , 107 Yoruba , 99 Luhya , 60 African Americans , 96 Barbadians ; [2] South Asia encompasses 84 Bengali , 103 Gurjarati , 99 Indian Telugu , 93 Punjabi , 99 Tamil ; [3] East Asia consists of 93 Dai , 103 Han , 105 Han , 104 Japanese , 99 Kinh ; [4] Europe comprise 90 Great British , 99 Finnish , 107 Iberian , 104 Toscani Italians  [We analyzed the 9637 bps, encompassing the , Italy) . Althougregion 741 bps on LTα gene and the adjacent 5′ region in the 1000 Genomes sequencing dataset using vcftools [LTα and 5′ region, in Neandertal and Denisovan samples from the UCSC genome browser [We extracted variant calls  from the vcftools . The stavcftools . The min browser .D (DT) [H (H) [LTα and the adjacent 5′ region, separately, in each population. Significance of the test statistics was assessed by comparing the observed values to expected values generated from 10,000 neutral coalescent simulations incorporating different models of growth using the ms software [DT and H statistics under a 2-, 4-, 6-, 8-, and 10-fold increase in population size  beginning 70,000 ya until the present [DT and H values under a range of demographic scenarios that included a population bottleneck at 60,000 ya , followed by 10-, 20-, and 40- fold growth beginning at 50,000 ya until the present [LTα and the 5′ region are different sizes, we also incorporated sequence length as a parameter in these simulations.We calculated the Tajima’s D (DT)  and Fay ) [H (H)  statistisoftware . If a sa present , 143–145 present , 145. FoLTα coding region using DnaSP [N/DS) is expected to equal the ratio of replacement to silent polymorphisms within species (PN/PS).In addition, we applied the McDonald–Kreitman (M-K) test to the ng DnaSP . The M-Kh-diversity) was also calculated for each population using the DnaSP software [We extracted fully phased haplotype data from the 1000 Genomes Project for 2039 individuals using vcftools , and thesoftware .LTα region spanning ~ 10 kbs, and separately 2) in an ~ 35.5-kb genomic region, encompassing neighboring genes , using the Haploview software [D′ statistic [We examined pairwise LD: 1) in the software . LD was tatistic , which itatistic . The boliHS statistic [iHS| > 2 represent the most extreme 5% of scores. We also estimated the length of haplotype homozygosity with another haplotype-based statistic, nSL, using selscan [iHS statistic, making it robust to recombination rate variation [iHS. To complement these scans for selection, we quantified the decay of identity of haplotypes with distance by calculating the EHH statistic using loci with extreme iHS and/or nSL scores as core SNPs [We characterized long-range LD on chromosome 6 for each population using the tatistic , which itatistic . SNPs wi selscan . This stariation . The outore SNPs .FST derived from genomic data using the Weir and Cockerham method implemented in vcftools [FST at individual polymorphic sites across the ~ 10-kb region of interest and at ~ 7.5 million randomly selected SNPs from across the genome. The observed FST estimates for SNPs in and/or near LTα were then compared with the empirical distribution of FST values derived from genome-wide SNPs in order to identify outlier values (≤ 2.5th percentile or ≥ 95th percentile of the distribution). FST values at individual sites in or near LTα, and FST values at the ~ 7.5 million genome-wide polymorphisms were calculated using the same 21 populations.To measure the degree of genetic divergence among global populations, we calculated average vcftools , 148. Weθ over 1,000,000 runs [θ and our estimate of μ, we calculated the effective population size (Ne) parameter, which was determined to be 18,367 based on the formula Ne = θ/4μ. Then, the TMRCA of the gene tree and the ages of individual polymorphisms were estimated from the weighted average of simulated ages over 100,000 independent runs [We used a coalescent-based approach to estimate: 1) the expected TMRCA of the gene tree and 2) the expected ages of individual polymorphisms. This method requires an outgroup that provides the ancestral state at each polymorphic site. We applied the GENETREE software  to our s000 runs . Using tent runs . GENETREent runs . We remoLTα on protein function were inferred using the SIFT algorithm implemented in the Ensembl Variant Effect Predictor toolset. SIFT predicts the functional impact of amino acid substitutions  based on sequence homology, the physical properties of amino acids and multiple alignment information [G is the difference in the Gibbs free energy for folding between the final state (the mutant) and the reference state (the wild-type). For each polymorphic site, ΔΔG was computed from the free energy of the wild type protein  minus the Gibbs free energy of the mutated protein . In other words, ΔΔG = ΔG WT – ΔG MUT. Given this definition, mutations resulting in negative ΔΔG values were classified as “destabilizing”, while mutations resulting in positive ΔΔG values were categorized as “stabilizing”. We constructed the protein structure of LTα, which served as the input for FoldX, with the homology modelling server SWISS-MODEL [rs2229092 (H51P) and rs1041981 (T60N), but excluded rs2229094 (C13R). Therefore, the ΔΔG could not be calculated for rs2229094 using the FoldX tool.The effects of nonsynonymous SNPs at ormation . In addiormation , phyloP ormation  and phasormation  to evaluormation . We alsoormation  and I-Muormation  tools. ΔSS-MODEL . The resLTα gene using the SNP2TFBS tool [We also analyzed polymorphisms in and near the FBS tool , which pFBS tool , 56. In FBS tool , we searAdditional file 1 : Figure S1. contains pairwise LD plots (spanning ~9.64 kbs) for African and non-African populations that were not included in Fig. Figure S2. contains pairwise LD plots for loci located across a larger genomic region (spanning ~ 35.5 kbs) for each population. Figure S3. displays Manhattan plots of standardized |iHS| values in populations that were not included in Fig. Figure S4. shows Manhattan plots of standardized |nSL| scores for all populations. Figure S5. presents the remaining EHH plots for populations that were not included in Fig. Additional file 2 : Table S1. contains the frequency of alleles at polymorphic sites across LTα and 5′ regulatory region in global populations, and estimates of per site FST. Tables S2. gives summary statistics for the 5′ regulatory region only. Table S3. lists the observed Tajima’s D (DT) statistics for the LTα gene and their corresponding P-values under different scenarios of population growth. Table S4. shows the observed Fay and Wu’s H (H)  statistics for the LTα gene and their corresponding P-values under different scenarios of population growth. Table S5. lists the observed DT statistics for the 5′ regulatory region and their corresponding P-values for different scenarios of population growth. Table S6. contains the observed H statistics for the 5′ regulatory region and their corresponding P-values under different scenarios of population growth. Table S7A. presents haplotype frequencies in global populations. Table S7B. lists the polymorphic sites where the highest-frequency haplotypes in the network differ from one another and also indicates the alleles (on these haplotypes) that are shared with Neandertal and Denisovan genomes. Table S8. presents the frequencies of common haplotypes in non-African populations. Table S9. shows the frequencies of common haplotypes in African populations. Table S10. gives the genomic coordinates for markers in the LD plots (spanning ~9.64 kbs) in Fig. Table S11. gives the genomic coordinates for markers in the LD plots (spanning ~35.5 kbs) in Additional file Table S12. lists outlier standardized |iHS| and |nSL| scores for SNPs in each population. Table S13. presents the inferred ages of mutations across LTα and the 5′ regulatory region.


Ecological speciation can sometimes rapidly generate reproductively isolated populations coexisting in sympatry, but the origin of genetic variation permitting this is rarely known. We previously explored the genomics of very recent ecological speciation into lake and stream ecotypes in stickleback from Lake Constance. Here, we reconstruct the origin of alleles underlying ecological speciation by combining demographic modelling on genome-wide single nucleotide polymorphisms, phenotypic data and mitochondrial sequence data in the wider European biogeographical context. We find that parallel differentiation between lake and stream ecotypes across replicate lake-stream ecotones resulted from recent secondary contact and admixture between old East and West European lineages. Unexpectedly, West European alleles that introgressed across the hybrid zone at the western end of the lake, were recruited to genomic islands of differentiation between ecotypes at the eastern end of the lake. Our results highlight an overlooked outcome of secondary contact: ecological speciation facilitated by admixture variation. Ecological speciation can proceed rapidly, but the origin of genetic variation facilitating it has remained elusive. Here, the authors show that secondary contact and introgression between deeply diverged lineages of stickleback fish facilitated rapid ecological speciation into lake and stream ecotypes in Lake Constance. However, the origin of genetic variants underlying rapid speciation and reproductive isolation has remained unknown in all but a few cases8, e.g., whether loci that contribute to reducing gene flow between populations are derived from de novo mutation, from standing genetic variation or from admixture variation acquired through introgression between divergent lineages. Understanding the process of speciation and its constraints requires knowledge on the origin of alleles under ecological, sexual or incompatibility selection. In turn, understanding the origin of alleles requires reconstructing the history of populations undergoing speciation.Contemporary speciation studies have shown that speciation can sometimes be surprisingly fast even in the face of gene flow, allowing for sympatric divergence or persistence of incipient species. Many of these cases involve divergent natural selection or habitat-dependent sexual selectionGasterosteus aculeatus complex) of Lake Constance, Central Europe3. Lake- and stream-adapted ecotypes differ in predator defense and feeding morphology, ecology, nuptial coloration, migration behavior, and life history: lake stickleback grow larger than stream stickleback12, are covered with larger or more lateral bony plates and possess longer spines as protection from predators14, have longer gill rakers and jaws adapted to feeding on zooplankton instead of benthic invertebrates13, migrate to lower reaches of streams to breed in contrast to resident stream stickleback3 and start breeding 1 year later and die older than stream stickleback12. In a South-Eastern tributary of Lake Constance, both ecotypes breed in sympatry and they maintain phenotypic10 and genomic differentiation despite ongoing gene flow3. Sympatric breeding of stickleback ecotypes is very rare and occurs here at a surprisingly early stage of speciation, given that historical records document the presence of stickleback in the Lake Constance catchment for the past 150 years only, and their prior absence from the basin18. The genomic architecture of this case is characterized by an undifferentiated genomic background interspersed by strong differentiation across multiple chromosomes, especially in low recombination regions and inversions enriched with quantitative trait loci (QTL) for divergent traits19. The lack of reduced diversity within genomic islands and of genomic background differentiation led us to hypothesize that ecotypes likely diverged in situ in Lake Constance, either from selection on standing genetic variation or admixture variation. From Lake Constance data alone, however, we were unable to distinguish these alternative origins of alleles.We recently documented a case of contemporary ecological speciation in threespine stickleback  primary divergence in situ, in which lake and stream ecotypes have recently diverged from standing genetic variation in a single lineage that colonized Lake Constance; (2) ecological vicariance (outlined above); (3) secondary contact, in which ecotypes correspond to West and East European lineages that have diverged in allopatry, recently met and sorted between lake and stream habitats with exchange of genes at the lake-stream boundaries; and (4) hybrid origin, in which one of the ecotypes has recently evolved through hybridization between divergent West and East European lineages or sorting of admixture variation following introgression from one divergent lineage into the other lineage.Here, we re-evaluate the population history of Lake Constance stickleback and investigate the origin of genetic variants underlying contemporary ecological speciation and early persistence in sympatry. We place all previously studied lake and stream populations from the Lake Constance catchment in a wider European phylogeographic context, using new and published genome-wide single nucleotide polymorphism (SNP), mitochondrial, microsatellite and plate morph data25 and uncover an unexpected outcome of secondary contact: repeated ecological speciation beyond the contact zone. Our results imply that caution should be taken when inferring modes and times of speciation from population genome sequence data in the absence of a thoroughly sampled phylogeographic context.Our analyses reveal admixture variation as genetic source of ecotype differentiation and thus contemporary ecological speciation, derived from hybridization between two divergent lineages from at least two European watersheds. We find that stream ecotypes in Lake Constance show a gradient of admixture between divergent European stickleback lineages: near the zone of secondary contact, stream ecotypes show a 50:50 hybrid origin, while far from the zone of contact, novel stream ecotypes evolved in situ from predominantly one genomic background, aided by sorting of introgressed alleles. Our analyses provide evidence for the hypothesis that admixture variation can be an important facilitator of rapid ecological speciation and adaptive radiation26  sequencing confirm the position of Lake Constance stickleback among the central European lineages  haplotypes in the Lake Constance catchment Fig. , all of 26 Fig. . All fiv 29 Fig. . Phylogeges Fig. . Among ts31 Fig. . In Lakes31 Fig. . Thus, mImportantly, contributions are not equally distributed among populations and ecotypes in the Lake Constance catchment Fig. . Lake anSbfI-RAD sequencing clusters the stream population NID with West European lineages rather than with East European lineages, with very high bootstrap support . When Constance and East European populations are treated as sister lineages in a phylogenetic tree, all Lake Constance populations show a significant excess of derived allele sharing with West European populations from the Rhine and the upper Rhone , Fig. , AGS1, F32. We compared models on three hierarchical levels of complexity  and stream  populations, and an NsiI-RAD dataset14 including Constance lake (ROM) and stream  populations (see Methods).We compared different modes of stickleback ecotype divergence in Lake Constance using a coalescent demographic modeling framework, and taking advantage of the West and East European sister lineages as discussed above. We tested the fit of multiple neutral demographic models to the observed site-frequency spectra (SFS) computed from sites in high recombination rate regions in order to minimize effects of selection on the SFSity Fig. . This ality Fig.  and minoity Fig.  contribuity Fig. . We optiWe infer West and East European lineages to have split ~3665 : 3636–4877) generations ago, while the upper Rhone and Rhine populations split 1710 1710–2239) generations ago according to our best 3-population model  generations for S2, 1276 (1015–1446) generations for NID, 2819 (1626–2819) generations for BOH and 1065 (888–1232) generations for GRA Fig. . Effecti473 Fig. , while e473 Fig. .In summary, demographic modeling suggests that West and East European lineages diverged ~4000 generations ago, translating to ~8000 years assuming 2 years average age of reproduction as generation time. After several thousand generations of isolation, these West and East European lineages met in the Lake Constance system, perhaps at lake-stream boundaries North and West of the lake. Stream stickleback there are of hybrid origin, having received most of their genome from a West or East European lineage depending on the population. Introgression across the secondary contact zone contributed some West European alleles to the lake ecotype and also to stream ecotypes in South-Eastern tributaries.Gasterosteus aculeatus), while tributaries North and West of the lake are of hybrid origin with the ‘classical freshwater’-type, low-plated stickleback from West European streams (Gasterosteus gymnurus). Even though stream ecotypes differ in admixture proportions and plate morph distributions  and one with parapatric reproduction (S2). These genomic islands are thus candidate loci for reproductive isolation or ecological adaptation to either the lake or the stream habitat due to their persistence in sympatry, parallel allele frequency shifts, habitat associations and their enrichment with QTL controlling adaptive traits divergent between ecotypes3. Before identifying the origin of alleles in genomic islands, we aim to confirm that these genomic islands represent regions that resist gene flow between ecotypes due to either divergent selection or reproductive isolation, rather than representing regions that diverged due to background selection35.We seek the answer to this question by identifying the origin of alleles in genomic islands of differentiation between lake and stream ecotypes breeding in sympatry. In a previous studyFST) across the genome will arise as by-product of background selection in the absence of gene flow41. Background selection removes proportionately more linked variation in low recombination regions, leading to negative correlations of differentiation (FST) with recombination rate, absolute divergence (dXY) and diversity levels (π), and correlated genome-wide differentiation across populations and taxa41. Allopatric stickleback populations from different European watersheds lack gene flow, but we find no correlation of FST with recombination rate or absolute divergence, even though differentiation landscapes are weakly correlated and FST and diversity show negative correlations in some populations  between allopatric European stickleback populations, even in the absence of gene flow.A rich literature has shown that heterogeneous differentiation  = 0.50, FST (S1 vs. AGS1) = 0.46, FST (S2 vs. AGS1) = 0.48, Fig. FST (L1 vs. PLS1) = 0.31, FST (L1 vs. PLS1) = 0.30, FST (S2 vs. PLS1) = 0.33), consistent with a largely East European genomic background of both ecotypes. In genomic islands of ecotype differentiation, however, stream stickleback are significantly less differentiated from West European stickleback  = 0.38, two-sided permutation test p < 0.01, FST  = 0.32, p < 0.01) than genome-wide, in contrast to the lake ecotype for which both genomic islands and genomic background show similar levels of differentiation from West European stickleback  = 0.55, Fig. If admixture variation facilitated ecotype divergence in South-Eastern tributaries of Lake Constance, we expect differential sorting of West- and East-derived alleles in genomic islands of differentiation between lake and stream ecotypes. We thus compared genome-wide differentiation and differentiation in genomic islands between Constance lake/stream stickleback and West European populations Fig. , 11. GenHere we have demonstrated that secondary contact between divergent lineages and the re-assortment of introgressed alleles into ecotypes underlie recent ecological speciation across lake-stream habitat boundaries in Lake Constance. Our analysis reveals an unexpected outcome of secondary contact between old allopatric lineages: rapid in situ ecological speciation outside the secondary contact zone fueled by introgression of admixture variation beyond the hybrid zone. It also explains and reconciles contrasting conclusions of previous studies regarding the origin, age and mode of ecotype divergence in Lake Constance. Finally, our results raise interesting questions about the evolutionary potential arising from recombining old alleles into new combinations and how distinctive the new combinations might be from parental combinations.14. Our analyses of mitochondrial, microsatellite, and genome-wide SNP data in a larger phylogeographic context using an array of different methods such as phylogenetic reconstruction, demographic modeling, D-statistics, hybrid index, and cluster analysis clearly reject an origin from a single lineage and identifie more than one source of origin. The colonization of the Lake Constance catchment by multiple lineages is consistent with historical accounts reporting introductions of stickleback into streams of North-Western Lake Constance between 1920 and 1940, in which the introduction of both fully plated and low-plated individuals was documented17. This is additional to historical accounts of introductions from unknown sources into streams South of Lake Constance in the late 19th century16. Consistent with an earlier mitochondrial phylogeography22 and the biogeographic context of a highly plated freshwater phenotype42, our analyses based on genome-wide SNPs identified East Europe as the major source of origin. The same lineage is also found in the nearby upper Danube drainage12 and it is one of the central European lineages28 as opposed to the highly divergent Black Sea or Mediterranean lineages. Such a central European rather than Black Sea origin is also in agreement with the historical ichthyologic record, which documents the absence of stickleback from the upper Danube until the early 20th century18 and thus rather a more recent colonization of the upper Danube and the Lake Constance basin from Northern catchments aided by human introductions instead of a natural colonization from the Black Sea.Analyzing the incipient radiation of Lake Constance stickleback in a European-wide biogeographic and phylogeographic context revealed contributions of at least two old central European lineages of freshwater stickleback that had evolved in isolation for several thousand generations before coming into secondary contact. Secondary contact has not been invoked previously in this system and explains the old, early Holocene  divergence times that had been estimated between Lake Constance ecotypes by applying a primary divergence model representing an ecological vicariance scenario17 and ecotype divergence time estimates hundreds of generations ago. Our parameter estimates are still associated with high uncertainty, as reflected by their wide confidence intervals in each model and variation for the same parameters between different datasets , outcomes varied from persistence with leaky reproductive isolation in steep tension zones48, through classical ‘mosaic hybrid zone’ patterns where each species occurs in habitat patches rich in the habitats they are best adapted to49, to situations resembling a hybrid swarm in which allelic combinations are sorted between parental species habitats on very small spatial scale, reminiscent of ecological speciation from a hybrid population50. In Lake Constance stickleback, we document a similar continuum of outcomes where secondary contact and environmental adaptation interact in diverse ways. Outcomes range from partial collapse at the zone of contact of the old lineages at habitat boundaries  to the assembly of alleles into new combinations in islands of genomic differentiation (S1 and S2 stream populations) that persist in sympatrically breeding ecotypes (S1 stream population versus lake ecotype).Secondary contact between old lineages can result in a complex mosaic of evolutionary outcomes. In classical work on contact zones between the West and East European species of firebelly toads  of one of the two old lineages. Our finding that genomic islands of sympatric ecotype divergence are derived from introgression of West European alleles suggests that introgression has likely facilitated or promoted early sympatric coexistence of ecotypes. The new stream ecotype populations carry a new combination of West and East European-derived alleles distinct from both original East and West European lineages 22. Accessions are given in Supplementary Table 22 and from populations across Europe58, resulting in a combined dataset of 254 individuals from the Lake Constance catchment, each sequenced for 253 overlapping base pairs of the mitochondrial control region 21, featuring lake and stream populations from Lake Constance, populations from upper Danube, the upper Rhone catchment (Lake Geneva) and the River Rhine  with 10 replicates for each K. We identified the best number of genetic clusters supported by the data using the Evanno method63.We combined data for four microsatellite markers in common between two previous studies64, in which individuals were scored as low, partially or fully plated morph, based on the presence of 0–3 lateral plates posterior the pelvic girdle (low plated), more than three later plates but with a gap of at least two plates to the caudal peduncle  or the presence of a continuous series of lateral plates up to the caudal peduncle (fully plated)21. We embedded this lateral plate morph data in the context of historical phenotype distributions65.We combined lateral plate morph data from lake and stream populations around Lake Constance, from the upper Rhone drainage (Lake Geneva), the Rhine and the upper Danube66 restriction-site-associated (RAD) DNA sequence data for East (Vistula) and West  European stickleback populations  data due to non-overlapping restriction sites. The NsiI dataset includes three tributary populations from North and West of Lake Constance and adjacent lake sites, including two sites overlapping with the SbfI dataset , three individuals each for European lineages and two each for North American outgroups  model with optimized substitution rates and a gamma model of rate heterogeneity. We further applied an ascertainment bias correction to account for the fact that we only used polymorphic SNP positions. Significance was assessed using 100 bootstrap replicates.For phylogenetic analysis, we used subsets of the ups Fig. , by seleSbfI subset containing all individuals from populations L1, L2, ROM, S1, S2, NID, PLS1, AGS1, and FRS4 . We reduced the dataset to 3030 biallelic SNPs with a minor allele frequency >5%, <5% missing genotypes and no linkage disequilibrium exceeding r2 = 0.95 within 100 kbp windows. To avoid effects of allelic dropout due to a high proportion of PCR duplicates in some populations , we randomly sampled one allele at each genotype to mimic the same level of allelic dropout for each sample, using a custom bash script 3. We then ran Admixture v1.3.074 for haploid data with otherwise default settings assuming 2–10 clusters (K), followed by cross-validation to identify the number of clusters with the lowest cross-validation error.To infer population structuring, we used another SbfI dataset to test for excess allele sharing between Lake Constance stickleback populations and European sister lineages, resulting in a dataset of 23,277 SNPs. We added the alleles of three different outgroups, Japan Sea stickleback Gasterosteus nipponicus, Black-spotted stickleback G. wheatlandi and Ninespine stickleback Pungitius pungitius to this dataset, inferred from previously published whole-genome data75 . We aligned all three outgroups to the improved threespine stickleback reference assembly68 using Stampy v1.0.2276 with default settings except for adding the option ‘--substitutionrate’ to add the expected divergence from the reference genome as estimated by Yoshida et al.75. We used Freebayes v1.1.0-3-g961e5f3-dirty77 to call outgroup genotypes for the 23,277 SNP positions in our SbfI dataset for genotypes with minimal depth 6 and phred-scaled site and genotype quality 20, then merged variants with the SbfI dataset and removed any multiallelic sites or indels using Bcftools. We then computed the D-statistic using Admixtools v4.178 for different quartets featuring each of the three outgroup taxa .We obtained rough estimates for admixture proportions of Lake Constance populations derived from West and East European lineages by computing the hybrid index with the R-package Sbf1 and Nsi1 datasets into multidimensional site-frequency spectra (SFS). The Sbf1 data subsets included one lake population (L2), one South-Eastern tributary stream (S2) population, one Western tributary stream (NID) population, the East European lineage (PLS1), and two West European lineages from the Rhine (AGS1) and the upper Rhone (FRS4), depending on the hierarchical level tested  and three Northern and Western tributary stream populations , with NID overlapping between NsiI and SbfI datasets. We excluded individuals with a strong allele imbalance in heterozygous genotypes, i.e., a minor/major read number imbalance strongly deviating from 1:1 . In each population, we identified loci out of Hardy–Weinberg equilibrium using Vcftools and discarded sites with evidence of heterozygote excess plus/minus a buffer of 100 bp left and right of each site from the dataset in order to eliminate putative duplicated regions in the genome and removed the sum of all sites. Then, we removed sites in low recombination rate regions of the genome (<1.5 cM Mbp−1) to avoid biases due to linked selection32. We estimated local recombination rates based on the FTC cross recombination map68 by cubic spline smoothing of the genetic on the physical map for each chromosome with a spline parameter of 0.7 and calculating first derivatives (= recombination rates) for positions of interest3. Next, we randomly subsampled a fixed number of genotypes from each population  to a dataset without missing data, discarding sites with too few genotypes, with a custom python script . We further reduced the SNP portion of the datasets to unlinked SNPs by removing sites with r2 > 0.95 within 200 bp distance. After this, we added again N monomorphic sites proportional to the retained number of SNPs post linkage-pruning by randomizing the order of monomorphic sites and retaining the first N sites. Finally, we converted the resulting linkage-pruned datasets without missing data into multidimensional SFS using a custom python script , folded them with another custom python script  and converted them to joint 2D-SFS with a custom R script . We also generated 100 non-parametric block-bootstrap replicates of the observed multidimensional SFS by randomly resampling blocks of 10,000 sequenced sites adjacent to each other on a chromosome to the observed number of such blocks as implemented in vcf2sfs.py.For demographic modeling, we converted subsets of both 81 and a hierarchical modeling approach to reconstruct the demographic history of Lake Constance stickleback by comparing the fit of different demographic models to observed SFS and to estimate parameters for the best-fitting models. First, we optimized three population models , Rhine (AGS1), and upper Rhone (FRS4). For each model, we maximized the likelihood from 100 random starting parameter combinations and minimal 10 to maximal 50 ECM cycles with a stopping criterion of 0.00181. The expected minor allele (folded) SFS for each model and parameter combination was approximated with 100,000 coalescent simulations. We used a mutation rate of 1.7e−8 in all simulations82. The likelihood and parameter estimates of each model were obtained from the run with the highest likelihood among 100 optimizations. To identify the best-fitting model, the likelihoods of models were compared using the Akaike information criterion (AIC)81. We estimated confidence intervals for parameters of the best model from non-parametric block-bootstrap replicates of the observed 3D-SFS: we performed 10 parameter optimizations with each bootstrap replicate, starting from the best parameters inferred from the observed data, and the parameter estimates from the run with the highest likelihood for each bootstrap sample was used to compute 95% confidence intervals using empirical percentiles. Furthermore, we assessed whether other models than the best model explained the data similarly well or not by computing likelihood distributions from 100 SFS simulated under each model given the maximum-likelihood parameters inferred from the observed data and comparing the overlap of these likelihood distributions  and two South-Eastern tributary populations , one East European (PLS1) and two West European populations . We filtered the SbfI-data subset for each pairwise comparison to include only biallelic SNPs with at least three sequenced genotypes per population using Bcftools. We estimated pairwise F-statistics for each SNP by performing locus-by-locus AMOVAs in Arlequin v3.5.23. SNP-level F-statistics were averaged over non-overlapping windows containing at least 2500 sequenced bases without splitting RAD loci or over sliding windows of 1 Mbp size with 200 kbp step size. Non-overlapping windows were on average 344 kb (73–1564 kb) wide. We computed an average recombination rate for each window by computing the mean over 10 recombination rate estimates at equally spaced positions across each non-overlapping window. We computed weighted average F-statistics by averaging variance components83 and calculating the ratio of averages84. We used permutation tests to assess, whether differentiation in windows overlapping with genomic islands of differentiation persisting among sympatric populations identified in an earlier study3 differed from the genome-wide distribution of differentiation. For this, we permuted the position of genomic islands 10,000 times across the genome, generated a null distribution for the mean and computed empirical quantiles for the observed mean differentiation in genomic islands.We used a subset of the 3 rather represent genomic regions resisting gene flow between ecotypes or regions that diverged due to background selection in the absence of gene flow, using a similar subset of the Sbf1 dataset as above with one South European (SOR) and one West European population (CHA) added, as well as the full NsiI dataset. In non-overlapping windows containing at least 2500 SbfI-RAD sequenced sites, we computed differentiation (weighted average FST) for all pairwise comparisons as outlined above. In addition, we estimated nucleotide diversity (π) within population and pairwise absolute divergence (dXY) for the same non-overlapping windows. For the latter two statistics, we subsampled SbfI- or NsiI-data subsets of population pairs to N genotypes per population corresponding to 75% of a population’s individuals in order to get a dataset without missing data, discarding sites with too few genotypes, with the custom script sampleKgenotypesPerPop.py. We summarized the subsampled VCF files into 2D-SFS for each non-overlapping window with the custom script vcf2sfs.py and computed both π and dXY with custom scripts , respectively. Pearson’s correlation coefficient (r) between statistics was computed in R for windows without missing values, for pairwise statistics as FST or dXY using statistics from non-overlapping population pairs to avoid autocorrelation. All data analysis was performed on the servers of the Genetic Diversity Centre (GDC), the Ubelix computer cluster, University of Bern and the Euler cluster, ETH Zurich, Switzerland.We formally tested whether parallel genomic islands of differentiation between Lake Constance ecotypes identified previouslyFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


Cassia tora is a plant of medicinal importance. Medicinal plants from different localities are believed to differ in their therapeutic potency. In this study, six populations of C. tora with different eco-geographical origins were investigated genotypically (ISSR) and phytochemically (FTIR) to establish an integrated approach for population discrimination and authentication of the origin of this medicinal herb. CHS gene expression analysis and determination of flavonoid content were carried out to substantiate the study. A total of 19 population-specific authentication bands were observed in 11 ISSR fingerprints. Authentication codes were generated using six highly polymorphic bands, including three authentication bands. FTIR spectra revealed that the peaks at wavenumber 1623 cm−1 (carbonyl group) and 1034 cm−1 (>CO- group) were powerful in separating the populations. These peaks are assigned to flavonoids and carbohydrates, respectively, were more intense for Ranchi (highland) population. Variation in the transcript level of CHS gene was observed. The findings of FTIR and RT-PCR analyses were in agreement with the TFC analysis, where, the lowest amount of flavonoids observed for Lucknow (lowland) population. All the populations of C. tora have been authenticated accurately by ISSR analyses and FTIR fingerprinting, and the Ranchi site was observed to be more suitable for the potential harvesting of therapeutic bioactive compounds. Cassia tora L. Syn. Senna tora (L.) Roxb. verna. Chakwad, commonly known as sickle senna, belongs to family Caesalpiniaceae . It is the wild annual herbal crop, indigenous to palaeotropical region (Africa and Asia to eastward Polynesia) and distributed throughout the tropical and sub-tropical regions of the world4. The plant is widely consumed as a potent source of sennosides (laxative), and enlisted in the World Health Organisation’s ‘List of Essential Medicines’5. It's medicinal potentials have been described in the traditional Chinese medicine (TCM) and Ayurvedic practices with the special reference to cure psoriasis and other skin degenerative disorders6. In addition to this, the plant was also used traditionally to cure diabetes, dermatitis, constipation, cough, cold and fever, etc7. The plant harbors anti-proliferative, hypolipidemic, immunostimulatory, and anticancerous properties8. Earlier, It has been reported that C. tora possessed a large amount of flavonoids, the potent antioxidants10. The production of flavonoids in plants is linked with the expression of chalcone synthase (CHS) gene encoding CHS enzyme which is the first committed enzyme in flavonoid biosynthesis12. CHS is ubiquitous to higher plants and belongs to the family of polyketide synthase (PKS) enzymes (known as type III PKS). It is believed to act as the central hub for the enzymes involved in the flavonoid pathway13. The expression of CHS gene is the important step in the biosynthesis of flavonoids15 and CHS transcription is regulated by endogenous programs in response to environmental signals16. Plant samples from different geographical origins have different biochemical compositions due to variations in the environmental conditions and genetic reasons19. Therefore, it is crucial to identify the medicinal herb at the locality level.The therapeutic potential of plants has been utilized in traditional medicines such as Chinese, Ayurveda, Siddha, and Unani etc. Being relatively nontoxic and easily affordable, there has been resurgence in the demand for medicinal plants20. Additionally, medicinal plants are processed for use as crude drugs, which affect many morphological and anatomical characteristics, as well as resulting in changes in some chemical constituents21. Therefore, it is difficult to identify the crude herbs through anatomical and chemotaxonomical studies. The established DNA barcoding approaches to authenticate plant species were based on either a short, and standardized DNA sequence region or DNA polymorphism using genetic markers like ISSR, and SNP etc.24. In plants, variations among the plastid gene sequences, for example, rbcL, matK and trnL genes, and ITS regions were used in identification, population discrimination and authentication of various plant species29. However, the lack in the prior information of genomic regions and low evolutionary rate of change in the coding regions are serious limitations to such analysis. A DNA based polymorphism assay may be the suitable alternative for the population authentication of herbal medicines. Earlier studies based on genetic markers  could significantly identify the plant populations31, however ISSRs were found highly reproducible, more variable and efficient than the currently used DNA marker34 in being the more robust to even slight changes in DNA concentrations. In addition, they retained the benefits over other PCR-based techniques such as the need for very little template material. Nevertheless, DNA genotyping also has limitations such as within species variation. Furthermore, the technique does not reveal the composition of the active ingredients or chemical constituents. DNA remains the same irrespective of the plant part used, while the phytochemical compositions may vary with the plant parts used, physiology, and the environment21. Therefore, proper integration of DNA based techniques like ISSRs and analytical tools like FTIR for chemo-profiling would be more efficient to authenticate the population and will lead to the development of a comprehensive system of characterization that can be conveniently applied at the industry level for quality control of herbal drugs.The general approach to identification is dependent on morphological, anatomical, and chemical features, but such characteristics are often affected by environmental and other developmental factors during plant growthC. tora and b) discriminate the same populations based on the variability in phytochemical (mainly flavonoid) content. For the aforementioned purposes, we used the ISSRs and FTIR as the rapid and efficient techniques. In addition, we also employed a qPCR based approach to test the expression level of CHS gene and total flavonoid content (TFC) analysis to substantiate the study. To the best of our knowledge, this study is the first attempt of its type.Therefore, the aim of this study was focused upon a) development of molecular markers to distinguish the test populations of C. tora which were collected from different geographical locations  specific authentication bands observed which were present in one population but absent in other  and CT-5 (Ranchi) populations which showed intense peaks compared to rest of the populations 35  and Pearson’s correlation were carried out between the selected spectral regions (2000–900 cm−1)  Fig. . PCA rev−1) Fig. .Figure 4The total flavonoids content (TFC) in extracts, was determined using the formula (y = 0.005x + 0.085), derived from the calibration curve, and expressed as mg/g leaf dry wt . High yield (21.53 mg/g) of TFC was observed for CT-5 (Ranchi) population. However, lowest yield (13.87 mg/g) was found in CT-2 (Lucknow) population  population and the high quantity of transcripts was observed for the CT-6 (Puri) population. CHS2 analysis disclosed the lowest transcript level for CT-2 (Lucknow) population however, relatively high expression was observed in CT-5 (Ranchi) population  percentage reduction of relative epidermal thickness compared to positive control36. The environment has influences on plant development and metabolism, and can alter the plant’s chemical compositions and therapeutic potency19. Therefore, selection of the genuine populations for potent application has become the key issue in the modernization of traditional medicines. It is, however, difficult to authenticate genuine from among the wild populations accurately using the conventional techniques, as they are similar in morphological, anatomical characteristics, and also, sometimes in chemical components too37. Hence, the precise identification and authentication of genuine population is a prerequisite for the chemical and pharmacological investigations of traditional medicines, as well as for their clinical applications.Traditionally, C. tora, and highly polymorphic. The results are consistent with the view point that level of genetic diversity as affected by the species distribution38. At the same time, we have detected 19 population-specific authentication bands, and established ISSR authentication codes involving three authentication bands for the each population of C. tora  for Dehradun population proved the declination of genetic functions of a species at higher altitude and low temperatures41. Bary-Curtis cluster analysis of all populations favored the above findings and split Dehradun population from the rest  which indicates that the simple sequences were abundant and highly dispersed throughout the genome of est Fig. . However43. It provides more information through the fingerprint regions of herbal medicines, rendering the technique direct and simple44. Previously, FTIR has been used in identification and population discrimination studie46. Samples from the different populations can be discriminated based on the functional group absorption. Peak absorbance at the particular wavenumber is presented in (Supplementary Table −1) were similar for all the populations with the variable intensity, which implies the presence of similar major chemical components in all the samples obtained from different locations. Previously, the methanolic extract of C. tora had been analyzed using FTIR, and flavonoids were reported as a major phenolic component in the plant10. Carbonyl (>C=O) group constitutes the functional group of flavonoid, in which, stretching vibrations in carbonyl compounds lie between 1750-1600 cm−1 of mid-IR47. The sharp peaks in this region indicated that the all extracts were flavonoid-rich. Nevertheless, the high absorbance of O-H, C=C, and C-O-C functional group in methanol extracts of the leaf indicated that the phenol and flavonoids could be the dominant compounds10. The absorbance by C-O, C-H, C=C, C=O, and C-N functional groups between 1800–900 cm−1 are indicative of benzene, aldehyde and carbohydrate groups 48 had also reported the high amount of phenolics in highlands population of ginger. The high flavonoid content in Dehradun and Ranchi populations may be due to ecological stresses like a decrease in soil moisture and nutrients availability or the decreasing temperature49. These stresses could have led to oxidative damages, and as the antagonistic response, plants synthesized abundant antioxidants especially the phenolics50.FTIR has been proven to be an accurate, fast and simple method for phytochemical screening45. In this study, several peaks overlapped together at a single wavenumber making their appearance incoherent; therefore, SD-IR was used to resolve the peaks and to reveal the weaker spectral features (1800-800 cm−1) for interpreting the components with a low concentration and weak absorption peaks44. Results of SD-IR of the C. tora leaf illustrated the two distinct and sharp peaks in-between the wave region (1500-900 cm−1) assigned to benzene (1384 cm−1) and carbonyl group (1034 cm−1)  and (1100–900 cm−1) assigned to the carbohydrate region35. Patna population had the highest absorption peak at 1384 cm−1, and lowest at 1034 cm−1. Low-intensity peak indicated the presence of low amount of the carbohydrates of this population compared to all the populations. According to the above findings, Patna population could be easily discriminated from rest of the populations. Earlier, similar studies had also been used for population discrimination of Polygonum minus, species discrimination in between Tephrosia tinctoria and Atylosia albicans, and identification of genuine American ginseng population46.The analysis based on SD-IR provides a better way to distinguish the populations when the peaks overlapped as SD-IR spectra enhanced the apparent resolution and amplified the tiny differences in the IR spectrum−1) Fig. , which c51. Higher altitude like that of Dehradun, exposed the plants to intense solar radiation than the rest . However, with relatively lower polymorphic DNA (81.9%) compared to other populations, Dehradun population estimated high amounts of flavonoids after Ranchi, and it might be due to the geographical elevation and other physical and physiological stresses50. Therefore, it is emphasized that Ranchi population produced secondary metabolites in greater abundance and is more genetically affluent than the others. Hence, this population can be better exploited for the germplasm conservation and breeding purposes. The high TFC content of Ranchi populations along with Dehradun and Puri populations  had highest flavonoid content  followed by lowland populations Fig. . The simons Fig.  could beons Fig.  where, tsis Figs . Dehradusis Figs  and 6. BCHS, leads to increased phytochemical (i.e. flavonoids) concentrations in plants54. Genes encoding CHS constitute a multigene family in which the copy number varies among the plant species, and functional divergence appears to have repeatedly occurred55. CHS gene expression has been studied extensively in relation to flavonoids production in many plant species15. However, there are few reports about the CHS gene analysis in sub-tribe Cassiinae. Panigrahi et al. (2013)56 correlate the flavonoid content with the presence of CHS gene in between C. laevigata and C. fistula, and Samappito et al. (2013)57 studied the expression of CHS gene in C. alata roots and correlates their role in the synthesis of flavonoids. In our study, a preliminary attempt was made to access the expression level of CHS among all the tested populations of C. tora because this plant is rich in flavonoids which are a major source of bioactive compounds10. CHS1 analysis showed higher transcript level for Ranchi and Puri populations compare to the rest  and was also in agreement with TFC analysis  in greater quantity than lowlands ones.ISSR fingerprinting was the suitable method for estimating the genetic differences among the populations and the authentication codes developed during analysis, will be helpful in differentiating the C. tora plants were collected from their natural habitats in August 2014 at six different locations in India , Varanasi, India. For the each population, herbarium specimen was prepared and deposited at the ‘Herbarium’ of the above-mentioned institution with the voucher specimen number . These taxonomically authenticated samples are referred to as Biological Reference Material (BRM)59. The plant collection sites with eco-geographical details are given in (Supplementary Table 60 with the given modifications. 30 mg of Polyvinylpyrrolidone (PVP) was added to remove polyphenols and extracted sample was treated with RNase  for 30 min. DNA concentration and purity were determined by spectrophotometry  and electrophoresed on 0.8% agarose gels. The final concentration of each DNA sample was diluted to approx 20 ng/ml with Mili-Q water and stored at 20 °C till further use.The genomic DNA was extracted from lyophilized young leaves using the cetyl trimethyl ammonium bromide (C-TAB) method of Wang (2010)2, 2 µl 10 mM dNTPs, 0.32 mM primer, 2.5 unit of Taq polymerase, and Mili-Q water. The reactions were performed in a Mastercycler thermocycler . The program consisted of an initial denaturation at 94 °C for 4 min, followed by 35 cycles of 45 s at 94 °C, 45 s at 48–52 °C (depending on the primer), 2 min at 72 °C, and the final extension of 10 min at 72 °C. A negative control, with the template DNA omitted, was included in each PCR. Amplification products were electrophoretically separated at a constant voltage of 60 V for 3 h, in 1.5% agarose gels with 0.5X TAE buffer, stained with ethidium bromide and visualized under UV. The 100 bp and 1 kb DNA ladders were used to estimate the molecular size of the fragments. Twenty-five primers were tested to identify those that produced sharp and reproducible bands. Three individuals from each population of C. tora were randomly chosen for the experiment. The eleven primers selected for this study were used to amplify all the C. tora DNA . Primers were designed using the software Primer 3 , referring to accessed sequences in the Genbank 61. Triplet of all sample reactions were carried out and negative control of master mix in addition to primers was performed in all RT-PCR runs. GAPDH was taken as control because of its constitutive expression. The accuracy of primers was tested using genomic DNA of the plant as positive control. The intensities of the PCR products on agarose gels were quantified with the Gel Doc 2000 system and volume tool of the Quantity one software .Total RNA was isolated from the leaf samples (100 mg) using TRIZOL reagent (GIBCO-BRL) as per instructions given in the manufacturers’ protocol. The total RNA was digested with DNase at 37 °C for 15 min and then reverse transcribed into cDNA using M-MLV Reverse Transcriptase RNaseH (Bio-Rad CFX-96The amplified products were scored in terms of the binary code as present (1) or absent (0), each treated as the unit character regardless of its intensity. Polymorphism at the population level was calculated as the ratio of polymorphic loci to the total number of loci scored in all accessions of the same population. The pair wise genetic distance matrix was computed using UPGMA, NTSYSpc version 2.02e 63 with slight modifications. Each dried leaf samples of C. tora (weight 5 g) was lyophilized in liquid nitrogen and ground to fine powder. The powder was then defatted with hexane (50 ml), and extracted using a soxhlet extractor  (30 min). Hexane was discarded through rotary evaporation. Finally, extracts were dissolved in 50 ml of methanol, incubated overnight (25 °C), filtered through 0.2 µm Millipore filter and stored (4 °C).Leaves of C. tora was applied on KBr. The infrared spectra were obtained at the resolution of 1 cm−1 in the mid-IR range of 4,000–400 cm−1 using a FTIR spectrophotometer . All determinations were in five technical replicate, and data analyzed using statistical software OriginPro 8.0  pellets. The fraction (10 µl) of each extract (100 mg/ml) of 8.0 Fig. . Since, 8.0 Fig.  to compaet al. (2011)64. An aliquot of diluted leaf samples (1 mg/ml) as well as the standard solution of quercetin were added to 75 ml of NaNO2 solution, and mixed (6 min), by adding 0.15 ml AlCl3 (100 g/L). After 5 min, 0.5 ml of NaOH was added and the final volume adjusted to 2.5 ml with distilled water, and thoroughly mixed. Absorbance of the mixture was read at 510 nm against the blank using a UV-VIS spectrophotometer . TFC concentration is expressed (mg/g dry wt) . Differences between combined data of highland and lowland populations were analysed using a Student’s t-test analysis in SPSS software v12.0.1 . Changes with P<0.05 were considered to be significant. Calibration curve of quercetin  was plotted using Microsoft Office Excel (2007).Polymorphic data obtained by ISSR analyses were analyzed for Bary-Curtis differentiation in the software BioDiversity Pro (version 2.0). Correlation and PCA analysis were done in PAST software (version 2.1) for clustering the transmittance data of six populations of different origins.Supplementary Information


Aedes (Stegomyia) albopictus (Skuse) is an important worldwide invasive species and can be a locally important vector of chikungunya, dengue and, potentially, Zika. This species is native to Southeast Asia where populations thrive in both temperate and tropical climates. A better understanding of the population structure of Ae. albopictus in Lao PDR is very important in order to support the implementation of strategies for diseases prevention and vector control. In the present study, we investigated the genetic variability of Ae. albopictus across a north-south transect in Lao PDR.The Asian tiger mosquito, c oxidase subunit 1 gene (cox1), to assess the population structure of Ae. albopictus in Lao PDR. For context, we also examined variability at the same genetic locus in samples of Ae. albopictus from Thailand, China, Taiwan, Japan, Singapore, Italy and the USA.We used variability in a 1337-bp fragment of the mitochondrial cytochrome Ae. albopictus from 9 localities in Lao PDR and Thailand populations. Significant differences were observed between the Luangnamtha population and other locations in Lao PDR. However, we found no evidence of isolation by distance. There was overall little genetic structure indicating ongoing and frequent gene flow among populations or a recent population expansion. Indeed, the neutrality test supported population expansion in Laotian Ae. albopictus and mismatch distribution analyses showed a lack of low frequency alleles, a pattern often seen in bottlenecked populations. When samples from Lao PDR were analyzed together with samples from Thailand, China, Taiwan, Japan, Singapore, Italy and the USA, phylogenetic network and Bayesian cluster analysis showed that most populations from tropical/subtropical regions are more genetically related to each other, than populations from temperate regions. Similarly, most populations from temperate regions are more genetically related to each other, than those from tropical/subtropical regions.We observed very high levels of genetic polymorphism with 46 novel haplotypes in Aedes albopictus in Lao PDR are genetically related to populations from tropical/subtropical regions . The extensive gene flow among locations in Lao PDR indicates that local control is undermined by repeated introductions from untreated sites. Flaviviridae, Flavivirus), is one of the most challenging public health problems in the Greater Mekong Subregion (GMS) composed of Cambodia, China, Myanmar, Thailand, Vietnam and Lao People’s Democratic Republic (PDR) , northern [Luang Prabang (LP) Province], central  and southern [Champasak (CH) Province] regions of Lao PDR Fig. . Aedes able keys  and conf® Tissue kit  according to manufacturer’s instructions. The fragment of mtDNA cytochrome c oxidase subunit 1 (cox1) gene was amplified using two sets of primers, 1454F (5′-GGT CAA CAA ATC ATA AAG ATA TTG G-3′) and 2160R (5′-TAA ACT TCT GGA TGA CCA AAA AAT CA-3′); and 2027F (5′-CCC GTA TTA GCC GGA GCT AT-3′) and 2886R (5′-ATG GGG AAA GAA GGA GTT CG-3′), following the polymerase chain reaction (PCR) protocol explicitly detailed in Zhong et al. [® . All sequencing reactions were carried out in both directions using an ABI Big Dye Terminator Kit v.3.1  and analyzed on an ABI Prism 3500xL—Avant Genetic Analyzer  at the Institut Pasteur du Laos sequencing facilities in Vientiane.Total genomic DNA was extracted from single whole mosquitoes using a NucleoSping et al. . Aliquotcox1 gene sequences were edited using Sequencher® version 5.4.6  and automatically aligned in Geneious v.9.1.6. [The v.9.1.6. .Hd), nucleotide diversity (π) and (K) average of nucleotide differences within each site were generated using DnaSP v.5.0 [FST was calculated to estimate population differentiation based on differences in haplotype frequencies, whereas Nei’s Nm estimated gene flow is based on GST [The number of haplotypes (H), haplotype diversity , using the AMOVA approach, into K groups to maximize the between group variation. SAMOVA estimates were computed for K = 2–8, with 1000 simulated annealing steps from each of 100 sets of initial starting conditions. Isolation by distance (IBD) was checked using a Mantel tests [Analysis of molecular variance (AMOVA) was conducted to determine the distribution of genetic variation within and among populations using 1000 permutations implemented in Arlequin v.3.5 . Additioel tests . IBD wasel tests , 40 betwAe. albopictus populations from Lao PDR was examined using the statistics D [FS [The hypothesis of strict neutrality among istics D  and Fu’scs D [FS , calculacs D [FS . The miscs D [FS –45. Statcs D [FS .FST, Nm and AMOVA using the same methodology described earlier.To make broader comparisons among haplotypes from Lao PDR and other geographical regions, we analyzed samples from Thailand and downloaded available data in GenBank from China, Taiwan, Japan, Singapore, Italy and the USA . The parK (K = 1 to 21). In this analysis, the most likely number of genetic clusters (K) in the dataset is determined without prior information of the sampling locations, and then assigns proportion of the ancestry of each individual into the different clusters implemented in the program. The method of Evanno et al. [ad hoc quantity, based on the second rate of change of the likelihood function between successive values of K. Poterior probability values were estimated using a Markov Chain Monte Carlo (MCMC) method and 1,000,000 interactions of each chain following the 100,000 iteration burn-in period were performed, as recommended by Pritchard et al. [In addition, a Bayesian clustering algorithm implemented in the program STRUCTURE v.2.3 was used to investigate genetic structure of individuals. The program was run under varying assumptions on Hardy-Weinberg (HW) and linkage equilibriums , with teo et al.  was usedd et al. . We visud et al. .cox1 (1337-bp) were amplified from 172 specimens, representing populations from Lao PDR (n = 155) and Thailand (n = 17). No insertions, deletions or stop codons were detected across all samples, which minimizes the likelihood of pseudogene amplification.Partial sequences of the mtDNA A total of 44 haplotypes were identified among the Lao populations Table ; of thescox1, and identified 66 haplotypes of Ae. albopictus in 6 different countries . The trimmed fragment we used is 96-bp smaller (1337-bp) than that of Zhong et al. [Zhong et al.  amplifieg et al. ; howeverg et al.  and 46 nAe. albopictus in Lao PDR populations ranged from 0.537 (LN) to 3.105 (KM), corresponding with the range of the nucleotide diversity (π) 0.00040 (LN) to 0.00232 (KM). Haplotype diversity (Hd) ranged from 0.416 ± 0.116 (mean ± SD) (LN) to 0.942 ± 0.029 (VC) . Gene flow (Nm) was > 1 among all populations, except LN and XB  and JS    in Lao PDR. When we added all samples including that of Zhong et al. [Ae. albopictus , with 56.8% of genetic variation found within-population and 43.2% among-populations. The spatial analysis of molecular variance (SAMOVA), based on mtDNA data, showed no genetically distinct population groups. Partitions of the sampling areas for each K value were not informative. FCT values presented a narrow range between 0.18 and 0.23. .Global AMOVA tests indicated a high proportion of the total genetic variance was attributable to within-population variation (85.98%), suggesting low and significant genetic structure among populations  and raggedness index (0.09) were not statistically significant in almost all the populations, except the SSD value for BO and CH and Rag for CH  and 45 (n = 52) , except CH (Champasak), may be due to climate  and found evidence for IBD.The higher and significant differentiation among LN (Luangnamtha) and other locations in Lao PDR  community. This has led to a better road infrastructure throughout the country and has increased connectivity between all the provinces, which has the potential to facilitate human-assisted movement of Aedes mosquitoes  and their pathogens [Ae. albopictus including latex-collection cups [Aedes albopictus is highly adaptable and successfully spread from its preferred forested environments to different rural and urban habitats, which has increased its potential as a vector and, consequently, arboviruses transmission risk in these more populated areas.Signs of recent expansion observed in athogens , 67. In ion cups , 69. Accion cups , the risAe. albopictus were analyzed from many different geographical regions, three haplotypes were observed to be shared between Lao PDR and other countries. H45 and H46 were shared with the USA  and Thailand, and H56 with the USA (Texas). H45 and H46 are shared haplotypes from Los Angeles, California, where samples were collected in 2001. Similarly, Zhong et al. [FST was lower when comparing Lao PDR with the 2001 California samples  than the ones collected 10 years later in 2011   and east-west (Japan to India) pattern of genetic differentiation [Although no study has performed a comprehensive analysis of the species’ full native range , the genntiation ; our resntiation .Ae. albopictus . Similar results were observed in Zhong et al. [cox1 of Ae. albopictus from China, Taiwan, Japan, Singapore, Italy and the USA. Maynard et al. [Ae. albopictus of Indo-Pacific regions.Overall, we observed significant population structure in g et al.  and Mayng et al. . As mentg et al.  analyzedd et al.  using boAe. albopictus populations were found to be very genetically related to the tropical Thailand strain. An allozyme study suggested that populations of Ae. albopictus in the eastern USA possibly originated from temperate Asian regions [Laotian  regions , while m regions , 76 and Ae. albopictus in Lao PDR. Laotian Ae. albopictus are genetically related to populations from tropical/subtropical regions. The high polymorphism but shallow population structure across Lao PDR and signs of a recent population expansion in Ae. albopictus may be the result of recent economic development that facilitates human-mediated movement of Ae. albopictus. We suggest that extensive movement and likely common reintroductions of Ae. albopictus to treated sites represent a major challenge to dengue control in Lao PDR.To our knowledge, this study represents the first genetic analysis of Additional file 1: Table S1. Pairwise differentiation, FST, among populations of Ae. albopictus. Abbreviations: China: GZ, Guangzhou; XM, Xiamen; JS, Jiangsu; Taiwan: TW, Xinzhu; Japan: JP, Nagazaki; Singapore: SG, Helios Block; Italy: IT, Trentino; USA: LA01, California; LA11, California; NJ, New Jersey; TX, Texas; HW, Hawaii; Lao PDR: BK, Borikhamxay; BO, Bokeo; CH, Champasak; KM, Khammuane; LN, Luangnamtha; LP, Luang Prabang; VC, Vientiane Capital; XB, Xayabouly; Thailand: TH, Thailand. #Samples available in GenBank from Zhong et al. [P ˂ 0.05).g et al. . *SignifAdditional file 2: Figure S1. Fixation indices obtained by SAMOVA for the best-clustering option at each pre-defined values of K. Abbreviations: FCT, variation among groups of populations; FSC, variation among populations within groups; FST, variation among population among groups.Additional file 3: Table S2. Geographical distances (in km) among Ae. albopictus from Lao PDR. Abbreviations: BK, Borikhamxay; BO, Bokeo; CH, Champasak; KM, Khammuane; LN, Luangnamtha; LP, Luang Prabang; VC, Vientiane Capital; XB, Xayabouly.Additional file 4: Figure S2. Mismatch distributions showing the frequencies of pairwise differences of Ae. albopictus in Lao PDR.Additional file 5: Table S3. Assignment of the Bayesian clustering analysis of Ae. albopictus populations. Abbreviations: China: GZ, Guangzhou; XM, Xiamen; JS, Jiangsu; Taiwan: TW, Xinzhu; Japan: JP, Nagazaki; Singapore: SG, Helios Block; Italy: IT, Trentino; USA: LA01, California; LA11, California; NJ, New Jersey; TX, Texas; HW, Hawaii—are samples from Zhong et al. [#Samples available in Genbank from Zhong et al. [g et al. ; Lao PDRg et al. . The coe


Theropithecus gelada Rüppell, 1835), endemic primates of the Ethiopian highlands, are largely unstudied both in genetic diversity and intrageneric phylogeny. Currently, a northern and central subspecies and one isolated southern population are recognized, of which the central is classified as Least Concern, the northern as Vulnerable, and the southern is not yet assessed. The distribution and taxonomy of the subspecies remain poorly defined. Here, we estimate the mitochondrial DNA (mtDNA) diversity and phylogenetic relationships among gelada mtDNA lineages based on samples across the entire species range. We analysed 1.7 kb-long sequences of the mtDNA genome, spanning the cytochrome b gene and the hypervariable region I of the D-loop, derived from 162 faecal samples. We detected five major haplogroups or clades  which diverged between 0.67 and 0.43 million years ago, thus suggesting a rapid radiation, resulting in largely unresolved intrageneric phylogenetic relationships. Both, the northern and central demes contain two similarly valid haplogroups, each with little or no geographic segregation among respective haplogroups. Effective population sizes of the northern and central demes decreased during and after the last glacial maximum but remained stable for the southern deme, although on a very low level. The distribution of haplogroups within the geographic ranges of the putative gelada subspecies indicates that mtDNA sequence information does not allow reliable taxonomic inferences and thus is not sufficient for solving the taxonomic rank of the three demic populations, with the possible exception of the southern population. Nevertheless, due to the genetic differences all three populations deserve conservation efforts, in particular the smallest southern population.The large-bodied, terrestrial primates in the tribe Papionini are among the most intensely studied animals in the world, yet for some members of this tribe we know comparatively little about their evolutionary history and phylogeography. Geladas ( Capra walie, [Tragelaphus buxtoni, [Canis simensis, [Chlorocebus djamdjamensis, [An essential precondition for effective species conservation is recognizing what exactly we need to preserve, making species delimitation and the definition of evolutionary and management units crucial for conservation planning –4. Endema walie, ; mountaibuxtoni, ; Ethiopiimensis, ; Bale moamensis, ).Theropithecus gelada Rüppell, 1835), also endemic to the Afro-alpine grasslands of the Ethiopian Plateau, are the only extant taxon of a once species-rich genus formerly widely distributed in Africa and Eurasia [Theropithecus is closely related to baboons (Papio), kipunjis (Rungwecebus), and crested mangabeys (Lophocebus); however, the phylogenetic relationships among these genera remain uncertain [Theropithecus gelada–Theropithecus gelada gelada Rüppell, 1835 and Theropithecus gelada obscurus, Heuglin, 1863, but their validity and geographic distributions are unclear [T. g. gelada is classified as “Vulnerable” [T. g. obscurus is classified as “Least Concern” [Geladas ( Eurasia –13. The ncertain –17. Curr unclear . Althoug unclear –27), the unclear –30. Acconerable” , whereasConcern” , though Also, recent observations conflict with historic records for several gelada populations. For example, a few of the northernmost populations reported from Tigray, which were mentioned by Yalden et al.  see als and GippT. g. gelada occurs north of Lake Tana and west of the Tekeze River (the “northern” population), whereas T. g. obscurus occurs south of Lake Tana and east of the Tekeze River , rather than the geographically more proximate T. g. obscurus  [Theropithecus gelada arsi. In a recent study, Shotake et al. [T. g. gelada) and central (T. g. obscurus) clades. Shotake et al. [Morphologically, the southern population resembles the geographically distant ulation) ,29,36. Bulation) ,40; and ulation)  tentative et al.  found gee et al.  reconstre et al.  regardedT. gelada. We were able to take advantage of new data from a country-wide survey conducted from 2014–2016 where our team obtained 174 geo-referenced gelada DNA samples with extensive representation from the “northern”, “central”, and “southern” gelada populations. We were able to generate and analyse 1.7 kb mtDNA sequence information from 162 samples. In particular, we were interested in the geographical distribution of gelada mitochondrial haplotypes, in the genetic diversity of respective demic populations and their effective population sizes, and in whether the newly available genetic information justifies any taxonomic inferences.The aim of our study was to conduct a comprehensive study on the phylogeography of Sample collection was exclusively non-invasive and complied with the laws of Ethiopia and Germany and with the guidelines of the International Primatological Society. During sampling of faecal material, no animals were harmed or disturbed.During nationwide surveys of gelada distribution and abundance between 2014 and 2016  S1 FileS1 File, The Ethiopian Wildlife Conservation Authority, and government officials in the following provinces/regions: North and South Shoa, North Gondar, North and South Wollo Zones, and Oromia and Tigray Regions, North Shewa Zone provided permissions to conduct this research.5’-AACCATCGTTGTATTTCAAC-3’ and 5’-CGTGTAGGAATAGTAAGTG-3’, 5’-AACCTCGTCCAATGAGTC-3’ and 5’-TAAAATATAATACAGATGCTAC-3’, and 5’-CAAAGCATGATATTCCGC-3’ and 5’-GAGGTAAGAACCAGATGCCG-3’. Primers were designed on the basis of available mitochondrial genome sequences from geladas. PCR reactions were carried out in a total volume of 30 μl containing 1 U Biotherm DNA Taq polymerase (Genecraft), 1x reaction buffer, 0.16 mM of each dNTP, 0.33 μM of each primer, 0.6 mg/ml BSA and ~100 ng genomic DNA. Thermo cycling conditions comprised of 94°C for 2 min, followed by 40–50 cycles of 94°C for 1 min, 54°C for 1 min and 72°C for 1 min, followed by 72°C for 5 min. We checked PCR performance on 1% agarose gels and excised PCR products with correct size from the gel. After purification with the QIAquick Gel Extraction Kit (Qiagen), PCR products were Sanger-sequenced on an ABI 3130xL sequencer (Applied Biosystems) using the BigDye Terminator 3.1 Cycle Sequencing kit (Applied Biosystems) and the amplification primers. To avoid and check for cross-sample contamination, all working steps were carried out in separate laboratories under routine precautions. Appropriate negative controls were included into all assays and 10% of randomly selected samples were re-analysed.We extracted total genomic DNA using the First DNA All Tissue Kit  according to the manufacturers’ protocols, with minor modifications as outlined in . After enucleobytes.com/4peaks/) and sequences were assembled in SeaView 4.5.4 [Obtained sequence electropherograms were checked with 4Peaks 1.8 .For phylogenetic tree reconstruction in IQ-Tree 1.5.2  and MrBaTheropithecus and Papio with an offset of 4.0 and a mean of 1.0, translating into a median of 4.69 million years ago (Ma) and a 95% highest probability density (HPD) of 4.03–7.69 Ma. The calibration point relies on the oldest known Theropithecus fossils with an age of ca. 4 million years [Theropithecus was already established and, thus, separated from Papio. The adequacy of a 10% burn-in and convergence of all parameters was assessed by inspecting the trace of the parameters across generations using Tracer 1.6 (http://beast.bio.ed.ac.uk/Tracer). Sampling distributions of independent runs were combined with LogCombiner 2.4.2, and trees with mean node heights were summarized with TreeAnnotator 2.4.2 using a burn-in of 10%. Trees were visualized in FigTree.We estimated divergence times using a Bayesian approach implemented in the BEAST 2.4.7 package . We perfon years ,56, suggFor the following analyses we used the complete gelada dataset with 162 sequences, all with 1733 bp in length. To further trace phylogenetic relatedness among gelada haplotypes, we built a haplotype network in POPART 1.7  using thS [S values suggest population contraction, whereas negative values suggest population expansion. Tajima’s D is considered more powerful at detecting older and steeper population declines, whereas Fu’s FS is more sensitive in detecting more recent and shallow population contraction [−8 substitutions/site/year as determined from the divergence time calculation (see above). For each dataset, we ran two independent analyses with 25 million generations, sampling every 1000 generations and a burn-in of 10%. The results of each run were checked to ensure convergence and stationarity using Tracer. The estimated effective population size was given as Ne multiplied by generation time. To get the effective population size, we divided the values by a generation time of 10 years, which is similar to those in drills  or rhes1 years, ).We successfully retrieved sequences from 162 (out of 174) faecal samples and found a total of 61 haplotypes . AccordiThe two north clades did not reveal any geographic structure. Haplotypes of both clades were found admixed within the sampling range. For the two central clades, a slight geographic pattern was observed; samples from the central-2 clade were found predominantly in the northern part of the range of the central clades. However, both clades intergraded spatially .Papio ursinus) and was almost completed prior to the first divergence of the gelada lineage (0.67 Ma).Monophyly of all five clades was strongly supported, but the phylogenetic relationships among them remained largely unresolved  Figs. Ach) and nucleotide diversity (π) for the various gelada demes are summarized in h = 0.462) is roughly half of that estimated for the northern (h = 0.938) and central populations (h = 0.964). For all three populations, nucleotide diversities  are relatively low, especially for the southern population. The haplotype and nucleotide diversities of the central and northern populations decrease, if we divide them into their respective two clades. However, the southern deme remains the one with the lowest genetic diversity.Haplotype diversity , before it started to decline around 10,000 years ago to the current female Ne of ~22,000 . For the southern deme, the BSP suggests that at least within the last 1500 years the population remained stable but on a very low level of only ~750 females.The recent population declines are also obvious in our BSPs. They suggest that the global gelada population experienced a prolonged period of demographic stability  as a second taxon, T. g. senex. An alternative explanation for the occurrence of the “senex” morphotype in the Noari region could be hybridization among T. g. gelada and T. g. obscurus. An indication for a hybrid origin of the “senex” type could be the occurrence of a central clade haplotype (T. g. obscurus) in the range of the north clade (T. g. gelada). The respective sample was collected in the Noari region, close to the type locality of “senex”. A genetic analysis of the type series of “senex” stored in the Strasbourg Natural History Museum would probably help to shed some light on the phylogenetic relationship of this presumed taxon.Our south clade corresponds to the Arsi region clade of Shotake et al. . In contountains . T. g. sWhether one of the northern lineages was historically also present in Tigray and Eritrea is not clear because geladas have most likely been exterminated from their former northern range . However, survey effort in Tigray has been relatively minimal, so it cannot be ruled out that small isolated populations still exist.obscurus” [Our two central clades correspond largely to Shotake’s “Blue Nile river basin” clade. Similar to our two north clades, the central clades show geographical admixture, with a tendency for central-2 clade occurring mainly in the northwest of the central range. In addition to , we discbscurus” , it woulIn general, we found genetic diversity distributed into three demes with one exception. One sample with a central haplotype (h60) was found within the geographic range of the northern haplotypes, east of the Simien Mountains but west of the Tekeze River. Identical haplotypes have been found at least 350 km south. With the currently available information, we can only speculate about possible causes for this geographical mismatch. It can be a relict of a former partly overlapping distribution of the central and north clades, or it is possible that humans have transferred single individuals from one area to another. It is known at least for infant baboons in Eritrea that they are kept as pets and transported over large distances, where they are occasionally released into wild populations (pers. observation DZ).Although the five clades are well supported, their branching pattern is not well-resolved  Figs. HoPapio, divergence ages within geladas are relatively recent  and morphological information are necessary for a proper internal taxonomic revision of Theropithecus.The taxonomy of geladas is disputed ,30,74, agression ,71,75,76Rhinopithecus although nucleotide diversity in geladas is smaller than in this Asian colobine genus [P. hamadryas; [h ± SD = 0.983 ± 0.003, p < 0.001; nucleotide diversity: π ± SD = 0.04251 ± 0.00088, p < 0.001).As reported by  and alsone genus . Comparemadryas; , geneticThe relatively low genetic diversity, in the southern population in particular, at first, might tempt conservation managers to increase genetic diversity by translocation of extra-demic geladas. However, such genetic rescue by translocation should be avoided here, since the preservation of the different evolutionary lineages of geladas will be undermined and any local adaptation would likely be lost, with possible negative consequences for survival in this marginal habitat –81. The Canis simiensis [The combination of relatively low genetic diversity and the apparent extirpation of several local populations over the last century, a trend that is suspected or confirmed in another endemic species to the Afromontane region–the endangered Ethiopian Wolf imiensis –supportsimiensis ,84. At tS1 FigNumbers at nodes refer to bootstrap values in %.(PDF)Click here for additional data file.S2 FigNumbers at nodes refer to posterior probabilities.(PDF)Click here for additional data file.S1 Table* Sample from northern range but with central haplotype.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S1 FileTheropithecus gelada across the Ethiopian Highlands. 2016; Unpubl. Final report to Margot Marsh Biodiversity Foundation.Determining the conservation status of gelada monkeys: Distribution, abundance and phylogenetic relationships of (PDF)Click here for additional data file.