Saussurea polylepis Nakai is an herbaceous perennial endemic to Korea and is highly restricted to several continental islands in the southwestern part of the Korean Peninsula. Given its very narrow geographical distribution, it is more vulnerable to anthropogenic activities and global climate changes than more widely distributed species. Despite the need for comprehensive genetic information for conservation and management, no such population genetic studies of S. polylepis have been conducted. In this study, genetic diversity and population structure were evaluated for 97 individuals from 5 populations  using 19 polymorphic microsatellites. The populations were separated by a distance of 20–90 km. We found moderate levels of genetic diversity in S. polylepis . This may be due to long lifespans, outcrossing, and gene flow, despite its narrow range. High levels of gene flow , especially from wind-dispersed seeds, would contribute to low levels of genetic differentiation among populations. However, the small population size and reduced number of individuals in the reproductive phase of S. polylepis can be a major threat leading to inbreeding depression and genetic diversity loss. Bayesian cluster analysis revealed three significant structures at K = 3, consistent with DAPC and UPGMA. It is thought that sea level rise after the last glacial maximum may have acted as a geographical barrier, limiting the gene flow that would lead to distinct population structures. We proposed the Heuksando population, which is the largest island inhabited by S. polylepis, as a source population because of its large population size and high genetic diversity. Four management units  were suggested for conservation considering population size, genetic diversity, population structure, unique alleles, and geographical location . Sorghum bicolor  in the Korean Red List [S. polylepis is edible, so it is very likely that the population size will decrease more rapidly by humans, such as S. lapa, a medicinal plant [S. polylepis. Thus, the aims of this study were to (1) assess the genetic diversity and population genetic structure of S. polylepis using microsatellite markers, and (2) develop and implement the conservation strategies based on genetic diversity. This study will improve our understanding of the demographic and evolutionary history of endemic species on the continental islands and provide fundamental baseline knowledge for developing and implimenting conservation strategies.sthonica , S. gnapphalodes , S. chabngsanica , and S. olucrata , the popRed List . In addial plant . DespiteSaussurea polylepis Nakai .The perennial herb is Nakai  is disti islands . Given tS. polylepis were sampled to analyze the genetic diversity and population genetic structure from five continental islands: Gwanmaedo (GM), Gageodo (GG), Hongdo (HO), Heuksando (HS), and Uido (UI). Although S. polylepis also occurs on Myoungdo located approximately 15 km west of GM . Since less than 10 individuals were observed and most were seedlings with a single leaf, only two individuals were collected. Although a greater number of samples are needed for more robust conclusions, additional individuals in GM could not be found over a 3-year survey period . GG is an island located at the far southwestern Korean peninsula and has an area of 9.18 km2. Approximately 50 individuals were found, but most of them were very young, allowing only 17 mature individuals to be collected. HO is located about 20 km west of HS and has an area of 6.47 km2. Thirty-three individuals were observed along the trail, and as with GM most were young. This allowed the collection of a total of 13 individuals. In HS, which has a 21.7 km2 area, S. polylepis was continuously distributed along the trail to the summit. A total of 50 individuals were collected, which represented more than 40% of the total observed individuals. UI is the second largest (10.7 km2) of the five islands, and of 30 individuals observed, we collected a total of 15 mature individuals. Therefore, the total number of individuals sampled in this study from five islands  represents reasonably good proportion of actual species distribution range and population size on each island. All samples were collected with permission issued from the Korea National Park Service and voucher specimens for each population were deposited in the Ha Eun Herbarium, Sungkyunkwan University (SKK). We thank the Korea National Park Service to permit sample collection of Saussurea polylepis from five islands.Distribution information for sampling was accessed and confirmed through literature and specimens from the Korea National Arboretum (Korea Forest Service) and National Institute of Biological Resources (Ministry of Environment). A total of 97 individuals of st of GM , it was st of GM . Of the 2) of PCR buffer, 0.5μL (each 10mM) of dNTPs, 0.1μL (5U/μL) of Taq DNA polymerase , and 14.9μL of distilled water. The PCR program consisted of initial denaturation at 95°C for 1 min, followed by 35 cycles at 95°C for 20 s, annealing at 58–60°C for 40 s, extension at 72°C for 60 s, and a final extension at 72°C for 5 min. The PCR amplicons were then detected using an ABI 3730XL DNA Sequencer  at the Macrogen company , and allele sizes were determined by comparing with a GeneScan 500 LIZ Size Standard using the software GeneMapper version 5.0 (Applied Biosystems).The total genomic DNA was extracted using the DNeasy Plant Mini Kit , following the instructions of the manufacturer. PCR was performed using 19 SSR primers  as previFst was additionally compared to Fst values corrected using the excluding null alleles (ENA) method.The presence of null alleles was investigated using Microchecker 2.2.3 , with a Na), number of effective alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (F), and proportion of polymorphic loci (P%), were estimated using GenAlEx v.6.5 [Fst) and number of migrants (Nm).Genetic diversity parameters, including mean number of alleles  was conducted using Arlequin version 3.5  to estimadegenet . An UPGMadegenet , and theadegenet .5 burn-ins and 1 x 105 MCMC iterations with 20 runs per K value. Best K was identified based on the approach of Evanno et al. [Bayesian model-based clustering was applied by STRUCTURE v.2.3.4  to charao et al.  by STRUCo et al. . The grao et al. .To detect evidence of recent bottlenecks in the populations, we tested the mode-shift based on allele frequency using BOTTLENECK v.1.2.02 software . WilcoxoS. polylepis. Null alleles were detected in all but one population  : 0.063–0.108, uncorrected Fst = 0.084, 95% CI: 0.066–0.108) (Na ranged from 1.47 (GM) to 6.32 (HS) across 19 loci, and Ne varied from 1.48 (GM) to 2.87 (HS). The average Ho value was 0.42, ranging from 0.35 (HO) to 0.47 (GM). The He value of GM was the lowest, whereas that of GG was the highest. Excluding GM, the four populations all had He values higher than Ho values, indicating heterozygote deficiency. Inbreeding coefficient ranged from -1.00 in GM to 0.20 in HS (Nm) across five populations was 1.76.In this study, 19 polymorphic microsatellite markers were used to determine the genetic diversity of .e., GM) , and fou6–0.108) , the ori20 in HS . On aver20 in HS . Allelic20 in HS . Private20 in HS , and theS. polylepis individuals formed three groups according to DAPC analysis. The scatterplot shows that most of the individuals from HO, HS, and UI populations were overlapped and HS consisted of highly variable individuals and widely positioned. The UPGMA result (Fst) showed that HO-HS had the lowest value (0.03), while GM-HO had the highest (0.31), indicating significant genetic differentiation  as well as mean heterozygosity values were similar or higher (He = 0.42 estimated by using microsatellite DNA data) of endemic species reported by Nybom [S. polylepis had similar values to Hypochaeris catharinensis of Asteraceae (He = 0.44) [S. polylepis had relatively low genetic diversity compared to endemics Michelia coriacea (He = 0.470) [Antirrhinum charidemi (He = 0.48) [S. polylepis can be assessed as relatively moderate.High genetic diversity is important because it can allow populations to more easily adapt to environmental changes. Therefore, understanding the level of genetic diversity within and among populations is essential to establish conservation strategies of species . In the r higher  when comby Nybom . In addi = 0.44) , which h= 0.470)  and Anti = 0.48) , demonstS. polylepis, the influence of inbreeding on the genetic diversity of S. polylepis still cannot be overlooked and further analyses about inbreeding are needed to get accurate conclusion.The genetic diversity of plant species is related to distribution range, population size, life cycle, mating system, and gene flow . Of thesS. polylepis can be explained by the perennial life history, mating system, and gene flow. Although the primary pollinator remains unclear, bees and flies were seen to be involved in the pollination of S. polylepis as with S. weberi [S. laniceps, and S. medusa [S. obvallata within Saussurea was reported as a self-compatible species [S. polylepis. Young and Pickup [Aster furcatus. The outcrossing and self-incompatibility system are important means for increasing genetic diversity and SI species tend to have higher diversity. However, the reduced genetic diversity including S-alleles due to small populations and bottlenecks (or founder effect) of S. polylepis can lead the lack of reproductive phase individuals (mates) and reduction of offspring density due to low seed set can cause the slow recovery of population size and subsequently increase extinction risk. Therefore, the reliance on outcrossing and self-incompatibility in S. polylepis within small populations can be obstacles to increasing or maintaining genetic diversity due to a lack of pollinators and other nearby reproductive phase individuals.The moderate genetic diversity of . weberi , S. lani. medusa . Asterac. medusa . Althoug species , the facd Pickup  demonstrd Pickup , which fNm = 1.76) across five populations can also support gene flow.The degree of gene flow can be confirmed from the AMOVA . AMOVA rHo value than He value. The Ho value was less than the He value in GG, HO, HS, and UI, indicating a deficiency of heterozygotes. The decreased Ho value might be explained by overlapping generation [S. polylepis is likely insect-pollinated and has self-incompatibility, assortative mating may not adequately explain heterozygote deficiency. The Wahlund effect may provide a better explanation, as it can be caused by the sampling of individuals from two or more subpopulations with different genotypic frequencies within a population presumed to be a single population. Also, the presence of null alleles in four populations may have led to an underestimation of observed heterozygosity.GM showed higher neration , inbreedneration , 76. Genneration , 76. BecHS was the largest population and had the most private alleles, in accordance with the results of previous studies, suggesting that genetic diversity is positively correlated to population size , 77. ThiS. polylepis (S. involucrata [Nm) was indirectly estimated based on Fst values across all populations for each locus. Wright [Nm > 1.0, genetic differentiation may be restricted. This is true for neutral genetic variation and selection can affect to either increase or decrease Fst [Nm value of 1.76 obtained from the Fst values across all populations of S. polylepis can be explained the gene flow and low levels of differentiation among populations.In this study, we found substantial genetic diversity within, rather than among populations in olylepis . This paolucrata . Gene flolucrata . In this. Wright  showed tease Fst . In addiXylocopa flavorufa (carpenter bees) visited wild and domesticated populations at a distance of up to 6 km. The shortest distances in this study was approximately 20 km between the HO and HS populations. Therefore, there is most likely a low probability of gene flow by an insect pollinator. On the other hand, the seed of S. polylepis has pappus, which enable seed dispersal by wind. Long-distance dispersal of seeds by wind has been reported [S. polylepis.Result generated from the DAPC showed three clear distinctions between populations , especiareported , 87, andreported , 88, 89.Fst values ranged from 0.03 to 0.31 . Considering that UI is about 40 km from GM, the barriers to seed dispersal are likely not only pure geographic distance, but also factors such as sea currents and wind direction. Excluding GM, genetic differentiation among the other four populations was low (Fst = 0.03–0.06). The Fst value between HO and HS was the lowest, suggesting that the closer the distance, the more active the gene flow. This is also consistent with the DAPC, UPGMA and STRUCTURE results. In particular, even thought Nm and AMOVA results suggested active gene flow and low levels of differentiation, GM and GG had different population structure compared to HO-HS-UI based on DAPC, UPGMA, and STRUCTURE analyses. These differences may be explained by the time of gene flow. The continental islands across which S. polylepis is distributed are contained within the Yellow Sea. The Yellow Sea is located between the Korean peninsula and mainland China, and was exposed during the Last Glacial Maximum  of the Pleistocene when the sea level was 85-140m lower [The pairwise  to 0.31 . GM and 0m lower , 91. Sea0m lower . In thisIt is necessary to first establish demographically independent management units in order to maintain adequate population size, especially of endemic or rare species. Based on genetic diversity, genetic differentiation, and geographical distances, GM, GG, HS-HO, and UI may indeed be acceptable management units. GM is composed of a genetic structure (dark blue) shared by the other populations, especially UI , it is nNe) for preventing inbreeding depression and having evolutionary potential. Based on their study, establishing a conservation strategy to retain more than Ne ≥ 100 as an initial step should be prioritized. The current small size of GM presents challenges to locally obtaining heterozygous seeds or seedlings, especially considering mate limitation, will therefore inevitably require transplanting or reintroducing nursery-grown S. polylepis. In addition, strategies for restoring genetic diversity of GM through artificial gene flow may be considered. As GM shared the most genetic structure with UI, we could even set up UI and GM as a conservation unit for artificial gene flow. Based on population size, genetic diversity, and private allele abundance, the HO-HS unit is indisputably the most critically in need of management. Given that gene flow exists between HO and HS, rescue effects may play a role in buffering these populations against extinction [Saussurea, are resistant to water stress and easy to store, so that genetic diversity can be preserved for relatively long periods at low temperatures [Saussurea, it was reported that seeds can be stored at -20 °C [ex situ strategies [Increasing the population size of GM should be a priority. Frankham et al.  suggestetinction . Howevereratures . In Saust -20 °C . GG and t -20 °C  and an it -20 °C . Thereforategies .S1 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableNa = No. of different alleles, Ne = No. of effective alleles, Ho = Observed heterozygosity, He = Expected heterozygosity, F = Fixation index, Fsta = global Fst without using the ENA correction, Fstb = global Fst using the ENA correction, * Significant departures from Hardy-Weinberg equilibrium at p < 0.05.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Tablep < 0.05); − = non-significant linkage disequilibrium; * = unavailable data.Note: + = significant linkage disequilibrium ((DOCX)Click here for additional data file.


Eokochia saxicola, a rare Mediterranean coastal rocky halophyte. Plastid genome analysis revealed very low intraspecific haplotype variation and partial admixture among Capri and Palinuro populations with at least two independent colonization events for the Strombolicchio islet. Nuclear SNPs variation consistently identified three distinct genetic clusters corresponding to our sampling localities. Furthermore, strong genetic isolation was confirmed by both historical and contemporary levels of migration among the three populations. The DIYABC analysis identified two introductions temporally separated from Palinuro to Capri (ca.25 Mya) and subsequently to Strombolicchio (ca.09 Mya) as the most likely hypothesis for the current distribution of E. saxicola. Regardless of their small population sizes, all study sites supported high-genetic diversity maintained by outcrossing and random mating between individuals owing largely to wind pollination, an exclusive trait among Mediterranean narrow endemics. In conclusion, the patterns observed confirm that some Mediterranean endemics are not necessarily “evolutionary dead-ends” but rather represent species that have extensive demographic stability and a strong evolutionary legacy.Several past and recent climatic and geological events have greatly influenced the current distribution of coastal species around the Mediterranean Basin. As a consequence, the reconstruction of the distributional history of these species is challenging. In this study, we used both chloroplast and nuclear SNPs to assess the levels of genetic differentiation, contemporary/historical levels of gene flow, and demographic history for the three only known (one mainland and two insular) populations of  The Mediterranean Basin is one of the Hotspots of Biodiversity on the Earth , largelyIn particular, the most recent quaternary glacial-interglacial events have greatly influenced the current distribution of many plant species around the Mediterranean Basin . The rolEokochia saxicola (Guss.) Freitag and G. Kadereith (Amaranthaceae) is a perennial evergreen shrub restricted entirely to coastal rocky environments exposed to the salt spray in very few locations around the South Tyrrhenian Sea , Capri , and Ischia . However, a new population has recently been discovered on the mainland coastal area along Campania and represents the only known mainland population of E. saxicola  Freitag and G. Kadereith] about 10.2 Mya  What are the primary factors that have shaped the phylogeographic structure? (3) What determines the genetic diversity and genetic structure of extant populations? (4) Can the combination of phylogeography and genetic diversity help define E. saxicola conservation priorities?nian Sea . Historisaxicola . Accordian Basin . To date10.2 Mya , i.e., l10.2 Mya  and is, 10.2 Mya . Intereslophytes . Geneticlophytes . This islophytes  by identlophytes . The maiEokochia saxicola is a multi-branched perennial evergreen shrub found exclusively on maritime rocks  close to the sea level in the area occupied by halophilic vegetation  (E. saxicola in three locations representing the only known populations of its current distribution: Capri Island near Naples (Campania), Strombolicchio islet , and Palinuro, along the Cilento and Vallo di Diano and Alburni Nature Park coastline (Campania). Field collections were conducted from 2015 to 2017. Due to the inaccessibility of the cliffs (getation . Charactgetation . Like otgetation . Accordired (EN) . The maie cliffs , only the cliffs .Eokochia saxicola individuals were extracted from preserved leaf material using the DNeasy Plant Mini Kit , following the protocol of the manufacturer. The quantity and the purity of DNA extractions were checked using the NanoDrop ND 1000 Spectrophotometer , whereas the concentration was assessed with the Qubit fluorometer system  and the Quant-IT ds-DNA BR Assay kit (Invitrogen). Samples were sequenced using a slight modification to the standard ddRAD protocol , a “genome skim” was performed , and orphan reads were removed through the python script FASTQ Combine Paired End.py2. The E. saxicola filtered reads were mapped to S. hirsuta plastome by using BOWTIE2 v2.3.5.1  was built by using the TCS method implemented in PopArt v1.7  setting of 0.70  trees were generated from both datasets. GTRCAT was used as the substitution model for nucleotide sequences, as implemented in RAxML v8.2.12 . The resThe R package AWclust  was implK) was then determined by the lowest Bayesian Information Criterion (BIC), and the DAPC plot was constructed with the function scatter.dapc.The vcf file  converted into a genind object with the function vcfR2genlight in the R package adegenet v2.02.  was subsvia shared ancestry in the ddRAD dataset, focusing on the most recent coalescence (common ancestry) among the sampled individuals , and the result was visualized in a heatmap plot with function heatmap 0.2. The fineRAD structure was implemented to infer population structure ividuals . SamplesThe following analyses, more sensitive to the excess of missing data, were conducted with the reduced dataset containing 25 individuals and filtered for a minimum number of ddRAD loci shared by 95% of individuals. The function basic.stats in R package hierfstat  was implWe focused on the timing of the connectivity change based on the estimates of historical and contemporary migration by using a coalescent-based method (MIGRATE) and a disequilibrium-based method (BayesAss), respectively. The historical migration rate  was estiE. saxicola populations were tested on the ddRAD dataset under the coalescent-based approximate Bayesian computation in DIYABC Random Forest v1.0 . Then, in accordance with the geological age of the sites-hosting current populations  processed on the summary statistics was used before processing Random Forests (RF) predictions . The 250ibutions .E. saxicola + S. hirsuta, 41,187,562 raw reads (150 bp for average read length) were generated; of which, 34,528,403 reads remained after performing the preprocessing procedure  differed largely from E. saxicola plastome both in size and number of SNPs.For the Illumina sequencing for the analysis of SNPs variation in the complete plastome sequences of 16 rocedure . Variantio islet . All othFrom ddRAD sequencing, a total of 30,022,310 raw reads were obtained. After which, 29,276,067 reads were identified through de-multiplexing and filtering using STACKS pipeline to remove any low-quality read, ambiguous barcodes, and sequences without cut sites. We obtained, on average, 609,918 raw reads per sample and 224,637 mapped reads per sample .de novo_map.pl generated 6,476,170 loci and assembled ∼ 99.9% of loci into contigs. The total length of pair-end contigs (the generated de novo assembly) was approximately 175 Mbp, as calculated with the software Bandage . Due to this low coverage, SNPs were called by adopting a variant calling strategy of 3×-minimum coverage. With our filtering approach, in the 48 individuals/3,962 SNPs datasets, we never observed any case of multiple variants in the same locus (suggestive of a diploid status for E. saxicola) or of variants called in less than five individuals. With this conservative approach, we may have lost some rare variants, but we have reduced the risk of calling false SNPs due to low coverage.The pipeline  Bandage . The aveAn ML tree was built with 3,962 SNPs present in at least 70% of samples. The 48 individuals clustered distinctly in three separate clades. The separation of three distinct clades was further emphasized when using the reduced dataset of 25 individuals with 120 SNPs shared by 95% of individuals .K = 3 as the optimal number, with each cluster corresponding to a population  concordantly revealed three distinct genetic clusters, which included the two island populations (Strombolicchio and Capri) and the mainland Palinuro population .The heatmap plot confirmed the existence of the three populations . OverallThe mean genetic diversity parameters among populations were 0.0941 for expected Heterozygosity (He), 0.0841 for observed Heterozygosity (Ho), and 0.0781 for the Fixation index (Fis), respectively. Interestingly, the Palinuro population had the highest inbreeding value (Fis = 0.2343) .Multiple runs of BAYESASS yielded low levels of contemporary gene flow  among the three populations. Concordantly, also, the historical rates of migration as calculated in MIGRATE were generally very low . Low mc E. saxicola dispersion was inferred to have occurred by temporally separated introductions from the Palinuro to Capri and by the latter, to Strombolicchio as revealed by DIYABC model selection, scenario 1 (p = 0.501). The alternative scenario (2) where Strombolicchio originated from Palinuro received 231 votes. Instead, the scenarios (3 and 4) suggesting the split of an ancestral population between Capri and Palinuro and the subsequent colonization of Strombolicchio from Capri or Palinuro received 308 and 180 of the votes, respectively.The enario 1 . This scThe colonization of Capri from the mainland population was predicted to have occurred around 24,615 generations ago while the following colonization of Strombolicchio only occurred 9,003 generations ago. Interestingly, the relatively narrow posterior parameter distribution of the population introduction from the mainland Palinuro to the Capri Island suggests this single event was followed by a bottleneck period (db) of at least 43 generations with a relatively small founding population of 10 individuals. The bottleneck size of the Strombolicchio population (Nb-Strombolicchio) was around nine individuals. However, at time 0, the effective population size of Strombolicchio is 32 individuals, while the effective population size of Capri is 369 (249–484), i.e., comparable with 379 (150–483) of Palinuro .E. saxicola. In particular, both markers contributed to disclosing the roles of contemporary versus historical processes in shaping the current genetic variation of E. saxicola populations. Despite its paleoendemic status, the present distribution and partition of genetic diversity in E. saxicola revealed a rather recent dispersion/fragmentation, most likely occurring during the last quaternary climatic oscillations, which resulted in the almost complete interruption of ongoing gene flow among living populations.The integration of different nuclear and plastid genetic markers provided novel insight into the temporal and spatial distribution, a colonization pathway, and dispersal patterns of FST and an increase in the average overall estimated co-ancestry .It is well-known that different filtering strategies influence both the estimation of genetic diversity and differentiation . Therefoancestry . Thus, oE. saxicola revealed a surprisingly low level of intraspecific variation (only three haplotypes differing for a maximum of two mutation steps) among the three isolated populations with a considerable number of differences  with the related S. hirsuta. Since large interspecific differences can be expected between paleoendemic species separated approximately 10 Mya , differing by a single-base mutation  further supports the evidence of a “hopping” dispersal ability along with these coastal rocky habitats.According to the morpho-bathymetric reconstruction of the Southern Tyrrhenian Sea  and conslophytes . The curE. saxicola on Strombolicchio. Surprisingly, despite the rarity of E. saxicola populations and the small size of the islet, at least two occasional events (two different plastid haplotypes) of colonization likely occurred as founder events for the Strombolicchio population  has been completed isolated from any mainland, even during the Quaternary seal-level oscillations. Accordingly, long distance dispersal or “jumping” is the only plausible scenario for the colonization of pulation . Hydrochpulation , may havnian Sea  that is ch other  and impeE. saxicola . The different analyses of nuclear markers, performed with different settings, concordantly shows three main genetic clusters matching the three sampling localities of Palinuro, Capri, and Strombolicchio . In contE. saxicola. One evolutionary implication of similar low contemporary and historical migration rates over time is that E. saxicola has a long history of living in relatively small, isolated populations (still preserving enough genetic variation). In contrast to other island narrow endemics  and contemporary (mc) levels of migration between the three genetically distinct populations are very small and similar in magnitude , as alsoendemics , for whiE. saxicola presents distribution. By assuming a generation time of 10 years, we can consider that the introduction to Capri, approximately 0.25 Mya, and the colonization of Strombolicchio (0.09 Mya) took place around the late Pleistocene. The presence of inaccessible known plant spots (on Capri) and the possible occurrence of other unknown plant spots along the Palinuro cliffs suggest we probably have underestimated the census population sizes (N) for the three populations. Still, Ne estimations are near and even larger than N  as the most likely hypothesis for r than N , i.e., mE. saxicola and Amarantaceae, whereas, in the few other known wind-pollinated endemics, this mechanism is rather a secondary adaptation to the low availability of biotic pollination  both in recent and ancient timescales. All sampled individuals, despite a clumped distribution due to the accessibility of the cliff faces, were genetically different, indicating that clonality in this species is rare. Moreover, at least at the intrapopulation level, the long lifespan of adult plants, together with their high resprouting ability and wind pollination, may contribute to maintaining a high level of outcrossing and multiple random mating among the few individuals . Interestingly, wind pollination is an almost unique trait among Mediterranean narrow endemics. Indeed, anemophily is a plesiomorphic trait in lination .Regardless of the extremely small number of individuals in each population, the moderate degree of intrapopulation co-ancestry  suggestsE. saxicola still maintains some degree of genetic diversity even at the intrapopulation level ; this genetic trait may have represented life insurance for the long-term survivorship of this rare species. This survivorship was further aided by the low level of anthropogenic disturbance and high stability typical of cliff plant communities  rocky shores  can be found below: BioProject SS, AS, and DC: conceptualization. SS and AS: plant material collection. SC and DC: data curation. SC, DC, and TG: formal analysis. DC and SS: funding acquisition. DC and TG: methodology. SS, SC, DC, and AS: writing – original draft preparation. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations.We developed https://github.com/smetam/pylae.The source code and installation manual are available at  In association studies, researchers combine samples with different  phenotypes and compare Single Nucleotide Polymorphisms (SNPs) frequencies in two groups. When enough samples are available , a significant difference in frequencies between the groups suggests an association between the position on the genome and the studied phenotype. However, there is a possibility that the association is due to inhomogeneity of the study group in terms of provenance/origin . In this case, two populations may have different frequencies of ancestry informative markers (AIM) that are not causal to the phenotype.An intuitive approach to solving this problem is determining the population structure first and then adjusting for it. However, this strategy is complicated for admixed populations. Due to meiotic recombination during transmission of genetic material to the offspring, individual segments of the genome may have different origins. Recombination makes it hard to determine the source of such individuals (or plant populations), especially locus-specific local origin.a priori estimates are sufficiently accurate. Therefore, ICM is faster than the EM approach. To construct an accurate a priori estimate, LAMP uses the MAXVAR algorithm, which works only for two populations. The size of the genomic window is selected to minimize classification errors. LAMP works fast, but its accuracy decreases if the ethnic admixture ratio is close to 1:1.Several solutions to this problem exist, such as LAMP, LAMP-ANC, and RFMIX. However, there is always room to develop a user-friendly, fast, accurate approach. The LAMP  algorithLAMP-ANC  is a modThe Machine Learning algorithm RFMIX  treats oPyLAE-a new tool for determining local origin along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). PyLAE does not involve the estimation of many parameters. PyLAE is a valuable tool to study admixed populations . We used the dataset of 2,504 worldwide individuals with the reported ethnic origin  based on K admixture components. Instead of solving an “exact admixture” problem, we aim to find the smallest subset of populations whose combined admixture components are close to those of the individuals within a small tolerance margin. Due to the range of natural variation, the admixture proportions can be considered exact neither for the reference populations nor for the tested individuals. The admixture proportions we use are merely maximum likelihood estimates and may fail to be exactly equal to the actual shares of ancestral genomes. Therefore, determining ethnic proportions is mathematically and computationally more challenging than finding a single most fitting bio-origin. From our experience with reAdmix, this tool accurately identifies all significant components; however, small proportions are frequently misassigned. When similar individuals are analyzed as a group, it is possible to estimate even the small proportions reliably.To confirm the reported origin, we have extracted 130,000 ancestry informative markers identified by a worldwide study contacted by the National Genographic consortium, as described in . The suphttps://ftp.1000genomes.ebi.ac.uk/vol1/ftp/. The phased data were obtained for the 1000 Genomes Project from the open repository We have developed two modes: “diploid” and “haploid”. For the “diploid” mode, we used the samples without pre-processing. For the “haploid” mode, we used the phasing information provided by 1000 Genomes and assumed that the parents were homozygous and simulated two homozygous “parents” for each sample. The admixture vector, used as an informative prior for “diploid” and “haploid” modes, is generated by applying the Admixture tool  to the oInput:Tested Individual: VCF file and  admixture vector obtained by Admixture in supervised mode, as described above.Reference: multi VCF file with putative ancestral populations corresponding to K component admixture.N is the number of positions overlapping between the reference and the individual.2 > 0.8 within sliding windows of 50 SNPs . It is a common practice to exclude positions with pairwise genotypic r 50 SNPs .Stage 1. Bayesian posterior probabilityPosterior probability L. Since the distance between two consecutive ancestry informative markers is sufficiently large, positions within the same window can be considered independent.The prior probability of a population iTherefore, we have K posterior probabilities for every genotype, and the matrix of posterior probabilities We are interested in determining the optimal sequence of the source populations along the genome. This problem is solved using the Viterbi algorithm . For comStage 2. Viterbi algorithmS consists of K ancestral populations. Population labels (i = 1..K) are the hidden states in our model. The initial probabilities th hidden state can either be assumed to be uniform with 1/K probability of each population or set to be equal to global admixture components. Transition probabilities i to j are inversely proportional to the TreeMix distances between corresponding ancestral populations. Transition probability from state i to jOur state-space For efficiency, the computation is performed in the log-space. The algorithm is implemented as a python script and can be adapted to analyze any organism using user-provided ancestral components, prior probabilities, and transition matrix.Most of the currently living individuals are admixed to various degrees. To compare genotypes between populations and analyze selection signals, it is essential to identify and exclude introgressed regions containing non-representative genotypes. We have determined local ancestry profiles for all 2,504 analyzed individuals from the 1000 Genomes project. With our approach, even the admixed individuals that are typically excluded from the analysis  can be retained after masking the introgressed regions.http://varianttools.sourceforge.net/Annotation/RefGene). The SNPs were classified by their location and function: 58% intergenic, 38% intronic, 5% ncRNA, and 1% exonic . Next, we counted the synonymous and nonsynonymous SNPs per each KEGG pathway  Find the number of (non)synonymous SNPs in groups A and B.I represent the total number of studied pathways, and i = 1,…, I, be the number of the (non) synonymous SNP per ith pathway are nS(i) and nA(i). The expected fraction of (non) synonymous SNPs in individuals from group A is given by nA is the amount of (non) synonymous SNPs in all KEGG pathways found in group A, nB is the amount of (non) synonymous SNPs in all KEGG pathways found in group B. The fraction pi of A (non)synonymous SNPs in the ith KEGG pathway is (a) Let (2) The enrichment D(N)SE scores are computed for every pathway with continuity correction:(a) P-values are calculated using Bonferroni and Benjamini–Hochberg corrections and used to identify differentially enriched pathways. A pathway is considered to be differentially enriched if the adjusted P-value < 0.005 ((3)  < 0.005 .P-value of the nonsynonymous test is required to be below the corresponding P-value of the synonymous test for each pathway.(4) To consider the excess of synonymous SNPs over nonsynonymous SNPs, we calculate enrichment scores for synonymous SNPs, DSSE. To be considered significant, the To analyze differences in numbers of SNPs per pathway between the two groups of populations (groups A and B), we need to calculate: (1) DSSE scores (synonymous) and (2) DNSE scores (nonsynonymous) . TherefoA different set of putative ancestral populations or a different set of markers require additional processing. First, we need to collect a database of putatively un-admixed individuals. If there is an existing validated set of ancestry informative features, these markers should run the admixture in supervised mode. For each self-reported ancestry, samples should be clustered based on their admixture profiles to identify subgroups within each self-reported ancestry. These subgroups are then examined using information about the studied population’s history, and the most representative subset is retained. Then, putative ancestral populations  are generated for every ancestry. The validity and stability of the ancestral populations are evaluated using (1) PCA, (2) leave-one-out supervised admixture, and (3) by application of supervised admixture to the original dataset.PyLAE can process both phased and unphased data, which is both a blessing and a curse. It is a blessing since some genomes may not have phased data, and it is a curse since the accuracy may suffer. Therefore, we first evaluate the constraints that are imposed by different components of the pipeline. The first component is the calculation of the Admixture  using supervised admixture. Every sample was analyzed first in diploid and then in haploid mode .Diploid and haploid admixture profiles were clustered within each reported ethnic origin  and 2. AWe have investigated the difference between inferred haploid parental admixture profiles and diploid admixture profiles of analyzed individuals. We have computed an average of parental haploid admixture vectors for every individual and calculated the Еuclidean distance between the vectors. The average distance for all individuals was 0.092. On average, the largest differences were observed for Toscani, Iberian, Columbian, Mexican Ancestry, and Puerto Rican individuals. In diploid mode, the average value of the Mediterranean component in Toscani individuals was 60%, South West Asian −23%, and Northern European −15%. In haploid mode, the Mediterranean component was 82%, and South-West Asian −18%. The same situation happened with the Iberian samples. In the diploid mode, the average value of the Mediterranean component was 55%, South West Asian −19%, and Northern European −24%. In the double haploid mode, the Mediterranean component was 72%, South West Asian −16%, and Northern European −12%. Similar inflation in the Mediterranean component was accompanied by a reduction in the Northern European components.To investigate the source of this difference, we have identified the most significant admixture component for every sample and calculated the difference between the average parental value of this component and its diploid value. The difference ranged from −0.0057 to 0.3063; the mean difference was 0.0684, indicating that haploid admixture tends to increase the value of the largest component. The magnitude of the increment grows approximately linearly for components <0.87 and then reduces linearly for components above 0.87. Worldwide populations show different trends . For indWe have investigated the accuracy limitations of the dataset and admixture-based approaches. First, for each individual’s admixture vector, we have calculated the nearest population, based on Euclidean distance between admixture vectors, using the leave-one-out approach; then we have computed fractions of self-hits , and fractions of super population self-hits (the agreement is at the level of super-populations). Admixed populations like African American, African Caribbean, Colombian, Mexican in the USA, Peruvian, and Puerto Rican have the lowest assignment accuracy at both population and subpopulation levels. We also noticed that West African populations, such as Yoruba, Esan, Gambian Mandinka, and Mende, are frequently misclassified. Although they speak languages from the same Niger-Congo group, their genomes are not identical . SimplifGPS and reAdmix , Fig. 4 PyLAE algorithm on 2,504 individuals of the 1000 Genomes dataset using: diploid and haploid versions, with informative and noninformative priors. Informative prior is assumed to be equal to the global admixture vector. The noninformative prior is uniform, assuming equal probabilities for a region to be from any of the K reference populations.We have tested the Since we do not know the origins of regions of the genome, we chose the agreement with the global admixture vector as a measure of performance. For each analyzed individual, we have computed a total fraction of a genome attributed to each of the K components and compared it to the global admixture vector. We have calculated Euclidean distance and Pearson correlation of aggregated results and admixture vectors. The results are shown in PyLAE with the current local ancestry gold-standard tool RFMix  phase ancestral samples (2) obtain genetic maps. The phasing was performed with Beagle  and without prior, only the ‘phased’ case. We have also tried various values of the transition penalty. The probability of changing the population assignment (transition probability) was modeled as n is the number of populations and x is the penalty, that was ranged from 0 (uniform prior), to 10,000.We compared ol RFMix . We usedh Beagle . PyLAE wPyLAE–we have computed a fraction of windows assigned to each ancestral population; for RFMix, we have calculated a fraction of markers assigned to each ancestral population. Then the results were compared with the global ancestry assignments calculated by Admixture  is determined by the ancestral composition of the analyzed sample . OverallPyLAE has a higher correlation with corresponding global ancestral composition compared to RFMix. For African, American, and South Asian individuals, RFMix had a higher correlation. It is important to notice that global and aggregated local ancestries were in agreement for both programs; the lowest correlation coefficient was 0.8676. In the 1000 Genomes dataset, African, American, and South Asian samples are more admixed than East Asian and European samples.PyLAE and RFMix have correctly determined the origin for 98.6% and 99% of genomic positions across all simulated samples, respectively. Predictions made by RFMix and PyLAE were highly concordant: the predictions agreed on 98.52% of the genome. Therefore, we propose that PyLAE and RFMix complement each other and be used as a part of the same local ancestry pipeline to improve prediction accuracy.Using the ANNOVAR  and snpESince we use a nine-component representation, all Africans in our datasets were assigned to the same Sub-Saharan African component. At the same time, African Americans have various admixture levels from other components. Therefore, local ancestry mode only partitions genomes of European and African American individuals.PyLAE has higher enrichment scores compared to the whole-genome approach. Comparing the enrichment scores between differentially enriched pathways , in 91 cases, PyLAE resulted in higher enrichment scores and 41-in lower scores for African Americans. If the significance cut-off is lowered, PyLAE results in a higher number of differentially enriched pathways , Phagosome, Malaria, Viral myocarditis, HTLV-I infection; Europeans: Nitrogen metabolism, Mismatch repair, Cell cycle, Glyoxylate and dicarboxylate metabolism, Porphyrin and chlorophyll metabolism, Insulin resistance, Tight junction, Selenocompound metabolism, Oocyte meiosis, Amyotrophic lateral sclerosis (ALS), Protein digestion and absorption.Application of Dependence on ethnicity efficiency has been reported in processes and conditions related to the above-listed pathways: vitamin B6 metabolism , fatty aPyLAE is an easy-to-use, accurate and fast tool for local ancestry analysis that can be used with minimal prior information about the genome or samples.In summary, PyLAE)Project name: Python Local Admixture Estimation (https://github.com/smetam/PyLAEProject home page: Operating system(s): Platform independent, Linux is required for pre-processingProgramming language: Python, ShellOther requirements: Python 3.5 and higher; bcftools (pre-processing)License: Creative Commons.


Cercis chuniana, a woody species endemic to the southern mountain ranges in subtropical China, to elucidate its population dynamics. We used genotyping by sequencing (GBS) to investigate the spatial pattern of genetic variation among 11 populations. Geographical isolation was detected between the populations located in adjacent mountain ranges, thought to function as geographical barriers due to their complex physiography. Bayesian time estimation revealed that population divergence occurred in the middle Pleistocene, when populations in the Nanling Mts. separated from those to the east. The orientation and physiography of the mountain ranges of subtropical China appear to have contributed to the geographical pattern of genetic variation between the eastern and western populations of C. chuniana. Complex physiography plus long‐term stable ecological conditions across glacial cycles facilitated the demographic expansion in the Nanling Mts., from which contemporary migration began. The Nanling Mts. are thus considered as a suitable area for preserving population diversity and large population sizes of C. chuniana compared with other regions. As inferred by ecological niche modeling and coalescent simulations, secondary contact occurred during the warm Lushan–Tali Interglacial period, with intensified East Asia summer monsoon and continuous habitat available for occupation. Our data support the strong influence of both climatic history and topographic characteristics on the high regional phytodiversity of the subtropical evergreen broadleaved forest in subtropical China.The mountains of subtropical China are an excellent system for investigating the processes driving the geographical distribution of biodiversity and radiation of plant populations in response to Pleistocene climate fluctuations. How the major mountain ranges in subtropical China have affected the evolution of plant species in the subtropical evergreen broadleaved forest is an issue with long‐term concern. Here, we focused on  The reason that we chose C. chingii as the outgroup is mainly based on the ML tree constructed for Cercis based on GBS data, which shows that C. chuniana is at the basal branch followed by C. chingii. Therefore, we chose C. chingii as the outgroup as the other congeneric species are phylogenetically much most distant. Additionally, we tested the result of the ML tree by randomly choosing one individual of C. chingii, which did not change the final topology.We collected 11 populations and 112 individuals of 2.2C. chuniana and locate potential distributional areas in conjunction with historical biological inferences. We based the analysis on high‐resolution paleoclimate data inferred for the Last Interglacial , Last Glacial Maximum , Middle Holocene , and current. Bioclimatic variables were downloaded from the WorldClim database  and our sample collection locations were used to determine the locations of populations considered to occur at present. The analysis pipelines and parameter settings, including the occurrence points, current/past bioclimatic variables and the convergence threshold and maximum number of iterations, were all as in Dai et al. . Total genomic DNA was extracted with the modified cetyl trimethyl ammonium bromide (CTAB) method  was used for paired‐end (PE) 150 sequencing. Further analyses and DNA library assembly were performed to remove low‐quality reads. Reads in fastq format were assembled by using STACKS v2.2  to reconstruct phylogenetic relationships among the 11 populations of i Figure  and MegaCercis. The fossil age of Cercis was originally estimated as 34 Ma  ranging from 1 to 11. The python script Choose K in FastStructure was used to choose the optimal K, that is, the value that maximizes the marginal likelihood. Results were graphically represented and edited with Adobe Illustrator. We performed principal component analysis (PCA) using the PCA function in SNPRelate  in the R package.We estimated population genetic structure with a Bayesian Markov chain Monte Carlo (MCMC) model implemented in FastStructure v1.0  analysis implemented in RASP v3.2  to transform SNPs into a folded site frequency spectrum (SFS) based on the construction of 10 demographic models with the two groups , time (T) and migration rates in individual migrants per generation (NLM‐ES and MES‐NL) for the two groups in each model from posterior distributions. To scale parameter estimates into real values, we calculated the substitution/site/generation mutation rate based on phylogenetic analysis and divergence time estimate of Cercis , because the genomic mutation rate has not been calculated for this genus. The clock rate was firstly estimated as 2.32 × 10−8 substitutions/site/year for nine species and 241 individuals of Cercis with Tracer v1.6  period, under higher temperature than current , finding that the three fragmented distribution areas were located mainly inside forest steppe (number 7) and partially in semi‐arid temperate woodland or scrub , occupying most of the Chinese subtropical region. No changes in the geographical distribution were evident between MH and current. Precipitation in April, May, and June was revealed to be the most influential climate factor for the suitable distribution range of C. chuniana . Results yielded a continuous geographical distribution of t Figure . In contt Figure . We compna Table .3.2C. chuniana with C. chingii as outgroup, among which 32,890 SNPs agreed with the SNP extraction criteria. The data have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.15283395).GBS produced 200 Gb clean reads after filtration. High‐quality tags were identified from 10,761,958 Gb‐PE reads. The sequence data were high quality with Q20 ≥ 92.23% and Q30 ≥ 85.00%. The mean G + C content was 37.84%. We detected 61,748 SNPs for 3.3C. chuniana was estimated as 2.39 (95% HPD = 1.97–2.74) Ma during the early Pleistocene  followed by NLW2 (0.33); the lowest was detected in LXS2 (0.19). On average, He and Ho in the Nanling Mts.  were comparable to those of the other populations in the east . In the FastStructure analysis, YDS separated from the remaining populations when K = 2. When K = 3, WYS and LXS2 clustered as one group and this group was separated from the remaining groups. Seven subpopulations (K = 7) were determined as the optimal clustering for C. chuniana , of which the variation among the seven groups accounted for 96.28% of the total variation (Table The highest He was detected in NLW2 (0.38) followed by NLW5 (0.35); the lowest was detected in LXS2 0.31; Table ; Table 1 NLW1 0.3 followed3.5Six vicariance events V1‒V6) among the geographical regions were inferred from the S‐DIVA analysis  was estimated at ca. TSEC = 0.10 Ma. This date is within the Lushan–Tali Interglacial period in China  than at present. In contrast, the ES population sizes remained more or less constant  was much higher than MES‐NL (0.33), with migration occurring after NL and ES divergence.The best‐fit model for the demographic analysis with FSC2 is SECEXP, indicating isolation followed by secondary contact (SEC) and demographic expansion  also indicates low inter‐population gene flow  and with smaller population sizes than within the Nanling Mts., where more recent and rapid population diversification occurred (0.35‒0.19 Ma) with larger population sizes  coincides with the Lushan–Tali Interglacial period in China  higher than MES‐NL  than the eastern ones (from 264–727 m). Seeds of Cercis are supposedly dispersed primarily by wind during the fall and winter  and eastern (ES) mountains, with the migration rate Dickson, . The mou5C. chuniana. The Nanling Mts. are considered an important glacial refugium for the preservation of genetic diversity during the glacial periods because of its complex physiography and long‐term stable ecological conditions. Our study provides molecular evidence on how topography and climate change affect the phylogeographic history of the representative species within STEBF of southern China. Study of additional plant groups with similar geographical distribution patterns is further required to assess whether the patterns from Cercis observed here apply more generally to the evolutionary history and past vegetation changes in the STEBF associated with physiography and climate fluctuation.We aimed to advance understanding of the roles of mountain ranges and glacial cycles on the geographical distribution pattern of genetic variation for the plant species within the subtropical evergreen broadleaved forest (STEBF) in southern China. The orientation and physiography of the mountain ranges and the climate fluctuations across glacial cycles in this region appear to correlate with the geographical pattern of genetic variation in The authors declare no conflict of interest to this work.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Table S1‐S5Click here for additional data file.


Salvelinus fontinalis) populations using whole‐genome pooled sequencing. The populations inhabit isolated alpine lakes in western Canada and descend from a common source, with an average of ~19 (range of 7–41) generations since introduction. We found some evidence of bottlenecks without recovery, no strong evidence of purifying selection, and little support that varying propagule pressure or differences in local environments shaped observed neutral genetic variation differences. Putative adaptive loci analysis revealed nonconvergent patterns of adaptive differentiation among lakes with minimal putatively adaptive loci (0.001%–0.15%) that did not correspond with tested environmental variables. Our results suggest that (i) introduction success is not always strongly influenced by genetic load; (ii) observed differentiation among introduced populations can be idiosyncratic, population‐specific, or stochastic; and (iii) conservatively, in some introduced species, colonization barriers may be overcome by support through one aspect of propagule pressure or benign environmental conditions.Understanding the drivers of successful species invasions is important for conserving native biodiversity and for mitigating the economic impacts of introduced species. However, whole‐genome resolution investigations of the underlying contributions of neutral and adaptive genetic variation in successful introductions are rare. Increased propagule pressure should result in greater neutral genetic variation, while environmental differences should elicit selective pressures on introduced populations, leading to adaptive differentiation. We investigated neutral and adaptive variation among nine introduced brook trout  populations from a common source using whole‐genome pooled sequencing. We found some evidence of bottlenecks without recovery, no strong evidence of purifying selection, and little support that varying propagule pressure or differences in local environments shaped observed neutral genetic variation differences.Understanding the drivers of successful species invasions is important for conserving native biodiversity and for mitigating the economic impacts of introduced species. We investigated neutral and adaptive variation among nine introduced high‐alpine brook trout ( To enhance sport‐fishing opportunities, 100,000's of brook trout were stocked between 1941 and 1973 into mountain lakes in several national Parks in the Rocky Mountains of Canada  and census population size estimates obtained in concurrent research and used in analyses below are described in Yates et al. .To examine whether environmental variables were positively correlated with neutral genetic diversity and adaptive differentiation, 14 abiotic and biotic environmental variables were quantified in each lake between May 2017 and August 2018 Table . VariablIn summer of 2017, depth profiles of temperature, dissolved oxygen, pH, and conductivity were measured twice in each lake at 1 m sequential depths to 0.5 m above bottom with a multiparameter YSI Professional series sonde . HOBO MX2202 Pendant wireless temperature/light dataloggers  were also deployed at the center of each lake and recorded measurements every 30 min at 0.5 m depth from beginning of July to mid‐September. Detailed descriptions of macroinvertebrate and zooplankton sampling are in Appendix 2.3Qiagen blood and tissue kits  and the manufacturer protocol. To ensure equal quantities of DNA in the pooled samples, DNA quality and quantity were initially assessed by 1% Agarose gel electrophoresis using HindIII digested Lambda DNA run at 100V to assess possible DNA degradation. Multiple quality tests per individual were conducted on a Qubit Fluorometer 2.0  selecting for quantity >20 ng/µl and confirmed in a NanoDrop spectrophotometer  as well as estimates of 260/230 and 260/280 ratios greater than >1.8 quality.DNA extractions from fin tissue were conducted using n = 17) and both McNair sexes (n = 8) due to low population and sample sizes. Twenty individuals per pool were chosen to ensure a balance between available population samples and to have equal representation between sexes (two pools of 20 per population), while maintaining accurate unbiased allele frequency estimates  with 20 individuals in each pool; exceptions were Cobb females . All pools passed quality and quantity requirements and were sequenced each on two lanes of NovaSeq 6000 S4 flowcell (Illumina) and paired‐end reads of 100 base pairs (bp). Coverage was estimated based on the assumption that the brook trout genome is approximately 3Gb, based on the Animal Genome Size Database (http://www.genomesize.com/).Genomic libraries of these pooled DNA samples were prepared by Génome Québec Innovation Centre, Montréal, Québec, Canada, via a shotgun approach with PCR with 2.4Salvelinus sp.) available from NCBI  to permit sequence alignment.A reference genome of charr ; these were used for all subsequent genomic analyses, except the Cochran Mantel Haenszel (CMH) tests described below.SNP discovery was performed using the PPalign module of the PoolParty pipeline v 0.8  between the 18 pools were determined with Poolfstat v 1.1.1  stocking and environmental variables was firstly run to remove potentially correlated variables at a cutoff of 0.7 with the psych R package v 1.9.12 . PoPoolation 2  bounded at .05. Applying a Bayesian framework, PCAdapt determines population structure using K z‐scores to fit SNPs to K principal components based on Cattell's rule  was used to determine the driving habitat and environmental factors ), raw sequence counts totaled 10,775,432,330 reads, of which unique reads averaged 431,983,622 in each pool (72.2%) with quality scores of 36 Figure . All poo3.2FST was effectively zero, averaging −0.020  across populations were similar between most populations; however, some populations were significantly different from others (Table Putatively highly deleterious mutations (297) had lower allele frequencies than moderate  and low‐impact 11,030) deleterious mutations, consistent with a role for purifying selection in these populations . Re‐analysis of the RDA running VIF analysis <10 to avoid overfitting the model remained non‐significant , when removing upstream and downstream water discharge, number of tributaries, surface area, depth, temperature variance, and number of discernable spawning sites.After filtering, PCAdapt identified 2,768 putatively adaptive loci 0.764%) of 362,493 tested SNPs. Male and female pools of each population grouped together in the score plot analysis ; data curation (lead); formal analysis (lead); funding acquisition (supporting); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing . Hyung‐Bae Jeon: Data curation (supporting); formal analysis (supporting); methodology (supporting); writing – original draft (supporting); writing – review and editing . Alison M. Derry: Conceptualization ; data curation (supporting); funding acquisition ; writing – review and editing . John R. Post: Conceptualization ; funding acquisition ; writing – review and editing . Sean Rogers: Conceptualization ; funding acquisition ; writing – review and editing . Shelley Humphries: Data curation (supporting); resources (supporting). Dylan J. Fraser: Conceptualization ; data curation (supporting); formal analysis (supporting); funding acquisition ; investigation (supporting); methodology (supporting); project administration (lead); resources (lead); supervision (lead); writing – original draft (supporting); writing – review and editing .Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


Y-chromosome DNA profiles are promising tools in population genetics and forensic science. Analysis of Y-chromosome variety was performed on a total of 191 unrelated males throughout different regions in Basrah. The Y-chromosome variety was explored utilizing 17 markers system. For the uniparental system, the large majority of the haplogroups observed in the Basrah population are  considered to have begun in the Middle East and to have later spread all over Western Eurasia. 30% of the Y-chromosomes, in all likelihood, represent landings from inaccessible distant geographic regions. The level of haplotype diversity and its implication for statistics are evaluated. The distinctive extent of long go genetic input observed for the Y chromosome shows that gene flow events to this area might have involved mainly males. The human Y-chromosome is male explicit, and the connected Y-STRs situating on the non-recombining region of the Y chromosome has a patrilineal inheritance mode. It is only inherited from fathers to sons. They are transmitted unchanged except for the mutations, making the Y-STR haplotype very useful in paternal lineage testing , 2. At pBlood samples (191) were collected from unrelated, healthy male volunteers born and living in different parts of Basrah from different ages. DNA was extracted using the gSYNC™ DNA Extraction Kit Quick protocol by the Geneaid company.®_Yfiler™ PCR Amplification kit  according to the manufacturer’s instructions. The PCR products were genotyped with capillary array electrophoresis on ABI 3500 Genetic Analyzer . Genotyping was carried out using GeneMapperR _ ID-X . Allele designations were based on comparisons with the allelic ladder provided in the Yfiler kit.Fifteen single copy Y-STR loci  and a multicopy locus (DYS385a/b) were amplified with AmpFLSTRY-STR haplotype data were set up for the analysis utilizing the MS Excel™ with Microsatellite Toolkit . The essThe haplotypes of the 17 Y-STRs of 191 male individuals were submitted to Whit Athey’s Haplogroup Predictor (http://www.hprg.com/hapest5/index.html), with equivalent priors [Gene diversity values for every Y-STR loci are given in Compared with the Turkish study on seventeen Y-STR loci from the Cukurova region of Turkey, the lowest gene diversity (0.5) was recorded in DYS 391, and the highest gene diversity (0.95) was found in DYS385. No vital variations were found with haplotype data of different Turkish populations .For the Kuwaiti population, the lowest value of GD was observed for DYS437 (0.409), while the highest one (0.957) was found in DYS385. The lowest genetic diversity value for the Failaka Island population found in DYS392 (0.236), and the highest one (0.976) is presented by DYS385 [The observed allele frequencies of the seventeen Y-STR loci are given in We compared the Haplotypes found in this study with seven different populations: Tunis (n=81) , German The results of Y haplogroup predictions and their probabilities for the Basrah population are shown in DYS385 had the highest diversity GD=0.8461), while loci DYS392 had the lowest GD=0.3547). The mean number of pairwise differences of the Basrah population is 16.745, and the gene diversity is. 0.6228. There was a low frequency (0.0052) in allele .Written informed consent was obtained from the participants before obtaining the sample.Further data is available from the corresponding author on reasonable request.I wish to express my deepest gratitude to the Head of Biology Department Dr. Munaff J.Abd Al-Abbas and to the Cell and Biotechnology researchers unit in the College of Science, Basrah University and Dr. Dhamia Kassim Suker, for their assistance.AIA suggested the main aims and objectives of the research. BMO contributed to most experimental work and data analysis. Both authors, AIA and BMO wrote and reviewed the manuscript.


Here, we use a multivariate Bayesian approach to assess the adaptive potential of invasive populations of ragweed (Ambrosia artemisiifolia), a serious allergen and agricultural weed. We compared several aspects of genetic architecture and the structure of G matrices between three native and three introduced populations, based on phenotypic data collected in a field common garden experiment. We found moderate differences in the quantitative genetic architecture among populations, but we did not find that introduced populations suffer from a limited adaptive potential or increased genetic constraint compared with native populations. Ragweed has an annual life history, is an obligate outcrosser, and produces very large numbers of seeds and pollen grains. These characteristics, combined with the significant additive genetic variance documented here, suggest ragweed will be able to respond quickly to selection pressures in both its native and introduced ranges.Invasive species are a global economic and ecological problem. They also offer an opportunity to understand evolutionary processes in a colonizing context. The impacts of evolutionary factors, such as genetic variation, on the invasion process are increasingly appreciated, but there remain gaps in the empirical literature. The adaptive potential of populations can be quantified using genetic variance–covariance matrices  One important way humans are changing the ecological landscape is through the accidental or intentional movement of organisms into novel locations. The ecological and economic impacts of alien plants continue to be immense  and covariation (G) in multiple introduced and native populations, given the many potential evolutionary genetic mechanisms that could potentially affect genetic variance and covariance in introduced populations.Despite the importance of heritable genetic variation for the ability of a population to respond to selection, there is a dearth of studies on invasive species from a quantitative genetics perspective variances reflect both genetic and environmental influences on traits  in ways comparable to evolved differences between populations . We focus on size and phenology traits that are likely to be under selection in the native and introduced ranges, and likely to lead to continued invasion success, agricultural problems, and allergenic symptoms. Specifically, we ask the following questions: (1) How do native and introduced populations differ in their mean phenotypes, genetic variances, and heritabilities in key phenotypic traits? (2) Are there correlations between traits that could accelerate or constrain adaptation? (3) Is there a divergence in the G matrices of native and introduced populations? (4) How would native and invasive populations differ in their responses to selection based on their genetic (co)variances?In this study, we examine how quantitative genetic architecture varies between introduced and native populations of the prolific invader 22.1Ambrosia artemisiifolia is an annual herb . Phenotypic diversity and neutral marker diversity are high in the invasive range, suggesting evidence of multiple introductions  and introduced (France) ranges Figure , from ov2.3Beginning in January 2013, we removed seeds from stratification (4°C) and placed them on filter paper in petri dishes. We placed all the petri dishes in the greenhouse and monitored them for germination and signs of desiccation. As seeds germinated, we planted them into a 75% Pro‐Mix, 20% sand, and 5% topsoil soil mix in seedling flats. Petri dishes and early germinants were moistened and watered as needed, and all dishes and plants were treated equally. After four weeks, we transplanted the plants into 4‐inch round pots. To keep plants small and accelerate time to flowering, we compressed the growing season by switching the lights from long‐day photoperiods (14:10) to short‐day photoperiods (10:14), mimicking natural changes in daylength experienced after the summer solstice. To prevent uncontrolled pollination, we used individual chambers and a purified air delivery system  during the summer of 2014. As with the parental generation, we stratified seeds and then placed them on petri dishes to germinate. We transplanted seedlings from flat trays four weeks after germination into three blocks. Prior to planting, we removed all vegetation and tilled the soil. At the end of June, seedlings were transplanted over three days into the field. Within each block, we arranged plants in square grids with 10‐cm spacing between plants in a grid. We transplanted 2700 germinants  into the field. Failed establishment and early mortality reduced sample sizes, although the median number of paternal half‐sibling families per trait ranged from 39 to 49  , final height, final number of branches, and date of first flower. We observed little variation in germination timing, suggesting that most variation in early height was due to establishment and growth over the first two weeks, although subtle differences in germination timing might lead to contributions of plant age in early height. Final height was measured in early October, after vertical growth appeared to have stopped, and prior to a killing frost, which would have made estimates of reproductive effort impossible. Most ragweed plants are monoecious  as response variables. Independent variables included the fixed effects of continent, and population nested within continent.2.7Historically, it has been difficult to estimate uncertainty around quantitative genetic parameters  and dams within sires (VD), separately for each geographic population. We used a burn‐in of 200,000 iterations, ran the model for 5,000,000 iterations, and sampled the posterior every 500 iterations to obtain 10,000 samples of the posterior distribution. We monitored autocorrelations between samples and convergence using the coda suite of tools , we estimated the narrow‐sense heritability for each trait within each population.We started by fitting a univariate model for each response variable, partitioning variation among sires (VA) was four times the variation in half‐sibling families within a population (VS) , and sire and dam covariance matrices equaling 1/4P. We compared the output of different prior specifications with the Gelman and Rubin diagnostic . We used 500,000 burn‐in iterations and 5,000,000 total iterations, and sampled the posterior every 500 iterations to obtain 10,000 samples of the posterior distribution of G.We also used G matrices separately for each population. The full model in matrix notation was as follows: μ and B(i) are the fixed effects of intercept and block, jS represents sires, k(j)D represents dams within sires, and le(ijk) represents the residual variance. For our design, i ranges from 1 to 3 for the blocks, j from 1 to 50 for sires, and k from 1 to 3 for dams within sires.In these models, block was treated as a fixed effect and dam was nested within sire; we estimated For some of the subsequent analyses, we removed the fitness proxy traits or treated them separately (noted explicitly below). We present results from analyses using traits standardized by the global standard deviation, following Hine et al.  and Gosd2.8G matrices , we used Krzanowski's common subspace analysis and the fourth‐order genetic covariance tensor. Krzanowski's method determines whether the majority of genetic variance in trait space is common among populations. The subspace analysis allows an investigation whether the leading directions of multivariate trait space  are in common among a set of matrices. Put broadly, it characterizes whether the leading principal components are in common among matrices. As described by Blows et al. , this apG matrix divergence for the evolutionary trajectories of the populations, we solved the multivariate breeder's equation and used the R metric, which predicts evolution with and without covariances , directions in multivariate space by which they diverged (fourth‐order tensors), how they affect the response to selection , and whether they constrain adaptation (R metric). These methods provide a thorough analysis of any divergences between G matrices, and can point to the traits underlying these differences, and the implications for future responses to selection. Specific details of each method are outlined below.To examine the implications of 2.8.1p populations ), we used the equation: T, and the subset kt of the eigenvectors of Gt are contained in At   G matrices.Some parts of multivariate trait space will have genetic variance, while others will not. We can examine whether the subspaces with the most genetic variation are similar for multiple populations using the Krzanowski subspace analysis ). The tensor can be represented as the matrix S, containing variances and covariances of all the elements among the G matrices—that is, the variances and covariances of genetic variances and covariances .The tensor method has the advantages of being compatible with various experimental designs and encompassing all the variation among matrices . We used the first principal component of seed mass and total inflorescence size as a composite fitness component; separate analyses of male and female fitness revealed similar results and are not presented. In these analyses, we held β fixed at point estimates from the multiple regression; differences in predicted responses to selection thus come from differences in the G matrices between each population. Each posterior sample of G in each population was multiplied by β to obtain a posterior distribution for Δ z¯; we compare populations by examining differences in the means and 95% HPD intervals of Δ z¯. To ensure that units were comparable, we performed calculations of unstandardized G matrices (units of traits2 for variances) and unstandardized selection gradients (units of traits−1) to obtain predicted responses to selection in original trait units. We then divided these responses to selection by the global standard deviation of each trait, to put each estimate of Δ z¯ into comparable standard deviation units.To implement this, we used two approaches. First, we used field data to obtain point estimates of the elements of β by implementing a Bayesian regression approach with the bas.lm function in R. We explored two priors (ZS‐null and AIC), and found that it did not affect the results, and report results using the ZS‐null prior. We performed 10,000 MCMC iterations to sample the posterior of the elements of β. We then combined a single posterior estimate of G with a single posterior estimate of β to estimate the response to selection, incorporating uncertainty in both components parts by sampling each of them from their posterior distributions.In the second approach, we incorporated uncertainty in our estimates of 2.8.4Covariances between traits can have important impacts on evolutionary trajectories by either constraining or accelerating adaptation. If introduced populations are subject to greater constraints to adaptation because of genetic covariances, this should be seen in lower rates of adaptation due to covariances, compared with native populations. Agrawal and Stinchcombe  developeG matrices and constructed G matrices where there are no correlations between traits . The “R value” is the ratio of the two. When R > 1, adaptation is accelerated by covariances between traits, and when R < 1, adaptation is slowed down by genetic correlations.To implement this method, we calculated the rate of adaptation given the observed G matrix. We compared the R values to determine whether the HPD intervals overlapped for each pairwise comparison of populations.As with the breeder's equation method, the R metric requires selection gradients. We ran separate analyses for male and female fitness, as well as a composite of the two, to calculate selection on the four nonfitness traits. To compare populations, we again took advantage of the Bayesian framework and the 10,000 estimates of each 33.1F4,1531 = 41.150, p < 0.0001) and populations nested within continents , indicating multivariate divergence in the traits  among the G, but there was large uncertainty in this dimension. The West Virginian population was the most divergent in terms of each populations’ contribution to the first eigentensor  accounted for 89% of the variation in this eigentensor, while the second and third eigenvectors explained 4% and 3.8% of the variation in the first eigentensor. The second eigentensor described 12% of the variation among G matrices. The first eigenvector of the second eigentensor accounted for 47.5% of the variation in this eigentensor. Altogether, the first three eigenvectors of the second eigentensor accounted for 82% of its variation.For the tensor analysis using all six traits and six populations, we found two significant eigentensors Figure —that is,r Figure . The fir3.3.3Estimated selection gradients differed in magnitude but not direction depending on the fitness metric that we used . Results from solving the breeder's equation were consistent across fitness metrics, so we only present those using composite fitness here Figure . We exam3.3.4We observed that the point estimates of the R metric were positive: In other words, the rate of adaptation is accelerated by genetic covariances, relative to not having them Figure . Individ4G matrices of introduced populations were not significantly or homogeneously diverged from native populations of ragweed. We found that introduced populations did not have lower additive genetic variance or diminished adaptive capacity when compared to native populations. Below we discuss the implications of these results for understanding ragweed's invasion in particular, and more generally the stability of G through space and time.Invasive species are an important component of anthropogenic global change  value for reproductive allocation suggest that ragweed has locally adapted across Europe , the multivariate framework introduces many more variances and covariances to be estimated, a challenge that gets harder with each additional trait considered (parameters estimated = n×n+12 for n traits).Ragweed appears to have evolved rapidly in its introduced range, and our results suggest that it has sufficient quantitative genetic variation for adaptation in traits that could allow further expansion of its range and abundance in Europe. Clines in flowering time and reproductive biomass, and a high The quantitative genetic variation we found is especially concerning given that ragweed also has several characteristics recognized as advantageous for invasion. Ragweed has a short generation time, small propagule size, and high propagule pressure, all of which will facilitate its spread (Dormontt et al., 4.2G over space and time, since the ability to predict evolutionary trajectories are contingent on G matrix consistency (Arnold et al., G matrices will be impacted by mutation, selection, drift, recombination and migration (Arnold et al., G will change over time have been intractable and the dynamics of G must be studied empirically (Revell, G, but results are equivocal, and the difficulty in rigorously estimating one G matrix, let alone multiple G matrices, has meant that we do not yet have a clear picture of how G varies in space and time (Aguirre et al., G matrices—while accounting for uncertainty in each—has increased the impetus and utility of more empirical research on G matrix variability (Delahaie et al., G matrix variation remain rare, especially for nonmodel organisms (Cano et al., Evolutionary biologists have long held an interest in the stability of  Revell, . There hG when compared to native populations. First, a bottleneck could cause a shift in the genetic architecture (Whitlock et al., G (Arnold et al., G matrices of two ecotypes of aquatic isopods. In two different lakes, the isopods have colonized a new habitat in the last few decades. While Eroukmanoff and Svensson (Hypericum canariense, Dlugosch and Parker (Introduced populations could face two main forces that could shift Svensson  found noSvensson . Likewisd Parker  found raG due to waves of migration. Ragweed's habitat preference for recently disturbed sites suggests that the species often experiences frequent bottlenecks in both its native and introduced ranges, which could introduce periodic bottlenecks to all populations. The effects of regular bottlenecks might be ameliorated by its prodigious pollen production, wind pollination with wide pollen dispersal, and outcrossing mating system. Despite the different evolutionary forces introduced ragweed populations have faced, all of which could have contrasting effects on levels of quantitative genetic variation, their G matrices have not substantially diverged from those of native populations. It may be that the interplay between these forces and the genetic architectures of complex traits such as size and phenology leads to a relatively stable G; alternatively, it may be that species such as ragweed become invasive because they have a relatively stable G that can lead to responses to selection new ranges despite the interplay of these forces.Our findings, along with past studies (Hodgins & Rieseberg, G matrix differences for native and introduced ragweed populations. Overall, the G matrices are largely stable across geography, consistent with studies on other taxa that have also found similarity in G across conspecific populations. While we did find some moderate differences between G matrices, most differences seem to be driven by the West Virginian population, which was highlighted by several of the analyses as a divergent population. Differences were not more apparent between populations from different continents than those from the same range. When confronted with the same selection scenario, responses of introduced populations would not be more different from native populations than from each other. There are too few studies of G matrix variability among populations for broad patterns to emerge, but past authors have argued that G matrices are stable across geography (Arnold et al., We used a variety of methods to assess the magnitude of 5It is increasingly appreciated that evolutionary factors are important in the invasion process and that there is value in approaching the study of invasive species from a quantitative genetics perspective. Data on the adaptive potential of wild populations are scarce (Delahaie et al., A. artemisiifolia populations are not limited in their adaptive potential when compared to native populations. Importantly, the availability of additive genetic variance seen here indicates that ragweed will be able to respond to selection pressures in the introduced range, whether from novel selection, global change, or eradication efforts. Combined with its annual life history and prolific production of seeds, ragweed is primed to adapt rapidly to selection pressures that arise in its introduced range.We used a multivariate Bayesian approach and found that introduced The authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.


Salmo trutta) have been widely introduced throughout the Southern Hemisphere, impacting endangered native fauna, particularly galaxiid fishes, through predation and competition. However, due to their importance for sport fishing and aquaculture farming, attempts to curtail the impacts of invasive salmonids have generally been met with limited support and the best prospects for protecting native galaxiids is to predict where and how salmonids might disperse. We analysed 266 invasive brown trout from 14 rivers and ponds across the Falkland Islands as well as 32 trout from three potential source populations, using a panel of 592 single nucleotide polymorphisms (SNPs) and acoustic tagging, to ascertain their origins and current patterns of dispersal. We identified four genetically distinct clusters with high levels of genetic diversity and low admixture, likely reflecting the different origins of the invasive brown trout populations. Our analysis suggests that many trout populations in the Falklands may have originated from one of the donor populations analysed (River Wey). The highest genetic diversity was observed in the rivers with the greatest number of introductions and diverse origins, while the lowest diversity corresponded to a location without documented introductions, likely colonized by natural dispersal. High levels of gene flow indicated widespread migration of brown trout across the Falkland Islands, likely aided by anadromous dispersal. This is supported by data from acoustically tagged fish, three of which were detected frequently moving between two rivers ~26 km apart. Our results suggest that, without containment measures, brown trout may invade the last remaining refuges for the native endangered Aplochiton spp. We provide new insights into the origin and dispersal of invasive brown trout in the Falkland Islands that can pave the way for a targeted approach to limit their impact on native fish fauna.Biological invasions are important causes of biodiversity loss, particularly in remote islands. Brown trout ( Controlling biological invasions is increasingly important because they impact native species and communities leading to loss of biodiversity and ecosystem functionality  is native to Europe, Western Asia and Northern Africa; however, since 1864, it has been widely introduced outside of their native range and is currently found on all continents except Antarctica  and Chile  and the Falklands minnow , has been severely impacted by brown trout . Data analysis was completed by Noahgene Ltd. Raw data were imported into the software package Genome Studio 2.0.4 (Illumina Inc.) for cluster analysis and SNP calling. SNP calls were exported as a Genome Studio FR.txt file and imported into SNP Variation Suite 8.9.0 (Golden Helix Inc.). QC parameter threshold call rate >0.85 and MAF >0.025 were applied and filtered SNP genotypes  exported to Microsoft Excel for downstream analysis. Invariant loci , SNPs with more than 3% missing data overall and any individuals with more than 10% missing data were removed.DNA from adipose fins was extracted using Qiagen DNeasy 96 plate tissue kits (Qiagen) following the manufacturer's protocol. Concentration and QC parameters were determined using a NanoDrop 8000 spectrophotometer (Thermo Fisher Scientific), and samples normalized to 50 ng/µl. SNP array analysis was completed by Neogen Europe, using a custom design 24k 2.3o), gene diversity (Hs) and FIS were calculated for each sampling site using the basic.stats function . Overall FST estimates were calculated using the betas function  using the linkage disequilibrium method implemented in NeEstimator v2  and geographic distance matrix was conducted using 999 randomisations in the ade4 package in R , a Mantel test between genetic distance  using the Jombart, . The optsnapclust function in the adegenet R package. Using the clusters defined by K‐means, we simulated F1 and F2 backcrosses between pairs of clusters and snapclust was run to reassign individuals to one of six possible classes: parental 1, parental 2, F1 hybrid, F2 or backcross with either parental population.Admixture between clusters was also examined using the divMigrate function in the diveRsity v1.9.90 R package using genetic diversity and differentiation statistics  and Howietoun hatchery (N = 6) and Loch Leven (N = 14) representing Pentlands. We were not able to obtain samples from Cobbinshaw Loch  or Germany . DNA extraction and genotyping were carried out as above except for 10 samples obtained from Loch Leven whose DNA had already been extracted. SNP data from putative origins and the Falklands’ samples were combined into a single database and analysed as above to examine genetic clustering and differentiation, using a maximum K of 17.We reconstructed the stocking history of brown trout in the Falkland Islands during 1948–1962 and identified the putative sources from the literature and historical records  and tagged 25 sea trout with size range between 175 and 545 mm from San Carlos River. San Carlos was chosen because it was accessible and surrounded by East and West Falkland rivers with brown trout presence, allowing us to detect movement between the two islands. All fish were tagged with 9 mm ID‐2LP9 acoustic transmitters (Thelma Biotel) inserted in the abdominal cavity, following standard surgical tagging procedures . The relatively low number of variable SNPs is likely the result of using an array designed for other salmonid species, possibly combined with the history of the introduced populations, that originated from limited number of stocks of hatchery/farm origin, as reflected in the current population structuring. Low intra‐population genetic diversity and high structuring had been also observed in the Falklands brown trout analysed using microsatellites  was negative for all sampling sites, indicating a small excess of heterozygotes  was observed between Green Hill and Herbert, in contrast the largest pairwise genetic distance (FST = 0.215) observed between Cobbs Pass and Colorado Pond  at Moody Brook to 489 (95% CI = 167‐infinity) at Doctors Creek  or Euclidean distance , for the Falklands together or for West Falkland . However, a significant correlation was observed for rivers in East Falkland between genetic and coastal distance  but not for Euclidean distance , suggesting a role for marine dispersal.No significant isolation by distance was found, using either geographic distance around the coast  and the largest between cluster 1 and cluster 2 , mostly between clusters 3 and 4 and between clusters 1 and 4, with the greatest number of admixed individuals being from Swan Inlet Figure .Estimates of migration rates were consistent with the DAPC results Table . The gre3.4ST = 0.047) and the greatest between clusters 1  and 5  in San Carlos Table , three o4e ranging from 12 to 489), with high 95% confidence intervals in several cases, potentially due to low sample sizes , as well as in the target populations. Yet, both our current and previous analysis identified strong population structuring, lack of isolation by distance and the presence of anadromous trout  that could have resulted from marine dispersal, but also from admixture with farmed escapees. One of the rivers in cluster 2 (Fitzroy) is close to the location where sea trout are being farmed in open net cages since 2013, initially with locally captured brood stock from Camilla Creek (cluster 4), and then with ova imported from Howietoun Hatchery UK in 2014 and 2015. Although we found no direct evidence of mixing between Fitzroy and Howietoun fish, escapes from sea pens are not uncommon in salmonid farming and are the main route of introduction of invasive salmonids in the Southern Hemisphere  as reported in the early literature (Arrowsmith & Pentelow, The popularity of brown trout as sport fish is common to other countries where it is also highly invasive, such as United States or New Zealand (Jones & Closs, None declared.Supplementary MaterialClick here for additional data file.