PNLIP, PPARD), pigmentation  and the immune response .The analysis of geographically specific regions and the characterization of fine-scale patterns of genetic diversity may facilitate a much better understanding of the microevolutionary processes affecting local human populations. Here we generated genome-wide high-density SNP genotype data in 425 individuals from six geographical regions in Lithuania and combined our dataset with available ancient and modern data to explore genetic population structure, ancestry components and signatures of natural positive selection in the Lithuanian population. Our results show that Lithuanians are a homogenous population, genetically differentiated from neighbouring populations but within the general expected European context. Moreover, we not only confirm that Lithuanians preserve one of the highest proportions of western, Scandinavian and eastern hunter-gather ancestry components found in European populations but also that of an steppe Early to Middle Bronze Age pastoralists, which together configure the genetic distinctiveness of the Lithuanian population. Finally, among the top signatures of positive selection detected in Lithuanians, we identified several candidate genes related with diet ( The current Lithuanian population resulted from a complex amalgam between the former Baltic tribes each with potentially different contributions from Finno-Ugric and Slavic sources2. Moreover, since the Neolithic, the native population of what would become Lithuania has not been substituted by other peoples. Thus, the origin of the contemporary Lithuanian population may be traced back to the Neolithic settlers, with little admixture hereafter3.Lithuania is a north-eastern European country where the most archaic Indo-European language is spoken5. These people were highly related to hunter-gatherers (HG) from western Europe4. Notably, according to Lazaridis et al. (2014)6 modern eastern Baltic populations carry the largest proportion of western hunter-gatherer (WHG) ancestry of all Europeans. Moreover, recent results from Mittnik et al. (2018)7 point to continued gene flow between groups in the southeast Baltic region, who were more closely related to WHG, and the northern or eastern groups, more closely related to the eastern hunter-gatherers (EHG)7. Archaeological findings suggest that Finno-Ugrians had a minor contribution on the Lithuanian population5. However, Y-chromosome biallelic markers showed similarities between both Lithuanians and Latvians and the Finno-Ugric Estonians and Mari8. On the other hand, while mtDNA diversity also show this connection of Lithuanians to Finno-Ugric speakers, it also revealed similarities with Slavic (Indo-European) speaking populations2. Finally, analysis of molecular variance using the mtDNA HV1 region and Y STR haplotypes showed that Lithuania is a homogeneous population2.The first settlement in the contemporary Lithuanian territory was founded in the late Palaeolithic in west Lithuania along the Baltic sea after the last glaciation around 11,000 years before present9. In addition, the establishment of the Baltic tribes lead to the development of the current Lithuanian dialects, resulting in the actual regional linguistic differentiation. Six dialects can be currently distinguished in Lithuania: three groups from Aukstaitija  and three groups from Zemaitija .The apparently long isolation of the Balts may have contributed to the preservation of an ancient social structure and of an archaic language10. Indeed, several genetic variants that were adaptive in the past have been suggested to currently associate to particular common immune response and metabolic disease phenotypes due to recent environmental and dietary shifts12. During the last years, the availability of high-throughput genotyping platforms and next generation sequencing techniques together with the increasing number of genotype-phenotype association studies and the development of new statistical and computational methods in the field of evolutionary genomics allow not only inferring putative selective events but also deciphering their potential phenotypic adaptive targets when analysing genomic patterns of variation from contrasting environmental conditions. To date many population genetics studies have been performed on large reference populations, but the need to analyse local patterns of population structure and adaptation remains. Without doubt, the analysis of geographically specific regions and the characterization of fine-scale patterns of genetic diversity may facilitate a much better understanding of the microevolutionary processes affecting local human populations.The characterization of the adaptive history of local populations is of great interest because it provides knowledge about the genes that have been targeted by positive natural selection at a regional geographical scale. Besides their biological role in our past survival, these genes could be involved in rare, severe Mendelian diseases and contribute to current differences in resistance or susceptibility to diseasePNLIP, PPARD), pigmentation  and the immune response .To the best of our knowledge, there are no previous studies describing fine-scale genetic structure and recent positive selection in the Lithuanian population. Thus, in this study, we aimed to analyse local patterns of population structure and signatures of adaptive positive selection from genome-wide high–density SNP genotyping data we generated with the Illumina HumanOmmiExpress-12v1.1 and Infinium OmniExpress-24 arrays in 425 individuals from six regions in Lithuania. Furthermore, by combining our dataset with ancient data publicly available we aimed to explore the prehistorical ancestry components present in the Lithuania population. Notably, our results display Lithuanians as a homogenous population presenting one of the highest proportions of distinct pre-Neolithic HG  and Early to Middle Bronze Age steppe pastoralist ancestry components found across contemporary Europeans. Moreover, among the top signatures of positive selection detected in Lithuanians we identified several candidate genes related to diet , and 19 pairs of individuals (including 1 duplicate) were inferred to present kinship coefficients higher than expected for 2nd degree relatives (0.0084). Moreover, a first exploratory principal component analysis (PCA) revealed 8 outliers  and PC2  were statistically significantly different among ethnolinguistic groups. If the main regions were compared to each other (Aukstaitija vs. Zemaitija), only PC1 scores were significantly different . Still, genetic differentiation assessed through calculation of pairwise FST values revealed that the fraction of the genetic variance due to differences among ethnolinguistic groups ranged from 0.19% to 0.39% , southern (n = 67), and eastern (n = 79)) and the Zemaitija (northern (n = 79), western (n = 43), and southern (n = 78)) regions of Lithuania Fig. . Geneticers Fig. , which wers Fig. , Fig. S2ers Fig. . Howeverers Fig. , a clearet al. (2016)6 including a wide range of modern and ancient samples from west Eurasia and north Africa. Principal component analysis (PCA) of all modern samples displayed a tight clustering of the Lithuanian samples partly overlapping the north European group and in close proximity towards one of the extremes of the eastern and western European clusters  revealed three ancestry components on the Lithuanian population 6 dataset, Lithuanians display the most extreme admixture f3 values when specifically testing the western HG ancestry in the form f3 , as well as the ancestry components of Scandinavian HG, the eastern HG, and the EMBA steppe pastoralists but not for the European Neolithic farmer component characterizing the genetically isolated Sardinians and Basques  displayed such a pattern in the same region.Before analysing signatures of positive selection, we merged our original genotyping data in the Lithuanian population to an additional, but much denser, external SNP dataset, namely the 1000 Genomes Project Phase3 dataseton Table . Among tCOL6A5 and COL6A6 genes coding for the collagen type VI alpha 5 and alpha 6 chains, respectively. Interestingly, a non-synonymous variant in COL6A5  with a CADD value of 23.2 was found among the top XP-EHH and FST outliers along the region. Moreover, the SNP is reported as an eQTL for COL6A5 in testis (P = 3.0 × 10−17) and for COL6A6 in heart (P = 4.1 × 10−9) with the derived C allele implying lower expression in both cases according the Genotype-Tissue Expression (GTEx) portal . Notably, even if the signal was only significant in the LT-YRI comparison, all European populations present high frequencies (above 0.748) for the derived C allele at rs12488457, which is found at very low frequencies in YRI (~0.013). Finally, an additional signal in the LT-YRI comparison mapping on chromosome 1 includes two extreme XP-EHH and FST outliers (rs274750 and rs274752) at the 3′UTR of the COL8A2 gene, which codes for the collagen type VIII alpha 2 chain. Again, the signal was only significant in the LT-YRI comparison but the derived alleles at rs274750 and at rs274752, C and A respectively, are nearly fixed in the three European populations and found at intermediate frequencies in YRI. Interestingly, polymorphisms in COL6A5 have been associated with body mass index19 and dermal phenotypes, such as eczema20, while mutations at COL8A2 have been linked to corneal endothelial dystrophies21.Another of the strongest signals detected in the LT-YRI comparison was found in a ~225 kb region in chromosome 3, which comprises the TYRP1 gene, which codes for a melanosomal enzyme that participates in the melanin biosynthetic pathway. However, no obvious functional variant was identified among the top outlier XP-EHH and FST values in that region. In the same population comparison we also identified two signals probably related to immunity: one in chromosome 12 comprising the IL26 and IL22 interleukin genes, and the other in chromosome 6 containing the BRD2 and HLA genes.As for the LT-CEU comparison, one of the strongest signals detected in Lithuanians was found at a ~184 kb region in chromosome 9 that comprises the SLC24A5 region related to light pigmentation which, besides being identified as target of recent selection in the LT-YRI and CEU-YRI comparisons, emerged also as an outlier for old signatures in all three European populations. Interestingly, at least two of the candidate regions detected in Lithuanians for old signatures of selection may be related to dietary and environmental pressures: one in chromosome 6 containing the PPARD gene and another in chromosome 10 comprising the PNLIP and PNLIPRP3 genes. Whereas PNLIP codes a pancreatic lipase required for the efficient digestion of dietary fats22, PPARD stimulates beta-oxidation not only in muscle and adipose tissue but also in the liver and other tissues and has been suggested to be involved in metabolic adaptation to environmental changes23. However, in none of these two cases any obvious functional variant specifically linked to the detected signatures was identified among the SNPs genotyped.A total of 35 candidate regions for older signatures of selection were identified in the Lithuania population when using the Tajima’s D statistic Table . NotablyGeographically specific microevolutionary processes can be inferred when exploring local patterns of population structure and adaptation within the global and historical genetic context established from large general population and ancient publicly available reference panels. In this study we aimed to do that by analysing genome-wide high–density SNP genotyping new data generated in six ethnolinguistic groups from Lithuania.24. Similarly, according to Kasperaviciūtė et al. (2004)2, when considering Y-chromosome and mtDNA uniparental markers, Lithuania is a genetically homogeneous population. However, the sample size (180–196 Lithuanian samples) used in that study could be too small to identify minor differences between regions of Lithuania. Here, by increasing sample size to 399 individuals and covering the whole genome, we had statistical power to detect weak signals of genetic structure. However, even if we detected statistically significant genetic differentiation among the six Lithuanian ethnolinguistic groups, genetic distances among these groups were found to be rather small.At regional level, we found small genetic distances and a rather homogeneous genetic landscape across the six ethnolinguistic groups present in Lithuania. Previous analyses showed only statistically significant differences in the allele frequencies of the P1 and LWb blood groups when comparing south Aukstaitija with the remaining Lithuaninan ethno-linguistic regions as well as on the Alu TPA25 allele distribution between north Zemaitija and south Aukstaitija25. In contrast, the middle Lithuanian land was unsuitable to live permanently due to the extreme cold climate before 11,000 years ago5. People in the different regions of Lithuania seem to have lived in relative isolation for a long time because of the inaccessible nature of the terrain, bounded on the landward side by vast forests and swamps27. Thus, if genetic differences between Lithuanians from different geographical regions existed, these disappeared in the contemporary population. Alternatively, the genetic composition of the Baltic peoples from which contemporary Lithuanians originated may have been relatively homogeneous.Archaeological findings locate the first settlement in the contemporary Lithuanian territory in the late Palaeolithic along the Baltic sea in west Lithuania (Zemaitija region), after the last glaciation around 11,000 years before present, probably favoured by the environment and the presence of marine food resourcesWhen combining the new data we generated with external datasets, we confirmed that Lithuanians locate within the expected European context, even though they also present particular genetic distinctiveness when compared to neighbouring populations. In addition, the inclusion of ancient individuals from different periods across western Eurasia in the analysis allowed us to distinguish the genetic signature of three main prehistorical sources shaping the distinctiveness of present-day Lithuanians: pre-Neolithic HG groups, the Early to Middle Bronze Age Steppe pastoralists and Late Neolithic Bronze Age (LNBA) Europeans. Moreover, up to three HG populations can be inferred to contribute to the main genetic component identified the Lithuanians being the contribution of the WHG and the Scandinavian HG greater than that of the EHG. On the contrary, earlier European Neolithic movements from Levant/Anatolia known to contribute to genetically differentiated populations in Europe such as Sardinians or Basques are not especially predominant in Lithuania.27. Moreover, after the fall of the Roman Empire in the 5th century, the eastern Baltic region was spared by the subsequent population movements of the Migration Period28, which allowed the most archaic of all the living speaking Indo-European languages1to survive. Thus, Lithuanians could retain their cultural identity.Partial genetic isolation of the Lithuanians is a possible explanation for the structure results observed. Until the late Middle Ages, the eastern Baltic region was one of the most isolated corners of EuropeCOL6A5 and COL6A6) as well as the TYRP1 gene involved in pigmentation and were detected in the LT-YRI and LT-CEU comparisons, respectively. Interestingly, an additional collagen gene (COL8A2) and a well-known target of positive selection related skin pigmentation (SLC24A5) also emerged among the top signals of recent selection detected in the LT-YRI comparison. Similarly, the two candidate regions for recent selection detected in the LT-CEU comparison comprising only genes related to the immune response  and two candidate regions for old signatures in LT with genes probably related to diet  may probably result from specific pathogen driven and local dietary selection pressures in the Lithuania population. Notably, a significant number of the candidate regions for positive selection detected in the Lithuania population were also identified in FIN and/or CEU in our analyses and thus point to common selection signals across Europeans. Some of these signals had already been described in previous genome-wide scans of positive selection in Europeans and include the aforementioned SLC24A5 gene as well as other candidates not always completely characterized such as LRRC3630 and PPARD31.After establishing the genetic homogeneity and distinctiveness of Lithuanians within the European context, we next investigated whether specific signals of positive selection could be identified using different statistical approaches for recent and old classical selective sweeps. The two strongest signals for recent selection in our analysis comprise two candidate genes coding for different collagen chains , which can be further subdivided ethnolinguistically into six dialectal groups. We sampled three groups from Aukstaitija (western (n = 79), southern (n = 67), and eastern (n = 79)) and three groups from Zemaitija (northern (n = 79), western (n = 43), and southern (n = 78)) Fig. . All theGenomic DNA was obtained from whole blood using either a standard phenol-chloroform method of extraction or the automated DNA extraction platform TECAN Freedom EVO . A NanoDropR ND-1000 spectrophotometer  was used to assess DNA concentration and quality. This work is part of the LITGEN project, which was approved by the Vilnius Regional Research Ethics Committee 235 No. 158200-05-329-79, date: 3 May 2011.−4) were eliminated from the study. After quality control 1 individual was excluded with more than 10% missing genotypes (MIND >0.1) and 532,836 autosomal SNPs remained out of 589,752.Genotyping was performed at the Department of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Lithuania with the Illumina HumanOmniExpress-12v1.1 (296 samples) and the Infinium OmniExpress-24 (129 samples) arrays , which include an overlap of 707,138 genome-wide SNPs. Genotyping data quality control was performed according to the standard manufacturer recommendations. Individuals and SNPs with >10% missing data and SNPs with minor allele frequency (MAF) <0.01 were excluded from the analysis. SNPs with deviations from Hardy–Weinberg equilibrium 6. Next, the whole Lithuanian dataset was randomly sampled to keep a sample size of 50 individuals and avoid any potential overrepresentation in subsequent analyses. After this procedure, we ended up with a total of 1,033 modern and 284 ancient samples, respectively, and 52,477 SNPs after LD pruning.To obtain a broader geographical and historical context for the genetic diversity of the Lithuanian population, we merged our genotyped autosomal Lithuanian data with that of the Human Origins dataset from Lazaridis 32. SNPs in linkage disequilibrium were removed with the indep-pairwise option of PLINK (v1.07) using a window size of 50 SNPs, a step size of 5, and a r2 threshold of 0.533. Genetic relationship and consanguinity were inferred through the kinship and the inbreeding coefficients, which were estimated with KING v.2.113 and PLINK v1.0733, respectively. Moreover, negative F values were converted to zero as they probably represent sampling errors34. Outlier samples on the PCA plot, as well as individuals with inbreeding coefficients higher than that expected for second cousin mating offspring  and 2nd degree relatives (kinship coefficient >0.0084) were removed for all subsequent analyses  for the six Lithuanian ethnolinguistic groups was carried out with independent pruned SNPs using SmartPCA from EIGENSOFT (v7.2.1)es Table .6 with the SmartPCA program (v. 13050) from EIGENSOFT package (v7.2.1)32. The analysis was first carried out on the present-day Eurasian samples, and subsequently we projected the ancient individuals in the analysis. PCA results were then plotted in R (v3.2.3).To visualize the closest ancient groups to our modern Lithuanians, we performed a PCA on the merged Lithuanian – Lazaridis dataset35. We used the unsupervised method implemented in the program including both ancient and present-day samples with K  ranging from 2 to 12. The results of 10 iterations were combined and plotted by using PONG (v1.4.7)36.Model-based individual ancestries were estimated with ADMIXTURE (v. 1.3.0)15. We considered Mbuti as outgroup in the analysis and calculated the shared drift between each putative ancient group and all the modern groups in the dataset in the form f3 . We have focused the analyses on the three main European ancestral components: pre-Neolithic HG , European Neolithic farmer and Bronze Age steppe components. These groups are represented in the analyses as in the reference6 WHG, EHG, SHG, Europe_EN, Steppe_EMBA, respectively.Outgroup f3-statistics were computed using the qp3Pop program from ADMIXTOOLS package (v4.1)16 generating a pooled dataset of 264,950 autosomal SNPs distributed genome-wide in a total of 2,928 individuals. In particular, we included 20 populations from the 4 main geographical regions as described in the 1000 Genomes Project Phase3 dataset16: Africa including the Yoruba in Ibadan, Nigeria (YRI), Luhya in Webuye, Kenya (LWK), Gambian in Western Divisions in the Gambia (GWD), Mende in Sierra Leone (MSL), and Esan in Nigeria (ESN) populations; Europe including Utah residents with ancestry from northern and western Europe (CEU), Toscani in Italy (TSI), Finnish in Finland (FIN), British in England and Scotland (GBR), and Lithuanians (LT); East Asia including Han Chinese in Bejing, China (CHB), Japanese in Tokyo, Japan (JPT), Southern Han Chinese, China (CHS), Chinese Dai in Xishuangbanna, China (CDX), and Kinh in Ho Chi Minh City, Vietnam (KHV); South Asia including Gujarati Indians in Houston, Texas (GIH), Punjabi from Lahore, Pakistan (PJL), Bengali from Bangladesh (BEB), Sri Lankan Tamil from the UK (STU) and Indian Telugu from the UK (ITU). Genotyping data was subsequently phased with SHAPEIT237.To characterize signals of positive selection specific of the Lithuanian population we merged our original SNP genotyping data to that downloaded from the 1000 Genomes Project Phase3 datasetFST)38 and the XP-EHH statistic39 which were computed between all possible pairs of populations among LT, CEU, FIN, and YRI. FST values were calculated with vcftools v.0.1.1340. Negative FST values were converted to zero. XP-EHH was run using selscan v1.2.0a41. For each comparison a XP-EHH score per SNP was obtained and XP-EHH scores >2 were considered as indicative of positive selection. We selected as candidates for positive selection any genomic region with two or more SNPs located at the 0.1% top extreme of the XP-EHH genome-wide empirical distribution and with at least one SNP presenting an FST p-value < 0.01.Signatures of recent or ongoing positive selection were next investigated using the locus fixation index , RefSeqGene, dbSNP14746 and CADD version 1.347.Finally, gene and variant annotation in candidate regions for selection was performed with ANNOVARThis research was approved by the Vilnius Regional Research Ethics Committee No. 158200-05-329-79, date: 2011-05-03. All the study participants provided written informed consent.Supplementary Material


To investigate the structure and rate of gene flow among populations of habitat‐specialized species to understand the ecological and evolutionary processes underpinning their population dynamics and historical demography, including speciation and extinction.Peruvian and Argentine Andes.Merganetta armata).Two subspecies of torrent duck  and Argentina , and sequenced the mitochondrial DNA (mtDNA) control region to conduct coarse and fine‐scale demographic analyses of population structure. Additionally, to test for differences between subspecies, and across genetic markers with distinct inheritance patterns, a subset of individuals  was subjected to partial genome resequencing, obtaining 4,027 autosomal and 189 Z‐linked double‐digest restriction‐associated DNA sequences.We sampled 156 individuals in Peru , including no shared mtDNA haplotypes. Demographic parameters estimated for mtDNA using IM and IMa2 analyses, and for autosomal markers using ∂a∂i (isolation‐with‐migration model), supported an old divergence , 95% HPD range = 1.2 Mya to 200,000 ybp; and autosomal ∂a∂i = 782,490 ybp), between the two subspecies, characteristic of deeply diverged lineages. The populations were well‐differentiated in Argentina but moderately differentiated in Peru, with low unidirectional gene flow in each country.Haplotype and nucleotide diversities were higher in Peru than Argentina across all markers. Peruvian and Argentine subspecies showed concordant species‐level differences (ΦWe suggest that the South American Arid Diagonal was preexisting and remains a current phylogeographic barrier between the ranges of the two torrent duck subspecies, and the adult territoriality and breeding site fidelity to the rivers define their population structure. Here, we focus on the two subspecies distributed in the highest regions of the Andes  in the Central Andes of Peru, and the larger (360–580 g) and darker‐colored subspecies M. a. armata in the Southern Andes of Argentina  and thousands of nuclear markers isolated using partial genome resequencing ), attained from altitudinal sampling transects in Peru and Argentina. Specifically, we estimated the time since divergence and gene flow between the two subspecies, and also characterized population structure and gene flow between riverine populations within subspecies. Furthermore, we calculated genetic diversity across the different populations to infer whether specific riverine populations have experienced demographic processes such as population expansion or contraction. Because the spatial linear configuration of the rivers restricts suitable habitat and constrains the population, we also examined evidence for altitudinal clines in mtDNA haplotype frequency. Finally, this genetic characterization across altitudinal gradients permits us to explore whether genetic variation is associated with recently described morphological and physiological differences of torrent ducks at high elevations, including changes in body size , Museo Argentino de Ciencias Naturales “Bernadino Rivadavia” , and CORBIDI . DNA was extracted using a DNeasy Blood & Tissue kit and following the manufacturer's protocols (Qiagen). Extractions were quantified using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific Inc.) to ensure a minimum concentration of 20 ng/µl.Blood samples were collected from 156 captured and released torrent ducks, during the 2010–2011 dry seasons, from two rivers in the western and eastern slopes of the Central Andes of Peru  and aligned by eye using se‐al v.2.0a11 .A 634‐base‐pair (bp) fragment (domains I and II) of the mtDNA control region (CR) was amplified and sequenced using specific primers for torrent ducks L100 (5′‐CATACATTTATGCGCCCCATAC‐3′) and H774 (5′‐CCATACACGCCAACCGTCTC‐3′) that prevented amplification of a known nuclear copy . PCR otherwise followed standard protocols  and Malargüe River in Argentina (n = 9) were subjected to partial genome resequencing via ddRAD‐Seq. Sample preparation for ddRAD‐Seq followed the double‐digest protocol outlined in DaCosta and Sorenson (SbfI and EcoRI restriction enzymes. Adapters containing sequences compatible for Illumina TruSeq reagents and barcodes for de‐multiplexing reads were ligated to the sticky ends generated by the restriction enzymes. The adapter‐ligated DNA fragments were then size‐selected for 300–450 bp using gel electrophoresis (2% low‐melt agarose) and purified using a MinElute Gel Extraction Kit (Qiagen). Size‐selected fragments were then PCR amplified with Phusion High‐Fidelity DNA Polymerase (Thermo Scientific), and the amplified products were cleaned using AMPure XP magnetic beads . Quantitative PCR using a Library Quantification Kit for Illumina (KAPA Biosystems) was used to measure the concentration of purified PCR products, and samples with compatible barcode combinations were pooled in equimolar concentrations. A multiplexed library was sequenced on an Illumina HiSeq 2,500 using single‐end 150 bp chemistry at the Tufts University Core Genomics Facility. Raw Illumina reads are deposited in NCBI's Sequence Read Archive (SRA data: SRP215991).While mtDNA was obtained across all samples, for more extensive genomic analysis of the two subspecies, a subset from Chillón River in Peru (Sorenson . In shor2.4http://github.com/BU-RAD-seq/ddRAD-seq-Pipeline). First, reads were assigned to individual samples based on barcode sequences using the ddRADparser.py script. Reads per sample were then collapsed into identical clusters using the CondenseSequences.py script with low‐quality reads  and with an average per‐base Phred score <20) filtered out with the FilterSequences.py script. Condensed and filtered reads from all samples were then concatenated and clustered with an –id setting of 0.85 in uclust. muscle v.3 . Manual editing increased the total number of retained markers by ~7%, while reducing bias resulting from discarding loci with indels or high levels of polymorphisms. Finally, output files for downstream analyses were created with custom python scripts  program “pegas” v.0.10  and nucleotide diversity  estimates were calculated in the R program “PopGenome” , Tajima's Paradis, . Analysi and Nei  model of2.6tcs v.1.21  tested, we used a block‐relaxation algorithm for the point estimation, with analyses terminated once the change  in the log‐likelihood of the point estimations increased by <0.0001. Final outputs were based on admixture proportions  per individual. All analyses were performed without a priori assignments.Analysis of population subdivision for ddRAD‐Seq data was done using two methods. First, we used a principal component analysis (PCA) as implemented in the adegenet R program  scaled to the mutation rate, and time since divergence for the populations (t)  Figure a. The IMim model to estimate levels and direction of gene flow (m) scaled to the mutation rate, and time since population divergence (t) between the two subspecies inhabiting Peru and Argentina, respectively  = 3.1 – 6.9 × 10–8 substitutions/site/year]. The im analysis was run in triplicate to check for consistency among runs, each with 20 Metropolis‐coupled chains of three million steps using a geometric heating scheme and a burn‐in period of 500,000 steps. Priors were defined as follows: ΘPERU = 80, ΘARGENTINA = 60, ΘANCESTRAL = 150, mPER.‐ARG. = 10, mARG.‐PER. = 10, t = 40, and s = 0–1.Meanwhile, we used the two‐population y Figure a. The di2.8∂a∂i . Using ∂a∂i, a site frequency spectrum was derived from all biallelic RAD‐Seq autosomal SNPs. Loci were concatenated, and SNPs extracted and formatted for ∂a∂i using the custom python script nex_mo.py  and Argentine  torrent duck populations, datasets were projected down to a total of 18 alleles per subspecies. We tested the empirical data against an isolation‐with‐migration evolutionary model as implemented in ∂a∂i *NAnc; Nref = reference effective population size; NAnc = ancestral effective populations size), migration rates (iM←j = 2NAncim←j), and divergence times (t = T/2Nref: T = time since divergence in generations).We further estimated rates and directionality of gene flow from the ddRAD‐Seq data with the program ∂a∂i to biologically informative values, we estimated generation time (G) and mutation rates . First, generation time (G) was calculated as G = α + (s/(1 − s)), where α is the age of maturity and s is the expected adult survival rate  with an average adult survival rate of 0.37 estimated for torrent ducks from mark‐recaptured data of the Chillón River (pers. obs.). Together, we estimated a generation time to be 2.5 years. Next, to obtain a mutation rate for nuclear genes, we multiplied a rate of 1.2 × 10–9 substitutions/site/year, previously calculated for nuclear genes in other ducks . A final mutation rate was calculated as the product of the above mutation rate and the total number of base pairs.To convert the parameter estimates from 33.1M. a. leucogenis in Peru had higher nucleotide diversity that was five times greater than M. a. armata in Argentina, although the haplotype diversity was similar between countries (Table Nucleotide (π/site) and haplotype diversities of the mtDNA CR differed among riverine populations and countries Table . M. a. les Table . These gST) of 0.82 and consistent with their current subspecies designations  and Arroyo Grande River (ΦST = −0.037), considering a 2,500 m divide along the rivers, or on the Pachachaca River system (ΦST = 0.029).A deep genetic distance corresponding to 4.1% uncorrected mtDNA CR divergence was observed between populations in Peru and Argentina, defining two monophyletic lineages. Specifically, among the four independent rivers we recovered 27 haplotypes characterized by 53 variable sites. Moreover, 16 private haplotypes were found in Peruvian rivers (Chillón and Pachachaca), and 11 private haplotypes were found in Argentine rivers  Figure , resulti3.2For ddRAD sequencing, a total of 22,662,951 raw reads were recovered from the HiSeq Illumina run of 10 Peruvian and 9 Argentine torrent duck samples. After quality filtering, we recovered 4,216 ddRAD‐seq loci, with 4,027 loci  assigned to autosomes and 189 loci to the Z chromosome ; two gametologs were also identified and excluded from analyses. On average, there was a median depth of 283 sequences per individual per locus .M. a. leucogenis of Peru as compared to M. a. armata of Argentina  than for autosomal (ΦST = 0.30) markers  and haplotype diversity (on average 1.6‐fold higher) in na Table . Considers Table a. A singa Figure . No addi3.3The effective population size parameters, Θ, scaled to the neutral mutation rate in IM and IMa2 for mtDNA were higher for the rivers in Peru than Argentina Figures b and 4b.∂a∂i analyses utilizing the genomic data in the isolation‐with‐migration model provided results that agree the mtDNA‐estimated results. Using autosomal markers, we estimated an averaged mutation rate of 1.52 × 10–3 s s−1 g−1  that was used to convert ∂a∂i results. Given the isolation‐with‐migration model, parameter estimates suggested different effective population sizes (Ne) for the two subspecies of torrent ducks .The D values for mtDNA for the populations in Argentina and Peru . Therefore, we did not observe a significant excess of rare nucleotide site variants compared to what would be expected under a neutral model of evolution, and each riverine population may be evolving as per mutation‐drift equilibrium. In contrast, Tajima's D values were negative for autosomal and Z‐linked markers for both subspecies populations in Peru and Argentina, although both markers had not statistically significant p‐values (Table Tajima's ru Table  were notes Table .m) between riverine populations within each country is effectively low and unidirectional  within Peru peaked at 0.28 from the larger Pachachaca population to the smaller Chillón population  as compared to gene flow from M. a. armata into M. a. leucogenis (2Nm21 = 0.21 migrants/generations).Estimating gene flow, the four‐population IMa2 analysis suggests that the number of migrants  with the IMa2 analysis between the two torrent duck subspecies peaked at 17.32 .For time since divergence, the posterior probability distribution of 4We provide the most comprehensive molecular analysis of torrent ducks to date and clearly identify limited or no gene flow and strong differentiation across the mitochondrial and nuclear genomes of torrent ducks found in the rivers of Peru versus Argentina. These results support the current taxonomic subspecies designation of Peruvian and Argentine torrent ducks. Moreover, we are able to conclude that these two subspecies probably originated during the early‐ to mid‐Pleistocene as a result of an old divergent nonvicariant event subsequent to the origin of the South American Arid Diagonal phylogeographic barrier  habitats , although not significant, possibly suggest a metapopulation dynamic. Specifically, our findings for Argentine populations prompt that these likely have been smaller and possibly have gone through multiple extinction–colonization events, whereas Peruvian populations have likely been large and resident to rivers that have had a prolonged period of water regime stability as compared to those in Argentina  and within countries , we found that populations inhabiting shorter rivers compared to populations inhabiting longer or branched rivers systems had the lowest levels of molecular diversity Figure  and 2. Tft Table b. Moreovft Table  can prod Figures .4.2M. a. leucogenis and M. a. armata  with similar distribution range and divergence time show relatively less genomic divergence and asymmetric gene flow  in New Zealand with a regional genetic differentiation (region around 100 km) associated with isolation‐by‐distance pattern  Strong territoriality and breeding site fidelity in adults increase the similarity of the individuals within a river, (2) geographic  or climatic barriers  isolate rivers preventing the dispersal of the individuals, and driving an independent evolution of each riverine population by genetic drift, and (3) individual dispersal capacity  describes an isolation‐by‐distance pattern that in short distance dispersal resembles a metapopulation model. For example, the year‐round territorial behavior, the long‐term pair bonds, and the strong site fidelity in adult torrent ducks, as mentioned in the first hypothesis, can prevent gene flow among rivers, but also contribute to the similarity of the individuals of a growing population in vacant or improved habitat, and thus increase the relatedness within each riverine population and develop an “extended family” , and Salvadori's duck  in New Guinea, or habitat specialist species.Torrent ducks are vulnerable to systematic pressure and stochastic perturbations because of their specialization to whitewater streams, low population densities, patchy distribution, low reproductive potential, and reliance on water quality and aquatic insect populations (Alvarez, Astie, Debandi, & Scheibler, None declared.L.A. analyzed the data and drafted the manuscript; L.A., M.S., C.K., and K.G.M. collected and acquired the data; P.L. acquired and analyzed the genomic data and helped to revise the manuscript; L.A. and G.C. conceived the ideas; J.P. interpreted the results and revised the manuscript; K.G.M. designed the study, interpreted the results, and helped to write the manuscript; and all the authors have read and commented on the final manuscript. Click here for additional data file.


Estimates of different ancestral proportions in admixed populations are very important in population genetics studies, especially for the detection of population substructure effects in studies of case-control associations. Brazil is one of the most heterogeneous countries in the world, both from a socio-cultural and a genetic point of view. In this work, we investigated a previously developed set of 61 ancestry informative markers (AIM), aiming to estimate the proportions of four different ancestral groups  in Brazilian populations. To the best of our knowledge, this is the first study to use a set of AIM to investigate the genetic contribution of all four main parental populations to the Brazilian population, including Asian contribution. All selected markers were genotyped through multiplex PCR and capillary electrophoresis. The set was able to successfully differentiate the four ancestral populations  and identify their genetic contributions to the Brazilian population. In addition, it was used to estimate individual interethnic admixture of 1050 individuals from the Southeast region of Brazil and it showed that these individuals present a higher European ancestry contribution, followed by African, Asian and Native American ancestry contributions. Therefore, the 61 AIM set has proved to be a valuable tool to estimate individual and global ancestry proportions in populations mainly formed by these four groups. Our findings highlight the importance of using sets of AIM to evaluate population substructure in studies carried in admixed populations, in order to avoid misinterpretation of results. These markers, also known as population-specific markers, are genetic markers with great variation of allelic frequency between different continental populations  in a sample of individuals from the São Paulo population.This study included 939 samples to represent the parental groups that contributed to the formation of the Brazilian population and 1050 individuals from the admixed population of São Paulo state, Brazil. The samples considered as parental groups included: 200 Sub-Saharan African individuals, from Angola, Mozambique, Zaire, Cameroon and the Ivory Coast; 290 European individuals, mainly from Portugal and Spain; 246 Native American individuals from tribes in the Brazilian Amazon ; and 203 Japanese individuals residing in the North region of Brazil that were either: i) immigrants born in Japan; or ii) Brazilian individuals with Japanese parents or grandparents. With the exception of the Japanese group, all samples were collected in the origin country and have been previously described by , African and Native American populations, as well as, more recently, by a significant amount of Japanese individuals. Therefore, the São Paulo population is suitable to be analyzed in this study.All participants have authorized the collection of their biological samples by signing a consent form and the ethical aspects of this study have been approved by the Ethics Committee . DNA quantification was performed by a spectrophotometer . Further, the samples were diluted to a 10 ηg/μL concentration and used for the multiplex PCR.et al. and Ramos et al. It is noteworthy that, as far as we know, this is the AIM panel with the highest number of INDEL markers to be applied in the Brazilian population to date.In this study, we used a set of 61 INDEL-type markers that was described by Santos et al. and Ramos et al. Technical features of the used primers can be found in these published works. For the multiplex amplifications, we used the following protocol for each sample: 5.0 μL of the QIAGEN Multiplex PCR kit , 1.0 μL of Q-solution, 1.0 μL of Primer Mix, 2.0 μL of water and 1.0 μL of DNA (concentration of 10ng/μL). PCR was performed in the ABI Veriti thermocycler  with the following program: 95° for 15 min; 35 cycles 94° for 45 sec, 60° for 90 sec and 72° for 60 sec; 70° for 30 min.Genotyping of the 61-AIM set was performed by Multiplex PCR (two runs), followed by capillary electrophoresis with fragment analysis, as described by Santos For the capillary electrophoresis and fragment analysis, we used the following protocol for each sample: 1.0 μL of the PCR product to each 8.5 μL of deionized formamide HI-FI (Life Technologies) and 0.5 μL of GeneScan 500 LIZ pattern size (Life Technologies). Separation of DNA fragments was performed using ABI PRISM 3130 Genetic Analyzer and GeneMapper ID v3.2 software (Life Technologies) was used for peak reading.adegenet package , which are positive values that correspond to the frequency differences between two populations. The delta values of the comparison of the four parental populations are presented in et al.The mean δ was higher in the comparison between NAM and AFR (0.40), and the lower mean δ was observed between ASN and NAM (0.24), which suggests a greater proximity between the NAM and ASN, corroborating the results by  or ancestry SE . Considering that the δ results indicate that this AIM panel is suitable to estimate interethnic admixture with Asian in individuals from admixed populations, we performed additional analyses. We quantified the level of standard error (SE) using data obtained in analysis performed with Structure v.2.3.4. software. As described by Halder The measure of ancestral SE was also very low (less than 1%), which means that none of the parental populations could have contributed with more than 1% in the formation of the other three populations. It suggests that the panel of 61 AIM could be employed in the estimates of individual interethnic admixture involving the Asian ancestral population. Furthermore, we performed the DAPC analysis in the four parental populations, which generated four distinct clusters . It is nFor the investigated markers, the FST values show a difference of 0.32 between European and Native American; 0.32 between European and African; 0.26 between European and Asian; 0.43 between Native American and African; 0.23 between Native American and Asian; and 0.35 between African and Asian. Considering the obtained values, the FST results indicate a great degree of differentiation between Native American and Asian populations, and an even greater degree in the analysis between the other populations . To further investigate this finding, we applied the 61-AIM set to estimate interethnic admixture in samples from 1050 individuals from the São Paulo population that have reported Asian ancestry. Then, we performed analyses considering three and four parental populations  . EuropeaIn addition, we present the estimates of individual interethnic admixture in Figures S1, S2 and S3. Both European and African proportions showed a great similarity in these estimates, when considering three or four ancestral populations.Here, we highlight that the NAM and ASN contributions show similar percentage when analyzed separately , but when they are considered in the same analysis, they are notably different , as shown in When we analyzed the contribution from all four populations, we observed that 0.6% of the studied individuals from the Southeast region presented at least 30% of Asian contribution. From these individuals, 3% had more than 70% of Asian contribution.In this study, we aimed to determine whether a 61-AIM set with reported efficiency in estimating individual interethnic admixture in three ancestral groups  , but it can also identify substructure in admixed populations. When applied to such populations, this panel allows estimates of the individual and global interethnic admixture regarding the genetic contributions of these ancestry groups. Moreover, it was able to efficiently separate Asian and Native American populations, despite their proximity when compared to the other continental populations. Therefore, we showed in this study that the 61-AIM panel is a useful tool that could be valuable in studies in Brazilian populations, which is extremely important to avoid misinterpretations of the findings in association studies. To the best of our knowledge, this is the first study to apply an AIM panel to estimate the individual contribution of these four main parental populations  in an admixed population from Brazil.


Pacifastacus leniusculus) is a highly invasive species, which has caused mass declines of native crayfish in Europe through displacement and transmission of the oomycete Aphanomyces astaci (crayfish plague), which is typically lethal to native European crayfish. However, whether Aphanomyces astaci may have facilitated the invasion of the signal crayfish is not known. We estimated the genetic diversity at microsatellite DNA loci, effective population size, and potential origins of seven infected and noninfected signal crayfish populations in Europe and one founder population in North America. Approximate Bayesian computation analysis and population structuring suggested multiple host introductions from diverse source populations, as well as higher heterozygosity among infected than uninfected populations, which could reflect a fitness advantage. Low effective population size, moderate heterozygosity, and lack of isolation by distance suggest that some invasive signal crayfish populations may not be fully established or that their genetic diversity may have been reduced by eradication attempts.Successful establishment of non‐native species is strongly influenced, among other factors, by the genetic variation of founding populations, which can be enhanced by multiple introductions through admixture. Coexisting pathogens can also facilitate the establishment of non‐native species by detrimentally impacting on the native fauna acting as novel weapons. The signal crayfish ( Yet, the extent to which novel pathogens drive invasion success is controversial  is currently widespread across Europe despite having gone through a large bottleneck following a single introduction event of 90 individuals in 1890  and hand netting  following the manufacturer's instructions  and 90 s of extension at 72°C. Twenty‐four additional cycles of PCR were then run as above at an annealing temperature of 56°C followed by a single final extension cycle of 30 min at 60°C. Microsatellites were resolved on an Applied Biosystems ABI3130xl Genetic Analyser , and fragment length was determined using the GeneScan 500–LIZ size standard and scored using GeneMapper v45.0 (Applied Biosystems).2.3NEF) and the populations’ expected (HE) and observed heterozygosities (HO) respectively. Deviations from Hardy–Weinberg equilibrium and tests for linkage disequilibrium were investigated using GENEPOP online v4.0.10 , which weighs the contribution of each locus to the homozygosity index depending on their allelic variability, was estimated for each individual in Cernicalin v1.0  values for these individuals were unknown.MICRO‐CHECKER v2.2.3 . The number of clusters tested ranged from K = 1 to 9, with 20 repetitions for each K value, and 60,000 MCMC steps discarding the first 10,000 as burn‐in  were generated from the parameters prior distributions. Mean gene diversity across loci and mean M index diversity across loci (one sample summary statistics) were calculated for each population. Pre‐evaluation of each scenario was carried out by generating Principal Component Analysis (PCA) plots based on summary statistics using 30,000 (1%) simulated data sets and the posterior distribution of the parameters was estimated using the logit function 2 distance) to assess whether the observed data was included within the distribution of the predictive posterior parameters of the simulated data. Confidence in each scenario was obtained from the highest posterior probability using logistic regression, estimated by comparing the summary statistics from simulated and observed results, and from calculating type I and type II errors using 1000 simulated datasets  and plague intensity  in individual crayfish using population of origin as a random factor and individual homozygosity (HL) as a predictor with either a binomial logit link (infection status) or a Gaussian link  with the lme4 package in R, version 3.3.2.Population heterozygosity and effective population size were compared between infected and noninfected populations using a Welch 33.1p = 0.001), however results of repeated analyses  removing the affected microsatellites showed no obvious deviations from the results including all nine microsatellites (Supporting Information Table HE between 0.5 and 0.7) across all the sites. All populations displayed a degree of deviation from Hardy–Weinberg equilibrium (HWE) across various loci due to lower than expected HE. Of 81 Chi‐square tests conducted (one per locus) 37 showed a significant deviation from HWE (Supporting Information Table p < 0.0001). Tests for linkage disequilibrium (LD) for each locus pair across all populations (Fisher's method) revealed only three significant associations of 36 pairwise comparisons after Bonferroni corrections, these were between LPL26 and LPL40, LPL6 and LPL45 and LPL26 and Scop9.MICRO‐CHECKER results indicated that four microsatellites had significant evidence of null alleles (HE) across all populations ranged from 0.46 to 0.69 respectively. The mean effective number of alleles ranged from 2.25 in the Dderw site to 5.45 in the Gavenny site and for Lea the mean was 4.55. Across all loci, there was no significant difference in number of effective alleles (NEF) between populations . Effective population size  ranged between 12.9 (Sirhowy) and 90.4 , with the exception of the Mochdre and Gavenny sites, and the Mochdre and Lea sites . The AMOVA results for all sites indicated that variation among populations accounted for 15% of the genetic differentiation, while variance among individuals within populations accounted for 31% and the remaining 54% was due to intraindividual variation , apart from Sirhowy and Dderw . Geographically distant populations  displayed high levels of genetic similarity despite distance, which was reflected in lack of IBD in the Mantel test for UK populations .Pairwise population Analysis of the most likely scenario of colonization suggested the simultaneous divergence of populations (Scenario 1), based on logistic regression and PCA results Figure . ObserveHE = 0.527) compared to populations which were more admixed . Results from BARRIER suggested that the most likely number of discontinuities in genetic connectivity was due to three barriers  did not have a significantly lower heterozygosity , while their effective population size did not differ significantly between them .Infected crayfish populations had a significantly higher mean expected heterozygosity than uninfected ones (HL for each population ranged from 0.50 to 0.68 (0 being heterozygous and 1 being completely homozygous). Crayfish populations differed significantly in individual homozygosity , with infected populations having a significantly lower homozygosity by locus. Crayfish populations also differed significantly in plague infection loads , but homozygosity did not explain the probability that an individual would be infected  or the intensity of infection  when controlling for population of origin , which is similar to what is observed in other invasive crayfish species populations,  in China  values and the most likely colonization scenario support the assumption that current crayfish populations in Britain are not genetically homogenous, a phenomenon common in invasive species originating from different origins  will be archived in Dryad upon acceptance. Click here for additional data file.


AbstractUsneasubfloridana, a widespread lichenised fungus, focusing on a comparison of genetic variation of its populations amongst three geographically remote and disconnected regions, in order to determine relationships amongst environmental data, variation in lichen secondary chemistry and microsatellite data in genotyped populations. In all, 928 Usnea thalli from 17 populations were genotyped using seven specific fungal microsatellite markers. Different measures of genetic diversity  were calculated and compared between lichen populations. Our results revealed a low genetic differentiation of U.subfloridana populations amongst three distant areas in Estonia and also a high level of gene flow. The results support suggestion of the long-range vegetative dispersal of subpendulous U.subfloridana via symbiotic propagules . Our study has also provided evidence that environmental variables, including mean annual temperature and geographical longitude, shape the genetic structure of U.subfloridana populations in Estonia. Additionally, a weak but statistically significant correlation between lichen chemotypes and microsatellite allele distribution was found in genotyped specimens.Understanding the distribution of genetic patterns and structure is an essential target in population genetics and, thereby, important for conservation genetics. The main aim of our study was to investigate the population genetics of  The disentangling processes which shape genetic patterns and structure of natural populations is of great importance in understanding basic questions concerning evolution, ecology and conservation biology of species. The distribution of genetic diversity, which is a significant part of overall biodiversity, could indicate patterns of gene flow, genetic drift and potential for local adaptation . The vasUsneasubfloridana Stirt. has a wide distribution across Eurasia, Macaronesia and North America (Usnea species in Estonia (Northern Europe) being frequently found on Norway spruce (Piceaabies), Scots pine (Pinussylvestris) and Silver birch (Betulapendula), as well as other deciduous trees and lignum  within the mainland of Estonia or had occurred at least in the past  are highly variable DNA sequences of short tandem repeats of 1–6 bp with co-dominant inheritance and appear as widely used markers for studying genetic variation and structure of natural populations , the genes for which have been found in clusters  regions. The main aims of our research were: (i) to study the genetic differentiation of U.subfloridana populations, growing in the south-eastern and northern regions of mainland and on a western island in Estonia, Northern Europe; (ii) to compare the measures of genetic diversity of U.subfloridana populations amongst the three study areas; (iii) to find whether allele frequencies in studied populations correlate with environmental variables; and (iv) to check if there were correlations between lichen chemotypes and microsatellite allele distribution in genotyped data.In the present research, we studied the population genetics of Pinussylvestris-dominated boreal forests, being classified as the Oxalis-Vacciniummyrtillus, the Vacciniummyrtillus, the Calamagrostio-Pinetum and the Vacciniumvitis-idaea forest site types. These forest types are also widely distributed in other Baltic states , Põlva County, the south-eastern region of mainland (hereafter SE) and Hiiumaa County, the second largest western island (hereafter W) of Estonia, located in the Baltic Sea Fig. . Accordic states , in Fennc states  and in nc states .Usneasubfloridana populations, sampled from 17 study sites, three in N, eight in SE and six in W  was recorded for each sampled tree at breast height (1.3 m). The stand age was taken from the Fieldwork was carried out during the summer of 2011 (in SE), the autumn of 2014 (in N) and the autumn of 2016 (in W). The potential study sites for sampling were selected from forest survey maps using comparable forest characteristics (stand age and site type) from their forest survey . UsneasUsnea thalli were air dried, cleaned to remove other lichen specimens and examined under a stereomicroscope. Thin layer chromatography (TLC) with solvent A , according to the manufacturer’s protocol. Seven fungal microsatellite loci  were amplified in two multiplex PCR using QIAGEN Multiplex PCR Kit, following the instructions described in All collected olvent A  was usedU.subfloridana populations were calculated in the Microsatellite Analyzer ver 2.65 (MSA)  and allelic richness (A)) for 65 (MSA) . The meaSA)  (MSA . The numSA)  with 1023 permutations to estimate genetic differentiation were performed using ARLEQUIN ver 3.5  from populations of W, genotyped individuals (112 multilocus genotypes and 32 trees) from populations of N and genotypes individuals (452 multilocus genotypes and 166 trees) from populations of SE were analysed separately where each tree was treated as a distinct population. The fourth AMOVA was undertaken using all genotyped individuals (928 multilocus genotypes) and the fifth without identical multilocus genotypes or clone corrected dataset (403 multilocus genotypes). The rate of gene flow (Nm) across seven loci amongst 17 populations was estimated using GenAlex ver 6.5  was performed, implemented with the programme package Canoco 5.0 (RDA)   was perfRDA was implemented in the same programme package. The data matrix of frequency of 62 alleles in 17 populations was used. The number of records of each allele in each population was counted and log-transformed for data analyses. The same environmental variables as in the first RDA were used as the explanatory variables. In both RDA models, the interactive forward selection procedure with randomisation tests was employed to select the most important environmental variables influencing variation in response data, retaining variables with an independent significant contribution at the p < 0.05 level. Subsequently, variation partitioning analysis (VPA) in the same programme was employed. The unique effects of statistically significant explanatory variables and the shared proportion of variation, explaining the distribution of multilocus genotypes (the first RDA) or allele frequency in populations (the second RDA), was calculated. The statistically significant contribution of variables was tested by the permutation test .To assess the significance of the associations between allele frequency in the populations and environmental variables, the second U.subfloridana populations were recorded. The minimum number of alleles was four in locus Us04 and the maximum was 15 in locus Us03 and, on average, 2.8–9.1 were detected per locus across 17 populations. The mean number of alleles per population varied from 5.3 to 6.1 in populations on the western island in Estonia (W), from 5.3 to 6.2 in populations in the south-eastern region of mainland (SE) and from 4.9 to 5.4 in populations in the northern region of the mainland (N). There were 403 different multilocus genotypes across 928 genotyped specimens in 17 lichen populations. Allelic richness (A) varied from 4.86 to 5.94 across all lichen populations and Nei’s unbiased genetic diversity (H) ranged from 0.58 to 0.67  = 10.74, p = 0.001); H was higher in populations from W and lower in populations from N. The clonal diversity (M) also depended significantly on the region  = 5.62, p = 0.02); M was higher in populations from N and lower in populations from W. The allelic richness  = 2.83, p = 0.09) and private allelic richness  = 0.18, p = 0.83 did not differ amongst the three regions .The results of ANOVA showed that Nei’s unbiased genetic diversity (H) depended significantly on the region  was due to the differences amongst individuals within study tree and 0.5%, 0.3% and 0.7% of genetic variation  was found amongst populations (Table AMOVA results (928 genotypes) revealed that most of the total genetic variation (97.7%) was due to the differences amongst individuals within studied Usnea populations; a low proportion (1.8%) of genetic variation was attributed to regional differences  showed that most of the total genetic variation (98.7%) was also due to differences amongst individuals within studied Usnea populations and 1.2% of genetic variation was found between the regions . The results of variation partitioning analysis (VPA) showed that two variables, ‘Lichen substance’ and ‘Temperature’, represent 5.5% of the variation of U.subfloridana multilocus genotypes (adjusted variation), the former being 4.1% and the latter 1.3% of the total variation, while the co-effect of both variables was less than 0.1% (p = 0.0005).In the RDA with allele frequency data in studied populations accounted for 36.1% of variation in the response data. According to the results of the interactive forward selection of explanatory variables, the variables ‘Temperature’ and ‘Longitude’ contributed significantly to the explanation of variation in the response data . The results of variation partitioning analysis (VPA) showed that the two explanatory variables, ‘Temperature’ and ‘Longitude’, represent 27.0% of the variation of allele frequency data (adjusted variation), the former being 8.2% and the latter 7.6% of the total variation, while the co-effect of both variables was 11.2% (p = 0.0005). Alleles, occurring only (e.g. 326 and 346 (Us05), 201) or being more frequent (e.g. 322 and 330 (Us05), 195(Us08), 345 (Us09)) in populations of western Estonia with an average higher air temperature, are located in the positive side of the ordination plot. The alleles, occurring only (e.g. 362 (Us09)) or being more frequent (e.g. 168 and 180 (Us02), 181 (Us04), 332 (Us05), 198 (Us08)) in populations of south-eastern Estonia, are located on the negative side of the ordination plot  and northern (N) regions of the Estonian mainland and Hiiumaa (W), an island in the Baltic Sea  that had to travel several kilometres over the sea.Lichen-forming fungi, reproducing purely sexually, are assumed to have a longer dispersal distance and exhibit less genetic structure than clonally reproducing species via isidia/soredia or fragments of thalli . HoweverSea Fig. ; Table 2ons Fig. . Our preulations . However Estonia . A possilly e.g. . AccordiLobariapulmonaria populations in the Iberian Peninsula . Nephromaparile (Ach.) Ach. and N.laevigatum Ach. were also related to measured environmental and habitat variables, indicating micro-evolutionary responses to the environment. Our studies showed that genetic diversity (H) of Usneasubfloridana populations was higher on Hiiumaa island while clonal diversity (M) was higher in the northern region (N) of Estonia. This is contrary to our a priori assumption that relatively isolated populations, occurring on islands, will exhibit a lower genetic diversity and higher levels of clonal diversity. Moreover, the allele distribution in studied populations correlated with a mean annual temperature and geographical longitude  indicated that, for example, several alleles occurring only or being more frequent are found in the populations of western Estonia with a higher average air temperature while other alleles, occurring only or being more frequent in populations of south-eastern Estonia, are related to a lower average air temperature  Bory, in the study delimiting species within the genus UsneasubgenusNeuropogon. However, the chemical variation across these species was not randomly distributed between the two morphotypes nor amongst the genetic clusters  regions in Estonia. We recorded a very low genetic differentiation of U.subfloridana populations amongst three distant areas, suggesting spatially unrestricted dispersal of individuals and unconstrained gene flow in U.subfloridana populations. Furthermore, geographical longitude and the mean annual temperature might play an important role in forming genetic variation in U.subfloridana populations in Estonia. This work contributes to the existing knowledge of population genetics of highly clonal and complex organisms, such as lichens.We studied the population genetics of


Picea species  at 400K SNPs using exome capture to infer the past demographic history of Norway spruce (P. abies) and estimate the amount of recent introduction used to establish the Norway spruce breeding program in southern Sweden. Most of these trees belong to P. abies and originate from the base populations of the Swedish breeding program. Others originate from populations across the natural ranges of the three species. Of the 1,499 individuals stemming from the breeding program, a large proportion corresponds to recent introductions from mainland Europe. The split of P. omorika occurred 23 million years ago (mya), while the divergence between P. obovata and P. abies began 17.6 mya. Demographic inferences retrieved the same main clusters within P. abies than previous studies, that is, a vast northern domain ranging from Norway to central Russia, where the species is progressively replaced by Siberian spruce (P. obovata) and two smaller domains, an Alpine domain and a Carpathian one, but also revealed further subdivision and gene flow among clusters. The three main domains divergence was ancient (15 mya), and all three went through a bottleneck corresponding to the LGM. Approximately 17% of P. abies Nordic domain migrated from P. obovata ~103K years ago, when both species had much larger effective population sizes. Our analysis of genomewide polymorphism data thus revealed the complex demographic history of Picea genus in Western Europe and highlighted the importance of material transfer in Swedish breeding program.Primeval forests are today exceedingly rare in Europe, and transfer of forest reproductive material for afforestation and improvement has been very common, especially over the last two centuries. This can be a serious impediment when inferring past population movements in response to past climate changes such as the last glacial maximum (LGM), some 18,000 years ago. In the present study, we genotyped 1,672 individuals from three  SSR markers define two well‐differentiated groups: southern and northern P. abies, on the one hand, and Siberian spruce, on the other hand , that is, before the Pliocene–Quaternary glaciation, which is consistent with estimates of dating based on the molecular clock  provide us with the opportunity to investigate genomewide pattern of diversity in hundreds or even thousands of individuals. Using whole‐genome polymorphism data, model‐based and nonparametric clustering methods allow detecting and quantifying subtle genetic admixture and migration  populations from southern Sweden that were used to establish the Swedish Norway spruce breeding program, (b) natural populations of P. abies across its natural range, and (c) two close relatives, the Siberian spruce (P. obovata) and the Serbian spruce (P. omorika).In this paper, we thus investigated past demographics and recent translocations in 22.1Picea species  with origins extending from 43.0°N to 67.3°N in latitude and from 7.3°E to 65.8°E in longitude  were collected after seed germination in growth chambers in 2015. Those individuals were used as reference for genetic cluster definition and genotype assignation. P. obovata samples were collected from four Siberian populations: three along a longitudinal gradient in Southern Urals: Shalya , Ekaterinburg  and Tugulym  and one in the Northern Urals: Krasnij Kamenj . Two additional populations that are part of the P. abies–P. obovata hybrid zone were also included as follows: Indigo, from high latitude  and Kirov, from a lower latitude , the former having a more even contribution of the two parental species , and INDEL realignment was performed by GATK . 40,018 probes of 20 bp long were designed based on scaffolds of π0 and π4, respectively). Their ratio was then calculated for protein coding sequences of P. omorika, P. obovata, and the three main domains of P. abies . Tajima's D  were also calculated using polymorphisms in noncoding regions. The above summary statistics were calculated with custom Perl scripts .Genetic diversity was calculated at all sites and also at 0‐fold  and fourfold sites  varied from one to eight, and the best value was chosen at the lowest value of cross‐validation error  were first estimated. P. omorika was used to polarize the derive allele frequency, and shared polymorphic sites between P. omorika and target populations were excluded for stringency. In pilot runs to maximize the likelihood for individual SFS, we noted that estimates of historical Ne for all three populations are much larger than current ones suggesting that current populations had gone through bottlenecks. Therefore, a divergence model was applied with all three populations going through a sudden size contraction at TBot. The Fennoscandian domain first split at time TFAC, followed by the split of the Alpine and Carpathian domains at TAC. To reduce model complexity, we used a constant migration rate  between all pairs of populations averaged along the whole divergence history. To examine whether migration could improve model composite likelihood, models with m = 0 and m = 1 × 10−6 were compared based on Akaike's weight of evidence. The aforementioned parameters were estimated by maximizing composite likelihoods based on observed 3‐dimensional joint SFS using Fastsimcoal2 v2.6.0.2 , where CLO and CLE are the observed and estimated maximum composite likelihood, respectively) was computed to evaluate model goodness of fit. A nonsignificant p‐value of this test indicates that the observed SFS is well explained by the model. In a second step, demographic inference was extended to all three spruce species using pairwise joint minor allele frequency spectra. For the three major domains of P. abies, parameter values that were estimated during the first step were used  and their ratio calculated for protein coding sequences of P. omorika, P. obovata, and the three main domains of P. abies . On average, we used around 30,000 SNPs in 7,453 genes for each calculation. P. omorika harbored the lowest diversity (π4 = 0.0066), while P. obovata and P. abies exhibited larger values, ranging from 0.0072 to 0.0079. We found less difference in π0 values among the three species (0.0029–0.0032). This led to large π0/π4 ratios in all three species but especially in P. omorika , for P. abies (−0.32 to −0.42) and for their hybrid population in Kirov (−0.296). The positive value of Tajimas' D observed in P. omorika (0.875) likely reflects the sudden and very recent population contraction experienced by the species.Genetic diversity was calculated at 0‐fold and fourfold sites (FST) in noncoding regions were highest between P. omorika and P. abies domains (average FST = 0.66). The Fennoscandian domain was more closely related to P. obovata (FST = 0.13) and to the hybrid population (FST = 0.1) than the two other P. abies domains were  was conducted on genetic variation of s Tables  and 3. We Figure a,b. SeveP. abies populations from which other clusters are derived through admixture. For instance, the Russian–Baltics populations include components from the Carpathians and Fennoscandia, as well as a small fraction from P. obovata. Central European populations derived from admixture between the Alpine and Carpathian domains. Trees from Northern Poland are similar to trees from the Russian–Baltics domain but also contain an additional contribution from the Alpine domain. The Swedish populations are particularly complex: Some trees clustered with Alpine ones, others with Fennoscandia and the Russian–Baltics domain, and a small fraction correspond to admixture between the Alpine and Fennoscandian domains.To understand the history of the clusters detected in the PCA, we turned to unsupervised clustering analysis . Each individual was assigned to one of the seven clusters identified in P. abies populations by PCA, and a confidence value was computed by bootstrapping over 100 subsets of total samples. In total, 560 out of 575 (97.4%) of the individuals whose ancestry was unknown  could be assigned to one of the seven P. abies clusters with an average probability over 0.905 based on genetic similarity . The results thus suggest that the genomic markers we used in this study are powerful enough to give a high‐resolution picture of the recent divergence history in P. abies. Hence, the method could serve as a rather efficient way to identify translocation material used for reforestation.As noted above, information on the exact origin of 575 individuals of the breeding population was missing. We therefore used the PCA coordinates of these individuals to assign them to one of the seven ty Table , the 15 P. abies , (b) from the hybrid to the Russian–Baltics cluster (39.0%), and (c) from P. obovata to the Fennoscandian cluster (12.7%)  indicating admixed components in the Russian–Baltics individuals while 11.5%–22% of 3f‐tests supported admixed components in Central Europe and in Fennoscandia populations (mainly from P. obovata and hybrid ancestry).Previous analyses revealed the importance of admixture event in s Figure . They we) Figure . Extensi3.4P. abies clusters, Alpine (ALP), Carpathian (CAR), and Fennoscandian (FEN), which also represent the three main ancestry components of P. abies in the previous analyses. Assuming an average migration rate of 10−6 leads to similar estimates of Ne for the three P. abies domains, around 6,000 to 8,000 , with CAR and FEN populations slightly larger than ALP  of models with migration free to vary.Average migration rates (m) were set to 10G‐statistic test. For multidimensional model fitting, the p‐value is highly significant which suggests that our model is certainly oversimplified and does not capture the full history of the species. However, likelihood ratio test based on a joint 2‐dimensional SFS supports our divergence model , which is expected as the species is endangered and is part of the IUCN Red List. The natural range of P. omorika is believed to have been under continuing contraction since the LGM, but our estimates suggested a severe and recent bottleneck  around 2,800 years ago. P. obovata has the largest Ne of the three species, with a value ~ 35,500. Unlike the other two species, no recent size contraction was detected in P. obovata after the end of LGM. The split of P. omorika occurred 23 mya, while the divergence between P. obovata and P. abies began 17.6 mya. Approximately 17% of P. abies FEN population migrated from P. obovata 103,000 years ago, when both species had much larger effective population sizes (6 × 105 for FEN and 5.6 × 106 for P. obovata). The hybrid population has a small effective population size around 400 and was established ~1,600 years ago, with almost equal contribution from both species   reaches equilibrium faster than synonymous diversity (π4), after a bottleneck , due to the strong bottleneck experienced by the species as confirmed from our demographic simulations. Interestingly, a similar decrease in genetic diversity was observed by Kuittinen, Muona, Karkkainen, and Borzan (P. omorika and P. abies (expected heterozygosity He = 0.13–0.15 in P. omorika versus 0.22 in P. abies). The estimates of synonymous diversity in the other two species were close to the value reported in interior spruce (0.0073) by Hodgins, Yeaman, Nurkowski, Rieseberg, and Aitken (Pinus contorta) may have led to strongly underestimate the genetic diversity at nonsynonymous sites compared to synonymous sites. In general, the π0/π4 ratio of spruce species is among the highest among perennial plants though it is still lower than in Quercus robur (~0.5) and other oak species   and the periods that followed  in 4.4The presence in central and southern Sweden, as well as Denmark, of trees with ancestry tracing back entirely, or almost entirely, to the Alpine or the Carpathian domain, reflects recent introductions. That these trees are recent introduction is supported by the fact that those individuals could be assigned to the same clade of Baltic and central European populations with very high confidence in both PCA and TreeMix analyses. Most likely, these genotypes with an important Alpine or Carpathian component reflect translocations from central European countries that took place during the massive reforestation of the twentieth century. Indeed, since the 1950s, Sweden and Norway, and to a lesser extent Finland, started to import seeds for forest reproduction material from the Belarus, Czech Republic, Germany, Slovakia, and the Baltic States .In contrast to other spruce species that had a negative Tajima's Aleksić, , whereass. Gigov  analyzedP. omorika, the three major domains of P. abies had similar effective population sizes, with the Alpine domain only slightly smaller. The three domains almost split at the same time and thereafter remained rather stable until the end of the GLM. P. obovata had a much larger population size and the bottleneck occurred earlier, around the end of the Eemian interglacial to the beginning of the last glacial. These results possibly reflect the fact that spruce species, as well as other tree species, especially in Siberia, actually occupied a much larger area before and during the LGM than assumed under the “southern refugia” paradigm that has dominated phylogeography until recently , much older than the 5–6 mya from Lockwood et al. (P. abies and P. obovata to be around 18 mya and the split with P. omorika to be around 23 mya. This latter estimate is consistent with a divergence of the Eurasian spruce clade around the early to the middle Miocene (13 ~ 23 mya), as inferred from molecular clock dating methods  and derived ones. As in Tsuda et al. (P. obovata extended as far west as Fennoscandia and the Baltics.Two main conclusions emerge from the present work. First, the current distribution of a et al. , we obseP. abies and this is the object of a companion paper (Milesi et al., Second, a large proportion of the trees selected in southern Sweden to establish the Swedish breeding program were shown here to be recent introductions from Central Europe. This agrees well with previous studies of the release of “alien” material in Sweden (Laikre, Palme, Josefsson, Utter, & Ryman, None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Analyses of the control region sequences of European brown trout populations’ mitrochondrial DNA have revealed five main evolutionary lineages  mostly relating to the main water basins; however, the hybridization between lineages were increasingly reported. Due to the hydrogeography of Hungary, wild populations should theoretically belong to the Danubian lineage, however, this has not been verified by genetic studies.In our study multiple molecular marker sets  were used to investigate the genetic composition and population genetics of the brown trout populations in two broodstocks, six wild streams in Hungary and one Serbian population.The admixture of Atlantic and Danubian lineages in these populations, except the Serbian population with pure Danubian origin, was observed by control region sequences of mitochondrial DNA and PCR-RFLP markers in the nuclear genome, and one unpublished Danubian haplotype was found in Hungarian populations. A sex-specific marker revealed equal gender ratio in broodstocks and Kemence stream, whereas in other wild streams the proportion of female individuals were less than 50%. Structure and principal component analyses based on the alleles of microsatellite loci also revealed overlapping populations, however the populations were still significantly different from each other and were mostly in Hardy-Weinberg equilibrium.Stocking and migration can have a significant genetic impact on trout populations of wild streams, however there are no guidelines or common practices for stocking of small streams in Hungary, thus the genetic background of these populations should be considered when developing conservation actions. The family Salmonidae consists of three subfamilies , which include a diverse group of fishes with sixty-eight species . In the  is a native salmonid species in Eurasia  with poorly known stocking history and varying degree of angling pressure in the drainage system of the Danube in Hungary between 2011 and 2014 , Table 14)2SO4 , 200 µM dNTP mix, 250 nM for each primers, 1,5 mM MgCl2, 100 ng template and 1 U Taq polymerase (Fermentas). The following PCR cycles were used: 3 min preamplification denaturation at 94 °C, then 30–60 s at 94 °C, 30–90 s at annealing temperature  and 1 min at 72 °C for 35 cycles. As a final step, products were fully elongated for 5 min at 72 °C. The reactions were checked on 1% of agarose gels, containing 0.5 µg/ml ethidium bromide. The microsatellite fragments were analysed on the ABI 3130 sequencer with POP7 polymer based on the various dyes.The control region (CR) sequences of mitochondrial DNA (CR mtDNA), and PCR-RFLP of the same region (CR) as well as two nuclear genomic loci  were used to distinguish the Atlantic and Danubian alleles/haplotypes. In addition, five microsatellite loci were used to analyse the genetic composition of the populations. Amplification and digestion methods were used according to 434 on the amplified CR mtDNA fragment of Atlantic haplotypes and no cut represents in Danubian haplotypes. Endonuclease BslI also generates a single cut on Atlantic lineage-specific allele of amplified LDH fragment at the polymorphic site G353 and no cut represents in Danubian alleles (EU672412) and Danubian (EU672413) sequences in GenBank, endonuclease MspI generates no cut on Atlantic and a single cut at 212 bp on the Danubian lineage-specific allele of the SL sequences in GenBank. The alleles were separated on 2% agarose gels, containing 0.5 µg/ml ethidium bromide.Following the amplification, CRmtDNA fragments were digested with Fnu4HI (SatI) , LDH-C1 fragments with BslI  and the SL fragments with MspI (Thermo Fisher Scientific) restriction endonucleases. Endonuclease Fnu4HI generates a single cut at the polymorphic site C alleles . Based oFor the comparison of populations based on haplotypes of CR mtDNA, randomly chosen samples (at least 10%) of each sample group were analysed by sequencing of the same control region used in RFLP . For bi-AY185568) and Atlantic (AY185577) reference sequences (π) of the control region per populations were analysed in DnaSP 5.10.01 , effective allele number (Neff), observed (Ho) and expected (He) heterozygosity per locus and per population were calculated using GeneAlEX 6.5 . In caseDigested fragment-length analysis of CR mtDNA revealed the pure Danubian origin of the Serbian control population, whereas the Atlantic lineage were found in all Hungarian sites . There whttps://www.ncbi.nlm.nih.gov/) with accession numbers from MG751088 to MG751096. The analysis of sequence variation has revealed nine haplotypes   has not been described previously and was represented in only one individual of the KE stream. This haplotype also belongs to the Danubian group based on the median-joining network. The average divergence within the Danubian group was 0.0021, within the Atlantic group 0.0017 and between the groups 0.0112. The divergence of the newly found Da6 haplotype was 0.0013 from Da3, Da5 and 0.0027 from other Danubian haplotypes (The median-joining network  indicateplotypes . The medThe most abundant Danubian haplotype (Da1) appeared, beside the Serbian control, in the wild populations and the SZV broodstock. Da2 haplotype was detected in both hatcheries and one wild population while Da3 in one wild stream as a private haplotype with low frequency. Da4 haplotype was represented only in the hatcheries and Da5 haplotype was detected only in the SZV broodstock. The most frequent Atlantic haplotype (At1) was found in all populations with the exception of the Serbian control. In addition, two further Atlantic haplotypes were detected in the Hungarian populations, At2 haplotype was represented in the JO and KO streams as well as the LF broodstock 1, and the At3 Atlantic haplotype was found only in the JO stream as a private haplotype of this stock. The haplotype diversity of the sample set was 0.670 and the nucleotide diversity 0.005.Within nuclear markers, LDH and SL loci were analysed using digested fragment-length polymorphism. These loci also verified the pure Danubian origin of the Serbian control population, whereas varying proportions of both lineages were found in all Hungarian sites . In bothFIS values of nuclear PCR-RFLP loci indicated a significant excess of heterozygosity in LF broodstock 1 and KE stream at p < 0.01 , which were mainly related to OMM1064 and Ssa408uos loci. The highest number (11) of private alleles was revealed in the two Lillafüred broodstocks  and intermediate levels between Hungarian populations and the Serbian control (0.222–0.369) . Beside the Serbian control, BI and KO streams were the most divergent sites among the Hungarian populations. All FST values of microsatellite analyses showed a significant divergence among populations. In case of the nuclear PCR-RFLP markers only the Serbian control was significantly divergent from all other populations and was highly distinct from them (0.558–0.920). Within the Hungarian populations low and intermediate levels of significant genetic divergence were found (0.027–0.231) and again, the BI, KO and KE streams were the most divergent sites.Pairwise 2–0.369) . The meaThe Nei’s Da genetic distance based NJ tree of populations  also supAnalysis of all populations by STRUCTURE did not give a clear ΔK result, revealing two, three and five possible clusters of the sampled area. The highest probability of cluster numbers was at two, while the lowest at the five value . Figure The PCA analysis showed very similar, but more structured clustering. The individuals were separated to three main groups by the horizontal axes while the distribution of the population were continuous along the vertical axes . Both thAMOVA supports the results of STRUCTURE and PCA displaying a low variance among populations  while 90% of the variance was found within populations . These in turn are mainly related to the variance within individuals  and only low variance was found among individuals .Very little information is available on the origin of the current brown trout populations in Hungary and what is known comes mostly from anecdotal information. The reason for this is that prior to World War I, these watercourses were mostly neglected by anglers and aquaculturists who preferred the more typical salmonid waters of the Carpathian mountains in today’s Slovakia, Ukraine and Romania. The interest in the salmonid waters of the low mountain ranges of today’s Hungary has increased following the Paris peace treaty of 1920. The Lillafüred trout farm has been founded in 1932 and the first shipment of trout eggs has arrived from Traismauer, Austria and Kláštor pod Znievom, Czechoslovakia (today Slovakia) in 1933. This broodstock was later the main supply of fish stocked into various streams in Hungary. Stocking of fish from this broodstock has been documented in the Bán and Jósva streams investigated in this study .This study is the first genetic diversity assessment of wild brown trout populations of Hungary. In addition, it offers further information regarding the two Hungarian broodstocks. The sample sizes in the case of some populations are low, especially in the KO, BI and SRB populations. These were however limited by the circumstances of sampling, hydrogeographical features and the abundance of fish in the given watercourses. For instance, in the Bittva stream (population BI) only three locations were available for sampling  at a distance of several km from each other. Effective analysis of genetic mixing using low-marker number has been proved in several studies, as well as in case of similar marker set as we used . HoweverThe mixed origin of the broodstocks and genetic admixture of Atlantic and Danubian lineages in the wild brown trout populations were revealed using multiple marker sets. High proportions of Atlantic haplotypes and alleles were found in the Danubian basin. All Atlantic haplotypes found in this study were frequently detected in other countries  as well as from Austrian, German and Danish hatcheries that held breeders of Atlantic origin. Better performance of breeders with non-native alleles in hatchery conditions would explain their presence in all broodstocks . Their fFST values, as it was found in other studies, too  based on ies, too . This caOn the other hand, none of the analysed populations displayed significant deviation from the normal composition according to the results of Hardy–Weinberg equilibrium analyses. Only the PCR-RFLP marker loci in all broodstocks and a few streams showed a significant excess of heterozygosity, but in most cases the microsatellites also showed a tendency of heterozygosity excess. This could have been caused by anthropogenic effects (mixing of populations) or due to the low number of the available samples. These results refer to the post-stocking analyses of domesticated fishes in different rivers where fishes with poor performance were found . ResultsMore detailed evidence of the admixture was shown by MJ tree, STRUCTURE plot and PCA results, which implies to anthropogenic transfer of fish among populations. However the STRUCTURE Harvester could not clearly determine the number of the genetic clusters, the colour plots of STRUCTURE showed the most possible differentiations of populations with five clusters. The genetically clustered groups are dispersed over the populations that correspond with the outcome of the Median-joining network analysis of mitochondrial haplotypes. It was confirmed by PCA analyses, that also showed five genetic groups , but the classification of the individuals was more clear than in STRUCTURE. Because of these we recommend the usage of PCA analyses next to STRUCTURE. Although, we have used only a few microsatellites, the admixture among most streams and the cultured Szilvásvárad stock is evident. Only the geographically district Serbian population is separated (as it was expected), and the Apátkut, Kölöntés and the Lillafüred broodstock show lower overlaps with the genotype of the Szilvásvárad broodstock. However, all populations carry the elements of their ancient genetic backgrounds, which is shown by the relatively large number of private alleles (42),  and the fact that we could not identify non-admixed individuals. The identification of admixed and non-admixed (if any) individuals would be possible by increasing the number of analysed loci as it was illustrated by Currently no regulations exist in Hungary on stocking of natural watercourses with non-indigenous populations beyond the level of species. Although stocking is considered an important conservation activity of autochthonous populations , but theThe first comprehensive study of Hungarian brown trout cultured broodstocks and wild streams revealed genetic admixture of the lineages in these populations, which could derive from natural or anthropogenic sources. Currently, there are no guidelines or common practices for stocking of small streams; however, the genetic background of these populations should be considered when developing conservation actions.10.7717/peerj.5152/supp-1Table S1The number of base substitutions per site from between sequences are shown, analyses were conducted using the Tamura 3-parameter model. Da, Danubian haplotypes, At, Atlantic haplotypesClick here for additional data file.10.7717/peerj.5152/supp-2Table S2∗P < 005, ∗∗P < 0.01, ∗∗∗P < 0.001.Pop, population, N, number of samples, Na, number of alleles, Nma, mean number of alleles, Neff, effective allele number, Ar, Allelic richness, He, expected heterozigosity, Ho observed heterozigosity, Sign, significant deviation, NA, not available, ns, not significant, Click here for additional data file.10.7717/peerj.5152/supp-3Table S3Click here for additional data file.10.7717/peerj.5152/supp-4Table S4Click here for additional data file.10.7717/peerj.5152/supp-5Figure S1Click here for additional data file.10.7717/peerj.5152/supp-6Supplemental Information 1Click here for additional data file.