Hordeum vulgare L. ssp. spontaneum) populations of the Southern Levant using genotyping by sequencing (GBS) of 244 accessions in the Barley 1K+ collection. The inference of population structure resulted in four genetic clusters that corresponded to eco-geographical habitats and a significant association between lower gene flow rates and geographical barriers, e.g. the Judaean Mountains and the Sea of Galilee. Redundancy analysis (RDA) revealed that spatial autocorrelation explained 45% and environmental variables explained 15% of total genomic variation. Only 4.5% of genomic variation was solely attributed to environmental variation if the component confounded with spatial autocorrelation was excluded. A synthetic environmental variable combining latitude, solar radiation, and accumulated precipitation explained the highest proportion of genomic variation (3.9%). When conditioned on population structure, soil water capacity was the most important environmental variable explaining 1.18% of genomic variation. Genome scans with outlier analysis and genome-environment association studies were conducted to identify adaptation signatures. RDA and outlier methods jointly detected selection signatures in the pericentromeric regions, which have reduced recombination, of the chromosomes 3H, 4H, and 5H. However, selection signatures mostly disappeared after correction for population structure. In conclusion, adaptation to the highly diverse environments of the Southern Levant over short geographical ranges had a limited effect on the genomic diversity of wild barley. This highlighted the importance of nonselective forces in genetic differentiation.Determining the extent of genetic variation that reflects local adaptation in crop-wild relatives is of interest for the purpose of identifying useful genetic diversity for plant breeding. We investigated the association of genomic variation with geographical and environmental factors in wild barley ( Local adaptation is an essential survival strategy for plants in stressful environments because they are sessile. Natural selection in heterogeneous environments leads to increased fitness of local genotypes. Gene flow can, however, offset genetic differentiation resulting from local adaptation and reduce fitness (Kawecki and Ebert Hordeum vulgare L. ssp. spontaneum) is a highly suitable model species for studying the local adaptation of crop-wild relatives, as it occurs over a wide geographical range in the Fertile Crescent and Central Asia  with a resolution of 90m, and (4) geographical coordinates of collection points  climate data from the n points ; File S1 climate n points , highly n points . We thenALStructure  as the rank of a matrix consisting of individual-specific allele frequencies  and neighbor-joining (NJ) were also performed. Missing genotypic values (~3% of the dataset) were replaced by the average number of alternative alleles at each SNP locus before performing PCA.The number of ancestors and ancestry coefficients were estimated using the model-based method ies Leek . OptimalFST and Nei genetic distance between genetic clusters defined by ALStructure. Accessions were assigned to genetic clusters according to the highest ancestry coefficient calculated by ALStructure with the optimal K value. FST values were calculated as ratio of average values , and the results of the three runs were averaged. Each MCMC chain encompassed 10 million burn-in iterations and 10 million post-burn-in iterations thinned by an interval of 5000 iterations. Outputs of EEMS were processed using the R package rEEMSplots. To examine whether geographical barriers contribute to genetic isolation, we separated map pixels into barrier and non-barrier pixels according to geographical elevation  as the model ranking standard with 1,000 iterations. Default parameters were used for the ResistanceGA framework.To account for non-independence between observations, we used Peterman  to assesEEMS, unbundled principal components  was constructed in line with the method of Forester et al.  were used in RDA  was evaluated as the pairwise K as covariates. A statistical framework proposed by Capblancq et al.  method, RDA has high detection power and a low false-positive rate in identifying adaptation signatures  enrichment analysis with gene annotations of the barley ’Morex v2’ genome Mascher . Over-rer2 threshold of 0.1. For analyses of B1K+ accessions, we selected 58,616 SNPs with an overall missing proportion of 0.029 and maximal individual missing proportion of 0.059. Further filtration resulted in 19,601 SNPs  and 27,147 SNPs  ≥ 0.05 among 72 IPK accessions originating from 13 countries  accessions had a highest ancestry coefficient of less than 0.9. The first three principal components (PCs) represented the clusters corresponding to the ALStructure results  if the HOH accessions were excluded (File S2).The inference of population structure among B1K+ accessions with FST and Nei’s genetic distances. The Southern Desert cluster was most strongly isolated from the other three clusters  to North (region B) in the western region  to South (region I) in the eastern region  to the surrounding areas of Mount Carmel (region E). However, the gene flow in the opposite direction (region E → H) was much weaker  and its adjacent regions indicated the isolation of Southern Desert accessions, consistent with the high genetic differentiation suggested by the FST values  of SNP variation while spatial autocorrelation captured by dbMEMs, which are eigenfunctions of a spatial network  was solely explained by environmental variables  as covariates in partial RDA when examining the effect of environmental variables on SNP variation. The partial RDA indicated that population structure explained 15.43% (p = 0.0002) of SNP variation, and environmental variables solely explained 8.71% (p = 0.0002) of SNP variation when conditioned on population structure  and “CoefVar_Rain” , were the most influential predictors on the first RDA axis  with RDA on ancestry coefficients. As expected, a high proportion of population structure that was explained by environmental variables  of population structure could be solely explained by environments whereas spatial autocorrelation accounted solely for 39.87% (p = 0.0002) of population structure , we first estimated LD decay to assess whether the marker density of the reduced-representation data was sufficient to accurately identify adaptive genes in these scans. We fitted the ly Fig. S. Given tBAYPASS, identified 279 candidate SNPs (XTX > 11.05). However, candidate SNPs detected by the four methods hardly overlapped, except simple RDA and BAYPASS with 125 common SNPs, 91 of which were located in pericentromeric regions of chromosomes 3H, 4H, and 5H  which encodes an ATP-dependent RNA helicase. The second gene HORVU.MOREX.r2.4HG0314300 is linked to SNPs associated with ’Elevation+Temperature’. It encodes a nucleolar GTP-binding protein , respectively, and the outlier method, Ps XTX > .05. HoweOur study indicated that geography and spatial autocorrelation were better predictors of genomic variation than environmental gradients even though the diverse environments of the Southern Levant are expected to impose strong natural selection  on a PCA Fig. . The PCAEEMS  were the most important drivers of genomic variation  and population structure (83%) were explained by spatial autocorrelation  among populations (Günther and Coop Arabidopsis lyrata (Berner and Roesti πa/πs) than other genomic regions (Baker et al. Significant polymorphisms hardly overlapped between methods Fig. B. This oWe observed a stronger effect of nonselective factors such as geography and isolation-by-distance on total genetic diversity in the wild barley populations of the diverse and stressful environments of the Southern Levant. Nevertheless, natural selection has a small but significant influence on genomic variation. This might be potentially valuable for barley breeding because water availability, i.e., precipitation and soil water capacity, was the most strongly correlated environmental variable. Outlier test and simple RDA identified genomic regions that might contribute to local adaptation, but these regions were not robustly identified by the different tests applied. One limitation of our study was therefore that only a small proportion of the wild barley genome was sequenced by the GBS approach. This was suitable for analyzing genome-wide patterns of variation and mapping of causal genes (Milner et al. Supplementary text, tables, and figuresFile S1File S2File S3File S4File S5


Diopatra (Onuphidae), which have a history of being transported through anthropogenic activities. Because they build tubes and act as ecosystem engineers, they can have a large impact on the overall ecosystem in which they occur. One conspicuous member, Diopatra biscayensis, which was only described in 2012, has a fragmented distribution that includes the Bay of Biscay and the Normanno‐Breton Gulf in the English Channel. This study explores the origin of these worms in the Normanno‐Breton region, which has been debated to either be the result of a historic range contraction from a relic continuous population or a more recent introduction.Evolutionary history of natural populations can be confounded by human intervention such as the case of decorator worm species Northeastern Atlantic, the Bay of Biscay, and the Normanno‐Breton Gulf.D. biscayensis biogeographic distribution. The reduced genomic data set was used to calculate standard genetic diversities and genetic differentiation statistics, and utilized various clustering analyses, including PCAs, DAPC, and admixture.We utilized a RAD‐tag‐based SNP approach to create a reduced genomic data set to recover fine‐scale population structure and infer which hypothesis best describes the D. biscayensis as a single population spanning the Bay of Biscay to the Normanno‐Breton Gulf in the English Channel, although unexpected genetic substructure was recovered from Arcachon Bay, in the middle of its geographic range. Consistent with a hypothesized introduction, the isolated Sainte‐Anne locality in the Normanno‐Breton Gulf was recovered to be a subset of the diversity found in the rest of the Bay of Biscay.Clustering analyses were consistent with D. biscayensis phylogeographic connectivity is the result of introductions, likely through the regions’ rich shellfish aquaculture, and not of a historically held range contraction.These results are congruent with previous simulations that did not support connectivity from the Bay of Biscay to the Normanno‐Breton Gulf by natural dispersal. These genomic findings, with support from previous climatic studies, further support the hypothesis that  Diopatra biscayensis in the Normanno‐Breton Gulf, a region with a rich aquaculture history. A RAD‐tag based SNP approach was used to assess the fine scale population structure and infer whether D. biscayensis’ distribution was likely the result of a historic range contraction from a relic continuous population or a more recent introduction. This study explores the origin of the decorator worm species  Large species of Diopatra have present‐day phylogeographic patterns that are particularly difficult to interpret as they have been moved for sale as fishing bait, in association with shellfish aquaculture, and have undergone natural shifts in ranges partly in response to climate change , to the Normanno‐Breton Gulf, near Champeaux, France , on the English Channel , but approximately 450 km of coastline further into the English Channel and around the tip of Brittany, there are a number of localities hosting D. biscayensis within the Normanno‐Breton Gulf. What is not well understood is whether the disjunct field sites inhabited by D. biscayensis are the result of natural or anthropogenic causes.Two conspicuous species of D. biscayensis are from collections from Spanish sites in the early 1900s. These were misidentified as D. neapolitana at the time of collection . However, available genetic data are based solely on mitochondrial, single‐locus markers, which have limited resolution over the timescale of interest. Thus, to distinguish between the human‐mediated transport hypothesis and the range reduction hypothesis , and it is heavily decorated with debris such as shell fragments, and the aperture typically points downward . Sample libraries were dual‐barcoded and sent to Hudson Alpha Institute for Biotechnology  for sequencing on an Illumina HiSeq using 50 bp single‐end chemistry.DNA was extracted from individuals of g et al. , which eAlfI recognition site (scripts from https://github.com/Eli‐Meyer/2brad_utilities). Retained filtered sequences were then assembled using the Stacks v 2.1 . Retain2.3BayeScan v.2.01 , a secondary run at both priors recovered no additional loci potentially under selection , found that over multiple simulations K = 1 had the highest likelihood, with K = 2 having the second highest support. In conjunction with the analyses of genetic distances, DAPC and Admixture analyses were influenced by the genetically distinct Arcachon locality, with sharp drop‐offs in support for K = 2 when this location was removed from the data set. We conservatively estimate, henceforth, that D. biscayensis, given its geographic range from the Bay of Biscay to the Normanno‐Breton Gulf, is a singular population.Both DAPC and D. biscayensis is a singular population over this geographic range. Figure Principal component analyses Figure  recoveredivMigrate further support these findings  genetic signature would have been expected. An anthropogenic‐based introduction to the Bay of Mont‐Saint‐Michel is further supported by the fact that the lecithotrophic larvae of D. biscayensis have limited dispersal capability, <<50 km , indicative of an introduction rather than an historic range contraction  retained the largest window space in the convex hull analysis Figure , indicatD. biscayensis’ reproduction  gigas. Consistent with this notion, there is other abundant evidence for the association between aquaculture and the introduction of exotic species into Europe and secondary introductions among areas with aquaculture (Goulletquer & Le Moine, Migration analyses Figure  suggesten et al. . In the D. biscayensis and migration analyses did not uncover even moderate connectivity to the surrounding localities. The recovered substructure of Arcachon within the singular population is unusual given its central placement in the Bay of Biscay. Arcachon is a semi‐closed bay with aquaculture efforts focused on oyster farming and is a popular tourist location, increasing the potential for anthropogenic transport by the live bait industry. One possible explanation for the limited connectivity with surrounding localities is the unique hydrodynamics of Arcachon Bay. Specifically, river flows and wind‐driven currents have only minor impacts on the hydrology, instead tidal flows account for the majority of water mass movement, which are somewhat restricted by a sill near the mouth at ~ 20 m depth; most of the water masses re‐enter the bay after moving seaward on ebb tides (Plus et al., D. biscayensis larvae are in the water column for 4–5 days, this likely limits the species’ capabilities to disperse in or out of the bay. Strong currents have been shown to influence the recovered phylogeography with multiple other marine species (Collins et al., Although our interests originated with the connectivity of Sainte‐Anne locality to the rest of the Bay of Biscay, the genetic distinctness of Arcachon necessitates further investigation. This bay, while centrally located, shows signs of genetic differentiation from the rest of the geographic range of D. biscayensis recovered from the Bay of Biscay up to the Normanno‐Breton Gulf serves as another example of how aquaculture can affect natural populations and influence ecosystems. Introduced species often can have major impacts on the local ecosystem, causing competition for local resources, interrupting food webs, and leading to an economic impact (Leung et al., Diopatra biscayensis is an ecosystem engineer that influences the overall habitat which it occupies and can have a major impact on the biodiversity within the region (Berke et al., Diopatra species can increase overall biodiversity for a locality, it also can result in displacement of endemic fauna via competition (Berke et al., D. biscayensis is strongly influenced by temperature (Berke et al., The single population of Matthew P. Galaska: Formal analysis‐Equal, Methodology‐Equal, Writing‐original draft‐Equal, Writing‐review & editing‐Equal; David S. Wethey: Conceptualization‐Equal, Funding acquisition‐Equal, Resources‐Equal, Writing‐review & editing‐Equal; Andrés Arias: Resources‐Equal, Writing‐review & editing‐Equal; StanislasF. Dubois: Resources‐Equal, Writing‐review & editing‐Equal; Kenneth M. Halanych: Funding acquisition‐Equal, Methodology‐Equal, Writing‐review & editing‐Equal; Sarah A. Woodin: Conceptualization‐Equal, Funding acquisition‐Equal, Resources‐Equal, Writing‐review & editing‐Equal.None declared.Supplementary MaterialClick here for additional data file.


Cynomys parvidens) are federally threatened due to eradication campaigns, habitat destruction, and outbreaks of plague. Today, Utah prairie dogs exist in small, isolated populations, making them less demographically stable and more susceptible to erosion of genetic variation by genetic drift. We characterized patterns of genetic structure at neutral and putatively adaptive loci in order to evaluate the relative effects of genetic drift and local adaptation on population divergence. We sampled individuals across the Utah prairie dog species range and generated 2955 single nucleotide polymorphisms using double digest restriction site‐associated DNA sequencing. Genetic diversity was lower in low‐elevation sites compared to high‐elevation sites. Population divergence was high among sites and followed an isolation‐by‐distance model. Our results indicate that genetic drift plays a substantial role in the population divergence of the Utah prairie dog, and colonies would likely benefit from translocation of individuals between recovery units, which are characterized by distinct elevations, despite the detection of environmental associations with outlier loci. By understanding the processes that shape genetic structure, better informed decisions can be made with respect to the management of threatened species to ensure that adaptation is not stymied.Utah prairie dogs ( Genetic variation can be rapidly eroded in these small populations by genetic drift, a process that goes unmitigated in isolated populations without gene flow . The Utah prairie dog is one of five species of prairie dog found in North America and is listed as Threatened under the United States' Endangered Species Act and as Endangered by the IUCN Red List of Threatened Species . Our genome‐wide approach allows us to harness information in both neutral and adaptive loci to tailor conservation activities to maximize the success of recovery efforts without incurring the potentially substantial costs that could result from translocating locally adapted individuals.22.1We trapped Utah prairie dogs and collected hair and whiskers during a field trial of a sylvatic plague vaccine . Fragments were paired‐end sequenced on an Illumina NovaSeq6000 at Texas A&M AgriLife Genomics. We aligned sequences to a Gunnison's prairie dog (Cynomys gunnisoni) genome , samples with >300 ng of total genomic DNA, quantified using a Qubit 2.0 Fluorometer (Invitrogen), were used for double digest restriction site‐associated DNA sequencing  were removed  SNP was used. Second, only loci represented in 80% or more of individuals were retained. Third, only loci present in all 14 sampling locations were retained. Fourth, individuals missing >30% of data (2.2K) present in our sampling sites: the Bayesian clustering program STRUCTURE v2.3.4  because we had unequal sampling among sites  using STRUCTURE, we used a combination of the LnP(D) and Δ K as outlined in Janes et al.  using the sequential K‐means clustering algorithm executed through the find.clusters function in adegenet . We used two methods to determine the number of genetic clusters  correction  based on the number of private alleles using the R package genepop , observed heterozygosity (HO), allelic richness (AR), the number of private alleles (AP), and inbreeding (FIS). We calculated HE, HO, and AP using the R package poppr v.2.8.2  less genetic variation, (b) fewer private alleles , and (c) a higher degree of inbreeding than populations in which genetic structure is shaped by other mechanisms. We characterized genetic variation in each site as well as each genetic cluster by measuring expected heterozygosity (Ne) and tested for evidence of recent genetic bottlenecks for each site, as these factors significantly impact the rate at which genetic variation is lost as well as the rate of increase of inbreeding and genetic drift  method implemented in the program NeEstimator v2.1  and tested for significant heterozygosity excess compared to the level predicted under mutation‐drift equilibrium using standardized differences tests . For the BayeScan method of outlier detection, we defined populations as the sampling sites (n = 7) and used the default parameter settings . We directly controlled the false discovery rate (FDR) by setting the target FDR  to limit the proportion of false positives to 5%  in PCAdapt as 5 based on where the amount of variation explained by each PC decreases (identified as the point of inflection in the PCAdapt ‘scree plot’). We defined outlier loci as those with q‐values <0.05, meaning that 5% or less of loci identified as outliers are potentially false positives. To generate the set of outlier loci, we retained loci that were identified as under divergent selection in both BayeScan and PCAdapt methods. We compared this outlier dataset to the neutral set of loci to assess the relative contribution of genetic drift and selection in shaping patterns of genetic structure. Specifically, we compared patterns of genetic structure for outlier and neutral loci using the program STRUCTURE and by using DAPC in adegenet  in the full genetic dataset , neutral loci only, and outlier loci only using Mantel tests between genetic and geographic distance carried out in the R package adegenet ) and geographic distance was calculated as the pairwise Euclidean distance between sites (km) using the pointDistance function in the R package “raster” . Using the RDA, we identified outlier loci and the environmental variables most associated with those loci . To infer how each SNP relates to the environmental variables, we identified the environmental variable that was most strongly correlated to each outlier SNP based on correlation coefficients. To illustrate these environment‐SNP associations, we created triplots of all RDA axes using the R package vegan , implemented in the R package ‘vegan’ . The final genomic dataset contained 2955 variable SNP loci with a mean depth of coverage of 20.08 for all individuals  was two, using both the program STRUCTURE (based on Ln(K) and the ∆K method) and the unsupervised clustering method using DAPC  were informative for describing genetic structure based on BIC  among CCUT1 and CCUT2 sites as well as among the HEUT sites . Overall, females and males showed comparable patterns of population differentiation  were higher in HEUT sites compared to CCUT sites , CCUT2 (AP = 2), CCUT3 (AP = 8), and HEUT1 (AP = 17). However, when individuals were grouped into genetic clusters, a large number of private alleles were detected (Table AP = 1326). This genetic cluster also had the highest amount of genomic variation in terms of HO, HE, and AR  and had large confidence intervals (Table Ne for this site are underestimated (linkage disequilibrium‐based methods for estimating Ne assume closed populations). We also detected significant bottlenecks  for all sites as well as for each genetic cluster.Estimates of ls Table . We founls Table . However3.4nloci = 51; 1.7% of total loci). For the neutral dataset, all loci as identified as an outlier in either program were removed . We found significant patterns of isolation by distance using the full SNP dataset , only neutral loci , and only outlier loci  based on 999 replicates  to elevation. Therefore, we removed highly correlated variables as well as variables with low variation across sites. We reduced the four landscape (three landcover and elevation) and 19 climatic predictor variables to 4 total variables – proportion of forested land (Forest), elevation (Elev), mean temperature of the driest quarter , and precipitation seasonality . To account for the strong population structure found using STRUCTURE and DAPC, we used the individual proportion of ancestry that was assigned to the first genetic cluster when K = 2 (Pr(Group Membership); Figure 2 = 0.09; p = 0.001). When we compared how well each component of the RDA explained genetic variation, we found that all four RDA axes were significant . The first constrained axis of the RDA explained the most variation (50.4%) followed by the second (27.5%), third (12.2%), and fourth (9.9%) axes. By using a ±3 standard deviation cutoff for SNP loadings on each RDA axis, we identified 141 outlier loci. Of the outlier loci, we found 46 SNPs were highly correlated with elevation, 2 with forests, 48 with precipitation seasonality, and 45 with temperature  sites and high‐elevation (HEUT) sites.D and FST metrics. Higher rates of migration between neighboring sites combined with strong evidence of isolation‐by‐distance indicate that when prairie dogs do disperse, it is likely to nearby colonies and that long‐distance dispersal is rare or nonexistent. These combined analyses lend support to Utah prairie dogs functioning as a metapopulation with the sites acting as subpopulations within the genetic clusters. Patterns of metapopulation dynamics, largely driven by plague epizootics, have also been documented in black‐tailed prairie dog (Cynomys ludovicianus) colonies  and low effective population size .The three RUs  of the Utah prairie dog are separated by physiogeographic features that could serve as potential barriers to movement, isolating populations. Our genetic structure results indicate that Awapa Plateau (where HEUT sites were located) is a distinct habitat in which individuals are isolated and diverging in allopatry, aligning well with its designation as a separate RU. In contrast, the CCUT genetic cluster encompassed sites within the Pansaugunt and West Desert RUs, indicating that these sites share historical or contemporary variation. Another study on Utah prairie dogs, that used microsatellite markers and broader geographic sampling, found Pansaugunt to be a separate genetic cluster  remains an important metric for characterizing the long‐term evolutionary potential of a species, as populations with a small Ne have an elevated extinction risk due to the fixation of deleterious alleles and through the loss of adaptive variation   between high‐elevation (HEUT) sites and low‐elevation (CCUT) sites. The RDA analysis expanded on this pattern of divergence by showing that outlier loci were associated with high‐elevation environments, a greater proportion of forests, colder temperatures during the driest quarter, and greater precipitation seasonality. Differences in habitat and climate between HEUT and CCUT sites indicate that local adaptation could occur, and our outlier and GEA analyses indicate that adaptation is ongoing. Other studies have also found that the HEUT sites harbor a greater diversity of flea and small mammal species compared to CCUT sites . This is somewhat larger than the proportion of SNP loci identified in studies of other mammals  small, isolated populations, (b) a history of bottlenecks/founder events, (c) low effective population size, and (d) limited gene flow among populations. Our range‐wide study on Utah prairie dogs, as well as another by Brown et al. , showed Translocations are currently used for prairie dogs, with considerable research focused on improving success (Curtis et al., None declared.Supplementary MaterialClick here for additional data file.


Triticum aestivum L.) accessions in soil and hydroponics at low and sufficient P. To identify the genic regions for P efficiency traits, the accessions were genotyped using the 35 K-SNP array and genome-wide association study (GWAS) was performed. The high-quality SNPs across the genomes were evenly distributed with polymorphic information content values varying between 0.090 and 0.375. Structure analysis revealed three subpopulations  and the phenotypic responses of these subpopulations were assessed for P efficiency traits. The C2 subpopulation showed the highest genetic variance and heritability values for numerous agronomically important traits as well as strong correlation under both P levels in soil and hydroponics. GWAS revealed 78 marker-trait associations (MTAs) but only 35 MTAs passed Bonferroni Correction. A total of 297 candidate genes were identified for these MTAs and their annotation suggested their involvement in several biological process. Out of 35, nine (9) MTAs were controlling polygenic trait . These multi-trait MTAs (each controlling two or more than two correlated traits) could be utilized for improving bread wheat to tolerate low P stress through marker-assisted selection (MAS).Soil bioavailability of phosphorus (P) is a major concern for crop productivity worldwide. As phosphatic fertilizers are a non-renewable resource associated with economic and environmental issues so, the sustainable option is to develop P use efficient crop varieties. We phenotyped 82 diverse wheat ( Triticum aestivum L.) is a major staple food crop world over. To meet the needs of an increasing population, agriculturists are facing the challenge of enhancing wheat production up to 70% by 20501. Increasing demand and the challenges of global climate change requires an urgent need for genetic improvement of wheat to attain maximum yield, adaptation and tolerance to various abiotic and biotic stresses. Bioavailability of phosphorus (P) is a major constraint in the wheat production system due to slow diffusion and high P-fixation in different soils. In acidic soils, P is fixed with Al and Fe oxides and hydroxides whereas in alkaline soils, it is precipitated as calcium phosphates thereby, limiting P availability and crop productivity2. Higher levels of P fertilization, a non-renewable resource, increase the chances of environmental pollution through eutrophication of water bodies3 as well as an economic burden on the resource poor agricultural sector in several countries4. Low bioavailability of P and less fertilizer recovery (> 30%) thus justifies the need to develop crop varieties that can acquire P more efficiently.Wheat  genes and gene products provide tolerance to plants to cope with the P stress condition as shown by transcriptomic, proteomic and metabolomic studies11. Further, genotypic differences in P stress tolerance have been reported in many crops, for example, in soybean 7, maize 11, wheat 12 and rice 13. To improve P use efficiency (PUE) through genetic approaches, identification of a few quantitative trait loci (QTLs) have been reported which are mainly based on the shoot P concentration and biomass production16. However, the traits governing PUE needs to be defined appropriately, correct phenotyping techniques and large number of population or diverse germplasm are some of the challenges faced by breeders. Screening for P efficiency includes a comparison in shoot biomass and grain yield17 at low and sufficient P levels. So, the phenotypic variability can be determined through relative performance such as relative biomass and relative grain yield. Apart from phenotypic variability, information about the amount and distribution of genetic diversity provides opportunity for genetic improvement based on exploitation of genetic resources that can be used in future plant-breeding programs. In wheat, mapping of QTLs for P uptake and utilization efficiency detected six and seven loci controlling P utilization and P uptake efficiency, respectively18. It was suggested that although P uptake and utilization efficiencies are negatively correlated, the presence of positive linkages between QTLs for P uptake and utilization at two loci would allow the possibility of improving both uptake and utilization efficiencies.In plants, inorganic P (Pi) is a key structural component of bio-membranes and nucleic acids. It has role in energy transfer reactions, signalling and regulation of carbon metabolism. In response to Pi starvation, plants possess several adaptive strategies at morphological, physiological, biochemical, genetic and genomic levels. Below ground traits such as increased root surface area, total root length, increased root-to-shoot ratio, root hair length and density, along with increased exudation of organic acids, protons and Pi-solubilizing enzymes from root enhances P-mining abilityTriticum urartu (AA), a progenitor related to Aegilops speltoides (BB), and Aegilops tauschii (DD). Large size (17 Gb) and complexity of genomes has hindered genomic analyses for elucidating the genetic diversity of wheat. However, with the sequencing of reference wheat genome, these challenges have been met. The genetic diversity may be determined by using pedigree data, morphological19 as well as molecular marker information20. Analysis of genetic diversity using morphological information is expensive, labour intensive, and may be affected by genotype and environment (G × E) interactions. Hence, deoxyribonucleic acid (DNA) markers have been used extensively to evaluate the population structure and genetic diversity of accessions22. The DNA markers such as amplified fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), microsatellites, and single nucleotide polymorphisms (SNPs) have been exploited to analyze the relationships and genetic diversity levels in wheat accessions25. Among these markers, SNPs are the most widely distributed sequence variations in plant genomes and are the most frequent type of sequence variation26. The SNP markers are highly fitted for genome-wide studies such as association mapping to detect the relationship between phenotypic variation and genetic polymorphisms27, population structure analysis28 and genomic selection. Development of SNP array was major improvement in wheat genotyping which permitted acquisition of exhaustive genotypic data in wheat29. The SNP array is a high-throughput and cost-efficient genotyping assay and is a robust genomic approach for elucidation of genetic variation on a genome-wide genetic diversity analysis of plants30.Wheat is an allohexaploid with three genomes , derived from three ancestral parental diploid species: 31. However, GWAS is usually carried out on a large number of populations, say more than 300. But in cases where the numbers are limited (< 100), there is still a possibility to associate the genotypically diverse and phenotypically superior lines to develop new cultivars with desirable traits for abiotic stress tolerance. Several GWAS studies have been reported on population sizes between 60 and 15034. Genome-wide analysis studies on elucidating the complex traits of P efficiency in rice 35, maize 36 and soybean38 have been reported but literature on bread wheat is limited.In order to dissect the genetic basis of complex traits such as P efficiency, genome-wide association study (GWAS) based on linkage disequilibrium (LD) is employed. GWAS is a high resolution and cost-effective method that associates variation across the entire genome with phenotypes by utilizing the benefits of a large number of marker polymorphismsIn the present study, we used 82 wheat accessions to determine the phenotypic variability for low P stress tolerance and the SNP array data for studying genetic diversity, population structure and marker-trait associations (MTAs). The aim of study was: (i) to describe the genome distribution of the SNPs and their information content based on the 35 K Wheat Breeders Affymetrix-SNP array, (ii) to analyze the population structure, estimate genetic diversity and marker-trait association using SNP markers, and (iii) to identify the most diverse and P efficient genotype(s) based on the phenotypic cluster associated with the diverse population group.Triticum aestivum L.) accessions procured indigenously as well as from Australian Winter Cereals Collection  and CIMMYT (Mexico) were used in this study. The collection comprised of 77 cultivars and 5 landraces (Supplementary Table 39 in low and sufficient P treatment soil were 2.67 and 42.2 mg P kg−1 soil respectively. The soil pH (in water) was 7.1 and the electrical conductivity was 1.4 mS cm−1. Recommended dose of nitrogen (120 kg N ha−1) and potassium (40 kg K2O ha−1) were added to the soil. Initially, six seeds were sown in each pot (size 30 cm diameter × 30 cm height) which were thinned to four healthy plants after emergence of 3–4 leaves comprising one experimental unit (one pot with four plants). Three such experimental units were used for recording data thus three replicates for each accession and P treatment. The weather data recorded on daily basis for the whole crop duration for two seasons are presented in Supplementary Fig. A set of 82 bread wheat (3 and HClO4 acid in 4:1 ratio following method of Murphy and Riley40. The shoot P percentage (SPP), shoot P uptake (SPU), grain P percentage (GPP), grain P uptake (GPU) and total P uptake (TPU) were calculated after analysing P concentration. The P acquisition efficiency (PAE) was calculated as the ratio of P content at low P to sufficient P. P harvest index (PHI) was computed as the ratio of P content in grain to the P content in grain plus shoot. The P use efficiency (PUE) was calculated as the ratio of grain yield to total P uptake41. The P stress susceptibility index (PSSI) was calculated according to Eq. LP denotes the mean yield of genotype under low phosphorus (LP) and YSP the mean yield of genotype under sufficient phosphorus (SP). XLP and XSP denotes the mean YLP and YSP respectively of all genotypes.The accessions were phenotyped for various P efficiency traits. The total biomass (TBM) and grain weight per plant (GWP) were recorded after harvest. Shoot and grain P concentration was estimated after digestion with diacid mixture containing HNO2 were germinated and after emergence of coleoptiles, seedlings were transferred to full strength nutrient solution at two concentrations of P: 500 µM (sufficient P) and 5 µM (low P). The composition of nutrient solution and method to grow plants were followed as mentioned earlier 42. The solution pH was maintained at 5.6 throughout the experiment using 1.0 N HCl or 1.0 N KOH. The growth conditions in the glasshouse were maintained at temperatures 22–12°C/day-night, 10 h photoperiod and 90% relative humidity. Four replications were maintained for each P treatment and accessions. Out of four, two trays were sown at staggered time interval of 15 days which served as repetition over time. Destructive sampling was done after 22 days of growth in nutrient solution and the plants were separated into root and shoot and dried in a hot air oven. Traits such as total biomass (TBM), shoot dry weight (SDW), root dry weight (RDW), root-to-shoot ratio (RSR), P concentration in whole plant (PCON), total P uptake and PAE were recorded.Surface sterilized seeds with 0.1% HgCl43. Eleven quantitative traits were used to perform PCA and the traits that differentiated accessions for P efficiency were identified by the loading scores. Analysis of genotypic variation and their interaction with traits were carried out in R (GGE Biplot GUI ver 1.0–9). Supplementary Figures 2) for the whole panel of 82 accessions was calculated by the ratio of genotypic variance and phenotypic variance. From ANOVA data, the genotypic variance (GV) and phenotypic variance were calculated.The design of experiment was completely randomized (CRD) with two factors, P and accessions. Pooled data was analysed by two-way analysis of variance (ANOVA). Principal component (PCA) and correlation of traits between different subpopulation were carried out using R ver 3.5.1  44 and all accessions were genotyped with the 35 K Wheat Breeders Affymetrix-SNP array  after standard Axiom sample preparation46. SNP allele calling was carried out using the proprietary software package ‘Axiom Analysis Suite’ (Thermo), following the Axiom Best Practices Genotyping Workflow. A total of 35,143 SNPs was scored on the array. Markers were eliminated from the data set if they showed more than 10% missing values or had a minor allele frequency (MAF) of < 5%. After performing these checks, a total of 10,019 high-quality SNP markers remained in the dataset which were used for population structure and diversity analysis. The frequency at which the second most common allele occurs in a given population is MAF while the polymorphic information content (PIC) indicates the genetic properties of SNPs in a population from different aspects and was calculated using the formula given by Botstein et al.47.The DNA was extracted from 8–10 days old seedlings (10 plants pooled) following CTAB method48. Population structure was evaluated by means of K-values (an assumed fixed number of subpopulations) from 1 to 10 in the population. Three independent analyses were used for each K-value and the STRUCTURE program was set on 50,000 as burn-in iteration followed by 100,000 Markov Chain Monte Carlo (MCMC) replications after burn-in49. The optimal value of K was detected by using STRUCTURE HARVESTER v0.6.9450 based on ad-hoc method described by Pritchard et al.48 in the user manual as well as Evanno’s method51. Expected heterozygosity (He)52, gene diversity (GD)53 and fixation index (Fst)54 values were derived from STRUCTURE analysis results. Expected heterozygosity is a basic estimate of genetic diversity in a population and describes the expected proportion of heterozygous genotypes under Hardy–Weinberg equilibrium. The Fst is the measure of genetic variance that can be described by population structure based on Wright’s F-statistics where a value of 0 indicates no differentiation between the subpopulations while a value of 1 indicates complete differentiation.The population structure was analysed using Bayesian approach. Model-based clustering was performed using software STRUCTURE version 2.3.455 to estimate genetic diversity and calculate the diversity indices among the 82 bread wheat accessions. The diversity indices including Shannon’s diversity index (I), diversity index (h), and unbiased diversity index (uh) were calculated for each population. In addition, genetic indices such as number of loci with private allele, number of different alleles (A), number of effective alleles (Ae), percentage of polymorphic loci (PPL), number of private alleles and gene flow or haploid number of migrants (Nm) were also calculated using GenAlEx v6.503.The number of subpopulations determined by STRUCTURE was used for AMOVA and GenAlEx v6.50357, multiple loci mixed model (MLMM) and Fixed and Random Model Circulating Probability Unification (FarmCPU) methods58 to select the best fitting model and reduced false discovery rate (FDR). The FarmCPU uses a fixed effect model (FEM) and a random effect model (REM) iteratively to remove the confounding between testing markers and kinship that results in false negatives, prevents model overfitting, and control false positives simultaneously58. Therefore, GWAS was performed on the adjusted BLUPs for each trait in each year to identify SNPs associated with both P levels and efficiency traits using FarmCPU with population structures (Q1 and Q2) or first three principal components  as covariates. It was done by looking at the model fit using Quantile–Quantile (Q-Q) plots and FarmCPU-calculated kinship implemented in GAPIT (Genome Association and Prediction Integrated Tool) of the R software 59. A uniform suggestive genome-wide significance threshold level of p-value > (− log10 p = 6.00) was selected for MTAs considering the deviation of the observed test statistics values from the expected test statistics values in the Q–Q plots from the two-year results of the present study. Each significant MTA was further subjected to Bonferroni multiple correction  for eliminating the false positives.GWAS was conducted using 31,926 SNPs markers, after removing the missing genotyping calls and very low MAFs, and the best linear unbiased predictors (BLUPs) for phenotypic traits measured under different environments among bread wheat 82 accessions for P efficiency in soil and hydroponics at two P levels. The traits used were TBM, GWP, SPP, SPU, GPP, GPU, TPU, PHI, PSSI and PUE under low and sufficient P soil. Apart from soil experiment, traits like TBM, SDW, RDW, RSR, PCON and TPU were measured under low and sufficient P hydroponics condition. The association mapping was performed by fitting four different models such as general linear model (GLM), mixed linear model (MLM)26. The squared allele frequency correlations (R2) at p-values < 0.001 for each pair of loci were considered to estimate significant linkage disequilibrium. The physical map depicting chromosomal localization of SNP markers was drawn using MapInspect software (https://www.plantbreeding.wur.nl/UK/software_map-inspect.html)  Fig. A. The R ml) Fig. B.In order to identify potential candidate genes, genes within 0.25 Mbp  on both the sides of each MTA were retrieved using BioMart form EnsemblPlant. These were annotated using InterPro and their biological function was analysed using gene ontology available at Biomart of Ensemble plant.In the analysis of data, out of total 35,143 SNPs, 0.31% (110) could not be scored as they gave insufficient signal, while 38.5%  showed more than 10% missing values. Hence, these markers were eliminated from the dataset. Apart from this, 32.5%  SNPs were excluded because they exhibited a MAF < 5%. After data processing and SNP filtering, a total of 10,019 high-quality SNPs were used for further analysis. The distribution of effective high-quality SNPs was 3271, 3823 and 2925 in the A, B and D genomes, respectively  was determined by plotting ‘K’ against ΔK that exhibited a sharp peak at K = 3  was noted among accessions in each subpopulation. Highest value of He was observed in C2 and the lowest was noted in C1  landraces.The intra-population genetic diversity analysis revealed the A and Ae allele numbers as 2.195 and 1.570, respectively (Supplementary Table I), diversity index (h), and unbiased diversity index (uh) were 0.503, 0.313, and 0.335, respectively. The Shannon diversity index value varies between 0 and 1, where 1 indicates complete evenness. We found I values of different subpopulations ranged from 0.453 (C1) to 0.530 (C2) whereas h ranged from 0.280 (C1) to 0.349 (C2). Among the three subpopulations, C2 was highly diverse , C1 was least diverse  while C3 exhibited moderate values of diversity indices . Further, C3 subpopulation exhibited the highest (96.73%) percentage of polymorphic loci (PPL) while lowest (82.30%) was noted in C2. The lowest private allele number was observed in C1 whereas C2 and C3 showed higher values of number private alleles. Thus, SNPs can act as useful markers for genetic diversity studies.The average values of genetic diversity indices such as Shannon’s information index (The pooled mean (two years) for all traits recorded on 82 wheat accessions were subjected to PCA at sufficient and low P , PHI (16.3%), shoot P uptake (14.7%) and P percentage in shoot (14.2%) (Supplementary Table P < 0.001) effect of genotype and P levels were observed for various P efficiency traits in wheat accessions  to 94.9% . The average values subpopulation-wise for various phenotypic traits recorded for years 2016 and 2017 showed significant (P < 0.001) effect in all subpopulations except for PHI in 2016 (Supplementary Tables H2) were estimated for each trait for all subpopulations. The genetic variance for most of the traits was found to be highest in C2. However, C3 subpopulation showed the minimum values of genetic variance for most of the traits while C1 was intermediate. The H2 ranged from 70% (grain weight per plant) to 92.9% (PHI) in C1, from 86%  to 98.37% (grain weight per plant) in C2, and from 81.8% (grain P percentage) to 93.79% (grain weight per plant) in C3 subpopulation. For most of the traits, the H2 was highest in the genetically diverse C2 subpopulations as compared to the other two subgroups. The descriptive statistics shows the mean, range, standard deviation and coefficient of variation (CV) for each trait under low and sufficient P levels in C1, C2, and C3 subpopulations  influenced the various traits in wheat accessions (Supplementary Tables H2 ranged from 72.15% (root-to-shoot ratio) to 100% (P concentration). The subpopulations C1, C2, and C3 showed significant (P < 0.001) effect of P and accessions for all traits (Table H2 varied from 77.59% (root-to-shoot-ratio) to 97.96%  in C2, from 78.98% (root-to-shoot-ratio) to 100% (P concentration) in C1, and from 75.61% (root-to-shoot-ratio) to 100% (P concentration) in C3. Similarly, the highest H2 was recorded in the genetically diverse C2 subpopulation for most of the traits. The descriptive statistics showed that the CV values obtained were very high (> 30%) in all subpopulations and not acceptable  positive or negative association in both soil Fig.  experimeP-value less than 0.0001) were found in both, soil  genome followed by B (26) and A (24) genome , GWP (9), SPP (10), SPU (8), GPP (4), GPU (4), TPU (3), PHI (8), PUE (9), PSSI (1), PAE (1) under SP while with six of eleven traits, that is, GPU (10), GWP (8), PAE (1), PUE (5), PHI (4) and PSSI (1) under LP. Similarly, in hydroponics experiment, the significant MTAs were associated with three of seven traits, they were TPU (1), SDW (2) and TBM (3) in SP and two of seven traits were PCON (5) and TBM (2) in LP. No significant MTAs were found for RDW and RSR in hydroponics culture. Out of 78 MTAs, only 35 MTAs passed Bonferroni correction which belonged to soil experiment only  of the 35 MTAs were identified. Out of 297, protein functions were available for 254 candidate genes which mainly belonged to carbohydrate metabolic process, fatty acid biosynthesis, protein phosphorylation/ dephosphorylation, vascular/transmembrane/protein transport and nucleotide biosynthetic process  but are less likely to have been between Triticum aestivum and Aegilops tauschii (DD)62. Earlier reports also provide evidence of lower degree of polymorphism in D genome63. The T. turgidum genome likely existed for 200,000 years prior to the addition of the D genome to become T. aestivum, this allowed it more time to develop diversity.In this study, we used a 35 K SNP array to genotype and analyse 82 bread wheat accessions. The distribution of effective high-quality SNP markers in the A, B and D genomes scored in the present study were in agreement with earlier reports64. Botstein et al.47 classified markers on the basis of PIC values into three categories-highly informative (PIC > 0.5), moderately informative (0.25 < PIC < 0.5), and slightly informative (PIC < 0.25). The PIC value of SNP marker was found to be moderate or low informative due to bi-allelic nature of SNPs confined to maximum PIC values of 0.50. In our study, the PIC value ranged from 0.090 to 0.375, with an average PIC value of 0.233 based on the selected high-quality effective SNPs, which is in agreement with the PIC value observed for a set of US wheat genotypes61. Würschum et al.60 reported 1,395 high quality SNP markers with average PIC value of 0.330 in winter wheat. Similar PIC value of 0.240 was observed for wheat population genotyped using 9 K SNP array65. Further, in F3:6 Nebraska winter wheat populations genotyped with 25,566 SNPs by GBS, an average PIC value of 0.250 was reported66.The PIC values are very effective for genetic studies in wheat breeding programs as it assesses the genetic variability among accessions. The PIC values are a sign of informative markers within a set of samples which can be utilized for analysing genetic diversity67. Further, lowest Fst value observed in C2, and highest Fst value in C1 indicates that accessions in the latter subpopulation have several different genotypic patterns or due to hybridisation with divergent parent as it is exhibited in allelic richness. Thus, the wheat accessions showed significant divergence within each of the subpopulations.Information regarding genetic diversity and population structure is important for characterizing the domestication history and genetic relatedness of wheat accessions. In our study, analysis of population structure resulted in three subpopulations. Population structure analysis indicated that wheat accessions can be categorized by geographical origin or be divided into landraces and modern varieties. The Fst value obtained for differentiation among three subpopulations was within the acceptable limit which agrees with earlier report stating that Fst value greater than 0.150 can be considered as significant in differentiating populationsHe) value depends on the allele richness and abundance of alleles in a population. It provides an estimate of the average heterozygosity and genetic distance among the genotypes in a population68. The C2 subpopulation exhibited highest He value indicating that it possessed greatest genetic diversity within the cluster as compared to C3 and C1. Further, the diversity index and Shannon’s diversity index were lowest in C1 and greatest in C2. Moreover, the lowest private allele number was observed in the C1 whereas C2 containing landraces showed a higher value of private alleles. Among subpopulations, in C1 no landrace was present while in C2, there were three  landraces thus exhibiting highest level of genetic diversity. This result is in accordance with previous reports in Iranian wheat where the landraces group showed high level of genetic diversity compared with the cultivar group63. The adaptability of landraces could be associated with this genetic diversity69. Lower level of genetic diversity is correlated with lower number of landraces as reported in a study with 90 Chinese winter wheat accessions that included 11 landraces70. Loss of biodiversity has occurred mainly due to substitution of traditional landraces in farmer’s fields with improved cultivars that has limited the wheat genetic diversity. Wild species plays a major role in genetic improvement of cultivars and conservation of genetic resources for crop breeding. Wheat landraces are important sources to widen the genetic base of cultivated wheat. Therefore, landrace populations can be effectively utilized for development of new cultivars to obtain adaptation to climate extremes and tolerance to abiotic and biotic stresses71. Further, inclusion of landraces in breeding program could enhance the genetic diversity for quality traits72.The gene diversity  value < 1.0 is associated with limited genetic exchange among subpopulations that finally leads to high level of genetic differentiation. In the present study, Nm was high (3.106) which led to gene exchange among subpopulations thereby resulting in low level of genetic differentiation among subpopulations. This result is in agreement with others66 who also reported that high Nm value is associated with high gene flow in wheat. Heritability of traits such as plant height, spike number per plant and thousand grain weight reported in wheat crop in field experiment for P efficiency is in close agreement with our results75. Similarly, in hydroponics, the heritability of traits such as shoot dry weight per plant, root dry weight per plant and total dry weight per plant agrees with our results.The AMOVA and genetic diversity indices calculated for subpopulations showed that within the population, diversity explained most of the genetic diversity as compared to among population diversity. Low level of genetic diversity among subpopulations was related to higher level of gene flowH2 was observed in C2 subpopulations for various traits as compared to C1 and C3. Further, the C2 subpopulation showed very strong correlation values between the traits under both sufficient P and low P in soil as well as hydroponics medium. Nevertheless, we found a few superior genotypes which performed better under low P stress and were common between soil and hydroponic screening in each subpopulation. For example, in C1 subpopulation, NI 5439 was a good performer; in C2, IC 534,271 and TURACO; and in C3 subpopulation, CARAZINHO, DL 784–3, and RAJ 3777 were superior accessions. In earlier reports, CARAZINHO was shown to possess the highest P utilization efficiency for dry matter at low P77. The genotypes identified in C2 (highly diverse) and C3 (moderately diverse) subpopulations, thus, may be included in the breeding programs to develop new cultivars tolerant to low P stress.We further assessed the phenotypic performance of subpopulations, obtained by genetic analysis, for P use efficiency traits. Analysis of phenotypic data derived from soil and hydroponic experiments showed that the highest value of genetic variance and P-value and passed Bonferroni correction) controlling two to four traits simultaneously. The high confidence candidate genes were identified for six of these nine MTAs. Some of these genes are known to be affected under low P conditions. For instance, FAD/NAD(P) are involved in plant photosynthesis, protein kinases participate in signal transduction and are known to be differentially expressed under P deficiency (Supplementary Table 79. MTAs each controlling two or more than two correlated traits could be recommended for marker-assisted selection (MAS). These MTAs will help not only in the study of genetic architecture of correlated traits, but also in simultaneous improvement of more than two correlated traits through MAS. Though it is not easy to find out whether such multi-trait MTAs are due to pleiotropism or due to close linkage49. The stability of an MTA across different years is very important. In the present study, the MTAs which were associated with a particular trait were different in different years. For instance, we found eleven unique MTAs in 2016 and 19 unique MTAs in 2017 under SP and five unique MTAs in 2016 and eight unique MTAs in 2017 under LP in soil experiment. This might be due to the genotype × environment (G × E) interaction because G × E interaction is known to reduce association between phenotypic and genotypic values and cause selections from one environment to perform poorly in another, thereby forcing plant breeders to examine genotypic adaptation80.In the present study, we found nine MTAs  may be included in the breeding programs to develop new cultivars tolerant to low P stress. From GWAS approach, we found 12 significant MTAs controlling two-four traits. These multi-trait MTAs could be utilized in MAS to develop wheat varieties with low P stress tolerance.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Below is the link to the electronic supplementary material.


Tillandsia recurvata (L.) L. populations, a common epiphytic weed with self-reproduction and clonal growth widespread in dry forests and deforested landscapes in the American continent. We introduce the TRec model, an individual-based approach to simulate the spreading of T. recurvata over time and across landscapes subjected to abrupt changes in tree density with the parameters adjusted according to the empirical genetic data based on microsatellites genotypes. Simulations with this model showed that the strong spatial genetic structure observed from empirical data in T. recurvata can be explained by a rapid increase in abundance and gene flow followed by stabilization after ca. 25 years. TRec model’s results also indicate that deforestation is a turning point for the rapid increase in both individual abundance and gene flow among T. recurvata subpopulations occurring in formerly dense forests. Active reforestation can, in turn, reverse such a scenario, although with a milder intensity. The genetic-based study suggests that anthropogenic changes in landscapes may strongly affect the population dynamics of species with ‘weedy’ traits.The rapid spread of many weeds into intensely disturbed landscapes is boosted by clonal growth and self-fertilization strategies, which conversely increases the genetic structure of populations. Here, we use empirical and modeling approaches to evaluate the spreading dynamics of  Ecosystems are currently subjected to unprecedented rates of human-induced changes that are fostering the sixth mass extinction2. However, not all organisms respond the same way to human disturbances. While many species become extinct or migrate, others thrive in anthropogenic environments and can invade natural environments4. Indeed, human activities, such as those related to deforestation, have allowed the dispersal and reproduction of many species4. Weeds are opportunistic species that can evolve from introduced or native species that grow within human-transformed landscapes without being cultivated and negatively affecting both the environment and the economy6.Intensive disturbances are inherent to human history and have substantial effects on natural communities6. However, native species possessing traits that pre-adapt them to establish in newly disturbed areas can also evolve into weeds7. In addition to landscape disturbances, propagule pressure, and individual density, the spreading rate of weeds is mainly driven by their high dispersal abilities and wide traits’ plasticity, which have enabled them to rapidly colonize large areas11. In particular, clonal growth and self-fertilization (hereafter referred to as ‘selfing’) are some of the most important adaptations that allow weeds to establish new populations after long-distance dispersal events13. Indeed, clonal growth and selfing favor invasiveness and weediness, even in native species, by mitigating the Allee effect of small populations and, therefore, enhance reproductive assurance and genetic transmission16. While selfing can increase species’ investment in seed production, clonal growth reduces the likelihood of genet death by sharing stochastic risk over multiple ramets18.Between 50 and 80% of all invasive plant species can be classified as weeds, depending on current impacts and human perception22. On the other hand, clonal growth may also lead to the maintenance of (once established) genetic diversity24, while continuous selfing can result in the absence of inbreeding depression as a consequence of purging of deleterious alleles over generations26. These effects lead to a strong population subdivision, creating a metapopulation dynamic with extinction in a site being balanced by recolonization. This dynamic may, therefore, favor selfing and clonal genotypes, due to their higher capacities of recolonization27.Despite the positive effect of clonal growth and selfing strategies on weed invasiveness, the aggregation of self-fertilized offspring and clonal individuals strongly influences the spatial genetic structure (SGS) of populations, reducing local genetic diversity and promoting inbreeding depression28. With an increase in abundance after colonization, the general kinship often reduces due to the greater overlapping of maternal seed shadows and successive introductions of new genotypes through seed dispersal from neighbor populations29. Therefore, we expect that density-relatedness dynamics reduce the high SGS of populations of selfing and clonal species throughout colonization stages31.Understanding local spread dynamics is essential to elucidate how native weeds opportunistically reach broad range distributions, despite their tendency to form highly aggregated populations with low genetic diversity. Individuals within populations with reduced abundance and limited seed dispersal, for instance, are usually close-related at the beginning of colonization34. Oppositely, we also expect that coordinated actions of reforestation, or afforestation, should be an efficient strategy to control the weed spreading. Here, we integrate empirical and simulated genetic data to study the spreading dynamics of Tillandsia recurvata (L.) L., an epiphytic weedy bromeliad with selfing and clonal growth strategies, widely distributed in the American continent37. This species is known by several popular names , which refer to the dry aspect of leaves and the intense clonal growth from leaf axils that forms a “ball-like” shape38  how such genetic structuring develops over time and varies in landscapes with distinct tree densities; and (III) whether abrupt human-induced changes in the landscape (deforestation or reforestation) affect the dynamics of T. recurvata. We hypothesize that T. recurvata exhibits high spatial genetic structure due to intrinsic features, such as clonal growth, selfing, and epiphytic habit. Such genetic structuring, however, might be reduced as subpopulations become denser and more connected by seed dispersal. Since this species is drought-adapted and its seeds are wind-dispersed, we also hypothesize that reductions in tree densities (i.e. deforestation) will lead to increased individual abundances and reduced genetic structure among subpopulations on trees.Here, we specifically aimed to investigate (I) whether subpopulations of 56. To overcome this difficulty, we adopted a modeling approach, which has been helpful to simulate spatio-temporal dynamics of species genetic structure in natural systems58. We introduce the TRec model, which takes advantage of the individual-based models (IBMs) by explicitly incorporating characteristics of each individual  and simulating interactions among them and with the surroundings in multiple landscapes, which may produce complex and distinct outcomes59. In the study presented, we employed the combined results from microsatellite markers and the TRec model to estimate and simulate the SGS of a T. recurvata population scattered on neighboring trees and to understand the emergence of the observed empirical patterns of genetic variation over colonization time and across landscapes with distinct tree densities.To achieve our goals and properly test the outlined hypothesis we might gather numerous and continuous empirical genetic data over a long period in similar landscapes with varying tree densities, which would be intractable and very time consumingHandroanthus spp. (Bignoniaceae) of similar ages (ca. 20 years) apart from each other from 2.5 to 47.5 m and surrounded by a grassland matrix , we observed moderate levels of genetic diversity within subpopulations, despite high endogamy and generally high genetic structure. The number of alleles (A) per subpopulation ranged from 19 to 30, while allelic richness (AR) ranged from 2.20 to 3.54, and the number of private alleles (AP) was up to 2 per subpopulation (see Table E) and observed (HO) heterozygosities per subpopulation ranged from 0.27 to 0.51, and from zero to 0.20, respectively (Table FIS) were significant and very high in all sampled subpopulations, ranging from 0.52 to 1.0, due to significant heterozygosity deficit under Hardy–Weinberg equilibrium (Table FST) ranged from 0.01 to 0.31 . AMOVA analysis, in turn, showed lower genetic variance among subpopulations (17.26%) than within subpopulations (82.74%) although it was significantly greater than zero (p < 0.001).The empirical data was collected in a human-transformed landscape of ca. 0.2 ha in southwest Brazil  composed by a grove of 20 cultivated ly Table . The inbum Table . Pairwis.31 Fig. . The Manij) was detected at the smallest distance interval (up to 2.5 m), which is significantly higher than expected by chance . We detected turnovers of alleles and multi-locus genotype (MLG) of 0.031 and 0.537, respectively. MLGs were evenly spaced among subpopulations, generally with few shared and one distinct dominant MLG in most of the subpopulations  that thrive on scattered trees ; whereas strong winds, massive regional seed rains, and low rates of seed capturing likely reduce the SGS within T. recurvata populations . Such analysis indicated wind speed and capture probability as, respectively the most and the least accurately predicted parameters  successfully constricted the prior distribution of the parameters. The posterior range of these parameters, as estimated by the ABC  into three distinct phases, here referred to as Lag (ca. 0 to 10 years), Log (ca. 10 to 25 years), and Stationary (higher than 25 years) phases , as evidenced by the highest values of FST and SP , and the lowest mean number of alleles per locus and subpopulation  and expected heterozygosity . The simulations followed to the Log phase as individuals from the same MLL (commonly with the same genotype) started to disperse seeds to other trees, leading to a boom in individuals abundance and a subpopulation differentiation decay . As the individual abundance saturated, at the Stationary phase, all measured variables from simulated genetic data suffered only small variations , we highlighted four key values of tree density in ascending order  showed the lowest individual abundance, A, and HE, and the strongest SGS . Conversely, landscapes with ca. 190 trees/ha (D2) and 275 trees/ha (D3) showed, respectively, the lowest SGS and the highest individual abundance . Finally, landscapes with increasing tree densities from 275 (D3) to 375 trees/ha (D4) tended to gradually hold fewer individuals and MLLs as long as the SGS enhances.From the simulations with distinct tree densities , we observed that removing up to 90% of the trees from a high-tree density landscape led to 80% less ground coverage, while the addition of new trees to lower-tree density landscapes led to smaller changes (ca. 10%) in the ground cover  in a small landscape (c.a. 0.2 ha) with low tree density and opportunistically colonized by a massive population of this species. Each tree in this system functions as habitat units, forming distinct subpopulations of T. recurvata that group specific sets of multi-locus genotypes (MLG). The data simulated by our TRec model indicate that the highest levels of SGS in T. recurvata occur at the very beginning of the colonization process and under extremely low or high tree densities. As expected, lower SGS occur at landscapes in which seed dispersal is limited neither by long distances among spaced trees nor by the low carry capacity of wind among dense canopies. The TRec model also shows that deforestation is a turning point for the opportunistic thriving of T. recurvata populations. Our results contribute to the understanding of the dynamics of species invasiveness and landscape invasibility and also on the consequences of human-induced changes for the spreading of opportunistic species, as we discuss below.In this study, we integrated empirical and simulated genetic data to provide insights into the spreading of the epiphytic weed T. recurvata at a fine-scale (ca. 2000 m2), as indicated by the significant Sp statistics and high subpopulation differentiation. Such SGS likely arises as an outcome of the breeding system and life form of T. recurvata, which limits local gene flow, resulting in a significant isolation-by-distance, persistence of private alleles in six out of 14 studied subpopulations, as well as high MLGs turnover. Indeed, fine-scale SGS often results from limited gene flow67, and other studies have reported similar genetic patterns for plants with, self-reproduction69, clonal growth70, epiphytic habit71, fast-growth73, and high-density populations74. Our results indicate, therefore, that specific habits and breeding systems can foster the structuring of populations, even for opportunistic and widespread plants.Our empirical study in an anthropic landscape showed a relatively high to moderate SGS for a population of T. recurvata spreading is gradually conducted by several multi-locus lineages (MLLs) following a phalanx pattern, rather than by a single leading MLL40. The relatively low SP observed for a selfing species66, in turn, seems to be related to its dense populations74. These results suggest that multiple and dense sources of T. recurvata propagules from the surrounding areas are responsible for the massively spreading over the studied landscape. Under such conditions, self-reproduction and clonal growth48 may lead to a partial, and perhaps transitory, isolation among subpopulations67. Beyond the effect of the breeding system and life form, the relatively high SGS of T. recurvata population may also result from founder events on each host tree67. Nevertheless, the genetic drift resulting from consecutive founder events, as well as the increasing number of seeds released from large subpopulations, may intensify the metapopulation dynamics of successive local extinctions and recolonizations, reducing the genetic structure of the population over time27.The few MLGs shared among subpopulations indicate that the T. recurvata population resembles the diffusion models of weedy species invasion in newly disturbed landscapes, with sequential Lag, Log, and Stationary phases75. During the Lag phase (up to the 10th year), only a few T. recurvata individuals successfully establish in the landscape, despite the constant input of new genetic lineages (MLLs) per reproductive season. At this stage, as clonal growth continues and the proportion of clones increases, leading to aggregated individuals holding the same genotypes within each subpopulation, the population reaches the highest genetic structure. The reproduction start of such large groups of genetically related individuals leads to a boom in the overall individual abundance, part of the newly produced seeds is exchanged among subpopulations and causes a gradual decrease in the genetic structure, as observed in other empirical studies66. It is notorious, at this point, the impact of clonal-growth in T. recurvata spreading, overcoming the demographic stochasticity and the Allee effect77, and leading the population to the following Log phase (10th to 25th year).The temporal dynamics of the simulated T. recurvata populations as a source of seeds for supporting the species spread into newly disturbed areas after ca. 25 years of colonization. Notwithstanding the specific characteristics of each species, this temporal pattern is typical for opportunistic organisms growing under optimal conditions75. As observed for T. recurvata, a fast increase and stabilization in the individual abundance of populations and communities are expected for static systems79. However, given their xerophytic preference, distinct patterns in the spreading of T. recurvata populations can came across in landscapes with varying tree densities, as we see in the following section.The rapid increase in individual abundance during the Log phase drives the interchange of seeds, and therefore MLGs, among subpopulations, leading to an overall reduction in subpopulation differentiation and genetic structuring. However, the saturation of the static system limits the establishment of a higher number of individuals and MLLs, maintaining all calculated statistics at a steady level after the 25th year (the Stationary phase). Such increment and further stabilization of gene flow at relatively high levels indicate a potential role of T. recurvata subpopulations occur in landscapes with ca. 190 trees/ha, which likely represents the ideal condition for the species spreading. Landscapes with fewer trees tend to harbor fewer individuals of T. recurvata, from which the seeds have to cross longer distances that likely prevent gene flow. On the other hand, landscapes with more than 190 trees/ha counterbalance the effect of increasing habitat amount for epiphytic hosting81 with the intensification of shading and wind friction for seed dispersal84. Indeed, as we observed here, studies implementing IBM approaches for distinct biological systems have shown that the abundance and genetic structuring of populations result from the interplay between habitat amount and resistance to gene flow across landscapes58. For T. recurvata, unlike other epiphytes85, the heavy overlapping of tree crowns in dense forests likely reduces the species' fitness, resulting in small, transient, and isolated populations constrained to the driest and upper canopy layers87. Further studies incorporating the genetic component of fitness into the models can give more insights into the colonization process and the invasiveness of this opportunistic species. The increasing number of genome-wide markers generated by high-throughput sequencing, as well as the constant development of computational methods, will also allow future investigation on the relative importance of natural selection on the genetic structure of such species.Our model indicates that the highest gene flow (i.e. the lowest SGS) among T. recurvata populations in formerly densely forested landscapes. Indeed, studies have suggested that conditions found in dense forests typically prevent the colonization and development of this species, given the limited carrying capacity of wind for seed dispersal84. Using our model approach, we observed an overgrowth of T. recurvata populations in recovering landscapes that suffered low to moderate tree removal, with a significant increase in gene flow. The massive deforestation , in contrast, reduces drastically the number of available host trees, leading, as expected, to smaller populations of T. recurvata and increasing gene flow among subpopulations as the forest regenerates.The combined effect of abrupt reductions in tree density and natural self-thinning have enabled the opportunistic thriving of T. recurvata populations in the opposite direction. In our model, the presence of only 20% of uncovered ground  under lower-tree densities constrained the active increment of trees, reducing the differences among the distinct levels of reforestation. Despite the milder effects in comparison to deforestation, the active increase in tree density and the continuous growth of the former trees likely reduce the abundance of T. recurvata and the gene flow among subpopulations. The larger and older trees in reforested landscapes, compared to the smaller and younger trees in landscapes following deforestation, could play a major role in increasing shading and reducing the carrying capacity of wind for seed dispersal. Therefore, our results indicate that the control of T. recurvata spreading depends not only on the tree density of landscapes but also on the stage of forest recovery. In other words, the positive effects of controlling weeds with posterior reforestation, besides costly, can be much more time-consuming than just avoiding deforestation.Reforestation, in turn, slowly leads 7. Here, we demonstrate that ‘weedy’ traits, such as selfing and clonal growth19, may lead populations to distinct outcomes depending on the landscape conditions. In some landscapes, such traits undermine gene flow and genetic diversity, potentially increasing inbreeding in populations22, but under ideal conditions, they can also mitigate the Allee effect by assuring the genet survival, seed dispersal, and genetic transmission16. Our study also shows that anthropogenic deforestation is the turning point to increase the invasibility of landscapes, enabling the rapid thrive and spreading of T. recurvata especially onto recovering landscapes, where smaller trees produce reduced cover on once heavily shaded ground by larger trees. As have been shown by other studies90, our findings reveal that reforestation has milder and slower effects on mitigating the impact of massive tree removal on the spreading of opportunistic species, highlighting the importance of maintaining forests instead of investing in restorations. On the other hand, landscape features determined by shading, as well as growth, reproduction, and competitiveness among trees likely lead to alternative outcomes for epiphytes. Testing the impact of distinct environmental conditions  on the assembling of tree groups would be a wise step toward a more global understanding of invasion ecology.Opportunistic species provide an excellent system to examine the process of successful colonization in novel environmentsHandroanthus spp. (Bignoniaceae) of similar ages (ca. 20 years) apart from each other from 2.5 to 47.5 m and surrounded by a grassland matrix  composed of a grove of 20 cultivated T. recurvata growing on 14 neighboring trees  and extracted the total genomic DNA from leaf samples according to Tel-Zur et al.91. The collection of plant material complied with national guidelines. Permits to collect in conservation units were granted by COTEC  and SISBIO . We characterized the multi-locus genotype (MLG) of each individual using seven microsatellite loci designed for other species and cross-amplified them as described by Chaves et al.92. We avoided sampling clones by collecting individuals at distinct branches of each host tree. Indeed, clonal individuals of T. recurvata  can be easily distinguished because they are formed on leaf axils and remain attached, conferring the typical "ball" shape of the species38.We sampled a total of 224 individuals of T. recurvata subpopulation, we calculated the number of alleles (A), allelic richness (AR), the private number of alleles (AP), expected (HE) and observed heterozygosity (HO), and inbreeding coefficient (FIS) and tested for departures from Hardy–Weinberg equilibrium using the R statistical package diveRsity93. Furthermore, we tested whether subpopulations were isolated-by-distance using a Mantel test with Slatkin’s linearized FST (FST/(1 − FST))94, Nei’s95, Edward’s96, and Reynold’s97 pairwise genetic distances and the logarithm of geographical distance using the R statistical packages poppr98 and pegas99. We also measured pairwise subpopulation differentiation (FST) and tested the hierarchical partition of genetic variance among and within sampled subpopulations by performing an analysis of molecular variance (AMOVA100), using the R statistical package poppr98.To describe the genetic diversity within each T. recurvata population sampled, we tested whether the distance among T. recurvata individuals (disregarding tree crowns) affects their relatedness. For this, we estimated the kinship coefficient (Fij101) between all pairs of individuals sampled within seven distance classes, each comprising the same number of pairs, using the R package RClone102. To quantify the SGS under this approach, we calculated the Sp statistics  and tested the statistical significance of the mean kinship coefficient values in each distance class  by randomly shuffle (1000 ×) individual locations66. We also quantified the partitioning of the genetic composition of the whole population by calculating the turnover of alleles and multi-locus genotypes among subpopulations (MLG104), using the R statistical package vegetarian105.To investigate the spatial genetic structure (SGS) within the 106 and outlined it following the ODD  protocol formulated by Grimm et al.108 . Such seeds annually reach the landscape from outside  by wind dispersal and generate mature individuals that reproduce by self-fertilization and grow by producing new ramets  on trees within the landscape , depending on the shading rate and competition of its attachment site  or whether they maintain a net-zero gain of energy, with expenses surpassing gain . For the TRec model, we assume that each seed that successfully develops and thrives in the landscape forms a multi-locus lineage (MLL) of multiple descendants, which can be traced back to the ancestor. Each new seed produced within the landscape can also be wind-dispersed by chance from the mother to other trees, but new ramets are always attached to the same host tree. Each MLL often groups multiple MLGs due to heterozygous loci . Another source of genetic variation within MLLs is simulated by a varying mutation rate (from 10–2 to 10–6) that simulates DNA replication slippages of SSR loci.We implemented the TRec model in NetLogo 6.0.1ape Fig. . The winite Fig. . The eneson Fig. . In the son Fig. . T. recuE), mean inbreeding coefficient (FIS), global subpopulation differentiation (FST), and mean modified Garza-Williamson statistic114—as well as T. recurvata abundance and the total number of MLL, are differently affected by the variation of each parameter. To improve the TRec model accuracy, we estimated posterior distributions of the model parameters in an ABC framework (‘Approximate Bayesian Computation’60). We employed the network algorithm implemented in the ‘abc’ R package115 to estimate such distribution based on 0.1% retained simulations that most resembled the empirical SSR data according to the six above-mentioned summary statistics .The simulation process using the TRec model was comprised of three subsequent phases Fig. E: (I) paT. recurvata populations (hereafter referred to as 'time simulation’) and the tree densities values for which the lowest and highest SGS values are achieved (hereafter referred to as ‘density simulation'). To reduce the bias effect inherent to dynamic landscapes , we performed both time and density simulations in static landscapes. We performed 2500-time simulations that emulate 50 years of spreading of T. recurvata individuals on the empirical landscape representation  with random heights and same crown sizes. Yearly (for the time simulations) and after 30 years (for the density simulations), we recorded the individual and MLL abundances, as well as the MLG, MLL, and ramets’ origins  of up to 15 random T. recurvata individuals established on each host tree representation of the empirical study (for the time simulations) or on up to 15 random trees for density simulations (for the density simulations). Based on the MLGs of each simulation, we calculated the average number of alleles (A), the expected heterozygosity (HE), the inbreeding coefficient (FIS), and the global subpopulation differentiation (FST), using the arlsumstat console version of Arlequin 3.1; the MLG and MLL turnovers among subpopulations (T/OMLG and T/OMLL), using the R package vegetarian105; and the Sp statistics considering seven distance classes as in the empirical study66 using the R package RClone102.For the second phase of the simulation process see Fig. E, we useion Fig. , and 500T. recurvata populations  of T. recurvata spreading over static landscapes with low  and high tree densities . After these first 30 years, the model emulates distinct levels of anthropogenic reductions  and increments in tree density  in high and low tree density landscapes, respectively. Thereafter, we yearly calculated individual and MLL abundances, A, HE, and FST using the arlsumstat console version of Arlequin 3.1 over 30 additional years in landscapes allowing tree density variation as the result of tree-growth (an average DBH increase of 0.13 cm/year116), self-thinning (according to the Yoda’s law), and natural regeneration . Here, as in the time and density simulations, we also included the estimated posterior range of the IBM parameters (see Table To emulate the effect of human-induced changes of tree density on the SGS of Supplementary Information 1.Supplementary Information 2.


The general picture of human genetic variation has been vastly depicted in the last years, yet many populations remain broadly understudied. In this work, we analyze for the first time the Merchero population, a Spanish minority ethnic group that has been scarcely studied and historically persecuted. Mercheros have been roughly characterised by an itinerant history, common traditional occupations, and the usage of their own language.Here, we examine the demographic history and genetic scenario of Mercheros, by using genome-wide array data, whole mitochondrial sequences, and Y chromosome STR markers from 25 individuals. These samples have been complemented with a wide-range of present-day populations from Western Eurasia and North Africa. Our results show that the genetic diversity of Mercheros is explained within the context of the Iberian Peninsula, evidencing a modest signal of Roma admixture. In addition, Mercheros present low genetic isolation and intrapopulation heterogeneity.This study represents the first genetic characterisation of the Merchero population, depicting their fine-scale ancestry components and genetic scenario within the Iberian Peninsula. Since ethnicity is not only influenced by genetic ancestry but also cultural factors, other studies from multiple disciplines are needed to further explore the Merchero population. As with Mercheros, there is a considerable gap of underrepresented populations and ethnic groups in publicly available genetic data. Thus, we encourage the consideration of more ethnically diverse population panels in human genetic studies, as an attempt to improve the representation of human populations and better reconstruct their fine-scale history.The online version contains supplementary material available at 10.1186/s12864-021-08203-y. Genetic studies focused on human populations have been intensely boosted in the last decades owing to the increasingly affordable genome-wide platforms, expanding the amount of high-quality genetic material considered in the studies, and allowing their availability in public genomic repositories. This has enabled to vastly depict the human genetic landscape both from an anthropological and biomedical point of view –3. Yet, The Iberian Peninsula genetic scenario is the complex result of several demographic processes related to different historical events characterized by diverse population contacts, including multiple ethnic and geographic origins , 7. The quinqui, an unclassified language. They are nowadays distributed across the whole Iberian Peninsula, although the total number of Mercheros has not been properly estimated [Despite the exhaustive genetic coverage of the Iberian Peninsula, being also part of the 1000 genomes project [stimated , 16. Litstimated , 17, 18.stimated , 17, 18.In the present study, we aim to genetically characterize the Merchero population in order to complete the genetic landscape of Iberian Peninsula, by analysing genome-wide autosomal and uniparental data from 25 samples. These are the first available Merchero genetic samples, which will reduce the gap of the underrepresented populations and ethnic groups in the reference panels. In order to assess the genetic origin of the Merchero population, their ancestry profiles and admixture levels with external groups are examined with allele-frequency and haplotype-based methods. In addition, we describe their demographic history and genetic substructure, through the inference of runs of homozygosity (ROHs), identity by descent (IBD) segments, and effective population size (Ne) dynamics.To assess the genetic origin of Mercheros, a large genome-wide database including West Eurasian and South Asian populations individuals was analyzed . We first computed a Principal component analysis (PCA) Fig. A, where p-value = 0.0018). The higher Roma proportion together with a large standard deviation of this component in the Merchero’s groups suggest that few Merchero individuals might have experienced gene flow from Spanish Roma. This result is also supported by the UMAP analysis of the Iberian Peninsula context, where some Merchero samples are placed between the Roma and the rest of the Spanish samples  are observed in Mercheros Fig. S, which aThe global pairwise sharing of IBD segments was explored to test for the genetic relationship among populations. These analyses mirror the results presented above within the Iberian Peninsula context Fig. S. SpanishThese results, in agreement with the previous section, suggest a very recent scenario of genetic inbreeding and substructure in Mercheros, that might be the result of a subtle admixture with an already inbred population such as Roma Fig. B.We further examined the genetic demographic history of Mercheros focussing on their Ne dynamics. Long-term Ne (200 to 2000 generations ago) was estimated from genome-wide patterns of linkage disequilibrium, showing slightly lower values in Mercheros than in the Spanish general population Fig. SA, althouSeveral hypotheses have been suggested regarding the origin of Mercheros. However, these hypotheses have not been supported by robust data and they have mostly spread by word of mouth . Our stuquinqui exhibits an influence from Spanish and Portuguese Romani  and Basque Romani (Erromintxela) [n = 4) present a higher probability of randomly sharing segments  , 16. Hows Table S. These IThe Merchero population does not represent a genetically isolated and homogenous group within the Iberian Peninsula, which is consistent with previous literature about the social aspects of this population , 17, 18.The present study represents the first comprehensive genetic analysis of the Spanish minority ethnic group known as Mercheros. Since ethnicity is influenced by both genetic ancestry and cultural identity, further studies are needed to reconstruct the history of the Merchero population from different scientific disciplines, such as linguistics, anthropology, and genetics. Despite the large amount of publicly available genetic data, there is a considerable gap regarding underrepresented populations and ethnic groups. Overcoming this limitation is pivotal to properly expand the diversity frame of human population genetic studies. This will broaden the knowledge about the complete human population history and the demographic processes that have shaped the genetic variation in fine-scale details, and thus, to improve and facilitate the interpretation of biomedical studies as well , 5. Therp < 10− 5), and a minor allele-frequency (MAF) below 0.05 were filtered out, remaining a total of 599,176 SNPs. Linkage disequilibrium (LD) pruning was performed with a window size of 200 SNPs, a sliding shift of 25 SNPs, and an r2 of 0.5 keeping 231,582 SNPs. Three Merchero samples were removed from the dataset due to high level of relatedness .Twenty five samples were collected from self-reported Merchero volunteers, whose written informed consent was obtained. The parents and grandparents of all volunteers self-identified as Mercheros from several locations in Spain. Moreover, eleven new Spanish Roma samples were collected in order to test for a common origin between Mercheros and Roma, as previously suggested . DNA extTo have a reference panel, our dataset was merged with the West Eurasian and North African samples from Lazaridis et al. 2016 study ; Utah reumap method [A PCA was performed using the SmartPCA program from the EIGENSOFT 6.0.1 package . Uniformp method  in R [29p method . The unsp method  was usedpoppr v2.1.0 R package [P-values were obtained through the empirical distribution under the null hypothesis from 1000 permutations with the ade4 v2.0.1 R package [nnls v1.4 R package [In order to better assess population structure and detect fine-scale ancestry patterns, haplotype-based methods were applied. SHAPEIT v2  was firs package  to analy package . After g package . GLOBETR package  was usedROHs were identified using PLINK v1.9b software  with theggplot2 R package [prcomp function in R base [IBD segments were identified using IBDSeq (version r1206) software  with def package  showing n R base  with then R base , assuminn R base .To estimate the Ne trajectories (0 to 200 generations ago), we used IBDNe version 23Apr20.ae9) software Apr20.ae9. In addiIllumina mtDNA Genome [Illumina MiSeq guidelines [Mitochondrial DNA (mtDNA) was PCR-amplified in four fragments using 4 pairs of primers under identical conditions . GeneticA Genome  and Illuidelines , respectidelines  and indiidelines . Only twidelines  (Table Shttp://www.hprg.com/hapest5/) [Seventeen STRs present in the commercially available AmpFLSTR Yfiler PCR Amplification Kit (Thermo Fisher Scientific) were genotyped following manufacturer’s recommendations. Y-chromosome haplogroups were predicted from YSTRs using Whit Atheys’ haplogroup predictor (apest5/) .Additional file 1. Supplementary Information file. Additional Figures  and Tables (Table S1- S7) with their references.