Coregonus lavaretus) is native to only two lakes and vulnerable to extirpation. Six new refuge populations were established over the last 30 years as a conservation measure. In this study, we examined whether there is a predictable ecological and evolutionary response of these fishes to translocation. We found eco‐morphological differences, as functional traits relating to body shape differed between source and refuge populations. Dual isotopic analyses suggested some ecological release, with the diets in refuge populations being more diverse than in source populations. Analyses of up to 9117 genome‐mapped SNPs showed that refuge populations had reduced genetic diversity and elevated inbreeding and relatedness relative to source populations, though genomic differentiation was low (FST = 0.002–0.030). We identified 14 genomic SNPs that showed shared signals of a selective response to translocations, including some located near or within genes involved in the immune system, nervous system and hepatic functions. Analysis of up to 120,897 epigenomic loci identified a component of consistent differential methylation between source and refuge populations. We found that epigenomic variation and genomic variation were associated with morphological variation, but we were not able to infer an effect of population age because the patterns were also linked with the methodology of the translocations. These results show that conservation‐driven translocations affect evolutionary potential by impacting eco‐morphological, genomic and epigenomic components of diversity, shedding light on acclimation and adaptation process in these contexts.Identifying the molecular mechanisms facilitating adaptation to new environments is a key question in evolutionary biology, especially in the face of current rapid and human‐induced changes. Translocations have become an important tool for species conservation, but the attendant small population sizes and new ecological pressures might affect phenotypic and genotypic variation and trajectories dramatically and in unknown ways. In Scotland, the European whitefish ( These populations were colonized postglacially and are genetically closely related relative to other British populations , Glashan , Tarsan , which form the Eck translocation system, and Lomond , Allt na Lairige , Shira , Carron Valley Reservoir , Sloy , which form the Lomond translocation system. Sampling occurred between August and October 2017 using multi‐panel, Nordic‐pattern gillnets. Fish collection was undertaken under licence from Scottish Natural Heritage (now NatureScot) and Marine Scotland. Individuals were photographed on the left side. White muscle tissue from the left side, underneath the dorsal fin and above the lateral line, was taken for genomic and epigenomic analyses and stored in absolute ethanol at −20°C. For stable isotope analysis (SIA), we collected ~1 cm3 of muscle tissue from the right side of the fish, underneath the dorsal fin and above the lateral line, and the stomach contents, and both were stored at −20°C. Due to different sampling schemes, we could not collect stable isotope data from the source population of Eck.European whitefish individuals were collected from eight Scottish lochs Figure  in two ln = 40), Loch Sloy (n = 17) and Loch Eck (n = 41) that were used to establish the refuge populations between 2009 and 2011. For the morphometric analyses, we added photographs from: a sampling of the parent fish from Loch Lomond (n = 89) and Loch Eck (n = 118) that were used to establish the refuge populations between 2009 and 2011; samples from refuge populations Allt na Lairige (n = 4), Lochan Shira (n = 15), Loch Glashan (n = 33) and Loch Tarsan (n = 60) collected during a survey in 2014 and 2015 , Carron Valley Reservoir (n = 11) and Loch Sloy (n = 20) collected in a survey in January 2018.In addition to the samples collected in 2017, we included previously collected samples in the genomic and morphometrics analyses when available. For the genomic analyses, we included a subset of the parent fish from Loch Lomond . Landmarks were digitized using TPSDig2 v.2.16  * Lake, with Shape being the combination of all principal components, and Size the centroid size (the square root of summed squared distances of landmarks from the configuration centroid). We implemented the procD.allometry function for each lake system separately. When the interaction term was significant, we performed a pairwise test for homogeneity of slopes using the advanced.procD.lm function, to test if populations differed in allometric slope. If the interaction term was not significant, that is if different populations have common or parallel trajectories, we performed pairwise tests for shape difference. Significance was assessed with a randomized residual permutation procedure with 1000 iterations. We performed a principal component analysis (PCA) on the Procrustes coordinates of all individuals to explore the major axes of variation.All photographs used for morphometric analyses were taken with the same protocol and using graph paper or ruler for scale. Body shape was captured with 14 fixed landmarks Figure  chosen b6 Rohlf, . Statist6 Rohlf,  with thegeomorph to quantify the level of parallelism, or deviation from it, in body shape change in response to the translocations across the two lake systems. Significant difference in trajectory direction (θP: differences in the direction of phenotypic change) and trajectory lengths (ΔLP: differences in the magnitude of phenotypic change) was assessed using 1000 permutations.We performed a phenotypic trajectory analysis (PTA) . Traits were chosen based on previous publications  correction for multiple testing. A PCA was carried out to determine the major axes of phenotypic variation between source and refuge populations, across and within lakes.Linear measurements of nine body traits plus fork length were obtained from distance between landmarks Figure b . Stable isotope ratios of carbon (13C/12C) and nitrogen (15N/14/N) were measured with an IsoPrime  isotope ratio mass spectrometer.Lipid extraction of tissue and stable isotope measurement methods followed Yohannes et al. . Isotopisi_div.R set of functions  and the epiRADseq libraries used PstI‐HF and the methylation‐sensitive HpaII enzymes. The enzymes MspI and HpaII have the same recognition site. Libraries were sequenced on an Illumina NextSeq500 with 75‐bp paired‐end reads at Glasgow Polyomics to a depth of 400 M reads each.Samples collected in 2017 were prepared using epiRADseq  split among three libraries, and the ddRADseq library was composed of 96 individuals (2.5–9.1 M reads per sample) Table .2.6process_radtags in Stacks v.2.4.1 . A principal component analysis (PCA) revealed the presence of a batch effect between the epiRADseq and ddRADseq libraries on PC2. To identify and exclude the loci responsible, we used two approaches: (a) we ran a PCA and calculated the correlation between eigenvectors and SNP genotype using the snpgdsPCACorr function in the R package SNPRelate v. 1.16 , with the following criteria: ‐p 6 (minimum number of populations genotyped), ‐r 0.75 , ‐‐min‐maf 0.05 , ‐‐max‐obs‐het 0.6 (maximum observed heterozygosity required to process a site at a locus) for the combined data set; ‐p 4, ‐r 0.75, ‐‐min‐maf 0.05, ‐‐max‐obs‐het 0.6 for the Lomond system data set; and ‐p 2, ‐r 0.75, ‐‐min‐maf 0.05, ‐‐max‐obs‐het 0.6 for the Eck system data set. One SNP per locus was retained.We generated three data sets for population genomic analyses: a combined data set with all eight populations specifically for outlier analyses, and a data set for the Eck system  and for the Lomond system  separately that were used for the genetic diversity, inbreeding and relatedness analyses. An initial vcf file was generated for each in vcftools v.0.1.15  and with the script pop_missing_filter.sh (available at https://github.com/jpuritz/dDocent/blob/master/scripts/pop_missing_filter.sh) removed sites with more than 33% missing data per population. After filtering, the genomic combined data set comprised 184 individuals and 5116 SNPs, the Lomond system data set comprised 110 individuals and 6333 SNPs, and the Eck system data set comprised 77 individuals and 3712 SNPs.Each data set was then filtered in LD‐kNNi method implemented in Tassel v.5 .To assess the quality of the ddRADseq and epiRADseq data for combined genomic analyses we: (a) calculated the heterozygous miscall rate, which measures putative genotyping errors by estimating deviation from HWE, with the R package  Goudet, , using e2.9E, observed heterozygosity HO), nucleotide diversity π and number of private alleles per population were calculated by the population module of Stacks for each lake system separately. For these analyses, we retained all SNPs present in the loci from the Lomond  and Eck  system data sets, as these metrics do not need to account for linkage disequilibrium. Genomic measures of pairwise relatedness, xyR, and individual inbreeding coefficient, HF, were estimated in Plink v.1.9  was calculated using the function RsquareAdj in vegan. SNPs with a loading greater than ±2.5 standard deviation, or z‐score,  on the lake type RDA axis were considered to be outliers.To detect genomic outlier SNPs associated with translocations, we used two approaches. First, we applied a redundancy analysis (RDA) to the combined data set as a multilocus genotype–environment association (GEA) using the R package BayPass  to identify SNPs associated with covariates based on a calibration procedure using pseudo‐observed data sets  <0.05 were then excluded from the count table. After removing the library effect, we identified a weak batch effect associated with the Illumina adapter barcode. We reiterated the same procedure and excluded loci affected by this bias. Finally, we excluded loci that had nonzero read counts in fewer than 33% of individuals from each lake system separately to remove uninformative loci present in only a small number of individuals. After filtering, the epigenomic Lomond system , Eck system (N = 45) and combined data sets (N = 111) had totals of 120,897, 117,395 and 114,565 loci, respectively.Preliminary analyses using multidimensional scaling (MDS) in the R package 2.12glmFit and glmLRT functions in edgeR. All loci with an FDR < 0.05 for each comparison were considered to be differentially methylated (DM). Excess of DM loci sharing between source‐refuge population comparisons was calculated with the R package SuperExactTest v.1.0.7  annotations. Genes overlapping the SNPs and DM loci and genes within 3000 bp upstream and downstream of these outliers were retained for the analysis. Protein sequences from the European whitefish genome , and proportion of morphological variation explained by epigenomic effects while accounting for genomic variation, morpho~epi + Condition(gen), following a similar approach by Rougeux, Laporte, Gagnaire, & Bernatchez  to partition the percentage of morphological variation due to genomic and epigenomic effects together, rnatchez . We ran rnatchez . Estimat33.1F2,277 = 5.72, p‐value = 0.001), with the refuge populations having different allometric trajectories compared with the source population , but not when Shira was excluded , indicating that Shira fish had a different allometric trajectory compared with the other populations . However, the direction of phenotypic change between source and refuge populations differed significantly across systems ; the different directions resulted in a convergence of the source to refuge trajectories on PC1 , with the Loch Lomond and Carron populations showing the highest δ15N levels; Glashan and Tarsan intermediate levels; and Allt na Lairige, Shira and Sloy the lowest  and isotopic divergence (IDiv) were higher in most refuge populations compared with source Table . This su3.3The heterozygous miscall rate was <0.1% for the ddRADseq and epiRADseq samples Table . Rarefie3.4Genetic diversity and heterozygosity were generally lower in the refuge populations than the source; the difference is very small but significant in most cases Table . No refuFH were higher in the refuge populations; significantly so in all but one instance , and showed lower inbreeding  and lower relatedness   Figure . Due to 3.5K = 2) as the best‐fitting scenario; the Sloy population significantly differentiated from Lomond, and three clusters as the second best‐fitting scenario, with the Carron population further splitting from Lomond but no further genetic structuring by refuge lake   (PC1 19%) Figure . Individe Figure . Admixtu) Figure .FST = 0.002–0.003, p‐value < 0.05), and the two refuge populations were not differentiated from each other  . There was a trend of age effect, with differentiation being higher between the source and two 30‐year‐old refuge populations than between the source and the two 7‐ to 9‐year‐old ones, and there was no significant differentiation between the two 7–9 refuge populations  of the total variance . Of this, lake system explained 96.6% on axis 1, and lake type explained 3.4%  and lake system as explanatory variables, the genomic RDA explained 18.7% , five were found in or near genes and so could be putatively associated with functions Figure . These g3.7Across all lakes, 1294 loci were differentially methylated (DM loci) between source and refuge populations. Most of the variation in the data set was explained by lake system. Specifically, PC1  separated Eck system from Lomond, and refuge populations separated from the source populations on PC3  Figure a,b. The p‐value < 0.0001, Figure p‐value < 0.0001) . In the Eck system, there were 139 DM loci between Eck and refuge population Tarsan, and 858 DM loci between Eck and refuge population Glashan. Of these, 81 loci overlapped, which was more than expected by chance  Figure , one of ) Figure , DPYSL5.p‐value < 0.001, fold enrichment = 8.96–90.65) included neural functions  and ion transport  (Table There were no significantly enriched GO terms (FDR > 0.1) from the genes associated with DM loci (Eck system), but the top 11 GO terms (based on uncorrected t) Table . The gent) Table .R2 = 0.03) of the total variance , of which lake system separated on the first axis (65%) and lake type on the second (35%)  Figure . We iden) Figure , of whic) Figure . The RDA) Figure . These r3.8R2 = 0.18) of the variance in body shape . Genomic and epigenomic components separately explained 7%  and 16%  of the variance in morphology, respectively , while when controlling for genomic variation, epigenomic variation explained 11%  of the morphological variance. This pattern differed between systems and refuge population ages  of the variance in body shape, genomic variation explained none , while epigenomic variation explained 27%  and 28%  of the variation separately and when controlling for genomic variation, respectively . In the group containing Lomond and the older, that is 30‐year‐old refuge populations, genomic and epigenomic together explained 16%  of the variance in morphology, genomic alone and when accounting for epigenomic effects explained 13%  and 10% , respectively, and epigenomic variation explained none .We applied RDA to partition the variance in morphology that was explained by genomic and epigenomic components. When considering the full data set, genomic and epigenomic effects together explained 18%  and muscle (long‐term) stable isotope analysis. Refuge populations had the highest isotopic richness and with a greater inter‐individual range in δWhitefish are renowned for high levels of polymorphism and adaptive eco‐morphologies in body shape, gill rakers and physiology  offspring from thermally stressed mothers, with their fear‐related locomotor response and spatial learning abilities impaired  explained substantially more variance in the epigenomic models than it did in the genomic models (35% vs 3% of the variance captured in the RDA). This agrees with previous findings that individuals reared in different environments exhibit higher epigenomic differentiation than genomic differentiation due to phenotypic plasticity (Artemov et al., 5We identified consistent ecological and morphological responses in whitefish refuge populations, suggesting that the ecological and evolutionary consequences of these conservation‐driven translocations might be predictable. In the refuge populations, genetic diversity was reduced while relatedness and inbreeding increased; this was related to both the number of founder individuals and time since translocation and are difficult to separate. Genomic and epigenomic analyses suggested roles of neural development, immune system and metabolism in response to translocation. This demonstrates that transgenerational molecular mechanisms might facilitate acclimation and rapid adaptation to new environments and response to divergent selection that accompany these human‐mediated colonization. In addition, our findings suggest that translocated populations can adapt to their new environments at the genomic level despite a reduction in diversity. Our findings shed light on processes behind recent and rapid differentiation, acclimation and adaptation in populations of high conservation concern and that are targets of management effort. This highlights the value of combining genomic and epigenomic approaches to understand ecological and evolutionary responses to novel environments, but also the need for experimental work to better understand the role, and potential transgenerational effect, of epigenomic mechanisms.None declared.6Fig S1‐8Click here for additional data file.Table S1‐11Click here for additional data file.


Ceratocystis manginecans complex are important fungal pathogens of plantation trees globally. The most important hosts include species of Eucalyptus, Acacia, Mangifera, and Punica. Despite their relevance and widespread occurrence, little is known regarding their population genetics and how this might relate to their host associations or geographic regions in which they occur. A global collection of 491 isolates representing the C. manginecans complex, from four different plant hosts and nine countries, were genotyped using microsatellite markers. Population genetic analyses using numerous tools were conducted to interrogate how their genetic diversity and structure might be affected by host or areas of occurrence. Results of genetic diversity studies showed that when grouping isolates into populations based on their host associations, the population on Eucalyptus was most diverse, and it also has a broad global distribution. When considering countries of origin as a basis for defining populations, the gene and genotypic diversity were highest in populations from China, Indonesia, and Brazil. In contrast, populations from Oman and Pakistan collected from Mangifera had the lowest genetic diversity and were clonal. Molecular variance, population differentiation, and network and structure analyses showed that the genetic structure of isolates in the C. manginecans complex is influenced by both host association as well as geographical isolation. Furthermore, the results reflected the movement of genotypes between plant hosts and geographic regions that have implications regarding the broad global distribution of this pathogen.Species in the  Tree diseases caused by fungal pathogens are increasing in incidence and impact globally. This is especially true in the case of trees grown for plantation forestry and fruit tree production ,2,3,4. ACeratocystis spp. are amongst the most important causal agents of emerging tree diseases [C. albifundus, the causal agent of a canker and wilt disease of Acacia mearnsii in Africa [C. manginecans, associated with wilt disease of Mangifera indica in Pakistan and Oman [C. platani, the causal agent of canker stain and die-back of Platanus spp. in Europe and the USA [C. lukuohia and C. huliohia, which are associated with the rapid ‘ōhi’a death of native Metrosideros polymorpha trees in Hawaii [diseases ,17,18. Tdiseases ,19. Impon Africa ,21, C. mand Oman , C. plat the USA , and C. n Hawaii ,25.Ceratocystis manginecans and C. eucalypticola have been reported to cause serious diseases on several plant hosts, including Acacia mangium [Eucalyptus spp. [Mangifera indica [Prosopis cineraria [Punica granatum [Ceratocystis manginecans was first described from Oman and Pakistan causing a devastating wilt disease on mango trees [Prosopis cineraria and Dalbergia sissoo [Ceratocystis acaciivora was described as the causal agent of wilt and die-back disease of the legume tree Acacia mangium used to establish forest plantations in Indonesia [C. manginecans based on additional multi-gene phylogenetic analyses [Ceratocystis eucalypticola, a species closely related to C. manginecans, was first described from Eucalyptus in South Africa [Ceratocystis from Eucalyptus trees in Brazil, Indonesia, Congo, Thailand, Uganda, and Uruguay were closely related to this species and most likely represent the same species [ mangium ,26,27, Etus spp. ,29,30, Ma indica ,32,33,34ineraria , and Pungranatum . Ceratocgo trees , and whea sissoo . Ceratocndonesia . This spanalyses . CeratocCeratocystis manginecans and C. eucalypticola are phylogenetically closely related species residing in the Latin American Clade and for which species boundaries remain poorly defined. This is largely due to a paucity of informative taxonomic markers to clearly define species in this clade and the presence of multiple ITS types in single isolates of C. manginecans and C. eucalypticola [C. manginecans and C. eucalypticola should be treated as the same species [C. eucalypticola and C. manginecans reside in two discrete clades, justifying their recognition as distinct species. Based on the results of previous studies [C. manginecans complex.ypticola ,32,36,37 species ,36,38. I studies ,36,37,38C. manginecans complex [C. manginecans from Oman, Pakistan, Indonesia, and Vietnam suggested that C. manginecans could be native to South East Asia. This was based on a significantly higher genetic diversity observed amongst isolates from that area, especially in Vietnam. A very low genetic diversity was observed on Chinese populations from Punica, indicating that this pathogen has experienced a severe genetic bottleneck [Punica was indirectly derived from introductions of a population on Eucalyptus from Brazil [Several population genetic studies have been conducted on isolates of the  complex ,33,36,39 complex  or South complex  using poC. manginecans complex, these have mostly considered isolates from a limited number of plant hosts and regions. As a consequence, knowledge regarding the global population genetic diversity, genetic structure, and distribution of isolates in this complex is limited. Thus, the aims of this study were (i) to investigate the genetic diversity of the C. manginecans complex using a large collection of isolates from nine countries and four plant hosts using 10 polymorphic microsatellite markers, (ii) to consider whether there are patterns of population genetic structure reflecting geographic isolation, host association, or species boundaries of the isolates, and (iii) to determine possible patterns of distribution for isolates from different plant hosts or geographic regions.Although several population genetic studies have been conducted on isolates residing in the C. manginecans complex  were obtained from various countries and sources during the period 2003 to 2014  and from diseased stem tissue from Punicagranatum trees in SiChuan and YunNan provinces. In YunNan province, the isolates from Punica and Eucalyptus were collected approximately 200 km from each other , which were collected and used in previous studies [C. manginecans from Mangifera indica and Acacia spp. used in the study of Fourie et al. [Isolates representing the  to 2014 . Isolatech other . Additio studies ,29 . DNA samples were diluted in 10 mM Tris-HCl (pH 8.0) to a final concentration of approximately 50 ng/μL and stored at −20 °C until being used.Single hyphal tip cultures generated for all isolates used in this study were subjected to DNA extraction and SSR genotyping. Isolates were grown on 2% Malt Extract Agar  for 2–3 weeks at 25 °C. Mycelium was scraped from the surfaces of the agar using a sterile scalpel and transferred to 2 mL Eppendorf tubes. Genomic DNA was extracted using a modified cetyl trimethyl ammonium bromide (CTAB) protocol described by Möller et al. . DNA conC. manginecans based on molecular data. The remaining isolates, newly collected for the present study, were identified based on DNA sequence analyses. These included sequences of the ITS region using primers ITS1 and ITS4 [MS204 gene region using primers MS204F.ceratoB and MS204R.ceratoB [RPBII gene region using primers RPB2-5Fb and RPB2-7Rb [Isolates utilized in the study of Fourie et al.  had prevand ITS4 , the MS2.ceratoB , and theRPB2-7Rb .TM PCR Product Clean-up Reagent  to remove unincorporated primers and dNTPs, which was followed by sequencing with the BigDye Terminator v3.1 sequencing premix kit . An ABI PRISM™ 3100 Autosequencer  was used for sequencing.The PCR reaction mixtures and thermal cycles for all three gene regions were the same as those described by Liu et al. . PCR proMS204 and RPBII gene regions, using the program PhyML v. 3.1 [Ceratocystis pirilliformis (CMW 6579) was used as the out-group taxon.Raw sequence data were trimmed and assembled using Geneious v. 7.0; then, they were aligned using MAFFT v. 7  and thenL v. 3.1 . ConfideC. manginecans [TM 3500xl Autosequencer . The GeneScan 500 LIZ molecular size standard  was used as the internal size standard. GeneMapper® v. 5 software  was used for allele scoring. Different fragment sizes were considered to represent different alleles. The combination of alleles from all genotyped markers for each isolate was presented as a multi-locus genotype (MLG).Ten microsatellite markers, specifically developed for ginecans , were usginecans , with miOne to two representative isolates for every allele determined from fragment size analysis were selected for Sanger sequencing to confirm the allele size obtained with GeneScan. Collectively, 85 isolates were selected, and PCRs and sequencing reactions were conducted using unlabeled microsatellite primers. The sequences obtained from the forward and reverse primers were assembled in Geneious v. 7. Sequence data for each of the observed alleles from all markers were deposited in GenBank.To calculate the various population statistics, isolates were divided into populations based on countries of origin. Additionally, in countries (China and Indonesia) where isolates were obtained from more than one host, these were also divided into sub-populations by taking both country of origin and host plants into consideration. This made it possible to consider the relationship between genetic diversity and host associations where possible. The total number of alleles (Na), the number of effective alleles (Nef) , as wellAnalysis of molecular variance (AMOVA) was investigated in GENALEX v. 6.5  to test To investigate possible evolutionary relationships among the observed multilocus genotypes (MLGs), minimum spanning networks (MSN) were constructed using Bruvo’s distance with the R packages poppr and ape ,51.△K and the median value of lnPr (K) generated from STRUCTURE HARVESTER [The model-based Bayesian clustering approach, used to study genetic differences in populations, was implemented in STRUCTURE v. 2.3.3. This detected the most likely number of sub-populations (K), based on allele frequencies per locus ,53. The ARVESTER .Since STRUCTURE tends to detect the uppermost level of genetic differentiation within a given dataset , subsequA) statistic [p < 0.05 was assessed by comparing the observed rBarD value with that obtained from 999 randomization samplings of the same dataset. Clone-corrected datasets were prepared and used for this calculation using the ia function in the poppr R package [Mode of reproduction was investigated using multilocus linkage disequilibrium in the form of the Index of Association  and occurring on four hosts  were included in the analyses . In addition, data generated (using the same microsatellite markers) for 131 isolates of C. manginecans from Acacia spp. and Mangifera indica by Fourie et al. [Collectively, data for a total of 491 isolates of the analyses . Of these et al.  were incAll ten microsatellite markers utilized in this study were polymorphic with the number of alleles per locus ranging from two to 14. A total of 50 different alleles were generated with the ten markers. The sequencing of all alleles observed in this study from 85 representative isolates  than that from Acacia (30 MLGs), Punica (8 MLGs), and Mangifera (1 MLG).For the geography-associated populations, gene diversity (Hexp) values obtained for nine geographic populations ranged from 0 to 0.351 . The higEucalyptus population were higher compare with Indonesia Acacia, Indonesia Eucalyptus, and China Punica populations  and genotype diversity (G) of China ulations . For Chipulation . Likewispulation . In thesulations .p ≤ 0.001). Based on geographic origin, a higher genetic differentiation was detected among populations (56%) and lower variation within populations (44%). In terms of host, of the total molecular variation, 51% of the genetic difference was observed among populations and 49% was within populations  with highest gene flow (Nm = 3.209) were recorded between the population on Eucalyptus in China and the population on Eucalyptus in South Africa, followed by Eucalyptus in Uruguay . These results also showed that the genetic differentiation between the China Eucalyptus population and the Eucalyptus populations from other countries (φPT = 0.135 to 0.361) were all lower than that between China Eucalyptus population and populations from other countries that were not from Eucalyptus (φPT = 0.524 to 0.648)  and gene flow (Nm)  showed to 0.648) . These rC. manginecans complex  analyses revealed two distinct clusters for the 491 isolates residing in the  complex a,b. Thes complex a,b. Basepulation a,b. In af Punica b.Acacia (165) and Mangifera (21), and 31 isolates originally collected from Eucalyptus in China and Indonesia  and Punica (80)  .In the subsequent STRUCTURE analyses conducted on isolates from each of the two clusters identified in the first structure run, the optimal K values of 2 and 4 were suggested for cluster 1, while the optimal K values of 2 and 5 were suggested for cluster 2 . These fCeratocystis. China  in Oman and Dalbergia sissoo (Shisham) in Pakistan [Mangifera to the native hosts, but the source of the introduction onto Mangifera was unknown. The authors further suggested that this could have come from South East Asia or South America [C. manginecans  on Acacia mangium, which, similar to Prosopis cineraria and Dalbergia sissoo is a legume, suggests a host link between the diseases caused by C. manginecans in the Middle East and South East Asia. The results of the present study add further support for the hypothesis that C. manginecans on Mangifera is linked to the disease caused by C. manginecans on Acacia in South East Asia.Host range expansion events in the  studies ,33,36. TPakistan . These a America ,33. The C. manginecans on Punica and Eucalyptus emerging from this study is as intriguing as that linking C. manginecans from the legume hosts. Eucalyptus residing in the Myrtaceae  is native to Australia and some nearby islands but has been widely planted globally for plantation development for more than 100 years [Eucalyptus having been moved globally, such as from the native area to the new environment [Eucalyptus where these trees have been established as non-natives [Eucalyptus to Punica in the case of C. manginecans seems very probable given the fact that Punica is also a member of the Myrtales [C. manginecans on Punica could have originated from Eucalyptus.The genetic association between isolates of 00 years ,13,59. Tironment ,60,61. H-natives ,63. Nota-natives ,65. ThusMyrtales . This miMyrtales  showing C. manginecans complex were found in isolates from more than one geographic location provides evidence that this pathogen has been accidently moved globally. Moreover, some introductions have apparently occurred recently. It is well-recognized that Ceratocystis species are well suited to long-distance dispersal by insects and human activities [C. manginecans complex. Thus, the emergence of devastating diseases of Acacia in Asia [Eucalyptus in Congo [Punica in China [Mangifera in Oman and Pakistan [Eucalyptus. The origin of this source population has yet to be determined. The answer to this question is complicated by the fact that there is also evidence of hybridization in Ceratocystis spp. (including C. manginecans) and possibly horizontal gene transfer [The fact that some genotypes of the tivities ,67. The tivities ,21,68, a in Asia ,26, Eucain Congo ,69, Puniin China , and ManPakistan ,33,35 altransfer ,70,71.C. manginecans and C. eucalypticola, are currently recognized based on multiple gene phylogenic analyses. Their species boundaries become unclear when a large number of isolates are included in analyses. The existence of two major Eucalyptus-  and Acacia- (viz. C. manginecans) associated clusters provides an indication of an ongoing process of speciation driven by host associations in these fungi. The results provide a foundation for future studies on genetic variation of other globally distributed populations in the C. manginecans complex. Collectively, these should lead to a better understanding of the genetic relationships and pathways of introduction of Ceratocystis spp. Furthermore, the results will contribute to managing future invasions resulting from different genotypes of the C. manginecans complex on different host tree species in different countries and regions.Although the available cultures from different populations considered in this study varied in number, intriguing patterns of global spread emerged. There remains substantial opportunity to expand the knowledge gained in this study with larger numbers of isolates from areas or hosts that could not be sampled or where sampling was not optimal. Some genotypes occurred only on specific hosts. The results of this study suggest that host-associated genotypes should be taken into consideration in defining quarantine measures and in the assessments of invasive risks of these fungi. The two closely related species,


Hepatozoon canis is a protozoan transmitted to dogs and other wild carnivores by the ingestion of ticks containing mature oocysts and is considered the principal cause of canine hepatozoonosis in the world. Here, we examined ribosomal RNA 18S gene sequence variation to determine the genetic differences and phylogeographic diversity of H. canis from various geographical areas around the world.H. canis from 46 countries to assess haplotype relationships, geographical structure, genetic diversity indices, and relationships among populations. We performed neutrality tests and pairwise comparisons of fixation index (FST) values between groups and pairwise comparisons of FST values between populations. To determine whether populations are structured, analyses of molecular variance (AMOVAs) and spatial analysis of molecular variance (SAMOVA) were performed.We used 550 publicly available sequences of H. canis yielded 76 haplotypes. Differentiation among populations indicated that there is no phylogeographical structure (GST = 0.302 ± 0.0475). Moreover, when samples were grouped by continents a significant FST was obtained, meaning that populations were genetically differentiated. The AMOVA showed that 57.4% of the genetic variation was explained by differences within populations when all locations were treated as a single group and revealed that there is no population structure when populations are grouped into two, three, and four groups , suggesting that dispersal between populations is high. SAMOVA revealed significant FCT values for groups K = 5. The Tajima’s D and Fu’s Fs show that populations have undergone recent expansion, and the mismatch distribution analysis showed population expansion .The dataset of H. canis does not show phylogeographic or population structure. The haplotypes exhibit low genetic differentiation, suggesting a recent expansion due to gene flow among populations. These results provide pivotal information required for future detailed population genetic analysis or to establish control strategies of this parasite.The current molecular data confirmed that The online version contains supplementary material available at 10.1186/s13071-021-04924-x. Hepatozoon infect a wide range of species of mammals, reptiles, amphibians, and birds [Hepatozoon species affecting domestic and wildlife animals has increased worldwide; therefore, the study of Hepatozoon has gained relevance in the veterinary field [Hepatozoon americanum and H. canis are protozoa transmitted to dogs and other wild carnivores by ingestion of ticks containing mature oocysts [H. americanum and H. canis are reported in canids, the latter is widely spread and is considered the principal cause of canine hepatozoonosis in the world [H. canis is the brown dog tick Rhipicephalus sanguineus [Haemaphysalis flava and Haemaphysalis longicornis in Japan [Amblyomma ovale in Brazil [Amblyomma mixtum and Rhipicephalus turanicus in Mexico [Ixodes ricinus ticks in Italy [H. canis sporozoites spread via the bloodstream and lymph to several organs including the spleen, bone marrow, lung, liver, and kidney, infecting leukocytes and parenchymal tissue cells.The vector-borne pathogens of the genus nd birds –5. Thesend birds –8. In thry field . Hepatoz oocysts , 10. Althe world . The mainguineus ; howeverin Japan , Amblyomn Brazil , Amblyomn Mexico , and Ixoin Italy . FollowiH. canis, thus representing a possible health hazard to domestic dog populations [H. canis infects a wide range of carnivorous hosts around the world including dogs, jackals, foxes, opossums, and domestics cats [Wild canids may be an important reservoir for ulations . Wild caulations , 17. Curics cats , 18–20.Vibrio vulnificus [Corynosoma australe [Angiostrongylus cantonensis, and A. malaysiensis [Plasmodium knowlesi [Toxoplasma gondi [Molecular sequences obtained from collected samples can provide information about past evolutionary events. Relevant probabilistic models of molecular evolution enable reconstructing ancestral sequences to a sample of taxa, and phylogenetics provides an adequate framework for the reconstruction of ancestral sequences . Phylogelnificus , canine lnificus , Corynosaustrale , Angiostysiensis ) includiknowlesi  and Toxoma gondi ).H. canis is widely reported in different studies in several regions around the world; however, the genetic diversity and phylogeography of these hemoparasites is understudied [H. canis from various geographical areas around the world. For H. canis, we can infer that dispersion around the world could be related to the movement of dogs associated with human migration; hence, we hypothesized there is no population or phylogeographic structure. Thus, we aimed to test whether the migratory movements in humans is associated with the population structure of eukaryotic parasites, using Hepatozoon as a model of study.Despite the relevance of many parasitic diseases, the studies in genetic and phylogeographic diversity are few compared with other taxa like mammals, amphibians, birds, and plants. Molecular detection of rstudied , 15, 16.Hepatozoon canis” to discard any sequences not identified to species level (Hepatozoon sp.); (2) 55 sequences without complete metadata lacking host and country of origin were discarded; (3) sequences with duplicated accession number (redundant) or missed annotations from parasites different from H. canis were not selected; (4) considering an observed wide range in sequence length between 140 bp and 3.1 kbp, sequences < 500 bp were omitted from further analysis. The remaining sequences had a median of 602 bp (533–677). During this step, sequences from genetic regions other than the ribosomal 18S gene were discarded. (5) Finally, the remaining 618 sequences were aligned, and sequences with coverage < 50%  were discarded. The custom R scripts used for the sequence selection are available at: https://github.com/VictorHJD/Hepatozoon_Phylogeography. The script for selection was run in R version 4.0.3 [To define the final dataset of sequences included in this study, several filtering steps were applied as follows: (1) a total of 1170 sequences were downloaded from the ENA/GenBank database using as searching criteria “on 4.0.3 .H. canis (Table Canis lupus familiaris) (n = 339), wildcats (Felis silvestris) (n = 2), red foxes (Vulpes vulpes) (n = 94), pampas fox,  (n = 1), black-backed jackals (Canis mesomelas) (n = 7), golden jackals (Canis aureus) (n = 15), opossums  (n = 2), and capybara (Hydrochoerus hydrochaeris) (n = 1) and several tick vector species: Rhipicephalus sanguineus (n = 46), Rhipicephalus (Boophilus) microplus (n = 1), Rhipicephalus tiranicus (n = 7), Rhipicephalus spp. (n = 3), Haemaphysalis adleri (n = 2), Haemaphysalis bispinosa (n = 6), Haemaphysalis concinna (n = 2), Haemaphysalis longicornis (n = 1), Ixodes canisuga (n = 2), Ixodes hexagonus (n = 1), Ixodes ricinus (n = 10), Dermacentor marginatus (n = 2), Dermacentor reticularis (n = 1), Dermacentor spp. (n = 1), Amblyomma mixtum (n = 1), and three unidentified ticks. All sequences were automatically aligned using Clustal X [The final cured dataset contained 550 sequences (samples) from the rRNA 18S gene spanning the V4 hypervariable region of lustal X  and manulustal X .Table 1NH. canis haplotypes, statistical parsimony networks (haplotypes network) were constructed using the program TCS 1.2.1 [To infer genealogical relationships among CS 1.2.1 . The aliCS 1.2.1 . The fulK = 2 to K = 10) under the independent loci model was evaluated. Two independent runs with ten replicates for each K, accepting the partition with the K value that had the highest likelihood and posterior probability (PP) provided the final result.The most likely number of genetically differentiated clusters was estimated using BAPS  . BAPS ashS, hT) and ordered haplotypes  and differentiation parameters  were estimated using software PERMUT 1.0 [hS), total diversity (hT), geographical average haplotype diversity (vS), geographical total haplotype diversity (vT), level of population differentiation at the species level (GST), and an estimate of population subdivisions for phylogenetically ordered alleles (NST) were calculated. GST and NST are often used to assess the geographical structure affecting population differentiation. Significant differences between NST and GST parameters were tested with 10,000 permutations. If NST is significantly larger than GST, this means that two alleles sampled from the same region are phylogenetically more closely related than two alleles sampled from different regions, evidencing the presence of a significant phylogeographical signal in the data [Population diversity indices for unordered  four groups  similar to the episode where the four geographic regions (continents) have been isolated long enough to differentiate populations. The AMOVAs were performed using ARLEQUIN 3.11 with 16,000 permutations to determine the significance of each AMOVA. Additionally, spatial analysis of molecular variance (SAMOVA) was performed using SAMOVA 1.0 [To determine whether populations were structured, analyses of molecular variance (AMOVAs)  were perMOVA 1.0  to identNH), gene diversity (h), nucleotide diversity  [FS) [DT and FS that result from an excess of rare alleles present at low frequencies indicate that populations have undergone recent expansion.Molecular diversity indices, including the number of haplotypes (UIN 3.11 . We used [D (DT)  and Fu’sDT) [FS) . NegativNeutrality tests were performed in ARLEQUIN 3.11, with 10,000 permutations . MismatcR2 statistics were calculated based on the difference between the number of singleton mutations and the average number of nucleotide differences, where small positive values of R2 are expected under a scenario of population expansion [Ramos-Onsins and Rozas xpansion . Signifixpansion .H. canis populations . The 550 sequences were used to run the analysis using the coalescent Bayesian skyline tree prior. This coalescent-based approach estimates the posterior distribution of effective population size (Ne) at intervals along a phylogeny, thereby allowing one to infer population fluctuations over time. The haplotype sequence alignments were imported in the program BEAUti2 (included in BEAST 2.6.4) in nexus format. The best nucleotide substitution model was estimated using Jmodeltest 2.0 [H. canis, while that for Asia was TPM3uf. Finally, when populations were treated as a single group, the best substitution model was TPM3uf + G. Since the literature lacks estimations of substitution rates for H. canis, a substitution rate of 1.5% per 100 million years for the 18S rRNA used to study the lineage evolution of Toxoplasma gondii [Finally, the Bayesian skyline plot  method itest 2.0 . The HKYa gondii  was implH. canis (mean length: 586 bp) yielded 76 haplotypes with different abundance along the geography  correspond to tick while 84.8% (95/112) belong to dogs and two sequences (1.7%) to red foxes. In H7, 9.09% (9/99) belong to ticks, and the 90.91% are distributed in sequences from other vertebrate hosts . For H9, 12.32% (9/73) belong to ticks, 53.42% (39/73) to dogs, 30.13% (22/73) to red foxes, 2.73% (2/73) to opossum, and 1.38% (1/73) to pampas fox. Moreover, the haplotype network shows various singletons for each host    indicated that H. canis is genetically subdivided. Genetic diversity across all populations  was higher than the average within-population value , indicating that populations are similar among them. PERMUT analysis showed that NST was not significantly different  than GST, indicating no phylogeographical structuring.Differentiation among populations based on 18S rRNA gene variation , Asia , and Europe , and a small, statistically non-significant, negative value for Africa . When populations are treated as a single group, the Tajima’s D and Fu’s Fs had a small and statistically significant negative value , indicating that populations have undergone recent expansion, often preceded by a bottleneck , considering all populations as one group  and grouping America , Asia , and Europe populations Asia , providing evidence for sudden demographic expansion in the past for all groups of populations  sequences), Angiostrongylus cantonensis and A. malaysiensis [Plasmodium knowlesi [cox-1 and 18S RNA sequences), which showed haplogroups defined in the network and with geographic congruence. However, these studies used DNA fragments shorter than the data used in our present study. On the other hand, our results agreed with a previous study that reported that dogs carried the same or very similar H. canis haplotypes as red foxes, suggesting that both hosts participate in the same epidemiological cycle [Since its first description in 1905 , H. canistralia, , 52–54. iagnosis , which caustrale  , a measure of species evenness, was high for all populations  [H. canis do not appear to be correlated with the geographical regions  , 60. Infons Fig.  and coulFST pairwise comparisons, AMOVAs, and BAPS  [S Tables , Fig. 1Cepatica) –26, 59. H. canis are affected by anthropogenic activities.Human activities have affected the geographical ranges of many host species, shrinking by loss of habitat or increasing through introduction of individuals into new regions and promoted spillover events. In parasitic nematodes, the impact of human activity is well documented, offering an opportunity to study how changes in host population size and connectivity shape the population genetics structure . In thisH. canis populations had a recent expansion , at least that shows the Bayesian skyline plots analysis for African population and when all population were analyzed like a group  or high gene flow between populations [H. canis to spillover among host species is feasible given the wide range of carnivores that can be infected by these hemoparasites worldwide [Our results showed that on Table , Fig. 3.nes Fig.  and Bayeequences , 40. Neuelection . Hepatozulations . Howeverulations . Moreoveorldwide , 18–20.H. canis presented a multimodal pattern suggesting recent expansion. Furthermore, non-significant values for SSD mean that the observed data do not deviate from that expected under the model of expansion, and the non-significant raggedness index also indicates population expansion. These results suggest that population expansion occurred recently, except to Africa [h, Table H. canis host favors dispersion and gene flow among populations. The latter is supported by several studies done with dogs and wild canids worldwide, which observed the interactions networks among parasites, vectors, and hosts [In the mismatch distribution result, o Africa . Demograh, Table . Owing th, Table , high mond hosts , 52, 54.H. canis in felids and capybaras highlights the lack of information on the transmission dynamics of H. canis into new hosts [A. mixum, as potential vectors has been [H. canis moves between different host geographic areas and vectors favoring gene flow.The detection of ew hosts , 8, 66. has been . AlthougH. canis in the world. The current molecular data confirmed that H. canis does not show phylogeographic and population structure, which could be due to its wide range of definitive host (domestics and wild canids), lack of clear understanding of the role of other non-canid species in the transmission dynamics, and the impact of human activities. Our results showed identical H. canis haplotypes co-occurring in several geographical regions and host species, indicating wide distribution of the parasite. The haplotypes exhibit low genetic differentiation, suggesting a recent expansion due to gene flow among populations. The reasons are mainly still unknown. However, we consider that the role of human migrations might cause the dispersion of vector and host. Finally, using more variable target regions such as mitochondrial and apicoplast genomes might improve resolution. We suggest these more variable regions should be applied in future studies to understand the natural history of H. canis and approach it from an eco-epidemiological vision.This study provides novel and significant insights into the phylogeography and population differentiation of Additional file 1: Dataset S1. GenBank accession numbers for the sequences used on this study.Additional file 2: Figure S1. Statistical parsimony network of 76 rRNA 18S haplotypes found in 46 populations of H. canis worldwide. Black dots represent unsampled haplotypes. The frequency of each haplotype is represented by the size of the circle. Each haplotype is coded with a different color according to the continent. The numbers inside the haplotypes in the network indicate the number of individuals that share that haplotype. The four rectangles represent the four haplogroups reported in this study. Figure S2. Geographical distribution of host and statistical parsimony network of 76 rRNA 18S haplotypes found of H. canis in the world. a Distribution map of the hosts. Black dots represent sampled populations and pie charts represent hosts found in each sampling population. Section size of pie charts corresponds to the proportion of individuals with given hosts. b Haplotype network. Black dots represent unsampled haplotypes. The frequency of each haplotype is represented by the size of the circle. Each haplotype is coded with a different color according to the hosts. The numbers inside the haplotypes in the network indicate the number of individuals that share that haplotype. Table S1. Number of analyzed samples (n) for molecular marker (18S rRNA gene) and hosts found of H. canis.


Ivesia webberi, a United States federally threatened Great Basin Desert using six microsatellite loci. We assessed the effects of Euclidean distance, landscape features, and ecological dissimilarity on the pairwise genetic distance of the sampled populations, while also testing for a potential relationship between I. webberi genetic diversity and diversity in the vegetative communities. The results show low levels of genetic diversity overall  and high genetic differentiation among populations. Genetic diversity was structured along a geographic gradient, congruent with patterns of isolation by distance. Populations near the species’ range core have relatively high genetic diversity, supporting in part a central‐marginal pattern, while also showing some evidence for a metapopulation dynamic. Peripheral populations have lower genetic diversity, significantly higher genetic distances, and higher relatedness. Genotype cluster admixture results suggest a complex dispersal pattern among populations with dispersal direction and distance varying on the landscape. Pairwise genetic distance strongly correlates with elevation, actual evapotranspiration, and summer seasonal precipitation, indicating a role for isolation by environment, which the observed phenological mismatches among the populations also support. The significant correlation between pairwise genetic distance and floristic dissimilarity in the germinated soil seed bank suggests that annual regeneration in the plant communities contribute to the maintenance of genetic diversity in I. webberi.For range‐restricted species with disjunct populations, it is critical to characterize population genetic structure, gene flow, and factors that influence functional connectivity among populations in order to design effective conservation programs. In this study, we genotyped 314 individuals from 16 extant populations of  Ivesia webberi, the study species during the flowering season.One image was uploaded with the submission, which was tagged "not for review and publication." The photograph illustrates  Though its historical range is unknown, the species is now narrowly distributed along the western edge of the Great Basin Desert, near the Sierra Nevada Mountain Range , and the rate and probable direction of gene flow for I. webberi populations; (b) to estimate the effect of Euclidean distance, landscape features, and ecological dissimilarity on the genetic structure in the sampled populations; and (c) investigate a relationship between pairwise I. webberi genetic diversity and floristic diversity in the vegetative communities in order to assess potential impacts of non‐native and invasive species on maintenance of genetic diversity. Due to the spatial configuration of these populations, we also (d) tested the central‐marginal hypothesis (CMH), which predicts decreased gene flow and increased pairwise genetic differentiation among populations towards the edge of the species range  were processed using a TissueLyser II . Genomic DNA was extracted using the protocol described in the DNeasy96 Plant Extraction Mini kit (QIAGEN). DNA per sample was quantified at the Nevada Genomics Center (https://www.unr.edu/genomics) using the PicoGreen dsDNA assay . DNA concentration was determined using a standard curve equation following DNA detection under the Fluoroskan Microplate Fluorometer .Five mg of leaf tissue from each plant sample  in 10.0 µl reaction volumes in a 96‐well format using the Qiagen Multiplex PCR kit, which contains HotStarTaq DNA polymerase, dNTPs, and PCR buffer at a 2× concentration. Loci were amplified in single or multiplexed PCRs with a final concentration of 0.05 μM of each tailed forward primer and 0.1 μM of each reverse primer. Each PCR included 5 µl of Multiplex Mix, 20 ng of DNA, between 0.1–0.2 µl of primer and approximately 4.8 µl of ultrapure molecular grade water. PCR parameters included a 15‐minute hot start at 95°C, then 41 cycles of 95°C for 30 s, followed by a touchdown annealing temperature that ranged between 65 to 55°C for 90 s with a final elongation step of 72°C for 30 s. The touchdown annealing temperature begins with 7 cycles at 65°C, 7 cycles at 61°C, 7 cycles at 58°C, and 20 cycles at 55°C.No microsatellite loci have been developed for Appendix . PCR amphttps://naes.unr.edu/genomics). All alleles generated were scored, binned, and genotyped using the ABI GeneMapper software . We also re‐amplified 30% of the sample (~115 samples) to validate genotyping reliability. Individual leaf samples that failed to amplify were removed from the analysis, thus reducing the sample size from 384 to 314 . Fragment analysis was done on an Applied Biosystems (ABI) Prism 3730 DNA analyzer at the Nevada Genomics Center (2.32.3.1Na), allelic richness (RS), the inbreeding coefficient (FIS), and to determine whether linkage disequilibrium among loci was present within populations. The outcrossing rate (t) was calculated using the inbreeding coefficient (FIS) and the formula t = (1–FIS)/(1 + FIS)  using Microsatellite Toolkit in Excel. MICROCHECKER v.2.2.3  among individuals within populations was calculated using the Lynch and Ritland  Weir, . We esti Ritland  equation Ritland . We test Ritland  and the 2.3.2FST) and calculate the number of migrants between populations (Nm) based upon FST estimates. STRUCTURE  across all I. webberi populations, using a 100,000‐iteration burn‐in followed by ten 500,000 Markov chain Monte Carlo (MCMC) replications per K, for K = 1–10. The optimal number of genotype clusters was determined using the ΔK method  was calculated for each population and Bayesian genotype cluster identified using the linkage disequilibrium (LD) method in NeEstimator v.2.0 , land‐cover, inverse of habitat suitability , and ecological dissimilarity  on pairwise genetic distance among the 16 ResistanceGA uses a genetic algorithm from the GA R package to optimize the conversion of predictor variables into resistance surfaces and testing the effect of the parameterized resistances on gene flow , an IBD model , and an IBR model . Following the 10 MLPE model replicates, we conducted bootstrapping to assess the sensitivity of the MLPE models to the spatial distribution of I. webberi populations. Here, we randomly resampled 75% of the data without replacement, fitted the MLPE models again using 10,000 iterations, and selected the best models using the average AICc values  and predictor weight (relative contribution of each predictor to the model). The percentage contribution of each surface within the multisurface optimization was calculated by dividing each transformed resistance surface by the sum of the composite resistance surface  variables following three consecutive feature reduction analyses , both of which are indicators of genetic diversity, was used to investigate the predictions of the central‐marginal hypothesis. Additionally, Mantel test was used to investigate the relationship between a matrix of the latitudinal degrees and the pairwise genetic distance (FST) among the sampled populations.The range center of the 2.3.6I. webberi genetic diversity and the floristic dissimilarity across the sampled sites  of the aboveground flora and the soil seed bank across the 10 sites. The floristic dissimilarity matrices were generated using the Bray–Curtis method. MRM analysis was conducted with 10,000 permutations in the phytools R package  ranged from 3–13 alleles, while allelic richness (correction for sample size) per locus (RS) ranged from 2.002 to 4.073 , indicating a heterozygote deficit. Locus PMS1438 had significant negative FIS values in multiple populations indicating heterozygous excess   , WLO (p = .011) and RAH (p = .015), while the MER population was close to being significant (p = .088), suggesting a potential bottleneck  per population was the highest in WLO (RT = 15.63), which is located in the cluster of populations at the center of I. webberi range  per population were observed. Similar to the distribution of allelic richness per population, expected and observed heterozygosities were the highest among centrally located populations, but the lowest in the peripheral populations .Allelic richness summed over all loci  included infinity, we do not report those values here. For the populations that we could calculate both an Ne and 95% CI, the values ranged from 0.9 to 11.6 . The nonsignificant pairwise FST values were found primarily among the spatially proximate populations at the center of the range. The global FST was 0.158 . The majority of these population pairs were separated by <5 km. However, the distance between population pairs with Nm ranging from 8 to 19 ranged from 7 to18 km. The population pairs with high Nm values were primarily among core populations and populations on the eastern edge of the distribution. These data suggest that there may be extant intervening populations that have not been sampled or have been recently extirpated that could represent stepping stones for dispersal among more distantly spaced populations. Dispersal among spatially distant populations may also be facilitated by specific environmental conditions or unidentified dispersal agents. Analysis of molecular variation (AMOVA) showed that 71% of the molecular variance was within individuals, while 11% and 18% of the molecular variance were among individuals and populations respectively (Table Pairwise FST) validates the genetic distance in the DMR population from the rest of the sampled populations  were identified as the best fit of the data   and lowest in genotype cluster 1  among the 16 sampled I. webberi populations indicating isolation by distance and significant spatial genetic structure  or observed heterozygosity . In contrast, we found a significant positive relationship between pairwise latitudinal degrees and genetic distance among the 16 populations .Genetic diversity was generally higher in the centrally located populations than in the peripheral populations. However, despite this spatial genetic diversity pattern, we did not observe a significant relationship between range center index (RCI) and allelic richness  models and the bootstrap analysis showed that land cover, representing urban settlements and highways, did not pose any barrier to gene flow among ns Table . The majns Table . TherefoI. webberi genetic distance and the dissimilarity matrices for actual evapotranspiration (AET) and summer seasonal precipitation  and accounted for more than 50% of the deviance in the data structure, with three GDMs explaining 76% of the deviance  of rs Table . However4I. webberi populations. We found evidence of isolation by distance, by environment and by resistance as well as environmental correlates of standing genetic variation. These patterns appear to be largely driven by geographic distance, where complementary analyses  provide strong support for the isolation by distance model, but some of the variance is also explained by evapotranspiration and precipitation, and to a smaller degree by latitudinal gradient and habitat suitability.The evolutionary potential of species under changing environmental pressures is strongly tied to the maintenance of genetic variation, which can be directly tied to gene flow and connectivity among populations. The results of this study reveal contrasting patterns of significant population genetic structure and isolation in addition to dispersal and gene flow among the sampled FST estimates were found among spatially proximate populations at the center of the range. Evidence of both genetic bottlenecks and high levels of genetic variation among centrally located populations suggest that these populations may have a metapopulation dynamic defined by an extinction‐colonization patch dynamic  ranging from 0.192 to 0.605, with the exception of two neighboring populations at the center of the range (MER and IVF), which had higher observed heterozygosity . Not surprisingly, the highest levels of heterozygosity and allelic richness as well as nonsignificant pairwise  Hanski,  and gene Hanski, , as well Hanski, .However, the Bayesian genotype clustering analysis reveals a more complex movement pattern. Membership in the individual genotype clusters was not confined to specific populations, but was spread among multiple populations across the species range supporting movement among the spatially discrete sites. We see a gradual decrease in assignment to the yellow genotype cluster in the western portion of the range and increased membership in the green cluster moving from west to east consistent with a pattern of isolation by distance. However, the easternmost populations  have few or no individuals that assign to the other genotype clusters . Individuals which assign to blue genotype cluster are found primarily in the three most centrally located populations  would result in pollination and hence admixture, whereas seed dispersal would not. Only through future sexual reproduction would dispersed seeds colonizing a new population lead to admixture. Once seeds are established and if the adult plant reproduces vegetatively, no admixture would be observed and distinct genotype cluster assignments within populations would persist. Individuals which assign to the blue, gray, and orange genotype clusters show very little evidence of admixture. I. webberi is known to reproduce vegetatively, which could explain the high proportional membership of individuals in the same population to distinct genotype clusters. In fact, negative FIS values for some of the loci, indicating a heterozygous excess, in multiple locations, together with high within individual genetic variation is consistent with vegetative regeneration and clonality in I. webberi , but rather we found evidence of a complex interplay among isolation by distance, by environment, and by resistance. Isolation by resistance was driven by the inverse of the projected habitat suitability, not land cover. This indicates that potentially suitable areas from the niche models may play an important role in genetic structure and among population gene flow as undiscovered populations may act, or did act if currently extirpated, as stepping stones for gene flow among more spatially distant populations as suggested by estimates of Nm. This study showed that land cover may be a less important driver of genetic structure in this species, which may be partly due to the fact that the habitat suitability map has already explained the isolation by resistance pattern that occurs within the land‐cover layer. Moreover, most urban settlements within I. webberi's range are in lower elevations, whereas I. webberi populations are found in higher elevations and forest vegetation, which are under federal and state protections. Theoretically, pollinator‐driven gene flow among I. webberi populations would not pass through the unsuitable urban landscape. This is particularly true for the spatially aggregated populations in the center of the species’ range.The spatial genetic structure of I. webberi have responded to these varying microclimatic conditions across the sites through variation in phenology. For example, populations at lower elevations were observed to regenerate earlier than those in the higher elevations and this could result in a temporal mismatch in flowering which can impede successful gene flow via pollen transfer among the populations. Previous studies also show significant influence of water availability, temperature, and precipitation on genetic diversity in different plant species . This highlights significant ecological dissimilarity among the sites which correlates with genetic distance and may indicate isolation by environment. Both actual evapotranspiration and precipitation represent water availability and climatic stress, challenges to persistence for native flora in the Great Basin Desert. The differences in water availability among these sites may be attributed to their varying elevation and topographic positions which also determine the duration of their exposure to sunlight. Field observations suggest that Ivesia webberi sites, revealed positive but not significant (p > .05) results, which may be attributed to the small sample size (n = 10). Similarly, species beta‐diversity of the soil seed bank, but not aboveground flora, was significantly associated with the pairwise genetic distance. Most of the sites where I. webberi is located have undergone varying degrees of anthropogenic habitat modifications, including one site that served as part of a trail for westward expansion of European colonizers during the late 19th and early 20th centuries. Moreover, these sites have been affected by frequent wildfires and non‐native and invasive plants. However, field observation and natural history indicate that this species has high potential for recovery postdisturbance, which may be linked to the dormant deep taproot caudices buried and firmly protected in the argilic subsurface soil horizon. This may explain field observations that suggest that the abundance of the invasive plant species does not prevent the annual vegetative regeneration of established matured I. webberi individuals. However, invasive alien species can hinder new recruitment of native plants by outcompeting the young and delicate seedlings ; Data curation (lead); Formal analysis (lead); Funding acquisition ; Investigation ; Methodology (lead); Software (lead); Validation ; Visualization ; Writing‐original draft (lead); Writing‐review & editing (supporting). Kelly B. Klingler: Data curation ; Formal analysis ; Methodology ; Software ; Writing‐review & editing . Mary M. Peacock: Conceptualization ; Funding acquisition ; Project administration (lead); Supervision ; Visualization ; Writing‐original draft ; Writing‐review & editing (lead).Appendix S1‐S7Click here for additional data file.


Astragalus exscapus L. subsp. transsilvanicus (Schur) Nyár. (Fabaceae) is a rare plant endemic to the Transylvanian Plateau, represented by 24 identified populations. Limited information on the genetic variation and population structure is available, which obstructs efficient measures for conservation strategy. The present study aimed to analyze the genetic diversity and population structure of eight populations of A. exscapus subsp. transsilvanicus revealed by sequence-related amplified polymorphism (SRAP) markers. A total of 164 bands were amplified, 96.7% of which (159) were polymorphic. Nei’s gene diversity index (He) was estimated to be 0.228 at the population level and 0.272 at the subspecies level. The genetic differentiation among populations (Gst) was 0.165, which indicated a low proportion of total genetic diversity. The analysis of molecular variance (AMOVA) indicated that 17% of the total variation of A. exscapus subsp. transsilvanicus is found among the populations, while 83% was found within the populations. A UPGMA dendrogram, principal coordinate analysis, and the STRUCTURE software grouped the populations into two clusters uncorrelated with the provenience of the 125 individuals, which might be attributed to fragmentation processes, insect pollination, population size, and specific environmental conditions of the habitats. Astragalus exscapus L.) is a perennial herb native to dry grasslands of Central Europe. It has small populations and a relatively high habitat specificity; therefore, it is framed a special rarity type according to Rabinowitz [n = 16) and self-compatible; however, it requires pollination by insects, as, without pollinators, the flowers do not produce seeds, indicating a self-compatible breeding system dependent on insect pollination [A. exscapus plants have a reduced stem, and their lemon-yellow odoriferous flowers appear in middle May gathered in racemes. The species has a swivel root system, which plays an important role in preventing soil erosion. Considered as a relict species of the Pleistocene steppe vegetation, A. exscapus is threatened with extinction in many countries in Europe because of pollen limitation and inbreeding depression [A. exscapus demands sustainable conservation strategies to overcome the risk of extinction [Plebejus sephirus Kovacsi Szabo (Hungarian Zephyr Blue) is closely linked to the ecology of A. exscapus, given that it constitutes the predominant food-plant of the butterfly’s larval stage [Stemless milkvetch .Interestingly, the root system of the plant under study is very similar to that of charides ,7, saponcharides ,9,10, ancharides , having A. exscapus presents high interest for bio-conservational purposes, and it can be considered important from the pharmaceutic, economic, and ecologic perspectives. One of the known subspecies of A. exscapus is endemic to Transylvania, Romania, namely, Astragalus exscapus L. subsp. transsilvanicus (Schur) Nyár., identified in small and isolated populations of the Transylvanian Plateau. Presently, 24 populations of A. exscapus subsp. transsilvanicus have been recorded, and most studies of the subspecies focused on the ecological significance and biological traits [Broadly, l traits ,13, wherAstragalus sericeocanus Gontsch., an endangered species endemic to the region of Baikal lake, used inter simple sequence repeats (ISSRs) as molecular markers to evaluate the genetic diversity and population structure of the species, aimed at developing appropriate management strategies directed toward species conservation [Astragalus nitidiflorus Jiménez Mun. et Pau, a critically endangered endemism of Murcia province, Spain, was investigated for future conservation strategies using molecular markers [Astragalus subrecognitus Bagheri, Maassoumi & F.Ghahrem., a very rare and narrow endemic species native to the northwestern region of Iran, was analyzed using ISSR molecular markers and sequence-related amplified polymorphism (SRAP) to determine the genetic structure and the conservation status of the plant [Earlier studies regarding ervation . Astraga markers . Astragahe plant .A. exscapus subsp. transsilvanicus populations, SRAP molecular markers [To accomplish genetic analysis of  markers  were sel markers ,19. SRAP markers . Due to  markers ,22,23, h markers ,25, link markers , systema markers ,28,29,30 markers .A. exscapus subsp. transsilvanicus populations, (ii) the degree of genetic variation among populations, and (iii) the genetic relationships and population structure of 125 individuals analyzed from eight populations of A. exscapus subsp. transsilvanicus in order to recommend conservation measures for the efficient germplasm management of this endemic species.In this study, SRAP markers were employed to assess (i) the levels of genetic diversity within and among eight investigated Several morphologic characteristics of the subspecies were measured in the field during sample collection for the genetic analysis. Traits such as plant height, the number of leaves per plant, the number of flowers per plant, and the inflorescences’ diameter were evaluated for 125 individuals from eight different locations, and the results are presented in A representative individual from the Gădălin population is presented in Fourteen SRAP primer combinations presented clear, highly polymorphic patterns and generated a total number of 164 bands with an average of 11.71 bands per primer set. The levels of polymorphism and the size of the range of PCR products amplified by SRAP primers are presented in Astragalus exscapus subsp. transsilvanicus samples collected from eight populations located on the Transylvanian Plateau. For dominant markers, such as SRAP markers, the PIC values ranged from 0 to 0.5, where 0 indicates the fixation of one allele and 0.5 denotes equal frequencies of alleles [The highest number of total bands (16 bands) was attained using the primer combinations Me4/Em5 and Me6/Em1 and the lowest number (nine bands) was attained using the primer combinations Me2/Em6 and Me8/Em3. The number of polymorphic bands detected by each primer combination ranged from eight (Me2/Em6) to 16 , with a mean value of 11.36 polymorphic bands. The primer average polymorphic information content (PIC) was 0.34, with values ranging between 0.23 for Me2/Em6 and 0.48 for Me4/Em4 . These r alleles .An example of an electrophoretic profile generated using the combination of the primers Me4 and Em5 is shown in A. exscapus subsp. transsilvanicus populations, the polymorphic band percentage (PBP %), number of different alleles (Na), number of effective alleles (Ne), Shannon’s information index (I), and expected heterozygosity (He), which is also called Nei’s gene diversity, were calculated. The results of the evaluated genetic diversity indices are presented in In order to investigate the levels of genetic diversity within and among As shown in The Gădălin (G) population recorded the highest number of effective alleles (1.403) and the highest Shannon index (0.382). Furthermore, it presented an expected heterozygosity value of 0.247 and a polymorphic percentage of 85.42%.The Beleni population recorded the lowest number of effective alleles (1.349), and Shannon index (0.324), and expected heterozygosity values (0.247).Both molecular variance analysis (AMOVA) and pairwise population differentiation (PhiPT) were performed to investigate the distribution of genetic diversity within and among populations.A. exscapus subsp. transsilvanicus populations according to POPGENE results.The total genetic diversity at the species level (Ht) was 0.2727, while the average genetic diversity within populations (Hs) evaluated by SRAP markers was 0.2279. The coefficient of genetic differentiation among populations (Gst) was 0.1645, which indicated that approximately 17% of the total genetic diversity was distributed among A. exscapus subsp. transsilvanicus populations, which was significantly higher than that among populations (17%), as summarized in p < 0.001) and 2.5395, respectively.Furthermore, analysis of molecular variance analysis (AMOVA) revealed that 83% of total genetic variations were contributed by differences within The pairwise PhiPT provided estimates of genetic differentiation among the populations. The highest differentiation (0.480) was observed between the Săbed and Silivaşu de câmpie populations, and the lowest (0.113) was observed between the Sărmăşel Gară and Viişoara populations .A. exscapus subsp. transsilvanicus populations from Cluj County , where the pairwise population PhiPT values ranged between 0.166 and 0.264  showed a higher genetic differentiation  compared to nd 0.264 .The data generated by SRAP molecular markers were used to construct a dendrogram based on the unweighted pair group method with an arithmetical average (UPGMA) algorithm  and JaccA. exscapus subsp. transsilvanicus individuals into two main clusters illustrated in In this study, a high cophenetic correlation coefficient of 0.8417 between the Jaccard (J) similarity matrix and the cophenetic matrix was obtained, indicating a good fit between the built dendrogram and the similarity matrices. The UPGMA-based dendrogram grouped the On the other hand, the results of PCoA were largely in agreement with those obtained by the cluster analysis, further supporting the separation of individuals into two main groups, as shown in One group consisted of individuals belonging to Săbed, Viişoara, Valea Florilor, and Sărmăşel Gară populations, and the other group contained the individuals from Gădălin, Milăşel, Silivaşu de câmpie, and Beleni populations.A. exscapus subsp. transsilvanicus populations presented different levels of genetic similarity. These findings are in accordance with the similarity levels observed in Jaccard’s similarity matrix which is presented in In summary, the phylogenetic tree and PCoA showed that A. exscapus subsp. transsilvanicus individuals, the population structure was analyzed using STRUCTURE software. The optimal cluster value (K) of analyzed 125 individuals was 2, with the highest value of both LnP (K) and delta K recorded from the Structure Harvester v. 6.0.  contained 59 pure individuals from Săbed, Viişoara, Valea Florilor, and Sărmăşel Gară populations, while cluster II (red) included 57 pure individuals from Milăşel, Beleni, Gădălin, and Silivaşu de câmpie populations. Individuals with admixed population assignment were from Milăşel (1), Beleni (1), Gădălin (3), Valea Florilor (2), and Viişoara (2).Astragalus is the largest plant genus in the world, including approximately 3000 species, with about one-third of existing species considered vulnerable, endangered, or critically endangered, restricted to narrow geographic and ecological regions [ regions .Astragalus species, there are few studies on the assessment of genetic diversity through DNA-based molecular markers, and further studies on genetic diversity are necessary, particularly for endemic species [Astragalus exscapus subsp. transsilvanicus with the aim of providing valuable and additional information for the authorities involved in developing strategies for the sustainable conservation of local endemic species from Transylvania, Romania.Despite the great scientific interest in the evolution and conservation of  species . In thisAlthough the relatively low level of accuracy of results for estimating genetic diversity using dominant markers has generated controversial opinions, it has been well documented that SRAP is a valuable tool for detecting DNA polymorphisms and assessing genetic variation in many plant species ,28,29. SAstragalus species. For example, Bagheri et al. reported that SRAP proved to be an adequate and inexpensive technique to compare the genetic diversity between Astragalus subrecognitus Bagheri, Maassoumi & F.Ghahrem. populations from the eastern and western sides of the Qezel Ozan River in the Iranian province of Zanjan, as the nine combinations of selected primers generated 135 fragments in total, of which 93.3% were polymorphic [Astragalus exscapus subsp. transsilvanicus.To the best of our knowledge, there are very few reports of the use of SRAP markers in ymorphic . SimilarGenetic diversity in wild plant species depends on a variety of environmental factors such as life history, breeding system, seed dispersal, population size, and ecological traits that are considered mainly responsible for the level and distribution of genetic diversity within and among populations. Furthermore, to know the genetic diversity is important for the conservation of the most relevant populations .A. exscapus subsp. transsilvanicus populations revealed a relatively high level of genetic diversity at the species level   was higher compared to that of Astragalus cremnophylax Barneby var. cremnophylax (He = 0.135) [Astragalus jaegerianus Munz (0.266) [Astragalus crassicarpus Nutt. var. trichocalyx (Nutt.) Barneby (He = 0.55) [Astragalus bibullatus Barneby & E.L.Bridges (He = 0.63) [In the present study, SRAP analysis of eight investigated  99.31%) . Noticea= 0.135) , nearly  (0.266) , and low = 0.63) .A. exscapus subsp. transsilvanicus populations could be predominantly explained by life form and breeding system. A. exscapus subsp. transsilvanicus is considered a subspecies with a mixed breeding system; it is a self-compatible subspecies but requires pollination by insects, as, without pollinators, the flowers do not produce seeds [A. exscapus and its adaptability to different environmental conditions [A. exscapus subsp. transsilvanicus should also be able to adapt to environmental changes. As reported in previous studies [Although rare plants are usually represented by small, isolated populations spread in naturally isolated habitats, the considerable existing genetic diversity in ce seeds . This brnditions . Our res studies , the breIt is worth mentioning that naturally isolated habitats also provide an opportunity to examine the accumulated effects of isolation and fragmentation on the levels and distribution of genetic variation among populations. According to Travis et al., , habitatAstragalus species [The proportion of genetic diversity attributed to among-population variation (Gst) has been used to estimate the genetic variation among the populations in the case of  species ,15,16 usA. exscapus subsp. transsilvanicus was 0.16, which indicated that a low proportion of total genetic diversity was observed among populations, similar to that reported for long-lived perennial species (Gst = 0.19) [In the present study, the Gst of the eight populations  = 0.19) . MoreoveA. exscapus subsp. transsilvanicus is found among the populations, while 83% is found within the populations. These results are in concordance with those obtained by Vicente et al. [Astragalus nitidiflorus Jimenez & Pau, an endemic species threatened with extinction. Our results are also in accordance with the results obtained previously by Wall et al. [Astragalus michauxii (Kuntze) F.J. Herm. performed using codominant SSR markers, where the percentage of interpopulation variability was determined to be 9.66% of total genetic variation.The AMOVA results of this study indicated that 17% of the total variation of e et al.  in a stul et al.  regardinA. exscapus subsp. transsilvanicus populations showed that the populations are not yet affected by genetic drift. These results are consistent with those reported by Alexander et al. 2004 [Astragalus oniciformis Barneby (Nm = 3.93) using ISSR markers.For endemic species, maintaining the genetic diversity is very important because it helps to increase the population fitness in a changing environment . Gene flal. 2004  in a stuA. exscapus subsp. transsilvanicus individuals evaluated by STRUCTURE software indicated two clusters , Viişoara (V), Săbed (S) and Sărmăşel Gară (SG) populations and individuals from Gădălin (G), Beleni (B), Silivaşu de câmpie (SI), and Milăşel (M) populations. It is noteworthy that geographically distant populations were genetically related, as revealed by both the UPGMA dendrogram and the PCoA graphical representation, based on Jaccard’s similarity coefficient.Correspondingly, the principal coordinate analysis and UPGMA built dendrogram similarly indicated two main clusters of A. exscapus subsp. transsilvanicus was found to be influenced mainly by its geographic distribution, which might not be directly attributed to geographic distances between the eight analyzed populations.The population structure of A. exscapus populations from central Germany, rare plant species may be limited in abundance not only due to habitat specificity, but also due to biological attributes, such as low fecundity and/or low dispersal potential [A. excapus has a disjointed distribution in Europe, being restricted to dry regions, and it can be found on south-facing slopes with high irradiation and a pronounced summer drought period. This is also the case for A. exscapus subsp. transsilvanicus; therefore, its rarity might be partially caused by limited dispersal potential together with habitat fragmentation and grazing disturbances.According to previous work related to otential . A. excaA. exscapus is pollinated by bumblebees such as Bombus hortorum or B. pascuorum, which gather both nectar and pollen from the flowers; after successful pollination, one pod is produced per flower, which contains between one and 18 bean-shaped seeds. Considering that the species reproduces solely via seeds and that 90% of the seeds are dispersed less than 50 cm distance from the mother plant, there is clear indication of the low dispersal potential of the species, which further affects the construction of the population.Related to the species biology and reproductive system, the plants start to flower in mid-April and finish flowering at the end of May, while the lemon-yellow odoriferous flowers appear in five to over 100 racemes, each with four to eight flowers a few centimeters above the ground . AccordiAstragalus exscapus subsp. transsilvanicus existed, which gradually suffered a process of adaptation to the new conditions of growth and development due to the progressive isolation of the habitats [On the other hand, it is assumed that, before the fragmentation process of the habitats by human intervention, a single homogeneous population of habitats .Astragalus exscapus subsp. transsilvanicus reflect the recent isolation of the populations, as well as the fragmentation of the species habitat (represented by well-preserved xeric to mesic grasslands from the forest–steppe of the Transylvanian Plain). Apparently, the gene flux between populations was recently interrupted, and the remaining populations are relics resulting from the fragmentation and constriction of much larger populations which were once close to another. This is of major importance for conservation principles and strategies applied to this endemic subspecies and its Natura 2000 habitat, which is also endemic to Transylvania (6240 * Sub-Pannonian Steppe Grasslands) [The low level of variation among the populations and the relatively high level of intra-population variation in Serratula radiata  M. Bieberstein, Salvia nutans L., Astragalus dasyanthus Pallas, Pontechium maculatum (L.) Böhle et Hilger, Crambe tataria Sebeok, Peucedanum ruthenicum M. Bieberstein, and Plantago argentea Chaix. Their present situation is a consequence of the last two agrarian reforms in Transylvania: the first from 1856 applied in the former Habsbrug Empire, and the second from 1921 in post-World War I Romania [Astragalus exscapus subsp. transsilvanicus and species alike in Transylvania, while the steppe grasslands of the 6240 * habitats were destroyed by plowing or drastically degraded by sheep overgrazing.The results of the study highlight the idea proposed by Bădărău et al.  that the Romania . Before  Romania . As this Romania . The intCentaurea ruthenica Lam., Centaurea trinervia Stepham., Iris pontica Zap., Colchicum versicolor Ker Gawl., Nepeta ucranica L., Scutellaria supina L., Goniolimon tataricum (L.) Boiss., etc.). For this latter category of species, different conservation principles and strategies must be applied, focused on the maintenance of the population at the local level, according to the principles of “Peripheral Populations Biology” [The conservation strategies for the steppe species of the region, which were confirmed to be much more widespread in the past, should be based on ecological reconstruction of the habitat 6240 * and re-expansion of their populations in the reconstructed areas, resulting in the re-establishment of their metapopulational complexes. This is necessary because they were characteristic elements of the ancient Transylvanian forest–steppe landscape, which is intended to be reconstructed via diverse projects . StudiesBiology” ,47.Astragalus exscapus L. subsp. transsilvanicus (Schur) Nyár. Young leaf samples were collected from eight different locations shown in The biological material was represented by 125 individuals of the subspecies A. exscapus subspecies, as shown in Eight populations were sampled in May 2014 representing the range of census size, area sizes, number of analyzed individuals, and geographical spread of the For DNA isolation, 5–6 young leaves were collected from the plants; the samples were coded, placed into plastic zip-lock bags, and maintained at −20 °C until further examination. The genomic DNA was extracted using the CTAB-based method according to a previously published protocol . DNA conA. exscapus subsp. transsilvanicus samples with SRAP markers, a primer combination screening process was assessed; eight forward and eight reverse primers  were tested on the basis of PCR amplification products as described in a previous study [To perform genetic analysis of us study . Of the 2, 0.2 mM of dNTPs, 0.3 µM of both forward and reverse primers , 1 U of Taq DNA polymerase , nuclease-free water , and genomic template DNA (2 µL).PCR reactions were carried out in a 96-well Gradient Palm-Cycler  following the protocol extensively described by Li and Quiros . The reaThe PCR temperature cycling conditions were (i) initial denaturation at 94 °C for 5 min, (ii) 5 cycles of 1 min of denaturation at 94 °C, 1 min of annealing at 35 °C, and 1 min of elongation at 75 °C, followed by 35 cycles of denaturation at 94 °C for 1 min, annealing at 50 °C for 1 min, and elongation at 72 °C for 1 min, and (iii) a final elongation step of 10 min at 72 °C. PCR amplifications were repeated twice for each primer combination to ensure the reproducibility of the results.Separation of the amplification products was performed on 1.6% agarose gels  in 1× TAE at 100 V and 176 mA for 1.5–2 h. The gels were visualized in a UVP Biospectrum AC Imaging System  after staining with 0.5 μg/mL EtBr  for 20 min.SRAP gel images were analyzed using TotalLab TL120 software . The band size was determined using a 100 bp DNA step ladder .The electrophoretic patterns of each primer were scored as band present (1) or band absent (0) and transferred to an original binary matrix using MS Excel. The total number of bands (TNB), number of polymorphic bands (NPB), and percentage of polymorphic bands (PPB) were counted. In order to estimate the allelic variation of each marker combination, the polymorphic information content (PIC) was calculated using the following equation:l allele . The oriN = 9999.Pairwise comparisons between individuals were used to calculate the Jaccard (1908) coefficient of genetic similarity in PAST (v 3.15) software . The PASPrincipal coordinate analysis (PCoA) was used to generate a coordinate graph in which the points in the Cartesian plane represent the individuals from the eight analyzed populations based on the Jaccard’s genetic similarity index and using the PAST program.The genetic diversity indices were calculated using POPGENE1.32 software , i.e., tDistribution of the genetic variation at two levels—within populations and among populations—based on SRAP marker patterns was determined by analysis of molecular variance (AMOVA) while presuming the Hardy–Weinberg equilibrium in the sampled populations after 999 permutations of a significance test. Furthermore, the pairwise population differentiation (PhiPT) was calculated using the GenAlEx 6.51 package ,51.The genetic structure of the analyzed populations was evaluated using a Bayesian model in STRUCTURE software, version 2.3.4 . K (unknA. exscapus subsp. transsilvanicus, including the habitat loss due to grazing disturbance, have not yet affected the genetic diversity of this species. However, there is a certain need for bio-conservation of populations to preserve the genetic integrity and the genetic diversity found in this species. Recommended conservation measures include monitoring population genetics, restoring the suitable habitats, and maintaining the effective population size.The findings of this study suggest that current threats of