Salvelinus alpinus). We also assess the utility of sequenced microsatellites for fisheries applications by quantifying the spatial scales of movement and exploitation through genetic assignment of fishery samples to rivers of origin and comparing these results with a 29‐year tagging dataset. Self‐assignment and simulation‐based analyses of 111 genome‐wide microsatellite loci and 500 informative SNPs from 28 populations of Arctic Charr in north‐eastern Canada identified largely river‐specific genetic structure. Despite large differences (~4X) in the number of loci surveyed between panels, mean self‐assignment accuracy was similar with the microsatellite loci and the SNP panel (>90%). Subsequent analysis of 996 fishery‐collected samples using the microsatellite panel revealed that larger rivers contribute greater numbers of individuals to the fishery and that coastal fisheries largely exploit individuals originating from nearby rivers, corroborating results from traditional tagging experiments. Our results demonstrate the efficacy of sequence‐based microsatellite genotyping to advance understanding of fine‐scale population structure and harvest composition in northern and understudied species.The resiliency of populations and species to environmental change is dependent on the maintenance of genetic diversity, and as such, quantifying diversity is central to combating ongoing widespread reductions in biodiversity. With the advent of next‐generation sequencing, several methods now exist for resolving fine‐scale population structure, but the comparative performance of these methods for genetic assignment has rarely been tested. Here, we evaluate the performance of sequenced microsatellites and a single nucleotide polymorphism (SNP) array to resolve fine‐scale population structure in a critically important salmonid in north eastern Canada, Arctic Charr ( Our study is the first to evaluate the efficacy of sequence‐based microsatellite genotyping for population identification, genetic assignment and estimates of dispersal in a northern fish species and has broad implications for the management of other vital understudied northern species.22.1Salmo salar) in 2016, but no Arctic Charr were encountered  , prior to extracting DNA using the Qiagen DNeasy 96 Blood and Tissue extraction kit (Qiagen) following manufacturer's guidelines, and quantifying with either Qubit (ThermoFisher Scientific) or QuantIT PicoGreen (Life Technologies).We collected 915 juvenile baseline samples for microsatellite and SNP genotyping from 28 rivers in northern Newfoundland and Labrador between 2005 and 2017 Figure . Additioed Table . ElectroSalvelinus reference genome from Christensen, Rondeau, et al. (n = 37) and comprised dinucleotide loci with 20 repeats and trinucleotide loci with 10–14 repeats. We tested 384 loci (219 dinucleotide and 165 trinucleotide) in pools of 22–23 loci per multiplex PCR in eight individuals to identify useful microsatellites following the protocol of Zhan et al.  in R to check for family structure. This program calculates probabilities of related and unrelated genotype pairs and computes log‐likelihood ratios comparing a hypothesis of related versus unrelated  for mixed stock fisheries analysis in 996 individuals because some loci failed to amplify in all samples. Pairwise FST was highest between Parker's Bay Brook (PBP) in Newfoundland and Kiyuktok Brook (KIY) in northern Labrador with both microsatellite and SNP panels  and lowest between Ikarut River (IKA) and River 103 (R103) with microsatellites and R103 and River 104 (R104) with SNPs    distributed across the genome Figure a with pe8 Figure b and a my) Table . Relatioy) Table . Howevery) Table  demonstr3.2R = .86), with mean accuracy ranging from 77.9% to 94.6%. A final panel of 500 SNPs chosen for downstream analysis represents an optimal trade‐off point between assignment accuracy (>90%) and cost, and previous studies have used similarly sized panels for genetic assignment in salmonids     Figure . Because3.3R2 = .44, p = .004)  Figure . Overall3.42 = 0.14, p < .0001) relationship between the number of individuals recaptured and distance from tagging site   exceeded that of Atlantic Salmon from the same geographic region using a similar number of microsatellites (FST ~ 0.05; Bradbury et al., The population structure detected in Arctic Charr in Labrador was largely hierarchical and generally similar across methods with both suggesting a latitudinal cline in structure with southern populations being most divergent. Nonetheless in both cases, highly accurate assignment of individuals to most rivers was achievable and clearly suggest Arctic Charr are structured at the river scale, extending earlier genetic studies of anadromous Arctic Charr in the Canadian Arctic and Labrador (Bernatchez et al., 4.3We further demonstrate the utility of sequence‐based microsatellite loci to disentangle the composition of mixed stock fisheries and spatial scale of movement in Arctic Charr in Labrador. The results of genetic assignment using the microsatellite loci indicate that these fisheries primarily target local populations, with most individuals genetically assigning to rivers within 200 km of the fishery. Comparison of these microsatellite‐based assignments with extensive tagging data allowed independent estimates of movement. The tagging data indicate capture of most individuals within 100 km of their tagging site, corroborating our genetic assignments and past tagging studies of Arctic Charr in other regions of Canada (Gyselman, Although genetic and tagging‐based estimates were largely congruent, the genetic estimates suggest slightly larger spatial scales of movement than the tagging experiments, even when excluding populations in the south. This difference suggests that genetic‐based estimates may resolve rarer straying events, which may be missed with tagging‐based estimates of movement, further demonstrating the utility of sequenced microsatellites for fisheries applications. However, it is worth noting that the tagging experiments did not necessarily tag fish in natal rivers, and tagging in overwintering rivers near fishery locations could potentially reduce our estimates of movement. Furthermore, the genetic component of our study did not include all anadromous Arctic Charr populations in the region. Although low assignment probability of some individuals may indicate that populations in the region remain unsampled (Waples & Gaggiotti, 5Our comparison of marker types validates the efficacy of a sequence‐based, genome‐wide microsatellite panel for detecting river‐specific structure in salmonids and for elucidating fine‐scale patterns of exploitation and movement. Our results demonstrate river scale population structure and limited gene flow in Arctic Charr, despite evidence of some straying among rivers. We show general agreement between both genetic assignment based on sequenced microsatellites and long‐term tagging data suggesting that coastal fisheries generally exploit individuals within 100–200 km of their natal river. The current study demonstrates the efficacy of sequence‐based microsatellite genotyping to advance understanding of fine‐scale population structure and harvest composition in northern and understudied species and provides a valuable tool for fisheries management and conservation.None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Hypogeophis rostratus, to investigate intraspecific morphological and genetic  variation across the archipelago (782 samples from nine islands) to identify patterns and test processes that shaped their evolutionary history within the Seychelles.Island systems offer excellent opportunities for studying the evolutionary histories of species by virtue of their restricted size and easily identifiable barriers to gene flow. However, most studies investigating evolutionary patterns and processes shaping biotic diversification have focused on more recent (emergent) rather than ancient oceanic archipelagos. Here, we focus on the granitic islands of the Seychelles, which are unusual among island systems because they have been isolated for a long time and are home to a monophyletic radiation of caecilian amphibians that has been separated from its extant sister lineage for ca. 65–62 Ma. We selected the most widespread Seychelles caecilian species, Overall a signal of strong geographic structuring with distinct northern- and southern-island clusters were identified across all datasets. We suggest that these distinct groups have been isolated for ca. 1.26 Ma years without subsequent migration between them. Populations from the somewhat geographically isolated island of Frégate showed contrasting relationships to other islands based on genetic and morphological data, clustering alternatively with northern-island (genetic) and southern-island  populations.H. rostratus across the Seychelles is explained more by isolation-by-distance than by adaptation, the genetic-morphological incongruence for affinities of Frégate H. rostratus might be caused by local adaptation over-riding the signal from their vicariant history. Our findings highlight the need of integrative approaches to investigate fine-scale geographic structuring to uncover underlying diversity and to better understand evolutionary processes on ancient, continental islands.Although variation in  Anolis lizards e.g. [Islands have had an important influence on the understanding of diversification and adaptive evolution since the landmark publication of “On The Origin of Species by Means of Natural Selection”  see 2]..2]. Thesrds e.g. –11, Galárds e.g. –14, and rds e.g. . In cont2 , with no migration occurring between them, despite multiple low stands in sea level. The data support the recognition of a single but variable species.Genetic and morphological data support a geographically structured northern island and southern island clade within the Seychelles caecilian Hypogeophis rostratus were collected on nine granitic islands in the Republic of the Seychelles between 1981 and 1991, and between 2013 and 2015: Mahé, Sainte Anne, Cerf, Silhouette, Frégate, La Digue, Praslin, Curieuse, and Félicité  gene from 100 individuals sampled from nine islands  and brain-derived neurotrophic factor (bdnf)) and two anonymous nuclear markers (brev5 and rost5) [The polymerase chain reaction (PCR) was used to amplify a portion of the mtDNA cytochrome d rost5) . See AddH. rostratus. AFLP marker profiles were generated for a total of 274 individuals from seven islands . Uncorrected pairwise (p-) molecular genetic distances and Tajima D values were estimated using MEGA X [We inferred relationships among individuals by constructing haplotype networks and by inferring phylogenetic trees. Individual alleles for diploid nuclear sequences were reconstructed using PHASE v.2.1.1 . Input flgorithm  as impleg MEGA X .cytb using Bayesian inference (BI) methods. PartitionFinder v.2.1.1 [cytb, sampling every 10,000 generations over 106 generations with one cold and three heated chains. Tracer v.1.7 [Cytb sequence data for a congeneric endemic Seychelles caecilian, H. brevis, were obtained from GenBank (HQ444110), and used as a closely related outgroup [Phylogenetic trees were inferred for  v.2.1.1  was used v.2.1.1  was useder v.1.7  was usedoutgroup  to root outgroup .H. rostratus clades found  and ). The null hypothesis is that, since divergence, there has been no gene flow between the two (northern and southern island-group) lineages because of a saltwater barrier separating the two groups [cytb gene was used in the analyses  but after multiple independent runs, double peaks were observed in several parameters, which were likely a consequence of reciprocal monophyly between the two groups for cytb data. Final IMa analyses therefore were carried out with only the nuclear data. No molecular rate was provided for the nuclear data because of a lack of comparative evolutionary rates for these markers. The IS model was used for the nuclear loci. Posterior density curves were inspected for clear peak estimates and appropriate prior distributions boundaries. Priors were defined based on summary statistics and adjusted following initial runs. The final analysis was run five times with different starting seeds and checked for consistency between runs.Based on results from the initial molecular analyses, the Isolation-with-Migration (IM) model  as impleo groups –32. Initcytb, following other studies of amphibians and reptiles [cytb. PartitionFinder v.2.1.1 [cytb partition, and strict clock priors were used for the nuclear partitions . Analyses were run for 106 generations, with sampling every 10,000 generations. Tracer was used to investigate that good mixing was occurring between all parameters and that ESS values were > 200 as per previous analyses.Time estimates for the split between the northern- vs southern-island lineages were made using *BEAST in BEAST2, defining each clade as a “species”  for all reptiles , 103. Nu v.2.1.1  was agai v.2.1.1 . As a teTwo different approaches were used to quantify within-island AFLP diversity. First, the program TFPGA  was usedST) of Wright’s FST in Arlequin v.3.5 [ST were obtained via bootstrapping over loci. Given that other analyses of our genetic data indicated the presence of two distinct island groups (see Results), we also estimated θST and its associated confidence limits for a posteriori grouping of islands. To further refine our inferences about patterns of genetic structure among islands, we used TFPGA to generate a UPGMA dendrogram based on pairwise Nei’s [Genetic structuring among islands and island groups was characterized using Wier and Cockerham’s  estimatein v.3.5 . Ninety-se Nei’s  unbiasedse Nei’s ). To furK) that occur within the sampled H. rostratus we used a Bayesian clustering algorithm implemented in STRUCTURE v.2.3.4 [K values were specified based on the number of islands for which data were generated for (i.e. K1 – K7 for the full dataset) in each analysis. An analysis of H. rostratus genetic variation within the largest, highest and geographically most complex Seychelles island of Mahé, which has the most sampled populations (seven) and specimens (n = 97), was carried out using the admixture model to investigate intra-island population structure. To infer the most likely K for each dataset STRUCTURE HARVESTER [K [K value was then subjected to independent runs on STRUCTURE of 1 × 106 with a burn-in of 100,000 steps. Final summary figures of STRUCTURE results were created using distruct v.1.1 [To identify the number of discrete populations  with TL as the covariate implemented using PROC GLM [Nussbaum & Pfrender  presentePROC GLM . Using AMultivariate analyses for measures and counts of 16 phenotypic traits in 170 males and 146 females were conducted separately for each sex to determine patterns of morphological variation among islands. Principal components analysis (PCA) of metric characters  and prii) and error (Ve) for each trait using PROC VARCOMP [ST, using the formula Vi/(Vi + 2(Ve)). This measure is analogous to the standard measure of quantitative trait subdivision QST [ST includes both genetic and environmental sources of variance. We used a nested design, with islands nested within groups  to estimate the levels of subdivision at the level of island group. Because the univariate ANCOVA demonstrated significant sexual dimorphism we examined the correlation between males and females in population subdivision across all phenotypic traits using a least-squared regression of PST values.To examine patterns of phenotypic divergence on a trait-by-trait basis we obtained restricted Maximum Likelihood (ML) estimates of the variance components explained by island of origin  was used to generate a pairwise distance matrix for each geographic sampling locality. The probability of the observed correlation was estimated by comparison with a distribution of correlation coefficients generated with 1000 random permutations of matrix elements.To produce distance matrices for the Mantel tests, appropriate analyses for each dataset were completed. For the morphometric data, Gower similarity coefficients for males and females were used and generated by PAST v.3.05 . For AFL v.6.502 , 127. Fo v.6.502  was used v.6.502  with parAdditional file 1. Fig. S1. a) UPGMA dendrogram of AFLP data. Support values are represented on the branches, where applicable. b) PCA plot of AFLP data for PCs 1 and 2. Fig. S2. a-e) correlation plots between datasets; f) Morphological trait subdivision (PST) among islands. The average level of subdivision among islands for AFLP markers (θst) is denoted by the solid line with the upper and lower confidence limits shown as dotted lines. See text for an explanation of the trait abbreviations and methodology; g-h) male vs. female correlation plots. Fig. S3. Fig. S3. PCA plots for: a) females, b) males. Colors and symbols are the same as those used in Fig. Additional file 2. List of specimens used in this study and associated GenBank accession numbers for sequence data.Additional file 3. Results of the final five independent IMa runs.


Sweet cherry production faces new challenges that necessitate the exploitation of genetic resources such as varietal collections and landraces in breeding programs. A harmonized approach to characterization is key for an optimal utilization of germplasm in breeding. This study reports the genotyping of 63 sweet cherry accessions using a harmonized set of 11 simple sequence repeat (SSR) markers optimized in two multiplexed PCR reactions. Thirty-eight distinct allelic profiles were identified. The set of SSR markers chosen proved highly informative in these germplasm; an average of 6.3 alleles per locus, a PIC value of 0.59 and above-average expected and observed heterozygosity levels were detected. Additionally, 223 amplified fragment length polymorphism (AFLP) markers derived from eight selective primer combinations were employed to further differentiate 17 closely related accessions, confirming the SSR analysis. Genetic relationships between internationally known old cultivars were revealed: SSR fingerprints of “Schneiders Späte Knorpelkirsche” and “Germersdorfer” were found to be identical to those of the standard cultivar “Noire de Meched”, among others, whereas four accessions known as “Hedelfinger Riesenkirsche” and four known as “Große Schwarze Knorpelkirsche” showed allelic differences at various loci. The genetic diversity of locally-grown cultivars worldwide might be currently underestimated. Several autochthonous Austrian sweet cherry germplasm accessions were genotyped for the first time and their genetic relationships analyzed and discussed. Interestingly, seven Austrian sweet cherry landraces were shown to be clearly genetically separated from international and modern varieties, indicating that Austrian germplasm could include valuable genetic resources for future breeding efforts.  Autochthonous varieties and their wild relatives are valuable genetic resources for the breeding and development of any domesticated crop ,2,3,4. GDespite a long-standing history of sweet cherry growing , the ecoRecent surveys of sweet cherry landraces in the provinces Burgenland ,12 and UEnvironmental and phytosanitary factors can lead to morphological variation in plants , potentiThe deployment of genetic markers such as simple sequence repeats (SSRs), or single nucleotide polymorphisms (SNPs) to reveal a characteristic genetic fingerprint, considerably accelerates and simplifies the process of cultivar grouping and identification ,18,19,20SSR markers inherently cover a very limited part of the target genome. They are not universally polymorphic. A defined set of markers could reveal genetic polymorphism within one population but be less informative for a second group of genotypes, e.g., closely related landraces from another gene pool ,26,27. AThis is the first study reporting genetic fingerprints of autochthonous Austrian landraces and germplasm accessions of sweet cherry. The research questions we wanted to address were the following: (1) Are the SSR marker set and the multiplex method we chose effective? Do they provide fast and reliable results that can be harmonized? (2) Can Austrian sweet cherry landraces be successfully differentiated by the selected set of SSR markers? (3) Have the selected accessions and landraces been correctly identified? n = 29) representing old landraces and probably regional selections of known cultivars in the traditional high-stem meadow orchard growing system; and (2) Sample Set BOK: University of Natural Resources and Life Sciences, Vienna (BOKU) germplasm collection, Vienna, AT (n = 23) including Austrian landraces and some modern varieties. Additionally, ECPGR reference genotypes from East Malling Research, United Kingdom (GB) (n = 5) and United States of America (US) reference genotypes (n = 6) were included in Sample Set IS. These reference genotypes were used to standardize the SSR data as described by [Plant material for SSR-analysis was collected at two locations. (1) Sample Set STB: Stoob, Burgenland, AT  (2) Scharten, Upper-Austria (n = 5) and (3) BOKU germplasm collection (n = 8), Vienna. ECPGR reference genotypes “Noire de Meched” and “Noble” were included in the analysis  Leithaberg, Burgenland  and diluted to a concentration of 2.5–4 ng/µL. For the reference accessions, lyophilized leaf-samples were extracted and diluted accordingly. The genomic DNA for the US reference accessions was kindly provided by Dr Amy Iezzoni’s team in Michigan State University.For AFLP analysis, DNA was extracted from lyophilized leaf-samples according to  with minTwelve primer pairs recommended by the ECPGR  and suitPrimer concentrations in PCR reactions were optimized to produce similar intensity of chromatogram peaks to facilitate scoring. The final volume of PCR reactions was 13 µL containing 5–8 ng genomic DNA, 2× Type-it Multiplex PCR Master Mix , 1.25–5 nmol of primer, the exact quantity depending on each primer .PCR was started with a denaturing step at 95 °C for 5 min, followed by 10 circles of touchdown-PCR: 95 °C for 30 s, 60 °C for 1.5 min (−1 °C per cycle) and 72 °C for 30 s, followed by 18 cycles: 95 °C for 30 s, 50 °C for 1.5 min and 72 °C for 30 s, with a final elongation step of 60 °C for 30 min.® and GENOTYPER® . Scored fragment sizes were harmonized with those of the reference genotypes using the allele sizes published by [The amplicon sizes were measured against a LIZ 500 standard with an ABI 3130 Genetic Analyzer . Subsequent scoring was done with GENESCANished by . 2, 0.2 mM of dNTPs. The pre-amplified template was diluted 1:10 and 4 µL were incubated in a 10µL-PCR reaction with 0.5 pM of each of the selective primers. For this final selective amplification, 18 different primer combinations were tested on nine varieties. The eight most promising combinations were selected for the analysis: ATC/ATC, ACC/ATC, ACC/CAG, AGG/AGT, AGG/CAG, ATA/ATC, ATA/AGT, ATA/CAG. AFLP-fragments were run on a LI-COR (NEN Model 4300) analyzer  and scored for presence (1) or absence (0) by hand with SagaTM (Version 3.0) . For all samples, except two, biological replicates  for estimation of clonal variation were done and run side by side. Additionally, a technical control was run for four samples . Three types of negative controls, one from the start and one for each of the PCRs were included. Only clearly visible bands between 90 and 400 bp of length were scored.AFLP analysis was performed following the protocol by Vos et al. with the following modifications: Genomic DNA (0.3 µg) was incubated with 3.6 U Tru1I and 45 U EcoRI in 25 µL Tango-Buffer  for 1 hour at 37 °C, followed by two hours at 65 °C and 15 min at 85 °C. 5 µL of the adapter-ligation-solution as described by Vos et al.  were addFor SSR data and allele frequencies, the identification of unique alleles was done in GenAlEx version 6.5 ,42. polyThe calculation of the genotype association curve , frequenThe genotype association curve  reveals The DAPC was done in R, package adegenet version 2.1.3  for SSR For AFLP-scores the PCoA was done in GenAlEx ,42. ThisPrunus avium, obtaining reliable results [Eleven of twelve SSR markers gave unambiguous results in the multiplex approach, which could be standardized. Scores of BPPCT034  could no results ,19.The number of alleles per locus ranged from 3 to 9, with an average of 6.3; PIC values ranged between 0.22 (EMPa017) and 0.78 (BPPCT037), with an average of 0.59. The observed and expected heterozygosity ranged between 0.06 resp. 0.23 (EMPA017) and 0.89 resp. 0.81 (BPPCT037) depending on the SSR marker . Eight different genotypes  show unique alleles in up to three markers. The best markers in terms of detecting unique alleles in different genotypes were CPPCT022 and EMPaS10, with unique alleles amplified in six and five genotypes, respectively.The genotypes obtained by SSR fingerprinting allowed us to confirm or reject the morphological identification of landraces and germplasm accessions and revealed the occurrence of homonyms and synonyms in otherwise well-known varieties. Some phenotyping, grafting or labelling errors were also detected . Four trees in Stoob were phenotyped as cultivar “Große Schwarze Knorpelkirsche”; only two showed the same genotype and all of them differed compared to the reference accession in the BOKU germplasm collection. Two of three trees phenotyped as “Hedelfinger Riesenkirsche” and collected in Stoob had the same fingerprint, but all of them differed from the two accessions of the same name in the BOKU germplasm collection, which also turned out to show two distinct genetic profiles. Some clonal variants were also identified such as the samples phenotyped as landrace “Butterkirsche” and the two types of “Kritzendorfer Einsiedekirsche”. Six of the sampled Austrian landraces showed to be identical to several germplasm accessions and standard cultivars from different origins. This group of seemingly identical genotypes includes the accessions “Germersdorfer”, “Schneiders Späte Knorpelkirsche”, the ECPGR reference cultivar “Noire de Meched” and the US-reference “Schneiders”, as well as traditional Austrian cultivars like “Melker Riesenkirsche” and “Horitschoner Herzkirsche”. For simplification purposes this group of accessions will be mentioned as the “Schneiders-Group” throughout this study.An AFLP-analysis including further samples from two other sampling sites was conducted to confirm if these cultivars are true clones i.e., synonyms (see below). The Discriminant Analysis of Principal Components  was used to identify groups of genetically related individuals in all three sample sets. This analysis has been developed and is suitable for clonal or partly clonal populations, since it does not rely on Hardy–Weinberg or linkage disequilibria . The varCluster 1 consists of nine multi-locus-genotypes (MLGs) and contains landraces from Burgenland  as well as the rootstocks “NY54” and “F12/1” . Only onAccording to Simpson’s diversity index lambda, , DAPC-clThe expected heterozygosity (Hexp) of all three clusters were very similar, with values ranging from 0.595 (cluster 2) to 0.63 (cluster 1).The dendrogram  was calcDAPC-cluster 1 (red) is represented on the upper end missing “Kritzenorfer Einsiedekirsche”, which groups with the varieties of the cluster below. NY45 groups together with Goodnestone Black (uppermost end), and F12/1 appears separated from all other varieties on the bottom end of the dendrogram. The other two DAPC clusters appear to be admixed, whereas notably all genotypes of “Hedelfinger Riesenkirsche” appear in one cluster and all genotypes of “Große Schwarze Knorpelkirsche” in another. A separate cluster consists of “Hybrid 222”, i.e., “Burlat VG”. Unlike DAPC analysis, the dendrogram shows the rootstock “F12/1” as more genetically distant from the rest of the varieties . This min = 7), “Horitschoner Herzkirsche” (n = 3), “Melker Riesenkirsche” (n = 2), “Schneiders Späte Knorpelkirsche” (n = 3) of three origins and the ECPGR standard “Noire de Meched” were tested. We also included the clearly distinct phenotypes “Noble” , rootstock “F12/1” as well as the Austrian landrace “Rainkirsche” (K02)  . Four ruAn AFLP analysis with eight selective primer combinations and 17 samples of four different origins resulted in 223 markers, of which 64 (28.7%) were polymorphic. The error-rate was moderate, with 1.36–1.79% of difference in band-occurrence between technical replicates. Biological replicates differed up to 6.73%. The samples of “Noble” could clearly be separated from the other samples with PCoA . The perThe optimized multiplex-PCR approach to SSR genotyping was broadly successful; 11 of the 12 SSR-markers were easily scored and standardized against published data for five reference accessions (data not shown). This gives us confidence in the quality of the data generated which should prove straightforward to compare with similarly standardized data for other germplasm collections in the future The number of markers was comparable to that of similar studies ,23,27,51The chosen primer combination was found to be effective for differentiating between most of the Austrian sweet cherry landraces tested. Regarding the accurate identification of duplicates (i.e. synonyms) and homonyms , both could be detected by SSR-analysis, providing essential information for the optimal management of the germplasm collection and future breeding approaches.In DAPC cluster 2 , there aFour of five samples phenotyped as “Hedelfinger Riesenkirsche” showed differences in allele sizes. The cultivar “Hedelfinger Riesenkirsche” originates from Hedelfingen in Germany where it was selected from seedlings and taken to Hohenheim (Germany) around 1850. Thereafter, this cultivar has been widely distributed by tree nurseries [“Große Schwarze Knorpelkirsche” as well as “Hedelfinger Riesenkirsche” were assigned to the same DAPC cluster; both have been referred to as “population cultivar”, indicating that the cultivar consists of several phenotypes . This coOn the other hand, three not previously identified genotypes of distinct phenotypes found in Stoob showed the same fingerprint  are found in this group, along with the ECPGR reference cultivar “Noire de Meched” and the US-standard “Schneiders”. Although these findings could be due to limited SSR-marker resolution, suspicions that some of the cultivars could be clones and their names therefore synonyms have been raised before. Braun-Lüllemann and Bannier  found thThis finding is especially intriguing since this latter cultivar was found to be comparable in fruit size to widely distributed modern cultivars like “Regina” or “Kordia”, and thus represents a potential candidate for breeding. Considering that maximizing fruit size still is one of the most important objectives in sweet cherry breeding , it is eTo our knowledge, until now there has been no such study. To gather additional evidence on the correct genetic identity of the above-mentioned varieties which have identical SSR profiles, an AFLP analysis was conducted.We compared the ECPGR standard “Noire de Meched”, accessions of the Austrian germplasm collection “Germersdorfer”, “Schneiders Späte Knorpelkirsche”, “Melker Riesenkirsche” and “Horitschoner Herzkirsche”, as well as samples from two different sites consisting of several landraces identified as one of the just mentioned cultivars. Three genetically and phenotypically different accessions were included for comparison. Prunus avium varieties, which puts more weight on such technical errors. To sum up: based on the results shown in this study genetic differences among the tested varieties exist. These genotypes might represent valuable resources for future breeding efforts, if they have superior traits, e.g., disease resistance or superior fruit size. Based on the results of the AFLP analysis, the tested varieties in general show low genetic variation; only about 29% of markers were shown to be polymorphic. This is in agreement with reported polymorphic rates of 21% for AFLP markers in sweet cherry . TechnicThe genetic diversity of the Austrian landraces evaluated was subsequently compared to that of international standard cultivars based on the results of the DAPC and calculated diversity indices. The DAPC sorted the samples into three clusters. In cluster 2, all samples of “Hedelfinger Riesenkirsche” and “Große Schwarze Knorpelkirsche” group together with “Goodnestone Black”, “Napoleon”, “Stella Spur”, “Lambert”, “Lapins”, “Ulster”, “Tavriczskai” and “Sarga Dragan”. “Chelan” groups with “Noble”, “Early Burlat”, “Burlat”, “Jaboulay” and “Früheste der Mark” in DAPC cluster 3, whereas DAPC cluster 1 comprised only varieties from Burgenland, i.e., landraces not mentioned in the available literature. These autochthonous varieties seemed clearly distinct from the other groups and may therefore constitute a valuable germplasm for breeding as members of a regional gene pool. Details on valuable phenotypic and physicochemical characteristics of these varieties such as unique taste, low susceptibility to rain-induced cracking or high content of polyphenols in the fruit have been recorded in prior studies ,12,58. TA successful method of fast and easy-to-use multiplex SSR analysis for international harmonization of sweet cherry accessions was presented. The investigated collection of autochthonous Austrian sweet cherry landraces is highly diverse and could constitute a valuable germplasm for future breeding programs, since Austrian landraces were shown to represent a regional gene pool. They exhibit interesting traits that might be valuable for breeders  and are Concerning the various genotypes of “Große Schwarze Knorpelkirsche” and 2Hedelfinger Riesenkirsche”, marker assisted selection (MAS), field trials and cultivar evaluations should be conducted to identify the most valuable of the clones for breeding purposes.It would be interesting to compare the genetic diversity of Austrian landraces with those from the French collection described by Mariette et al. . Does itPhenotype-based surveys on sweet cherry diversity have been conducted for some Austrian regions ,11,12,13Part of these gaps could effectively be filled by genetic evaluation, as has been shown in this study. Homonyms, synonyms and labeling errors were detected. The genetic data help to evaluate the genetic diversity, identity and trueness to type of Austrian sweet cherry accessions and thus serves as an important and valuable tool for the management of Austrian germplasm collections.


Vitis vinifera and V. labrusca, followed by their separation from wild grapevine. A core collection of 120 genotypes captured 100% of all detected alleles. The accessions selected for the core collection may be used in future phenotyping efforts, in genome association studies, and for conservation purposes. Genetic divergence among accessions has practical applications in grape breeding programs, as the choice of relatively divergent parents will maximize the frequency of progeny with superior characteristics. Together, our results can enhance the management of grapevine germplasm and guide the efficient exploitation of genetic diversity to facilitate the development of new grape cultivars for fresh fruits, wine, and rootstock.The management of germplasm banks is complex, especially when many accessions are involved. Microsatellite markers are an efficient tool for assessing the genetic diversity of germplasm collections, optimizing their use in breeding programs. This study genetically characterizes a large collection of 410 grapevine accessions maintained at the Agronomic Institute of Campinas (IAC) (Brazil). The accessions were genotyped with 17 highly polymorphic microsatellite markers. Genetic data were analyzed to determine the genetic structure of the germplasm, quantify its allelic diversity, suggest the composition of a core collection, and discover cases of synonymy, duplication, and misnaming. A total of 304 alleles were obtained, and 334 unique genotypes were identified. The molecular profiles of 145 accessions were confirmed according to the literature and databases, and the molecular profiles of more than 100 genotypes were reported for the first time. The analysis of the genetic structure revealed different levels of stratification. The primary division was between accessions related to  Vitis spp.) is considered to be a major fruit crop globally based on hectares cultivated and economic value [Grapevine  thrive because of their vegetative characteristics, which are best adapted to the country's environmental conditions, with generally high humidity. In addition, due to their relatively high robustness, they are resistant to many diseases that affect grapevine in the country, resulting in production of relatively high volume, although of low quality, and have become dominant on Brazilian plantations [In Brazil, the American ntations , 4.Vitis have contributed evolutionarily through interspecific crossings, accidental or planned, to the adaptation of grapevine to the highly different conditions that its expansion has demanded. Hybrid varieties are characterized by greater resistance to pests and diseases than V. vinifera and by producing fruits with better organoleptic characteristics than American grapes. Crosses and natural mutations have greatly benefited from the possibility of vegetative propagation among grapevines, enabling the exploitation of different characteristics over time, with noticeable variations in berries, flowers, and leaves, further increasing the number of cultivars planted [The wild species of the genus  planted , 5.The starting point of any breeding program of a species is genetic variability, whether spontaneous or created. The manipulation of this variability with suitable methods leads to the safe obtainment of superior genotypes in relation to agronomic characteristics of interest . Germplaex situ field collections. The germplasm banks involved in breeding programs are fundamental to the development of new materials. These collections generally have a large number of accessions, but only a small proportion of these resources are used in practice. The management of such collections becomes complex when many accessions are involved. Redundancy should be reduced to a minimum, the use of “true-to-type” plant material must be ensured, and the introduction of new accessions should be optimized [Most germplasm is derived from seeds, but for highly heterozygous plants, such as grapevines, this method is not suitable, with conservation most commonly occurring through the use of ptimized . Therefoptimized , 11. Theptimized .Information on the genetic diversity available in germplasm banks is valuable for use in breeding programs because such information assists in the detection of combinations of accessions capable of producing progenies with maximum variability in characteristics of interest, guiding hybridization schemes .The identification of grapevine cultivars has traditionally been based on ampelography, which is the analysis and comparison of the morphological characteristics of leaves, branches, shoots, bunches, and berries , but as Vitis [Microsatellites, or simple sequence repeats (SSRs), are among the most appropriate and efficient markers for genetic structure and conservation studies . SSRs arVitis . Since SVitis , they haVitis , 20, parVitis , 22, genVitis , 24, detVitis , and assVitis .Because SSRs are highly reproducible and stable, they have allowed the development of several reference banks with grapevine variety genetic profiles from around the world. Access to these reference banks allows the exchange of information between different research groups, significantly increasing international efforts related to the correct identification of grapevine genetic resources .Vitis spp. germplasm bank including wild Vitis species, interspecific hybrids, and varieties of the main cultivated species  and varieties developed by the IAC.Considering the importance of viticulture and winemaking in Brazil, the Agronomic Institute of Campinas (IAC) has a Vitis spp. available in this germplasm bank using microsatellite markers. The accessions were characterized, and their molecular profiles were compared with the use of different literature and online databases. Here we quantify the genetic diversity of this Brazilian germplasm and describe its genetic structure, and we suggest the composition of a core collection that would capture the maximum genetic diversity with a minimal sample size. We discuss perspectives related to the use of this information in germplasm management and conservation.Our objective in the present study was to describe the diversity and genetic structure of the Vitis spp. Germplasm Bank of the IAC in Jundiaí, São Paulo (SP), Brazil, were analyzed. This germplasm encompasses more than ten species of Vitis, including commercial and noncommercial varieties of wine, table, and rootstock grapes. Each accession consisted of three clonally propagated plants, sustained in an espalier system and pruned in August every year, leaving one or two buds per branch. For sampling, were collected young leaves of a single plant from each accession. Detailed data on the accessions are available in A total of 410 accessions from the Total genomic DNA was extracted from young leaves homogenized in a TissueLyser  following the cetyltrimethylammonium bromide (CTAB) method previously described by Doyle (1991) . The quaA set of 17 grapevine SSR markers well characterized in previous studies , 29–32 wPolymerase chain reaction (PCR) was performed using a three-primer labeling system  in a finO), expected heterozygosity (HE), and fixation index (F). GenAlEx software was also used to identify private (Pa) and rare alleles (frequency < 0.05).Descriptive statistics for the genotyping data were generated using GenAlEx v. 6.5  to indicj), and null allele frequency (r) were calculated to evaluate the efficiency and discriminatory potential of each microsatellite marker. Polymorphism information content (PIC) was calculated using Cervus 3.0.7 [n is the number of alleles, and pi and pj are the frequencies of the ith and jth alleles [j) values were estimated to compare the efficiencies of microsatellite markers in varietal identification and differentiation. This parameter was calculated in accordance with the formula as follows: j is the probability that two randomly selected samples have different and distinct banding patterns, pi is the frequency of the ith pattern revealed by each marker, N is the number of samples analyzed, and I is the total number of patterns generated by each marker [The polymorphism information content (PIC), discriminating power  was estimated using Cervus 3.0.7. By definition, a microsatellite null allele is any allele at a microsatellite locus that consistently fails to amplify to detectable levels via the polymerase chain reaction (PCR) [The null allele frequency (on (PCR) . Cervus on (PCR) , in whicon (PCR) .a priori assignment of individuals to groups were used: a Bayesian model-based approach, a distance-based model using a dissimilarity matrix, and discriminant analysis of principal components (DAPC).To assess the overall germplasm structuring, three approaches with different grouping criteria that do not require The model-based Bayesian analysis implemented in the software package STRUCTURE v. 2.3.4  was usedAll simulations were performed using the admixture model, with 100,000 replicates for burn-in and 1,000,000 replicates for Markov chain Monte Carlo (MCMC) processes in ten independent runs. The number of clusters (K) tested ranged from 1 to 10.ad hoc statistics [The online tool Structure Harvester  was usedatistics . The optatistics , and theatistics . Based opoppr [Distance-based methods proceed by calculating a pairwise distance matrix, the entries of which provide the distance between every pair of individuals. This matrix may then be represented using some convenient graphical representation, such as a dendrogram, and clusters may be identified by eye . Geneticpoppr . The pripoppr . The finpoppr .adegenet 2.1.2 [find.clusters function was used to detect the number of clusters in the germplasm, which runs successive K-means clustering with increasing numbers of clusters (K). We used 20 as the maximum number of clusters. The optimal number of clusters was estimated using the Bayesian information criterion (BIC), which reaches a minimum value when the best-supported assignment of individuals to the appropriate number of clusters is approached. DAPC results are presented as multidimensional scaling plots.DAPC as implemented in the R package et 2.1.2 , 61 was et 2.1.2 . The finwww.vivc.de), Italian Vitis Database (http://www.vitisdb.it), “Pl@ntGrape, le catalogue des vignes cultivées en France” (http://plantgrape.plantnet-project.org/fr) and the U.S. National Plant Germplasm System . For this comparison, the molecular profile of seven microsatellite loci  adopted by the databases was used.To verify the trueness to type and identify misnamed genotypes, the molecular profiles obtained in this study were compared with the data contained in the following online databases: Vitis International Variety Catalogue , São Paulo's Agency for Agribusiness Technology , and the State University of Campinas . Additionally, we confirm that this study did not involve endangered or protected species.Vitis spp. were analyzed at 17 SSR loci  was 0.75 (ranging from 0.48 to 0.88). The expected heterozygosity (HE) was higher than the observed heterozygosity (HO) for most loci, except for VVIv36. Among these loci (HO<HE), the probability of null alleles (r) was significantly high (>0.20) only for VVIp25b and VVIn52. The analysis revealed a high HE level, ranging from 0.58 (VVIq57) to 0.91 (VVIp31), with a mean of 0.83.Across all the accessions, the mean observed heterozygosity (Hj) was greater than 0.80 for 13 of the 17 loci, with the highest value for the VVIp31 locus (0.91). The Dj values were high for 76.5% of the SSR markers used (>0.80). When the PIC and Dj of each locus were analyzed together, 12 loci presented the highest values for both indexes (>0.80). In this study, the largest amount of information was provided by VVIp31, for which 24 alleles were detected showing a PIC and a Dj ≥ 0.90.The PIC estimates varied from 0.52 (VVIq57) to 0.90 (VVIp31), with a mean value of 0.81. The discrimination power  is related to the main accession species. Cluster 1 was formed by accessions with the greatest relation to  hybrids .V. vinifera and V. labrusca, respectively, had the smallest number of private alleles . The largest number of private alleles was found in cluster 3 (53), constituting 72.60% of the total private alleles.Of the 304 observed alleles, 227 were shared among the groups; the remaining 77 represented private alleles (Pa) in different groups of accessions . The VVMV. labrusca hybrids were allocated to subgroups L1 (n = 18) and L2 (n = 10). In cluster 3, the second round also divided the accessions into two subgroups (K = 2), the V. rotundifolia accessions were assigned to the VR subgroup (n = 11), and the others accessions related to wild Vitis species were allocated to the WV subgroup (n = 40).A subsequent round (second round) of STRUCTURE allowed the identification of secondary clusters within the three main genetic clusters . In ClusAlthough the Admixture group contained a large number of heterogeneous accessions, a subsequent round of STRUCTURE was also performed on this set to identify possible clustering patterns. As a result, the analysis revealed the presence of two subgroups (K = 2). Accessions of the Seibel series and hybrids including cultivars of this complex in their genealogy were separated from the other hybrids and assigned to the SS subgroup (n = 31). The remaining 67 accessions of the Admixture group were in the OH subgroup.Additionally, DAPC was performed with no prior information about the groupings of the evaluated accessions. Inspection of the BIC values  revealedInitially, the DAPC scatterplot based on the first and second discriminant functions showed the formation of three main distinct groups, with great genetic differentiation of clusters 8 (dark green) and 9 (green) from the others . In a suThe allocation of individuals into clusters according to the DAPC showed several similarities to those achieved in the second round of STRUCTURE, and both analyses showed the same pattern of clustering. Essentially, clusters 1 (magenta), 2 (yellow), 8 (dark green), and 9 (green) of the DAPC reflected the subgroups SS, TG, VR, and WV detected by the STRUCTURE second round, respectively, and the WG subgroup corresponded to DAPC clusters 5 (red) and 3 (orange).V. labrusca hybrids, the analyses resulted in a slightly different division. DAPC separated these accessions in clusters 6 (blue) and 7 (purple), basically assigning ‘Niagara’ accessions in cluster 6 and the other V. labrusca hybrids in cluster 7. The STRUCTURE second round also identified ‘Niagara’ accessions as a separate group (NG); however, a more refined division was performed in the other hybrids, separating them into 3 subgroups. DAPC cluster 4 (brown) did not correspond to any subgroup identified by the STRUCTURE second round; this cluster was formed mostly by hybrids developed by the IAC breeding program used as table grapes.In the case of the V. rotundifolia accessions and the other wild species was clearly separated from the cultivated Vitis species, as seen in the DAPC. There was also a strong separation between accessions related to V. labrusca and other accessions. The wine grape accessions of V. vinifera were mainly concentrated at the top of the dendrogram, while the table grape accessions of this species were found at the bottom. However, the other hybrids  were scattered among all the groups formed by the dendrogram.Finally, we constructed a dendrogram using the neighbor-joining method from the distance matrix based on Rogers’ distance to confirm the relationships among the accessions . The denThe identification of 145 accessions was validated through matches with data available in the literature and databases. The results also confirmed matches to reference profiles of clones based on somatic mutations. Another 42 accessions showed molecular profiles that matched a validated reference profile of a different prime name, indicating mislabeling .The molecular profiles of the remaining 223 accessions did not match any available reference profile. This accession group included wild species and cultivars from grapevine breeding programs in Brazil (the IAC and Embrapa), the United States, and France (Seibel series). The molecular profiles of more than 100 hybrids developed by the IAC were reported for the first time.The accessions ‘101–14’, ‘Bailey’, ‘Black July’, ‘Carlos’, ‘Carman’, ‘Castelão’, ‘Catawba Rosa’, ‘Elvira’, ‘Moscatel de Alexandria’, and ‘Regent’ showed a different profile than the reference profile of the same name and did not match any other available reference profile. However, additional morphological and source information is needed to validate their identification. To avoid possible confusion, these accessions were indicated as “Unknown”.After correcting the mislabeling, 22 cases of duplicates were identified, all with accessions of the same name and the same molecular profile. Accessions identified with different names but having the same molecular profile were classified as synonyms. Thirty-one synonymous groups were elucidated in this study . Some acO values ranged from 0.64 (Core 1) to 0.70 (Core 3); the value for Core 3 was similar to that detected for all 410 accessions (0.75). The three samples showed HE and Ne values higher than those observed for the entire dataset. The HE values for all samples were 0.85, while Ne ranged from 134.53 (Core 2) to 137.69 (Core 3). The values of Ne and HE are related to allele frequencies, and low values of allele frequencies generate even lower values when squared. With a reduction in the number of accessions (N), the low-frequency alleles  and rare alleles (frequency less than 0.05) showed an increase in frequency, resulting in an increase in Ne and HE.Three independent sampling proportions were constructed with a size ranging from 10 to 30% of the entire dataset to identify the smallest set of accessions that would be able to represent as much of the available genetic diversity as possible . Core 3,Core 3 sample was the only one that managed to capture 100% of the alleles, being the best option for use in breeding as a core collection. All clusters detected in the STRUCTURE analysis and DAPC are represented in Core 3. In particular, in the STRUCTURE analysis at K = 3, 49 accessions were in cluster 1, 12 were in cluster 2, 29 were in cluster 3, and 30 were in the admixture group, representing 41, 10, 24, and 25% of Core 3, respectively.The results of this study revealed high levels of genetic diversity among the evaluated accessions. The observed high genetic diversity was expected since the grape germplasm from the IAC includes varieties with very diverse origins, wild species, and different intra- and interspecific hybrids.E of 0.83 across the entire accession set in the 17 evaluated loci  was likely due to the taxonomic amplitude of the germplasm since a relatively large number of low-frequency alleles were found in wild species accessions. Lamboy and Alpha (1998) , when anO values than those expected from the randomized union of gametes (HE), except for VVIv36. For these loci, the probability of null alleles was positive but significantly high (> 0.20) for only VVIp25b and VVIn52. This finding suggests that at these loci, some of the apparent homozygotes could be heterozygous, with one allele being visible and the other not. Such null alleles can occur when mutations prevent the linking of primers to the target region [Most loci had lower Ht region .j). PIC is an indicator of a marker’s informative ability in genetic studies , and its value reflects the polymorphism of the marker in the population studied. According to the classification of Botstein et al. (1980) [j values demonstrate that the microsatellite markers used in this study can be considered very effective for grape cultivar discrimination and could be valid to distinguish other accessions that could be introduced into the collection.The high number of alleles obtained by the 17 SSR primer set positively impacted the PIC and discrimination power (D. (1980) , all theV. vinifera, V. labrusca, and wild Vitis. This first structural level is also evidenced in the DAPC analysis and neighbor-joining dendrogram, where it is possible to observe a clear distinction of the accessions associated with V. labrusca and wild species.The genetic structure was mostly impacted by two factors that are difficult to separate: clear discrimination based on species and human usage as wine, table, or rootstock grapes, as previously noted by Laucou et al. (2018)  and EmanV. riparia Michaux, V. rupestris Scheele Michx, and V. labrusca L., and a number V. vinifera L. cultivars from Europe. The intra- and interspecific crossings carried out during breeding cycles in search of novelties and hybrid vigor promote the miscegenation of grapevine cultivars, resulting in hybrids with a heterogeneous genetic composition.However, a large number of accessions were not assigned and remained in a large admixed group, evidencing the genetic complexity of the analyzed plant material. Many of these accessions are crossbreeds between native vine species found in North America such as V. vinifera and the wild species V. ruprestris. The mixture of two genomes was detected by STRUCTURE, which assigned a membership probability threshold of 0.55 and 0.45 to clusters 1 and 3 respectively, representing the genetic clusters of the two parental species. A similar situation was observed for the accession ‘IAC 0457–11 Iracema’ developed from the cross between the species V. vinifera and V. labrusca, represented by genetic groups 1 and 2, respectively. The hybrid presented an intermediate membership of 0.5 to the two groups. The other accessions from Admixture group exhibited a similar or even more complex origin than these examples, and some of them were derived from crosses between more than three species, having associations with the three clusters simultaneously.The assignment of these hybrids to groups based on species is often difficult, as these individuals certainly carry alleles from different gene pools, being in an intermediate position and belonging simultaneously to more than one cluster. The accessions ‘Campos da Paz’ and ‘IAC 0457–11 Iracema’ are examples of this condition. ‘Campos da Paz’ is an interspecific hybrid resulting from the cross between the cultivated species Our results demonstrated the largest number of private alleles in cluster 3 composed of the wild germplasm . This fiV. vinifera were separated according to their use. The WG subgroup was composed mainly of wine grapes, such as the accessions ‘Syrah’, ‘Merlot Noir’, ‘Chenin Blanc’, ‘Petit Verdot’, and ‘Cabernet Sauvignon’, which showed associations with a membership greater than 0.95, corresponding to DAPC clusters 5 (red) and 3 (orange). The V. vinifera accessions of table grapes as ‘Centennial Seedless’, ‘Aigezard’, ‘Moscatel de Hamburgo’, and ‘Italia’ and their mutations ‘Benitaka’, ‘Rubi’, and ‘Brazil’ were found in the TG subgroup. This subgroup corresponded to cluster 2 (yellow) in the DAPC. In the neighbor-joining dendrogram, the V. vinifera accessions were also completely separated in terms of use; the wine grapes were located at the top, and the table grapes were located at the bottom. This result showed that the strong artificial selection based on human usage with wine or table influenced the genetic structure within the cultivated compartment of grapevine, as previously identified in previous studies [The second round of STRUCTURE  identifi studies , 73.V. vinifera genome [V. rotundifolia accessions constituted the most divergent group. This species is the only one in the germplasm belonging to the Muscadinia subgenus, which contains plants with 2n = 40 chromosomes, while the others belong to the Euvitis subgenus, with 2n = 38 chromosomes. A high genetic divergence between V. rotundifolia and the species in the Euvitis subgenus was also observed by Costa et al. (2017) [V. rotundifolia is resistant to several grapevine pests and diseases [In the DAPC and neighbor-joining dendrogram, two groups were differentiated to a greater extent than the others Figs  and 5, wa genome , while p. (2017)  through . (2017)  through diseases  and is aV. vinifera varieties and wild American Vitis species to develop phylloxera-resistant cultivars with characteristics of fine European grapes [Vitis species clearly created unique genetic pools, with many related accessions, since they were developed using the same breeding program, which explains the grouping and genetic distinction.The DAPC cluster 1 was formed by only accessions of the Seibel series and hybrids with varieties of this series in their genealogy. The Seibel series is in fact a generic term that refers to several hybrid grapes developed in France at the end of the 19th century by Albert Seibel from crosses between European n grapes . As thesV. labrusca hybrids formed distinct groups in the three analyses. In the DAPC, this accession group was subdivided into two clusters (6 and 7) indicating the presence of a secondary structure between them  can generate a deviation from Hardy-Weinberg equilibrium; however, in our study, STRUCTURE analysis provided a very consistent attribution of genotypes to clusters. The Admixture group reflects the crossing among genotypes of the three groups identified in the first round of STRUCTURE corresponding to breeding activities in search of novelties and hybrid vigor. Furthermore, this analysis provides important information regarding the genetic composition of the hybrids, providing information about the proportion of each species in their genome. The three primary genetic groups of STRUCTURE were easily distinguished in the other analyses; however, in the DAPC analysis, new levels of structure were revealed within these primary groups. The DAPC analysis also provided information about the genetic divergence between the clusters, allowing the identification of related ones.The STRUCTURE second round was carried out to investigate the presence of subclusters within the primary clusters and simultaneously validate the levels of structure obtained in the DAPC analysis. Most of the subgroups found in the STRUCTURE second round corresponded to the division obtained by the DAPC analysis, although some structural levels were different. These differences between analyses do not invalidate their results but rather bring complementary information that enhances understanding of the genetic structure and genetic relationship of germplasm accessions. The grouping based on the species of accessions was also evidenced by neighbor-joining dendrogram, but the differential of this analysis further provided visual information on the genetic relationship of the accessions within the groups. In the dendrogram, the genetic distance between two specific individuals was easily verified, providing a useful tool in breeding programs, mainly for the selection of divergent parents.The information obtained by the STRUCTURE, DAPC, and neighbor-joining dendrogram provides important knowledge for the management of germplasm diversity. The identification of divergent groups guides crossings in breeding programs, facilitating the appropriate combination of parents to obtain progeny with wide genetic variability, allowing the maximization of heterosis and making it possible to obtain individuals with superior characteristics. Information about the available genetic diversity is valuable because if properly explored, it can reduce vulnerability to genetic erosion through the avoidance of crosses between genetically related genotypes while also accelerating genetic progress related to characteristics of importance to grape growth .Considering the vast diversity of names for the different varieties of grapevine, standardization is necessary. Errors due to homonyms, synonyms, differences in spelling, and misnamed accessions impede estimation of the real number of different accessions that are present in grapevine collections, with a negative impact on grapevine breeding programs. Therefore, the verification of true-to-type accessions is indispensable . For graIn this study, 42 cases of misnaming were found by comparing the molecular profiles obtained with the information available in the literature and databases . The molThe molecular profile of the accession ‘Magoon’ matched that of ‘Regale’ in the present study, and ‘Regale’ had a similar molecular profile to those obtained by Schuck et al. (2011)  and RiazThe accessions ‘Armenia I70060’ and ‘Armenia I70061’ were labeled according to their country of origin, Armenia, during their introduction. Through microsatellite marker analysis, these accessions were identified as ‘Aigezard’ and ‘Parvana’, respectively. A similar situation was observed for the accession ‘Moscatel Suiça’, corresponding to ‘Muscat Bleu’ from Switzerland; this accession was likely also labeled according to its country of origin.VvmybA1, a homolog of VlmybA1-1, is the molecular basis of the loss of pigmentation in a white grape cultivar of V. vinifera due to the lack of anthocyanin production [Additionally, 31 synonymous groups were identified . Cases ooduction .The detection of somatic mutations is very difficult with a small number of microsatellite markers, especially when they are located in noncoding regions of the genome . This waV. doaniana and V. berlandieri have the same molecular profile, indicating a case of mislabeling; certainly, some mistake was made during the acquisition of these materials, and the same genotype was propagated with different names.The accession ‘Tinta Roriz’ was identified as a synonym of the cultivar Tempranillo Tinto in this study; this synonym is already registered in the VIVC and is widely used in regions of Portugal . The wilVitis spp., and it can occur during any stage of accession introduction and maintenance. It has been observed that 5 to 10% of the grape cultivars maintained in grape collections are incorrectly identified [The occurrence of misidentification is common, especially for old clonal species such as entified , 99. In entified . The corentified  and in hIn this study, 223 accessions with molecular profiles did not match any available reference profile. The largest subset of accessions was from the Brazilian grapevine breeding program of the IAC, with 109 molecular profiles described for the first time. The identification and description of unreported molecular profiles is important for regional and national viticulture and ensures the institution's intellectual property rights over these cultivars. The information obtained in this study will contribute to international cooperation to correctly identify grape germplasm and will allow the inclusion of new molecular profiles of Brazilian grapevine cultivars in the database.The intention for the development of a core collection is to represent the genetic diversity of the entire germplasm in a reduced set of accessions that is feasible to handle. The efficiency of the approach based on SSR profiles in identifying a core collection was already demonstrated for grapevine by Le Cunff et al. (2008) , CiprianV. vinifera subsp. sativa core collections, the same result was obtained with smaller percentages of individuals, from 4 to 15% [V. vinifera subsp. sativa is one of the reasons for the small number of individuals in the core collection since cultivated genotypes tend to be less diverse than wild counterparts [In this study, 120 accessions (Core 3) were necessary to capture all the allelic diversity of the whole collection, which is equivalent to approximately 30% of all accessions . In V. v4 to 15% , 102. Ac4 to 15% , the useterparts .Vitis species and the high genetic variability detected. These are likely the same reasons for the need for a high number of genotypes in our core collection, since the Vitis spp. Germplasm Bank of the IAC also includes accessions belonging to different Vitis species and many interspecific hybrids that have complex pedigrees (derived from crosses among three or more species). The comparison of different methods used to form core collections is not easy, as the analyses are rarely performed in the same way, and the original collections rarely include the same global diversity of species [Migliaro et al. (2019)  analyzed species .vinifera varieties. This large number of interspecific hybrids in the core collection can be explained by their predominance in germplasm; in addition, many of them have a complex pedigree, which certainly combines alleles of different species of Vitis. Regarding the other genotypes in Core 3, 31 were identified as V. vinifera cultivars and seven as wild Vitis.Among the 120 genotypes in Core 3, 82 were identified as interspecific hybrids, with 13 being non-The core collection was constructed to provide a logical subset of germplasm for examination when the entire collection cannot be used. Complementary criteria, such as phenotypic, agronomic, and adaptive traits, should be associated with the core collection to make it more fully representative. Finally, this core collection will be useful for the development of new breeding strategies, future phenotyping efforts, and genome-wide association studies.A wide range of genetic diversity was revealed in the studied germplasm, ensuring the conservation of a large portion of grapevine genetic resources. The genetic diversity showed a pattern of structuring based on the species and use of accessions, as evidenced in a manner similar to the three structuring analyses. In addition, each of the analyses provided different information that was be complementary and equally valuable for breeding.Taken together, our results can be used to efficiently guide future breeding efforts, whether through traditional hybridization or new breeding technologies. The obtained information may also enhance the management of grapevine germplasms and provide molecular data from a large set of genetic resources that contribute to expanding existing database information.S1 TableDetailed characteristics: SSR matches, usage, country of origin, species, group assignments according to the DAPC, and STRUCTURE first and second round, core collection composition, and genotyping results obtained with the 17 microsatellite markers.(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 FigThe accepted true number of clusters was nine.(TIF)Click here for additional data file.S2 FigGraphics for the detection of the most probable number of groups (K) estimated based on the method described by Evanno et al. (2005) . (A) Clu(TIF)Click here for additional data file.


Sargassum muticum, an emblematic species for circumglobal invasions. Albeit demonstrating polymorphism in the native range, microsatellites failed to detect any genetic variation over 1,269 individuals sampled from 46 locations over the Pacific–Atlantic introduction range. Single‐nucleotide polymorphisms (SNPs) obtained from ddRAD sequencing revealed some genetic variation, but confirmed severe founder events in both the Pacific and Atlantic introduction ranges. Our study thus exemplifies the need for extreme caution in interpreting neutral genetic diversity as a proxy for invasive potential. Our results confirm a previously hypothesized transoceanic secondary introduction from NE Pacific to Europe. However, the SNP panel unexpectedly revealed two additional distinct genetic origins of introductions. Also, conversely to scenarios based on historical records, southern rather than northern NE Pacific populations could have seeded most of the European populations. Finally, the most recently introduced populations showed the lowest selfing rates, suggesting higher levels of recombination might be beneficial at the early stage of the introduction process , whereas uniparental reproduction might be favored later in sustainably established populations .Twenty years of genetic studies of marine invaders have shown that successful invaders are often characterized by native and introduced populations displaying similar levels of genetic diversity. This pattern is presumably due to high propagule pressure and repeated introductions. The opposite pattern is reported in this study of the brown seaweed,  Despite a long‐standing scientific interest in the understanding of what makes a species successful once introduced in a new range, this is still an unresolved and pressing issue  Fensholt 1955, also displays the typical high genetic diversity reported in other successfully introduced marine taxa. So far, most marine species studied are animals , and yes et al. . Among tc Figure .S. muticum has mostly been explained by accidental introductions through deliberate imports of oysters on which the species grows  gigas from Asia to North America and Europe which started in the 1940s. They can be responsible for both primary introductions  and secondary introductions , or Polysiphonia morrowii . A previous study based on mitochondrial sequencing  across Japan and 16 sites across the two areas of introduction, with nine sites in the NE Atlantic (N = 138) and seven sites (N = 87) in the NE Pacific , by the Genoscreen Company  according to a procedure coupling microsatellite enrichment and pyrosequencing with 454 GS‐FLX titanium technology  according to the manufacturer's instructions. For multiplex PCR amplifications Table , 2 µl of2.3Double‐digest RAD‐seq individual libraries were produced following Brelsford, Dufresnes, and Perrin . Brieflyprocess_radtags module of Stacks v1.32  2, a maximum of two stacks per locus, and SNP calling bounded model type (high bound 0.01). Catalog loci were filtered according to mean log likelihood scores >−30 using the rxstacks module and according to their missing rate . Haplotype‐based genotypes (RAD_loci) were exported from Stacks. Repetitive sequences can be confounded with alleles in denovo analyses of RAD‐seq data, leading to an uneven coverage among alleles in a given locus. The mean allelic coverage ratio was calculated for all heterozygote RAD‐seq loci and tested against the expected coverage ratio using Student's t test. The distribution of p‐values was used to set a threshold for excluding RAD‐seq loci with imbalanced allelic coverage. Finally, loci with a minor allele frequency (MAF) lower than 0.01 and observed heterozygosity higher than 0.5 were filtered out. The final dataset was made of 8,788 polymorphic loci in a sample set of 300 individuals from 21 sampling sites, with 20% missing data across the whole dataset. In addition, for selfing rates, multi‐locus genotypes (MLGs), and genetic structure analyses (see below), we used an even more conservative dataset made of 256 individuals, with <10% of missing data. In this conservative dataset, only one individual from the San Francisco—Pier 39 (no. 13) locality was retained and used in individual‐based analyses .A total of 1.16 billion reads were obtained across the three libraries. Raw sequences were quality checked and demultiplexed using the 2.4f and theta estimates of Fis and Fst indices were estimated with Genepop v4.2.2  was estimated from the distributions of multi‐locus heterozygosity in each population  was computed following the equation 9 in David et al.  where pi is the genotypic frequency of the ith genotype. For RAD‐seq loci, MLGs were clustered into multi‐locus lineages based on the distribution of pairwise genetic distance using the R package poppr  at microsatellites and RAD‐seq loci. For the 14 microsatellites, the analysis was straightforward and MLGs were directly compiled through the dataset and the number of private MLG per population counted. Genotypic diversity was computed by N/(N−1) . The best value for the number of cluster K was determined by the deviance information criteria (DIC). Graphical output was obtained using CLUMPAK.Genetic structure was estimated with two approaches: a non‐model‐based approach using a principal component analysis (PCA), a multivariate method implemented in the package adegenet 1.4–2  ranged from 0.001 to 0.242 with the highest values observed in the native range Table . Averages = 0.765) as compared to either the NE Pacific (s = 0.261) or the NE Atlantic (s = 0.244) introduction ranges .Population selfing rates were also variable both among and within geographic ranges. The average value across populations was more than three times higher in the native range ( s = 0.26 or the N s = 0.26 or the N3.3N = 6), Arida ) from the native populations and (b) the Kashikojima population (no. 3) from the rest of the native populations  revealed genetic differentiation among and within the three areas Figure . The firtion no.  from thetion no.  from thetion no.  from thetion no.  from theK = 5. The five clusters defined four different groups of individuals  over the whole dataset was obtained for s Figure : two gens Figure , sometims Figure B. Runnins Figure A.Pairwise Fst values and associated dendogram Figure  supporte4Sargassum muticum. The extremely low polymorphism  however prevented such investigations. In this study, we aimed at using supposedly polymorphic nuclear markers, namely microsatellite loci, to investigate the population genetic diversity and structure of this worldwide invader. Despite an extensive sampling  encompassing the native (North East Asia) and introduced ranges (North East Pacific and the North East Atlantic), unexpectedly, no genetic diversity was detected over the two introduced ranges using 14 microsatellite markers. RAD‐seq loci, however, revealed 1,988 polymorphic loci across the two introduction ranges, compared with 8,580 in the studied part of the native range. These results and their implication on our understanding of the introduction processes and success of the study species are discussed in turn below, notably regarding what has been documented in other marine introduced species.Despite its status of an emblematic worldwide invader, only one previous study based on nuclear, plastidial, and mitochondrial sequencing  which might have originated from multiple introduction events and vectors . However, polymorphism was also detected in the introduced range with RAD‐seq loci, even though the genetic diversity was much lower than in the native range. As expected following a founder event  history of introduction. When considering the dates of first report of n et al. ), the eln et al. . This arn et al. , in partS. muticum could, therefore, have been introduced accidentally in the southern NE Pacific during these deliberate oyster imports well before its date of first report in this area. One should be cautious on considering the date of first observation to infer the introduction history of NIS, especially in the marine environment where field surveys are sometimes hard to carry out or with insufficient observation pressure. In addition, they could largely be biased by the recording interest that can be triggered by the discovery of a new NIS in a specific area. Invasion histories are typically challenging to reconstruct with confidence, certainly when historical information is sparse and genetic data of the initial stages of the invasion process are not available  the reported date of introduction  and the genetically based scenario  could be that dates of the first report in California poorly represent the actual dates of the first establishment of the species. A “Report from California oyster culture” Barrett,  indeed rOcinebrellus inornatus; Martel et al., Regarding the introduction in the NE Atlantic, previous assumptions based on surveys . We thus cannot ascertain the origin of any of the genetic entities revealed by the RAD‐seq data. It is, however, noteworthy that part of the Anan individuals shares an identical genetic background with individuals in the southern NE Pacific and the NE Atlantic  the southern populations could have seeded most of the European populations. Massive sampling in the native range might be helpful to investigate these scenarios, although, with longer time elapsed since the introduction, the likelihood to identify the sources is fading rapidly in highly genetically structured species  three independent introductions occurred at a global scale, 2) northern and southern populations of 4.3Carcinus maenas in Newfoundland . As compared to other sexually reproducing marine invaders, and regarding the absence of genetic diversity in introduced populations, phenotypic plasticity or epigenetic factors are probably important mechanisms by which the species could accommodate a broad range of environmental conditions  high selfing rates, usually associated with limited inbreeding depression and (b) high fertility . We first hypothesized that selfing rates should be higher in introduced populations as compared to native ones, but the RAD‐seq dataset actually documented the opposite , these S. muticum. Examining herbarium specimens dating from the first reports would have been extremely valuable to determine whether many lineages were initially introduced or whether some new MLGs evolved and increased in frequency over time, and, if this is the case, which ones were the “winner” of the introduction lottery. We attempted to use such herbarium specimens. Unfortunately, we could not get enough good quality DNA, even for genotyping microsatellites, to examine these specimens. Whatever the mechanisms initially involved, the ability to self and restore populations with a single or a few individuals seems to be one of the most important factors explaining the rapid establishment of S. muticum over large scales. For preventing its future spread, surveillance programs should thus pay particular attention on drifting individuals, which, if fertile, might seed a new population: such drifting individuals should be rapidly removed when found in new places. In addition, campaigns removing S. muticum should take extreme care not to produce fertile drift material. More generally, our results suggest that self‐compatible marine NIS should be particularly well monitored, as already documented in terrestrial introduced plants  marine introduced species which exhibits almost no genome‐wide genetic variation over most of its circumglobal introduction range. Our study also demonstrates that RAD sequencing is a powerful tool to detect genetic variation for nonmodel species, in the case of extremely reduced genetic diversity, following severe founder events. Our result does not mean that genetic diversity is not important for the long‐term establishment of invasive species but emphasizes the need to be extremely cautious in interpreting neutral genetic diversity as an indicator of adaptive genetic diversity and potential success of an invader. Epigenetic processes should be investigated in detail in S. muticum as they may also explain the acclimatization of this seaweed to large latitudinal gradients and a diverse set of habitats, without adaptive genetic diversity.The spread of F.V. conceived the study and analyzed the data. S.L.C., S.B., and C. D‐T conducted the molecular experiments and analyzed the data. A.E. and N.M. contributed to the sampling design and to the sampling. F.V., S.L. C., and C.D.‐T wrote the first draft of this paper. All authors reviewed the manuscript.https://doi.org/10.5061/dryad.1rj6442 (Le Cam,, Daguin‐Thiébaut, Bouchemousse, & Viard, ddRAD sequencing and microsatellite dataset are available as a VCF file and a multi‐locus genotype file, respectively, uploaded on Dryad Digital Repository at  Click here for additional data file.