Centaurium erythraea and C. littorale, readily cross-fertilize, resulting in hybrids of various ploidy. In northern Serbia, two stable populations of a hexaploid taxon C. pannonicum have been documented. It has been proposed previously that this taxon emerged after an interspecific hybridization event between two tetraploid sister-species: C. erythraea and C. littorale subsp. compressum. The existing populations of the hybridogenic taxon, as well as neighboring populations of the two parental taxa were here characterized by both morphometrics and molecular markers (EST-SSR and trnL-F). Three leaf and two flower characteristics were found to be informative in delimitation of the parental taxa and in their discernment from hybrid individuals, the latter having intermediate values. Eight microsatellite markers were found to have good ability to distinguish studied taxa, placing C. pannonicum in closer relationship with C. erythraea. Conversely, trnL-F plastid marker nominated C. littorale subsp. compressum to be the donor of the C. pannonicum plastid DNA. Reproductive isolation of the hexaploid hybrid individuals from the parental species should be examined as the next logical step in describing the new species.Interspecific hybridization is one of the major actuators of evolutionary changes in plants. As the result of allopolyploid hybridization, offspring may gain different ploidy levels in comparison to parental species, which can provide them instant reproductive isolation. Two tetraploid sister species,  The significance of interspecific hybridization in the “origin” of plant species and evolution has been debated for decades, with opposing views on it regarding it as a creative evolutionary force or as an evolutionary noise . Since tIt is a well-known fact that a new hybrid line can be formed through allopolyploidization or homoploid hybrid speciation, the former being more common and more feasible . A recenHuge numbers of plant species have been reported to have passed through at least one whole genome duplication event , and immCentaurium Hill (family Gentianaceae) includes about 25 annual or biannual species having either diploid or polyploid genomes [Centaurium are poorly featured in species determination and delimitation [Centaurium, such as morphometrics and cytogenetics [Centaurium and proposed a new classification, while RAPD [trnL-F regions; [The genus  genomes . They ar genomes . Differe genomes ,11,12,13 genomes ,17,18,19 genomes ,21,22. B genomes  noticed mitation ,20,24. Hgenetics , or phytgenetics ,30,31,32genetics . Accordigenetics  introducregions; ) have beC. erythraea Rafn and C. littorale (Turner) Gilmour  Kirschner)  Gilmour contains two subspecies: Centaurium littorale (Turner) Gilmour subsp. littorale and Centaurium littorale subsp. compressum (Hayne) Kirschner, with Centaurium uliginosum  Beck being a synonym for the latter one). They are both found in the continental areas of the central and east Europe [C. pannonicum, grow in stable populations in a few locations in the northern Serbia and were, however, found to be hexaploid [Centaurium [Here we studied the background and the outcomes of the interspecific hybridization between two sister-species: lantbase , the taxt Europe  and, as t Europe , both ofexaploid . With a ntaurium  and analntaurium , the divPrincipal component analysis (PCA) biplot based on five morphological characters of 127 individuals provided good discrimination between two parental species, particularly along the principal component 2 (PC2) . The firC. littorale subsp. compressum, which was hardly discoverable by PCA. Based on the sequential backward search, the highest classification accuracy (91.5%) in repeated k-fold cross validation was achieved when LDA used four out of the five provided features (omitting sin(leaf tip angle)). The top features in order are: length/width ratio of floral stalk leaves  + calyx/corolla-tube ratio (77.8% classification accuracy with the addition of this feature) + length of corolla lobes (89.5% classification accuracy with the addition of this feature) and + cos(leaf tip angle) achieving the observed 91.5% accuracy. The highest proportion of falsely classified individuals was from C. pannonicum which was predicted as C. erythraea on average in 3.2% of total cases and C. littorale subsp. compressum which was predicted to be C. pannonicum on average in 2.7% of total cases  showed that the selected morphological characters were very useful for taxa diversification , particual cases . C. erythraea originating in Bački Vinogradi (C.ery.BV) with average 4.25 alleles per locus. The lowest number of alleles was recorded for two populations of C. littorale subsp. compressum, originating in Majdan and Palić (C.l.c.MJ and C.l.c.PA), both averaging 2.5 alleles per locus. Throughout the populations, 27.33 alleles per population were recorded on average.Population-genetics parameters are summarized for nine populations in C. littorale subsp. compressum (PA) to 0.9912 in the C. pannonicum (MJ) with an average value of 0.9121 throughout the studied populations. Observed heterozygosity was evidently lower for all populations: from 0.5588 in the C. erythraea (BE) to 0.8125 in the C. littorale subsp. compressum (AS), with an average of 0.6750 , Palić (PA) and Hajdukovo (HA), followed by two C. erythraea populations from Fruška Gora Mountain (BE and IV). According to these results, the three populations of C. littorale subsp. compressum differ at the most from two C. erythraea populations from Fruška Gora Mountain and slightly less from the neighboring C. erythraea population BV. Each of the two C. pannonicum populations had considerably lower FST values vs. all the three populations of C. erythraea than vs. each of C. littorale subsp. compressum. The same trends can be observed within the Jost’s D values.The FC. littorale subsp. compressum. The cluster is quite coherent, and four populations of this taxon are spread more or less evenly, with no population differentiation visible except that the individuals from the population AS tend to cluster together. The second cluster, also having a strong (0.997) bootstrap support, contains two other taxa. A substantial genetic variation of C. erythraea is visible in this cluster. Two individuals (C.ery.BV 3184 and C.ery.IV 3138), originating in different populations, are the first to segregate at the Bruvo distance of 0.4. Most of the remaining individuals of the same taxon are further clustering together in a fairly heterogeneous sub-cluster without population diversification. However, the other sub-cluster of the second cluster, comprising mainly hybrid individuals, also contains three “interlopers” from C. erythraea  turned out to be misclassified at the beginning of the study (see below). In any case, the mentioned sub-cluster is made of the hybrid C. pannonicum individuals with obvious differentiation between its two populations.UPGMA dendrogram constructed from Bruvo genetic distances, which were obtained from the allele frequencies at eight EST-SSR loci , consistrythraea . Two of C. littorale subsp. compressum group of individuals from the two other taxa, upholding the results obtained by unsupervised UPGMA. Likewise, the second group is comprised of C. erythraea and C. pannonicum genotypes, the latter one forming its own subgroup.Non-metric multidimensional scaling  was usedtrnL-F region of 43 individuals included in research ranged between 504 and 514 bp. Within this research, it was not possible to read the sequences with primer “c”. The final alignment was done with 43 sequences . This haplotype was harbored by 5 more individuals characterized as hexaploid C. turcicum and tetraploid C. erythraea × C. littorale. Haplotype A also appeared in four individuals of C. erythraea, two of which were from BE locality, and one from each IV and BV. A maximum-likelihood phylogenetic tree of  dataset  revealedC. erythraea individuals as well as another two sequences belonging to the tetraploid hybrid individuals of C. erythraea × C. littorale and one sequence from a possible hexaploid hybrid taxon, C. turcicum.Haplotype B owned all the remaining sequences belonging to Centaurium, C. erythraea and C. littorale subsp. compressum. Hybrid individuals having a hexaploid genome were only recently noticed in two localities, namely Majdan—MJ and Palić—PA. The aims and the directions of the presented research have been conceived on the basis of several facts and observations. Firstly, literature data indicate great phylogenetic relatedness of C. erythraea and C. littorale [C. erythraea and C. littorale in geographically remote localities from our study site led us to presume that the hybrids might arise after independent hybridization events, which certainly deserves our attention. Finally, a research on interspecific hybridization would possibly raise public awareness on importance to further protect Subotica Sands and Nature Park Palić, particularly the locations MJ and PA, since the ubiquitous arable land of these plain landscapes, roads, and other obstacles derived from human activity represent barriers for species distribution and cause strong fragmentation of the habitat .As can be seen from ocenosis a–c. They cm tall . Leaf wi cm tall . TherefoC. littorale subsp. compressum and C. pannonicum  DNA, which may give false variations when non-specific markers are used. In the previous study , we conf species  in compaC. erythraea populations was recorded for Bački Vinogradi (BV). This might happen because BV locality may be from time to time populated with hybrid individuals and also with C. littorale subsp. compressum, a few of them spotted in 2018, which certainly promotes the gene flow between the taxa. This may be the main reason for misidentification of the individual C.ery.BV 3190, which turned out to group with C. pannonicum  intron and trnL-trnF spacer region from the plastid DNA have been previously analyzed in order to estimate phylogenetic relationships [trnL (UAA) intron and trnL-trnF spacer region. Nevertheless, within this research, it was not possible to read the sequences with primer “c”, probably due to the existence of polyA region (about 17–20 bp), which was observed on the forward part of the sequences. PolyA regions are unstable, causing polymerase slips during extension, and thus making sequencing impossible [trnL-trnF sequences of closely related taxa and potential hybrid individuals retrieved from GenBank. Although the resulting matrix of the aligned sequences consisted of just 380 characters, the phylogenetic analysis showed two distinctive haplotypes present among all analyzed individuals. The same haplotypes and affiliations were recorded with 43 longer sequences (504–514 bp), which were obtained by sequencing the amplicons in only one direction (results are not shown but are available upon request). Within the ionships ,43,44,45ionships ,24. The ionships ) was notpossible . BecauseCentaurium [C. pannonicum. Plastid introgression after intrageneric interspecific hybridization is a well-documented event [C. pannonicum were some population(s) of C. littorale subsp. compressum from the same or nearby areas, since all analyzed individuals of C. pannonicum, as well as all individuals of C. littorale subsp. compressum, have been shown to possess the same plastid haplotype. Three individuals of C. erythraea share the same haplotype but these individuals belong to distant populations  also showed a haplotype characteristic for hybridogenic taxon and C. littorale subsp. compressum. However, for this individual, with very high certainty, we can infer it was incorrectly determined. As already mentioned, the analysis of microsatellite markers also grouped this individual with C. pannonicum. In general, two haplotypes can be noticed within the sample set made up from the studied individuals and previously reported GenBank sequences, but in some cases, it was not possible to link haplotypes to a particular species. It can be rather said that different haplotypes could be linked to different populations. This claim can be supported by the repeatedly underlined (and studied) fact that interspecific hybridization within the genus Centaurium occurs very commonly [Plastid DNA is maternally inherited in the genus ntaurium ,48 and ted event ,50. The commonly ,16,51,52C. erythraea subsp. erythraea and C. littorale subsp. littorale, Ubsdell [C. littorale subsp. littorale individuals due to slightly different flowering period and difference in maturity of stigmas and pollen grains between the two taxa. On the other hand, when we “switched off” the ecological factors and the species-specific biology by performing artificial hybridization under in vitro conditions [C. erythraea. When talking about the direction of interspecific hybridization, the choice of which species would act as the pollen donor may be influenced by various ecological factors , as well as the biological characteristics of two species which hybridize . In his extensive studies on hybridization between  Ubsdell ,12,13 handitions , seeds wC. pannonicum with C. erythraea than with C. littorale subsp. compressum. On the contrary, plastid trnL-F marker haplotyping pointed at C. littorale subsp. compressum to be the maternal species of C. pannonicum. The similar situation of discordant data obtained with differently inherited markers is often found among plant species [Among the populations studied here, the analysis with microsatellite molecular markers showed closer phylogenetic relationship of hybridogenic taxon  species ,53. AddiCentaurium. Our earlier study [C. pannonicum at two locations (PA and MJ) and determines with high confidence its tetraploid progenitors  of the hexaploid hybrid C. pannonicum, and consequently, the tetraploid taxon C. erythraea as the pollen donor  for the comparative study of morphological characters  and linear discriminant analysis (LDA) using the R programming language packages ‘stats’, ‘MASS’  and ‘carFor the purposes of DNA extraction, young leaves were collected from the upper half of the flower stem and immediately stored in plastic zip-bags containing silica gel. Samples were kept in darkness, at the room temperature until use. In total, 136 individual plants were included in the ESR-SSR genotyping .Total genomic DNA was isolated from dried leaf samples (20 mg leaf tissue) using a modified CTAB method . DNA conPrimer pairs  for amplification of eight EST-SSR loci , previously published by Banjanac et al. , were emO) and the expected heterozygosity (HE), and the effective number of alleles in a population based on the expected heterozygosity. HO of a population was obtained by counting heterozygous individuals, while HE was calculated on the basis of allele frequencies generated by ‘polysat’ using formula:th allele of a given locus. Data obtained by eight EST-SSR molecular markers of both tetraploid and hexaploid plants were first analyzed using the package ‘polysat’  in R proST and Jost’s D values using the function ‘calcPopDiff’ of the same R package.Given genetic data, allele frequencies by population were calculated using the ‘simpleFreq’ function in ‘polysat’ package for R, assuming polysomic inheritance. In the following step, using the data frame of allele frequencies and population sizes, we calculated a matrix of pairwise FThe obtained Bruvo genetic distances among the individuals were visualized using two-dimensional nonmetric multidimensional scaling (NMDS) which was performed employing the package ‘vegan’ for R .Further representation of relationships within the sample set included hierarchical cluster analysis (HCA), using UPGMA method. The dendrogram was constructed on the basis of the Bruvo genetic distances between individuals, which were obtained from the allele frequencies at eight EST-SSR loci, calculated by ‘polysat’ package. Assessment of the clusterwise stability of the clustering was performed by bootstrapping using the R package ‘fpc’ .trnL-F haplotyping. Analysis of the variation in plastid trnL-F region also included 16 GenBank accessions of the taxa of interest previously reported by other authors . The PCR reaction program used for the amplification of this combined plastid non-coding regions was as follows: 94 °C for 10 min; 35 cycles of 94 °C for 1 min, 55 °C for 45 s, 72 °C for 1.5 min, and 72 °C for 10 min as a final extension step.Two non-coding regions of plastid DNA, t et al. : “f” (AThttp://www.epochlifescience.com/). They were run on a 1% agarose gel stained with ethidium bromide, in order to evaluate the quality and quantity of the amplified templates.Amplified PCR products were purified using off-the-shelf absorption, washing and elution DNA buffers, and EconoSpin DNA spin columns according to the manufacturer’s instructions (https://www.ebi.ac.uk/Tools/msa/muscle/). Selection of conserved blocks from the alignment was performed using Gblocks v0.91b [Direct sequencing was performed on an ABI 3130 Genetic Analyzer , using BigDye Terminator v. 3.1 Cycle Sequencing Kit  for cycle sequencing reactions following the manufacturer’s instructions. All samples were sequenced with “f” primer. The obtained results were visualized and analyzed using the Sequencing Analysis Software 6  and BioEdit . Sequences alignment was done with MUSCLE program (s v0.91b  with theCentaurium pulchellum. Local support values were estimated using the Shimodaira-Hasegawa test on the three alternate topologies around the specific split [The resulting alignment was visualized with FastTree v2.1.10  using GTic split . A totalCentaurium [C. pannonicum habitats. In the end, but certainly not less important, is the unavoidable need to design and suggest a conservation strategy for new hybridogenic taxon C. pannonicum by evaluating its genetic diversity and the evolutionary potential of parental species in the zones where the hybrids may arise, as well as to define the current status of the habitats and to recognize possible threats. As a means of ex situ conservation strategy, we have established a seed collection representing the genetic diversity of C. pannonicum in its natural populations, which will enable the long-term preservation of its germplasm. On the other hand, as a means of in situ conservation, we may propose raising the level of protection of the sites and the prohibition of further habitat disturbance by humans.Plenty of evidence reveals the importance of both polyploidization and interspecific hybridization as evolutionary mechanisms responsible for speciation within the genus ntaurium ,10,34,51


Switchgrass breeders need to improve the rates of genetic gain in many bioenergy-related traits in order to create improved cultivars that are higher yielding and have optimal biomass composition. One way to achieve this is through genomic selection. However, the heritability of traits needs to be determined as well as the accuracy of prediction in order to determine if efficient selection is possible.F statistics which ranged from 0.3952 between lowland and upland accessions to 0.0131 among the lowland accessions. Prediction accuracy ranged from 0.57–0.52 for cell wall soluble glucose and fructose respectively, to insignificant for traits with low repeatability. Ratios of heritability across to within-population ranged from 15 to 0.6.Using five distinct switchgrass populations comprised of three lowland, one upland and one hybrid accession, the accuracy of genomic predictions under different cross-validation strategies and prediction methods was investigated. Individual genotypes were collected using GBS while kin-BLUP, partial least squares, sparse partial least squares, and BayesB methods were employed to predict yield, morphological, and NIRS-based compositional data collected in 2012–2013 from a replicated Nebraska field trial. Population structure was assessed by Accuracy was significantly affected by both cross-validation strategy and trait. Accounting for population structure with a cross-validation strategy constrained by accession resulted in accuracies that were 69% lower than apparent accuracies using unconstrained cross-validation. Less accurate genomic selection is anticipated when most of the phenotypic variation exists between populations such as with spring regreening and yield phenotypes.The online version of this article (10.1186/s12870-018-1360-z) contains supplementary material, which is available to authorized users. Panicum virgatum L.) if genomic estimates of breeding values (GEBV) are sufficiently accurate and the generation interval can be shortened.The ability to accurately predict performance and breeding values of crops and livestock based on large numbers of genetic markers in the absence of phenotypic data has been the focus of intense research efforts and is now being applied in advanced breeding programs , 35, 56.− 1  were applied at rates of 1.96, 1.48, and 1.26 kg ha− 1 respectively once in late spring or early summer. In 2011 two ramets were dug from each genotype in the original plots and used to establish two additional randomized plots per population, keeping the populations separate blocks. Residual plot stubble was burned in April 2011 and again in April 2012 prior to the onset of spring regrowth.Experiments were established from greenhouse-grown seedlings in 2009 in the field at the Eastern Nebraska Research and Extension Center (ENREC) near Mead, NE. One hundred twenty-five seedlings were planted for each of five populations in 5 rows of 25 seedlings on 1.1 m centers. Populations were randomly assigned to plots and genotypes within populations were randomly assigned to individual rows and positions within the plots. No fertilizers were applied in the establishment year while in post-establishment years 112 kg haThe five populations used in this study were either obtained from NRCS Plant Material Centers or were harvested from isolated polycross blocks near Mead, NE which were maintained at least 500 m from other switchgrass fields. All populations were assumed to be tetraploid based on breeding history. A summary of the populations is presented in Table Spring emergence (GRN) was determined in 2013 as the day of year (DOY) when green shoots visibly emerged from winter dormancy.All plots were harvested on a single clone basis after a killing frost between November 2 and 7, 2012 using a Carter Harvester  set at a cutting height of 10 cm. Fresh weights of individual clones were determined and subsamples were taken to determine percent moisture and provide an estimate of dry weight. Yield (YLD) was reported as dry weight corrected for the weight of the subsample.The anthesis date (ANT) was determined as the day of year when 50% of the mature tillers of each clone reached the anther emergence/anthesis R4 stage .Harvested samples were dried in a forced air oven at 60 °C. These samples were then ground in a Wiley mill to pass a 2 mm test sieve and later in a Cyclone Mill to pass a 1 mm test sieve. Ground samples were scanned using a Model 6500 near-infrared spectrometer . Estimates of over 20 biomass components, including theoretical yields of simple sugars and ethanol, were obtained from broad-based near infrared spectroscopy (NIRS) calibrations. Descriptions of the traits can be found in Table σG2), error variance (σe2), and repeatability (H2) were obtained using the restricted maximum likelihood method in the “lme4” package [Estimates of best linear unbiased predictors (BLUP) values for each trait across replicates, genotypic variance σG, error vn refers to the average number of replicates per genotype. Data was centered and scaled by the standard deviation prior to analysis to allow simpler comparisons among methods and traits and some outliers were removed.PstI and ligated to Illumina adapters with a custom set of sequence index barcode tags. Barcoded DNA was subsequently adapted with Illumina sequencing primers and purified with a Qiagen PCR cleanup kit. DNA libraries were pooled (96 samples per flow-cell land) and single-end reads were generated on an Illumina HiSeq 2000 at the Vincent J. Coates Genomics Sequencing Laboratory, University of California, Berkeley.Leaf tissue samples from the original plots prior to establishing replicates were collected and genomic DNA was extracted using a CTAB method . Genotyp9 100-bp single-end reads that were fed to the TASSEL-GBSv2 pipeline [https://www.phytozome.jgi.doe.gov) using BWA [Sequencing generated 1.25 × 10pipeline  and genesing BWA . For theSNP filtering was then performed. First, genotypes with a depth of 1 were set to missing which eliminated 2.33% of the individual SNP calls. Second, individuals were filtered out if they contained missing information for > 75% of the SNP positions. This resulted in the loss of 104 individuals (16% of the population). Third, sites with a minor allele frequency of < 0.025 and with genotype calls in fewer than 50% of all individuals were eliminated. Fourth, sites in complete LD with other sites were pruned. After applying these filters, a total of 19,342 SNPs remained across 483 individuals with 71% of the potential genotype calls present.l sites most in LD with the site to be imputed are used to determine the nearest neighbors and the weightings to be used when imputing [k = 13, l = 8 were applied. Accuracy was determined by masking and imputing 10,000 known genotypes and was estimated at 88.5%.For evaluating genomic selection methods and heritability imputation of missing genotypes was necessary and was conducted using the LD-kNNi algorithm where the imputing . In the FST statistic using the method of Weir and Cockerham [Population differentiation was measured on the imputed dataset with the ockerham . Individockerham .σG2) and residual variance (σe2) were derived from applying ridge-regression mixed modeling kinship-BLUP used a restricted maximum likelihood (REML) approach implemented in the R package rrBLUP version 4.5 [n x m) genotype matrix and p is the allele frequency [X is a full-rank design matrix for the fixed effects (β), Z is the design matrix for the random effects u, K is the additive relationship matrix A, and e is for the residuals ) with I being the identity matrix.For the purposes of predicting genomic breeding values (GEBV) and estimating genomic heritability within and across populations, additive genetic variance  were derived based on the BLUP phenotypes and on orthogonal decomposition of the genetic variance components by reparameterizing the basic model above. This was accomplished in a manner similar to that of Guo et al. [U is an n x (n-1) matrix of the eigenvectors of K with Ui the column I  representing the individual principal component loadings, and D is an (n-1) x (n-1) diagonal matrix with each diagonal element representing eigenvalues of K, arranged in decreasing value λ1, λ2, . λn - 1. The basic model is then rewritten asα becomes an (n-1) × 1 vector of random genetic effects that have a normal distribution N  with the principal components used as random variables that has the same distribution as the basic model yet allows separation of subpopulation structure explained by the dominant principal components (d) into across-population genetic variance,The matrix o et al.  based ono et al. , 27. AftThe total genetic variance was calculated asCorresponding marker-based trait heritability were obtained as :\documenFour methods were used to model the data: (1) a kinship-BLUP method (kin-BLUP); (2) a partial least squares regression method (PLS), (3) a sparse, partial least squares method (SPLS), and (4) BayesB (BB).r  was assessed for all methods using modified 5-fold cross-validation (CV) where the VS was constructed from specific populations or combinations of populations and divided into 5 sets that each comprised a single fold. The calibration set (CS) then consisted of the remaining individuals from all populations. Initial broad screening of all traits using 5-fold CV across the entire dataset (“All”) enabled us to focus on traits with higher prediction accuracy. In general, analysis was limited to those 22 traits with prediction accuracies greater than 0.5 with the kin-BLUP method.Prediction accuracy y) were sequentially used as a validation set (VS) by masking and predicting the values Each analysis, except for BayesB, was conducted using 20 replications of a 5-fold CV approach to estimate prediction accuracy within a single population or combination of populations. The subjects were divided at random into five sets. For each set, the phenotypic BLUP values (y) and GEBV n is the number of individual VS genotypes used for each fold. Reported MSE values were averaged across folds and replicates.Accuracy was determined from the average phenotypic correlation W in orthogonal scores T called latent variables and loadings P that are obtained by projecting the predicted variables and the observable variables to a new space and regressing y not on W but on the first a columns of the scores T. The latent variables are obtained individually in an iterative process which are then used as new variables of a linear regression. This allows to account for collinearity among the predictor variables and instances with many more predictor variables than observations. The PLS method was implemented with the R packages ‘pls’ [The partial least squares method (PLS) introduced by Wold  decomposes ‘pls’ . In this1. Values for this parameter as well as the optimal number of latent variables to use for each trait were obtained through a cross-validation approach. SPLS prediction was implemented using the ‘SPLS’ package [The sparse partial least squares method (SPLS) , like PL package , 8.This method allows a portion (π) of the markers to have large effects while most markers have no effects. Marker variance is unique for each marker. Marker effects were estimated with Monte Carlo Markov Chan simulations. The BayesB model was run with the ‘BGLR’ package in R that employs a Gibbs sampling algorithm , 35. A v− 1, of which 19,769 (13%) overlapped or intersected with annotated gene regions. This is comparable to the 15% of the total switchgrass genome annotated as gene region and demonstrates little ascertainment bias using GBS. Within these sequences, 19,342 SNPs were identified after filtering. These were primarily biallelic and are summarized in Table − 1 detected with a strong significant relationship between the total number of SNPs and the annotated length of the chromosome , and between SNP density and distance to the end of the chromosomes . A total of 5118 SNPs were annotated in coding sequences with 65% of the non-reference alleles caused by either missense or nonsense mutations, 2216 were in introns in gene regions, 786 were annotated in the 3’ UTR region, and 670 were annotated in the 5’ UTR region.The sequence tags aligned to a total of 148,557 unique genome loci or approximately 104 Mbp 19,769 1% overlapr between YLD and GRN was 0.22 while r between YLD and ANT was 0.43  were evaluated over all SNPs weighted by the population size and are reported in Table FST ~ 0.39–0.40). The K, N1, and N1EM populations were all closely related (FST ~ 0.01–0.03) to each other. The hybrid-based KxS population share more similarities with both S and K-based populations (FST ~ 0.13–0.16) than these were to one another,. This indicated that there was significant genetic differentiation between the lowland-derived  and the upland (S) populations.Population fixation statistics (df) were found whereby the lowland populations would be homozygous for one allele and the upland population homozygous for another. However, 3715 sites were fixed in either group and segregating in the other.Comparing the K, N1, and N1EM populations as a group with the S population, no fixed differences  for unordered markers. This approach uses pair-wise LD information from the markers that are the most tightly linked (i.e. in LD) in the genotypes to be imputed. Imputation resulted in 3,139,549 genotypes being added or 28% of the dataset. Prediction accuracy of the missing data was estimated to be 88.5% based on masking and imputing 10,000 known genotypes.Inference of population structure in the five populations based on PCA of the molecular data showed that the first three principal components accounted for 4.6–22.4% of the variation to jointly explain 40.4% Fig. . The pophg2) for the entire data set by orthogonal decomposition of the genetic variance components. Separate genomic heritability estimates  for selected traits are presented in Table hg2 ranged from 0.57 for ANT and SC to 0.15 for YLD. The highest estimate for hgA2 was 0.43 for ANT while the lowest was 0.14 for ASH and YLD. More important for the accuracy of genomic selection applications, hgW2 estimates ranged from 0.01 for YLD to 0.31 for NSC. The hgW2 for ASH represented 62% of the total while the hgW2 for NSCE represented 60% of the total. Collectively, the phenological traits (ANT and GRN) and YLD were most influenced by population structure while the NIR-compositional traits were influenced to a variable degree. The hgA2 for NIR compositional traits ranged from 38 to 74% of total heritability.The first three PC were used to assess the impact of population structure on genomic heritability  was 58 bp. The KxS population had a lower threshold than either the S or K populations.Filtering the data, based on criteria including depth of sequence coverage required per genotype, data-missingness, or minor-allele frequency, had nominal effects on r2 is known to vary across the genome we attempted to look at trends across single chromosomes, but the data was too sparse to detect these variations reliably. Overall the threshold value ranged from 33 bp (Chr7N) to 82 bp (Chr3N) across all 18 chromosomes. Threshold values were not significantly different between the N and K subgenomes.Because r = 0.88) while the following traits  were above 0.5 under the same conditions. When using a CV method where the VS was constrained to consist exclusively within one of either K, N1, N1EM, S, or KxS populations, ANT accuracy ranged from 0.46 in the N1 population to 0.06 in the K population and across different traits r ranged from 0.57 for Fructose (FRU) (MSE = 0.18) in the KxS population to − 0.23 (MSE = 0.05) for GRN in the KxS population. On average, across all traits and populations, accuracy was reduced 69.6% from the unconstrained CV strategy using kin-BLUP and similarly for the other prediction methods. GEBVs were slightly more accurate when the “Low” population was used as a VS rather than the individual K, N1, or N1EM populations and accuracies were reduced only 66.9% from the unconstrained CV strategy.Prediction accuracy results are summarized in Table The influence of increasing SNP density as well as the training population size on prediction accuracy was analyzed as before using kin-BLUP with 5-fold CV across all individuals for three test traits: IVDMD, YLD, and ANT. The results are shown in Fig. r = 0.74, MSE = 0.31) while PLS produced the lowest . When the CV strategy was constrained by individual population or within the lowland (“Low”) populations collectively, the results among regression techniques were more variable. The greatest variation among methods occurred for the trait·population combinations where maximum prediction accuracy was below 0.3. However, the coefficient of variation was greater than 20% between methods for some trait·population combinations with accuracies above 0.3. These included the NSCE·S, PPEN·KxS, NSC·S, PCA·Low, GRN·S, and SC·S combinations. In these cases, SPLS or PLS were the most reliable prediction methods, while BB was the least reliable.When the CV approach was unconstrained by population different regression techniques produced very similar prediction accuracies for all traits except GRN. For GRN, the SPLS method produced the highest accuracy  and wheat  [PstI with a recognition sequence of ‘CTGCAT’ to assemble the libraries may have resulted in fewer markers aligned to repetitive elements of low GC content and in more markers associated with the coding or regulatory regions of the reference sequence located in subtelomeric regions. This uneven distribution is difficult to separate from the current state of the available switchgrass reference genome which is fragmentary, but consistent with known gene distribution in other grass species such as sorghum  , 43. Thebicolor; ).Zea mays) and rice (Oryza sativa) populations where most heritability could be explained within populations [Many of the trait phenotypes and the molecular variation displayed moderate to severe population structure. This was expected given the breeding history and origination of these lines. The lowland populations K, N1, and N1EM were less differentiated among themselves. Large differences in the estimation of genomic heritability occurred depending on population and trait. Except for a few traits , most of the heritability existed across populations rather than within populations. This contrasts with some findings in maize  exists between repeatability and GEBV accuracy and more focus was given to the 21 traits that had both reasonable repeatability and accuracy above 0.5 for further analysis using different prediction approaches. These traits included total hexoses and ethanol potential, which are both important parameters for bioenergy, as well as IVDMD which is predictive of daily weight gains in cattle. Some traits that resulted in poor GEBV accuracy included CAL, GAL, N, STA, MAN, FUC, GLC and DM. One or more of these traits may have had better accuracy using a restricted VS. However, these traits were not examined due to the poor overall accuracy. Although yield was included in this study, yield data were collected in a spaced-planting trial whic is not an accurate indicator of yield in densely-planted (sward) plots with higher plant competition and mortality rates [The repeatability of measured traits was between 0.34 and 0.94. A strong positive relationship (Rty rates . This far2 ≤ 0.2). LD was found in this study to decline rapidly over short distances < 1000 bp. This rapid LD decay could be explained by the outcrossing nature of switchgrass [Zea mays [Picea abies [Similar to previous examples of genomic prediction based on GBS , reducintchgrass , large etchgrass . These rZea mays  and in Pea abies . IncreasF statistics.Differences between prediction methods were of interest as there are very few empirical examples of direct comparisons among these methods using actual data rather than simulated data. The PLS method employs dimensionality reduction while the sparse PLS combines both dimensionality reduction and variable selection. BayesB also uses variable selection as it allows some markers to have large effects and most to have a genetic variance of 0. Previously, results with switchgrass showed no discernible performance differences with three different prediction methods . This stThe prediction accuracies in this set of data are comparable to those found among morphological and compositional traits in a Northern upland diversity panel . In thatThis study emphasizes the possible benefits of using genomic selection in switchgrass. There are still many improvements that can be made in both phenotyping and genotyping methods (such as efficient indirect methods to predict yield in highly competitive environments) to improve genetic gains more rapidly in switchgrass . HoweverAdditional file 1:Figure S1. Correllelogram depicting positive (blue) and negative (red) correlations among whole plant traits. Color scale on right indicates Pearson correlation coefficient r. Figure S2. Correllelogram depicting positive (blue) and negative (red) correlations among wall composition traits determined by NIR. Color scale on right indicates Pearson correlation coefficient r.Figure S3. Boxplots of (a) ANT, (b) IVDMD, and (c) YLD for each population. Bottom and top of each box represent the first and third quartiles. Horizontal line represents the median, whiskers extend to the most extreme data point that is no more than 1.5 times the interquartile range from the box. Table S1. kin-BLUP regression statistics from 20 replicates of 5-fold CV. Table S2. Partial Least Squares regression statistics from 20 replicates of 5-fold CV. Table S3. Sparse Partial Least Squares Regression statistics from 20 replicates of 5-fold CV. Table S4. BayesB Regression statistics from 5-fold CV. Using 5000 iterations and a 1500 iteration burn-in period (see Methods Section). Table S5. Variance components for selected traits after partitioning based on dominant principal components 1–3. Table S6: ANOVA of factors influencing prediction accuracy. (DOCX 442 kb)


Taxus cuspidata, which has extremely small populations distributed mainly in Changbai Mt. in Northeast (NE) China, has recently endured severe habitat fragmentation. Here, we investigate the pattern of genetic diversity and structure, identify risk factors, predict the future distribution and finally provide guidelines for the conservation and management of this species. We used three chloroplast and two mitochondrial DNA fragments, which are both paternally inherited in yews but differ in mutation rates, to genotype a total of 265 individuals from 26 populations covering the distribution of the species in China. Both chloroplast and mitochondrial data showed high degrees of genetic diversity, extensive gene flow over the entire geographical range and historical stability of both effective population size and distribution of the species. However, ecological niche modeling suggests a decrease in suitable areas for this species by the years 2050 and 2070. The maintenance of high genetic diversity and the existence of sufficient gene flow suggest that recent fragmentation has not affected the genetic composition of the long-lived tree T. cuspidata. However, severe impacts of anthropogenic activities are already threatening the species. Conservation and management strategies should be implemented in order to protect the remnant populations.Forestland fragmentation caused by overexploitation of forest resources can in principle reduce genetic diversity, limit gene flow and eventually lead to species developing strong genetic structure. However, the genetic consequences of recent anthropogenic fragmentation of tree species remain unclear.  Forest trees, which cover a vast part of the world’s land surface, provide habitats for about two thirds of terrestrial species. They account for about 82% of terrestrial biomass and play important roles in the biodiversity and functioning of forest ecosystems e.g., . HoweverVarious molecular markers have been used to address genetic diversity, gene flow and geographic structure of forest tree species. Among them the mitochondrial DNA (mtDNA) and chloroplast DNA (cpDNA) in Cupressaceae and Taxaceae are both paternally inherited e.g., . The twoin situ cryptic refugia was demonstrated in phylogeographical studies on the widely distributed tree species in this region, such as the ash tree (The temperate coniferous and broadleaf mixed forest of Northeast (NE) China covers about half of the area in NE China, making it the largest forest region and a major refuge of forest resources . Within ash tree , walnut ash tree , Acer  for conservation and restoration in the conservation program “Research on protection and restoration of typical small populations of wild plants”. This program aims to conserve those plant species that are most-at-risk because their populations are considered to be “extremely small”  understand how environmental change has influenced the demographic history of the species, and finally (4) evaluate its probable future distribution and determine the conservation implications for the species.Here we report the results of a large-scale field survey of the existing populations throughout their range in China, evaluate their genetic resources using both chloroplast and mitochondrial genetic markers, and predict their potential distribution by ecological niche modeling (ENM). We aim to: (1) determine and compare the genetic diversity and genetic differentiation present within and among natural or transplanted populations by means of cpDNA and mtDNA markers, (2) investigate whether recent fragmentation has had a significant effect on the genetic resources of T. cuspidata adult trees from 26 populations (separated from each other by at least 30 km) covering the entire range of the species in NE China . 25 pairs of universal chloroplast primers and 32 pairs of universal mitochondrial primers were tested on sixteen individuals from eight distantly separated populations to assess their polymorphism. Three chloroplast DNA markers (h), haplotype diversity (Hd), nucleotide diversity (π), and number of segregating loci (S) of cpDNA and mtDNA sequences for all populations, the natural populations and the transplanted populations were estimated by DnaSP v.5 (HS), total genetic diversity (HT), and population differentiation  were calculated using the program PERMUT with 1,000 permutations (NST > GST) was evaluated by comparisons between the levels of NST and GST.The number of haplotypes  was performed in ARLEQUIN v.3.5  with 1,0D; Fu and Li’s D∗; Fu and Li’s F∗) was firstly carried out to infer potential population growth and expansion  model. Both neutrality tests and mismatch analysis were implemented in DnaSP v.5. Next, changes in effective population size were investigated by means of a coalescent Bayesian skyline plot (BSP) method (-10 substitution/site/year (s/s/y) for Taxus , sampling every 1,000 generations and discarding the first 10% as burn-in. The posterior distribution results were visualized in TRACER v.1.5  method  in BEAST) method  to recon) method , and a por Taxus . IndepenER v.1.5 .T. cuspidata was implemented in MAXENT  data were selected from the Model for Interdisciplinary Research on Climate (MIROC)  from c. 3 Ma) data from the original author Dr. Dan Lunt at Bristol University were adopted. We also projected future  data which were retrieved from the Atmosphere and Ocean Research Institute (The University of Tokyo), the National Institute for Environmental Studies and the Japan Agency for Marine-Earth Science and Technology. Resolutions of 2.5-arc minutes for the present, LGM and future climatic scenario(s), 30-arc seconds for the LIG scenario and 5-arc minutes for the Pliocene scenario were used for analysis. Highly correlated climatic variables  were discarded (A species distribution model for n MAXENT  to prediorldClim . Last Gl (MIROC)  of the Piscarded  and six iscarded .trnH-psbA, psbD-trnT, and trnL-trnF) had a total length of 2431 bp. We found 18 chloroplast haplotypes (C1–C18) resulting from six indels and nine substitutions , with a large proportion of the genetic variation partitioned within populations for total and natural populations . A negative FST value (-0.03) was measured in locally transplanted populations  of rrn5/rrn18-1 . There was a single nucleotide substitution at site 374 (A–C) of Appendix . Two comAppendix .Hd was found in transplanted population BXF (Hd = 0) and the highest value was found in natural population LJB . For transplanted populations, GST was not significantly different from NST, showing that the geographical distribution of haplotypes was independent of their genetic distances  in total and natural populations, while transplanted populations showed a non-significant FST value , mean (SD)] and was similar to the species current distribution   was in line with that of T. cuspidata in Russia . In addition, significant phylogeographic structure was only detected in natural populations by mtDNA, suggesting that the similarity among haplotypes was greater than the differentiation based only on the frequency of the haplotypes in mtDNA  and the other three populations in Tonghua, Linjiang, and Baishan . In addition, the population located in Changbai Mt. should be the pivotal site for conservation because the limited gene flow within this region. For ex situ gene conservation, the adaptive potential of these populations may favor appropriate responses to environmental change and their germplasm resources should be given priority for collection, in particular by taking advantage of established protected areas like Zhenfengling Provincial Nature Reserve in order to carry out restoration work for this fragmented species. However, we must bear in mind that process of artificial selection for afforestation might reduce the overall genetic diversity and the population size. In addition, actively publicizing the true nature of Taxus, i.e., the low concentrations of taxol in trees of this genus and the severity of toxicity (Conservation strategies for toxicity  to the pT. cuspidata in NE China. The stability of the effective population size during the demographic history of T. cuspidata demonstrated that past climatic oscillations had not severely affected population viability. The size of natural populations has substantially decreased, mainly because of recent fragmentation events, and future environmental change would further put at risk the genetic resources of this species, despite its long-term persistence. In this study, relevant conservation strategies for the endangered T. cuspidata are discussed, and strategies are suggested for prioritizing the conservation and management of all extant fragmented natural and transplanted populations in situ and for limiting human disturbance while coping with future climatic threats.Our results for both cpDNA and mtDNA markers indicate high levels of genetic diversity and low genetic differentiation among both natural and transplanted populations of FD and JL designed the research. FD, JYS, YY, and JS collected the samples and performed the experiments and analysis. FD and JYS wrote the manuscript. All authors revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Isocladus armatus, an endemic New Zealand marine isopod. Analysis of the genetic variation in 4,000 single nucleotide polymorphisms (SNPs) within and among populations and colour morphs revealed large differences in gene flow across two spatial scales. Marine isopods, which lack a pelagic larval phase, are typically assumed to exhibit greater population structuring than marine invertebrates possessing a biphasic life cycle. However, we found high gene flow rates and no genetic subdivision between two North Island populations situated 8 km apart. This suggests that I. armatus is capable of substantial dispersal along coastlines. In contrast, we identified a strong genetic disjunction between North and South Island populations. This result is similar to those reported in other New Zealand marine species, and is congruent with the presence of a geophysical barrier to dispersal down the east coast of New Zealand. We also found some support for a genetic basis to colouration evidenced by positive FST outlier tests, with two SNPs in particular showing strong association to the expression of a striped morph. Our study provides one of the first population genomic studies of a marine organism in New Zealand, and suggests that genotyping-by-sequencing can be a good alternative to more traditional investigations based on traditional markers such as microsatellites. Our study provides a foundation for further development of a highly tractable system for research on the evolutionary maintenance of CP.Understanding how genetic diversity is maintained within populations is central to evolutionary biology. Research on colour polymorphism (CP), which typically has a genetic basis, can shed light on this issue. However, because gene flow can homogenise genetic variation, understanding population connectivity is critical in examining the maintenance of polymorphisms. In this study we assess the utility of genotyping-by-sequencing to resolve gene flow, and provide a preliminary investigation into the genetic basis of CP in  A key challenge in evolutionary biology is to understand the mechanisms maintaining genetic diversity in natural populations . MaintenThe spatial scales over which organisms and genes interact in the terrestrial and marine environments are often highly divergent. For example, the lack of physical barriers to gene flow, combined with the biphasic life cycles of many marine organisms, mean that the scales over which genetic structure may occur in the oceans can be considerably larger than in the terrestrial environment . These pNevertheless, population genetic differentiation is generally expected to be greater in species with direct development than in organisms exhibiting biphasic life cycles, because of the reduced dispersal abilities of juveniles in these species . The lacIsocladus armatus (Sphaeromatidae). The endemic I. armatus is abundant in intertidal rock pools on semi-sheltered shores throughout New Zealand. I. armatus presents an interesting case study for dispersal because, unlike other direct developing marine invertebrates studied in New Zealand  within populations    in invesng (GBS) . Becausevariegated, striped, . Final numbers of each morph and population are listed in To investigate population connectivity at two different spatial scales, we collected 31–32 isopods from two North Island populations 8 km apart (Hatfield’s Beach and Stanmore Bay Beach north of Auckland), and one South Island population (Kaikoura) around 1,000 km away . From eastriped, , white, -pattern . No-pattK were added to the lysis solution at least two hours apart. Total yields of genomic DNA were quantified using a Quantifluor ST fluorometer.Total genomic DNA was isolated from the head and pereopods of each sample. All DNA extractions were preceded by a step to deactivate nucleases modified from a protocol for bone samples in DArTseq™ represents a combination of a DArT complexity reduction methods and next-generation sequencing platforms . Therefop < 0.01) from HWE in one or more populations.The DArTseq GBS data resulted in a dataset of 26,215 loci. All putative SNPs possessed only two alleles. Further quality control filtering  was applFST methods. First, we employed the FDIST approach in ARLEQUIN. This method uses simulations to generate a null distribution of F-statistics, in which P-values are conditioned on observed levels of heterozygosities across loci  according to q-values (FDR 5%) were calculated using the R package qvalue. The q-value of a locus defines the expected proportion of false positives among all loci with P-values equal to or less than the observed locus  were estimated for each population in the R package diveRsity using the function basicStats (r = 0.998) and was used to estimate differences in relatedness between groups. TrioML uses the genotype of a third individual as a reference in estimating the pairwise genetic relatedness between dyads, reducing the chance that genes identical in state are mistaken for genes that are identical by descent. We also used COANCESTRY to assess whether the mean relatedness within the two North Island populations is significantly different to the mean relatedness between the two North Island populations. Significance was assessed by bootstrapping over individuals from both populations using 10,000 resamples. The difference in mean group relatedness was deemed statistically significant if it fell outside the 2.5 and 97.5 percentiles generated by the permuted distribution (α = 0.05).Global and per-locus observed heterozygosities, expected heterozygosities, and the population coefficient of inbreeding  in the R package SNPRelate  to visuaK = 1 to K = 5 to be tested for the analysis with all three populations, and K = 1 to K = 4 to be tested for the analysis of two North Island populations only. Markov Chain Monte Carlo simulations were run for 100,000 iterations with a 50,000 burnin and 10 independent replicates. Assignment of individuals to clusters was inferred based on the inspection of a bar plot arising from the consolidation of results over the 10 replicates of K produced in CLUMPP (K were selected following the method of K to infer the true K.The number of genetic clusters (K) was inferred from individual assignments under a Bayesian framework implemented in STRUCTURE v.2.3.4 . We ran n CLUMPP  and visun CLUMPP . OptimalM = m∕µ where m = immigration rate per generation, assuming a generation time of one year, and µ= mutation rate per site) between populations using coalescent modelling in MIGRATE (θ) and the migration rate (M) based on FST values produced good posterior distributions, thus the mean values of 0.1 and 2500 on the priors for θ and M respectively were used in further analyses. We employed Metropolis sampling with a static heating scheme with four chains set at the default values, and ran each model for 1 million generations with 20 replicate chains sampling every 100 steps (to produce 20 million generations) with a burnin of 200,000 steps. Bayes factors were used to select the most appropriate model for the data  was not significantly different to the mean relatedness within populations (r = 0.0171).Of the 5,236 SNPs in our dataset, 2,461 of these were fixed for one island while being polymorphic for the other island. Of these 2,461 SNPs, 1,822 were fixed in the Kaikoura population, with the remainder being fixed in one, or both, of the North Island populations. This resulted in lower levels of observed heterozygosity in Kaikoura than in the North Island populations , Fig. 3AFST indices indicated strong population divergence both globally (FST = 0.31 p < 0.0001), and between the Hatfield’s Beach and Kaikoura (FST = 0.42 p < 0.0001), and Stanmore Bay and Kaikoura (FST = 0.42 p < 0.0001). In contrast, no genetic differentiation was identified between the North Island populations . A PCA of genetic distances revealed a clear genetic distinction between North Island and South Island populations, with the first principal component (PC) explaining 27.8% of the total variance and only 1.2% of the variance explained by the second PC  and between groups  was strong and directly comparable. No significant genetic structure was identified between the North Island populations . The amova function in the R package pegas produced similar results with the variance among groups accounting for 68.1% of the total variance (p < 0.0001), while the variance within groups accounted for only 0.19% of the total variance (p = 0.9).Hierarchical AMOVA analysis in Arlequin was concordant with the PCA and showed that 39.8% of the genetic variation occurred between the North and South Island groups and 59.9% within populations. In contrast, genetic variance between the North Island populations explained only 0.3% of the total variance. The genetic structure found between populations  of South Island individuals into the North Island was 88.3 (95% Highest Posterior Density (HPD): 3.3, 166.7). When we considered only the two North Island populations, the full model with bidirectional gene flow between the two populations provided the best fit. Estimated migration rates were similar in both directions, with M = 1271.7  from Stanmore Bay into Hatfields Beach, and M = 1205.0  from Hatfields Beach into Stanmore Bay , apart from one individual with an uncommon striped pattern from Stanmore Bay  of high quality SNPs. Our results show evidence of highly contrasting levels of population connectivity and gene flow associated with different spatial scales in In order to glean deep insight into the processes influencing genetic variation within and between populations, the geographical scale of population sampling regimes should equate to the scale at which dispersal occurs . By usinJaera albifrons, significant differentiation occurs across five metre distances due to the patchy distribution of suitable habitats  between the North and South Island were 14× smaller than those between the North Island populations. Estimates of M can be converted to the number of effective migrants per generation (Nm) using the equation χNm = Θi × Mi→j in which Θ = Neµ; where Ne = the effective population size, and µ= mutation rate per generation per locus). This equates to around 31 (95% Highest Posterior Density (HPD): 27, 35) migrants per generation in each direction between North Island populations, and 2.1  migrants per generation between the North and South Island populations. These values are consistent with values of Nm estimated at two similar spatial scales in other direct developing marine invertebrates; the scleractinian Balanophyllia elegans  populations. Loci are listed in order of decreasing significance.Click here for additional data file.10.7717/peerj.5462/supp-3Figure S1Click here for additional data file.10.7717/peerj.5462/supp-4Figure S2Colours indicate proportional membership to a genetic cluster summarised over 10 replicates in the program CLUMPP. Individuals are ordered by population of origin with vertical bars representing a single individual.Click here for additional data file.10.7717/peerj.5462/supp-5Figure S3Click here for additional data file.10.7717/peerj.5462/supp-6Figure S4Grey shaded area shows the 95% HPD intervals. Dashed line represents the mode of the posterior.Click here for additional data file.10.7717/peerj.5462/supp-7Figure S5Grey shaded area shows the 95% HPD intervals. Dashed line represents the mode of the posterior.Click here for additional data file.10.7717/peerj.5462/supp-8Figure S6Grey shaded area shows the 95% HPD intervals. Dashed line represents the mode of the posterior.Click here for additional data file.10.7717/peerj.5462/supp-9Figure S7Grey shaded area shows the 95% HPD intervals. Dashed line represents the mode of the posterior.Click here for additional data file.10.7717/peerj.5462/supp-10Figure S8Grey shaded area shows the 95% HPD intervals. Dashed line represents the mode of the posterior.Click here for additional data file.10.7717/peerj.5462/supp-11Fugure S9Grey shaded area shows the 95% HPD intervals. Dashed line represents the mode of the posterior.Click here for additional data file.10.7717/peerj.5462/supp-12Figure S10The first and second principal coordinates explain 2.1% and 2.1% of the variance respectively.Click here for additional data file.


Oncorhynchus mykiss) are widely distributed throughout the freshwaters of southern Chile. In this study, analysis of the mitochondrial DNA control region (CR) marker was conducted to investigate the level of genetic divergence among populations and their phylogenetic relationships with respect to native lineages. This information provided a framework to interpret the genetic structure and origin that was shaped during historical trout introduction efforts. To this end, we analyzed eleven naturalized populations of lakes and rivers from five basins. The CR marker revealed five haplotypes. The overall haplotype (H) and nucleotide (Π) diversities were 0.684 ± 0.030 and 0.00460 ± 0.00012, respectively. Global FST was 0.169, with several pairwise FST estimates showing significant differences (P < 0.05). The exact test of population differentiation corroborated this result (P < 0.001). Significant geographic structure was found (P < 0.05), with variations explained primarily by differences within populations (61.65%) and among group basins (20.82%). Maximum likelihood phylogenetic analysis resolved two distinct clades with medium bootstrap support when naturalized populations were aligned in conjunction with reference native lineages. The haplotype network revealed a close association between naturalized populations and four main haplotypes representative of three native ecotypes or lineages from western North America . These results indicate a genetic population structuring for naturalized rainbow trout from southern Chile and an origin probably represented by multiple lineages sources. Thus, mitochondrial DNA data strongly suggest that stocking of rainbow trout from different origins may have occurred during or after the initial introduction efforts.Numerous self-sustaining naturalized or introduced populations of rainbow trout ( Oncorhynchus mykiss  variabilities in naturalized populations distributed in the south and north of the country . These smtDNA has many attributes that make it particularly suitable for population genetic analysis, including rapid rate of evolution, lack of recombination, and maternal inheritance . Due to In Chile, the efforts to stock rainbow trout strains in natural environments, as well as the policies supporting these activities, have mostly not been documented. Thus, unravelling the origin of naturalized populations is relevant to determine the existence of different lineages, to assess their distribution pattern across different hydrographic basins and to support the stocking efforts aimed at reinforcing valuable varieties, for example, for recreational fishing purposes. To assess the genetic structure and the origin of the naturalized population of rainbow trout distributed in southern watersheds of the country (39°–41° S latitude), we examined the CR sequence variations. With this aim, sequence data were compared with reference sequences from western North America rainbow trout strains recovered from public databases to calculate genetic divergence parameters and the phylogenetic relationships to help to clarify its probable origin. We hypothesized that introduced populations of rainbow trout in southern Chile are derived from multiple sources. This characteristic could be attributable to either the co-existence of different strains or the interbreeding of escaped cultured strains with existing naturalized populations.O. mykiss were collected in eleven localities from southern Chile (39°–41° S latitude) from 1999 to 2017 (Specimens of 2 (1.5 mM), 0.9 µl of each primer (0.2 µM), 0.18 µl of Taq DNA polymerase (0.02 U/µl) (Kapa Biosystems), 9.0 µl of template DNA (20 ng/µl), and 22.77 µl of DNAse-free and RNAse-free distilled water (Gibco). The thermal profile was performed as follows: initial denaturation at 94°C for 2 min followed by 25 cycles at 94°C for 45 s, 62°C for 45 s with −0.5°C per cycle and 72°C for 55 s, then 15 cycles at 94°C for 45 s, 54°C for 45 s, 72°C for 55 s and a final extension step at 72°C for 5 min. PCR products were visualized on 2% agarose gels, and prior to sequencing, these products were cleaned with a QIA-quick Gel Extraction Kit (Qiagen). PCR products were bi-directionally sequenced on an Applied Biosystems ABI377 automated sequencer. The sequences from forward and reverse reads were aligned and edited using GENEIOUS 4.0.2 software (Biomatters Ltd.) to obtain consensus sequences for all individuals. The sequences were deposited in GenBank under accession numbers MN166836–MN166903.The control region sequences were amplified using the primer pair of Ctrl Reg-L19 (5’-CCACTAGCTCCCAAAGCTA-3’) and Ctrl Reg-03-R (5’-GTGGGTAACGGGCAATAAGA-3’). PCR amplification was carried out in 45 µl using a reaction mix composed of 9 µl Taq polymerase buffer A (1×), 0.9 µl of dNTPs (0.2 mM), 1.35 µl of MgClFST was calculated according to FST; and significance was tested using 10,000 permutations with a level of significance of α = 0.05. The exact test of population differentiation  was used as an outgroup for rooting purposes. The best-fit nucleotide substitution model was determined using jModelTest 2.1 (O. mykiss according to O. mykiss stonei), interior redband (O.m. gairdneri), Volcano Creek golden trout (O.m. aguabonita), Kern River rainbow trout (O.m. gilberti) and coastal steelhead (O.m. irideus). The CR reference sequences involved twelve haplotypes and most present high frequency in native populations. These haplotypes are representative of the five mtDNA haplogroups reported by Phylogenetic relationships for haplotypes were reconstructed using maximum likelihood (ML) by heuristic search methods in MEGA 5.05 software . The corTest 2.1  based onTest 2.1 . RainbowH) of 0.684 ± 0.030 and a nucleotide diversity (Π) of 0.00460 ± 0.00012 (Π was found in CLA (0.01149), while the lowest value was found in BON (0.00263) and in sampling localities with only one haplotype. The highest level of H was found in CLA (1.000), while RAN, PES and PICH had the lowest H (0.400), not considering populations with only one identified haplotype . The average number of pairwise nucleotide differences was k = 1.999, and ranged from k = 0.0000 in CAL-S and MAU to k = 5.0000 in CLA.A 435-bp fragment of the mtDNA control region sequences was obtained from 68 samples. Five variable sites were identified (1.15%), including four parsimony informative sites and one singleton variable site. The nucleotide composition was A, 32.5%, C, 20.8%, G, 15.2%, and T, 31.5%. The A–T content (64.0%) was higher than the G–C content (40.0%), showing an overall transition/transversion ratio of 2.94. Five haplotypes were defined Table 2,FST was 0.169, indicating moderate genetic differentiation among populations. Pairwise FST estimates indicated that within basin groups  most differences among populations were not significant (P >0.05). However, among basin groups, especially between the N- and C- or S-basins, several populations showed significant differences (P <0.05) (P <0.001). AMOVA based on haplotype frequency  and among groups (20.82%) . Only 17.53% of the variance was attributed to differences among populations within groups (FSC = 0.22137), a result that was in accordance with the pairwise FST estimate.Global O. mykiss of the Pacific Northwest from North America ; and haplotype RTDL32 found at low frequency in a rainbow trout lineage of the Eagle Lake from California  clustered into the two different groupings in the haplotype tree. The haplotype network revealed four main haplotypes, RTDL20, RTDL16, MYS03C-RTDL34-EU and MYS01K-1-EU, separated by one or two mutations  of the 68 sequences from naturalized rainbow trout populations from southern Chile in conjunction with 14 reference haplotypes of native populations, revealed the existence of five different haplotypes in Chilean population  Table 2.Our results based on the mtDNA CR marker, which involved the analysis of eleven populations belonging to five basins from southern Chile, indicate moderate but significant genetic differentiation among populations. The AMOVA results also support that this genetic variation originates primarily from differences within populations. However, among group differences were also significant, i.e. differences between northern and central or southern basins, which suggests that genetic divergence has a geographic structure. Considering that sample sizes per population were limited in some cases , estimation of genetic variation among populations should be considered with caution. Nevertheless, this result suggests the occurrence of a substantial degree of differentiation of populations among different basins, which concurs with the possible multiple origin hypothesis of naturalized rainbow trout populations from southern Chile.FST = 0.169) is higher than that reported in other studies of naturalized populations of rainbow trout distributed in southern Chile. Since in previous studies the genetic analysis was usually limited to one or few basins located within a more restricted geographic area (FST = 0.052) collected in tributaries at two Andean lakes (Puyehue and Rupanco lakes), both located in the same river basin (Bueno River basin). FST = 0.073) in fifteen naturalized populations of this species that inhabit a relatively restricted geographic area from the mainland and Chiloé Island of the Región de Los Lagos. In fact, no significant isolation by distance was detected for these populations. In addition, FST = 0.102–0.156). It should be noted that since the mtDNA Control Region is a more variable genetic marker than those used in the aforementioned reports, due to its higher mutation rate and smaller effective population size (FST = 0.169 vs. 0.383), which reveals that the populations studied present a relatively moderate level of structuring.The level of genetic structuring recorded in our study  was in the upper limit of that reported in native populations from western North America (0.352–0.825) , which pOncorhynchus tshawytscha, that has been transplanted from California to New Zealand, this process seems to have played an important role in shaping the genetic differentiation among populations . The protocol was approved by the Chilean Undersecretary of Fisheries and Aquaculture (permits #2750-20-09-07 and #1580-20-05-2016).This study was carried out in accordance with the recommendations of the Guidelines for the Use of Fishes in Research  and by the Dirección de Investigación, Universidad de Los Lagos (grant 0107F). The publication fee of this work was supported by the Dirección de Investigación and the Departamento de Ciencias Biológicas y Biodiversidad of the Universidad de Los Lagos.Author FE was employed by company Piscícola Huililco Ltda.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Zingiber species, with sympatric distribution in Hainan Island, and obtain a result contrary to previous studies. The results indicate that selfing Z. corallinum can maintain high genetic diversity through differentiation intensified by local adaptation in populations across the species’ range. In contrast, outcrossing Z. nudicarpum preserves high genetic diversity through gene exchange by frequent export of pollen within or among populations. Contrary to expectations, the major portion of genetic variation of outcrossing Z. nudicarpum may exist among populations, depending on the dispersal ability of pollen and seed. Our results also reveal that the main factor affecting population structure of selfing Z. corallinum is mountain ranges, followed by a moist climate, while that of outcrossing Z. nudicarpum is likely moisture, but not mountain ranges, due to gene flow via pollen.How mating system impacts the genetic diversity of plants has long fascinated and puzzled evolutionary biologists. Numerous studies have shown that self-fertilising plants have less genetic diversity at both the population and species levels than outcrossers. However, the phylogenetic relationships between species and correlated ecological traits have not been accounted for in these previous studies. Here, we conduct a comparative population genetic study of two closely related selfing and outcrossing  Evolutionary theory predicts that selfing will evolve when two distinct advantages (transmission advantage and reproductive assurance) outweigh the evolutionary costs associated with inbreeding depression and reduced fitness caused by the increased homozygosity of deleterious recessive alleles7. Nevertheless, a selfing strategy is expected to come at the cost of reduced genetic variation10. Numerous studies on genetic variation have shown that within-population diversity is typically reduced in selfing species relative to outcrossing species, but genetic differentiation among populations is strengthened14. When patterns of diversity are examined across a species’ entire range, selfers clearly show lower genetic diversity11, but these previous studies did not take into account the phylogenetic relationship between the species concerned. Comparisons of the genetic structure of species from unrelated taxa may be confounded by the effects of correlated ecological traits or phylogeny2. Recent comparative population genetic studies of related plant species have also verified the expectation that genetic variation within populations of selfing plants is lower than that of outcrossing plants, such as Arabidopsis16, Amsinckia17, Capsella6, Leavenworthia18, Lycopersicon19, Mimulus20, Miscanthus21 and Shorea22. In most cases, the species level of genetic diversity is also lower in selfing plants, compared to closely related outcrossing species, but less pronounced than within-population diversity23. Contrary to these studies, there are a few reports of the opposite results, namely that species-wide diversity is not significantly different in (predominantly) selfing plants compared with (predominantly) outcrossing relatives24. Thus, comparisons of closely related species with different mating systems are needed and could contribute to an improved understanding of the effect of mating system on population genetic diversity and structure3.The mating system used by plants is considered to be a major factor affecting the genetic variability of species and how it impacts the genetic diversity of plants has long fascinated and puzzled evolutionary biologists31. Isolated populations must utilize their inherent evolutionary potential to adapt to the changing environment32, resulting in local adaptive divergence. However, it can be difficult to fully understand what drives genetic diversity because its roots lie in a combination of within- and among-population effects and a complex mixture of climatic, historical and geological influences33. Comparing patterns of genetic variation in sympatric species that experience the same environmental influences can disentangle the relative influences on a species’ genetic structure due to biological properties of the species versus those linked to habitat features34. So far, most comparative studies on the population genetic structure of co-distributed taxa have focused on animals34. There are also a few comparative studies of the effects on genetic differentiation of sympatric and allopatric plant distribution40. However, empirical studies on the impact of mating system on genetic diversity and genetic differentiation of closely related plants with sympatric distribution are rare . In this study, we compare the effect of mating system on genetic diversity and genetic differentiation in two closely related Zingiber species with sympatric distribution in Hainan Island, selfing Z. corallinum41 and outcrossing Z. nudicarpum42, using ISSR (inter-simple sequence repeat) data. Both Zingiber species are perennial herbs belonging to the sect. Zingiber of the genus Zingiber46. We focus on the following questions: (1) whether selfing Z. corallinum show less genetic diversity than outcrossing Z. nudicarpum? (2) whether most of the total genetic variation is due to individual differences within populations of outcrossing Z. nudicarpum as theory predicts? (3) whether there are differences in factors affecting the population genetic structure of Zingiber populations that relate to mating system?After mating system, environmental and ecological factors  are considered the most important factors shaping the spatial genetic structure within speciesZingiber corallinum and Z. nudicarpum, respectively. Of these, 208 (92.44%) and 324 (95.86%) bands were polymorphic, and 13 (5.8%) and 0 were specific, in Z. corallinum and Z. nudicarpum, respectively. At species level, Nei’s genetic diversity (h) and the Shannon index (I) for Z. corallinum were almost identical to those of Z. nudicarpum . However, the population values for h and I in Z. corallimun were significantly lower than in Z. nudicarpum . There was marked variation in h and I among populations of Z. corallimun , but the opposite was true for Z. nudicarpum .The ISSR polymorphism and genetic diversity data are shown in Table Z. corallinum and Z. nudicarpum accounted for the highest proportion of amplified fragments, i.e. 60.89% and 65.98%, followed by medium-to-high gene frequency loci (50%< gene frequency <100%) and rare loci (gene frequency ≤5%), i.e. 16.44% and 15.11%, 12.13% and 17.75%, respectively. Common loci (gene frequency = 100%) in Z. corallinum and Z. nudicarpum accounted for the lowest proportion (7.56% and 4.14%) of amplified fragments. However, among all populations of Z. corallinum, common loci accounted for the highest proportion (72.89%) of amplified fragments. Both low-to-medium gene frequency loci (7.56%) and rare loci (7.04%) were less prevalent than medium-to-high gene frequency loci (12.51%). Among all populations of Z. nudicarpum, low-to-medium gene frequency loci accounted for the highest proportion (36.87%) of amplified fragments. Common loci were more prevalent (29.88%) than medium-to-high gene frequency loci (17.92%) and rare loci (15.32%).The patterns of gene frequency distribution are shown in Fig. Zingiber species are shown in Supplementary Table GST) for the populations of Z. corallinum (0.872) was significantly higher than that of Z. nudicarpum (0.580), which indicates that 12.8% and 42.0% of total genetic variability were distributed within populations of Z. corallinum and Z. nudicarpum, respectively. The level of gene flow (Nm) among populations of Z. corallinum and Z. nudicarpum were estimated to be 0.073 and 0.362, respectively. The result of AMOVA analyses (Table ΦST = 0.917) and 65.3% (ΦST = 0.653) of the total variation was partitioned among populations of Z. corallinum and Z. nudicarpum, respectively. That is, of total genetic variation, only 8.3% was due to individual differences within populations in selfing Z. corallinum, while 34.7% was attributable to individual differences in outcrossing Z. nudicarpum.Nei’s genetic differentiation statistics for all populations of the two inum 0.87 was signK = 2, all populations of Z. corallinum were assigned to two genetic clusters , all populations of Z. corallinum could be assigned to five genetic clusters , all populations of Z. nudicarpum were assigned to three genetic clusters , which comprised three populations  and four populations , respectively. However, the UPGMA dendrogram was in conflict with the NJ tree within the two clusters. In the NJ tree, cluster I further formed two well-resolved clades , which comprised population HNWN-D and the other two populations , respectively , with cluster I consisting of clade A only, which comprised population HNCJ. Cluster II further formed two well-resolved clades , which comprised populations HNBT and the other four populations , respectively. STRUCTURE analysis  and Z. nudicarpum (p = 0.070) , despite the population genetic diversity in selfing Z. corallinum being significantly lower than that in outcrossing Z. nudicarpum . This indicates that selfing Z. corallinum can maintain a high level of genetic diversity, similar to that of outcrossing species, albeit by using different strategies. The population genetic diversity of selfers is expected to be reduced by a factor of two compared to outcrossers49, but the loss of genetic diversity has been shown to be more severe in a number of comparative studies on closely related selfing and outcrossing species51. In the present study, the average population genetic diversity of selfing Z. corallinum was four times lower than in outcrossing Z. nudicarpum, consistent with the literature on closely related species. Our results also showed that common loci are very highly represented within populations of Z. corallinum, indicating that the corresponding individuals are highly homogeneous. The test of multilocus linkage disequilibrium . Thus, our study confirms the hypothesis that a selfing species is expected to show reduced genetic diversity within populations and increased homozygosity compared with a related outcrossing species.Is genetic diversity in a selfing 53. This is reflected in our demonstration that the populations of outcrossing Z. nudicarpum are highly homogenous, as evidenced by the absence of specific bands in populations across the range of the species. On the contrary, pollen migration among populations is rare in selfing plants and a specific locus that arises in an individual population cannot spread to other populations, resulting in a high level of genetic divergence among populations54. An increase in genetic differentiation among populations accompanied by a decrease in pollen flow may lead to an increase in species-wide genetic diversity55. Therefore, selfing plants can still preserve a comparable level of genetic variation across their range, as the various populations may fix different alleles56. This is the case for selfing Z. corallinum in our study. There is a high degree of genetic variation among populations of Z. corallinum (CV = 0.37) compared with Z. nudicarpum (CV = 0.10), which indicates that population habitat heterogeneity may result in a higher fluctuation of genetic diversity in selfing Z. corallinum. Thus, ecological heterogeneity always causes an increase in genetic variation in natural plant populations57, and this is preserved in selfing Z. corallinum due to lack of pollen migration within and among populations. The greater number of specific ISSR bands in selfing Z. corallinum populations is evidence of this phenomenon, and shows that the populations of Z. corallinum are highly heterogeneous. Moreover, our result reveals that there is a high differentiation value among populations of selfing Z. corallinum (ΦST = 0.917) compared with outcrossing Z. nudicarpum (ΦST = 0.653), which indicates that strong isolation among populations and/or isolation, intensified by local adaptation3, leads to fixation of different loci58 in populations of selfing Z. corallinum. The proportion of common loci within populations of selfing Z. corallinum is significantly higher than in outcrossing Z. nudicarpum. However, at the species level, the pattern of gene frequency distribution of selfing Z. corallinum is consistent with that of Z. nudicarpum, in which common loci account for the lowest proportion and low-to-medium gene frequency loci account for the highest proportion. These results show that there are very different allele frequencies among populations, thus resulting in a high level of species-wide diversity55 in selfing Z. corallinum, like outcossing Z. nudicarpum. In conclusion, we suggest that a selfing Zingiber species can maintain high genetic diversity through isolation intensified by local adaptation in populations across the species’ range, while in contrast an outcrossing Zingiber species preserve high genetic diversity by frequent export of pollen within or among populations.Previous studies have shown that outcrossing plants can preserve a degree of genetic diversity through frequent gene flow among populations, while genetic differentiation can effectively be eliminated when gene flow per generation is very lowZingber species? The pattern of genetic diversity distribution within and among plant populations is determined by two main characteristics, mating system and gene flow59. Because pollen migration in selfing plants is rare, there can be a lack of gene flow among populations, thereby increasing genetic differentiation among populations due to genetic drift and fixation of different loci within genetically isolated populations61. Thus, selfing plants theoretically have lower genetic diversity within populations and higher differentiation among populations8, which has been confirmed by many empirical studies63. Our result is also consistent with the theoretical prediction that selfing Z. corallinum harbors only limited genetic variance (8.3%) within populations, but a high degree of differentiation among populations . On the other hand, outcrossing plants typically should show greater genetic variation within populations and a low level of differentiation among populations64. However, our AMOVA analysis revealed that most of the genetic variation in outcrossing Z. nudicarpum was also found among populations (65.3%), rather than within populations (34.7%). The observed genetic diversity distribution pattern of outcrossing Z. nudicarpum populations can be attributed to the short distances of pollen movement via parasitic bees42 and restricted seed dispersal by gravity (our observation), which is confirmed by the low level of gene flow (Nm = 0.362) and relatively high genetic differentiation  among populations in Z. nudicarpum compared with that (ΦST = 0.28) in most outcrossing plants12. The Mantel tests also show that neither outcrossing Z. nudicarpum nor selfing Z. corallinum exhibits a pattern of isolation by distance among populations, suggesting that the stochastic force of genetic drift is much stronger than gene flow in determining the structure of populations49 in both the outcrossing Z. nudicarpum and the selfing Z. corallinum. We suggest that most of the genetic variation resides among populations in a selfing Zingiber species, while the major portion of genetic variation in an outcrossing Zingiber species may exist within or among populations, depending on the degree of isolation and the dispersal ability of pollen and seed.Whether most of the total genetic variation is due to individual differences within populations of an outcrossing Zingiber plants? The results of both NJ and UPGMA analysis reveal a clear pattern of population structure in Z. corallinum, with two clusters corresponding to two mountain ranges in Hainan, Wuzhi mountain range  and Limu mountain range . The two mountain ranges are separated by the Changhua river valley. Our result is consistent with that obtained for two members of the Gesneriaceae (African violet) family endemic to Hainan Island, Metapetrocosmea peltata65 and Oreocharis dasyantha66, in which genetic structure is concordant with the isolation pattern of the two mountain ranges, due to very weak gene flow among populations (Nm = 0.04). This indicates that topography is the major factor affecting population structure in selfing Z. corallinum in Hainan island, due to the absent of pollen movement41 and restricted seed dispersal by gravity (our observation). In the NJ tree, each cluster (I and II) forms two further groups (clades), which is completely congruent with the climate regionalization delimitation scheme67. In cluster I, clade A (population HNWN-D) and clade B (populations HNBT and HNLD) locate in the humid region and the semi-humid region, respectively. In cluster II, one group  lies in the semi-humid region, while the other group  is located in the humid region. This implies that moisture level in the environment also plays an important role in determining patterns of genetic structure in Z. corallinum. The Bayesian genetic structure and PCoA also confirm the partitioning results of the NJ analysis. We suggest that the major factors affecting population structure in a selfing Zingiber species are topography  and climate (i.e. moisture), which are responsible for the absence of pollen movement (gene flow) and restricted seed dispersal. Both the NJ tree and the UPGMA dendrogram divide six populations of Z. nudicarpum into three well-resolved clades , whose origin can be attributed to the different climate in the regions they inhabit, i.e. a semi-humid region , a semi-humid and humid boundary region  and a humid region  in Hainan island. Bayesian genetic structure analysis and PCoA also confirm the partitioning results of the UPGMA clustering and the NJ tree. Together, these data demonstrate that population structure in outcrossing Z. nudicarpum is likely to be mainly driven by climate (i.e. moisture) and topography  seems not to be the influence factor of genetic structure like selfing Z. corallinum, due to gene flow via pollen among neighbouring populations in outcrossing Z. nudicarpum.Whether there are differences in factors affecting the population genetic structure of selfing and outcrossing Zingiber corallinum and Z. nudicarpum are two closely related46 perennial herbs with contrasting mating systems. Z. corallinum is predominantly self-pollinated41 and Z. nudicarpum is usually cross-pollinated by a parasitic bee42. Seeds of both species spread mainly by gravity (our observation). Z. corallinum is endemic in south China and Z. nudicarpum is mainly distributed in south China, Vietnam and Thailand. Both species show sympatric distribution in Hainan Island, China.Z. corallinum and 182 individuals from six populations of Z. nudicarpum were collected from Hainan Island  and three populations of Z. nudicarpum  are located in the Limu mountain range, while three populations of Z. corallinum  and three populations of Z. nudicarpum  are located in the Wuzhi mountain range. Four populations of Z. corallinum  and four populations of Z. nudicarpum  lie in a humid region, and the five remaining populations  are located in a semi-humid region. Except for population HNLS of Z. nudicarpum, in which fewer individuals were available .DNA was extracted from leaf tissues following the modified CTAB method described by Doyle and DoyleThe images of the gels were analysed using Image Lab Software (Bio-Rad) to score for the presence (1) or absence (0) of bands and to assign a fragment size to each band. The presence or absence of bands was further confirmed by eye. To ensure the results were reproducible, duplicate PCR amplifications were performed and only clear and reproducible bands were scored.69 to estimate percentage of polymorphic loci (PPL), number of observed alleles (Na), number of effective alleles (Ne), Nei’s gene diversity (h), Shannon’s information index (I), and coefficient of genetic differentiation (GST). To explore the partitioning of genetic variation and Φ value, analysis of molecular variance (AMOVA) was performed in GenAlEx v6.50270 based on 999 permutations. Gene flow among populations was estimated indirectly based on the formula: Nm = 0.5(1-GST)/GST71. Bayesian cluster analysis was implemented in the program STRUCTURE 2.172 and was used to assign an individual to K genetic clusters with five runs each comprising a burn-in length of 100,000 and a run length of 1,000,000 Markov chain Monte Carlo (MCMC) replications under the admixture model. The optimal value of K was calculated according to the method of Evanno et al.73. To reveal the genetic relationship between populations, Nei’s genetic identity matrix was used as input for a cluster analysis by the unweighted pair-group method of averages (UPGMA) to generate a dendrogram in NTSYSpc-2.1074. The program MEGA v775 was implemented to generate a dendrogram from Nei’s genetic distances with the neighbour-joining algorithm. Principal coordinate analysis (PCoA) was performed as an alternative means of detecting and visualizing the genetic structure implemented in GenAlEx. Mantel tests implemented in GenAlEx were performed to analyze the effects of geographical distance on genetic structure. Moreover, multilocus linkage disequilibrium was also estimated using the index of 76. Calculation of statistics and tests of significance by randomization (1000) were implemented in the program Multilocus v1.2 (http://www.bio.ic.uk/evolve/software/multilocus).The presence/absence data matrix was analyzed in POPGENE v1.31Supplementary Information