Robinia pseudoacacia L., a North American tree which is now one of the worst invasive tree species in Europe. We realized large‐scale sampling in both the invasive and native ranges: 63 populations were sampled and 818 individuals were genotyped using 113 SNPs. We identified clonal genotypes in each population and analyzed between and within range population structure, and then, we compared genetic diversity between ranges, enlarging the number of SNPs to mitigate the ascertainment bias. First, we demonstrated that European black locust was introduced from just a limited number of populations located in the Appalachian Mountains, which is in agreement with the historical documents briefly reviewed in this study. Within America, population structure reflected the effects of long‐term processes, whereas in Europe it was largely impacted by human activities. Second, we showed that there is a genetic bottleneck between the ranges with a decrease in allelic richness and total number of alleles in Europe. Lastly, we found more clonality within European populations. Black locust became invasive in Europe despite being introduced from a reduced part of its native distribution. Our results suggest that human activity, such as breeding programs in Europe and the seed trade throughout the introduced range, had a major role in promoting invasion; therefore, the introduction of the missing American genetic cluster to Europe should be avoided.The role of evolution in biological invasion studies is often overlooked. In order to evaluate the evolutionary mechanisms behind invasiveness, it is crucial to identify the source populations of the introduction. Studies in population genetics were carried out on  Actually, 1601 seems a very unlikely date since black locust is absent  and in 1620 (“histoire des plantes nouvellement trouvées en l'isle de Virginie”). To our knowledge, the first citations of the species appeared in England in the John Tradescant list in “plantarum in horto Iohannem Tradescanti” (1634)  by Vespasien Robin's friend, Jacques Philippe Cornuti, under the name of Acacia americana robini. Moreover, Tradescant's son travelled to Virginia before 1640  and as it has been documented that he brought several plants back with him, it is likely that he was the one who first introduced black locust seeds to Europe from Virginia. It has been established that Tradescant the elder corresponded and exchanged seeds with the Robins from 1601 ).The native range of this species consists of two disjoint areas on both sides of the Mississippi watershed Little, ; the larome III” , or by Come III” . Such diome III” –1759, Miome III” . To dateome III” . It was Gunther, ; thus helettre sur le robinier” (1807). In the middle of the 18th century, American explorers returned to Europe and promoted the use of black locust in forestry: For instance, Michaux  and the European invasive range (34 populations). Sampling was conducted between spring 2014 and fall 2016 Table  by diffeBlack locust propagates through sexual and asexual reproduction. In common gardens, since trees were grown from seeds of known origin, there was no risk of collecting clones. However, in natural populations, a minimal distance of 25 m was kept between two sampled trees in order to minimize the risk of collecting clones.Either leaves, cambium, buds, or seeds were harvested depending on the season. For leaf sampling, a few leaflets on a green healthy leaf were collected using a manual tree pruner. For cambium sampling, external bark was removed from the trunk with a knife, then five rings of wood were collected using a 1‐cm‐diameter punch. In the field, samples were put into referenced tea bags and then placed into plastic boxes containing silica gel in order to dry the samples. The silica gel was renewed after 24 hr and 48 hr and then until it no longer changed color. The plastic boxes were then stored at ambient temperature in closed cupboards.In natural populations, GPS coordinates of either the population or each sampled tree were recorded using a portable GPS . On the campus of Michigan State University, the geographic origins of each mother tree were known and were used for the coordinates of the sampled trees, and the populations were defined by gathering trees from a close geographic location . For four populations , a few seeds from ten sampled mother trees were scarified and grown in the laboratory . It is assumed that the SNPs used in the present study were located within neutral regions since we did not use restriction enzymes targeting nonneutral restriction sites.SNPs have recently been developed on black locust , and using the iPLEX Gold chemistry genotyping kit according to the manufacturer's instructions. SNP data were visualized and validated using ViClust, a R program that we implemented for Galaxy ; the program was also made available as a standalone R script for Linux or Windows  All collected samples  were genotyped using 113 SNPs, which were selected according to the procedure presented in Bouteiller et al. ; (b) aft2.32.3.1For the analysis of genetic diversity and structure within and between ranges, we chose to identify and remove clones from the analysis using R version 3.3.1 , developed in the R adegenet library Jombart, .Markov Chain Monte Carlo (MCMC), developed in the STRUCTURE v2.3.4 software , then it was performed for each range separately (K varying from 1 to 15). The most probable number of clusters was determined according to Evanno, Regnaut, & Goudet  using the StrAuto script  interpolation with a power value of 1 was carried out using the ArcGIS v10.2.2 geostatistical analyst tool  was analyzed using  Wright, . Within  Goudet,  accordinFIS within each population that measures the proportional deviation of observed from expected heterozygosity within subpopulation; and the total number of alleles (TNA) per range, calculated by summing within each range the number of allele over all loci.Two datasets were analyzed in order to compare genetic diversity between ranges: the initial dataset  and the additional dataset . The second dataset specifically aimed to test for a potential bias in allelic frequency due to SNP selection. First, allelic frequencies were evaluated by plotting the MAF (Minor Allelic Frequency) distributions per locus and per range. Using the R package Hierfstat v 0.04‐28 Goudet,  and FstaDifferences in AR were determined by performing a nonparametric Wilcoxon paired test among loci between ranges. In order to evaluate differences in total number of alleles between ranges, a bootstrap over all loci and individuals was computed using 1,000 simulations and the differences were determined using a nonparametric Mann–Whitney test using R version 3.3.1  using pairwise FST plotted against the logarithm of the pairwise geographic distances among populations . The matrix of pairwise genetic distances was estimated using the Cavalli‐Sforza and Edwards Chord distance with hierfstat v 0.04‐28 . IBD was tested within each range, using two approaches: (a) The genetic distances between populations were calculated as the ratio Rousset,  and the 33.1χ2 = 29.04, df = 1, p = 7.10 × 10−8). As expected, no clone was found within the common garden populations, nor in the populations obtained from seedlings germinated in the laboratory. When removing these populations from the analysis , 98 genotypes were found with a least one duplicated version : 68 clones out of the 280 European samples and 30 clones out of the 356 American samples. Keeping only one sample per genotype resulted in a dataset that contained 720 genotypes out of the 818 sampled individuals after clone removal, which was distributed as 334 genotypes in 34 European populations and 386 genotypes in 29 American populations.Overall, a higher clonality was detected in the European populations compared to the American ones, with a significant range effect (SD = 0.26), with significant differences in clonality between populations . The most clonal populations (R < 0.5) were Munchenberg Germany (R = 0.27), Valencia Spain (R = 0.28), Gafos Galicia Spain (R = 0.36), Brno Czech republic (R = 0.4), La Flotte France (R = 0.4), and Uden the Netherlands (R = 0.4). Among American populations, the index of clonal diversity R ranged from 0.67 to 1  and there was no significant effect of population on the clonality . The lowest R value was observed for Pleasant Hill, in Arkansas (R = 0.67).Among European populations Table , the indFIS that was calculated among European populations of the “clonal dataset”  was significantly lower than the overall FIS calculated among American populations of the “clonal dataset” . At the population level, negative FIS values (Table R ranging from 0.28 to 0.67). Significantly positive FIS values were estimated among 22 of the 34 European populations (ranging from 0.066 to 0.161) and among 23 of the 29 American populations (ranging from 0.082 to 0.186).The overall 3.2Introduced European individuals of black locust are genetically close to only a few native individuals located in four populations from the northeastern part of its native range. Among the 29 sampled American populations, only four  showed individuals with a high level of genetic relatedness to the European individuals. The structure and multivariate analyses gave congruent results.First we used a PCA Figure  to analyK = 2 clusters . Overall, within the native range, black locust populations were clearly genetically differentiated, matching with geographic structure, whereas in the introduced European range, the differentiation between populations was low and no structure was detected across the continent.There was a significant genetic differentiation among all populations: Estimated FST of 4.46% (95% CI: 3.94%–5.06%). Clear signals of isolation by distance (IBD) were observed between populations in the native range, as both correlation tests were significant , which was significantly lower than the FST observed in the native range. No significant signal of IBD was found either by testing the correlation between pairwise FST/(1−FST) ratio and the logarithm of pairwise geographic distances (Supporting Information Appendix r = −0.013 p = 0.75) or by realizing a Mantel test . The correlation was not significant within each cluster either .Genetic differentiation among European populations was significant with an estimated K = 2 clusters  in both the native and invasive ranges, confirming that with this initial set we had oversampled high MAF SNPs, biasing the evaluation of diversity indexes (Supporting Information Appendix p = 0.00453), as well as a lower total number of alleles in Europe compared to North America  , number of individuals , and number of markers (113 SNPs). Furthermore, historical reviews and two types of analyses  led to the same conclusion concerning the limited origin of European populations.The results obtained using a molecular approach are congruent with historical records pointing to the original sources of black locust in the northeastern part of its native range in the Appalachian Mountains. By reviewing historical studies, we were able to conclude that the first black locusts introduced to Europe during the early 17th century were likely to have come from Virginia and further black locusts introduced during the 17th and 18th centuries from Pennsylvania and West Virginia Cobbett, . Consequ4.2Given that there are a few American populations close to European ones, a bottleneck is expected in European populations. The decrease in genetic diversity was only observed when using a larger number of SNPs, due to some particular properties of SNPs.SSRs and SNPs are two widely used markers for genotyping non‐model species , which confirmed the sampling bias. We partially corrected this bias by using the additional dataset (251 SNPs), where actual MAF distribution is closer to the expected MAF distribution , which exhibited two distinct genetic clusters . Most of the individuals were admixed between these two clusters (approx. 75%) and within each cluster one outlying population drove the signal of differentiation: Meppen  and Munchenberg . Although weak, three signals confirmed that the observed genetic structure was significant: (a) Within each cluster, some individuals were significantly assigned to the cluster, (b) the STRUCTURE admixture parameter (α) was checked for convergence (α should be relatively constant with a range of 0.2 or less −)as recommended in the STRUCTURE manual  and in Western and Eastern Europe , but with a very weak signal . A lower FIS in European populations than in American populations indicated an excess of heterozygosity within the former. Clonality usually produces this pattern .In general, we found that European populations of black locust were more clonal than American populations. This was demonstrated by comparing the number of clones detected within each range, as well as the higher index of clonal diversity R, and the analysis of the inbreeding coefficient FIS (−0.180), which indicated an excess of heterozygosity potentially due to a high level of clonality.Moreover, within the European range, the two outlying populations, Meppen and Munchenberg, were clearly differentiated no matter which of the analyses was applied. The results in a previous study on the Munchenberg population (code N° 7 – Hasenholz in Liesebach et al., FST and to slowly decrease genotypic diversity in purely clonal populations (Balloux, Lehmann, & De Meeûs, Eichhornia crassipes (Barrett et al., Fallopia japonica (Hollingsworth & Bailey, A. artemisiifolia (Friedman & Barrett, In Japan, both clonal and sexual reproduction have been found to promote the spread and invasion of black locust (Kurokochi & Hogetsu, 5We found a remarkable congruence between our genetic analysis and historical records regarding the geographic origins of the European black locust, with both approaches pointing to European populations originating from the Northern Appalachian Mountains. The history of black locust introduction is thus a unique pattern among invasive trees, which are commonly characterized by multiple introduction events. As a consequence, only a small part of black locust genetic diversity was introduced to Europe from its native American range.Furthermore, in spite of the fact that black locust suffered a genetic bottleneck and a loss of diversity following introduction, this did not prevent its successful colonization throughout its European range. Moreover, we found some evidence for a shift in mating systems between ranges with an increase in clonality in Europe, resulting from either natural or artificial selection. However, our sampling method was not specifically designed to investigate changes in clonality during and after introduction. Therefore, further studies involving the sampling of extensive populations and plots would be needed to better understand the role of clonality in the success of this species, conjointly with studies on the role of sexual reproduction in the spread of the black locust.None declared.AM, AJP, LL and SM conceived the experiment. XB, CFV, PB, CM, AM, MVL and SM sampled the populations. XB, CFV, AD, EG, MP and SM made the lab work. XB analyzed the data, with the contribution of CFV, EA, KD, ODT and SM. XB wrote the paper, with the contribution of AJP and SM. All authors read and approved the submitted version. XB and SM are co‐corresponding authors. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Scirpus mariqueter is a perennial grass almost limited in the Changjiang River estuary and its adjacent Qiantang River estuary. Here, using amplified fragment length polymorphism (AFLP), a moderate‐high level of genetic differentiation among populations (range FST: 0.0310–0.3325) was showed despite large ongoing dispersal. FLOCK assigned all individuals to 13 clusters and revealed a complex genetic structure. Some genetic clusters were limited in peripheries compared with very mixing constitution in center populations, suggesting local adaptation was more likely to occur in peripheral populations. 21 candidate outliers under positive selection were detected, and further, the differentiation patterns correlated with geographic distance, salinity difference, and colonization history were analyzed with or without the outliers. Combined results of AMOVA and IBD based on different dataset, it was found that the effects of geographic distance and population colonization history on isolation seemed to be promoted by divergent selection. However, none‐liner IBE pattern indicates the effects of salinity were overwhelmed by spatial distance or other ecological processes in certain areas and also suggests that salinity was not the only selective factor driving population differentiation. These results together indicate that geographic distance, salinity difference, and colonization history co‐contributed in shaping the genetic structure of S. mariqueter and that their relative importance was correlated with spatial scale and environment gradient.Estuarine organisms grow in highly heterogeneous habitats, and their genetic differentiation is driven by selective and neutral processes as well as population colonization history. However, the relative importance of the processes that underlie genetic structure is still puzzling.  To disentangle the relative roles of the different evolutionary forces acting on genetic structure of this species, it is useful to combine the information on levels of gene flow obtained from neutral loci with the information from outliers that are likely to be of selective signs. In this sense, it is hypothesized that if outlier loci do not show the same IBD or IBE pattern as neutral loci, but showing a direct correlation between genetic differentiation and geographical distance or environmental variables, or colonization history, the evidence for the relative importance of above processes in shaping the structure of this species will be found.In this study, using amplified fragment length polymorphism (AFLP), we quantified the distribution of genetic variations and the migration among populations of 22.1S. mariqueter has been considered as an endemic species of China, occurring in the CRE and the QRE  of Taiwan, China. Since the locations in the same region were suffered simultaneous reclamation process, and new population could be founded outside the seawall; thus, the populations from the same region have the same colonization history. According to colonization histories of these locations, these 14 populations were defined to eight groups from different geographic region: group 1 (QD1 and QD2 from Qidong), group 2 , group 3 (NH and JS from Shanghai), group 4 (HS from Hengsha Island), group 5 , group 6 (HZ1 and HZ2 from Hangzhou), group 7 (YY and BL from Ningbo), and group 8 , where only 13 individuals were available due to very limited population size of this population. Each individual was, respectively, stored in a plastic bag and then dried with silica.In this study, samples were collected from fourteen discrete locations along the coastal lines Figure , which cGenomic DNA was isolated from 5‐cm dried leaf tissue using CTAB (hexadecyltrimethylammonium bromide)‐based method  were investigated under the assumption that loci with uneven distribution are expected to be more genetic differentiation between populations than neutral alleles. To minimize the risk of detecting false positives, two different basic approaches were applied for detection of loci that are putatively under selection. First, we used the hierarchical Bayesian method described in Beaumont and Balding  as imple2.4PPL), Nei's gene diversity (Hj), and the Bayesian estimate of gene diversity (hS). The first two calculations were carried out using AFLP‐SURV 1.0  were used to obtain θB. θB statistics were calculated with different models using program Hickory 1.0: (a) a full model with noninformative priors for f, (b) a model in which f = 0, (c) a model in which θB = 0, and (d) a f‐free model. These models were compared using the deviance information criterion (DIC). The model with the smallest value was chosen. We set default sampling parameters: burn‐in = 50,000, sampling = 250,000, thin = 50.We characterized the overall level of genetic diversity within population, estimating: the proportion of polymorphic loci  was selected as an environment factor to test the effect of IBE. These correlations were performed using SPSS 15.0 software. All of above analysis were carried out with and without outlier loci to determine whether or not our results were being influenced by loci which may be under selection.To investigate the relative importance of spatial distance and salinity difference on genetic structure, we tested for IBD and IBE based on the three datasets: all loci, positive, and neutral dataset. For IBD test, we applied traditional method of a Mantel test as implemented in GENALEX 6.5 , allele frequencies are estimated for each of the K clusters, and each individual is then reallocated to the cluster that maximizes its likelihood score. Twenty repeated reallocations are performed within each run, and fifty runs are carried out for each K. Strong consistency among runs  is used to indicate the most likely number of clusters . MCHEZA detected only 21 loci  ranged from 49.3% to 66.8% . Both estimates of gene diversity varied only a little among populations. The range of estimates was from 0.1592 to 0.2075 assuming H–W equilibrium  between populations varied widely, ranging from 0.0310 (HZ1–HZ2) to 0.3325 (QD2‐TW), and all FST values were significantly different from zero (p < 0.001) (Table θB whether we using the full model (0.3870 ± 0.009) or f‐free model (0.3554 ± 0.019). This was higher than the traditional estimate of the overall FST between all populations (FST = 0.1565 ± 0.106) when assuming H–W genotypic proportions or AMOVA estimate (FST = 0.1857). Comparison of the deviance information criterion (DIC) in different models, the highest DIC value was attained with a model of no structure (θB = 0) provided further evidence that there is relatively high level of population genetic differences  was due to variation between groups with different population colonization history  kept increasing with increasing K. Separate STRUCTURE analysis including all individuals for each dataset after applying posterior ΔK statistic  or K = 3  as the most likely solution Table . AccordiThese 14 populations had different degrees of admixture and different genetic components  and geographic distance. A significantly positive relationship was found  for all 14 populations  based on all loci  with 21 outliers . A concave curve was obtained with all loci , but for those with a high salinity difference (>4.0 ppt), the correlation was significantly positive . When selecting the geographic distance as the controlling factor, similar results were obtained . However, no significantly positive relationship was found as expected when only considering outlier dataset, but also a concave curve was obtained  or with a high salinity difference (>4.0 ppt) . In contrast, based on neutral dataset, none‐liner pattern but significantly negative correlation was found with a low salinity difference (≤4.0 ppt)  and a significantly positive correlation with a high salinity difference (>4.0 ppt)  among populations was found although all locations hydrologic linked and indeed amount of migrants were detected. Considering these results, population genetic structure and its correlates with spatial distance, salinity difference and population colonization history were analyzed. The outlier loci analyses suggested that some of the AFLP loci were putatively under divergent selection, and the analyses of population genetics considering only these outliers revealed a very different pattern of differentiation from results obtained with the entire dataset or neutral dataset according to STRUCTURE, AMOVA, IBD, and IBE tests. The results suggest that the ecological forces, including environmental factors like surface salinity, spatial distance, and colonization history all play important role in shaping population structure of S. mariqueter and the effects may be compound.In this work, non‐Bayesian clustering analysis using FLOCK assigned all individuals to thirteen genetic clusters and revealed a complex genetic structure of 4.1S. mariqueter, beyond the transport by water flow, the long‐distance dispersal mostly via achenes and vegetative propagules can also be done by many birds . But migrations often happened more frequently among many neighboring populations, for example, HZ1 and HZ2, BL and YY, NH and JS  was detected although all locations hydrologic linked. The greatest interregional differentiation was found between TW and QD1 population (FST = 0.3325), which was responded to the very long distance between them (~800 km), but when TW was excluded, the overall FST was 0.1263, and also exhibited a relatively high level of genetic differentiation even in the absence of physical barriers at this regional scale. Population differentiation represents a historic gene flow rather than current value  were only limited in these populations while there was hardly positively selected loci attached to the central populations of 4.2S. mariqueter populations at the CRE or the QRE were deviate from the migration–drift equilibrium, which may be disrupted by the divergent selection process  or with outlier dataset , but showed a concave curve . However, this result does not mean the habitat heterogeneity of S. mariqueter cannot result in the genetic differentiation of this species, because a significantly positive correlation with the salinity difference >4.0 ppt  was found. However, we cannot explain why the genetic differentiation was significantly and negatively correlated with the salinity difference when the latter was equal to or less than 4.0 ppt . A potential reason is that when the salinity difference was low, the effect of natural selection on the genetic differentiation might be diluted by other factors, such as gene flow and phenotypic plasticity . Some population pairs  with similar salinity  but were located at different estuaries, respectively, also existed a high level of genetic differentiation  and Jiuduan shoal (JD) are alluvial islands in the CRE only with a short history (~160 years for the former and <100 years for the latter, Yang, Genetic structure in S. mariqueter has a small distribution range with no geographical barrier, a relatively high level of genetic differentiation was found among populations of this species, and this differentiation was proved to be affected by the interaction between geographic distance and environmental variability, as well as population colonization history. The results may help to disentangle the relative contributions of the underlying processes in the formation of genetic structure among estuarine populations, especially for high plants. Besides, this study provides a signal of local adaptation occurred in peripheral populations although high migration in these estuaries, suggesting that effective conservation of S. mariqueter should include maintaining all populations cover its distribution range regardless of population size, thus promoting preservation.To date, the patterns of genetic structure of estuarine populations with molecular markers cover a wide range of possible outcomes among and within species. Such observations may reflect varying degrees of isolation driven by selective and neutral processes. In this study, though The authors declare no conflict of interests.M. Y. and W. Z. conceived and designed the research; M. Y., W. Z., F. L., and Q. M. collected samples; M. Y. and Y. F. performed the experiments; M. Y., P. D., W. Z., C. X., and G. Y. analyzed the data; M. Y. and W. Z. wrote the manuscript. Click here for additional data file.


Bemisia tabaci (Gennadius) MED . Then, we used resampling techniques to create simulated populations with a random subset of individuals and 3,000 SNPs to determine how many individuals should be sampled for accurate estimates of intra‐ and interpopulation genetic diversity. We calculated the intrapopulation genetic diversity parameters  and pairwise genetic differentiation FST; finally, an ad hoc statistic, ΔK, was used to determine the optimal value. Our results showed that a sample size greater than four individuals (n ≥ 4) has little impact on estimates of genetic diversity within whitefly populations; moreover, precise estimate of FST can be easily achieved at a very small simple size (n = 3 or 4). Our results will provide in‐depth understanding of the optimization of sampling schemes in population genomics of invasive species.Analysis of population genetics provides insights into the evolutionary processes, among which the sample size choice is per se a crucial issue in the analysis. Genome‐wide high‐throughput techniques based on RADseq have been increasingly used in studies on the population genomics of invasive species. However, there is little information available regarding optimal sample sizes for analyzing population genomics of invasive species. In this study, we first use type IIB endonucleases restriction site‐associated DNA (2b‐RAD) to mine thousands of single nucleotide polymorphisms (SNPs) for native and introduced populations in Q1 clade (SPB and 17JN) and Q2 clade (ISQ and UAS0601) of the whitefly,  n ≥ 4) has little impact on estimates of genetic diversity within whitefly populations; moreover, precise estimate of FST can be easily achieved at a very small simple size (n = 3 or 4).We evaluated the optimal sample sizes for population genomics of invasive species. Our results showed that a sample size greater than four individuals ( While B. tabaci Q is a native species in the Mediterranean regions (Israel and Spain), it is an introduced species in the United States and China. Ten females were selected for sequencing from each population. The specimens were then preserved in 95% ethanol and stored at −80°C until DNA extraction. The DNA of individuals was extracted using a TIAMamp Micro DNA Kit  according to the protocol. Prior to sequencing, the DNA concentration of each individual was quantified by NanoDrop and Agarose Gel.The samples of 2.2The mitochondrial cytochrome c oxidase I (mtCOI) gene was amplified using forward primer 2195MF  as an outgroup, multiple mtCOI sequences were aligned using the ClustalW algorithm in MEGA7  in a 15 µl reaction at 37°C for 45 min. A 4 µl sample of digested DNA (~50 ng) was run on 1% agarose gel to verify the effectiveness of digestion. A total of 20 µl of ligation master mix consisted of 0.2 µM of each specific adaptor (five pairs of adaptors per five samples), 0.5 mM ATP (New England Biolabs), 200 U T4 DNA ligase (New England Biolabs), 2 µl 10 × T4 ligase buffer, 5.9 µl nuclease‐free water, and 10 µl digestion product. Each reaction tube was incubated at 16°C for 1 hr. The ligation products were amplified in 50 µl PCRs. Each sample was composed of 0.16 µM of each primer, 0.24 mM dNTP, 10 µl 5 × HF buffer, 0.8 U Phusion high‐fidelity DNA polymerase (New England Biolabs), 18.8 µl nuclease‐free water, and 18 µl ligation products. PCR was conducted in MyCycler thermal cyclers (Bio‐Rad) with 16 cycles of 98°C for 5 s, 60°C for 20 s, and 72°C for 10 s. Fifty microliter of each PCR product was run on an 8% polyacrylamide gel, with the DNA diffused into nuclease‐free water at 37°C for 30 min. For each tube, 12 µl of supernatant was used as a template and the above PCR steps repeated for 4–6 PCR cycles to improve the yield. The PCR products from five samples were combined, and the mixture purified using a MinElute PCR Purification Kit. Thirty microliter of digestion master mix was prepared containing 1 mM ATP, 3 µl 10 × CutSmart buffer, 2 U SapI (New England Biolabs), 10 µl purified mixed PCR product, and 13.8 µl nuclease‐free water. The mixture was then incubated at 37°C for 30 min. The digested product was added to the tube containing pretreated magnetic beads and the mixture incubated at room temperature. After a magnet was applied and the supernatant transferred to a new tube, 200 U T4 DNA ligase was added to the supernatant and incubated at 16°C for 45 min. Gel purification was then performed as described above. Barcodes were introduced by PCR with barcode‐bearing primers. PCR products were purified using a MinElute PCR Purification Kit and pooled for sequencing using the Illumina Hiseq Xten Paired‐end sequencing platform.The 2b‐RAD libraries were prepared at Qingdao OE Biotech Co., Ltd., as described in Wang, Meyer, Mckay, and Matz . For eac2.4N bases >8% and without restriction recognition sites, the filtered high‐quality sequences are referred to as enzyme reads. Then, mapping the enzyme reads to the B. tabaci MEAM1 and MED reference genomes using SOAP program  were deleted. In 2.5K, were used to identify the effect sample size has on estimates of genetic diversity and differentiation. Prior to evaluating the optimal sample size directly, we conducted a power analysis to find out the minimum number of resampling replicates that would be demanded to ensure exact estimation of genetic parameters. For each population, we first randomly selected 3,000 SNPs using data tools in Excel, we then constructed simulated data sets comprised of different numbers of resampling replicates  each represented by all combination of the different sample sizes . To construct each simulated population, a macro in Excel designed to assign each individual in the empirical data set a random number  was used to select a random subset of individuals from the empirical data set (n = 10). After sorting the data set by the random numbers, then we assigned the first two  to a new worksheet multiplied by 10 , bringing about simulated “populations” that were random, independent subsamples of the empirical data set. Sampling was carried out without alternate, so in the same replicate no B. tabaci individual was included more than once . Since replicates were independent of each other, however, the same individual may be contained in more than one replicate of the simulated data set. GenALEx 6.5 was used to calculate unbiased expected heterozygosity (uHe), observed heterozygosity (Ho), the number of effect alleles (Ae), and pairwise genetic differentiation (FST) for each replicate at each sample size  and B. tabaci subclade Q2 in USA likely originated from the Israel  were identified. SPB and 17JN populations were identified as Q1 clade while USA0601 and ISQ populations were Q2 clade  in the B. tabaci populations ranged from 1.179 (SPB) to 1.103 (USA0601). The expected heterozygosity (He) in the B. tabaci populations ranged from 0.063 (USA0601) to 0.109 (SPB). The observed heterozygosity (Ho) in the four populations ranged from 0.059 (ISQ) to 0.106 (17JN). The FST distance of SPB and 17JN calculated from all SNPs was 0.0778. The FST distance of USA0601 and ISQ calculated from all SNPs was 0.3457, indicating that a substantial genetic differentiation exists between the two populations.A total of 40 individuals were used for the next sequencing. Sequencing of 40 individuals generated 211,661,317 clean reads, with an average of 5,291,533 clean reads per sample. After deleting the low‐quality reads, a total of 142,621,503 enzyme reads were obtained (67.38% of the clean reads). These reads were aligned to the 3.2B. tabaci populations. Precise estimates of population genetic parameters were obtained in our simulations with x = 20, 30 or 40 resampling replicates  and the number of SNPs to 3,000, we detected no statistical difference for the mean values of Ae, Ho and uHe while the number of replicates was set to x = 20, 20, and 40, respectively  or x = 100 . Concurrently, the ΔK line chart shows a peak at x = 20, 20, and 40 separately  were obtained in our simulations with x = 20, 30, or 40 resampling replicates  individuals appears to have slight impact on the mean uHe. Such as, the mean value of unbiased He for n = 3 was 0.077  and for n = 8 it was 0.080   with 3,000 SNPs was adequate to accurately reflect the genetic diversity found in the ISQ population  Figure . At the size n =  with 3,0He when using 3,000 SNPs. The mean values of unbiased He for n = 4 was 0.056 , and for n = 8, it was 0.059 . Simultaneously, the ΔK line chart shows a clear peak at n = 4  were enough to recover the Ae and Ho for the USA0601 population , and for n = 8, it was 0.120   were enough to recover the Ae and Ho for the 17JN population  and using 1,000 SNP. The results showed that a sample size greater than four individuals has little impact on estimates of genetic diversity within JN population  Figure . Simulta3 Figure . Moreoven Figure . The ΔK e Figure . Furthern Figure .He when 3,000 SNPs are used. The mean value of unbiased He for n = 3 was 0.092 , and for n = 8, it was 0.106 . Simultaneously, the ΔK line chart shows a peak at n = 3  were enough to recover the Ae and Ho for the SPB population  was fixed to three and the number of SNPs were fixed to 3,000. The result showed that compared with x = 100, no statistical difference was tested for the mean values of FST when we set the number of replicates to x = 50  and for x = 100 were 0.246 . At the same time, the ΔK line chart showed a peak at x = 50  to three and the number of SNPs to 3,000, compared with x = 100, we detected no statistical difference for the mean values of FST when the quantity of replicates was set to x = 30  and for x = 100 were 0.091 . At the same time, the ΔK line chart showed a clear peak at x = 30  is smaller than Nazareno et al. (n = 35), too limited sampling space would increase overlaps between iterations. A similar study evaluated 30 individuals each population to identify the optimal sample size  and the results showed that a sample size greater than four individuals has little impact on estimates of genetic diversity within JN population. The results demonstrated that our conclusion was not an artifact.Genome‐wide high‐throughput techniques based on RADseq have been widely used in ascertaining the population genomics of invasive species  differ from those needed for the native whitefly population in Spain. This may be associated with the effects of invasion being coupling with bottleneck effects, founding effects, etc.n = 3 or 4), unbiased estimates of population differentiation can still be obtained (Figure B. tabaci MED, the actual minimum sample size needed for invasive genomics is not dramatically affected by the invasion process.Compared with traditional molecular markers, the finer population differentiation can be obtained with lower sample sizes when using genome‐wide high‐throughput techniques (Jeffries et al., d Figure . Howeverd Figure  but highd Figure ; Table 2B. tabaci has been studied using multiple molecular marker methods such as microsatellite loci (Chu et al., B. tabaci using the RAD sequencing method by Elfekih et al. (B. tabaci is a species complex which includes numerous cryptic species (De Barro, Galapagos tortoise (Gaughran et al., The population genetics of h et al.  did not Our studies suggest that a relatively small sample size can be used for an accurate estimation of genetic diversity and differentiation of alien species, which will help to provide a foundation for future research on the population genomics of invasive species. The necessity of resorting to small sample sizes in invasion genomics is helpful in revealing the invasion genetics of alien species, especially since most invasions consist of limited sized populations during their initial colonization and establishment phases (Lockwood, Hoopes, & Marchetti, None declared.DC conceived the ideas and designed the study; ZKW and YGZ provided the data analysis methods; WMQ and NL analyzed the data and wrote the manuscript; all authors edited the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Paphies ventricosa), an Aotearoa  endemic surf clam, was assessed to determine levels of inter-population connectivity and test hypotheses regarding life history, habitat distribution and connectivity in coastal vs. estuarine taxa. Ninety-eight toheroa from populations across the length of New Zealand were sequenced for the mitochondrial cytochrome c oxidase I gene with analyses suggesting a population genetic structure unique among New Zealand marine invertebrates. Toheroa genetic diversity was high in Te Ika-a Māui  but completely lacking in the south of Te Waipounamu (the South Island), an indication of recent isolation. Changes in habitat availability, long distance dispersal events or translocation of toheroa to southern New Zealand by Māori could explain the observed geographic distribution of toheroa and their genetic diversity. Given that early-Māori and their ancestors, were adept at food cultivation and relocation, the toheroa translocation hypothesis is plausible and may explain the disjointed modern distribution of this species. Translocation would also explain the limited success in restoring what may in some cases be ecologically isolated populations located outside their natural distributions and preferred niches.The population genetic structure of toheroa ( Pelagic larval duration has been shown to be a reasonable indicator for dispersal potential4, which is further modified by spawning or larval behaviour and the physical properties of dispersing propagules7. Within the constraints imposed by a species’ biology, the physical environment also plays a role in determining the distances over which populations are connected. Local and regional hydrodynamics are important8, as is the geographic distribution of suitable habitat9. For example, estuarine taxa may exhibit greater genetic structure, indicative of limited connectivity, because they live in geographically discrete habitats11. In contrast, open-coast taxa are often better connected, through either larval or post-settlement dispersal, over similar geographic scales because their habitat is more continuous, allowing greater mixing among populations12. Consequently, estuarine species may be more vulnerable to overharvesting than coastal taxa as recruitment from distant estuaries may be infrequent or insufficient to restore or sustain impacted populations. A similar situation may exist for open-coast taxa with disjunct distributions. For species which occur under a relatively narrow range of environmental conditions, distances between populations may be great and inter-population dispersal rare13. One such open-coast organism that occurs in geographically discrete populations and is hypothesised to experience limited inter-population connectivity is the toheroa (Paphies ventricosa)14, a large intertidal surf clam endemic to Aotearoa .Dispersal and connectivity among populations of marine organisms are strongly influenced by a species’ life history characteristics16. Toheroa inhabit exposed open-coast surf beaches and are primarily found in the middle of the intertidal zone. Juveniles are located at the upper end of this range (nearer the top of the beach) with adults located lower down the shore and buried up to 15–20 cm beneath the beach surface. The exact parameters that determine habitat suitability are uncertain (J. Cope unpublished research). However, in contrast to other New Zealand bivalves, such as the surf clam Paphies subtriangulata (tuatua) or the estuarine clam Paphies australis (pipi), toheroa appear to have habitat requirements that limit their geographic distribution17. Common features of the beaches on which toheroa occur include high wave energy conditions, a wide shallow gradient (dissipative beach) usually backed by sand dunes or cliffs, fine uniform sand with an average grain size of 0.21–0.33 mm, high levels of fresh water seepage onto the beach and high concentrations of phytoplankton19.Toheroa are broadcast spawners with a pelagic larval duration of around three to six weeks14. Toheroa were a staple food for Māori  in these areas and remain a species of great cultural importance. Toheroa began to be harvested more extensively by Pākehā  from the late 1800s and intense harvesting over the next 60–70 years resulted in toheroa populations declining to levels where their harvest was no longer viable. Commercial and recreational fisheries were closed between 1969 and 198014. Since that time only limited harvest for Māori customary purposes have been permitted. Despite having been protected for over 40 years, toheroa populations nationwide have, for unknown reasons, failed to recover, with some populations continuing to decline14.At the start of the 20th century, extensive toheroa populations were present on a handful of exposed west and south facing surf beaches Fig. 14. Toher17  gene. Our predictions, based on prior ecological knowledge, were for North Island populations to be well connected and for isolation and genetic divergence between northern and southern toheroa. Connectivity was expected to be less than that previously recorded for widely distributed open-coast taxa, such as P. sutriangulata, and more similar to levels of connectivity reported in studies of estuarine species such as P. australis10.In this study we assessed population genetic structure and levels of connectivity among toheroa across their entire distribution using the mitochondrial cytochrome n = 16) and Ripiro Beach (n = 16) in Northland, Waitarere Beach (n = 30) on the Kāpiti-Horowhenua coast and Oreti Beach (n = 36) in Southland were sequenced generating a 485 nucleotide COI alignment. Twenty-three nucleotide positions were variable leading to the delineation of 20 haplotypes  and 10 at Waitarere . In contrast, only a single haplotype (H1) was recorded at Oreti . Haplotype H1 was also the most abundant haplotype across North Island populations, accounting for 59% and 66% of sequences from Taitokerau and Waitarere , Ripiro (n = 2) and Waitarere (n = 1). The remaining 18 haplotypes were private (unique to one location) with all but one of these recorded in just one specimen. A rarefaction curve generated from haplotype frequencies suggested that greater sampling effort could yield many more haplotypes, a notion supported by the number of missing intermediate positions indicated in a haplotype network . In contrast, population subdivision (including divergence between northern and southern populations) was evident in the genetic structure of estuarine P. australis10 (microsatellites) and Austrovenus stutchburyi32 (mtDNA COI) with multiple alleles recorded in all populations. For A. stutchburyi, a number of COI haplotypes were found throughout New Zealand, but at different frequencies in northern and southern populations, while other haplotypes were found exclusively in either the north or the south.Only two of the 20 haplotypes recorded in this study were found in more than one population and our analyses indicate statistically significant genetic differences between all regions. Genetic differences between North and South Island toheroa were the result of a complete lack of genetic diversity at Oreti in the South Island, a stark contrast to the relatively high levels of diversity recorded at each sampling location in the North Island Fig. ; Table 137 which is expected for the southern toheroa on account of their remoteness to North Island populations. If isolation had occurred in the distant evolutionary past we would expect some degree of divergence to have resulted after a period of isolation, and be manifested in the form of a set of unique genetic variants. Molecular clock estimates for marine molluscs suggest an evolutionary rate of between 2.3 and 4.6% per million years for the COI gene39. For the toheroa sequenced here, the six substitution steps between the central H1 haplotype and those at the farthest extent of the haplotype network could therefore represent somewhere between 270,000 and 539,000 years of toheroa evolution in northern New Zealand. In contrast, the lack of divergence or genetic diversity in southern toheroa suggests a far shorter evolutionary history and supports the notion of a more recent isolating event with no subsequent opportunity for divergence. Based on the above rates of evolution, divergence in Oreti toheroa from an ancestral population should be detectable after c. 45,000–91,000 years of isolation. Accordingly, the absence of unique haplotypes in the south suggests these populations were founded more recently.A lack of within-population genetic diversity, as we observed for Oreti toheroa, could result from isolation and inbreeding41. However, while toheroa shells are found both in middens and in natural deposits in the vicinity of present day toheroa populations , they have rarely been reported elsewhere. There is no archaeological evidence for toheroa populations at other locations and the habitat along east and west coasts of the South Island is, at present, largely unsuitable for toheroa (P. Ross pers. obs.).Three scenarios could explain the geographic distribution of genetic diversity recorded in toheroa: First, that toheroa were formerly present along east or west coasts of the South Island (connecting northern and southern populations) and have disappeared from these areas due to factors such as over harvesting, the loss of suitable habitat or a changing climate. The occurrence of toheroa in Māori shell middens (domestic refuse heaps that indicate sites of human occupation or food processing activity) in the upper and central South Island would support this hypothesis if a range reduction had occurred after the settlement of New Zealand by Māori in approximately 1250 AD22. However, if a founding dispersal event had occurred in the distant past , genetic divergence from northern toheroa would be expected42. Alternatively, a more recent dispersal and founding event could have occurred, explaining the lack of divergence recorded in this study. However, there is currently no evidence to suggest that hydrodynamic conditions have changed such that long distance dispersal events would have only become possible in the recent past and this explanation seems unlikely.A second possible explanation is that rare, long distance larval dispersal events from northern populations may have seeded toheroa in the south. While it is impossible to rule out this scenario, our understanding of coastal circulation patterns suggests that the delivery of toheroa larvae over distances of 800 km or more is unlikely44 and were adept at food cultivation and translocation. The Polynesian explorers who settled New Zealand (the first Māori) brought with them plants 46 and animals (Pacific dogs and rats)47. Post settlement, Māori also domesticated and translocated numerous endemic plants48 and are thought to have translocated freshwater fish into lakes where they did not occur naturally38. Not surprisingly, it has been argued that translocation and active management of wild populations are part of Māori culture51. Despite the overwhelming evidence that cultivation and translocation were commonly used by early-Māori, the concept of pre-European translocation of marine species has received little attention and no attempts have been made to determine whether practices similar to those employed on land were also used in the marine estate – in this case, the cultivation of shellfish through translocation.A third possibility is that toheroa are not native to the South Island, but were translocated there by Māori. Early-Māori were prolific users of aquatic resourcesc. 1880 AD55. Based on this evidence, we now know that toheroa translocations took place. Translocations may even explain the small numbers of toheroa or their shells that have been reported at sites such as Pakiri or Karamea where toheroa are not found today  has provided numerous accounts of toheroa translocation within both North and South Islands dating back at least as far as 23. Population genetic analyses of New Zealand bivalves are limited to A. stutchburyi32, Paphies australis, Paphies subtriangulata10, and Perna canaliculus – a mussel with a population genetic structure that has to some extent been modified through modern aquaculture-related movements of broodstock and/or spat56. Consequently, we caution that other presently unstudied New Zealand bivalve taxa could have similar population genetic structures to that of toheroa and that these patterns may have arisen through natural biogeographic processes.While the genetic structure of toheroa appears to be unique, there are few other relevant taxa for which similar data are available2 adds to the intrigue surrounding this iconic species. While limited gene flow is just one of many factors potentially influencing toheroa population dynamics57, our study provides support for the hypothesis that South Island toheroa are genetically isolated. Furthermore, the limited sharing of haplotypes among northern populations may indicate limited connectivity at smaller spatial scales. As genetic diversity in northern toheroa is high, analyses that capture a greater proportion of this diversity will be required to provide estimates of larval exchange and will allow for comparisons with coastal and estuarine taxa. Such comparisons, and future population genetic studies, should incorporate thinking around the potential for humans to have influenced the distribution of marine species. If the translocation hypothesis is accepted, it could explain the disjointed modern distribution and population dynamics of toheroa and the limited success achieved in restoring what may, in some cases, be ecologically isolated populations located outside their natural distribution and preferred ecological niche.The reasons for the failure of toheroa to recover despite over 40 years of protection remain a mystery. The detection of a population genetic structure that is unique among New Zealand marine invertebratesn = 10) and Ripiro Beach (n = 16), on the Kāpiti-Horowhenua coast from Waitarere Beach (n = 30) and in Murihiku from Oreti Beach (n = 31). Toheroa were collected during surveys commissioned by the Ministry for Primary Industries in 2009 (Oreti), 2010 (NMB) and 2011 (Ripiro) and under a customary harvest permit in 2015 (Waitarere). Toheroa were stored at −20 °C prior to DNA extraction.Specimens for genetic analysis were obtained from the three main toheroa regions Fig. . In nort2 piece of adductor muscle was dissected from each specimen and genomic DNA extracted using the Zymo Research Genomic DNA II Kit . The mitochondrial COI gene was amplified using the universal primers LCO1490 and HCO219858. PCR amplifications were conducted in 10 µl reactions containing 4.8 µl Intron i-Taq 2x PCR master mix, 5 pmol of each primer and 1 µl of unquantified template DNA. PCR reactions consisted of an initial denaturing phase of 94 °C (4 min.), followed by 35 cycles consisting of 94 °C (60 s), 52 °C (90 s) and 72 °C (90 s) and a final extension period at 72 °C (5 min.). Unincorporated nucleotides and primers were removed by adding 2 units of Exonuclease I, 0.1 unit of Shrimp Alkaline Phosphatase and 2.7 µl H2O and incubating at 37 °C (30 min) then 80 °C (15 min). Sequencing reactions used Big Dye terminator sequencing chemistry (Applied Biosystems) on an Applied Biosystems 3130 Genetic Analyzer. DNA traces ends were generally of low quality and were trimmed and edited in Geneious Version 5.1.7 to produce a high quality alignment of 485 base pairs. Sequences have been deposited in the Barcode of Life Datasystems (BOLD) database under dataset DS-NZTOH (dx.doi.org/10.5883/DS-NZTOH) and cross-referenced to Genbank (accession numbers MH622204-MH622290). An additional 11 sequences from Oreti Beach (n = 5) and NMB (n = 6) were retrieved from the Barcode of Life Database .A 0.25–0.50 cm59 and Arlequin Version 3.5.2.260. For each population (Taitokerau (NMB and Ripiro), Waitarere and Oreti) we calculated the number of segregating sites (S), numbers of transitions (TS) and transversions (TS), the number of COI haplotypes (NH), the number of private COI haplotypes in a population (HP), haplotype diversity (HD), mean number of pairwise differences (k) and nucleotide diversity (π). A rarefaction analysis was generated in Analytic Rarefaction  to approximate the proportion of haplotype diversity captured by the current sampling regime. PopART Version 1.7 (http://popart.otago.ac.nz) was then used to generate a minimum spanning network to allow for visualisation of relationships between haplotypes and the geographic distribution of genetic diversity. Estimates of population pairwise FST values were then calculated in Arlequin to determine if any two populations differed significantly in their genetic composition. Tajima’s D61 and Fu’s Fs62 were calculated in DnaSP to test for deviation from the Wright–Fisher model of neutral evolution which can be indicative of either non-neutral evolution or population expansion or contraction under neutral evolution.Indices of genetic diversity were quantified using DnaSP Version 5


FST = 0.009) between sampling locations, and the occurrence of six major genetic clusters within the studied system. Redundancy analyses (RDAs) revealed that spatial and environmental variables explained 3.1% and 4.9% of the neutral genetic variation and 38.6% and 12.2% of the putatively adaptive genetic variation, respectively. These results indicate that these environmental factors play a role in the distribution of both neutral and putatively adaptive genetic diversity in the system. Moreover, polygenic selection was suggested by genotype–environment association analysis and significant correlations between additive polygenic scores and temperature and salinity. We discuss our results in the context of their conservation and management implications for the eastern oyster.Interactions between environmental factors and complex life‐history characteristics of marine organisms produce the genetic diversity and structure observed within species. Our main goal was to test for genetic differentiation among eastern oyster populations from the coastal region of Canadian Maritimes against expected genetic homogeneity caused by historical events, taking into account spatial and environmental  variation. This was achieved by genotyping 486 individuals originating from 13 locations using RADSeq. A total of 11,321 filtered SNPs were used in a combination of population genomics and environmental association analyses. We revealed significant neutral genetic differentiation (mean  For decades, most marine species were assumed to be genetically homogeneous due to their high dispersal potential and to the apparent absence of physical barriers in the oceanic environment  has historically been the most important bivalve species. This species occupies the shallow waters of bays, lagoons and estuaries along the east coast of North America , Canada, and are thought to have been caused by transfer of oyster spat from New England in 1913–1914, in an effort to rebuild overharvested stocks    to create a stack, the SNP model with an alpha of 0.05 and a minimum percentage of alignment of 85% to keep a read. A catalog of putative loci was created (cstacks) based on alignment position and allowing one mismatch between sample loci. The populations module was then used to call genotypes, applying several filtering steps to ensure quality of the data. First, a SNP had to be genotyped in at least 80% of the individuals in at least 12 of the 13 sampling sites. This criterion was used to minimize missing data, while keeping a substantial number of informative SNPs in our system. Second, in order to have sufficient power to call heterozygote genotypes, a SNP had to have a minimum read depth of 5. Third, a SNP had to have a global minor allele frequency (MAF) ≥0.01 or a local MAF ≥0.05. This filtering step is used to exclude putative sequencing errors and keep SNPs that are the most informative in our system. Only SNPs with a heterozygosity ≤0.6 for at least 12 of the 13 sampling sites were kept in order to avoid assembling homeologs as single loci. Individuals with more than 12% of missing data were excluded from the pipeline. Finally, we removed SNPs originating from the mitochondrial genome and SNPs sharing the same position within a single chromosome (duplicated SNPs resulting from sequencing and pipeline artefacts).Read quality was evaluated using FastQC v0.11.5 Andrews, . We usedhttp://www.python.org/) scripts, PGDSPIDER v.2.1.1.3 . Patterns of identity by missingness (IBM) were examined using a multidimensional scaling analysis implemented in PLINK to evaluate clustering bias that could be related to missing data shared between sequencing lanes or sampling sites.We generated two data sets: (a) a haplotype data set for analyses of genetic diversity and (b) a SNP data set for analyses of population genetic structure. For the haplotype data set, we excluded loci for which there were more than two haplotypes in five individuals or more  and a maximum of 20% missing haplotypic data. We added consensus sequences with a maximum of 20% missing data to this data set. For the SNP data set, we kept only the SNPs with the maximum MAF at each locus. Details of the number of loci and SNPs remaining after each filtering steps are presented in Supporting information Table 2.4FST) outlier detection methods, which use different underlying models: (a) BAYESCAN v2.1  ARLEQUIN v3.5   using VCFTOOLS. For each pairwise comparison with an associated R2 > 0.8, we randomly removed one linked SNP until no R2 value ≥ 0.8 was observed.Loci under selection are expected to behave differently from neutral loci in terms of population‐related patterns of diversification and are thus inappropriate to infer population demographic history , using the default options of the “on‐the‐fly” Random Forest algorithm  and the number of consensus reads . The average Pi values were compared between sampling sites using a t test in R.The haplotype data set and the R package STACKR v2.0.4  and expected (He) heterozygosity using GENODIVE 2.0b27  using the R package HIERFSTAT . Differentiation between sampling sites was first evaluated using pairwise estimates of FST .All analyses related to neutral population genetic structure were performed using the imputed data set of putatively neutral SNPs and excluding SNPs with strong LD . The optimal α‐score was used to choose the optimal number of principal components (n = 45), and we used all discriminant functions (n = 12) for both analyses.Isolation by distance (IBD) was evaluated with a Mantel test of all pairwise FST values (pairs of sites with FST < 0.001 were considered as a single population). A second grouping (hereafter “clusters”) was based on the groups observed using the combined results of the dendrogram and DAPC approaches (see Results).A hierarchical analysis of molecular variance (AMOVA) was performed using GENODIVE. Sampling sites were first grouped according to pairwise LH) to detect putative migrants, and a threshold alpha value of 0.002 was used to determine whether individuals were tagged as migrants .Finally, an individual assignment test was conducted using the approach developed by Paetkau, Calvert, Stirling, and Strobeck  and impl2.8pcnm function in the R package VEGAN . With this approach, physical distances are decomposed into a new set of independent spatial variables (dbMEMs) that can be used as explanatory variables in subsequent analyses , only one variable was retained. The resulting set of variables included seven variables related to temperature , two to turbidity (maximum monthly turbidity and minimum monthly turbidity) and one to salinity . Missing values (0.4%) for environmental data were replaced with the median using the function na.roughfix in the R package RANDOMFOREST  to evaluate multicollinearity of all retained variables . The global significance of the final RDA was assessed using an ANOVA, and marginal ANOVAs  were performed to assess the contribution of each explanatory variable. Partial RDAs were subsequently conducted to assess the proportion of genetic variation explained by environmental factors after controlling for the effect of spatial variables and vice versa. All RDAs were performed using the function rda in the R package VEGAN.We evaluated the influence of spatial distribution of environmental factors on putatively neutral and adaptive genetic variation throughout the studied system using redundancy analyses (RDAs)  multilocus signatures of selection for multiple environmental predictors, especially compared to differentiation‐based outlier detection methods  annotation of SNPs with nonsynonymous mutations. First, as we used only a single SNP for each locus, we extracted all SNPs having passed our quality filters located on loci for which a SNP has been identified as being putatively under selection by at least one method . The flanking regions (100 bp) of these SNPs were extracted from the eastern oyster reference genome that we previously used for the bioinformatic pipeline and BLASTed (minimum of 80% of similarity on 30 amino acids) on the protein sequences of 33.1Sequencing of libraries resulted in a total number of 1,514,266,246 raw reads, and the median number of filtered reads obtained per individual was 2,991,344. After removing individuals with low‐quality data, 486 individuals were retained for downstream analyses Table . Prelimi3.2R2 value ≥ 0.8) SNPs. By keeping only one of the strongly linked SNPs, we removed 348 SNPs, thus retaining 8,246 SNPs for the putatively neutral data set. Combining BAYESCAN, ARLEQUIN and OutFLANK, 23 SNPs were identified as being putatively under divergent selection. Of these SNPs, six were detected by all three methods, constituting the putatively adaptive data set.To identify putatively neutral and selected loci, we used the filtered SNP data set . BAYESCAN identified 8,597, 20 and 2,704 SNPs being putatively neutral, under divergent selection and under balancing selection, respectively. ARLEQUIN identified 11,310 SNPs being putatively neutral, 11 SNPs putatively under divergent selection and no SNPs under balancing selection, while OutFLANK detected nine outliers . In total, 8,594 were identified to be putatively neutral using both BAYESCAN and ARLEQUIN. The evaluation of LD between markers identified 380 pairs of strongly linked  to 5.99 x 10‐4 (MAL) between sampling sites (Table Ho) and mean expected heterozygosity (He) were similar between sampling sites. All Ho values were 0.23, and He ranged from 0.28 to 0.29 for nonimputed data  to 0.211 (CRB) for nonimputed data  ranged from 181 (4%) to 364 (8%) and 4,126 (94%) to 3,943 (90%), respectively, between sampling sites, and 2% of reads were tagged as consensus (Table es Table . Using t3.4FST between sites were significantly different from zero (95% CI), except for CRQ and MIS. Missing data were thus imputed using CRQ‐MIS as a single group and all other sampling sites as unique groups. Pairwise estimates of FST using the putatively neutral data set  between sampling sites ranged from 0.0007 (CRQ‐MIS) to 0.0228 (MAL‐RIC) . The results of the Mantel test showed a weak but significant pattern of IBD  that were highly supported with bootstrap support varying between 65% and 100% (median = 99.5%)  and the six clusters showed that 0.4% of the genetic variation could be explained by populations (p <0.0001) and 0.6% by clusters (p <0.0001)  to 94% (CRB)  to 77% (RIC), and 52% of individuals was correctly assigned to their sampling sites Figure . However3.5ordistep procedure, a single spatial vector (dbMEM‐3) and two temperature variables (number of weeks with temperature between 10 and 14°C and mean surface temperature) were selected for the RDA. The global RDA was highly significant and explained 7.7% of the genetic variation , following the 6 SNPs, fordistep procedure, a single geographic vector . Results of the ANOVA used to test the significance of each constrained axis showed that the first six axes were significant (p <0.05). These axes explained a cumulative proportion of 68.3% of the genetic variation  to detect candidate loci under selection was significant (nt p <0.0. These ap <0.001), with the exception of the relationship between polygenic scores and maximum (p = 0.344) and minimum (p = 0.707) monthly turbidity. The adjusted R2 of the significant correlations ranged from 0.082 (minimum monthly turbidity) to 0.544 (number of weeks with temperature between 10 and 14°C)  had significant hits and 36 SNPs, located on 21 loci and nine chromosomes, were nonsynonymous mutations  were positive, but were not as high as observed in other oyster species, such as the black‐lip pearl oyster using SNPs  was slightly lower than or in the same range as what has been observed using SNP data sets in Pacific  suggest that RIC and MIR‐TAB have less diversity than MAL, CRQ‐MIS‐SSI, SHD‐SHM and CRB, which could potentially reflect a bottleneck in these clusters of populations. However, our results do not suggest that the studied natural populations suffer from a high level of inbreeding.Genetic diversity of the studied populations was not excessively low despite the historical decline of populations in our study area. Inbreeding levels  has been suggested in eastern oysters  of 0.0018 and 0.003 in American lobster  in the same region (0.010)  formed a single cluster, followed by two central NB populations (TAB and MIR). Richibucto (RIC) formed a distinct cluster, and the four southern NB sampling sites  formed another cluster. The last two sampling sites, outside of NB (CRB and MAL), each formed independent clusters. Significant population structuring in the system was also supported by the relatively strong assignment success of 77% among the six resolved genetic clusters. Using balanced sampling sizes led to a higher and more uniform assignment success between clusters, corroborating the importance of sample size in assignment analyses.FST values ranging from 0.0157 (RIC‐SHD) to 0.0228 (RIC‐MAL), well above the global average pairwise FST of 0.009.While each regional cluster was formed of geographic neighbours, the overall pattern of genetic structure does not have a simple relationship to physical distance. For example, the northern NB cluster and the southern NB cluster form a group with Malpeque Figure , to the 4.3Using monogenic and polygenic approaches, we identified environmental factors that are correlated with changes in allele frequencies for both putatively neutral and adaptive loci in our study area. In particular, our results suggest that temperature has a significant impact on both neutral and putatively adaptive genetic variation throughout the system, with two temperature variables  being significantly correlated with neutral genetic variation using the RDA approach. For adaptive genetic variation, both temperature and salinity appear to be important based on the results of the monogenic and polygenic approaches.Environmental variables can shape neutral variation through their impact on gene flow, for example, by acting as barriers to long‐distance dispersal. Oysters disperse uniquely during the larval period, a particularly vulnerable life‐history stage that is characterized by extremely high mortality rates  and contemporary anthropogenic influences  of oysters in our study area may have impacted extant population genetic structure. Indeed, Malpeque disease is the primary reason oyster populations in the Gulf of Saint Lawrence were assumed to be genetically homogeneous. In New Brunswick, Malpeque disease occurred in the 1950s, with massive transplantation of PEI oysters during the 50s and 60s. Oysters in our study area can spawn in the year following settlement, although the number of gametes is very small initially and increases with oyster size/age , which has often been suggested as a major feature of highly fecund marine species (including oysters) subject to high larval mortality (reviewed in Hedgecock and Pudovkin ). It impRather, it is plausible that extant oyster populations reflect a mixture of “ancestral” populations and introgressed alleles from PEI oysters. In northern and southern NB, oyster populations seem to be more related to MAL (in PEI) and thus probably more related to the resistant oysters used for repopulation. However, the populations from central NB  and NS (CRB) do not cluster with the “Malpeque clade.” A possible explanation for this result is that these oysters may have independently acquired resistance to Malpeque disease or that there was limited introgression of resistance‐related genes of PEI oysters in these populations, due either to the presence of a larger number of oysters having survived the Malpeque outbreak  or to countervailing local selection pressure. As our results suggest that environmental conditions are at least partially responsible for the observed neutral and adaptive genetic variation, it is possible that a part of the “ancestral” genetic variation was preserved through selection.Evaluating the impact of ongoing human‐induced movement is also very difficult with our data set. Within NB, transfer permits are issued with an emphasis on mitigating the risks associated with aquatic invasive species and diseases, but there is no tracking of oyster movements by industry. The oyster aquaculture industry is spread out throughout NB, with aquaculture leases in all of the defined regional clusters. Oyster movements frequently occur between the northern and southern NB clusters, and between the Richibucto Estuary and southern NB, though most oysters tend to be moved within a cluster due to the logistics of wild spat collection . Importantly, the scope of activities carried out by the oyster aquaculture industry in NB has been relatively limited until the last 10 years. We were careful to avoid aquaculture leases in our sampling and sampled mature oysters, most of which are likely to be more than 5 years old, so it is also distinctly possible that insufficient time has passed for widespread hybridization to be detectable.4.5Crassostrea species  were found to be located within genes with at least one nonsynonymous mutation. Among the gene functions identified, several were related to ATP and ion binding, and one was related to immunity. Mitochondrial oxidative phosphorylation, in which energy is stored as ATP, is the major aerobic energy production process  by high temperatures , which could be associated with differential selection across temperature and salinity regimes. However, further investigation is clearly needed to elucidate the physiological basis for putative adaptive divergence in the eastern oyster.4.6Considering the ecological and economical importance of the eastern oyster in Canada, studying the extent of genetic divergence between populations and the factors responsible for the observed differentiation is a crucial step towards better management. Despite massive mortalities and human‐mediated translocations, our results demonstrate a clear pattern of genetic divergence related, at least partially, to spatial proximity in oyster populations within the studied region. Moreover, we found evidence that environmental conditions  were associated with putatively adaptive genetic variation in the system. The presence of local adaptation may argue for management measures aimed at limiting introgression from divergent populations Conover, , includiWhile we found correlative evidence that environmental variables were linked to changes in allele frequencies at both putatively adaptive and neutral loci, much work remains on elucidating the causative links and relevant scales for selection. For example, we relied on a government database  and complex . A better understanding of genotype–environment associations and the targets of selection in Maritime populations of the eastern oyster will contribute to mitigating these impacts and ensuring the long‐term viability of fishery and aquaculture activities.None Declared. Click here for additional data file.


Genetic polymorphisms in numerous pharmacogenetics studies were regarded as the essential factors involved in the response to or metabolism of drugs. These genetic variants called very important pharmacogenetic (VIP) variants played a role in drugs metabolism, which have been summarized in the PharmGKB database. In this study, we genotyped 80 VIP variants from the PharmGKB in 100 members of Blang volunteers from Yunnan province.2 tests to evaluate the significant loci between Blang and the other populations, including ASW, CEU, CHB, CHD, GIH, JPT, LWK, MEX, MKK, TSI, and YRI. The global variation distribution of the significant variants was observed from the ALlele FREquency Database. And then, we used F‐statistics (Fst), genetic structure, and phylogenetic tree analyses to ascertain the genetic affinity among 12 populations.Based on the PharmGKB database, we genotyped 80 VIP variants loci located in 47 genes. We used χnuclear receptor subfamily 1 group I member 2  was the most significant variant, followed by rs1540339 (NC_000012.12:g.47863543C>T) of vitamin D receptor . Furthermore, we found that genotype frequency of rs3814055 in the Blang was closer to the populations distributed in Miao. And genetic structure and F‐statistics indicated that the Blangs had a relatively closer affinity with CHD, CHB, and JPT populations. In addition, the Han nationality in Shaanxi was closer to it.Comparing the Blang with the other 11 populations from the HapMap Project, the statistical results revealed that rs3814055 (NC_000003.12:g.119781188C>T) of Our results will complement the pharmacogenomics information of the Blang ethnic group and provide a theoretical basis for safer drug administration for Blang. Recently, a large number of pharmacogenomics studies focused on genetic variations considered to be involved in response to or metabolism of drugs , most of whom live in Mount Blang, Xiding, Bada, Mengman, and Daluo areas of Menghai County in Xishuangbanna Dai Autonomous Prefecture of Yunnan province of Southwest China. The others distribute in ***Lincang, Simao, and Baoshan areas  regulations for human research subject protection.2.2We randomly recruited about 100 unrelated, healthy Blang people from the Yunnan Province of China. Each participant has undergone rigorous screening criteria. None of the subjects had any diseases including self‐reported cancer history and other diseases. Moreover, despite the influence of the Han and Dai people whose economy and culture development are relatively rapid, they still maintain the characteristics of the nation. They can be seen as representatives of the Blang population.2.3We chose 80 VIP variants loci located in 47 genes from the PharmGKB database. Genomic DNA was extracted from peripheral blood sample using the GoldMag‐Mini Whole Blood Genomic DNA Purification Kit  according to the manufacturer's protocol. NanoDrop 2000C spectrophotometer  was used to measure the DNA concentration. We utilized the Sequenom MassARRAY Assay Design 3.0 Software  to design Multiplexed SNP MassEXTEND assays  analysis by the Microsoft Excel  and SPSS 19.0 statistical software platform . The genotype frequencies of 80 variants in the Blang population were separately compared with those of the other populations, including the Chinese Han in Beijing, China (CHB); the Chinese of metropolitan Denver, Colorado, USA (CHD); the Japanese in Tokyo, Japan (JPT); a residents population in Utah with Northern and Western European Ancestry (CEU); the Gujarati Indians in Houston, Texas, USA (GIH); people with Mexican ancestry living in Los Angeles, California, USA (MEX); the Tuscan people of Italy (TSI); a population of African ancestry in the southwestern USA (ASW); the Luhya people in Webuye, Kenya (LWK); the Maasai people in Kinyawa, Kenya (MKK); and the Yoruba in Ibadan, Nigeria (YRI). All p values of less than 0.05 obtained in this study were two‐sided and Bonferroni's multiple tests were used to calculate the level of significance. After Bonferroni's multiple adjustment, we attempted to discover significantly different sites (p < [0.05/(80 × 11)]). Subsequently, we downloaded significant SNP allele frequencies from the ALlele FREquency Database  and analyzed the global genetic variation patterns from the HapMap database  to observe the variation of the selected VIP variants. On the basis of the Bayesian clustering algorithm approach, we performed structural analysis to assign the samples within a hypothetical K number of populations hypothesized by Pritchard, Stephens, and Donnelly (K = 3–10) was run for 10,000 steps after an initial burn‐in period of 10,000 steps. And we used △K to calculate to identify the most likely number of clusters by STRUCTURE HARVESTER   of PCR primers (listed in Table 3.2p < 0.05), we found that some loci were different (not shown). When compared to the 11 groups  and Blang without adjustment, the number of significantly different variants in the Blang population was 23, 30, 17, 30, 30, 21, 26, 21, 25, 22, and 35, respectively (data no shown). After adjustment (p < [0.05/(80 × 11)], listed in Table The average variants call rate of the results was over 95%. All selected loci meet the HWE. Using chi‐square test, we compared the Blangs and the 11 populations of the genotype frequencies distribution of 80 loci. Before adjustment  and rs4680 (HGVS: NM_000754.3:c.472G>A) in populations has changed. After correction for multiple tests, rs1801133 became less significant in ASW, JPT, LWK, MEX, MKK, TSI, YRI, except CHB. Besides, rs4680 were detected significant differences between CEU, MEX, TSI, and Blang. In the populations of ASW, CHB, JPT, LWK, MKK, and YRI, its differences disappeared. Nonetheless, some variants varied little, not even a bit, such as rs11568820, rs1544410, and so forth.VDR and NR1I2. There were rs10735810, rs11568820, rs1540339, rs1544410, rs2228570, rs2239179, rs2239185, rs731236, and rs7975232 distributed in VDR (vitamin D receptor), which encodes the nuclear hormone receptor for vitamin D3. Although failing to make amino acid changed, rs1540339 was also very significant among the nine populations except CHB, JPT, and MEX. Although rs2228570 (HGVS: NM_000376.2:c.2 T>G) was, the only one SNP changing amino acid, located in exon 2 of VDR, it was still prominent in the CHD.After analysis of Table NR1I2 changed little, significant differences still existed. We downloaded the associated data of rs3814055 from the website . As seen from the Figures Although rs3814055 in 3.3K values ranging from 2 to 10 were hypothetically in structure analysis. And, the results of K = 2,3 among global populations and the results of K = 3,4 ethnic groups from China were shown in Figure K = 3, the group was divided into three subgroups  based on relative majority of likelihood to assign individuals to subgroups. The results illustrated that Blang had a relatively closer affinity with CHB, CHD, and JPT. In accordance with the Table We used Structure 2.3.1 Software to analyze the genetic structure of the 23 populations in order to further identify the relationships between them throughout the world. Different Based on genetic structure, we further assessed the genetic relationship among 12 populations by using pairwise Fst values Table . As ment4NR1I2 rs3814055 was the most significant variant among the 12 selected populations, followed by VDR rs1540339. Using genetic structure analysis and Fst values, we also concluded that the genetic backgrounds of the Blang were similar to CHB.There is increasing interested in personalized medicine, because of genetic variations leading to each person's different metabolism of and reactions to some drugs. In our results, we genotyped the pharmacogenomic VIP variants in the Blang population. The conclusion was that that NR1I2, belongs to the nuclear hormone receptor superfamily, whose major role is to promote the detoxification and clearance of drugs and toxic xenobiotics from the body as a transcription factor  (Wang et al., Considering the above results, ethnicity is an important factor for the frequency distribution and the genotype of rs3814055 can be used as a marker for detecting IBD and UC. And the Blang may have a lower susceptibility to T1DM. Although rs1540339 has not been found to be relevant in the Blang, it is noteworthy in future studies. At present, there are more teams, including Jin TB et al., devoted to disease research of SNPs (Du et al., The authors have no conflicts of interest to declare. Click here for additional data file.