Bemisia tabaci is one such pest for which a large body of research has examined patterns of genetic variation. We review this literature to address the question: What spatial scales define B. tabaci populations? These studies are global in coverage and draw from a variety of genetic marker types. We found that genetic differentiation among populations is typically low, and that patterns of genetic diversity suggest that groups of migrating whiteflies from divergent populations are typically being sampled together. Overall, these results suggest that there is high ongoing gene flow over large spatial extents, but recent invasion by most populations could obscure genetic markers’ ability to detect geographic isolation. Genome-wide data collected across finer spatial and temporal scales hold great promise to clarify the spatial extent of a B. tabaci population, and could reveal whether insecticide rotations can be tailored to specific commodities or if coordination across commodities and regions linked by B. tabaci gene flow is justified.Pest management can be greatly enhanced by basic knowledge about pest dispersal patterns in agroecosystems, which for insects often relies on comparisons of genetic variation among populations. The globally invasive sweet potato whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is considered a weak flier whose life history nonetheless predisposes it to frequent dispersal, but the scale over which populations exchange migrants, and should therefore be managed, is uncertain. In this review, we synthesize the emergent literature on B. tabaci population genetics to address the question: What spatial scales define B. tabaci populations? We find that within-species genetic differentiation among sites is often low, and evidence of population structuring by host plant or geography is rare. Heterozygote deficits prevail among populations, indicating that migrants from divergent populations are frequently sampled together. Overall, these results suggest that there is high ongoing gene flow over large spatial extents. However, genetic homogeneity typical of recently invading populations could obscure power to detect real isolation among populations. Genome-wide data collected systematically across space and time could distinguish signatures of invasion history from those of ongoing gene flow. Characterizing the spatial extent of B. tabaci populations could reveal whether insecticide rotations can be tailored to specific commodities or if coordination across linked commodities and regions is justified.Effective pest management depends on basic knowledge about insect dispersal patterns and gene flow in agroecosystems. The globally invasive sweet potato whitefly  Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) cryptic species complex. The insect’s small body size  that are genetically distinct but morphologically identical [Basic knowledge about insect dispersal patterns in landscapes is foundational to effective agricultural pest management. This is because insects must regularly disperse to colonize crops, and knowing the pathways, timing, and severity of these dispersal events can reveal where, when, and how intensely to apply control measures. Such knowledge can be difficult to obtain; however, for widely dispersing insects whose populations cannot readily be tracked across agricultural landscapes ,2,3. In  another ,11. Furtdentical , and whidentical ,15,16,17dentical .B. tabaci complex have emerged over the last four decades as major economic pests of field and horticultural crops worldwide, named for their geographic origins: Middle East-Asia Minor 1  and Mediterranean  [B. tabaci species [B. tabaci is capable of sustained flight over long distances [B. tabaci will typically fly close to the ground  . Though er, MED) ,12, theser, MED)  and are er, MED) , mating er, MED) , virus ter, MED) , vector-er, MED) ,24, and er, MED) ,26. MED er, MED) ,27 and ser, MED) ,29. Inva species ,31,32,33 species , B. tabaistances , even pristances ,36. Yet, (<10 cm ) and set  has been used to delineate cryptic species , detect ulations ,74. WhilB. tabaci populations [B. tabaci populations, we examine four themes among B. tabaci microsatellite datasets that can shed light on the extent of gene flow among B. tabaci populations.Microsatellites are highly polymorphic, nuclear markers that can parse levels of genetic diversity and differentiation among populations at relatively fine scales . Unlike ulations ,55,56,64ulations  or is inulations . Some stulations  or decreulations ,45,58 geB. tabaci populations in a landscape. The fixation index (FST) [ST among populations within cryptic species, eight (53%) include ranges of FST that bound zero [ST among studies sometimes exceed 0.5 [ST among studies. Studies reporting relatively low genetic differentiation between populations typically cover small geographic areas within individual countries [ST among populations, and supporting the occurrence of high levels of gene flow among populations within cryptic species.Genetic differentiation arises when populations are isolated in space over time, due to the random generation and loss of genetic variation by mutation and genetic drift, respectively, and the action of natural selection in their respective environments. Importantly, gene flow among divergent populations tends to erode genetic differentiation between them . The degex (FST) , is a cound zero ,65,66,67ceed 0.5 ,64,65  applying STRUCTURE to the study of B. tabaci genetic differentiation [K = 1 is not evaluated by STRUCTURE, an optimal value of K = 2 can sometimes imply the near-complete absence of genetic differentiation among populations, especially when ancestry proportions for the two genetic clusters are evenly mixed among most individuals [K > 2  with the geographic distance between pairs of populations. An absence of evidence of isolation by distance can follow from frequent long-distance migration events erasing any association between genetic differentiation and geography. Of nine studies testing for evidence of isolation by distance [When migration among populations decreases as a function of increasing distance between them, a pattern of isolation by distance can arise , which idistance ,60,67,68distance   among populations. This likely offered greater statistical power for subsequent tests of isolation by distance. Lastly, the density of sampling at each site was quite high , again lending itself to greater resolution of any genetic differences among populations. So, while isolation by distance may appear rare in B. tabaci, we suggest that sampling more densely among sites that are well-distributed across a region  might reveal more cases where gene flow is geographically restricted among populations.The one case where a significant pattern of isolation by distance was found  seems toident in ,17,57,67ident in , essentiident in ,57,60, aB. tabaci populations is the observation of widespread heterozygote deficits (IS (FIS > 0 indicates heterozygote deficit). Of eighteen studies reporting population estimates of observed and expected heterozygosity, 14 (78%) demonstrated heterozygote deficits in most populations [B. tabaci, can result from large differences in allele frequencies between sexes [A common theme throughout microsatellite-based studies reporting on genetic diversity among deficits , which oulations ,67,68,70ulations ,55,58,59en sexes , but is en sexes . While sen sexes ,55,57, tB. tabaci microsatellite dataset. One interpretation of heterozygote deficits is that a population is undergoing substantial inbreeding, a phenomenon where heterozygosity erodes due to frequent mating between closely related individuals [B. tabaci movements over the landscape may predominate, but could also arise if sampling schemes result in high rates of sibling sampling. Additional evidence of inbreeding in microsatellite datasets is strong linkage disequilibrium among loci, wherein allele frequencies among loci are correlated because they share a very recent common ancestor and are still being inherited together. Importantly, though, linkage disequilibrium across loci was rare among B. tabaci microsatellite studies. Without further evidence from genome-wide data (such as long homozygosity tracts), it is difficult to conclude that the observed heterozygote deficits stem from inbreeding. Lastly, consequences of Wolbachia infection have been proposed to explain heterozygote deficits, whether due to sex-ratio distortions [Wolbachia infections can be quite common among MEAM1 and MED, but also extremely rare, depending on geography [Wolbachia infections will only grow in importance with time [Wolbachia infection status on B. tabaci population structure have so far found no causal relationship [What is the cause of such pervasive heterozygote deficits and what can they tell us about the extent of gene flow among populations? One cause of heterozygote deficits could be genotyping error, a common challenge among microsatellite datasets , whereinividuals . This mitortions  or asymmtortions . Wolbacheography ,57,84,85ith time . Howevertionship ,68.B. tabaci populations within cryptic species  among individuals collected at a site, referred to as the Wahlund effect . Populats issue, ,57). Insy extent  [17,27,4B. tabaci are connected over large spatial extents: temporal shifts in genetic cluster assignment, rapid spread of symbiont infections, and widespread distributions of insecticide resistance mutations. Invasive MED populations exhibited large shifts in genetic cluster assignment over a period of six years in China [B. tabaci, distributions of native populations can also exhibit broad shifts over time, with the viruliferous SSA 1 spreading throughout much of Sub-Saharan Africa over a period of 20 years [Rickettsia infection spread to nearly 100% of B. tabaci in 6 years over an area spanning southern Arizona, USA [ST, AMOVA, and STUCTURE analyses that there is potential for ongoing gene flow among B. tabaci populations over broad spatial extents.Three other lines of evidence provide some support for the idea that in China , as a siin China . In Austin China . In addi20 years . Second,ona, USA . Lastly,ona, USA , though ona, USA  within wB. tabaci population genetics, and none were designed to address questions about gene flow among populations on small spatial and temporal scales [B. tabaci from cassava (Manihot esculenta) in 8 countries in Sub-Saharan Africa, and found relatively high levels of genetic differentiation  according to 7453 SNPs. However, analyses of population structure were complicated by the inclusion of multiple B. tabaci cryptic species. Chen et al. [ST among MED populations between 0.078–0.346, but here comparisons were made among populations from different continents. While these studies were well designed for characterizing the species composition of economically important B. tabaci communities and genetic differences at continental and global scales, there remains a need for studies that leverage genome-wide datasets to address questions about the importance of local movements for B. tabaci ecology and evolution. Such studies in other insect systems have revealed colonization of crop fields from specific non-crop host plant reservoirs [Only four studies have so far used single nucleotide polymorphisms (SNPs) to examine l scales ,70,71,90n et al.  obtainedn et al.  genotypeservoirs , and shoservoirs .B. tabaci population? While our synthesis of the current B. tabaci population genetics literature cannot provide a quantitative answer, it does suggest that there is potential for frequent and widespread gene flow on the scale of an agricultural region. The limited resolution provided by small numbers of genetic markers in most studies, combined with the recent invasion history of many economic B. tabaci populations cast doubt on this interpretation and complicate the task of linking population genetic patterns with ecological and evolutionary processes. Low genetic differentiation and pervasive heterozygote deficits among populations could be a consequence of recent introduction of a small number of founding individuals, which would obscure any ability to detect evidence of geographic isolation or widespread gene flow among populations based on patterns of genetic differentiation. Moving forward, higher-resolution genetic data, like that offered by genome-wide single nucleotide polymorphism (SNP) datasets, combined with systematic sampling over landscapes and over time will play an important role in distinguishing whether populations are primarily local in nature or connected over wide spatial extents. Studies of B. tabaci population genetics are beginning to make use of SNP datasets [B. tabaci populations.What is the spatial extent of a datasets ,70,71,90B. tabaci migration among cropping systems? Sampling whiteflies in a region over time as they first arrive and depart from emerging and senescing crops, respectively, could reveal important ongoing constraints on B. tabaci genetic variation that could potentially be modulated by crop diversity in landscapes. Second, what can whole-genome data reveal about the spatial extent of gene flow? The relative ease with which whole-genome data can now be generated for non-model systems opens the door for a powerful set of tools to be applied to the study of B. tabaci ecology and evolution. Pertinent to B. tabaci gene flow, genome-wide patterns of linkage disequilibrium can be used to make precise inferences about the spatial extent and timing of gene flow among populations [B. tabaci particularly amenable to this kind of analysis . Lastly, is anything impeding movement of B. tabaci among crops? Landscape genetics approaches, which examine associations between genetic variation and environmental variables [B. tabaci populations should be considered on local versus regional scales is important for designing management schemes of all kinds, including, but not limited to, insecticide resistance management strategies, which could benefit from coordinated efforts to rotate insecticide modes of action across commodities and regions where B. tabaci population linkages are evident.We suggest three questions to guide future research along these lines. First, what are the genetic consequences of ulations , and hapariables , provideariables , and areariables ,7,96. Di


Esox lucius) to assess the relative performance of microsatellites and RADseq with respect to resolution and conclusiveness of estimates of population differentiation and genetic structure. To this end, the same set of individuals (N = 64) were genotyped with both RADseq and microsatellite markers. To assess effects of sample size, the same subset of 10 randomly chosen individuals from each population  were also genotyped with both methods. Comparisons of estimated genetic diversity and structure showed that both markers were able to uncover genetic structuring. The full RADseq dataset provided the clearest detection of the finer scaled genetic structuring, and the other three datasets  provided comparable results. A search for outlier loci performed on the full SNP dataset pointed to signs of selection potentially associated with salinity and temperature, exemplifying the utility of RADseq to inform about the importance of different environmental factors. To evaluate whether performance differences between the markers are general or context specific, the results of previous studies that have investigated population structure using both marker types were synthesized. The synthesis revealed that RADseq performed as well as, or better than microsatellites in detecting genetic structuring in the included studies. The differences in the ability to detect population structure, both in the present and the previous studies, are likely explained by the higher number of loci typically utilized in RADseq compared to microsatellite analysis, as increasing the number of markers will (regardless of the marker type) increase power and allow for clearer detection and higher resolution of genetic structure.Population genetic studies reveal biodiversity patterns and inform about drivers of evolutionary differentiation and adaptation, including gene flow, drift and selection. This can advance our understanding and aid decision making regarding management and conservation efforts. Microsatellites have long been used in population genetic studies. Thanks to the development of newer techniques, sequencing approaches such as restriction site associated DNA sequencing (RADseq) are on their way to replace microsatellites for some applications. However, the performance of these two marker types in population genetics have rarely been systematically compared. We utilized three neutrally and adaptively differentiated populations of anadromous pike ( Investigations of population genetic structure reveal patterns of biological diversity and inform about the relative importance of underlying evolutionary drivers, such as gene flow, genetic drift and selection . For exaDifferent types of genetic markers have been widely used to delineate populations. One of the most frequently used marker types in studies of population structure is microsatellites, which are short tandem repeats (of 1–6 bp) in the DNA that are abundant in the genome of most taxa . Most miThanks to technological advances in next-generation sequencing (NGS), alternative sequencing approaches for studying population genetics and genomics are becoming readily available, such as restriction site associated DNA sequencing (RADseq) methods  in the Kalmar Sound region of the Baltic Sea  using both methods (10 microsatellite loci and 1580 SNPs derived from RADseq). In addition, both microsatellite and RADseq genotyping was performed on the same subset of 10 randomly chosen individuals from each population to evaluate whether and how sample size influenced resolution or conclusiveness of the estimates of population genetic structure. The obtained data were first used to investigate and compare population genetic structure and connectivity (gene flow) among the populations as estimated by both marker types and for the different sample sizes. Next, we utilized the RADseq data to search for loci under selection to identify candidate genes putatively responsible for the phenotypical variation observed among the populations. We also investigated whether selection was correlated with two environmental variables  to which these populations have been found to be locally adaptated  that spawn in the Southeastern part of Sweden were included . Two of Nfemales = 12, Nmales = 10, Lervik Nfemales = 9, Nmales = 12, Okne Nfemales = 11, Nmales = 10) were captured using fyke nets placed in the inlet of the streams leading up to the spawning locations during 4 days of the spawning migration in the spring of 2016 , and alleles were scored in the Microsatellite Plugin 1.4.4 in Geneious® Pro 11.0.3 .Tissue from each individual was ground using stainless steel beads  and a Bullet Blender, and DNA was extracted using DNeasy blood and tissue kit  according to the manufacturer’s instructions. All 64 samples were genotyped for 10 microsatellite loci  at 37°C for 16 h. Size-selection of the fragments, paired-end library construction and sequencing was conducted by Science for Life Laboratory . The 64 samples used in this study were sequenced along with additional samples  on two lanes of Illumina HiSeq2500 with a 2 × 126 bp setup, which yielded a total of 89.1 M raw reads for the 64 samples (mean 1.4 M per sample).For RADseq, the extracted DNA was digested with HF De novo assembly, SNP calling, alignment to the reference genome and annotation were performed using the integrated approach of the Stacks de novo pipeline , M , and n  to use in the Stacks pipeline  and the subsetted sample size (N = 30) respectively (henceforth referred to as “full SNP dataset” and “subset SNP dataset”). For this, a minimum read depth of 10, minimum minor allele frequency of 0.05, and a maximum observed heterozygosity of 0.70 was used. To avoid linkage between markers, only one random SNP per locus was kept. After this relatively strict filtering with Populations, the final dataset consisted of 1580 and 1670 biallelic SNPs for the full as subset SNP datasets respectively. Both datasets were separately used in the subsequent analyses of population structure, genetic diversity and migration, and the dataset for the full SNP dataset was additionally used for outlier analyses. All the analyses were also performed when excluding individuals with >10% missing data . The exclusion resulted in only minor changes in the specific values of the estimates but did not qualitatively change the results; and because the results were robust we chose not to exclude the individual from the analyses.Demultiplexing, quality control  with and without excluding null alleles. The frequency of null alleles for each loci in each population was relatively low , expected heterozygosity (HE), and departures from Hardy-Weinberg equilibrium (HWE) were estimated in Arlequin v3.5 (Ar) and the fixation index Fis were calculated with the software FSTAT (version 2.9.4) (HO), expected heterozygosity (HE), and fixation index (Fis) were estimated using the Populations software in the Stacks pipeline .For each of the two microsatellite datasets, mean number of alleles, observed heterozygosity  , and numn 2.9.4) . For botFST (K) STRUCTURE v2.3.4  to use. The m values suggested by the software  were chosen and used in the further CAP analyses. The null hypothesis of no difference between the centroids of the populations was tested with CAP using 9,999 permutations  based on pairwise genetic similarity  between individuals and a K-means clustering analysis in Discriminant Analysis of Principal Components (DAPC)  were perRecent migration rates (m) among the populations were estimated from all four datasets (full and subset for both microsatellites and SNPs) with a Bayesian Markov Chain Monte Carlo (MCMC) resampling method in BAYESASS v3.0.4 , which eq-values 0.53–0.89, Neutrality of the microsatellite markers was tested using the default settings in BayeScan, and the results suggested that none of the microsatellite were under selection  performed with the LEA package in R . BayeScEnv 1.1 aims to differentiate between signals of selection and signals of purely demographic processes on allele frequencies , by idenage in R  were useage in R , and werage in R , and theP-values, to adjust for multiple testing, was performed using the method by P-values  = 0.05 for all outlier analyses. To identify candidate genes, all loci suggested as putatively being under selection or associated with the environmental factors in the outlier analyses were annotated using the reference genome  using the search strings listed in HO = 0.40–0.57, subset microsatellite dataset OH = 0.43–0.64), and all three populations were in agreement with Hardy-Weinberg expectation at the majority of the loci  than those obtained for the microsatellite datasets. The majority of the loci in the SNP datasets were in HWE , and yet Fis was close to zero for all populations  based on all four datasets  suggested differentiation among all three populations  was three , and revealed that the three clusters were mainly population-specific   were assigned to the same genetic cluster (K = 3 indicated that the populations mainly belong to population-specific clusters (Genetic differentiation estimates (specific . For theelihood)  or threeelihood) , and theelihood) . When ruaK plots  three cl cluster , whereasclusters . The resclusters . For theclusters , and theclusters .a priori population information  supported a significant differentiation between the centroids of the populations for all datasets . The cross-validation with the leave-one-out method showed that the assignment success was 100% (64/64) with the full RADseq SNP dataset, and 96.7% (29/30) for the subset SNP dataset – which mis-assigned LF3  to Okne. For the microsatellite datasets, assignment success was also high: 93.8% (60/64) and 93.3% (28/30) for the full and subset datasets respectively. For the full dataset three individuals from Lervik were assigned to Okne, and for the subset dataset one individual from Lervik was assigned to Okne, and one individual from Okne was assigned to Lervik.Because additional separation among clusters  could be hidden in another dimension of the PCAs we also utilized two ordination methods, one with ion CAP, , and oneion CAP, , S8. TheK = 5  for all BAYESASS runs. Gene flow estimates for all population pairs were low for both of the full datasets  or Fdist , and BayeScEnv did not find any signals of selection associated with either salinity , and for all of these five studies, the estimates of heterozygosity were equal or lower for RADseq compared to microsatellites. Three of the five showed lower values for RADseq compared to microsatellites, and the other two studies found no significant differences (Five of the seven studies reported within population heterozygosity (ferences . This isN = 64) provided clearer results and a more conclusive picture of the population structure.Regarding genetic structure, the findings from the studies revealed that both marker types produced comparable results for highly divergent populations, but that RADseq was more successful than microsatellites in revealing fine-scaled population structure in studies where > 400 loci were utilized . This isHO 0.21–0.29 for the full SNP dataset and 0.20–0.28 for the subset; HO 0.40–0.57 for the full microsatellite dataset and 0.43–0.64 for the subset; When analyzing the RADseq SNP data, estimates of heterozygosity were similar for the full sample size and for the subset of individuals  revealed significant differentiation among all three populations. Both marker types also revealed similar patterns of the degree of pairwise population differentiation  and the island population (Harfjärden) . To evaluate whether separation in other dimensions affected the estimated population structure, we used DAPC, which effectively collapses the dimensions of the PCA into a single measure of separation. The results from DAPC revealed that both SNP datasets clearly suggested three clusters, whilst the results for the microsatellites were not as conclusive, and did not clearly suggest any specific value of K . In CAP cross-validation for the full SNP dataset, all individuals were assigned to their respective population. For the subset SNP dataset one individual was mis-classified, which was most likely a first generation migrant (suggested by GeneClass). The cross-validation test for the microsatellite datasets mis-classified three and two individuals for the full and subset datasets respectively, and these were not the same individuals as those identified as migrants using GeneClass.When analyzing population structure, the different approaches yielded somewhat mixed results. Based on ntiation . Consistntiation , 4C. Thentiation , 4D. Likntiation , 4A,B. Blue of K . The CAPlue of K . This diThe results of the migration analyses (with BAYESASS and GeneClass) for all four datasets indicated low levels of gene flow for all population pairs  that the three populations included in the present study are adaptively differentiated. The local adaptations have resulted from environmental differences among the spawning locations, and can for example be attributed to differences in the salinity and temperature regimes , 2019, aDespite the already existing evidence of phenotypic local adaptation in the populations, the two analyses of locus-specific effects (Bayescan and Fdist) did not identify any SNPs under selection. This finding was somewhat surprising, because adaptive differentiation between the populations should be reflected in the genome. However, the LFMM analyses did identify signatures of selection, when salinity and temperature were introduced as environmental factors .Of the SNPs identified as outliers by the LFMM analyses , one SNP was associated with both of the environmental variables, which indicates that it might not be directly associated with a single environmental variable, but rather stress in general. The annotation revealed that of the four SNPs identified, three were located within known genes . It is pConclusions about the importance of different environmental factors should also be done cautiously when based on results from outlier analyses alone, as responses to selection are further complicated by developmental plasticity, phenotypic flexibility and crossing norms of reaction affecting the phenotypic expression of genetic variation in the study populations, especially during early stages of the development . For exaFST values) for both the full and subset datasets. In addition, both marker types were able to detect genetic structuring for both datasets – though the full SNP dataset provided somewhat more conclusive results. The previous studies that have compared the two marker types, invariably report that RADseq SNPs outperform microsatellites in detecting fine-scaled structuring. However, the number of microsatellite loci that have been used in those studies have generally been low, and to evaluate the relative performance studies using higher numbers of microsatellites should also be investigated. Estimates of within population diversity (heterozygosity) based on RADseq SNPs were lower than those based on microsatellites, consistent with the findings in previous studies, and likely reflecting the high mutational rate of microsatellites. Both marker types generally indicated low levels of gene flow between all populations. Results from the outlier analyses suggested signs of selection associated with salinity and temperature, two environmental factors that have been implicated as drivers of local adaptation of these pike populations also in previous studies using different approaches.When comparing the relative performance of microsatellites and RADseq SNPs in the present study, both markers indicated genetic differentiation between the populations  are available in the NCBI Sequence Read Archive , and the study (including capture and sampling procedures) was granted ethical approval , by the Ethical Committee on Animal Experiments in Linköping, Swedish Board of Agriculture, Sweden.JS and AF conceived the study. JS, AF, and PT conducted the sampling of fishes and collection of DNA samples. JS and YY performed the laboratory work, bioinformatical analyses, and drafted the first manuscript. JS, AF, and YY contributed to interpreting the results. All authors commented on and agreed to the final version of 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.


Mytilus mussels, sampled in Europe and genotyped with 77 ancestry‐informative markers. We identified a type of introduced mussels, called “dock mussels,” associated with port habitats and displaying a particular genetic signal of admixture between M. edulis and the Mediterranean lineage of M. galloprovincialis. These mussels exhibit similarities in their ancestry compositions, regardless of the local native genetic backgrounds and the distance separating colonised ports. We observed fine‐scale genetic shifts at the port entrance, at scales below natural dispersal distance. Such sharp clines do not fit with migration‐selection tension zone models, and instead suggest habitat choice and early‐stage adaptation to the port environment, possibly coupled with connectivity barriers. Variations in the spread and admixture patterns of dock mussels seem to be influenced by the local native genetic backgrounds encountered. We next examined departures from the average admixture rate at different loci, and compared human‐mediated admixture events, to naturally admixed populations and experimental crosses. When the same M. galloprovincialis background was involved, positive correlations in the departures of loci across locations were found; but when different backgrounds were involved, no or negative correlations were observed. While some observed positive correlations might be best explained by a shared history and saltatory colonisation, others are likely produced by parallel selective events. Altogether, genome‐wide effect of admixture seems repeatable and more dependent on genetic background than environmental context. Our results pave the way towards further genomic analyses of admixture, and monitoring of the spread of dock mussels both at large and at fine spacial scales.Human‐mediated transport creates secondary contacts between genetically differentiated lineages, bringing new opportunities for gene exchange. When similar introductions occur in different places, they provide informally replicated experiments for studying hybridisation. We here examined 4,279  It constitutes a model for investigating the genetic and evolutionary consequences of marine invasions  and Mediterranean (Med.). , and compared these to mussel populations established in the vicinity. This genetic survey allows us to report, for the first time, an unexpected and extensive introduction of a nonindigenous lineage of M. galloprovincialis in Norway  M. edulis lineage, resulting from an anthropogenic introduction. We also combined our data with multiple samples of admixed populations from natural hybrid zones and laboratory crosses. This allowed us to compare multiple independent events of admixture, with a variety of ecological and genomic contexts.To place these populations in a wider context, we additionally analysed published and new samples of putative The similarities and differences between these various admixed populations help to clarify the factors that determine the outcome of an introduction with hybridisation. In particular, we show that similar outcomes sometimes reflect shared colonisation history, but can also arise in genuinely independent colonisations. However, this predictability is highly background‐dependent, and replicated outcomes only appear when the same parental backgrounds are involved.22.1M. galloprovincialis ancestry in the port of Cherbourg (France), as sampled in 2003  Mathiesen et al. . Our aim was to further assess the origin of the M. galloprovincialis ancestry. (b) Coolen .As part of our sampling process, we re‐genotyped samples from several previous studies that reported the presence of n et al.  who stud) Coolen  studied Samples originating from another oil platform from the Norwegian Sea  and one Norwegian sample  were also included. We note that the MCH oil rig was free of settled mussels at the time of deployment.M. edulis and Med. M. galloprovincialis, produced in Bierne, Bonhomme, Boudry, Szulkin, and David  and M. galloprovincialis from the western Mediterranean Sea . F2s were produced by crossing one F1 female and five F1 males. Additionally, sex‐reciprocal backcrosses to M. galloprovincialis were made, they are named BCG when the females were M. galloprovincialis and BCF1 when the female was F1  in combination with a Kingfisher Flex  extraction robot to extract DNA. We followed the kit protocol with modified volumes for the following reagents: 2 × diluted magnetic beads, 200 μl of MB3 and MB4, 300 μl of MB5 and 100 μl of MB6. The extraction program is presented in Figure Genotyping was subcontracted to LGC genomics  and performed with the KASP™ array method , showing that the genotyping method is mostly accurate. A few individuals identified as affected by a The data set was filtered for missing data with a maximum threshold of 10% for markers over all individuals and 30% for individuals over all markers. This filtering yielded 4,279 individuals genotyped at 77 loci . We separated nuclear (76 loci) and mitochondrial (1 locus) markers for downstream analyses. The mitochondrial marker (named 601) is located on the female mitochondria.M. edulis species complex , M. galloprovincialis  and M. trossulus (tros). L2 defines allopatric lineages in each species: (a) American  and European (edu_eu) M. edulis; (b) Atl.  and Med.  M. galloprovincialis; (c) Pacific (tros_pac), American  and European  M. trossulus. Finally, L3 defines subpopulations where the differentiation is mainly due to local introgression following historic contacts between lineages  were included  and mussels from Brittany ; and (c) West  and East  Med. M. galloprovincialis, the limit being set at the Siculo‐Tunisian strait.Most analyses required reference population samples. A list of reference individuals and groups was set a priori using the literature and our knowledge of the x Figure . We defiAdmixture  was estimated in each L3 level for all markers. edu_eu_south was separated in two groups, corresponding to the Bay of Biscay , and to estimate time since admixture.Estimates of linkage between markers allow us to account for admixture linkage disequilibrium in ancestry estimation  and 132 F2 offspring. Markers that were not heterozygotic in all F1 parents, or with an allele frequency difference between species lower than 0.2 were removed to avoid spurious distortions and orientation. We also removed two markers with >10% missing data. This left a final data set of 40 informative markers and 114 F2 offspring. Alleles were oriented according to their frequencies in reference samples. We then used the R package qtl to produce a genetic map  was performed in R, using the Jombart, . The genhierfstat  and a separate and inferred α for each population , and the major clustering output of the most parsimonious K was used . Additionally, to allow a better classification of individuals at bay scales, Structure analyses were performed on a “local data set” with the ports and surrounding populations, with and without admixture, and without including the reference populations (K in 2–5). Finally, specific Structure runs with the linkage model were used to estimate the age of the admixture . Briefly, admixture linkage disequilibrium allows the estimation of the number of breakpoints per Morgan since the admixture event, r, which can be interpreted as an estimate of the number of generations since a single admixture event  were classified using the reduced data set without admixture, using the yellow and grey clusters corresponding to pure Atl. M. galloprovincialis and admixed M. galloprovincialis, respectively  were selected and native individuals were removed. The Structure ancestry estimates with admixture, identifying the four clusters edu_eu_south, gallo_atl, gallo_med and edu_am, were used  between populations of each admixture type. Additionally, a nonparametric post hoc pairwise comparison test was carried out, using the Dwass–Steel–Critchlow–Fligner test  mussels distant from the Bay of Brest, Northern Brittany population ; (b) individuals outside the Bay of Brest (the limit being the entrance straight), taken as reference local individuals; and (c) individuals inside the Bay of Brest classified as Atl. M. galloprovincialis with the local Structure without admixture result . A raster of costs was built for each port from polygon shapefiles  modified to include small port structures that could stop larval dispersal or to exclude inaccessible parts. Locks inside ports were considered as opened for the purposes of distance calculation between isolated points. We used the program QGIS to handle polygons and raster creation. Land was coded as missing data, and water was set to have a conductance of 1. The R package hzar  with the R package 2.7Our data include multiple admixture events. To ask if outcomes were similar across events, we compared the deviations of marker allele frequencies from their expected values in each situation.fexp. This expected value is calculated from the observed allele frequencies in pure‐lineage reference populations, and from the mean ancestry values across all markers for the focal population, as estimated from Structure.We denote the expected frequency of an allele in an admixed focal population as Structure with K = 4 clusters  and summed ancestries from South‐Eu. and American M. edulis, giving the composite ancestry estimation Qedu for each individual:Admixed population frequencies are calculated only with admixed individuals in each population . For LOF and GAS admixed populations, we used the North‐Eu. M. edulis lineage (edu_eu_north) to estimate parental allele frequencies  while using the usual Q¯edu estimation.For laboratory crosses, the parental Med. fobs from the expected frequency fexp is defined as:The deviation of the observed frequency r to estimate the strength of the correlation and tested the significance with a permutation test (5 × 104 permutations). The classic t test was not used due to the distortions not following normality.This computation allows us to estimate a distortion by locus from the average genomic expectation given the population ancestry and parental allele frequencies. The correlation of distortions by locus is then computed between admixed populations, corresponding to different admixture events (e.g. between one dock mussel and one Norway admixed population). For each correlation, we used Pearson's p‐values, developed by Poole, Gibbs, Shmulevich, Bernard, and Knijnenburg , and datasets contained possible nonindependence (e.g. from migration of hybrids between ports), we used a modified Fisher's method to combine jnenburg  and impl33.1Mytilus mussels , between 0.38 and 0.48 for L2 levels within species, and between 0.0024 and 0.31 for L3 levels within L2 , consistent with previous knowledge of its range , 16% American M. edulis (SD = 15.35), 41% Atl. M. galloprovincialis (SD = 13.91) and 3% Med. M. galloprovincialis (SD = 3.83) . These admixed mussels involve Atl. s Figure b, and ars Figure . LOF ands Figure . These a Figures  and S23.d Figure . On averM. galloprovincialis lineage. Nearly, all pairwise comparisons of the Atl. M. galloprovincialis ancestry are significantly different, with the exception of the GAS/JER comparison . This implies that no natural hybridisation is observed between these two lineages in our data set. This is in accordance with the distribution of the Mytilus lineages  Pure Atl. M. galloprovincialis from Brittany around Brest, Saint‐Malo and Saint‐Nazaire. We also observed intermediate individuals between Atl. M. galloprovincialis and M. edulis corresponding to admixed individuals or hybrids in the Bay of Brest area, Saint‐Nazaire and Saint‐Malo. All of these locations are in or close to natural hybrid zones between those two species, while the aquaculture of M. edulis in the Bay of Brest, imported from the Bay of Biscay, is an additional source of M. edulis in this area, especially since dispersing larvae from aquaculture sites are common , dock mussels are on average composed of 25% Eu. M. edulis , 69% Med. M. galloprovincialis (SD = 11.85), 4% Atl. M. galloprovincialis (SD = 6.08) and 2% American M. edulis (SD = 3.04)  have locked basins where the dock mussels were found. Importantly, dock mussels are nearly all localised inside port infrastructures, and we observed a sharp shift at the port entrance Figure . For theM. edulis migrants, yet Le Havre is the only one where F1 hybrids between dock mussels and M. edulis have been observed , we mainly find native migrants close to the port entrance inside Le Havre, Cherbourg and Saint‐Nazaire Figure . Le HavrM. galloprovincialis lineage, contrasting with the other ports where the native background is M. edulis , and exhibiting higher sympatry inside port infrastructure than anywhere else. Second, mussels with a typical dock mussel admixed genetic background have been detected outside port infrastructures, which motivated an extensive sampling. Contrary to the other ports, dock mussels extensively colonised the local environment, mainly inside and close to estuarine areas.The Bay of Brest is of particular interest for two reasons Figure e1‐e2. FiM. galloprovincialis populations. We compared several groups of Atl. M. galloprovincialis from Brittany  to assess the potential introgression from dock mussels to the local populations. While levels of M. edulis ancestry increased and levels of Atl. M. galloprovincialis decreased significantly from distant populations to inside the Bay of Brest, the levels of Med. M. galloprovincialis ancestry did not differ significantly  and 1.30 km (SD = 0.52) for the North and South transects of Le Havre, respectively  in Cherbourg  in Saint‐Nazaire  in the Bay of Brest , or if admixture events are independent, but lead to repeatable patterns of natural selection, then we would expect to see the same alleles over‐ or under‐represented in different locations.We cannot compare allele frequencies directly, because different locations are characterised by different overall levels of ancestry. Therefore, for each marker, in each location, we calculated its deviation from expected values. These expected frequencies were calculated from the allele's frequencies in the reference parental populations, combined with the overall levels of ancestry in the sampled location  towards the Med. M. galloprovincialis lineage in Le Havre, Cherbourg and Saint‐Nazaire , while displaying smaller distortions in Brest and Saint‐Malo (0.03 in both cases).Examination of these allele frequency deviations showed some suggestive similarities between admixture events. For example, the mitochondrial marker (601) is differentiated between the Med. and the Atl. s Figure . This loMore formally, the repeatability of admixture events can be assessed by correlating the complete set of allele frequency deviations between events. Four types of comparisons corresponding to differences in implicated lineages are presented in Figure M. galloprovincialis genetic background , contained an individual with pure Med. M. galloprovincialis in the port of Le Havre  despite the fact that neither of these lineages are found in this area. We also analysed two samples from a ferry hull collected in 2011 and 2013. The ferry crosses the English Channel between a M. galloprovincialis region in Brittany (Roscoff) and a hybrid zone in the UK (Plymouth) where M. edulis and M. galloprovincialis are found in sympatry , suggests that the admixture with M. edulis happened before the introduction of dock mussels in these ports.The evidence of limited natural dispersal outside ports, presented in this study, provides a strong case for a saltatory colonisation of ports through human‐mediated “jump dispersal.” In our view, the most parsimonious hypothesis of colonisation involves an initial admixture between pure Med. s Figure , where tShip traffic is thus likely to be the main source of these introductions to ports. The five studied infrastructures are large commercial and military ports that may have facilitated the primary introduction of mussels  with allozymes . In New Zealand, Gardner, Zbawicka, Westfall, and Wenne , where introgression impacts the fitness of native populations and selection against introduced alleles in wild populations seems to be acting , a haplotype‐based method showed that residual introduced tracts are present in native populations and go undetected by classical ancestry estimation methods . However, this observation may be more related to space competition with oysters on natural sites than to habitat selection, as finding mussels of any type on natural rocky shores in the bay was difficult.M. galloprovincialis when compared to M. edulis might facilitate hybridisation by avoiding stronger reproductive incompatibilities (both pre‐ and postzygotic). Therefore, the prediction of the invasion by dock mussels will require a thorough understanding of the reproductive incompatibilities between nonindigenous and native mussels . Second, compared to other ports, habitats suitable for dock mussels might have been available. Third, the closer genetic distance with Atl. M. galloprovincialis. Conversely, hybrid breakdown would have impeded both the introduction of the hybrid background, which would then have required a tremendous propagule pressure from maritime traffic. The same applies to the subsequent spread of dock mussels, even if fitter , but also the comparison of port samples to experimental backcrosses.Shared colonisation history cannot be the whole story, however, because we also found repeatable patterns between admixture events that must be considered independent. This includes not only the comparisons of natural admixture to the introduced Atl. M. galloprovincialis admixtures includes populations with a wide variety of contributions from the parental lineages. These range from a high M. edulis contribution in JER to a high Atl. M. galloprovincialis contribution in MCH. The high positive correlations of distortions observed between all Atl. M. galloprovincialis admixture, despite variable contributions of the two parental backgrounds, are particularly interesting. The calculation of D corrects for ancient introgression of parental backgrounds, and we are unlikely to have missed a hidden parental population given our broad geographic survey , where there is a high probability of strong reproductive isolation, either due to physical constraints or due to sufficient accumulation of incompatibilities (Roux et al., Mimulus, Aeschbacher, Selby, Willis, & Coop, Heliconius, Martin, Davey, Salazar, & Jiggins, Our comparison of Atl. M. galloprovincialis lineages are involved, equally notable is the lack of repeatability with different lineages. A possible explanation is that different sets of incompatible loci may be implicated in the reproductive isolation between M. edulis and the two M. galloprovincialis lineages. However, the history of divergence between the two M. galloprovincialis lineages is much younger than the divergence with M. edulis and most of the fixed mutations are expected to be shared by the two lineages (Fraïsse et al., M. galloprovincialis is in contact with M. edulis, while Med. M. galloprovincialis is not. Atl. M. galloprovincialis has experienced a punctuated history of introgression possibly swamped by bistable incompatibilities with an asymmetric advantage to the M. edulis allele (Fraïsse et al., M. galloprovincialis backgrounds. This hypothesis requires further theoretical and experimental investigation. Finally, given that karyotypic differences have been suggested between the two M. galloprovincialis lineages (Martínez‐Lage, González‐Tizón, & Méndez, While patterns of hybridisation are strongly repeatable when the same 5Mytilus mussels, with their introduction and hybridisation potential, are a particularly useful model for studying the parallelism of admixture events, and the range of outcomes of introductions with hybridisation. Our study shows that admixture between the same genetic backgrounds is highly repeatable. This repeatability can be explained by both a shared history of preintroduction admixture and parallel genomic processes. One category of anthropogenic hybridisations, the “dock mussels,” exhibit homogeneous patterns of admixture among all studied populations, and appear to be restricted to environments of large commercial ports. Follow‐up investigations will be needed to understand how selection, hybridisation, environmental conditions and dispersal are shaping the distribution and genomic architecture of these dock mussels and similar introductions.None declared. Click here for additional data file.


Echinometra sp. EZ inhabits both of these seas and plays a critical role in coral reef health as a grazer and bioeroder, but, to date, there have been no population genomic studies on this or any urchin species in this unique region. E sp. EZ’s life history traits , in theory, should homogenize populations unless nonneutral processes are occurring. Here, we generated a draft genome and a restriction site-associated DNA sequencing data set from seven populations along an environmental gradient across the Persian/Arabian Gulf and the Gulf of Oman. The estimated genome size of E. sp. EZ was 609 Mb and the heterozygosity was among the highest recorded for an echinoderm at 4.5%. We recovered 918 high-quality SNPs from 85 individuals which we then used in downstream analyses. Population structure analyses revealed a high degree of admixture between all sites, although there was population differentiation and significant pairwise FST values between the two seas. Preliminary results suggest migration is bidirectional between the seas and nine candidate loci were identified as being under putative natural selection, including one collagen gene. This study is the first to investigate the population genomics of a sea urchin from this extreme environmental gradient and is an important contribution to our understanding of the complex spatial patterns that drive genomic divergence.Extreme environmental gradients represent excellent study systems to better understand the variables that mediate patterns of genomic variation between populations. They also allow for more accurate predictions of how future environmental change might affect marine species. The Persian/Arabian Gulf is extreme in both temperature and salinity, whereas the adjacent Gulf of Oman has conditions more typical of tropical oceans. The sea urchin  It is not yet well understood how population structure arises in marine systems, where there are generally few barriers to gene flow and organisms often have a high dispersal potential. In this study, we investigated the population genomics of an ecologically important sea urchin from the Persian/Arabian Gulf and the Gulf of Oman. We found that despite the high capacity for gene flow, there was still evidence of weak population structure. This may be a result of the extreme environmental gradient between the two seas. Further, we found that gene flow is likely bidirectional and characterized a collagen gene under putative selection. Thus, our study reveals how adjacent environments with opposing conditions could result in population genetic structure for ecologically important marine species with high dispersal potential.The mechanisms governing evolutionary divergence are not well understood in marine systems, where clear barriers to gene flow are uncommon and organisms generally exhibit high dispersal capabilities . Indeed,Platygyra daedalea (Diadema setosum (The Persian/Arabian Gulf (hereafter PAG) is an example of an extreme marine environment, which is separated from the Gulf of Oman and the wider Indian Ocean by the narrow (42 km) Strait of Hormuz . The PAGdaedalea , the sea setosum , and sev setosum . The maj setosum  and the  setosum .−2 across eight sites between 2015 and 2019; Burt JA, unpublished data) and they play a significant role in the health and dynamics of coral reef ecosystems in the region as major bioeroders  and have been found in waters up to 20 m deep  to characterize patterns of genetic diversity and population structure of E. sp. EZ adult from Dhabiya reef in the southern PAG (24°21′55.8″N 54°06′02.9″E) was collected, preserved in RNAlater, and subsequently stored at −20 °C.A gonadal tissue sample from a single Total genomic DNA was extracted from the gonadal tissue sample using the DNeasy Blood and Tissue Kit (Qiagen). DNA quality was visualized on an agarose gel and concentration was determined with a 2000 Nanodrop spectrophotometer . High-quality DNA was submitted for PCR-free library preparation and whole-genome sequencing on one lane on an Illumina HiSeq3000 (150-bp paired-end reads) and one lane on a NextSeq500 (150-bp paired-end reads) at the University of Florida Interdisciplinary Center for Biotechnology Research.k-mer length of 21 using Jellyfish v2.2.6 . We performed de novo genome assemblies using Platanus v1.2.4  to construct artificial mate-pair libraries for five insert sizes  with MateMaker v1.0 (k-mer=85) using the previously generated artificial mate-pair libraries. We removed contigs smaller than 200 bp for our RAD-seq analysis, however, the uploaded final assembly (https://doi.org/10.5061/dryad.c59zw3r40) still contains these reads. The commands and parameters used for the genome assembly are available in a github repository (Approximately 302 million paired-end (PE) reads were obtained from the HiSeq and NextSeq sequencing. We performed adaptor trimming and quality filtering using Trimmomatic v0.36  with a ph v2.2.6 . This hih v2.2.6 . Mitochoh v2.2.6  with thes v1.2.4  with def plat.pl , was useker v1.0 . SSPACE ker v1.0  was subspository . We checpository  and BUSCpository  through pository .E. sp. EZ individuals were collected between 2017 and 2018 from seven sites along the northeastern Arabian Peninsula, for a total of 94 samples  following the manufacturer’s protocol for DNA extraction from tissues. DNA concentrations were normalized to 1 ng/μl for a total of 50 ng per reaction. Library preparation and sequencing of RAD markers were performed by Floragenex Inc.  using the restriction enzyme Stacks v1.46 (flags: -e sbfI -c -q -r), which removes any reads with an uncalled base and discards reads with low-quality scores  between populations and their significance was calculated in Arlequin v3.5.2.2 (HO), expected heterozygosity (HE), and the nucleotide diversity of variable sites (Pi) were estimated using “populations” in Stacks v1.46.Pairwise genetic differentiation  was set from 1 to 10 with 20 independent runs for each fixed number of K. Each run included a burn-in period of 100,000 iterations, followed by 100,000 iterations of the Monte Carlo Markov Chain (MCMC) algorithm. The admixture model was run with correlated allele frequencies. To identify the most probable number of groups (K) that best fit the data, we used STRUCTURE HARVESTER  within the smartpca program in EIGENSOFT v6.0.1 . We thenTreemix v1.13  was usedpcadapt (K) was defined by running a PCA with K = 1–20, and applying Cattell’s graphical rule  of α = 0.1 .To identify outlier loci, we used a Mahalanobis distance-based approach in the R package pcadapt , which hcal rule  to the sE value cutoff of 10−8 was used and only outlier SNPs which were within 1 kb of the BLAST hit were retained.We performed a search using the Nucleotide Basic Local Alignment Search Tool (BlastN) on the genomic scaffolds which contained outlier loci against NCBI’s Nucleotide collection (nr/nt) database in order to functionally annotate the identified outliers. An k-mer=85) resulted in a genome assembly with 4,487,317 scaffolds, measuring a total of 1.59 Gb. The assembly had an N50 of 1,006 bp and a mean coverage of ∼27×. We recovered 60%  of the core eukaryotic genes and 75%  of the core metazoan genes with CEGMA and BUSCO, respectively. The low recovery rates for conserved genes in the E. sp. EZ genome are due to the fragmented nature of the genome, which is likely a direct consequence of high heterozygosity and repeat content (25% of the genome comprised repeat regions). The estimated genome size was 609 Mb, the heterozygosity rate was 4.54%, and the duplication levels were 0.6%.The optimal assembly  were retained after initial quality filtering steps. Of the 59,446,179 discarded reads, 0.05%, 1.6%, and 15.4%, were discarded due to low-quality, ambiguous RAD-tags, and ambiguous barcodes, respectively. After mapping RADtags to the draft genome assembly and filtering for mapping quality, 378,775 SNPs were called. After stringent SNP filtering, 918 SNPs from 85 individuals across seven populations were kept. These 918 SNPs were used in downstream analyses unless a program did not allow triallelic SNPs, in which case we used a reduced VCF file containing 901 SNPs. This low SNP retention rate was likely due to extremely high heterozygosity and an abundance of repeat regions in the E. sp. EZ genome  were found when comparing Al Fiquet (GO) to Saadiyat (S-PAG) and Dhabiya (S-PAG), respectively; these PAG sites are the most geographically distant from the GO. When all samples in each respective Gulf were pooled, the FST between the PAG and the GO was 0.0057.The only significant pairwise n the GO . The higK, identified K = 2  and subsequently, the number of populations within the data set. We found that only the first eigenvector was significant  and explained 2.46% of the total genetic variation. All population structure analyses showed a slight degree of population differentiation.To further characterize population structure, we used STRUCTURE, smartpca, and a Tracey–Widom test. The Evanno method, which evaluates the second-order rate of change of the likelihood function with respect to ΔWe ran TreeMix with 85 sea urchin samples from seven populations to identify patterns of divergence and add migration edges to the phylogenetic model. The proportion of variance began to asymptote at 0.999 when six migration edges were fit  online. pcadapt was performed by retaining loci correlated with the first principal component axis after a 10% FDR correction. Of the 918 SNPs generated across 85 individuals, we identified nine candidate outliers. Of these nine outliers, we eliminated the loci that did not have a clear BLAST match , and those whose associated genomic scaffold had a query length <5,000 bp. This resulted in one outlier on scaffold 0012050  that was located in an E. sp. EZ COLP5α (5α collagen-like chain) . In adult sea urchins, this gene is expressed in mineralized regions and in the adult mutable collagenous tissues (Outlier detection in e chain)  gene, wh tissues . However tissues .E. sp. EZ and generated a population genomic data set consisting of seven populations from two environmentally distinct seas. With these genomic tools, we analyzed population dynamics of individuals living across dramatically divergent thermal and salinity environments. This study contributes to the growing body of literature characterizing population dynamics in environmentally extreme marine systems, and represents the first genome-wide investigation of a sea urchin from northeastern Arabia. Our findings are relevant to predicting how species will respond to future and ongoing climate change.We sequenced and assembled a draft genome for the sea urchin Echinometra is a pantropical genus with geographical distributions across the Indo-Pacific, Caribbean, and Atlantic. Echinometra is widely studied and recognized for their distinct patterns of population structure and speciation dynamics . This is likely a result of either large population size or an elevated mutation rate. In the sea squirt, Ciona savignyi, high heterozygosity (4.49%) was shown to be driven by a large effective population size, not elevated mutation rates  should result in a lack of genetic structuring between geographically distant populations . Modern FST results shown in FST values were found when comparing sites within the PAG to sites within the GO. The only FST values that were not significant between the two seas were found when comparing Dibba Rock to Musandam and to Ras Ghanada (the two PAG sites closest to GO). No significant FST values were found when comparing between sites within the same sea. These findings are congruent with studies on other marine organisms in the region, which also describe significant population differentiation between the two seas. One study on the sea urchin Diadema setosum used mitochondrial DNA to investigate population structure around the Arabian Peninsula and found that FST=0.05 , we were still able to detect weak but significant population differentiation. Together, these studies support a hypothesis of a consistent genetic break for many species between the PAG and GO.The FST=0.05 . A study to 0.29 . Finallyas 0.015 . The FSTE. sp. EZ, each corresponding to their respective Gulf. Interestingly, the STRUCTURE analysis revealed that assignment probabilities of individuals varied greatly within and between populations. For example, in every collection site in the PAG except Saadiyat, there are some individuals whose assignment probabilities more closely associate with the GO than the PAG. This same subtle pattern can also be seen for individuals from the GO whose assignment probabilities more closely resemble individuals from the PAG. These results are in contrast to a similar study on the coral P. daedalea by Through multiple analyses, we revealed the presence of two populations of E. sp. EZ between the two seas could be a result of a high degree of unidirectional or biased migration events. Oceanographic models have shown reduced mixing between the seas through subsurface outflow that prevents the transport of buoyant larvae, as well as long residence times of ∼1–3 years for seawater in the PAG (The patterns of admixture in  the PAG . HoweverCOLP5α. It is currently unclear what the exact role of this gene is in sea urchins but it has been implicated in collagen formation. Collagen genes have been shown to respond transcriptionally to thermal stress in other marine invertebrates (Our outlier analysis was preliminary and only resulted in nine significant outliers under putative selection. We could only functionally annotate one of these outliers; tebrates . FurtherE. sp. EZ, and the large effective population sizes, should all act to homogenize population differentiation. The RAD-seq dataset and E. sp. EZ draft genome assembly presented here will provide a platform for future studies on this ecologically important and understudied species.Our study contributes to recent efforts to characterize the population dynamics of organisms across extreme environmental gradients. The most striking result from this analysis was the presence of population structure given that the young age of the PAG, the dispersal capability of evaa150_Supplementary_DataClick here for additional data file.


These regions were suspected to have undergone natural selection since the time of admixture. Our results suggest that admixture had affected adaptation of AN-speaking Melanesians in the Solomon Islands.People in the Solomon Islands today are considered to have derived from Asian- and Papuan-related ancestors. Papuan-related ancestors colonized Near Oceania about 47,000 years ago, and Asian-related ancestors were Austronesian (AN)-speaking population, called Lapita, who migrated from Southeast Asia about 3,500 years ago. These two ancestral populations admixed in Near Oceania before the expansion of Lapita people into Remote Oceania. To understand the impact of the admixture on the adaptation of AN-speaking Melanesians in Near Oceania, we performed the genome-wide single nucleotide polymorphism (SNP) analysis of 21 individuals from Munda, the main town of the New Georgia Islands in the western Solomon Islands. Population samples from Munda were genetically similar to other Solomon Island population samples. The analysis of genetic contribution from the two different ancestries to the Munda genome revealed significantly higher proportions of Asian- and Papuan-related ancestries in the region containing the  They are Papuan-related ancestors, non-Austronesian (NAN)-speaking indigenous Melanesians, living in Near Oceania. Probably because of the large ocean lying to the east of Near Oceania, they did not extend into Remote Oceania, which includes all islands in Polynesia and Micronesia. The first colonization of Remote Oceania occurred in the Late Holocene by Austronesian (AN)-speaking people from Southeast Asia. They are called Lapita people after their culture, Lapita, which is characterized by high navigation skills and potteries decorated with distinctive motifs. Remains of their characteristic pottery suggest that they originated in Taiwan and arrived in the Bismarck Archipelago about 3,500 years ago4.The first immigrants into Oceania colonized Near Oceania, which comprises mainland New Guinea and surrounding islands such as the Bismarck Archipelago and the main Solomon Islands, about 47,000 years ago8. The dates of admixture in various Oceanian populations from the Solomon Islands, Bougainville and Bismarck Archipelago have been shown to mostly fall between 2300 and 3100 years ago8.Genetic studies of Near and Remote Oceanian populations demonstrated that most Oceanian people had both Asian- and Papuan-related ancestry components; therefore, it is considered that the Lapita people, Asian-related ancestors, admixed with NAN-speaking indigenous people, Papuan-related ancestors, in Near Oceania, before their expansion into Remote Oceania6. In addition, a recent study using a genome-wide dataset of 823 individuals from 72 populations, including 50 populations from Oceania also found signals of admixture in populations from the Solomon Islands8. The results of these two studies indicate that people in the Solomon Islands have Asian- and Papuan-related ancestries. Previously we studied mtDNA variations in AN-speaking Melanesian populations in the New Georgia Island, the western province of the Solomon Islands to examine the genetic affinity between AN-speaking Melanesians in the Solomon Islands and AN-speaking Polynesians9. The people of Munda, one of the studied populations living in the main town of the New Georgia Islands, had mtDNA haplogroups of both Near Oceanian and Asian origins as found in other populations from the Solomon Islands, suggesting that Munda people also have Asian- and Papuan-related ancestries.The Solomon Islands consist of a chain of six large islands  and approximately, 900 small islands. In a large-scale study on mitochondrial DNA (mtDNA) and Y chromosome variations in Near and Remote Oceania, haplogroups of Asian origin and Near Oceanian origin are both represented in different populations of the Solomon Islands10, which was recently applied for the detection of potential selection signals in the genomes of Malagasy, an AN-speaking population in Madagascar who have AN-related and Bantu-related genetic ancestries11. Our results revealed that: (1) the Munda people were genetically related to other populations from the Solomon Islands, (2) the Munda genomes consisted of both Asian- and Papuan-related ancestry components, and (3) the significant amounts of Asian- and Papuan-related ancestries were observed in the region containing the annexin A1 (ANXA1) gene and in the HLA class II region, respectively. These genomic regions are considered to have experienced natural selections since the time of admixture. Although the possibility that these regions were shaped by only genetic drift still remains, we found that positive selection acted over the HLA class II region in modern Papuans. This observation indirectly supports the scenario that the HLA class II region of the Munda genomes has been subjected to selection. Considering that HLA class II molecules and annexin A1 protein both play important roles in immunity, infectious diseases may have been a strong selective pressure in Munda.In this study, we investigated the following genetic characteristics of Munda population using the genome-wide single nucleotide polymorphism (SNP) data: genetic relationship between Munda and other Oceanian populations, a sign of the admixture in Munda genomes, and signals of positive selection based on the excess of either Asian-related or Papuan-related ancestry observed in specific genomic regions of Munda. To assess the positive selection , we performed a genome-wide analysis of local ancestry of Munda individuals using the Efficient Local Ancestry Inference (ELAI) algorithmA principal component analysis (PCA) plot of 51 Oceanian and two Taiwanese populations is shown in Fig. Since Asian-related ancestors for the Solomon Islands populations are considered to have originated in Asia, probably Taiwan, we calculated f3 statistics for populations in the Solomon Islands using Han Chinese (CHB) or Aboriginal Taiwanese as a proxy for Asian-related ancestors and Gidra for Papuan-related ancestors to test if Munda population descends from a mixture of the two ancestral populations. Concordant with the previous studies, majority of the population of Solomon Islands, including Munda, showed significantly negative f3-statistics, indicating that they are a result of admixture between Asian- and Papuan-related ancestors Table . The datet al. (2011), assuming a phylogeny shown in Fig. The contribution of Asian- and Papuan-related ancestry across Munda genomes (Papuan versus Asian ancestry) was measured by the ELAI algorithm using CHB and Gidra as proxies for Asian- and Papuan-related ancestors. In this study, instead of Aboriginal Taiwanese, CHB were used as Asian-related ancestors, since the number of SNPs in the dataset including CHB was approximately three times larger than the one including Aboriginal Taiwanese. Figure P < 2.2 × 10−16). The former position was located in the high Asian-related ancestry region spanning ~1.4 Mb on chromosome 9 (chr 9: 74835176–76273853) and the latter was in the high Papuan-related ancestry region spanning ~1.8 Mb on chromosome 6 (chr 6: 32233886–33976742). The proportions of Asian- and Papuan-related ancestries in these regions fell outside 3 standard deviations (SDs). The high Asian-related ancestry region (>82.6%) contained guanine deaminase (GDA), zinc finger AN1-type containing 5 (ZFAND5), transmembrane channel like 1 (TMC1), aldehyde dehydrogenase 1 family member A1 (ALDH1A1) and annexin A1 (ANXA1), and the high Papuan-related ancestry region (>85.4%) contained human leukocyte antigen (HLA) class II genes, such as HLA-DRB1, HLA-DQA1, HLA-DPA1 and HLA-DRA. Although the genes directly targeted by natural selection are hard to be inferred, annexin A1 protein and HLA class II molecules play important roles in immunity.The proportion of genetic ancestries is expected to be approximately normally distributed across the genome, when only random genetic drift operates. If natural selection acts, the values of the genetic region targeted by selection would be outliers in the distribution. Thus, if an excess of local ancestry related to either of parental group is observed in the specific genome region, the region is regarded as a potential target of natural selection. As seen in Fig. To investigate the effect of the assumption about the generation on the proportion of the Papuan-related component at each position, ELAI analyses were conducted with setting generations since the time of admixture as 50, 100 and 150. The highest position (chr 6:32430975) was consistent Fig. , while tHLA region is known to be lower than the average rate of the human genome12. The lower recombination rate may have resulted in more extended linkage disequilibrium (LD). Therefore, the simulation, assuming the recombination rate of the corresponding HLA region , was also conducted. The average length of the regions that continuously have proportions of Papuan-related ancestry larger than +3 SD was 490 kb, and the maximum length was 1.1 Mb in the simulation  was observed at the position of chr 6:32487913 , and SNPs with low P-values were accumulated in the HLA class II region 13, developed for detecting recent positive selection based on the degree of extended haplotype homozygosity (EHH), was calculated across chromosome 6 in modern Papuans (n = 14). The most significant signal  of Munda estimated in this study fell within the range of admixture date  of various Oceanian populations estimated by Pugach et al.8. These results suggest that Munda share population history with other AN-speaking populations in the Solomon Islands.A PCA plot for Oceanian populations Fig.  reflectents Fig. . The Solda Table , when we14. Although the coalescent simulation revealed that genetic drift could cause the excess of either of local ancestry to the observed extent, the maximum length of the regions that continuously have proportions of Papuan-related ancestry larger than +3 SD or smaller than −3 SD in the simulation was shorter than the observed ones (1.8 Mb in HLA class II region and 1.4 Mb in the ANXA1 region). The extended LD is one of signatures of recent positive selection15. Thus, the co-existence of long-range LD and significant excess of local ancestry in real data could be interpret as a sign of positive selection. Besides, a signal of recent positive selection was independently detected in the HLA class II region in modern Papuans , according to the IPD-IMGT/HLA Database16. Additionally, the HLA polymorphisms were considered to be formed by pathogen-driven balancing selection17. For example, a previous study on the HLA-DRB1 diversity for a Mongolian population who moved from the north to the south of China indicated that the difference of pathogens in the environment could alter the frequency of the HLA-DRB haplotypes18. New Guinea and the Solomon Islands belong to tropical regions and a number of tropical infectious diseases such as malaria have been prevalent until today19. The pathogen-rich environment may have influenced the HLA polymorphisms of populations in New Guinea and the Solomon Islands over tens of thousands of years and the HLA haplotype variations in Papuan-related ancestors would have been advantageous for the pathogen-rich environment better than those in Asian-related ancestors. Corresponding with the above scenario, we hypothesized that the adaptive HLA haplotype variations derived from Papuan-related ancestors have been adaptively conserved even after admixture, which resulted in high proportion of Papuan-related ancestry in the HLA class II region in Munda. Our hypothesis is consistent with a previous study20 that reported similar allele frequencies of HLA-DRB1 among NAN-speaking Melanesians, AN-speaking Melanesians and Polynesians. In addition, several studies that investigated the distribution of local ancestries also reported signatures of natural selection on the HLA regions in admixed populations; an excess of African ancestry in the HLA regions was found in Mexicans and Latino populations recurrently21. The ANXA1 gene, which is present in the high Asian-related ancestry region, encodes a membrane-localized protein that binds phospholipids and is also known to have functions involved in innate and adaptive immune systems23. One of the possible driving forces of the selection that acted over the two genomic regions of Munda may be infectious disease.The high Papuan-related ancestry region contained the Blood was sampled in Munda, the main town of New Georgia Island, after obtaining informed written consent from each participant. All methods were performed in accordance with the relevant guidelines and regulations. This study was approved by the Health Research Ethics Committee, Ministry of Health, Solomon Islands, the Ministry of Education and Training, Solomon Islands, and the Research Ethics Committees of the Faculty of Science, and the Faculty of Medicine, The University of Tokyo.24. Next, we obtained a genome-wide dataset comprising 21 individuals from Munda population and 231,049 autosomal SNPs. Although all individuals were sampled as unrelated, we checked Identical-By-Descent (IBD) values of each pair of samples. The calculation of IBD values was performed after LD pruning using PLINK software v1.90b5.2 (www.cog-genomics.org/plink/1.9/)25. LD pruning was performed with the following settings, which define window size, step and the r2 threshold:–indep-pairwise 50 5 0.5. As a result, the number of SNP markers was reduced to 69,577. Since one pair of individuals showed IBD value higher than 0.2 , we excluded one individual in that pair in 3-Population Test26, F4 Ratio Test26 and Effective Local Ancestry Inference analysis (Guan 2014) but retained the individual in Principal component analysis (PCA), ADMIXTURE27 analysis and admixture time estimation using ROLLOFF13.Genomic DNA was extracted from peripheral blood using a QIAamp Blood Kit . SNP genotyping was performed for 21 individuals from Munda with the Affymetrix GeneChip® Human Mapping 250 K Nsp SNP array according to the protocol described in a previous study24, and a dataset of 443 individuals from 48 Oceanian populations and two Taiwanese populations extracted from the genome-wide dataset comprising 823 individuals and over 620,000 autosomal SNPs30. After merging the datasets using PLINK software, a genome-wide dataset comprising 512 individuals from 53 populations and 49,523 autosomal SNPs were obtained (dataset 1). The list of the populations used in this study is shown in Supplementary Table 31 were further added to dataset 1 (dataset 2). Cross-validation procedure implemented in ADMIXTURE package was performed to find the best value of K. The results were drawn using POPHELPER Structure Web App v1.0.1032.We merged the genome-wide dataset of Munda population obtained in this study with previously reported dataset comprising 24 individuals from Tonga, AN-speaking Polynesians, and 24 individuals from Gidra, NAN-speaking Melanesians in the lowlands of Western Province, Papua, New Guinea 26. To test if the admixture occurred in Munda populations, the 3-Population Test26 was conducted. The f3 statistics should be negative if a population C has descended from a mixture of populations A and B. Assuming CHB and Gidra as a proxy of Asian-related and Papuan-related ancestors, respectively, f3 was calculated; population C was assumed to be one of the Solomon Islands populations. The f3-statistics were also calculated assuming Aboriginal Taiwanese  as a proxy of Asian-related ancestors instead of CHB since Lapita people are suspected to have originated in Taiwan33. The numbers of SNPs used for f3 calculation are listed in Table 3-Population Test, F4 Ratio Test and admixture time estimation by ROLLOFF were conducted using the AdmixTools package version 4.134. The dataset 2 and the HapMap data of 60 unrelated individuals from Yoruba in Ibadan, Nigeria (YRI)31 were also used for this analysis.To estimate the proportion of admixture, we conducted F4 Ratio Test assuming the population relationships shown in Fig. 13 implemented in Admixtools package26 was performed for a genome-wide dataset comprising 21 individuals of Munda, 24 individuals from Gidra and 45 individuals from CHB and 231,049 autosomal SNPs (dataset 3) to estimate the date of admixture experienced by the ancestors of Munda. In brief, ROLLOFF estimates the date of admixture based on the rate of exponential decline of admixture-induced linkage disequilibrium. Genetic distance (cM) between SNPs was determined assuming that 1 cM was equal to 1 Mb. Next, ROLLOFF analysis was conducted with the settings which define a bin size as 0.1 cM and maximum distance as 20 cM.ROLLOFF analysis10. The admixture generations were set as 77 based on the dates of admixture for the Solomon Islands populations estimated in this study. In addition, to investigate the effect of assumed generations since the admixture on the local ancestry estimation, the dates of admixture were set as 50, 100 and 150 generations ago, which covered the whole range of the previously estimated date of admixture in Oceanian populations: the dates of admixtures fell between 3,300-1,800 years ago with 95% confidence intervals ranged from ~1,600 to more than 3,600 years ago assuming a generation time of 30 years8. Statistical analysis was conducted using R version 3.5.3 and the mean values of local ancestry across the Munda genomes were plotted using R package ggplot2 version 3.1.135. To detect outliers on opposite tails, we conducted Grubbs’ test on the mean values of local ancestry across each position in Munda genome using R package “outliers“ version 0.1437.To detect signals of positive selection that might have operated after admixture, we conducted ELAI analysis across Munda genomes. Using PLINK software, one individual with high IBD value was excluded from dataset 3 and SNPs were filtered by genotyping rate higher than 0.95 in each population. The final dataset (dataset 4) used for ELAI comprised 89 individuals and 146,090 autosomal SNPs. ELAI analysis was performed with the ELAI version 1.00 software with the settings which define the number of EM steps as 20, the upper layer number of clusters as 2, and lower layer number of clusters as 10 according to the manual38. To reproduce the population history of Gidra, CHB and Munda, simulations were performed assuming the following population history. First, two subpopulations (Anc1 and Anc2) diverged from one ancestral population 1667 generations ago, which corresponds to 50,000 years ago when generation time is 30 years. Next, subpopulations Anc1’ and Anc2’diverged from Anc1 and Anc2, respectively 77 generations ago . Then, these subpopulations were mixed at the same generation, 77 generations ago  was used as the admixture rate. Next, the genotype data for 24, 20 and 45 individuals from hypothetical Gidra, Munda and CHB populations were generated from the obtained sequences. Considering SNP ascertainment bias observed in real data, 146,090 SNPs, that exhibited the same distribution of minor allele frequencies as the real data, were randomly extracted from the simulated genotype data. ELAI analysis was then conducted with the same settings as described above.We used coalescent simulations to address if genetic drift alone could produce observed patterns of admixture in the Munda genomes. Coalescent-based simulations were performed using R package “scrm” version 1.7.3.1ago Fig. . The des43 across the whole chromosome 6 were calculated for modern Papuans (n = 14) in Papua New Guinea investigated in the Simons Genome Diversity Project44. The data were filtered by PLINK software with the following settings which define the minimum of minor allele frequency (maf), threshold of missing calls per variant, threshold of missing calls per sample, excluding variants with multi-character allele, and including only biallelic sites: –maf 0.01–geno 0.05–mind 0.05–snps-only–biallelic-only. Information on ancestral alleles was obtained from 1000 Genomes Project Phase 345. After excluding variants without ancestral allele information, 295,531 SNPs were remained and the genotype data were phased with Beagle 5.0 (beagle.28Sep18.793)46. The phased genotype data were used to calculate iHS with R package rehh version 2.0.447.To detect signals of natural selection in modern Papuans, integrated haplotype scores (iHS)http://www.pophelper.com, Outliers, http://CRAN.R-project.org/package=outliers, The IPD and IMGT/HLA database, https://www.ebi.ac.uk/ipd/imgt/hla/, UCSC Genome Browser, https://genome.ucsc.edu/index.html, PANTHER, http://www.pantherdb.org, and HapMap Phase II recombination rate, ftp://ftp.ncbi.nlm.nih.gov/hapmap/recombination/2011-01_phaseII_B37/.Additional studies and information can be found at POPHELPER, Supplementary Information.