Fucus vesiculosus invaded this recently formed postglacial sea 8000 years ago and shortly thereafter Fucus radicans diverged from this lineage as an endemic species. In the Baltic Sea both species reproduce sexually but also recruit fully fertile new individuals by asexual fragmentation. Earlier studies have shown local differences in morphology and genetics between the two taxa in the northern and western Bothnian Sea, and around the island of Saaremaa in Estonia, but geographic patterns seem in conflict with a single origin of F. radicans. To investigate the relationship between northern and Estonian distributions, we analysed the genetic variation using 9 microsatellite loci in populations from eastern Bothnian Sea, Archipelago Sea and the Gulf of Finland. These populations are located in between earlier studied populations. However, instead of bridging the disparate genetic gap between N-W Bothnian Sea and Estonia, as expected from a simple isolation-by-distance model, the new populations substantially increased overall genetic diversity and showed to be strongly divergent from the two earlier analysed regions, showing signs of additional distinct populations. Contrasting earlier findings of increased asexual recruitment in low salinity in the Bothnian Sea, we found high levels of sexual reproduction in some of the Gulf of Finland populations that inhabit extremely low salinity. The new data generated in this study supports the earlier conclusion of two reproductively isolated but very closely related species. However, the new results also add considerable genetic and morphological complexity within species. This makes species separation at geographic scales more demanding and suggests a need for more comprehensive approaches to further disentangle the intriguing relationship and history of the Baltic Sea fucoids.Closely related taxa provide significant case studies for understanding evolution of new species but may simultaneously challenge species identification and definition. In the Baltic Sea, two dominant and perennial brown algae share a very recent ancestry.  The Biological Species Concept defines species as reproductively isolated units , and curFucus vesiculosus L. and Fucus radicans Bergström & Kautsky, share a very recent (<8000 years) common ancestry [Contrasting the general marine paradigm of efficient dispersal of propagules, many macroalgal species show clear patterns of isolation by distance and strong geographic population genetic structures . This isancestry  resultinancestry , and shaancestry . Despiteancestry –15. Thisancestry .F. vesiculosus and F. radicans may, in addition to sexual recruitment, recruit new attached and fully fertile individuals asexually, by release of small fragments (adventitious branches) [F. radicans in Estonia, sexual recruitment is almost exclusive [As a rare feature among fucoid species, Baltic Sea populations of ranches) . Notablyxclusive , 19.Fucus vesiculosus and F. radicans overlap in distribution in large parts of the northern Baltic Sea. Careful analyses using microsatellite genetic variation have repeatedly shown that, at a local scale, barriers to gene flow separate the two species [F. radicans and F. vesiculosus within each geographic cluster [ species –11. This cluster .F. radicans as a separate species, [Fucus "vesiculosus" inside Gulf of Finland [The main aim of this study was to further investigate the reason for the geographic subdivision into two separate geographic clusters. We hypothesized that if isolation-by-distance causes this division, populations located between the N-W Bothnian Sea and Estonia would show an intermediate position in a genetic analysis. Thus we analysed genetic variation in populations from, the coasts of E Bothnian Sea, Archipelago Sea and Gulf of Finland and integrated the new data with earlier data from N-W Bothnian Sea and Estonia , 18, 19.species, ) have sh Finland , 21.Fucus vesiculosus and F. radicans in the northern Baltic Sea is due to extremely low ambient salinity in the Bothnian Sea [Fucus in the Gulf of Finland where Fucus is present in very low salinities towards the east end of the gulf.According to an earlier suggested hypothesis, high level of asexual recruitment of populations of nian Sea , 19. Thinian Sea . In the Our results largely supported the earlier conclusion of two locally distinct species, although in part of the Gulf of Finland we failed to assign individuals to species based on morphological criteria. Populations providing a geographic link between the Estonia and N-W Bothnian Sea populations did not appear as genetic intermediates. Instead the new populations added to the complexity of the genetic structure observed in each species. Finally, the low-salinity effect on asexual recruitment was not supported in the Gulf of Finland where some populations remained highly sexual despite extremely low salinities.The Baltic Sea is one of the largest brackish water bodies in the world. It has a young and complex postglacial history with passing through various stages of brackish- and freshwater during the past 15,000 years. Its current status as a large brackish water sea was established as recently as 8000 years ago , 24. CurF. radicans or F. vesiculosus, or when possible both, from 12 populations in the northern and eastern parts of the Baltic Sea that has not been genetically analysed earlier. The sampling of this species did not require permission from any local or national authority as sampled only in areas with full access to anyone to pick seaweeds. The sampled species are not classified as endangered, and are not under any protection in the sampled area. The same sampling strategy was used for the new samples as for the old ones included in the population genetic analyses. That is, in each site, individual ramets were sampled either along a transect of up to 100 m, or from within an area of 50-200m x 50–200 m. Sampling ramets grown from the same (or close) holdfasts (<1m distance) were strictly avoided. From morphology we assigned each sampled individual to either F. vesiculosus or F. radicans, using thallus width and overall appearance, following the species description [F. vesiculosus and F. radicans from other areas. The morphological identification was done prior to genotyping of all individuals.We sampled 20–60 thalli  of either F. radicans and the second from the same site being F. vesiculosus , were included in this study. These data were from previously published studies . Ten of iculosus . DespiteGenomic DNA was extracted using the NucleoSpin® Plant II-kit  according to standard kit protocol. DNA samples were amplified in a thermal cycler following the same procedure as , and genFIS) and for each locus separately following Weir and Cockerham [LD) for each pair of loci were also performed separately on each population. We also calculated between-population divergence using pair-wise estimates of FST obtained on genet level from GENEPOP between populations of each species but we allowed the four unassigned populations to be included in both matrixes.The microsatellite data sets from the 12 new populations were checked for errors such as null alleles, stuttering, and large allelic drop-out, by means of 1000 randomizations using the software MICRO-CHECKER v. 2.2.3 . For cloockerham  and withockerham . Tests oThe program GENCLONE 2.0  was usedF. vesiculosus and F. radicans individuals living in sympatric populations. For the latter analysis we used a subset of five local pairs of sympatric populations, each pair was sampled at the same coordinates. In all STRUCTURE analyses, preliminary simulations were executed to determine burn-in length and full run lengths. For the final analyses, three repeat simulations at each K-value were used, with a burn-in period of 10,000 and a run length of 100,000 generations.To describe the population genetic structure among all populations we applied several different methods in order to test the robustness of our result under different assumptions (of the different methods). We used a conservative approach in that we performed the population-level analyses on data including only the genets of each population. To test if removing all ramets except one copy would make a difference to the results, we rerun several of the tests on data including all ramets. As the results were indifferent to analyses including only genets, we present here only results based on genets. The principal component analysis (PCA) estimates the genetic relationships among populations in a multidimensional space, depicting the genetic relatedness among populations regardless of historical or other causes underlying this structure . Using tFinally, we applied a new approach that groups populations according to the directional relative migration between populations . This apAnalyses from MICRO-CHECKER on data from the 12 new populations (genotype copies excluded) showed no evidence of null alleles, except occasionally in some populations for the locus Fsp2. With low null allele frequency (0.20) and expected minor effects on detection of genetic differentiation , this loL85) to 30 (Fsp2). Only taking into account genet variation, seven of the nine loci showed populations with departures from Hardy-Weinberg equilibrium  varied greatly among populations . In low FST estimates from 0.0 to 0.35 in F. radicans and from 0.0 to 0.31 in F. vesiculosus . Overall, the genetic map unveiled some intriguing patterns: Firstly, there was no strong global separation of the two species. Secondly, separation was obvious in all local sympatric populations, although weak in the Russian Gulf of Finland site , while instead N and Q were genetically similar (F. vesiculosus (A) appeared closely related to F. vesiculosus populations from the Baltic Proper (B), Estonia (Y), and W Bothnian Sea , and the unassigned Gulf of Finland populations seemed genetically related to the Estonian populations of both species , and Q (Archipelago Sea) of  similar . One add species .F. vesiculosus  and to some extend also by the two Russian populations (V1 and V2), we did a second PCA analysis, excluding these six populations in order to resolve the separation among the remaining populations more clearly. This analysis showed a more obvious separation between the two species with all populations of F. radicans (except the two from Estonia) well separated from all the F. vesiculosus populations along the first PC axis that now explained 34.5% of the variation  and the Baltic Proper (B) was minor compared to the separation among the F. vesiculosus populations inside the Baltic Sea. Finally, the unassigned populations now seemed to appear well within the F. vesiculosus cluster , one north to eastern Bothnian Sea group of F. vesiculosus  also including the population from the Archipelago Sea (Q), and one group of the unassigned Gulf of Finland populations  (F. radicans and V2—F. vesiculosus) were neighbours, while the sympatric Estonian populations of F. radicans (W1) and F. vesiculosus (W2) were clearly distinct in this analysis  . The twoanalysis .F. radicans and F. vesiculosus [With large populations and a very recent common ancestry (less than a few thousand generations ago), we would expect only weak impact of genetic drift and no large genetic differences in neutral (microsatellite) markers between iculosus , 42, 43.iculosus –11 and sF. radicans from the Bothnian Sea are very different from Estonian populations (F. radicans populations), as also earlier reported [F. vesiculosus there is a similarly strong subdivision between the four populations sampled along the SW Finnish coast and all other populations of this species analysed in the present study (F. vesiculosus populations). Indeed, all other populations of F. vesiculosus, except the Russian Gulf of Finland population, form a coherent genetic cluster.However, in both species populations from different geographic areas are in many cases strongly genetically distinct. For example, populations of reported . In F. vF. radicans populations. When the SW Finland and Russian populations  are removed from the PCA, the unassigned populations are instead more closely related to populations of F. vesiculosus , that could not be assigned using the morphological criteria that have been used to separate the two species , remaineiculosus . For botulations .Fucus species even inside the Baltic Sea. Indeed, genetic differentiation among the eastern and western Bothnian Sea populations of F. vesiculosus are of a similar magnitude or larger  and in nian Sea . If hybrnian Sea  for poteThe finding of relatively high levels of sexually recruited individuals in the inner parts of Gulf of Finland where salinity is only 2–3 PSU was unexpected, as earlier studies have argued that sexual reproduction is impeded in salinities below ~5 PSU due to polyspermy and subsequent failure of embryo development , 50. OccAlternatively, the distribution of asexual and sexual activity in the Baltic Sea fucoids, in general, may be a stochastic process following a recent colonization and spread into a new area by species capable of uniparental recruitment , 52.Fucus populations is complex and illustrates what happens when divergence within a species is strong enough to muddle separation to a closely related species. Earlier evidence of high rates of speciation in the Fucus, lineage suggests that this lineage is generally prone to local adaptation and ecological speciation [Fucus to an extreme level. As illustrated here, such a situation with rapid evolution of two recently diverged lineages may promote genetic structures that challenge the classical dichotomy of traditional systematics.In conclusion, the population genetic structure of the Baltic Sea eciation . The verS1 TableFIS calculated according to Weir and Cockerham [The ockerham . Signifiockerham .(DOCX)Click here for additional data file.S2 TableFST between all 14 F. radicans populations. (b) FST values between all 12 F. vesiculosus populations. The four unassigned populations  are included in both matrices. Bold figure indicates FST estimate is significant after Bonferroni correction.(a) (DOCX)Click here for additional data file.


Hypoplectrus spp), brightly colored reef fishes from the wider Caribbean. Close to 100,000 SNPs genotyped in 126 individuals from three sympatric species sampled in three repeated populations provide genome‐wide levels of divergence that are comparable among allopatric populations (Fst estimate = 0.0042) and sympatric species (Fst estimate = 0.0038). Population genetic, clustering, and phylogenetic analyses reveal very similar patterns for local adaptation and speciation, with a large fraction of the genome undifferentiated (Fst estimate ≈ 0), a very small proportion of Fst outlier loci (0.05–0.07%), and remarkably few repeated outliers (1–3). Nevertheless, different loci appear to be involved in the two processes in Hypoplectrus, with only 7% of the most differentiated SNPs and outliers shared between populations and species comparisons. In particular, a tropomyosin (Tpm4) and a previously identified hox (HoxCa) locus emerge as candidate loci (repeated outliers) for local adaptation and speciation, respectively. We conclude that marine populations may be locally adapted notwithstanding shallow levels of genetic divergence, and that from a population genomic perspective, this process does not appear to differ fundamentally from the early stages of speciation.Are the population genomic patterns underlying local adaptation and the early stages of speciation similar? Addressing this question requires a system in which (i) local adaptation and the early stages of speciation can be clearly identified and distinguished, (ii) the amount of genetic divergence driven by the two processes is similar, and (iii) comparisons can be repeated both taxonomically  and geographically (for speciation). Here, we report just such a situation in the hamlets ( Whether populations are adapted to local conditions and, if so, through what mechanisms are fundamental questions in evolutionary ecology Williams . This isCommon gardens and reciprocal transplants can provide direct evidence of local adaptation. These approaches suggest that local adaptation is not uncommon in marine species, even in the presence of planktonic dispersal, and sometimes at small spatial scales Sotka . NeverthGenome scans provide the opportunity to identify the genetic footprints of local adaptation in natural populations, even in the absence of a priori hypotheses about the selective factors and specific traits involved  constitute just such a system. These reef fishes from the wider Caribbean are known for their striking variation in color pattern  from three locations , with 14 individuals per sample . This sampling design provides the opportunity to explore the population genomic patterns of local adaptation  and speciation (between sympatric species) within a single system, and to repeat comparisons both taxonomically  and geographically (in three populations for speciation).This study is based on nine samples including three sympatric species (the barred hamlet Libraries were prepared following the restriction site‐associated DNA (RAD) sequencing protocol by Etter et al.  and sequSbfI restriction site, and reads including adapter sequence as detailed in Puebla et al.  was set to three and the number of allowed nucleotides mismatch between two stacks (M) to two, which is in line with the guidelines provided by Catchen et al. , retaining stacks with coverage ≥10x in ≥15 individuals per location in ≥2 locations, or considered individually , retaining stacks with coverage ≥10x in ≥5 individuals per group in ≥7 samples. Fst were estimated following a standard analyses of variance (ANOVA) approach  were run for each value of K. Structure Harvester (Earl and vonHoldt X|K) and the ad hoc statistic ΔK , between the 80th and 90th percentiles , between the 70th and 80th percentiles , between the 60th and 70th percentiles , and below the 60th percentile . This approach should be considered with caution, as there is some circularity in the process of selecting the most diverged SNPs to then explore genetic structure. Here, the most differentiated SNPs were selected to infer roughly how many and which SNPs were consistently differentiated among populations, and compare them with the number and identity of SNPs that were consistently differentiated among species  were filtered out. RAxML version 8.0.5  identifies outlier clusters. Outlier clusters that do not have any other outlier clusters nested within them are defined as single‐outlier clusters (SOCs). We hypothesized that population structure should result in admixture LD when considering the entire dataset, and that clusters of loci in LD should differentiate populations and species.Linkage disequilibrium network analysis , loci with heterozygosities >0.5, and that computation time for the calculation of the initial LD matrix becomes very long for >10,000 loci. LDna analyses were therefore restricted to black and barred hamlets from Honduras and Belize (which had highest sequencing coverage), filtering loci with coverage ≥20x in at least 11 individuals in all populations, removing loci with rare alleles and heterozygosities >0.5 and considering a single SNP per stack, which resulted in 10,734 SNPs. Global q‐value <0.2, corresponding to an expected false discovery rate of 20%.Outlier scans were performed to identify SNPs that may be under selection. Bayescan version 2.1 (Foll and Gaggiotti http://www.ncbi.nlm.nih.gov/blast) and Blastn searches to the teleost genomes available on the Ensembl genome browser . Blast searches were also performed for the consensus sequence of all stacks that included nonrepeated outlier SNPs.Paired‐end reads were used to assemble mini‐contigs around the repeated outlier SNPs using Velvet version 1.2.03 (Zerbino and Birney m = 0.5 (‘panmictic') and one with m = 0.02  that are similar to those observed in the real dataset). Each individual carried 80,000 diallelic unlinked loci with a mutation rate μ of 1E‐9. As for the real dataset, 14 individuals were sampled per population. Simulations were repeated three times and sampled 10 times each, resulting in a total of 30 datasets per scenario.In order to complement and better interpret our results, part of the analyses were repeated on randomized and simulated datasets. For the randomizations, the 126 samples were grouped into three random ‘species' from three random ‘locations' . Simulations were performed with SimuPOP version 1.1.4 (Peng and Kimmel m = 3 M = 2) provided an average of 53,811 stacks per sample, with a mean coverage per stack of 31x before SNP filtering. The number of stacks decreased with increasing m and M parameter values, which is expected  and proportions of outliers (0.06–0.07%) were provided by the five assemblies with different combinations of m and M parameters (Table S1).A total of 565,253,125 reads of 101 bp each were retained after filtering, corresponding to 83.9% of the raw reads  and heterozygosity were estimated to 0.00240 and 0.00178, respectively, close to the values of 0.0036 and 0.00187 reported for sticklebacks , removing loci with rare alleles , or applying more stringent filtering , respectively. The distribution of SNP Fst estimates presented a sharp mode close to zero and a shallow tail extending to a value of one . Population genetic statistics are presented in Table S2. Considering all nucleotides, global diversity (Fst estimates among populations ranged between 0.0053 (H. puella Belize/Honduras) and 0.0330  and 0.0132 (H. nigricans Belize/Panama (Table n of 17–25 per pairwise comparison (vs. 100–108 for microsatellites).When considering the nine samples independently, a total of 31,059 stacks were retained after filtering, providing 55,195 SNPs. X|K) was systematically higher for K = 1 than for any other value of K in the 10 replicate runs. Nevertheless, the black hamlets from Belize – the most differentiated sample according to the RAD and microsatellite Fst estimates – tended to form a distinct cluster in some runs with K = 2. This pattern became consistent when removing loci with rare alleles , in which case K = 2 was identified as the best clustering solution .The clustering analyses are summarized in Figures th–100th, 80th–90th, and 70th–80thFst percentiles provided strong evidence of clustering. The highest mean ln Pr(X|K) corresponded to K = 3 (90th–100th and 80th–90th percentiles) and K = 2 (70th–80th percentile). In each case, the ΔK statistic presented a clear peak at these K values, and the 10 replicate runs provided almost exactly identical groupings (including the three ‘misassigned' samples), although different seed numbers were used for each run. For the 90th–100th and 80th–90th percentiles, the three clusters corresponded to the three locations .The SNP subsets from the 90th–100thFst percentile grouped samples by location with a bootstrap support value of 49. The SNP subsets from the 80th–90th, 70th–80th, and 60th–70th percentile and below the 60th percentile did not reveal any clear phylogenetic signal, with trees similar to these obtained with the entire dataset (data not shown).A tendency to group samples by location and species was apparent when considering the entire dataset Fig. , but theφ and |E|min set to four and 16, respectively . The SOCs contained between nine and 43 loci each , representing 1.2% of the SNPs included in the analysis. Two of them (1149 and 1030) did not appear to distinguish the four samples, with global Fst estimates among samples of 0.0027 and −0.0191, respectively. SOC 1030 consisted of relatively tightly linked SNPs (median LD = 0.6 vs. ≤0.2 for the other four SOCs), which may reflect physical linkage (possibly an inversion). The other three SOCs  presented higher Fst estimates among the four samples  and diffuse linkage, that is, with a number of edges close to the number of loci. They tended to distinguish the black hamlets from Belize (the most differentiated sample) along the first DAPC axis and the barred hamlets from Honduras (471), barred hamlets from Belize (684), and black hamlets from Honduras (923) along the second axis, suggesting that these SOCs result from admixture LD.A small proportion of SNPs  presented LD values ≥ 0.8, the large majority of which involved a single pair of loci . These may be on flanking regions of the same restriction site, as a single SNP per stack was used for these analyses. Larger clusters emerged, grew, and merged at lower LD values. Five single‐outlier clusters (SOCs) were identified with H. puella, H. nigricans, and H. unicolor independently. Similar results (and the same repeated outliers) were obtained when running the Fst outlier analyses globally for each species instead of individually for each population pair (data not shown). Individual Fst estimates at the three outlier loci are highlighted in Figure Fst estimates among populations within species ranging between 0.348 and one. The latter Fst estimate of one corresponded to a SNP on locus 38,220 that was fixed in Belize (C/C) versus Honduras and Panama (G/G) in the three species. A total of 19 outliers were identified with the prior odds for the neutral model set to 100, which represents 0.01% of the SNPs analyzed. Loci 38,220, 55,313, and 39,894 were identified as outliers here again, as well as in the other assemblies (Table S1). All loci were not included in all analyses as they were below the minimum coverage threshold in some populations , to an intron situated 204 bp from exon 8 and 4826 bp from the repeated outlier.A mini‐contig of 467 bp and mean coverage of 1033x was obtained for the repeated outlier locus 39,894. The consensus sequence mapped uniquely to an intron in the Fst were estimated to 0.0001 and 0.0003, respectively, as opposed to 0.0042 for the real dataset. The distributions of SNP Fst estimates were similar to the real dataset , the simulations provided global Fst estimates ranging between zero and 0.0005 (mean = 0.0002) and no Fst outliers. For the ‘structure' scenario (m = 0.02), the simulations provided global Fst estimates ranging between 0.0031 and 0.0037 (mean = 0.0035) and 67 outliers, representing 0.02% of all the loci considered, and no repeated outliers. An example of each scenario is illustrated in Figure Two randomized datasets are illustrated in Figure Fst estimate = 0.0038–0.0042), Fst distributions , and numbers of repeated outliers (1–3) for the two processes. In both cases, about 20% and 10% of the most differentiated SNPs distinguished populations and species consistently when considered together in the clustering and phylogenetic analyses, respectively , only 7% were shared between populations and species comparisons. This pattern was equally true of outlier loci where, again, only 7% of the Fst outliers were shared between populations and species comparisons. In the same line, the three repeated outliers identified among populations differed from the single repeated outlier previously identified among species  among populations separated by >500 kilometers. Slightly lower Fst estimates are provided by microsatellites for the same species and populations , which is consistent with the higher diversity and larger sample size of the microsatellite dataset. The results are also consistent with the shallow Caribbean‐wide genetic structure reported for H. puella using microsatellites  and a simultaneous hermaphroditic mating system that implies a demographic sex ratio of 1:1  datasets. Two of the three repeated outliers did not map to any known sequence, which illustrates the limitations of RAD sequencing as a tool to identify candidate genes in the absence of a reference genome. On the other hand, one repeated outlier mapped uniquely to an intronic region of the Tpm4 gene in five teleosts  when exposed to cold temperatures  were identified in pairwise comparisons involving Belize specifically (Belize‐Honduras and Belize‐Panama). We hypothesize that the outlier signal observed at the Tpm4 locus is linked to local adaptation to periodic episodes of low temperatures in Belize. Fine mapping of the association between Tpm4 and population differences is needed to refine this hypothesis and establish to what extent the high levels of genetic differentiation observed in Tmp4 are due to reduced gene flow  was substantially higher than the mean coverage of 31x, suggesting that high divergence does not result from allelic dropout . This suggests that part of the nonrepeated outliers, possibly as many as 30% of them, may be false positives.Among the 107 Fst estimates are consistent with microsatellite data from the same species and populations and the relatively low levels of heterozygosity of the hamlets suggests that our Fst estimates are not disproportionately inflated by the occurrence of null alleles. In addition, the shallow levels of genomic structure reported here provide a very favorable scenario for the detection of loci under divergent selection  or the masked hamlet (Hypoplectrus providencianus) present both distinct color patterns and high levels of endemism, suggesting that local adaptation and color pattern may have both played a role in species divergence. Ultimately, whether divergence is considered within the framework of local adaptation or speciation may reflect more a question of perspective and levels of divergence than a fundamental difference between the two processes. We conclude that marine populations may be locally adapted notwithstanding very shallow levels of genomic divergence, and that from a population genomic perspective, this process does not differ fundamentally from the early stages of speciation.It is important to keep in mind that hamlets can be more diverged than the populations and species considered in this study, and that the distinction between local adaptation and speciation becomes blurred as populations and species diverge. For example, the hamlets from the Gulf of Mexico appear to be well diverged from similarly patterned Caribbean hamlets, and have been recently described as distinct species Victor . In this10.5061/dryad.nt722.RAD demultiplexed sequence data, SNP genotype calls and mini‐contig sequences: Dryad DOI: doi: None declared.Figure S1. Clustering results for adaptation , simulated data , and randomized data.Figure S2. Clustering pattern obtained with K = 2 when considering all the data and removing rare alleles .Figure S3. Patterns of linkage disequilibrium among the 10,734 SNPs considered in the LD analysis.Click here for additional data file.Table S1. Summary of the five assemblies with different combinations of m (stack depth) and M (mismatch) parameters.Click here for additional data file.Table S2. Mean number of individuals sampled per site, observed and expected heterozygosity, nucleotide diversity (π) and Fis in the three locations considered in this study.Click here for additional data file.Table S3. Highest blast hits for the stacks containing non‐repeated outlier SNPs.Click here for additional data file.


Habitat fragmentation has accelerated within the last century, but may have been ongoing over longer time scales. We analyzed the timing and genetic consequences of fragmentation in two isolated lake-dwelling brown trout populations. They are from the same river system  and have been isolated from downstream anadromous trout by dams established ca. 600–800 years ago. For reference, we included ten other anadromous populations and two hatchery strains. Based on analysis of 44 microsatellite loci we investigated if the lake populations have been naturally genetically differentiated from anadromous trout for thousands of years, or have diverged recently due to the establishment of dams.Divergence time estimates were based on 1) Approximate Bayesian Computation and 2) a coalescent-based isolation-with-gene-flow model. Both methods suggested divergence times ca. 600–800 years bp, providing strong evidence for establishment of dams in the Medieval as the factor causing divergence. Bayesian cluster analysis showed influence of stocked trout in several reference populations, but not in the focal lake and anadromous populations. Estimates of effective population size using a linkage disequilibrium method ranged from 244 to > 1,000 in all but one anadromous population, but were lower (153 and 252) in the lake populations.We show that genetic divergence of lake-dwelling trout in two Danish lakes reflects establishment of water mills and impassable dams ca. 600–800 years ago rather than a natural genetic population structure. Although effective population sizes of the two lake populations are not critically low they may ultimately limit response to selection and thereby future adaptation. Our results demonstrate that populations may have been affected by anthropogenic disturbance over longer time scales than normally assumed. Nearly all natural ecosystems, and the species that they encompass, experience anthropogenic pressure resulting particularly from habitat destruction, climate change, overharvesting and introduction of exogenous species-4. Land Populations within a species are often distributed across habitats that exhibit different environmental conditions. These differences may lead to variation in local selection regimes and potentially local adaptation, the degSalmo trutta), where several recent studies based on genetic markers and outlier scans and the ad hoc statistic ΔK  which measures the steepest increase of the probability of k, using STRUCTURE HARVESTER and for divergence time, t . We assumed a generalized stepwise mutation model[-4 to 10-3, a prior distribution of individual locus mutation rates from 10-5 to 10-2 following a Gamma distribution with mean determined by the mean mutation rate across loci. The number of repeats per mutational event across loci was assumed to follow a geometric distribution with a uniform prior for the parameter P ranging from 0.1 to 0.6, whereas P for individual loci followed a Gamma distribution with mean determined by mean across loci and prior ranging from 10-2 to 9 × 10-1. A number of summary statistics can be chosen for estimating posterior distributions of parameters, some of which are, however, partly redundant. We followed the approach of a previous study[M[FST between populations and across loci, and index of individual assignment[FST between pairs of populations, mean individual assignment likelihoods of individuals collected in one of a pair of populations and assigned to the other[We used Approximate Bayesian Computation (ABC) as implemented in DIYABC 2.0 to asseson model with a uus study and cond study[M within psignment across psignment; 2) meansignment; 3) FSThe other, the meahe other between he other.4 simulations, conducting a Principal Component Analysis (PCA) based on the summary statistics and checking if the observed data set and the cloud of simulated data were congruent. The analyses were based on simulating 106 data sets, and the posterior distribution of parameters was estimated using the logit approach based on the 104 (1%) data sets closest to the observed data. Following estimation of parameters we checked the model by simulating 103 data sets based on the predictive posterior distribution of parameters but using a new set of summary statistics, and conducting a PCA based on the summary statistics of these simulated data sets along with the 104 data sets simulated based on the prior distribution (see above) and the summary statistics from the observed data. These model checks were conducted by switching summary statistics among analyses. Hence, for analyses based on summary statistics set a) we checked the model using summary statistics set b), for the analyses using set b) we checked the model using set c), and for the analyses using set c) we checked the model using set a).We pre-evaluated each scenario and set of priors by performing 10t, the point in time when the ancestral population split into two, qA, the effective population size in the ancestral population prior to splitting; q1 and q2, the effective population size of population 1 and 2 after divergence; m1 and m2, the migration rate from population 2 into population 1 and from population 1 into population 2. The parameters are scaled by mutation rate. To provide unscaled parameters we assumed the microsatellite mutation rate estimated using DIYABC (see above). Based on initial trial runs we found that a heated chain approach encompassing 30 chains (parameters g1 = 0.7 and g2 = 0.4), an initial burn-in of 2 × 106 MCMC steps followed by 2 × 106 steps yielded convergence. We assumed a generation time of 3.5 years (see above) and the following upper bounds on scaled priors: q1 = q2 = 10, qA = 100, m1 = m2 = 175 and t = 2. We conducted three replicate runs for each population pair.We further estimated divergence time, effective population sizes and gene flow using the Bayesian, coalescence and Markov Chain Monte Carlo (MCMC) based method IMa. The metFST ranged from 0.002 (between the neighbouring populations RIB and KON) to 0.078 between HAL and the hatchery strain HAR. All pairwise FST estimates were statistically significant except for that between RIB and KON. The two lake populations HAL and MOS were significantly differentiated from all other populations with FST ranging from 0.025 to 0.062. Differentiation between HAL and the downstream anadromous LIL population within the Gudenå River system was 0.033, whereas FST between MOS and LIL was 0.037. Finally, FST between HAL and MOS was 0.053  are listed in Additional fileK was highest for k = 2, but with additional peaks for k = 4, 7 and 12  in the two hatchery strains, VOR and HAR, whereas the other cluster was prevalent (>90%) in KAR, SKA, MOS and HAL. The other populations showed significant proportions of both clusters (data not shown). Assuming k = 7 provided the most detailed separation of populations (see below), whereas assuming k = 12 did not provide further biologically meaningful separation (data not shown).Analysis of the STRUCTURE output showed that Δk = 7, the hatchery strains VOR and HAR were characterized primarily by the “red” cluster in Figure At Ne) using LDNE generally ranged from ca. 250 to > 1,000 in the anadromous trout populations, the exception being SKA, where the point estimate of Ne was 74 , but the lower 95% confidence interval was 480. In four anadromous trout populations (including RIB) the upper 95% confidence interval could not be determined. Specifically for LIL, representing anadromous trout from the lower Gudenå River system, Ne was 288. A comparable Ne estimate was obtained for MOS (252), whereas the estimate for HAL was lower (153).Estimates of contemporary effective population size  and an Isolation-with-migration model (IMa) led us to reject the hypothesis that divergence of HAL and MOS from the anadromous LIL population is natural and occurred several thousand years ago. Instead, divergence time coincided remarkably well with establishment of water mills and impassable dams in the Medieval from ca. 1200 to 1500. There was less congruence regarding the demographic impact of the establishment of dams, as DIYABC consistently provided higher estimates of effective population sizes than did IMa and estimates based on the linkage disequilibrium method (LDNE).Bayesian cluster analysis showed that the two lake populations HAL and MOS were genetically distinct, whereas the anadromous trout populations showed a regional genetic structure, reflecting ongoing gene flow and isolation-by-distance, as documented in other studies,46. ImpoIn the following, we first discuss the reliability of the estimates provided by DIYABC and IMa, and subsequently the impact of the impassable dams on genetic population structure and adaptive divergence.-4, estimated using DIYABC. This is quite close to estimates of mutation rate at dinucleotide loci in e.g. humans of 2.73 × 10-4[-4 in common carp (Cyprinus carpio)[-5) to change the estimate of divergence time for LIL-HAL from 789 years to, say, 12,000 years, coinciding with the end of the last Glaciation.The conclusion that the two lake populations diverged from anadromous trout due to habitat fragmentation during the Medieval relies on the results using the methods IMa and DIYA73 × 10-4 and 5.56 carpio). Hence, Whereas the congruence of results from different replicated runs of IMa suggests good convergence, a potential problem consists in the simplified model assumed, where LIL exchanges migrants with either HAL or MOS. In reality, both populations could simultaneously contribute to gene flow, but even more importantly LIL would be subject to gene flow from other anadromous populations. We are unable to resolve the magnitude of this potential problem, but we note that the results obtained generally make sense. For both lake populations the estimate of gene flow from LIL was virtually zero, as would be expected given the problems of passing the dams upstream. In contrast, there was a signal of downstream gene flow into LIL from MOS. Gene flow in this direction is certainly a realistic possibility, as fish can pass the dams downstream by simply being flushed with the current.Ne estimates of HAL and MOS obtained using IMa were of the same order of magnitude, albeit higher than estimates obtained independently using LDNE. In contrast, estimates of Ne in LIL obtained by IMa were more than 5 times times higher than those derived from LDNE. Although recent bottlenecks could explain this result, there was no evidence for this to have taken place. Alternatively, the result could reflect gene flow from other anadromous populations into LIL, thereby inflating historical Ne estimates (as in IMa) compared to contemporary estimates (as in LDNE) that pertain to the current or only a few generations back in time. The complexities of estimating Ne in populations showing some degree of geographical continuity are increasingly acknowledged[Ne estimate in LIL based on IMa should be interpreted more broadly as encompassing LIL and neighboring populations contributing to gene flow.owledged, and we Ne from the ancestral population prior to divergence and Ne of the separate populations after divergence, the latter thereby giving more weight to the recent past (in the present case the centuries since establishment of dams). Most summary statistics applied in DIYABC, such as expected heterozygosity, FST and numbers of alleles should primarily reflect historical Ne over a very long time scale[M[Ne is estimated could therefore explain the discrepancies of results. We emphasize the Ne estimates obtained by IMa as being most realistic and relevant in a conservation context; they are generally congruent with estimates of contemporary Ne obtained using LDNE and with estimates from Danish brown trout populations obtained using temporal methods, that for anadromous populations in Danish rivers generally range from ca. 250 to > 1,000 for large river systems[The DIYABC analyses appeared robust towards the choice of summary statistics, as evidenced by similar outcomes of the different analyses. However, unlike IMa it is assumed that there is no gene flow following splitting of populations. In the present case, this is probably not a major problem, as IMa suggested low gene flow between LIL and HAL/MOS, and divergence time estimates were quite similar between methods. Effective population size estimates, however, differed strongly between IMa and DIYABC, with the latter providing 5–10 times higher estimates than IMa. In both cases the estimates are “historical”, i.e. encompassing many generations, although IMa estimates me scale, with th scale[M which de systems,61.Ne estimates differ, probably reflecting the different time scales that they apply to.In total, we find the estimates of divergence time obtained by IMa and DIYABC to be robust, whereas Our results suggest that the two lake populations HAL and MOS were previously part of a coherent anadromous trout population inhabiting the Gudenå River system. The Gudenå River is the largest watershed in Denmark, and the effective population size of the ancestral trout population prior to fragmentation has presumably been high; > 1,000, given the estimates obtained from the KON and RIB populations  population found heritability of life-history tactics (anadromy versus residency) as high as 0.52-0.56[We suggest that the most important consequence of the dams concerns local adaptation and evolutionary potential. Even if trout ascend the dams downstream and migrate to sea, they will be unable to return to spawn. This could impose strong selection against anadromy, although the response to selection depends on heritability of the trait. There are few studies available that have estimated heritability of anadromy in salmonids. However, a study based on pedigreeing a wild brook trout  should be given by the expression R(∞) = 2Ne R(1), where R(1) denotes the initial response to selection. Moreover, the time until 50% of the response, t50% is roughly equal to 1.4Ne[Ne is 153, then t50% is 214 generations, approximately equal to the number of generations that have elapsed since isolation of the population. Although this is based on simplified assumptions, it nevertheless suggests that there should still be adaptive potential within HAL and the presumably larger MOS population. Of course, different conclusions could apply to other anthropogenically isolated populations with much lower Ne.In the long term, it is expected that reproductive isolation of the lakes combined with the relatively low effective population sizes should impose limits to the response to selection, due to the fact that loss of potentially adaptive variation is not counteracted by introduction of new variation through migration. This is all the more serious, as anthropogenic pressure increases the need for populations to adapt to environmental change,4,80, ano 1.4Ne. If we aLandscapes are presently being strongly modified by humans, but in some regions, such as large parts of Europe this is a process that has already been ongoing for centuries and even millennia. Hence, it is often difficult to ascertain that a given genetic population structure is natural rather than the result of anthropogenic modifications. In this study, we show that genetic divergence of lake-dwelling trout in two Danish lakes reflects establishment of water mills and impassable dams ca. 600 years ago rather than a natural genetic population structure. Also, the populations have historically been part of a larger system of anadromous brown trout. The reproductive isolation of the lakes is likely to have affected adaptive divergence among populations, in the short term by shifting the migration-selection balance. However, in the long term lack of gene flow combined with relatively low effective population size may compromise evolutionary potential.Pongo pygmaeus) is more recent and drastic than previously assumed[Martes pennanti) became fragmented and experienced population declines prior to European settlement[The results demonstrate that caution is warranted when analyzing genetic population structure and interpreting it as “natural”. Even though anthropogenic modification of habitats has accelerated during the past century, it may nevertheless be a process that has been ongoing over a much longer time scale with significant consequences for the affected populations. Our results exemplify the use of molecular dating for assessing anthropogenic factors affecting wild populations. This is similar to other recent studies demonstrating that population decline in Borneo orang-utans  represented by the sampled individuals, based on STRUCTURE,52 analyClick here for file


Acanthaster ‘planci’ L., are among the most important biological disturbances of tropical coral reefs. Over the past 50 years, several devastating outbreaks have been documented around Guam, an island in the western Pacific Ocean. Previous analyses have shown that in the Pacific Ocean, COTS larval dispersal may be geographically restricted to certain regions. Here, we assess the genetic structure of Pacific COTS populations and compared samples from around Guam with a number of distant localities in the Pacific Ocean, and focused on determining the degree of genetic structure among populations previously considered to be isolated. Using microsatellites, we document substantial genetic structure between 14 localities from different geographical regions in the Pacific Ocean. Populations from the 14 locations sampled were found to be structured in three significantly differentiated groups: (1) all locations immediately around Guam, as well as Kingman Reef and Swains Island; (2) Japan, Philippines, GBR and Vanuatu; and (3) Johnston Atoll, which was significantly different from all other localities. The lack of genetic differentiation between Guam and extremely distant populations from Kingman Reef and Swains Island suggests potential long-distance dispersal of COTS in the Pacific.Population outbreaks of the corallivorous crown-of-thorns seastar (COTS),  Acanthaster ‘planci,’ is a specialised coral predator and one of the most important biological threats to coral reefs throughout the Indo-Pacific , -Pacific . It has -Pacific , recent  species , all of  species . Since f to <10%  and cora to <10% . As a co to <10% .Although frequently studied, the origin, development and causes of COTS outbreaks remain largely unclear . DiffereLinckia laevigata; A. ‘planci’, a species with a long-lived planktonic larval stage  than those with longer planktonic stages, which are thought to have higher levels of gene flow and reduced population genetic structure. A correlation between the potential for migration and genetic structure has been observed in different marine groups, including other seastars  proposed by Initial studies using allozymes to investigate COTS population genetics seemed to provide evidence of strong gene flow and lack of genetic structure . HoweverAlthough mitochondrial markers show genetic differentiation between populations that have large scale geographic structure , these mThe differentiation between contemporary gene-flow patterns and evolutionary history is of importance for conservation biology as highlighted by several authors e.g., . The mutThis study aims to investigate the contemporary genetic structure of the Pacific crown-of-thorns seastar species using microsatellites and to test for isolation among distant geographical regions previously identified as a cohesive genetic unit by mitochondrial DNA. We especially focus on the genetic structure of populations around Guam, where recent COTS outbreaks have been observed. We compared samples from around Guam with a number of distant localities in the Pacific Ocean, and focused on determining the degree of genetic structure among populations previously considered to be isolated .A. ‘planci’ on this island has been reported since the early 1970’s  was used to extract total DNA from tube feet and pyloric caeca samples, following the protocol recommended by the manufacturer. As a preliminary step, the tissue was ground after freezing in liquid nitrogen, and incubated for 1 h at 35 °C in RLT lysis buffer (Qiagen). In the case of tube feet, DNA was extracted using the DNeasy Tissue Kit (Qiagen), according to the protocol recommended by the manufacturer.A. ‘planci’  with 1 µl of DNA template (around 20 ng of DNA). For fragment length analysis, the 5′ end of the forward primers used in the PCR were labelled with a fluorescent dye . The PCR cycling conditions were as follows: 10 min at 94 °C, 38 cycles of 30 s at 94 °C, 30 s at 56–60 °C , and 1min at 72 °C, and a final elongation of 5 min at 72 °C.All samples were genotyped using a set of 13 microsatellites previously identified for ‘planci’ . The setPCR products were mixed for genotyping in 3 different co-loading reactions as follows: co-loading 1 included loci Yukina01, Yukina05, Yukina06 and Yukina08; co-loading 2 included Maki01, Maki03, Tama01 and Hisayo01; and co-loading 3 included Aya02, Maki12, Maki11, Tama11 and AyU03. Samples were analysed on an ABI 3730 48 capillary sequencer (Applied Biosystems) using the dye set D and G5 and 400HD ROX size standard at the Sequencing Service of the Department of Biology at the Ludwig-Maximilians-Universität in Munich (Germany). The software GeneMapper v.4.1 was used to call allele sizes. The raw genotype data is given in The Markov chain algorithm implemented in the software GENEPOP v.4.2  was usedGenetic diversity within each locality was determined through the estimation of number of alleles per locus and locality, gene diversity, observed and expected heterozygosity and allelic richness using the software ARLEQUIN v.3.5.1.2  and GENEdiveRsity  loci by loci was carried out in ARLEQUIN v.3.5.1.2 . AMOVA , and by ate FDR, , and BoniveRsity .K) was evaluated using values for K from 1 to 10, with at least 12 independent runs for each value. Uniform priors in an admixture ancestry model were used in each run with a burn-in period of 200,000 generations, a posterior sampling chain of 1,000,000 generations and the assumption of correlated allele frequencies among samples. The determination of the most accurate value for K was evaluated using the statistic ΔK following the methodology of The estimation of the number of distinct populations and the assignment of individual samples to populations was done using the software STRUCTURE v.2.3.4 . The numadegenet (http://www.r-project.org/). Individuals with missing data were not included in this analysis.Finally, genetic structure and differentiation between localities was also determined by a discriminant analysis of principal components DAPC; . This laadegenet  implemenp-value < 0.05 after sequential Bonferroni correction). These loci were Tama01, Maki11 and Tama11, which deviated in more than 8 (out of 14) locations. Additionally, the analysis with Micro-Checker showed a consistent excess of homozygotes in the same markers, suggesting the presence of null alleles, polymerase stuttering or large allele dropout. Because these results were consistently biased for most of the sampled localities, those three loci were not included in subsequent analyses.Based on an exact test using one Markov chain for each locus per sampled location, three loci deviated significantly from HWE in almost all locations . In these cases the tests used did not show evidence of linkage disequilibrium, null alleles, or biases in the identification of genotypes. Thus, these loci were included in all subsequent analyses.The remaining markers showed HWE, with punctual deviations in some populations  for the entire set of samples and the lowest number of alleles per location (1–6) (Most of the microsatellite loci were highly polymorphic. Two exceptions were found: on (1–6) . On the The high genetic diversity suggested by the number of alleles in each location contrasted with the findings on allelic richness. The high genetic variability within localities precluded observing differences in allelic richness between localities . After cN = 5), the genetic similarity of Moorea with other localities cannot be precisely assessed and the status of Moorea as significantly different from all other localities must be taken with caution. Thus, to avoid artifacts, Moorea was not segregated as fourth group and was not included in some subsequent analyses .Pairwise to 0.480 . In genean 0.15) . Moorea lues see  obtainedWithin the first group, it is important to note that all the localities around Guam, except Tipalao Bay (G1), had statistically significant genetic similarities with Kingman Reef and/or Swains Island. This is noteworthy because the population in Tipalao Bay was the only aggregation in the southwest coast of Guam during surveys and COTS were almost absent in adjacent reefs . Moreover, the According to the results of the AMOVA, the percentage of genetic covariance explained by individual variation was 84%  value for the existence of 5 genetically different groups , Guam, Kingman Reef and Swains Island clustered together and were significantly different from all other locations sampled in this study . In the second group, the change in the number of predicted populations from 2 to 5 revealed the differentiation of islands such as Moorea and Johnston Atoll, and increased the heterogeneity observed inside the GBR.When samples were assigned to two genetic groups (is study . When thThe first two components of the DAPC explained 41.5% and 40.7% of the variation in the dataset and these results were consistent with the results found with STRUCTURE . In thisY axis) of the DAPC and the percentage of variance explained by this axis was low (6.4% of the variance).Because the genetic differentiation of Johnston Atoll with the other islands was strong, additional DAPC analyses were performed  and 3C. Localities within Guam  showed hA. ‘planci.’ Given the broad geographical distances separating the localities evaluated here, it is likely that this dispersal occurs in a stepping stone model involving intermediate localities not sampled in this study. The sampled localities in the Pacific were found to be structured in at least three large groups with apparently limited larval dispersal between them. The first group comprised Guam, Kingman Reef and Swains Islands; the second group included the Japan, Philippines, GBR and Vanuatu; and Johnston Atoll was isolated in a third group. Although a general high genetic diversity was found inside each sampled island, our analyses showed strong genetic similarities between localities in Guam, Kingman and Swains Island suggesting larval dispersal between these geographically distant regions. Gene-flow between these localities in the Pacific Ocean has been also inferred using allozymes from other marine organisms with high larval dispersal potential , the GBR, and the North Pacific Islands  belonged to different genetic groups. In contrast, when using mtDNA control region, A. ‘planci’ samples from Palau were closer to the western Pacific population, while Majuro and Pohnpei were more related to the GBR.Discordances between mtDNA and microsatellites data had been previously reported for Patterns of low control region mitochondrial divergence in Pacific COTS have been interpreted as a result of occasional exchange of larvae between distant areas, the retention of ancestral polymorphism or a signature of ancient gene flow . In addiDespite the apparent lack of genetic structure between some geographically distant regions, some evidence of genetic differentiation among localities around Guam were found. For example, it was found that some localities around Guam , Uruno Point (G2) and North Haputo Point (G4)) were genetically differentiated from Kingman Reef and/or Swains Islands , food quality , and population densities  around Guam was evaluated using microsatellites and compared to spatially distinct localities in the Pacific. Genetic structure was detected within the sampled Pacific localities, which suggests clustering of reefs into broad geographic groups, some of them consistent with previous findings based on the control region of the mtDNA. A lack of genetic structure was suggested between Guam Island and distant reefs, such as Kingman and Swains, previously considered isolated regions. Additional studies including a denser spatial sampling are needed to test the strength and direction of putative gene flow between these localities and whether such putative long-distance dispersal events have an impact at the local demographic level.In this study, the genetic structure of the crown-of-thorns seastar  in function of number of clusters.Click here for additional data file.10.7717/peerj.1970/supp-3Table S1Each line corresponds to one individual. The values are given in length (bp) of the fragment. Empty spaces are missing data.Click here for additional data file.10.7717/peerj.1970/supp-4Table S2N, number of individuals; Na, number of allele per locus; Ar, allelic richness; Ho, observed heterozygosity; He, expected heterozygosity; FIS, inbreeding coefficient; S.D., standard deviation.Click here for additional data file.10.7717/peerj.1970/supp-5Table S3CI, confidence intervals; BC, Bonferroni correction; FDR, False discovery rate.Click here for additional data file.


Lumbricus rubellus, which is a sentinel species in soil ecotoxicology, consists of two cryptic taxa that are differentiated in both the nuclear and the mitochondrial (mtDNA) genomes. Recently, several deeply divergent mtDNA lineages were detected in mainland Europe, but whether these earthworms also constitute cryptic species remains unclear. This information is important from an evolutionary perspective, but it is also essential for the interpretation and the design of ecotoxicological projects. In this study, we used genome-wide RADseq data to assess the reproductive isolation of the divergent mitochondrial lineages of L. rubellus that occur in sympatry in multiple localities in Central Europe.The accurate delimitation of species is essential to numerous areas of biological research. An unbiased assessment of the diversity, including the cryptic diversity, is of particular importance for the below ground fauna, a major component of global biodiversity. On the British Isles, the epigeic earthworm L. rubellus in sympatry. Because the clustering of the RADseq data was according to the population of origin and not the mtDNA lineage, reproductive isolation among the mtDNA lineages was not likely. Although each population contained multiple mtDNA lineages, subdivisions within the populations were not observed for the nuclear genome. The lack of fixed differences and sharing of the overwhelming majority of nuclear polymorphisms between localities, indicated that the populations did not constitute allopatric species. The nucleotide diversity within the populations was high, 0.7–0.8 %.We identified five divergent (up to 16 % net p-distance) mitochondrial lineages of L. rubellus in Central Europe were not reproductively isolated groups. The earthworm L. rubellus, which is represented by several mtDNA lineages in continental Europe, apparently is a single highly polymorphic species rather than a complex of several cryptic species. This study demonstrated the critical importance of the use of multilocus nuclear data for the unbiased assessment of cryptic diversity and for the delimitation of species in soil invertebrates.The deeply divergent mtDNA sympatric lineages of The online version of this article (doi:10.1186/s12862-015-0488-9) contains supplementary material, which is available to authorized users. Species delimitation aims to identify species-level biological diversity while delineating interspecific boundaries and estimating the number of species , 2. The COI), which is a standard in the DNA barcoding of animal species, under the sometimes questioned . We selected unique sequences that represented the primary haplotype groups, and we used 16 sequences that originated from individuals sampled in 11 European countries . We also obtained the COI sequences from a few Polish individuals for which no RADseq data were generated, and these individuals originated from the OL3 site  and from central Poland .The The genomic DNA was extracted from 25 individuals that were randomly selected from each population and was normalized to a concentration of 50 ng/μl using a Qubit® fluorometer. The RAD sequencing libraries were prepared according to the double-digest RADseq method described by Peterson et al. . For eacStacks software [process_radtags.pl  were calculated based on haplotype frequencies with Arlequin 3.5 . The model was selected using the Bayesian Information criterion. Additionally, a Bayesian tree was constructed in MrBayes .The earthworms that were sampled from the different sites were assumed to represent local populations. To assess the population genetic diversity in the mtDNA, we estimated the haplotype diversity, nucleotide diversity and the number of polymorphic sites using DnaSP . The meatations; ). The rework 4.6 . To show [MEGA 6 . To relapopulations program of Stacks. The population genetic statistics, such as the number of haplotypes, haplotype diversity and nucleotide diversity, were estimated. The pairwise differentiation between populations (RADseq FST) was estimated with Arlequin based on the SNP allele frequency . The number of polymorphic sites that were shared between the populations (Ss) and unique to the individual populations (Sx) as well as the number of fixed differences between the populations (Sf) were calculated in mstatspop [Structure software [Structure analysis, we used one randomly selected SNP per RAD tag (−−write_single_snp), which resulted in 1101 loci . The optimal K was selected based on the inspection of the change in the probability value of the data for a given K (L(K)), analyzed with Structure Harvester [MEGA 6.The RADseq data were analyzed with the statspop . A Bayessoftware –80, was arvester , assuminIBDWS . The following distance matrices were used in the study: pairwise FST values, log-transformed geographic distance (straight-line distance), and difference in Cd total soil concentration. To test the differences in the genome-wide genetic diversity between populations, we used t-tests with a strict Bonferroni correction for multiple comparisons.The isolation by distance was tested with a simple Mantel test. The effect of pollution on the degree of genetic differentiation between the populations was tested with a partial Mantel test while accounting for geographic distance . The tesIBDWS , and KT731501 – KT731525 (ATP6). The RADseq data (denovo_map.pl output tsv files) are available in the Dryad Digital Repository at the http://dx.doi.org/10.5061/dryad.8070m. Raw Illumina reads are available at NCBI BioProject number PRJNA296755 (http://www.ncbi.nlm.nih.gov/bioproject/296755) or upon request to the corresponding author.The mtDNA sequences of Additional file 1:Supplementary materials. This file includes supplementary materials to the main text: Note A1. Detailed description of the Illumina sequencing and Stacks analysis of RAD tags. Table A1. Variation at COI (453 bp) in Lumbricus rubellus populations. Table A2. Variation at ATP6 (563 bp) in Lumbricus rubellus populations. Table A3. Pairwise genetic distances between mtDNA haplotypes H1-H27 found in Poland. Table A4. Mantel test and partial Mantel test statistics for Lumbricus rubellus populations sampled at sites with different levels of metal pollution. Table A5. Information on mtDNA sequence markers of Lumbricus rubellus and the PCR conditions. Table A6. Quality control of Illumina reads from the HiSeq 2000. Table A7. The Stacks commands used to process the RADseq data. Table A8. Comparison of Stacks results for different analysis parameters. Figure A1. Bayesian tree based on the COI sequences of Lumbricus rubellus. Figure A2. The Ln P(D) in Structure analysis of Lumbricus rubellus. Figure A3. Relation between the genetic distance (RADseq FST) and the log(geographic distance) in Lumbricus rubellus populations; a reduced major axis regression based on the Mantel test. Figure A4. Final coverage per RAD tag (mean ± SD) for individual earthworms of Lumbricus rubellus. Figure A5. Effect of the Stacks parameters on the final number of usable RAD tags in Lumbricus rubellus. (PDF 650 kb)Additional file 2:Structure file. This is the main Structure input file generated by Stacks with the use of one SNP per RAD tag. (TSV 438 kb)


Picea abies [L.] Karst), at different sampling-scales and to investigate, accounting for population structure, the effect of environment on species genetic diversity. A total of 384 single nucleotide polymorphisms (SNPs) representing 290 genes were genotyped at two geographic scales: across 12 populations distributed along two altitudinal-transects in the Alps , and across 27 populations belonging to the range of Norway spruce in central and south-east Europe . At the macrogeographic scale, principal component analysis combined with Bayesian clustering revealed three major clusters, corresponding to the main areas of southern spruce occurrence, i.e. the Alps, Carpathians, and Hercynia. The populations along the altitudinal transects were not differentiated. To assess the role of selection in structuring genetic variation, we applied a Bayesian and coalescent-based FST-outlier method and tested for correlations between allele frequencies and climatic variables using regression analyses. At the macro-geographic scale, the FST-outlier methods detected together 11 FST-outliers. Six outliers were detected when the same analyses were carried out taking into account the genetic structure. Regression analyses with population structure correction resulted in the identification of two  and 38 SNPs  significantly correlated with temperature and/or precipitation. Six of these loci overlapped with FST-outliers, among them two loci encoding an enzyme involved in riboflavin biosynthesis and a sucrose synthase. The results of this study indicate a strong relationship between genetic and environmental variation at both geographic scales. It also suggests that an integrative approach combining different outlier detection methods and population sampling at different geographic scales is useful to identify loci potentially involved in adaptation.Forest tree species of temperate and boreal regions have undergone a long history of demographic changes and evolutionary adaptations. The main objective of this study was to detect signals of selection in Norway spruce ( Adaptation of forest tree species to their environment is of great interest in forest management, as climate change is considered to be a major threat to forest health and sustainability FST values than neutral expectations The development of forest tree genome sequences, single nucleotide polymorphisms (SNPs) databases and high-throughput genotyping platforms have facilitated the use of multi-locus scan approaches to identify loci involved in adaptation Molecular studies in conifers, incorporating both population history and landscape features, have identified numerous loci, likely to be involved in adaptation Picea abies L. Karst.) is a broadly distributed European conifer of great ecological and economic importance. Its range is divided into two major regions, a northern, boreal region and a central and south-eastern European region Norway spruce  and north (Mezzana) aspects in the Trentino province  . Six popThe macro-geographic scale sampling consisted of 27 putatively natural populations, distributed across the range of Norway spruce in central- and south-eastern Europe. Each population was represented by 15–24 individuals, for a total of 546 trees. Eight of the populations were sampled in the IUFRO 1964/68 provenance test In the micro-geographic scale study, two climatic variables were used. Ten years of average monthly mean temperature and monthly mean precipitation were obtained from the local spatial database http://www.worldclim.org). Based on the species distribution and its ecological preferences, and to describe the sampling site climate, five bioclimatic variables were integrated in the analyses: mean annual temperature (bio01), temperature seasonality (bio04), mean temperature of the warmest quarter (bio09), mean temperature of the coldest quarter (bio11), and annual precipitation (bio12)  . Climatihttp://dendrome.ucdavis.edu/NealeLab/crsp/overview.php). DNA was extracted from the haploid megagametophyte, obtained from one seed per sampled tree. Individual sequence alignment and SNP identification were performed using PineSAPSNP discovery was based on Sanger re-sequencing of a panel of 12 unrelated trees using primers derived from almost 1000 loblolly pine expressed sequence tags (ESTs), representing genes having various biological functions (HE) and observed (HO) heterozygosity, and the Wright's inbreeding coefficient (FIS) were calculated using the Genepop 4.0.5.3 program FIS values >0.25 were discarded. An individual call rate value was calculated for each sample, and samples with call rates lower than 95% were excluded.For each SNP, the percentage of individuals genotyped , the minor allele frequency (maf), the expected (FIS), among populations (FST) and for the total population (FIT) were calculated for each locus using Genepop (FIS) statistics per population were calculated over all loci with the gene diversity among individuals within population (1-Qinter). Differences among populations were tested using the pairwise FST analysis in Arlequin 3.5 For each geographic scale, values of genetic diversity among individuals  were estimated using the software Q-value Q-value <0.05 were considered as significant.To identify loci with extreme correlations between allele frequencies and climatic variables, regression analyses were carried out. We used linear regression models where the dependent variable was the arcsine-transformed major allele frequency (MAF) of each SNP, and the independent variables were climatic variables, ancestry coefficients, and an error term. The ancestry coefficients that describe the population structure were included as covariates, as suggested by Korves F-statistics and analysis of molecular variance (AMOVA) were applied k significant PCs, each PC eigenvalue was standardized and compared to the Tracy-Widom distribution (TW statistics) www.bioportal.uio.no). STRUCTURE runs were performed with a Markov Chain Monte Carlo (MCMC) burn in of 500,000 steps, followed by an MCMC of 600,000 steps. An admixture model was used in the simulations. Each analysis was replicated 10 times for each K, with K ranging from 1 to 12 and from 1 to 30 at the micro- and macro-geographical scale, respectively. The best K was assigned using the log likelihood value, and populations were assigned to each genetic cluster considering the assignment of the majority of individuals within each population.Patterns of population structure were analysed by principal component analysis (PCA) and by Bayesian cluster analysis. To further characterize population structures, hierarchical To investigate partitioning of genetic variation at different hierarchical levels, and to corroborate the results obtained with HIERFSTAT and STRUCTURE, an AMOVA analysis was performed at both geographic scales, assuming the presence of four genetic groups see  at the mThe 384 SNPs considered represent 290 genes S1A  encodingHO) per population ranged from 0.230 (M18) to 0.259 (C14) with a grand mean of 0.248 (± SD  = 0.168) (HO was between 0.206 (population X141) and 0.243 (X267) with a grand mean of 0.229 (± SD  = 0.159).At the micro-geographic scale, the overall genetic diversity  expressed as observed heterzygosity (= 0.168) . At the = 0.168) , HO wasFST value between population pairs at the micro-geographic scale  according to the permutation test  and 0.680 (BOE and MN).No significant differences were found in ic scale . At the ion test  and the FST values were calculated for each locus at both geographic scales following Weir and Cockerham SFT-values calculated among populations varied between -0.008 and 0.36 (FST = 0.024). The BayeScan simulation was run twice: using populations assigned according to their geographic position and according to STRUCTURE clustering. The first simulation detected 8 outlier loci , SNP locus 2_10483_01-340, encodes a haloacid dehalogenase-like hydrolase and was detected only in Alpine populations  with either temperature, precipitation, or temperature and precipitation variables combined (Pvar<0.01) in the model. The amount of SNP frequency variation explained by climatic variables ranged from 37% (CL3363Contig1_04-85) to 72% (2_9466_01-179), the latter locus encoding a hypothetical protein. Six of the identified loci overlapped with FST-outliers .For each of the 237 SNPs used in the macro-geographic scale study, 9 models were applied, which included one or two climatic variables related to temperature or precipitation . The anacombined . Twelve Ftransect/total = 0.0002, P = 0.587) and between sampling sites within transects   , and no , P = 1) . A furth, P = 1) .2r = 0.11; P = 1.9e−15) and distinguished between populations of the Alps from all other populations. The second PC highlighted the peculiarity of a population (MN) located in Montenegro in the Dinaric Alps; it represented the southernmost population included in the study, and explained the correlation between PC2 and latitude . The Bayesian cluster analysis with STRUCTURE detected four clusters (P<0.0001) portion of the total variation is explained by differences among clusters, as confirmed by the F-statistics analysis  and the application of different approaches in the data analysis provided new insights into the underlying genes that may be responsible for local adaptation. Some potential adaptive loci were found to be associated to temperature at both geographic scales, confirming the importance of this factor in driving adaptation in forest species.FST outliers. In the macro-geographic scale study, 13% of the variance was explained by population structure, presumably due to population demographic changes. Consequently, both BayeScan and Arlequin simulations were carried out with and without taking into account the population structure. BayeScan simulations without population structure correction detected eight outliers, whereas only one locus was detected with structure correction. The simulation considering the hierarchical island model detected seven outlier loci, with two of them being found only with this method. Altogether, seven FST outliers were identified taking into account the population structure, corresponding to 2.95% of the SNPs tested. This discovery rate is comparable to that observed in other conifers using similar approaches Picea mariana), where few SNPs were identified as outliers, and only within a specific lineage Theoretical Pinus taeda) using a different model Pinus contorta Dougl. ex Loud) field transplant experiment To account for variation along clines due to demographic processes and/or selection, population structure was included as a covariate in the linear regression models. In the 27 populations analysed in the macro-geographic scale study, 38 SNP loci were significantly correlated with temperature and/or precipitation variables, corresponding to a 16% discovery rate. A greater ratio (22%) was detected in loblolly pine  encodes a haloacid dehaolgenase-like hydrolase, an enzyme with a putative function in the biosynthetic pathway of the vitamin riboflavin, playing a role in a variety of redox processes affected in plant defence responses FST-outlier. An additional candidate locus (CL813Contig1_03) encodes a sucrose synthase, an enzyme of the primary metabolism and responsible for energy supply. The expressed sequence tag was isolated from Aleppo pine  and was shown to be induced by water stress Picea glauca), acetoacetyl-CoA thiolase was demonstrated to be involved in the up-regulation of transcripts in response to stress The six SNP loci that were identified by both correlation-based and Since genetic structure was detected only at macro-geographic scale, we assumed that both altitudinal transects sampled at micro-geographic scale belonged to the same gene pool. No structure effects at micro-geographic scale were observed.At the macrogeographic scale, the analysis of population structure using SNPs of functional genes revealed three major clusters, which were largely congruent with those delineated in previous studies In this study, we confirmed the confounding effect of genetic structure in the detection of outlier loci (see previous section). Therefore, the estimation of species genetic structure is a crucial step in the identification of adaptive loci, as previously reported FST-outliers and environmental associations led to the identification of several potential adaptive genes and corroborates previous suggestions that temperature is an important factor in shaping genetic diversity in conifers. A strong relation was found between genetic structure and environmental variables but this correlation does not allow the identification of the physiological function affected by the environmental factor. Therefore, in future studies it is crucial to complement genetic studies with transplant experiments, where the phenotypic variation or the effect of an environmental stress could be assessed. Finally, our original aspect of sampling at different spatial scales allowed us to provide insights into the effects of gene flow on local adaptation. Moreover, our results highlighted the importance of combining different approaches to investigate species adaptation This study indicates that genetic diversity of Norway spruce was shaped by both demographic and evolutionary processes, confirming the population structure identified with other marker types, but inferred from a much lower number of loci. The structure results were taken into account in the detection of selection and adaptation signs at the molecular level. The combined analyses of S1 FigBayeScan results at macro-geographic scale: populations assigned according to their geographic position (A) and according to STRUCTURE clustering (B).(TIFF)Click here for additional data file.S2 FigPlots of some loci significantly associated with bioclimatic variables; colours identify the locus minor allele frequency (m.a.f.) within each population.(TIFF)Click here for additional data file.S3 FigPlot of the first two significant principal components (PCs) at micro-geographic scale: one cluster was identified. (A). Plot of the two first PCs at the macro-geographic scale (B). Population labels are coloured according to the populations ID. Eingvalues for all PCs are in the bar plots.(TIFF)Click here for additional data file.S1 TableModels used for the regression analysis at the micro- and the macro-geographic scale. The letter “A” represents the population structure. The following variables were used: major allele frequency (MAF) with the arcsin transformation (asin(MAF)), annual mean temperature , annual precipitation , temperature seasonality (bio04), mean temperature of driest quarter (bio09) and mean temperature of coldest quarter (bio11).(DOC)Click here for additional data file.S2 TablePairwise FST between population-pairs at micro- (A) and macro-geographic scale (B). Population ID is described in P-value <0.0001) according to a permutation test (N = 1000).(DOC)Click here for additional data file.S3 TableAnalysis of variance at micro- and macro-geographic scales.F statistics were calculated at different levels using HIERFSTAT library in R: between transects or among clusters, among populations, among samples (A). AMOVA analysis calculated using Arlequin at micro- and macro-geographic scales. Fixation indexes statistically significant (*** P<0.000) (B).(DOC)Click here for additional data file.S1 Supporting MaterialSNP position and locus ID with BLAST-N (A).FIT, FST and FIS values calculated per locus at both scales (B). (XLS)(XLS)Click here for additional data file.S2 Supporting MaterialRegression model analysis at micro- (A) and macro-geographic scale (B).(XLS)Click here for additional data file.S3 Supporting MaterialSample by SNP genotyping matrix for micro- (A) and macro-geographic (B) studies.(XLS)Click here for additional data file.


Plutella xylostella, cause major economic losses to cruciferous crops, including cabbage, which is an important vegetable crop in China. In this study, we used the mitochondrial COI gene and 11 ISSR markers to characterize the genetic structure and seasonal migration routes of 23 P. xylostella populations in China. Both the mitochondrial and nuclear markers revealed high haplotype diversity and gene flow among the populations, although some degree of genetic isolation was evident between the populations of Hainan Island and other sampling sites. The dominant haplotypes, LX1 and LX2, differed significantly from all other haplotypes both in terms of the number of individuals with those haplotypes and their distributions. Haplotypes that were shared among populations revealed that P. xylostella migrates from the lower reaches of the Yangtze River to northern China and then to northeastern China. Our results also revealed another potential migration route for P. xylostella, i.e., from southwestern China to both northwestern and southern China.The larvae of the diamondback moth,  Plutella xylostella (Lepidoptera: Plutellidae), which originated in the Mediterranean region or South Africa [P. xylostella can cause crop losses of more than 90% [P. xylostella was recently estimated to be US$4–5 billions [P. xylostella has been a major pest of cruciferous crops since the 1970s and is now found in most cruciferous crop-growing areas. P. xylostella is considered a long-distance migratory species [P. xylostella cannot survive in the cold winter conditions of regions such as western Canada, northern Japan, or northern China. However, re-colonization from warmer regions where populations persist year round occurs annually [The diamondback moth, h Africa ,2, is noh Africa . P. xylothan 90% . The tot species . P. xyloannually –9.P. xylostella. Caprio and Tabashnik [P. xylostella populations. Simple sequence repeat (SSR) markers have been used to determine the dispersal of P. xylostella populations in Australia where no population structure was observed [et al. [P. xylostella from different areas of the world was genetically distinct. Another molecular marker that is widely used to study population structure is mitochondrial DNA (mtDNA) [COI) gene is highly variable at the DNA level [COI haplotypes [P. xylostella, the mtCOI gene has 1531 bp [COI marker to characterize the genetic structure of P. xylostella in South Korea [P. xylostella. Hence, knowledge of P. xylostella migration routes and of geographical landscapes that act as barriers to dispersal remains unclear.Many approaches have been used to determine the genetic structure of abashnik  in Hawaiabashnik  in Southobserved . Roux et [et al.  used ISS (mtDNA) . mtDNA i (mtDNA) ,16. AmonNA level . Diversiplotypes ,19. In P 1531 bp  and grea 1531 bp . Previouth Korea , China [th Korea , Australth Korea . BecauseCOI markers are particularly suitable for evaluating the genetic structure and dispersal routes of insects [COI marker to determine the migration pattern and population genetic structure of P. xylostella sampled from 23 sites spread throughout China. We also compared the mtCOI gene sequence data with published sequences from previous studies of P. xylostella in other regions.Because of their high variability, ISSR and mt insects –27. In tPlutella xylostella, is the most significant insect pest of cruciferous plants. The study of this pest will provide information needed for its forecast and control. Sample collection did not involve endangered species. Thus, no specific permissions were required.The diamondback moth, P. xylostella individuals from 23 sampling sites . A 704-bp fragment of the mti et al. . After v2+, 2.5 U of LA Taq DNA polymerase (Takara), 0.2 mmol/L of dNTPs mixture, 1.25 μmol/L of each primer, and 20 ng of DNA template. The cycling conditions were: initial denaturation of 5 min at 94°C; 35 cycles of 45 s at 94°C, 1 min at 40–58°C, and 1.5 min at 72°C; and a final extension of 10 min at 72°C, followed by storage at 4°C.Inter-simple sequence repeat (ISSR) PCR amplification was performed using 11 primers  and the PCR products were subjected to electrophoresis on a 2% agarose gel using 1x Tris boric acid EDTA buffer at 150 V. The gels were visualized with ethidium bromide under UV.P. xylostella populations was constructed by cluster analysis using the unweighted pair group method of the arithmetic averages (UPGMA) in the software POPGENE 1.32.ISSR DNA bands were scored qualitatively for the presence (1) or absence of bands (0) for each sample . This information was used to create matrices of the 11 primers. The software POPGENE 1.32  was usedST) [P. xylostella haplotypes, a parsimony haplotype network with 95% statistical support was obtained using TCS 1.21 [To assess how genetic diversity differed among geographic populations, we calculated haplotype diversity (Hd), nucleotide diversity (Pi), and the mean number of pair-wise differences using DnaSP5 . ArlequiST) . To deteTCS 1.21 . The staTCS 1.21 .mtCOI gene sequences of P. xylostella were downloaded from GenBank. A total of 296 sequences with 658-bp homologous regions were used for further analysis (GenBank accession numbers KC154937–KC155152 and DQ076332– DQ076411). Thirty of 296 sequences were sampled from South Korea, and the remaining sequences were sampled from China. With the program MEGA 4.0 and the K2P model [To compare the relationship of the haplotypes between China and neighboring country, the available 2P model , a neighA Mantel test for isolation-by-distance, as revealed by a correlation between Fst values and log-geographic distances, was performed using IBDWS 3.23 . We usedP. xylostella, Tajima’s D [S [S test is very sensitive to demographic population expansion, which generally leads to larger negative values [S tests were calculated by Arlequin 3.5.To detect evidence of range expansion in the populations of jima’s D  and Fu’sa’s D [S  tests wea’s D [S . Similare values . Both TaP. xylostella mtCOI gene from 620 individuals revealed 193 haplotypes (GenBank accession numbers KM588399–KM588591) among the 23 populations , the number of haplotype increased to 324. Two South Korean haplotypes were identical to haplotypes LX37 and LX39 from China, but the most common haplotype in South Korea (LX194) was absent in China. In addition, neither haplotype LX1 nor LX2 was found in South Korea  was 5.3277, which suggests a high level of gene flow among the sampled populations (The percentage of polymorphism (Ppl), Shannon’s information index (I), Nei’s gene diversity (He), and effective number of alleles (Ne) within population are summarized in ulations .P. xylostella populations. The populations BJ, SY, YQ, and TJX in seasonally suitable areas clustered together . Based on these finding, we infer that P. xylostella populations in China originated from one female founder moth with haplotype LX1.Of the 38 shared haplotypes found in our study, LX1 and LX2 were found in almost all of the P. xylostella has historically been considered to be capable of long-distance migration [P. xylostella had similar dispersal abilities and that fewer than 1% moved more than 200 m from their release point within 5 to 9 days [P. xylostella are separated by less than 10 km [P. xylostella can migrate only short distances. Short distance migration enables P. xylostella to move in a step-by-step manner in search of seasonally suitable areas. Annually, P. xylostella is able to migrate from regions where it can overwinter to regions where it cannot overwinter because the winter temperatures are too low [igration . More reigration ,53. A mao 9 days . In Hawa too low –9,21,54.Plutella xylostella persists year-round in subtropical and tropical regions. The widely distributed shared haplotypes suggest the gene flow in those region. Furthermore, in the temperate zone, where the JS and ZJK populations are located, shared haplotypes become more common because of migration. Besides the widely distribution haplotypes, the point-to-point haplotypes (haplotypes shared by only two populations) revealed the migration direction between P. xylostella populations. Three point-to-point haplotypes of the JS population were shared with other temperate zone populations located in northern China . Similarly, the ZJK population also shared point-to-point haplotypes with SX, NM, and HRB populations. From these results, we infer that one possible migration of P. xylostella begins in southeastern China  and progresses north (ZJK and BJ populations) into northeastern China (SY and HRB populations). In addition, the YNMD population in southwestern China shared point-to-point haplotypes with QHHZ, HN, and GDFY populations, which suggests another migration route that begins near the location of the YNMD population. The YX, YNMD, QHHZ, and HN populations clustered together in the UPGMA dendrogram, which is highly consistent with the hypothesis stated above. Recent studies have shown that the Qinling Mountain is a barrier for P. xylostella migration and gene flow [Several shared haplotypes were distributed in different regions of China, and this information reveals potential relationships between populations. ene flow . InteresP. xylostella are complex. Wind-enabled flight enables these insects to fly further than would be possible by powered flight alone [P. xylostella with an annual opportunity to re-colonize from southern to northern regions [P. xylostella outbreak in Yunnan Province. This indicates that P. xylostella may use the strong southwesterly winds caused by the monsoon to migrate into northwestern and southern China. Alternatively, however, the distribution of P. xylostella might simply reflect the extended cultivation of its host plants [P. xylostella than natural gene flow [The factors affecting dispersal and gene flow of ht alone ,57. In C regions . In soutt plants . Indeed,ene flow .P. xylostella dispersal. This is supported by the results of the ISSR data, which showed that the Hainan populations were genetically distinct from the other populations. Further evidence that oceans can act as barriers for P. xylostella dispersal was that both LX1 and LX2 were absent from South Korea and that the most common haplotype of South Korea (LX194) was not found in China. This indicates that P. xylostella populations in China and South Korea are isolated by the barrier of the East China Sea. A former study of P. xylostella populations suggested that there were barriers to gene flow and some degree of genetic isolation between Australia and New Zealand [In this study, the two most common and widely distributed haplotypes, LX1 and LX2, were absent from Hainan Island, and no point-to-point haplotype was found between the populations on Hainan Island and other sites. These findings indicate that the ocean is an effective barrier for  Zealand .P. xylostella populations in China and found high haplotype diversity and high gene flow among geographic populations. Nevertheless, the high pairwise FST values between Hainan Island and mainland China populations indicate strong genetic differentiation when dispersal is limited by oceanic barriers. The distribution of shared haplotypes among our sampled populations reveals two primary migration routes for P. xylostella in China; the first route begins in southeastern China (in the lower reaches of the Yangtze River) and progresses through northern and northeastern China, and the second route begins in southwestern China and progresses through northwestern and southern China.In conclusion, we used both mitochondrial and nuclear markers to analyze the genetic structure of S1 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.