Callinectes sapidus spans Nova Scotia to northern Argentina. In the US, it constitutes a keystone species in estuarine habitats of the Atlantic coast and Gulf of Mexico (GOM), serving as both predator and prey to other species, and also has historically represented a multi-billion dollar fishery. Knowledge relevant to effective management and monitoring of this ecologically and economically important species, such as levels of population genetic differentiation and genetic diversity, is necessary. Although several population genetics studies have attempted to address these questions in one or more parts of its distribution, conflicting results and potential problems with the markers used, as well as other issues, have obscured our understanding on them. In this study, we examined large-scale genetic connectivity of the blue crab in the US, using 16 microsatellites, and genotyped individuals from Chesapeake Bay, in the US Atlantic, and from nine localities along the US GOM coast. Consistent with the high long-distance dispersal potential of this species, very low levels of genetic differentiation were detected for the blue crab among the ten US localities examined, suggesting it constitutes a large panmictic population within this region. Estimations of genetic diversity for the blue crab appear to be high in the US, and provide a baseline for monitoring temporal changes in this species. Demographic analyses indicate a recent range expansion of the US population, probably during the Holocene. In addition, capitalizing on published microsatellite data from southern Brazil, our analyses detected high genetic differentiation between localities in the US and Brazil. These results point to the need for examination of genetic diversity and differentiation along the area spanning the US to southern Brazil.The native range of the blue crab  Callinectes sapidus Rathbun, 1896, whose natural range spans from Nova Scotia to northern Argentina  regions. In 2016 alone, 74,258 metric tons were caught in the US, worth ~$214 million . Large lLong-distance dispersal, and thus, low levels of genetic differentiation at large geographical scale, may be expected for the blue crab due to its extended pelagic larval duration that ranges for 4–7 weeks, followed by a postlarval megalopal stage of 1–3 weeks . Thus, oPrevious large-scale studies of population genetic differentiation of the blue crab in the US have been conducted using allozymes, mitochondrial restriction fragment length polymorphisms (RFLPs) and sequences, and nuclear protein-coding genes sequences. The degree of connectivity of the blue crab in its US range, however, remains uncertain, due to conflicting results among studies. Several sources of bias or lack of power  could have affected one or more of these studies.C. sapidus and C. similis ; Port Lavaca ; Galveston ; Avery Island ; Slidell ; D’Iberville ; Apalachicola ; and Cedar Key . Lower Laguna Madre  and Chesapeake Bay  were sampled in 2015. In seven GOM localities, crabs were sampled using double ring mesh nets with chicken as bait. In Rockport and D’Iberville, live crabs were purchased from local fishermen. Crabs from Chesapeake Bay were sampled by Midge Kramer . Sampled crabs were stored in a cooler with dry ice, when available, or regular ice. A chela from each crab was dissected and stored in 100% ethanol for DNA preservation.Adult blue crabs were collected in the US from nine localities across the GOM and one in the Chesapeake Bay . Specimec traits , 1984. EDNA was extracted from muscle tissue dissected from the chela with Quick-gDNA™ MiniPrep kit , according to the manufacturer’s “Solid Tissue” instructions. DNA quality was visually checked following electrophoresis on a 2% agarose gel stained with 0.1× GelRed .For each individual, amplifications of a total of 16 microsatellites were attempted using the PCR. Nine of these  reported by Due to the varying degree of amplification success for each marker, different PCR reaction mixes were utilized. The seven markers ; 1.25 μM forward primer; five μM of reverse and fluorescent M13 universal primer; 200 μM of each dNTP, for a final volume of 15 μL. The marker Pen23472 had 1.6 mM MgCl2 added to each reaction. The thermocycler conditions consisted of: a denaturation step at 95 °C for 5 min, followed by 30 cycles of 95 °C for 30 s, 53–63 °C for 35 s . These were subsequently analyzed on an ABI 3130x1 Genetic Analyzer at the DNA Technologies Lab and Institute for Plant Genomics at Texas A&M University. Two researchers (DM and ICC) independently performed allele calling with GeneMarker v.1.6  and STRand v.2.4.109 , respectHE, HO), test conformity to the expectations of Hardy-Weinberg Equilibrium proportions (HWP), and calculate FIS for each marker. To control for the occurrence of false positives due to multiple comparisons, significance of LD and HWP p-values was determined using: (1) the Bonferroni correction; and (2) the Benjamini–Hochberg procedure (HO), mean expected heterozygosity within populations (HS), total heterozygosity (HT), inbreeding coefficient (GIS), allelic richness (AR), and number of alleles (NA) for each locus and group were measured with FSTAT v.2.9.3.2 (NP) per locus was determined in GenAIEx v. 6.5  while accounting for null alleles (n), allelic dropout (b) and inbreeding (f). INEST was initially run using the six models available, with 50,000 burn-in cycles and 500,000 cycles overall, to determine the best models for our dataset, according to the deviance information criterion (DIC). The best models were then run to 1,000,000 cycles with 100,000 burn-in cycles. Mean paired genetic relatedness values (r) within each locality were estimated using allele frequencies according to the mean estimate (r), as described by r values on which the computed r was compared to assess its significance, and 10,000 bootstrap replicates were run to obtain 95% confidence intervals (CI) for each r.MICRO-CHECKER v. 2.2.3  was usedGenetic diversity estimations obtained for US localities were compared with those obtained by FST under the island model. Loci that do not fit neutral expectations are considered candidates of selection. Simulations assumed 100 demes with 20,000 simulated loci. Analyses were conducted considering either ten groups  or three groups , the eastern GOM , and the western GOM ). The Bayesian simulation-based test of FST values into locus-specific components (α) and population-specific components (β). It uses a reversible jump Markov Chain Monte Carlo (MCMC) algorithm and calculates the posterior probability that a locus is under selection by assuming two alternative models . Analyses were based on 20 pilot runs, each consisting of 5,000 iterations, followed by 100,000 iterations with a burn-in of 50,000 iterations.Presence of loci under putative selection was tested with the method of t): FST = 0.01 , FST = 0.007 , FST = 0.005 , and FST = 0.001 .The statistical power of the datasets used for detecting population structure was evaluated using POWSIM v. 4.1 . For eacDST), which quantifies relative degree of allelic differentiation, and fixation index (GST), which quantifies nearness to fixation, were calculated using FSTAT. Population differentiation was also estimated using FST, another measure that quantifies nearness to fixation, calculated in GenAlEx, GENODIVE , inbreeding coefficient (-f), and migration rate (-m) were set to 0.8. This value was selected after performing preliminary runs implementing different mixing parameter values (0.2 through 0.8), and examining the acceptance rates and mixing patterns of the chains . Multiple runs and the convergence of chains were checked by plotting traces in Tracer v.1.7. The method implemented in BayesAss assumes migration rates are relatively low and the proportion of non-migrants within a population  is bound at a minimum of 2/3. Estimated non-migration rates of approximately 2/3 may indicate that the actual value is lower than this, suggesting that populations may not be distinct (BayesAss 1.3 Documentation).s method , as impls method . Values Locus-by-locus analyses of molecular variance (AMOVA) and significance tests with 10,000 permutations were also performed in Arlequin. The data were grouped in multiple ways to examine patterns of variation: (1) all US localities; (2) all GOM localities; (3) GOM and Chesapeake Bay grouped separately; and (4) western and eastern GOM grouped separately.K values from 1 to 10 were tested in three iterations, with 500,000 steps and a burn-in of 125,000 steps. Four models were used: admixture with correlated allele frequencies; admixture with independent allele frequencies; no admixture with correlated allele frequencies; and no admixture with independent allele frequencies. All other settings were set to default. Values of Ln Pr(X|K)  , ΔK usino method , and Medo method , which aSelector . The LOCSelector , which iKmax ranging from two to five for 50,000 sweeps, discarding the first 10,000 sweeps, and each K was repeated five times. A discriminant analysis of principal component analysis (DAPC) with the R package adegenet v.2.1.1  was performed to identify population clusters. Finally, GENETIX v. 4.05  within the US and GOM were conducted using the program ISOLDE in Genepop on the Web. Two kinds of analyses were performed: (1) between localities, and (2) between individuals. Geographic distances were estimated in Google Earth Pro v.7.3 following the contours of the coastal margin between localities. Statistical significance based on the Spearman’s rank correlation coefficient was evaluated using Mantel tests .The aforementioned set of analyses of population genetic differentiation (except IBD) were also conducted on the genotypic data of the study by Ne) was estimated with NeEstimator v. 2.01 , which is the current effective population size; θanc (2Nancμ), which is the ancestral effective population size; Dg/2N, where Dg is the time of the demographic change in generations and N is the effective population size; and Dg * μ, where μ is the mutation rate per generation per locus. Inferences on population contraction or expansion are based on the Nratio (θcur/θanc), which is the ratio of the current effective population size divided by the ancestral one; a ratio > 1 is interpreted as a signal of population expansion, whereas a ratio < 1 as a signal of a bottleneck. Unscaled parameters of N, Nanc, and Dg were converted using a microsatellite mutation rate of 0.0005 per locus per generation, which represents a classical average value derived from many different species , nor with the BH FDR method (FDR ≤ 0.05). Both selection tests identified locus Pen23472 as an outlier, suggesting it may be under putative selection  per locus for each of the 10 US localities (16 loci). For comparison, we also show these data for the four Brazilian localities , no significant deviations from HWP were detected in 10 of them at any US locality after Bonferroni correction (p < 0.0003), whereas two loci (Tri24376 and Tet1886) showed deviations of HWP in only one locality, one locus (CSA035) in three localities, and two loci (CSC004 and CSC001) in five localities  and CSA073 (4%).For the 15 putatively neutral loci  to 11.3% (Tet1886), with an overall average of 3.7% (HO) and expected heterozygosity (HS) per locus within localities was 0.59 and 0.73, respectively (HT) per locus in the US was 0.74.The number of alleles per locus for the 16 loci in the US ranged from six to 47; average 18.1 . Percent of 3.7% . Averageectively . AverageHS were observed for all but one (CSC007) of the loci in common between the US and southern Brazil. Average HO, HS and HT in Brazil was 0.54, 0.58, and 0.58, respectively , hereafter referred to as the “US-seven-loci-dataset” , which were suggested in four or more populations. GIS estimation for the outlier locus Pen23472 was −0.040  and nfb , with DIC values of 19,383.164 and 19,384.124, respectively; and their estimated inbreeding coefficients were 0 and 0.021, respectively. Of the two models, the best one was nb , implying that inbreeding was not important. Mean paired genetic relatedness values (r) within each locality were very low  and GST (fixation index) values were obtained for each locus, with the exception of Pen23472, which was suggested to be under putative selection  and G′ST (measure of fixation) values per locus for the US-seven-loci-dataset, which contains the seven loci that exhibited the least deviations of HWP and the least presence of null alleles, were 0.004 and 0.006 respectively. Global average corrected DST′ and GST′ values per locus for the 15 putatively neutral loci  were 0.002 and 0.004 respectively. In contrast, corrected global DST′ and GST′ values for the outlier locus Pen23472 were higher: 0.049 and 0.062, respectively.Within the US, very low global election . Global FST was 0.008 ; Apalachicola vs. D’Iberville ; Cedar Key vs. Galveston ; and Galveston vs. Lower Laguna Madre . Without correction for null alleles, average pairwise FST estimations was 0.006. In this analysis, three pairwise comparisons did not include zero in the 95% CI: Apalachicola vs. D’Iberville ; Cedar Key vs. Galveston ; and Rockport vs. Slidell ; only the comparison SER vs. Port LaVaca was significant according to the FDR test (FDR ≤ 0.05). POWSIM indicates that this dataset has a 100% probability to detect differentiation for FST = 0.01; 98.7% to detect differentiation for FST = 0.007; 89.7% to detect differentiation for FST = 0.005; and 15.2% to detect differentiation for FST = 0.001.Using the US-seven-loci-dataset and correcting for null alleles, average pairwise FST estimations was 0.006 ; Apalachicola vs. Lower Laguna Madre ; Avery Island vs. Cedar Key ; and Cedar Key vs. Lower Laguna Madre . Without correction for null alleles, average pairwise FST estimations was 0.003. Only the Apalachicola vs. Cedar Key comparison did not include zero in the CI ; and none was significant according to the FDR test (FDR ≤ 0.05). POWSIM indicates that this dataset has a 100% probability to detect differentiation for FST = 0.01 and 0.007; 99.6% to detect differentiation for FST = 0.005; and 30.4% to detect differentiation for FST = 0.001.Using all the 15 putatively neutral loci and correcting for null alleles, average pairwise FST using the private alleles method for the US-seven-loci-dataset was 0.002 and for the 15 neutral markers 0.005 , five comparisons were significant, whereas 14 comparisons were significant with the BH FDR method (FDR ≤ 0.05). The frequencies of the seven alleles observed in this locus show pronounced differences across populations, but a geographic pattern is not clear , followed by the “among individuals within localities” component (~21%), and the “among populations within each group” component was very small (~0.3%). F-values for the genetic differentiation within the US, and within the Gulf, were 0.003. F-values for the genetic differentiation between Chesapeake Bay and the GOM, and between the west and east Gulf, were −0.003 and −0.0001, respectively.Analyses of molecular variance analyses considering different groupings do not suggest genetic structure within the US, within the GOM, between Chesapeake Bay and the GOM, nor between the west and east Gulf (K) was higher for K = 1 in all STRUCTURE analyses. K values ranging from three to nine were suggested by the Evanno method, whereas the K = 2 in all cases. STRUCTURE plots, however, do not show evidence of population genetic structure within the US in any of the analyses  and significant , large values for the lower limit of the 95% CI , and infinite for the upper limit of the 95% CI. These values can be interpreted as indicative of a very large Ne . The Nratio point estimate obtained for the US-seven-loci dataset using the GSM model (MaxLogLik = −483.3) was 10.27 . The Nratio point estimate for the 15 loci dataset under the GSM model (MaxLogLik = −1226.7) was 5.31 (95% CI [2.48–15.44]), whereas under the combined SMM/GSM model (MaxLogLik = –1176.3) this value was 3.05 (95% CI [1.70–5.46]). Relatively precise point estimates were obtained for the different parameters, especially for the 15 loci dataset analyses, as inferred by the clear peaks in the MIGRAINE pairwise likelihood profiles  are: N = 17,650  diploid individuals; Dg obtained from Dg/2N = 1,087  generations; Nanc = 5,785  diploid individuals; Dg obtained from Dgμ = 1,088  generations.Analyses of past demographic history using MIGRAINE suggest an expansion of the US blue crab population , with esThis study is the first to investigate microsatellite-based population structure at a large geographic scale for the blue crab within its US range. Prior studies of this species based on a smaller number of microsatellites examined smaller areas restricted to the US Atlantic coast . Our resFST estimations, this is consistent with the prediction that although failure to correct for the presence of null alleles can lead to overestimation of FST when population differentiation is significant, such FST estimates are regarded as unbiased when population structure is absent . After correction for null alleles, the 95% CI of only four comparisons, in both the 15-loci and US-seven-loci datasets  excluded zero, but their lower interval values were very close to zero, and one was negative. Thus, we interpret that in general, the FST results indicate a lack of, or very weak, genetic differentiation for the blue crab in the US.Results of genetic differentiation were highly consistent between the dataset that excluded and the dataset that included the loci with high frequency of null alleles and hence deviations of HWP . Thus, the conclusion of a lack of, or very weak, genetic differentiation for the blue crab in the US seems very robust, even in the presence of loci with null alleles. In the case of s absent , which aFST estimations, GST , another measure of fixation; and DST , which quantifies allelic differentiation; were close to zero. Fixation and allelic differentiation measures quantify complementary aspects of population structure, although they do not necessarily correspond to each other , whereas DST approaches zero if, and only if, all demes are identical  was higher for K = 1 in all analyses, and STRUCTURE plots did not show evidence of any genetic structure within the US. Performance of the Evanno method has been questioned . These results also suggest that there is no population structure, with r-values much higher than the values interpreted as indicative that locations are informative. AMOVA, DAPC, PCA, FCA, and BayesAss results also did not suggest any genetic structure within the US. IBD also does not appear to be occurring for the blue crab within the US.Population structure was also not detected using assignment tests , which have been shown to be highly insensitive to the presence of null alleles , even inestioned , whereasFST using the private alleles method was also very low , suggesting substantial gene flow.Conformance to HWP for pooled data at six loci is also consistent with a large panmictic population in the US. Five of these loci were not suggested to have null alleles, and the remaining one had a low percentage of potential null alleles. Null alleles could have contributed to deviations of HWP in the other loci, as they artificially inflate homozygosity. Similarly, low frequency of private alleles in the populations sampled in our study is also consistent with substantial gene flow. Average pairwise Remarkably similar values of genetic diversity to those reported in our study were found in a study in Charleston Harbor estuary, South Carolina , that usCSA-121, 0.004 for CSA-035, 0.012 for CSA-073, 0.059 for CSC-007 (this is the only marker they found deviating from HWE), and 0.093 for CSC-094. Pooling data across localities in our study, MICRO-CHECKER suggests a frequency of null alleles of 0.005 for CSA-121, 0.127 for CSA-035, 0.041 for CSA-073, 0.077 for CSC-007 and 0.037 for CSC-094. Differences in the sampled range could have contributed to the observed differences between the two studies. In addition, the South Carolina study used CERVUS 3.0  between both datasets, with an average number of alleles per locus (26 vs. 28.14) and expected heterozygosity (0.58 vs. 0.82) for southern Brazil and US, respectively. Genetic diversity estimations for the blue crab in the US are also higher than those reported for the brown swimming crab ectively .n = 213) of C. sapidus with 216 variable sites for a 622-bp COI fragment. For comparison, a study of C. bellicosus along most of its distribution in the Gulf of California and Pacific Baja California reports only 23 haplotypes (n = 67) with 26 variable sites for a 658-bp COI fragment . Null alleles, which can inflate inbreeding estimations, are not expected to largely bias inbreeding estimations for these loci, because they were absent, or in low frequency. A low average inbreeding value was also obtained for these loci when considered a single panmictic population (0.015). Similarly, unbiased estimations of the inbreeding coefficient for all 15 putatively neutral loci using INEST resulted in a value of 0 for the “null alleles and genotyping failures” (nb) model, and 0.021 for the “null alleles, inbreeding and genotyping failures” (nfb) model. Low mean relatedness values within localities (average = −0.0023) are consistent with low inbreeding and high gene flow. In the South Carolina study, estimated FIS values are low (average = 0.06), which is similar to the value estimated in southern Brazil (0.056). Similarly, low FIS values were reported in the Louisiana coast  implies temporal genetic stability. Similarly, the samples in C. sapidus from C. similis were later deemed unreliable by C. similis and C. sapidus megalopae that parallels changes in the allozyme allele frequencies reported for blue crab megalopae at the same localities studied by n = 32 individuals) and adults (n = 49) at a locality in Chesapeake Bay have also been reported by Two previous studies are at odds with the findings of high levels of gene flow for the blue crab in the US. On the basis of three moderately polymorphic allozymes, The second study that reported significant population structure for the blue crab within the US, examined mitochondrial COI sequences and found differences between the eastern and western GOM, and among some localities within the western GOM . Based oFST = 0.0103) between blue crabs collected in Panama City, Florida (GOM), and Agawam River, Massachusetts, at the northern US Atlantic (only these two US populations were included in that study). Thus, it is possible that the use of high-throughput sequencing methods could reveal low levels of genetic differentiation that may be present for this species in the US.The picture emerging from independent studies using different types of markers is that the blue crab experiences high levels of gene flow in the US Atlantic and GOM region, likely corresponding to a very large panmictic population. This is remarkable considering the variation in environmental factors , potential barriers for dispersal, and genetic breaks observed for other marine species in this region, which include differentiation between the Atlantic and GOM, the East and West GOM, and differentiation between the Laguna Madre and other GOM localities . A recenHigh levels of gene flow have also been determined for the blue crab along 740 km in southern Brazil . DespiteNe suggest a very large effective population size for the blue crab in the US, which is congruent with the extremely large population size inferred from the thousands of tons that are harvested each year for this species in this region. It has been suggested that precise Ne estimates using the LD method can be obtained with this method for relatively small populations (Ne < 200), and small populations are not likely to be mistaken for large ones; but it is very difficult to obtain reliable estimates for large populations with this method  Ne has been challenged. A recent study indicates that for medium-sized populations  and common sample sizes , negative estimates with the LD method are likely to occur, and that on average Ne estimates are negatively biased , which is consistent with the previous analyses of Ne. The expansion appears to have occurred recently, based on the point estimate for Dg  of 1,088 generations ago, and given that blue crabs live between 2 and 3 years. Considering the upper 95% CI value of Dg , which was obtained from Dg/2N, this expansion could have occurred several thousands years ago. Although mutation rates can be very variable, changes in one order of magnitude still result in large Ne and a recent time of demographic change estimations. Assuming mutation rates of 5 × 10−3 and 5 × 10−5, conversions of Ne result in 1,765 and 176,500 individuals, respectively, and of Dg in 108.7 and 10,880 generations, respectively. The demographic expansion of the blue crab probably occurred after the end of the last glacial period , during the Holocene, when the seas became warmer. Several marine and coastal taxa also exhibit signatures consistent with range expansions since the Last Glacial Maximum (C. sapidus is estimated to have diverged from its sister lineage C. toxotes 0.6 to 6.4 Mya (Callinectes crabs in the US Atlantic coast have been reported from the Pleistocene and Miocene (Our demographic analyses suggest a recent expansion of the US blue crab population. According to the analysis of the 15 loci dataset with the SMM/GSM model, which had the best fit for the data, the population grew between 1.7 and 5.5 times. Using a mutation rate of 5 × 10 Maximum . The lin 6.4 Mya . Fossils Miocene .Our population genetics analyses based on microsatellites indicate substantial gene flow, high genetic diversity, a large effective population size, and no indication of inbreeding or recent bottlenecks for the blue crab in its US distribution. Lack of genetic structure in the US is consistent with other studies using different types of markers that also suggest a panmictic population in this region, with rare instances of some local temporal genetic differentiation. Detection of population structure in marine organisms characterized by extremely large populations and high dispersal potential, and/or with recently diverged populations, however, may be difficult using neutral markers, and the use of non-neutral markers has been recommended . Therefo10.7717/peerj.7780/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.7780/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.7780/supp-3Supplemental Information 3Genotypes for each individualClick here for additional data file.


Symphodus melops) that occurs on a large scale in the Norwegian salmon industry. Parentage assignment analysis of mesocosm's offspring revealed 30% (195 out of 651 offspring) interbreeding between the two populations, despite their being genetically  and phenotypically differentiated. Moreover, our results suggest that reproductive fitness of the translocated western population doubled that of the local southern population. Our results confirm that human translocations may overcome the impediments imposed by natural habitat discontinuities and urge for immediate action to manage the genetic resources of these small benthic wrasses.Translocation of organisms within or outside its native range carries the risk of modifying the community of the recipient ecosystems and induces gene flow between locally adapted populations or closely related species. In this study, we evaluated the genetic consequences of large‐scale translocation of cleaner wrasses that has become a common practice within the salmon aquaculture industry in northern Europe to combat sea lice infestation. A major concern with this practice is the potential for hybridization of escaped organisms with the local, recipient wrasse population, and thus potentially introduce exogenous alleles and breaking down coadapted gene complexes in local populations. We investigated the potential threat for such genetic introgressions in a large seminatural mesocosm basin. The experimental setting represented a simulated translocation of corkwing wrasse ( Individual genotypes were assessed with GENEMAPPER v. 4.0 (Applied Biosystems). As a guard against potential genotyping errors, all samples were run with the same size standard and on the same machine. In addition, approximately 5% of the samples were randomly subjected to repeated genotyping.Total genomic DNA was extracted from ethanol‐preserved tail samples using E.Z.N.A® Tissue DNA kit (Omega Bio‐Tek), resuspending the DNA in TE buffer. The ability of microsatellite loci to resolve parentage assignment depends on their number as well as their degree of polymorphism , and calculating allelic richness (Ar), observed (HO), and expected heterozygosity (HE), both within samples and for the total over all samples (HT), based on Nei and Chesser  among all pairs of loci for each sample. MICROCHECKER v.2.2.1  with CERVUS v.3.0 , an index representing the probability of finding two individuals sharing a multilocus genotype. In addition, we determined the cumulative success rate of parentage allocation ranking the markers according to the exclusion probability of both parents, Excl. P2 option, based on 1,000 offspring simulations using the PFX_Mchoice macro implemented in PARFEX v1.0  and the exclusion probability , the average number of offspring (k`), and the variance in the number of offspring (Vk) from contributing breeders, as assessed from the parental assignments (above):Once all parental pairs were identified, we estimated the inbreeding effective number of breeders, Nb  and female (f) parents and combined to obtain the inbreeding effective Nb:We further investigated whether the presence of sneaker males would contribute to increase Nb or not. The analysis was performed by comparing the estimates when offspring from all breeding pairs were included  to those obtained excluding the offspring produced by sneaker males.2.5We used parentage assignment results to test the hypothesis of nonrandom mating between south and west breeders using a 2 × 2 contingency table with Yates correction. The analysis was performed taking into account parental origin (south and west) and sex . In this analysis, nesting and sneaker males were pooled together due to the small number of sneakers in the samples , sex , and whether both parents were from same origin or not. Furthermore, we also considered the effects of parental weight/length ratio  to analyze reproduction success. Since most possible male–female pair combinations did not produce offspring . The variables iL and jL are the weight/length ratio of the ith male and jth female, respectively. The variable iS is a factor variable considering the effect of sneaker behavior on the mating success and takes on a value of 1 if the i'th male is a sneaker and 0 if it is a nesting male. The ijε is an independent, identical distributed error term for each breeding pair of the i'th male and j'th female. The coefficients were estimated using maximum likelihood estimation.In the formula above, For those breeding pairs that actually produced at least one sampled offspring, we further analyzed the number of offspring in the sample, by using a general linear model with a poison distributed response variable:ijN is the number of offspring produced by the breeding pair of the i'th male and the j'th female. Similarly to model Equation  and heterozygosity (HE = 0.740), than those from the south (Ar = 11.5 and HE = 0.670). Average genetic difference between the two parental groups from different origins  was observed in 10 of the 3*11 cases generated from the two parental groups and the offspring at 11 loci, with eight of them remaining statistically significant at the 5% level after the FDR correction  of pairwise tests for LD were statistically significant after the FDR correction . Except for the locus pair SMD121‐SMB101 in the breeders from the west coast, all significant pairwise tests were detected in the offspring samples. As deviations from HW and from LD are expected in the heterogenous offspring group, representing a mix of parental stocks and hybrids, no action was taken in response to these findings, except as noted above for locus SMB118.3.2Q estimates, and low I index, were observed for the set of microsatellite loci (Table Vk), up to more than 2,000 for western nesting males . The logistic regression gave an intercept of the model equaling a basic probability of exp(−5.361)/(1 + exp(−5.361)) = 0.0047 for any random breeding pair to produce at least one offspring. Parameter estimates (in logit) are presented in Table Mating between corkwing wrasse breeders of south and west origin appears to occur randomly with no evidence for assortative mating by population origin , such aggressive encounters between males have likely occurred in the present experimental setting (Halvorsen et al., Paternal care marine fish species, such as corkwing wrasse, often display territorial behavior during the spawning season when large nesting males show aggressive behavior to guard their nests against small sneakers (Myhre, Forsgren, & Amundsen, Intraspecific latitudinal variance in life‐history traits generally is strongly correlated to environmental gradients (Munch & Salinas, Our findings provide information on the mating behavior of corkwing wrasse and insight into the putative consequences of intentional large‐scale wild fish translocations on non‐salmonid marine species. We confirmed that translocated wrasses with significantly different phenotypic and genetic profiles to local populations can and do interbreed with local specimens. Faust et al.  suggesteNone declared.EBG designed the study, conducted the sampling, and performed the microsatellite analyses. EBG, SHE, and PEJ analyzed the data. EBG wrote the original draft with contribution from all the co‐authors.


Diabelia  is a genus endemic to Asia, mostly in Japan, but two recent discoveries in China raised questions over the role of the East China Sea (ECS) in these species' disjunctions. Chloroplast DNA sequence data were generated from 402 population samples for two regions  and 11 nuclear microsatellite loci were screened for 549 individuals. Haplotype, population-level structure, combined analyses of ecological niche modeling, and reconstruction of ancestral state in phylogenies were also performed. During the Last Glacial Maximum (LGM) period after the Tertiary, Diabelia was potentially widely distributed in southeastern China, the continental shelf of the East China Sea and Japan (excluding Hokkaido). After LGM, all populations in China have disappeared except those in Zhejiang which may represent a Glacial refuge. Populations of Diabelia in Japan have not experienced significant bottleneck effects, and populations have maintained a relatively stable state. The observed discontinuous distribution of Diabelia species between China and Japan are interpreted as the result of relatively ancient divergence. The phylogenetic tree of chloroplast fragments shows the characteristics of multi-origin evolution (except for D. sanguinea). STRUCTURE analysis of nuclear Simple Sequence Repeat (nSSR) showed that the plants of the Diabelia were divided into five gene pools: D. serrata, D. spathulata, D. sanguinea, D. ionostachya (D. spathulata var. spathulata-Korea), and populations of D. ionostachya var. ionostachya in Yamagata prefecture, northern Japan. Molecular evidence provides new insights of Diabelia into biogeography, a potential glacial refuge, and population-level genetic structure within species. In the process of species differentiation, ECS acts as a corridor for two-way migration of animals and plants between China and Japan during glacial maxima, providing the possibility of secondary contact for discontinuously distributed species between China and Japan, or as a filter (creating isolation) during glacial minima. The influence of the ECS in speciation and biogeography of Diabelia in the Tertiary remains unresolved in this study. Understanding origins, evolutionary histories, and speciation will provide a framework for the conservation and cultivation of Diabelia.The Sino-Japanese Floristic Region (SJFR) is a key area for plant phylogeographical research, due to its very high species diversity and disjunct distributions of a large number of species and genera. At present, the root cause and temporal origin of the discontinuous distribution of many plants in the Sino-Japanese flora are still unclear.  The second species, D. serrata (Sieb. & Zucc.) Landrein, is widely distributed in southern Japan (from Shiga to Kagoshima Prefectures) and also contains a number of infra-specific varieties and forms  Sugimoto, and f. tomentosa (Koidz.) Nakai). These two species also have scattered distributions along the southeast coast of East China  Landrein, is a narrow endemic from Japan (from Fukushima to Fukuoka Prefectures), where its range partly overlaps with both D. spathulata and D. serrata  Landrein  Landrein) based on morphological characters . Diabelia serrata is distributed in Shikoku, Kyushu and the central and southern regions of Honshu. Whereas, Diabelia ionostachya is mainly distributed in Honshu and Shikoku, but rarely in Kyushu. Diabelia sanguinea grows mainly in central and northeastern Honshu. Diabelia spathulata is widely distributed in Honshu and Shikoku, but rare in Kyushu and Korea. A small number of populations of D. serrata and D. ionostachya are distributed in Zhejiang, China Landrein and Farjon, D. serrata and D. spathulata. Abelia spathulata var. sanguinea is recognized at species level as D. sanguinea (Makino) Landrein. The overlooked name Abelia ionostachya Nakai is recombined in Diabelia and two varieties are recognized, var. stenophylla Landrein and var. tetrasepala. For convenience and uniformity, the samples used in our analyses were assigned names based on Landrein and Farjon's proposed morphology-based taxonomy.do Hara,  and threta Hara, . The numin Hara, . Howeverd Farjon  have proDiabelia are likely to be wind dispersed , and while Diabelia is mainly distributed in Japan, its origin and subsequent dispersal patterns remain uncertain  of Diabelia taxa to determine when these species diverged, and which processes  most likely led to the current disjunct distribution of D. ionostachya and D. serrata across the ECS. We utilized population-level sampling to determine the relations between species and potential hybrid populations in Diabelia. Our objectives were (i) to refine the timeframe in which Diabelia populations diverged by fitting both datasets (nSSR and cpDNA) to an Isolation with Migration (IM) model; (ii) to model the species' potential distributions in response to past climatic changes (LGM) and to the present day using Ecological Niche Modeling (ENM); (iii) Through a comprehensive analysis of nSSR and cpDNA data we aimed to determine whether the current biogeography of Diabelia is due to the spread from China or Japan by long-distance dispersal  or during the LGM. Finally, we questioned whether D. ionostachya var. tetrasepala could be a hybrid of D. serrata  and D. spathulata or D. ionostachya . We used these data to assess infra-specific variation within Diabelia species.In the present investigation, we assessed the phylogenetic relationships and the phylogeographic history of Diabelia individuals from 69 sites throughout the range of this genus in Japan (64 populations), East China  and Korea  which showed consistent amplification and have also been reported to be the most variable markers for subfamily Linnaeoideae  protocol of Doyle and Doyle . We amplo et al. . We seleThe final PCR products (chloroplast DNA and microsatellite sequences) were purified with PEG8000 (Polyethylene Glycol) and sequenced using ABI Prism BigDye Terminator Cycle Sequencing Kits v. 3.1 on an ABI 3730xl DNA Sequencer  following the manufacturer's instructions.99); mean number of alleles per locus (A); observed heterozygosity (Ho); unbiased expected heterozygosity or Nei (e); genetic diversity indices of the populations of Diabelia; sum of squared variation within populations; and source of variation. GenePop v.4.2.2  correction for null alleles; Chapuis and Estoup, S) and Roger's (R). These distance matrices were converted into UPGMA (unweighted pair-group method using arithmetic averages) dendrograms  employing the programs Populations v.1.2.30  assuming an admixture model with correlated allele frequencies, and with a priori grouping of individuals into populations (but not into species). The length of the burn-in period and the MCMC runs were set to 50,000 and 500,000, respectively. The most likely value of K was determined both by choosing the smallest K after the log probability of data [ln Pr(X|K)] values reached a plateau . Third, a Principal Coordinates Analysis (PCoA) at the population level was carried out with GenAlEx. Fourth, putative genetic barriers between populations were detected with the software Barrier v. 2.2  was run with the aid of GenAlex and Arlequin, calculating the fixation indices and percentage of molecular variance at four hierarchical levels and the percentage of molecular variance at five hierarchical levels : (i) among species; (ii) among populations; (iii) calculation of the sum of squared variation within populations; and (iv) intraspecific variance. Similar to the first level, several possible combinations of populations were used, and based on the STRUCTURE analysis the clusters were divided into five geographic regions: (A), West Japan; (B), South Japan (including Nara and Wakayama); (C), Southeast Japan ; (D), Northwest Japan , China (Wenzhou) and Korea (Gyeongsangnam-do); and (E), Northeast Japan ; where the study populations occurred , and the four species  were estimated by using MIGRATE-N v. 3.6.4 , with uniform prior distribution both for Θ and M . The effective number of migrants per generation (Nm) among populations was estimated using the formula 4 Nm = ΘM  migration rates between the clusters identified with STRUCTURE; between the three geographical regions where the populations under this study occurred; and between the four species using a Bayesian assignment test with the software BayesAss v. 3.0 , representative sample sequences of haplotypes are shown in Dipelta is a sister genus of Diabelia . We aligned the newly generated sequences and sequences from GenBank with Clustal W implemented in MEGA X version 10.0.5 software  that was most appropriate for all the data was selected according to the corrected Akaike Information Criterion (AICc) implemented in jModelTest . We employed the maximum entropy algorithm, as implemented in MaxEnt v. 3.3 , from the collection records of Laboratory for Plant Systematics in Chiba University  for all four species under current conditions (1950–2000) were downloaded from the WorldClim website  variables were made based on their relative contribution to the model , making sure that the most influential variables for the two outgroup species were selected.Ecological niche modeling (ENM) was performed to evaluate the potential distribution of the four extant species of rom Hara  and Landrom Hara . Data wehttp://www.mpimet.mpg.de/en/science/models/mpi-esm/). Replicate runs (20) of MaxEnt (using the “subsample” method) were performed to ensure reliable results. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic plot, with 25% of the localities randomly selected to test the model. AUC scores may range between 0.5 (randomness) and 1 (exact match), with those above 0.9 indicating good performance of the model  surveyed across the 402 individuals of Diabelia was 1,127 bp long. Thirty-seven haplotypes (H1–37) were identified across the 51 populations of Diabelia , and the diversity ranged from 0 to 0.087  and Saitama (Honshu), and the diversity ranged from 0 to 0.083 , D. serrata formed a monophyletic group . The first diverging clade included D. serrata in Kagoshima  were found to be sister to D. ionostachya var. wenzhouensis (H37), sharing a common ancestor. Diabelia spathulata showed a pattern of ancient divergence between populations. The earliest diverging clade of D. spathulata in Japan is distributed in Kochi (D. spathulata var. spathulata Kochi6 (H18). Diabelia spathulata from Korea formed a clade with D. ionostachya var. ionostachya from Yamagata  ( PP = 1) , likely P < 0.05) was observed for 117 of 549 validity tests, mostly showing a higher than expected frequency of homozygosity. The values of null allele frequency at all loci were very low, averaging 0.051 [range = 0.027 (locus 8)−0.114 (locus 7)]; low percentages of null alleles are not expected to cause significant biases in genetic diversity estimates  and across populations , indicating that the significant positive deviations from H–W equilibrium can be at least partly attributed to the presence of null alleles.A total of 549 individuals from 66 populations across the range of the genus were genotyped with nSSR . All the 11 surveyed microsatellites were polymorphic across populations. Significant linkage disequilibrium was detected in 158 out of 1,207 locus–locus comparisons (about 13%), although only nine persisted after the implementation of Bonferroni's correction Rice, . A signiD. sanguinea and D. ionostachya compared to D. serrata and D. spathulata, both for allelic richness and for heterozygosity  when only populations with N > 10 were considered   and D. spathulata from Yamagata (SP-YAM2) , whereas the population with the largest value of He was also SP-FUK1. Populations outside Japan showed low genetic diversity, especially D. serrata (the only non-Japanese population) (WEN) (He = 0.273)  to 25 (locus 11) and averaging 17 alleles per locus . The numzygosity , even thnsidered . The mos= 0.273) .Diabelia based on FST was significant [mean FST (66 populations) = 0.419; FST values for individual species were also higher  showing low interspecific differentiation. The “among-species” component was also low (8.9%) in AMOVA (FST (66 populations) was found to be 0.437 based on AMOVA analysis. The Fst value of each gene-pool was lower than the four species concept , indicating a significant correlation between genetic differentiation and geographical distance, thereby suggesting that there is an isolation-by-distance across all studied populations. The greatest variation was found among all the populations of D. serrata (1568), followed by the populations of D. spathulata (1082), D. sanguinea (762), and D. ionostachya (710), respectively. The population of D. spathulata from Fukui (SP-FUK1) had the highest mutation value (240), followed by the population of D. sanguinea from Tochigi  and the population of D. serrata from Kagoshima . At the population level, the four species were found to be diverging at different rates, and D. ionostachya was more variable than other species  as well as D. ionostachya var. ionostachya  does not match current taxonomic concepts , which supported the predictive power of the model. According to the MaxEnt jackknife tests of variable importance, the precipitation variables were more informative for the model than the temperature ones under the present climate conditions , although bio9 (mean temperature of driest quarter) was also important for all the models. The present-day distributional predictions for the four species were largely congruent with the known occurrences, although additional areas appeared as suitable  and nucleotide levels , when the genus may have been widely distributed in southern Asia. However, after the Quaternary Period climate shocks and more recent environmental constraints, the main distribution of Diabelia in Asia was confined to Japan. The main distribution in southern China (during the LGM period) had disappeared except in Zhejiang, and a large number of populations are likely to have gone extinct , suggesting that extant populations likely differentiated well before the LGM. Compared with the LGM, the Tertiary climate and the relative location simulation of the ECS, China and Japan are not developed enough to predict the role of the ECS in the species differentiation of Diabelia. Although the phylogenetic tree shows that the populations of China and Japanese have significant genetic differentiation . In the haplotype network in Diabelia, haplotype H23, H29, H36, H37 were found to be connected with haploidy of external groups (Diabelia had an extensive distribution in southeast China and Japan (excluding Hokkaido) during the LGM. The predicted results of ENM in LGM also support this conclusion. The distribution records of Diabelia in China are all from one area in Zhejiang Province. In addition, plants growing in glacial refugial areas are generally expected to exhibit corresponding ecological characteristics, such as low dispersal capacity, niche specialization  further confirmed the inference that the alpine mountains in the middle of Honshu Island were refugees of the interglacial age  results from hybridization between D. serrata  and D. spathulata . In results of nSSR structure, D. ionostachya var. tetrasepala individuals from R4, R5, gene infiltration is low (D. ionostachya var. tetrasepala is the result of hybridization between D. serrata and D. spathulata. Intermediate individuals produced by interspecific hybridization do make it difficult to classify species accurately in the field. Additional data may enable the underlying discordance between taxonomic concepts and our molecular data to be correctly interpreted.External morphological differences and similar environmental niche among is nSSR, , only D.tructure . In the n is low . So it iDiabelia using phylogeographic data. Furthermore, the role of the ECS in speciation and species migration was explored with Diabelia as a case study. Combining chloroplast, nSSR with ecological niche modeling, we assessed phylogeny and population dynamics in Diabelia. Based on our results, a distinct population of D. ionostachya was identified in the Yamagata prefecture of northern Japan. The divergence of Diabelia species dates back to the mid to late Tertiary. Diabelia presents a typical multi-origin pattern of species evolution. Diabelia species in China represent primitive populations and the isolating function of the East China Sea in Diabelia speciation remains unresolved.This study presents the first comprehensive analyses of the phylogeny and biogeography of taxa within The datasets generated for this study can be found in H-FW, SL, and SS conceived the ideas and designed methodology. SS and MM collected the materials. K-KZ, HL, and W-XM constructed the species phylogeny cladogram. K-KZ, W-XM, Z-XZ, and TY analyzed the data. H-FW, SL, and RB led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. All authors contribute equally to the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Petrosia ficiformis is one of the species most affected by temperature-related diseases. Our study aimed to assess its genetic structure, connectivity, and bottleneck signatures to understand its evolutionary history and to provide information to help design conservation strategies of sessile marine invertebrates.Knowledge about the distribution of the genetic variation of marine species is fundamental to address species conservation and management strategies, especially in scenarios with mass mortalities. In the Mediterranean Sea, P. ficiformis in the Atlanto-Mediterranean region at 10 microsatellite loci. High levels of inbreeding were detected in most locations  and bottleneck signatures were only detected in Mediterranean populations, although not coinciding entirely with those with reported die-offs. We detected strong significant population differentiation, with the Atlantic populations being the most genetically isolated, and show that six clusters explained the genetic structure along the distribution range of this sponge. Although we detected a pattern of isolation by distance in P. ficiformis when all locations were analyzed together, stratified Mantel tests revealed that other factors could be playing a more prominent role than isolation by distance. Indeed, we detected a strong effect of oceanographic barriers impeding the gene flow among certain areas, the strongest one being the Almeria-Oran front, hampering gene flow between the Atlantic Ocean and the Mediterranean Sea. Finally, migration and genetic diversity distribution analyses suggest a Mediterranean origin for the species.We genotyped 280 individuals from 19 locations across the entire distribution range of Petrosia ficiformis showed extreme levels of inbreeding and population differentiation, which could all be linked to the poor swimming abilities of the larva. However, the observed moderate migration patterns are highly difficult to reconcile with such poor larval dispersal, and suggest that, although unlikely, dispersal may also be achieved in the gamete phase. Overall, because of the high genetic diversity in the Eastern Mediterranean and frequent mass mortalities in the Western Mediterranean, we suggest that conservation efforts should be carried out specifically in those areas of the Mediterranean to safeguard the genetic diversity of the species.In our study The online version of this article (10.1186/s12862-018-1343-6) contains supplementary material, which is available to authorized users. The Mediterranean basin is one of the most diverse places on Earth in terms of animal and plant diversity, yet one of the most threatened  ficiformis  has been recurrently reported to be affected by necrotic processes that decimated their populations in several areas of the Mediterranean ),hipelago  does nothipelago , and deeciformis , 76, potciformis , 37, 73,ciformis  that couIn our study, the closest populations geographically  did not appear in the same genetic cluster, and showed diverging proportions of individuals of 3 (when k = 6) or 4 (when k = 8) clusters. Some degree of larval retention could play a role in the case of ULL. That site is located in a submarine pinnacle that goes down to 65 m and is separated few km from the coast. It was identified as a highly-isolated population in K = 10, which could partially explain the low allelic richness of the site. The rest of microscale patterns of differentiation observed here are difficult to explain. In other sessile organisms with low dispersal abilities such as gorgonians, microscale differentiation has been explained mostly by a higher degree of self-recruitment . In our e) were always highest in the Mediterranean populations and lowest in those of the Macaronesian region  and the Paleogene (23 Mya) [P. ficiformis likely pre-dated the Messinian Salinity Crisis (MSC) during the Miocene  , when th(23 Mya) . Therefo5.6 Mya, ), when sciformis . Even thciformis , most arciformis , but theciformis –87. Altene caves  where it5.33 Mya , is not P. ficiformis  harbours the greatest diversity, and we suggest the possibility that those could act as a reservoir in case population decimation affects other areas in the Mediterranean. However, that area shows higher levels of connectivity with the Adriatic Sea, and therefore in light of our results efforts should be made to protect populations of P. ficiformis not only in already existent Marine Protected Areas –MPAs– in the Eastern Mediterranean (or to create new MPAs to preserve them), but also in the Adriatic and Western Mediterranean in order to provide westward “gene flow corridors” for the species. In addition, the Atlantic populations deserve special attention, since they show the highest degree of reproductive isolation and if impacted, they will not receive recruits from closely related areas, which could end in their disappearance.Our study provides a detailed account of the distribution of genetic diversity across the geographic natural range of Paramuricea clavata, for which genetic diversity and connectivity data are available , an, anParamP. ficiformis: the Atlanto-Mediterranean region that includes the Macaronesian islands of Azores, Madeira and the Canary Islands, and the entire Mediterranean Sea except for the Southern Mediterranean coastline  following the manufacturer’s indications with minor modifications on the lysis time (performed overnight) and the elution step (performed twice using 50 μl of buffer EB each time).A total of 280 sponges were sampled by the authors between 2008 and 2013 using SCUBA diving in 19 sites spanning the entire distribution range for All 280 individuals were genotyped at 10 unlinked microsatellite loci  previously described , using to) and expected (He) heterozygosity, and the fixation index (FIS). Differences between He values among basins  were calculated using two-tailed T tests in Statplus 6 . In addition, Genepop on the web version 4.0.10 [2, where ∑π2 is the sum of the squared population allele frequencies) were calculated with GenAlEx and GENODIVE version 2.0b23 [GenAlEx 6.5  was usedn 4.0.10 , 94 was n 2.0b23 . In orden 2.0b23  was usedP. ficiformis were used: two based on clustering approaches  and four distance-based methods: FST estimations, Isolation by Distance (IBD), BARRIER, and Analyses of the Molecular Variance (AMOVA).Several methods to assess population structure and differentiation in admixture, since although we had no previous knowledge on the origin of the populations studied, we assumed that a proportion of individuals can have recent ancestor coming from multiple populations, no locprior, since no additional sample-characteristic data was available, and correlated allele frequencies, because we had no previous knowledge on the correlation levels across populations [The assignment of individuals to each population was performed using a Bayesian clustering approach in STRUCTURE 2.3.4 , that caulations . The proulations . Convergulations . We thenulations  to find ulations  and the adegenet 2.1.1 package [k-means on transformed data with Principal Component Analysis (PCA), which is then run sequentially with increasing values of k. The resulting clustering solutions are compared using Bayesian Information Criterion (BIC), with the optimal cluster solution corresponding to the lowest value of BIC. Before performing the analysis, the optimal number of PCs to be retained was explored by a cross-validation method as implemented in the same package.For further assessment of population differentiation, the multivariate DAPC method was applied using the  package  implemen package . DAPC deFST statistic using an infinite allele model (IAM) in the software Arlequin 3.5 [FST values was analysed with 20,000 permutations and corrected using the B-Y [FST values after it, using the ENA method [FST and average FST per population following [adegenet 2.1.1 [hierfstat v0.04–22 [Population differentiation between pairwise sampling sites was estimated with the quin 3.5 . Signifi the B-Y  False Di the B-Y . In addi the B-Y . Only thA method  describeA method . Global ollowing  with UPGet 2.1.1  and hierv0.04–22 .FST estimates (FST /1- FST) were correlated against log-transformed geographical distances between samples [P. ficiformis , pairwise FST values and coordinates for sampling sites were implemented in the software BARRIER v2.2 [FST), and applies the Monmonier’s maximum distance algorithm to identify ‘barriers’ to gene flow among sites, namely the zones where differences between pairs of sites are the largest.To determine whether genetic differentiation was driven by geographical distance creating a pattern of IBD, linearized pairwise  samples  using a  samples . GeograpIER v2.2 . BARRIERFinally, an Analysis of Molecular Variance (AMOVA) was performed to determine the hierarchical distribution of genetic variation in GENODIVE version 2.0b23 . To reveP. ficiformis [Given the documented episodes of mass mortalities for ciformis , 10 in tciformis . The sofciformis . We usedciformis  which cahttps://diversityinlife.weebly.com/) in R [In addition, a population assignment analysis was performed calculating the likelihood ratio thresholds for all 19 populations based on the Monte Carlo test with an alpha of 0.002 and 1000 replicated datasets using GENODIVE version 2.0b23 . This mem/) in R , which um/) in R  to plot m/) in R : we seleAdditional file 1:Petrosia ficiformis. Abbreviations: Ns, number of genotyped individuals; A, number of alleles; Ae, Number of effective alleles; rA, number of alleles after rarefaction; Ho, observed heterozygosity; He, expected heterozygosity; FIS, inbreeding coefficient; HWE, deviation from Hardy-Weinberg equilibrium: ns, not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001. Population codes as in Table Descriptors of genetic diversity for all 19 locations of Additional file 2:Allele frequencies per locus and population for the entire study. (PDF 1776 kb)Additional file 3:adegenet for Petrosia ficiformis. Using Bayesian Information Criterion, the optimal clusters correspond to the lowest values, here shown in red circles. D. Individual assignment to each of the 10 clusters inferred using BIC. (PDF 331 kb)A. Graph depicting delta K and likelihood of K obtained from STRUCTURE. B. Individual genotype assignment to clusters (K) as inferred by STRUCTURE for all studied sites with K = 10. C. Number of clusters obtained by Additional file 4:FST comparisons between 19 populations of Petrosia ficiformis. Bold numbers reflect significant p-values after correction (p < 0.008). (XLSX 54 kb)Additional file 5:Petrosia ficiformis. Values with no overlapping CIs are shown in bold and green shading. (XLSX 19 kb)Migration rates (median and 95% CIs) obtained with MIGRATE-N for


Daphnia as well as environmental change are not well known. The genetic diversity and phylogeography of seven D. similoides sinensis populations located in the middle and lower reaches of the Yangtze River were investigated based on the combination of mitochondrial (COI gene) and nuclear (14 microsatellite primers) markers. Based on the mitochondrial gene markers, D. similoides sinensis from the middle and lower reaches of the Yangtze River had one ancestral haplotype and two evolutionary clades. In addition, D. similoides sinensis population deviated from neutral evolution, showing signs of a bottleneck effect followed by population expansion. Based on the microsatellite markers, the seven D. similoides sinensis populations formed three main groups. The dendrogram (NJ/ME) showed that D. similoides sinensis based on the mitochondrial genes marker were obviously clustered two main clades, whereas there were three clades based on the microsatellite markers. Our results suggested that the habitat fragmentation due to the barrier of the dams and sluices promoted the genetic differentiation and phylogeography of D. similoides sinensis populations in the middle and lower reaches of the Yangtze River.Geographical patterns, climate, and environmental change have important influences on the distribution and spread of aquatic organisms. However, the relationships between the geographical pattern and phylogenetics of  Avise eCOI and NDI genes, Ober, Matthews, Ferrieri, and Kuhn  located in the middle and lower reaches of the Yangtze and Huai Rivers is not yet clear.The Huai River historically drained directly into the Yellow Sea, but it is now connected to the lower reaches of the Yangtze River after many floods. Along the middle and lower reaches of the Yangtze River and Huai River of China, many tributaries of the river and lakes are distributed Figure . There w Dam Li, . MoreoveCOI gene) and nuclear (14 microsatellite primers) markers were jointly used to study the genetic diversity of D. similoides sinensis. This study aims to compare the differences in phylogenetics and population genetics of D. similoides sinensis located in the middle and lower reaches of the Yangtze River and to explore the influence of the geographical pattern on the phylogenetics of D. similoides sinensis. Specifically, we have made a hypothesis that the construction of dam and sluices in the middle and lower reaches of the Yangtze River promoted the genetic differentiation of D. similoides sinensis.In this study, both mitochondrial .er Table . In the er Table  and then2.2COI gene was amplified with the LCO1490 (5′‐GGTCAACAAATCATAAAGATATTGG‐3′) and HCO2198 (5′‐TAAACTTCAGGGTGACCAAAAAATCA‐3′) , 2.5 µl of 10 × LA‐Taq Buffer II, 4.0 µl of dNTPs (2.5 mM) , 0.5 µl of MgCl2 (25 mM), 1.0 µl of each primer (10 mM) , 0.25 μl of DNA polymerase TaKaRa‐LA‐Taq (5 U/µl) , and 14.75 µl of double‐distilled H2O. The PCR system (25 µl) of the SSRs contained 1.0 µl of genomic DNA (100 ng/μl), 1.0 µl of each primer (10 mM) , 12.5 µl of 2 × Taq Master Mix , and 9.5 µl of double‐distilled H2O.The PCR system (25 µl) of the COI gene amplification included an initial denaturing step of 1 min at 95°C, 35 cycles of 40 s at 95°C, 40 s at 45°C, and 1 min at 72°C, as well as a final extension of 72°C for 10 min. The conditions of the SSR amplification included an initial denaturing step of 3 min at 95°C, 35 cycles of 45 s at 95°C, 45 s at 54°C, and 45 s at 72°C, as well as a final extension of 72°C for 10 min.The conditions of the 2.3COI gene were checked by gel electrophoresis and then purified by the AxyPrep DNA Gel Recovery Kit  and sequenced with the forward and reverse primers . The sequence alignment was carried out using the SeqMan software in DNAStar. DNAspV5 was used to analyze the site variations, haplotype diversity, and nucleotide diversity of D. similoides sinensis among the COI sequences, as well as Fst among populations. Fu's Fs test, Tajima's neutrality test, and mismatch distribution were used to detect the evolutionary history of D. similoides sinensis populations  estimates was constructed with MEGA 4.1 and bootstrap resampled 1,000 times.The PCR amplification products of the ions Fu,  using DnD. similoides sinensis samples from seven populations located in the middle and lower reaches of the Yangtze River were amplified using the 14 microsatellite fluorescent labeled primers  iterations were run, and then 100,000 iterations after burn‐in were performed. The most likely value for K was estimated from the greatest rate of change in the likelihood function (ΔK) of successive K values  among seven geographical populations of D. similoides sinensis with tools for population genetic analyses (TFPGA) version 1.3  and haplotype diversities (Hd) were, respectively, 0.0053 and 0.784. A total of 24 haplotypes were detected, including 8 shared haplotypes and 16 specific haplotypes. The haplotype networks indicated that seven D. similoides sinensis populations had one ancestral haplotype (H‐2) and two main clades  and nucleotide diversity (π) ranged from 0.553 to 0.936 and from 0.0014 to 0.0092, respectively .Based on the microsatellite markers, there was lower ns Table , especiagh Table , implyiner Table . The Fs3.4D. similoides sinensis populations were obviously divided into three clades  of D. similoides sinensis individuals showed also two major evolutionary clades (clade A and clade B)  of only six populations  were constructed based on both mitochondrial and microsatellite markers. Based on the microsatellite markers, the dendrogram (NJ/ME) showed that six s Figure a. In thes Figure b. The JS) Figure . Clade AD. similoides sinensis population located in the middle and lower reaches of the Yangtze River experienced an expansion process  and Tajima's test  showed that the JSTH population deviated from neutral evolution, whereas the other six populations did not deviate from neutral evolution.Based on the mitochondrial gene sequences, the distribution of base mismatches indicated that there was a single peak, implying that the s Figure . Both Fu44.1π) are two important parameters to study population genetic diversity of organisms  and nucleotide diversity  based on the mitochondrial genes marker showed that six D. similoides sinensis populations were obviously clustered into two main clades, whereas there were three clades based on the microsatellite markers. One reason for the differing results between the mitochondria and nuclear data is significant differences in the evolutionary rate and level of polymorphism between different molecular markers (Bai & Zhang, D. similoides sinensis located in the middle and lower reaches of the Yangtze River show significant genetic differentiation.In this study, seven 4.2Luciobarbus and then affected the phylogenetic differentiation and geographical distribution of Luciobarbus population. Slechtova, Bohlen, Freyhof, Persat, and Delmastro (Daphnia magna had a high degree of provincialism with a patchy occurrence of specific lineages. Based on the mtDNA gene sequences, several Daphnia species in North America showed the similar phylogeographical pattern that topographic barriers generated population divergence of Daphnia (Hebert, Witt, & Adamowics, D. similoides sinensis along the two rivers was also isolated. Similarly, the connection of Lake Junshan with Lake Poyang and the Yangtze River was cut off after the construction of the lake embankment in 1958. In our study, there were two evolutionary clades of D. similoides sinensis located in the middle and lower reaches of the Yangtze River, namely the Yangtze River clade and the Huai River clade. Based on the genetic structure of the population, further analysis revealed that the JSH population had significant differences from the other four populations in the Yangtze River clade. The Lake Junshan was interconnected with Lake Poyang before 1958, but it was separated from Lake Poyang after the construction of an embankment in 1958. The lake ecosystem changed from an open to closed status, which weakened or even interrupted gene flow between the lake and other geographical populations of D. similoides sinensis. Therefore, the geographic isolation affected the phylogeography of D. similoides sinensis located in the middle and lower reaches of the Yangtze River.The influences of geographic isolation on the phylogeography of aquatic organisms have extensively been researched in the world. Machordom and Doadrio  found thelmastro  thought elmastro  found thDaphnia magna populations from small water bodies showed a stronger among‐population genetic differentiation than the populations inhabiting larger water bodies. In the study, the Fst values based on the mitochondrial marker were not significantly correlated with the geographical distance among populations, but long‐term habitat fragmentation caused by dams and sluices could restrict the movement among the D. similoides sinensis populations after the 1950s and enhance the genetic differentiations of D. similoides sinensis populations located in the middle and lower reaches of the Yangtze River.Avise  argued tD. similoides sinensis populations in the middle and lower reaches of the Yangtze River. The D. similoides sinensis distributed in the region had formed two clades, namely the Yangtze River clade and the Huai River clade. The D. similoides sinensis habitats in the middle and lower reaches of the Yangtze River have been fragmented because of the construction of dams and sluices since the 1950s. Habitat discontinuity and geographic segregation could accelerate regional genetic differentiation of D. similoides sinensis in the middle and lower reaches of the Yangtze River.In conclusion, there is significant genetic structure of the None declared.J.‐X.W., D.‐G.D., and Z.‐Z.Z conceived and designed the experimental plan. J.‐X.W., K.Z, W.‐P. Z. S.‐X.P, and X.‐X.X. performed the experiments. J.‐X.W. and Y.‐N.Z. analyzed and interpreted the sequence data. J.‐X.W. and D.‐G.D. drafted the manuscript. All authors have read and approved the final manuscript.


Aedes aegypti, the vector of dengue fever, was first reported in Yunnan in 2002. Now, this species is found in nine counties in border areas of south-west Yunnan. Related dengue fever outbreaks have been reported since 2013. The population genetics of Ae. aegypti in these areas were studied to explain the expansion history of this species.Ae. aegypti were sampled from six counties of Yunnan, and two laboratory populations from Guangdong and Hainan were also included in this study. A total of 12 microsatellite loci and three mitochondrial genes were analysed.Fifteen natural populations of Ae. aegypti populations from Yunnan show similar genetic diversity. The 17 populations could be divided into three groups: the first group included populations from Longchuan, Ruili and Gengma, which are located in the southwest of Yunnan; the second group included populations from Jinghong and Menghai, in the south of Yunnan; and the third group included populations from Mengla and the two laboratory populations from Guangdong and Hainan. Both microsatellite and mtDNA data revealed that the genetic relationships of the populations corresponded to their geographic relationships.The results indicate that Ae. aegypti from northern Myanmar and Laos to southern and southwestern Yunnan was a natural process. The effect of human activity on expansion was not obvious. Surveillance efforts should still be focused on border areas where Ae. aegypti does not occur, and a powerful control strategy should be applied to prevent outbreaks of dengue fever.The results suggested that the expansion of  Aedes aegypti representing the most important vector [Dengue fever (DF) is the most rapidly spreading arboviral disease in the world, with t vector . In 2009t vector  estimatet vector .Ae. aegypti in China was recorded in areas below 22° north latitude, including Hainan, Guangxi, Guangdong and Taiwan, and no Ae. aegypti were recorded in Yunnan [Ae. aegypti was found in the county city of Ruili, which is approximately 5 km away from Jiegao Port. In 2008, and 2009, Ae. aegypti was found at the Guanlei wharf on the Mekong River and in the Mohan Port of Mengla County, respectively. In 2011, Ae. aegypti was found in Jinghong, Xishuangbanna. There were records of Ae. aegypti in Ruili, Mangshi, Mengla, Menghai, Jinghong, Yingjiang, Longchuan and Gengma counties by 2014 [Before 2000, the distribution of n Yunnan . This spn Yunnan . In 2006 by 2014 . Dengue via the Mekong River. These countries of Southeast Asia are all threatened by dengue fever transmitted by Ae. aegypti [Ae. aegypti populations to border areas of Yunnan. However, the real reasons for the appearance of Ae. aegypti populations in Yunnan should be examined to develop an effective survey and control strategy for Ae. aegypti in related areas. There are two possible scenarios for the invasion of Ae. aegypti in Yunnan, China: (i) the mosquito expanded to Yunnan naturally, and populations from different areas are genetically independent; and (ii) the mosquito invaded Yunnan due to increasing transportation among countries, and populations from different areas are therefore linked by the highway system between countries and counties of Yunnan.Yunnan is located in southwestern China in areas contiguous with Myanmar, Laos, and Vietnam and connected to Cambodia and Thailand  aegypti . Most im aegypti . Human tAe. aegypti from South America and Southeast Asia and on a global scale [Ae. aegypti are located directly within or closely linked to repetitive elements, which could reduce the possibility of using these microsatellite sequences in genetic studies [Ae. aegypti populations of China.Because of the high variability, codominant expression, and broad genome distribution of microsatellites, they have been widely used as classic genetic markers in population genetic studies, providing information on population structure, patterns of gene flow, histories of introduction and colonization, or pathways of the expansion of invading species in new areas , 13. Theal scale –17. Howe studies . Therefoc oxidase subunit 1 (cox1), NADH dehydrogenase subunit 4 (nad4), and NADH dehydrogenase subunit 5 (nad5) are frequently used to infer the phylogenetic relationships of mosquitoes. cox1 is the most conserved of the three cytochrome oxidases encoded by mtDNA [nad4 and nad5 loci are highly polymorphic and have often been used to conduct genetic analysis of Ae. aegypti [Mitochondrial DNA (mtDNA) replicates independently from nuclear genomes and is materially inherited without recombination, so it can allow sharper genetic differentiation than nuclear DNA , 19. Theby mtDNA  and showby mtDNA . The nad aegypti –24.Ae. aegypti population in Yunnan, China: (i) the scenario of Ae. aegypti invasion of Yunnan, to develop an effective strategy for the surveillance and control of the Ae. aegypti population; and (ii) considering the expansion of Ae. aegypti, the possibility that this species will continue to spread within and outside of Yunnan. Molecular markers that can be used for population identification are needed to distinguish Ae. aegypti populations from Yunnan, Guangdong and Hainan.Based on population genetics analysis with microsatellite and mtDNA loci, we sought to study two issues in the Ae. aegypti: four from Ruili, one from Longchuan, two from Mengla, one from Menghai, one from Gengma, and six from Jinghong  following the manufacturer’s protocol. The DNA samples were stored at − 20 °C until further analysis.Total nucleic acids were extracted from each individual of via a genome-based method [2O). The PCR conditions were as follows: 95 °C for 10 min, followed by 30 cycles at 95 °C for 1 min, a different annealing temperature for each locus for 30 s, and 72 °C for 45 s. A final extension step was conducted at 72 °C for 10 min. The PCR product standard mixture was analysed on the ABI3730XL Genetic Analyzer , and the data analysed using GeneMapper software.Twelve polymorphic microsatellite loci were used in this study: 11 pairs of primers were obtained d method , and 1 pd method . The priNa), number of effective alleles (Ne), Shannonʼs information index (I), observed heterozygosity (Ho), expected heterozygosity (He) and positive inbreeding coefficient (FIS) with GenAIEx (version 6.501) [Nm was calculated with the formula 1/4(FST-1). PIC-Calc 0.6 [The genetic diversity of every locus was characterized by estimating the number of different alleles (n 6.501) . The valCalc 0.6  was usedCalc 0.6 . Fstat  was calculated to infer possible genetic bottlenecks in the more distant past  in ArleqΚ (K = 1 to 10), with a ‘burn-in’ period of 50,000 iterations and 250,000 replications. The method of Evanno et al. [K were combined using the Greedy algorithm of Clumpp 1.1.1 [K were displayed graphically using Distruct v1.1 [via regression of pairwise FST/(1 − FST) on the natural logarithm (Ln) of straight-line geographical distance [Genetic distance was calculated using GenAIEx (version 6.501) , and theo et al.  was usedpp 1.1.1 , and sumuct v1.1 . Isolatidistance .cox1, nad4 and nad5, were used to explore the polymorphism of all samples. The three fragments were amplified and sequenced with previously reported primers [cox1, nad4 and nad5 sequences were constructed with the Network program (version 5.0) [Three mitochondrial genes,  primers , 43. The primers . Phylogeion 5.0) . SimilarHo was found in GEM (0.309), and the maximum Ho was found in LYC (0.464) (Table He was observed in LBH (0.447), and the maximum He was observed in CAJ (0.630) , the maximum value was found in CAJ  ; the samples from Jinghong and Menghai made up the second cluster Group 2); and the populations from Mengla, Guangdong and Hainan belonged to the third group, which showed a close relationship among these populations (Group 3) , and the minimum FIS value was found in FTC (0.071). The FST and Nm values are shown in Additional file FST value among the 17 populations was found between MML and NAM (FST= 0.017), and these two populations were both from Ruili city. The pairwise FST values were always lower between populations from the same city than between those from different cities. The FST values between LGD, LHN and other populations were especially high compared to those for other populations from Yunnan. The value of Nm was between 0.807 (LHN and NKK) and 14.339 (MML and NAM), and most of the Nm values among the populations were greater than 1, indicating that there was a certain degree of communication among these populations of Ae. aegypti. According to the AMOVA results, the percentage of variation between groups was only 8.71%, while the percentage of variation within individuals was 63.48%, and the FIT  value was 0.3652. The results of the Mantel test showed a significant positive correlation between Nei’s standard genetic distance and geographical distance , as shown in Fig. The in GEM 0.86, and tcox1 (507 bp), 544 mtDNA nad4 (553 bp) and 534 mtDNA nad5 (414 bp) nucleotide sequences obtained from the 17 populations.There were 549 mtDNA cox1 gene had 11 haplotypes. The haplotype diversity index of the total population was 0.5670, and the nucleotide diversity index was 0.0034. The average number of nucleotide differences (k) was 1.687. The LYC population harboured the most haplotypes (n = 4): Hap1, Hap9, Hap10 and Hap11. There were 5 populations  exhibiting only one type of haplotype. The nad4 gene had 15 haplotypes. The haplotype diversity index of the total population was 0.5603, and the nucleotide diversity index was 0.0224. The MML population from Ruili and the DAL population from Menghai exhibited the most haplotypes (n = 5). Four of the 5 populations with only one haplotype of nad4 were the same as those with only one cox1 haplotype; these populations were LGD, LHN, NAM, RJL and HHY. The nad5 gene presented 10 haplotypes, and Hap1 was detected in all geographical populations. The indices of the total population were 0.503 for haplotype diversity, 0.005 for nucleotide diversity and 1.920 for the average number of nucleotide differences.The mitochondrial DNA results showed that the P-values with negative test values were all less than 0.05, indicating that these populations had not expanded during their history. The haplotype network maps , as shown in Fig. The results of the population expansion test showed that the Aedes aegypti is a mosquito species distributed worldwide in tropical and subtropical regions. No Ae. aegypti were found in any survey conducted before 2000 in Yunnan, and Ae. aegypti larvae were captured for the first time at the Jiegao Port of Ruili in 2002. The larvae of Ae. aegypti are mainly found in jars and flowerpots indoors and outdoors. Female mosquitoes usually suck blood near their breeding grounds. The flying distance of adults is usually short, and the average diffusion radius is within 100 m [Aedes aegypti does not enter diapause in low temperatures and short photoperiods [via diapause eggs, which is the main reason that Ae. albopictus expanded from Asia to North America and Europe [Ae. aegypti expansion in border areas of China and Southeast Asian countries could be revealed via population genetics research with microsatellite and mtDNA markers.in 100 m . Aedes aoperiods  and therd Europe , 49. TheHo values ranging from 0.309 to 0.467, with an average value of 0.389. All the natural populations conformed to Hardy-Weinberg equilibrium. These results indicated that there was no obvious decline in genetic diversity among these populations, which indicated that the Ae. aegypti populations from these areas were not the offspring of one or two invasion events from Southeast Asian countries but the result of successive expansions via different routes.Genetic diversity is one of the most important attributes of populations. The decline in genetic diversity among geographical populations could be an indicator of the bottleneck effect or founder effect that occurred in the history of populations expanding or invasion . AccordiAe. aegypti of Yunnan. The M-ratio test indicated that five populations had experienced slight bottlenecks in their history because the M-value of these populations was just below the critical value of 0.68 proposed by Garza & Williamson [Ae. aegypti expanded naturally from Southeast Asia to Yunnan, rather than being imported with human activities, and the second indicated that only some Ae. aegypti populations in urban areas have been under pressure from mosquito control measures applied during dengue fever outbreaks in recent years.The results of the bottleneck tests Table  showed tlliamson  and thatAe. aegypti from Guangdong and Hainan are relatively closely related to populations of Mengla.The results of population clustering were consistent with the geographical distribution of the populations. Longchuan, Ruili and Gengma are located in the southwestern part of Yunnan, contiguous with Myanmar. Populations from these areas were clustered in group 1 Figs. , 3. The R2= 0.375)  Fig. a. This fAe. aegypti in Yunnan by public health authorities and academic institutes. Local CDC entities carry out specific surveillance in border areas of Yunnan [Ae. aegypti populations collected from six counties of Yunnan. Their results showed that all populations could be divided into three groups, two of which included populations from Jinghong and Ruili, respectively, and the third included populations from the other four counties , which was different from the results of the present study. This relationship was not in accordance with the geographical relationship of populations, and could not be explained with any reasonable scenario of the expansion of Ae. aegypti in this area.Because of the close relationship with dengue fever, considerable attention has been paid to the occurrence of f Yunnan . Shi et f Yunnan  studied Ae. aegypti in high-latitude areas has not only been reported in southern Yunnan, China. Positive breeding sites of Ae. aegypti were noted at Darjeeling, West Bengal, India in 2006 [Ae. aegypti in northern South and Southeast Asia suggested that the northward expansion of this species was related to climate change on a large spatial scale. From 1954 to 2006, the annual mean temperature of Jinghong increased by approximately 1.3 °C, from 21.8 °C to 23.1 °C, and the increase was more obvious from the 1980s onward [Ae. aegypti in these areas. However, because this species does not enter diapause, its occurrence in other areas of Yunnan will be determined by the critical low temperature threshold for the survival of this species [The occurrence of  in 2006 . The syns onward . TherefoAe. aegypti populations from mainland Southeast Asia. The results suggested that human transportation routes have resulted in passive long-distance migration of Ae. aegypti in these areas . However, the results of the IBD test in the present study  indicated the role of human transportation in the expansion of Ae. aegypti in southwestern Yunnan was not as significant as in Southeast Asia. Aedes aegypti occurred independently in different areas in southwestern Yunnan from 2003 onward, indicating only a short history of these invasions. Because of the complex topography of Yunnan, transportation among these areas  is not convenient. Thus, the expansion of Ae. aegypti among these areas has not been found according to the results of the present study, and the distribution areas of the species were still restricted to border areas as of 2017 [Hlaing et al.  studied  of 2017 .Ae. aegypti in Yunnan, dengue fever outbreaks were reported from four county areas of Yunnan  [Ae. aegypti in Yunnan, this species has only been found in border areas, although it was reported in eight county areas in 2017 [Ae. aegypti invaded different areas of Yunnan independently, and there is no evidence of expansion among these areas. Therefore, the most important measures that can be adopted by the public health authority of Yunnan are surveillance and control of Ae. aegypti in border areas, especially in counties in areas that are currently negative for Ae. aegypti.After the invasion of  Mengla) . Dengue  Mengla) . Since t in 2017 . There hAe. aegypti in Yunnan can be divided into three groups according to population genetics. The genetic relationships of these populations were concurrent with their geographical relationships. This result suggested that the occurrence of Ae. aegypti in Yunnan is a result of the expansion of its distribution areas, rather than imported events closely related to human activity. Thus far, populations of Ae. aegypti have only occurred in border areas of Yunnan, and the species has not colonized inland counties of Yunnan. According to the topographic and climatic differences between border areas and inland areas of Yunnan, it should be difficult for the population of Ae. aegypti to expand to inland areas of Yunnan in a short time. Therefore, efforts aimed at the surveillance of Ae. aegypti and control of dengue fever should focus on the border areas of Yunnan, especially the counties and ports that are currently free of Ae. aegypti.The populations of Additional file 1: Table S1. The microsatellite short tandem repeat data.Additional file 2: Table S2. Results of Hardy-Weinberg equilibrium. Table S3.FIS per population. Table S4. Population FST (lower left) and Nm (top right) matrix table.


Colluricincla harmonica). We employed multi-locus phylogenetic, population genetic and coalescent analyses to (1) assess whether nuclear genetic diversity suggests a history congruent with that based on phenotypically defined subspecies ranges, mitochondrial clade boundaries and putative biogeographical barriers, (2) estimate genetic diversity within and genetic differentiation and gene flow among regional populations and (3) estimate population divergence times. The five currently recognized subspecies of grey shrike-thrush are genetically differentiated in nuclear and mitochondrial genomes, but connected by low levels of gene flow. Divergences among these populations are concordant with recognized historical biogeographical barriers and date to the Pleistocene. Discordance in the order of population divergence events based on mitochondrial and nuclear genomes suggests a history of sex-biased gene flow and/or mitochondrial introgression at secondary contacts. This study demonstrates that climate change can impact sexes with different dispersal biology in different ways. Incongruence between population and mitochondrial trees calls for a genome-wide investigation into dispersal, mitochondrial introgression and mitonuclear evolution.Understanding how environmental change has shaped species evolution can inform predictions of how future climate change might continue to do so. Research of widespread biological systems spanning multiple climates that have been subject to environmental change can yield generalizable inferences about the neutral and adaptive processes driving lineage divergence during periods of environmental change. We contribute to the growing body of multi-locus phylogeographic studies investigating the effect of Pleistocene climate change on species evolution by focusing on a widespread Australo-Papuan songbird with several mitochondrial lineages that diverged during the Pleistocene, the grey shrike-thrush ( The relationship between environmental change and species evolution is convoluted due to the plethora of ways in which species are impacted by and can respond to change. Understanding the population processes affected by environmental change is important for informing predictive models of species’ responses to future climate change. Climatic oscillations throughout the Pleistocene  impacted the evolution of biota across the globe through fragmentation, displacement and extinction Hewitt . PopulatBased on contemporary species’ ranges and taxonomic treatments, numerous putative historical biogeographical barriers, thought to reflect rising sea levels and expanded arid areas that formed during the Pleistocene, have been mapped across mainland Australia Ford . Two conThe biological influence of some Australo-Papuan barriers, that is, how they limited migration and affected local adaptation and lineage divergence, remains to be fully explored with molecular data  is an ideal species in which to investigate the complex evolutionary effects of Pleistocene climate: it is widespread and common throughout Australia and eastern coastal New Guinea and divergence among its mitochondrial clades has been dated to the Pleistocene  test for congruence between patterns of nuclear genetic differentiation and those based on phenotypically defined subspecies ranges  but are mostly synonymized with superciliosa of Cape York Peninsula ; for each K, 100 replicates of 30,000 burn-in sweeps followed by 100,000 sweeps were run. The calculated cluster probabilities of the 10 replicates with the lowest deviance information criterion (DIC) values of each K were averaged using CLUMPP 1.1.2  substitutions per site per million years (s/s/my) for codon position one, 1.6 × 10−3 (1.3–2.0 × 10−3) s/s/my for position two and 5.8 10−2 (5.2–6.3 × 10−2) s/s/my for position 3. Four runs of 100 million generations were conducted, sampled every 10,000 generations and checked for convergence using Tracer 1.6.0  s/s/my for AB4, DRD4, MUSK-I4, RI2 and TGFb2, and 1.2 × 10−3 (0.78–1.7 × 10−3) s/s/my for GAPDH11 , a P-value corrected using a modified false discovery rate procedure /4; where MA→B is the mutation-scaled migration rate from A to B, and θB is the effective population size B. The lower and upper bounds of confidence intervals around the number of effective migrants per generation were estimated by inputting, respectively, the 2.5th and 97.5th percentile estimates of MA→B and θB into the same equation. The Tasmanian regional population was excluded from this analysis due to its contemporary isolation  and to prevent problematic over-parameterization of the model. Among the Australian mainland populations, 12 asymmetric migration parameters were estimated. The dataset was subsampled using a random sample size of 12  so that an equal number of individuals was analyzed from each regional population. Analyses were run with gamma-distributed priors on M (mean 0.5) and θ (mean 10). This low migration rate prior ensured that the Markov chains were started in regions of parameter space that are consistent with the clear mitochondrial genetic structure seen among regional populations of the species , four genetic clusters were identified Appendix . Three oter Fig. .For nuclear intron sequence data , five genetic clusters were identified across the entire range of the species Appendix  and eachEach of the four BEAST runs converged and combined ESS values were >200 for all parameters. The splitting order and divergence dates of Australian mitolineages Fig.  were conThe four *BEAST runs converged, and combined ESS values were >200 for all parameters. The topology of the species tree Fig.  did not P < 0.011)  were detected among the grey shrike-thrush regional populations Fig. . BidirecIn using nuclear and mitochondrial genetic data to assess the roles of biogeographic barriers in the evolution of a widespread Australo-Papuan songbird, the grey shrike-thrush, we have generated four key findings. First, the geographic arrangement of nuclear genetic diversity was broadly concordant with mitochondrial genetic diversity, phenotypically defined subspecies ranges and contemporary or putative historical biogeographical barriers. Two exceptions to this were that the Trans-Fly population and the Cape York Peninsula/east PNG populations are distinguishable based on mitochondrial but not nuclear sequence data, and the Cape York Peninsula and north-west populations are distinguishable based on mitochondrial data and cluster analysis of the nuclear intron sequence data, but not cluster analysis of the microsatellite data. Second, genetic differentiation across the implicated barriers was significant for mitochondrial, microsatellite and some nuclear intron data and dated to the Pleistocene (0.01–2.58 Ma). There was one exception to this: nuclear genetic differentiation was not significant across the Torres Strait. Third, the order of population divergence inferred from mtDNA and nuclear DNA (nDNA) is not consistent, suggesting a history of sex-biased gene flow and/or mitochondrial introgression. Finally, parapatric regional populations appear to be connected by low levels of gene flow (<1 migrant per generation).D and Fu and Li’s F statistic estimates for the mitochondrial locus indicate a selective sweep or purifying selection and/or recent population expansion in the east Australian and south-west/central regional populations. Furthermore, mitochondrial sequence variation within grey shrike-thrush has previously been associated with aridity, and a candidate amino-acid target of positive selection was identified in ND6 for the south-west/central mitolineage through comparative analysis  and diversity within the mulga parrot (Psephotellus varius)  dates to 0.04–0.18 Ma while divergence within a genus of earthworm  dates to 31–84 Ma . This suggests that gene flow was limited between these two regions despite LGM land bridges connecting them, as has been observed for the butterfly species C. h. superciliosa and suggests they maintained male-mediated gene flow during the LGM across then-exposed savannah woodlands and grasslands  comprise five geographically and phylogenetically discrete clades for nuclear and mtDNA. Marki et al.  are included in Supporting Information Appendix S1Appendix S2Appendix S3Appendix S4Appendix S5Appendix S6Appendix S7