Drosophila melanogaster, high-altitude melanism has evolved in separate mountain ranges in sub-Saharan Africa, potentially as an adaptation to UV intensity. We investigated the genetic basis of this melanism in three populations using a new bulk segregant analysis mapping method. We identified 19 distinct QTL regions from nine mapping crosses, with several QTL peaks overlapping between two or all populations, and yet different crosses involving the same melanic population commonly yielded distinct QTL. The strongest QTL often overlapped well-known pigmentation genes, but we typically did not find wide signals of genetic differentiation (STF) between lightly and darkly pigmented populations at these genes. Instead, we found small numbers of highly differentiated SNPs at the probable causative genes. A simulation analysis showed that these patterns of polymorphism were consistent with selection on standing genetic variation. Overall, our results suggest that, even for potentially simpler traits like pigmentation, the complexity of adaptive trait evolution poses important challenges for QTL mapping and population genetic analysis.Unraveling the genetic architecture of adaptive phenotypic divergence is a fundamental quest in evolutionary biology. In  And yet, genome-wide association studies (GWAS) of female abdominal pigmentation variation  within four temperate populations all revealed major effects of the two melanin synthesis genes tan and ebony and the transcription factor bric-a-brac 1 (bab1), although other minor effect genes were also detected  and showed evidence of a strong partial selective sweep (Pigmentation displays strong geographic trends in Drosophila (ebony in Ethiopia) was found to be consistent with natural selection acting on standing genetic variation.Here, we investigate the genetic basis of melanic flies in three populations from Ethiopia, Cameroon, and Uganda, presenting the first application of a bulk segregant analysis (BSA) approach designed for osophila . Many ofThe populations in the present study were all studied by 1 flies produced from a cross between a darkly pigmented EF, CO, or UG line and a lightly pigmented ZI line, respectively . All parental lines had been inbred for eight generations. Each mapping cross between a melanic population and Zambia involved distinct inbred lines . To choose the parental lines, we tested lines from each population for the presence of eight common chromosomal inversions by PCR using primers and amplification conditions from In(2L)t, In(2R)NS, In(3L)P, In(3R)P, In(3R)K, and In(3R)Mo), and two sub-Saharan inversions on the X chromosome (In(1)A and In(1)Be). We also used the pigmentation score data of the fourth abdominal segment of females that we generated in We set four, three, and two experimental crosses each seeded with F1 males and 125 random F1 females were mixed together (N = 500). After combining the F1 flies from both reciprocal crosses, offspring were allowed to interbreed for 20 nonoverlapping generations at a population size of ∼1500 individuals in 28 × 14 × 15-cm plastic cages, each provided with 14 vials with standard Drosophila medium  and kept at ∼20°. Each generation, adults were allowed to lay eggs on the food for 1 week and then discarded, and food vials were replaced when the first new adult flies in the cage were 7–10 days old. After 20 generations , we allowed adult flies to lay eggs on fresh food for 2 days before replacing the vials and waiting for F20 flies to emerge. We then visually phenotyped 3–5-day-old females (N = 600) for pigmentation on the fourth abdominal segment under CO2 anesthesia, focusing on either pigmentation intensity near the anterior margin  or the width of the posterior black stripe , as in N = 60) and the 10% lightest (N = 60) females.We followed the experimental design described in 20 pigmentation subgroups for each cross. For each library, genomic DNA was extracted from a pool of 30 females by chloroform extraction and ethanol precipitation. DNA was then fragmented using the Bioruptor sonicator (Diagenode), and paired-end libraries with approximately 300-bp inserts were prepared using the NEBNext DNA Library Prep Reagent Set for Illumina (New England Biolabs no. E6000L). Library concentration and quality were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies) and were sequenced at the UW–Madison Biotechnology Center on the Illumina HiSequation 2000 platform with 100-bp paired read lengths.Genome libraries were independently prepared for the parental lines and the four FD. melanogaster reference genome (release 5.57) using default parameters in BWA v0.6.2-r126 (http://picard.sourceforge.net). Alignment around indels was then improved using GATK v3.2  summarized the difference in parental strain ancestry between the high and low phenotypic pools. With respect to the melanic parental line allele, da was estimated asHp is the frequency of the major allele in the melanic parental line, Lp the frequency of the melanic allele in the nonmelanic parental line, Hf the frequency of the melanic allele in the F20 dark subgroup, and Lf is the frequency of the melanic allele in the F20 light subgroup. Only sites with parental strain frequency difference H – pLp ≥ 0.25 were considered. The five chromosomal arms  were subdivided into 2728, 3131, 2357, 2956, and 2935 windows, each of roughly 8.4-kb on average, whose boundaries were determined according SNP density in ZI genomes  and secondary QTL peaks that may flank them  for its effect size (the proportion of phenotypic variance explained) and genomic location. Empirically-observed secondary peaks are also compared against these single QTL simulations, asking how often the observed “secondary deviation”  is generated when no secondary QTL is actually present. That analysis yields a P-value for each secondary peak (the null probability that the associated primary QTL would generate the observed secondary deviation by chance). If significant secondary peaks are present, simulations of multiple linked QTL are conducted. Rejection sampling conditioned on each peak’s height is used to refine the strength and location estimates of the primary and secondary peaks while accounting for these linked peaks’ effects on each other’s da values  are conducted to assess the significance of primary peaks. Peaks with at least a 95% true positive probability are carried forward in the analysis. For each of these significant primary peaks, simulations with a single QTL are conducted, with rejection sampling conditioned on the peak’s d values .STF for these windows between Zambia and the melanic population in question. Genomes from ZI (n = 197), CO (n = 10), EF (n = 68), and Uganda (n = 40) were obtained from the Drosophila Genome Nexus  of the window STF relative to the empirical distribution of STF of all windows per chromosomal arm, where Q denotes the proportion of windows with an equal or greater STF value. Strong selective sweeps from new mutations are expected to produce high window-based STF values due to hitchhiking effect, but such outliers may not include soft sweeps targeting alleles that already occur on multiple haplotypes; STF in each window and the quantile of this value according to the empirical distribution of this estimate for all windows per chromosomal arm.To test whether significant QTL contained highly differentiated windows between the two populations from which the parental lines were drawn, we estimated D. melanogaster populations  implies an instantaneous growth of the ancestral population (ZI) prior to an initial split, with a bottleneck taking place in the ancestor of the RG and EF populations. A second split between the two derived populations takes place followed by a bottleneck and a growth phase in each descendant population, with ongoing migration occurring among all populations. Parameters were optimized using δαδι and several runs were conducted to confirm that convergent parameter estimates were obtained. We then used nonparametric bootstrapping to infer parameter uncertainties. For this, we generated 100 bootstrapped AFS from the empirical data, and parameter SD were estimated using the Godambe Information Matrix (GIM) approach as implemented in δaδi version 1.7. Ancestral effective population size (eN) was inferred by dividing δaδi-estimated Watterson’s θ over four times the mutation rate, using an estimate from D. melanogaster of 3.27 × 10−9  and the frequency of the favored allele at the start of the sweep. In these simulations, we explored the hypothesis that the most differentiated SNP observed at ebony was a beneficial mutation (in Ethiopia only), and we set the target of selection to be the middle of the simulated 5001-bp locus. We simulated 10 different selection strengths and 6 starting beneficial allele frequencies for a total of 60 scenarios of selection. The 10 selection coefficients we studied were 0.00005, 0.000075, 0.0001, 0.0025, 0.005, 0.0075, 0.001, 0.0025, 0.005, and 0.0075. The six starting beneficial allele frequencies were 1/2Ne, 0.005, 0.01, 0.025, 0.05, and 0.1.We first ran 10,000 neutral simulations to test whether the values observed at STF as low as that observed at ebony. To emulate the observed SNP frequencies in simulations with selection, we employed a subsampling strategy. We initially simulated larger samples of chromosomes for the Zambia (n = 250) and Ethiopia (n = 50) populations, then pared them down to 189 and 33 chromosomes, respectively. Subsampling was then pursued to achieve a nontrivial probability of a simulation matching our observed allele counts in both populations. Simulations lacking sufficient numbers of both alleles in either population to achieve these subsamples were rejected. Although we used the –SFC flag of msms to condition on the beneficial allele’s persistence in the Ethiopian population at the time of sampling, simulations could still be rejected if this allele was unsampled or sampled at low frequency. For each selection scenario, we ran a maximum of 2,500,000 simulations in an attempt to get 1000 accepted simulations.In these simulations, our primary interest was to ask which models could replicate the observed maximal SNP frequency difference while generating a window Ne (corresponding to new mutations), only hard sweeps could be generated. For all other initial frequencies, we required at least two unique copies of the beneficial allele to be present at sampling, to study soft sweeps specifically.We also filtered simulations to clearly distinguish between the predictions of hard sweeps and soft sweeps. For the case of an initial beneficial allele frequency of 1/2Table S1.Raw sequence data are available from the NIH Short Read Archive with accession numbers given in i.e., SIBSAM. We performed nine mapping crosses, each pairing an inbred line from one of our melanic populations  with an inbred line from a lightly pigmented ancestral range population (Zambia). Mapping focused on two female pigmentation traits, A4 abdominal background color and A4 stripe width. For each cross, offspring interbred for 20 generations at a population size of ∼1500. In the final generation, the 10% darkest and lightest flies among 600 scored females were collected and sequenced as bulk pools for each cross.We used a newly-developed BSA method  to map pMaterials and Methods; A key goal of SIBSAM is to distinguish two or more linked QTL with overlapping mapping signals, which may occur when complex traits are mapped on this experimental scale. SIBSAM uses detailed experimental simulations in a three phase approach to make QTL inferences , each with a false positive probability below 5% (Table S2). Effect sizes ranged from 6.29 to 37.11% for primary peaks and from 5.66 to 25.92% for secondary peaks. Based on overlapping genomic confidence intervals, these can be summarized into 19 major QTL regions, ranging from 4-kb to 14-Mb long, of which 10 were unique to different single crosses  was shared among five crosses. This region consists of the 2.4-Mb telomeric end of chromosome X containing the important melanin synthesis gene volution , althougjing, which has been shown to affect abdominal pigmentation (bab1 and was shared by one cross from each of the three melanic populations. The other region (Q18) was shared by two crosses from Ethiopia and one cross from Uganda, and includes the melanin synthesis gene ebony. In Uganda, a strong effect of ebony on pigmentation has previously been illustrated  centered on cramped, which can influence abdominal pigmentation in a temperature-sensitive manner . While outliers for window STF could indicate loci subject to population-specific natural selection, some of these may be unrelated to pigmentation evolution. In light of the strong trend toward major QTL occurring near well-known pigmentation genes, we discuss the genetic differentiation of these genes in particular, in addition to the full QTL regions that contain them.To investigate whether pigmentation-associated peaks harbor genes that have been strongly differentiated between the lowland ZI population and each of the melanic populations, we estimated their differences in allele frequencies using ebony in Uganda ; however, genetic differentiation tends to be broadly elevated across this low recombination telomeric region, so genetic variation at yellow should be interpreted with caution. Elevated STF was also detected for a window upstream of the two transcription factor paralogs bric-a-brac , which may affect pigmentation , Q10 . Three peaks of differentiation (Q < 0.05) were found at Q8 surrounding the melanin synthesis gene black but no peak at the gene itself . None of these peaks contain any known pigmentation-related genes.In Cameroon, significant differentiation was found within five QTL detected in this population, including windows similar to that found in Uganda within Q1  in the QTL of this population did not show strong signals of window genetic differentiation, including ebony in spite of the major effect of this QTL in the cross with the darkest phenotype.In Ethiopia, strong differentiation was found in all but 2 (Q10 and Q16) of its 12 QTL, but in general these peaks did not fall near pigmentation candidate genes. The four melanin synthesis genes  for Ethiopia vs. Zambia. In spite of the lack of strong window STF signal cited above, all four of these genes had one or more individual SNPs with strong differentiation, yet without any strong pattern of differentiation at linked SNPs  fell in the first intron. Although this region is not known to affect abdominal pigmentation in cosmopolitan D. melanogaster, in D. pseudoobscura and D. virilis, two species that are completely dark and whose phenotype resembles the dark Ethiopian phenotype, intronic enhancers affect body pigmentation  which had often been detected in within-species studies focusing on the posterior abdominal segments in Drosophila , with an initial bottleneck to ∼1% of the ancestral population size followed by exponential growth. Although we could not investigate all possible historical models, and the effects of natural selection on genomic variation could impact the precision of these estimates, the demographic estimates obtained here serve our primary objective of providing a neutral model capable of generating population genetic data resembling that of our empirical populations.To identify a plausible null model, we used δaδi , from ouSTF and maximum SNP STF under this null model. Compared with neutral simulations based on this demographic model, we found that our window STF observed at ebony was only marginally elevated (Q = 0.07), but maximum SNP STF deviated more strongly from neutral predictions . Thus, simulation replicates in which the beneficial allele was fixed or else failed to rise much in frequency were rejected. For each simulation scenario, we tested how often the SNP STF entailed by the above counts was paired with a window STF at least as low as we observed for ebony.Next, we conducted simulations based on the hypothesis that the most differentiated SNP at STF criteria were soft sweeps with higher initial frequencies (Table S6). Weak hard sweeps could also match the STF disparity, but these scenarios almost never allowed the beneficial allele to rise to high frequency on the time-scale of the Ethiopian population (Table S6). Hence, selection from standing genetic variation appears to be the strongest hypothesis for the patterns of genetic variation observed at ebony in the Ethiopian population.The scenarios that best matched the above frequency and ebony is indeed related to pigmentation evolution, and whether other ebony variants may contribute as well. Contributions of multiple SNPs or haplotypes at a locus have previously been described for ebony in Uganda , or contributions to Ethiopia melanism from other SNPs not highlighted by our population genetic analysis. Different mutations at the same genes drive parallel pigmentation changes in humans . These QTL have estimated effect sizes above 0.33, a strength that resulted in 100% detection power in SIBSAM test simulations , none of these loci showed a clear window signal of high genetic differentiation. Instead, we found a recurring pattern of strong frequency differences limited to just one or a few SNPs. For ebony in Ethiopia, simulations confirmed that this pattern seems best explained by selection on standing genetic variation. Although the functional significance of our high STF variants remains to be confirmed, these narrow intervals of high genetic differentiation support the examination of individual SNPs in scans for local adaptation (e.g., While QTL often occurred near well-known pigmentation genes (D. melanogaster. These results provide one partial set of answers to the basic questions posed at the beginning of this article concerning the genetics of adaptation. However, it will be important to conduct similar studies of different adaptive traits in different organisms to assess the generality of our findings.Our work begins to provide one case study of the genetic architecture of adaptive trait evolution, while offering a foundation for detailed molecular studies to confirm the relevant genes and mutations responsible for parallel melanic evolution in Overall, our work highlights significant challenges to mapping the genetic basis of adaptive population differences. Even when relatively large effect loci are present, their detection may depend upon the specific genetic backgrounds used for QTL mapping and genetic targets of local adaptation may not be detected in typical population genetic scans. Nevertheless, continued advances in the analysis of genomic data and molecular methods for confirming adaptive variants have the potential to expand our understanding of the nuanced process of adaptive trait evolution.


Cyrtomium falcatum (Japanese holly fern) shows mating system variation, i.e., it displays two different types of sexual expression (gametangia formation) in gametophytes: mixed (M) type and separate (S) type. We examined whether there is variation in the selfing rate among populations of this species, and evaluated the relationship between mating system, genetic diversity and effective population size using microsatellites. In this study, we developed eight new microsatellite markers and evaluated genetic diversity and structure of seven populations (four M-type and three S-type). Past effective population sizes (Ne) were inferred using Approximate Bayesian computation (ABC). The values of fixation index (FIS), allelic richness (AR) and gene diversity (h) differed significantly between the M-type  and the S-type  populations . Although evidence of past bottleneck events was detected in all populations by ABC, the current Ne of the M-type populations was about a third of that of the S-type populations. These results suggest that the M-type populations have experienced more frequent bottlenecks, which could be related to their higher colonization ability via gametophytic selfing. Although high population differentiation among populations was detected , there was no clear genetic differentiation between the M- and S-types. Instead, significant isolation by distance was detected among all populations. These results suggest that mating system variation in this species is generated by the selection for single spore colonization during local extinction and recolonization events and there is no genetic structure due to mating system.The impact of variation in mating system on genetic diversity is a well-debated topic in evolutionary biology. The diploid sexual race of  The diversity of mating and sexual systems in land plants is of great interest to evolutionary biologists. For seed plants, the presence of both male and female reproductive organs within a single individual (i.e. monoecy) is a common and ancestral state . Thus, sCyrtomium falcatum (L.f.) C.Presl (Dryopteridaceae), to evaluate the relationship among sexual expression of gametophytes, mating system and genetic diversity. Matsumoto (2003) [Cyrtomium falcatum in Japan into northern and southern types. The northern type is distinguished from the southern type by smaller blades, fewer pairs of pinnae, and grayish indusia without a blackish brown center [C. falcatum. Sporophytes called mixed type (M-type) simultaneously produce gametophytes with both antheridia  and archegonia  at frequencies of 90% or greater after three months in cultivation. In contrast, sporophytes of the separate type (S-type) produce these bisexual gametophytes at frequencies of 10% or less. This sexual expression is equivalent to dichogamy, in which an individual temporally staggers the development of gametangia, because the gametophytes produce antheridia in the early stages of development and then produce archegonia after releasing sperm. Sporophytes that produce intermediate frequencies (10% < frequency < 90%) of gametophytes that produce antheridia and archegonia simultaneously are considered to be the intermediate type (I-type). Matsumoto (2003) [Sadleria, and suggested that the occurrence of simultaneously hermaphroditic  and unisexual gametophytes (S-type) in the laboratory would also be a good predictor of their occurrence in nature. Therefore, the S- and M-types observed in C. falcatum could be considered to reflect mating system variation in nature. Understanding this morphological variation is important because it helps clarify the role of mating systems in shaping genetic diversity of populations and may provide clues to the causes of mating system evolution. In the present study, we developed eight new microsatellite markers to evaluate genetic variation within and among populations of the northern type of C. falcatum. The specific aims of the present study were to: 1) test whether the wild populations of M-type individuals show higher selfing rates than those of the S-type individuals, 2) test correlations between high selfing rates, low genetic diversity and reduction of effective population size, if the selfing rate varies among populations, 3) estimate the past and current effective population size, and 4) test whether the M-type and the S-type populations are genetically differentiated.In this study, we focused on Japanese holly fern, o (2003)  divided n center . The norn center , while tn center  observedo (2003)  confirmeo (2003) . In suppo (2003) . Ranker o (2003)  comparedC. falcatum populations we examined are as determined by Matsumoto (2003) [C. falcatum from 20 localities  . Brieflyo (2003)  sowed spo (2003) . Becauseo (2003)  could beo (2003)  experimecalities  were exaC. falcatum that were previously examined by Matsumoto (2003) [For this study, we collected 233 sporophyte samples from seven populations  in five localities of sexual diploid populations of o (2003) . The sevo (2003) .Voucher specimens of the samples (ri010001–ri010233) were deposited in the Herbarium of the National Museum of Nature and Science (TNS), Tsukuba, Ibaraki, Japan. For sampling in Esan Prefectural Natural Park, we obtained permission from the Hokkaido Government Biodiversity Division.Total DNA was extracted from silica-dried leaves using the HEPES/CTAB method . The DNAet al. (2006) [HaeIII, PvuII, AluI, SspI, EcoRI, and ScaI) and ligated with a specific blunt adaptor [5(AG)8 and an adaptor primer AP2 (5′-CTATAGGGCACGCGTGGT-3′) [Firstly, following the method of Lian . 2006)  genomic  adaptor  using a TGGT-3′) . The PCRTGGT-3′) . A PIG1-06  genomCyrtomium falcatum, collected from the Tsukuba Botanical Gardens  and fragmented by nebulization. A DNA library was constructed using the GS FLX Titanium Rapid Library Preparation Kit (Roche/454 Life Sciences). The DNA library was purified using the MinElute PCR Purification Kit  and its quality was checked using the Agilent High Sensitivity DNA kit . Emulsion PCR was carried out using the GS Junior Titanium emPCR Lib-L Kit (Roche/454 Life Sciences), and pyrosequencing was conducted on a Roche 454 Genome Sequencer Junior instrument at the Tsukuba Botanical Gardens, with the GS Junior Titanium Sequencing Kit (Roche/454 Life Sciences). Contigs were assembled to over 500bp with GS Newbler De Novo Assembler (Roche/454 Life Sciences), implementing the default parameters and heterozygotic mode. The program QDD v.2.1 [Secondly, we developed microsatellite markers using an NGS method with a Roche 454 Genome Sequencer Junior . Genomic DNA was isolated from a pinna of the southern type of diploid DD v.2.1  was usedDD v.2.1 , the U19DD v.2.1 .et al. (2006) [6(AG)10 primer  under the following conditions: initial denaturation for 15 min at 95°C, followed by 30 cycles at 95°C for 30 s, 55°C for 90 s, 72°C for 1 min, and final extension at 60°C for 30 min. The PCR reaction mixture for the other six primer sets contained 0.2 μM reverse primer, 0.04 μM forward primer, and 0.2 μM of the fluorescent dye-labeled U19 primer , which acted as the second forward primer for the cycles following the touchdown stage. Touchdown PCR was performed with initial denaturation for 15 min at 95°C, followed by 25 cycles at 95°C for 30 s, 63–53°C (with a 0.5°C decrease for every subsequent cycle) for 90 s, and 72°C for 1 min, followed by 20 cycles of 95°C for 30 s, 53°C for 90 s, and 72°C for 1 min, and final extension at 60°C for 30 min. The PCR products were analyzed on an ABI 3500 Genetic Analyzer (Applied Biosystems) with the internal size standard, GeneScan 600 LIZ (Applied Biosystems), and fragment sizes were determined with GeneMapper 3.1 (Applied Biosystems). The original sequences for the markers were deposited in GenBank under the accession numbers LC055975—LC055982   and 72 bLC055982 .h; [AR; [FIS values within populations, taking into account the effect of underestimating heterozygosity in the presence of null alleles. As mentioned previously, two types of self-fertilization may occur in homosporous ferns: gametophytic (SI) and sporophytic (S) selfing. Hedrick (1987) [FIS and the rates of two types of selfing (SI and S).Gene diversity  and allh; [AR; ) were cah; [AR; . FSTAT wh; [AR; , with thk (1987)  derived FIS values as an indicator of the relative contributions of selfing sensu-lato (both S and SI) to outcrossing [FIS), gene diversity (h), allelic richness (AR), relatedness [FST [P-values to test whether the value in one group is significantly larger than the other. Our STRUCTURE analysis (see below) indicated that cluster 6 at K = 6 was admixed in both SAND and SADO populations, although these two populations are located far apart from each other  and the natural logarithms of direct minimum geographic distance among populations. Genetic structure was also investigated with the model-based clustering algorithm implemented in the software STRUCTURE v. 2.3.3 [K) varying from 1 to 15, were evaluated under the correlated allele frequencies model by running 100,000 burn-in Markov Chain Monte Carlo (MCMC) repetitions and 1,000,000 subsequent repetitions based on the LOCPRIOR model [K were averaged over 10 runs. We employed the CLUMPAK server [K. The optimum K value was determined based on ΔK [K value using STRUCTURE HARVESTER [Genetic differentiation among populations was evaluated by calculating the overall and pairwise FST  values aand F’ST  were alsand F’ST . Patternand F’ST ) were evand F’ST , accordiand F’ST ; a Mantev. 2.3.3 ,36. A nuK server  to evaluK server  among rued on ΔK , evaluatARVESTER . Bar chaARVESTER .Cyrtomium falcatum based on the Approximate Bayesian Computation (ABC) approach. DIYABC provides flexibility for the mutation models of microsatellite loci in coalescent simulations, allowing both the generalized stepwise mutation model  and theNa) was changed at t1 to the modern effective population size (N1) and N1 was set to be smaller than Na.Scenario 1. Bottleneck model: the ancestral effective population size (Nb) and the modern one (N1) were set to be equal, assuming the effective population size has not changed.Scenario 2. Constant model: the ancestral effective population size (Nc) was changed at t1 to the modern effective population size (N1) and N1 was set to be larger than Nc.Scenario 3. Expansion model: the ancestral effective population size , number of alleles (A), allele size variance across loci and M index across loci [t1 to time scale by year, we assumed a generation time of 3 years, as Matsumoto (2003) [C. falcatum produces spores at 1 year in cultivation tests.In these scenarios, oss loci ,46 were o (2003)  showed tEight new microsatellite markers were developed in this study . The numFIS) values, ranging from 0.220 to 0.794 over all loci  was significantly higher than S-type  of the M-type populations (1.999) was significantly lower than that of the S-type populations  of the M-type populations (0.152) was significantly lower than that of the S-type populations . The average of the pairwise FST values among the M-type populations was higher than that among the S-type populations, and the difference was nearly significant (P = 0.067). Significant IBD was detected among the 7 populations, both with  and without  the admixed individuals included  in Wakayama Prefecture and one S-type population (SADO) were separated from IZU1 and IZU2. At K = 4, KANT was differentiated. At K = 5 and greater, five clusters corresponding to the five main sampling localities . At K = 7, cluster 6 was further divided into two clusters and cluster 7 corresponded to the admixed cluster in SAND. The NJ tree for the seven clusters revealed two groups. In one group, cluster 1 (ESAN 1 and 2), 2 (IZU 1 and 2), 4 (KANT) and 5 (major cluster in SAND) were grouped together, while cluster 3 and 6 in SADO and cluster 7 for the admixed cluster in SAND were in the other group. The F values of each cluster  showed that clusters corresponding to M-type populations had larger values (0.627–0.751) than S-type populations  than the M-type ones . However, the posterior distribution pattern suggested that other parameters were poorly estimated (t1) in the SADO population. In the SADO population, the median value of t1 was 2,940 generations ago , corresponding to 8,820 years ago . In all populations, all of the summary statistics showed no significant differences between the observed and simulated data, based on the posterior distributions , and its 95% confidence interval (CI) did not overlap with those of the other two scenarios in each of the 7 populations, regardless of gametangia formation type . For scestimated , with thibutions , and theibutions , suggestC. falcatum, as all but one population (SADO) had significantly positive FIS values, ranging from 0.2 to 0.8, indicating it is highly likely that gametophytic selfing, sporophytic selfing, and outcrossing are all occurring in these populations. In contrast to our results, Chung et al. (2012) [C. falcatum populations along the southern shores of South Korea, and showed that the FIS values for these populations did not significantly deviate from zero, suggesting the prevalence of outcrossing. The C. falcatum individuals described in Chung et al. (2012) [C. falcatum types provide an interesting experimental opportunity for future evolutionary studies pertaining to the transition between obligate outcrossing and mixed mating in homosporous ferns.We found evidence for mixed mating in the northern type of diploid . (2012)  examined. (2012)  may be t. (2012)  reported. (2012)  suggesteK = 6, cluster 6, C. falcatum.A cluster specific to the SADO and SAND populations, which are located far apart from one another, was detected in the STRUCTURE analysis  and gene diversity (h) values of M-type populations were lower than those of S-type ones and the effective population size estimated by DIYABC also showed the same pattern  [The s Tables  and 2. An Tables  and 2. W. (2013)  employedC. falcatum follows these general trends. The observed patterns could be due to several factors. Firstly, inbreeding is expected to reduce effective population size: Ne = N / (1+FIS) [FIS values alone [FIS, average gene diversity (h) of the M-type populations (0.152) was about half that of S-type populations (0.367), and the average Ne of the M-type populations (215) was about a third of that of S-type populations (675). Although the 95% CIs of the inferred Ne values in the ABC should be taken into consideration, these levels of reduction in h and Ne are comparable to the case of complete inbreeding, and seem to be greater than those expected from the intermediate FIS values observed in M- and S-type populations (0.626 vs. 0.208). One possible explanation for population bottlenecks is colonization. The gametophytic selfing in ferns is hypothesized to be an advantage for long-distance colonization, as it enables a single spore to establish a new population [FST, genetic diversity and effective population sizes between M- and S-type populations.To our knowledge, this is the first study to show that the type of gametangia formation of gametophytes affects the levels of inbreeding and genetic diversity in natural populations of homosporous ferns. It is well known that inbreeding species have lower neutral genetic diversity within populations compared to outcrossing taxa . The cor (1+FIS) . Secondles alone , possibles alone , and/or pulation ,54,55. Fpulation  examinedpulation . DIYABC F’ST values are strongly variable in homosporous fern species (as they are in seed plant species), e.g. F’ST = 0.068 for Odontosoria chinensis in Hawaii [F’ST = 0.761 for Dryopteris aemura in Spain [F’ST = 0.589 for Cyrtomium falcatum in Korea [F’ST = 0.414 for Selliguea hastata in Japan [F’ST values calculated by R.I.). The F’ST values (0.739) of the present study are relatively high. Interestingly, Korean outcrossing populations of C. falcatum [F’ST value (0.589) when compared to the northern type of C. falcatum, despite their putative outcrossing. This may indicate that this level of population differentiation is typical for the species and could be due to species dispersal ability and/or habitat preferences. For example, it is possible that a discontinuous geographical distribution of suitable habitat, such as crevices in sea cliffs or rocks near the seashore, is responsible for population differentiation. Significant IBD was detected in this study , this timing corresponds to a post-LGM recolonization scenario.Previous studies using allozymes or microsatellites showed that the standardized n Hawaii , F’ST = in Spain , F’ST = in Korea , and F’Sin Japan  , genetic diversity, and effective population size. These results suggest that reproductive and demographic differences exist between the two types, despite the lack of genetic structure between them. The more severe population bottlenecks were inferred to have occurred in the M-type populations rather than the S-types. This implies that local extinction and recolonization events could provide opportunities for the maintenance of the M-type populations, and possibly for increasing the frequency of the M-type individual in some cases, even under occasional immigration of the S-type individuals. Evolution of selfing via transmission advantage operates only under a low genetic load, and fixed selfing is expected [The M- and S-types of expected . TherefoC. falcatum in these areas, although this was not expected given the morphological observations of Matsumoto 2003 [In this study, we discuss results obtained after removing putative admixed individuals in SADO and SAND populations. Subpopulation genetic structure within these two populations may be one of the reasons why a clearer difference in genetic diversity between the S- and M-types was detected only after the removing admixed samples. However, it is difficult to discuss the origin of cluster 6 and 7 in detail with the current dataset. One possible explanation is migration and hybridization of northern and southern types of oto 2003 . HoweverS1 FigNa) was changed at t1 to the modern effective population size (N1) and N1 was set to be smaller than Na Scenario 2. Constant model: the ancestral effective population size (Nb) and the modern one (N1) were set to be equal, assuming the effective population size has not changed. Scenario 3. Expansion model: the ancestral effective population size (Nc) was changed at t1 to the modern effective population size (N1) and N1 was set to be larger than Nc.Scenario 1. Bottleneck model: the ancestral effective population size ((EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 FigX axis indicates values for the parameter described in the title of each graph; pmic: the parameter of the geometric distribution to generate multiple stepwise mutations; smic: Mean mutation rate of single nucleotide indel; μmic: mean mutation rate of SSR. Y axis indicates probability of prior and posterior.(PPTX)Click here for additional data file.S4 FigPCA plots of prior, posterior and observed data set for summary statistics.(PPTX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Carex moorcroftii, a dominant species on the QTP to test the effects of geographic barriers on plant population divergence. We sampled 18 C. moorcroftii populations along a north-south transect crossing the TM to investigate the correlations of genetic variation and morphological traits with climate variables. The results showed this species holds high genetic diversity (He = 0.58) and the surveyed populations can be genetically clustered into two groups: populations from the north face of TM, and the other from the south. Gene flow between populations within groups is higher than those between groups. The traits, number and mass of seeds, mass of root and infructescence significantly varied among populations. Mantel-tests detected a weak but significantly positive correlation between genetic and geographic  and climatic distance , indicating both isolation by distance and isolation by environment. These findings together suggest high-altitude mountains of TM interrupt habitat continuity, result in distinct climatic conditions on both sides, increasing population divergence of plant species.High-altitude mountains are often geographic barriers to gene flow and play important roles in shaping population divergence. The central Qinghai-Tibetan Plateau (QTP) stands the location of the Tanggula Mountains (TM). We use the TM as a case, using  Both habitat heterogeneity and geographical barriers may directly affect evolutionary processes and genetic diversity of species3. Specifically, geographical barriers can limit dispersal ability leading to interrupted gene flow and increased population divergence, which in extreme cases may result in reproductive isolation4. However, in the absence of a geographical barrier, genetic divergence may still occur, primarily due to the effect of isolation by distance (IBD), which acts through genetic drift and dispersal limitation5. Alternatively, isolation by environment (IBE) is expected under the action of natural selection, which is influenced by habitat divergence6. Gene flow may counteract the effects of IBE and increase intra-population genetic variations and inter-population genetic connectivity7. This may enhance species persistence and adaptive ability under climate change6. For instance, gene flow from populations pre-adapted to warmer environmental conditions is expected to increase the recipient populations’ ability to adapt to increasing temperatures8. In a Swedish population of Pinus sylvestris, Nilsson9 found that the progenies sired by long-distance pollen had higher freezing resistance compared with that produced by local pollen. A comparison between pine and birch also showed that long-distance gene flow mediated by pollen and/or seed along the climatic gradient might moderately promote populations’ adaptation to climate warming10.High-altitude mountains, due to their physical characteristics, would alter the environment around them, leading to increased habitat heterogeneity  and possible geographical barriers to gene flow11. Intraspecific variation might be associated with environmental variables that fall along a latitudinal gradient, and the effects of these variable factors on population dynamics is increasingly recognized13. Uncovering the morphological and genetic variations of plant populations associated with particular environments along latitudinal gradients is an effective method to answer this question12.Morphological characters or genetic variation can be altered in plants in response to different environmental conditions. Revealing how environmental heterogeneity influences genetic variation and plant performance is of paramount significance in understanding the capacity of plant to respond to global climate change in real time15. With the uplift of the Himalaya, precipitation and air temperature increase from northwest to southeast on this plateau16. The snow-covered, high-altitudinal mountains and deep valleys affiliated with the QTP characterize the environmental gradients that act as geographic barriers to gene flow between populations at the regional scale. For instance, the Tanggula Mountains (TM) lie east to west on the central QTP with a mean altitude over 5,000 m a.s.l. The south side of the TM has more precipitation than the north, resulting in differences in the soil and ambient humidity18. Such environmental differences contribute to habitat heterogeneity, which can have strong effects on community assemblage by influencing the local populations. Additionally, the ridge of TM is always covered by snow or glacier, forming a vegetative gap and disrupting connections between populations on both sides21. These features make the TM an ideal model to test the effects of geographic barriers on population divergence, since the effects of the TM on the evolutionary process of plant species dispersal on this mountain has not been fully investigated.The Qinghai-Tibetan Plateau (QTP) is the highest place in the world, making it one of the most vulnerable places to global climate changeCarex moorcroftii is an ideal study system because it is a widespread dominant species of alpine steppe and meadow on the QTP. Additionally, It is a wind-pollination and gravity seed-dispersal species, which can clonally grow and propagate by stolons below ground. The distribution of C. moorcroftii crosses diverse climatic gradients on the QTP, especially crossing the TM. We hypothesize that (1) the north-south connection between C. moorcroftii populations is interrupted by the physical barrier of the TM; and (2) both morphological and genetic variations of C. moorcroftii will correspond to the different habitats found on either side of the TM. To test these hypotheses, we sampled 18 C. moorcroftii populations along a north-south transect crossing the TM, estimated morphological and genetic variations of these populations and correlated these variations with environment gradients. We would specifically answer the follow questions: (1) Do morphological and genetic variations of populations correlate with environmental changes? (2) Does the TM disrupt gene flow and enhance population divergence between the north and south sides? Such knowledge is useful to assess the ability of long distance dispersal of C. moorcroftii, which is important for the alpine plants to track the distribution shift caused by climate changes.To investigate the effects of the TM on population divergence, MTWaQ (Bio10), with a contribution of 34.5%. Additionally, MTWaM (Bio5) and Temperature Seasonality (standard deviation*100)  were the next most important contributors to the distribution of C. moorcroftii , Mean Temperature of Driest Quarter , Min Temperature of Coldest Month , Annual Mean Temperature , Annual Precipitation , Precipitation of Wettest Quarter , Precipitation of Warmest Quarter , Temperature Seasonality , and Isothermality . The variables with highest loading values for PC2 and PC3 were MTWaM and temperature range (Bio2), respectively  was 2.679 and ranged from 1.842 to 4.024. We noted that C. moorcroftii populations showed high genetic diversity, where HO ranged from 0.480 to 0.806, with a mean value of 0.664 and He showed a mean value of 0.579, ranged between 0.409 and 0.732 . Interestingly, Our STRUCTURE analysis assigned the 18 populations into two genetic groups Fig. S: the popThe autocorrelation analysis showed that there was significant spatial genetic structure (SGS) up to a distance class of 200 km Fig. , which iFst among populations from the north group was 0.143, and that of the south group was 0.269, both were lower than the value (0.278) between south and north groups  (Table Svs. 0.65 (between groups)).The mean value of pairwise C. moorcroftii populations have relatively large effective population sizes with Ne > 890 (Table Nm = 1.119), which supports the estimate based on Fst values (Nm = 0.889)  Table S. Estimat) Table S. In geneWleaf, ranging from 1.58 mm to 2.33 mm, whereas Mrhizomes showed a 15-fold difference and ranged from 0.81 g to 12.26 g  invested more to aboveground organs during growth, with a slope significantly <1.0, whereas south group  displayed a reverse trend, with a slope >1.0  showed some type of allometric allocation in logMroots − logMrhizomes. The north populations had slopes <1.0 , and the south ones >1.0 (P10), indicating that the former invested more in roots than the later.Interestingly, most populations of GD) and geographic distance (GGD)     while the genetic-environmental association was also significant , MTWaM (Bio5), MTCM (Bio6), MTDQ (Bio9), MTWaQ (Bio10), MTCQ (Bio11) were main contributors to genetic divergence (Table SMantel-tests showed a weak but significant positive correlation between genetic distance (32) Fig.  and betw5) Table . When th MTCM Bio, MTDQ  or ED  on morphological differentiation (MD)  Table , whilst MD and three climatic principal components difference matrices (PCclim1 to PCclim3) controlling for GGD or GD revealed that Mroots, Minfructescence, M1000seeds, Nseeds and RAB were significantly influenced by climatic variables (PC1clim and PC2clim) (Table Mroots and Nspikelet were significantly influenced by AMT (Bio1), Hinfructescence and Mroots by MTWaM (Bio5), Mroots and M1000seeds by MTDQ (Bio9) and MTCQ (Bio11)  Table . Further) Table S, Fig. S622. Geographical barriers cause distributional gaps, leading not only to disjunct populations but also to cascading effects related to interrupted gene flow and habitat heterogeneity23. This study addressing these issues, showed that the high-altitude mountains of the TM acted as a geographical barrier to gene flow that enhanced population divergence in C. moorcroftii. Our results emphasize the importance of both gene flow and local adaptation in shaping population genetic structure.While population divergence is broadly driven by IBD or/and IBE, both theoretical and experimental studies suggest that in certain cases, genetic variation proportioning between populations reflects the consequence of the interactions of multiple factors24. We illustrated that C. moorcroftii held rich genetic variation (He = 0.58). The high level of genetic variability within populations of C. moorcroftii is similar to other outcrossing perennial grasses  and comparable to those of the perennial and woody species that range across most areas of QTP . The high genetic diversity of C. moorcroftii may be due to its biological characteristics such as perenniality, wind-pollination, and generalist characteristics, allowing it to persist in a range of environmental conditions on the QTP. The relativly large effective population size 25, whereas the seeds of C. moorcroftii were dispersed mainly by gravity, resulting in limited gene flow. The differences of population genetic structure between the two co-occurred sepcies additionally suggested the effectiveness of our sample strategy of avoiding the effects of human transport on genetic estimation for the plant species nearby a highway.Interestingly, TM’s effects on gene flow were not found in another dominant species of C. moorcroftii populations revealed the existence of morphological variations between populations. Further analyses found significant allometric allocations between several morphological traits, e.g. logMshoots and logMinfructescence, and logMroots and logMrhizomes  and climatic variables (PC1clim & PC2clim)  changed with latitude. These findings together suggest the morphological response of plant populations to environmental change. Further common garden trials may provide more direct evidence of morphological adaption or/and phenotypic plasticity in response to environmental variation.The PCA of 15 morphological traits for s Table S, suggestm) Table . Other sC. moorcroftii populations was positively correlated with PCclim1 both when GGD was controlled or not  is a member of the section Racemosae G. Don in the subgenus Carex Linnaeus38. It grows in alpine areas of Central Asia and Tibet, with the core of its distribution on the QTP. The altitudinal range of this plant covers 3,400–5,700 m39 and is one of the dominant species of alpine grasslands and meadows on the QTP. C. moorcroftii is a perennial sedge and reproduces both asexually, by elongating rhizomes and forming clonal ramets, and sexually, where it flowers and produces seeds from July to September16.C. moorcroftii was estimated based on current climatic layers used in the maximum-entropy algorithm (MaxEnt 3.3)40. MaxEnt is a common modeling algorithm used for presence-only data, which calculates the probability of presence, based on occurrence locations relative to random background conditions called pseudo absences. We used occurrence data from three sources; 86 records from the Chinese Virtual Herbarium (CHV) (http://www.cvh.org), 188 from the Global Biodiversity Information Facility (GBIF) (http://www.gbif.org), and 25 from the published studies  or the authors’ collections for a total of 299 sampling points. We used records from CHV and GBIF with the taxonomic annotation “Carex moorcroftii” with the exception of records with unclear location descriptions. The environmental layers used were the 19 bioclimatic variables (1950–2000) obtained from Worldclim (http://www.worldclim.org/), with a resolution of 2.5 arc-minutes (5 km). We defined the extent of QTP according to the commonly accepted range of the QTP, between 73.31°E to104.78°E, 26.00°N to 39.78°N41.The range of 40, where a perfect model has an AUC of 1, although a good model has an AUC > 0.7. To test the relative importance of each climatic variable, we applied both percentage contribution and permutation approaches. Maxent creates marginal response curves and single variable response curves for each bioclimatic variables. Additionally, jackknife tests were used to determine the relative importance and co-linearity among variables using the training gain, test gain, and test AUC.To avoid potential over-parameterization of the model, we based the summary model predictions on 20 replications using a subsample method. For each replicate a 75% random selection of the occurrence locations were used for model training and the remaining 25% for model testing, with 20 replicates for each. We also set a regularization multiplier of 10, 10, 000 as background points, 5,000 numbers of maximum iterations and 0.00001 of convergence threshold. We quantified overfitting by comparing threshold-dependent omission rates with theoretically anticipated levels of omission. We used AUC (the area under ROC (the receiver operator characteristic curve)) to evaluate model performanceMTWaM, Bio5), Mean Temperature of Warmest Quarter  and Temperature Seasonality (standard deviation*100)  were the most important factors associated with the distribution of C. moorcroftii (date not shown). A north-south transect crossing the TM on the QTP was designed to sample C. moorcroftii populations. Along this transect, we sampled a total of 18 populations, ranging from N 28° to 35° in latitude, E 86° to 97° in longitude and 4,100–5,100 m in altitude  and 6.2 °C difference in MTWaQ (4.9–11.1 °C) a seasonality gradient, TS varied from 6.25 to 9.03 °C across the sample sites by using the raster data . To measure morphological traits, we sampled using the quadrat method following Sun et al.42. For each population, we first set up a transect 100 m long with one quadrat plot of 1.0 m × 1.0 m for every 10 m along this transect. After evaluating the number of ramets and flowering shoots for each plot, we collected all underground plant material for C. moorcroftii in each plot to a depth of 1.0 m. Thus, each population included 10 samples for phenotypic trait variation analysis. Of the 18 DNA-sampled populations, seven (Suonandajie (P3), Wudaoliang (P4), Tuotuohe station (P6), Naqu (P13), Everest 4900 (P16), Yela mountain (P17), Chuanzangxian (P18)) did not have adequate fruiting samples (<3). To avoid the statistical bias associated with small sample sizes, the seven populations were not included in the analysis of morphological variations , rhizome (Lrhizome) and infructescence (Hinfructescence); weight of leaf (Wleaf); and the number of leaves (Nleaf), Spikelet (Nspikelet) and seeds (Nseeds). We dried the plant material in an oven at 60 °C for 48 h and then weighed it. All measured parameters and methods are listed in Table Plant material for each plot was separated and classified as: leaves, roots, spikes, or rhizomes. We measured traits related to performance: length of leaf . PCR was performed in 10 μl volume, which included ca. 20 ng of DNA, 0.2 μM of primers, 2.5 mM of MgCl2, 0.2 mM of dNTP ), 1 × Taq of buffer, and 0.5 U of Taq polymerase. The PCR was carried out in a 2720 Thermal Cycler  and proceeded as follows: 4 min at 94 °C; then 35 cycles for 45 s at 94 °C, 30 s at 55–62 °C, 45 s at 72 °C; then a final extension step of 10 min at 72 °C. Alleles were sized on a Hitachi ABI 3730 (ABI) automated sequencer using LIZ 500 (ABI) as a ladder and analysed in Genemapper 4.0 (ABI).DNA was extracted from 25–30 mg of dried leaf material following the methods from Doyle & DoyleMTWaM and MTWaQ were the two most important environmental variables for C. moorcroftii, the habitat heterogeneity at a regional scale can also be associated with the other 17 bioclimatic variables. The top contributing variables are the variables where the average at the occurrence locations is the most different from the background. Thus, these two variables alone do not adequately describe variation in the climatic aspect of the habitat. However, that being said the least contributing variables have averages at occurrence locations that are not different from the background, so they probably have a similar range of values and therefore may not be a good choice to explain variation in the climatic conditions of the species’ distribution. Thus, we used principal component analysis (PCA) to reduce the dimensionality of all 19 of the bioclimatic variables for the original locations of C. moorcroftii populations to infer habitat heterogeneity.Although the ENM forecasted that Descriptive statistical analyses were performed to characterize the morphological variations of the 11 trait-measured populations Table . A princ45, where parameter a is referred to as the ‘allometric coefficient’ and b the ‘allometric exponent’. An exponent significantly different from 1.0 indicates an allometric  relationship. We used standardized major axis (SMA) regression to fit the allometric relationship and estimate the parameters, and tested whether the slope of each population statistically differed from 1.0. We used the software package Standardised Major Axis Tests and Routines (SMATR)46 to conduct the SMA analyses. A p < 0.05 was used to determine statistical significance for heterogeneity slopes and slope differences.We determined the proportion of reproductive biomass (R) to vegetative biomass (V) and also shoot biomass (A) to root biomass (U) by using the classical allometric model, R = aVb, fitted as log R = log a + b log V and A = aUb, and fitted as log A = log a + b log U47 to detect the presence of null alleles and genotyping errors such as large allele dropout or stuttering. The effective number of alleles (Ae), observed heterozygosity (HO), and expected heterozygosity (He) were calculated by using GenAlex 6.41 software48. Hardy-Weinberg Equilibrium (HWE) and linkage disequilibrium tests (LD) were conducted for each population, and p-values were subjected to the sequential Bonferroni test for multiple comparisons by using GENEPOP v4.049.We used 1,000 randomizations in the program MICROCHECKER v2.2.3C. moorcroftii populations in three distinct analyses. First, we estimated the genetic differentiation of C. moorcroftii populations using STRUCTURE 2.350 to explore potential substructure by evaluating the number of subpopulations (K). Population numbers from 1 to 18 and each was tested 20 times at the population level based on 25,000-step burn-in and 100,000 MCMC iterations. We estimated the most probable number of gene pools (K) in the data by comparing the log probability of the data [Pr(X/K)] for each value of K across all 18 runs of STRUCTURE. The most probable number of subpopulations (K) was calculated using the ΔK statistic, which calculates the log-probability rate of change between successive analyses of K51. Secondly, we constructed Nei’s genetic distance matrices after creating 1,000 bootstrapped matrices using the program, Microsatellite Analyser (MSA)52. Thirdly, overall and pairwise estimates of differentiation (Fst), were calculated by FSTAT53 and the significance of AMOVA output was tested with 999 permutations by using GenAlex 6.41software48.We analyzed the genetic structure of the F-statistics between all combinations of north - south population pairs.To compare the level of gene flow between both sides of the TM, we estimated pairwise 54. This method makes no assumptions of HWE within the sample and is based on transient multi-locus disequilibrium in multi-locus disequilibrium in multi-locus genotypes of migrants relative to the host population. It calculates inbreeding coefficients for each population separately and the joint probabilities are used to estimate recent migration rates. For our analysis we used the following parameters: a run length of 3,000,000 iterations with a 1,000,000 burn-in. a sampling frequency of 2,000, δp  at 0.30, a δm of 0.15 (maximum change of migration rate) and a δF of 0.15 (maximum change of inbreeding coefficient). The dataset was run 10 times with different starting seeds to ensure consistency of results and the average of these runs is reported.To quantify average recent migration rates (last few generations) among populations, we applied a Bayesian approach using the software BAYESASS 1.355 was applied. The ML model assumes that populations are at drift-migration equilibrium, have a constant size and migration occurs over the coalescent period (approx. 4Ne generations). However, unlike F-statistics, this method allows non-symmetrical migration and differences in size among populations, both of which biologically plausible scenarios. MIGRATE jointly estimates the mutation-scaled effective population sizes  and the mutation-scaled effective immigration rate  between geographical groups. MIGRATE accomplishes this by estimating allele genealogies and approximating the sum of probabilities across possible genealogies using the Metropolis-Hastins Markov chain Monte Carlo sampling. The Brownian motion model for microsatellite as an approximation of the ladder model was used as the mutation model. As a search strategy, ten short chains with 10,000 recorded trees, followed by three long chains with 50,000 recorded trees with a burn-in of 20,000 and a sampling increment of 100 was used. The first run estimated θ and M from Fst values, whereas subsequent runs incorporated the ML estimates of θ and M from the previous run as the starting parameter values converged. We used the results of the fifth run for interpretation. To avoid problems due to different sample sizes among groups, a reduced random subset  of each population was used in a parallel computation.To estimate the direction and magnitude of historical gene flow between populations, the maximum likelihood (ML) approach implemented in MIGRATE version 3.2.7C. moorcroftii populations. The distance class was set to 50 km, and the autocorrelations were performed using 1,000 permutations.In addition, we used GenAlex v6.41 to estimate spatial autocorrelation at the landscape scale distribution of gene flow in MD), genetic (GD), geographical (GGD), and environmental (ED) distances among populations, Mantel tests were performed using the Spearman rank correlation coefficient. We calculated Euclidean distances on standardized values  and the Euclidean distance calculated with the values of the PCA axes was considered the ED. To perform the Mantel test between MD and GD, we only used populations shared by each of these distances, which excluded populations P3, P4, P6, P13, P16, P17, P18. If both GGD and GD were significantly correlated with MD, then partial Mantel tests were performed to evaluate their relative importance to morphological traits using the three climatic principal components difference matrices (PC1clim to PC3clim) after controlling for GGD or GD.To test for correlations between morphological (R using the “ecodist” package. Multiple matrix regression with randomization (MMRR) is a novel and robust approach for estimating the independent effects of potential factors, especially in situations of low-to-moderate gene flow. We implemented MMRR with 10,000 permutations in R with making use of the MMRR function script.In addition, to test for possible spatial effects of genetic structure, isolation by distance (IBD) and isolation by environmental distance (IBE) were estimated. The correlations between genetic differentiation and geographic/environmental factors were determined by a combination of partial mantel tests and matrix regression analysis on distance matrices. Specifically, partial mantel tests between populations were performed between two factors of interest given the other variables in the model, as implemented in Dataset 1


We found a remarkable association of HERC2 rs12913832 GG with blue color (p < 0.01) but the other markers did not show any association with iris color. The results for the Buenos Aires population differ from those of other populations of the world for these polymorphisms . The differences we found might respond to the admixed ethnic composition of Argentina; therefore, methods of analysis used in European populations should be carefully applied when studying the population of Argentina. These findings reaffirm the importance of this investigation in the Argentinian population for people identification based on iris color.The aim of this work was to describe the phenotypic and genotypic variability related to iris color for the population of Buenos Aires province (Argentina), and to assess the usefulness of current methods of analysis for this country. We studied five Single Nucleotide Polymorphisms (SNPs) included in the IrisPlex kit, in 118 individuals, and we quantified eye color with Digital Iris Analysis Tool. The markers fit Hardy-Weinberg equilibrium for the whole sample, but not for rs12913832 within the group of brown eyes (LR=8.429;  Iris color is a conspicuous phenotypic trait in humans, and its variation is one of the most relevant manifestations of the physical aspect among individuals. This phenotypic trait is polygenic, and its genetic background is variable across different populations. Eye color is subjected to certain changes along the lifetime  generateHERC2, IRF4, SLC24A4, SLC45A2 and TYR among the most important ones. Within these genes, particular SNPs have been reported as responsible for the pigmentation variation, allowing the determination of iris color in European populations with small levels of error. These SNPs are included in the IrisPlex  from Buenos Aires. This project was approved by the Ethics Committee for Biomedical Research at IMBICE, and donors provided individual written informed consent. The age of participants ranged between 18 and 50 years to avoid changes in iris color due to late age of individuals , and we Taq polymerase  in a thermocycler . For rs12203592, rs12896399 and rs1393350, we performed allele-specific PCR followed by electrophoretic separation in 1.8% agarose  electrophoresis and staining with GelRedTM . The SNPs rs12913832 and rs16891982 were genotyped by PCR-RFLP, using DdeI  and HinfI enzymes  respectively for restriction digestion, followed by 8% polyacrylamide  electrophoresis. Bands were recorded in a Gel DocTM XR+ system .We designed oligonucleotides for genotyping the SNPs rs12896399, rs1393350, rs12913832 and rs1689198, while those for rs12203592 were obtained from a previous publication  by comparison of observed and expected heterozygosity values through exact test (Markov chain) with Arlequin software 3.5 . We alsoFst and AMOVA were analyzed with Arlequin 3.5 for comparison with previous literature data for Native Americans , Africans , East Asians , total 230 individuals) and Europeans , and Russia, total 151 individuals) , while TYR rs1373350 also showed a tendency to HW disequilibrium within the blue group .Fis values were under 15% and non-significant, with rs12203592 in the limit of significance , which could be explained by the low frequency of the T allele for this marker.Nucleotide diversity for the five SNPs was 0.3306 ± 0.2287; the mean observed heterozygosity was 0.3178 ± 0.0758, and the mean expected heterozygosity was 0.3335 ± 0.1277. HERC2 rs12913832 GG with blue iris color (p < 0.01). No association with iris color was found for the other markers.When calculating genotype-phenotype association by a median regression, after discarding the underrepresented genotypes , we onlyp=0.367), leading to conclude that for this sample size it was not possible to confirm an association between sex and iris color as has been previously proposed  which emerged at the East or Northwest of the Black Sea, about 6,000 to 10,000 years ago (As expected, the relationship genotype-phenotype for rs12913832 was the same as that observed in other populations (ears ago . The bluears ago . Howeverears ago . AdditioFis values were non-significant, suggesting that important genetic drift, selection or inbreeding processes are not occurring at present in the population under study.The other markers fit HWE, and As the Buenos Aires population has a certain Native American contribution (IRF4 gene, the reduced frequency observed for the T allele might interfere in the possibility of finding an association with iris color. Another factor might be the sample size, and we also have to consider that, in the population under study, the number of individuals with blue eyes is much lower than individuals with brown eyes. It is important to note that iris color depends, in part, on light scattering and absorptive properties of extracellular components, especially in lightly pigmented irises (In this work, an association with iris color was not detected for four markers, contrary to previous reports from other authors (d irises , so thisIn contrast to other studies , 2014, iFst consistently showed that the Buenos Aires population is genotypically different from other populations of the world, reaffirming the importance of studying these genes in our population for people identification based on iris color. The structure analysis also pointed out this differentiation. The results for the Buenos Aires population were more similar to native populations from other American countries, and in second place, to European populations, suggesting a considerable contribution of Native Americans to the current genetic composition of the Buenos Aires population. However, the percentage of Native contribution in this province is much lower than in other regions of the country (Results obtained for  country , could bAccording to our results, Africans were the most distant from Native American, European, East Asian and Buenos Aires populations, in agreement with the age of divergence of African populations from the rest of the world  that shaOur results suggest that iris color SNPs should be analyzed for the population in which the system IrisPlex is going to be implemented. Another option is to select the SNPs to use in identification after an evaluation of the population under consideration. The differentiation found among populations is consistent with the fact that these markers are related to a phenotype with an important regional adaptive value.We conclude that the genetic background for iris color in the Buenos Aires population is genetically different to that of other countries. The differences we found might be due to the admixed ethnic composition of Argentina. Buenos Aires presents a population identity that differentiates it from other populations of the world, and for that reason, the methods of analysis used in European populations should be carefully applied in this case. Our results emphasize the importance of analyzing the variation of these genes and their relationship with iris color, not only in this province, but along the Argentinian country for people identification based on iris color.


In the present study, thirty autosomal insertion and deletion polymorphic loci were simultaneously amplified and genotyped in a multiplex system, and their allelic frequencies as well as several forensic parameters were obtained in a sample of 236 unrelated healthy Tujia individuals. All the loci were in Hardy-Weinberg equilibrium after applying a Bonferroni correction and all pair-wise loci showed no significant linkage disequilibrium. These loci were observed to be relatively informative and discriminating, quite efficient for forensic applications. Allelic frequencies of 30 loci were compared between the Tujia group and other reference populations, and the results of analysis of molecular variance indicated the Tujia group showed the least significant differences with the Shanghai Han at one locus, and the most with Central Spanish population at 22 loci. We analyzed the population genetic structure by the principal component analysis, the clustering of STRUCTURE program and a Neighbor-Joining tree, and then evaluated the genetic relationships among Tujia and other 15 populations. The study was conducted in accordance with the human and ethical research principles of Xi’an Jiaotong University Health Science Center and approved by the ethics committee of Xi’an Jiaotong University Health Science Center. We have obtained written informed consent from all volunteers for the purpose of research. The investigation was conducted in order to ensure that any two individuals didn’t share a common ancestry within at least three previous generations; all individuals were born and lived in the same prefecture; and their ancestors married no any other ethnic people.® method  according to the manufacturer’s instructions® PCR System 9700 thermal cycler  under the recommended reaction condition. PCR products of all loci were separated and detected by capillary electrophoresis on the ABI 3500 Genetic Analyzer (Applied Biosystems). Genotyping of InDel loci was analyzed using the BTO 550 (Qiagen) as internal lane standard and by GeneMapper® ID software v3.2 (Applied Biosystems). Experiments were carried out according to the kit control and the ISO 17025 standard in this study.Genomic DNA was extracted from bloodstain cards by using the ChelexFst and p values for pairwise interpopulation comparisons were calculated based on allele frequencies of 30 InDels by analysis of molecular variance (AMOVA) performed with ARLEQUIN version 3.1 software (http://cmpg.unibe.ch/software/arlequin3). Principal component analysis (PCA) in two forms and phylogenetic reconstruction were employed in MATLAB 2007a , R statistical software v3.0.2http://pritch. bsd.uchicago.edu), respectively. The detailed population genetic structure was performed with the STRUCTURE program v2.2 (http://pritch.bsd.uchicago.edu.) to analyze the structure of Tujia and the other populations previously published based on the same 30 InDels.Hardy-Weinberg equilibrium (HWE), allelic frequencies and forensic statistical parameters of 30 InDels were calculated by the modified powerstat (version1.2) spreadsheet . Linkage disequilibrium (LD) analysis for all pair-wise InDel loci was performed using the SNPAnalyzer v2.0 p < 0.05 was only observed at the HLD88 locus (p = 0.0382). P values were adjusted after applying a Bonferroni correction for all 30 InDel loci analyzed and P > 0.00167 was considered statistically insignificant. Then, the genotype frequency data for all loci showed no deviations from HWE expectations in the sample of Tujia group. Allelic frequencies and forensic statistical parameters of 30 InDels based on the raw genotype  to 0.9987 (HLD97), and shown in  were sho2r and |D’|. As shown in 2r less than 0.8, and no crimson box was coated by a thick black curve. The present LD tests suggested that 30 InDels were independent for the following statistical analyses, and also suited for forensic cases in the Tujia group.Linkage disequilibrium tests of these pairwise InDels were analyzed using the SNPAnalyzer version 2.0 and obtained several indexes: LOD, DA distance by examining the differences between allelic frequencies at the same set of 30 InDel loci of different populations. DA distances between the 16 groups with each other based on allelic frequencies of the 30 InDel loci were shown in Genetic distance is a measure method of the genetic divergence between different populations, used for understanding the origin of biodiversity and reconstructing the history of different ethnic groupsp < 0.05). As shown in Population differentiations for 30 InDels were compared between the Tujia group and other populations previously published based on AMOVA method  and R statistical software v3.0.2The genetic relationships among Tujia, central Asian (Uigur and Kazak populations), western Eurasians  and other eastern Eurasians  were also discerned with the aid of abovementioned InDel datasets at the individual level. Results of individual PCA were presented by the plots of the first two PCs shown in , which tWe constructed a neighbor-joining (N-J) phylogenetic tree shown in . The braK = 2, three clusters were highly visible and easily distinguishable basically by red, green and mixture of the two. When K = 2–7 .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Despite being a central issue in evolutionary biology, few studies have examined the stasis of characters in populations with no gene flow. A possible mechanism of such stasis is stabilizing selection with similar peaks in each population. This study examined the evolutionary patterns of morphological characters with and without strong selection in ant populations. We show that compared to a character that seems to be less important, characters that are more important were less variable within and among populations. Microsatellite analyses showed significant genetic differences between populations, implying limited gene flow between them. The observed levels of genetic differentiation cannot be attributed to recent population separations. Thus, the observed differences in morphological variance seem to reflect the degree of selection on each character. The less important character changed proportionately with time, but such a pattern was not observed in more important characters. These results suggest that stabilizing selection maintains morphological stasis between populations of the same species with minimal gene flow independent of divergence times. Understanding the forces responsible for generating biodiversity is one of the most important goals of evolutionary biology. Consequently, the mechanisms underlying diversification among populations have attracted considerable attention1234567891012131015161718Among populations with minimal gene flow, a common stabilizing selection pressure can result in stasis in the status of a character20In this study, we compared the evolutionary patterns of characters under strong or weak selection among ant populations with minimal gene flow. For each character with a different functional importance, we investigated its degree of variation within and among populations. Then, the amount of evolutionary change, along with the phylogenetic branching pattern, was compared with branch lengths using a phylogenetic tree reconstructed from genetic distances among populations, which were estimated from 4 microsatellite loci. Based on the results, we discuss the factors affecting the evolutionary patterns for characters experiencing different strengths of selection.Formica yessensis workers collected from a population in Ishikari  by using 10 kari see . During One may think that the frequency of use is not useful for estimating the adaptive significance of organs because, for example, insects’ copulatory organs are adaptively important but are used only a few times in their life. Thus, we provide an additional explanation for the logical basis of our assumptions (strong selection on FTS length (FTSL) and weak selection on HTS length (HTSL)) in detail. First, worker ants from all the populations examined used their FTS for cleaning their antennae. The antennae of ant workers are their most important sensory organ, and many chemical sensory receptors exist on its surface. If a worker ant goes without cleaning her antennae for long time, she loses her most important sense (ants are not highly dependent on vision). Thus, FTSL in all populations must be optimized for efficiently cleaning antennae. In fact, the shape of the FTS is quite different from that of other homologous traits (MTS and HTS). The FTS has a brush-like shape to clean the surface of antennae see , but theF tests. After a Bonferroni correction for multiple comparisons, the residual variance of FTSL was found to be significantly smaller than that of MTSL and HTSL. Whereas, there was no difference between MTSL and HTSL  from an allometric equation with head width. Differences in the residual variances were compared for each pair of the three characters using Another measured character, the length of tibia of the right hind leg (HTL), could also experience strong selection based on the fact that the walking speed of a worker was reduced to approximately 48% after cutting the HTL at an intermediate point  will be correlated with the genetic distance irrespective of the geographic distance. The partial correlation analysis showed no significant correlation between the species distance and the genetic distance when the geographic distance was controlled for . This result indicates that the present taxonomic status is not robust  after arriving at this area. A combination of the data for F. truncorum and F. yessensis in the far-east populations is thus further justified.A COI gene sequence of 1041 nt  was used to reconstruct a molecular phylogeny. All three methods  resulted in a branching pattern in which the Far-East (Japan + Korea) populations were derived from European runcorum . While tHead width (HW) was used as an index of body size as this character was found to be the most isometric for PC1 in an analysis including all the four characters−5, 1.86 × 10−5 and 11.17 × 10−5, respectively. HTSL exhibited a considerably larger variance than the other two characters among the populations. Simple comparisons revealed significant differences in the degree of variance between FTSL and HTSL  and between HTL and HTSL , but the difference between HTL and FTSL was not significant . These results show that although HTL and FTSL were significantly different among the populations . Thus, a geographic cline was not detected for any character.t-test, df = 7: HTL vs. FTSL, t = 4.379, P = 0.0032; HTL vs. HTLS, t = 4.884, P = 0.0018; FTSL vs. HTSL, t = 4.504, P = 0.0028). These results suggest that although HTL shows the strictest allometric constraints, FTSL is more strictly constrained than HTSL. As a result, the ranking of characters in relation to the strength of selection pressure was estimated to be as follows: HTL > FTSL > HTSL.The residuals from an allometric regression of a character relative to HW were calculated for each character in each population, and the variance in the residuals  was calculated for each population . In all t-test, t = 2.131, df = 4, p = 0.0385), there may be differences in the strength of selection on HTSL among populations (selection is stronger in the populations with S < 1). However, even in the populations with S < 1, the average residual variance in HTSL is marginally larger than that of FTSL . This marginality difference could be due to the small sample size (n = 3). These results support the following view: although there are differences in the strength of selection on HTSL among populations, the selection strength is consistently stronger on FTSL than on HTSL in each population.There are variances in the strength of selection on HTSL among the populations because HTSL significantly varies among the populations see . In factProsopocoilus inclinatus, the lengths of their mandibles (weapons) show a strong allometric constraint within each morph, i.e., the residual variance is small within each morphAnother possibility is that the HTS is used as a weapon because insect weapons are selectively important but frequently show a large variance within populations, such as in the stag beetles, which shows dimorphism in its mandibleFIS) were not significantly positive in 9 of the 10 populations, with the exception of the values for the Furano population , indicating that inbreeding does not occur in the most populations. The average relatedness between workers in a nest was not significantly different from zero in 7 of the 10 populations, with the Ishikari, Furano and Chunggyesan populations being the exceptions. However, even in these populations, the estimated values were close to zero , suggesting that multiple workers from a nest reflect each queen’s genotype.We determined the genotypes of a total of 911 workers from the 10 populations for 4 microsatellite loci. Significant linkage disequilibrium was not detected for any pair of loci, indicating that every locus is independent of each other. Inbreeding coefficients (FST) among the populations ranged from 0.023 to 0.488. All except one of the values were significantly different from zero after a Bonferroni correction for multiple comparisons with a strict significant level of p = 0.001 . In the Gotemba-Norikura pair, the FST scores did not significantly differ from zero after the Bonferroni correction . F. yessensis populations (Ishikari and Samsung) formed a robust clade with F. truncorum . Thus, the order of characters in relation to stabilizing selection strength was again estimated to be as follows: HTL > FTSL > HTSL.We reconstructed the ancestral states of each of the three characters  at each branching point within the above tree using the Kutsukake and Innan’s (KI) methodNext, we examined whether the amount of evolutionary change in each character is correlated with branch lengths. Because there is a large gap in the distance between Clade I and II, we conducted the analysis on each the clade. This treatment is required because the ancestor of Clade I is assumed to have come to Japan through the Korean peninsula, whereas that of Clade II probably arrived in Japan via Sakhalin. No characters showed a correlation with branch length within Clade I . A significant positive correlation was detected for HTSL in Clade II , but HTL and FTSL did not show such a trend . These results show that only HTSL in Clade II has evolved in a manner corresponding to the time after branching.The independent contrast analysis did not reveal any evolutionary correlation among the characters, implying that the three characters evolved independently from each other. Thus, we can interpret the observed evolutionary pattern of each character independently. We found that the magnitude of the residual variance within a population corresponded to the degree of divergence among the populations see . While tFew studies have focused on the evolutionary forces responsible for maintaining stasis within a species between distant populationsA shortage of time after population separation is another factor that could explain minimal genetic differentiation between populations. However, significant genetic differentiations were observed in this study, suggesting that enough time has passed for the accumulation of genetic mutations after the separation of each population. We therefore cannot attribute the observed stasis in HTL and FTSL to either sufficient gene flow between populations or a shortage of time after population separation. It therefore remains likely that a narrow range in the stabilizing selection peaks maintains HTL and FTSL at similar states between genetically diverse populations. This view is also supported by the facts that the variances among populations in the allometric slopes of these two characters were smaller than that of HTSL, suggesting that the stabilizing selection peaks for HTL and FTSL were similar among the populations.There is a large gap in the Dc between the southern (Clade I) and northern populations  to allow for a robust statistical analysis. In addition, if the ancestral population of Clade I arrived in Japan through the Korean peninsula and that of Clade II came to Japan from Sakhalin, it is not appropriate to analyse the data by combining both clades. When only Clade II was analysed, a positive correlation was observed between the degree of change along a branch and branch length for HTSL but not for HTL and FTSL, suggesting that only HTSL has evolved in proportion to the divergence time. As discussed in the previous section, stabilizing selection with similar optimal peaks might act on HTL and FTSL. The narrow ranges of the optimal peaks among the populations would permit only small changes along a phylogenetic branch regardless of the branch length. Our results support the hypothesis that morphological similarities between far distant populations within a species (without gene flow) are maintained by similar stabilizing selection pressures in each population. When a species extends its distribution to a new area, they are likely to inhabit an area with a suitable ecological niche. In such a place, stabilizing selection pressures similar to those of their previous habitat are likely to act on the species. In other words, individuals of a species are more likely to survive in new habitats where selection pressures similar to those of their previous habitat are present as they are already adapted to such an environment. Thus, stabilizing selection would preserve similar morphologies among far distant populations in a species without gene flow, as is shown by the present study. More studies using more characters are required to determine the robustness of the above hypothesis. In particular, MTSL is a good subject for testing this hypothesis because MTSL shows similar degrees of residual variance to those found in the allometric relationship for HTSL, suggesting weak (or no) stabilizing selection on this character. Whether MTSL shows a pattern of evolution that correlates with time as indicated by branch lengths on the phylogenetic tree is worth testing in the future.In the absence of selective pressures, allele frequencies in a population change in relation to divergence time via random genetic driftFormica truncorum (Fabricius) and F. yessensis (Forel), have huge polygynous colonies consisting of numerous adjacent nests that are shared by all colony members37The ants used in this study, F. yessensis is found in southwestern Hokkaido and the highlands of Honshu in Japan, on the Korean peninsula and in Taiwan, while the range of F. truncorum extends from northeastern Hokkaido to EuropeF. yessensis and F. truncorum population is reproductively well-isolated, we should detect a clear genetic differentiation between the two species when the geographic distances among populations is controlled for. This hypothesis was tested statistically using a partial correlation analysis. We assigned a pseudo-species distance to each population pair (0 to pairs of the same pair and 1 to pairs of different species). Then, Kendall’s partial rank correlations between the species distances and genetic distances were calculated while controlling for geographic distances.F. truncorum and F. yessensis, we considered it is reasonable to combine data for these two species for Far-East populations.Using mitochondrial DNA sequences, we also conducted a phylogenetic analysis among the populations to test whether combining the data was possible (see the Results section for details). When no clear genetic differentiation was found between Five populations of each species were collected in Far-East Asia see . Except For additional experiments and measurements, we collected 50 workers of various sizes from the Ishikari population on September 6, 2016.We measured the following four characters, the tibial spur (FTS) length of the right foreleg (FTSL), the tibial spur (HTS) length of the right hind leg (HTSL), the tibial length of the right hind leg (HTL) and head width (HW) see . For theF. yessensis workers collected from the Ishikari population for an hour and recorded the frequency of FTS and HTS use.Preliminary observations using many ants from multiple populations showed that the FTS is used frequently by the ants to clean their antennae, while the MTS and HTS have no apparent function except for the HTS being rarely used to scratch the back surface of the abdomen, where no specific organ exists. We observed 10 We measured the four characters  of 30 workers from each of the 10 nests in each population. The head and right leg of each collected worker were removed from the body, and each character was measured to the nearest 0.001 mm using a binocular microscope  fitted with an ocular micrometer. The measurements were ln-transformed to allow for comparisons between characters with different mean variancesSince both the species are continuously polymorphicTo examine the strictness of the allometric constraints for the characters examined, we calculated the variances in the residuals relative to the allometric line for each character in each colony from each population. When a character has a strong allometric constraint, the residual variance is expected to be small and vice versa4142We used two types of genetic information to generate phylogenies for the populations, mitochondrial DNA (mtDNA) sequences and allele frequencies of genomic microsatellite loci. A DNA extraction kit  was used to prepare DNA for mtDNA sequencing. Genomic DNA for microsatellite analyses was prepared using a modified Chelex methodFormica (Coptoformica) exsecta from Finland and Formica (s str.) pratensis from Germany were used as far and near outgroups, respectively. F. truncorum from Finland was also included in the analysis to determine the relationship between Japanese F. yessensis and F. truncorum populations.We sequenced a section (1041 nt) of the cytochrome oxidase subunit I (COI) gene encoded on the mtDNA. The target region was amplified via PCR using the primer pair CI13 and CI2446We used four polymorphic microsatellite loci  to characterize the genetic composition of the populations. The primer sequences and amplification conditions have been published elsewhere50F. yessensis and F. truncorum are highly polygynous, and because workers move freely between nestsFormica (s str.) spp. have shown that relatedness is not different from zero within castes (queens or workers) in nests37Genetic differentiation among populations was examined via an analysis of molecular variance using Arlequin ver. 2.000In the phylogenetic analysis, we employed the chord distance (Dc)We used the random Ornstein-Uhlenbeck model described by Kutsukake & Innan57How to cite this article: Imai, S. et al. Difference in evolutionary patterns of strongly or weakly selected characters among ant populations.. Sci. Rep.6, 39451; doi: 10.1038/srep39451 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.