Lynx lynx throughout north-eastern Europe using microsatellite, mitochondrial DNA control region and Y chromosome-linked markers. Using SAMOVA we found analogous patterns of genetic structure based on both mtDNA and microsatellites, which coincided with a relatively little evidence for male-biased dispersal. No polymorphism for the cytochrome b and ATP6 mtDNA genes and Y chromosome-linked markers were found. Lynx inhabiting a large area encompassing Finland, the Baltic countries and western Russia formed a single genetic unit, while some marginal populations were clearly divergent from others. The existence of a migration corridor was suggested to correspond with distribution of continuous forest cover. The lowest variability (in both markers) was found in lynx from Norway and Białowieża Primeval Forest (BPF), which coincided with a recent demographic bottleneck (Norway) or high habitat fragmentation (BPF). The Carpathian population, being monomorphic for the control region, showed relatively high microsatellite diversity, suggesting the effect of a past bottleneck  on its present genetic composition. Genetic structuring for the mtDNA control region was best explained by latitude and snow cover depth. Microsatellite structuring correlated with the lynx's main prey, especially the proportion of red deer (Cervus elaphus) in its diet. Eurasian lynx are capable of maintaining panmictic populations across eastern Europe unless they are severely limited by habitat continuity or a reduction in numbers. Different correlations of mtDNA and microsatellite population divergence patterns with climatic and ecological factors may suggest separate selective pressures acting on males and females in this solitary carnivore.Due to their high mobility, large terrestrial predators are potentially capable of maintaining high connectivity, and therefore low genetic differentiation among populations. However, previous molecular studies have provided contradictory findings in relation to this. To elucidate patterns of genetic structure in large carnivores, we studied the genetic variability of the Eurasian lynx,  Various studies on population genetics in large carnivores show diverse patterns of genetic structure. Some research emphasizes that, due to their high mobility, carnivore populations are maintained through long distance dispersal, which contributes to high gene flow across large spaces and low differentiation among populations Some of these discrepancies may result from using markers of different heritability, e.g. microsatellites and mitochondrial DNA (mtDNA) – the markers most frequently used in population genetic studies. For instance, a microsatellite-based study on the genetic structure of Scandinavian brown bears Lynx lynx), two studies Lepus timidus)  or domestic reindeer (Rangifer tarandus) (Norway) and the south-western populations feeding on Cervids From the research concerning the population genetic structure of the Eurasian lynx  and used three types of genetic markers: maternally inherited mtDNA, Y-chromosome markers and nuclear microsatellites. Our primary goal was to understand the relative role of factors affecting the genetic diversity in different populations. We predicted that: 1) lynx are capable of maintaining high rates of gene exchange providing that habitat is continuous; 2) genetic variability and distinctiveness of contemporary lynx populations in Eastern Europe are affected by both human activity  and past climatic events ; 3) genetic differentiation between lynx populations is correlated with climatic factors and/or 4) it is correlated with the main prey type of lynx. This knowledge will help in interpreting the importance of current and historical processes in shaping the distribution and genetic structure of the lynx population, as a model species of large, mobile carnivores.The license for lynx live-trapping and blood sampling was obtained from the National Ethics Committee for Animal Experiments (no: DB/KKE/PL – 110/2001) and the Local Ethics Committee for Animal Experiments at the Medical University of Białystok (no: 52/2007). No animals were harmed during live-trapping and handling. An effort was made to minimize the time animals spent immobilized in the traps. All traps were equipped with radio- or GSM-alarm systems that allowed us to remotely control trapping and release the captured animals from the traps within 15 minutes to 1 hour.We analysed samples of Eurasian lynx from northern, central and eastern Europe bordered by Norway, the Carpathian Mountains and Russia (Kirov Republic) . We arbiAltogether, 332 lynx individuals were sampled between 1992 and 2011. Sample sizes per population are given in 2O , 7 µl primer mix (containing 2pmol of each primer). Aliquots of 8 µl of this mix was then added to 2 µl of isolated DNA template. The thermocycling profile was as follows: 95°C for 15 min, followed by 30–45 cycles of denaturation at 94°C for 30 s, annealing at 57°C (Panel 1 and 3) or 56.5°C (Panel 2) for 90 s, extension at 72°C for 60 s and final extension at 60°C for 30 min. The PCR products were mixed with 10 µl ultragrade formamide and 0.2 µl GeneScan 500-LIZ size standard , denatured at 95°C for 5 min, immediately cooled and then separated using a four-capillary ABI 3130 Genetic Analyser . Allele sizes and genotypes were scored using GeneMapper 4.0 software . PCRs were repeated twice to test the consistency of the results for a subset (5%) of randomly chosen individuals. A total of 54 hair samples from Lithuania and Belarus were analyzed. Hair samples which gave low amplification rate and displaying contamination within hair traps were excluded. Microsatellite panels in the remaining hair samples (20) were amplified between two and four times until they gave reliable genotypes, e.g. heterozygotes at least three times out of four. Samples with inconsistent genotypes were discarded from the analysis. Possible genotyping errors due to stuttering, short allele dominance and null alleles were tested by MICRO-CHECKER 2.2.3. Genomic DNA was extracted using the Genomic Mini Kit  according to the manufacturer's protocol and was stored at −20°C until used. The amplification of 13 microsatellite markers (twelve nuclear and one Y-linked) was carried out in three multiplex panels . The above-mentioned microsatellite loci were developed for domestic cats G GAA TTT AAC CAT GAC C -3′ and LcytbR: 5′-GAC TCT TCA TTT GAG GAG ACG. The entire 681 bp of the ATP6 gene (LATP_F: 5′-TCC AGA ACC TAA ATC CAC AAC C-3′ and LATP_R: 5′-GCA TGA GTT TGG TGG GTC ATT A-3′) was amplified in 8 lynxes from Norway, Finland, Latvia and Poland. All laboratory procedures and quality test controls for cr mtDNA followed DBY4, which had an annealing temperature of 48°C. We obtained clear PCR products for DBY4 (185 bp), DBY7 (275 bp), DBY8 (115 bp), SMCY7 (625 bp) and SMCY17 (125 bp) but no clear products were obtained for UBE1Y6 and UTY11. The five Y-chromosome markers that gave clear PCR products were sequenced in 43 males from Russia, Finland, Estonia, Latvia, Poland, Romania and Belarus according to A portion (613 bp) of the mtDNA control region (cr mtDNA) was amplified in 122 lynxes using the primer pair LGL283 and ISM015 A), allelic richness (AR), gene diversity (Hs) and inbreeding coefficient (FIS) in the populations. Differences among groups of samples in AR, Hs, expected heterozygosity (He), relatedness coefficient (r), and FST were tested using a permutation procedure (10000 iterations) in FSTAT. The same software was used to tests for linkage disequilibrium among 12 microsatellite loci within each population and for the total sample (13200 permutations). Hardy–Weinberg equilibrium probabilities were calculated using GENEPOP 4.0 π), number of segregating sites (s) and mean pairwise difference were calculated using ARLEQUIN v 3.1 FST (for microsatellites) and ΦST (for mtDNA) values for the 10 populations were estimated using ARLEQUIN and their significance was tested using 1000 permutations corrected for multiple tests by Bonferroni correction. For comparative purposes, we have also calculated pairwise DESTST/(1- FST) http://ibdws.sdsu.edu/~ibdws/For the autosomal microsatellite loci, FSTAT 2.9.3 http://www.goldensoftware.com/demo-downloads). Contour lines of the first axis of the PCA were interpolated and superimposed onto a geographic map of the study area. By interpolating the PC1 scores for each individual lynx throughout the study area, we tried to identify regions where genetic dissimilarity between individuals was considerably higher or lower than would be expected from the IBD effect alone. Such geographic areas may represent dispersal barriers or migration corridors, respectively.Principal coordinate analysis (PCA) was performed based on a sample-wise matrix of genetic distance for microsatellite and mtDNA data in GenAlEx v. 6.0 ΦST and FST). To explore patterns of genetic divergence in more detail, we applied the spatial AMOVA procedure using SAMOVA ver. 1.0 CTΦ values (among group variance) and minimized ΦSC values (among populations within group variance). The significance of Φ-statistics was tested using 10000 permutations for K = 2 to K = 9 partitions of the sampling sites. The pRST - RST tests between identified groups were performed in SPAGEDI Analysis of molecular variance  replicates after the burn-in period. The program was run 5 times for each number of clusters (K) between 1 and 10 to verify the consistency of estimates across runs. To determine most likely number of genetic clusters we evaluated the probability logarithm of the data (lnP(D); Contemporary migration rates (within the past one to three generations) between lynx populations were evaluated using a Bayesian approach implemented in BayesAss ver. 1.3 −7 substitutions/site/year with SD  = 2.28×10−7 substitutions We applied Approximate Bayesian computation , average snow cover depth (SCD) in January www.worldclimate.com), To identify correlations between genetic distance between lynx populations and abiotic climatic, as well as biotic factors (lynx basic prey), we performed multivariate multiple regression analysis using DISTLM ver. 5 We obtained control region mtDNA sequences for 332 lynx samples. Sixteen 613 bp haplotypes of the cr mtDNA were observed, of which four  were previously unreported . All newATP6 gene among 8 lynxes from different populations (GenBank accession no.: KM000083).The cytochrome b gene (1094 bp) showed no polymorphism among 12 individuals from different populations (GenBank accession no.: KM000081). Likewise, no polymorphism was found among 681 bp of the entire No sequence polymorphism was found in the five Y-linked chromosome markers , among 43 male lynx from seven sampling locations  and the Y-linked microsatellite locus 278g21-4 possessed only a single allele (146 bp long) in all males studied.R and He) were lowest for the Norwegian and BPF populations, and largest for lynx from Russia  than for the remaining lynx populations, possibly due to bottlenecks. Interestingly, the Carpathian population did not show any signs of reduced levels of polymorphism at the microsatellite loci; however it exhibited a positive and significant FIS value, as did the population from Belarus  linkage disequilibria between loci within any of the populations studied, as well as the frequencies of null alleles low . Measures of genetic diversity for the microsatellite loci  and the majority of comparisons were significant , with an x-intercept at 498 km and 586 km for females and males, respectively . For fem2 = 0.028, p = 0.28) nor for microsatellite loci  among our sampling locations  but not for the cr mtDNA   pattern for mtDNA (rocations . When we = 0.08) .ΦST = 0.570, p<0.001, FST = 0.464, p<0.001). SAMOVA was subsequently used to identify the subdivision that most likely explains the mtDNA structure observed in lynx. The data were best explained assuming four groups of lynx populations: (1) Norway, (2) BPF, (3) Carpathians and (4) the remaining 7 sampling sites .Geographical structuring among lynx cr mtDNA haplotypes in the study area was highly supported by AMOVA results where all sampling sites were treated as a single group . The data were best explained in SAMOVA analysis assuming the same four groups of lynx populations: (1) Norway, (2) BPF, (3) Carpathians and (4) the remaining 7 sampling locations . RST did not differ significantly from pRST for any pairwise between-group comparisons .For the microsatellite data, AMOVA supported the geographical structuring of the populations  is correlated with latitude  but not with longitude. Similarly, the number of days with snow cover, snow cover depth, and the NAO index, as revealed by marginal tests, had significant effect on mtDNA differentiation among sampling locations  were separated into three clearly distinct genetic entities. Based on the different heritability of mtDNA and microsatellites, as well as previous research that suggested different genetic structure in the brown bear with use of the same types of markers et al. RST and pRST values for microsatellite loci suggest genetic drift as the primary reason for observed genetic divergence among the lynx populations at microsatellite nuclear loci.Spatial autocorrelation analysis provided relatively little evidence for male-biased dispersal in the lynx. This is not fully concordant with the available telemetry data, suggesting that similar to other large felids The strong genetic structure in the Eurasian lynx revealed by our analyses corresponds well with our previous study on mtDNA that showed large differentiation among the populations at the westernmost peripheries of its range Our study has also indicated that the genetic structure in the Eurasian lynx may be explained, at least in part, with climatic conditions, particularly the depth of snow cover, which was significantly correlated with mtDNA population differentiation, even when geographic coordinates were included as co-variables in the conditional tests. The lack of a barrier on the synthetic genetic map for mtDNA in northern Scandinavia throughout Finland and Russia coincides well with similar climatic conditions (e.g. high snow cover depth) in all northern lynx populations. Very similar results have recently been reported based on mtDNA for a smaller dataset of lynx samples from the western part of its range The relationships between microsatellite population differentiation and climatic factors were less clear, with the only significant effect of snow conditions found in the sequential test. On the other hand, a significant effect on microsatellite differentiation in the marginal and sequential tests were found for prey, especially the proportion of red deer in the lynx's diet. Although drawing firm conclusions from the latter result can be somewhat speculative, red deer are killed more often by lynx in North-Eastern Poland (BPF and KARPF) than in other areas (Lynx canadensis) The results of multivariate multiple regression, along with the IBD effect may suggest that despite generally concordant patterns of population subdivision, there might be different factors such as climate and prey type affecting mtDNA and the microsatellites genetic structure of lynx population, respectively. The effect of climate may have resulted in the matrilineal clusters of lynx being specifically adapted to hunting prey under local snow conditions. Moreover, as male lynx are more likely to kill larger prey This study revealed differing levels of gene flow within the range of the Eurasian lynx. Low genetic divergence and high rate of migrants identified between the eastern part of European Russia and some western lynx sampling locations suggested high gene flow along the east-westward direction, despite the distance between locations. Several independent analyses of our microsatellite data indicated possible migration corridors situated between Russia (Kirov and Arkhangelsk Oblast), Finland and Belarus. In contrast, little genetic exchange appears to have occurred between neighboring populations situated at the westernmost peripheries of the lynx range, e.g. Norway, the BPF and the Carpathians.The possibility of recent gene flow between lynx inhabiting the far east of European Russia and the westernmost edge of the lynx range (approximately 1100–1300 km) may not be surprising considering the long distances large carnivores (including lynx) are capable of covering during dispersal and migrations et al.Studies documenting long-distance, cross-continental movements in wildlife are rare due to difficulties in collecting representative material The lynx population inhabiting eastern European Russia was characterized by high genetic variation, and therefore one may expect it to constitute a possible source population for these cats in Europe. However, we found present-day gene flow between Russian and Finnish lynx in both directions with migration rates slightly higher from Finland to Russia than in the opposite direction. The pattern of lynx population history revealed with DIYABC analysis of the mtDNA data supports the scenario of a Russian origin for the European lynx populations.The highest variability at both types of markers was found in sampling locations grouped into clusters situated within the core lynx range (Russia) or being directly connected to it through the continuity of the habitat present . Whereas high genetic variability in this cluster can be directly linked with the central location and habitat continuity Among the three most divergent and least variable populations, the Norwegian and Carpathian lynx present different patterns of genetic variability. Whereas the lynx in both areas showed a complete lack of polymorphism in mtDNA, they had different levels of microsatellite variability. As the Norwegian lynx had one of the lowest reported values for allelic richness and heterozygosity, it is clearly concordant with the relatively recent bottleneck experienced by this population during the twentieth century In the case of the BPF population, it is most likely that both its peripheral location and strong isolation from the core range were the main reasons for the low microsatellite and mitochondrial variability. A similar relationship between genetic variability and location relative to the core of the species range was reported for the Canada lynx The Eurasian lynx is capable of maintaining a panmictic population across eastern Europe unless they are severely limited by habitat continuity or a reduction in numbers. The effect of climatic conditions (snow cover) on the genetic divergence between populations with respect to mtDNA, but not nuclear microsatellite loci, may suggest different selective pressures acting on males and females in solitary carnivores. However, further research is needed to identify such selective pressures.S1 FigResults of isolation by distance (IBD) analysis of the Eurasian lynx populations. Two upper panels show IBD for microsatellite data for all ten putative populations (A) and excluding Kirov (Russia) (B). Two lower panels show IBD for mtDNA data for all populations (C) and excluding Kirov (Russia) (D). A significant relationship was only obtained for microsatellite data after excluding the Russian population.(PDF)Click here for additional data file.S2 FigNumber of genetic clusters of the Eurasian lynx population in north-eastern Europe. Evaluation of the most likely number of genetic clusters with the ΔK method (A) and the probability logarithm of the data (lnP(D) (B). ΔK clearly suggests that K = 4 is the most likely number of clusters, while lnP(D|K) shows leveling off from K = 4 and the highest likelihood at K = 8.(PDF)Click here for additional data file.S1 TableHaplotype frequencies of the mtDNA control region in ten populations of Eurasian lynx Lynx lynx.(DOC)Click here for additional data file.S2 TablePairwise differentiation between Eurasian lynx populations.FST values based on 12 microsatellites  and FST values based on the mtDNA control region . Nonsignificant values are given in italics.(DOC)Click here for additional data file.S3 TablePairwise differentiation between Eurasian lynx populations.DEST  and RST  values based on 12 microsatellites.(DOC)Click here for additional data file.S4 TableHabitat type, climate and lynx diet composition of the ten lynx populations studied. * References used for data on lynx diet: Norway (DOC)Click here for additional data file.S1 AppendixList of Eurasian lynx genotypes based on 12 microsatellite loci.(XLS)Click here for additional data file.S2 AppendixProcedures adopted in the Approximate Bayesian Computations (DIYABC 2.0.4 software) and the list of all scenarios tested.(DOC)Click here for additional data file.


ST FST analysis) can be used to determine to what degree differentiation among populations can be attributed to selection or genetic drift. Traditionally, QST FST analyses require a large number of populations to achieve sufficient statistical power; however, new methods have been developed that allow QST FST comparisons to be conducted on as few as two populations if their pedigrees are informative. This study compared genetic and morphological divergence in three strains of brook trout Salvelinus fontinalis that were historically or currently used for stocking in the Lake Superior Basin. Herein we examined if morphological divergence among populations showed temporal variation, and if divergence could be attributed to selection or was indistinguishable from genetic drift. Multivariate QST FST analysis showed evidence for divergent selection between populations. Univariate analyses suggests that the pattern observed in the multivariate analyses was largely driven by divergent selection for length and weight, and moreover by divergence between the Assinica strain and each of the Iron River and Siskiwit strains rather than divergent selection between each population pair. While it could not be determined if divergence was due to natural selection or inadvertent artificial selection in hatcheries, selected differences were consistent with patterns of domestication commonly found in salmonids.Fitness related traits often show spatial variation across populations of widely distributed species. Comparisons of genetic variation among populations in putatively neutral DNA markers and in phenotypic traits susceptible to selection (Q Fitness related traits often vary across populations of widely distributed species. Across its range, a species may be exposed to variation in both biotic and abiotic factors such as differences in resource availability, habitat structure, and temperature regimes that select for local adaptation, as well as repeatable temporal variation that may be unique to each population. As a result, selection may vary temporally during ontogeny as well as spatially across populations. Besides opportunities for divergent selection, spatial  separation between populations also fosters reproductive isolation which allows for genetic drift to be a driver of divergence.ST) to the analogous measure of quantitative genetic variation underlying phenotypes between populations (QST) ST is based on neutral markers, FST can be viewed as the amount of neutral genetic divergence between populations and is used as a null expectation for the level of quantitative trait variation (QST) that is expected in the absence of selection ST estimates differ significantly from FST estimates.Evaluating whether quantitative trait differences are a result of selection is important in understanding whether adaptive differences exist between populations ST to FST are an effective way to assess the cause of population differentiation, there are several limitations to traditional analyses. QST FST comparisons typically require data from at least 10 populations to have the statistical power to differentiate drift and selection ST FST comparisons cannot differentiate selection from drift when QST and FST values are equal, even though selection can be acting ST) against which the probability distribution of observed mean additive genetic values  for each population can be compared to test if the observed pattern in quantitative trait differences could be generated by drift alone. As a result, driftsel has greater power for distinguishing between drift and selection, enabling comparisons between few populations as well as the ability to distinguish between drift and selection even in cases where QST and FST are equal Although comparisons of QS. fontinalis coupled with population subdivision provides an ideal situation for evolutionary change through natural selection in wild populations ST estimates in S. fontinalis populations show a moderate to high degree of population subdivision even for spatially close populations Brook trout in the Lake Superior basin have historically exhibited diverse ecological life history diversity, ranging from stream dwelling populations, to those that are born in streams and migrate to Lake Superior where they obtain very large sizes before returning to streams to spawn. The varied environments inhabited by ST FST analysis to determine whether quantitative trait diversity is a product of selection  that were historically or currently used for stocking in the Lake Superior Basin, and vary in morphology and life history with both migratory and resident forms. We examined these populations to determine if extant diversity in morphology and size over several time points during development show patterns of variability consistent with neutral markers that are subject only to genetic drift, or if morphological traits show patterns of variability that are more consistent with patterns of natural or artificial selection.In this study, multivariate quantitative trait divergence was examined in hatchery strains of brook trout .S. fontinalis) that were previously used to quantify heritability of morphological traits S. fontinalis were collected in late summer near the outlet of Lake Assinica and were presumed to be migrating from Lake Assinica into the Broadback River to spawn S. fontinalis are a resident strain founded from 1,400 fish collected in 1993 from the Iron River, Michigan Three strains of brook trout . Gametes from the Siskiwit strain were provided by the U.S. Fish and Wildlife Service Iron River National Fish Hatchery . For the Assinica strain 30 families were created using 10 sires and 15 dams in a partial factorial breeding design. Twenty eight Iron River strain families were created from 10 sires and 20 dams in a partial factorial breeding design. Twelve Siskiwit strain families were produced from five sires and 12 dams in a modified full sib nested half sib design. Families were created on 09 and 15 November 2006.Crosses were created and used for a previous study on the heritability of morphological traits, and full details of mating design and rearing can be found therein To assess neutral marker variation, 1714 individuals from the three study populations were genotyped with fourteen microsatellite markers Sampling took place on three separate occasions: from 13–17 August 2007 (sampling period one), 22–26 October 2007 (sampling period two), and 21–24 January 2008 (sampling period three) S. fontinalisS. fontinalis associated with habitat specialization Reflectance is associated with smoltification in migrating salmonids where the pelagic habitat makes it advantageous to have a silvery coloration. Increased reflectance has been shown in anadromous Reflectance was quantified using SigmaScan Pro . The pixel intensity of the measured area was standardized by calibrating each picture with either black and white or blue and white patches on the acrylic box Body shape, evaluated by thin plates spline analysis ST between populations was calculated with Fstat ST was used to provide basic information on population differentiation and was not used further in the driftsel QST FST analysis.FMean trait values for length, weight, and morphology were previously summarized in P generated by RAFM was used as the prior for the function MH in driftsel ST to a distribution of FST as has been done in traditional QST-FST analyses The R package driftsel ST estimates for the population comparisons are as follows: global FST was 0.164, FST between Iron River and Assinica was 0.1453, FST between Siskiwit and Assinica was 0.2, and FST between Siskiwit and Iron River was 0.1627. These FST values indicate moderate divergence in neutral markers and are in the range previously found between other, natural populations of Lake Superior S. fontinalisF2,651 = 67.89, p<0.001) . The Assinica strain had greater reflectance and was significantly different from both Siskiwit (p = 0.001) and Iron River (p = 0.001), Iron River and Siskiwit were not significantly different from each other (p = 0.198). At the third time point, reflectance was significantly different among strains , and was significantly different in all possible pairwise comparisons between populations . The Assinica strain had the greatest reflectance, followed by Iron River and Siskiwit. Reflectance increased between the first and second time point for all populations, and the third time point is not directly comparable as a different intensity standard was used.Length, weight, and morphological trait variation have been previously described in these samples p<0.001) . The AssIn the multivariate analysis of all traits at each time point, population divergence was attributed to divergent selection . To visualize population divergence based on multivariate traits, 95% credible intervals around the ancestral mean genotypes under random genetic drift are drawn for pairs of traits. The observed mean phenotype is plotted as a point to visualize where each population falls with respect to expectations under drift, and with respect to current populations see . Each poS. fontinalis strains that have been used currently or historically for stocking in conservation and management efforts in the Lake Superior Basin. The goal of this study was to determine the pattern of divergence in morphological characters over time, and whether there were differences in the patterns or trends for selection on these multivariate quantitative traits for size, shape, and body coloration over the course of development.In this study quantitative genetic variation was examined in morphological traits within and among The present study builds on a prior study examining heritability of morphological traits relative to the ecology and history of the putative source populations from which the hatchery strains were derived Historically, tests for quantitative trait divergence between populations were largely limited to the analyses of single traits, and many populations were required to gain sufficient power needed to reject the null model of divergence due to neutral processes .The univariate analysis of single traits in this framework showed an overwhelming signature of divergence in quantitative traits that was consistent only with neutral expectations. There are several possible explanations for this. First, our study may simply not have had the power to detect divergent selection. Although driftsel yielded results showing divergent selection, simulation tests in ST FST analyses, or more generally studies on quantitative trait divergence, have examined traits across multiple developmental stages in an organism. Quantitative trait divergence, the result of which is consistent with selection, was evident in the first year of life, and persisted in the three developmental time points examined here. The power of the multivariate approach for examination of quantitative trait divergence is much improved over univariate analyses and traditional QST FST analyses. Multivariate analyses suggest that most all traits in the Assinica and Iron River populations have diverged due to selection, but univariate analyses suggest that divergence due to selection is the most evident for length and weight. Selected differences could be due to domestication effects in hatcheries but without testing the progenitor populations; however it is impossible to determine if this divergence is reflective of wild populations and natural selection in the wild or arose over time by artificial selection in hatcheries.Currently, few QTable S1Primer and PCR details for S. fontinalis microsatellites used in this study.(DOCX)Click here for additional data file.Table S2Raw genotype data.(XLSX)Click here for additional data file.Table S3Phenotypic raw data for time period 1.(XLSX)Click here for additional data file.Table S4Phenotypic raw data for time period 2.(XLSX)Click here for additional data file.Table S5Phenotypic raw data for time period 3.(XLSX)Click here for additional data file.


Macrobrachium nipponense) is a non-obligatory amphidromous prawn, and it has a wide distribution covering almost the entire Taiwan. Mitochondrial DNA fragment sequences of the cytochrome oxidase subunit I (COI) and 16S rRNA were combined and used to elucidate the population structure and historical demography of oriental river prawn in Taiwan. A total of 202 individuals from six reservoirs and three estuaries were separately collected. Nucleotide diversity (π) of all populations was 0.01217, with values ranging from 0.00188  to 0.01425 . All 76 haplotypes were divided into 2 lineages: lineage A included individuals from all sampling areas except SMR, and lineage B included specimens from all sampling locations except Chengching Lake Reservoir (CLR) and Liyu Lake Reservoir (LLR). All FST values among nine populations were significantly different except the one between Jhonggang River Estuary  and Kaoping River Estuary . UPGMA tree of nine populations showed two main groups: the first group included the SMR and Tamsui River Estuary (TSE) (both located northern Taiwan), and the second one included the other seven populations . Demographic analyses implied a population expansion occurred during the recent history of the species. The dispersal route of this species might be from China to west-central and west-southern Taiwan, and then the part individuals belonging to lineage A and B dispersed southerly and northerly, respectively. And then part individuals in west-central Taiwan fell back to and stay at estuaries as the sea level rose about 18,000 years ago.The oriental river prawn ( The present population genetic structure of a species may be fully interpreted if the influence of historical events and the complex interactions of biology, geography, and climatic shifts were considered . Stern cThere were drastic changes in areas and configurations in marginal seas of the Western Pacific during the late Quaternary glacial cycles . For exaThe spatially disconnected river systems form unique natural geographical constraints on freshwater fauna . The disMacrobrachium nipponense) distributes broadly over the East Asian regions  [The oriental river prawn ( Taiwan) , 12, and Taiwan) , Uzbekis Taiwan) , souther Taiwan)  and Iran Taiwan) . In Taiw Taiwan) . Orienta Taiwan) , 19. A n Taiwan) , 21. The Taiwan) , 23.Only one paper analyzed morphological characters in an attempt to determine the population structure of this species in Taiwan . The finAll samples were collected in open and public waters, and thus specific permission is not necessary. The specimens were collected by five bait traps each reservoir and each estuary from the evening 17:00 to next morning 7:00 during successive 3 days in winter season, from December 2013 to January 2014. A total of 202 individuals were separately collected from six reservoirs, Shihmen Reservoir , Mingte Reservoir , Tsengwen Reservoir , Chengching Lake Reservoir , Liyu Lake Reservoir  and Yangming Lake Reservoir , and three estuaries, Tamsui River Estuary , Jhonggang River Estuary  and Kaoping River Estuary  ; Table 1Total genomic DNA was extracted from abdominal muscle tissue following the treatment of a standard proteinase K, phenol–chloroform extraction, and ethanol precipitation . Two difAll sequences were aligned using MegAlign . The number of variable and parsimony informative sites, base composition, and transition/transversion ratios, and haplotype diversity and nucleotide diversity  were calwww.fluxus-engineering.com. Rough dates of population expansion were estimated with the formula τ = 2μT [T is the time since expansion, τ is the expansion time, and 2 μ is μ (the mutation rate) x generation time x number of bases sequenced. The average divergence rate of 1.17–1.66% per million years and a generation time of one year were used here [Part of sequences of the 16S rRNA and COI genes were concatenated in following analyses. There were used 16S and COI fragment in network methods. Phylogeographic analyses were carried out by the neighbor-joining (NJ) and maximum likelihood (ML) methods based on 16S rRNA and COI gene respectively by MEGA . We used τ = 2μT , where Tsed here .FST statistics were estimated by ARLEQUIN 3.5 [FST values. The population structure was also assessed by analysis of molecular variance  of all nine populations was 0.945, with values ranged from 0.658 (SMR) to0.964 (MTR) (π) of total populations was 0.01217, with values ranged from 0.00188 (SMR) to 0.01425 (MTR)  . The nuc25 (MTR) . A totalMacrobrachium asperulum were also analyzed in NJ and ML reconstruction as outgroups. Phylogenetic tree of all haplotypes is shown in h) of lineage A and lineage B were 0.966 and 0.733, respectively. Nucleotide diversities (π) of lineage A was 0.00834, and the lineage B was 0.00450  were found in the grouping 2, and supported these nine populations could be divided into two main groups: the first group included the SMR and TSE, and the second one included the other seven populations. Significant ΦCT values were also found in different groupings, indicating that an additional genetic discontinuity may also have occurred among populations.Five different groupings for nine populations were suggested by UPGMA trees. The results of AMOVA are shown in D found in this population  from that predicted by the growth expansion model  in the lineage B. This suggested that lineage A was older than lineage B. The lineages A and B might originate from the same ancestor in mainland China, and then dispersed to Taiwan in different time. The lineage A moved to Taiwan earlier than lineage B, and the viewpoint was supported that haplotyps from YLR population has been formed unique sub-lineage in lineage A  profile, with a steeper wave indicative of a smaller initial population before the expansion , 56. An Populations with ancestral genotypes tend to preserve higher nucleotide and haplotype diversities because of long-term accumulation of mutations , 58, 47.Acrossocheilus paradoxus [Macrobrachium asperulum [Fs values of lineage A and B, MTR, TWR and JGE populations support above viewpoint , composed of many mountain peaks above 3000 m in elevation, is an obvious geographical barrier for dispersal of freshwater species from west-to-east and vice versa in Taiwan , 50, 52.Our study indicated a high level of genetic structure among the oriental river prawn populations in Taiwan. Two main lineages (A and B) were found, and lineage A was older than lineage B. The west-central and southwestern Taiwan and estuaries were mixing areas of these two lineages. According to genealogic analysis, the dispersal route of this species was from China to west-central and southern Taiwan, and then the part individuals belonging to lineage A and B dispersed southerly and northerly, respectively. And then part individuals in western coasts fell back to and stay at estuaries as the sea level rose. Demographic analyses implied a population expansion occurred during the recent history of the species.S1 Dataset(FASTA)Click here for additional data file.S2 Dataset(FASTA)Click here for additional data file.S3 Dataset(FASTA)Click here for additional data file.


Citrus medica L.) is a medicinally important species of citrus native to India and occurs in natural forests and home gardens in the foothills of the eastern Himalayan region of northeast India. The wild populations of citron in the region have undergone rapid decline due to natural and anthropogenic disturbances and most of the remaining individuals of citron are found in fragmented natural forests and home gardens in the region. In order to assess the genetic structure and diversity of citron in wild and domesticated populations, we analyzed 219 individuals of C. medica collected from four wild and eight domesticated populations using microsatellite markers. The genetic analysis based on five polymorphic microsatellite loci revealed an average of 13.40 allele per locus. The mean observed and expected heterozygosity values ranged between 0.220–0.540 and 0.438–0.733 respectively among the wild and domesticated populations. Domesticated populations showed close genetic relationships as compared to wild populations and pairwise Nei's genetic distance ranged from 0.062 to 2.091 among wild and domesticated populations. Analysis of molecular variance (AMOVA) showed higher genetic diversity among‐ than within populations. The analysis of population structure revealed five groups. Mixed ancestry of few individuals of different populations revealed exchange of genetic materials among farmers in the region. Citron populations in the region show high genetic variation. The knowledge gained through this study is invaluable for devising genetically sound strategies for conservation of citron genetic resources in the region.Citron ( Citrus medica L., commonly known as citron, is native to India , citron contributed to the origin of lemon (Citrus limon), bergamot (Citrus bergamia), and key lime (Citrus aurantifolia)  at the University of California, Riverside. Barkley et al.  determine the levels of genetic diversity in wild and domesticated populations of C. medica, (2) determine whether the domestication process led to a reduction in genetic diversity (3) assess genetic structure and diversity of C. medica in its native habitat and (4) infer genetic relationships among wild and domesticated populations.The present study, based on an extensive sampling from northeast India, is the first to assess the genetic variability of C. medica  and 0.5 μL of the M13 universal forward primer (1 pmol/μL), 0.5 μL DMSO and 6.3 μL sterile dH2O. Thermal cycling parameters consisted of initial denaturation at 94°C for 4 min followed by 35 cycles of 94°C for 1 min, 50–55°C for 45 sec . The fragment size corresponding to each SSR marker of each sample was scored using the e‐seq software and the bands recorded as 1 (present) or 0 (absent) on an EXCEL sheet for further analysis.Each forward oligonucleotide primer consisted of M13 tail sequence (5ʹ‐ CACGACGTTGTAAAACGAC‐3ʹ) at the 5ʹ end for visualization of the PCR product using M13 primers labeled with IRD700 and IRD800. The amplified PCR products were diluted (1:20) with loading dye (Formamide and Bromophenol blue), denatured at 94°C for 5 min and cooled on ice before loading onto the 6% polyacrylamide gel on a LI‐COR IRAR), private allele (AP), genetic differentiation (FST), and inbreeding coefficient (FIS) in each population and locus were calculated using software programs POPGENE Version 1.31  among the 12 domesticated and wild populations were calculated following Nei's unbiased measures of genetic distance  and linkage disequilibrium (LD) using POPGENE Version 1.31  over 10000 randomizations. The distribution of genetic variation within and among wild and domesticated populations was estimated using Nei's standard genetic variation ] and a pairwise matrix of geographic distances (Mantel ST) /4FST.Analysis of molecular variance (AMOVA)  values of 1–15. Simulations were carried out with the following settings: admixture model, correlated allele frequencies, and MCMC repetitions of 10,000 iterations. The final results were based on a run length of 100,000 and five iterations for each K using admixture model with the independent frequency and correlation model. We examined ΔK values, which are derived from the second‐order rate of change of the likelihood function used to determine K , independent of sample size, ranged between 3.83 in the Tinsukia wild population to 2.48 in the Sairang2 domesticated population (Table AP = 4), as well as in the Tinsukia wild, Banskandi domesticated, Aizawl domesticated and Sairang1 wild populations, each with two private alleles, and in the Sairang2 and Motinagar1 domesticated populations, which had one private allele each. No private alleles were found in any of the other populations  within the populations (Table 2 domesticated population (He = 0.438 ± 0.217). The He values for wild populations ranged from 0.500–0.733, and for domesticated populations it ranged from 0.438–0.706. This wide range of heterozygosity values indicates high diversity within the populations. In all cases, average observed heterozygosities were lower than the expected heterozygosities under HWE (Table The mean observed (Ho) and expected (He) heterozygosity values varied significantly (ns Table . The higFST) values were calculated for each locus and population separately and slight variation was observed among loci  and Sairang2 (domesticated). All other population pairs were significantly differentiated and the significance level in the most of the population pairs was P < 0.001  values were significantly positive  for all the populations except for one wild population in which it was positive but insignificant   values are summarized in Table DS vales between populations ranged from 0.062 between the Sairang1 wild and Sairang2 domesticated populations in Mizoram to 2.091 between two domesticated populations, Sairang2 (Mizoram) and Neairgram (Assam). Similar results were observed when the genetic distances of the populations in the study were determined using Nei's DA index (Nei's unbiased genetic distance) of genetic distance. The smallest DA was observed between the Sairang1 wild and Sairang2 domesticated populations (0.049) and largest DA was observed between the Neairgram and Sairang2 domesticated populations (2.074) (Data not shown here). The AMOVA showed significant total genetic variation among the populations and individuals (P < 0.001) for all variance components. The genetic differences were 27.49% among individuals within populations, 24.98% among populations, and 47.53% at the individual level  and Banskandi (domesticated); the second cluster consisted of distant populations Itanagar and Aizawl (both domesticated); the third cluster contained the Sairang2 (wild), Sairang1 (domesticated), and Motinagar1 (domesticated) populations, which are located in the same geographic region; the fourth cluster was formed by the Motinagar2 (wild) and Lakhipur (domesticated) populations; and the fifth cluster was made up of two proximate domesticated populations Sonai and Neairgram and the distant, wild Namsai population  represented by the individuals having posterior probability values above the threshold value q = 0.75 ] for C. medica populations in the region. Thus, genetic distances between populations are independent of the corresponding geographical distances.Correlation between geographic distance (km) and Nei's genetic distance among the citron populations of NE India was insignificant. The geographic distance among the populations ranges from 0.01 to 535 km. Mantel test also showed no significant correlation between geographic distance and genetic differentiation  among the 12 populations of wild and domestic C. medica. FST was calculated according to Weir and Cockerham (ockerham .Click here for additional data file.


Saccharina japonica is a commercially and ecologically important kelp species widely distributed along the coast of Japan Sea. However, it is still poorly known about population genetics and phylogeographic patterns of wild S. japonica populations on a large geographic scale, which is an important contribution to breeding and conservation of this marine crop.Population structure and genetic diversity of marine organisms in the Northwestern Pacific Ocean exhibited complex patterns. COI and trnW-trnL sequences. Diversity indices suggested that S. japonica populations along the coast of Hokkaido exhibited the highest genetic diversity. Bayesian Analysis of Population Structure (BAPS) revealed four clusters in the kelp species . The network inferred from concatenated data exhibited two shallow genealogies corresponding to two BAPS groups (cluster 2 and cluster 3). We did not detect gene flow between the two shallow genealogies, but populations within genealogy have asymmetric gene exchange. Bayesian skyline plots and neutrality tests suggested that S. japonica experienced postglacial expansion around 10.45 ka.We collected 612 mitochondrial S. japonica. Gene exchange among S. japonica populations could be caused by anthropogenic interference and oceanographic regimes. Postglacial expansions and gene exchange apparently led to more shared haplotypes and less differentiation that in turn led to the present shallow phylogeographical patterns in S. japonica.The coast of Hokkaido might be the origin and diversification center of The online version of this article (doi:10.1186/s12862-015-0517-8) contains supplementary material, which is available to authorized users. Saccharina japonica (Aresch.) C.E. Lane, C. Mayes, Druehl & G.W. Saunders is widely distributed along the coast of Japan Sea. Taxonomically, this species was initially named Laminaria japonica by Areschoug in 1851 [Saccharina Stackhouse from the genus Laminaria Lamouroux based on multiple lines of molecular data and proposed to use S. japonica to replace L. japonica. The systematic survey in Laminariales from the Far Eastern Seas of Russia also supported to transfer L. japonica into the genus Saccharina [S. japonica along the coast of Hokkaido, integrative morphological and phylogenetic analyses have identified four varieties, S. religiosa (Miyabe) C.E. Lane, C. Mayes, Druehl & G.W. Saunders, S. ochotensis (Miyabe) C.E. Lane, C. Mayes, Druehl & G.W. Saunders and S. diabolica (Miyabe) C.E. Lane, C. Mayes, Druehl & G.W. Saunders [The commercially and ecologically important seaweed  in 1851  reportedccharina . For S. Saunders , 5. In tS. japonica in recent years [S. japonica populations will aid in the management, conservation and breeding of this marine macroalga. However, current genetic researches of S. japonica mainly focused on cultivated populations, and involved wild populations only on a limited geographic scale [S. japonica populations in the Japan Sea needs clarification, especially regarding population genetic and phylogeographic patterns on a large geographic scale.Global climate change and over exploitation has drastically reduced the resource of nt years . Understic scale , 8. The e.g. reproductive strategies and intrinsic life-history characteristics) and abiotic factors  [Laminaria digitata (Hudson) J.V. Lamouroux and Macrocystis pyrifera (Linnaeus) C. Agardh [S. japonica influenced dispersal and gene flow of brown seaweed, especially economic seaweeds [S. japonica are rarely known.Population structure and genetic diversity of marine organisms in the Northwestern Pacific Ocean exhibited complex patterns which mainly resulted from biotic factors (nuities) –13. The nuities)  nuities) –17 and Pnuities) , 19. In nuities) –23. Alonnuities) . However. Agardh , 22. Incseaweeds , 25. In COI and trn DNA markers have been intensively used to explore intra-specific phylogeographic patterns of brown seaweeds [S. japonica [COI marker has been demonstrated to has more polymorphism sites than nuclear (ITS) and plastid (rbcLS), and has been verified in S. japonica [COI and trnW-L and applied them in 26 S. japonica wild populations to explore intraspecific genetic diversity, population structure and demographic history and to find potential abiotic and/or biotic factors associated with genetic differentiation in S. japonica.Mitochondrial seaweeds , 26–32, japonica . In partjaponica . In the S. japonica  were collected, ranging from Sakhalin, Russia (48°50′ N) to Shandong, China (37°09′ N)  according to manufacturer’s instructions and mitochondrial markers COI and trnW-L were adopted. One COI partial region was amplified with primers SacCOIF and SacCOIR [trnW-L partial region from the 3′ end of the trnW gene to the 5′end of the trnL was amplified with primers 5′-GGTTCAAGTCCCTCTCTTTCTGT-3′  and 5′-AACCTAAACCCAGCGTGTAT-3′  based on the mitochondrial genome sequence of S. japonica [Taq buffer, 0.4 μM of each primer, 0.5 unit Taq polymerase and sterile water  was amplified on a Takara thermocycler  with initial denaturation of 95 °C for 5 min, 35 cycles of 95 °C denaturation for 30 s, 52 °C (COI)/55 °C (trnW-L) annealing for 30 s, a 72 °C extension for 2 min (COI) or 1 min (trnW-L) and final extension of 72 °C for 5 min. Amplified products were sequenced on an ABI PRISIM 3730 automatic sequencer  and sequenced data were edited and aligned with BioEdit v7.1 software [ SacCOIR  and the software .S. japonica. To test mutation rate, COI and trnW-L sequence data were concatenated and the combined mtDNA haplotype network was constructed.The number of segregating sites (S), average number of nucleotide differences (K), nucleotide diversity (Pi) and haplotype diversity (Hd) were calculated using DNASP v5 . NETWORKFST values of pairwise populations and pairwise BAPS groups were conducted for genetic differentiation analysis using 10,000 permutations with Arlequin v3.5.With concatenated data of two mtDNA loci, Bayesian analysis of population structure was conducted using BAPS v6.0 with a spatial model to define groups , 38. CalF/ST(1–FST)) were regressed against the logarithm of geographic distances within location distances (estimated with Google Earth 6.0). To visualize IBD patterns [http://ibdws.sdsu.edu/~ibdws/).The mantel test for isolation-by-distance (IBD) established relationships between genetic and geographic distances  and genepatterns , 1000 raTo further test migration rate and divergence time between neighboring populations in Far Eastern Russia and Hokkaido, an isolation-with-migration model was applied using software IMa2 , 43. MigSaccharina occurred approximately 20 Ma ago. Therefore, 626 bp COI was used to re-construct the phylogeny of Laminariales (3 genera and 5 species) and 20 Ma was used as the ancestral time of the common ancestor (tMRCA) of Saccharina, Pelagophycus Areschoug and Nereocystis Postels & Ruprecht, which all are implemented in BEAST v1.7.4 [According to the study of Silberfeld et al.  on brownT v1.7.4  with theT v1.7.4 . The maxT v1.7.4 .S. japonica in China and Korea is short compared with terrestrial crops, so neutrality tests and mismatch distribution analysis were used to explore the demographic history of the 22 S. japonica populations  was used for data analysis, with mean substitution/site selected at 1.0 for the time estimation to the units of substitution/site. The HKY + I substitution model was selected for COI, the HKY substitution model was applied for trnW-L and the HKY + G substitution model was selected for combined sequences as identified in PartitionFinder v1.1.0 [7 and 108. For each marker, multiple analyses were conducted with different random seeds to test convergence, and results from replicate runs were pooled with LogCombiner v2.1.2 and parameters were assessed using Tracer v1.4.1 [The duration of transplantation and domestication to jima’s D  and Fu’s’s D [FS  were est’s D [FS . A BayesT v1.7.4 . COI or r v1.1.0 . The numr v1.4.1 .COI and 362 bp for trnW-L. In the COI and trnW-L sequences from 612 individuals, 35 and 20 segregating sites respectively were identified, possessing 30 COI haplotypes (GenBank accession number: KT963115-KT963144) and 22 trnW-L haplotypes (GenBank accession number: KT963093-KT963114).With alignment and trimming, the remaining sequences were 1528 bp for COI marker, nucleotide diversity (Pi) range was 0.00000–0.00191 and haplotype diversity (Hd) range was 0.0000–0.68254. For the trnW-L marker, haplotype diversity was 0.00000–0.0042 and nucleotide diversity was 0.00000–0.76190. Data from both COI and trnW-L showed highest diversity indices in Hokkaido, Japan. For the whole dataset , there were 55 segregating sites and 53 haplotypes in all the 26 populations  defined by BAPS, which accounted for 54.79 % of variation  to 0.87016 (cluster 2 and cluster 3) . Pairwise FST values indicated that pop24 and pop25 in China diverged significantly from the other 24 populations (FST: 0.5304–1.000)  revealed four groups ) and geographical distance (22 populations except populations in China and Korea)  indicated that the population genetic structure of S. japonica did not fit the IBD model.The mantel test indicated no significant correlation between genetic distance  reaching convergence with high ESS values across all parameters. Migration between cluster 2 and cluster 3 (pair 3) seemed to be negligible in both directions . Effective population sizes  were approximately two times higher in cluster 2 than in cluster 3 . Peak posterior distribution of migration rate estimated asymmetrical gene flow between WA and SH, where migration to SH was significant  and migration to WA was negligible . A similar asymmetrical gene flow was observed between VL and WS, with gene flow from VL to WS was significantly higher than in the opposite direction. Estimated split time between cluster 2 and cluster 3 was 0.0554 Ma , with all statistics sharing broadly overlapping 95 % HPD intervals  C.E. Lane, C. Mayes, Druehl & G.W. Saunders was assumed to be 4.84 Ma. Calculated COI region divergence between S. japonica (AP011493) and S. augustata (AP011498) was 4.633 % and estimated divergent rate was 0.9572 %/Ma  [COI suggested that S. japonica populations started to expand at 10.45 Ka  . Since matively) , 54, estS. japonica, with many shared haplotypes in four genetic clusters  significantly influenced the demography of seaweeds [S. japonica could survive severe ancient conditions and colonized suitable coastal territories when the sea level increased after the LGM. Judging from the constructed haplotype network  Lamouroux [S. japonica in the present study has high gene flow among most populations. In S. japonica, due to limited gametes and spores dispersal ability, the floating or drifting thallus by oceanic currents was regarded as the main dispersal factor to enhance the gene flow in the kelp populations [Low genetic differentiation in the populations of amouroux , 58 due amouroux , 22, 24.ulations . The warUndaria pinnatifida (Harvey) Suringar [Marine transportation also promotes kelp population genetic homogeneity. Pop 21  and pop 15–18 in Sakhalin shared also one haplotype H33), and pop19  and pop22  shared one haplotype (H27), indicating gene exchange in Far Eastern Russia populations. In addition, IMa analyses indicated that an asymmetric gene flow existed between WS and VL in Far Eastern Russia  Papenfuss [S. japonica exhibited the highest genetic diversity in Hokkaido. Among the 55 haplotypes analyzed with the COI and trnW-L marker systems, H1 was the most common in almost all populations (20/26), so H1 might be the ancestral haplotype. Moreover, pop7 and pop9 presented the highest genetic diversity among all populations  Papenfuss [Codium fragile (Suringar) Hariot [Caulerpa taxifolia (M. Vahl) C. Agardh [S. japonica is not native to China and this kelp was firstly found in the vicinity of Dalian harbor around 1930s [S. japonica in China.The invasive populations have lower genetic diversity than the source populations as reported in other introduced seaweeds, including apenfuss , Codium ) Hariot  and Caul. Agardh . S. japond 1930s , 65, posnd 1930s , 8. In tS. japonica in its native range. The shallow phylogeographic architecture suggests a complex interaction of anthropogenic interference (marine transportation and aquaculture) and post-LGM population expansion. The coast of Hokkaido might be the origin center for S. japonica in the northwestern Pacific. Extensive selection and multi-generation inbreeding have already reduced genetic diversity of S. japonica and caused genetic degeneration of cultivars in China. The generated knowledge about the levels of diversity of wild S. japonica populations is an important contribution for efficient breeding and rejuvenation of cultivated Saccharina by improving and enhancing domesticated genepool of S. japonica in China.This study examined the phylogeographic architecture and population genetic diversity of COI and trnW-L haplotype sequences: GenBank accessions number: KT963115-KT963144 for COI; KT963093-KT963114 for trnW-L.The newly obtained


Bathysa australis (Rubiaceae) is a widespread tree along the elevation gradient of the Serra do Mar, SE Brazil. This self-compatible species is pollinated by bees and wasps and has autochoric seeds, suggesting restricted gene dispersal. We investigated the distribution of genetic diversity in six B. australis populations at two extreme sites along an elevation gradient: a lowland site (80–216 m) and an upland site (1010–1100 m.a.s.l.). Nine microsatellite loci were used to test for genetic structure and to verify differences in genetic diversity between sites. We found a marked genetic structure on a scale as small as 6 km (FST = 0.21), and two distinct clusters were identified, each corresponding to a site. Although B. australis is continuously distributed along the elevation gradient, we have not observed a gene flow between the extreme populations. This might be related to B. australis biological features and creates a potential scenario for adaptation to the different conditions imposed by the elevation gradient. We failed to find an isolation-by-distance pattern; although on the fine scale, all populations showed spatial autocorrelation until ∼10-20 m. Elevation difference was a relevant factor though, but we need further sampling effort to check its correlation with genetic distance. The lowland populations had a higher allelic richness and showed higher rare allele counts than the upland ones. The upland site may be more selective, eliminating rare alleles, as we did not find any evidence for bottleneck.Distance and discrete geographic barriers play a role in isolating populations, as seed and pollen dispersal become limited. Nearby populations without any geographic barrier between them may also suffer from ecological isolation driven by habitat heterogeneity, which may promote divergence by local adaptation and drift. Likewise, elevation gradients may influence the genetic structure and diversity of populations, particularly those marginally distributed.  The problem of speciation in tropical rain forests has long intrigued ecology researchers Federov . TropicaProtium subserratum, across a mosaic of soil types. They pointed out higher levels of genetic differentiation between adjacent populations in different soil types than between geographically distant populations in the same soil type.However, nearby populations with no discrete geographic barrier between them may also suffer from ecological isolation driven by habitat heterogeneity, which may promote population divergence by local adaptation and drift as well  Hook. f. ex K. Schum. (Rubiaceae) is a tropical tree endemic to the Brazilian Atlantic forest, widely distributed along the altitudinal gradient of the Serra do Mar mountain range, Southeast Brazil. For this reason, together with B. australis biological features, we believe this species may serve as a model organism to study the effect of altitude on genetic structure, and diversity. B. australis displays great phenotypic variability in leaf attributes between lowland and upland populations at this gradient  and is mainly pollinated by bees and wasps , could generate an upward pollen flow. Because the stigmas from a tree at a given altitude will be receptive earlier than pollen donors are available at the same altitude, the outcrossed pollen pool of this tree might be partly composed of migrant pollen from lower altitudes  are on the upper slopes  Is there a fine-scale spatial genetic structure within B. australis populations, as would be expected, considering B. australis dispersal mode?; (2) On a larger scale, Is there a genetic structure between the upper and lower B. australis populations of the Serra do Mar mountain range?; (3) Are the upper mountain populations more genetically diverse than the lower ones?In this study, we aimed to investigate within- and between-population genetic structure and diversity for Bathysa australis is a tree species of the Rubiaceae family that belongs to the lower strata of the forest, reaching up to 15 m in height. It is an endemic species of the threatened Atlantic rain forest, distributed predominantly in south and southeast Brazil  of the Atlantic forest of the Serra do Mar mountain range in São Paulo State, SE Brazil  were found in the six 1-ha plots, accounting for the whole population, that is, seedlings, juveniles, and adults , 1.5 mmol/L MgCl2, and 1 U Taq DNA polymerase. A PTC-100 thermal cycler (MJ Research) was used with the following program: 96°C for 1 min, followed by 30 cycles of denaturation at 94°C for 1 min, 1 min at a specific annealing temperature, and a final extension of 72°C for 5 min. Amplified products were checked by electrophoresis on 3% agarose gels containing 0.1 mg ethidium bromide per milliliter in 1 × TBE buffer. The amplicons were electrophoretically separated using an ABI 3500 automated sequencer  with GeneScan 600 LIZ marker as the size standard (Applied Biosystems). Fragment size and allele identification were determined using the software GeneMarker version 2.2 .We extracted genomic DNA from leaf tissue samples using the DNeasy Plant Mini Kit . The genetic variation of s et al. . PCR ampWe tested for linkage disequilibrium (LD) using FSTAT with a Bonferroni correction for multiple comparisons. The presence of null alleles was checked using the software FreeNA (Chapuis and Estoup A), effective number of alleles (Ae), observed heterozygosity (Ho), expected heterozygosity (He), and fixation index (F). We also calculated allelic richness using the program FSTAT  by Loiselle et al. , varying from 1 to 7 under the admixture model, with no prior population information. To verify the robustness of our results, we performed 20 independent runs per K value with 200,000 burn-in periods and 500,000 Markov Chain Monte Carlo iterations. The statistics described by Evanno et al. (K). The K value that best represents the structuring of populations can be identified by the peak value of ΔK.The population structure inference as well as the number of existing genetic clusters and individuals assigned to each cluster was performed using the software STRUCTURE v.2.3  after 10,000 permutations in FSTAT. Furthermore, we counted exclusive and rare alleles (frequency ≤ 0.05) in each locus to complement diversity comparisons between the upland and lowland sites. These comparisons were made with a Wilcoxon paired test in R  fluctuated from −0.208 to 1.0, with a mean of 0.186 ± 0.04 and 72% of positive values. All populations had at least two loci with a significantly positive F value in the Hardy–Weinberg equilibrium test, indicating an excess of homozygotes. The locus BA30 from the L2 population, for example, did not show any heterozygous individual. All fixation index values were significantly positive using a 95% confidence level (P < 0.05), indicating deviations from HWE in all six populations . Allelic richness ranged from 2.0 to 11.1 (mean 6.9 ± 2.2). The observed heterozygosity ranged from 0.0 to 0.926 (mean 0.578 ± 0.03), and the expected heterozygosity in HWE ranged from 0.381 to 0.888 (mean 0.706 ± 0.02). The fixation index  than between populations . The pattern of isolation by elevation, on the other hand, proved consistent even after discounting the effect of geographic distance . This result suggests that geographic distance is not a factor influencing the genetic structure among sites. The elevation distance between sites seems to be a more relevant element. However, in order to confirm the correlation between elevation and genetic distance, we must increase our sampling effort to include B. australis populations from intermediate elevations , but not for the upland site , suggesting that the strongest structure observed for the lowland site is a result of geographic distance. However, the partial Mantel test indicates that this correlation disappears again when the effect of altitude is discounted . The same occurred with the correlation between genetic and elevational distance, so neither site showed an elevation effect , which seems reasonable, because of the low altitudinal variation within sites.Within the altitudinal groupings, we found a correlation between genetic and geographical distance for the lowland site (P = 0.027). Contrary to our expectations, the lowland populations showed higher allelic richness (AR = 7.169) than the upland ones , although the lowland populations had 20 more exclusive alleles than the upland populations  may facilitate the occurrence of crossing events between relatives. The fine-scale spatial genetic structure has shown that nearby individuals are closely related.In natural populations of plants, a heterozygote deficiency at any locus can be driven by two mechanisms. The first is related to the mating system and possible deviations from panmixia. The second derives from the structuring of populations and the fixation of alleles.  Andrich  suggesteBathysa australis have shown a restricted gene flow between the lowland and upland populations, which, associated with random genetic drift, might promote the loss of some alleles and the fixation of others in different populations.Genetic structuring among populations is another way of increasing homozygote frequencies. Although a heterozygote deficiency may have negative implications on population fitness, as a result of inbreeding depression, and may narrow a species’ capacity to cope with environmental changes by the loss of genetic variability . It is thus possible that the restricted seed dispersal can lead to limited pollen dispersal by creating higher local tree densities, increasing the positive correlation between pollen and seed dispersal distances  at the same study site, observed that the availability of pollinators varied considerably with altitude and that the presence of effective pollinator bees was up to 200 times lower at the upland site in the Serra do Mar.B. australis populations at the study site, may restrict pollen dispersal by concentrating the pollinator’s resource on a single spot. Besides, synchrony leads to pollinator satiation, causing it not to visit other plants or to visit only very close ones , which contributes to a limited pollen flow and, consequently, to a limited gene flow between these populations.Animal pollination is strongly influenced by local density and flowering synchrony. High local densities, as we observed for B. australis is another life-history feature that might strengthen the genetic structure of its populations. Self-compatibility may enhance inbreeding rates that, together with genetic drift, can lead to increased genetic divergence even between close populations (Young et al. Finally, the mixed reproductive system of B. australis is continuously distributed along the elevation gradient, we observed no gene flow between the lowland and upland populations, isolating these extreme groups. This creates a scenario for potential adaptation to the different conditions imposed by the elevation gradient. Behind such segregation, we have not found an isolation-by-distance pattern. Elevation difference was a relevant factor though, but we need further sampling effort to check its correlation with genetic distance.In the Serra do Mar mountain range, we found a marked genetic structure on a scale as small as 6 km. Even though B. australis, which has a limited gene flow among the populations from the altitudinal extremes of the Serra do Mar. Still, we did not find higher genetic diversity in the upland populations, as would be expected, considering the optimum of environmental conditions for this species. On the contrary, the lowland populations exhibited greater allelic richness and higher exclusive and rare allele counts.There is a significant variation in the distribution patterns of genetic diversity within populations along altitudinal gradients (Ohsawa and Ide Podocarpus parlatorei genetic decline toward higher elevations reflected forest migration during glacial periods. In the same way, Ohsawa and Ide (A possible explanation might be the existence of forces reducing upper mountain genetic diversity, as it would be the case for bottlenecks during forest movements (Ohsawa and Ide  and Ide  found th and Ide , and dur and Ide , and it We could say that both populations might be already adapted to their local conditions, and each has its own set of optimum conditions, both experiencing high genetic diversity levels. However, the upland site might be more selective and the survival of individuals carrying new mutations would be compromised, keeping only alleles of high adaptive value. This may have led to the elimination of rare alleles in the upland populations, explaining the presence of more exclusive and rare alleles in the lowland, as well as its higher allelic richness. Further controlled transplant experiments might elucidate this question.


Gasterosteus aculeatus) is a well-studied model organism with a short generation time, small genome size, and many genetic and genomic tools available. Within this originally marine species, populations have recurrently adapted to freshwater all over its range. This evolution involved extensive parallelism: pre-existing alleles that adapt sticklebacks to freshwater habitats, but are also present at low frequencies in marine populations, have been recruited repeatedly. While a number of genomic regions responsible for this adaptation have been identified, the details of selection remain poorly understood. Using whole-genome resequencing, we compare pooled genomic samples from marine and freshwater populations of the White Sea basin, and identify 19 short genomic regions that are highly divergent between them, including three known inversions. 17 of these regions overlap protein-coding genes, including a number of genes with predicted functions that are relevant for adaptation to the freshwater environment. We then analyze four additional independently derived young freshwater populations of known ages, two natural and two artificially established, and use the observed shifts of allelic frequencies to estimate the strength of positive selection. Adaptation turns out to be quite rapid, indicating strong selection acting simultaneously at multiple regions of the genome, with selection coefficients of up to 0.27. High divergence between marine and freshwater genotypes, lack of reduction in polymorphism in regions responsible for adaptation, and high frequencies of freshwater alleles observed even in young freshwater populations are all consistent with rapid assembly of G. aculeatus freshwater genotypes from pre-existing genomic regions of adaptive variation, with strong selection that favors this assembly acting simultaneously at multiple loci.Adaptation is driven by natural selection; however, many adaptations are caused by weak selection acting over large timescales, complicating its study. Therefore, it is rarely possible to study selection comprehensively in natural environments. The threespine stickleback ( Adaptation to novel environments is a keystone of evolution. There is only a handful of natural and experimental systems in which the process of adaptation has been studied in detail, and each studied system brings its own surprises with regard to the number of loci involved, dynamics of adaptation, extent of interactions between loci and of parallelism between different adapting populations. The threespine stickleback is an excellent model organism for evolutionary studies. Marine-derived freshwater populations of this species have consistently acquired a specific set of morphological, physiological and behavioral traits allowing them to reside in freshwater for their whole lifespan. Previous studies identified several genomic regions responsible for this adaptation. Here, using whole-genome sequencing, we compare the allele frequencies at such regions in four derived freshwater populations of known ages: two natural, and two artificially established in 1978. Knowledge of population ages allows us to infer the strength of selection that acted at these loci. Adaptation of threespine stickleback to freshwater is typically fast, and is driven by strong selection favoring pre-existing alleles that are likely present in the ancestral marine population at low frequencies; however, some of the adaptation may also be due to young population-specific alleles. Studies of adaptation in nature are complicated by the typically long timescales at which evolution proceeds, and therefore are rather rare e.g. . PositivThe study of the genomics of adaptation has experienced a recent upheaval since the advent of population-level next-generation sequencing, which enables identification of selected loci and detailed studies of divergence and polymorphism within them in a wide range of model systems Gasterosteus aculeatus) has become a widely used model organism for studying adaptation and speciation G. aculeatus likely lived in the sea, and colonization of new freshwater habitats, followed by evolution of freshwater populations, occurred repeatedly all over the Northern Hemisphere. While fish from marine populations utilize freshwater lakes and streams only as temporary spawning grounds, thousands of isolated freshwater resident populations have been independently established, and they have diverged in morphological, physiological and behavioral traits allowing them to survive in the freshwater for their entire lifespan. Independent origin of freshwater populations of G. aculeatus in different locations in the Northern Hemisphere provides an opportunity to study adaptive evolution under similar environments Threespine stickleback  facilitated studying the genetic basis of the differences between the two forms, and several large genomic regions with high concentrations of nucleotide substitutions between the forms were found by comparing individuals from marine and freshwater habitats in a RAD-Seq analysis G. aculeatus from Atlantic and Pacific basins used whole genome sequencing to reveal more than two hundred small genetic regions throughout the stickleback genome that differ between the forms Freshwater and marine forms of G. aculeatus adapted to freshwater inhabit lakes and streams that originated after the retreat of the Pleistocene glaciers, indicating that adaptation can be fast Populations of G. aculeatus in the White Sea and its basin were initiated by Valery Ziuganov in the 1970s Studies of G. aculeatus from the White Sea basin, including two artificial populations. We aimed to detect the genetic differences between the ancestral marine and the derived freshwater populations, and to measure the rate of adaptation, and the strength of positive selection which drives it, at divergent genomic loci. Whole-genome comparisons of multiple artificial and natural derived populations allow detailed analysis of selection acting simultaneously at multiple loci. Using multiple populations of different ages allows studying the process of adaptation at these loci at a range of time points, from tens to hundreds of years, and the uniformity of the process of selection. Finally, whole-genome analysis reveals the detailed patterns of divergence and polymorphism within the selected loci and in their vicinity.Here, we use whole-genome sequencing to study eight populations of G. aculeatus in the White Sea and the derived freshwater populations in its vicinity. To identify such markers, we compared the genome sequences of two samples of marine individuals with two samples of freshwater individuals . We defined “marker” single-nucleotide polymorphisms (SNPs) as polymorphic nucleotide sites where both marine samples contained a particular allele at frequencies above 80%, while both freshwater samples contained another allele at frequencies above 80% (“strong criterion”) or above 50% (“weak criterion”). For comparison, we also identified marker SNPs according to the strong criterion using only one marine-freshwater pair of populations (Nilma vs. Mashinnoye).G. aculeatus chromosomes , which, although identifiable both by the strong and the weak criteria, contains twenty times as many weak markers as strong markers. Furthermore, five DIs  could be identified only by the weak criterion, indicating that the frequency of freshwater-specific alleles in one or both freshwater populations is below 0.8.In the majority of the DIs, the number of weak markers is only slightly above the number of strong markers, indicating that the freshwater-specific alleles have usually reached the frequency of 80% in both lake populations. Two exceptions are DIs IV-5 and XI-1  population  was started from 20 marine  and 20 freshwater  individuals. The Quarry Malysh population  was started from 1 marine female and 1 freshwater male Schistocephalus solidus are dominant in residential individuals, and nematodes in anadromous individuals G. aculeatus populations to freshwater is likely to still be ongoing.In addition to lakes Mashinnoye and Lobaneshskoye, we also sampled hovskoye , all locIn all four young populations, frequencies of freshwater alleles at marker SNPs within DIs have been increasing rapidly . These iFreshwater alleles also increased in frequency at the marker SNPs located outside DIs. This was observed for all chromosomes in the two natural populations, except chromosomes XIV and XVI, each carrying only one marker SNP ; and forG. aculeatus is two years . Only one selection coefficient s was ascribed to a DI, on the basis of the average frequency of freshwater marker alleles in it. As the allele frequencies within a DI are non-independent, the strength of selection cannot be estimated with precision, and the estimates for individual DIs should be treated with caution; still, this analysis allows us to appreciate the selection in effect.We estimated the selection coefficient s were high: out of the 19 DIs, fifteen in Lake Ershovskoye, and twelve in Quarry Goluboy, had s>0.1  and Quarry Goluboy (s = 0.13) were rather similar. In particular, DI IV-1, carrying the EDA allele, experienced s = 0.19 in Lake Ershovskoye, and s = 0.09 in Quarry Goluboy, consistent with the previously reported data on dynamics of armor phenotype over an even shorter time period s = 0.255. This DI is centered on several nonsynonymous substitutions in gene HTR3A, a subunit of serotonin ligand-gated ion channel receptor with a wide spectrum of physiological functions. The close second was DI I-1 , a 470 kb long chromosomal inversion overlapping 27 genes, including the ATP1A1 gene which encodes a well-studied Na+/K+ transporting ATPase; the differences between the freshwater and the marine allele of this gene include 6 amino acid substitutions, and may be responsible for the differences in osmoregulation between the two forms s is hard to estimate). This region overlaps the upstream region of the gene OVGP1 (estrogen-dependent oviduct protein or mucin-9). This gene is involved in reproduction Overall, estimated values of ad s>0.1 . The selG. aculeatus from the White Sea basin from the ancestral marine population. The great majority of these markers cluster within 19 short genomic regions, or DIs , implying rapid adaptation of G. aculeatus to the lacustrine environment  is somewhat lower than that inferred previously from short-term experimental populations  in European lakes Labeobarbus (Cyprinidae: Teleostei) in lakes and rivers of Ethiopia Salvelinus  The very high evolutionary rate observed at several of the DIs during transition from marine to residential form could be attributed to pre-existing genomic regions, recruited from the standing variation of the marine population. Such “precast bricks” allow emerging freshwater population of sticklebacks to build rapidly a phenotype adapted to various challenges  which it faces in the new environment. Plausibly, this form of evolution may be widespread beyond the stickleback model. Rapid emergence of parallel well-differentiated autochthonous flocks in the genus Fish were collected in June–August 2011 by scoop-net or landing-net, anaesthetized and euthanized with a tricaine methane sulphonate solution (MS222), and then immediately fixed in 96% ethanol on site. Fish euthanasia was conducted under the supervision of the Ethics Committee for Animal Research of the Koltzov Institute of Developmental Biology Russian Academy of Sciences. Location of lakes and quarries, estimated age of population, and sample size are presented in AATGATACGGCGACCACCGAGAT and I-qPCR-2.1: CAAGCAGAAGACGGCATACGA) and diluted to final concentration of 9 pM. Diluted libraries were clustered on a paired-end flow cell using cBot instrument and sequenced using HiSeq2000 sequencer with TruSeq SBS Kit v3-HS (Illumina), read length 101 from each end. Sequences for each population are available at the NCBI Short Read Archive .Total genomic DNA was extracted from each individual using Wizard genomic DNA purification kit (Promega). Prior to library preparation, DNA samples of between 8 and 20  fish froG. aculeatus downloaded from the UCSC (http://genome.ucsc.edu/) using bwa aln program of the BWA (Burrows-Wheeler Alignment Tool) package (http://bio-bwa.sourceforge.net/). Output was then converted to SAM format using bwa sampe. Next, data were processed with picard (http://picard.sourceforge.net/) in order to remove duplicated reads. We identified SNPs in all populations using program mpileup of the samtools package (http://samtools.sourceforge.net/). For SNP calling, different depth cutoffs were used for different populations due to differences in read coverage among populations: >10 for Nilma, Malysh, Goluboy and residential fish from Ershovskoye, and >5 for Mashinnoye, Lobaneshskoye, Martsy, and anadromous fish from Ershovskoye. To minimize sequencing errors, positions with base qualities lower than 40 within a population were excluded from the analysis. As an alternative approach to SNP calling, we used UnifiedGenotyper program from GATK package (http://www.broadinstitute.org/gatk/) with the same coverage cutoffs. This led to a larger pool of marker SNPs, but similar clustering patterns: all the DIs identified with the program mpileup were observed, as well as several new clusters Positions of genes were derived from Ensembl database release 72 . Each set consisted of three primers: two allele-specific, and one anchor primer. Additionally, we used previously published primers Stn382 As a proxy for the time of formation of natural residential populations, we use the time of desalination. Before complete desalination, a lake is meromictic, and contains two water layers – a higher freshwater layer, and a lower saltwater layer, forming a halocline. This halocline prevents proper oxygenation; as a result, the lake becomes anoxic every winter, causing extirpation of residential populations s. The ages of Goluboy and Malysh populations are known to be 34 years (17 generations). The Ershovskoye freshwater population is known to be 34 years old or younger s.For each DI, we calculated the average frequency of a freshwater allele over all marker SNPs . We estip0∼0.1. Selection coefficient s was calculated from the per generation change in allele frequency under the assumption that this change is driven by selection alone  Natural populations. The horizontal axis shows the approximate ages of populations, ranging (left to right) from two marine populations (∼0 years) to recent (∼34 and ∼250 years) and older lake populations (∼600 and ∼700 years). Whiskers, standard deviation. (B) Experimental populations. For each of the DIs of marker SNPs, the assumed initial 50% frequency of freshwater alleles (black line) and their current frequencies are shown for two experimental populations: quarries Malysh (left) and Goluboy (right), each started in 1978. Dashes, boxes and whiskers correspond to the median, standard deviation, and 5th and 95th percentiles, respectively.Mean frequencies of freshwater alleles at marker SNPs obtained under the strong criterion and located outside identified DIs, at freshwater populations of different ages. ((EPS)Click here for additional data file.Figure S2Mean frequencies of freshwater alleles at marker SNPs obtained under the strong criterion and located outside identified DIs depending on the distance from the DI. The horizontal axis shows, in logarithmic scale, the distance of the marker SNP from the nearest DI in bp and the y axis shows the frequency of freshwater allele in marker SNP.(EPS)Click here for additional data file.Figure S3th and 95th percentiles, respectively. Red, marker SNPs (under the strong criterion) located within identified DIs; purple, marker SNPs (under the strong criterion) located outside DIs.Comparison of mean frequencies of freshwater alleles at marker SNPs located within and outside of DIs defined by only one marine-freshwater population pair, at freshwater populations of different ages. Each two boxes correspond to the four young freshwater populations . Dashes, boxes and whiskers correspond to the median, standard deviation, and 5(TIF)Click here for additional data file.Table S1Genes overlapping the 19 DIs of marker SNPs, their functions, and types of substitutions associated with them.(XLSX)Click here for additional data file.Table S2Average frequencies of freshwater alleles in each population for each of the 19 DIs found in our study, mean ± st. dev. Asterisks denote the DIs identified under the weak criteria.(PDF)Click here for additional data file.Table S3List of allele-specific primers used for DIs validation.(PDF)Click here for additional data file.Table S4Comparison of freshwater allele frequencies based on allele-specific PCR and on calculation of marker SNP frequencies.(PDF)Click here for additional data file.Table S5Comparison of the two programs mpileup and GATK for identification marker SNPs. Bold font denotes DIs identified under the strong criteria, non-bold font denotes DIs identified under the weak criteria.(PDF)Click here for additional data file.Table S6Average frequencies of freshwater alleles in each population for each of the 35 DIs defined with the GATK in our study, mean ± st. dev.(PDF)Click here for additional data file.Table S7Mean frequencies of freshwater alleles in each population for DIs found in our study without merging procedure, mean ± st. dev.(PDF)Click here for additional data file.