A total of 823 alleles were detected. The observed allele numbers (Na) per locus, Nei's gene diversity (He) and the polymorphism information content (PIC) ranged from 2 to 9, 0.061 to 0.869 and 0.060 to 0.856, with an average of 5.344, 0.624 and 0.586, respectively. Five SSR loci, RM1350, RM1369, RM257, RM336 and RM1374, provided the highest PIC values and are potential for exploring the genetic diversity of rice cultivars in Northeast Asia. Molecular variance analysis showed that a significant difference existed both among groups (91.6%) and within each group (8.4%). The low genetic variation within each group indicated that the gene pool is narrow and alien genetic variation should be introduced into the rice breeding program in Northeast Asia. Based on the He and PIC values, the nine groups were ranked in a descending order: Heilongjiang landraces, Jilin landraces, Japanese improved varieties, Heilongjiang improved varieties, Russian Far East district of the Russian Federation improved varieties, Liaoning improved varieties, Jilin improved varieties, Korean improved varieties and Democratic People's Republic of Korea improved varieties. The nine groups were further divided into three subgroups and the 288 varieties into five clusters. This study provided information for parent selection in order to broaden the gene pool of the japonica rice germplasm in Northeast Asia.Genetic diversity and the relationship among nine  Oryza sativa L.) is one of the most important crops in the world. Northeast Asia includes the areas of Northeast China , Japan, Korea, Democratic People's Republic of Korea and Mongolia, along with the area of the Russian Far East district of the Russian Federation.[Rice , with the advantages of allele specificity and co-dominance, have been extensively used to assess genetic diversity and the relationships of subspecies among rice cultivars.japonica rice cultivars and landraces from different geographical regions in Northeast Asia have been reported until now. In this study, 154 SSR markers on 12 rice chromosomes were randomly selected to investigate the genetic diversity and the genetic relationships among the 288 accessions (included landraces and improved varieties) in different geographical areas . This research will acknowledge the present situation, characteristics, developing trend and improvement emphasis of the germplasm resources in Northeast Asia, and provide references for protecting the genetic diversity, broadening the genetic pool and the effective utilization of germplasm resources.Few research studies on the genetic diversity and genetic relationships of different In this study, 288 rice accessions (landraces and improved varieties) were collected from the Crop Science Research Institute, Chinese Academy of Agricultural Sciences, Liaoning Academy of Agricultural Sciences, Heilongjiang Academy of Agricultural Sciences and Northeast Agricultural University. Based on their geographical distribution and variety types, these accessions were divided into nine groups: Heilongjiang landraces (HL), Jilin landraces (JL), Heilongjiang improved varieties (HIV), Jilin improved varieties (JLIV), Liaoning improved varieties (LIV), Japanese improved varieties (JIV), Korean improved varieties (KIV), Democratic People's Republic of Korea improved varieties (DPRKIV) and the Russian Far East district of the Russian Federation improved varieties (RFERIV). Each group consisted of 53, 51, 25, 26, 29, 60, 28, 9 and 7 accessions, respectively .Total genomic DNA was extracted and purified from the young leaves by a modified CTAB method described by Edwards et al. . The DNAhttp//www.gramene.org, 600 primer pairs on rice chromosomes were designed and synthesized by the Sangon Biotech Co. Ltd. (Shanghai). Twelve genotypes from the nine groups  were selected for testing the polymorphism of the primers. Finally, 154 out of 600 primers with higher amplification rate and distinct polymorphism were selected for the genetic diversity study .Based on the SSR-contained sequence information at 2 (25 mmol/L), 0.3 μL of dNTP mixtures (10 mmol/L), 2 μL of 10 × PCR buffer, 2 μL of SSR primer (2 μmol/L), 0.2 μL of Taq polymerase (10 U/μL) and 12 μL of ddH2O. The amplification profiles were 94 ºC for 2 min, followed by 35 cycles of 94 ºC for 30 s, 47 ºC for 30 s, 72 ºC for 30 s, and then extended at 72 ºC for 5 min.PCR reaction was conducted in 20 μL volumes mixed with 2 μL of genomic DNA (25 ng/μL), 1.5 μL of MgClPCR products were mixed with loading buffer  and denatured at 94 ºC for 5 min, and then put on ice for 5 min. The denatured PCR products were separated in a 6% denaturing polyacrylamide gel and directly detected by silver straining.Clearly detectable polymorphic bands were scored for the analysis. Each amplified polymorphic band was assigned as one allele for each SSR locus. Band presence or absence was scored as 1 or 0 within each accession. POPGENE 1.32  was usedI) varied from 0.042 to 2.070, with an average of 1.144. Five loci, RM1350, RM1369, RM257, RM336 and RM1374, on chromosomes 3, 6, 9, 7 and 10 ranked the top five on He, PIC and Na, indicating that these primers have potential to explore the genetic diversity for other rice germplasm in Northeast Asia. RM207, RM264, RM1306, RM501 and RM1379 showed the sixth to tenth largest PIC value, and RM207, RM264, RM1306, RM501 and RM1379 presented the sixth to the tenth largest He, while RM1306, RM1353, RM1347, RM207 and RM264 had the sixth to the tenth largest Na.A total of 823 allelic variations were detected using the 154 SSR primers . Na ranged from 2  to 9 , with an average of 5.34. He ranged from 0.061 to 0.869, with an average of 0.624. PIC ranged from 0.060 to 0.856, with an average of 0.586. Shannon's information index  in this study were smaller than those in the aforementioned studies, indicating that the genetic diversity of these selected rice accessions in Northeast Asia was relatively low.It is essential to understand genetic diversity for the effective conservation and utilization of rice germplasm. Previous studies on the genetic diversity of natural rice populations of improved varieties and landraces have been reported. Zhao et al.  used 29 P < 0.01). The variation within each group accounted for 8.40% and that among the groups for 91.60%. Therefore, it is necessary to do further analysis of the genetic diversity among different groups.Molecular variance analysis showed  that a sBased on the He and PIC value , the ninindica rice cultivars was higher than that of japonica cultivars, although the number of accessions and Na of indica accessions were less than those of japonica cultivars. Sun et al. [Na, He and PIC are widely used to quantify the level of the genetic diversity of plant species. However, the explanation of these parameters is conflicting. For example, Xu et al.  found thn et al.  also repPrevious studies have compared the genetic diversity among improved varieties, landraces and common wild rice from different countries and regions. The results of Shu et al.  indicateCompared with the genetic identity and genetic distance  among diA comparison was made on the Fst and Nm in order to elucidate the differences of the genetic distance among the nine groups . The resSimilarly, the results in The following facts can be concluded according to the Nm of one group vs. each of the other eight groups : the larDifferent Nm values reflect the difference in genetic information exchange among different groups in Northeast Asia: the HL and JL groups are greatly influenced by each other; RFERIV is greatly influenced by HL; HIV, JLIV, LIV, KIV and DPRKIV bear the largest influence by JIV, but the least by RFERIV; JL and JIV bear the least influence by RFERIV; HL and RFERIV bear the least influence by DPRKIV.japonica rice cultivars from Yunnan, China and Korea, using SSR markers. The results showed that there was a significant difference in genetic variation between Yunnan and Korea rice cultivars. They suggested that the Korean varieties can be used to expand ‘Yunnan varieties’ genetic base and improve rice quality. Based on the genetic relationship of the nine groups, it is suggested that (1) HL and JL can be used more frequently to improve HIV, JLIV and LIV; (2) unfavourable influence caused by similar genetic background must be fully considered when using JIV to improve HIV, JLIV and LIV, especially for HIV; (3) RFERIV can be used to broaden the genetic base of rice cultivars from other countries or regions in Northeast Asia.The genetic background of the parents must be fully considered in order to expand their gene pool in breeding programmes. Zhao et al.  studied Based on genetic identity, the nine groups can be divided into three subgroups with a threshold of 0.8212. The first subgroup included HL and JL; the second subgroup included JLIV, HIV, LIV, JIV, DPRKIV and KIV; and the third subgroup included only RFERIV. In the second subgroup, HIV, JLIV and LIV were closely related to JIV, especially for LIV .Figure Figure 2. japonica rice accessions based on Nei's (9) coefficient of genetic similarity, using the UPGMA and SHAN routine in the NTSYS 2.1 program. Panels 1 to 4 display 72 accessions, respectively. The vertical dashed line indicates the coefficient of genetic similarity value, 0.6925, dividing the 288 varieties into five clusters.Dendrogram of 288 Based on the coefficient of genetic similarity, the 288 varieties can be divided into five clusters with a threshold of 0.6925 , consistThe first, second and fifth clusters contained 39 HIV and 45 JIV, accounted for 76.5% of HIV and 75.0% of JIV, respectively. The first and third clusters included 25 JLIV and 49 JIV, accounting for 88.5% of JLIV and 81.7% of JIV. The first, third and fifth clusters included 28 LIV and 57 JIV, accounting for 96.6% of LIV and 95.0% of JIV. This agreed with the above results that JIV had the most frequent genetic information exchange with HIV, JLIV and LIV, but it cannot explain that JIV had significant influence on HIV.japonica rice cultivars in Northeast Asia was low, compared to previous studies. Molecular variance analysis showed that a significant difference existed both among and within groups. The nine groups were ranked in a descending order as: HL, JL, JIV, HIV, RFERIV, LIV, JIV, KIV, DPRKIV. The genetic relationships among different groups showed that genetic information exchange happens frequently.In this study, the values of Na, He and PIC showed that the level of the genetic diversity of


Capreolus sp., is one of the most widespread meso-mammals of Palearctic distribution, and includes two species, the European roe deer, C. capreolus inhabiting mainly Europe, and the Siberian roe deer, C. pygargus, distributed throughout continental Asia. Although there are a number of genetic studies concerning European roe deer, the Siberian roe deer has been studied less, and none of these studies use microsatellite markers. Natural processes have led to genetic structuring in wild populations. To understand how these factors have affected genetic structure and connectivity of Siberian roe deer, we investigated variability at 12 microsatellite loci for Siberian roe deer from ten localities in Asia.The roe deer, HE = 0.522 to 0.628) were found in all populations except in Jeju Island, South Korea, where the diversity was lowest (HE = 0.386). Western populations showed relatively low genetic diversity and higher degrees of genetic differentiation compared with eastern populations (mean Ar = 3.54 (east), 2.81 (west), mean FST = 0.122). Bayesian-based clustering analysis revealed the existence of three genetically distinct groups (clusters) for Siberian roe deer, which comprise of the Southeastern group , Northwestern group  and Jeju Island population. Genetic analyses including AMOVA (FRT = 0.200), Barrier and PCA also supported genetic differentiation among regions separated primarily by major mountain ridges, suggesting that mountains played a role in the genetic differentiation of Siberian roe deer. On the other hand, genetic evidence also suggests an ongoing migration that may facilitate genetic admixture at the border areas between two groups.Moderate levels of genetic diversity  contains supplementary material, which is available to authorized users. Capreolus Gray, 1821) is one of the most widespread meso-mammals in Cervidae and includes two species, the smaller European roe deer  and the larger Siberian roe deer . The two species of deer are distinguished mainly by differences in morphology and karyotype. The Siberian roe deer is distributed in the Palaearctic throughout continental Asia  size standard and analyzed GeneMapper version 3.7 .A total of 12 microsatellite loci were used and tested for genotyping and genetic analysis of HO) and expected heterozygosity (HE) in Hardy-Weinberg equilibrium were estimated for each locus using the Microsatellite Toolkit, version 3.0  was estimated for given K between 1 and 10 with ten independent runs set by 1,000,000 Markov chain Monte Carlo (MCMC) iterations followed by burn-in period of 100,000 iterations. The “real” value of K within the dataset was estimated from the Ln Pr (X/K) according to the method of Evanno et al. [K were used to calculate ∆K. An ad hoc statistic test in this parameter was used in simulations to identify the true number of genetic clusters, which offers accurate means to selecting K instead of choosing a K with the highest log probability that could lead to overestimated K [FST / (1-FST)) on geographic distance (Ln-Km) between pairs of populations. The correlations for two variables and probability were carried out using the Mantel’s test in GENALEX version 6.1 and significance was determined based on 999 permutations [Existence of population genetic structuring was evaluated using the model-based Bayesian clustering method in the program STRUCTURE version 2.3.4 , which io et al. , in whicimated K . Existenimated K  was obtautations .FST genetic matrix. We conducted the analysis using FST for each of the eleven microsatellite loci; exclude IDVGA29 due to low polymorphism, to make sure that the barriers were not verified with strong differentiation at only few loci. Each locus indicates how many support a given barrier and putative genetic boundaries were identified across the geographical landscapes. Pairwise FST, RST and pRST  were calculated per each population and locus to estimate the main causes of population differentiation in Siberian roe deer using program SPAGeDi [RST was compared against the distribution of pRST values.We also applied Monmonier’s maximum difference algorithm to highlight geographical features with obvious genetic discontinuity between populations using the program BARRIER version 2.2 . The dat SPAGeDi , 72. RSTHe) relative to heterozygosity expected at drift-mutation equilibrium (Heq) by Wilcoxon sign-rank tests  [He becomes larger than Heq, because Heq reflects allele number and sample size. We used a two-phase mutation model (TPM) [l-shaped distribution under the mutation-drift equilibrium, expecting distorted distribution under the recent population bottleneck [Three different approaches were used to detect molecular evidence of historical population bottleneck. First, we tested for deviations of expected heterozygosity ( = 0.01)  using th = 0.01) , 75. Dur = 0.01) . Thus thel (TPM)  using a ttleneck .M value of Garza & Williamson’s [M is the mean ratio of the total number of alleles to the range of allele size. This test is useful for detecting a bottleneck further in the past (>100 generations). Meta-analysis for natural populations revealed that historically reduced or founded populations had M-ratio < 0.68, but stable populations showed M > 0.82.Lastly, iamson’s  was calciamson’s . M is th


Senecio (ragwort) species that form a natural hybrid zone with respect to elevation on Mount Etna, Italy, using a quantitative trait locus (QTL) mapping approach. We found signals of divergent selection with increased genetic differentiation close to QTLs. Extensive interactions between QTLs and traits suggested a QTL architecture that is resistant to hybridization.Hybrid zones that occur across environmental gradients provide excellent opportunities for studying the maintenance of divergent adaptations in the presence of gene flow. They also provide insight into the biodiversity implications of future species contact and hybridization in a changing world. We studied divergent morphology between two  Senecio aethnensis and S. chrysanthemifolius on Mount Etna, Sicily, provides a well-studied example of species divergence in response to conditions at different elevations, despite hybridization and gene flow. Here, we investigate the genetic architecture of divergence between these two species using a combination of quantitative trait locus (QTL) mapping and genetic differentiation measures based on genetic marker analysis. A QTL architecture characterized by physical QTL clustering, epistatic interactions between QTLs, and pleiotropy was identified, and is consistent with the presence of divergent QTL complexes resistant to gene flow. A role for divergent selection between species was indicated by significant negative associations between levels of interspecific genetic differentiation at mapped marker gene loci and map distance from QTLs and hybrid incompatibility loci. Within-species selection contributing to interspecific differentiation was evidenced by negative associations between interspecific genetic differentiation and genetic diversity within species. These results show that the two Senecio species, while subject to gene flow, maintain divergent genomic regions consistent with local selection within species and selection against hybrids between species which, in turn, contribute to the maintenance of their distinct phenotypic differences.Knowledge of the genetic basis of phenotypic divergence between species and how such divergence is caused and maintained is crucial to an understanding of speciation and the generation of biodiversity. The hybrid zone between  Speciation commonly proceeds through genetic divergence between populations that ultimately become reproductively isolated from each other due to intrinsic and/or extrinsic breeding barriers . PhenotyQuantitative trait locus (QTL) analysis is a powerful way of analysing the genetic basis of divergent traits between species , especiaComplementary insights into the relationship between QTL architecture and divergent selection can be obtained by investigating genetic diversity and differentiation among mapped molecular marker loci . Heterogn = 20), short-lived perennial, self-incompatible, herbaceous species of Senecio (Asteraceae), S. aethnensis and S. chrysanthemifolius, which grow at elevations above 2000 m and below 1000 m, respectively, on Mount Etna, Sicily. Whereas S. aethnensis produces large flower heads (capitula) and fruits, and entire (spathulate) leaves, S. chrysanthemifolius has smaller flower heads and fruits, and highly dissected (pinnatisect) leaves. The two species hybridize and form a hybrid zone at intermediate elevations on Mount Etna (S. chrysanthemifolius flowering 6 weeks earlier (April–June) than S. aethnensis (July–September) . A previous analysis of the hybrid zone showed that leaf shape, flower head structure and fruit structure exhibited steeper clines and/or shifts in cline position relative to a molecular genetic cline  were distributed across multiple linkage groups. This frequency of TRD was similar to that found in other crossing studies involving distinct ‘species’  of a reciprocal cross between two cross-compatible F1 progeny derived from a reciprocal cross between S. aethnensis (A) and S. chrysanthemifolius (C) was produced as described in S. aethnensis and three of S. chrysanthemifolius representing the elevational extremes of each species’ range and also the source locations of the mapping family parents (NIC1 and PIC1) Supporting Information—Table S1][see . Significant outlier loci were distributed across most linkage groups of the genetic map . However, significant negative associations between measures of species differentiation for marker loci and the genetic map distance from the nearest QTL peak were present . A breakdown in the complex genetic architecture of these traits following hybridization would be expected to reduce the fitness of most hybrid offspring and therefore contribute to introgression barriers between the two Natural Environment Research Council (NERC) Grant NE/D014166/1 to R.J.A. as principal investigator. A.C.B. was supported during part of the writing of this paper by funding from European Commission 7th Framework Programme Capacities Work Programme (FP7-REGPOT 2010-1), Grant No.264125EcoGenes.The research was funded by a R.J.A., A.C.B. and S.J.H. designed the research. A.C.B. performed the experiments and analysis. A.C.B. wrote the first draft and A.C.B., R.J.A. and S.J.H. contributed to revisions.None declared.The following additional information is available in the online version of this article —Table S1. Information on wild sampled populations of S. aethnensis and S. chrysanthemifolius.Table S2. Summary quantitative trait results for S. aethnensis, S. chrysanthemifolius and a reciprocal F2S. aethnensis and S. chrysanthemifolius mapping family.Table S3. Paired-trait correlations in (a) F2AC progeny, (b) Senecio aethnensis, (c) S. chrysanthemifolius and (d) all three samples.Table S4. Comparison of summary QTL results for a CIM and MIM analysis of a reciprocal F2S. aethnensis and S. chrysanthemifolius mapping family.Table S5. Summary QTLs results from a MtCIM analysis compared with single trait QTL analyses of a reciprocal F2S. aethnensis and S. chrysanthemifolius mapping family.Table S6. (a) ‘Sampling without replacement’ test results for paired-trait QTL coincidence, (b) permutation tests of overall paired-trait QTL coincidence using different QTL and TRDL datasets and genetic map interval sizes.Table S7. Summary population genetic statistics for AFLPs and other dominantly scored molecular genetic markers from S. aethnensis and S. chrysanthemifolius samples.Table S8. Summary population genetic statistics for codominantly scored molecular genetic markers from S. aethnensis and S. chrysanthemifolius samples.Table S9. Expressed sequence tag loci showing evidence for divergent or convergent selection between S. aethnensis and S. chrysanthemifolius.Figure S1. Boxplots summarizing quantitative trait results for S. aethnensis, S. chrysanthemifolius and a reciprocal F2 mapping family that were included in the QTL analysis. Trait numbers in titles correspond to the trait numbering system of Table Figure S2. Genetic map of a reciprocal F2S. aethnensis and S. chrysanthemifolius mapping family showing quantitative trait loci identified by CIM and marker loci that were significantly divergent or convergent between species. Map distances in Kosambi centiMorgans are shown in the scale bar to the left of linkage groups. Linkage groups are represented by vertical bars with mapped locus positions indicated with horizontal lines. Weakly linked linkage groups (<4 LOD or >20 cM) that probably belong to the same chromosome are aligned vertically. Grey shading on linkage groups indicates regions exhibiting significant TRDLs. Locus names are listed to the left of linkage groups and mapped QTLs are listed to the right. ‘c’ or ‘d’ listed to the left of locus names indicates if that locus was identified as significantly convergent or divergent based on genetic differentiation analysis across sample populations, while greater than symbol to the left of locus names indicates if the locus was included in QTL analysis. Quantitative trait loci were identified by CIM with significance determined if the LOD score exceeded the 0.95 quantile of 1000 data permutations. Quantitative trait loci 2-LOD interval ranges are indicated with vertical lines with a bold horizontal line indicating the highest LOD score position. Quantitative trait locus summary information includes trait names, ‘a’ or ‘d’ each followed by ‘+’ or ‘−’ indicating additive or dominance effects and their direction of effect supporting or opposing the observed species difference, respectively, and the PVE.Supplementary information. Additional text describing the genetic map, TRD analysis, composite interval mapping, QTL sign tests, genetic diversity analyses and discussion of the functions of significantly divergent and convergent loci.


Rhacophorus omeimontis) were amplified in this study.Pleistocene climatic oscillations and historical geological events may both influence current patterns of genetic variation, and the species in southern China that faced unique climatic and topographical events have complex evolutionary histories. However, the relative contributions of climatic oscillations and geographical events to the genetic variation of these species remain undetermined. To investigate patterns of genetic variation and to test the hypotheses about the factors that shaped the distribution of this genetic variation in species of southern China, mitochondrial genes (cytochrome b and NADH dehydrogenase subunit 2) and nine microsatellite loci of the Omei tree frog  haplotypes and the Bayesian genetic clustering analysis based on microsatellite data both revealed that all populations were divided into three lineages . The two most recent splitting events among the lineages coincided with recent geological events  and the Pleistocene glaciations. Significant expansion signals were not detected in mismatch analyses or neutrality tests. And the effective population size of each lineage was stable during the Pleistocene.The genetic diversity in the populations of R. omeimontis lineages. Each diverged lineages survived in situ with limited gene exchanges, and the stable demographics of lineages indicate that the Pleistocene climatic oscillations were inconsequential for this species. The analysis of genetic variation in populations of R. omeimontis contributes to the understanding of the effects of changes in climate and of geographical events on the dynamic development of contemporary patterns of genetic variation in the species of southern China.Based on the results of this study, complex geological events (the recent dramatic uplift of the QTP and the subsequent movements of the YGP) and the Pleistocene glaciations were apparent drivers of the rapid divergence of the The online version of this article (doi:10.1186/s12862-015-0572-1) contains supplementary material, which is available to authorized users. Recently, two primary drivers, namely, Pleistocene climatic oscillations and geological events, have been used to explain the contemporary genetic variation within Northern Hemisphere species. In the Pleistocene, climatic cycles generally caused populations to diverge and produce new lineages, additionally affecting the demographics of the populations. In concrete terms, the species were restricted to refugia during glacial periods and subsequently expanded into new habitats during interglacial periods. As a result, the geographic distributions of populations and the genomes of species were markedly affected , 2. HoweThe indigenous species of southern China are diverse and have complex evolutionary histories. The unique geographic and climatic features in this area most likely shaped the distributions of genetic variation for these species , 7, 9. FParus major)  and a final extension at 72 °C for 10 min. After a distinct band of the expected fragment size was confirmed, the PCR products were sent to a commercial sequencing company  for sequencing in both directions using an ABI 3730XL DNA Analyzer . The identical PCR primers were used for the sequencing.The total DNA was extracted from the toe tips using a TIANamp Genomic DNA kit  according to the manufacturer's protocol. We designed two pairs of primers to amplify the fragments of mitochondrial cytb  and ND2 . The PCR was conducted in a 30 μl reaction volume that contained 1 μl of genomic DNA, 15 μl of Premix R. omeimontis loci based on the polymorphic evaluations of each locus  and the ΔK statistic for a given K [The genetic relationships of these populations based on the microsatellite data were deduced using a Bayesian clustering method implemented in STRUCTURE 2.3.1 . The adm given K .To estimate the divergence time between lineages, a suitable molecular clock was established using the relative rate tests in PHYLTST . The besRag) and the degree of fit between the observed and simulated data  [p-values for the SSD implies a significant expansion signal for a population. Second, the neutrality tests including Tajima's D [Fs [D and Fu's Fs imply sudden demographic growth. These two analyses were implemented in Arlequin 3.1 with 10,000 bootstrap replicates. Third, Bayesian skyline plots (BSPs) were generated for all combined sequences of each lineage excluding the out-groups in BEAST 1.5.4, which is a method that reliably reconstructs changes in effective population size across evolutionary time scales from sequence data [We applied three common methods to assess the demographic histories of the three lineages that were inferred from the phylogenetic trees. First, a mismatch distribution was simulated for each lineage to test the hypothesis of recent population growth. A goodness-of-fit test was used to determine the smoothness of the observed mismatch distribution  . Generaljima's D  and Fu’s's D [Fs  were connce data . The suih = 0.95, π = 1.91 %). Among the seven populations, the haplotype and nucleotide diversity of the BX, LS and PB populations were lower than those in the other four populations  ranged between 6.71 and 12.14, and the ranges of the Ho, He and AR value were 0.57–0.79, 0.69–0.83 and 6.45–10.37, respectively.For the mtDNA sequencing, a combined mtDNA sequence that included 672 bp cytb and 988 bp ND2 sequences was obtained from each of the 196 individuals. No insertions, deletions or stop codons were found in the entire sequences. Thirty-nine haplotypes were identified and deposited in GenBank (Accession Nos. KP895611-KP895688). For the entire population, the haplotype and nucleotide diversity were relatively high , indicating detectable genetic differentiations (Table Фst (Rst) matrices were detected  were shared by two populations (EMS and HY), with the remaining haplotypes (34 of 39) restricted to one population   Fig. . These g51) Fig. . Because51) Fig. . This raR. omeimontis was estimated at 13.37 Ma [95 % Highest Posterior Density (HPD), 10.14–17.55 Ma; Fig. B. buergeri and R. omeimontis was estimated to be 42.59 Ma , which is close to the previous estimation  [Because the hypothesis of clock-like evolution was not rejected at the 5 % level in the relative-rate tests, we set a strict molecular clock model for the analyses. As in the phylogenetic tree analyses, the substitution model was set to GTR + I + G. The time since the most recent common ancestor (tmrca) of the entire in-group of D and Fu's Fs were negative, but not significant  is restricted to subtropical forests in southwest China [R. omeimontis, we found that the entire population was highly genetically diverse. The high level of mtDNA haplotype diversity for R. omeimontis  is comparable with that of other amphibians in China, such as Quasipaa boulengeri  [Buergeria robusta  [R. omeimontis  than that in other amphibians such as the moor frog  [Rana sylvatica, Ho = 0.60 and He = 0.64 for the highest polymorphic locus) [The Omei tree frog (st China . In this 1.14 %) . However = 0.38)  and the c locus) . These hF-statistics were calculated to evaluate the genetic differentiation between populations based on the mtDNA sequences and the microsatellite markers. A significant deviation from zero was detected for both Фst and Rst in all population pairs except for the pairwise Rst between populations obtained from Sichuan Province, which indicated detectable genetic differentiation between R. omeimontis populations. The incongruence of differentiation pattern between the mtDNA and microsatellite data could be attributed to the different types of inheritance and rates of evolution for the two types of markers and to the unique dispersal pattern of R. omeimontis. The polyandry mating system of R. omeimontis and the observation that the return rate of R. omeimontis females is much higher than males [Pairwise an males , 60 suggan males .R. omeimontis were divided into three genetic lineages  . Each phase of these movements possibly generated fragmented habitats, which could facilitate local divergence because of limited gene flow among populations for a species with such a low mobility. Similar patterns are also reported in Leiolepos reevesii [Leptobrachium ailaonicum [Rhynchocypris oxycephalus [Babina pleuraden [Based on the broadly consistent results inferred from the mtDNA markers and the microsatellite loci, the seven populations of HG) Fig.  with deeina Fig. . Specifiina Fig. . Moreoveina Fig.  and the ina Fig. . These rreevesii , Leptobrlaonicum , Rhynchocephalus  and Babileuraden . Hence, R. omeimontis were estimated to have occurred during the Pleistocene  which c0.012 Ma . The repR. omeimontis apparently survived in situ to shape the current geographically structured genetic pattern. The gene flow was most likely limited among lineages based on the low genetic similarity and the deep genetic differentiation revealed by the genetic structure analyses. When combined with the low migration capability of this species, the presumption is geographic barriers hindered gene flow and thereby resulted in large genetic differentiation and local evolution. Furthermore, each lineage remained stable during the local evolution. As shown by the demographic history reconstruction, the effective population sizes of all lineages remained constant through evolutionary time. Both the mismatch analyses and neutrality tests did not detect significant signals of rapid expansion, which are the typical responses to climatic oscillations [Quasipaa boulengeri, the long-term demographics of lineages during the Pleistocene were also stable in the southern China [in situ in southern China [After diverging from one another, the lineages of llations , 18–20. rn China . Additiorn China , 66. Thurn China , 67, 68.R. omeimontis and evaluated the relative roles of Pleistocene climatic oscillations and geological events in shaping the current patterns of genetic differentiation of the species in southern China. The levels of genetic diversity were high, and the populations of R. omeimontis were clearly differentiated. The phylogenetic reconstructions and the estimation of divergence time indicated that complex geological events (including the recent uplift of the QTP and the subsequent movements of the YGP) coupled with the Pleistocene glaciations drove the genetic divergence of the lineages of R. omeimontis. Following the divergence, each lineage was restricted in situ and diverged independently without distinct population expansion. Therefore, the Pleistocene climatic oscillations did not appear to influence the demographics of this species, and this phenomenon might be unique for species in general in southern China because of the complex topography. The results of this study will increase our comprehensive understanding of the relative contributions of geographical events and the Pleistocene climatic changes to the current genetic patterns of species in southern China.In this paper, we investigated the genetic structure of the populations of The sequence dataset generated herein is available in the GenBank repository with Accession numbers KP895611-KP895688.


Parmelina tiliacea has been analyzed through its geographical range, including samples from Macaronesia (Canary Islands), the Mediterranean, and Eurosiberia. DNA sequences from the nuclear ribosomal internal transcribed spacer, the mitochondrial large subunit ribosomal RNA gene, and the translation elongation factor 1-α were used as molecular markers. The haplotypes of the three markers and the molecular variance analyses of multilocus haplotypes showed the highest diversity in the Canary Islands, while restricted haplotypes occurred at high frequencies in Mediterranean coastal samples. The multilocus haplotypes formed three unevenly distributed clusters (clusters 1-3). In the Canary Islands all the haplotypes were present in a similar proportion, while the coastal Mediterranean sites had almost exclusively haplotypes of cluster 3; cluster 2 predominated in inland Mediterranean sites; and cluster 1 was more abundant in central and northern Europe (Eurosiberian area). The distribution of clusters is partially explained by climatic factors, and its interaction with local spatial structure, but much of the variation remains unexplained. The high frequency of individuals in the Canary Islands with haplotypes shared with other areas suggests that could be a refugium of genetic diversity, and the high frequency of individuals of the Mediterranean coastal sites with restricted haplotypes indicates that gene flow to contiguous areas may be restricted. This is significant for the selection of areas for conservation purposes, as those with most genetic variation may reflect historical factors and biological properties of the species.The genetic diversity and population structure of the foliose lichenized fungus  Many lichenized fungi have long been considered to have exceptionally wide areas of distribution. However, detailed molecular studies are increasingly showing that, in some cases, different lineages are hidden under one species name, allowing recognition of more than one species . FurtherParmelina tiliacea is a foliose lichen occurring throughout Europe, North Africa, and the Canary Islands, and extending eastward to Asia. We decided to investigate the genetic structure within this species to ascertain if it showed any genetic variation. We hypothesized that this could be the case as the lichen occurs from sea level to 1500 m (and even to 3200 m in India), and under different ecological conditions. It is found on nutrient-rich bark of broad-leaved deciduous or coniferous trees, acid or siliceous rocks, as well as roofing tiles and memorials in well-lit situations. In Europe the species is particularly common around the Mediterranean, especially in central Iberia, extending through central into northern Europe, becoming rare in northern and western Scotland and with scattered localities in Ireland and southern Scandinavia. This species has asexual propagules , and also reproduces sexually by ascospores, although the spore-producing apothecia are often scarce or absent.P. cryptotiliacea [We used samples from a wide geographic range and selected three molecular markers of the fungal partner to build haplotype networks, analyze the population structure through clustering models and molecular variance analyses, and explore the geographic and climatic distribution of the global genetic variation of this widespread species. We did not use material previously recognized as a separate cryptic species, tiliacea , in thisP. tiliacea consists of a single widely distributed lineage or shows any population subdivision; (2) determine if any genetic differentiation found corresponds with different geographical areas or other discontinuities that might be barriers to dispersal; and (3) ascertain if the distribution of any group of panmictic individuals correlates with, for example, macroclimatic variables or spatial trends. The analyses detected differences in haplotype composition between the regions, and allowed recognition of three clusters of populations in P. tiliacea. These clusters were partly explained by geography, local spatial structure, and climate variables. We also discuss the possible influence of past climate and vegetation.The study aimed to: (1) establish whether Parmelina tiliacea from 62 localities representing different altitudes and habitats. No specific permissions were required for collecting at these locations because this species is very common and neither endangered nor protected. Sampling sites were from three broad geographical areas: Macaronesian (MA), Mediterranean s.l. (sensu [http://www.worldclim.org/) with Diva-GIS 7.5 [We studied 364 samples of . (sensu ) and Eur. (sensu . Sites f-GIS 7.5 .EF1-α) was amplified using primers EFA-F and EFA-R [DNA extraction and amplification of the nuclear ribosomal internal transcribed spacer (ITS) and partial large subunit mitochondrial ribosomal rRNA gene (mtLSU) followed the procedures described in  with somnd EFA-R , with sind EFA-R . Three snd EFA-R , and manhaplonet function of the pegas package [Measures of genetic diversity were calculated for each sampling site and for the four geographic areas: the number of polymorphic sites (s), number of haplotypes (h), haplotype diversity (Hd) and nucleotide diversity (π) were estimated with DnaSP v5  for each package , availab package . The disEF1-α loci could be combined in a joint analysis, we used the congruence among distance matrices (CADM) test [CADM.global implemented in the R library ape [W [To assess whether the ITS, mtLSU and DM) test  based onDM) test  with funrary ape  which cay ape [W –30. The y ape [W , and they ape [W .R, Rogers distance) and geographic distances of the sampling sites with the mantel.randtest function of the adegenet 1.4–1 package [kde2d) and the results displayed with a customized color palette using image in MASS package [A matrix with the multilocus haplotypes (MLHs) of the three combined loci was built for each sampling site and AMOVA analyses were run with Arlequin 3.5.1.3  to estim package –35. Corr package .5 iterations after a burn-in period of 104 iterations. Analyses were run with a data set that included all 364 individuals, and with a collapsed data set excluding redundant haplotypes within sites. The best k value was selected based on the rate of change in the log likelihood of the data between successive k values runs (ΔK) using the methods in [http://taylor0.biology.ucla.edu/structureHarvester/). We used Distruct V1.1 [2 tests of heterogeneity to assess whether the observed frequencies of clusters differed significantly from the null expectation of equal frequency across areas.Clustering analyses were performed with Structure v2.3  using ththods in , throughuct V1.1  to graphP. tiliacea explained by the macroclimate and geography. As descriptors of spatial relationships we used principal coordinates of neighbor matrices (PCNM) obtained by principal coordinate analysis of a truncated matrix of Euclidean (geographic) distances among the sampling sites using the PCNM R package [decostand function in the R package vegan [forward.sel of the packfor R package, retaining only those that were significant following the double stopping criterion [varpart and rda coupled with a permutation test (anova) implemented in vegan [We used redundancy analysis (RDA)-based variation partitioning –43 to de package . The PCN package . For the package  using thge vegan , and cliriterion . The sigin vegan .EF1-α) and downloaded the rest (up to 364 sequences per locus) from GenBank. The alignments included 462 bp from ITS, 716 bp from mtLSU and 546 bp from EF1-α. No recombination events were detected in the single matrices with either RPD3 or Imgc programs. Genetic diversity indices obtained for each marker globally and by geographical area are detailed in EF1-α haplotypes. ITS and EF1-α had similar values of haplotype diversity (Hd) in all samples and in the different areas, while mtLSU values were lower and heterogeneous in the different areas. The lowest values of nucleotide diversity for the three loci were in the MC area. Globally, the three loci appeared to be selectively neutral according to Tajima’s D values. The significant positive D value for the ITS from the MA samples and the mtLSU from the MI and EU samples might suggest sudden population contraction or balancing selection at these loci. The frequency of the haplotypes in the four regions, a summary of the number of restricted haplotypes (i.e. haplotypes found only in a single area) for each marker, and the percentage of samples with those haplotypes, are shown in We generated 1086 new sequences  and by pairs, so we combined the loci into a multilocus haplotype (MLHs) matrix. Of the 78 MLHs found (RT = 0.17) when considering all four areas, but a very substantial structure (ФRT = 0.31) when MI sites were compared with MC ones. MC also had the highest values of pairwise differences (FSTs) against the MA, MI, and EU  rejected the incongruence among the three matrices, globally  and without redundant haplotypes . However, the scatterplots of local densities of distances  detectedStructure analyses based on the distribution of the MLHs of the different sites showed that the best estimate for the number of clusters was k = 3. Similar groups were obtained when the analysis was run excluding redundant MLHs from each site. The proportion of the three clusters at the different sites is shown in A preliminary redundancy analysis (RDA) of the cluster data showed that the X and Y geographical coordinates were not significant, whereas three PCNM variables  had a positive Moran’s I index of spatial autocorrelation at low distances . Five biEF1-α) demonstrated genetic variability within Parmelina tiliacea. The ITS region is frequently used for phylogenetic studies in fungi and it has been adopted as a universal DNA barcode marker for species [P. tiliacea this marker detected only three main haplotypes, one absent and another predominant in the MC area, as well as many restricted haplotypes at low frequencies in MA. The EF1-α marker, which has not often been used in studies of the population genetics of lichenized fungi [EF1-α haplotypes, but none of mtLSU. In contrast, MA had a high proportion of mtLSU restricted haplotypes.The three molecular markers of the fungal partner we used  can be expected to be dispersed within short distances from the parents as in other lichens [The combined MLHs also indicated that the Canary Islands (MA) had the highest number of restricted and rare MLHs, but most of the individuals had haplotypes widespread in other areas. On the contrary, the high number of exclusive MLHs found in the coastal Mediterranean sites (MC) were present in many individuals. Although the relative contribution of asexual and sexual processes in  lichens  forming Parmelina tiliacea were subdivided into three clusters with heterogeneous distributions between regions, the extreme cases being the Canary Islands (MA) and the coastal sites (MC) around the Mediterranean Basin . In the Canary Islands (MA area), the three genetic clusters occurred in similar proportions, and there were also many restricted MLHs at very low frequencies, most individuals sharing MLHs with other areas. Our data indicate that these islands retain the greatest amount of diversity in this species and suggest that they may have served as a refugium for it. The Canary Islands, as other oceanic islands at low latitudes, are generally considered to have been buffered from the pronounced climate changes of the last 2 Ma [ast 2 Ma , and to ast 2 Ma ).In the coastal Mediterranean sites (MC), however, in all those around the Mediterranean Basin, cluster 3 was dominant and a very high proportion of the individuals had MLHs restricted to this area. This may indicate a geographic barrier for dispersal, as seems to occur with the Pyrenees and the Alps in the case of some plants and insects –58. ThesP. tiliacea that remains unexplained, and which could perhaps be related to intrinsic biological properties or ecological requirements of the species. Factors related to the historical biogeography of the species  could also have been involved in the initiation of differentiation in this widespread species.The geographical genetic differentiation found cannot be explained by isolation by distance (IBD) alone, although a weak pattern of spatial autocorrelation at low distances was detected. Part of the variation of the cluster distribution can be explained by the macroclimatic variables alone, independently of any spatial structure. Another fraction reflects the interaction between climate and the local spatial structure generated by other natural processes, factors that are often mixed together. Both variables have been found to explain population structure in other lichens , 7. NeveParmelina [P. tiliacea must be a relatively young process. Thus, the present structure of the continental populations (i.e. the non Macaronesian sites) could well have been influenced by Miocene and Pleistocene events, as suggested for some other organisms [The macroclimatic variables we used are only rough approximations for the ecological requirements of the individuals, as they do not take into account either local microclimatic conditions that may be important for the establishment of lichens , or pastarmelina , the divrganisms  and alsorganisms .Parmelina tiliacea populations in the Canary Islands and in the Mediterranean coastal sites in relation to the differentiation of lineages within species and the conservation of its genetic diversity. These two areas serve as significant reservoirs of its genetic diversity and so merit preservation. More discriminating markers and more detailed information on microclimatic variables are needed to establish the contribution of clonal vs sexual reproduction to the fine-scale structure of the populations. The contribution of the algae component to the establishment of a particular fungal genotype in a location has been demonstrated in other species [Our study highlights the special position of the  species , but thiS1 Table(PDF)Click here for additional data file.S2 TableHaplotypes exclusive of a single area in bold. PH: private haplotypes/number of haplotypes in the area. SPH: samples with private haplotype/number of samples in the area.(PDF)Click here for additional data file.S3 TableThe analyses were run with an admixture model and uncorrelated loci. Locality codes as in (PDF)Click here for additional data file.


Rhythmic clock widely occurs in biological systems which controls several aspects of cell physiology. For the different cell types, it is supplied with various rhythmic frequencies. How to synthesize a specific clock signal is a preliminary but a necessary step to further development of a biological computer in the future.This paper presents a genetic sequential logic circuit with a clock pulse generator based on a synthesized genetic oscillator, which generates a consecutive clock signal whose frequency is an inverse integer multiple to that of the genetic oscillator. An analogous electronic waveform-shaping circuit is constructed by a series of genetic buffers to shape logic high/low levels of an oscillation input in a basic sinusoidal cycle and generate a pulse-width-modulated (PWM) output with various duty cycles. By controlling the threshold level of the genetic buffer, a genetic clock pulse signal with its frequency consistent to the genetic oscillator is synthesized. A synchronous genetic counter circuit based on the topology of the digital sequential logic circuit is triggered by the clock pulse to synthesize the clock signal with an inverse multiple frequency to the genetic oscillator. The function acts like a frequency divider in electronic circuits which plays a key role in the sequential logic circuit with specific operational frequency.A cascaded genetic logic circuit generating clock pulse signals is proposed. Based on analogous implement of digital sequential logic circuits, genetic sequential logic circuits can be constructed by the proposed approach to generate various clock signals from an oscillation signal. Synthetic biology is an emerging interdisciplinary research field, which concentrates on understanding the behaviors of biological system from system-level as well as creating an artificial genetic circuit based on the principles of systems biology, mathematics and engineering-4. AnaloInspired by electronic circuits, several synthetic genetic circuits have recently been created, such as toggle switch, genetic oscillator, pulse generator, genetic counter, logic evaluator, sensor, filter, and cell-cell communicator. The former twos are based on protein-protein interaction without any external input to control their behaviors. Toggle switch applies two repressor genes repressing each other to cause bi-stable phenomenon, like as a memory device. By cascBoolean logic gate is an essential unit of a computer in digital logic circuits. To bring the insight of digital logic circuit design in electronic systems into biological systems, the more complicated bio-computing processes can be easily constructed by combining a variety of genetic logic gates. The genetic logic gates constructed are based on different genetic transcriptional reactions to express various logic behaviors-22. To uH∞ control theory by regulating degradation rates of mRNAs and proteins in stochastic perturbational environments[In biological systems, there are different rhythm frequencies depending on cell types. A 12-hour rhythm has been recently found in the mouse liver. For this reason, there are many engineered approaches proposed to synthesize the specific oscillation signals. In, the freronments,34. For ronments,37. To cronments,39.This paper proposes an artificial genetic sequential logic circuit with a function of frequency divider based on the periodic oscillation signal from a repressilator and analogous topology of the digital logic circuits in electronics. The proposed genetic sequential logic circuit is triggered by a clock pulse signal to generate a clock signal whose frequency is an inverse integer multiple to the genetic oscillator. Similar to an electronic waveform-shaping circuit, a genetic waveform-shaping circuit constructed by several genetic Buffers in series is designed, which regulates time duration of logic high/low levels of an oscillation signal in the basic sinusoidal cycle and reshapes the oscillation signal into a pulse-width-modulated (PWM) signal with different duty cycles by regulating the different threshold levels of the Buffer. The PWM signal can be regarded as a pulse signal with the frequency is coherent to that of the genetic oscillator. The clock pulse signal is served as the rising or falling triggered edges of a clock signal with base frequency. In the digital logic theory, Karnaugh map is applied to determine the input signals of the rising or falling edge-triggered genetic JK flip-flops in each level. A synchin silico show performance of the synthesizing clock pulse signal, and the clock signal with double, triple, quadruple basal periods while operating at the same genetic oscillator.For our proposed genetic pulse generator design, the periodic property of genetic oscillator is considered and the clock pulse signal is generated by utilizing the existing synthetic genetic oscillator constructed by three repressor genes which repress each other in the closed loop. Different from the genetic counter circuit design,29, we iBy applying mathematical models to describe the biochemical reactions of genetic systems, a synthetic genetic circuit with a specific function can be synthesized from the system's perspective.L genes described by a class of nonlinear Hill differential equation[Consider the dynamic model of the synthetic genetic logic circuit with equationmi and pi denote, respectively, concentrations of mRNA and protein for the gene i, λi and γi are, respectively, the degradation rates of mRNA and protein, αi is the transcription rate of mRNA, βi is the synthesis rate of protein, αi,0 is the basal production rate, fi(⋅) is the promoter activity function which describes the nonlinear transcriptional behavior and reflects the strength of the interaction between regulated protein and RNA polymerase (RNAp), and u is the concentration of transcription factor (TF) which is produced from other gene(s) or inducer(s) to control the transcription rate of target genes.where For a gene with an operator site which can bind a repressor or activator TF, the promoter activity functions are described asandfNOT and fBuffer are promoter activity functions for logic NOT and Buffer[n is the Hill coefficient which denotes the binding cooperativity between TF and the corresponding operator, and K is the Hill constant which are proportional to the lengths or affinities of the TF binding sites inserted into the promoter region of the target genes. For logic NOT gate, the input is a repressor and the gene produces a protein only in absence of the repressor; otherwise, the presence of the repressor obstructs the bound of RNAp and promoter. For genetic Buffer, the input is an activator which advances the bound of RNAp and promoter to produce protein. The frameworks for the two logic gates are illustrated in Figures where d Buffer,30, respFor genes with two operator sites, which can bind two repressor TFs or activator TFs, the promoter activity functions are described in accordance with their logic functions asandfAND, fOR, fXOR, fNAND and fNOR are, respectively, promoter activity functions of logic AND, OR, XOR, NAND and NOR gates, u1 and u2 are concentrations of repressor or activator TFs, K1 and K2 are Hill constants for u1 and u2, respectively, and n1 and n2 are the corresponding Hill coefficients. For logic AND, OR and XOR gates, the transcriptional behaviors are regulated by two activator TFs with different binding sites. Two repressor TFs control the genetic expressions of logic NAND and NOR gates. Their construction frameworks are shown in Figure where In,39, the whereρi and ρi0, are new synthesis and basal production rates of the protein. The dynamic model of 2L differential equation (1) is reduced to the dynamic system with L differential equation (9). For real-world implementation, fetching the corresponding promoter-RBS parts from the promoter-RBS library, the synthetic genetic circuit can be realized in the genetic systems.Here, In digital logic circuits, the output of sequential logic circuits depends not only on the present inputs but also on the past inputs. For synchronous sequential circuits, a clock signal is utilized as a metronome to coordinate actions of circuits, which oscillates between high-level and low-level states. The circuits with triggered clock signals become active either in the rising edge, the falling edge, or in both of the rising and falling edges. For the sequential logic circuit triggered at the rising edge of the clock signal, it becomes active when its clock pulse goes from low to high (0 to 1), and ignores high-to-low (1 to 0) transition.In genetic logic circuits, oscillation signal produced from a repressilator is not ideal as a clock for use in the kind of circuits relying on the change of rising or falling edge of the clock signal for state transition. Our proposed approach is to introduce the idea of a waveform-shaping circuit in electronics to genetic logic circuits, and reshape the synthesized genetic oscillation signal into a crisp clock signal or a PWM signal with different duty cycles. By regulating the size of duty cycle, the clock pulse can be generated with a rising edge or a falling edge whose frequency is coherent to the oscillation frequency. To use the clock pulse, the designed genetic counter based on the topology of an electronic sequential logic circuit is triggered to generate a clock signal with its frequency is inversely integer multiple to the genetic oscillation.lacI, tetR, cI) which represses each other in the cycle chain. The product of the first repressor gene, lacI from E. coli, inhibits the transcription of the second repressor gene, tetR from the tetracycline-resistance transposon Tn10, whose protein product in turn inhibits the expression of the third repressor gene, cI from the λ phage. Finally, cI inhibits lacI expression, completing a negative feedback cycle[Oscillation phenomenon in biological systems has been discovered at various levels of biological organization. Its practical function is to control the dosage of drugs or as a synchronous mechanism for cell-cell communication. The oscillation capability depends not only on the network topology but also on the system parameters. Currently, the simplest synthetic genetic oscillator can be synthesized from a single gene repressing itself with a delayed negative feedback loop. An extension of the simplest oscillator, called a repressilator, consists of three genes  ≡ ,  or . For other design, the oscillation behavior can be generated by a number of repressor and activator genes in which the number of repressor genes must be odd.where To design the genetic oscillator with desired oscillations, one can realize a gene regulatory network to track a reference sinusoidal signal given byyd is the oscillation signal with the desired amplitude A, basal frequency ω0, phase φ and yd,0 is the base level to ensure nonnegative protein concentration. For more details regarding synthetic genetic oscillator design by optimization algorithms one is referred to[where erred to.yT is used in electronics. For the input signal with its value larger than the threshold level, it is treated as “logic high”. Otherwise, it is referred to “logic low”. However, in biological systems, an ideal step function doesn’t exist. A sigmoid function  1 because it ensures that the input which is larger or less than the threshold level will be amplified or shrunk. By cascading the next sigmoid function, the oscillation input signal will gradually reach the saturation region and remain in the high or low level.In electronics, a waveform-shaping circuit is designed to shape the input signal to the desired form according to an input and output (I/O) characteristic curve. For the oscillation input and the clock output, the I/O characteristic curve of the desired waveform-shaping circuit is displayed in Figure According to this idea, a waveform-shaping circuit can be used to regulate the period of the logic high/low levels of an oscillation signal in a sinusoidal cycle and generate a PWM signal with different duty cycle defined byD is the duty cycle, T0 is the basal period of oscillation signal (11) with 2π/ω0 and Ton being the period of “logic high” in a basal period. For the PWM signals with different duty cycles, the threshold is obtained by consideringwhere withyd,0 + A, a clock pulse served as a rising triggered edge is generated and shown in Figure yd,0 - A. Similarly, a PWM signal with 50% duty cycle, i.e. the clock signal with its frequency is consistent to the genetic oscillator, is synthesized and shown in Figure yd,0. In other words, the PWM signals with different duty cycles can be synthesized from an oscillation signal via a waveform-shaping circuit in different threshold levels.To select the threshold level approaching to In engineered genetic logic circuits, a genetic Buffer is propoIts steady-state solution is easily obtained aspk is the output concentration of the kth Buffer, pk,ss denotes its steady-state concentration, uk, Kk and nk are, respectively, the input concentration, Hill constant, and Hill coefficient of the kth Buffer and ρk, γk and ρk0, are, respectively, synthesis, decay and basal rates. The second term of the right-hand side of (16) is the minimal level and ρk/γk is the difference between the minimal and maximal levels. Output concentration of the genetic Buffer is the half maximal output concentration when the input concentration equals Kk and thus Kk refers to the threshold level yT.where In each stage, the corresponding inputs and the threshold levels are given byandKk. To resolve this problem, one can again cascade a Buffer with the design parameters of (16) in the last stage of genetic waveform-shaping circuit to compensate the output level.In the first stage, the input signal is the oscillation signal in (11) and the threshold level is chosen according to the desired duty cycle in (13). For the next stage, the input signal is the output concentration of the previous Buffer and the threshold level is the half maximal output level in the previous one. The topology of our proposed genetic waveform-shaping circuit is displayed in Figure Kk is obtained byThe gain at the operating point Ak is the gain of the kth Buffer. The gain is proportional to the Hill coefficient nk and the synthesis rate ρk and is inversely proportional to the Hill constant Kk and the decay rate γk at the operating point uk = Kk. To ensure that the necessary condition of the gain at the operating point, Kk should be exceeding 1. At first, one chooses the appropriate Hill constant for the desired synthesized PWM signal and then selects a suitable Hill coefficient nk, synthesis rate ρk and decay rate γk satisfying (19). From the system parameters in the previous stage, one proceeds to choose the appropriate system parameters in the next stage satisfying (18) and (19). From[where 9). From,39, to rFrequency divider in electronics is a device that generates an output signal whose frequency is an inverse multiple to that of the input signal. A sequential logic circuit, counter, is used to achieve this function, which is constructed by a series of flip-flops and triggered by the clock pulse to generate the clock signals with multi-fold basal period. Figure Genetic JK flip-flops based on the topology of digital logic circuits in electronics divide into the rising edge-triggered one and the falling edge-triggered one shown as in Figure pCLK is the concentration of clock pulse from low to high, pW, pV, pR, pS, pQ, andW, V, R, S, Q andpK and pCLK activate the transcription of the gene W. The proteins pJ and pCLK activate the transcription of the gene V. The productions of the genes W and Q activate the transcription of the gene R and the productions of the genes V andS. The proteins pR andQ and the proteins pS and pQ inhibit the transcription of the genewhere For the falling edge-triggered genetic JK flip-flop, the model is described bypCLK is the concentration of clock pulse from high to low. This circuit is composed of six genetic NAND gates with its topological structure shown in Figure where δ -fold basal period in which δ is a positive integer, a synchronous genetic counter circuit with δ number of rising edge-triggered genetic JK flip-flops is constructed byTo synthesize the clock signal with an inversely multiple of frequency of genetic oscillator, a synchronous genetic counter circuit can be used. The counter circuit in electronics works on the rising or falling edge of the clock and count the number of clock pulses. Based on the feature, one first generates a series of clock pulses using our proposed genetic waveform-shaping circuit, and uses the clock pulse signal to trigger the genetic counter. According to the Karnaugh map in the digital logic theory, the input signals of each genetic JK flip-flop and the topology of genetic counter circuit can be determined. To synthesize the clock signals with 2with the input of each genetic JK flip-flop given byδ-fold basal periods. Figure δ-fold basal period.whereTo synthesize the clock signal with triple basal period, the synchronous genetic counter with two rising edge-triggered genetic JK flip-flops and a falling edge-triggered genetic JK flip-flop is constructed bywith the input of each genetic JK flip-flop given bywhereThe above approach is generic, by an analogous way, one is able to determine the corresponding inputs of each genetic JK flip-flop based on the engineering digital logic theory and cascTo demonstrate the proposed synthetic genetic sequential logic circuit is effective to realize the function of frequency divider, the following numerical examples are illustrated to confirm the performance of the proposed method.Consider the dynamic model of the synthesized genetic oscillator constructed by three repressive genes given byχ is the regulation coefficient. The period of oscillation increases when the regulation coefficient χ decreases. Otherwise, the period decreases. Figure χ. Oscillation with the basal period T0 = 32 sec, the amplitudes A = 0.63, and the base level yd,0 = 1.1731 is displayed in Figure χ = 0.5.where pcI is the desired oscillation input. To design the clock pulse signal with D = 10%, the designed genetic waveform-shaping circuit is described bySuppose protein concentration p6 is the clock pulse signal with D = 10%. The circuit has six genetic Buffers. In the first Buffer, the threshold level 1.76 is selected and the corresponding parameters including rate constants, Hill constants, and Hill coefficients and satisfying (18) and (19) are chosen for the second to the fifth Buffers. The last Buffer is to compensate the output of maximal level via choosing an appropriate rate constants in (16). In each stage, the I/O characteristic curves are shown in Figure where D = 90% is generated by the following genetic waveform-shaping circuitSimilarly, the clock signal with p6 is the clock pulse signal with D = 90%. The first Buffer is designed with the threshold level 0.6761 and the second to the fifth Buffers choose the corresponding threshold level 0.5 satisfying (18). The Buffer in the last stage functions to compensate the output with the maximal level. In each stage, the I/O characteristic curves are shown in Figure where D = 100% is described byAccording the proposed approach, the oscillation signal can be rectified and filtered as a direct current (DC) output. The genetic waveform-shaping circuit with p4 is the DC signal with D = 100%. There are four genetic Buffers. In the first Buffer, the threshold level 0.5 is chosen and the second to fifth Buffers choose the threshold level 0.5. The Buffer in the last stage functions to compensate the output of maximal level. In each stage, the I/O characteristic curves are shown in Figure where δ = 2, all synthesis and decay rates are 1, all Hill constants are 0.5, all Hill coefficients are 4 in (22),To design the clock signals with the double and the quadruple basal periods, we suppose Escherichia coli[The goal of synthetic biology is to design genetic circuits with specific functions by using the approaches of mathematics and engineering. Several genetic logic gates have recently been developed and experimentally realized-22. In-18, simpchia coli,10. The Reporter protein or green fluorescent protein (GFP) has been used in real-world experiments to reflect genetic expression. By usinIn sequential logic circuits, the triggered signals could be rising or falling edge of a clock signal. One can use clock pulse signals to replace the rising or falling edge to trigger the sequential logic circuits such as our proposed genetic counters. The proposed genetic counters with a function of frequency divider based on the topology of electronic circuits can be used to generate clock signals with multi-basal period. In other words, one can utilize the proposed approach to generate clock signals whose periods is an integer multiple of 24-hour from a cell with circadian rhythm.in silico show the synthesizing genetic clock with double, triple, and quadruple basal periods while operating based on a genetic oscillator. By extending the proposed design principle, a class of multi-basal period clock signals can be generated in a straightforward manner.This paper has proposed a synthetic genetic sequential logic circuit as a frequency divider. The synthesized clock frequency is inversely multiple to that of the genetic oscillator which generates the fundamental sine wave. Through controlling threshold level of the genetic Buffer, the proposed waveform-shaping circuit regulates time duration of logic high/low levels in a basic sinusoidal cycle for an oscillation input and generates ideal pulse signals with the coherent frequency to the genetic oscillator. Regarding the generated pulse signal as rising/falling edge of the clock signal with base frequency, a genetic synchronous counter circuit based on the topology of digital sequential logic circuit is triggered by a pulse wave form to synthesize a clock signal with the inverse multiple frequency to the genetic oscillator. Experimental results The authors declare that they have no competing interests.CLL formulated the research topic. CHC developed the method, performed the simulation and wrote the manuscript. Both authors read and approved the manuscript.


Although the theoretical framework for the genetic founder effect on islands was proposed in 1973, we are the first to quantify it in relation to island size, dispersal distance, and plant traits. In addition, our genetic results are mainly coherent with post-glacial colonisation rather than  Long-distance dispersal (LDD) processes influence the founder effect on islands. We use genetic data for 25 Atlantic species and similarities among regional floras to analyse colonization, and test whether the genetic founder effect on five islands is associated with dispersal distance, island size and species traits. Most species colonized postglacially via multiple dispersal events from several source regions situated 280 to >3000 km away, and often not from the closest ones. A strong founder effect was observed for insect-pollinated mixed maters, and it increased with dispersal distance and decreased with island size in accordance with the theory of island biogeography. Only a minor founder effect was observed for wind-pollinated outcrossing species. Colonization patterns were largely congruent, indicating that despite the importance of stochasticity, LDD is mainly determined by common factors, probably dispersal vectors. Our findings caution against a priori assuming a single, close source region in biogeographic analyses. Long-distance dispersal (LDD) of plants is a complex process ; therefoOceanic islands represent good models to study LDD, as every species (or its ancestor) on such islands must have arrived by LDD. According to the equilibrium theory of island biogeography, the number of species on an island increases with island size and decreases with distance to source regions , althougThe role of LDD in shaping the current northern flora, which contains species that typically are widely distributed across a naturally fragmented biome, is debated . Neverthin situ glacial persistence  glaciation(s) in local ice-free refugia in different Atlantic regions  support the prevalent hypothesis of postglacial long-distance colonization or, alternatively, local glacial survival, (ii) determine the source areas for postglacial island colonization in the North Atlantic region, (iii) quantify the intensity of the genetic founder effect and investigate how it relates to distance, island size and plant species traits and (iv) compare genetic and floristic relationships among regions.2 as delimited by 2), Svalbard (24 453 km2 of non-glaciated area), the Faroe Islands (1396 km2) and Jan Mayen (377 km2). Although East Greenland is only part of an island, we treated it as an island because only narrow strips of land disrupted by glaciers connect it to North and South Greenland, and the Inland Ice Sheet forms a firm dispersal barrier to West Greenland  datasets for Arctic and north-boreal species of vascular plants present in the five recipient islands. Most data originate from published studies Supporting Information—Table S1]). For each recipient island and each species, we calculated the proportion of individuals allocated to each source region according to the assignment test  resulting in 51 recipient islands × species combinations. The source region with the highest proportion of allocation was considered the main source region. As we are addressing historical colonization and not present day immigration, the results of the assignment test should not be interpreted as revealing individual immigrant individuals. Nevertheless, as the number of generations since colonization is small in evolutionary time for these mainly long-lived plant species , (ii) proportion of intrapopulation genetic diversity observed in the recipient island relative to that in the main source region , (iii) proportion of regional  genetic diversity in the recipient island relative to that in the main source region , (iv) proportion of AFLP markers observed in the recipient island relative to those in the main source region (markers), (v) number of source regions inferred in order to account for all markers observed in the recipient island (sources markers) and (vi) number of source regions estimated from the assignment test . All six measures are also influenced by potential effective dispersal events occurring after initial colonization; in the following we therefore use the term ‘founder effect’ to encompass the overall reduction in genetic variability in a population in a colonized area as compared with its source. Correlations among the measures and differences in intensity of the founder effect among species and recipient islands were investigated using principle component analysis (PCA) as implemented in the R-package ade4 (http://cran.r-project.org/web/packages/ade4/) and R version 3.01 . To determine to what extent distance between source and recipient island, island size, dispersal and pollination mode were correlated with the intensity of the founder effect, we carried out a Principal Component Analyses with Instrumental Variable . As growth form and dispersal type were significantly correlated, we chose to use dispersal type because we assume it to be directly relevant to the founder effect (in situ) was evaluated based on the amount of genetic diversity in the recipient island relative to that in potential source regions, as well as on the number of private markers  found in the recipient island . Although these numbers contain uncertainties, they provide a rough estimate useful for comparison with other islands. For Jan Mayen, where no palaeobotanical records existed, we assumed a time period of 12 800 years, similar to East Greenland (12 700) and Iceland (13 000).Sagina caespitosa had relatively high numbers of private markers (5) in a recipient island, but this was not combined with high levels of genetic diversity, and thus not interpreted as indicating in situ glacial survival. Only Arenaria humifusa and Saxifraga rivularis showed a genetic pattern consistent with glacial survival on Svalbard Supporting Information][see . Thus, 92 % of the species analysed were assumed to have colonized postglacially.In most cases, we observed less genetic diversity in the recipient islands than in the source regions both at the population and regional levels and in terms of number of markers, reflecting a founder effect Table . We obseArctous alpina had unique groups and Cassiope tetragona had both a western and an eastern genetic group  or amphi-Atlantic/East-Atlantic (7) genetic groups. Both eastern and western genetic groups were observed in Saxifraga rivularis having a unique group in addition), four to East-Atlantic groups, one to West-Atlantic groups and Vaccinium uliginosum had both a western and an eastern genetic group , two (n = 17) or more (n = 7) source regions . Lack of adaptations to dispersal was indeed associated with a higher proportion of markers but fewer propagules , which explained 47.1 % of the variation Fig. A–D. The les Fig. F.FigureSupporting Information—Table S4) as fixed effects. For insect-pollinated species, the proportion of regional diversity decreased by 0.076  for reduction by one of the natural logarithm of island size (for instance from 1096 to 403 km2 or from 162 755 to 59 874 km2), but this was not the case for wind-pollinated species  a model including island size and pollination mode with (best model) or without interaction (AICc difference to the best model 1.05) or (ii) a model with island size, distance from source as well as their interaction , making it hard to distinguish dispersal by wind from dispersal by ocean currents. The main bird migration routes connect Iceland and the Faroe Islands to Great Britain .The work was supported by the I.G.A. conceived the idea and drafted the manuscript together with D.E.; I.G.A., D.E., P.B.E., K.B.W., P.S., A.T. and S.B. analysed the genetic data; C.B. lead the project compiling the genetic data; H.S. and R.E. compiled the floristic data; I.G.A. and D.E. did the statistical analyses and all co-authors commented on the manuscript.None declared.The following additional information is available in the online version of this article –File S1. Supporting information containing details of genetic and statistical analyses, R script for estimating number of propagules.Table S1. Data used for estimating founder effect and dispersal routes and traits of the 25 species analysed.Table S2. Significant values of pair-wise association among the size of the island, distance to source region and four species traits.Table S3. A compiled list of vascular plant taxa in recipient regions and occurrences of recipient region taxa in the potential source regions.Table S4. Model selection using Akaike's information criterion.


Estimating the rate of exchange of individuals among populations is a central concern to evolutionary ecology and its applications to conservation and management. For instance, the efficiency of protected areas in sustaining locally endangered populations and ecosystems depends on reserve network connectivity. The population genetics theory offers a powerful framework for estimating dispersal distances and migration rates from molecular data. In the marine realm, however, decades of molecular studies have met limited success in inferring genetic connectivity, due to the frequent lack of spatial genetic structure in species exhibiting high fecundity and dispersal capabilities. This is especially true within biogeographic regions bounded by well-known hotspots of genetic differentiation. Here, we provide an overview of the current methods for estimating genetic connectivity using molecular markers and propose several directions for improving existing approaches using large population genomic datasets. We highlight several issues that limit the effectiveness of methods based on neutral markers when there is virtually no genetic differentiation among samples. We then focus on alternative methods based on markers influenced by selection. Although some of these methodologies are still underexplored, our aim was to stimulate new research to test how broadly they are applicable to nonmodel marine species. We argue that the increased ability to apply the concepts of cline analyses will improve dispersal inferences across physical and ecological barriers that reduce connectivity locally. We finally present how neutral markers hitchhiking with selected loci can also provide information about connectivity patterns within apparently well-mixed biogeographic regions. We contend that one of the most promising applications of population genomics is the use of outlier loci to delineate relevant conservation units and related eco-geographic features across which connectivity can be measured. Inferring population connectivity from molecular data within a population genetic framework can shed light on the evolutionary and ecological processes that shape patterns of genetic diversity  that produce weak patterns of genetic differentiation or even no differentiation at all  is not justified in such species. On the contrary, m ≫ μ would imply that the variation detected in one deme is mostly replenished by migration from the metapopulation rather than by locally arisen mutations. Under this assumption, the small differentiation generally observed in most marine species would not be an artifact of using multi-allelic markers, but the consequence of high migration. This has two consequences for our interpretation of genetic variation in high gene flow species: (i) homoplasy is likely to play a very limited role because homoplastic alleles will be almost equally distributed throughout the metapopulation by migration, (ii) high heterozygosity values reflect large metapopulation effective size rather than locally high population size, a pattern that remains true even with relatively low migration between populations.Since the advent of multi-allelic loci in population genetics, it has been pointed out that the maximum value taken by Wright's σ). The mean dispersal distance (μ) can be obtained from σ by assuming a normal distribution of dispersal distance (blue line). (B) The three sampling strategies commonly used in marine population genetic studies: 1. A geographically subdivided species range is discretely sampled, but some populations are not sampled (gray dotted circle); 2. A continuously distributed species is sampled discretely, and the geographic distance between samples is of the same order as σ; 3. Continuous sampling of individuals separated by distances of the same order as σ. Colored points are sampled individuals, gray points indicate nonsampled individuals. (C) The information about dispersal that can be obtained from indirect and direct methods for each sampling strategy.(A) The distribution of dispersal distances can be estimated in two ways: through the direct detection of discrete dispersal events (blue bars), or the indirect estimation of dispersal parameters like the standard deviation of parent–offspring dispersal distances (Nem) from FST in the island model (Wright Nem or m under various extensions of the island model or other more refined population structures (Broquet and Petit FST does not necessarily mean that migration is strong as genetic differentiation is influenced by both effective size (Ne) and migration rate (m)  or moments of the distribution of dispersal distances  Fig. . For ins(m) Fig. . Second,(m) Fig. . The maiσ have been reported for a variety of species . In the case of parentage analysis, an additional constraint stems from the necessity to sample a large fraction of the potential parents. Assignment methods specifically applied to detect immigrants without identifying their origin require less extensive sampling, but their efficiency reduces quickly with decreasing genetic differentiation  if the selection coefficient (s) can be measured, which actually represents a serious challenge to most case studies. However, a measure of selection can sometimes be obtained using experimental populations or genotype frequency comparisons between larvae and adults sampled from the same cohort. By contrast, inferring dispersal from a neutral hitchhiker locus only requires the recombination rate with the selected locus  along the chromosome is measured between spatial coordinates −500 and 500. (A) A local adaptation cline lying at the frontier between two environments where selection acts in opposite directions . The cline width parameter (w) is defined as the inverse of the maximum slope at the cline center, and k is a coefficient that depends on the selection regime (Slatkin r with the selected locus makes a shift (Δp) in the central region of the cline, and an external gradient of allele frequency (∂p/∂x) directly outside the cline (Barton s = 0.5). The amount of linkage disequilibrium (D) between selected loci is measured after dispersal at the center of the overlapping clines. (C) A tail of introgression produced by the inflow of foreign alleles entering a subdivided population  appears on the left side of a chain of demes  and then propagates to the right side from deme to deme (m = 0.01), leaving behind a complex allele frequency pattern at a neutral hitchhiking locus (r = 0.001). Local connectivity between adjacent demes is transiently revealed by the structure of the neutral hitchhiking locus, as long as gene flow re-homogenizes allele frequencies. The chromosomal signatures of selection can take the form of narrow regions of differentiation (A), large genomic islands (B), or shoulders of differentiation (C and D) centered on the selected loci.Plots show the chromosomal and geographic signatures of selection under four different selective processes. Selected and neutral loci are colored in red and green, respectively. Genetic differentiation ( Slatkin . A neutre Barton . (B) HybDrosophila melanogaster revealed the existence of several latitudinal clines  takes advantage of the substantial genetic differences existing between populations or species that are on both sides of the hybrid zone. Minimal dispersal distances can be obtained through the identification of parental genotypes that crossed a hybrid zone and successfully settled in a foreign parental population or species. An even more precise estimation of larval dispersal distance can be made when the source of dispersing larvae is known, as for first-generation hybrids dispersing outside a hybrid zone. Using this strategy, patterns of larval movement among neighboring patches of blue mussels have been examined by measuring realized larval dispersal based on the genetic identification of recently settled juveniles , other adjacent demes being otherwise highly connected in the standard linear stepping-stone model. During a few thousands of generations postcontact, introgression generates a step in allele frequency between the two populations of the introgressed species, and the step then disappears as allele frequencies equilibrate between species . Importantly, the gradient only appears if dispersal is spatially limited, otherwise spatial homogenization occurs immediately as foreign alleles enter the introgressed population. In order to illustrate how this mechanism can be used to detect a local barrier to dispersal, we simulated a contact zone between two partially reproductively isolated species and the introgression of foreign alleles within one of the two species which is geographically subdivided Fig. . The extTails of introgression may be also influenced by selection acting outside the tension zone. In this case, the gradient of allele frequency within the range of the introgressed species may be steepened by a gradient of selection . Because secondary contact zones commonly coincide with environmental gradients , as well as the position of the selected locus (−3 kb 5′ of the start codon of the EF1α gene), the selection coefficient (s = 0.01), and incidentally the local recombination rate of the chromosomal region (ρ = 1.7 cM/Mb, Bierne Another scenario that generates outlier loci happens during the spread of an unconditionally favorable allele in a spatially subdivided population. This process leaves a transient footprint at neutral markers in the chromosomal vicinity of the sweeping allele. When the overall genomic differentiation is low, as it is typically the case in marine species, this process generates an elevated level of differentiation on both sides of the selected locus Bierne , which cSubstantial progresses in our understanding of connectivity in nonmodel organism can be achieved with large population genomic datasets. High-density genome scans have reached the power to detect outlying patterns of genetic differentiation at different spatial scales, enabling conservation geneticists to identify genetic differences reflecting restriction to gene flow where classical neutral markers were hitherto most often largely uninformative, as in high gene flow species. The scope of the applications of outlier loci for assessing connectivity patterns in marine species needs further investigations, in particular through gathering a larger set of empirical data. Some of the methodologies that were proposed in this review are still underexplored, and we hope that our work will stimulate new research to test how broadly they are applicable to nonmodel marine species. Although spatially explicit methods are directly applicable to continuously distributed sessile species, selected and hitchhiker loci also have the potential to reveal cryptic genetic structure in migratory species with natal homing (Gagnaire et al.


Picea asperata and P. crassifolia occurring on the Qinghai–Tibet Plateau (QTP). We analyzed sequences of three chloroplast, two mitochondrial DNA fragments and a further 13 nuclear loci from 216 individuals of the two species. Both species showed a low level of genetic diversity in contrast to other congeners occurring in the QTP and adjacent regions. The estimated population sizes of P. asperata and P. crassifolia are less than the ancestral population size before splitting. These results together with multiple statistical tests  suggest that these two species underwent recent bottlenecks. Based on approximate Bayesian computation (ABC), we also determined that the period of the population shrinkage was consistent with the interspecific divergence during the late Pleistocene. The reduced population sizes and the divergent selection may together have triggered the initial divergence under high gene flow between these two species. Our results therefore highlight the importance of climatic oscillations during the late Pleistocene in promoting speciation through changing demographic sizes of the ancestral species on the QTP and in adjacent regions.Divergence during the early stage of speciation can be driven by a population bottleneck via reduced gene flow and enhanced lineage sorting. In this study, we aimed to examine whether such bottlenecks occurred during the initial speciation of two closely related spruce species  Divergence can be promoted by population bottlenecks because of reduced quantity of gene exchange and that small populations with increased rates of genetic drift can fix more derived mutations than large populations , but P. crassifolia extends to a more northerly latitude. Phenotypic differences between P. asperata and P. crassifolia are obvious. The leaf apices of P. asperata are always acute; in contrast, the leaf apex of P. crassifolia is obtuse and the length/width ratio of the leaf is always lower than that of P. asperata . Seeds collected from 20 trees (10 trees per species from different populations) were planted in a common garden at Lanzhou University (Xiaguanying), in order to observe whether their morphological traits were stable under common environmental conditions.We sampled needles from 11 and 15 populations, respectively, through the main natural distribution range of KX212991‐KX213062). Chloroplast and mitochondrial DNA variation data  were obtained from previous studies  were sequenced using an ABI 3130xl Genetic Analyzer  following the methods in Li et al. . SequencS), Watterson's parameter θw , we performed 20 independent runs with a burn‐in of 200,000 and 500,000 iterations, respectively. The program Distruct version 1.1  approach implemented in ABCtoolbox , the population size was Nb. Four statistics, including the segregating sites, Tajima's D, nucleotide diversity, and Fu's Fs, were used to summarize the genetic information in the sequence dataset. We used the standard algorithm in ABCsampler  and compared. The differences in leaf apex between species were obvious, and the needles of CL (Table S2). A total of 71 and 78 polymorphic sites were detected in P. asperata and P. crassifolia, respectively (Table S1). The silent nucleotide diversities within P. asperata and P. crassifolia were 0.00334 and 0.00393, respectively. Similarly, the total nucleotide diversity in P. asperata (0.00264) was slightly lower than in P. crassifolia (0.00304).Genetic diversity was detected at 12 loci . No variation was detected at nuclear locus 4ΦST between P. asperata and P. crassifolia (0.053) was significantly higher than in the random scenario (P < 0.001), indicating clear population differentiation between the species. However, genealogies constructed based on the variations in cpDNA, mtDNA, and at each nuclear locus revealed that P. asperata and P. crassifolia shared most of their genetic variations  is 2 . When K had a value of 2–4, we found that these two species could not be grouped into distinct clusters  and 5.2 × 104 (95% HPDI: 0.4–69.1 × 104) individuals, and their ancestral population size (N) was 2.4 × 105 (95% HPDI: 0.1–59.3 × 104) individuals  years per generation  per generation, and the reverse migration rate from P. crassifolia to P. asperata was estimated at 26.26 (95% HPDI: 0–169.7) per generation.We used the ABC approach to estimate the parameters of the IM model based on the sequences at 13 nuclear loci. The estimated current effective population sizes of P. asperata and P. crassifolia accompanied with the bottleneck. Thus, the bottleneck likely began earlier than the divergence between P. asperata and P. crassifolia, and the reduced population size should have restricted gene flow between P. asperata and P. crassifolia when splitting occurred.Next, using ABCtoolbox  and P. wilsonii (0.0087) are much higher than either P. crassifolia (0.00304) or P. asperata (0.00264). Recently, P. likiangensis and P. wilsonii have been shown to have experienced population shrinkages during the late Pleistocene period . Picea asperata and P. crassifolia bear stable phenotypic differences despite of high level of genetic sharing  obtained with Structure.Table S1. Nucleotide variation at 13 loci in P. asperata and P. crassifolia.Table S2. The number of segregating sites at 13 loci for P. asperata and P. crassifolia.Click here for additional data file.