There is considerable ethno-linguistic and genetic variation among human populations in Asia, although tracing the origins of this diversity is complicated by migration events. Thailand is at the center of Mainland Southeast Asia (MSEA), a region within Asia that has not been extensively studied. Genetic substructure may exist in the Thai population, since waves of migration from southern China throughout its recent history may have contributed to substantial gene flow. Autosomal SNP data were collated for 438,503 markers from 992 Thai individuals. Using the available self-reported regional origin, four Thai subpopulations genetically distinct from each other and from other Asian populations were resolved by Neighbor-Joining analysis using a 41,569 marker subset. Using an independent Principal Components-based unsupervised clustering approach, four major MSEA subpopulations were resolved in which regional bias was apparent. A major ancestry component was common to these MSEA subpopulations and distinguishes them from other Asian subpopulations. On the other hand, these MSEA subpopulations were admixed with other ancestries, in particular one shared with Chinese. Subpopulation clustering using only Thai individuals and the complete marker set resolved four subpopulations, which are distributed differently across Thailand. A Sino-Thai subpopulation was concentrated in the Central region of Thailand, although this constituted a minority in an otherwise diverse region. Among the most highly differentiated markers which distinguish the Thai subpopulations, several map to regions known to affect phenotypic traits such as skin pigmentation and susceptibility to common diseases. The subpopulation patterns elucidated have important implications for evolutionary and medical genetics. The subpopulation structure within Thailand may reflect the contributions of different migrants throughout the history of MSEA. The information will also be important for genetic association studies to account for population-structure confounding effects.  The human population genetic history of Asia is complex, which is highlighted by the controversy surrounding the earliest migrations through Asia. One school of thought is that Asians are descended from two major ancestral groups, the earliest who migrated via a southern coastal route and a later group who spread across northern and eastern Asia . An alteth Century AD [th Century AD) and the Ayutthaya dynasty (established 15th Century AD), although the southern region of Thailand was essentially autonomous and ruled by Malay vassals until the 19th Century AD. During this most recent phase of Thai history, a large influx of migrants from southern China occurred [Thailand lies at the heart of mainland Southeast Asia (MSEA), the region in which peoples speaking Tai-Kadai, Austroasiatic (Mon-Khmer), Sino-Tibetan, Hmong-Mien and Austronesian languages are present. The contemporary populations of this region are dominated by Tai language speakers (Thai and Laotian) and Austroasiatic speakers (Cambodian and Vietnamese). Most importantly, Thailand is located at the crossroads of ancient human migration paths between North and East Asia and Island Southeast Asia. Therefore, the genetic footprints of ancestral migrants may be present among people in this region. The earliest archaeological evidence of humans in MSEA was obtained in southern Thailand, dating to approximately 25,000 Years Before Present (YBP) , which intury AD . Later Toccurred . Within occurred . Despite the strategic location of Thailand in MSEA, there has been no large-scale study of its population’s genetic variation. Previous studies of human genetic diversity in Thailand were done with limited marker sets ,23, and/The recruitment of human subjects was approved by the ethical review committee for research in human subjects : Ministry of Public Health, Thailand (CCA No. Si 32/2009). P< 5.5×10-8) and missing data >10%). The second dataset is a case-control association study to identify genetic factors of major depressive disorder. Human subjects for genotyping were recruited according to the ethical statement mentioned above. The dataset comprises 374 individuals (186 cases and 188 controls) collected from North, Northeastern, Central and Southern regions of Thailand. The DNA samples were genotyped using the Illumina Human 610-Quad BeadChips Array at RIKEN, Japan. The total number of genotyped SNPs is 593,542. SNPs were filtered to remove markers in high LD (linkage disequilibrium r2 > 0.5), high deviation from HWE (P<10-3) and missing data >5% using the PLINK tool. After filtering, 438,503 SNP markers remained for further analyses. Disease association test was performed using the PLINK tool. No marker passed the threshold for Bonferroni-corrected significance (P<10-7). The top 50 ranked markers are shown in 0-thalassemia/hemoglobin E with different spectrums of disease severities. The study collected 383 severe patients and 235 mild patients and performed case-control association. The data and association study were previously published in [independent case-control association studies of Thais where individuals’ samples were collected from different regions in Thailand by different Principal Investigators. For datasets 2 and 3, individuals were asked to assign a geographical label for themselves  based on their place of birth, or their parent’s place of birth. We tested for systematic differences of allele frequency caused by sampling bias between datasets 2 and 3 for 438,503 SNPs. A Bonferroni corrected P-value of 10-7 was used as the significance threshold. In accordance with PLoS policy on data availability, requests to access datasets 2 and 3 should be sent to Dr. Verayuth Prapanpoj and Prof. Suthat Fucharoen, respectively.Three SNP genotyping datasets were analyzed in this study. The first dataset is from a worldwide population study of 850 individuals from 40 populations published in . The genished in . GenotypThe analyses were done in two stages. First we observed the relationship between Thais and other related populations. The common polymorphic SNPs from all three datasets  were used for population structure analysis. This marker set includes only SNPs that have the same reference SNP identification code (rs-id) between the Affymetrix and Illumina SNP array platforms. For some of these SNPs in common, the SNP calling on one platform is the complement of the other platform, i.e., A/G versus T/C. In these cases, the Affymetrix SNP calls were complemented to be the same as Illumina’s. Common SNPs in which the base identity of the variant SNP was ambiguous on either platform were excluded. Finally to ensure that no hidden technical bias may exist between the two platforms for the common marker set, minor allele frequencies (MAF) for each SNP were calculated from a control population with 136 samples from Affymetrix  and 1,18seqboot, gendist, consense and neighbor programs within the PHYLIP program suite (with default parameters) [seqboot . The dissimilarity matrix was calculated from the matrix of allele frequencies using the gendist program. The neighbor module was used to construct NJ-trees from these matrices. Finally, consense was used to generate the consensus tree with bootstrapping values using the Pygmy population as an out-group. The unrooted phylogram was plotted using Dendroscope [Population structure was analyzed first by bootstrapping neighbor-joining (NJ) tree of the three combined datasets  using the ameters) . Allele droscope . The ipPCA program ,35 was uHigh-resolution study of population substructure within the Thai population was performed on the combined datasets 2 and 3 . Subpopulations were assigned using ipPCA with stopping criterion EigenDev=0.21. ADMIXTURE was used to estimate individual ancestries from K=2 to K=4 ancestors. Genome-wide Fst values  were caldistinctive positions (clades with 100% bootstrap support) from other populations except for Thai Moken and Cambodian people who occupy positions in the tree with weaker bootstrap support. It is striking that the Thai subpopulations  are also distinct.In order to frame the Thai population in a worldwide context, the Thai genetic data were combined with the worldwide population data published in . The comNext, subpopulation genetic structure was analyzed using the ipPCA algorithm ,35. SubpNext, admixture ratios of inferred ancestry (K=2 to 10) for each individual (ipPCA outliers excluded) were determined using the ADMIXTURE program . When inNext, having shown substructure among the mainland Thai population with relatively few markers, a higher resolution analysis of 992 Thai individuals was performed using 438,503 SNP markers. Subpopulation assignment by ipPCA revealed four subpopulations labeled SPA, B, C and D . 20 outlHaving demonstrated substructure among the Thai population, an investigation of the genomic regions most diverged among the subpopulations was performed. The markers were ranked according to their Fst values in pairwise subpopulation comparisons . Among tIn this study, we have attempted to fill an important gap in the knowledge about human population genetics in MSEA. Consensus NJ tree  and ipPCFrom the Admixture analysis at K=7, MSEA people in SP19-22 were shown to be represented by one major ancestral component . This coth Century AD Ayutthaya period. Although SP22/SPD constitutes a minority of Central Thais, SP22/SPD individuals are concentrated in this region. Several Chinese, Vietnamese and a Cambodian individual were assigned by ipPCA with Thais in SP22. One explanation for this pattern, given the modern history of MSEA is that the Thais, Vietnamese and Cambodian in SP-22 may be descendants of recent Chinese migrants. In support of this conjecture, Admixture analysis showed that these individuals share a prominent ancestry with predominantly Chinese SP18 individuals . Further sampling of genetic variation in other MSEA populations, particularly Vietnamese and Cambodians may shed further light on this pattern. File S1List of SNP-ids for the 41,569 SNP markers common to the Illumina Human 610-Quad BeadChips Array and the Affymetrix Human SNP Array 6.0 platforms.(ZIP)Click here for additional data file.Figure S1MAF correlation of 41,569 SNPs between Illumina and Affymetrix platforms. MAFs for each SNP were calculated from a control population of European ancestry with 136 samples from Affymetrix [fymetrix  and 1,18fymetrix  platform(TIFF)Click here for additional data file.Figure S2ipPCA clustering decision tree for analysis of combined datasets 1, 2 and 3 (worldwide datasets). The terminal nodes boxed in red represent ipPCA resolved subpopulations labeled SP1-24. The internal nodes represent groups of individuals with unresolved population structure. Terminal nodes marked with asterisks represent outlier individuals. The EigenDev value for each iteration of ipPCA is shown in each node; values >0.21 indicate the present of substructure. (PDF)Click here for additional data file.Figure S3ipPCA clustering decision tree for analysis of combined datasets 2 and 3 . The terminal nodes boxed in red and labeled as SPA, SPB, SPC, and SPD represent ipPCA resolved subpopulations. Terminal nodes marked with asterisks represent outlier individuals. The numbers of individuals for each regional origin label  are indicated in each node. The intermediate nodes represent groups of individuals with unresolved population structure. The EigenDev value for each iteration of ipPCA is shown in each node; values >0.21 indicate the present of substructure. (TIFF)Click here for additional data file.Table S1Major depressive disorder GWAS top 50 associated SNP data.(XLSX)Click here for additional data file.Table S2Correspondence of individual ipPCA-assignments of SP19-22 with SPA-D.(XLSX)Click here for additional data file.Table S3Top 50 rank SNP from Chi-squared analysis between Thalassemia dataset and the Major depressive disorder dataset from the expected ratio for all markers.(XLSX)Click here for additional data file.Table S4Top 200 ranked SNPs based on Fst values for all pair-wise comparisons between SPA, SPB, SPC and SPD.(XLSX)Click here for additional data file.


Liquidambar styraciflua), a tree species with a disjunct distribution between northeastern Texas and Mexico, by analyzing sequences of two chloroplast DNA plastid regions in Mesoamerica. Our results revealed phylogeographical structure, with private haplotypes distributed in unique environmental space at either side of the Trans-Mexican Volcanic Belt, and a split in the absence of gene flow dating back ca. 4.2–1.4 million years ago (MYA). Species distribution modeling results fit a model of refugia along the Gulf and Atlantic coasts but the present ranges of US and Mesoamerican populations persisted disjunct during glacial/interglacial cycles. Divergence between the US and Mesoamerican (ca. 8.4–2.8 MYA) populations of L. styraciflua and asymmetrical gene flow patterns support the hypothesis of a long-distance dispersal during the Pliocene, with fragmentation since the most recent glacial advance  according to coalescent simulations and high effective migration rates from Mesoamerica to the USA and close to zero in the opposite direction. Our findings implicate the Trans-Mexican Volcanic Belt as a porous barrier driving genetic divergence of L. styraciflua, corresponding with environmental niche differences, during the Pliocene to Quaternary volcanic arc episode 3.6 MYA, and a Mesoamerican origin of populations in the USA.We investigate the genetic variation between populations of the American sweetgum ( Mesoamerican cloud forests encompass an extremely diverse and heterogeneous mixture of temperate and tropical species. Several of the temperate species (ca. 50 plant lineages) exhibit a disjunction between the temperate floras of the eastern USA and the mountains of eastern Mexico  to the cpDNA dataset in the study by Morris et al.  in Mexico, as compared to L. styraciflua from eastern North America, which ranges from sea level to 300 m in elevation  region revealed almost no sequence variation across 18 samples from geographically distant locations of L. styraciflua in Mexico . PCR and sequencing protocols of the chloroplast DNA plastid regions can be found in Data S1. Forward and reverse sequences were assembled using Sequencher 4.9  and subsequently aligned with SE-AL ver. 2.0a11 . The sequences reported in this study are available from GenBank . We additionally obtained GenBank sequences of L. styraciflua from Morris et al. (Two chloroplast DNA plastid regions with the highest base pairs—327 base pairs (bp) of s et al.  and becas et al.  to be usA statistical parsimony network of haplotypes was constructed using TCS ver. 1.2.1  analyses on the combined dataset to infer evolutionary relationships among haplotypes using MRBAYES ver. 3.1.2 . For the L. styraciflua crown group, we set an age of 3 MYA on the basis of fossil material , making sure that parameter values were fluctuating at stable levels. Based on these results, the first 3000 trees were discarded as burn-in, and the remaining samples were summarized as a maximum clade credibility tree with mean divergence times and 95% highest posterior density (HPD) intervals of age estimates in TREEANNOTATOR. Finally, these results were summarized in a single tree visualized in FIGTREE ver. 1.3.1 (http://tree.bio.ed.ac.uk/software/figtree/).To relate genetic differentiation found among s et al.  plus L. π) and haplotype diversity (h) for the resulting groups, and computed pairwise comparisons of FST values between populations and groups in ARLEQUIN ver. 3.01  and ordered haplotypes  and differentiation parameters  were estimated according to the methods described by Pons and Petit . The most likely number of groups (K) was determined by repeatedly running the software SAMOVA with two-to-five groups and choosing those partitions with a maximum ΦCT value, as suggested by Dupanloup et al.  implemented in SAMOVA ver. 1.0  for chloroplast-wide, synonymous substitution rates described for most angiosperms . The results of each run were visualized using TRACER (http://tree.bio.ed.ac.uk/software/tracer/) to ensure that stationarity and convergence had been reached (ESS > 200). BSPs were generated for the groups north and south of the TMVB.Three methods were used with the concatenated cpDNA dataset to infer the historical population expansion of D Tajima  and Fu'sma Fs Fu  neutralima Fs Fu  calculatma Fs Fu  with thema Fs Fu  with 10,ma Fs Fu  were perma Fs Fu  to descrL. styraciflua genetic groups  to estimate the time of divergence (t) between populations, effective number of migrants per generation (m1 and m2), and the effective population size of the ancestral (qA) and descendant populations (q1 and q2). The isolation-with-migration (IM) model  divided by two times the divergence time (11.9 Ma) among Liquidambar species  to compare estimation of effective population sizes with those based on general rates from Wolfe et al.  and an assumed high annual adult survival of 0.9 or 0.99 based on typical estimates for long-lived tree species (Franklin and Debell T) is calculated according to T = a + [s / (1–s)] , a lower survival rate (0.9) and therefore a lower average generation time (34 years) would be more likely. We primarily assumed an average generation time of 34 years, but also explored the effects of generation times as high as 124 years. To convert the time since divergence parameter of IMa to years, t, we divided the time parameter (b) by the mutation rate per year (u) converted to per locus rate by multiplying by the fragment length in base pairs, and calculated for the 1.0–3.0 × 10−9 Wolfe's et al.  hypothesis assumed that the US and Mesoamerica populations persisted isolated in two refugia since the Pliocene , in two Pleistocene refugia for the duration of the glacial episodes , or that populations persisted in two refugia only since the most recent glacial advance . This hypothesis predicts that population structure of L. styraciflua would reflect the effects of colonization from multiple refugia or long-term geographic isolation and divergence between groups. The alternative hypothesis (B1–B4) tested fragmentation of a single refugial population with the number of years in the refugium differing . Effective population sizes (Ne) of 10,000, 50,000, 100,000, or 1,000,000 were used for all the simulations based on the values calculated with IMa, and absolute time (years) was converted to coalescent time (generations) assuming a generation time of 34 or 124 years for L. styraciflua using the equation from Lande et al.  was compared with the s-values of the simulated genealogies to evaluate model fit , (2) only the Mesoamerican populations (L. macrophylla sensu Oerst.), and (3) both US and Mesoamerican populations (L. styraciflua sensu lato) to predict where L. styraciflua resided at the LGM and whether range expansion and population connectivity are observed between groups of populations. A total of 218 unique occurrence data were assembled for L. styraciflua using records gathered from The World Biodiversity Information Network  and the Global Biodiversity Information Facility , supplemented with our georeferenced records from field surveys and collection. Distributional records were input into the maximum entropy machine-learning algorithm in MAXENT ver. 3.2.2 , and MAXENT was set to randomly use 30% of values for training and 70% of values for testing the model. After removing variables that exhibited a strong correlation (Spearman's rank correlation >0.9), ten variables (see Results) were used to generate the SDM model under current climatic conditions using MAXENT with the default parameters for convergence threshold (10−5) and 1000 iterations. We evaluated model performance using the area under the curve (AUC) values of the threshold independent receiving operating characteristic curve (ROC). Resulting distributions were projected with QUANTUM GIS ver. 1.8.0-Lisboa. To explore distributions earlier in time, the SDM under current climatic conditions of L. styraciflua was projected to past climatic scenarios, at the LGM  and Last Interglacial . Past climate layers were also drawn from the WorldClim webpage for two LGM past climate scenarios developed by the Paleoclimate Modeling Intercomparison Project Phase II . Haplotype diversity (h) was high for most localities ranging from 0.18 to 0.86, reflecting the presence of different haplotypes within each site (Table S2). Nucleotide diversity (π) was low (0.0001–0.0019) for most populations, indicating little variation between sequences from the same population (Table S2). Ten substitutions were detected, and three regions with indels of 1–3 bp were treated as fifth character state after adjusting the length of the sequences to the length of those from the US populations of the Morris et al. . Haplotype H2 is shared among individuals from populations in Chiapas , Oaxaca , Veracruz , and one individual from Louisiana; H11 was exclusively found in the two populations of the TUX region; H4–H5 and H7–H10 were private to populations north of the TMVB; H3 was private to Coapilla, Chiapas; and H6 was private to population in Chayotepec, Oaxaca, both localities south of the TMVB; and H12–H24 were located exclusively in US populations .The statistical parsimony network of US and Mesoamerican samples recovered 24 haplotypes. The most frequent haplotype (H1) is shared by populations of L. styraciflua yielded 29 haplotypes among 160 sequences (Table S1). Statistical parsimony retrieved a well-resolved network, in which three haplogroups can be distinguished  in a single, well-supported clade (PP 0.92), supporting the monophyly of L. styraciflua. The basal split between clade containing haplotypes H15, H19, and H24 distributed in the Cumberland Plateau and the Southern Appalachians  grouped all L. styraciflua occurred at ca. 11.9 MYA .The BEAST analysis suggests that the basal split between clade containing haplotypes H15, H19, and H24  indicated that L. styraciflua is genetically highly subdivided, which is corroborated by the high fixation index (FST) of 0.86. Genetic diversity  across all populations was higher than the average within-population , and, consequently, population differentiation was high across the entire range of the species in Mesoamerica. The level of NST  was significantly higher (P < 0.05) than that for GST, indicating phylogeographical structuring.Differentiation among populations based on FCT value obtained when samples were grouped by north and south of the TMVB and TUX region . In the mismatch distribution, sudden demographic expansion  was not rejected for regional groups (Table S2). The BSPs of Ne through time were flat across time, with no significant increase in population size in the groups north and south of the TMVB postdating the LGM .Patterns of demographic history inferred for Mesoamerican populations of −9, 1.59 × 10−9, and 3.0 × 10−9 s/s/y, the ancestral population size (NA) is estimated to be higher than population sizes of descendant populations for both USA/Mesoamerican and north/south of TMVB splits (Table −9 s/s/y), the divergence between the US and Mesoamerican populations occurred at 5.3 MYA, and 2.7 Ma between populations located north and south of the TMVB. When testing for migration following the split between US versus Mesoamerican populations, extremely low-effective migration rates (close to zero) from USA to Mesoamerica are indicated with higher migration in the reverse direction. Effective migration following the split between populations north and south of the TMVB was close to zero in both directions, suggesting that the unsorted tree we observed is the result of incomplete lineage sorting and incomplete coalescence and not due to significant levels of gene flow across the TMVB  for the time as the most recent glacial advance  rejected the two-refugia hypothesis but not the fragmentation hypothesis using a generation time of 34 years (Table S3). For other Ne values, both the two-refugia and fragmentation hypotheses were rejected at any divergence time (T1–T3). This pattern upheld when considering a generation time of 124 years except that both hypotheses were not rejected at low divergence time values . Thus, our coalescent simulations support the null hypothesis of the current disjunct distribution being the result of fragmentation of a single widespread ancestral lineage.We calculated L. styraciflua, and the models performed well . The ENM for the current climate variables using both the US and Mesoamerican records predicted well the Mesoamerican distribution and under predicted some known localities in the USA  revealed a similar fragmented scenario to the current ENM predicted, with a reduction in suitable habitat in eastern Mexico when using the Mesoamerican dataset. As predicted by the palaeodistributional pattern, both the US and Mesoamerican populations persisted disjunct in their present ranges through the later Pleistocene rather than a pattern of expansion to the south.The current distribution predicted by MAXENT Fig.  closely USA Fig. . When ouUSA Fig. . PredictL. styraciflua (US and Mesoamerican records). The first niche axis (62.1% of variation) was associated with temperature. The second niche axis (27.7%) was associated with annual precipitation and precipitation of coldest quarter, whereas the third axis (5.4%) was associated with coldest months. Tests of niche divergence and conservatism on these three niche axes showed evidence for niche conservatism on niche axis 1 and 2  were only found in the cloud forests of the Sierra Madre Oriental north of the TMVB, haplotypes H15–H17 formed the second group of private haplotypes corresponding to the TUX region, and the remaining private haplotypes (except H20) clustered in a third group south of the TMVB. This pattern of the haplotype network was also supported by the AMOVA and SAMOVA analyses. However, patterns of haplotype sharing between the groups separated by the TMVB suggest historical gene flow, as haplotype H20, which is located in populations north of the TMVB, is descendant of haplotype H19, from south of the TMVB. Nonetheless, gene flow close to zero among populations on both directions of the TMVB was confirmed by the IMa results, supporting the idea that the TMVB is a porous barrier. Lastly, the private haplotypes retrieved in the populations of the TUX region are indicative of allopatric fragmentation and constitute genetically unique populations. Conservation of the TUX region is particularly important due to the restricted distribution of the cloud forest, and because of the accelerated deforestation rates in the TUX region that threaten the endemic genetic diversity of these and possibly other codistributed taxa  within a presumably recently colonized area (Mesoamerica) are not expected under the long-distance dispersal model. Considering the high haplotype and genetic diversity observed in Mesoamerican populations, a vicariance scenario should be the most likely explanation and nonreciprocal monophyly of the US and Mesoamerican populations likely the result of incomplete lineage sorting due to insufficient time. Nonetheless, our coalescent simulations support the null hypothesis of the current disjunct distribution being the result of fragmentation of a single widespread ancestral lineage since the most recent glacial advance. The coalescent simulations used in this study provide a means of inferring population history even in the face of incomplete lineage sorting by assessing the stochasticity inherent in the coalescent process  suggests a Mesoamerican colonization hypothesis for L. styraciflua in the USA, although phylogenetic relationships that would clarify this remain unresolved  in eastern Mexico, and the authors found genetic differentiation between populations south of the TMVB  from those north of the volcanic belt , and the ENM reconstructions agreed with a scenario of expansion south to north of the TMVB during the LGM  is a volcanic chain of mountains that virtually cuts Mexico into northern and southern halves, and its geological development had a profound impact as a biogeographical barrier between Nearctic and Neotropical biotas Halffter . Recent L. styraciflua populations distributed north and south of the volcanic belt. Substitution rates of 1.0 × 10−9, 1.59 × 10−9, and 3.0 × 10−9 s/s/y indicated that divergence of populations separated by the TMVB occurred during the Pliocene to mid-Pleistocene . The most likely explanation for this disjunction is one of the most recent episodes of the geologic evolution of the TMVB, the formation of the Pliocene to Quaternary volcanic arc that occurred at ca. 3.6 MYA, and subsequent climatic changes in the region (Gómez-Tuena et al. L. styraciflua separated by the volcanic belt would have been exposed and eventually adapted to their differing environmental conditions. Populations of the Mesoamerican sweetgum separated by the TMVB are distributed in unique environmental space, implying that the differing environmental conditions of on either side of the TMVB would have reduced gene flow, as shown by IMa results, that would have reinforced the divergence of the two cpDNA haplogroups following initial spatial separation (see also Liu et al. L. styraciflua, combining a phylogeographic and species distribution modeling approach, suggest that the observed patterns of genetic variation and divergence in Mesoamerica are best explained by a combination of isolation imposed by the TMVB and reinforcement by subsequent differential climatic conditions. Although the latter may be true for species that disperse poorly or that are reluctant for physiological reasons to cross regions of less-hospitable habitat, niche divergence for other species with poor seed dispersal may equate to enhanced opportunities for isolation and reduced gene flow, thereby increasing the likelihood of speciation.Our phylogeographic analyses highlight the importance of the TMVB in driving isolation and genetic divergence between


Stenocereus stellatus, managed under traditional agriculture practices in central Mexico. Microsatellite diversity was analyzed within and among populations, between geographic regions, and among population management types to provide detailed insight into historical gene flow rates and population dynamics associated with domestication. Our results corroborate a greater diversity in populations managed by farmers compared with wild ones (HE = 0.64 vs. 0.55), but with regional variation between populations among regions. Although farmers propagated S. stellatus vegetatively in home gardens to diversify their stock, asexual recruitment also occurred naturally in populations where more marginal conditions have limited sexual recruitment, resulting in lower genetic diversity. Therefore, a clear-cut relationship between the occurrence of asexual recruitment and genetic diversity was not evident. Two managed populations adjacent to towns were identified as major sources of gene movement in each sampled region, with significant migration to distant as well as nearby populations. Coupled with the absence of significant bottlenecks, this suggests a mechanism for promoting genetic diversity in managed populations through long distance gene exchange. Cultivation of S. stellatus in close proximity to wild populations has led to complex patterns of genetic variation across the landscape that reflects the interaction of natural and cultural processes. As molecular markers become available for nontraditional crops and novel analysis techniques allow us to detect and evaluate patterns of genetic diversity, genetic studies provide valuable insights into managing crop genetic resources into the future against a backdrop of global change. Traditional agriculture systems play an important role in maintaining genetic diversity for plant species.Microsatellite markers (N = 5) were developed for analysis of genetic variation in 15 populations of the columnar cactus  The evolution of key characteristics in crops through domestication marks one of the most important shifts in ecology and life history for certain plant species. Descriptions of plant domestication and patterns of inheritance have been used to infer more general evolutionary processes since the early development of evolutionary theory Darwin , 1875. NClassical models of plant domestication involve artificial selection and propagation of plants with desirable characteristics, resulting in shifts in the frequency of desirable traits Doebley  and bottMesoamerica is an important domestication center for hundreds of plant species including crops of worldwide importance, such as maize, squash, beans, and peppers, and numerous semi-domesticated species Harris . The TehThe TV and the Balsas River Basin are among the main areas of plant domestication in Mesoamerica MacNeish . CharactStenocereus stellatus Riccob., throughout its natural distribution range in central Mexico. This study develops our previous research which identified three different management systems of S. stellatus found in wild populations, managed in situ among pastures and agroforestry fields, and cultivated in home gardens  and the TV, which have significant environmental differences. LMB is more humid than the TV, and such differences are relevant in understanding both natural and cultural histories of the species since seedling recruitment differs significantly according to water availability  should both be evident.If traditional farmers increase genetic diversity in their home gardens through cultivating multiple clones of desirable individuals  unit. Samples were stored at Rancho Santa Ana Botanic Garden (RSABG) for DNA extraction.Mature individuals of , Mexico . Wild poS. stellatus vendors in markets (N = 55) to provide information on cultivation and trade practices that might influence patterns of genetic diversity and gene movement. Interviewees included both males and females of various occupations, ages, and ethnic groups. Responses to the questions were summarized qualitatively to augment interpretation of quantitative genetic results.Interviews were also conducted with home gardeners in each town and 2O, precipitated with 2 volumes of 95% ethanol, and then dried and resuspended in 100 μL 1 × TE buffer.A modified silica-based CTAB extraction protocol  using a modified CTAB protocol , as well as four other species of columnar cacti (data not shown). The fragments were separated on 2% agarose gels. Florescent-labeled reverse primers were obtained for five loci , 0.3 μL primer (at 1 pmol/L), 0.4 μL dNTPs (at 2.5 mmol/L), 0.5 U GoTaq Flexi DNA polymerase  and 1.5 μL 10 × GoTaq Flexi amplification buffer (Promega). Annealing temperatures and MgClh primer . Forty cAO , AE , HO (observed heterozygosity), HE (expected heterozygosity), and f (the fixation index), were calculated using GenAlEx ver. 6.41  to geographic distance between population pairs. GenAlEx was used to estimate FST and regression parameters, to evaluate model significance with Mantel's tests, and to test for deviation from Hardy–Weinberg expectations. Migration between populations was estimated using BayesAss+ ver. 1.3  were calculated using GenAlEx ver. 6.41 with 999 total data permutations and pairwise population permutations. Estimates of Nei's genetic distances were made with GenAlEx, and an unweighted pair-group classification based on arithmetic averages (UPGMA) was generated with NTSYSpc 2.2 Rohlf . IsolatiBOTTLENECK 1.2.02  and HE (P = 0.011), with managed populations having greater diversity in both cases. The cultivated and wild population types showed a significant difference in the fixation index, f (P = 0.007), with wild populations having less inbreeding than cultivated ones.Populations in the two regions did not differ significantly overall in mean genetic diversity or levels of inbreeding . SeveralfB, were similar to estimates of more traditionally calculated methods . There was also no statistically significant difference between the TV and LMB .There was a trend for more deviations from Hardy–Weinberg expectations for individual loci in the cultivated populations, then the managed populations, followed by the wild populations , but these differences were not statistically significant (P = 0.047) under the infinite alleles model; no population showed a significant departure in the appropriate direction from mutation drift equilibrium under the other mutation models tested. Many populations were characterized instead by a heterozygosity deficiency. With 60 separate tests, we would expect three to be significant by chance based on a probability level of 0.05; therefore the significant result for CoxW may or may not be meaningful and is being examined further as part of a separate analysis .Populations showed little evidence of bottlenecks, regardless of the mutation model used. The only population with a significant excess heterozygosity based on a one-tailed Wilcoxon test was CoxW (P < 0.001). At the sub-regional scale, twice as much genetic variation was evident among populations in the TV (8%) as in LMB . Within both regions, all remaining variation was maintained within populations , with no variation among management categories in either case. The mean value for Nei's genetic distance was greater in the TV than in LMB . The three managed populations examined in LMB had the lowest mean D value to all other populations, but in the TV, managed populations had two of the greatest mean D values.The AMOVA indicated that significant genetic variation was evident both among populations and between regions, but not among management categories. For all populations, 93% of the variation was found within populations, 6% among populations, and 1% between regions ; the mutation model; and the statistical test used to detect heterozygosity excess  would reinforce existing differentiation among populations, as has been reported for other species characterized by asexual recruitment.At the intraregional spatial scale, populations did not show a clear geographic pattern of similarity between wild and nearby human manipulated populations, and isolation by distance was not evident. Within regions, migrants only originated in cultivated and managed populations, not wild populations. Some gene flow was from a centrally located population to nearby populations, but in other cases, gene flow occurred between some of the more distant populations. We detected no clear relationship between population size or density and gene flow.S. stellatus in managed and cultivated populations. Although managed populations have historically been primarily on communal land accessible to all town residents, part of one managed population we visited (CaM) had recently been developed, with conversion to home gardens as private residences filled in the population. Another (MeM) is now under the ownership of several town residents who have augmented the managed population with additional stem pieces to curb soil erosion and supply fuel for pottery firing . Patterns of gene flow and genetic similarity involve human agency where there are close cultural ties between the populations and farmers move stem pieces or seeds from one population to another. They may also reflect pollinator-mediated gene movement, as the distance between many of our populations is within the range that bats may move in a night of foraging. Superimposed on these more predictable processes are the idiosyncrasies of individual towns and farmers pertaining to land ownership and goals of management, interactions with local and regional markets, and individual preferences for different fruit traits. Together they constitute a complex web of cultural, physical, and biological interactions that have shaped the composition of the populations we examined.As is occurring elsewhere in Mexico  selective pressures imposed by both agricultural and natural environments (Allaby et al. S. stellatus, S. pruinosis [Parra et al. This analysis of Since the mid-20th century, growing concerns about conservation of crop genetic resources and increasing alarm over food security, especially in areas of low agricultural productivity, have resulted in incorporation of these issues into national policies and international agreements Gepts . As mole


Monochamus alternatus Hope (Coleoptera: Cerambycidae), is an important forest pest as well as the principal vector of the pinewood nematode (PWN), Bursaphelenchus xylophilus (Steiner et Buhrer), in mainland China. Despite the economic importance of this insect-disease complex, only a few studies are available on the population genetic structure of M. alternatus and the relationship between its historic dispersal pattern and various human activities. The aim of the present study was to further explore aspects of human activity on the population genetic structure of M. alternatus in mainland China. The molecular data based on the combined mitochondrial cox1 and cox2 gene fragments from 140 individuals representing 14 Chinese populations yielded 54 haplotypes. Overall, a historical  expansion that originated from China’s eastern coast to the western interior was revealed by the haplotype network, as well as several recent, long-distant population exchanges. Correlation analysis suggested that regional economic status and proximity to marine ports significantly influenced the population genetic structure of M. alternatus as indicated by both the ratio of shared haplotypes and the haplotype diversity, however, the PWN distribution in China was significantly correlated with only the ratio of shared haplotypes. Our results suggested that the modern logistical network  in China is a key medium by which humans have brought about population exchange of M. alternatus in mainland China, likely through inadvertent movement of infested wood packaging material associated with trade, and that this genetic exchange was primarily from the economically well-developed east coast of China, westward, to the less-developed interior. In addition, this study demonstrated the existence of non-local M. alternatus in new PWN-infested localities in China, but not all sites with non-local M. alternatus were infested with PWN.The Japanese pine sawyer,  Monochamus alternatus Hope (Coleoptera: Cerambycidae), is a major pest of coniferous forests, especially pines (Pinus spp.), and is also the key vector of the exotic pinewood nematode (PWN), Bursaphelenchus xylophilus (Steiner et Buhrer) (Nematoda: Aphelenchoididae), in eastern Asia The Japanese pine sawyer, M. alternatus is believed to be responsible for the spread of the PWN in Asia given that PWN is transmitted to new hosts during maturation feeding and oviposition by nematode-laden M. alternatus adults M. alternatus and possible influencing factors could provide critical clues to the spread of PWN in China. It is important to note that M. alternatus is native to China, and in the early report by Chen et al. M. alternatus can be influenced by multiple factors, among which human-mediated movement of the pest is believed to be a significant component. Like many bark- and wood-boring insects M. alternatus can be commonly moved to new locations through inadvertent transport of infested wood, including logs, lumber, and wood packaging material. In the case of M. alternatus, the portions of a tree trunk or branch that are most commonly infested are the cambial region between the bark and the sapwood and the outer sapwood M. alternatus and that some larvae and pupae can survive the milling process, wood products made from infested trees, especially the sapwood portion, could serve as a pathway by which M. alternatus is moved within China. Although M. alternatus could be transported long distances within China via logs and lumber, we believe that wood packaging material such as pallets and crating that are used in the transportation of commercial products is the most likely pathway for long-distance human-assisted transport of M. alternatus across mainland China as well as internationally M. alternatus in China.Mainland China is one of the areas most highly impacted by PWN in eastern Asia M. alternatus and PWN from eastern China. Similarly, molecular techniques have been used to explore the origins of other invasive forest pests such as Agrilus planipennis Fairmaire (Coleoptera: Buprestidae) and Tomicus piniperda (L.) (Coleoptera: Curculionidae: Scolytinae) in North America M. alternatus in mainland China, and to elucidate the relationship between the genetic structure of M. alternatus and both human activity and PWN dispersal in China. The results of the present research should be helpful in explaining how M. alternatus has spread in mainland China, formulating integrated management strategies to control M. alternatus and PWN, and providing new insights in the field of invasion ecology.Nowadays, molecular techniques are frequently used to study the dispersal history of invasive organisms or non-local populations of native organisms. For example, Fu et al. M. alternatus and PWN in mainland China M. alternatus adults were collected from each site with assistance from the local forestry administrations and forest academies using flight-intercept traps  baited with either the M99-1 bait (Chinese Academy of Forestry) or FJ-Ma-02 bait (Fujian Academy of Forestry Sciences) α-pinene, β-pinene, and ethanol. The traps were placed in the same pine forests at each location from late March to early October in 2010 and 2011, and were checked weekly for adult beetles.Fourteen Chinese provinces were selected to cover the major distribution range of cox1 and cox2 sequences of Monochamus galloprovincialis (Olivier) from GenBank (EU556596) To minimize the chance of sampling closely related individuals, such as individuals from the same parents, 15 ∼ 20 traps were placed 2 ∼ 3 m above ground at a given site and spaced at least 100 m apart. The beetles used from each site were selected from all traps at a location and from both years of sampling. Captured adults were preserved in 95% ethanol and preserved at −40°C until processing in the Laboratory of Biological Invasion and Ecosecurity, Yunnan University. The partial No specific permits were required in the present study for collection of this widespread native forest insect. The authors confirm that the sampling sites were not privately owned or protected. This study did not involve any endangered or protected species.M. alternatus adults from each of the 14 sampling locations were used in the present research for DNA extraction. All samples were washed with double distilled water and dissected to expose the mesothorax muscle tissues that contain abundant mitochondria. For each individual, 20 mg of muscle tissues were obtained and homogenized in a labeled 1.5 mL Eppendorf tube, and then digested for 12 h with protease buffer, containing 450 µL STE , 75 µL SDS , and 25 µL Proteinase K .Ten g. Next, 550 µL isopropanol was added and chilled at −20°C for 12 h, and then centrifuged at 9,000×g to pellet DNA. The pellets were washed with 70% ethanol, dried at 37°C, and dissolved with 60 µL Tris . The dissolved DNA was quantified on an Eppendorf Biophotometer , and preserved at −40°C in the same laboratory. The ≈100 ng/µL dilutions were used as templates in polymerase chain reactions (PCR).The phenol-chloroform method was applied to the resultant mixture to isolate DNA, and the supernatant was recovered through centrifugation at 5,000×cox1 gene and a ≈ 700 bp fragment of the cox2 gene were amplified by PCR on a Biometra T-Professional Standard thermocycler . The thermal profile consisted of an initial denaturation at 93°C for 3 min; followed by 35 cycles of denaturation at 93°C for 30 sec, annealing at 50°C (cox1) or 47°C (cox2) for 1 min, and elongation at 72°C for 2 min; then a final elongation at 72°C for 10 min.For all samples, a ≈ 800 bp fragment of the µL system which contained 2.6 µL of 10× PCR buffer , 2.6 µL of MgCl2 , 4.1 µL of dNTP mixture , 0.5 µL of Taq DNA polymerase , and 0.5 µL of each of forward and reverse primers . The primers for the cox1 fragment were C1-J-2183  (5′-CAA CAT TTA TTT TGA TTT TTT GG-3′) and TL2-N-3014  (5′-TCC AAT GCA CTA ATC TGC CAT ATT A-3′) cox2 fragment were tRNALeu−F (5′-GTG CAA TGG ATT TAA ACC CC-3′) The PCR reaction was applied in a 25 The PCR products were purified using TaKaRa Agarose Gel Purification Kit (version 2.0)  and sequenced by Sangon Biological Engineering Technology & Services Co., Ltd. . Sequencing reactions were carried out in both forward and reverse directions on an ABI Prism 3730×l automatic sequencer .Numts (nuclear copies of mtDNA fragments), and then aligned into contigs. The number of polymorphic sites and nucleotide composition were analyzed in MEGA 4.0 Fst values, nucleotide diversity of each population, and the degree of gene flow among populations (Nm) were calculated by Arlequin 3.11 Fst values were tested using 1,000 replications.Raw sequences were proofread and aligned using Clustal W M. alternatus, an AMOVA  analysis http://ports.com/browse/asia/china), regional economic status FCT value and the smallest P value was considered optimal. The optimal gamma value for AMOVA analysis was estimated using jModelTest 0.1.1 Transportation (or logistics network) data are difficult to use as a quantified criterion in analysis, but they can be well represented by port distribution and frequency of freight transportation, and are strongly associated with regional economic status M. alternatus, we used each factor in correlation analysis against three key population genetics indices. The first index was the ratio of shared haplotypes (denoted as Rshr) because it can indicate a recent introduction/establishment of a non-local population of a native organism K) and the haplotype diversity (denoted as Dhap) which reflect the genetic diversity within populations were also used in correlation analysis to estimate the influence of human activity on population genetic diversity. Correlation analysis was performed with SPSS 13.0 , using Kendall’s τ and Spearman’s ρ methods To analyze the relationship of the above three factors with the population genetic structure of A 3D multidimensional scaling (MDS) analysis http://nfgis.nsdi.gov.cn/), while the spatial data for port coordinates were extracted from Ports.com (http://ports.com/browse/asia/china).The contours of the significantly correlated population genetics indices were mapped in Surfer 10.3  using the Kriging gridding method cox1 fragment and a 565 bp cox2 fragment, which corresponded to the 2,247 ∼ 2,881 bp portion and the 3,079 ∼ 3,643 bp portion in the mitogenome of Drosophila yakuba Burla, respectively. In the combined 1,200 bp sequences, 63 sites (5.25%) were polymorphic and 41 sites (3.42%) were informative parsimony. Overall, 40 C–T transitions, 20 A–G transitions, and five A–T transversions were found, consisting of 63.5%, 31.8%, and 7.9% of the total polymorphic sites, respectively. No deletions or insertions were detected. The nucleotide composition showed a high A–T bias of nucleotide usage comprising 73.2% of the total average nucleotide composition.The alignment yielded a 635 bp cox1 and cox2 sequences were defined based on polymorphic sites and designated in numerical order, with 43 private haplotypes included. Haplotypes derived from the Guangxi and Yunnan populations were locally private, while those derived from Fujian and Zhejiang populations were commonly shared by other populations, with four shared haplotypes present in each population, and the remaining populations containing one to three shared haplotypes .Fifty-four haplotypes of the combined plotypes . The numplotypes . The coxM. alternatus populations varied from 0.0018 to 0.0082, with the distance between the Guizhou and Yunnan populations being smallest while the distance between the Guangxi and Yunnan populations was the largest. Most of the K2P distances between populations from Jiangsu, Shandong, Zhejiang, Fujian, Guangdong, and Anhui were below 0.0040, while the distance between the two populations mentioned above (Guangxi and Yunnan) and those from Henan, Hunan, or Jiangxi were higher. Close genetic connections between geographically distant M. alternatus populations were observed, particularly between the populations from Shandong, Henan, or Guizhou and those from Jiangsu, Zhejiang, Fujian, Guangdong, or Anhui , Zhejiang and Hunan (6.5625), Jiangsu and Hunan (7.7254), Zhejiang and Fujian (8.5542), Fujian and Anhui (9.8026), Jiangsu and Fujian (13.6057), Shandong and Fujian (18.5227), and Zhejiang and Anhui (25.5357). By contrast, relatively low values were mostly associated with d Yunnan .M. alternatus populations were divided into three groups based on regional economic status, with 19.9% of variation explained among groups , 32.9% among populations within groups , and 47.2% within populations . The AMOVA analysis was also significant when the 14 populations were divided into three groups based on the following RFT ranking, <100 billion tkm , 100 ∼ 200 billion tkm, and >200 billion tkm. The results showed that 20.1% of the variation was explained among groups , 32.7% among populations within groups , and 47.2% within populations . By contrast, when the 14 populations were divided with respect to port and PWN infestation status, the FCT values were not significant and the variation among groups was much lower   .Rshr) and the PWN infestation status , as well as the RFT . Correlation analysis also showed significant positive correlations between haplotype diversity (Dhap) and port distribution , as well as the regional GDP . However, the ratio of shared haplotypes (Rshr) was not significantly correlated with the port distribution nor the regional GDP, and also the haplotype diversity (Dhap) was not significantly correlated with the PWN infestation status nor the regional RFT. Similarly, no significant correlations were detected between any human-activity related factor and Nei’s average number of pairwise differences within populations (K), or between the K value and PWN infestation status . All populations from the economically developed regions and most M. alternatus populations from the less developed regions formed one major cluster, while populations from Hubei, Chongqing, and Guangxi formed a second group, and those from Guizhou and Yunnan formed a third group , and no defined haplogroups. When categorized by the geographical regions of mainland China, the median joining network clearly showed that H10 or H34 from the eastern populations were the ancestry haplotypes based on their direct connection to the outgroup, and a natural  population expansion that occurred from eastern China to central China and then to southern and southwestern China. This network also suggested the existence of human-mediated (recent) dispersal as evidenced by a haplotype shared by geographically distant populations (H42) .M. alternatus populations expanded from the economically developed regions of China to the less developed regions and then to the underdeveloped regions. Six haplotypes were shared by M. alternatus populations from economically developed regions and the less developed regions, while only one haplotype was shared by the populations from the economically developed regions (Shandong) and the underdeveloped regions (Guizhou), and none were shared by populations from the less developed regions and the underdeveloped regions. Twenty-two haplotypes  were found in populations from the less developed regions, followed by 16 (37.2%) from developed regions, and only five (11.6%) from the underdeveloped regions  were associated with sites with PWN infestations and ports, especially those along the coastal area and Yangtze River. And three, areas with higher haplotype diversity (Dhap) were also associated with sites with PWN infestations and ports, especially the coastal area and the area stretching from the Pearl River delta upward into central China  and port distribution as well as regional GDP, and between the ratio of shared haplotypes (Rshr) and the RFT  (Rshr) than the neighboring areas  in the study area became a reductive gradient from the east coast westward  , which cng areas , demonstwestward . OverallM. alternatus haplotypes (Rshr)  , indicatM. alternatus in China in the present paper, and the similar spread pattern of PWN in China it can be inferred that westward spread of PWN in mainland China likely resulted from the introduction of non-local M. alternatus that originated from areas already infested with PWN.Since the first report of the PWN infestation in China in Nanjing  in 1982,M. alternatus in the spread of the PWN, it is important to realize that not all non-local M. alternatus populations carry PWN. This is supported by our finding that there was no significant correlation between the genetic structure and the haplotype diversity (Dhap) in M. alternatus with the infestation pattern of PWN  while two other nearby sites (Wanding and Ruili) were both infested with PWN. Overall, the present research confirms that the inadvertent introduction of non-local M. alternatus can lead to the spread of PWN, but that the invasion and spread of PWN would not necessarily affect the genetic structure of M. alternatus.However, despite the causative role of n of PWN ; Table 4M. alternatus populations across mainland China in recent years. Advancements in combining molecular biology and socioeconomic indices to address research on the dispersal of cryptic pests will be useful in developing and improving pest management and regulatory strategies, especially when human activities are linked to pest dispersal.Although the present analyses were carried out with a limited sample pool, the composite analyses using molecular and economic data suggested a strong linkage between human activity and the dispersal of Table S1Results of AMOVA analyses with significance based on different grouping criteria of regional freight turnover (RFT) in China (see text for details).(DOC)Click here for additional data file.Table S2Matrix of the gene flows (Nm)  and the Kimura two-parameter (K2P) distances  among 14 populations of M. alternatus from mainland China.(DOC)Click here for additional data file.Table S3Nei’s average number of pairwise differences between populations , Nei’s average number of pairwise differences within populations (K) , the pairwise Fst values , and the nucleotide diversity (the last row) of the 14 populations of M. alternatus from mainland China.(DOC)Click here for additional data file.


Myotis davidii, an endemic Chinese bat. We used a combination of nuclear and mitochondrial DNA markers to investigate genetic diversity, population structure, and the demographic history of M. davidii. In particular, we compared patterns of genetic variation to glacial oscillations, topography, and environmental variation during the Pleistocene in an effort to explain current distributions in light of these historical processes.Global climatic oscillations, glaciation cycles and the unique geographic topology of China have profoundly influenced species population distributions. In most species, contemporary distributions of populations cannot be fully understood, except in a historical context. Complex patterns of Pleistocene glaciations, as well as other physiographic changes have influenced the distribution of bat species in China. Until this study, there had been no phylogeographical research on M. davidii comprises three lineages  based on the results of molecular variance analysis (AMOVA) and phylogenetic analyses. The results of a STRUCTURE analysis reveal multi-hierarchical population structure in M. davidii. Nuclear and mitochondrial genetic markers reveal different levels of gene flow among populations. In the case of mtDNA, populations adhere to an isolation-by-distance model, whereas the individual assignment test reveals considerable gene flow between populations. MDIV analysis indicate that the split of the MEP and SWP/SH lineages, and from the SWP and SH lineages were at 201 ka BP and 158 ka BP, respectively. The results of a mismatch distribution analysis and neutrality tests indicate a population expansion event at 79.17 ka BP and 69.12 ka BP in MEP and SWP, respectively.M. davidii appear associated with climatic oscillations, topography and eco-environmental variation of China. Additionally, the three regions are genetically differentiated from one another in the entire sample set. The degree of genetic differentiation, based on the analysis of mtDNA and nDNA, suggests a male-mediated gene flow among populations. Refuges were in the MEP, SH and the lower elevations of SWP regions. This study also provides insights for conservation management units .The complex demographic history, discontinuous extant distribution of haplotypes, and multiple-hierarchy population structure of  Global climatic oscillations and associated glaciation cycles have profoundly influenced species population distributions . PleistoMyotis davidii) is endemic to China, occurring in a small province of middle and northern China .Total genomic DNA was isolated from tissue using the Qiagen DNAeasy Tissue Kit. Hypervariable region I (HVI) of mitochondrial control region was amplified by polymerase chain reaction using primer pair P-F . In ordeMyotis myotis [M. daubentonii [M. davidii , 20 mM (NH4)2SO4, 0.01% (v/v) Tween-20, 0.2 mM dNTP mix, 1 μM of each primer and 0.5 U of Bioline Taq polymerase. Touchdown PCR was used with all primers, with an initial denaturing step of 3 min at 94°C. The annealing temperature of the reaction is decreased 0.5°C each cycle from 60°C to a touchdown at 50°C, at which temperature 20 cycles are carried out. An additional 25 cycles were then performed with 30 s denaturing at 91°C and 30 s annealing at 50°C. No extension steps were included [Subsequent to screening 16 microsatellite loci originally isolated from either s myotis  or M. dabentonii , 8 polymii Table . All forii Table , and PCRincluded , apart fA), haplotype diversity (h) and nucleotide diversity (π). All estimates were made using ARLEQUIN 3.1 [Estimates of mitochondrial haplotype variability within different regions and populations included the number of haplotypes , observed heterozygosity (HO) and average allelic richness (RS) determined with FSTAT 2.9.3 [Fis was calculated for each population and adjusted for multiple tests using a Bonferroni's correction [Microsatellite genotype data were scored using the Genemarker software 1.75 (SoftGenetics Inc.). Genetic diversity was assessed for each population by calculating expected heterozygosity  and maximum likelihood (ML) algorithms performed in PAUP 4.0b10 . A GTR +ayes 3.1  using deSSD) and the Harpending raggedness index (r) with parametric bootstrapping (10000 replicates). Generally, a smaller and non-significant value (PSSD and Pr > 0.05) indicated population expansion. We estimated the time since expansion (t) with the equation τ = 2 μkt [τ (tau) is a parameter of the time to expansion in units of mutations, μ is the mutation rate per nucleotide and k is the number of nucleotides of the sequence. Tajima's D and Fu's Fs (based on the infinite-site model) were sensitive to the population expansion. Significant negative values of Tajima's D were interpreted as non-neutral conditions. Negative values of Tajima's D and Fu' Fs significantly deviated from "0", inferred as a signal of demographic expansion. All analyses were conducted with ARLEQUIN 3.1 [The population demographic expansion was tested by mismatch distribution analysis and neutrality tests. Mismatch distribution analyses were implemented to detect historical demographic expansions. Significant difference from a model of sudden expansion was assessed using the sum of squared deviations  were estimated using the formula t = popT*(θ/2 μk), where pop Tis the time of population divergence, θ is the population mutation rate, μ is mutation rate per nucleotide, and k is the number of nucleotides of the sequence. Likelihood values for pop Tand θ were calculated and the value with the highest posterior probability accepted as the best estimate [With a coalescent-based approach, the program MDIV was used to estimate the timing of population divergence . MDIV waestimate . The proNyctalus [We used a mutation rate of 20% per million years (Myr) in our study, which was previously applied to bats of the genus Nyctalus . The genst for mtDNA using ARLEQUIN 3.1 [Fst was calculated with GENALEX 6.0 for microsatellites [Genetic differentiation among populations was quantified by computing pairwise differentiation ΦQUIN 3.1 . Fst wastellites .CT), between populations within regions (ΦSC) and between all populations (ΦST) by using ARLEQUIN 3.1 [To test for geographical genetic structure, analyses of molecular variance (AMOVA) with 10000 permutations were assessed according to the degree of differentiation between regions  to assess the statistical significance of the correlation between genetic and geographical distances was performed with IBDWS 3.14 , to undeYY carried out the molecular genetic analysis, participated in manuscript's design and drafted the manuscript. KS, LX, LW, TJ, SL, GL, JF participated in the design of the study and performed the statistical analysis. Sean W. Berquist participated in manuscript editing. All authors read and approved the final manuscript.mtDNA haplotypes. Distribution of dloop region haplotypes shared in the same and different local populations of Myotis davidii.Click here for fileAMOVA in mtDNA and microsatellite. The molecular variance analysis (AMOVA) in mtDNA and microsatellite of Myotis davidii were in three geographical regions. Regions chosen: Middle East Plain, Southwest Plateau, South Hills. Asterisks highlight hierarchical levels explain a significant proportion of the overall variance (P < 0.001).Click here for fileEstimates of genetic difference derived from both mtDNA and microsatellites. Above diagonal is Φst from mtDNA. Below diagonal is Fst from microsatellites. The significant values are marked with asterisks. The pairwise genetic difference within regions is in bold. Significance level = 0.05.Click here for file


Patterns of genetic diversity between and within natural plant populations and their driving forces are of great interest in evolutionary biology. However, few studies have been performed on the genetic structure and population divergence in wild emmer wheat using a large number of EST-related single nucleotide polymorphism (SNP) markers.FST-outlier method. Significant correlations between loci and ecogeographical factors were observed.In the present study, twenty-five natural wild emmer wheat populations representing a wide range of ecological conditions in Israel and Turkey were used. Genetic diversity and genetic structure were investigated using over 1,000 SNP markers. A moderate level of genetic diversity was detected due to the biallelic property of SNP markers. Clustering based on Bayesian model showed that grouping pattern is related to the geographical distribution of the wild emmer wheat. However, genetic differentiation between populations was not necessarily dependent on the geographical distances. A total of 33 outlier loci under positive selection were identified using a . This adaptive genetic diversity is significantly associated with ecological factors.Natural selection appears to play a major role in generating adaptive structures in wild emmer wheat. SNP markers are appropriate for detecting selectively-channeled adaptive genetic diversity in natural populations of wild emmer wheat The analysis was conducted using 21 of the examined wild wheat populations. Populations from Turkey including W. Siverek, E. Siverek, and N. Diyarbakir with many missing data and Mt. Hermon, a cold desert with the highest rainfall were excluded from this analysis in order to minimize the errors or the bias caused by extreme climate conditions.Multiple regression analysis was performed to investigate the relationship between environmental variables and SNP allele frequencies, and detect the best predictors of gene diversity and PIC index ,15. Nei’Genotyping of 200 wild emmer wheat accessions with multiplexed 1,536 Illumina Golden Gate SNP assay generated 307,200 genotypic data points. Out of the 1,536 SNPs presented in our oligonucleotide pool assay (OPA), 1,371 (89.3%) SNPs with high quality genotype calls were obtained, while the other 10% failing to generate clear genotype clustering were removed. Out of the 1,371 scoreable SNP markers, 266 were monomorphic across all the 200 accessions and the overall polymorphism rate was 80.6%. Marker distribution, Nei’s gene diversity, and PIC values calculated for each chromosome and genome were presented in Table P < 0.05) for gene diversity and PIC  and PIC . In the genome A, chromosome 3A and 6A had higher genetic diversity and chromosome 1A and 5A had lower genetic diversity than the genome-wide average in the analyzed germplasm , followed by N. Diyarbakir (55.75%) and Yehudiyya (51.49%), whereas the polymorphic loci of Rosh-Pinna and Qazrin were the least (31.49-32.49%).Proportion of polymorphic loci, gene diversity, and PIC of the 25 wild emmer wheat populations were summarized in Table r = 0.014, P = 0.543; Figure Genetic distances (D) were calculated for all the population pairs, based on the shared-allele distance . The results suggested that the optimal value of K was 2  assigned to a specific cluster with a probability higher than 80% was obtained, and only 6% were classified as admixed. However, percentage of unassigned genotypes, classified as admixed, increased continuously with K, and this percentage is 8.5%, 14%, and 42% when K = 3, 4 and 5, respectively. Hence, the clustering diagrams with K ranging from 2 to 4 are presented in Figure SNP genotyping data were used for genetic structure analysis, using the Bayesian clustering model implemented in the STRUCTURE software. The estimated log probability (LnP(D)) increased continuously with increasing s Figure A. We app2 Figure B. When KK = 2, the analyzed wild emmer wheat populations can be divided into two genetically distinct groups (Group I and Group II)  , and accessions from south marginal populations including Taiyiba, Bet-Meir, Sanbedriyya and Jaba were clustered together  Figure C. Group ) Figure C. When KP < 10-5) portion of the variation resided between populations (Table FST = 0.098) was highly significant (P < 10-5) as indicated by permutation test. These results indicate that differentiation between populations has truly occurred.Population differentiation was assessed with an analysis of molecular variance (AMOVA). The AMOVA revealed that individuals within populations are highly genetically differentiated in relation to individuals among populations, which is reflected by a higher proportion of variance within populations than among populations. Ninety percent of the genetic variation resided among accessions within populations, while a small (9.82%) but significant (ns Table . MoreoveFST) were also calculated for pairwise comparisons of the 25 populations . Forty-four out of 126 pairs showed strong genetic differentiation (FST > 0.2). However, genetic differentiation between populations was independent of geographical distances between the sites of collection, as revealed by the Mantel test  Water factors : 316;b) Geographic factors : 30;c) Temperature factors : 69;d) Geographic factors + water factors : 121;e) Water and temperature factors : 80;f) Geographic factors + temperature factors : 70; andg) Geographic factors + temperature + water : 69.Average Nei’s gene diversity and PIC of the 25 populations of wild emmer wheat in this study were 0.1841 and 0.1530, respectively. Compared to those obtained previously with EST-SSR , SSR 15, and allK = 2, 3 and 4, respectively  populations. These results suggest that ecological variables play an important role in shaping the genetic structure of wild emmer wheat.This study presents the first genome-wide analysis on population structure of SNP genetic variation among natural populations in wild emmer wheat. Clustering based on Bayesian model showed that the grouping pattern is related to the ecogeographic distribution of the wild emmer wheat populations. All central populations collected from warm and humid environments in the Golan Plateau  and near the Sea of Galilee  were separated from marginal populations when r = 0.014, P = 0.543; Figure Indeed, SNP-based genetic distances were found to be independent on the geographical distances, as revealed by the Mantel test  indicated that a large number of multiple fungal disease-resistance genes exist in chromosomes 1B, 2A, 3B, 4A and 7A, such as Lr17, Lr20, Lr27, Lr28, Lr30, Lr38, Sr2, Sr7, Sr15, Sr21, Sr22, Sr38, Yr17, Pm1, Pm4, and Hd. In addition, three genes including P-EA[GBP-1[SPDS[In the present study, we identified 33 candidate loci under positive selection based on l Figure . These ll Figure . The locl Figure . This obding P-EA, GBP-1[4-EA[GBP-1 and SPDSBP-1[SPDS,50, whicBP-1[SPDS-50, appeBP-1[SPDS.The organization and evolution of genetic diversity in nature at global, regional, and local scales are nonrandom and heavily structured; and are positively correlated with, and partly predictable by, abiotic and biotic environmental heterogeneity and stress , as showFST-outlier method. The markers and genome segments we identified as outliers under positive selection were consistent with known patterns of selection. These results suggested that ecological factors plaid an important evolutionary role in generating adaptive structures in wild emmer wheat. SNP markers are appropriate for detecting selectively-channeled adaptive genetic diversity in natural populations of wild emmer wheat. However, it will be greatly helpful to conduct functional studies to confirm the role of these outlier loci or genome segments in wild emmer wheat.The present work, using genome scan approach, presented strong evidence for adaptive genetic divergence in wild emmer wheat associated with ecological factors. Ecological factors, singly or in combination, explained a significant proportion in variation of SNP allele frequency. The SNPs could be classified into several categories of ecogeographical predictors. We identified a total of 33 loci under positive selection by using an All the supporting data are included as additional files.The authors declare that they do not have any financial or non-financial competing interests exist.JR carried out DNA extraction and data analysis, and drafted the manuscript. LC and DS carried out DNA extraction. FMY designed the SNP markers and worked on finalizing the manuscript. JW performed the SNP genotyping. YP and DFS carried out the field experiments. EN and AB worked on the manuscript; MCL was in charge of SNP design and genotyping work and participated in drafting the manuscript. JP was in charge of the entire research including experimental design, germplasm collection, outlining and finalizing the manuscript. All the authors read and approved the final version of the manuscript.Shared-allele distance between populations of wild emmer wheat.Click here for fileFst among populations of wild emmer wheat.Differentiation coefficients shown as pair-wise Click here for file2) of genetic indices  and environmental variables (as independent variables) in 25 populations of wild emmer wheat, T. dicoccoides, in Israel and Turkey.Coefficient of multiple regressions (RClick here for file


Ursus maritimus) must adapt to a rapidly changing landscape. This process will necessarily alter the species distribution together with population dynamics and structure. Detailed knowledge of these changes is crucial to delineating conservation priorities. Here, we sampled 361 polar bears from across the center of the Canadian Arctic Archipelago spanning the Gulf of Boothia (GB) and M'Clintock Channel (MC). We use DNA microsatellites and mitochondrial control region sequences to quantify genetic differentiation, estimate gene flow, and infer population history. Two populations, roughly coincident with GB and MC, are significantly differentiated at both nuclear (FST = 0.01) and mitochondrial  loci, allowing Bayesian clustering analyses to assign individuals to either group. Our data imply that the causes of the mitochondrial and nuclear genetic patterns differ. Analysis of mtDNA reveals the matrilineal structure dates at least to the Holocene, and is common to individuals throughout the species’ range. These mtDNA differences probably reflect both genetic drift and historical colonization dynamics. In contrast, the differentiation inferred from microsatellites is only on the scale of hundreds of years, possibly reflecting contemporary impediments to gene flow. Taken together, our data suggest that gene flow is insufficient to homogenize the GB and MC populations and support the designation of GB and MC as separate polar bear conservation units. Our study also provide a striking example of how nuclear DNA and mtDNA capture different aspects of a species demographic history.As global warming accelerates the melting of Arctic sea ice, polar bears ( Ursus maritimus), affecting the spatial and seasonal distribution of the sea ice that they rely on to travel and hunt , that extends over ∼500,000 km2. These areas are separated by a land barrier (the Boothia Peninsula) for the majority of their length and connected by water only through the narrow Bellot Strait, an ∼25 km long passage with fast tidal currents that prevent the formation of stable pack ice , discarding genetic data from cubs, yearlings and second-year bears that were related to the sampled mother.Samples for genetic analyses were collected by the Nunavut Department of Environment during surveys carried out in the spring (March–June) between 1998 and 2000 in two previously defined polar bear populations: GB and MC Fig.  for a deJMP version 10 .We used mark-recapture data to compare movement patterns between sexes and populations. Movement was estimated by calculating the straight-line distance between sites where each individual was captured in different years using the great circle distance calculator in Google Maps. When a bear was captured in three different years, we only used the maximum distance between capture sites to avoid pseudo-replication. Three females moved from MC to GB and could not be a priori assigned to either population (and thus be confidently used to compare movement patterns between GB and MC). We analyzed the data in two ways, first excluding these three females and then again considering them part of the population where they were initially captured (MC). In total we obtained 56 independent observations of distances traveled by individual bears during the study period. We compared movement patterns between sexes and populations by performing a two factor analysis of variance (ANOVA) in Cervus version 3.0.3  following the manufacturer's procedures. We genotyped 531 individuals  for nine previously published dinucleotide microsatellite loci  for the amplification and sequencing of an ∼472 base pair (bp) fragment of the mitochondrial control region . Amplification was carried out using primers PBCR1 (5’-AGCTCCACTACCAGCACCC-3’) and PBCR 4 (5’-AAATGCATGACACCACAGTTATGTGTGATC-3’) . Polymerase chain reaction (PCR) cocktails contained 2 μL of genomic DNA, 2.5 mmol/L MgCl2, 0.1 mmol/L dNTPs, 0.1 μmol/L of each primer, and 0.75 U taq DNA polymerase  in a final volume of 25 μL PCR ViBuffer A . The thermocycling profile included an initial denaturation step at 94°C for 5 min; followed by 34 cycles of 94°C for 30 sec, 50°C for 30 sec, and 72°C for 45 sec; and a final extension at 72°C for 5 min. An aliquot of the PCR products was visualized on an ethidium bromide-stained 2% agarose gel and successful reactions were purified using the QIAquick PCR purification Kit (QIAGEN). Sequencing was carried out in both directions using the above mentioned primers at the London Regional Genomics Centre  using an Applied Biosystems 3730 Analyzer. All sequences were deposited in GenBank (Accession Numbers KF192517 – KF192602).We selected a subset of 86 subadult and adult individuals evenly distributed throughout the study area , or taking into account the difference among haplotypes together with their frequencies (ΦST). Statistical significance was assessed through 1000 random permutations. We calculated the genetic diversity within populations by estimating p-distances in Arlequin and FIS values in FSTAT version 2.9.3.2 . We explored values of K = 1 through 4 (two more than the a priori reported number of populations) with ten iterations per value of K each with 2,500,000 generations, discarding the initial 500,000 as burn-in. The most likely K value was determined using the Evanno et al. .We assessed population structure using individual genotypes for the nine microsatellite loci and two programs that implement Bayesian clustering algorithms: o et al.  method iGeneland runs per group analyzed , each consisting of 2,500,000 iterations and a thinning of 100. The number of genetic populations was set to K = 2 based on results of Structure runs, using correlated allele frequencies and the spatial model. After discarding 5000 steps (25%) as burn-in, we divided the study area into 100 quadrats . The HKY , the effective number of migrants per generation, were obtained from the migration (m/μ) and θ (4Nμ) parameters calculated with IMa2. By calculating 2Nm = 4Nμ × 1/2 × m/μ, we estimated 2Nm independent of the mutation rate .Based on our mark-recapture data, the average distance traveled was slightly larger for males than for females (71 ± 60 km vs. 57 ± 61 km), and for MC than for GB individuals (89 ± 90 km vs. 54 ± 45 km), although these differences were not statistically significant. The maximum distance traveled was larger for females (317 km) than for males (194 km), and for MC (317 km) than from GB (185 km). In three instances, females that were initially observed within the MC boundaries were subsequently captured in the area encompassed by GB . When these three females were included in the population from which they had initially been captured (MC), MC individuals were found to have traveled larger distances compared to those of GB (101 ± 88 km vs. 54 ± 45 km), and this difference was statistically significant (two factor ANOVA: K) identified by Structure was two, corresponding to differentiation between the GB and MC populations, although individuals show varying levels of admixture , causing Structure to fail in assigning individuals to either genetic population without incorporating sampling locality as a prior in the Bayesian analysis. Using information from individual genotypes and sampling coordinates, Geneland assigned most quadrats in the GB to the GB genetic cluster, and most MC quadrats to the MC cluster, with an FST value of 0.013 between clusters . Altogether, these results suggest nuclear genetic differentiation between GB and MC with a low level of admixture.Bayesian clustering analyses performed using data from nine microsatellite loci suggest GB and MC polar bear populations are genetically differentiated Fig. . The mosure Fig. A. Using ure Fig.  method,  = 2 see . Howeverers Fig. , suggestStructure assigned individuals to two genetic clusters corresponding to GB and MC (when capture location was used as a prior); however, differentiation was higher in females than males  were both marginally higher for females than for males . As before, for both sexes Geneland assigned most quadrats in GB to a different genetic cluster than those in MC and vice versa  among 86 individuals that differed from each other by up to nine mutational steps , with haplotype e being most common among GB individuals and haplotype a most abundant among MC individuals . Figure a and e) were captured; again showing how individuals carrying the e haplotype are more common in GB and those with the a haplotype are mainly found in MC.The CR statistical parsimony network analysis implies genealogical distinction between GB and MC Fig. . The neteps Fig. A. Haplotals Fig. A. The geThe divergence between individuals from GB and MC is comparable to the divergence among CR sequences from individuals sampled across the species’ range Fig. . When thONeSAMP . IMa2 analysis of mtDNA suggests that there have been significant levels of gene flow since the establishment of the GB and MC populations , whereas the converse is true when the analysis was performed using nuclear data (Table U. arctos) CR to diverge at a rate of 29.8%  per million years. Huang et al. , while nuDNA implies GB and MC began to diverge very recently, ca. 85 years ago (95% HPD: 14–350 years ago). Posterior density curves for parameters estimated in the IMa2 analyses and details for the calculations regarding the splitting time parameter are presented in ca. 1200% per million years or an extremely low mutation rate of 2.5 × 10−6 per generation  would be required for time estimations to be congruent . The differentiation between our two focal populations is moderate compared to that found by Paetkau et al.  to be often markedly smaller than census population sizes (N), particularly for bears where Ne/N was between 0.28 and 0.69. In a study conducted on polar bears from the Beaufort Sea (Alaska), Cronin et al. . Thus, with relatively small effective population sizes  it is likely that genetic drift has played a role in shaping differences in allele frequencies. Although the results from our IMa2 analysis must be interpreted carefully, the data suggest that the genetic differences between these populations arose within the last hundreds of years. This time frame suggests that the land barrier that separates GB and MC (the Boothia Peninsula) is sufficient to at least partially restrict polar bear movement across these areas, and that the Bellot Strait does not constitute a corridor connecting GB and MC. We agree with Paetkau et al. , yet no clear phylogeographic pattern when compared to the CR sequences obtained by Edwards et al. . Direct estimates of current census population sizes in the study area  calculated using CR sequences was significantly different from zero. As we argued above, we do not think that this represents the signature of contemporary gene flow between GB and MC, but rather the vestige of genealogical events that occurred elsewhere. Our estimates of the effective number of migrants per generation using nuDNA did not differ significantly from zero suggesting that contemporary gene flow between these populations is insufficient to homogenize them. This implies that low microsatellite differentiation and the admixture shown by the Bayesian assignment analyses (Fig. IMa2 analysis.Our indirect estimates of gene flow using ses Fig.  reportedIMa2 results did not show evidence of gene flow between populations either for analyses with sexes combined or when analyzed separately. These results are consonant with those of Taylor et al. (tμ in the IMa2 analyses. This result would be consistent with male-biased gene flow or could simply be spurious, reflecting the lower sample sizes for males.Finally, we found no compelling evidence for sex-biased dispersal or gene flow. Our mark-recapture data suggest that males and females do not differ in the distances traveled between years. The r et al.  who did r et al.  combinedr et al.  concludeBoth nuDNA and mtDNA show that polar bears from the Canadian Arctic Archipelago spanning GB and MC comprise two genetically distinct populations. The level of differentiation between GB and MC is moderate when compared to polar bears from other populations (Paetkau et al.