Pungitius kaibarae, is a small gasterosteid fish occurring in freshwater systems on the Korean Peninsula and in southeast Russia. On the Korean Peninsula, P. kaibarae populations are distributed in three geographically separated regions: the NE (northeast coast), SE (southeast coast), and a limited area in the ND (Nakdong River). In this study, we used mitochondrial loci and microsatellites to investigate the evolutionary history of P. kaibarae populations by assessing their pattern of genetic structure. Our analyses revealed a marked level of divergence among three regional populations, suggesting a long history of isolation following colonization, although ND individuals showed relatively higher genetic affinity to populations from SE than those from NE. The populations from NE showed a great degree of interpopulation differentiation, whereas populations from SE exhibited only weak genetic structuring. Upon robust phylogenetic analysis, P. kaibarae formed a monophyletic group with Russian P. sinensis and P. tymensis with strong node confidence values, indicating that P. kaibarae populations on the Korean Peninsula originated from the southward migration of its ancestral lineage around the middle Pleistocene.The contemporary distribution and genetic structure of a freshwater fish provide insight into its historical geodispersal and geographical isolation following Quaternary climate changes. The short ninespine stickleback,  The contemporary distribution of species has been shaped by historical dispersal events following Quaternary climate fluctuation , southeast coast (SE), and a limited area (the Geumho tributary) in the Nakdong River  is one of the major watersheds draining south into the ocean.The Korean Peninsula is an excellent model for detecting historical events of dispersal during Quaternary climatic oscillations. This area was never completely covered by ice sheets during the multiple episodes of glaciation and therefore served as a refuge for many species  than those from SE  were collected from nine independent rivers from July 2012 to February 2013 using a kick net . Tissue samples for genetic analyses were obtained by removing 1 × 1 mm fin clip from the tip of each tail fin, and the individuals were released back to their habitat. Populations were classified as NE , SE , and the Nakdong River  in accordance with the geographic location . Thermal cycling consisted of a preliminary denaturation step at 94°C for 10 min followed by 35 cycles of 94°C for 30 sec, 54–58°C for 30 sec, and 72°C for 30 sec and final extension at 72°C for 10 min. PCR products were purified using a Primeprep PCR Purification Kit (GenetBio) for direct sequencing. The amplified products were sequenced by Genotech  on an ABI 3730XL DNA Analyzer .We used PCR (polymerase chain reaction) to amplify a partial fragment of the mitochondrial control region (CR), Cyt b (cytochrome b), and COI (cytochrome oxidase subunit I) from a total of 128 individuals in eight ulations  using prulations , Cyt b , haplotype diversity  of each population pair using Arlequin 3.5  was used to test the geographical effects on population structure using Arlequin, which partitions the genetic variance among regions, among populations, and within populations. To examine the intraspecific pattern of phylogenetic relationships, a haplotype network for each mitochondrial gene was constructed based on the median-joining algorithm in SplitsTree 4.13  + brook sticklebacks and threespine sticklebacks based on the fossil record  and observed (HO) and expected (HE) heterozygosities. In addition, allelic richness (AR) and fixation indices (FIS) were estimated using Arlequin and Fstat 2.9.3.2  with a setting of 70% single-step mutations. The second approach was to test a mode-shift away from the typical L-shaped distribution of allelic frequencies  for each population. An M below Mc of a certain population indicates that the population has experienced a significant decline in size  representing Ne of 500 and 5000, respectively, with constant mutation rate of 5 × 10−4 and default values of Δg  and Ps (0.9).Three different approaches were used to determine whether populations may have undergone significant declines in size. Bottleneck 1.2  and variance of allele sizes (pairwise-RST), while testing their significance by Fisher’s exact tests after 10,000 permutations in Arlequin. A randomization  procedure was used to evaluate whether stepwise mutation has contributed to population divergence  ranging from one to nine, with ten independent MCMC runs each consisting of 4 × 105 iterations after a burn-in of 105 iterations made for each K. The most reliable number of distinct genetic clusters was inferred using the delta K method described by Evanno et al. , among populations and within populations using AMOVA in Arlequin.Measures of multilocus differentiation among populations were calculated based on the difference in allele frequency , although only a single haplotype was observed in the two NE populations  and 45 polymorphic sites, 42 of which could be regarded as parsimoniously informative. The mean haplotype diversity and mean nucleotide diversity of CR sequences revealed egion SE .D and Fu’s Fs tests of deviation from neutrality with Cyt b sequences  and 57 polymorphic and parsimoniously informative sites. The haplotype and nucleotide diversity of Cyt b sequences were 0.907 ± 0.008 and 0.0269 ± 0.0003, respectively. In Cyt b, NE and SE exhibited similar levels of diversity . No signequences . The seqequences . Tests o Fu’s Fs .Omono12) to 41 (Ppu1), while the observed and expected heterozygosity ranged from 0.160 (Omono12) to 0.858 (Ppu1) and from 0.164 (Omono12) to 0.804 (Ppu1), respectively. Ppu2 was found to be out of HWE after Bonferroni adjustment , and Ppu3 was out of HWE when considering α = 0.05 owing to significant heterozygote deficiency  showed significant heterozygote deficiencies when considering pairwise comparisons of populations and loci . At α = 0.05, three population-locus combinations additionally showed significant heterozygote deficiencies . Fisher’s exact test showed the evidence of significant linkage disequilibrium from a single pair  after sequential Bonferroni correction  and revealed significant disequilibrium from three pairs  when considering α = 0.05.Among a total of 288 individuals, extensive polymorphism was observed at the 12 microsatellite loci examined Tables. The totficiency . At the A and AR) and TH (HO and HE), while they were relatively low in populations OH, YG, and GH . Upon maximum likelihood estimation of relatedness by ML-Relate, a low level of family relationships (1.81–11.29%) was exhibited consistently across the populations examined  and showed evidence of bottleneck in populations OH and GH at α = 0.05 , 0.966 (Cyt b: 0.843–0.996), and 0.983 (COI: 0.914–1.000) being observed  and 2.67 Mya (1.91–3.07), respectively , suggesting a contribution of stepwise mutations to population divergence at the microsatellite loci. Significant FST and RST values were observed in most of pairwise comparisons  at K = 7 on CLUMPP. One cluster corresponded to SE populations including GG, HS, and TH, while the other six clusters were represented by each population  showed relatively higher genetic affinity to populations from SE than NE, despite its genetic distinctness from all other populations.The main objective of this study was to estimate genetic variability using nuclear and mitochondrial loci in an attempt to characterize the population structure of P. kaibarae can be obtained by considering their placement within the phylogenetic tree of Gasterosteidae. While all P. kaibarae haplotypes detected in the present study composed a monophyletic group, Russian P. sinensis was resolved to be a sister taxon of P. kaibarae, and Russian P. tymensis was placed at the basal position of the Russian P. sinensis and P. kaibarae group by strong node confidence values. Thus, it appears that P. kaibarae was derived from the north with dispersal of its ancestral lineage. The separation age of Russian P. sinensis and P. kaibarae from P. tymensis represents the early Pleistocene, while the age of divergence between Russian P. sinensis and P. kaibarae was dated back to the time representing the middle Pleistocene. Around those periods, the glacial maxima formed vast areas, including the Sakhalin and the Far East Russia, tundra-like environment, which had little vegetation coverage and were unsuited for many freshwater species  may also represent the signature of isolation between the Geumho River and east coast streams. How have the three east coast stream and the Geumho River spatially been isolated following the dispersal? It is well known that the zone of Yangsan Fault is located between the Geumho River and SE region  in our phylogenetic analysis with CR sequences. However, a complex pattern of relationship was observed. For example, Russian P. sinensis did not cluster with other P. sinensis samples from Korea and Japan, but instead clustered more closely with P. kaibarae and P. tymensis. We suggest a comprehensive reevaluation based on morphological and genetic investigation of Asian and Russian Pungitius species with the background of the phylogeny obtained in the present study. First, a taxonomic revision is required for Korean P. sinensis and Japanese P. sinensis and P. pungtius, because those three were not clearly resolved to support separate clustering  was slightly higher in NE than SE in CR and COI but not in Cyt b. Although the results of mitochondrial and microsatellite analyses were generally comparable, microsatellite analyses revealed a slightly different pattern in the distribution of genetic diversity, as population OH showed relatively lower levels of diversity. SE populations also showed diversity estimates slightly higher than those of NE populations. Population GH (the Nakdong River) exhibited a lower level of genetic diversity than those from NE and SE. As with the results from mitochondrial analyses, the lowest level of diversity was observed in population YG. In addition, southward expansion to the Korean Peninsula might have involved a limited number of individuals, leading to loss of genetic diversity in the founding population.In agreement with our prediction, a low level of mitochondrial diversity was detected, because most of the populations exhibited only a couple of haplotypes (mean number of haplotypes per population = 2.458). Population OH showed the highest mean number of haplotypes (3.667), while population YG harbored a single haplotype in all three mitochondrial loci. Haplotype diversity  is lower than those previously reported in studies with other α = 0.05. However, a signature of population decline was identified across all NE populations, because they had average M ratios lower than the critical values for individual populations calculated by Critical M. Instead, SE populations and population GH exhibited larger average values ranging from 0.696 to 0.895. The M ratio is likely to indicate the signature of reduction and recovery in population size that dates back 100–300 generations , and distinct future conservation plans should be prepared specifically for each group. Accordingly, we suggest that a comprehensive reevaluation of the conservation status of this species including the background of our genetic results be conducted.Although


Strongylocentrotus droebachiensis from discrete populations on the NE Atlantic coast. By using eight recently developed microsatellite markers, genetic structure was compared between populations from the Danish Strait in the south to the Barents Sea in the north (56–79°N). Urchins are spread by pelagic larvae and may be transported long distances by northwards-going ocean currents. Two main superimposed patterns were identified. The first showed a subtle but significant genetic differentiation from the southernmost to the northernmost of the studied populations and could be explained by an isolation by distance model. The second pattern included two coastal populations in mid-Norway (65°N), NH and NS, as well as the northernmost population of continental Norway (71°N) FV. They showed a high degree of differentiation from all other populations. The explanation to the second pattern is most likely chaotic genetic patchiness caused by introgression from another species, S. pallidus, into S. droebachiensis resulting from selective pressure. Ongoing sea urchin collapse and kelp forests recovery are observed in the area of NH, NS and FV populations. High gene flow between populations spanning more than 22° in latitude suggests a high risk of new grazing events to occur rapidly in the future if conditions for sea urchins are favourable. On the other hand, the possibility of hybridization in association with collapsing populations may be used as an early warning indicator for monitoring purposes.We compared the genetic differentiation in the green sea urchin The online version of this article (doi:10.1007/s00227-015-2801-y) contains supplementary material, which is available to authorized users. Strongylocentrotus droebachiensis (O.F. Müller 1776) (Echinoidea) is a key species with severe impact on coastal ecosystems. By large-scale overgrazing of kelp forests in the Pacific  has dominated the coast of mid-Norway and North Norway and Russian Kola coast for more than four decades as a result of a sudden growth in the sea urchin populations  of ocean currents of the NE Atlantic do not exist, we created shape files in a GIS made from approximate, manually drawn paths following the main currents across Kattegat, along the Swedish and Norwegian coast, and crossing the Barents Sea to Svalbard Fig.  based onS. droebachiensis  and formatted for analysis in GenAlEx . Results of 10 independent runs with K = 1–10 clusters were input to Structure Harvester  as well as FST, based on null-allele-corrected allele frequencies. For Jost’s D, we used null allele corrections as decribed above, while FST values were computed in the FreeNA software (Chapuis and Estoup FST matrix applying the ENA correction (Chapuis and Estoup Genetic differentiation between populations was estimated as Jost’s FIS) were analysed with INEST version 2.0  and migration parameters between populations under specified dispersal scenarios (models) using genetic data. Posterior distributions of parameters (effective population sizes and migration rates) were generated by Bayesian inference using Markov Chain Monte Carlo runs. Basically, we ran three migration models: migration from A to B only, from B to A only or both ways. Posterior model probabilities were compared using Bayes factors. Parameters were free to vary over intervals specified as priors, set for each run. We applied uniform prior distributions of effective population size Θ from 0 to 200 and for migration rate M from 0 to 100. Mutation rate was constant over all loci. We applied a static heating scheme of 4 chains with temperatures proposed by the program , with swapping intervals set at 10. Only Group 1 individuals  were included in the datasets, since Group 2 individuals only occurred in a few areas, whereas Group 1 was present at all but one sampling stations. Since there was evidence for selection on five out of eight loci  and 26 (Strdro-5563) alleles detected per locus (Table FST and Jost’s DEST estimates (Table DEST < 0.06) over a wide geographical range (56–79°N). However, two coastal stations (NH Helløya and NS Skogsholmen at 65°N in the middle of the distribution area) stood apart, showing a much higher level of divergence  between populations in an overall calculation, resulting in an overall DEST of 0.085 (SE 0.050). Among these five loci, Strdro-97 and 7209 contributed little to differentiation (DEST 0.009 and 0.012), while the remaining three loci showed stronger differentiation (DEST 0.116–0.471). This was due to marked differences in allele frequencies at these loci, particularly at Strdro-5563 and 1356. Figure Only five loci  differentiated significantly  to 0.22  with Group 1 animals partitioned most of the genetic variance 96.4 %) to within populations and the remaining 3.6 % among populations  and mean log probability of K levelled off at higher K. Concordant with the genetic differentiation estimates, one cluster dominated stations from south to north, except the NH and NS station in mid-Norway, which were mainly allocated to the second cluster, and the FV station where individuals were evenly divided among clusters ) were then plotted against estimated distances from the southernmost site DV, to test a hypothesis of IBD given an assumed dominant northwards transport of pelagic larvae.Based on the expectation that larval transport occurs primarily from south to north following the Norwegian coastal current, we plotted an estimate of genetic differentiation between the southernmost population from the Belt Sea (DV) and all other populations versus estimated distance between stations Fig. . LineariS. droebachiensis mitochondrial partial COI sequences from this study are available at the European Nucleotide Archive: http://www.ebi.ac.uk/ena/data/view/LN828950-LN828961. Our sequences clustered with another sequence from Norway  and by the clear indication of two distinct clusters in Structure. Punctual presence of high diversity patches in the midst of genetically homogenous populations of a species covering large geographical areas is not uncommon, especially among marine invertebrates  from the Pacific for marine cultivation purposes during the 1960s and has since the 1990s extended its range westwards into East Finnmark waters , NH and NS, as well as the northernmost population of continental Norway (70°N) FV, were very different genetically from the other populations and showed high degree of isolation from all other populations. This was indicated both by population differentiation estimates Below is the link to the electronic supplementary material.


IL1A), interleukin 4 (IL4), nuclear factor kappa B1 (NFKB1) and protease-activated receptor 1 (PAR1) genes may influence not only the inflammatory response but also susceptibility to cancer development. Among major ethnic or continental groups, these polymorphic variants present different allelic frequencies. In admixed populations, such as the Brazilian population, data on distribution of these polymorphisms are limited. Here, we collected samples of cancer-free individuals from the north, northeast, midwest, south and southeast regions of Brazil and from the three main groups that gave rise to the Brazilian population: Native Americans from the Brazilian Amazon, Africans and Europeans. We describe the allelic distributions of four IL1A (rs3783553), IL4 (rs79071878), NFKB1 (rs28362491) and PAR1 (rs11267092) gene polymorphisms, which the literature describes as polymorphisms with a risk of cancer or worse prognosis for cancer.The inflammatory response plays a key role at different stages of cancer development. Allelic variants of the interleukin 1A (PAR1 gene polymorphism allelic distribution was similar among all Brazilian regions. For the other three markers, the northern region population was statistically distinct from other Brazilian region populations.The genotypic distribution of the four polymorphisms was statistically distinct between Native Americans, Africans and Europeans. For the allelic frequency of these polymorphisms, the Native American population was the most distinct among the three parental populations, and it included the greatest number of alleles with a risk of cancer or worse prognosis for cancer. The IL1A, IL4, NFKB1 and PAR1 gene polymorphism allelic distributions are homogeneous among the regional Brazilian populations, except for the northern region, which significantly differs from the other four Brazilian regions. Among the parental populations, the Native American population exhibited a higher incidence of alleles with risk of cancer or worse prognosis for cancer, which can indicate greater susceptibility to this disease. These genetic data may be useful for future studies on the association between these polymorphisms and cancer in the investigated populations.The  External factors that modify tissue homeostasis, such as microorganism infection, tissue injury and exposure to contaminants may induce inflammatory processes . ChronicThe inflammatory response may be modulated through gene expression variations due to structural genetic polymorphisms or regulatory regions. Therefore, an individual genetic composition may influence not only the inflammatory response but also susceptibility to cancer development , 4.IL1A), interleukin 4 (IL4), nuclear factor kappa B1 (NFKB1) and protease-activated receptor 1 (PAR1)  for association studies on cancer, data for which is limited on Brazilian populations. Thus, the aim of this study is to characterize allelic distributions in a representative sample of the Brazilian population from all geographic regions of the country.We must know the distribution of allelic frequencies of these polymorphisms [NFKB1 gene marker in the European sample (p = 0.003).The allele and genotype frequencies of the investigated polymorphisms  in samples from the five Brazilian geographic regions and in the parental populations are presented in Table IL1A (40 %) followed by IL4 (34 %), PAR1 (30 %) and NFKB1 (17 %).When we compared allelic distributions in parental populations, all markers exhibited a significantly different allele frequency between the three groups, and the Native American populations exhibited more differences from the continental populations Table . Among tPAR1 gene) showed no significant difference between the Brazilian regions. The distributions of the other three polymorphisms  were statistically similar between the northeast, south and southeast regions.For the geographic region comparisons Table , rs11267The analyses showed statistically significant differences in the rs3783553, rs79071878 and rs28362491 polymorphism distributions between the northern population and the populations in the other regions, except the rs3783553 polymorphism in the midwest region (p = 0.543). This polymorphism exhibited a significantly different distribution in the midwest population compared with the northeast, south and southeast regions.All polymorphisms investigated here have been previously described as associated with a predisposition to some form of cancer , 24, 25,Based on this analysis, the proportion of potentially deleterious alleles is higher in the Native American population (50 %) than the African (44 %) and European (35 %) populations. Among Brazilians, the proportion of potentially deleterious alleles is 40 %. Likewise, the proportion of individuals who are carriers of six or more alleles associated with cancer is greater among the Native Americans (8.6 %) than the Africans (5.2 %) and Europeans (1.9 %), and the proportion of Brazilians (4.2 %) is intermediate among the ancestral population values. The proportion of carriers is not evenly distributed between the geographic regions. The proportion is smaller in the Northeast Brazil population (2.7 %), intermediate in Midwest and South Brazil populations (mean = 3.4 %) and more elevated in the North (5.0 %) and Southeast Brazil populations (6.5 %).This is the most comprehensive study on the rs3783553, rs79071878, rs28362491 and rs11267092 polymorphism variations in Brazil and among its ancestral populations. This is also the first study to describe the variability of these markers in Native American populations.We evaluated the distribution of allele and genotype frequencies for these four markers among the pairs of ancestral and Brazilian populations using the Chi-square test with the due statistical corrections  to avoid spurious correlations. This test is adequate to compare two or more populations for a qualitative variable . FurtherNFKB1 gene polymorphism. In this set of markers, the Native American populations were the most differentiated among the investigated continental populations.The data from the ancestral populations investigated here reveal considerable heterogeneity between continental populations. All investigated markers present δ values greater than 30 %, except for the IL1A, NFKB1 and PAR1 markers (the three markers for which data were available) in the African and European populations are similar to the frequency observed herein. Thus, we discard the hypothesis and assume that our samples are representative of the European, African and Native American populations.Given the differences in the observed allelic distribution between the parental populations, we tested the hypothesis that these differences were due to population sampling. Therefore, we used data published in the 1000 Genomes project Table . We veriDespite the frequency differences between the parental populations and the intense and heterogeneous process of admixture that formed the current Brazilian population, the allelic distribution is relatively homogeneous throughout most of the country. In general, only the northern population exhibits an allele distribution that significantly differs from the other geographic regions for three of the four investigated markers.We understand that these differences may be explained by a greater contribution of Native Americans to the northern populations. Previous estimates using different types of genetic markers show that the greatest Native American genetic contribution among Brazilians occurs in North populations , 39, 43.Our analyses involving potentially deleterious alleles demonstrate that the Native American population presents a higher proportion (50 %) of these alleles compared with the other parental populations . This analysis holds when considering the proportion of carriers with six or more (among eight possible) potentially deleterious alleles.However, we cannot evaluate how this genetic composition may be associated with a higher incidence of cancer in this population because Native American populations form traditional communities that reside in outlying areas of urban centres. Epidemiological data are not available on the incidence of different types of cancer among these populations.Despite the absence of epidemiological data, recent studies demonstrate that different forms of cancer are associated with a higher (or lower) contribution from Native American ancestry in admixed populations from South America. The investigations were associated with acute lymphoblastic leukaemia , breast IL-1A (rs3783553), IL4 (rs79071878), NFKB1 (rs28362491) and PAR1 (rs11267092) gene polymorphisms differs between European, African and Native American populations. Further, the same heterogeneity is not observed between regional populations in Brazil, except for the northern region, which significantly differs from the other four Brazilian regions.In summary, our study shows that the allelic distribution of the Moreover, the results show that the Native American population includes a greater proportion of carriers with six or more alleles associated with cancer, which suggests that this population may have a higher risk of developing (or worse prognosis for) diseases associated with these alleles. The presented genetic data may be useful for future studies on the association between these polymorphisms and cancer in these populations.The study population consists of 928 non-related and cancer-free adult individuals, recruited in ten Brazilian Federal Units, between the years of 2009 and 2010. In the present work, 180 individuals  residing in the states of Pará (60), Amazonas (60) and Rondônia (60) represented the North region; 187 individuals  residing in the states of Ceará (135) and Pernambuco (52) represented the Northeast region; 186 individuals  residing in the states of Goiás (101), Mato Grosso do Sul (49) and Distrito Federal (36) represented the Midwest region; 184 individuals  residing in the state of São Paulo represented the Southeast region; and 191 individuals  residing in the state of Rio Grande do Sul represented the South region. Additional details may be found in a previous study .In addition, we investigated a sample of 701 individuals representative of the main ethnic groups that originated the Brazilian population: 222 Native Americans from nine tribes of the Brazilian Amazon  ; 211 AfrInformed consent for DNA analysis was obtained from healthy individuals for research purposes. Ethics approval was obtained from the local committee of Instituto de Ciências da Saúde, Universidade Federal do Pará.® Multiplex PCR kit  and the primers described in Table Four polymorphisms were genotyped by a single multiplex reaction with Master Mix QIAGEN®3.7 software (Applied Biosystems).Multiplex PCR products were separated and analyzed by capillary electrophoresis on the ABI 3130 Genetic Analyzer instrument (Applied Biosystems), using GS-500 LIX as pattern of molecular weight (Applied Biosystems), G5 virtual filter matrix and POP7 (Applied Biosystems). After data collect, samples were analyzed in GeneMapper2 test, df = 2) in BioEstat software [P value was considered significant if lower than 0.05.Allelic and genotypic frequencies were obtained by direct counting and δ value  was determined by substracting values of allele frequency in the studied parental populations, as described by Santos et al. . Hardy–Wsoftware . FDR (Fasoftware . These a


Pseudotsuga menziesii) is one of numerous wide-range forest tree species represented by subspecies/varieties, which hybridize in contact zones. This study examined the genetic structure of this North American conifer and its two hybridizing varieties, coastal and Rocky Mountain, at intervarietal and intravarietal level. The genetic structure was subsequently associated with the Pleistocene refugial history, postglacial migration and intervarietal hybridization/introgression. Thirty-eight populations from the USA and Canada were genotyped for 13 nuclear SSRs and analyzed with simulations and traditional population genetic structuring methods. Eight genetic clusters were identified. The coastal clusters embodied five refugial populations originating from five distinct refugia. Four coastal refugial populations, three from California and one from western Canada, diverged during the Pleistocene (56.9–40.1 ka). The three Rocky Mountain clusters reflected distinct refugial populations of three glacial refugia. For Canada, ice covered during the Last Glacial Maximum, we present the following three findings. (1) One refugial population of each variety was revealed in the north of the distribution range. Additional research including paleodata is required to support and determine whether both northern populations originated from cryptic refugia situated south or north of the ice-covered area. (2) An interplay between intravarietal gene flow of different refugial populations and intervarietal gene flow by hybridization and introgression was identified. (3) The Canadian hybrid zone displayed predominantly introgressants of the Rocky Mountain into the coastal variety. This study provides new insights into the complex Quaternary dynamics of this conifer essential for understanding its evolution (outside and inside the native range), adaptation to future climates and for forest management.Douglas-fir ( Abies lasiocarpa, A. grandis, A. concolor, Larix sibirica, Pinus radiata) with hybrids and introgressed individuals in zones of contact  Franco) is a tree species of temperate and boreal forests with both a wide native distribution range and a large artificial range outside its natural distribution, where it regenerates naturally  and the interior variety (P. menziesii var. glauca)  into central California. The var. glauca extends 4500 km from central BC across the Rocky Mountains into southern Mexico. Both varieties differ morphologically and ecologically. The var. menziesii grows faster and is considerably larger in size than the var. glauca, which is more shade tolerant, more cold-hardy, and more drought resistant  and the Mexican lineage  (Li and Adams Douglas-fir (Divergence estimates of Douglas-fir in its three main lineages (with the Rocky Mountain lineage being the oldest) differ among recent studies , this study aimed at investigating the geographical genetic structure at the intervarietal level as well as at the level within coastal and Rocky Mountain varieties separately in order to obtain a more detailed picture on the phylogeographic pattern, selected historical demography, intervarietal hybridization and introgression patterns of Douglas-fir. Instead of using new and/or a larger number of dominant organelle loci (cpDNA and mtDNA) than used in previous range-wide phylogeographic and demographic studies  Did the coastal variety expand from (at least two) different glacial refugia located in the current distribution range; (2) which refugia contributed to the spread of the investigated Rocky Mountain variety after the LGM; and (3) did intervarietal F1 hybrids rather than backcrosses to one of the variety exist within analyzed populations of the hybrid zone in BC?A total of 766 individuals representing 38 populations from the USA and Canada and covering the natural distribution range of the coastal and Rocky Mountain variety were collected . Higher ηg/μL. A total of 17 highly polymorphic dinucleotide nuSSRs were selected from Slavov et al.  according to the manufacturer’s instructions. The quality and concentration of the extracted DNA were measured on a 1.5% agarose gel electrophoresis and the Implen OD600 DiluPhotometer. For PCR reactions, the DNA was subsequently diluted with PCR water to an equal concentration of 30 v et al. . These wv et al. . For simv et al. . PCR ampv et al. . The PCRv et al. .Na), mean number of alleles (A), effective number of alleles (Ne), observed heterozygosity (HO), expected heterozygosity (HE), inbreeding coefficient (FIS), and allelic richness (AS). They were corrected for sample size by rarefraction, with the software FSTAT v. 2.9.3.3 (Goudet AS) using the approach of Szpiech et al. (HE), observed heterozygosity (HO), and inbreeding coefficient (FIS) using the GenAlEx v. 6.5 software  using Bayesian admixture analysis in STRUCTURE v.2.3.4.  of each individual and population. An individual/population was declared as coastal with Qvar (membership coefficient) > 0.9, Rocky Mountain with Qvar < 0.1 and intervarietal admixed with 0.9 > Qvar > 0.1.The genetic structure was analyzed at two levels, at the intervarietal level, and at the intravarietal level. For the former, analyses of genetic structure were run on the entire dataset including both varieties, whereas for the latter all analyses were run on data subsets representing populations either of the coastal variety or the Rocky Mountain variety. The populations of the whole dataset were divided into two variety subsets (Qvar > 0.1) as well as from individuals of the opposite variety when present. Each K was run 20 times. As ΔK  detects the uppermost level of genetic structure where several hierarchical levels exist  for each individual and population of the 20 runs per K were computed with the software CLUMPP v.1.1.2 using the Greedy option and random input orders  was the highest. This assignment was also checked visually on DISTRUCT plots in order to evaluate the estimated Qintra value of populations and their composition. Although running STRUCTURE on a population level underestimates the actual number of clusters within the dataset, we preferred this strict and controllable procedure over reshuffling the individuals among clusters, which might overestimate the number of existing clusters and cause assignment problems especially for populations resulting from gene flow between refugia. If Qintra for one or more populations within the studied dataset was 0.2 < Qintra < 0.8, the procedure was additionally rerun in STRUCTURE under the locprior using the ΔK value recommended by Evanno  to those without this option, the assignment of populations was accepted. Populations with 0.2 < Qintra < 0.8 were assigned to reflect cluster-mixed populations.The genetic population structure of both varieties was inferred using two different clustering approaches . The Bayesian STRUCTURE analysis was run on both variety subsets separately using identical settings as described before. In addition, each subset was cleaned from possible intervarietal-admixed individuals . In more detail, intervarietal-admixed individuals of R18, R21, and R39 were used to assemble such new populations a18, a21, and a39.As a second approach, population clustering and the relations between populations were calculated with a pairwise codominant genotypic distance followed by a covariance standardized PCoa using GeneAlEx 6.5 , effective population size of each population , and effective population size of ancestor (Na). As t was expressed in the number of generations, we used an approximate average generation time of 100 years to convert it into years, following Gugger et al.  analysis in DIYABC, version 2.0  from studied populations in BC have been selected. Subsequently, values of the intervarietal heterozygosity (IH) and maximum likelihood hybrid index (HI) were estimated following the approach as described by Lexer et al. (IH and intermediate HI), or represent rather recombinant hybrids and introgressants of subsequent generations . In the analysis, genotypes of 500 F1 individuals were simulated with the HYBRIDLAB software v.1.1 , all intervarietal-admixed individuals with 0.2 < r et al. . In moreNa) observed per locus and variety (HO) and expected (HE) heterozygosities varied from 0.255 to 0.924 among 13 nuSSR loci (HO and HE were found in the population of the Rocky Mountain variety R24 (New Mexico) located in the southernmost area of this variety analyzed in the present study. This population exhibited 100% missing values for three loci . The highest values of As (6.5) were found in four populations of the coastal variety. The smallest values for allelic richness (As = 5.4 or 5.7) were calculated in populations of both varieties with the northernmost  or the southernmost  distribution in the studied native range. The inbreeding coefficients (FIS) were significantly positive indicating deviations from Hardy–Weinberg expectations at both population level, and also on the locus level within variety , a central  and a southern cluster (R34-37). Nine populations  represented cluster-mixed populations between the central and southern cluster and two  between central and northern cluster Fig.. The souHO and HE) were similar to those calculated for populations. The highest values of HO, HE, and As were found in the populations of cluster I, located in the northern area of Oregon and the south of Washington to the west of the Cascades. From cluster I, a decline of diversity (HE and As) was found toward the southernmost cluster III and the northernmost cluster II within the coastal variety. The same is true for the Rocky Mountain variety when comparing the values HE and As of the cluster VI with the cluster VII in the north and the cluster VIII in the south  situated in the south of the Rocky Mountains and a cluster with the remaining populations Fig.. The lathe south . As mentD differentiation values among all clusters estimated by the hierarchical STRUCTURE analysis were significantly different displaying larger values in intervarietal than in intravarietal comparisons  revealed more structure than the Bayesian analysis by STRUCTURE at this level. In total, three clusters were separated; two of the Rocky Mountain variety and one of the coastal variety with two populations (R35 and R38) clearly detached from the rest of this cluster Fig.. The firAt the intravarietal level of the coastal variety, the Californian clusters  and the northern cluster II (R38) in BC were separated from the rest Fig.. The firN1 and N2 have diverged in the past from an ancestral population Na and in scenario 2 and scenario 3, a classic invasion history was simulated where population A was derived from population B or vice versa, respectively. More complex histories could of course be simulated, but simplicity of the model is an important criterion when selecting among competing hypotheses . To estimate and to clarify the intravarietal demographic history of the eight genetic clusters revealed by STRUCTURE and PCoa, both varieties were treated separately. We designed putative historical scenarios for seven different groups . Within For the coastal variety, the results of ABC analysis clearly favored the hypothesis that the population group I′ and genetic clusters II, III, IV, and V were derived from the cluster I as reflected in scenario 2 . The posQvar < 0.8) have been assigned to represent intervarietal-admixed genotypes by STRUCTURE  for intervarietal-admixed individuals (0.15 < Qvar < 0.85), 12 admixed individuals could be additionally recognized within beforehand mentioned populations  with just one additional population R16 located close to R10. All are positioned in existing or possible contact regions between both varieties.In total, 25 individuals  versus maximum likelihood HI, the majority of the individuals with intervarietal-admixed genotypes from populations R17, R18, R21, R39 had a HI larger than 0.5  were distributed below the 95% confidence intervals of simulated F1′ intervarietal hybrids  and their reduced IH pointed to their not recent origin and backcrossing to the coastal variety. Although both varieties might have come into contact at different times in Canada, there was no apparent pattern/grouping concerning IH and HI of these intervarietal recombinants and introgressants among populations , a more recent period when compared to the older estimates of two recent reports based on cytoplasmic (cpDNA and mtDNA) data  in western OR and WA was consistent with the existence of a former refugium on the Pacific coast of these states and with postglacial colonization to the south , to the east, where both slopes of the Cascades have primarily been colonized by it and to the north colonizing southern BC Fig.. This glNotholithocarpus densiflorus (tanoak), a forest tree associated with the Douglas-fir in California  situated in the north of the current distribution area of Douglas-fir in BC Fig. led to tTsuga mertensiana  which expanded from four separate refugia. The postglacial colonization which brought both varieties to a contact at the present eastern border of the hybrid zone  followed by intervarietal hybridization and predominant unidirectional introgression into the coastal variety rather than assortative mating of F1 hybrids  led to a 450-km wide hybrid zone in BC  the estimation of glacial history and divergence times for six distinct genetic clusters (populations), (2) the identification of so far the most comprehensive phylogeographic structure of the coastal variety, and finally, (3) the revealing of a complex postglacial recolonization in BC.Existing phylogeographic patterns allow light to be shed on the impact of past climatic cycles on species distributions, which in turn may help to facilitate predictions on how species will respond to future climates. Recent distribution models which incorporated intraspecific variation  resulted in a different (and more optimistic) projection of future suitable habitats than the models that treated species as single entities  with extensive introduction outside their native ranges, where they sometimes naturalize or even become invasive (Call and Nilsen And finally, the Douglas-fir is only one of several broadly distributed and economically important forest tree species (such as


Ammodramus maritimus) along the Gulf of Mexico are currently recognized as four subspecies, including taxa in Florida (A. m. juncicola and A. m. peninsulae) and southern Texas (Ammodramus m. sennetti), plus a widespread taxon between them (A. m. fisheri). We examined population genetic structure of this “Gulf Coast” clade using microsatellite and mtDNA data. Results of Bayesian analyses  of microsatellite data from nine locations do not entirely align with current subspecific taxonomy. Ammodramus m. sennetti from southern Texas is significantly differentiated from all other populations, but we found evidence of an admixture zone with A. m. fisheri near Corpus Christi. The two subspecies along the northern Gulf Coast of Florida are significantly differentiated from both A. m. sennetti and A. m. fisheri, but are not distinct from each other. We found a weak signal of isolation by distance within A. m. fisheri, indicating this population is not entirely panmictic throughout its range. Although continued conservation concern is warranted for all populations along the Gulf Coast, A. m. fisheri appears to be more secure than the far smaller populations in south Texas and the northern Florida Gulf Coast. In particular, the most genetically distinct populations, those in Texas south of Corpus Christi, occupy unique habitats within a very small geographic range.Seaside Sparrows ( Isolated populations of organisms inhabiting patchily distributed habitat often form genetically or morphologically distinct populations that may be recognized as species or subspecies Melospiza melodiaButeo lineatusMyioborus miniatusChondestes grammacusAlthough the ESA does not specifically define diagnostic criteria Ammodramus maritimus) is largely endemic to - and often abundant in - salt marshes along the Atlantic and Gulf coasts of the United States; seven extant subspecies are currently recognized A. m. nigrescens, A. m. mirabilis) have been considered full species in the past Ammodramus m. maritimus, A. m. macgillivraii, A. m. mirabilis, A. m, nigrescens, and presumably A. m. pelonota form an “Atlantic” clade  describe population genetic structure of Seaside Sparrows along the Gulf Coast; (2) ask whether genetically distinct populations align with current subspecific taxonomy, and (3) discuss potential conservation risks based on a more refined understanding of the distribution and genetic variation among these populations.Birds were banded and bled under Federal Bird Banding Permit 22648, State Permits LNHP-11-06 and LNHP-12-023 (Louisiana), SPR-1011-351 (Texas), LSSC-11-00096 (Florida), 21553-12-0010 , 21540-12-112 , 4164-2011-002 , 01111210 (Florida Division of Recreation and Parks), and 2011-001 . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Protocols were approved by the Institutional Animal Care and Use Committee of the Louisiana State University AgCenter (Permit Numbers: AE2011-04 and A2012-05).A. m. fisheri). Captured birds were banded with numbered aluminum bands; a blood sample was taken from the brachial vein and stored in Queen's lysis buffer We captured 374 Seaside Sparrows with mist nets at nine locations across the northern coast of the Gulf of Mexico during 2012–2013 , Fig. 1.2, 0.16 mM dNTPs, 1X buffer (New England Biolabs), 0.04 µM forward primer, 0.38 µM reverse primer, 0.60 µM dye-labeled M13 primer, 0.5 units Taq polymerase, and 10 ng DNA. Amplification of some loci was improved with the addition of dimethyl sulfoxide  and betaine (1.0 µL) to the PCR . The thermal cycling protocol was 94°C for 60s, followed by 35 cycles of 94°C for 30s, annealing temperature  for 30s, 72°C for 30s, and a final extension cycle at 72°C for 5 minutes. PCR products were run on an Applied Biosystems 3730 capillary sequencer , and all genotyping was done by a single individual (SW) using GeneMarker . At least three reference individuals were included on each plate to ensure consistency between runs.DNA was extracted using Qiagen DNeasy Tissue and Blood kits (Qiagen Ltd.). We quantified extracted DNA concentrations with a NanoDrop 2000 . DNA from all individuals was amplified for 14 microsatellite loci: Aca01, Aca11 Taq polymerase, and 10 ng DNA. The thermal cycling protocol consisted of 94°C for 30 s followed by 34 cycles of 94°C for 30 s, 50°C for 30 s, 72°C for 60 s, and a final extension step of 72°C for 7 minutes. Sanger single-pass sequencing was performed at Beckman Coulter Laboratories . Sequences were aligned in Sequencher  and deposited in GenBank  was sequenced for 9–12 individuals from sampling locations 1, 2, 3, 6, 8 and 9  and Factorial Correspondence Analysis  were performed in GenetixDST (FST) was estimated in SMOGD FST/(1−FST) vs log km to explore the influence of some of the samples: (1) all samples; (2) all samples excluding putative A. m. sennetti (locations 1 and 2); (3) only samples of putative A. m. fisheri (locations 3–7). Effective genetic population size (Ne) was estimated for all nine populations using the LD method with a random-mating model, 0.05 as the lowest allele frequency used, and a jackknife approach to estimating 95% CIs in the program NeEstimatorrlequin  and pairwise differences (100 permutations) for comparisons. A 95% confidence interval haplotype network was constructed with TCS . Two additional GeneLand analyses were run to further explore data from locations in southern Texas and Florida. To examine an apparent hybrid zone near Corpus Christi, Texas we used a correlated allele model and the admixture model for hybrid zones . To examine weak differentiation (based on FST) of the two named subspecies sampled in Florida (locations 8 and 9) we used a correlated allele model . For all GeneLand analyses we set coordinate uncertainty to 0.3 to allow for the possibility of different individuals captured at the same point to be assigned to different populations. Because GeneLand algorithms may produce “ghost” populations , especially at large spatial scales, we limited our acceptance of GeneLand results to the most parsimonious estimation of K that did not include “ghost” populations We used Gigrate-N; ayesAss; igrate-n  and migration rates . We used a Bayesian framework and a Brownian motion microsatellite model, with a proposed connection matrix that included estimation of all θ and M parameters with the exception of migration between locations 1 and 3 , as direct migration between these two locations is biologically unreasonable considering the sedentary nature of Seaside Sparrows along the Gulf Coast M were estimated using FST, MCMC runs used one long chain, recording every 100 steps, and visiting 4×106 geneologies.To further explore the apparent admixture zone near Corpus Christi, Texas, we used two Bayesian approaches to estimate historic (MayesAss (v3.0) estimates recent immigration rates by estimating the proportion of migrant ancestry within population samples and individuals 7 iterations, with 106 as burn-in, with a sampling interval of 103. Mixing parameters were dM = 0.1, dA = 0.45, and dF = 0.45. We examined the trace outputs of the log-probabilities to insure the MCMC had converged using the program Tracer (v.1.6.0 st generation or 2nd generation migrant from each population) to characterize the likelihood of migrant ancestry.The program B v.1.6.0 . EstimatA. m. sennetti from location 1, but similar (range  = 9.0–11.4) among all other locations ; both FST and Dest were consistently higher in all comparisons involving location 1 (southernmost A. m. sennetti), and lowest within samples encompassing A. m. fisheri  in ocations . Estimat al. Ne . Althoug fisheri . The FCA fisheri .r = 0.5346, P = 0.0004; Fig. S1A in A. m. sennetti, a pattern of IBD was still evident from central Texas to Florida , and was also evident when excluding location 1 as an outlier with a very different distribution of haplotype frequencies . All pairwise comparisons of φST involving location 1 were significant ; no other pairwise comparisons were significant .All six locations sampled for ND2 contained at least one unique haplotype . Locatiotructure indicated a sufficient burn-in length and number of post-burn-in iterations. For the Gulf-wide analyses, Structure indicated the best support for K = 3, based both on evaluation of mean estimated ln probabilities for each hypothesis  as a separate population. GeneLand found a best estimate of K = 3 that corresponded well with Structure output .Inspection of plots of summary statistics provided by Spothesis  and 4, ae output , and theeneLand of locations 1–3 suggests that birds sampled in Aransas Co. have mixed ancestry of A. m. sennetti from further south and A. m. fisheri from further north (not shown), and this is congruent with the broader-scale output of the Structure analysis (see eneLand consistently recovered a single population among the two samples from Florida (populations 8 and 9), consistent with the low levels of differentiation observed via FST and Dest.The admixture analysis in Gigrate-N analysis of the admixture zone between A. m. sennetti and A. m. fisheri suggests that our samples from Aransas Co., Texas have historically experienced unequal gene flow from populations to the north and south. Migration rates (M) between populations 1 and 2 are roughly similar in each direction. Estimated migration rates between populations and 2 and 3 are asymmetric, with population 3 contributing a greater number of migrants into population 2 (ayesAss) also appears asymmetric, in that, in contrast to populations 2 and 3, population 1 has not received many recent migrants (nd generation (or greater) migrant , A. m. fisheri, and a combined group including A. m. juncicola and A. m. peninsuale. Ammodramus m. cf. sennetti from our southernmost site in Texas (location 1) are significantly diverged from all other population samples, including the apparent hybrid A. m. sennetti × fisheri population in Aransas Co., Texas (location 2). The two Florida subspecies we sampled (A. m. juncicola and peninsulae) are not significantly differentiated from each other at the 14 loci we sampled, and form a single cluster diverged from the other subspecies in Gulf-wide Bayesian analyses. The subspecies with the largest geographic range (fisheri) exhibits relatively weak geographic structuring and IBD throughout its range from the mid-Texas coast to eastern Mississippi.At the broadest geographic scale, our microsatellite and mtDNA analyses show that genetically differentiated Seaside Sparrow populations along the coast of the Gulf of Mexico are not entirely consistent with current subspecific taxonomy. Our microsatellite data are consistent with the recognition of three populations: Spartina and Juncus  reaches its southern limit in Texas A. m. sennetti and fisheri aligns with a shift to different coastal plant communities from north to south. The birds we sampled in Cameron Co. (location 1) were found in habitat dominated by Borrichia and Batis – a plant community quite unlike any other Seaside Sparrow habitat. This transition in plant communities in the Corpus Christi area is also concordant with Webb's A. m. cf. sennetti from location 1.; and (2) how much of this part of the Texas coast is occupied by Seaside Sparrows. Our data suggest that the geographic range of this taxon is smaller than that most recently described by Oberholser That Seaside Sparrows sampled near Corpus Christi, Texas (location 2) show genetic evidence of admixture is concordant with Griscom's A. m. sennetti” is needed. The type locality of sennetti is Corpus Christi A. m. sennetti to Nueces and Copano Bays  as A. m. cf. sennetti.Based on the foregoing, we suggest that a taxonomic reassessment of “Bays see . MicrosaA. m. juncicola and A. m. peninsulae has long been debated, generally because plumage variation between the two is slight relative to plumage variation within each area FST and DST for these two populations  are marginally greater than several estimates for populations of A. m. fisheri separated by greater geographic distances. GeneLand analyses - which are explicitly designed to assess fine-scale genetic structure - failed to distinguish between the two Florida populations we sampled. Based on our relatively limited genetic sampling, however, we are hesitant to make any taxonomic suggestions regarding these taxa, and genome-wide sampling, for example, could discover important differences between the populations , and additional data are needed to better understand the size, distribution, and demography of these populations. The limited extent of salt marsh within the geographic range of these taxa suggests that marsh loss in this region could decimate these populations The distinction between ions see . NonetheA. m. juncicola  “filling in the gaps.” Analyses of samples from the western Florida panhandle are needed to understand the population genetic affinities of these birds.We note that the western panhandle of Florida (Escambia Co. to approximately Bay Co.) remains a gap in our knowledge, at least partially because the limited amount of saltmarsh habitat means that there are very few Seaside Sparrows in the area la e.g., , this viA. m. sennetti and the situation in western Florida, the case of A. m. fisheri seems relatively straightforward from Brazoria Co., Texas eastward. Population genetic characteristics of Seaside Sparrows sampled from Brazoria Co. to Jackson Co., Mississippi (populations 3–7) were found to be relatively homogenous and essentially panmictic. This broad distribution and large (combined) population size suggests this taxon is of relatively low conservation concern at this time.In contrast to the case of A. m. nigrescens and A. m. pelonota) and current Federal Endangered status (A. m. mirabilis) of taxa within the Seaside Sparrow, our results support the consideration of Seaside Sparrows along the northern coast of the Gulf of Mexico as comprising three “distinct population segments:” (1) Ammodramus m. cf. sennetti, (2) A. m. fisheri, which ranges from at least Brazoria Co., TX, east to Jackson Co., MS, and (3) a combined A. m. juncicola + A. m. peninsulae group which ranges from Bay Co. south to Pasco Co., FL. Additional data are needed to better understand the situation in Texas south of Brazoria Co., but at the very least our data indicate that a highly distinct taxon (A. m. cf. sennetti) occurs south of about Corpus Christi. This distinct and isolated taxon may be in need of additional protection and management.In light of recent extinctions  and S2 (showing and ND2 haplotype network).(DOCX)Click here for additional data file.


Breonadia salicina (Rubiaceae) is a critically endangered plant at the local scale native to southwestern Saudi Arabia. To understand the levels and partitioning of genetic variation across populations and geographical regions of this species, we assessed its genetic diversity using inter-simple sequence repeat (ISSR) markers.h) and Shannon’s information index (I) were 0.086 and 0.125, respectively. At the species level, PPL was 32.2%, while h and I were 0.116 and 0.172, respectively. A hierarchical analysis of molecular variance revealed a high level of genetic differentiation among populations , consistent with the gene differentiation coefficient (GST = 0.256). Nevertheless, the evaluated genetic diversity was very low within populations; while relatively high among populations, levels were insufficient for long-term survival. Saudi Arabian accessions were also compared to accessions of a population from Yemen, where the species is more widespread. The Yemeni population also showed low genetic diversity but clustered separately.Fourteen ISSR primers selected from 43 primers gave rise to 211 amplified loci, of which 68 were polymorphic. The percentage of polymorphic loci (PPL) at the population level ranged from 17.1 to 23.7%, with an average of 21.3%. Nei’s gene diversity (Breonadia salicina in Saudi Arabia is characterized by low within-population genetic diversity and high among-population genetic differentiation. Based on our findings, this locally endangered species is on the verge of local extinction. The species’ survival depends on successful implementation of suggested strategies for its long-term conservation. B. salicina and many other endangered species.The conservation of threatened and endangered medicinal species is indispensable for the future of humankind. The flora of Saudi Arabia is rich in diversity, with numerous rare and endangered plant species. The number of threatened species is increasing annually as a result of adverse environmental conditions and anthropogenic activities . Over-exBreonadia Ridsdale (Rubiaceae) is a monotypic genus distributed in tropical Africa and the southern Arabian Peninsula. Breonadia salicina (Vahl) Hepper & JRI Wood  marker system uses repeat-anchored primers to amplify DNA sequences between two inverted simple sequence repeats . ISSR maBreonadia is a monotypic genus; to solve this problem, comparison with widespread B. salicina in Yemen can be used as an alternative for better understanding of genetic diversity.Comparisons with common congeners may decrease the contradictory effects of phylogeny ,23. To dB. salicina is limited, and information regarding its genetic basis is lacking. As a consequence, the purpose of this study was to investigate patterns of genetic diversity within and among natural populations of B. salicina in Saudi Arabia using ISSR markers and to compare their levels of genetic diversity with those of a population from Yemen, where the species is more widespread. Informed by the results, we also aimed to address possible strategies for long-term conservation of B. salicina.Literature on B. salicina population levels and localities was insufficient. Therefore, to understand the spatial distribution and current state of this endangered plant species, we initially conducted searches at sites where B. salicina had been most recently collected and for which usable locality information was available. Information was obtained from King Saud University Herbarium records, site data from vegetation mapping, and botanists. A qualified botanist subsequently conducted a thorough survey of similar habitats in other areas for B. salicina. In the case of B. salicina, the most suitable habitat—along 500- to 2000-m high escarpments and above permanent streams and rivers—is characteristic of many sites in southwestern Saudi Arabia.Previous information regarding B. salicina were collected from most individuals present in three fragmented natural populations located in southwestern Saudi Arabia and a single individual plant found in Al-Baha, Saudi Arabia. The remaining individuals  in the same populations were not sampled because of the difficulty of reaching their positions. For better comparison of genetic diversity, we also sampled a population from Yemen, as the tree is widely distributed in that country  and Sigma (UK). A set of 43 ISSR primers were screened for reproducibility and suitability for genetic diversity analysis after optimization of primer annealing temperatures according to their Tm values. DNA was diluted to a working concentration of 20 ng/μl. Amplifications were performed in 25-μl volumes using PCR beads   on a Verh), the Shannon index (I), percentage of polymorphic loci (PPL), population diversity (HS), total gene diversity (HT) within populations, and inter-population differentiation (GST) [For determination of genetic diversity, banding patterns generated by the ISSR primers were analyzed by comparing bands present in various lanes. We scored the most prominent and distinct bands, assigning for each band a ‘1’ if present and ‘0’ if absent. To assess genetic diversity, basic parameters including Nei’s gene diversity index (on (GST)  were calon (GST) . A non-pon (GST) ,30. The on (GST)  to evaluon (GST) .B. salicina in Al-Baha Province. On the other hand, two wild populations were newly identified and sampled from Asir Province in southwestern Saudi Arabia. The few known relict populations in the southwestern part of the country contained less than 30 individuals; no young seedlings were observed in these populations, except in Wadi Lejib (Jizan Province)  from previously reported locations, and that one had vanished in Wadi Fadah (Jizan Province). Similarly, our recent exploration discovered only one surviving individual of the formerly  thin (the) Table . Although varied from 0.0714 to 0.0968, and I ranged from 0.1031 to 0.1405  than that obtained in many common species using RAPD markers (h = 0.22 or 0.23) [B. salicina may be due to its breeding system, restricted geographic attribution, gene flow, genetic drift, and/or natural selection. The type of breeding system is the main factor affecting genetic diversity both among and within populations [B. salicina mating system have been conducted. The genetic diversity found in B. salicina at the population level (h = 0.0861) is lower than the average for both outcrossing-plant species and mixed breeding  and closely similar to that of self-pollinating plants (h = 0.091) using the same data source [B. salicina is more compatible with selfing behavior, which often reduces within-population genetic variation and increases it among populations. In addition, habitat fragmentation leads to decreased genetic diversity and increased inter-population divergence, likely the result of restricted gene flow among fragmented populations, increased inbreeding, and random genetic drift within populations [At the species level, PPL, ly Table . This leubiaceae –39. Meanor 0.23) . The lowulations ,42. At pa source . Based oulations . Small-sulations –46.Tableh, and other evaluated parameters, similar to the Saudi Arabian populations. PPL, h, and I in the Yemeni population were 24.2%, 0.094, and 0.137, respectively  and 50 (56.3%); the lowest number, 1 (8.3%), was obtained using primer UBC 25. Upon combination of ISSR data from Yemeni and Saudi Arabian populations, a high level of genetic diversity was expected within the Yemeni population. The opposite was true, however, with low genetic diversity observed within the Yemeni population with respect to PPL, GST values is classified as low (<0.05), medium (0.05–0.15), or high (>0.15) [GST value uncovered for B. salicina (0.256) would thus be considered high; by indirect comparison, however, it was much lower than values previously reported using ISSR markers (0.34) [In population genetics, gene differentiation based on  (>0.15) . The GSTs (0.34) .B. salicina populations were formerly part of a single population that became fragmented owing to anthropogenic activities and various ecological factors. An alternative, more likely hypothesis is that these populations were larger in the past and were probably bordered by intervening populations  that ensured low levels of gene diversity coupled with this level of genetic differentiation.These findings raise the possibility that all present-day GST value for B. salicina was supported by an AMOVA, which indicated that 17% of the total variation was partitioned between populations among regions. When populations were analyzed without grouping, AMOVA indicated that 84.86% of the total variation in the studied populations was due to differences within populations, with the remaining 15.14% accounted for by among-population differences  values of the Saudi Arabian populations had a very narrow range, varying from 0.9514 to 0.9705, and pairwise genetic distances were between 0.0299 and 0.0498 Table . GeneticB. salicina populations share similar habitats; such a situation tends to result in similar population characteristics, as a clear association exists between a population’s characteristics and its environment [These close genetic identities suggest that all three Saudi Arabian populations had a recent common ancestor, an inference further supported by the results of the UPGMA analysis Figure . Moreoveironment ,49. The B. salicina accessions from Saudi Arabia along with the eight Yemeni accessions based on Jaccard’s similarity coefficient using UPGMA cluster analysis [B. salicina accessions were divided into two major clusters. Cluster I comprised 10 accessions from populations 1 and 2, and cluster II contained seven accessions from population 3 and 2 outliers from population 1 (Breonadia_S4 and Breonadia_S6). In addition, a sample from Al-Baha (Breonadia_S7), the geographically most distant locality, was also the most genetically distant accession, forming its own clade. One accession (Breonadia_S5) from population 1 was widely separated from the rest of the Saudi Arabian accessions, suggesting that population 1 was the ancestral population of B. salicina. Although genetic similarity based on Jaccard’s similarity coefficients was highest between populations 1 and 2 and lowest between populations 1 and 3, genetic similarities among all three populations were nearly identical. Jaccard’s similarity coefficients ranged from 82.76 to 94.82% among the B. salicina accessions, indicating a narrow genetic diversity (Table Breonadia_S15) had the lowest genetic similarity (82.76%) to the other studied accessions. The cause of this higher genetic diversity is unknown, but is presumably due to environmental factors prevailing at the higher altitude  of population 3.We evaluated the genetic relationships of the 19 analysis   without any external disturbance. In addition, the plant is cultivated in Yemen for wood and timber [On the other hand, based on genetic distance Tables  and 7, td timber ,51.FigurB. salicina. The first component of our proposed strategy concerns the mating system of B. salicina, suggested in this study to involve self-pollination. To verify this finding, further investigation is urgently needed. The outcome of such a study would have important implications for B. salicina conservation and management, as knowledge of the mating system is a major factor in the elucidation of genetic variability levels at both species and population levels [Drawing on our study results, we suggest a possible management strategy for the conservation of n levels .B. salicina to reduce the impact of human activities would allow its habitats to increase in size through natural regeneration to reach effective population sizes. The construction of an in situ conservation area to preserve all extant populations would also maintain most existing genetic variation. Although losses of any one population or individual tree at specific locations may not cause an immediate decrease in genetic diversity, such losses have dangerous long-term consequences.A second important consideration for restoration of endangered and rare species is population size . EstabliB. salicina have included seed germination, which is more successful under moist conditions, and placing cuttings and wildlings in mud, the most frequently used method. Wildlings appear to have a higher survival rate than nursery seedlings, which need to be at least 1 m high to survive transplantation [Third, plants can be introduced from other populations via appropriate propagation and seedling management to increase the chance of gene exchange and recombination and to improve the level of genetic diversity over time. In Tanzania, effective management procedures for antation .ex situ conservation program to capture most of the detected genetic variability and to increase genetic diversity by crossing different populations. Fifth, collection of seeds and germplasm in botanical gardens or other institutions may be of significant practical value for the conservation of genetic diversity. Sixth, establishment of a regeneration system for this endangered plant via tissue culture would not only guarantee its ex situ conservation and sustainable survival, but would also enhance its in situ conservation. A seventh component of the action plan is the education and involvement of local people. Finally, as genetic information alone will still not provide a sufficient solution, demographic and ecological perspectives must be taken into consideration before drawing up and implementing a conservation strategy.A fourth suggested management practice is establishment of an B. salicina genetic diversity. Observed low levels of within-population genetic diversity coupled with the relatively high genetic differentiation detected among Saudi Arabian B. salicina populations suggest the hypothesis that these populations were formerly part of a single population that has become fragmented as a result of biotic and abiotic factors in the natural habitat. Alternatively, current populations were larger in the past, with intervening, now-extinct populations  ensuring lower levels of gene diversity. While both Saudi Arabian and Yemeni populations are marked by low genetic diversity, the presence of optimal growing conditions and cultivation in Yemen has contributed to the widespread distribution of B. salicina in that country. To broaden the genetic base of B. salicina and conserved this critically endangered plant species in Saudi Arabia, appropriate management and conservation strategies require implementation.The molecular markers used in this study were found to be effective for the assessment of


The Malay people are an important ethnic composition in Southeast Asia, but their genetic make-up and population structure remain poorly studied. Here we conducted a genome-wide study of four geographical Malay populations: Peninsular Malaysian Malay (PMM), Singaporean Malay (SGM), Indonesian Malay (IDM) and Sri Lankan Malay (SLM). All the four Malay populations showed substantial admixture with multiple ancestries. We identified four major ancestral components in Malay populations: Austronesian (17%–62%), Proto-Malay (15%–31%), East Asian (4%–16%) and South Asian (3%–34%). Approximately 34% of the genetic makeup of SLM is of South Asian ancestry, resulting in its distinct genetic pattern compared with the other three Malay populations. Besides, substantial differentiation was observed between the Malay populations from the north and the south, and between those from the west and the east. In summary, this study revealed that the genetic identity of the Malays comprises a mixed entity of multiple ancestries represented by Austronesian, Proto-Malay, East Asian and South Asian, with most of the admixture events estimated to have occurred 175 to 1,500 years ago, which in turn suggests that geographical isolation and independent admixture have significantly shaped the genetic architectures and the diversity of the Malay populations. The Malay people, who generally inhabit the Malay Archipelago, particularly the Peninsular Malaysia, is a group of genetically diverse populationshttp://www.hapmap.org), the Human Genome Diversity Projecthttp://www.hagsc.org/hgdp) and the 1000 Genomes Projecthttp://www.1000genomes.org). Some recent studies on Asian populations have included a few Malay samples from Malaysia, Indonesia and Singapore814In Malaysia, the Malays consist of several sub-groups, e.g., Melayu Minang, Melayu Jawa, Melayu Bugis and Melayu Kelantan, based on their respective geographical origins. Malay sub-groups have various historical origins and genetic affinities134625The present study aimed to investigate the genetic relationship and population structure of the Malays from both Southeast Asia and South Asia to extend our knowledge about population history and the genetic landscape of human populations in Southeast Asia. Analyses were conducted by using 288,660 single nucleotide polymorphisms (SNPs) shared by 133 Malay individuals from Peninsular Malaysia, Singapore and Sri Lanka , as well as approximately 50,000 SNPs shared by 12 Indonesian Malays (IDM) located in Sumatra Island, 30 SGM and 38 PMM  from the HUGO PanAsia SNP Consortium (PASNP)FST, SLM showed a closer relationship with South Asian populations (FST = 0.004–0.024) than with Southeast Asians (FST = 0.010–0.057). In addition, the genetic difference between SLM and the other Sri Lankan ethnic groups (FST = 0.007–0.012) was larger than that between any other Sri Lankan ethnic groups (FST = 0.001–0.003).As shown in the population phylogenetic tree , populat−16), and between those from the west (SLM) and the east   . At the global scale, the four Malay groups were considerably distinct from Africans, and similar to other non-Africans . South A× 10−16) .An unsupervised ADMIXTURE analysis, without prior information of individual ancestry, was performed to investigate patterns of admixture in Asia. Nine ancestral clades were assumed to reflect the major genetic components in Asia . CollectMalay populations shared four major components, e.g., East Asian, South Asian, Austronesian and aboriginal Southeast Asian. East Asians, which contribute 4%–16% of the Malay genomes, had interactions with Malays at very recent time  . We obseWhile the genetic makeup of the four Malay populations shared similar ancestral components, the proportion of these components in each population substantially differed. In PMM, SGM, and IDM, the Austronesian and Southeast Asian aboriginal components had larger proportions than the East Asian and South Asian components. Specifically, very little genetic difference was found between PMM and SGM, in which the two major components were of nearly equal proportions and together comprised 60%–70% of the entire genome. IDM also showed a very similar genetic pattern, but was more marginally affected by the Austronesian population . However, the genetic admixture pattern of SLM was unique compared to the other Malay populations, exhibiting admixture between South Asians and Malays. The proportions of different components were relatively more comparable in SLM. For instance, the proportions of Central Asian and South Asian components in SLM were intermediate between those observed in Southeast Asians and South Asians, and a similar pattern was detected for the Southeast Asian component  and East Asian component.−16), whereas the East Asian component showed a much less significant correlation . Furthermore, the European and Central Asian components, although they were not major components in the Malays, also largely contributed to the Malay differentiation .To quantify the contribution of each ancestral component to the differentiation of Malay populations, we calculated the correlation between the proportions of each ancestral component and the values along PC1 of the 4 Malay populations in In this study, we conducted a genome-wide analysis of four geographical Malay populations from Malaysia, Singapore, Indonesia and Sri Lanka. We have analyzed the genetic diversity of the Malay populations and revealed substantial population differentiation and gene flow among these different geographical populations.th–13th centuries, the Malay culture reached its golden age, and influenced the entire Malay Archipelago2th century, the Malacca Sultanate initiated a major revolution in Malay history by promulgating its languages, religions and traditions, which has also recently influenced the immigrants from other countriesth centuryth–17th century202th century, European countries, including Portugal, Netherlands, and Britain, colonized the majority of Southeast and South Asian countries, bringing about mating and gene flow across continents. More recently, due to the development of transportation and the globalization of the entire human society, the Southeast and South Asian populations have thus had more frequent gene flow from each other and from their surrounding neighborhoods, e.g., East Asians. Although previous genetic studies investigated less comprehensive Malay samples covering a smaller geographical range of Malay, the results indeed concurred with our findings of extensive genetic diversity among the Malays1In general, all the four Malay populations were inferred to be substantially admixed with multiple ancestries from East Asian, South Asian, Austronesian, and Southeast Asian aboriginal people around 175–1,500 years ago , Table 1The present study provides the first identification of the distinct genetic makeup of SLM compared to the other three Malay populations. SLM inherited much more South Asian ancestry, whereas PMM, SGM, and IDM are genetically similar to most of the other Southeast Asian populations, with minor fluctuations in the proportions of each genetic component across populations. Two factors have facilitated to shaping SLM to be a genetically intermediate population between South Asians and Southeast Asians  and 2. FUnlike those isolated or ancient populations in Southeast Asia , the Malays do not have a representative genetic component. Instead, four major ancestral components constitute the Malay genomes, making the Malays distinguishable from most of the other Southeast Asian populations . The Aus2324The major limitation of the present study lies in the possible bias of the estimated admixture time and fraction. For some populations, the sample size and SNP density were not highly appropriate for use in the analyses. On one hand, most of the Southeast Asian genotypes, including IDM, from PASNPIn summary, the present study provides the first demonstration of the genetic relationship and genetic makeup of various Malay populations and indicated a complex human population admixture history in Southeast Asia. We propose that geographical isolation and independent admixture history have significantly shaped the genetic architectures and the diversity of the Malay populations. An important direction for future work is to extend to a wider coverage of Malay groups and increase the SNP density, to allow a more detailed investigation of the population genetic diversity in Southeast Asia.http://www.statgen.nus.edu.sg/~SGVP/), 30 SGMs, 12 IDMs and 38 PMMs from PASNPet al. (2014)The present study focused on 213 unrelated Malay individuals from 4 Malay groups that were collected from different datasets. All the 27 SLM samples, as well as 538 Sri Lankan samples from other ethnic groups  were genotyped on an Illumina 2.5 M array. The other 186 Malay samples were previously reported, including 89 SGMs from the Singapore Genome Variation ProjectTo characterize the genetic variation of the 4 Malay groups on a global scale, we also included 3,170 unrelated individuals from 90 worldwide populations reported in HapMap Phase 3http://pngu.mgh.harvard.edu/~purcell/plink/) to control the data quality. Stringent sample filtering was conducted within each of the examined populations, and in total, 113 samples with missing rates of >5% were excluded from the subsequent analyses , and were visualized by MEGA v6.06http://www.megasoftware.net). The bootstrap value was based on the 1,000 repeats of FST calculation by randomly sampling SNPs. PCA was conducted by using EIGENSOFT v3.0http://www.hsph.harvard.edu/alkes-price/software/). Prior to analysis, 24 SNPs were removed by LD pruning with a threshold of r2 = 0.8 for pairwise SNPs, and 14 individuals  were filtered out with a PC1 value >2-fold standard deviation in the PCA for each independent population. Population genetic clustering was conducted by using ADMIXTUREhttps://www.genetics.ucla.edu/software/admixture/) specifying K = 2 to K = 12. Cross validation (CV) was used to assess the number of clusters best-fitting the data . We used the one-reference model in ALDER, assuming only a single reference population. To circumvent the uncertainties about the exact ancestral source, we have performed the same admixture test using all the populations from each ancestral group. Because various numbers of SNPs have been identified in the populations that are used in this analysis, which ranged from ~50,000 to ~2,000,000, in the independent analysis of each donor-receptor pair, we used the SNPs shared by the pair of populations to maximize the SNP density. Similarly, when estimating the ancestral contributions of the Sri Lankan ethnic groups to Sri Lankan Malays, we examined all possible combination of the two sources . Ten individuals were randomly sampled from each population to generate a fair sample size. The supervised STRUCTURE analysis is based on the admixture model with pre-assigned ancestral individuals and two predefined clusters (K = 2). We obtained locus-specific ancestry estimations by using the linkage model implemented in STRUCTURE version 2.3http://www.r-project.org/) based on the Pearson’s correlation model.Wright’s fixation index the data . To quanHow to cite this article: Deng, L. et al. Dissecting the genetic structure and admixture of four geographical Malay populations. Sci. Rep.5, 14375; doi: 10.1038/srep14375 (2015).


Detecting and quantifying the population substructure present in a sample of individuals are of main interest in the fields of genetic epidemiology, population genetics, and forensics among others. To date, several algorithms have been proposed for estimating the amount of genetic ancestry within an individual. In the present review, we introduce the most widely used methods in population genetics for detecting individual genetic ancestry. We further show, by means of simulations, the performance of popular algorithms for detecting individual ancestry in various controlled demographic scenarios. Finally, we provide some hints on how to interpret the results from these algorithms. The genetic variability among the human species is known to be relatively low compared to other primate species . There aInferring population substructure in the human genome is cumbersome and is the main goal for the large number of genetic ancestry algorithms and approaches that have been proposed in the last decade. A basic assumption is that any current individual genome or population is a mixture of ancestries from past populations . Therefoa priori unknown ancestry components, the quantification of the proportions of these components, or the identification of the assumed population of an individual. Individual ancestry methods can be classified depending on the assumptions of the method, the scope of the algorithms , and the use of prior ancestry information, among others (see Table n m K2) for ADMIXTURE [n m K) for sNMF [Methods for estimating ancestry have traditionally focused on populations; their main interests are to establish the relationship among populations and to quantify the admixture proportions in the admixed populations ,16. AdmiDMIXTURE  and O or Mult [et al. ,29). For [et al. . Neverth [et al. ). In con [et al. ) in the ved data ,24. For ved data , the genf in the ancestral populations for all the loci and the ancestry q proportions in each individual include Bayesian  and maxTRUCTURE  and ADMITRUCTURE ).Recently, new types of global ancestry methods have been proposed. These methods take advantage of the spatial dependence of human population substructure  to estimK) of ancestral populations from the data . In the case of model-free methods, using their output, a classifier algorithm can be applied in order to identify the number of genetically homogeneous population clusters . In mod example ,42, or [ example ). One ex example , a new ad n  and model-based methods  to the same dataset ,46. PlotFurther improvements on genotyping technology, with the description of millions of single nucleotide polymorphisms (SNPs) in the human genome , have alIndividual ancestry methods can depict a detailed picture of the genetic landscape of human populations . Furtherad hoc plausible explanation for any estimated admixture pattern  populations can also bias the output of some algorithms, such as PCA [In principle, averaging the fragments of local ancestry over the genome of one individual computes the global ancestry estimation in that individual; similarly, averaging all of the global individual ancestries in one population provides a migration/admixture estimation in that population. Moreover, the mean and variance in the length of the ancestry fragments and the global ancestry proportions can be used to estimate parameters such as the time or migration rate of the admixture event in particular demographic scenarios . Neverthple, see ). The prh as PCA ,56; the h as PCA ). Furtheh as PCA . UltimatWe exemplify some of these caveats using unsupervised analyses from four ascertained global-based algorithms on simulated and real data using the default parameter settings from each algorithm. In particular, we consider ADMIXTURE , sNMF 2, fastSTRt generations ago in two populations. In one version of the model, the two descendent populations start evolving independently. In another version, migration between the two populations is allowed. The second model comprises an ancestral population that splits in two, which after a certain number of generations evolving with a genetic barrier, create a new population by admixture , whereas uneven sampling does not influence the estimates of the ancestry components with any of the programs. The number of SNPs has a strong impact on all programs. When only very few sites are available , fastSTRUCTURE produces the best outcome. This is not surprising, as ADMIXTURE and sNMF have been particularly developed to consider a dense number of markers [t > 0.0125 that correspond to Fst > 0.0124 [Nm > 10 , the populations become distinguishable if the migration starts before 100 generations backward in time . Therefore, the migration rate cannot be inferred from this analysis.In addition, we studied the parameter range where migration becomes detectable depending on the start time and rate of migration in the continuous gene flow (CGF) model  as previously described. In each simulation, a fraction of heterozygote genotypes was decreased proportional to the Fis , we observe that sNMF and fastSTRUCTURE indicate correctly the effect of migration in their estimates , the genetic variation is more divergent in sNMF and fastSTRUCTURE; in contrast, both populations appear more similar with ADMIXTURE. Therefore, sNMF and fastSTRUCTURE seem to provide better ancestry estimates compared to ADMIXTURE, particularly when inbreeding is high (Fis > 0.1). If migration is absent, inbreeding has a minor effect on the ancestry estimates from the different algorithms (data not shown).We further investigated how the presence of hidden inbreeding affects the estimated genetic ancestry proportions from each algorithm. We used the two-population model with constant migration  by replK = 2 or K = 4 assumed ancestral populations ; nevertheless, even in that case, the mean difference between the estimated and the real admixture proportion can reach 5% in the case of sNMF and MDS, and 6% in the case of fastSTRUCTURE. Unequal sample sizes of the parental populations also affect the performance of the different algorithms. ADMIXTURE and fastSTRUCTURE show a systematic error bias in the estimation of the admixture proportions in the hybrid population when there is unequal sample size in the parental populations, independently of the amount of population differentiation among the parental populations.As seen in Figure Simulations from synthetically generated admixed populations from African (YRI) and European (CEU) as ancestral populations were produced  can be misleading; except for cases such as the admixture of European and Sub-Saharan African populations in the US , admixtuad hoc interpretation; in particular, any demographic interpretation from these methods should be further validated by means of demographic simulations, showing that the proposed demographic model can produce the observed output of genetic ancestry.The number of SNPs and sample size seem to be a limiting factor in all the algorithms that we have tested; therefore, it would be recommended to use as many markers  and samples as possible. However, in our simple model, we observe already good estimates for >10 samples and >1,000 markers. In case fewer markers are available, fastSTRUCTURE provides the best estimates followed by ADMIXTURE and sNMF. Furthermore, it is recommendable to run more than one algorithm on the same data at the same time given the observed diversity of results, different sensitivity to biased sample size of the different algorithms, and ancestry noise. In this sense, combining global ancestry and population ancestry methods , or usi