Rhizophora species are the most widely distributed mangrove trees in the Indo-West Pacific (IWP) region. Comparative studies of these species with shared life history traits can help identify evolutionary factors that have played most important roles in determining genetic diversity within and between populations in ocean-current dispersed mangrove tree species. We sampled 935 individuals from 54 natural populations for genotyping with 13 microsatellite markers to investigate the level of genetic variation, population structure, and gene flow on a broad geographic scale in Rhizophora apiculata, Rhizophora mucronata, and Rhizophora stylosa across the IWP region. In contrast to the pattern expected of long-lived woody plants with predominant wind-pollination, water-dispersed seeds and wide geographic range, genetic variation within populations was generally low in all the three species, especially in those peripheral populations from geographic range limits. Although the large water-buoyant propagules of Rhizophora have capacity for long distance dispersal, such events might be rare in reality, as reflected by the low level of gene flow and high genetic differentiation between most of population pairs within each species. Phylogeographic separation of Australian and Pacific island populations from SE Asian lineages previously revealed with DNA sequence data was still detectable in R. apiculata based on genetic distances, but this pattern of disjunction was not always evident in R. mucronata and R. stylosa, suggesting that fast-evolving molecular markers could be more suitable for detecting contemporary genetic structure but not deep evolutionary divergence caused by historical vicariance. Given that mangrove species generally have small effective population sizes, we conclude that genetic drift coupled with limited gene flow have played a dominant role in producing the current pattern of population genetic diversity in the IWP Rhizophora species, overshadowing the effects of their life history traits. Recent population fragmentation and disturbances arising from human activities could further endanger genetic diversity in mangrove trees. Rhizophora, the most representative genus in the mangrove family Rhizophoraceae with broad distribution in both Indo-West Pacific (IWP) and Atlantic-East Pacific (AEP) regions.Mangroves consist of a diverse group of species of trees, shrubs, palms, vines, and ferns from 20 families .To cover geographic distributions of the three on Table . The preon Table , with muThe dried leaf pieces of each individual sample were disrupted in a 2 ml microcentrifuge tube using QIAGEN TissueLyser II , and total genomic DNA was extracted using the standard CTAB method . The exact p-values of HWE tests were estimated with a Markov chain algorithm described by Guo and Thompson  was tested for each of the locus—population combinations using GENEPOP , rarefied number of alleles per locus (AR), and inbreeding coefficient (FIS) within populations were estimated using FSTAT 2.9.3  with GenAlEx version 6.5  and expected heterozygosity (HE) within populations, and the total gene diversity (HT) at the species level, inbreeding coefficient within individuals (FIS) and genetic differentiation among populations (FST) were also computed using GenAlEx version 6.5  was estimated based on the mean FIS over loci within each population using the equation t = (1 − FIS)/(1 + FIS)  with pRST values obtained from 10,000 allele size permutations using SPAGeDi 1.4  iterations with a burn-in period of 5 × 105, employing the admixture model with correlated allele frequencies between clusters  between populations and between species, we used BayesAss v3.0.3 , averaged 0.315, 0.315, and 0.305 in R. apiculata, R. mucronata, and R. stylosa respectively, although the range of the estimates among populations was much broader, from 1.85 (AUS.TRI) to 4.54 (THL.MNP) in R. apiculata, from 1.92 (SRL.RKW) to 4.31 (PHP.PNL) in R. mucronata, and from 1.77 (CHN.DNZ) to 6.15 (AUS.CTR) in R. stylosa. At the species level, similar amount of total gene diversity (HT) was found in R. apiculata (0.621), R. mucronata (0.648), and R. stylosa (0.620) , China (CHN.QLH and CHN.TLH), and Pacific islands , whereas relatively high estimates of allelic richness and heterozygosity were found in South East Asia, such as populations from Indonesia (IDN.NSL and IDN.TRK), Malaysia (MLS.BLG), Philippines (PHP.PNL), and Thailand (THL.RNG and THL.PNB). In R. stylosa, high levels of heterozygosity were found in two populations from North Australia , whereas much lower levels were found in populations from China, Fiji, Guam, and Kiribati. However, there were exceptions to the general pattern, such as in R. mucronata the highest level of heterozygositywas found in East Australia , while the lowest estimate was found in a population from Thailand .Compared to populations located in or near the centers of these species' geographic ranges, the levels of genetic diversity were often lower in peripheral populations. For example, in FIS found in four populations  of R. apiculata, four populations  of R. mucronata, and five populations  of R. stylosa , must represent an underestimate of outcrossing or an overestimate of inbreeding within each population. The estimates of t ranged from 0.441 to 1.567 among populations of R. apiculata, from 0.338 to 1.225 among populations of R. mucronata, and from 0.504 to 1.894 among populations of R. stylosa. The average outcrossing rate over populations was 0.821, 0.776, and 1.051 in R. apiculata, R. mucronata, and R. stylosa, respectively, indicating that they are predominantly outcrossing species, consistent with a primarily wind-pollinated mating system.Among a total of 702 locus-population combinations , 164 of the combinations were monomorphic as some of the loci were not variable in all 54 populations. Significant deviations from HWE were detected in 30 of the remaining 538 locus-population combinations, and the global test of heterozygosity deficiency revealed that 13 out of 54 populations significantly deviated from HWE, corresponding to the significantly positive P < 0.05 after Bonferroni correction; Supplementary Table FST values were highly heterogeneous, ranging from 0.01 (between AUS.DTR and AUS.TRI) to 0.75 (between AUS.DTR and CHN.QLH) in R. apiculata; from 0.07 (MLS.KPS and THL.PNB) to 0.72 (AUS.DTR and SRL.RKW) in R. mucronata; and from 0.03 (AUS.TDR and AUS.MTB) to 0.76 (USA.GM and FJ.SVP) in R. stylosa. As expected, the high FST values were found between geographically distant populations, whereas the low FST values were found between geographically adjacent populations. At the species level, the mean FST values over all 13 loci were similar, 0.461 in R. apiculata, 0.452 in R. mucronata, and 0.484 in R. stylosa  were clearly divergent from those of East Coast.The NJ analysis based on allele frequencies in populations of all three species showed three major groups Figure , with eaR. apiculata; from 0.0088 to 0.2052 in R. mucronata, and from 0.0062 to 0.1513 in R. stylosa  and from SRL.RKW to SRL.PBL (0.1425) in R. mucronata; from AUS.EMB to AUS.CTR (0.1419) and from AUS.CDR to AUS.TDR (0.1513) in R. stylosa. Furthermore, strong directional differences in gene flow were detected between some of these adjacent populations, e.g., gene flow from AUS.EMB to AUS.CTR was much higher than that from the opposite direction in both R. apiculata (0.1285 vs. 0.0077) and R. stylosa (0.1419 vs. 0.0072); gene flow from AUS.TRI to AUS.DTR was up to 18 times higher than that from AUS.DTR to AUS.TRI and the same pattern of directionality was observed in all three species . Strong directional differences in gene flow were detected in seven population pairs in R. apiculata, four in R. mucronata, and seven in R. stylosa . Repeated founder effects or bottleneck events associated with geo-climatic history in the IWP region and genetic drift in small and geographically isolated populations are most likely the major cause of the low genetic diversity within populations and within species of Rhizophora, especially in marginal populations of the species' distribution ranges. With rare exceptions, the lowest estimates of genetic diversity were found in the peripheral populations of each species. Low genetic diversity in marginal populations is also shown in other studies of R. stylosa. For example, Wilson , and reported much lower estimates of heterozygosity (HO = 0.19 and HE = 0.26). Islam et al. (HO (0.130) but higher HE (0.338) in 16 populations of R. stylosa from Sakishima islands of the Japanese archipelago . These marginal populations of Rhizophora may represent descendants of a few ancestral propagules dispersed from the more centrally located populations after the last glacial maximum (LGM), resulting in further reduction of genetic variation due to the founder effect. Low genetic diversity in peripheral populations was also detected in other mangrove tree species, such as Bruguiera gymnorhiza (Rhizophoraceae) from its northern geographic limit in Japan  in the AEP region , which is often a small fraction of the census population size  data . It can be concluded that genetic drift might have played a dominant role in the evolutionary history of mangrove trees, as evidenced at both SNP and microsatellite loci across the genome.Geographic distributions of mangrove trees are strongly influenced by air temperature and availability of intertidal habitats, with the latter being tightly linked to sea level fluctuations  were reported in Wee et al. , self-compatible plant species can be further characterized as predominantly outcrossing (t > 80%), mixed mating (t = 20–80%), or predominantly selfing (t = 0−20%). Although t is commonly computed using progeny arrays /(1 + FIS) as described by Weir , the levels of allozyme variation were extremely low at both population and species levels in comparison with other plant species with mixed-mating system. Random genetic drift and founder effects are most likely the driving force in mangrove populations, especially in the species' distributional margins, overriding the effect of breeding system on the level and pattern of genetic diversity.Several previous studies reported various levels of inbreeding in populations of FIS have been commonly used to infer inbreeding in many mangrove studies. This is probably due to the difficulties to assay progeny arrays with mangrove propagules. However, microsatellite-based estimates of FIS should be interpreted with caution if null alleles are present at one or more of the sampled loci, because null alleles could result in an excess of observed homozygosity compared to HWE expectation and consequently result in an overestimate of FIS at the locus. Null alleles are commonly present at microsatellite loci . We found highly significant correlations (p < 2.2e−16) between the two estimates in both of the studies  with those from FREENA . We found that the estimates of genetic differentiation were comparably high whether or not the null alleles were included in the data analyses  showed that d Szmidt  providedR. mucronata and R. stylosa based on morphological keys  and a CRCG grant to MS, and a postgraduate fellowship from the University of Hong Kong to YY.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The evolutionary reasons for variation in nose shape across human populations have been subject to continuing debate. An import function of the nose and nasal cavity is to condition inspired air before it reaches the lower respiratory tract. For this reason, it is thought the observed differences in nose shape among populations are not simply the result of genetic drift, but may be adaptations to climate. To address the question of whether local adaptation to climate is responsible for nose shape divergence across populations, we use Qst–Fst comparisons to show that nares width and alar base width are more differentiated across populations than expected under genetic drift alone. To test whether this differentiation is due to climate adaptation, we compared the spatial distribution of these variables with the global distribution of temperature, absolute humidity, and relative humidity. We find that width of the nares is correlated with temperature and absolute humidity, but not with relative humidity. We conclude that some aspects of nose shape may indeed have been driven by local adaptation to climate. However, we think that this is a simplified explanation of a very complex evolutionary history, which possibly also involved other non-neutral forces such as sexual selection. The study of human adaptation is essential to our understanding of disease etiology. Evolutionary investigations into why certain disease phenotypes such as sickle-cell anemia and lactose intolerance occur at different rates in different populations have led to a better understanding of the genetic and environmental risk factors involved. Similarly, research into the geographical distribution of skin pigmentation continues to yield important clues regarding risk of vitamin D deficiency and skin cancer. Here, we investigate whether variation in the shape of the external nose across populations has been driven by regional differences in climate. We find that variation in both nares width and alar base width appear to have experienced accelerated divergence across human populations. We also find that the geospatial distribution of nares width is correlated with temperature, and absolute humidity, but not with relative humidity. Our results support the claim that local adaptation to climate may have had a role in the evolution of nose shape differences across human populations. The shape of the nose, like many other parts of the face, varies both within as well as across human populations. For example, the distance between the nasal alare (wings of the nose) are significantly larger in individuals of West African, South Asian, and East Asian ancestry as compared to persons with European ancestry . The nasA vital function of the nose is to warm inspired air to core body temperature and saturate it with water vapor before it reaches the lower respiratory tract . In factThere are several challenges in testing this hypothesis. We know there is substantial nose shape variation among human populations, in both the external morphology of the nose as well as the underlying cranial morphology ,2,9. WhiSeveral studies have used this approach and found that while most aspects of the skull seem to be evolving neutrally, the shape of the nasal aperture appears to be more differentiated across human populations than expected under genetic drift ,12,13. MHere, we carried out an exploration of external nose shape variation in light of quantitative genetic theory to investigate whether nose shape variation among populations exceeds neutral expectations. In doing so, we discuss the methodological challenges involved in tackling such questions in humans, and show how the limitations of previous studies can be addressed with recent advances in statistical genetics. As proof of principle, we compare the differentiation of nose shape with two highly heritable morphological traits, which are known to have a polygenic basis; namely height and skin pigmentation –18. Both3dMD Atlanta, GA). Five positioning landmarks  were placed in order to establish facial orientation. A spatially dense mesh of 7,150 quasi-landmarks (QLs) was mapped onto each image and its reflection. Generalized Procrustes Superimposition .,27.l), v(sn–prn) . We alsoril area   to 1 . Eq  West African (N = 40), ii) North European (N = 236), iii) East Asian (N = 127), and iv) South Asian (N = 73) . This is in accordance with the idea that differences in skin pigmentation, at least across continental populations, have been driven by positive selection. Height, on the other hand, does not show signals of accelerated divergence . While height seems to be under positive parately ,34, it dc/h2 = 1. In other words, we assume that the proportion of between-population variance in the trait due to additive genetic effects is the same as the proportion of within-population variance due to additive genetic effects. This can be an incorrect assumption if a large proportion of the phenotypic variance between populations is due to direct environmental effects . If the true value of c/h2 is drastically lower than 1, phenotype-derived estimates of the among-population genetic variance, and hence, Qst will be inflated, resulting in false-positive conclusions regarding the role of selection in driving phenotypic differentiation. This fact has largely been ignored in previous studies, in which Qst-based approaches were used to explore craniofacial divergence . In. In39].  (2014)) , might p. (2010) , using lulations . Thus, thy2 from local ancestry at 623,625 autosomal SNPs in a sample of 409 Cape Verdeans, who derive their ancestry primarily from W. Africans and Europeans  is quite heritable. Results for skin pigmentation data are not presented here as they were unavailable. Differences in many aspects of nose shape are also heritable . Other aspects of nose shape may not be as heritable between these two populations . Another interesting observation from hy2 are generally lower than estimates of hg2 for all traits. This suggests that for most human traits, the additive genetic variance between populations might be less than the additive genetic variance within populations, which agrees with the fact that most of the genetic variation in humans exists within populations. However, we are cautious of over-stating this conclusion since the heritabilities were estimated in individuals with W. African and European ancestry only and do not reflect the variation within and across other populations. Altogether, our results show that genetic differences underlie the variation in many aspects of nose shape, both within- and between-populations.Following this reasoning, we estimated Previously, several studies have shown that the shapes of the nasal aperture and nasal cavity are correlated with climate variables related to temperature and humidity such that individuals from cold-dry climates exhibit narrower nasal cavities compared to individuals from warm-humid climates ,42. We wper se but the width of the nares that is under selection pressure. However, even nares width is not as strongly correlated with temperature and humidity as skin pigmentation is to UVB, suggesting that the strength of selection exerted by temperature on nose width is likely to be much weaker than the selection pressure imposed by UVB exposure on skin pigmentation.As expected, we see a strong relationship between skin pigmentation and UVB . The positive values of the slope and t-statistic indicate that people tend to be darker in regions with higher exposure to UVB and vice versa. This pattern provides further support to the notion that adaptation to ultraviolet radiation has had an important role in the evolution of skin pigmentation in humans. Nares width is significantly correlated with temperature  and absolute humidity . The positive slopes indicate that individuals from warm-humid climates, on average, tend to have wider nares whereas individuals from cool-dry climates tend to have narrower nares. This is consistent with previously reported patterns of ecogeographic variation in the width of the nasal aperture and nasal cavity ,42. NareFollowing a suggestion by one of the reviewers, we investigated whether the observed phenotype-climate correlations were driven by any one population. To do so, we re-estimated the phenotype-climate slopes (skin pigmentation vs UVB and nares width vs temperature) after removing each population in turn. The diversity of facial features across human populations has fascinated scientists for a long time. Even though genetic drift has played a predominant role in human evolution, external physical traits such as facial shape and skin pigmentation, because of their proximity to the environment, have also likely been influenced by natural selection. Substantial evidence has accumulated supporting the hypothesis that differences in skin pigmentation across human populations have evolved largely in response to selection pressures imposed by exposure to ultraviolet radiation . How selh2) is equal to the among-population heritability (c). Qst calculated this way is probably best considered to be the ‘minimum’ Qst proposed by Relethford (1994) [h2 always being less than c, which is anticonservative as has been noted previously [c = h2. However, we used sensitivity curves to evaluate the behavior of Qst to cases where c < h2. In addition, we demonstrate that both within- and between-population variation in nose shape are heritable.To test the first hypothesis, we used Qst–Fst comparisons to investigate whether the mean difference in nose shape among populations is greater than that expected under genetic drift alone. Estimation of Qst relies on stipulating the within- and among-population components of phenotypic variance that are due to additive genetic effects. These variance components are ideally calculated through experiments in which the effects of environmental variables can be controlled . This pod (1994) . Howevereviously . In factc = h2 assumption. This suggests that the width of the nares and alar base may have evolved across populations due to divergent selection.We carried out Qst–Fst comparisons for seven nose shape traits: nares width, alar base width, nasal height, length of the nasal ridge, nasal tip protrusion, external surface area, and nostril area. We found that the divergence in the width of the nares and alar base deviate from neutral expectations, and that these results are robust to the Next, we hypothesized that the divergence of these two traits is driven by climate adaptation. To investigate this, we tested whether the spatial distribution of these traits is correlated with mean annual temperature and humidity. Our results show that nares width is strongly correlated with temperature and absolutely humidity. The positive direction of the effects indicate that wider noses are more common in warm-humid climates, while narrower noses are more common in cold-dry climates. Nares width is not, however, correlated with relative humidity. This is not surprising since absolute humidity levels are likely more important for respiration than relative humidity . Alar baAnother hypothesis regarding the function of the nose that has received less attention compared to the air-conditioning hypothesis is that of selective brain cooling (SBC) . Some laClimate may not have been the only factor in contributing to nose shape differences across populations. In fact, we show that temperature is only weakly correlated with nares width, especially when compared with the correlation between skin pigmentation and UVB. What then could be the selective agent driving the divergence of nose shape? We mentioned earlier that all aspects of nose shape studied here are sexually dimorphic, which raises a number of questions. Why does this sexual dimorphism exist? Is it merely a by-product of circulating hormones leading to differences in growth and development in early adulthood, or does it have an adaptive function, such as signaling sex to other males and females? Sexual selection has likely played an important role in human evolution, as evidenced by the presence of sexual dimorphism in many physical traits . Could cultural differences in perceptions of dominance and attractiveness have had a role in the divergence of nose shape ? Could tThe investigation of nose shape evolution with respect to climate adaptation, while interesting anthropologically, is also relevant medically. As humans are becoming more of a global community, the study of local adaptation is becoming more important to understanding health risks involved in living in ‘foreign’ climates. Obvious examples of such health risks are of increased risk of sunburn, skin cancer, and folate deficiency in light-skinned individuals exposed to high UVB, and of low birth weight and chronic mountain sickness associated with hypoxia at high altitudes ,55. DoesData collection was carried out with informed consent of participants and with approval from the institutional review boards (IRBs) at The Pennsylvania State University  and at The University of Illinois Urbana-Champaign (IRB# 13103). All participants provided written informed consent.et al. [The data used in this paper are part of a larger dataset that were collected in various locations through studies based at the Pennsylvania State University and at the University of Illinois at Urbana-Champaign. The majority of these data were collected in the United States at the Pennsylvania State University, at the World Science Festival in New York, and at the University of Illinois at Urbana-Champaign. Data collection in Europe took place in Dublin (Ireland), in Rome , in Warsaw (Poland), and in Porto . The Cape Verdean data were collected in collaboration with Dr. Sandra Beleza as described in Beleza et al. . All dat3dMD System, Atlanta, GA). Skin pigmentation was measured with melanin index using Derma Spectrometer . Melanin index is defined as the 100 x log10 (1/percentage reflectance at 650 nM) [The phenotype data collected include three dimensional (3D) images, skin pigmentation, standing height, and body weight. 3D images of participants were taken using the 3dMD system ( 650 nM) . We tookThe total number of participants was 4,257. Of these, we selected 2,637 individuals based on genetic ancestry and availability of phenotypic and covariate data, as described in the following sections. A summary of the sample sizes used in different analyses is provided in Illumina, Inc., San Diego, CA), and the 23andMe v3 and v4 arrays . In order to select individuals based on genetic ancestry, we needed to merge genotypes across platforms into a single database. We took several steps to ensure there were no batch effects due to genotyping platform, which could potentially lead to biases in ancestry estimation and Fst calculation. First, we removed SNPs that were not present on all four platforms to ensure there were no systematic differences due to missingness. Second, we performed quality-control separately for each platform by removing individuals and SNPs with a genotyping rate of less than 90%. Finally, we removed palindromic (AT/GC) SNPs and recoded the data in the Illumina A/B format [2 cutoff of 0.5). This resulted in 118,420 autosomal SNPs. Related individuals were identified using identity-by-state (IBS) analysis (IBS > 0.8). Individuals were removed to minimize relatedness and maximize sample size . These QC steps were carried out in Plink 1.9 [Out of the 4,257 participants, we had genotype data for 3,746 individuals. Since the data were collected as part of different studies over a long period of time, the samples were genotyped on four different genotyping arrays, namely the Illumina HumanHap300v1 BeadChip, the Illumina Infinium HD Human 1M-Duo Beadarray  using an unsupervised clustering approach in ADMIXTURE. To assist with the clustering and interpretation of the resulting clusters, we merged this dataset with genotypes from the HapMap dataset for the same SNPs . We ran ADMIXTURE for three values of k , and visually determined the optimum clustering scheme to be k = 6 for our purposes. To further visualize fine-scale structure, we carried out unsupervised principal component analysis (PCA) on the genotype data in Plink 1.9 . A combiNorthern European below), self-reported ancestry information, corroborated through the birthplace of all four grandparents, was used where genotype data were unavailable.For the Qst–Fst analyses, we were interested in selecting individuals with ancestry primarily from one of four populations: i) North European, ii) West African, iii) East Asian, and iv) South Asian. Much of this was based on genetic ancestry calculated from dense genotype data. However, for some individuals (see West African (N = 40): We identified 40 individuals who had close to 90% or greater ancestry from ADMIXTURE cluster 1. ADMIXTURE cluster 1 represents ancestry from West Africa, given that individuals from the HapMap YRI  derive most of their ancestry from ADMIXTURE cluster 1 (luster 1 . These 4East Asian (N = 127): Comparison with the HapMap CHB , HapMap CHD , and HapMap JPT  samples shows that cluster 4 represents genetic ancestry from East Asia (ast Asia . We seleast Asia . We furtSouth Asian (N = 73): 98 individuals in our dataset had more than 60% ancestry from ADMIXTURE cluster 2, which seems to represent the South Asian cluster : Our full dataset had a large number of individuals with European ancestry from different regions of Europe. In order to minimize structure within Europe, we only retained individuals with Northern European ancestry. Thus, we restricted the analysis to Europeans collected in Ireland (N = 151) and Poland (N = 85) who reported that the ancestry of all four of their grandparents was from the region of sampling. More details for these samples are described in Candille et al. [e et al. . Of thisMSB is the mean square among populations, MSW is the mean square within populations, ni is the number of individuals in the ith populations, and a is the number of populations, Estimation of the variance components,  follows ,66,67:nθ [Wright’s Fst measures the genetic divergence between two or more populations. We estimated the Fst across the four populations for the LD-pruned set of 118,420 autosomal SNPs, described above, using Weir and Cockerham’s θ . The genWe used a non-parametric bootstrap approach to test whether the Qst of a phenotype is significantly greater than the Fst. This was done by computing Qst from subsamples generated by randomly selecting individuals with replacement, within population and sex such that the number of males and females in each population remains unchanged. Values of Fst were simulated by randomly sampling from the Fst of 118,420 SNPs . We genehg2) for height, skin pigmentation, and nose shape aspects. The top four genetic PCs were included as covariates in the model to correct for population structure, which can inflate heritability estimates [For the calculation of E output . This cuE output . We usedstimates ,69,70. Ac) in an admixed population from Cape Verde, who derive their ancestry primarily from W. Africans and Europeans  for a total of 1,016,423 SNPs. This was reduced to 623,625 autosomal SNPs after removing palindromic SNPs, SNPs that did not overlap with the HapMap dataset, and other SNPs based on filters described in Beleza et al. [2 ∼ 1) (hy2). Global ancestry, calculated from the mean of local ancestry across all SNPs, was included as a covariate to correct for ancestry stratification and any environmental variables that might covary with ancestry [We estimated the proportion of phenotypic variance between populations due to additive genetic effects  . We usedancestry . Sex, agThe primary focus here was to answer the question: Does nose shape variation between geographically distant populations show signals of local adaptation? To answer this, we needed to ascribe a climate value to every individual that can serve as a proxy of their ancestral environment. For this reason, we selected individuals used in the Qst–Fst analyses who reported at least one parent’s birthplace. These were further filtered to retain individuals whose parents’ birthplaces coincided with their continental ancestry, for example, East Asian individuals whose parents were born in the United States were removed. Finally, we restricted the analyses to females since genetic data were not available for males sampled in Ireland and Poland. This resulted in 140 females with genetic, phenotypic, and geographic data. We have plotted the geographic distribution of the parents of these individuals in https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.24/) [2), for the period between 1996 and 2005, generated by the Earth Probe Total Ozone Mapping Spectrometer (EP/TOMS) Team at 0.5° resolution, were downloaded from the Goddard Earth Sciences Data and Information Services Center (GES DISC) website (https://disc.gsfc.nasa.gov/) [et al. (2016) [Gridded climate data were obtained in latitude/longitude coordinate system. Climate grids with mean monthly values for temperature (measured in degrees Celsius), and vapor pressure  at 0.5° resolution for the period between 1901 and 2015 were obtained from The Climate Research Unit TS v3.24 Dataset (s_3.24/) . Ground-sa.gov/) . Relativ. (2016) . For eac. (2016)  and Rast. (2016)  were useβ are fixed effects and ε is the random error term. Thus, ε ∼ N, where G is the genome-wide kinship matrix, which was constructed from the genotypes at 118,420 SNPs in GCTA [We used a linear mixed model to test the relationship between phenotype and climate variables, while correcting for age and BMI. We took non-independence among observations due to genetic similarity into account by allowing the observations to be correlated according to the SNP-based genome-wide kinship matrix. In matrix notation, the linear model takes the following form: in GCTA . The slo in GCTA ,79. P-va3dMD Atlanta, GA). Five positioning landmarks  were placed on each face in order to establish facial orientation. A spatially dense mesh of 7,150 quasi-landmarks (QLs) was mapped onto each image and its reflection. As a result of this mapping, the vertices and triangles are homologous across individuals [We captured high resolution 3D images of participants’ faces using the 3dMD Face system , and v. left and right alar base (acl and acr). These landmarks have been defined previously [Linear distances were calculated using three mid-line landmarks; i. nasion (n), ii. pronasale (prn), iii. subnasale (sn), and two sets of paired landmarks; iv. left and right alar curvature  for each replicate. This was then averaged across replicates for the same observer, then across observers to obtain the mean nostril area for each individual.To measure area of the nostrils, the observers defined regions around the nostrils (approximate location shown in 2) was calculated by summing the areas of polygons bounded by the 709 QLs, defining the entire nose region  made by the two observers to estimate the degree of intra-observer error. The quantiles for these standard deviations are shown in We calculated the standard deviation in the measurement of linear distances (in mm) and nostril area . ICC for each measure was calculated using linear mixed models where the observer was treated as fixed effect and subject was treated as random effect. This was carried out using the nlme package in R ,81. WithS1 FigMDS plots (k = 2) showing clustering of genotyped individuals by A) genotyping platform and B) by ancestry. In B) the genotyped individuals from the study set are shown in black, while the HapMap samples are shown in color for reference. The plot shows that individuals cluster by ancestry and not by genotyping platform. Thus, batch effects due to genotyping platform are unlikely to lead to artifacts in ancestry inference.(TIF)Click here for additional data file.S2 FigPCA plots showing clustering of genotyped individuals by A) South Asian and B) East Asian ancestry around HapMap reference samples. The colored points are reference samples from the HapMap dataset and the grey points are individuals from our dataset. A) South Asian individuals are shown with the HapMap GIH samples and B) East Asian individuals are shown with the combined East Asian sample (CHB+JPT+CHD) from the HapMap dataset. The red lines indicate the cutoff for removing individuals who appear to cluster far away from the main cluster. The individuals who were removed, based on clustering, are shown as triangles, whereas all other individuals are shown as circles.(TIF)Click here for additional data file.S3 Figθ.The distribution was generated from 118,420 autosomal SNPs using Weir and Cockerham’s (TIF)Click here for additional data file.S4 FigADMIXTURE-based ancestry estimates were calculated using a k = 2 assumption. LAMP-LD-based ancestry estimates were calculating the mean local ancestry across 623,625 autosomal SNPs.(TIF)Click here for additional data file.S5 Fig2.A) Mean annual temperature is measured in degrees Celsius. B) Annual aridity index is a ratio. C) UVB irradiance is measured in J/m(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 FigQQplots showing that the residuals for A) the inverse of melanin index are more normally distributed than B) untransformed melanin index. The points are residuals from linear models where population, sex, age, and BMI were used as predictors.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S1 File(ZIP)Click here for additional data file.


Human cytomegalovirus (HCMV) is a member of the  Human cytomegalovirus (HCMV) is a ed world ,2. Thoug virus ~25,000 bp,ed world . HCMV ised world .HCMV is exquisitely adapted to its human host, and cytomegaloviruses in general demonstrate a high level of host-specificity, with non-human CMV being absent even in populations with high levels of opportunity for zoonotic transmission . The lonA large body of work has previously shown that HCMV exhibits high levels of genetic diversity within individuals ,10,11,12Renzette et al. performed population genomic sequencing from urine samples collected within two weeks of birth from three congenitally HCMV-infect neonates and found that intrahost pairwise diversity across all known open reading frames (ORFs) averaged 0.22%, similar to levels observed in genome-wide analyses of human immunodeficiency virus (HIV) and ORF-specific analyses of other RNA viruses, such as dengue and West Nile ,29. At tSTF, a measure of differentiation between populations, was roughly the same between populations from the urine and plasma compartments of the same patient as between populations from the urine compartments of two unrelated individuals, suggesting evolutionary forces of sufficient strength to generate rapid divergence between compartments within a single host [Further work by Renzette et al. used similar population genomic analysis of urine and plasma samples from five congenitally infected infants to investigate divergence between individuals as well as within individuals across sequentially sampled time points and between different tissue compartments . HCMV isgle host . Comparagle host . AdditioHaving established that there are high levels of within-host genomic diversity in HCMV across compartments, we must next seek to understand what combination of neutral and selective processes are generating these patterns, given that the observed levels of genetic diversity are on par with those of high-mutation rate RNA viruses.en route to the kidney compartment, followed by subsequent population expansions within each compartment . Applying demographic modeling methods to time-sampled genomic HCMV data from five infants, Renzette et al. found support for a demographic model of transplacental infection and dissemination, wherein HCMV populations in congenitally infected neonates experience at least two bottlenecks Multiple studies have also found support for the presence of gene flow between compartments after initial dissemination and colonization throughout the host as well as recurrent maternal admixture or reinfection, possibly seeding new diversity into individual compartments ,28. Poka−7 new mutations per base pair per generation, ten-fold higher than estimates for herpes simplex virus type 1, but comparable to estimates for murine cytomegalovirus [In addition, an important factor contributing to variation in genome-wide diversity observed in HCMV appears to be attributable to genome-wide variation in mutation and recombination rates. Relatedly, there is accumulating evidence for variable levels of linked selection as well—namely, background selection effects modifying levels of variation owing to the recurrent removal of linked deleterious variation, and genetic hitchhiking effects modifying variation owing to linkage with positively selected alleles ,42,43. Ralovirus ,44,45. Valovirus . Relatedalovirus ,24. Thisalovirus ,47. Given that the genomic impacts of selection are largely determined by local recombination rates ,42, receHigh levels of diversity and multiple-strain infection have been found to be associated with higher viral loads ,50,51, dHage et al. recently demonstrated the ability to detect the presence of mutations conferring resistance to antiviral drugs in clinically sampled HCMV populations, suggesting the possibility of tracking the presence and frequency of a suite of known resistance mutations during the course of treatment during HCMV infection . They alGiven that no current methods exist to prevent maternal-fetal transmission or reduce the severity of fetal infection , acting HCMV generally persists in highly diverse populations, with diversity ranging by up to two orders of magnitude across the genome . ComplexGiven what we now know regarding the frequency of mixed infection, reinfection, and gene flow between compartments, the development of successful vaccines and treatments for HCMV may be very difficult and require a knowledge and incorporation of variants not just from the targeted compartment, but from all compartments contributing to within-patient diversity. Furthermore, Pokalyuk et al. demonstrated that genetic sampling at individual loci can result in the misclassification of mixed infections as single infections, and Garrigue et al. have also recently shown that even when looking at individual loci, traditional Sanger sequencing can miss even relatively high-frequency resistance mutations, highlighting the utility of high-throughput, genome-level characterization of HCMV infection. This is of particular importance given the emerging links between genetic diversity and patient outcome ,58. Yet,


Apis mellifera found in the Americas. AHBs originated in 1956 in Rio Clara, Brazil where imported African A. m. scutellata escaped and hybridized with local populations of European A. mellifera. Africanized populations can now be found from Northern Argentina to the Southern United States. AHBs—often referred to as ‘Killer Bees’— are a major concern to the beekeeping industry as well as a model for the evolutionary genetics of colony defence. We performed high coverage pooled-resequencing of 360 diploid workers from 30 Brazilian AHB colonies using Illumina Hi-Seq (150 bp PE). This yielded a high density SNP data set with an average read depth at each site of 20.25 reads. With 3,606,720 SNPs and 155,336 SNPs within 11,365 genes, this data set is the largest genomic resource available for AHBs and will enable high-resolution studies of the population dynamics, evolution, and genetics of this successful biological invader, in addition to facilitating the development of SNP-based tools for identifying AHBs.The Africanized honeybee (AHB) is a population of  Apis mellifera) was introduced to North and South America from Old World populations in the early 18th century1. In its native range, the honeybee is divided geographically and genetically into five ancestral lineages—the M and C lineages of Europe, the A lineage of Africa, and the Y and O lineages of Asia2–7—that encompass approximately 22 subspecies7.The European honeybee (A. m. mellifera and A. m. iberica) into North America9. By the 20th century, C lineage (A. m. ligustica and A. m. carnica) and some O lineage (A. m. caucasia) subspecies were introduced9. It was during this century that Brazilian beekeepers first imported honeybees, chiefly A. m. mellifera and A. m. carnica, followed by A. m. ligustica and A. m. caucasia10. These subspecies were used exclusively in Brazil until 1956 when A. m. scutellata was imported from Africa for genetic research. Mated A. m. scutellata queens arrived from South Africa11 to breeding stations in Rio Claro, São Paulo, Brazil. The intention of the breeding program was to crossbreed A. m. scutellata with commercial stock to serve as a base population in selection programs12. Famously, queens and drones escaped and hybridized with the existing population13. These hybrids became one of the most astounding insect invaders in recent history; feral populations of the ‘Africanized’ bees retained the highly defensive trait of their African ancestors and are now the most common honeybee found from Central South America (Brazil and Northern Argentina) to Mexico and the southern United States14.European settlers, in the early 18th century, introduced subspecies of the M lineage  with the manufacturer's recommended protocol to extract an average of 4.71±1.42 μg of high-quality DNA from each of the colonies.We collected 12 diploid worker bees from the brood frames of each of 30 Africanized honeybee colonies from four apiaries all located at Iaras city, São Paulo, Brazil , locatedDNA samples (one pooled sample of 12 workers from each colony) were submitted to The Centre for Applied Genomics  for library preparation and high-throughput sequencing. In brief, DNA was quantified by Qubit HS assay and 200 ng of DNA was used as input material for the TruSeq Nano DNA Sample Preparation protocol  following Illumina's recommendation. DNA was sheared to 550-bp on average using a Covaris S2 system . The sheared DNA was end-repaired and the 3' ends were adenylated prior to ligation of the TruSeq adapters. The library was enriched by PCR using different indexed adapters to allow for multiplex sequencing using the following conditions: 95 °C for 3 min followed by 8 cycles of 98 °C for 20 s, 60 °C for 15 s and 72 °C for 30 s, and finally an extension step at 72 °C for 5 min.Final TruSeq Nano DNA genomic libraries were validated on a Bioanalyzer 2100 DNA High Sensitivity chip (Agilent Technologies) for size and by qPCR using the Kapa Library Quantification Illumina/ABI Prism Kit protocol (KAPA Biosystems) for quantities. Ten libraries were pooled in equimolar quantities and sequenced on a HiSeq 2500 platform on a high throughput flowcell with the Illumina TruSeq V4 sequencing chemistry following Illumina’s recommended protocol to generate paired-end reads of 150-bases in length.N=30 colonies) were trimmed of Illumina Adaptors using Trimmomatic v0.32 then aligned to the most recent version of the honeybee genome (Amel_4.5 (ref. Each colony’s sequenced reads (4.5 ref. ) using B4.5 ref. . Paired 4.5 ref.  and re-a4.5 ref.  with div4.5 ref.   We used GATK with --ploidy 2 to identify only the location of variants and were unconcerned with specific genotype calls. We called all variant sites in GATK and VARSCAN using default parameters. We removed indels and all SNPs within 10 bp of indels, removed all unmapped scaffolds (Scaffolds 17.XXX or GroupUn) and mitochondrial sequence (Scaffold 18.1), and removed SNPs of low quality (Q<25) or in areas of low genomic complexity, thus reducing the potential for calling erroneous SNPs due to paralogous sequence or misaligned reads3  and Europe . The latter sequence data were obtained from a recent honeybee population genomics study3 and represents ancestral populations from which Africanized populations are derived. We generated a single MPILEUP file and extracted from it the 3,606,720 SNPs identified above in our Africanized honeybee samples. We estimated pairwise population differentiation on all sites with --min-count 6 --min-coverage 100 --max-coverage 800 --min-covered-fraction 0.8.To quantify differentiation among contemporary Africanized populations in Brazil and ancestral honeybee populations, we used POPOOLATION2 v1.201 ref. . We creahttps://github.com/harpur/afz/blob/master/AHBPipeline.sh).We have not used any custom code and relied on previously available, validated, software packages; however, we have left our general pipeline available for re-use at the author’s GitHub . The data consist of SNPs called across the most recent honeybee reference genome (Amel_4.5 ref. ) in Vari3. Africanized bees are known to be derived from three of the major honeybee population groups: A, M, and C16. We found that 99.8% of the 3,606,720 SNPs found in AHBs, were also found within one or more of these ancestral populations. Africanized populations are expected to have higher A lineage ancestry relative to C and M lineage ancestry16. Using a regression model30, we demonstrated that allele frequencies within Africanized bees are more correlated with A lineage allele frequencies  relative to both M lineage allele frequency  and C lineage allele frequency .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Palaemon capensis and one marine/estuarine species Palaemon peringueyi, in the historical biogeographical context of South Africa. A total of 103 specimens of P. capensis collected from 12 localities and 217 specimens of P. peringueyi collected from 24 localities were sequenced for the mitochondrial cytochrome oxidase one (CO1) locus. Results from analyses of molecular variance (AMOVA), pairwise ΦST comparisons and haplotype networks demonstrate weak to moderate genetic differentiation in P. capensis and P. peringueyi respectively. P. peringueyi exhibits partial isolation between populations associated with distinct biogeographic regions, likely driven by the region’s oceanography. However, there is minimal evidence for the occurrence of discrete regional evolutionary lineages. This demonstrated lack of genetic differentiation is consistent with a marine, highly dispersive planktonic phase in both the amphidromous P. capensis and the marine/estuarine P. peringueyi. Bayesian skyline plots, mismatch expansions and time since expansion indicate that both species maintained stable populations during the Last Glacial Maximum (LGM), unlike other southern African aquatic species.We compare the genetic structuring and demographic history of two sympatric caridean shrimp species with distinct life history traits, one amphidromous species  An understanding of the genetic structuring of organisms is pivotal to a broad range of biological disciplines, including species distribution studies , conservNevertheless, life history strategy alone does not necessarily determine scales of connectivity and population structure, as realized dispersal patterns are also influenced by physical connectivity among coastal and riverine habitats . This inAside from the interplay between life-history strategy and contemporary barriers to connectivity, long-term historical climatic and oceanographic change are often considered to be drivers of population structuring and demographic changes , 22], , , 30]. I. I30]. IPalaemon is ideally suited for the exploration of the role of life history strategy on genetic structure and demography, as this genus represents a complex array of life history strategies, from fully marine to fully freshwater with abbreviated larval development, and with a high proportion of amphidromous species . N. NPalaemcus; see  for a dicapensis . Occasiocapensis . Palaemo Namibia  to Kosi st coast , which cer study , indicatdings in , and shoPalaemon capensis (n = 103) and P. peringueyi (n = 217) were collected from 12 and 24 localities respectively along the South African coastline between 2015 and 2016, encompassing the entire distribution of each species in South Africa  following the manufacturers’ protocol. Extracted DNA was stored at -20°C until required for polymerase chain reaction (PCR). The mitochondrial CO1 gene was selected for the current analysis as it is widely used in studies on palaemonid shrimp , and is P. peringueyi the CO1 region amplified with the primer pair LCO11490 and HCO2198 ) and analyzed on a 3730 automated sequencer (ABI).http://www.ebi.ac.uk/emboss/transeq/). No stop codons were detected.Sequences were visualised using BioEdit v7.1.3.0 , and cheH), haplotypic diversity (h), number of private haplotypes (Nph), number of polymorphic sites (Np), and nucleotide diversity (πn). In addition allelic richness (haplotype richness) was estimated by rarefaction using CONTRIB v 1.02 for each biogeographic region, where possible . O. OP. caprganisms . Both sprganisms , Wood peP. capensis and P. peringueyi do differ to some degree, which is likely a reflection of species specific life history strategies. The star-burst like network of P. capensis and lower level of haplotypic diversity observed in this species can potentially be explained by environmental tolerance. Whilst P. peringueyi is a generalist and thus able to survive in greater portions of available habitat during periods of climatic change, P. capensis is far more specialist and thus far more susceptible to environmental disturbance.However, it is important is note that the demographic signals exhibited by S1 FigStatistical values above the nodes represent the posterior probablity (pP) values for the Bayesian analyses. Values below each node represent the bootstrapping values for maximum likelihood. Only bootstrap values >75% and pP values > 0.95 are shown. An asterisk (*) indicate clades that were not statistically supported.(TIF)Click here for additional data file.S1 TableTemperatures in bold under PCR profile indicate the annealing temperatures. The final extension was at 72°C for 10 minutes. * Protein coding.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


The molecular basis of evolutionary change is assumed to be genetic variation. However, growing evidence suggests that epigenetic mechanisms, such as DNA methylation, may also be involved in rapid adaptation to new environments. An important first step in evaluating this hypothesis is to test for the presence of epigenetic variation between natural populations living under different environmental conditions.Geospiza fortis and G. fuliginosa, on Santa Cruz Island in the Galápagos. Using data collected from more than 1000 birds, we found significant morphological differences between populations of G. fortis, but not G. fuliginosa. We did not find large size copy number variation (CNV) genetic differences between populations of either species. However, other genetic variants were not investigated. In contrast, we did find dramatic epigenetic differences between the urban and rural populations of both species, based on DNA methylation analysis. We explored genomic features and gene associations of the differentially DNA methylated regions (DMR), as well as their possible functional significance.In the current study we explored variation between populations of Darwin’s finches, which comprise one of the best-studied examples of adaptive radiation. We tested for morphological, genetic, and epigenetic differences between adjacent “urban” and “rural” populations of each of two species of ground finches, In summary, our study documents local population epigenetic variation within each of two species of Darwin’s finches.The online version of this article (doi:10.1186/s12862-017-1025-9) contains supplementary material, which is available to authorized users. However, recent work suggests that heritable modifications to gene expression and function, independent of changes to DNA sequence, may also be involved in the evolution of phenotypes –3. One o- “CpG”) . Methyla- “CpG”) –8. Impor- “CpG”) –14. Inde- “CpG”) , they ma- “CpG”) .Environmentally-induced epimutations may be a component of the adaptive radiation of closely related species to new environments . For exaEpigenetic variation also occurs among populations within single species , 19–24. (Geospiza fortis) and the small ground finch (G. fuliginosa)  Fig. . Darwin’Epigenetic variation may contribute to the phenotypic diversity of Darwin’s finch populations that cannot be detected through genomic studies. As an initial test of this hypothesis, we compared components of the morphology, genetics, and epigentics in populations of finches living at El Garrapatero, a relatively undisturbed locality, to populations living near Puerto Ayora (Academy Bay), the largest town in the Galápagos Islands Fig. . HereaftG. fortis and G. fuliginosa living in urban and rural environments . The two localities, which are separated by about 10 km, are both in the arid coastal zone of Santa Cruz Island , during which more than 3700 finches were captured- and more than 300 individuals recaptured- only one bird  was shown to have moved between the two sites (J. Raeymaekers pers. comm.).We studied each of two populations of P-values were adjusted with a Bonferroni correction for multiple tests. Morphological analyses were run in the program RStudio using R version 3.2.1 with the packages pwr, plotrix, lme4, and lmerTest [Finches were captured at the two study sites January–April 2008–2016. The birds were mist-netted and banded with individually numbered Monel bands in order to track individuals. They were aged and sexed using size and plumage characteristics . MorphollmerTest –42.Blood and sperm samples for epigenetic and genetic analyses were collected from a subset of birds captured January–April 2009–2013 at the two study sites. Blood samples (<90 μl) were taken from finches via brachial venipuncture. The samples were stored on wet ice in the field and, within six hours of collection, erythrocytes were purified by centrifugation. Sperm samples (~5 μl) were taken from a subset of males. The sperm samples were obtained by gentle squeezing of the cloacal protuberance of reproductively active males. Blood erythrocytes and sperm samples were stored in a − 20 °C freezer in the Galápagos. Following each field season, they were transferred to a − 80 °C freezer in the USA for long-term storage. All field procedures were approved by the University of Utah Institutional Animal Care and Use Committee  and by the Galápagos National Park.2O was used instead of the buffer provided in the kit. Genomic DNA from finch sperm was prepared as follows: collected sperm suspension was adjusted to 100 μl with 1 x Phosphate Buffered Saline (PBS) then 820 μl DNA extraction buffer  and 80 μl 0.1 M dithiothreitol (DTT) were added and the sample was incubated at 65 °C for 15 min. Next, 80 μl Proteinase K (20 mg/ml) were added and the sample was incubated on a rotator at 55 °C for 2 h. After incubation, 300 μl of protein precipitation solution  were added, then the sample was mixed and incubated on ice for 15 min, then spun at 4 °C at 13,000 rpm for 20 min. The supernatant was transferred to a fresh tube, then precipitated over night with the same volume of 100% isopropanol and 2 μl glycoblue at −20 °C. The sample was then centrifuged and the pellet washed with 75% ethanol, then air-dried and re-suspended in 100 μl H2O. DNA concentration was measured using a Nanodrop Spectrophotometer (Thermo Fisher).Genomic DNA from finch red blood cells (erythrocytes) was prepared using the Qiagen DNAeasy Blood and Tissue Kit . The manufacturer’s instructions for nucleated blood samples were followed, but in the final DNA elution step HTo test for genetic differences between the urban and rural populations we sequenced DNA extracted from red blood cells (erythrocytes) and compared genetic copy number variation (CNV) . CNV, deThe pools were diluted to 130 μl with 1 x TE buffer and sonicated in a Covaris M220 with the manufacturer’s preset program to create fragments with a peak at 300 bp. Aliquots of the pools were run on a 1.5% agarose gel to confirm fragmentation. The NEBNext DNA Library Kit for Illumina was used to create libraries for each pool, with each pool receiving a separate index primer. The libraries were sent to the University of Nevada, Reno Genomics Core for NGS on the Illumina HiSeq 2500 using a paired end PE50 application. All 6 pooled sequencing libraries for each species were run in one sequencing lane to generate approximately 30 million reads per pool. The read depth across the genome was then assessed to identify CNV and statistically assessed with a Bayesian analysis. The genome-wide paired end read depth was approximately 2× with the CNV read depth being a total of 300 to 6000 reads per CNV detected.Following Skinner et al. , we usedn = 6 individuals per species, per site for sperm and n = 15 individuals per species per site for erythrocytes to consider biological variation of the pools and analysis). All pools were diluted to 150 μl with 1× Tris-EDTA  and sonicated with a probe sonicator using 5 × 20 pulses at 20% amplitude. Fragment size (200–800 bp) was verified on 1.5% agarose gel. Sonicated DNA was diluted to 400 μl with 1xTE and heated to 95 °C for 10 min, then shocked in ice water for 10 min. Next, 100 μl of 5 x immunoprecipitation (IP) buffer  and 5 μg of 5-mC monoclonal antibody  were added and the sample was incubated on a rotator at 4 °C over night. The next day Protein A/G Agarose Beads from Santa Cruz Biotechnology, Santa Cruz CA, were prewashed with 1xPBS/0.1% BSA and re-suspended in 1 x IP buffer. Eighty μl of the bead slurry were added to each sample and incubated at 4 °C for 2 h on a rotator. The bead-DNA-antibody complex was washed 3 times with 1 x IP buffer by centrifuging at 6000 rpm for 2 min and re-suspending in 1 x IP buffer. After the last wash the bead-complex was re-suspended in 250 μl of digestion buffer  with 3.5 μl Proteinase K (20 mg/ml) per sample and incubated on a rotator at 55 °C for 2 h. After incubation, DNA was extracted with the same volume of Phenol-Chloroform-Isoamyalcohol, then with the same volume of chloroform. To the supernatant from chloroform extraction, 2 μl glycoblue, 20 μl 5 M sodium chloride and 500 μl 100% cold ethanol were added. DNA was precipitated at −20 °C over night, then spun for 20 min at 13,000 rpm at 4 °C, washed with 75% ethanol, and air-dried. The dry pellet was re-suspended in 20 μl H2O and concentration measured in Qubit using a Qubit ssDNA Assay Kit .For analysis of erythrocytes, genomic DNA was extracted from the same individuals as used in the CNV pools. Each erythrocyte pool included five individuals and contained a total of 6 μg of genomic DNA. Sperm pools included two individuals and contained a total of 1.8 μg of genomic DNA. Three pools were generated per species, per site  at the University of Nevada, Reno Genomics Core Laboratory using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina®, starting at step 1.4 of the manufacturer’s protocol to generate double stranded DNA. After this step the manufacturer’s protocol was followed. Each pool received a separate index primer. NGS was performed at the same laboratory using the Illumina HiSeq 2500 with a paired end PE50 application, with a read size of approximately 50 bp and approximately 100 million reads per pool. Two separate sequencing libraries, one rural and one urban, were run in each lane. The read depth for identified differential DNA methylated regions (DMRs) ranged from approximately 100 to >1000 total reads per DMR.Taenopygia guttata) genome using Bowtie2 [Basic read quality was verified using summaries produced by the FastQC program . The rea Bowtie2  with def Bowtie2 . The cn. Bowtie2  was used Bowtie2 . The winp-value [p-value less than 10−3 were considered DMR. The DMR edges were extended until no genomic window with an edgeR p-value less than 0.1 remained within 1000 bp of the DMR. The DMR that included at least two windows with an edgeR p-value <10−3 (“multiple-window DMR”) were then selected for further analysis. Because no fully assembled or annotated genome exists for any Darwin’s finch species, we aligned DMR with the zebra finch genome. CpG density and gene associations were then calculated for the DMR, based on alignment with the reference genome. Though we previously found high (>98%) homology between Darwin’s finch and zebra finch genomes using tiling arrays [G. fortis genome [To identify DMR, the reference genome was broken into 100 bp windows. The MEDIPS R package  was usedp-value  was usedg arrays , some dis genome . All thewww.skinner.wsu.edu under genomic data) using a 2 Mb sliding window with 50 kb intervals. DMR were annotated using the biomaRt R package [www.skinner.wsu.edu under genomic data). To assess that the DMR were not false positives due to random biological variation within populations, a pairwise comparison analysis  on the genomic sequence data within the individual urban or rural sites and cell populations was performed [DMR clusters were identified with an in-house R script . Within species, urban G. fortis was significantly larger than rural G. fortis for all direct morphological measurements, except tarsus length (Table G. fortis body and beak size (PC1 body and PC1 beak) also differed between the two sites; however, there was no difference in beak shape (PC2 beak). In contrast to G. fortis, G. fuliginosa did not differ significantly between the urban and rural populations in any of the morphological measurements or composite measures ; largest effect size = 0.358 (beak depth)). Power for comparisons of G. fuliginosa appeared adequate for detecting similar effect sizes (0.69–0.91).We used 1097 birds captured between 2008 and 2016 for morphological analyses. We controlled for slight variation among years in traits by including year as a random effect in all analyses. At both sites, G. fortis pools and five of six G. fuliginosa pools. The total number of variants per pool ranged from 1 to 20. However, no variants were exclusive to all urban or all rural pools for either G. fortis or G. fuliginosa , there were no fixed differences between the urban and rural populations for either species. It is unclear why certain pools had more variants than others; however variation was consistent among chromosomes.Mean read depth genome-wide for pools used in CNV analysis varied between 1.08× and 1.30× . The total read depth of individual variants ranged from 300 to 6000. We identified unique CNV in three of six Geospiza fortis genome [G. fortis genome was higher (two-fold). However, we still did not find any differences in CNV between the urban and rural populations for either species of Darwin’s finch. A limitation of this CNV analysis is that only large variants (>24 Kbp) can be detected reliably; smaller variants (<10 Kbp or less) may have escaped detection.To control for underestimation of CNV differences due to reads that did not align to the zebra finch genome, we performed a similar analysis aligning reads to the un-assembled s genome . The avep-value cut-offs ranging from <0.01 to <1e-05 in Additional file P < 0.001. We evaluated differences on three “regional” scales  we estimated that most DMRs were 500–1000 bp in length  were found in 3–5 multiple windows Table . Based ogth Fig. . DMRs weG. fortis sperm cells  and marginally between the two cell types . Pathway analysis (KEGG) showed DMRs associated with several genes  in metabolic pathways of G. fortis sperm. DMRs were associated with different genes  in metabolic pathways of G. fuliginosa sperm. Geospiza fortis erythrocyte DMRs were associated with genes  in the MAPK signaling pathway. Geospiza fuliginosa erythrocyte DMRs were not associated with any particular pathway.We identified potential genes associated with DMRs through alignment with the zebra finch reference genome. DMRs within 10 kb of a gene (such that the promoter is included) have the potential to influence the gene’s expression and/or pathways associated with that gene . DiffereG. fuliginosa populations (erythrocytes and sperm combined): BMP5, BMP7 and FST  within each species and rural or urban populations for specific cell types. We identified a number of DMRs between individual pools, which suggests that there is epigenetic variation within the study populations. However, few DMRs were found in multiple pools from the same population. Moreover, almost none of these DMRs were also found between urban and rural populations  were larger than those at the rural site (El Garrapatero). In contrast, G. fuliginosa individuals did not differ morphologically between the sites. We did not find genetic differentiation between populations of either species based on CNV comparisons. However, we did find epigenetic (DMR) differences between urban and rural populations of both species of finches.Darwin’s finches are well known for their phenotypic variability and evolution in response to changing environmental conditions . In addiG. fortis were larger in nearly all morphological measurements compared to rural G. fortis .In contrast, es Table . Geospizz Island . As a relization . It is pnta Cruz . While wG. fortis, we found no genetic differences between the urban and rural populations, based on the CNV comparisons made. Because CNV sequence coverage was limited, we may have overlooked small CNV, but larger CNV should have been detected between the two populations. CNV is a sensitive index of genetic differentiation between populations; indeed, some studies have found that CNV accounts for more genetic variation than SNPs [Despite differences in morphology between populations of han SNPs –65. Recehan SNPs  and primhan SNPs .G. fortis or G. fuliginosa [G. fortis or G. fuliginosa.Our study is first to explore genetic variation between populations of Darwin’s finches using large–scale genomic features (CNV). Like our study, previous studies using smaller-scale genomic markers  detected little or no genetic structure within populations of either liginosa , 34, 68.liginosa , 30. HowG. fortis and G. fuliginosa. These data suggest that methylation patterns are species-specific, even when comparing closely related species. This may mean that G. fortis and G. fuliginosa are responding to environmental changes at the urban site in different ways. The lack of overlap in DMRs between the two species may reflect differences in their diets [G. fortis.In contrast to our genetic results, we found a large number of epigenetic differences between urban and rural populations in both species of finches and both cell types Fig. . Althougir diets . As discAlthough DMRs were also found in both cell types, few of the same genomic regions were differentially methylated in sperm and erythrocytes. Because methylation is involved with cell differentiation , 69, somWhile locations of DMRs varied between species and cell types Fig. , they haMany of the DMRs we detected were associated with metabolic and signaling genes Fig. . PreviouGeospiza finches [We also found DMRs associated with genes in the bone morphogenic protein (BMP) / transferring growth factor beta (TGFß) pathway Fig. . Express finches ; however finches . These dG. fortis between the urban and rural populations has been documented through mark-recapture studies; but it is not very common (J. Raeymaekers pers. comm.). Low levels of gene flow between populations would preclude divergence of the rural and urban populations due to drift. However, much more work is needed to understand the basis of epigenetic variation and its relationship to phenotypic variation in populations of Darwin’s finches.Our study compared just two populations - one rural and one urban – and therefore we cannot be certain that urbanization is the key environmental change influencing finch morphology and/or epigenetics in our study. Moreover, it is possible that differences between the two populations are the result of epigenetic drift, rather than differential selection. Some dispersal of We found epigenetic differences between adjacent populations of each of two species of Darwin’s finches. We do not know which of the DMRs are responses to environmental differences between the urban and rural sites, versus the result of random epigenetic drift. However, as the environmental differences between our sites are recent (<60 years) any methylation changes associated with urbanization have spread quickly. As in other recent studies , 20, 22,Additional file 1: Figure S1.Comparison of vegetative cover at the rural site (El Garrapatero) versus urban site  over the course of the study. Cover was dervied from Normalized Difference Vegetative Index (NDVI) values generated from satellite imagery . Values range from 0-1 with 1 reprensenting the highest vegetation cover. (PDF 850 kb)Additional file 2: Figure S2.G. fortis erythrocytes showing read depth and alignment, and CNV numbers per pool with chromosomes containing CNV indicated, and no overlap between rural and urban pools indicated. (B) CNV analysis summary for the G. fuliginosa erythrocytes with Read Mapping Summary, overall CNV per pool and chromosome, and no overlapping CNV identified. (PDF 20 kb)Copy number variation (CNV) between the rural and urban populations. (A) CNV analysis summary for the Additional file 3: Table S1.The number of DMR detected at single window and multiple window scales at increasing levels of significance. (PDF 61 kb)Additional file 4: Table S2.G. fortis sperm (A) and erythrocytes (B). Description includes DMR name, chromosome number, DMR start site, length in base pair (bp), number of multiple sites, minimum p-value, CpG number per sequence length, CpG density (CpG number / 100 bp) and DMR gene association. “NA” indicates DMR associated with a gene that did not align to the zebra finch reference genome. (PDF 126 kb)Description of multiple-window DMR detected in Additional file 5: Table S3.G. fuliginosa sperm (A) and erythrocytes (B). Description includes DMR name, chromosome number, DMR start site, length in base pair (bp), number of multiple sites, minimum p-value, CpG number per sequence length, CpG density (CpG number / 100 bp) and DMR gene association. “NA” indicates DMR associated with a gene that did not align to the zebra finch reference genome. (PDF 154 kb)Description of multiple-window DMR detected in Additional file 6: Table S4.G. fortis sperm (A) and erythrocytes (B) and G. fuliginosa sperm (C) and erythrocytes (D). Description includes DMR in cluster, chromosome number, cluster start site, cluster stop site, length in bp, and minimum p-value. (PDF 103 kb)Description of DMR clusters detected in Additional file 7: Table S5.G. fortis sperm (A) and erythrocytes (B) and G. fuliginosa sperm (C) and erythrocytes (D). Description includes DMR name, gene symbol, entrez gene identification, chromosome number, start position site, ensemble gene identification number, gene description and gene classification category. (PDF 225 kb)Gene associations with DMR detected in Additional file 8: Figure S3.MAPK signaling pathway. Genes associated with DMR are listed and outlined in red in the pathway. (PDF 109 kb)Additional file 9: Figure S4.Glycolysis metabolism pathway. Genes associated with DMR are listed and outlined in red in the pathway. (PDF 66 kb)Additional file 10: Figure S5.(A)G. Fuliginosa RBC urban analysis, (B)G. fuliginosa-RBC rural analysis, (C)G. fortis RBC urban analysis, and (D)G. fortis rural analysis. Numbers indicate DMRs between urban (U) or rural (R) individual pools (1-3). “Full analysis” are DMRs identified between urban and rural pools. DMRs identified in the full analysis were found independently of within-site variation. (PDF 98 kb)DMRs identified in pairwise comparison of pools within populations:


Caatinga and Cerrado phytogeographical domains. In particular, the effects of Quaternary climatic events on the demography of endemic species from xeric habitats are poorly understood. The present study uses an integrative approach to reconstruct the evolutionary history of Partamona rustica, an endemic stingless bee from dry forest diagonal in Brazil, in a spatial-temporal framework. In this sense, we sequenced four mitochondrial genes and genotyped eight microsatellite loci. Our results identified two population groups: one to the west and the other to the east of the São Francisco River Valley (SFRV). These groups split in the late Pleistocene, and the Approximate Bayesian Computation approach and phylogenetic reconstruction indicated that P. rustica originated in the west of the SFRV, subsequently colonising eastern region. Our tests of migration detected reduced gene flow between these groups. Finally, our results also indicated that the inferences both from the genetic data analyses and from the spatial distribution modelling are compatible with historical demographic stability.The South America encompasses the highest levels of biodiversity found anywhere in the world and its rich biota is distributed among many different biogeographical regions. However, many regions of South America are still poorly studied, including its xeric environments, such as the threatened  Caatinga and Cerrado phytogeographical domains. In particular, the effects of Quaternary climatic events on the demography of endemic species from xeric habitats are poorly understood.South America encompasses the highest levels of biodiversity found anywhere in the world  and its Caatinga, Misiones/Piemonte and Chiquitano refuges—were proposed [Caatinga refuge, which covers most of the present-day distribution of its area, is the largest stable area of SDTF. However, some Caatinga ecoregions have weak climatic stability [Cerrado and found evidence of two savanna corridors and predicted the presence of a large refuge in the north-eastern extreme of this area [Phylogeographic studies have indicated that Pleistocene climatic changes may have influenced the species richness, spatial distribution, endemism, genetic diversity, historical demography and biogeographic patterns of the Neotropical biota , 10. Preproposed . The Caatability . Previouhis area .Caatinga and Cerrado is complex and controversial [Caatinga and Cerrado using the stingless bee Partamona rustica [Caatinga of southwestern state of Bahia and areas between the Caatinga and the Cerrado in northern state of Minas Gerais [Constrictotermes cyphergaster, where they build its nests [The geological and paleo-climatic history of the oversial , and undoversial , 10. To  rustica  as a mods Gerais . Its nests nests , 16. ThiP. rustica in a spatial-temporal framework. Specifically, we investigated the genetic diversity and structure of its populations, and how climatic changes during the late Pleistocene may have influenced the demographic history of its populations. We also inferred species distribution patterns since the late Pleistocene to identify possible species-specific refugia. We then applied a coalescent-based Approximate Bayesian Computation (ABC) approach to test competing scenarios of the origin and dispersal of P. rustica populations across the species current geographic range.In the present study, we used an integrative approach to reconstruct the evolutionary history of P. rustica workers from 145 nests from 11 localities in the Caatinga of south-western Bahia  [Melipona bicolor (Mbi 232 and Mbi 254) [Total DNA was extracted from one worker per colony using Sheppard and McPheron’s protocol . The mitd Phel7)  and two Mbi 254) . The PCRMbi 254) . InformaThe electropherograms were edited and combined in contigs using the Codon Code Aligner v3.7.1 software . The sequences were aligned using CLUSTAL W in the BioEdit 7.0.9.0 program . All aliP. rustica, we estimated the number of variable sites (S), number of haplotypes (h), haplotype diversity (Hd) and nucleotide diversity (π) for each mitochondrial gene region and for the concatenated gene regions using DnaSP v5.10.01 [To assess genetic diversity in v5.10.01 .For the analyses of the SSR dataset, Microchecker v. 2.2.3  was firshttp://www.fluxus-engineering.com/). In order to verify the occurrence of population groups on the SSR dataset, we used the AMOVA-based K-means clustering method [http://www.patrickmeirmans.com/software/). We also conducted a spatial analysis of molecular variance (SAMOVA) using the SSR and mtDNA  datasets separately in SAMOVA 2.0 [We obtained a haplotype network for the concatenated mitochondrial gene regions using the median-joining network method  implemeng method  in kMeanMOVA 2.0 . For furST) and geographic (in kilometres) distances, considering all populations. The significance of this analysis was obtained with 10,000 permutations of both mtDNA  and SSR datasets, in analyses run separately.We also implemented the Mantel test using the Isolation by Distance Web Service 3.23 (IBDWS)  to invesP. rustica lineages based on haplotype of four concatenated mitochondrial regions. The best fit model was selected using MrModeltest v2 [http://beast.bio.ed.ac.uk/Tracer) and we accepted the results if ESS values were >200. On the other hand, the ML inference was run under the GTRAC model; invariable sites and gamma distribution were estimated independently for each partition during the run. Node supports of the maximum likelihood analysis were estimated with 1,000 bootstrap replications. This analysis was performed in RAxML program [Partamona helleri and Partamona seridoensis as outgroup taxa. Tree obtained for each inference was visualized and edited using FigTree 1.4 (http://tree.bio.ed.ac.uk/software/figtree/).We implemented Bayesian inference (BI) and maximum likelihood (ML) to estimate the phylogenetic between ltest v2  based onltest v2 . Chain c program . For botTo evaluate the population size dynamics over time we implemented the Bayesian Skyline Plot (BSP) for each population group using BEAST 1.8.1 , based oP. rustica groups were calculated based on the isolation with migration (IM) model [west>east and meast>west = mutation-scaled migration rate), and the effective population sizes of the eastern, western and ancestral populations .Divergence times, effective population sizes and migration rates between western and eastern M) model , 38 usinM) model , for theWe first conducted series of relatively short preliminary runs in the ‘MCMC mode’. These short runs were used to adjust the run duration and prior distribution for each parameter and the heating scheme for the subsequent, longer runs. For the final run, we used 40 MCMC chains with a geometric heating scheme (g1 = 0.975 and g2 = 0.75), with the maximum prior population sizes set at 30, migration rates at 20 and divergence time at 20. We used the HKY substitution model and an inheritance scalar of 0.25. We assumed the same mutation rate for COI (1.3–1.9% per lineage per million years) as in BEAST and a generation time of one year. The run had eight million steps. The first two million steps were discarded as burn-in. We also checked convergence by analysing the posterior distributions, and the ESS values that were higher than 200. We also tested the SSR dataset (on its own or together with the mtDNA data). We removed the ribosomal regions (12S and 16S) from the IMa2 and BSP analyses because no substitution rates have yet been estimated for these regions in the Hymenoptera. In all tests using only SSR dataset or combined dataset (SSR + mtDNA) we obtained poor values of ESS (ESS <11). Therefore, it was not possible to use the SSR loci  in IMa2 analysis because the runs did not converge and became stationary. Thus, we implemented this analysis only using mtDNA dataset.Finally, we tested for evidence of recent population bottleneck events in the SSR dataset in BOTTLENECK v. 1.2.0.2 . The runP. rustica populations, we implemented the Approximate Bayesian Computation procedure (ABC) [P. rustica populations by investigating five potential historical scenarios: scenario 1—at t1, the eastern population group gives rise to the western group; scenario 2—at t1, the western group gives rise to the eastern group; scenario 3—at t1, an ancestral population splits and gives rise to both groups; scenario 4—at t2, an ancestral population gives rise to the eastern group, and at t1, the eastern group gives rise to the western group; scenario 5—at t2, an ancestral population gives rise to the western group, and at t1, the western group gives rise to the eastern group  and SSR datasets both separately and together. For both datasets, the priors were set at a uniform distribution, with a generation time of one year. For the mtDNA dataset, the simulations were run for four million iterations, with a different set of summary statistics being generated for each population (the number of haplotypes and segregating sites) and between populations , with the mutation rate being left on the default setting. For the SSR dataset, the simulations were also run for four million iterations with summary statistics being generated for: (1) the number of alleles, (2) mean genetic diversity, (3) mean size variance, (4) FST, (5) the classification index and (6) the (dμ)2 distance. Once again, the mutation rate was left on the default setting. All other settings were configured as default settings of DIYABC for the SSR and mtDNA datasets. The posterior probability of each scenario was calculated by logistic regression, considering between 500 and 50,000 datasets that were closest to the observed values. The scenario that best explained the data was used to estimate the origin and dispersal of P. rustica.To evaluate the fit of the different historical scenarios to the observed data for the re (ABC)  availablre (ABC) . We assuroup see . We estiP. rustica was generated, we projected the model onto past climatic conditions to predict the potential distribution of the species during three distinct periods of the late Quaternary: the Last Interglacial period , the Last Glacial Maximum , and the Mid-Holocene . We modelled potential distributions using MaxEnt 3.3.3k [www.worldclim.org). Because the collinearity of variables can result in the overfitting of the model [Once a contemporary model  of the potential distribution of t 3.3.3k , 44 basehe model , we omitWe modelled the potential paleo-distribution of the species during the LGM and M-H periods using downscaled data derived from two Global Climate Models (GCMs) commonly used in recent analyses , 47, theWe obtained a total of 2,260 bp for the four mtDNA gene regions . No indeMicrosatellite loci revealed between two and 17 alleles. The allelic richness (standardised to a minimum sample size of eight) of the polymorphic loci ranged from 1.785 to 3.621, and the mean gene diversity (He—expected heterozygosity) was 0.328, ranging from 0.222 to 0.505 . The genWe used different approaches to estimate the degree of population differentiation. Our haplotype network, based on the four mitochondrial gene regions, revealed the presence of two groups separated by two mutations, the first being restricted to the west of the São Francisco River Valley (SFRV), with eight haplotypes at COC and CMA localities, and the second in the east of the SFRV, represented by all the other nine populations and 22 haplotypes .The evolutionary model selected for the BI based on the AIC, including the outgroups, was TIM2 + I + G for COI, TIM3 + I for COII, TrN for 12S and TIM2 + I for 16S. The topology based on both methods of phylogenetic reconstruction was similar, showing at least two groups (eastern and western groups). In this sense, the CT values in The AMOVA-based K-means cluster analysis of the SSR dataset indicated these two groups as the best clusters . The SAMp = 0.627), it revealed a significant positive correlation between genetic and geographic distances for SSR dataset , indicating isolation by distance among the populations.Whilst the Mantel test did not detect any significant correlation between genetic and geographic distances in the mtDNA data . The isolation with migration model also revealed low migration rates between groups (The estimate of the divergence time between the two groups of n groups . The IMaca. 258 ka (HPD 95%: 7.0–476 ka) and western group then diverged to form the eastern group at ca. 13 ka (HPD 95%: 10–26 ka). In scenario 2, western group gave rise to the eastern group at ca. 14 Ka   and scenario 2 , therefore, we can consider both scenarios as probable.The SSR dataset was omitted from the ABC analyses because it did not adjust to any of the models tested, neither alone or together with mtDNA dataset. The ABC analysis for the mtDNA dataset identified scenario 5 as the origin and dispersal scenario with the highest posterior probability, followed by the scenario 2 . Both sc–34,300) . There ip = 0.1250; heterozygosity excess, p = 0.9023). On the other hand, we found a non-significant heterozygosity excess (Wilcoxon: p = 1.0000) and a significant heterozygosity deficit (p = 0.0019) in the eastern group.The BSP did not reveal any marked fluctuations in the effective size (Ne) of the population in either group, although there was a slight reduction in the median Ne values at 5 kya , althougP. rustica, with an AUC of 0.96. During the LIG, few suitable areas were observed within the current range of P. rustica  analysis accurately predicted the current distribution of  rustica , whereas rustica , and a s rustica . All the rustica . Conside rustica , before  rustica . Zones oanalysed .P. rustica was identified to the east of the SFRV in all models. This zone covers central Bahia, including the Chapada Diamantina hills, and northern state of Minas Gerais , which then dispersed to the east of the SFRV, where the species probably colonised central and south-western Bahia  of both mtDNA and SSR datasets were consistent in reveal two groups of populations in y H10 in  and the enario 2 . Therefoenario 2 . These frn Bahia . TherefoP. rustica groups exhibit reduced levels of migration between them. This reflects the moderate degree of differentiation between groups in both the mtDNA and the SSR dataset, which contrasts with the within-group variation especially at the SSR markers, and may be accounted for by the relatively recent evolutionary history of P. rustica. However, the divergence time indicated by the ABC approach was shorter than that suggested by the IMa2, which may be due to the migration between groups, which is not contemplated by the DIYABC, which assumes an absence of migration among populations after they have diverged [Our analyses indicated that the two diverged .P. rustica, the probable gap of 200 Km between groups hampers the analysis of the potential role of the SFRV as a barrier to gene flow. Moreover, the SFRV region was determined to be an area of instability for the species  and isolation by distance, as found in the SSR data, since this stingless bee might have low vagility [While the present results reveal a moderate degree of differentiation between population groups of  species , which mvagility , 49, or Partamona queens are phylopatric, remaining in their place of origin [csd), in which individuals that are heterozygous at the csd locus are females, while in the hemizygous condition they develop into haploid males [csd system and the phylopatric queen, it is crucial for this insect to have a mechanism to prevent inbreeding and homozygosis at the csd locus to avoid the generation of diploid males.The Mantel test did not find evidence of isolation by distance in the mtDNA data, indicating that the differences found among populations cannot be accounted for by the physical distances among them. By contrast, strong evidence of isolation by distance was found in the SSR dataset. Similarly, while the mitochondrial genes indicated a high degree of differentiation between populations, only moderate differentiation was found in the SSR dataset . These af origin . These nf origin , 49. Morid males , 53. Howid males , 55, 56.id males , 58, 59 id males . Given tP. rustica, featuring a sex-biased dispersal pattern. So, the acquisition of new genes by populations should occur as a result of the dispersal behaviour of the males, as observed in others stingless bees [Therefore, this disagreement between the isolation by distance tests, together with the observed population differentiation, can be explained by the phylopatric females and dispersing males in ess bees , 62, 63,ess bees , 65, 66 ess bees . Alternaess bees .P. rustica  of Bahia [Chapada Diamantina and the SFRV as areas of instability for the species, as well as the north-eastern Bahia Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableThe test indicates two groups (eastern and western–see (DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.