Ornduffia are endemic to the Porongurup Range in the South West Australian Global Biodiversity Hotspot. The rare Ornduffia calthifolia is found exclusively on the summits, while O. marchantii is more widely dispersed across a greater range of elevation and is not considered threatened. Hybridisation in suitable overlapping habitat has been suspected between them for decades. Here we combine genotyping by sequencing to verify hybridisation genetically, and fine scale (2 m resolution) species distribution modelling (SDM) to test if hybrids occur in suitable intersecting habitat. From a study area of c. 4700 ha, SDM identified c. 275 ha and c. 322 ha of suitable habitat for O. calthifolia and O. marchantii, respectively. We identified range overlap between species of c. 59 ha), which enveloped 32 individuals confirmed to be hybrids. While the hybrids were at the margin of suitable habitat for O. marchantii, their preference for elevated habitat was closer to the more narrowly distributed O. calthifolia. The combination of genetic data and fine scale spatial modelling approaches enabled a better understanding of hybridisation among taxa of conservation significance. However, the level to which hybrid proliferation and competition for habitat presents as a threat to O. calthifolia is currently unknown and requires priority in conservation management given the threats from global warming and disturbance by tourism.Hybridization has an important and often positive role in plant evolution. However, it can also have negative consequences for species. Two closely related species of  For example, genetic and demographic swamping are two potential evolutionary processes that may cause extinction of one or both parents. Genetic swamping is the more frequent outcome, which occurs when hybrids replace one or both parents3. Demographic swamping results from outbreeding depression; where parent taxa expend reproductive energy on infertile or unfit hybrids, leading to population growth rates below what is needed for replacement4. Alternatively, hybridisation may increase diversity in previously isolated, inbred, populations (genetic rescue), increasing their viability5.Hybridisation of two genetically distinguishable populations can have constructive or destructive outcomes for taxa6. Global biodiversity hotspots contain exceptional concentrations of endemism, including almost half of all global plant biodiversity, yet cover a fraction of the Earth’s surface7. These hotspots are therefore priorities for conservation management. For example, high levels of plant diversity and endemism occur in the South West Australian Global Biodiversity Hotspot, which includes over 2500 threatened vascular species8. This flora includes many groups of closely related taxa with narrow distributions that are geographically proximal in the relatively subdued landscapes of south-western Australia. For these taxa, understanding interactions through hybridisation have until recently been limited by available technology.Narrow range endemic taxa are more vulnerable to extinction from the outcomes of hybridisation than common species10. Genotyping by sequencing can produce thousands of high-quality markers, enabling comprehensive genetic analyses even of non-model organisms12. Opportunities for hybridisation can be readily explored using SDM, by extrapolating the environmental correlates of the parent populations13 and delineating their overlapping niches. While there are multiple examples of SDM complementing genomic analyses (e.g.15), few have modelled endemic species at finer than 10 m resolution17.Recent advances in molecular biology (e.g. next generation sequencing) and fine scale species distribution modelling (SDM) means they can now be used together to confirm hybridisation and determine suitable habitat at unprecedented levels of detailOrnduffia are endemic to a single mountain range in south-western Australia. Ornduffia calthifolia is declared rare flora under the Commonwealth Environment Protection and Biodiversity Conservation Act 199918 and is found exclusively on the summits of the Porongurup Range19. Ornduffia marchantii is found at lower elevations but is not considered threatened. Hybridisation potential has been confirmed in greenhouse experiments20 and is thought to be occurring in the intervening zone21. Keppel et al. (22) suggested that asymmetric hybridisation, with O. calthifolia as the maternal parent, could threaten the survival of O. calthifolia.Two closely related species of Ornduffia species in south-western Australia. We have three specific aims and associated hypotheses:verify the occurrence of hybrid individuals in the wild;estimate the pattern of hybridisation across species and populations; andexamine whether hybridisation occurs in suitable overlapping habitat identified by distribution modelling. We hypothesised that the distribution of hybrid individuals would be spatially variable and occur more frequently in suitable overlapping habitatHere, we integrate genotyping by sequencing data and fine-scale SDM to confirm hybridisation between the two 23. The Porongurup Range  is Australia’s largest granite outcrop22 consisting of multiple granite domes about 1100 million years old24. It varies in elevation from c. 230–670 m over 26 km2 and has a Mediterranean climate, with cool, wet winters and hot, dry summers. Soils at the base of this range may remain moist all year. Higher elevations receive greater orographic rainfall than the base25, though no official measurements have been produced26.The Porongurup Range (670 m a.s.l.), along with the Stirling Ranges (1090 m a.s.l.), provide the only significant altitudinal relief within south-western AustraliaOrnduffia (Family: Menyanthaceae) includes eight semi-aquatic and wetland species with fleshy leaves, native to southern Australia22. Ornduffia calthifolia  elliptic seeds that demonstrate hydrophobic properties, assisting dispersal by water28. Additionally, ants are thought to be an important dispersal agent for both species28.The herbaceous genus lia Fig.  is arountii Fig. ., which tii Fig. . Ornduff22. The two parent species have different habitat preferences; O. calthifolia occurs mostly within moist, sheltered crevices at the highest-elevation (e.g. 640 m asl) granite peaks , whilst O. marchantii occurs at lower elevations (e.g. 350 m asl), often in disturbed habitat, under forest dominated by karri  where soils are wet and loamy29.Putative hybrids Fig.  have onlaks Fig. 18,20, whmy29Fig. 20.FigureO. calthifolia  and O. marchantii  were sampled  of putative hybrids Fig.  between We collected 187 samples, each population comprising 13 or 14 samples except for T2,4, which only had two individuals. The largest healthy leaf from each sample was collected for morphometric analysis, placed in an airtight bag and refrigerated in the field. Where there were ample individuals, samples were a minimum of 5 m apart. We recorded the coordinates of each sample with the aid of a GNSS receiver (Garmin Etrex 10).Ornduffia marchantii is identified as having a smaller narrower leaf and shorter stem than O. calthifolia20. Three leaf dimensions , and petiole length were measured on the largest healthy leaf of each plant. Sampling occurred out of flowering season and thus no reproductive variables could be obtained. We produced 2-dimensional multidimensional scaling (MDS) ordination plots in PATN V2.329 to visually assess trends in the resultant patterns. The goal of MDS is to faithfully represent the distance matrix in the lowest possible dimensional space, with level of stress measuring the resultant distortion. If stress is low, the chosen dimensional representation (in this case two) reasonably represents the objects relative positions. We used the Gower association measure30 to derive the distance matrix, due to its capacity to handle continuous data29, with 1000 random starts, a maximum 1000 iterations and a cut value of 0.938.Morphometric analysis was used to identify phenological intermediates. a priori groups in PAST3 software31. These functions operate directly on the dissimilarity matrix and are allied with MDS ordination in that it uses the rank order of dissimilarity values. If two groups of sampling units are different, then compositional dissimilarities between the groups ought to be greater than those within groups. The ANOSIM statistic R is based on the difference of mean ranks between groups (r_B) and within groups. In both cases the Gower dissimilarity measure and 10 000 permutations were applied.We ran analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) to test for significant morphological differences between these 32 as were loci with a minor allele frequency <0.05 (e.g.33) or average read depth <5 (e.g.34). Loci displaying departure from Hardy-Weinberg equilibrium and linkage equilibrium were removed with the dartR package in R35.Diversity Arrays Technology Pty. Ltd extracted DNA from 0.1 g dried leaf samples with the NucleoSpin Plant II method (Macherey-Nagel). Genome wide scans by Diversity Arrays Technology (DArTSeq) sequenced the isolated DNA fragments. DARTSeq is a genome complexity reduction method combined with next generation sequencing technologies, capable of identifying tens of thousands of single nucleotide polymorphism (SNP) loci. To ensure only high quality and informative markers were included, loci with more than 10% missing data  were removed from analysis36 to identify outlier loci, whose allele frequencies vary more between populations than expected under genetic drift. These loci may represent putatively non-neutral genetic regions and were removed from the analysis. We applied default parameters for iterations . We set ‘Prior odds for neutral model’ to 200, as higher odds are required for larger datasets37. The inbreeding coefficient was set as ‘FIS uniform between 0 and 0.5’ (default is uniform between 0 and 1) based on FIS results calculated for each locus using the R package ADEGENET38. We applied a false discovery rate (FDR) threshold of 0.05  to separate neutral and adaptive loci36.We used BAYESCAN V2.1E), observed heterozygosity (HO) and allelic richness (AR) values were produced in the R package HIERFSTAT39. Inbreeding coefficients (FIS) were calculated for each locus using R packages ADEGENET38. We grouped all samples into four populations for calculating genetic diversity; the two parent reference populations, Transect 1 and Transect 2.Expected heterozygosity  algorithm, calculating percentage admixture individuals to K genetic clusters. We tested for optimal number of clusters with 10 runs each of K = 1–6. We ran a burn-in of 50 000 iterations and 250 000 MCMC iterations, no previous population information and correlated allele frequencies (e.g.46). A subsequent analysis in STRUCTURE was performed with USEPOPINFO = 1 to allocate putative hybrid individuals to the parental populations. We used the ΔK method47 in STRUCTURE HARVESTER V0.6.9348 to delineate optimal K. Individual replicates were aligned using the ‘full search’ algorithm in CLUMPP 1.1.249. The optimal K value was then used in the delineation of hybrid individuals. Pure species were delineated by a threshold at 0.1, meaning individuals assigned a value > 0.90 to a species were categorised as pure (e.g.46).We generated a pairwise Nei’s genetic distance1 hybrids, we ran analyses on NEW-HYBRIDS50 to assign individuals to one of six genealogical classes  (e.g.46). We validated the accuracy at which both STRUCTURE and NEW-HYBRIDS correctly classified individuals using simulated populations created in HYBRIDLAB51. From each parent population we created 200 individuals, of which we simulated the creation of the other four genealogical classes identified by NEW-HYBRIDS. We then ran simulated samples through STRUCTURE and NEW HYBRIDS with the same parameters as the real data.To identify hybrid status of individuals as back-crossed or F13 for both parent species to predict opportunities for hybridisation in the overlapping suitable habitat. All ‘pure’ individuals , as delineated by STRUCTURE, were included as presence data. We supplemented this survey with 207 O. calthifolia and 55 O. marchantii samples from surveys conducted in February 2012 and November 2013. As no coordinates were duplicated over surveys, we assumed all samples were unique. To quantify model accuracy, we calculated mean area under the curve (AUC) of receiver operating characteristic (ROC) curves52 with 100 bootstrap iterations. Each iteration randomly selected 20% of the points for an independent validation dataset. We did not develop an SDM for the hybrid because its current limited geographic extent comprises of only a few clustered samples and is therefore likely to result in under prediction.We developed SDMs with MAXENT V3.3.3c. 0.63 last return heights per m2, which were interpolated into a 2 m resolution digital elevation model (DEM). Horizontal and vertical accuracies are <0.35 m and <0.15 m respectively. Elevation, slope (first derivative of elevation), aspect (degrees from north), curvature (second derivative of elevation), solar radiation (WH/m2) at monthly intervals and the topographic wetness index (TWI) defined by Gessler et al.53 were derived from the DEM using the Spatial Analyst Toolbox of ArcGIS v 10.554.Light detection and ranging (LiDAR) data for the Porongurup Range was captured in April 2011 with an airborne Leica ALS 50-II scanner. A flight height between 1700 and 2220 m resulted in 55. TRI56 is a measure of terrain heterogeneity, VDCN is a measure of local ridge heights. TPI57 identifies elevation relative to its neighbourhood. The SWI was used in addition to TWI because they were not significantly correlated (r = 0.4) and the SWI has predicted potential of soil moisture for cells with small vertical distance to a channel in valley floors more realistically than TWI58. As we recognised that O. calthifolia preferentially inhabits locations at or near the base of cliff faces we used a high pass filter to identify cliff edges and modelled for proximity. As O. marchantii is associated with the shade provided by karri over storey, we calculated a canopy height model (CHM).We calculated topographic roughness index (TRI), vertical distance to channel network (VDCN), topographic position index (TPI) and the SAGA Wetness Index (SWI) in SAGA v 2.1.459. We defined a threshold to classify suitable and unsuitable habitat by minimising misclassification of pseudo-absences created nearby traversed locations60.There is no accepted method for selecting model thresholds other than they should be data driven20. Samples from Transect 1 occupied broad ordination space with most individuals occurring in similar ordination space to that of O. calthifolia, yet also occurring with O. marchantii and in the space between the two species. Individuals from Transect 2 occupied ordination space between that of the two species, with some overlap with O. marchantii. Each class was significantly different  from every other class except for O. calthifolia with Transect 1. ANOSIM produced an overall R value of 0.2045 and PERMANOVA an F statistic of 39.76. Pairwise R values can be found in Fig. As expected, analysis of morphometric data using MDS showed separation of individuals from the two parent species classes with little overlap in ordination plot space Fig. . These tO. marchantii, had greater genetic diversity than O. calthifolia , with 8 489 loci retained after data cleaning. BAYESCAN identified 263 outlier loci that were removed from the analysis. The more common and dispersed taxon, O. calthifolia populations clustering separately from those of O. marchantii on the axis for coordinate 1. There was some variation within both species that was predominantly identified by coordinate 2, with the more isolated eastern populations (‘OC Castle Rock’ and ‘OM East’) exhibiting greatest separation from other populations within each species. Thirty-six individuals from Transect 1 were clustered with O. calthifolia from the Devil’s slide population but the majority occurred in intermediate space between the two species  were shared with hybrids.The ΔK method identified K = 2 to be optimal. Variation between replicate runs on STRUCTURE did not result in any changes to species assignment when K = 2. Admixture modelling in STRUCTURE detected hybridisation between tii Fig.  and indiO. calthifolia populations were above the threshold of 0.9 and classified as pure. Most of the samples from populations of O. marchantii were also identified as pure, except for three samples of O. marchantii from the ‘OM East’ population, which were below the 0.9 threshold. We sampled this population at two slightly disparate locations .Samples from Transect 1 showed a range of genetic affinities with some individuals assigned to each of the two parent species and the remainder showing a range of admixture. Samples assigned to either of the two parent species were located primarily at lower elevation, with assignment to t 1 Fig. . Further1 and F2 Hybrids were more prevalent at higher altitudes along Transect 1. Across the five populations of increasing elevation, the number of F1 and F2 hybrids were 0, 0, 2, 7 and 7. Perfect classification of simulated individuals from HYBRIDLAB was achieved in both STRUCTURE and NEW-HYBRIDS with 100% of the simulated individuals correctly classified.Results from NEW-HYBRIDS closely matched those from STRUCTURE. FO. calthifolia and O. marchantii, respectively. With elevation as the highest contributor  statistics across 100 bootstrap runs based on a 20% subset for validation were 0.98 and 0.92 for 0 m Fig. .Table 2SO. marchantii, and suitable areas of habitat can be found across the extent of the range. The extent of suitable habitat for O. marchantii (322 ha) was greater and far more widespread and fragmented than that of O. calthifolia , as opposed to merely bringing the taxa into contact (e.g.62). Plastic responses to habitat change (e.g. flowering time), are known to vary under conditions of anthropogenic disturbance64. Ornduff20 noted that there is some natural overlap in flowering and pollination times and was able to produce hybrids under laboratory conditions.An area of 59 ha suitable for both species was derived by intersecting SDMs of the parents to highlight the potential for co-occurrence and natural hybridization. All the Transect 1 sites and Transect 2, 3–4 were located within this overlap area. Therefore, the greater rate of hybridization may be due to greater overlap of the parent species in transect 1. However, given transect 1 is adjacent to a walking track it is possible that disturbance is opening new corridors for species contact and altering plant phenology, but this theory needs further explorationO. calthifolia since a 2013 study22. The rarity of this species therefore makes rigid sampling along elevational transect difficult. Limitations in study design mean we cannot rule out subtle differences in microhabitat between the two transects or that Transect 1 is located on a historic, geographically stable hybrid zone.Whilst hybrid individuals were largely restricted to Transect 1, which is located within relatively disturbed habitat, our sampling was not designed to determine whether disturbance drives hybridization. However, given that the study site is a prominent tourist destination, future studies should investigate the role of disturbance in hybridization. Such studies could be complicated by a marked drop in the abundance of 1 hybrids, and would display approximately 50% affinity to each parent65. Affinity to O. calthifolia increased with elevation in Transect 1 from the lowest population , to the highest . This change in admixture is consistent with the bounded hybrid superiority model66, whereby a zone of hybridisation occurs between two geographically separated species, creating a gradient of admixture. In this model, hybrids possess superior adaptability to the intermediate conditions, but parent species dominate in their niche habitats66.We found evidence of introgression and backcrossing, as hybrids exhibited a range of affinity to each species. Without introgression, all hybrids would be expected to be FO. calthifolia individuals. Persistence of O. calthifolia amongst hybrids in this population is noteworthy considering the proximity of hybrids. The site was adjacent to a sheer rock face  and the threats posed by global warming and disturbance through tourism, understanding the dynamics of hybridisation should be a priority in monitoring and conservation management.The combination of genetic data and spatial modelling approaches enabled a better understanding of hybridisation among taxa of conservation significance. Although the hybrids were at the margin of suitable habitat for


Buchenavia oxycarpa (Mart.) Eichler (Combretaceae), to evaluate Wallace’s hypothesis along the Rio Negro, a major Amazon tributary that has driven allopatric speciation for several animal taxa. We sampled six individuals from sixteen localities along both river banks, and used a modified ddRADseq protocol to identify SNP markers. Our population genomic data revealed strong genetic structure for B. oxycarpa sampled across banks of the Rio Negro , supporting the hypothesis that the Rio Negro acted as a significant genetic barrier for B. oxycarpa. Our study shows that gene flow for this large and well-dispersed Amazon tree is impeded by riverine barriers, though this has not yet resulted in speciation. Future studies focused on species with different life histories, including species restricted to non-flooded forests, are needed to further advance our understanding of Amazon rivers as drivers of biotic diversification.Wallace’s (1854) Riverine Barrier hypothesis is one of the earliest explanations for Amazon biotic diversification. Despite the importance of this hypothesis for explaining speciation in some animal groups, it has not been studied extensively for plant species. In this study we use a prominent Amazon tree,  For instance, ecological differences among species explains much of the interspecific variance in levels of genetic differentiation in birds across physical barriers6. These findings were also observed for other animal species10.The patterns of population structure can provide insights into the evolutionary history of individual taxa. Migration, a key evolutionary force, is expected to weaken the degree of genetic structure within a species range12. Indeed, strong genetic differentiation has been observed across physical barriers for plant species with restricted dispersal abilities15. For example, highland and mountain ranges acted as effective barriers to gene flow for the tree species Styrax sumatrana14, a species rarely dispersed by animals16. On the other hand, for plant species with high dispersal abilities, geographic barriers may not promote significant population structure18. In the Amazon Basin, where rivers are a potential cause of allopatric population differentiation for a plethora of taxa, high rates of cross-river gene flow have been documented for several different plant species19. Along the Rio Negro, one of the largest tributaries of the Amazon river broadly acknowledged as a biogeographic barrier since Wallace20, the first study to test this hypothesis for plants reported high rates of gene flow across river banks for two animal dispersed canopy-emergent trees17. A more recent population genomics study recovered weak genetic differentiation of a fish-dispersed shrub species spanning the Rio Negro18. Thus, extensive gene flow across large Amazon rivers may be expected for plant species with high dispersal abilities21. One limitation of most prior studies is that they involved species that are most common in the flooded forests and therefore able to endure flooding and benefit from seed dispersal by water. There are as yet very few studies of species that occur primarily in terra firme (unflooded) forest17.For sessile organisms such as plants, landscape features and life-history traits  can restrict migration and increase the amount of genetic variation among populationsBuchenavia oxycarpa (Mart.) Eichler is a prominent timber tree species (up to 30 m in height) distributed in lowland terra firme forests of South America . In Brazil, B. oxycarpa is found in the Amazon, Cerrado, and the Atlantic Forest22. In the Amazon Basin, B. oxycarpa occurs at relatively high densities in humid forests, often near rivers and periodically flooded terrains. Due to its high wood quality, B. oxycarpa has been overharvested in some areas, as in the Cerrado23. Buchenavia oxycarpa is pollinated by small bees24. In the Peruvian Amazon, B. oxycarpa is dispersed by two sympatric primate species, Saguinus mystax and S. fuscicollis25. Although these primate species are not found in the Central Amazon basin, it is very likely that seeds of B. oxycarpa are dispersed by other species of the genus Saimiri. The spongy mesocarp of the indehiscent fruits of B. oxycarpa may be associated with hydrochory, so water dispersal may be possible for this species26.The study species B. oxycarpa, we would expect high rates of gene flow between populations of this animal-dispersed plant species based on genetic studies of tree species with similar ecology21. As a consequence, populations of B. oxycarpa located on opposite margins of the Rio Negro – a putative riverine barrier – should present weak population genetic structure. This picture may be expected since results from recent studies have indicated that long-dispersal syndromes, such as vertebrate-dispersal, are responsible for low levels of genetic differentiation for riverine plant species with widespread distributions17.Even though no genetic information is available for B. oxycarpa . While most Amazon tree populations occur in low densities or have scattered distributions, B. oxycarpa is among the few plant species that are sufficiently common along both banks of the Rio Negro, allowing an adequate sampling to test whether the Rio Negro is an effective genetic barrier. To this end, we sampled B. oxycarpa at sixteen localities on opposite river-banks of the Rio Negro and used a modified high-throughput DNA sequencing methodology 27 to identify anonymous SNP markers from the nuclear genome. Through this study, we hope to contribute to our current understanding about the importance of Amazon rivers for the assembly and evolution of the plant biota.In this study, we aim to test the influence of the Rio Negro as a biogeographic barrier to gene flow for B. oxycarpa individuals ranged from 102 million to 154 million. Each read started with a barcode sequence that identified each sample (up to 10 bp long), which was followed by a 6 bp restriction site, and 85 bp of usable data. The mean number of retained reads that passed the default quality filters, including a Phred quality score > 33, and which contained an identifiable barcode, were 2,165,480 ± 124,807 SE. Throughout the B. oxycarpa genomes, further filtering  resulted in 3,298 unlinked polymorphic SNP markers with a mean coverage depth per locus of 18.2 ± 5.6x. The minor allele frequency (MAF) averaged 0.095 ± 0.0363 SD. No significant departures from HWE were observed in any population or locus after a Bonferroni adjustment (P > 0.000015). In addition, no LD was observed after a sequential Bonferroni correction for k tests .The number of single-end raw reads of 101 bp produced for each lane of HiSeq. 2000 Illumina containing 48 We detected 278 potential loci that were under diversifying selection with the false discovery rate (FDR) set to 0.05. Thus, a total of 3,020 neutral loci were used in genomic analyses.Using the MDS and Bayesian clustering methods, we identified one potential barrier to gene flow in the Rio Negro for both sets of SNP loci analyzed . An examination of the stress values  determined that two dimensions were sufficient to explain genetic patterns along the Rio Negro. The MDS plot indicated a clear separation of samples between the left and right banks of the Rio Negro Fig. . The genFST based on neutral loci ranged from 0.0086 to 0.6711 and all but 17 pairwise estimates were statistically significant (P < 0.05), indicating limited differentiation between B. oxycarpa population pairs from the Rio Negro  nor when applied between pairs of B. oxycarpa populations on opposite banks of the river .The 120 pairwise estimates of CT = 0.576, P < 0.001; Table SC = 0.027, P < 0.001, Table B. oxycarpa.The hierarchical multi-locus evaluation of genetic differentiation, performed using an AMOVA, agrees with our previous results indicating that the Rio Negro can be a barrier to gene flow. Most of the genetic variation was attributable to differences observed between banks  were added sequentially to the tree. The graph model  was also observed for Amphirrhox longifolia in a recent study conducted in the same location18.The multiple matrix regression analysis using neutral SNPs indicates that 93.2% of the variation in pairwise genetic distances is explained by historical isolation caused by the riverine barrier. This analysis also indicates that geographic distance is not a primary driver of population structure for 37 than plants that can move extensively across rivers18. The patterns of neutral genetic structure observed here seem to have resulted from species-specific traits. However, this trend is not consistent with what has been observed in other animal-dispersed plant species in wider Amazon rivers. As a matter of fact, the Rio Negro does not seem to pose a barrier for a low-density and widely distributed canopy-emergent tree species (Caryocar villosum) that grows in tierra firme forests, nor to a habitat-specific tree (C. microcarpum) that grows in seasonally flooded black-water forests17. These results are expected given the long distances of gene flow associated with bat-pollination and seed dispersal by fish17.Riverine plant species with low dispersal abilities are more likely to show hierarchical genetic structureB. oxycarpa is unknown in the study area, their seeds are dispersed by two sympatric primate species (Saguinus mystax and S. fuscicollis) in the Peruvian Amazon25. These findings suggest that other primate species  may act as dispersal vectors of the seeds of B. oxycarpa along the Rio Negro. Although some primates can eventually cross smaller Amazon rivers , the Rio Negro seems to be a dispersal barrier for primates31. Indeed, Boubli and co-authors31 showed that this wider river represents a genetic barrier that limits the distribution of the primate genera Ateles and Chiropotes to the left bank and Saguinus and Sapajus to the right bank in the upper Rio Negro. Although water can disperse seeds to greater distances than other dispersal mechanisms38, the strong genetic differentiation observed for B. oxycarpa in opposite banks of the Rio Negro are congruent with the distribution patterns of primates. Further studies of the seed ecology of B. oxycarpa may confirm this observation. Studies of this nature would also shed light on the role of plant-animal dispersal mutualisms in shaping patterns of population genetic structure. Given that fruits are a key component of primate diet in the Amazon Basin39 and that primates are important seed dispersal vectors in tropical forests41, an improved understanding of the actual role of rivers as barriers in this region will further improve our understanding of the role of allopatric speciation for the origin and maintenance of Amazon plant diversity.In this context, the behavior and movement of frugivorous animals seems to constitute an important evolutionary force shaping patterns of genetic structure of riverine plant species. Although the specific disperser of B. oxycarpa. The Rio Negro is subject to long periods of flooding42, especially in the lower portions where B. oxycarpa populations were sampled. In addition, inundation extent can vary among river banks, which can lead to differences in flowering and fruiting phenology, possibly restricting gene flow by pollen across river banks. Future studies evaluating the reproductive biology of B. oxycarpa in both banks of the Rio Negro may enable us to better predict genetic patterns for this species. Furthermore, river characteristics such the degree of river discharge and width can impact gene flow44. For instance, a recent population genomic study carried out in the core Amazon Basin showed that the strength of the riverine barrier for the plant species Amphirrhox longifolia depended on the width of the rivers separating populations18.Landscape features may also play an important role in structuring genetic diversity of B. oxycarpa individuals were not recorded in our study, these types of data can help infer explicit targets of selection and adaptation, especially when correlated with allelic frequencies. Studies of this nature would be extremely valuable for species of conservation concern such as B. oxycarpa, which has been drastically overharvested in some regions23.Although the environmental conditions, morphological, and reproductive traits of B. oxycarpa, though not enough to generate novel taxonomic diversity. Although this study provided new evidence about the role of rivers in shaping genetic structure on plants, our knowledge about landscape genetics and allopatric speciation history of Amazon plant diversity is still limited. Clearly, larger-scale population genomic studies on plants with varied ecological traits are greatly needed in order to test whether the patterns observed here are representative of the floodplain flora of the Rio Negro as a whole. Additional studies, conducted in Amazonian waterways with distinct features would complement our current understanding, which is focused on patterns observed along the Rio Negro. It would also be important to study plants that are restricted to the non-flooded terra firme, without any adaptations to flood tolerance. While this study focused on the analysis of historical genetic structure, detailed analyses based on contemporary gene flow would provide additional insights. More comprehensive studies are also needed to identify putative associations between ecological-environmental traits and allelic frequency. Studies of this nature would allow us to better understand how deterministic and neutral processes have contributed for the evolutionary trajectory of Amazon plant species.Overall, the population genetic structure data reported here supported the hypothesis that the Rio Negro acted as a significant genetic barrier for 45. This river runs for 1700 km throughout the Amazon Basin, with the mouth of the river near the city of Manaus, Amazonas State, Brazil. Although the geological history of this river is poorly understood, geomorphological studies suggest that the mouth of the river migrated 150 km eastward to its current location46. The floodplain forests bordering the river, known as igapó, are home to several hundred tree species48 and are mainly lowland rainforests. Sub-montane, montane, and other lowland vegetation, such as white sand-soil forests49, also occur throughout the basin. The region has a mean annual rainfall of 2200 mm (IBGE) and the climate is classified as tropical equatorial, with a dry season occurring between June and August, and the rest of the year considered the rainy season. Because its nutrient-poor soils are unsuitable for agriculture, deforestation along the Rio Negro has been minimal.The Rio Negro Fig.  is the fB. oxycarpa individuals were collected during the rainy season of 2016. Sixteen sampling sites (populations) located on both the left (L) and right (R) banks of the Rio Negro were included in the study  = 0.343, 95% CI ; FST (n = 2) = 0.321, 95% CI ].Leaf samples of flowering dy Table , Fig. 4.dy Table , and allet al.18. Briefly, we used the Macherey-Nagel kit (Macherey-Nagel GmbH & Co. KG) to extract genomic DNA from leaf samples. Two genomic libraries were constructed using a double digest RADseq protocol55 and following the adjustments outlined in Nazareno et al.18 related to minimizing variation in number of reads per individual. Before digestion, we used the Qubit dsDNA Assay Kit (Invitrogen) to quantify the concentrations of double-stranded DNA. Samples were adjusted to equal molar concentration, and the final DNA concentration for each sample was 500 ng.µL1. Two restriction enzymes, EcoRI and MseI (New England Biolabs), were used to digest each sample, and digestions were carried out in a total volume of 20 µL, using 17 µL of resuspended DNA, 5 units of EcoRI, 5 units of MseI, and 1X CutSmart buffer (New England Biolabs). The protocol consisted of 3 hours at 37 °C, with a final 20 min deactivation step at 65 °C. The Agencourt AMPure XP system (Beckman Coulter) was used to purify the reactions, following the manufacturer’s instructions, with elution in 40 µL TE buffer. The cleaned digests were quantified using Qubit to standardize the initial DNA mass to be added into an adapter ligation. Adapter ligations were conducted using a total volume of 30 µL, with 42 ng DNA, 0.22 µM of a non-sample specific MseI adaptor , 0.33 µM of a sample specific EcoRI double-strand adaptor for each DNA sample, 1U of T4 DNA ligase (New England BioLabs), and 1.3 X T4 ligase buffer which were incubated at 23 °C for 30 min. Restriction enzymes were then heat-killed at 65 °C for 10 min followed by a slow cooling to room temperature (23 °C).The preparation of the libraries and DNA sequencing followed the protocol described in Nazareno 2, 0.5 U of iProofTM High-Fidelity DNA polymerase (BIO-RAD), and 2X of iProof buffer. An Eppendorf PCR System was used for PCR using the following protocol: 98 °C for 30 s, 20 cycles of 98 °C for 20 s, 60 °C for 30 s, and 72 °C for 40 s, followed by a final extension at 72 °C for 10 min. Before pooling samples in each library, samples were purified using the Agencourt AMPure XP system and the DNA quantified using Qubit. The concentration of DNA for each sample ranged from 2.13 ng.µL−1 to 13.00 ng.µL−1. We prepared multiplexed libraries with generally the same amount of DNA per sample. The target size range to select genomic fragments was 375–475 bp, and automated size-selection was performed using a 2% agarose cartridge . We used the Agilent 2100 Bioanalyzer (Agilent Technologies) with the Agilent DNA 1000 Kit to measure size, quantity and quality of each individual library. Libraries were sequenced (100-bp single-end reads) at The Centre for Applied Genomics in Toronto, Canada, in a single lane  of an Illumina HiSeq. 2000 flow cell .After cleaning the reactions, ligation products were amplified in 20 µL PCRs, containing 13.5 µL of the ligation product, 0.2 µM of Illumina PCR primers, 0.2 mM dNTPs, 1.0 mM MgClet al.18. The program Stacks 1.3557 with the de novo assembly was used to analyze files containing the raw sequence reads. The program ustacks was used to produce consensus sequences of RAD tags, as it aligns short-read sequences from a single sample into exactly matching stacks. To estimate the diploid genotype for each individual at each nucleotide position, we used a maximum-likelihood framework58. The optimal minimum depth of coverage to create a stack was set to three sequences, with the maximum distance permitted between stacks set to two nucleotides, and the maximum number of stacks allowed per de novo locus set to three. We used an alpha value for the SNP model of 0.05. A catalog of consensus loci containing all loci and merging all alleles together was built using Cstacks; individual genotypes were then compared to the catalog using sstacks, and rxstacks was used to exclude problematic loci. Finally, we used the POPULATIONS program57 to identify the loci present in at least 85% of individuals (-r 0.85), with a minimum stack depth of 10 (-m 10), a Minor Allele Frequency (MAF) of 1% (–min_maf 0.01), and ddRAD tags requested to be present in all populations (-p 16). The final analysis included only the first SNP per locus.The procedure used to identify and genotype SNPs is fully outlined in Nazareno 59. HWE tests were done using the adegenet package61 implemented in R61. We used the Genepop 4.062 program to test for linkage disequilibrium (LD) between loci in each population, calculating exact probabilities with a Markov Chain consisting of 100 batches and 5000 iterations per batch. After adjusting the p value, SNPs that failed the HWE test and SNP pairs in LD in at least seven locations  were excluded from further analyses. Based on the final dataset, we calculated minor allele frequencies for B. oxycarpa using the adegenet61 package in R64.For all populations, we determined the number of raw sequence reads and unlinked SNPs. We also assessed deviation from Hardy–Weinberg equilibrium (HWE) using the exact test, which is based on Monte Carlo permutations of alleles and is the most appropriate for small sample sizes65. This software incorporates locus and population-specific regression terms, avoiding unrealistic assumptions such as island migration models, symmetrical gene flow, or equal population sizes66. Bayescan was run with 20 pilot runs of 10,000 iterations, a burn-in of 50,000, and a final run of 100,000 iterations. We set odds of the neutral model to 10,000  to minimize false-positives.We used the Bayescan software to identify SNP loci as having higher (divergent selection) or lower  levels of population divergence than strictly neutral lociB. oxycarpa population structure, we assessed the genetic structure and the historical connectivity patterns between populations along and across the river using complementary genetic analyses. The methods are described in Nazareno et al.19, and a brief overview is provided below.To investigate the effects of the river on A67) and visualized the results by applying multidimensional scaling (MDS) in XL-STAT (Addinsoft). This was done with the Scaling by MAjorizing a COnvex Function (SMACOF) method. As an ordination technique, MDS plots populations with similar genetic structure closer together in ordination space as established by a stress factor. No assumptions relating to the cause of structure, HWE, or gametic equilibrium are required. We used the GENELAND 4.0.268 package in R to develop a Bayesian model to better understand the geographic distribution of genetic variability. This approach incorporates spatial data while identifying spatially explicit genetic discontinuities, minimizing the Hardy-Weinberg and linkage disequilibrium that would result if individuals from different, randomly mating populations were incorrectly grouped. The spatial model with correlated allele frequencies proposed by Guillot et al.69 was used as it enables the inference of differentiation due to limited gene flow caused by physical barriers. We conducted 100 independent runs of 1,000,000 in length, discarding the burn-in of 500,000 iterations in post-processing. As the most likely number of k populations was unknown, it was treated as a simulated variable along with the MCMC simulations (1 ≤ k ≤ 16). The modal number of genetic groups of the best run  was considered as the number of genetic clusters (K).First, we calculated genetic distance among populations (DFST) following Weir & Cockerham70 and SPAGeDi71 to calculate FST. The significance of deviation of FST values were estimated using a jackknife procedure across loci. To assess visual similarity between genetic and geographic distances based on both the MDS and GENELAND methods, we used a Mantel test72 for isolation by distance (IBD) to verify if the overall pattern met the expectation of decreasing genetic similarity with increasing geographic distance. To test the riverine barrier hypothesis for the Rio Negro, we deconstructed the genetic structure using a multiple matrix regression, allowing us to assess the relative contribution of long-term historical divergence and the effects of IBD. The model proposed by Legendre & Legendre73 was used as it evaluates the relationship across three matrices: (1) pairwise genetic distances [FST/(1 − FST)74] between B. oxycarpa populations; (2) Euclidian distances representing the geographic distance between pairwise B. oxycarpa populations; and (3) a pairwise binary matrix of isolation by the river as an expression of long-term historical divergence. This binary matrix was constructed by coding each B. oxycarpa population pair in relation to the river, with populations on the same river bank as ‘0’ and those on different river banks as ‘1’27. Multiple matrix regression and a single Mantel test were performed in R64 using 10,000 permutation tests of significance for the correlation coefficient.We used ANOVA to assess pairwise genetic differentiation (75), defining two hierarchical levels of population differentiation: between populations from opposite river banks; and between populations along each bank. Arlequin 3.5.276 was used to calculate population differentiation estimates and their statistical significance based on 20,000 random permutations.To examine the effect of the river on genetic variation between populations, we used a nested hierarchical analysis of molecular variance , it improves the likelihood fit of a bifurcating phylogeny. The branch lengths of the resulting phylogeny are proportional to the amount of genetic drift per branch, based on a composite maximum likelihood of the local optimum tree78. Thus, inference is based on “shared genetic drift” between sets of populations, assuming that shared drift implies a shared evolutionary history79. We added stepwise migration edges, inspecting the results for consistency between runs, and we used R64 to visualize the population graph and residuals.To quantify historical connectivity based on neutral loci, we used TreeMix 1.12Supplementary information


The transcription factor STAT3 has a protective function in the heart. Until recently, the role of STAT3 in hypertension-induced cardiac hypertrophy was unsettled. Earlier studies revealed that global reduction of STAT3 activity reduced cardiac hypertrophy with hypertension, but caused a disruption of myofilaments and increased contractile dysfunction. However, newer studies with cardiomyocyte-specific deletion of STAT3 indicate that STAT3 does not cause cardiac hypertrophy with increased blood pressure. Rather, cardiac STAT3 is important for maintaining metabolic homeostasis, and loss of STAT3 in cardiomyocytes makes the heart more susceptible to chronic pathological insult, for example by disrupting glucose metabolism and protective signaling networks via the upregulation of certain microRNAs. This scenario has implications for understanding peripartum cardiomyopathy as well. In viral myocarditis, STAT3 opposes the initiation of the dilated phenotype by maintaining membrane integrity via the expression of dystrophin. STAT3 signaling was also found to attenuate myocarditis by polarizing macrophages to a less inflammatory phenotype. On the other hand, STAT3 contributes to immune-mediated myocarditis due to IL-6-induced complement component C3 production in the liver, as well as the differentiation of Th17 cells, which play a role in initiation and development of myocarditis. Besides canonical signaling pathways, unphosphorylated STAT3 (U-STAT3) and redox-activated STAT3 have been shown to couple to transcription in the heart. In addition, tissue signaling cytokines such as IL-22 and IL-17 have been proposed to have actions on the heart that involve STAT3, but are not fully defined. Understanding the novel and often protective aspects of STAT3 in the myocardium could lead to new therapeutic approaches to treat heart disease. The transcription factor signal transducer and activator of transcription 3 (STAT3) continues to excite much interest in heart research. A PubMed search on August 7, 2018 using the terms “STAT3 and (cardiac or heart)” shows a steady increase in the number of publications over time  . Moreove2+ channels with subsequent engagement of the hypertrophic transcription factor NFAT (nuclear factor of activated T-cells), as well as the loss of the pro-survival factor Bcl-xl. Acute stimulation was associated with reduced cardiac contractility due to the downregulation of β1-adrenergic receptors, protein kinase A, and several other downstream effectors  mice exhibited marked cardiomyocyte hypertrophy, as well as cell death and associated cardiac fibrosis, in response to chronic β-adrenergic stimulation with isoproterenol . The proComparable findings were reported in an AngII-induced model of hypertension . In thisFor a number of reasons the findings arguing against a role for STAT3 in cardiac hypertrophy that are based on its targeted KO in cardiomyocytes may not be definitive, and intriguing reports persists suggesting that STAT3 does play a more active role depending upon the circumstances. Granted many of these studies are also less than definitive as they base their conclusions on an association of STAT3 activity levels with cardiac hypertrophy and/or use a pharmacological inhibitor. Nonetheless, the choice of experimental model may be a determining factor in whether STAT3 contributes to hypertrophy. STAT3 was recently implicated in the mouse heart in ischemia-induced cardiac hypertrophy downstream of heat-shock transcription factor 1 . In a moIt cannot be discounted that while STAT3 may be pro-hypertrophic it normally inhibits another signaling pathway in cardiac myocytes that couples to hypertrophic growth. Thus, loss of STAT3 and its input into cardiac hypertrophy would be compensated for by activation of this pathway. In this context, the role of STAT3 in regulating mitochondrial function and reactive oxygen species (ROS) formation may be a consideration. In fact, mitochondrial dysfunction is linked to cardiac hypertrophy and heart failure through ROS generation . In cardThere is reason to think that endogenous STAT3 may attenuate cardiac hypertrophy in certain cases. An early study showed that IL-10 inhibited isoproterenol- and TAC-induced cardiac hypertrophy . EvidencOverall, the findings summarized here indicate that cardiac myocyte-specific STAT3 may not be responsible for hypertrophic growth of the heart in response to a rapid increase in blood pressure. Rather, STAT3 is important for maintaining contractile function and metabolic homeostasis with hypertension. Indeed, endogenous cardiac myocyte STAT3 may attenuate hypertrophy under certain conditions and, in cardiac myofibroblasts, STAT3 likely contributes to extracellular matrix remodeling.Besides an increase in the size of individual cardiomyocytes, pathological cardiac remodeling involves increased fibrosis, which is characterized either as reactive or as reparative when replacement of dead myocardium occurs. Multiple lines of evidence indicate that STAT3 is an important contributor to collagen synthesis and cardiac fibrosis , 26. NewIV-spectrin at intercalated discs, downstream of Ca2+/calmodulin-dependent kinase II (CaMKII) activation, was implicated in cardiac fibrosis and loss of cardiac function, but not hypertrophy  is a life-threatening condition, which may affect women in the last month of pregnancy or the first months after giving birth. Female mice with cardiac myocyte-specific STAT3 deletion develop a form of PPCM . This isLow STAT3 ventricular levels were also found to compromise glucose uptake and thereby sensitize the normal and peripartum heart to the toxic effects of chronic β-adrenergic signaling by contributing to a state of energy depletion and associated increased generation of ROS . This waMyocarditis or inflammatory cardiomyopathy is most often caused by a viral infection, commonly involving coxsackievirus. Although all ages are at risk, myocarditis commonly affects the young. In most cases, myocarditis resolves spontaneously, but more than 40% of affected individuals progress from increased cardiac hypertrophy, apoptosis, and fibrosis to a dilated cardiomyopathy with reduced contractility . Nearly In contrast, genetically prolonged and enhanced STAT3 activity in cardiac myocyte was associated with greater inflammation, left ventricular rupture, and worse outcome following subacute MI . Thus, tOther cell types are involved in the pathogenesis of myocarditis as well, further complicating the role of STAT3. Evidence was reported that STAT3 contributes to immune-mediated myocarditis in mice due to enhanced hepatic and cardiac IL-6 production, as well as IL-6-induced complement component C3 production in the liver . STAT3 iIn the heart, STAT3 uses two mechanisms, canonical and U-STAT3, to regulate the expression of two different pools of genes . CanonicU-STAT3 signaling occurs as a consequence of canonical STAT3 signaling-induced STAT3 expression and is proposed to prolong the inflammatory response initiated by canonical signaling . ElevateSome genes are activated by U-STAT3 via a complex with the unphosphorylated p65 subunit of NF-κB. Binding does not involve GAS elements, but rather a specific DNA element that supports binding of p65 homodimers and cooperativity with U-STAT3 . STAT3 SThe mechanism by which U-STAT3 activates target genes not requiring NF-κB is unknown , but mayU-STAT3 also binds to AT-rich DNA sequence sites and sequences implicated in chromatin organization, as well as DNA structures involved in nucleosomal structure and assembly . These oin vitro, oxidative stress was reported to inhibit canonical STAT3 transcriptional activity. We previously reported that levels of monomeric STAT3 measured under non-reducing conditions were decreased in a redox-sensitive manner in the Gqα model of heart failure ; however, its simultaneous activation of STAT3 and STAT3's binding to the promoter region damped iNOS gene induction  is pro-inflammatory and implicated in adverse remodeling of the heart associated with hypertension and viral myocarditis , 76. It nduction . The patRecent evidence indicates that endogenous levels of STAT3 in the heart are needed to maintain normal structure, contractile function, and metabolism under stress conditions. This conclusion has importance for hypertension, peripartum cardiac remodeling, and viral myocarditis. A growing theme is that STAT3 may also have benefit in the heart by repressing certain genes, and a better understanding of how that is accomplished is needed. The importance of STAT3 in immune cells and in crosstalk among the various cell types of the heart ought to be resolved, as well as how the various cellular subcompartments of STAT3 are integrated in controlling the genomic and non-genomic actions of STAT3 in response to stress or injury. Network analysis of STAT3 as a sentinel at intercalated discs, mitochondria, sarcoplasmic reticulum, and nuclei under stress conditions ought to be performed. In addition, novel signaling aspects of STAT3, such as U-STAT3, its redox-sensitivity, and the importance of STAT3 in chromatin organization have been described, but their implications for cardiac function and response to stress are not fully understood. Lastly, the role of STAT3 in the heart in response to tissue-specific cytokines is an area that requires further investigation. Understanding the novel and protective aspects of STAT3 in the myocardium could lead to new therapeutic approaches to treat heart disease.MK, CZ, and GB helped write the manuscript and create the figures. GB edited the text.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.


Vitis vinifera L.) were systematically sampled across the main grape-producing areas in a nationwide survey of China. Grapes from special regions, such as heavy metal polluted areas (e-waste dismantling area) and pesticide free areas (courtyard) were also collected to make a comparison. Grape skins and pulps were separated to evaluate influence of accumulation behavior, environmental transport and water cleaning efficiency to heavy metals. Levels of copper in grape skins (5.02 ± 3.18 μg/g) were higher than in pulps (3.74 ± 1.48 μg/g). Only high level of copper in two grape skins (sampled from an e-waste dismantling area) showed obvious decrease during water clean-up procedure, indicating the influence of air deposition. Statistical analysis showed no significant difference in the copper levels of grapes from markets, courtyards and e-waste dismantling areas. Concentrations and sources of chromium (Cr), manganese (Mn), nickel (Ni), cadmium (Cd), lead (Pb) and arsenic (As) were also analyzed. Higher levels of these heavy metals were observed in grape skins than pulps. Finally, we evaluated the risk of ingesting heavy metal through grapes using the estimated daily intake (EDI). No health risk was found by consuming grapes according to the data from this study.In this study, grapes ( Vitis vinifera L.) is one of the most widely consumed fruits around the world. It is not only a popular table fruit, but is also the raw material to produce wine worldwide. Recent studies have suggested that the good cardiac health condition of inhabitants around the Mediterranean area was related to rich content of phenolic compounds in grapes from their diet2, which might further promote grape consumption. As known, viticulture will be abundantly applied with copper-based pesticide, because this kind of plants is vulnerable to fungal diseases (e.g. downy mildew). In order to control downy mildew, about 400–800 kg per hectare of Bordeaux mixture  was sprayed several times during the whole grape growing season3. During the process of which, the sprayed copper pesticide can be deposited on the fruit surfaces and might find its way into the fruit tissues. Soil in vineyards was also considered to be a seriously copper polluted area due to year by year application of Bordeaux mixture7. The copper could also be able to transport to grapes through root adsorption. To limit copper concentration, the dose of copper in vineyard soil is limited to 8 kg Cu per hectare in vine cultivation8.Grape 22, and some of them discussed copper distributions in underground and aerial parts of the plants23, but few of them discussed the influence of pesticides application or other potential factors  on grapes. In this study, we not only sampled grapes from the main grape-producing areas, but also from e-waste dismantling areas with environmental heavy metal pollution and from home courtyard with pesticide-free situation. These sampling areas allow us to compare and analyze copper and other heavy metals in grapes across Chinese market.Normally copper is nontoxic to mammals25. It has been demonstrated that excessive intake of heavy metals could result in damage to organs and cause numerous diseases, which were characterized by osteomalacia, renal and nerve dysfunction, cardiovascular problems and so on26. The main exposure routes to heavy metals for humans are through dietary. Therefore, the tolerate daily intake of these elements in food were strictly restricted by authorities and countries28. European Commission (EC) have set up regulations to control lead (Pb) and cadmium (Cd) in foodstuffs29, and World Health Organization (WHO) also assessed heavy metal toxicity and recommended ingestion ranges to certain amounts30. As an important part of dietary, it is necessary to evaluate, monitor and control heavy metals in fruits.Some of the heavy metals are well known for their toxicity, non-biodegradable nature and potential bioaccumulative behavior in human tissuesCopper-based pesticide is intensively used in viticulture, but few studies have evaluated heavy metals in grapes with regard to the large-scale application of Cu-based pesticide. In this study, grapes from markets, home yards and e-waste dismantling areas were collected. The major objectives of this article were to assess the levels of copper in grapes across Chinese market and discuss the influential factors. Moreover, six more important heavy metals (chromium (Cr), manganese (Mn), nickel (Ni), metalloid arsenic (As), Cd and Pb were simultaneously analyzed. Grapes were separated into skins and pulps for analysis respectively. Factors such as pesticide application, soil pollution and water clean-up process were taken into consideration to trace the sources and fates of heavy metals in grapes. Further, the potential health risk of ingesting heavy metals by consuming grapes was also evaluated.In this study, contents of heavy metals in samples were expressed on dry weight base. The moistures of grape skins and pulps were in the range of 74.2–88.9% and 78.4–92.3%, and the geometric means were 82.8% and 85.2% respectively. The concentrations of copper in grape skins and pulps were in the range of 1.83–20.1 μg/g dw and 1.27–7.74 μg/g dw, with average mean values of 5.22 μg/g dw and 3.73 μg/g dw, respectively.Seven grape samples were collected from Taizhou. The mean values of copper in these 7 grape skins and pulps were 5.30 μg/g and 3.10 μg/g respectively, which were in the same level to the average of all samples. Four home grown grapes (pesticide free) showed mean concentrations of copper in skin and pulp to be 5.34 μg/g and 3.21 μg/g respectively, which also were comparable to the mean values obtained from all grape samples.The water clean-up process was conducted. The differences of copper levels in paired unwashed and washed grape skins and pulps were shown in Fig. The concentrations of Cr, Mn, Ni, Cd, Pb and As in grape skins n = 41) and pulps (n = 41) from different functional zones were summarized in Table  and pulp31, nor exceeded the restriction of European Commission (20 μg/g)32. The mean value of copper concentration in pulps was higher than some of the previous studies 18. In order to evaluate the influence of copper-based pesticide on copper levels, we further summarized the copper levels in some other fruits and listed the results in Table Of all the grape samples analyzed, no sample exceeded the maximum residue limit (MRL) of copper restricted by Chinese national tolerance limit of copper in fruit (10 mg/kg)mmission 0 μg/g32.18. However, it still need more evidence to discuss the influences of the other factors beyond application of pesticides.Concentrations of copper in grape skins and pulps from markets, ewaste dismantling area and courtyards were shown in Fig. 34, which were much higher than Beijing soil (29.0 μg/g)35. It was reported that the copper concentration would be around an average of 30 μg/g in the unpolluted soils36. However, copper level in Taizhou soil was quite comparable to the copper polluted soils in vineyards37. It has been reported that the concentrations of copper in grapes would increase with the age of the vines, but even though only few copper were transported from soils to grapes according to our study. Another study supported our suggestion that the amount of copper which was transferred from soil to plant could only explain a small part of the total copper concentration in plants4. Considering the air transport pollutions, levels of copper in total suspended particulate (TSP) in e-waste dismantling area (483 ± 167 ng/m3) were several times higher than that in Beijing (110 ± 110 ng/m3) and Taiwan (198.6 ± 385.4 ng/m3)39. This TSP-copper was also able to absorb on grape skins. Whether the air deposition will bring in heavy metal pollution to grapes will be discussed later in the clean-up procedure of this study.In this study, we collected grape samples from an intensive e-waste dismantling area to find out whether the environmental contamination (soil uptake or air deposition) would bring heavy metal pollution into grapes or not. Taizhou is a notorious and intensive e-waste dismantling center which located in Southeast China. The e-waste dismantling history of local area can be traced back to 1980s, almost focusing on scrapped transformers and other electrical equipment. Due to the dismantling activities, local soil was seriously polluted by heavy metals. However, not as expected, only two grapes had higher copper levels in grapes from e-waste dismantling area. The average level of copper in all 7 grape samples from Taizhou was in the same level compared to the average level of all grapes, implying that soil from e-waste dismantling area did not bring in general copper pollution to grapes when compared to the commercial vineyard. The copper concentration in local soil was more than 100 μg/g according to previous reportFour home grown grapes (pesticide free) were collected to make a comparison to the copper-based pesticide applied grapes. None of these four home grown grapes were sprayed pesticides. Relatively higher copper levels in grape skins were found than pulps. It seems like that grape skins play a better enrichment than grape pulps. Furthermore, the similarity of copper levels in home grown grapes and e-waste dismantling area collected grapes showed that the copper levels were not closely connected to the heavy metal levels of soil. From another point of view, our study suggested that grape might not be a good copper accumulation plant, or the middle environmental levels of copper were rich enough for grapes to absorb and combine to the tissue.In order to investigate whether copper was attached to the surfaces of grape skin or combined to the tissue, we evaluated the level variation of copper before and after fresh water cleaning. 4.12–72.3% of copper (average 7.62%) was removed after cleaning for all the six paired skin samples. As can be seen from Fig. 18. The rain will also be helpful to clean copper off from grape skins. Before the grape harvest, it is the most rainy days in China, which is helpful to remove heavy metals from grapes. As a result, although the application of pesticides was overdose from time to time, the attached copper on grape surfaces was not at a high level in most of grapes. However, it should also be noticed that washing may only reduce the levels of metals in a smattering part. Once metals were introduced and distributed into tissues, they were hardly to be thoroughly removed under traditional handling techniques40. Paradelo et al. investigated the loss of copper-based pesticides during complex physical washing processes, the result of which showed that mean copper loss was 27.0% for the Bordeaux mixture, and the washing time was not quite related41.The limited changes of copper concentrations after clean-up process in other four paired grapes suggested that the withdrawal period of pesticide was long enough. Generally, in order to ensure the fruit was safe to eat, the application of Bordeaux mixture will be stopped about fifteen to twenty days before the grape was sold on market. Previous investigation also suggested that safety withdraw time for grape and grape product was more than 20 days. Normally 40 to 50 days were completely enough to limit copper concentrations below 20 μg/g even in dry year42. Wilcoxon nonparametric test for matched pairs was conducted to analyze the significant difference of metal levels between grape skins and pulps , but the level of Cd was one order of magnitude lower (0.002 ± 0.001 μg/g dw)43. Without direct pesticide spray for these heavy metals, root absorption from water/soil and air deposition might be the two main sources of heavy metals in grapes. Root absorption is closely associated with heavy metal chemical fractions, which were quite different from one heavy metal to another. Pb is less distributed in exchangeable and soluble fraction44, which makes it hard to uptake by plant. In the same time, clean-up experiment showed that Pb levels in unwashed skins were 3.0 times higher than washed skins, indicating 66.7% of Pb was removed after the washing process. Differences in Pb content of unwashed and washed grape skins were also evaluated using the paired t-test. Although no significant difference was found (p < 0.05), the value was very close to it (p = 0.06). So it is conducted that a large portion of Pb may come from air deposition.The environmental pollution in the e-waste dismantling area was also evaluated for the other heavy metals to see if it has introduced the trace elements into grapes. The levels of Cr, Ni, As, Cd and Pb in grape skins and pulps from e-waste dismantling area were very close to those collected from market and courtyard. Pb was one of the most toxic heavy metals. Comparable Pb levels were found in our study (0.31 μg/g) and another urban waste dumping area, with an average copper level of 0.4 μg/g in grapes45. Copper was an essential element which was difficult to establish regulatory guideline, because the dose-response curve of copper was found to be a U-shape15. This implied that ingestion of too little or too much copper can both cause adverse health consequences46. Based on a variety of analytical data of daily diets, the WHO suggested a dose of 0.7 mg copper for women and 0.8 mg for men per day. This value would totally meet daily requirement of humans. The upper limits of TDI for adults were set to be 10 and 12 mg/day for woman and man respectively30. No grape samples exceeded the limitation in our study.In order to evaluate the intake of heavy metals, we introduced estimated daily intakes (EDI) to evaluate heavy metal ingestion in this study. EDIs of heavy metals were calculated by using the average levels of grape pulps and skins. The EDI equals to the metal concentrations in fruit multiplying by a man’s daily intake (presumed to be 500 g). In our study, the daily intake of copper through grapes was in the range of 0.11–0.96 mg, and the mean value was 0.28 mg. This result was lower compared to the WHO guideline, but was higher than an investigation of copper ingestion from fruit by Chinese people−1 kg−1 bw respectively. Even the maximum daily intakes of Pb, Cd and As were under the TDIs  set by the Food and Agriculture Organization/World Health Organization (FAO/WHO)27. Except one pulp sample with a concentration of Pb reaching 0.37 μg/g dw, all the other pulp samples were below the standards restricting the MRLs to 0.1 μg/g (Pb) and 0.05 μg/g (Cd) in China standard28 and European regulations29. Excessive ingestion of As is associated with skin and internal cancers, as well as various noncarcinogenic effects47. Therefore, the As exposure, which was generally through human diet, caused great public attention and need continuous monitoring. The digestion of As through grapes in the present study were two orders of magnitude lower than the levels of FAO/WHO limit. The results implied that the consumptions of these grapes did not pose a health risk to the consumers, but the contributions from total daily dietary consumption should not be ignored.The EDIs of toxic metals, such as Pb, Cd and As, were 0.44, 0.06 and 0.33 μg day48, the major grape producing areas did not change a lot over the past decades49. Bordeaux mixture is currently employed as a major pesticide for grapes in China for its good efficacy and cost-effectiveness50. Heavy metals are known unable to degrade as fundamental elements. Therefore, it indicated that the grape samples collected in the present study were still meaningful nowadays.Forty grape samples  were collected from 11 provinces in China Fig.  during 251:MC represents the moisture content, WW and DW were the wet weight and dry weight of samples.The samples were carefully stored and kept in a cooling box packed with ice. The box and its contents were then transported to the laboratory quickly. When the samples arrived, they were manually separated into skins and pulps immediately and stored in a refrigerator under −20 °C for 24 hours. The samples were later lyophilized at −56 °C under a pressure of 0.05 bar. The dry samples were then grinded to fine powders and sealed in polyethylene bags. Both the wet and dry weights of skins and pulps were recorded and the moisture contents were calculated for skin and pulp samples. The moisture content was defined by the following equationApproximately 0.2 g dry samples were weighed and carefully transferred to a polytetrafluoroethylene digestion container. Nitric acid  followed by 30% hydrogen peroxide  were added into the polytetrafluoroethylene container and the mixture was predigested for 3 hours at 60 °C. The mixture was then further digested using a microwave , with the following program: ramped at 160 °C in 10 min and held for 30 min, then raised to 180 °C in 10 min and held for 30 min. The resultant solutions were then diluted with Milli-Q water to approximate 40 g. The dilution factor could be calculated by the sample weight and the final dilution weight.45.The standard solutions were prepared from 100 mg/L stock  in 2% nitric acid. The samples were then analyzed by an inductively coupled plasma mass spectrometry . Determinations were performed in triplicate by following previously established methodsInternal calibration method was used to qualify and quantify real samples. The ranges of our linear standard curves were between 0.5 and 100 ng/mL for all the metals. 94 samples were determined in three batches. In each batch, two blank samples, two standard reference materials (SRM) and 31–32 samples were analyzed. All the 6 blank samples in 3 batches were under detection limits. Two SRMs including peach leaves (GBW08501) and tea leaves (GBW08513) were analyzed to guarantee the analytical procedures. The results showed a good agreement to the certificate values. Except Pb was 70.2 ± 5.1%, the recoveries of Cu, Mn, Cr, Ni and As were in the range of 79.0–83.4%, 96.8–102%, 81.2–89.4%, 79.9–85.7% and 97.0–119%. During the ICP-MS analysis procedure, the analytical accuracy was also evaluated by analyzing 10 ppb standard solution for every 20 samples. The mean value and relative standard deviation (RSD) of copper were 9.93 ng/g and 2.06%, and the RSD of other heavy metals were in the range of 0.9–6.32%.Statistical analysis was conducted to analyze the distribution and correlation of the studied parameters. The concentration relationships among the detectable heavy metals in grape skins and pulps were analyzed using the nonparametric Wilcoxon test. Paired t-test was conducted to see if there was significant difference between unwashed and washed skins. SPSS 17.0 for Windows  was performed and all the significance level for all the tests was set to p < 0.05 unless otherwise mentioned.Concentrations of copper in fruits around the world.


Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with the real data. A recent trend in the investigation of ancestral evolutionary processes of modern humans is the application of genetic gradients as a measure of fitting, since evolutionary processes such as range expansions, range contractions, and population admixture (among others) can lead to different genetic gradients. In addition, this strategy allows the analysis of the genetic causes of the observed genetic gradients. Here, we review recent findings on the selection among alternative scenarios of human evolution based on simulated genetic gradients, including pros and cons. First, we describe common methodologies to simulate genetic gradients and apply them to select among alternative scenarios of human evolution. Next, we review previous studies on the influence of range expansions, population admixture, last glacial period, and migration with long-distance dispersal on genetic gradients for some regions of the world. Finally, we discuss this analytical approach, including technical limitations, required improvements, and advice. Although here we focus on human evolution, this approach could be extended to study other species. Ind surfing ,17, wher surfing  recentlySince recent studies showed that genetic gradients can vary with different evolutionary processes, one can perform a selection among alternative evolutionary scenarios with data simulated under each scenario, followed by a fitting between simulated and real data based on genetic gradients. This strategy is not new in population genetics; for instance, the approximate Bayesian computation (ABC) approach ,20 is frHere, we review the application of genetic gradients simulated under spatially-explicit computer simulations to distinguish between alternative evolutionary scenarios of modern humans by their fitting with real genetic gradients. First, we present the commonly-used methodologies to perform this selection among alternative scenarios, including the simulation of genetic data and estimation of genetic gradients. Next, we describe previous studies applying this approach to investigate the influence of human range expansions, range contractions followed by range re-expansions , population admixture and migration with long-distance dispersal, among others, on genetic gradients of some regions of the world, and to perform a selection among alternative evolutionary scenarios. Finally, we discuss advantages and limitations of those studies, and we provide recommendations based on our experience.The simulation of genetic gradients usually consists of two main steps, namely: the simulation of genetic data under a given evolutionary scenario, and the estimation of the genetic gradient from the simulated data. Next, we describe the most frequently-used methodologies to perform both steps.i) simulation of the evolutionary history of a sample, and (ii) simulation of genetic data upon a given evolutionary history.A variety of approaches exist to simulate genetic data in population genetics, and they can be roughly classified in two types concerning the kind of simulation: , it is possible to simulate molecular evolution upon such evolutionary history to obtain genetic sequences for all the internal and tip nodes  ,52,53. TKERNELPOP [IBDSim [CDMetaPOP [SPLATCHE [SPLATCHE2 [It is known that the consideration of a 2-dimensional (2D) landscape with its particular geography may result in simulations which are more realistic than those obtained with models of a lower number of dimensions ,55. ThisERNELPOP , IBDSim  [IBDSim  and CDMeDMetaPOP ) have noDMetaPOP ,51 for cSPLATCHE  and its PLATCHE2  have beeSPLATCHE2 require a 2D landscape/map, which, for various regions of the world, can be imported from a Geographical Information System (GIS) [SPLATCHE2 simulates samples of genetic data by three main steps (i) A forward-in-time simulation of the evolutionary history of the entire population accounting for spatial and demographic information (ii) the application of the coalescent to reconstruct the evolutionary history of a sample (which is embedded in the history of the entire population) (iii) a simulation of molecular (sequence) evolution over the evolutionary history of the sample to obtain genetic data for the sample , and short tandem repeat (STR).Spatially-explicit computer simulations with em (GIS) . This main steps : (i) A formation A. Here, ulation) B and, . PCA identifies orthogonal axes  where objects show the highest variance of the information present in the original data. In population genetics, PCs provide an acceptable approximation of the covariance pattern among individuals of a given dataset . They wes et al.  and Brans et al. , which pRecently, more complex methods have been developed to estimate genetic gradients and population structures for a given landscape ,70,71. TThe European settlement by Paleolithic and Neolithic populations has been generally proposed with little admixture, yet studies still disagree. The estimated level of admixture varied with the applied genetic marker and the type of analyses performed, with Neolithic contributions below 25% , near 50The genetic gradient in the Americas based on real data follows the direction of the expansion (NW-SE) from Bering ,7,80. A i) absence of the last glacial period, (ii) presence of the last glacial period through the modeling of a range contraction towards all Southern Europe, followed by a period of time at refugia in all Southern Europe and a posterior re-expansion to recolonize the north and, (iii) presence of the last glacial period through the modeling of a range contraction towards only the Iberian Peninsula, followed by a period of time at refugia in only the Iberian Peninsula and a posterior re-expansion to recolonize the north (ii) and (iii) present a different direction of range re-expansion: a re-expansion with direction S-N in (ii) and a re-expansion with direction SW-NE in (iii). The range contractions were simulated by a series of progressive contraction events during which demes located in the most northern areas became uninhabitable by setting its carrying capacity to zero [i) a scenario based on a large Paleolithic ancestry (>95%) and absence of any range contraction, and (ii) a scenario with some Paleolithic ancestry that considered a range contraction towards the Iberian Peninsula caused by the last ice period. In contrast, pure Neolithic expansions (without admixture and without genetic signatures from the last ice period) produced genetic gradients that did not fit with the genetic gradients estimated from real data.A factor that has been frequently ignored in interpretations of the SE-NW European genetic gradient is the last ice age that occurred at 29–13 kya . During he north . Note th to zero ,86. In a to zero . They fo to zero  found twBranco et al.  studied In addition to population admixture and the last glacial period, some other processes were investigated for testing their influence on genetic gradients. In this Section, we also briefly present the application of PC2 and PC3 to identify genetically isolated regions.François et al.  investigArenas et al.  investigSome studies suggested that the expansion of modern humans throughout the world could present LDD events, for example traveling by boats . ActuallThe first few PCs from a PCA are often used to explore the structure and variance of the data. In the analysis of a genetic sample, the first PC (PC1) map provides a spatial genetic gradient and the following PC maps  indicates genetically isolated regions. PC2 and PC3 maps were estimated in analyses of European populations ,14 to hiComparisons between simulated and real genetic gradients showed that spatially-explicit computer simulations provide good approximations of real processes and can be used to perform selection among alternative evolutionary scenarios. However, so far all the studies testing alternative scenarios of human evolution through simulated genetic gradients only performed qualitative comparisons with real genetic gradients ,14,18,78Another important aspect in this strategy, as in any analytical strategy based on computer simulations, is that the computer simulations should be as realistic as possible.The studies discussed in previous sections analyzed genetic gradients of modern humans, ignoring some geographical barriers such as rivers and mountain ranges. We believe that these assumptions may not cause relevant biases when investigating large world regions (as done in such studies), but they could be crucial when investigating small regions. Moreover, the simulations should consider not only the current geographic landscape, but also its evolution from the beginning of the simulated evolutionary history . Of couAnother way to generate more realistic computer simulations is by improving the modeling of human evolution. The aforementioned studies performed computer simulations based on evolutionary parameters  estimated in previous works. However, the real processes were probably more complex, presenting multiple expansion waves and admixtures , and with the application of robust statistical methods for quantitatively evaluating the fitting between simulated and real genetic gradients. We believe that the application of genetic gradients for testing among alternative scenarios will increase in interest and use in the coming years.