Tropical mountains hold more biodiversity than their temperate counterparts, and this disparity is often associated with the latitudinal climatic gradient. However, distinguishing the impact of latitude versus the background effects of species history and traits is challenging due to the evolutionary distance between tropical and temperate assemblages. Here, we test whether microevolutionary processes are linked to environmental variation across a sharp latitudinal transition in 21 montane birds of the southern Atlantic Forest in Brazil. We find that effective dispersal within populations in the tropical mountains is lower and genomic differentiation is better predicted by the current environmental complexity of the region than within the subtropical populations. The concordant response of multiple co-occurring populations is consistent with spatial climatic variability as a major process driving population differentiation. Our results provide evidence for how a narrow latitudinal gradient can shape microevolutionary processes and contribute to broader scale biodiversity patterns. There are many hypotheses for why the tropics are more biodiverse than higher latitudes. Phylogenomic analyses of 21 montane birds finds that tropical birds disperse less and have more genetically structured populations than their counterparts at higher latitudes, possibly due to a larger elevational climate gradient in the tropics While the overall mechanisms causing this pattern are still debated3, in mountains, the latitudinal disparity in species richness is tightly coupled to the interplay between species ecologies and climate4. Montane taxa that inhabit temperate environments tend to have broader thermal tolerance and higher dispersal capacities6. In contrast, tropical montane taxa are subjected to strong elevational zonation, having narrow thermal niches, increased habitat specialization, and consequently limited dispersal8. These differences should facilitate geographic isolation, differentiation, and promote higher species accumulation in tropical mountains4. The link between seasonal climatic variation, thermal stratification, and dispersal limitation across mountains was initially made by Janzen7, who proposed that “mountain passes are higher in the tropics” after observing that elevational gradients have greater thermal overlap in temperate regions. Numerous studies support this mechanism, suggesting higher levels of genetic differentiation10, reduced niche breadths13, and higher species turnover across elevation gradients in tropical mountains14. This substantial support was borne out with comparisons of regions at very different latitudes and highly divergent taxa , whereas continuous systems where species are distributed across tropical and subtropical climates are far less explored. It remains unclear at what point in the diversification process the major assumptions associated with Janzen’s hypothesis start manifesting, in particular, whether these predictions are observable between populations within species. Determining how the initial stages of diversification contribute to the latitudinal diversity gradient in montane regions could be critical to understanding how global patterns of diversity are generated and maintained over deeper scales.The tropics harbor more species than temperate regions15, occur in habitats that were drastically affected by historical climatic oscillations16, and have a distinct evolutionary origin, belonging to clades that have been more successful at dispersing than tropical lineages17. By focusing on a shallow temporal scale it is possible to explicitly test Janzen’s mechanism while controlling for these confounding effects of history. If the predictions of Janzen’s hypothesis are supported within species, populations should progressively occupy wider elevational ranges and broader environmental space as latitude increases4. This pattern should lead to more interconnected populations in higher latitudes when compared to the populations in the tropics that are predicted to occur in narrower elevational bands. A key assumption of this model is that regional differences in climate directly affect elevational distributions. Although topography might differ between sampled localities, which could affect the elevational range of species, populations of higher latitudes should progressively occur at lower elevations and occupy a larger proportion of the elevations available in a given region. The evolutionary consequence of these ecological conditions would be that low elevation, high latitude individuals would be more genetically similar than those with restricted, high elevation distributions at lower latitudes. For instance, we expect higher rates of gene flow and larger effective population sizes for the low elevation, high latitude populations. Alternatively, if population differentiation is not associated with latitudinal climatic variability, differences between tropical and temperate montane lineages may only be observable at macroevolutionary scales where taxa occur in contrasting latitudes and have substantially diverged in their traits and histories.Temperate versus tropical comparisons are usually discrete observations that reflect the extremes of a continuum. A sampling at this scale makes it challenging to isolate the potential effects of climate variation on the latitudinal diversity gradient. In general, temperate species are youngerHere, we test whether intraspecific genetic structuring varies across a narrow latitudinal transition, and explore how current and historical environmental variation shapes microevolutionary processes across the gradient. We examine a community of montane birds distributed across the southern Atlantic Forest sky islands in Brazil, a region characterized by two main mountain massifs, the Serra da Mantiqueira and the northern portion of the Serra do Mar, and the Serra Geral and southern portion of the Serra do Mar. These two mountain regions are separated by a low elevation gap of ~250 km wide, overlapping with the formal geographic delimiter of the southern tropical and subtropical regions, the Tropic of Capricorn Fig. . First, This study provides a community-wide assessment of how climatic variability affects population differentiation and habitat occupation across a continuous latitudinal gradient. Our approach allows us to isolate the effects of historical and current habitat configuration and species biology on the genetic diversity of a heterogeneous group of birds to suggest that ecological constraints in the tropics cause greater population differentiation at the onset of speciation.Araucaria (pine), cloud and montane ombrophilous forests above 900 m, while in the subtropical region species are typically found in a multitude of environments, including the relatively low elevation  semi-deciduous forests in the interior of the continent and the lowland habitats in the southernmost part of their distribution. Variation in the elevational distributions of species across the latitudinal gradients is predicted to be due to climate variability under Janzen’s hypothesis. However, in the Atlantic Forest mountains, it could also be caused by subtropical mountains having lower elevational profiles , indicating that species could occur at higher elevations if they were available, the lower elevation limit was predicted by temperature seasonality . Wider elevational ranges towards the more thermally seasonal latitudes might affect how individuals disperse across topographically complex terrains, affecting their level of differentiation18.We found that annual temperature variation progressively increases with latitude across the southern Atlantic Forest, leading to a greater overlap of annual temperature ranges across elevation at higher latitudes Fig. . The stules Fig. . We builN = 227 individuals) and generated species distribution models (SDMs) for current and past climates. To test the effect of geographic distance (Euclidian), environmental resistance, seasonality, and historical environmental resistance on genetic differentiation, we used mixed-effect models as implemented in ResistanceGA19. The most frequent best-fit model AIC weight >0.999 in ten species) included seasonality, environmental resistance, and historical environmental resistance  had a strong effect on the occupied environments and individuals’ connectivity. This pattern leads to contrasting levels of genetic differentiation between populations occurring in mountains with distinct climates. We genotyped thousands of SNPs using ddRAD for 21 species , habitat preference (the major environments occupied by the species22), and the minimum time that the species occurred in both regions (represented by the divergence time between populations obtained with our demographic parameter estimation approach) as covariables. For the tropical mountains, the best-fit model contained environmental resistance as a significant predictor of the genetic distance between individuals . In all tested cases, divergence time, Kipp’s Index, and occupied environment were not significant variables  in each mountain region per species. Under both models, our results support a significantly lower effective dispersal for the populations in the tropical mountains  than for populations of the subtropical mountains  approach that allowed us to simulate data under distinct migration regimes. In the first model, migrations were scaled by the linear geographic distance between sampled localities (IBD-BSSM), while in the second model, a single value was assigned to all migration parameters (Island-BSSM). We then used a neural network algorithm to explicitly estimate the effective dispersal  models , our results remained consistent among species and between independent approaches. We analyzed our dataset using three alternative methods by (1) looking at individual species considering their whole distribution within the Atlantic Forest, (2) combining all the species in a single analysis testing environmental predictors for tropical and subtropical regions, and (3) developing a coalescent-based method that allowed us to explicitly estimate the effective dispersal of individuals between sampled localities. By analyzing individual taxa we were able to explore the variation in the main predictors among species, and by combining many species in the same analyses we reduced the effect of low sampling for individual taxa, capturing the major process driving community-wide genetic differentiation across a latitudinal gradient. In addition, our coalescent-based method was an accurate way to assess how effective dispersal varies between populations using a reduced number of samples.Analyzing localities at different geographic scales or with uneven sample sizes could lead to biased results. For example, sampling at a closer geographic distance within the subtropical region than the tropical region could artificially produce lower estimates of genetic differentiation in the former. However, we used a similar number of samples in both regions Fig. . Additio30. Although we cannot rule out that Atlantic Forest birds are locally adapted to distinct climates, our results likely suggest that phenotypic plasticity and elevational compensation following climatic variation can lead to similar patterns to the ones observed in studies comparing highly divergent organisms with contrasting thermal tolerances10. Biotic interactions could also limit the elevational distributions of montane species31. Little is known about interactions between species of birds in the Atlantic Forest32, but interspecific aggression is a pervasive behavior in birds33, and competitive interaction likely drive elevational divergence34. Testing for both, spatial variation in physiological traits and biotic interactions could produce stronger support for the effects of climate variability driving local patterns of gene flow. However, the presence of a strong climatic gradient and the concordant response of multiple co-occurring populations are consistent with spatial climatic variability as a major factor driving greater population differentiation in tropical than subtropical populations (but see ref. 35), confirming Janzen’s predictions within species.Species distributed across latitudinal gradients often display variation in physiological traits, with higher-latitude populations being more plastic and having wider thermal tolerances8, which runs counter to the emerging idea that diversification was faster in temperate regions38. However, we did not directly estimate rates of differentiation, and not accounting for time in the landscape, beyond population split times, could bias interpretations. At broader geographic scales, the degree of phylogeographic structuring in birds was previously shown to be best predicted by species age15, which applied to this system could indicate tropical populations have greater structure, in part, because they have been in lower latitudes longer. Despite the possibility of differential time in the landscape, genetic differentiation within the Atlantic Forest tropical mountains was likely formed after the divergence between most tropical and subtropical populations, following the upslope movement of montane environments associated with past climatic cycles39. Future studies that model the deeper biogeographic history of these montane taxa will be able to clarify the role of time-dependency in interpreting rates of genetic structuring across the latitudinal gradient.Our results show higher differentiation in tropical mountain populations, consistent with the view that the historical stability and strong elevational zonation promotes higher rates of geographic isolation and subsequent speciation40, such as ecological competition41. Recent work has supported an association between differentiation and species formation, with higher predictability of population structuring and speciation in the tropics42, indicating a link between micro and macroevolution to some degree. Regardless of the evolutionary outcomes of this genetic structuring, our results suggest that mountains of southeastern Brazil, a small part of the biodiverse rich Atlantic Forest, have an ideal geographic configuration to explore the effects of climate in population differentiation, and harbor multiple isolated populations, some of which may be cryptic species awaiting discovery and protection.The contribution of the microevolutionary processes reported in this study to macroevolutionary patterns is unclear, but our results show a substantial disparity in the initial stages of speciation between tropical and subtropical regions. The greater differentiation within tropical populations might not lead to higher species formation, which could be limited by time and diversity-dependent processesPstl and Mspl) to produce a reduced genome library that was sequenced in an Illumina platform by the University of Wisconsin Biotechnology Center . Sample demultiplexing, de novo assembly, and SNP calling for each species was performed in ipyrad v0.9.1843, using the same set of parameters for all species. To minimize biases that can arise in comparative RADseq datasets due to variation in genetic diversity among species44, we used relatively loose filtering for the clustering threshold (0.90 clust_threshold). This setting allowed for a 10% sequence divergence between reads, which reduces the impact of allele dropout on species with more genetic diversity. Additional post-sequencing filters were not implemented because they have marginal impacts on estimates of genetic differentiation, admixture, and demographic parameters45. Only loci with less than 25% missing information were retained. To reduce the possibility of linked SNPs, one variant per locus was randomly selected for downstream analyses. A detailed description of our bioinformatics pipeline is available in the “A total of 227 tissue samples from 21 species were obtained in the field or requested from natural history collections . ConStruct simultaneously estimates continuous and discrete patterns of population structure by assuming a rate of decay in the relatedness among individuals as a function of the geographic distance. Thus, discrete groups are only assumed when genetic variation significantly deviates from isolation-by-distance. We used conStruct to explore the presence of genetic structure between the two main mountain blocks of the Southern Atlantic Forest by testing if a model with two layers (K) could better explain genetic variation than a model including a single layer. For each conStruct run, we performed 50,000 iterations discarding the first 50% as burn-in. We assumed layers with a relative contribution >5% to the total covariance of the model were significant. To obtain pairwise genetic distance matrices among individuals across the entire distribution of the species, we estimated Nei’s genetic distance46 with Adegenet v1.347 in R v3.6 using SNP matrices.We tested for the number of ancestral populations (K) for each species complex and clustered individuals to populations, controlling for the effects of geographic distance, using conStruct24, which uses the site frequency spectrum (SFSs) as a summary statistic to approximate the likelihood of a demographic model given the data. We used easySFS v1.0  to estimate the projection of SNPs that maximize the number of segregating sites in the Joint SFSs (considering two populations) for each species. To estimate demographic parameters, we built a model with two populations  allowing for an instantaneous demographic size change after divergence (expansion or contraction) in each population, and constant gene flow between populations. Search intervals for the approximate likelihood computations are available in Supplementary Data ST between populations, we used PipeMaster v0.0.948 in R. Species with less than three individuals per population were excluded from the analyses.To estimate historical demographic parameters, we used Fastsimcoal v2.649 with a spatial resolution of 30 s. Current and past habitat suitability was estimated in Maxent v3.4.150 . After filtering, our final dataset had 13,986 occurrence records  in nlme v3.153 and corMLPE v0.0.2 (https://github.com/nspope/corMLPE) in R. This approach fits a mixed-effect model with a correlation structure designed to account for the nonindependence of pairwise distance matrices, treating the residuals of the pairwise comparisons as the sum of two random populational level effects53. We normalized the response and predictor variables, genetic distance, and environmental distances, respectively. To test tropical and subtropical mountains’ specific drivers of genetic differentiation, we fitted a model including all individuals for all species as well as models for each mountain region independently. We performed model ranking using AIC weights. A detailed description of our landscape genetics approach is available in the “Based on the environmental analyses, our approach produced four variables that capture different mechanisms potentially affecting individuals’ connectivity, namely the geographic distance, environmental resistance, temperature seasonality resistance, and historical environmental resistance. The environmental resistance was derived from the current climate projections and reflects the degree of resistance for individuals to move in space according to the suitability values of SDMs. The seasonality resistance was derived from the fourth bioclimatic variable of the CHELSA database . In this variable, higher values of seasonality are translated into less resistance among individuals. Lastly, the historical environmental resistance matrix was generated by averaging the binary presence-absence layers of each projected time slice: (1) Pleistocene: Last Interglacial (ca. 130 ka), (2) Pleistocene: Last Glacial Maximum (ca. 21 ka), (3) mid-Holocene: Northgrippian (8.326–4.2 ka), and (4) late-Holocene: Meghalayan (4.2–0.3 ka). Landscape resistance for each variable was obtained by calculating the cost distance along the least-cost path between samples using gdistance v1.3. We assessed the individual and the combined effects of landscape resistance layers on genetic distance using ResistanceGA v4.054: number of segregating sites, nucleotide diversity, pairwise FST, pairwise percentage of shared polymorphism, fixed polymorphism and private polymorphism, and Tajima’s D. We ensured that the simulated models fitted the observed data by performing a PCA and plotting the first ten PCs of simulated statistics vs observed , these were used to train a neural network (NN) regression model, using the R v3.6 interface to Keras v2.3 (https://github.com/rstudio/keras), to estimate the two main parameters of interest: Ne and De. We simulated an additional 1000 datasets for testing the model. We built a NN with two hidden layers with 32 nodes each and an output layer with a single node, all with a relu activation. We used the rmsprop as an optimizer and a Mean Absolute Error (MAE) for loss and accuracy metrics. For each parameter, we trained the NN for 1000 epochs with a validation split of 0.1. For additional evaluation metrics, we plotted and calculated the correlation coefficient of true versus estimated values for the testing data. We performed four replicates of the NN regression and used the median as the final value. We also calculated the standard deviation to evaluate variation among replicates.To simulate the effect of distance we built for the tropical and subtropical populations of each species, a bidimensional stepping stone model (BSSM) where each locality represented a population with assigned effective population size (Ne). Populations were connected through migration (Nm) to two of their nearest neighbors according to the linear geographic distance among them. We only included species with at least three sampled localities. We assigned the same Ne to all localities, and values were sampled from a uniform distribution . The amount of migration among localities varied. We built two versions of the BSSM which parameterized migration in different ways. In the first model, migrations were scaled by linear geographic distance (IBD-BSSM). To do so, we included an effective dispersal parameter (De), which represents the movement of alleles in space per generation. To calculate the number of migrants, we divided De by the linear distance. For instance, if De = 50 Km and the linear distance between two localities, Ne1 and Ne2, is 100 Km, there will be 0.5 Nm per generation for each migration direction (i.e. Nm1 < 2 and Nm2 < 1). The De values were sampled from a uniform prior distribution ranging from 1–200 Km. It is unrealistic to expect a perfect correlation between migration and linear distance, thus we added a noise parameter, represented by a percentage sampled from a uniform distribution (0–1). The noise was generated by randomly making the scaled migrations higher or lower by that sampled percentage. In the second model (Island-BSSM), a single Nm value was sampled from a uniform distribution (0.25–5) and assigned to all migrations between localities. Each Nm varied according to the sampled noise parameter as above. In this case, De was conditional on the sampled Nm and was calculated by multiplying the average linear distance by Nm. For each region, tropical and subtropical, and each species, we generated the two models and sampled parameters using R 3.6.3 . We then used the sampled parameters to simulate the following summary statistics using msABC56 in R. We ran 12 independent univariate analyses varying the response variables with mountain region as the predictor variable  models using caper v1.0.1Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Arthrobotrys oligospora. However, their large-scale geographic structure, patterns of gene flow, their potential phenotypic diversification, and host specialization remain largely unknown. In this study, we developed a new panel of 20 polymorphic short tandem repeat (STR) markers and analyzed 239 isolates of A. oligospora from 19 geographic populations in China. In addition, DNA sequences at six nuclear gene loci and strain mating types (MAT) were obtained for these strains. Our analyses suggest historical divergence within the A. oligospora population in China. The genetically differentiated populations also showed phenotypic differences that may be related to their ecological adaptations. Interestingly, our analyses identified evidence for recent dispersion and hybridization among the historically subdivided geographic populations in nature. Together, our results indicate a changing population structure of A. oligospora in China and that care must be taken in selecting the appropriate strains as biocontrol agents that can effectively reproduce in agriculture soil while maintaining their nematode-trapping ability.Maintaining the effects of nematode-trapping fungi (NTF) agents in order to control plant-parasitic nematodes (PPNs) in different ecological environments has been a major challenge in biological control applications. To achieve such an objective, it is important to understand how populations of the biocontrol agent NTF are geographically and ecologically structured. A previous study reported evidence for ecological adaptation in the model NTF species  Meloidogyne, are widespread pests that cause crop yield losses worth more than US $157 billion worldwide each year [Meloidogyne spp. has heavily relied on chemical nematicides. However, resistance to chemical nematicides has emerged, and the environmental and human health impacts of nematicide residues are becoming increasingly recognized [Plant-parasitic nematodes (PPNs), especially the root-knot nematodes in the genus cognized . Therefocognized .A. oligospora has generally been considered to be the most common nematode predator in temperate soils [A. oligospora naturally encounters a broad range of nematodes and behaves as a generalist predator with the characteristic ability of forming adhesive trapping nets when its mycelia are in contact with nematodes. Unlike endoparasitic fungi, NTFs are able to grow saprophytically. However, how they maintain and balance their saprophytic and predatory lifestyles are not known. In addition, how geographic populations interact with each other and how ecological factors impact population dynamics remain poorly understood. Such knowledge is important not only for understanding the diversity and evolution of these fungi but also for identifying the most appropriate isolate(s) for commercial biological-control applications [At present, about 700 fungal species are known to be capable of attacking living nematodes . These fungi are taxonomically diverse but traditionally divided into five groups based on the mechanisms by which they attack nematodes: (i) nematode-trapping fungi (NTF), (ii) endoparasitic fungi, (iii) egg- and cyst-parasitic fungi, (iv) toxin-producing fungi, and (v) fungi with special nematode-attacking devices . Among tte soils . In soilications ,6.A. oligospora strains from several geographic locations and ecological niches in China using DNA sequences at three gene fragments: its , tub (β-tubulin), and rpb2 (RNA polymerase II subunit). That study identified a large number of unique alleles and genotypes, as well as their limited geographic distributions, consistent with a large effective population size of A. oligospora and its potential for genetic differentiation among geographic populations, likely driven by ecological adaptation [A. oligospora phenotypic variation in the response to nematodes was assessed by analyzing the genetic variation of genomic SNPs using 18 wild isolates [A. oligospora’s overall patterns of genetic variations, the relationships between genetic variations and phenotypic variations, and its mode of reproduction remain to be fully described.Most ecological studies of nematophagous fungi have been restricted to surveys on their geographical and seasonal distributions, including examining the effects of abiotic  and biotic (mainly nematode density) factors on their distributions ,13,14,15aptation . Furtheraptation . Recentlisolates . HoweverA. oligospora in China. These samples were analyzed with a panel of 20 short tandem repeat (STR or microsatellite) markers newly developed for this study. Combined with the DNA sequences of six housekeeping genes , and subtilisin-like serine protease (sp)) and mating type (MAT) genes (mat 1-1 and mat 1-2), the population structure, phylogenetic relationships, and mode of reproduction of the nematode-trapping hyphomycete A. oligospora in nature were investigated. In addition, we analyzed the patterns of dispersal and potential ecological niche adaptations of strains in China. Furthermore, representative strains from each of the robust phylogenetic lineages [A. oligospora as a biocontrol agent against PPNs in agricultural fields are discussed.For this study, we conducted a broad range of sampling on the model NTF species lineages  were useA. oligospora, including 104 previously analyzed strains [A. oligospora were based on the standard method described by Zhang et al. [Panagrellus redivius nematodes were added onto the culture plate and inoculated at room temperature. Each sample was screened three independent times, resulting in 1500 agar plates for a total of 500 soil/water samples. After one month, nematodes were examined using a dissecting microscope. Single spores were directly isolated from the mycelia that trapped nematodes and then cultivated on a CMA Petri dish at 25 °C. The taxonomic characters were examined from cultures on CMA at 7–14 days after inoculation. The initial identification of A. oligospora was based on species-specific microscopic and macroscopic colony morphological features. The sizes of conidia and conidiophores were observed using an Olympus BX51 microscope .This study analyzed a total of 239 strains of  strains ,31 and 1A. oligospora strain ATCC24927 (GenBank accession ADOT00000000), using the MISA software (Available online: https://webblast.ipk-gatersleben.de/misa/ (accessed on 12 April 2021)). From the candidate STR markers in the genome, we selected three di-, tri-, and tetranucleotide repeats based on loci with the highest repeat numbers and an interruption (max difference: 2 SSRs) of 100 bp within the boundaries of potential PCR primer regions [http://pgrc.ipk-gatersleben.de/misa/primer3.html (accessed on 13 April 2021)), and primers with lengths of 20–23 bp, a Tm value about 60 °C, and product sizes in the 150–300 bp range were selected for further screening. For selected primer pairs, fluorescent tags were added to the forward primers (http://www.appliedbiosys-tems.com/Genemapper (accessed on 15 June 2021)) was employed to determine the polymorphism information content (PIC) for each STR marker.STRs (or microsatellites) have been extensively used for molecular ecology and population genetic studies due to their high-level polymorphisms and high reproducibility in eukaryotic genomes ,34,35,36 regions . Primer  primers . The fluits [tub [rpb2 [tef-1), mitogen-activated protein kinase (mapk), and subtilisin-like serine protease (sp) [mat1-1 and mat1-2 were amplified using the primer pairs MAT1F (5′CCAGAGCAAAGTTCACGG3′) and MAT1R (5′GTAGGAGGTTCTAGGGCG3′), MAT2F (5′GCTTGCCTTCTGTTTACGG3′) and MAT2R (5′GTTTATTGAGAGGTGGGTC3′). The obtained its sequences were compared with known sequences of A. oligospora using BLAST to confirm their species identification first for all candidate strains [Genomic DNA was extracted from the mycelia collected from single-spore cultures growing on cellophane membrane on potato dextrose agar (PDA) according to the modified CTAB method . Fragmenits , tub [40ub [rpb2 , translaase (sp) . Similar strains .2SO4; 20 mM MgSO4; 1% Triton X-100; 1 and mg/mL BSA), 1.5 mM MgCl2, 2.5 mM dNTPs, and 1.5 mM primers) under the following amplifying conditions: 1 min of initial denaturation at 94 °C, followed by 30 cycles of 1 min of denaturation at 94 °C, 1 min of primer annealing at 50 °C, 1.5 min of extension at 72 °C, and a final extension period of 10 min at 72 °C. The PCR products were visualized under UV light exposure, and the purified fragments were directly sequenced on both strands with the same primers that were used for amplification.PCR reactions were performed using 1 µL of genomic DNA template in a 50 µL PCR mixture A. oligospora isolates was performed with both selected STR loci and single nucleotide polymorphic (SNP) loci from multilocus sequence typing (MLST). Alleles at the each STR locus were combined with SNPs to generate the multilocus genotype for each strain. All genotypes were entered into Microsoft Excel to conduct population genetic analyses by GenAlEx version 6.1  [The genotyping of stralia) . For thestralia)  was usedA. oligospora is Orbilia auricolor [A. oligospora. Thus, its importance in nature remains largely unknown. In a previous study, a population in a stressful environment was shown to have significant evidence of recombination, likely due to sexual reproduction [mat1-1 and mat1-2) were analyzed from our A. oligospora strains and populations, and the following two complementary tests were conducted. First, we calculated the index of association (IA) and rBarD. The null hypothesis for IA is that there is random association  among alleles at different loci [A was standardized by the number of loci, with the rBarD algorithm adjusting for the numbers of loci. In the second test, we calculated the proportion of pairwise loci that were phylogenetically compatible (PrC). PrC is an indicator of recombination at the population level. In contrast, a lack of phylogenetic incompatibility implies asexual reproduction. Both tests were conducted using MultiLocus version 1.3  [It has been reported that the corresponding teleomorph  of uricolor . Interesuricolor . Theoreturicolor . Thus faoduction . Here, tent loci , and IA cot, UK) .In our previous study, three divergent lineages (cryptic species) were suggested for samples from China, and these were also included in this study . The comCaenorhabditis elegans employed in this study were cultured on nematode growth media (NGM) with Escherichia coli (OP50) as food source and maintained following standard procedures [The a coli OP as food 4) of strains of A. oligospora from the three different lineages were evenly spread on water agar (WA) and incubated at 28 °C for 3–4 days. About 200 nematodes were added to each WA plate to induce trap formation. Traps and captured nematodes were examined and quantified (per plate) under a microscope at 12 h intervals according to our previous observation of trap formation: the appearance of immature traps and mature ones could be found at every 12 h after nematode induction. Nematodes crawling on the wall of the plate were excluded because they were not in contact with the fungal hyphae. The experiments were performed in triplicate.To investigate their nematode-trapping abilities, freshly harvested conidia  for 15 d at 28 °C and 180 rpm. The fermentation broth was then centrifuged at 11,000 rpm for 30 min. The supernatants were collected, and their nematicidal effects were measured in 24-well microtiter plates. In each well, 1 mL of a supernatant was mixed with 5 μL of live nematodes (at 50–100 L2 nematodes per μL). At room temperature, the number of dead nematodes and the total amount were counted at 2, 4, 8, 12, 24, 48, and 72 h, respectively. A nematode was considered dead if it was stiff or bent irregularly for a long time. A PDB medium and an M9 buffer were used as controls. The mortality of nematodes was defined as the ratio of dead nematodes over the tested nematodes, and the corrected mortality rate was calculated by the ratio of tested mortality minus those of controls over “1-mortality of controls”.p values < 0.05 were considered statistically significant. All statistical analyses were conducted using IBM SPSS statistical software V22.0 .To compare the differences among strains and populations, we calculated the means and standard deviations for each treatment. A one-way analysis of variance followed by Tukey’s multiple comparison test (when necessary) were used to analyze data, and A. oligospora (ATCC 24927), we selected 20 to genotype strains of A. oligospora. Information about these 20 STR markers in our sample is shown in A. oligospora were found to have a moderate to high level of polymorphism, with 13 having high polymorphism, six having medium polymorphism, and only one having a low PIC value  to 0.0995 (mapk), with an average 0.0594 (125/2106) (p = 0.106) among these six sequenced fragments. Based on MLST, a total of 59 multilocus genotypes were found among the 239 isolates. Among these 59 multilocus genotypes, 47 were only found in one geographical population each, while the remaining 12 were shared among geographic populations. Of the 59 MLST genotypes, Genotype #23 was the most frequently found at 29.7% (71/239), and it was found in 13 of the 19 geographic populations. The second and third most frequent MLST genotypes were Genotype #7  and Genotype #1 , which were found in five and seven geographic populations, respectively. Except for populations in Zhejiang, Jilin, Kanas Lake and Urumqi Botanical Garden from Xinjiang, Guangxi, Gejiu in Yunnan, and Sichuan, private genotypes were found in 12 geographic populations (n = 10), followed by Hainan and Heijing (Yunnan) populations, both containing seven private genotypes. Among the 19 geographic populations, the diversity of multilocus genotypes ranged from 0 to 0.981. Overall, the Heijing, Yunnan population had the highest genotypic diversity (0.981), followed by Qinghai (0.956).The length of the combined sequences of the six gene was 2106 bp, with individual lengths ranging from 184 to 465 bp. Among the 2106 aligned nucleotides, 125 were polymorphic among the 239 strains of 25/2106) . Here, wulations . PopulatA. oligospora geographic populations from China, we analyzed the genetic differentiation of these isolates by using the STR and MLST types of molecular markers . After clonal correction, the sample sizes in the STR and MLST datasets were 186 and 100, respectively. The populations of Jilin and Xinjiang had only one genotype in the MLST dataset and thus could not be used for further population genetic analysis based on DNA sequences. However, based on the combined STR alleles and SNPs at all six MLST loci, 188 genotypes were found, and the combined genetic information was used for clonal correction for the following population genetic analyses.To assess the relationships among the 19 p = 0.006). The pairwise comparisons of the same dataset showed that among the 120 local population pairs, 29 pairs showed statistically significant differentiation (p < 0.05). The biggest differentiation was found between the Tibet and Hubei populations , followed by that between the Tibet and Guangxi populations  (p = 0.01) (p = 0.001), indicating a higher level of genetic differentiation among populations by the new STR markers that that by sequence variation at the house-keeping gene based MLST. This pattern was similarly observed in the pairwise comparisons of genetic variance between populations, where the STR dataset showed that 95 out of 171 local population pairs were significantly differentiated (p < 0.05), which was more than those indicated by the MLST dataset. Based on STR markers, the Sichuan population was significantly different from all other geographic populations (p < 0.05) , followed by that between Hubei and Sichuan populations  (p = 0.24)  . The Man = 0.01) . However < 0.05) . Moreove= 0.001) . However = 0.24) , which wp < 0.05); more of these pairs were found from populations from Southwest China  and Hainan Island, as compared to those from other parts of China (p = 0.16)  .A. oligospora, and most geographic populations contained alleles and genotypes of both genetic clusters. The population structure indicated by STRUCTURE software and PCoA was very similar, and both the MLST and STR datasets identified two distinct genetic clusters in all 239 A. oligospora isolates  in the Chinese population of isolates . Specifiisolates a–c. Popuisolates d,e. Howeisolates e. Intereisolates d,e were A. oligospora from 19 geographic populations in China (A. oligospora samples from different geographic populations (except for Guizhou) were widely distributed across Clade ⅠI. Interestingly, different geographical populations had different distribution patterns between the two clades: five geographic populations  were only found in Clade ⅠI, and samples from Tibet were only found in Clade I. Two populations (Hainan and Heijing from Yunnan) were widely distributed in both clades.Phylogenetic analyses based on SNPs and MLST identified two distinct clades (Ⅰ and Ⅱ) within in China , consistmapk, rpb2 and sp fragments, suggesting they belonged to populations that are diverging from the other two clades. However, the same strains had mixed relationships with others based on sequences at its, tub, and tef-1 loci. Together, these differences in phylogenetic relationships among strains by different gene fragments are consistent with recombination in nature.Significant differences in the relationships among strains among the analyzed gene fragments were identified based on the pairwise comparisons of topologies among all single gene phylogenetic trees . The resA. oligospora strains were randomly selected, and their mating type (MAT) genes were successfully amplified. Among the 197 strains, 84 and 113 belonged to mat1-1 and mat1-2, respectively, in a ratio of ~1:1.3, consistent with A. oligospora being a heterothallic fungus. Furthermore, the minimum spanning tree (MST) with the 197 strains based on the STR dataset showed that the A. oligospora strains containing mat1-1 and mat1-2 were both overall broadly distributed on the MST  and six MLST loci  in the total sample, including all 239 A. oligospora isolates. As indicated by the phylogenetic incompatibility test, variable levels of recombination were found within different sample groups. Interestingly, population groups from Southwestern China had the highest levels of phylogenetic incompatibility. It is worth noting that phylogenetic incompatibility was also identified in all samples where random recombination was rejected, indicating that low levels of recombination are common in samples of A. oligospora in China.The rBarD and phylogenetic incompatibility tests for detecting recombination were conducted for (i) the total sample, (ii) samples from each geographical sample groups by both the STR and MLST datasets, and (iii) samples representing MLST genotypes in each of the phylogenetic clades ; the allA. oligospora—including 30, 6, and 2 strains from the divergent Clades A, B, and C identified in our previous study [For conidial morphology analyses, 38 strains of p value of greater than 0.05 was obtained, suggesting there was no significant difference in the total variance among the three clusters. Second, an analysis of variance was conducted. The L/W ratio of Clade C strains was found to be significantly higher than those of Clades A and B by Duncan’s multiple comparison test  from different geographic populations were selected to investigate their potential differences in mycelial growth rate and colony morphology on three different media at 25, 28, and 30 °C. The fungal colonies on the CMA medium were generally very loose and their aerial hyphae were sparse. By contrast, those on TYGA medium produced extremely dense mycelia and their aerial mycelia grew most robustly. The growth status of hyphae on the PDA medium was intermediate between CMA and TYGA b.A. oligospora strains was 25 °C. At 25 °C, all strains could grow normally and showed the fastest growth rate among the three tested temperatures. However, the growth rates of isolates from Clades B and C (except YMF1.02797 from Clade C) were slower than those of Clade A at 28 °C, and the aerial mycelia deviated from radial spread. In fact, isolates from Clades B and C were unable to grow at 30 °C —YMF1.03101 (Clade A), YMF1.03135 (Clade B) and YMF1.03254 (Clade C)—were found to have significantly higher conidial yields than all other tested strains from either the same or different clades.A. oligospora was most distinguishing when exposed to C. elegans for 12 h. The highest number was produced by strain YMF1.03063 of Clade B at >47% more than others, while the numbers produced by YMF1.03056 of Clade A and YMF1.02797 of Clade C were the least, only accounting for about 20% of that produced by YMF1.03063. The number of trappers produced by other tested strains were similar , YMF1.03095 (Clade B), and YMF1.03254 (Clade C) had obvious nematicidal effect, and the corrected mortality caused by strain YMF1.03095 was as high as 71.5%. However, strains YMF1.03056 and YMF1.02797, which showed the distinguishing ability to form traps and capture nematodes on the solid media, produced fermentation supernatants with weak pathogenicity  were similar. However, differences concerning genetic differentiation and the level of genetic exchange (hybridization) were also found. Specifically, more genetic variation among populations and frequent gene exchanges were revealed by STRs, which was likely due to the faster evolutionary rate of STRs than MLST. Indeed, the STR markers allowed for the detection of hybridization in most geographic populations, suggesting the hybridization events likely occurred very recently. Similarly, STRs failed to identify a statistically significant positive correlation between genetic and geographic distances or to discriminate the ancient population divergence that the MLST revealed. In addition, though the STR markers showed overall higher population differentiation than the MLST markers, they also showed greater gene flow among many of the 19 geographical populations. Together, due to the high mutation rate, the STR markers allowed us to capture the recent, fine-scale population genetic events that the MLST markers failed to reveal. Future studies should focus on analyzing the potential factors contributing to the differences in population structures, as revealed by MLST and STR markers, including factors such as ecological niche adaptation and host nematode distributions and preferences.A. oligospora strains in China, indicating historical population divergence in this species. One of the major genetic cluster was composed of samples from Southwestern China and Hainan Island. Geographic populations from this region had a large number of private genotypes and unique alleles. The widespread genetic diversity and uniqueness in these areas suggested historical geographic isolation and, likely, the accumulation of locally adapted genotypes within and among local populations. Factors such as landscape features , climate, and habitats in different geographic populations could all impact local ecology, genetic drift, selection, and adaptation. Specifically, Southwest China has a large number of mountain ranges and river systems that create a diversity of ecological niches, making it one of the most important biodiversity hot spots on earth [Aspergillus fumigatus pathogenic hyphomycetes in Yunnan, and they were significantly different from those in other parts of the world [Our study identified two distinctly differentiated genetic clusters among on earth . Indeed,on earth . For exahe world ,57. FurtA. oligospora, most likely due to hybridization. In addition to inter-lineage hybridization, we also found unambiguous evidence for recombination within individual lineages, though most of the samples showed evidence of non-random recombination. The frequent sharing of alleles but limited or no overlap in multilocus genotypes were further consistent with frequent recombination in nature.Our analyses revealed evidence for genetic exchanges among the strains and populations in Southwestern China and Hainan Island. As indicated by the population structure and gene genealogical analyses, there is extensive evidence for allele-sharing among lineages of mat1-1 and mat1-2 idiomorphs in our samples were also consistent with sexual reproduction and heterothallic lifecycle of A. oligospora in nature. Indeed, the widespread distributions of both mat1-1 and mat1-2 idiomorphs across geographic populations made mating between strains with different genetic elements possible. The finding that a couple of strains with different mating types shared the same STR genotype is also a strong piece of evidence for their recombinant origins. Our results here complement those in an earlier study that showed A. oligospora populations from stressful environments to be in greater linkage equilibrium than those from other environments in the same geographic areas [The similar frequencies of ic areas .Aspergillus fumigatus in Yunnan [Candida tropicalis in Hainan Island [Fusarium oxysporum in South-Central China [Russula virescens Ally from Southwestern China [A. oligospora population from China. As indicated in our previous studies, the long-distance dispersal of asexual spores by natural forces, anthropogenic activities, the transport of agricultural and forestry products, and animal (soil nematode and insects) vectors all could have contributed to the spread of genetic elements of A. oligospora to different ecological and geographic niches [A. oligospora will likely increase, which could further facilitate hybridization and recombination, potentially accelerating the distribution of adaptive alleles originating in one area to broader geographic reaches [In addition to extensive allelic and multilocus genotypic sharing (hybrids) in our samples, our other analyses such as gene genealogical comparison and STRUCTURE also suggested frequent hybridizations, with different strains showing variable proportions of genetic elements from the two genetic clusters. Similar population structures have been found in several other fungal populations in Southern China, such as those of the opportunistic human fungal pathogens n Yunnan  and Candn Island , the plaal China , and thern China . Our resc niches . With in reaches .Duddingtonia flagrans have a single recent common ancestor with limited genetic differentiation among geographic populations [Arthrobotrys and Monacrosporium have been found to contain significant genetic variation and cryptic species among morphologically similar isolates [The Fungal kingdom is among the most diverse and specious groups of eukaryotes. At present, we are still far from knowing the full extent of fungal diversity. One major finding based on DNA sequences over the last two decades has been the existence of multiple genetically divergent cryptic species within many previously defined species ,63,64,65ulations , severalisolates .A. oligospora was associated with phenotypic characters such as morphology and nematode-trapping abilities. As indicated by shape of conidia (L/W), the morphological differentiation of A. oligospora strains was found to be associated with phylogenetic lineage differentiations. However, we also observed large variations among strains within each of the lineages, a result consistent with significant observed genetic variations within individual lineages of this species. The observed variations suggest in field applications as biocontrol agents against plant-parasitic nematodes, a mixture of genotypes from the locally sourced strains might be more effective than a single strain.Over the last two decades, the phylogenetic species concept (PSC) has been frequently used for identifying fungal species . AccordiA. oligospora. At the same time, the identification of hybrids in most geographic populations suggests that strains in different clusters/lineages are sexually compatible. Indeed, the comparable distributions of mat1-1 and mat1-2 genes suggest that sexual mating is likely common in natural populations of this species. Analyses of laboratory crosses between strains from the same and different lineages could help reveal the underlying genetic mechanisms responsible for the observed phenotypic variations. Indeed, we are in the process of identifying strains of opposite mating types and conducting laboratory crosses for strains between lineages to examine their cross-fertility and the extent of potential reproductive isolation. Species and cryptic species have been defined based on many different criteria [The genetic clusters and phylogenetic lineages identified here represent speciating populations within criteria , includicriteria , ecologicriteria ,70, and criteria . Host prcriteria ,73. At pA. oligospora from China will allow us to test the importance of some of these factors for future biocontrol applications.In selecting strains for field applications as biocontrol agents against PPNs, a common approach is to select strains with the strongest nematode-trapping ability, as demonstrated in laboratory settings. However, we believe many other factors, including the specificity of NTF–PPN interactions and the growth and reproductive abilities of the selected strains in field conditions, should be considered. The large number of strains and genotypes identified here for


The mitochondrial DNA (mtDNA) complete control region coverage of 111 individuals from Kalash population of Pakistan has been presented for forensic applications and to infer their genetic parameters. We detected in total 14 different haplotypes with only five unique and nine shared by more than one individual. This population has come up with quite lower haplotype diversity (0.8393) and very higher random match probability (0.1682), and ultimately lower power of discrimination (0.832). Additionally, haplogroup distribution reveals the genetic ancestry of Kalash, mainly from West Eurasia (98.8%) and very little from South Asia (0.9%). Neither African lineages nor East Asian genetic segments were detected among these Kalash. This study will contribute to the database development for forensic applications as well as to track the evolutionary highlights of this ethnic group. On the basis of culture and language, Pakistan is usually divided into 16 ethnic groups of miscellaneous pedigrees. The major ethnic groups include the Punjabis, Pathans, Sindhi, Saraiki, Muhajir, Balochi, Kalashi, and Makrani . DNA quantitation was done using NanoDrop 1000 spectrophotometer . After the quantification of the genomic DNA, the mtDNA complete control region was amplified by the same condition as described in . We observed the 14 different haplotypes (five unique and nine shared) with only 47 polymorphic sites. The detected haplotypes, their respective frequencies, and haplogroups have been presented in Table S1). The majority of observed variance (96.11%) was attributable to differences within populations, and only 3.89% represented differences among populations. The FST pairwise differences calculated were found comparatively higher between the populations from East and South East Asia, whereas lower between South and West Asian populations (Table S2). The most dominant haplogroups in the mtDNA of Kalash population are U4, R0a, U2e, and J2 indicating the occurrence of Western Eurasian influence  has been presented with total only 14 haplotypes (nine shared and five unique haplotypes). The haplogroup R0a + 60.1T (West Eurasian was observed most frequently 28.8% in this population affirming the previous studies as far as West Eurasian dominance is concerned. This data is in accordance with Rahman et al.  study ou


However, the extent to which the observed genetic structure of the Huis was mediated by the movement of people or the assimilation of Muslim culture also remains highly contentious. Analyses of over 700 K SNPs in 109 western Chinese individuals  together with the available ancient and modern Eurasian sequences allowed us to fully explore the genomic makeup and origin of Hui and neighboring Han populations. The results from PCA, ADMIXTURE, and allele-sharing-based f-statistics revealed a strong genomic affinity between Sichuan Huis and Neolithic-to-modern Northern East Asians, which suggested a massive gene influx from East Asians into the Sichuan Hui people. Three-way admixture models in the qpWave/qpAdm analyses further revealed a small stream of gene influx from western Eurasians into the Sichuan Hui people, which was further directly confirmed via the admixture event from the temporally distinct Western sources to Sichuan Hui people in the qpGraph-based phylogenetic model, suggesting the key role of the cultural diffusion model in the genetic formation of the Sichuan Huis. ALDER-based admixture date estimation showed that this observed western Eurasian admixture signal was introduced into the Sichuan Huis during the historic periods, which was concordant with the extensive western–eastern communication along the Silk Road and historically documented Huis' migration history. In summary, although significant cultural differentiation exists between Hui people and their neighbors, our genomic analysis showed their strong genetic affinity with modern and ancient Northern East Asians. Our results support the hypothesis that the Sichuan Huis arose from a mixture of minor western Eurasian ancestry and predominant East Asian ancestry.The ancestral origin and genomic history of Chinese Hui people remain to be explored due to the paucity of genome-wide data. Some evidence argues that an eastward migration of Central Asians gave rise to modern Hui people, which is referred to as the  Archeologically and historically supported transcontinental exchange in North China and Northwest China during the past 5,000 years played an important role in the formation of the modern genetic, linguistic, and cultural diversity in East Asian people , and III) SNPs with a minor allele frequency of >0.01 (--maf 0.01) and a genotyping success rate per SNP higher than 0.01. We merged our 109 newly generated genomes with previously published modern and ancient population data included in the Human Origins dataset  or recent publications  to genotype ~6,992,479 SNPs from the autosomes , the Y chromosome , the X chromosome , and the mitochondrial genome . Genotype calling was carried out with the default parameters. PLINK 1.9  using the smartpca package built-in EIGENSOFT  with 10 replicates and found K = 13 as the best resolution for ancestry dissection  to evaluate the shared genetic drift among 366 Eurasian modern and ancient populations using the default parameters. Next, we used the qp3pop package  to explore the admixture signatures with different Eurasian ancestral source candidates, where significant negative-f3 values with Z-scores < −3 denoted that the target population was a result of admixture between two parental populations designated as source1 and source2. We finally used the qpDstat package , and the standard error was estimated using the block jackknife  among 366 Eurasian populations to explore the genetic relationships among modern and ancient populations and newly focused populations and other reference groups. We ran TreeMix  algorithm to reconstruct phylogenetic relationships based on different genetic distance matrixes using MEGA 7.0 . To validate the concordance of our results, we also estimated the admixture times of Uyghur populations, and our observed admixture patterns were consistent with previous reports   data from 109 Sichuan Hui and Han individuals in Sichuan Province and co-analyzed them with publicly available modern and ancient DNA data for comprehensive population genetic history reconstruction. We grouped modern reference individuals from Eurasia according to their language family categories  and classified ancient people based on their geographical affinities . We firsK value (K = 13) revealed multiple sources of East Asian-dominant ancestry that were enriched in both inland (Hmong) and coastal (Ami) Southern East Asians, and others were dominated by inland (Tibetan) and coastal (Japanese) Northern East Asians (Z = −6.513 and p < 10−3), Iranian farmers , and Anatolian farmers . The patterns of genetic relationships revealed by model-based ADMIXTURE analysis were consistent with the PCA results.Model-based ADMIXTURE results with the optimal t Asians . Our stut Asians . We alsoTo quantitatively evaluate the genetic differences between Hui and Han populations from Sichuan Province and 170 modern and 67 ancient Eurasian populations with a population size larger than five, we calculated the pairwise Fst genetic distances  accordinf3 statistics in the form f3 to explore the shared genetic drift between two studied populations and Eurasian reference populations, including 188 modern and 176 ancient populations, which may have contributed genetic materials to the Hui and Han gene pools in Sichuan Province , Fujian (0.3025), and Chongqing (0.3020) provinces and ancient Iron Age Luoheguxiang (0.3013) and late Neolithic Pingliangtai (0.3004). Small amounts of shared genetic drift between the studied groups and archaic peoples  were identified. There was a strong statistically significant correlation of shared ancestry between Hui and Han individuals in the outgroup f3 values . The correlations of shared genetic drift with latitude (0.1817 in Hans and 0.1691 in Huis) and longitude (0.6992 in Hans and 0.6777 in Huis) showed an association between shared genetic drift and longitude, which was consistent with the gradient changes in the f3-based heatmap (f3_Bohui_Hui = 0.941*f3_Guizhou_Hui + 0.0139). However, the Guizhou Huis had a smaller amount of shared genetic drift (0.2918) with Boshu Huis than with other East Asian reference groups.We subsequently used a formal test of outgroup-Province . We foun heatmap . Furtherf3-based genetic matrixes (1/f3). We found that the Neanderthal and Denisovan populations were fitted at the root in the reconstructed phylogeny and detected genetic differentiation between modern eastern and ancient Eurasian populations  tree to date among 190 modern and 176 ancient Eurasian populations based on inverted ulations . Ancientulations . Among aulations . Howeverulations . These of3 statistics in the form f3, which would be expected to yield statistically negative values (Z-scores < −3) if the allele frequencies of Huis/Hans were intermediate between the frequencies of source1 and source2. We found signals supporting two-way or three-way admixture models for the genetic formation of Sichuan Huis and Hans , suggesting the possible westward movement of Han Chinese over historical time. Focusing on the Chinese Huis, we obtained most admixture signals among the source pairs of Northern East Asians (Tibetans and southern Siberians) and Southern East Asians (Tai-Kadai and Austronesian speakers), such as f3 = −7.216*SE (standard error). Western Eurasian admixture signals could also be obtained when we used Han Chinese as one source and Ingushian (−5.748), Lak (−5.508), or Adygei (−5.429) as the other source . Excess shared alleles between Nanchong Hans and modern and ancient Southern East Asians were identified through significant positive values, suggesting that neighboring Han individuals had more East Asian ancestry than Hui people  = 12.351*SE in the Human Origin dataset and f4 = 13.599*SE in the 1240K dataset. However, no statistically significant negative f4 values for f4 were identified in the Human Origin dataset, suggesting that the studied populations formed a clade compared with western Eurasians based on the lower-density dataset. Although weak statistical significance for negative f4 values is shown in f4 values with absolute Z-scores larger than 2. This pattern could be validated via further whole-genome sequencing data with more SNPs and more rare variants. Indeed, we observed statistically significant f4 values when we used Iran_C_TepeHissar (−3.924*SE), Russia_Srubnaya_Alakul (−3.88*SE), Iran_IA_Hasanlu (−3.449*SE), Russia_Srubnaya (−3.129*SE), and Russia_MLBA_Sintashta (−3.019*SE) as the western Eurasian sources. Furthermore, we also detected relatively close genomic affinity between Huis and geographically close Hans relative to western Eurasians via significant negative f4 values for f4 based on two merged datasets , we first calculated i people , especiadatasets .f4 statistics  and f4. As shown by the significant positive values in f4 values are shown in Additionally, we calculated these types of atistics  to explof4 values observed among modern Eastern panels based on the merged 1,240 K dataset demonstrated that the Boshu Huis harbored more East Asian-derived ancestry than their respective predefined comparative populations, which is consistent with the genetic affinity identified via the outgroup f3 statistics and pairwise f4 analysis based on the Human Origin dataset  using all possible East Asian pairs  and ancient Hanben peoples shared more alleles with Huis than other East Asians  suggested that other East Asians shared more alleles with Han Chinese than with Hui people. Significant positive values for f4 showed a similar affinity pattern, but Sino-Tibetan and ancient northern East Asians shared more alleles with Hui people than with others. Thus, we further chose the Austronesian-speaking Ami, Austroasiatic-speaking Htin, Tai-Kadai-speaking Li, Tibeto-Burman-speaking Sila, and ancient southern East Asians of Hanben as the surrogates in the f4 statistic calculations. As detailed in f4 values for f4 . We also found that Neolithic-to-Iron-Age populations from the Yellow River basin shared more derived alleles with Boshu Huis than with ancient Southern East Asians, which also pointed to predominant East Asian assimilation and the stronger Neolithic-to-modern Northern East Asian affinity of the modern Huis as supporting evidence from f4  . Further, Mbuti) , higher , Mbuti) . These g, Mbuti) . Interes, Mbuti) . Overallf4 values in f4 statistics for the Nanchong Han population in f4 also documented more Southern East Asian-like ancestry in Nanchong Hans than in Boshu Huis  and then evaluate their corresponding ancestral proportions. The best-fitting qpAdm model was dependent on the following criteria: (1) the p-value of the rank test was larger than 0.05, (2) there was no negative ancestry proportion, and (3) the minimum mixture proportion was larger than the corresponding standard error. To better visualize the differentiated population structure of Hui and Han populations, we applied the same analysis strategies to estimate their admixture proportion. We first simulated three different two-way admixture scenarios and then analyzed the three-way admixture models with 44 western Eurasians, 17 Northern East Asians, and three Southern East Asians as the potential ancestral sources.Considering the admixture events and sources that we observed in our studied Han and Hui populations, we applied qpAdm models focused on the western and eastern sources. We found that all pairs focused on Nanchong Hans failed, and 95 out of 880 pairs focused on Boshu Huis yielded fitted models  and Southern East Asians (Yangtze River farmer-related people), we found that 0.314 (Mongolia_N_North-like) ~0.891 (Haojiatai_LBIA-like) of the ancestry of Nanchong Hans related to Northern East Asians mostly associated with Yangshao and Longshan people from Henan Province . A higheThird, to directly assess the genetic relationship between Boshu Huis and Nanchong Hans, we used Nanchong Hans as the East Asian source to fit two-way admixture models. We obtained 17 fitted models with five modern European and 12 western Eurasian steppe pastoralist populations as the western Eurasian source . The HuiqpWave-based admixture models with diverse modern or spatiotemporally different ancient western sources to explore the full landscape of the admixture process  and 40,000-year-old Tianyuan people as the early eastern Asian lineage; Mongolian Neolithic people (Mongolia_N_East), late Neolithic Qijia people (Qijia_LN), and ancient Tibetan Plateau people (Chokhopani) as proxies for the Northern East Asian lineages; and Iron Age Hanben as a Southern East Asian proxy. We obtained the best-fitting model (Model A) for the Nanchong Hans with an absolute Z-score of 2.417 and a likelihood of 19.834  and X chromosomes . The sex-bias Z-score was calculated using the formula A and SEX denoted the standard errors of the admixture proportions. Positive ZSexBais scores suggested a male-driven admixture process due to autosomes possessing more western Eurasian ancestry than X chromosomes, and negative values supported a female-driven admixture process. As shown in sexBais scores in different three-way admixture models focused on both Guizhou Huis and Boshu Huis, which suggested a male-dominated admixture of western Eurasians and East Asians.We assigned 109 tested mitochondrial genomes based on 4,198 maternal mitochondrial lineage-informative SNPs (MLISNPs) and 60 Y-chromosomal genomes (28 Huis and 32 Hans) based on 22,512 paternal Y-chromosomal lineage-informative SNPs (YLISNPs). Among 49 studied Hui people , we idenf4 statistics showed differentiated demographic histories between Huis and Hans: compared with geographically close Hans, Boshu Hui people possessed more ancestry related to modern western Eurasians  calculation and historically documented migrations from southeastern China to western China. This needs to be further explored via historic ancient DNA from these areas. Second, in addition to the admixture signatures from Northern and Southern East Asians in the Nanchong Hans, we also identified a small amount of western Eurasian ancestry in this group, which was consistent with our recent findings for Shaanxi Hans and Gansu Hans  = −2.784*SE, also suggested the possibility that some of this western Eurasian signature may have been indirectly introduced via Hui people or other gene flow events into East Asia. In summary, modern northern Han Chinese, modern northwestern Han Chinese, and some modern western Han Chinese result from the admixture of ancient northern and southern sources within East Asia and some gene flow from western Eurasia.We provided newly generated genome-wide SNP data from Hui and Han Chinese individuals from western China and performed a comprehensive population genetic analysis to investigate their origin and admixture history. Shared genetic ancestry inferred from https://en.wikipedia.org/wiki/Hui_people#cite_note-71). Currently, Hui people have unique cultural beliefs but use the same language as Han Chinese  and modern and ancient Eurasian populations to date. Our results suggested that neither hypothesis can simply fit the complexity of Huis' admixture history. Both East Asians and western Eurasians contributed to the Sichuan Hui people, with a major influence on assimilation from East Asians, which suggested that cultural diffusion played the dominant role in the Hui admixture history. We used different methods and western sources as potential ancestral sources in our comprehensive admixture model reconstructions and found a small amount of western Eurasian gene flow in the Hui people but more than that in geographically close Han Chinese. First, we observed western Eurasian ancestry in Chinese Huis in the model-based ADMIXTURE result, which was maximized in both western steppe pastoralists and modern Central Asian people and deviated toward western Eurasians in the PCA plots. Second, admixture-qpAdm and qpGraph analyses  because of different datasets or the limitations of some analyses. However, the overlapping ancestral sources provided consistent admixture scenarios.The complex admixture and migration history in western Eurasians complicated the inference of the ancestry of Chinese Hui people. Recent ancient DNA studies have demonstrated that modern western Eurasians descended from three main ancestral populations: one related to local European hunter–gatherers, one related to incoming Anatolian farmers, and the other related to Early Bronze Age pastoralists from the Pontic-Caspian Steppe  and western Eurasian origin (demic diffusion) hypotheses. We first identified significant genetic differentiation among geographically different Hui, as well as between Hui and adjacent Han populations, suggesting their different demographic structures. Findings based on the The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by this project was inspected and approved by the Medical Ethics Committee of the North Sichuan Medical College. The patients/participants provided their written informed consent to participate in this study.GH, YXL, GC, XZ, and MW conceived the idea for the study. YL, JY, YXL, RT, DY, YW, PW, SD, SZ, and HL performed or supervised wet laboratory work. GH, GC, XZ, YL, JY, YXL, RT, DY, YW, PW, SD, SZ, and HL analyzed the data. GH, YL, MW, and XZ wrote and edited the manuscript. All authors contributed to the article and approved the submitted version.YXL was employed by company AnLan AI, Shenzhen, China. The remaining 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.


ST) and quantitative trait (PST) differentiation to assess local adaptation among three natural populations of Eruca sativa (Brassicaceae) distributed along a climatic range representing desert, semi-arid and Mediterranean habitats.The aridity gradient in the eastern Mediterranean offers an opportunity to investigate intra-specific genetic differentiation and local adaptation in plant populations. Here we used genetic  analysis revealed high genetic diversity in each population, but low genetic differentiation between populations and relatively high gene flow. Further phenotypic evaluation in a common garden experiment (conduced in a Mediterranean habitat) showed clear differences in phenological traits among populations (day of flowering and duration of the reproductive stage), shoot and root biomass, as well as fitness-related traits . FE. sativa. Furthermore, the FST–PST comparison supports the hypothesis that these were subjected to past diversifying selection. Thus, the results clearly demonstrate adaptive divergence among populations along an aridity gradient, emphasize the ecological value of early flowering time in arid habitats, and contribute to our understanding of the possible impact of climate change on evolutionary processes in plant populations.Overall, our results support the identification of genotypic and phenotypic differentiation among populations of The online version contains supplementary material available at 10.1186/s12862-022-01996-w. Adaptation to local environmental conditions—a perquisite for a plant’s long-term survival in any geographic region—requires intra-species variation in life-history traits –4. IndeeST) with quantitative trait differentiation (QST) assessed in common garden conditions ; 500 permutations were applied with 95% confidence intervals.The Fy Wright  was furtMilligan . In thisST was calculated using the equation: PST = c/h2 × VC/(c/h2 × VC + 2 × VCerror), in which VC is the phenotypic variance component of the population, VCerror is the residual variance, h2 is the proportion of phenotypic variance that is due to additive genetic effects within population, and c is the proportion of total variance that is presumed to be due to the additive genetic effect among populations. The c/h2 range of 0 to 2 was used for estimation of PST, which is generally smaller than 1.0, since trait heritability is usually lower among populations than within populations [ST, were obtained from a random intercept of the generalized linear mixed model (GLMM). The GLMM model was implemented using the lmer function in the R-package lme4 version 1.1-21 [ST are given in Additional file ST confidence intervals (95%) were obtained by 1000 bootstrap iterations of the original data. Finally, for inferring past diversifying selection, the following criteria were used: (1) PST > FST, for diversifying selection; (2) PST = FST, for inferring that the trait differentiated neutrally; and (3) PST < FST, for stabilizing selection [Following Morente-López et al. , PST wasulations . Variancn 1.1-21 . In GLMMelection .Additional file 1: Table S1. The investigated populations of E. sativa, their location, the annual precipitation, soil salinity and average temperature at the growing season. The conditions at the agricultural experimental field site (ARO) is also provided. Table S2. Analysis of molecular variance in populations of E. sativa. Table S3. Log-rank test applied on Kaplan–Meir survival curves for the measured phenological traits: the onset, duration and end of flowering (*P < 0.05). Table S4. Variance components of the tested phenological traits calculated using the generalized linear mixed model (GLMM). Figure S1. Annual rainfall (mm) and the average daily temperature in January at the natural sites of E. sativa in the 20 years that preceded the sampling. Data was obtained from meteorological stations located in vicinity to the studied sites.Additional file 2. An Excel file with the list of the AFLP scored loci.


Anastrepha obliqua, that plague different host plants, present differences at the genome level; these differences are associated with adaptive processes related to the exploitation of the resource and can lead to speciation in sympatry, first with the formation of host races. In this studio, we used pooled RAD-seq to assess genomic differentiation and population structure in sympatric populations of Anastepha obliqua that infest three different host plants. The results obtained support the effect of host plants on genomic differentiation in populations of the species A. obliqua. We identified a small group of candidate genes that could be under divergent selection, a product of the selective pressure mediated by the host plants.Individuals of a polyphagous species, such as Anastrepha obliqua (Macquart) (Diptera: Tephritidae) is an important pest in the neotropical region. It is considered a polyphagous insect, meaning it infests plants of different taxonomic families and readily colonizes new host plants. The change to new hosts can lead to diversification and the formation of host races. Previous studies investigating the effect of host plants on population structure and selection in Anastrepha obliqua have focused on the use of data from the mitochondrial DNA sequence and microsatellite markers of nuclear DNA, and there are no analyses at the genomic level. To better understand this issue, we used a pooled restriction site-associated DNA sequencing (pooled RAD-seq) approach to assess genomic differentiation and population structure across sympatric populations of Anastrepha obliqua that infest three host plants—Spondias purpurea (red mombin), Mangifera indica (mango) of the family Anacardiaceae and Averrhoa carambola (carambola) of the family Oxalidaceae—in sympatric populations of the species Anastrepha obliqua of Inter-Andean Valley of the Cauca River in southwestern Colombia. Our results show genomic differentiation of populations from carambola compared to mango and red mombin populations, but the genetic structure was mainly established by geography rather than by the host plant. On the other hand, we identified 54 SNPs in 23 sequences significantly associated with the use of the host plant. Of these 23 sequences, we identified 17 candidate genes and nine protein families, of which four protein families are involved in the nutrition of these flies. Future studies should investigate the adaptive processes undergone by phytophagous insects in the Neotropics, using fruit flies as a model and state-of-the-art molecular tools. A fundamental challenge in contemporary evolutionary biology is to understand the genetic mechanisms that determine how organisms can adapt to different environments . AlthougAnastrepha Schiner is the most diverse and economically important in the Neotropics, with more than 300 species [Anastrepha . This family of flies is currently composed of approximately 4500 species , many of species . Few stua , the West Indian fruit fly. This species is distributed from northern Mexico to southern Brazil and the Caribbean, and is occasionally found in Texas and California. In addition, it is part of the taxonomic group fraterculus and is considered an important pest of 60 species in 24 plant families of tropical and subtropical areas [A. obliqua has been found to be infesting fruits of mango , red mombin , carambola , and araza  [A. obliqua has been reported to infest other fruits, both of commercial and wild interest [In this study, the study organism was the species al areas ,27. In tinterest . This apA. obliqua with its host plants, some molecular studies have provided relevant information on this topic. For example, Aguirre-Ramirez et al. [A. obliqua populations in the state of Veracruz, Mexico, from five host plant species and 52 geographic collections using data from mitochondrial DNA sequences and microsatellite nuclear DNA markers. They found significant differences between samples collected from different host plants in one locality (Apazapan), which could imply a certain degree of fidelity for the host plant (S. purpurea vs. M. indica var. Criollo) that restricts the ongoing gene flow between populations.Although few studies have been conducted to understand the association of z et al.  found diz et al.  analyzedTraditionally, studies that seek to understand the basis of an adaptive process in nature use common garden experiments, where individuals from contrasting environments are exposed to a common laboratory environment  and statAnastrepha the genetic-molecular mechanisms are mostly unknown. To investigate this issue, we sampled sympatric populations of A. obliqua infesting three host plants in the Inter-Andean Valley of the Cauca River in southwestern Colombia and measured the differentiation between populations through a subset of RAD-seq data. Finally, we used three approaches to identify those significantly differentiated loci that are correlated with the use of host plants.Although members of the family Tephritidae have been used as models to understand the effect of host shift on population dynamics and the evolutionary history of populations ,40, in tA. obliqua larvae: mango (M. indica) and red mombin (S. purpurea) of the family Anacardiaceae, and carambola (A. carambola) of the family Oxalidaceae. These three fruits were sampled because:Fruits were collected from three host plants species infested with A. obliqua;(i) the red mombin is a native species of America and natural host of A. obliqua is its main pest;(ii) the mango, a species native to Southeast Asia introduced into Colombia in the 16th to 17th centuries , belongi(iii) the carambola an introduced species around the 18th century and brought to Colombia in 1930 , belongiA. obliqua associated with geographic distance in the Inter-Andean Valley of the Cauca River. To choose the populations of fruit flies in the study, the estimated dispersal capacity of wild flies of A. obliqua was also taken into account, which has been reported to be approximately 151 m in radius [These plants were all found in localities of southwestern Colombia located in the north and south of the Inter-Andean Valley of the Cauca River. The reason why this region was chosen is because Aguirre-Ramirez et al.  reportedn radius .A. obliqua obtained from each host plant for each locality, for a total of six populations  and south  of the Inter-Andean Valley of the Cauca River were selected as the sampling sites . In eachulations . ObtainiA. obliqua using the DNeasy Blood and Tissue kit . DNA quality was evaluated in a 1% agarose gel. Only nondegraded DNA samples were processed and quantified in a NanoDrop ND-1000 spectrophotometer. Each sample was diluted to 20 ng/µL, re-quantified, and pooled by population  (accessed on 14 January 2020) was used. The raw reads were end-trimmed using a minimum Phred quality score of 20 and a minimum read length of 75 bp.To reduce the error rate towards the 3′ end of Illumina reads, BBMAP v.37.93  (accessed on 14 January 2020), and ambiguously mapped and unaligned reads were removed by SAMTOOLS v.1.5 [As semi-global alignment and realignment of unmapped reads is recommended for a pooled sample , we usedLS v.1.5 .π) and population mutation rate  with POPOOLATION v.1.2.2 [π and Wθ were calculated across a non-overlapping 1000 bp sliding window with the ‘variance-sliding’ command, taking into account a minimum minor allele count of two. Additionally, the significance for π and Wθ differences among populations was first assessed by conducting a one-way ANOVA. As part of a post-hoc analysis, populations were then assigned significance groupings using Tukey’s honestly significant difference tests. One-way ANOVA and Tukey’s test were performed with REAL STATISTICS v.6.8  (accessed on 14 January 2020).To characterize genome-wide patterns of genetic variation and population differentiation, we calculated the nucleotide diversity (Tajima’s  v.1.2.2 . Because v.1.2.2  were folSTF) for each pairwise population comparison was calculated for each SNP as follows: (i) with BCFTOOLS v.1.5 [STF for each pairwise comparison was calculated at each of the SNPs using the ‘fst-sliding’ command of POPOOLATION2. Confidence intervals for the STF estimates were inferred by bootstrapping 1000 times in the R package BOOTSTRAP v.2017.2 [To determine the degree and pattern of the population genetic structure, the fixation index  (accessed on 14 January 2020) based on average STF values. Additionally, a hierarchical clustering tree was constructed [To visualize the multilocus patterns of population differentiation, a Principal Coordinates Analysis (PCoA) plot was generated using the R package LABDSV  [XtX values for each SNP were estimated based on the core model implemented in BAYPASS v.2.2 [XtX, a pseudo-observed dataset (POD) was simulated using the R function ‘simulate_baypass’. SNPs with XtX estimates greater than the 99.9% threshold determined from POD were identified, resulting from adaptative divergence [Second, the identification of overly differentiated SNPs was based on the atistics , which and SNPs) . The XtXSS v.2.2  with thevergence .dB) via the transformation 10log10 (BF). As a decision rule, we then followed the popular Jeffrey’s rule that quantifies the strength of evidence using the criterion of dB > 20 as strong evidence [A. obliqua populations within the sampled sites (north and south), separately, using core model and AUX model.Finally, we relied on the Bayes factor (BF) calculated under the auxiliary covariate model (AUX), also implemented in BAYPASS, to identify differentiated SNPs associated with the host plants. The AUX model involves the introduction of a binary auxiliary variable to classify each locus as associated or not. This allows us to easily compute posterior inclusion probability (and BF) for each locus while explicitly accounting for multiple testing issues. For each SNP, the Bayes factor was expressed in deciban units  term mapping was also performed in BLAST2GO under default parameters. Visualization of GO annotations was carried out with WEGO [To identify candidate genes associated with the outlier SNPs found with the AUX model and at least one of the other two methods, each of the contigs associated with each outlier SNP were annotated with BLAST2GO . We giveith WEGO .A total of 258.3 million paired reads were obtained from NGS sequencing, with an average of 43 million per population. After filtering, 240.8 million paired reads were retained, with an average of 40.1 million per population . The de W ranged from 0.0105  to 0.0108 (population CaS) and from 0.0112 (population CaN) to 0.0117 (population CaS), respectively.To estimate the levels of genome-wide genetic variation, POPOOLATION identified between 481,206 and 565,527 SNPs in the populations evaluated . The witSTF across all SNPs were similar between the populations. The values varied between 0.0105 (MS vs. CaS) and 0.0162 (CaN vs. CaS). Slightly higher STF values were obtained between the CaN population and the other populations  were subsampled across all populations with POPOOLATION2. The average pairwise ulations . This reulations . Additioulations  and poolulations , revealeSTF values  in 44 contigs. To our surprise, some overly differentiated SNPs  were not associated with the use of the host plant (dB below expectations), which indicated the presence of other selective pressures. From the results of all three methods, 233 SNPs were identified in 113 contigs. Of these, 54 SNPs in 23 contigs were detected by the AUX model and at least one of the other two methods  were found at least once in the top 0.1% of pairwise  methods . The dis obliqua . The SNP obliqua . Four SN obliqua .Of the 23 contigs that contained SNPs that were shown to be correlated with the use of host plants, we identified 17 contigs that presented homologous sequences with the NCBI arthropod protein database in a BLASTx search . Of the We also determined the protein families related to the identified sequences. For this, we used the InterProScan tool of the BLASTO2GO program to retrieve information about the protein families .Anastrepha obliqua associated with the use of host plant. To date, genetic studies that have provided information on the association of A. obliqua with its host plants have focussed on the use of data from mitochondrial DNA sequences and microsatellite markers of nuclear DNA [A. obliqua. This study is the first to provide information at this level on the population differentiation of A. obliqua associated with its host plants. The most interesting findings of this work are as follows: (i) the analysis of the genetic differentiation between the populations yielded low values of STF based on the genomic SNPs examined. However, (ii) a subset of them showed signs of adaptive differentiation, suggesting the presence of selective pressures in these populations. Of the SNPs identified as being putatively adaptive 54 were associated with host plant specificity. Finally, (iii) some of the adaptive genomic regions linked with host plant specificity are associated with protein families involved in nutrition.In this study, we used a pooled RAD-seq approach to evaluate genomic differences between six wild populations of the species lear DNA ,26, and π and Wθ between the analyzed populations were very similar, with a variation of 0.0105 to 0.0108 for π and from 0.0112 to 0.0117 for Wθ. However, we found evidence, with Tukey’s test, of significant differences between the patterns of genomic variation in the populations, for both π and for Wθ. According to our analysis, the differences found between the patterns genomic variation were more pronounced between the populations of A. obliqua from carambola fruits than between mango and red mombin populations  among the populations of A. obliqua of different hosts plants species. This result is similar to that found by Bakovic et al. [Rhagoletis cerasi populations from 2494 SNPs. This fruit fly uses two species of host plants, Prunus spp. and Lonicera spp., which can be found in sympatry. These researchers revealed that the genomic differentiation between the populations of this species was low (STF ranged from 0.004 to 0.143), especially when they compared populations of different host plants found in sympatry. Based on the average pairwise STF values obtained, we can suggest that the populations analyzed in this study show low genetic differentiation. The level of genetic differentiation observed is likely due to that ongoing genetic flow persists between the populations or due to a recent descent of the populations.The ulations . We did c et al.  when estA. obliqua with the greatest degree of genomic differentiation from the others was CaN, since its average pairwise STF values were higher. This result is consistent with the differentiation inferred from the patterns of genetic variation of the populations. What could have caused this? Studies of the chemical composition of fruits of different plant species have shown that they can vary widely in both nutrients and secondary metabolites, which could be toxic to fruit flies. For example, Oroño et al. [A. fraterculus associated with three species of host plants that were in sympatry and differed in chemical composition, with the aim of testing the hypothesis of sympatric speciation mediated by chemical substances. The researchers found that the adaptive processes related to the plant chemistry of the host plants seem to have promoted genetic differentiation between the populations of A. fraterculus, since the individuals analyzed were pooled according to the host plants and, in addition, extremely low gene flow was discovered between populations. In this sense, it is possible that the chemical composition of the fruits of carambola plants, which belong to the family Oxalidaceae, exerts selective pressure on the larvae that feed on the carambola pulp, promoting their genomic differentiation more than the red mombin and mango plants, which are part of the family Anacardiaceae. This differentiable pattern was also found when the bacterial community was analyzed in populations that infest the same three host plants  in the same geographic region analysed by us [A. obliqua of carambola fruits was significantly different from that of the same fruit fly in mango and red mombin. On the other hand, Aguirre-Ramirez et al. [A. obliqua populations that were associated with different host plants. Particularly for carambola fruits, these authors reported a lower haplotype diversity than was found on mango and red mombin fruits. Because the number of individuals in the CaN population is lower than that of the other populations  of the principal coordinate analysis and the hierarchical clustering tree suggests the possibility of a genetic relationship among the populations of z et al. , the avez et al. , who didz et al. , the anaA. obliqua that infest three different host plants. Additionally, we tried an isolation by distance test as described in [A. obliqua in Veracruz, Mexico. They mention that the genetic structure of populations of the species A. obliqua in this locality is determined more by geography than by the host plant.It should be noted that the main objective of this research was to assess genomic differentiation and population structure in sympatric populations of ribed in , but it ribed in , in the ribed in  and withribed in , who worA. obliqua.We do not rule out the participation of host plants in the differentiation of populations, since we identified a subset of SNPs that presented differences associated with the host plant. Research on insects phytophages that manage to form host races has helped to understand how the accumulation of small changes in the genome that show some association with the use of the host plant can act in combination to reduce the effective rate of gene flow and promote the formation of host races and subsequent speciation . For thiWe identified 54 SNPs that showed significant differentiation associated with host plant specificity. BLAST search queries and the annotation of the SNP-containing 23 RAD contigs allowed us to identify 17 candidate genes and nine protein families. Among the most interesting annotated protein families, the MFS transporter superfamily and major facilitator superfamily stands out; they are related to the digestive system of herbivorous insects  and are A. obliqua. We cannot say with certainty that this species presents host races. However, our results support the hypothesis that the species A. obliqua could be in an incipient stage of population differentiation mediated in part by its host plants. Our results add to a growing list of possible examples of ongoing ecological divergence. We identified a small group of candidate genes that could be under divergent selection; a product of the pressure mediated by the host plants. We believe that in future research, an analysis based on the sequencing of the complete genome of individuals of the species A. obliqua would allow us to detect more important genomic regions that support the adaptation of A. obliqua to their host plants. Our study is a first step to better understand the population genomics of an important pest of fruit crops, A. obliqua.In conclusion, we present evidence that supports the effect of host plants on genomic differentiation in populations of the species