Cobitis vettonica, an endangered freshwater fish species endemic to the Iberian Peninsula, in order to propose a biogeographic model of the responses of species to the multiple changes that occurred in the Iberian hydrological system during the Quaternary period. We also deciphered the relationship of C. vettonica with its sister species C. paludica, particularly in sympatric areas, and provide genetic information for conservation purposes. To achieve this goal, we analyzed both mitochondrial and nuclear data (the cytochrome b and the nuclear recombination activating 1 genes) and a battery of single‐nucleotide polymorphisms (SNPs) of 248 individuals of C. vettonica or C. paludica from 38 localities, including some sympatric ones, covering the entire distribution area of C. vettonica. We highlight the important role played by the hydrogeomorphological processes and climatic changes that occurred in the Iberian Peninsula during the Quaternary on both the population structure of C. vettonica and its relationship with its sister species C. paludica. Our results support the genetic introgression of populations at the eastern limit of the distribution of C. vettonica. Furthermore, we postulate genetic introgression in sympatric areas. Finally, we propose the establishment or expansion of four Operational Conservation Units (OCUs) for C. vettonica, and highlight the threat faced by its populations due to the low level of genetic diversity detected for some of its populations and genetic introgression with C. paludica, which could eventually displace C. vettonica, resulting in a loss of diversity in this species.We studied the population genetic structure of  Cobitis vettonica to propose an area‐specific biogeographic model of the species’ responses to the multiple changes that occurred in the Iberian hydrological system during the Quaternary. We also decipher the relationship of C. vettonica with its sister species C. paludica, particularly in sympatric areas, and provide genetic information for conservation purposes.We study the population genetics of  The samples of C. paludica were collected from 13 localities, including its type locality : of these, 11 were either adjacent to those of C. vettonica or in sympatric localities in the Tagus Basin, and two were in the Duero Basin , following the manufacturer's protocol. Polymerase chain reaction (PCR) was used to amplify 1140 bp of MT‐CYB from 208 individuals , and 1500 bp of RAG1 from 90 individuals   Table . Amplifia) Table  for MT‐Cu et al.  for RAG1MT‐CYB , build a catalogue (cstacks), match all generic samples against the catalogue (sstacks), and reconstruct loci using R2 reads and identify SNPs using the metapopulation information (gstacks). Prior to running the STACKS modules, several tests were used to identify which parameters maximized the number of SNPs obtained in at least 80% of the individuals  was set to 5 due to the depth of coverage values obtained. The M parameter  has to be carefully set. If it is set too high, paralogous or nonhomologous loci can be incorrectly merged into the same locus and if it is set too low, homologous loci can be lost. For this reason, the parameter M was explored between 3 and 5. Following the indications of Paris et al.  were explored from 2 to 4  values were used to assess the reliability of the phylogenetic hypothesis. Two sequences of other species of Cobitis were used as outgroups: C. bilineata (EF605321.1) and C. zanandreai (AF263089.1). The genetic divergence between the lineages obtained in the MT‐CYB analysis was evaluated through uncorrected p‐distances with 1000 permutations using MEGA v.6.0  through the Maximum Likelihood (ML) method in the RaxML program implemented in CIPRES Science Gateway v3.3. The evolutionary model selected was ASC_GTRGAMMA, as recommended in the program manual for data that contain only variable sites, and the Lewis ascertainment bias correction was used  using different groupings based on basin, sub‐basin, and evolutionary lineage. These analyses were implemented in Arlequin v.3.11  was also calculated in Arlequin v.3.11. For the localities in which mitochondrial haplotypes of both C. vettonica and C. paludica were found, the Φst was calculated by treating the individuals of each species as separate populations.To examine the genetic structure of all studied populations of C. vettonica matrix and 4500 loci in the C. vettonica + C. paludica matrix. STRUCTURE  for each analysis was equal to the number of populations studied + 1 . We performed 10 independent simulations for each K with a burn‐in length of 50,000 and 50,000 MCMC repetitions after the burn‐in. The most probable number of subpopulations for each matrix was estimated by taking into account the results of both the ΔK , haplotype or allelic diversity , nucleotide diversity (π), and number of polymorphic sites (S). For MT‐CYB, the populations with shared haplotypes between C. vettonica and C. paludica were analyzed considering only the individuals identified as C. vettonica. For the SNP data, a different set of genetic diversity parameters was estimated using the populations program in STACKS 2.4 ; however, only the results for C. vettonica are shown. In addition, a third matrix was studied taking into account the populations of both species, except the populations of the Aurela and Salor rivers of C. paludica, due to the close relationship that these localities showed with the localities of the Erjas sub‐basin of C. vettonica.Different genetic diversity parameters were estimated for both C. vettonica, deviations from a model of mutation‐drift equilibrium for MT‐CYB were tested using Fu's Fs , Last Glacial Maximum , and Last Interglacial . The quality of the model was evaluated by the Area Under the Curve (AUC) derived from the Receiver Operating Characteristic (ROC).To identify the potential niche of 2.7C. vettonica were reconstructed using both the S‐DIVA  and C. paludica (S2) with high support  was not monophyletic since many individuals identified morphologically as C. vettonica from the Alagón sub‐basin populations were found in both lineages . The lineage of C. paludica (S2) was also not a monophyletic group since one individual identified morphologically as C. paludica from the Salor sub‐basin was included in the C. vettonica lineage (S1). Within C. vettonica (S1), there were two well‐differentiated lineages  included populations from the Ponsul, Aravil, and Erjas sub‐basins located along the western limit of the species’ distribution range, and one individual from Salor River  ranged from 0.3% to 1.2%  were more related to those of C. paludica from the populations in the Salor, Tamuja, Ibor, Huebra, and Yeltes rivers. The remaining populations of C. vettonica included in the SNP analysis belonging to the V2 and S2 lineages  did not show a clear phylogenetic structure with several intermediate individuals.The unrooted phylogenetic tree of 3.2MT‐CYB was consistent with the phylogenetic reconstruction for MT‐CYB. The network showed two main groups separated by 20 mutational steps corresponding to the species C. vettonica and C. paludica , the network was more informative than the phylogenetic reconstruction since there were three well‐differentiated haplogroups . The first haplogroup (V1) clustered populations from the Águeda sub‐basin in the Duero Basin and in the Tagus Basin, those from the western part of Alagón sub‐basin, plus one individual belonging to Jerte River (eastern Alagón sub‐basin)  presented the most common haplotype of the haplogroup. Haplogroups V1 and V2 were separated by three/four mutational steps, and V3 was separated from V1 by 10 mutational steps. In the group comprising haplotypes found mainly in C. paludica (S2), a subset was composed of individuals identified as C. vettonica from the Alagón and Erjas sub‐basins  along with other individuals of C. paludica from other Tagus and Duero tributaries. However, no evidence of a geographic structure was found within S2. The haplotype network for the nuclear marker (RAG1) showed a single group in which all studied populations of both species were represented and therefore was of low resolution  Figure . These p) Figure . The secn Figure .MT‐CYB data, the highest percentage of variance among groups was when the groupings were comprised of the lineages found in the previous analysis , and no significant differences were found, except for the Acebo River, which showed a high level of differentiation with the Árrago river .According to the AMOVAs with the C. vettonica matrix was K = 4 and K = 2 for Puechmaille and ΔK methods, respectively  and Caparro and Erjas rivers in the Tagus Basin  , Caparro to the second group (blue), and the rest of the populations presented a mixture of both groups. When we analyzed the populations of both species, the most probable number of subpopulations was K = 6 and K = 2 for Puechmaille and ΔK methods, respectively , with Gata having a small contribution from the third group (purple) in all individuals , which also comprised approximately 29% and 15% of the total composition of the Aurela and Salor rivers, respectively, with very small contributions from the second (blue) and fourth (pink) groups in some individuals. The results for K = 2 were consistent with those obtained for K = 6. Mayas and Gata belonged to the first group (orange), Ibor, Tamuja, Huebra, and Yeltes to the second group (blue), and the rest of the populations presented a mixture of both groups.The results of the SNP structure analyses showed that the most probable number of subpopulations (genetic groups) for ly Table . For K =) Figure . The othly Table . For K =3.3C. vettonica based on MT‐CYB were high  and Caparro  rivers  and Gata (1 vs. 0), whose numbers remained nearly the same as when only C. vettonica was considered. In the upper Erjas and Caparro rivers, the number of private alleles decreased to eight and two, respectively  and SNP data, was detected for C. vettonica, in line with previous molecular studies of MT‐CYB  is greater than between populations from the two main basins (Duero and Tagus basins). Similarly, greater differentiation within the same sub‐basins than between different sub‐basins has been found for Achondrostoma salmantinum, which has a restricted distribution area that partially overlaps with that of C. vettonica  than species of Luciobarbus and Squalius .The population structure of 4.1.1C. vettonica, which are currently assigned to single OCU  showed the lowest diversity values of all the populations. Two possible explanations may account for this low diversity: in the first case, these populations may have not yet recovered from a recent bottleneck that occurred following their separation from the western Alagón sub‐basin, and in the second, they may have not yet diversified following a founder event that occurred after the estimated dispersal of a small number of individuals from the rivers of the western Alagón sub‐basin .The Duero populations of 4.1.2MT‐CYB, and in the SNP analysis, presented a high level of admixture with C. paludica. These results suggest extensive genetic introgression of C. vettonica with its sister species C. paludica, except in Hurdano and Ladrillar rivers, where only one individual from each was studied. Therefore, regardless of the different levels of genetic introgression, we propose that these populations all be assigned to the OCU that was recently established by Doadrio et al.  with those in the Salor and Aurela sub‐basins, particularly the latter with which it was most closely related phylogenetically. The relationship between these populations was more evident when the Aurela and Salor populations were removed from the SNP genetic diversity analysis, which resulted in an increase in the number of private alleles from eight to 141. However, in the SNP structure analysis, only one individual belonging to the Erjas population had a very low contribution of the genetic group assigned to C. paludica. Altogether, the results do not support the presence of genetic introgression in the upper Erjas, where no mitochondrial haplotypes of C. paludica were found. Moreover, higher values of genetic diversity would be expected for the SNP data if there were introgressions, as occurs in the eastern Alagón sub‐basin populations.According to the mitochondrial analyses, the Ponsul, Aravil, and Erjas sub‐basin populations of C. vettonica and C. paludica but also other species of European Cobitis , which split from the two Alagón sub‐basin lineages (V1‐V2) during the Pleistocene . Based on the ancestral area reconstructions, the diversification of these lineages occurred in the area currently comprising both the Erjas and Alagón sub‐basins. This implies that the Erjas acquired its current configuration during this period of time, interrupting any gene flow that may have been established by connections between tributaries on the right bank of Erjas River and the left bank of Árrago River. Subsequently, following the isolation and diversification of the Erjas sub‐basin population from the rest of the populations, individuals from this sub‐basin dispersed to downstream areas of the Tagus Basin . These dispersal events could have taken place as a consequence of climatic changes associated with the LGM, as supported by our niche models which indicated the Ocreza, Nisa, Sever, Ponsul, Aravil, and Salor sub‐basins as more suitable habitats than the rest of the rivers in the current distribution range of the species. These events gave rise to both the colonization of new areas to the west (Ponsul and Aravil sub‐basins) and the sympatry with C. paludica, which led to introgression events.The earliest divergence in C. vettonica in the lower Alagón sub‐basin. In the second, they diverged after the union of the banks due to climatic changes during the LGM. According to our niche models, the optimal ecological conditions for C. vettonica were located further downstream and, as predicted by phylogeography theory, the latitudinal distribution of organisms retracted southwards  also occurred during the Pleistocene . The complex geology of the Alagón sub‐basin is evident by the anomalous current trajectory of some of its rivers  and those of S. pyrenaicus (distributed in the Tagus Basin) in the Árrago River   would be affected by this phenomenon. It could lead to the extinction of C. vettonica in these areas or to adaptive variations, resulting in a major loss of the genetic diversity of this species.For OCU IV, there was no genetic distinction between the non‐introgressed (upper Erjas) and putatively introgressed  populations. Despite this, any management plan must take into consideration this potential introgression as the indiscriminate mixing of individuals from these two groups could lead to genetic introgression with The authors declare no conflict of interest.Andrea Corral‐Lou: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Methodology ; Resources (lead); Software (lead); Validation (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing (lead). Silvia Perea: Conceptualization ; Data curation ; Formal analysis (supporting); Methodology (supporting); Supervision (supporting); Writing – review & editing . Anabel Perdices: Resources (supporting); Supervision (supporting); Writing – review & editing (supporting). Ignacio Doadrio: Conceptualization (lead); Data curation ; Funding acquisition (lead); Investigation (lead); Methodology (lead); Resources ; Supervision (lead); Validation (lead); Visualization ; Writing – review & editing .Supplementary MaterialClick here for additional data file.


Sorbus aucuparia (Rosaceae) is a small tree species widely distributed in Eurasia. The Hyrcanian forest is the southernmost distribution limit of this species. Severe habitat degradation and inadequate human interventions have endangered the long-term survival of this species in this region, and it is necessary to develop and apply appropriate management methods to prevent the loss of its genetic diversity. In this study, we used 10 SSR markers in order to evaluate the genetic diversity of this taxon. Leaf samples were collected from five known populations of S. aucuparia throughout its distribution area in the Hyrcanian forest. Expected heterozygosity ranged from 0.61 (ASH) to 0.73, and according to the M-ratio, all populations showed a significant reduction in effective population size, indicating a genetic bottleneck. Global FST was not statistically significant and attained the same values with and without excluding null alleles (ENA) correction (FST = 0.12). Bayesian analysis performed with STRUCTURE defined two genetic clusters among the five known populations, while the results of discriminant analysis of principal components (DAPC) identified three distinct groups. The average proportion of migrants was 22. In general, the gene flow was asymmetrical, with the biggest differences between immigration and emigration in Barzekoh and Asbehriseh. The Mantel test showed that there was no significant correlation between genetic distance (FST) and geographic distance in S. aucuparia. The best pathway for theoretical gene flow is located across the coast of the Caspian Sea and significant spatial autocorrelation was observed in only one population. In order to reduce the extinction risk of very small and scattered populations of S. aucuparia in the Hyrcanian forest, it is very important to establish and/or enhance the connectivity through habitat restoration or genetic exchange. The Hyrcanian forest, located along the southern coast of the Caspian Sea in Iran and Azerbaijan, is one of the most important biodiversity centers on our planet . The areSorbus aucuparia L.) is one of the most important species of the genus Sorbus, which has a medicinal value, and has a wide natural range in areas with low and high altitudes from the Atlantic coasts of Europe to the Kamchatka Peninsula and East China in Asia [S. aucuparia, with only a few and small populations of this species remaining in this region. The rowan tree is distributed in the Hyrcanian forest at higher altitudes in mountainous regions, reaching the upper forest limit (1800–2800 m a.s.l.) and often growing on rocky slopes [The rowan tree  What is the genetic diversity of S. aucuparia within and between its natural populations in Iran? (2) What is the spatial genetic structure of natural populations of S. aucuparia in the study area? (3) What is the migration rate and gene flow between the populations of this species? Finally, based on our results, we are discussing the conservation implications and measures needed for the long-term conservation of this species in the Hyrcanian forest.This study was designated to investigate the genetic diversity and population genetic structure of S. aucuparia throughout its distribution area in the Hyrcanian forest . The PCR program involved an initial denaturation step of 5 min at 94 °C, followed by 30 cycles at 94 °C for 30 s, the appropriate annealing temperature for 30 s, 72 °C for 40 s, and an extension cycle of 1 min at 72 °C. PCR product was run on 8% polyacrylamide gel and dyed with silver nitrate protocol [DNA was extracted from the leaf tissue using the CTAB methods ,32 with protocol . The mulST were estimated using FREENA and tested with bootstrapping over loci [The null allele frequencies of each locus were assessed using Microchecker 2.2.3 software . The avever loci . The sigST) and Nm (haploid number of migrants) within the population were obtained. The Bayesian algorithm implemented in STRUCTURE [5 MCMC iterations, 104 burn-in, and 10 independent runs with the maximum number of clusters set to K = 6. Evanno’s delta K method from CLUMPAK software was used to choose the best K. Function ‘find.cluster,’ implemented in the adegenet package in R, was used to estimate the optimal number of clusters for the DAPC. Next, the ‘dapc’ function was used to perform this analysis. To estimate the contemporary dispersal patterns and determining the degree of connectivity in populations under study, assignment analysis was done by GENEALEX 6.501 software. In order to infer historical gene flow (Nm) patterns, MIGRATE-N v3.6 [5). The software CIRCUITSAPE [Analysis of molecular variance (AMOVA) among and within populations was performed using GenAlex ,40. FromTRUCTURE  was usedTRUCTURE  providedE-N v3.6  was usedE-N v3.6 ,45. FourCUITSAPE ,47 was uST with ENA correction) was tested against the matrix of spatial distance between populations created using the program QGIS.Patterns of isolation by distance  were inSpatial autocorrelation analysis  was perfAnalysis of 10 microsatellite loci in 78 individuals (genets) showed 41 different alleles. The number of different alleles per locus ranged from 3 (MSS5) to 5.83 (MSS9). The values of He and Ho per locus varied from 0.47 (MSS5) to 0.73  and from 0.23 (MSS9) to 0.82 (MSS1), respectively. The highest and lowest frequency values of null alleles were in MSS9 (0.28) and MSS16 (0.003), respectively. The mean null allele frequency for all examined populations was 0.10 .Genetic diversity estimates obtained for each population at the genet level are summarized in M-ratio, all populations showed a significant reduction in the effective population size, indicating a genetic bottleneck . This result suggests that the presence of null alleles does not influence the level of differentiation. The pairwise FST ranged from 0.005 (between BAN and LOM) to 0.08 (between BAN and ASH), indicating a varied level of differentiation among populations  and geographic distance in S. aucuparia . However, a resistance analysis made in CIRCUITSCAPE, with elevation as a matrix of resistance against the FST matrix, indicated that elevation could be a significant barrier for gene flow across the eastern and western range of the species. CIRCUITSCAPE models the connectivity between stands as a landscape resistance distance (isolation-by-resistance). In our analysis, altitude was used as a resistance raster, and paths without topographical barriers were estimated as best ways for a gene flow. The map generated by CIRCUITSCAPE showed the path of high conductance that represents possible pathways of gene flow among populations; theoretical conductance is presented in The Mantel test showed that there was no significant correlation between genetic distance  with a small area and high tree density that is separated about 100 km from the main region of the Hyrcanian forest, showed the highest heterozygosity, allelic richness, and number of private alleles. An appropriate interpretation is that this population is the nearest to the European populations, and it may has acted as a receiver of genes from western and south-eastern Europe, especially from countries with access to the Black Sea (Turkey and Georgia). On the other hand, the BAN population, as the easternmost population of S. aucuparia in the Hyrcanian forest, showed high heterozygosity and private alleles. There are several examples where genetic diversity within populations showed an increase towards species distribution margins [S. torminalis. Additionally, Jankowska-Wroblewska et al. demonstrated that peripheral populations of S. torminalis have relatively high levels of genetic diversity [Our study revealed that the Hyrcanian populations of s Sorbus ,51,52 anf Europe . An inte margins ,55. Kuce margins  found hiiversity .S. aucuparia populations in the Hyrcanian forest are considered as a range-edge population of this species in the Northern Hemisphere. These range-edge populations are isolated from the populations in Europe and are vulnerable to genetic drift [Sorbus [S. aucuparia populations in the Hyrcanian forest. Additionally, because of their often high levels of heterozygosity, outcrossing trees such as S. aucuparia can be disproportionately vulnerable to a reduction in pollen-mediated gene flow, which can mask deleterious recessive alleles that, if expressed, can lead to a reduction in population’s fitness [On a global scale, the ic drift . Inbreedic drift ,58. Thesic drift . All fivic drift . Given tic drift , it seem [Sorbus , positiv fitness .S. aucuparia using the M-ratio with positive inbreeding. Genetic drift is inversely related to the effective population size  and typSorbus aucuparia is usually found above the timberline and in the rocky and steep habitat of the Hyrcanian forest, while the KH habitat is a dune forest with relatively suitable soil and a much smaller habitat slope than other habitats. This could be the reason for the higher genetic diversity and completely different genetic structure of S. aucuparia in this habitat. Due to the distance of at least 500 km of the population of BAN from the other four Hyrcanian populations and possibly the complete cessation of gene flow over time, it has been differentiated from other populations.In the periphery of a species range, abiotic and biotic environments may differ from those in the center, and there are likely less suitable habitats . HabitatS. aucuparia populations in the Hyrcanian forest, showing evidence of a past bottleneck. To reduce the extinction risk of very small and isolated populations of S. aucuparia in this region, there is a need to establish and/or enhance the connectivity between isolated populations through habitat restoration or genetic exchange. In fact, gene movement via seedling could provide a pathway for dispersal and, as a result, greater genetic diversity retention through increased effective population size, reducing the effects of drift [S. aucuparia should be identified to reduce the geographical distance for gene flow among the main populations. Additionally, improving the habitat quality and increasing the density of trees by planting additional seedlings should be used as another alternative to increase connectivity among neighboring trees and reduce the inbreeding depression within the populations.The results of our study demonstrate a positive inbreeding in of drift ,64. To a


Lolium multiflorum in Oregon. We used phenotypic approaches, as well as population genomic and gene expression analyses, to determine whether known mechanisms were responsible for glyphosate resistance and whether resistance phenotypes evolved independently in different populations, and to identify potential loci contributing to resistance. We found no evidence of genetic alterations or expression changes at known target and non‐target‐site resistance mechanisms of glyphosate. Population genomic analyses indicated that resistant populations tended to have largely distinct ancestry from one another, suggesting that glyphosate resistance did not spread among populations by gene flow. Rather, resistance appears to have evolved independently on different genetic backgrounds. We also detected potential loci associated with the resistance phenotype, some of which encode proteins with potential effects on herbicide metabolism. Our results suggest that Oregon populations of L. multiflorum evolved resistance to glyphosate due to a novel mechanism. Future studies that characterize the gene or genes involved in resistance will be necessary to confirm this conclusion.The multitude of herbicide resistance patterns that have evolved in different weed species is a remarkable example of the rapid adaptation to anthropogenic‐driven disturbance. Recently, resistance to glyphosate was identified in multiple populations of  Gulf has been used widely as a reference susceptible population for L. multiflorum herbicide resistance characterization , acetyl‐CoA‐carboxylase , and EPSPS (glyphosate) inhibitors was the most common. Populations susceptible to all herbicides tested were also included in the study. However, in this study, we focused only on glyphosate because of its importance in agricultural and noncropping areas, and because the NTSR mechanisms of resistance are largely unknown.A set of 16 y Figure . The popy Figure . From ea2.2L. multiflorum population. When plants reached the 23‐BBCH (three tillers) growth stage , which is within the recommended field rate for most crops where glyphosate is used.To confirm glyphosate resistance in the sampled populations, we performed a shikimate accumulation assay . Survival data were also collected 30 days after glyphosate treatment by giving a “0” for plants that survived, and “1” to individuals that died.Forty‐eight hours after glyphosate application, the youngest fully expanded leaves from a different tiller were collected, weighed, and stored in Eppendorf tubes in a −80°C freezer until shikimate accumulation quantification was performed as described in Shaner et al. . Briefly2.3L. perenne sites, we included a mean of 15 samples per site (range 13–16 samples). DNA was extracted with a commercial kit following the manufacturer's recommendations , followed by sample preparation according to the method developed by Elshire et al. (ApeKI (New England BioLabs Inc.) for 2 h at 75 C. Barcodes (4–8 bp) and common adapters were ligated with 400 U T4 DNA ligase (New England BioLabs Inc.) at 22°C for 1 h, followed by enzyme inactivation at 65 C for 30 min. Samples were then multiplexed  and purified using a commercial kit . A PCR amplification step was performed using P1 and P2 as primers with 14 cycles  and a final extension at 72°C for 5 min . A final library clean‐up step was performed before sequencing . Library quality control was performed with qPCR and a bioanalyzer . Sequencing (three libraries of 96 multiplexed samples each) was performed with a HiSeq 3000 in 150‐bp paired‐end mode at the Center for Genome Research and Biocomputing (CGRB) at Oregon State University.A genotype‐by‐sequencing study was performed to identify SNPs for population genetic analyses. From each of the 16 Oregon populations, as well as the Gulf, PRHC, and e et al. . Brieflyprocess_radtags module, with the ‐‐paired, ‐c, ‐q, and ‐r flags. De novo assembly of loci was optimized as recommended by Paris et al. . Second, cstacks was used to build catalog loci. Third, sstacks was used to align the de novo loci to the catalog. Fourth, tsv2bam was implemented to transpose sequencing data to be oriented by locus, and paired‐end reads were integrated to each single‐end locus assembled. Finally, SNPs were called with the gstacks module. An integrated approach was used to align the catalog loci to the draft genome of Lolium perenne, a close relative to L. multiflorum , followed by vcftools , and exclude sites that were missing in more than 50% of the individuals (‐‐max‐missing 0.5) This dataset was used for the population genetic analyses described below.SNPs were filtered with the 2.4EPSPS gene also played a role in glyphosate resistance in the Oregon populations of L. multiflorum. DNA from 5–6 samples per population was used to amplify a 338‐bp fragment of the EPSPS gene containing positions 102 and 106, which is located in exon 2 of this 1536‐bp‐long gene (based on the coding sequence of Oryza sativa). We used primers described by Adu‐Yeboah et al. (2014) for PCR amplification  following the manufacturer's recommendations. BigDye Terminator v3.1  was used for sequencing in an ABI 3730 sequencer (Applied Biosystems Inc.). To determine whether resistant individuals were differentiated from susceptible individuals, we performed a multiple alignment in Geneious Prime 2020.0.4 (www.geneious.com) with a reference EPSPS sequence from L. multiflorum  Link . This transporter is localized to the plasma membranes and is believed to be involved in the efflux of glyphosate from the cytoplasm into the apoplast.Copy‐number variation of EPSPS from the L. multiflorum populations from our study , 0.25 µl of forward and 0.25 µl of reverse primer at 10 µM, and 2 µl of genomic DNA normalized to 5 ng µl−1, and were performed in a StepOnePlus™ qRT‐PCR System (Applied Biosystems Inc.). Amplification was carried out with an initial denaturation cycle at 98°C for 3 min, and 40 cycles of 98°C for 15 s, followed by 64°C for 60 s. Melt curves were generated to assess specificity of primers, and reaction products were run in a 1% agarose gel to assess fragment size and number. Primer efficiency was also performed with both primer sets. EPSPS copy number from resistant populations, as well as lm_105, was compared with Gulf.Primers were designed to amplify a 68‐bp fragment in the coding region of dy Table , as wellABCC8, we designed several primer pairs to amplify a region of this gene based on the available sequence from E. colona (NCBI accession number MT249005.1). After Sanger sequencing a 420‐bp fragment from L. multiflorum, we designed shorter, L. multiflorum‐specific primers for quantitative real‐time PCR . RNA was extracted using a commercial kit , followed by cDNA synthesis . The qRT‐PCR was performed as described for EPSPS, with three technical replicates. The experimental runs were pooled into one dataset based on a Levene test of homogeneity of variance (p = 0.64). Copy‐number variation and gene expression were quantified using the 2‐ΔΔCt method  in R with a Bonferroni correction considering 10 populations. ABBC8 gene expression from resistant populations, as well as lm_105, was compared with Gulf.For quantifying the gene expression of the ABC transporter gene, CR Table . ALS was2.6stacks to compute the nucleotide diversity (π), expected heterozygosity (HE), observed heterozygosity (HO), and FIS (inbreeding coefficient) for each population. To test whether levels of genetic diversity were different between resistant and susceptible populations, we used a two‐sample Wilcoxon test in R for each measure of genetic variation.To quantify levels of genetic variation within populations, we used prcomp function was applied to a scaled SNP dataset using a custom R script, and eigenvalues were plotted with ggplot2  was implemented to obtain an overview of the population structure among samples. PCA is a model‐free data summary technique, enabling the identification of population structure regardless of the historical underlying process shaping present levels of genetic variability McVean, . SeparatWickham, .vcf was sorted and converted to HapMap format using TASSEL  as outlined in Liu et al. , whereas 100% survival was observed in the known glyphosate‐resistant population (PRHC). Out of the 16 Oregon field populations analyzed, eight were glyphosate‐resistant and were characterized by an exceptionally low accumulation of shikimate . PRHC was included as a positive control for the presence of a functionally relevant amino acid substitution in EPSPS at position 106 that confers resistance. As expected, a mutation was found in EPSPS at position 106 from PRHC, causing a proline‐to‐alanine substitution. There were synonymous mutations found among the sequenced individuals from Oregon, but no mutation was found in common among all resistant individuals sequenced and no nonsynonymous mutations were observed.The sequence analysis of EPSPS copies across the surveyed populations relative to the housekeeping gene ALS  reads per lane that were fed to the Stacks pipeline. The average number of reads per sample was approximately 1,800,000 . The gstacks module indicated the mean coverage was 12.5X . Following the filtering steps in the populations module and vcftools, 2,193 SNPs were retained for population genetic analyses.After removing reads with adaptor sequence, without barcodes and restriction enzyme cut sites, and discarding low‐quality reads, the ST indicates little differentiation among populations in Oregon   Figure .L. multiflorum samples share little ancestry with plants from the perennial species (L. perenne)  Figure . HoweverL. perenne, PRHC, and Gulf are removed from the analysis, additional differences are observed among the Oregon populations. Consistent with the PCA, at K = 2, the resistant and susceptible samples are not assigned to separate clusters. Rather, there is extensive shared ancestry among these samples, further supporting a recent common ancestor among all of the Oregon populations. However, at higher values of K, multiple distinct groups become evident among the glyphosate‐resistant populations. There is some shared ancestry between the resistant and susceptible populations, as well as unique population structure among some susceptible populations, but there is no evidence that all resistant populations have the same historical pattern of ancestry. These results suggest that glyphosate resistance likely has evolved independently on different genetic backgrounds. Moreover, if resistance evolved once and was shared among populations, we might expect that resistant populations would show reduced levels of genetic diversity. However, we did not observe statistical differences when population genetic estimates  were compared between the resistant and susceptible populations  included 28,078 sites, with imputation accuracy of 0.9506. Between each pair of resistant and susceptible populations, overall levels of population differentiation were quite low. Median FST varied from 0.022 to 0.032, depending on the population, with most of the third quartile of the distribution below 0.067  in Australia  J. D. Sauer in Mississippi  A. J. Scott plants had up to 10 copies of the gene  in the dataset, strong and recent natural selection should result in long blocks of LD between SNPs reasonably tightly linked to functional mutations. By contrast, it is likely that the diverse patterns seen in these resistant Oregon populations are due to mutations (maybe at different loci) that pre‐dated the widespread use of glyphosate and segregated at low frequency in the ancestral population. To test this scenario, a different approach aimed at detecting selection on standing genetic variation would be better suited than one that searches for shared outliers. However, the lack of shared outliers is consistent with our analyses of population structure, further supporting the conclusion that resistance does not have a single origin in these Oregon populations. The assembly of a reference genome for L. multiflorum may aid in future studies as well. Given the apparent complexity of the origin of resistance in these populations, future studies that sequence whole genomes from these populations will be necessary to test these alternate hypotheses.Consistent with a multiple‐origins model, our analysis of genetic divergence across the genome of ST that were in common between individual resistant populations and each of the susceptible populations. Gene annotation and ontology analyses of genomic contigs containing these genes identified several molecular functions potentially associated with resistance. For example, we detected genes involved in the detoxification of xenobiotics, most notably cytochrome P450 genes. Genes in this family have been suggested to be involved in glyphosate resistance by enhancing herbicide degradation in other populations of L. multiflorum, L. rigidum Gaudin, and L. perenne outside of Oregon (Suzukawa et al., L. multiflorum and other weed species, the genetic and molecular mechanisms remain largely unknown (Suzukawa et al., Even though no outliers were consistently found between all pairs of resistant and susceptible individuals, we did find SNPs with high FL. multiflorum populations from Oregon. Elucidating the molecular mechanisms of herbicide resistance is crucial for the improvement in weed management practices. Although target‐site resistance has been described frequently for many herbicides with different mechanisms of action, the underlying molecular characteristics that confer non‐target‐site resistance remain largely unknown (Baucom, This research suggests that glyphosate resistance evolved multiple times in  Baucom, . Althoug Baucom, , a more The authors have no conflict of interest to declare.CAB designed the research and performed the experiments. CAB and MAS performed the statistical analysis, data interpretation, and manuscript preparation. CAB and MAS contributed equally.Supplementary MaterialClick here for additional data file.


The demographic history of South and Southeast Asia (S&SEA) is complex and contentious, with multiple waves of human migration. Some of the earliest footfalls were of the ancestors of modern Austroasiatic (AA) language speakers. Understanding the history of the AA language family, comprising of over 150 languages and their speakers distributed across broad geographical region in isolated small populations of various sizes, can help shed light on the peopling of S&SEA. Here we investigated the genetic relatedness of two AA groups, their relationship with other ethno-linguistically distinct populations, and the relationship of these groups with ancient genomes of individuals living in S&SEA at different time periods, to infer about the demographic history of this region.We analyzed 1451 extant genomes, 189 AAs from India and Malaysia, and 43 ancient genomes from S&SEA. Population structure analysis reveals neither language nor geography appropriately correlates with genetic diversity. The inconsistency between “language and genetics” or “geography and genetics” can largely be attributed to ancient admixture with East Asian populations. We estimated a pre-Neolithic origin of AA language speakers, with shared ancestry between Indian and Malaysian populations until about 470 generations ago, contesting the existing model of Neolithic expansion of the AA culture. We observed a spatio-temporal transition in the genetic ancestry of SEA with genetic contribution from East Asia significantly increasing in the post-Neolithic period.Our study shows that contrary to assumptions in many previous studies and despite having linguistic commonality, Indian AAs have a distinct genomic structure compared to Malaysian AAs. This linguistic-genetic discordance is reflective of the complex history of population migration and admixture shaping the genomic landscape of S&SEA. We postulate that pre-Neolithic ancestors of today’s AAs were widespread in S&SEA, and the fragmentation and dissipation of the population have largely been a resultant of multiple migrations of East Asian farmers during the Neolithic period. It also highlights the resilience of AAs in continuing to speak their language in spite of checkered population distribution and possible dominance from other linguistic groups.The online version contains supplementary material available at 10.1186/s12915-021-00981-x. The peopling of South and Southeast Asia (S&SEA) is complex and contentious. Occupying about 5% of the total landmass, S&SEA is home to over 26% of the world population  of diverIt has been estimated that the Austroasiatic language family consists of 150 languages with approximately 65 million speakers . AAs areMost previous studies on AAs of S&SEA have focused on studying uniparental markers , 24, 29 To address these questions, we investigated the genetic relatedness of the AA groups (AAI and AAM) along with their relationship with other ethno-linguistically distinct populations in South, Southeast and East Asia. Subsequently, we studied the relationship of these groups with genomes of 43 archaic individuals living in S&SEA at different time period (200 to 8000YA) to infer about the demographic history of this region.The AAs are scattered discontinuous population isolates, surrounded by many other populations. In order to study the genetic relationship of the AAs, with respect to their geographical neighbors, we looked into the population genetic structure that exists among them. We pooled common SNPs genotyped in three different platforms  . We also observe distinct ancestries being identified for some subgroups: like Seletar and MahMeri [Seletar as a separate subgroup from other ANS when we increase K from 4 to 5 ]. Identifying such small isolated populations as separate ancestries (brown for Seletar and gray for MahMeri in the figure) is probably because of the large impact of random genetic drift upon their small effective population sizes, resulting in a quick change to their allele frequency distribution. The impact of drift is also evident in the elevated identical by state (IBS) segments between individuals of these subgroups and a higher proportion of Runs of homozygosity (ROH) within the genome of each individual of these subgroups. , indicating similarity among the groups. The individuals classified as ANI are genetically similar to individuals of populations from Central South Asia , while the individuals classified as ATB are similar to population groups from EA (pink). We also observe two distinct ancestries among the EA based on their geographic location: ancestry predominant among the “Southern” East Asian (pink) and increasing proportion of “Northern” East Asian ancestry (orange), as we move northwards.We estimated the genomic ancestries and admixture proportions at an individual level using model-based clustering analysis as implemented in ADMIXTURE  Fig. 1cc1. We raulations  and showFor a closer perspective, we conducted a PCA Fig. d on all We investigated whether the clustering pattern observed in our PCA analyses is robust if we take linkage disequilibrium (LD) and haplotype structure into consideration. Using fineSTRUCTURE , the hapFst . Populations like Seletar, MahMeri, and CheWong, exhibiting high overall Fst with all populations, strengthen the possibility of these populations being strongly drift affected  compared to AAM (mean = 0.065).To investigate substructure at the population level, on the same set of populations  betweenockerham   shows an overall concordance of genetics and geography. As we are primarily interested in exploring the diversity and evolutionary history of the AA speakers, we repeated the same with the AA populations (AAI + AAM). When we look into the correlation by choosing each population from AAM group compared pairwise with each population from the AAI group, the correlation turns out to be negative (Correlation coefficient = − 0.446); in agreement with our previous claim that the genetics-geography concordance is not uniform. We repeated the same analysis by masking non-AAI ancestry within the AAI subgroups  we used the African Mbuti Pygmy from the HGDP dataset as the outgroup (the results are invariant to the choice of other African populations as outgroup and are not shown). We measured the f3 values between AAM, AAI, and TB  between AAM and AAI groups of populations, indicative of an exclusive and recent shared genetic history. The mean f3 values were higher (mean = 0.063) for TB-AAM than between TB-AAI (mean = 0.018).We recollect that in our PCA Fig. d, the TB f3 test . In our f3 calculations, we used the Mbuti Pygmy population as an outgroup. The AAM populations, especially the MahMeri, and the Seletar among the ANS, with long branch lengths from the leaf to the preceding node, show strong evidence of being affected by random genetic drift. Comparatively, the effect of drift is much less apparent in the AAI populations. Consistent with our other analyses, we observe that the TB populations share clade with the Malaysian populations, while AAI formed a distinct cluster. To indicate possible admixture between populations, TreeMix adds migration edge to the phylogeny. Assuming 3 admixture events in the TreeMix model (which explained 98.95% of the variance), we found a migration edge joining Tharu and the ancestral population of Jamatia and Tripuri (indicating admixture within the TB subgroups). We also observed another migration edge originating in the branch common to Temuan and MahMeri and terminating at the node common to Jamatia and Tripuri. The third edge on this tree is between the node common to Birhor and Korwa (AAI) and the node common to Jehai, Bateq, Kintaq, and Mendriq (AAM). The position of this edge suggests that this gene flow happened prior to the complete separation of all the AAI and AAM lineages, indicative of deep common ancestry.We also generated a phylogenetic tree with AAI, TB, and Malaysian populations using the program TreeMix v1.12   [. , share as low as 253 Mb and as high as 1745 Mb  , we esti , we estPopulations which currently live in geographical proximity, show a lot of sharing of genetic components as is evident in our ADMIXTURE analysis Fig. c. The prD, W), X), for all subpopulations of AAI (Y), AAM, and ATB (Z), a southern EA subgroup (W=Cambodian and Dai) and Mbuti Pygmy , i.e., D, Southern EA), Mbuti Pygmies)  between Southern EA and both TB  and AAM . Among the TBs the highest evidence of geneflow from Southern EA was obtained for Jamatia and Tripuri (mean for each = − 0.06) and among AAM, for MahMeri (mean = − 0.07). Unlike the AAM who had similar D values when compared to both Cambodian and Dai (mean = − 0.05), the absolute D values were higher  for TBs with Dai (mean = − 0.05) than with Cambodian (mean = − 0.04). This observation is in agreement to the fact that the Dai subgroup resides geographically more close to the TBs than the Cambodians do.Further evidence of gene flow between “Southern EA populations” , TB speakers of India, and AAM comes from D statistics , which mes) Fig. . D statip < 2.2e−16)  [Ne  followed by a gradual decrease from 400 generations onwards  . Using MNe)  as impleIn addition to genotypes of extant samples, we analyzed genotypes of 43 ancient DNA samples, excavated from different locations of SEA , 50, dat). These ancestries roughly correspond to AAI (purple in color) and EA (pink in color) while the AAM populations are split into 2 ancestries, one comprising mainly of the MahMeri like ancestry (gray in color) and the other comprising of Bateq, Mendriq, Jehai, and Kintaq (orange in color). On this we projected the ancient genomes    Group Anc_8K comprises the genome La368 which is nearly 8000 years old (8KYO), (b) Group Anc_4K comprise of ~ 4KYO genomes (I1859 and I1137), (c) Group Anc_3K comprise of ~ 3KYO genomes (I4458 and La364), (d) Group Anc_2K comprise of ~ 2KYO genomes (I1680 and In661), and (e) Group Anc_R comprise of ~ 250 years old genomes (Ma555 and Vt719). D statistics was computed for the five ancient genome groups with AAM subgroups, EA (Cambodian), and Mbuti Pygmies in separate branches , along with central and eastern India through peninsular SEA, overlaps with the pre-Holocene modern human settlement in Sunda and has been hypothesized as the corridor of first modern human migration to Sahul , 59. It From our initial population structure analysis , the existence of a genetic relationship between AAI and AAM is not clearly apparent. Rather, we find clear and separate clustering of AAI and AAM. Our findings are consistent with the inferences drawn by Chaubey et al. , who fouOur analysis of autosomal data indicates that in pre-Neolithic times, the ancestors to today’s Austroasiatic speakers had a widespread distribution, as tentatively claimed by Lipson et al. , possiblNe post-separation. The period of decline coincides with the advent of agriculture in this region and corroborates with findings of Chakraborty et al. [Our estimate of the separation time of AAI and AAM lineage, approximately 470 generations ago, predates the advent of agriculture in this region, suggesting Austroasiatic ancestry as a pre-Neolithic phenomenon and not associated with the spread of agriculture as has been widely suggested , 60–62. y et al. . This fuy et al. , the souNe of AAM and AAI 350 generations ago onwards  points towards forced isolation of foraging societies.Demographic factors are known to have a huge impact on culture and behavior of populations . LanguagWe also found clear differences in the genomic relatedness of ancient genomes to that of present-day AAs, before and after agricultural expansion. The oldest ancient genome (La368) in our study, nearly 8000 years old, was more similar to present-day AAI while the genomes from the period around 4 KYA and later bore more resemblance to present-day AAM. The agricultural expansion brought the EA farmers whose genetic ancestry got incorporated into the genomes of the original inhabitants of SEA. La368, however, lived in times when the migration of EA farmers had only just begun and therefore had minimum East Asian ancestry. Our results, on the other hand, show that present-day AAI also has negligible EA ancestry. This negligible EA ancestry (2.77% Southern Eastern ancestry and 0.41% Northern East Asian estimated using ADMIXTURE) can be a residual of the ancient admixture of Southern EA-like ancestry that we find in La368 and other ancient genomes. We also note the conspicuous similarity between AAI and AAM in the absence of Northern EA component in their genomes unlike the ATB. However, the EA component of AAI can also be derived from more recent admixtures with the contemporary TB populations living in close geographical proximity with the contemporary AAI populations. Thus, the similarity between the oldest genome in our study and contemporary AAI can be attributed to the fact that genomes of both were not influenced by East Asians. On the other hand, the genomes of the remaining ancient individuals were most likely influenced by gene flow from the incoming EA farmers to a varying degree, contributing to the similarity observed with genomes of present-day AAM who have a substantial EA ancestry. We also find that with a decline in the age of our ancient samples, there is an increase in EA-like ancestry indicative of a temporal shift in ancestry. Even previous analyses comparing AA-like and EA-like ancestry in ancient genomes , 50 reveOur findings agree with previous reports , 50 thatThe current study emphasizes the importance of both geography and language in reconstructing the population structure of India and SEA. Our study suggests that the ancestors to present-day AA speakers were the resident native population extending from Central India to mainland Southeast Asia. They were hunter-gatherers and spoke possibly some proto-AA language that has given rise to the present-day AA language family. The present-day Indian Austroasiatics and Malaysian Austroasiatics shared a common ancestor until about 10.5 KYA. Post-separation they had a disparate genetic history. Around 7 KYA, with the advent of agriculture, there was an ancestry shift in Southeast Asia. The distribution of AA hunter-gatherers started to shrink and their population size kept declining. As farmers from EA began migrating southwards to Mainland Southeast Asia, the population size of AA hunter-gatherers residing in SEA decreased further. Moreover, the East Asians interbred with local AA hunter-gatherers introducing “East Asian ancestry” in SEA. With subsequent migration waves, substantial EA ancestry was added to the native Austroasiatics residing in MSEA, including AAM in our study. The continuous migration also resulted in rapid fragmentation and isolation of the AA hunter-gatherer population. On the other hand, since the East Asians did not enter peninsular India, the “East Asian ancestry” was not introduced in AAs in India, who mostly retained their genetic ancestry while interbreeding locally with populations of ASI ancestry. The incoming wave of East Asians in SEA may not only have influenced the language of present-day TBs but also contributed to the temporal change in the genetic ancestry of Southeast Asia.We also report a unique phenomenon where, despite notable changes in genetic identity of individuals, the linguistic identity remains intact. The shared genetic ancestry of the AAI and the AAM that predates the arrival of East Asians and their isolation post local admixture with linguistically distinct neighbors, have contributed to their linguistic similarity. However, the language of AAI and AAM may have changed over time following their separation and isolation giving rise to the Mundari and Mon-Khmer branch of the Austroasiatic language respectively. Thus, this study not only sheds light on genetic history but also provides new insights to the linguistic history of India and Southeast Asia.http://pngu.mgh.harvard.edu/purcell/plink/), we merged this dataset with genotype data of 144 unrelated Malaysian individuals. The data was generated on Illumina Human Omni 2.5 array and was already in hg19 assembly [We obtained genotype data on 367 unrelated individuals belonging to Indian populations from the archives of National Institute of Biomedical Genomics . This daassembly . This isassembly , genotypassembly . We inclassembly , 32 geneassembly . These aassembly  comprisep < 10− 6) in any of the 14 subpopulations and were out of Hardy Weinberg equilibrium (p < 10− 2) in 2 or more subpopulations. The final dataset [We included only biallelic loci in our analysis. We removed all monomorphic variants and SNPs with alleles A/T and G/C from our analysis. To address the issue of insufficient data, we removed SNPs with missingness of more than 5% in the entire dataset, or SNPs that were missing in more than 25% of individuals in any of the 14 subpopulations listed in Supplementary Table 1c in Additional file  dataset  had 324,In order to understand the overall population structure and the genetic affinities of the individuals in our dataset, we performed principal component analyses (PCAs) using smartpca program of EIGENSOFT package . We perfIn order to compare the EIGENSOFT result with other Population Structure visualization methods, we did fineSTRUCTURE(v0.0.2)  analysisTo further explore the genetic relatedness, we used Beagle FastIBD (v4.1)  to identThen we looked at the distribution of these “normalized IBD segment lengths.” We also computed the number of IBD segments shared between each pair of subpopulation. The same method was applied for estimating “normalized IBD segments” between AAI and ATB and between AAM and ATB.The detailed results of the pairwise sharing between individuals from these disparate subgroups is available in Supplementary Table 3 in Additional file In order to understand how populations were related to each other through a common ancestor, the impact of drift and to obtain evidence of gene flow, we built ancestry graphs using TreeMix  version K possible ancestries in each run of the analysis on a given dataset, ADMIXTURE computes a cross-validation error (CVE) and estimates the proportion of each of the K ancestry in the genome of each individual of the dataset. The run with the minimum CVE error is considered to be the optimum number of K ancestries that best explains the data. We ran ADMIXTURE with all Mainland Indian and Malaysian populations along with HGDP dataset populations namely EA and CSA. This was done in 3 different ways (a) without LD pruning of SNPs, (b) with LD pruning of SNPs at r2 = 0.1, and (c) with LD pruning at r2 = 0.5. For each SNP set , (b), and (c)), we estimated the Standard error of the CVE estimate by running ADMIXTURE multiple times. Minimum CVE error in each case was observed at K = 9 but the lowest CVE was when r2 = 0.1 and K = 9. Plots were generated with results of LD pruned dataset at r2 = 0.1. Standard error was estimated for the ancestry proportion estimates at K = 9 using the moving block bootstrap approach implemented in ADMIXTURE. Standard error of each ancestry proportion estimate was generated by running 1000 replicates with K = 9 and r2 = 0.1.To infer the different ancestral components present in admixed populations and the proportions of each such component in an individual’s genome, we performed ADMIXTURE  (v1.3.0)Fst between each subpopulations of mainland India and Malaysia was estimated. These values were rounded to the third decimal place.Using PLINK  version Using PLINK, we estimated identity by state between individuals and homozygosity runs in the genome of each individual for 17 subpopulations. These subpopulations included all the five AAI subgroups, six AAM subgroups, and three ANS subgroups. Two subgroups of Andaman Island (Jarwa and Onge) who were known to have highly inbred and another subgroup from ANI (WBR) who were outbred from previous reports , 74 weref3 statistics measures the shared drift between two populations relative to an extremely diverged population outgroup. Using ADMIXTOOLS (v5.1) [f3 statistics of the form f3  where Mbuti Pygmy was the outgroup. Keeping X as an ATB subpopulation, Y was AAI or AAM subpopulation. With X being AAI subpopulation, Y was an AAM subpopulation. This allowed all pairwise comparison between subpopulations of ATB and AAM, ATB and AAI, and AAI and AAM.Outgroup S (v5.1) , we calcFst = 0.067), as source populations of AAM- and EA-like ancestry respectively. We chose Mendriq, CheWong, Cambodian, Tu, and Jamatia as recipients (having ancestry from each of the source populations as a result of admixture) and estimated the admixture time for each of them by creating coancestry curves. The rate of decay in the curves was calculated which was equal to the number of generations since admixture took place.Segments resulting from admixture follow an exponential distribution from which number of generations since admixture can be estimated . To dateThe variation in dates of East Asian admixture with AAM was further estimated by keeping the recipient population the same (Cambodians). For this, MahMeri and Jehai, two homogeneous AAM populations, were chosen as source for AAM ancestry. Yakut, Dai, Naxi, Japanese, and Tu who were located in different geographical locations were chosen as EA ancestry source.To estimate admixture between AAI and ASI, Birhor or Korwa and Paniya were selected respectively as references and Kadar or Ho as recipient. For AAI and TB admixture, Birhor and Jamatia were chosen as references for AAI and ATB ancestry respectively and Tharu as the admixed recipient.Z score value was indicative of gene flow between W and Z while a positive Z score would indicate gene flow between W and Y. Absolute Z score value greater than 3 was considered to be significant. We estimated D statistics using ADMIXTOOLS [The tree topology of our D statistics analysis is shown in Fig. MIXTOOLS . First wLocal ancestry estimation was done using RFMix  version Fst values and the geographic distances were used in the form of a matrix to estimate the Pearson correlation using the Mantel test.The longitude and latitude coordinates of each subgroup of mainland India and Malaysia were obtained. Haversine method was used to determine the geographical distance between each subgroup. The weighted Fst was calculated using PLINK. These new weighted Fst values and the same geographic distance were used to perform the Mantel test and to calculate the Pearson correlation.In a separate analysis, the non-AAI ancestry was masked using RFMix from the AAI individuals. For this, Birhors were kept as reference for AAI ancestry while TB and ASI represented their respective ancestry. The genomes of the rest of the AAI subgroups  were masked for the ASI and TB like ancestry using RFMix. Similarly EA ancestry was masked in AAM subgroups , considering MahMeri, CheWong, and Jehai as AAM-like ancestry and all EA population as EA ancestry. The genomes of the rest of the subgroups from mainland India and Malaysia remained same. For the unmasked and newly masked genomes, new pairwise weighted e) over time was estimated for all subgroups of AAI, AAM, ANS, TB, and EA. Fst values previously calculated using PLINK version 1.9, between subgroups of AAI and AAM, were subsequently used to estimate population divergence time between them using the method described by McEvoy et al. [Using “McEvoy” method implemented in NeON , effectiy et al.  and UPGMy et al. .We also analyzed 43 ancient genomes from Southeast Asia , 50 spanK value was obtained, we projected the 43 ancient genomes on the inferred ancestries (using the –P parameter).Using AAI, AAM, TB, and the same East Asian populations, we ran ADMIXTURE (v1.3.0) to calculate the CVE. Once the best DW)X), we used all AAM subpopulations as Z while W and X remained East Asian and Mbuti Pygmies respectively. For Y, we used 5 ancient genome groups (the details of the groups are in the main text) namely Anc_8K, Anc_4K, Anc_3K, Anc_2K, and Anc_R one at a time.In a separate D statistics analysis of the form Additional file 1:Supplementary Figure 1. ADMIXTURE analysis on populations from mainland India, Malaysia, East Asians (EA) of HGDP and Central South Asians (CSA) of HGDP. Supplementary Figure 2. IBS and ROH distribution. Supplementary Figure 3. Haplotype based clustering using fineSTRUCTURE on the mainland Indian and Malaysian population. Supplementary Figure 4. Fst estimation. Supplementary Figure 5. Genetic distance and geographic distance correlation Supplementary Figure 6. Population separation, drift and geneflow. Supplementary Figure 7. IBD estimation between AAM and AAI. Supplementary Figure 8. IBD estimation between TB and AAI. Supplementary Figure 9. IBD estimation between AAM and TB. Supplementary Figure 10. Admixed segment length. Supplementary Figure 11. Change in effective population size. Supplementary Figure 12. The age distribution of the ancient genomes, Supplementary Figure 13. ADMIXTURE analysis on all subgroups of AAI, AAM and TB and a few subpopulations of EA. Supplementary Figure 14. Ancestry correlation . Supplementary Figure 15. D statistics value estimation.Additional file 2: Supplementary Table 1. Classification of populations used in the study. Supplementary Table 2. Values of outgroup f3 statistics. Supplementary Table 3. Minimum maximum and total number of bases and total number of discrete segments of IBD shared. Supplementary Table 4. Ancestry proportion estimation. Supplementary Table 5. D statistics with AAM, AAI, Southern-East Asians and an African population. Supplementary Table 6. Admixture time estimation. Supplementary Table 7. Metadata on the 43 ancient genomes. Supplementary Table 8. D statistics with Ancient genomes, AAM, Southern-East Asians and an African population.


Pseudophryne guentheri) on wet and dry soils and quantified fitness-related traits upon hatching. TGF produced mixed outcomes in hybrids, which depended on crossing direction (origin of gametes from each sex). North-south crosses led to low embryonic survival if eggs were of a southern origin, and high malformation rates when eggs were from a northern population. Conversely, east-west crosses led to one instance of hybrid vigour, evident by increased fitness and desiccation tolerance of hybrid offspring relative to offspring produced from within-population crosses. These contrasting results highlight the need to experimentally evaluate the outcomes of TGF for focal species across generations prior to implementing management actions.Targeted gene flow (TGF) could bolster the adaptive potential of isolated populations threatened by climate change, but could also lead to outbreeding depression. Here, we explore these possibilities by creating mixed- and within-population crosses in a terrestrial-breeding frog species threatened by a drying climate. We reared embryos of the crawling frog ( Pseudophryne guentheri among geographically distant populations to investigate fitness impacts of targeted gene flow in counteracting a drying environment. When reared on wet and dry soils, embryonic survival depended on the origin of gametes from each sex, highlighting the need to experimentally evaluate the outcomes of TGF prior to implementing management actions.Rudin-Bitterli et al. created pure and reciprocal crosses of the Australian crawling frog  Yet, as habitats become fragmented, gene flow between isolated populations is constrained5, reducing genetic diversity and adaptive capacity. To mitigate these problems, targeted gene flow  is suggested for future management of threatened plant8 and animal species11. TGF involves the deliberate translocation of individuals or gametes within a species’ indigenous range to increase the genetic variation (and concurrently the fitness) of recipient populations, and to promote their adaptation to anticipated local conditions12. Many species show geographically based variation in adaptive traits15, and therefore TGF can potentially increase genetic variation of recipient populations in a desired direction16. In this way, TGF can potentially reinstate or increase natural gene flow between populations to facilitate adaptation17.Genetic variation in traits related to environmental tolerance can allow populations to rapidly adapt to environmental change21. Outbreeding depression can arise in F1 or later generations as a result of genetic incompatibilities  or via the dilution of local adaptation in the recipient population . While the risks of outbreeding depression may be overemphasised for some taxa24, the consequences of mixing populations are difficult to predict25. It is therefore important to assess the potential risks and benefits of TGF empirically, something only a handful of studies have done to date30.The current enthusiasm for TGF is tempered by concerns over risks associated with mixing previously disjunct populations and subsequent outbreeding depression, where offspring of local and introduced parents have a lower fitness than pure-cross offspring of the recipient population32. Many species have undergone climate-related extinctions or declines35, and species distribution models suggest that a further 12-47% of anuran species are at risk of extinction due to climate change36. Reduced precipitation is an obvious threat, as unshelled eggs and highly permeable skin render all amphibian life stages susceptible to desiccation and subsequent mortality33. However, as some amphibian populations show greater tolerance to desiccation stress than others37, TGF could be strategically employed to enhance the resilience of populations adapted to higher-rainfall regions that are now drying.Amphibians are among the most vulnerable taxa to climate changePseudophryne guentheri, a species in which populations are threatened by habitat loss and declining winter rainfall (see Methods). Application of TGF could potentially mitigate these dual threats to population persistence, but as outbreeding depression has been shown in laboratory crosses of a congeneric species30, TGF could also be ineffective, or have deleterious consequences. We evaluated TGF within a laboratory setting by creating pure and reciprocal crosses among four geographically distant populations. We then assessed phenotypic traits in the resulting offspring, comparing individuals reared on wet soils  to those reared on drier soils (−400 kPa) that significantly reduce survival and hatchling fitness, particularly in mesic populations38. Our experiments were designed to answer three questions: (1) do offspring from between-population crosses show hybrid vigour or reduced fitness; (2) does the effect depend on geographic or genetic distance; and (3) does the direction of the cross influence TGF outcomes?Here, we explore the potential of TGF to mitigate declines in population-level fitness using the crawling frog, P. guentheri from low-rainfall regions at the northern edge of the species’ range and from higher-rainfall regions close to the centre the species range . The population origin of females was a significant effect in six of the nine traits examined (Supplementary Tables 37. Offspring from pure crosses of the wettest population (AA) were most sensitive to dry conditions, with embryonic survival, hatchling wet weight, and developmental stage decreasing markedly, and hatchling malformations becoming more prominent if embryos were reared on dry soils  crosses differed in their desiccation tolerance in a pattern consistent with site water availability, in agreement with a related studyils Fig. . Furtherils Fig.  and 3.Embryonic survival was very low (~15%) when eggs from southern populations A or B were crossed with sperm from northern populations C or D, both in the dry and wet soil treatments Fig. . OffspriOffspring originating from long-distance crosses in the opposite direction (eggs from northern population C crossed with sperm from southern populations A or B), also had reduced survival in dry and wet treatments compared to pure CC crosses, although these effects were much less severe, with at least 55% of embryos hatching Fig. . ApproxiOffspring originating from hybrid crosses of two populations located near the centre of the species’ range  showed enhanced desiccation tolerance compared to AA crosses in all traits studied. As such, embryonic survival increased, and the time required to hatch and the number of hatchling malformations decreased in AB offspring reared on dry soils Fig. . AdditioOffspring originating from short-distance crosses in the opposite direction (eggs from population B crossed with sperm from population A) took longer to hatch after inundation relative to BB crosses, and their developmental stage at hatching was reduced slightly, both in the wet and dry treatments Fig. . OtherwiOffspring originating from short-distance crosses between the two populations at the northern edge the species’ range  barely differed in desiccation tolerance and fitness traits compared to CC offspring. Wet mass at hatching was increased slightly in the dry treatment, and developmental stage at hatching was marginally higher in the wet treatment in CD crosses, compared to pure-cross counterparts (CC).P. guentheri populations resulted in exceptionally diverse outcomes for traits putatively linked to fitness  was achieved both under benign developmental environments, and under more stressful conditions that may simulate future environments. This result is in stark contrast to outbreeding depression inferred in a related species (P. bibronii), where populations just 20 km apart were mixed and produced largely inviable offspring30. We discuss our key findings in turn below, and conclude with commentary on the potential application of TGF in this genus.Reciprocal crosses between three of four ess Fig. . Reduced41 and is frequently the result of Dobzhansky-Muller incompatibilities, where two species accumulate complementary genes that have no deleterious effects within species, but cause inviability or sterility when combined with genes from another species (complementary epistasis between loci)44. Additionally, cytonuclear incompatibility (=discordance) is likely to explain asymmetrical outcomes in reciprocal crosses of two species of newt (Triturus cristatus and T. marmoratus)45, and differential growth performance across mitotypes of the salamanders Ambystoma macrodactylum46. In our current study, reproductive isolation is positively correlated with pairwise genetic distance 47, and it appears that the drier range edge (northern) and mesic (southern) populations of P. guentheri have sufficient genetic divergence for reproductive isolation to occur.Our demonstration of reduced fitness in F1 hybrids originating from long-distance (~460 km) north-south crosses is consistent with asymmetric reproductive isolation, as reciprocal crosses produced different levels of fertilisation success and hybrid inviability. Asymmetric reproductive isolation is common at the species levelDA) of 0.348. Genetic analysis of 12  P. guentheri populations, based on > 12,000 single-nucleotide polymorphism (SNP) markers, revealed that the genetic distance between the northern  and southern  populations used in this study was well below this threshold (DA = 0.14)39. However, strong sexual selection might facilitate rapid evolution of reproductive isolation, even when the genetic distance between populations is low49. Furthermore, the relationship between hybrid inviability and genetic divergence can differ greatly, even between closely related taxa18. For example, crosses between Drosophila species that diverged 0.35 Myr ago resulted in outbreeding depression in the F250, while hybridisations between other Drosophila species isolated for much longer (3–4.3 Myr51) resulted in F2 heterosis52. This makes it difficult to predict the severity of outbreeding depression in advance, and builds the case to experimentally evaluate the outcomes of TGF for focal species across generations16.In frogs, the lower threshold for the evolution of hybrid inviability is estimated to be a Nei’s genetic distance  with broader heads53. Eggs from populations A and B also took substantially longer to show signs of fertilisation when mixed with sperm from populations C and D (see Methods). Therefore, atypical spermatozoa–egg interactions54 may have contributed to slow fertilisation rates in long-distance crosses, highlighting that non-genetic constraints may affect the application of TGF in conservation programmes.Recent findings for 39, short-distance (~100 km) crosses between populations A and B led to asymmetric hybrid vigour. When sperm from population B fertilised eggs from population A (the most mesic site in this study), the resulting offspring showed enhanced desiccation tolerance relative to AA offspring. These effects were very strong in most traits measured, to the extent that trait responses of AB hatchlings resembled those of BB offspring in the drier treatment. For example, embryonic survival of AB hybrids was greater (22% difference), time to hatching was faster (37% difference) and the number of hatchling malformations was reduced (46% difference) in the drier treatment relative to AA offspring , which lowers risks such as predation or fungal infestation of eggs57. Finally, malformations and body size affect swimming performance at hatching, which in turn influences predator avoidance and foraging efficiency63. As malformations can persist past metamorphosis64, they are likely to have long-term consequences on survival and fitness.These trait differences are likely to provide fitness benefits in a drier climate. For example, if hatching-stage embryos breach their egg capsules more quickly when flooded, the resulting larvae are more likely to be washed into pools of standing water where they can feed and seek refuge. Furthermore, faster development rates allow embryos to hatch earlier if an appropriate stimulus is received  in BA hybrids in the dry treatment. In pure crosses, both traits varied among populations in a pattern consistent with local water availability, with AA offspring (from the most mesic population) being the slowest to hatch, and at a less-advanced developmental stage, and CC offspring  hatching and developing the fastest in the drier treatment. If males carry locally adapted genes for these traits, we would expect their expression to be altered in hybrids in a pattern that reflects annual rainfall (or related abiotic variables) at the sites from which males were sourced. Accordingly, AB, AC and AD hybrids should hatch more quickly in the drier treatment compared to AA offspring. Analogously, BA offspring would be expected to take longer to hatch in the drier treatment than BB offspring (whereas BC and BD offspring should hatch more quickly), and CA and CB offspring should take longer to hatch compared to CC offspring. Our data Fig.  show thi71, consequently leading to different offspring fitness and desiccation tolerance amongst hybrid crosses. However, given that positive outcomes were observed in some crosses , despite genetic incompatibilities , this latter explanation is less likely. Another possibility is that locally adapted phenotypes produced in different environments reflect interactions between mitochondrial genes  and nuclear genes, and so mitotypes might modulate the expression of traits passed on by males. Mitonuclear/environmental interactions have recently been shown in insects72 and birds73, but mitonuclear co-evolution has received limited attention in amphibian populations46. In contrast, polyploidy is well documented in anuran amphibians74, and dosage effects in polyploid lineages could also potentially explain asymmetry in phenotypes created by crosses of the same populations in reciprocal directions. As polyploidy may be generated via hybridisation74, future research using this model system could investigate chromosome numbers in parents and offspring to refute or confirm the possibility that polyploids exist or can be created in P. guentheri.The strength of male population origin effects, which exceeded treatment effects in both traits  could be pipetted onto egg masses freshly stripped from females within a field setting, effectively seeding a population with new genetic material if most females can be intercepted before mating. Indeed, it has been shown in some Pseudophryne populations that almost all females entering a breeding chorus can be captured76, so the logistics of achieving TGF in this species are tractable, and less complex than those for internally fertilised amniotes .The ideas presented above are necessarily speculative, and we are a long way from understanding how admixture of populations will play out in the short and longer term. An encouraging result in our focal system is that desired traits (in this instance those related to desiccation tolerance) can be achieved by mixing male gametes from the ‘pre-adapted’ population with female gametes in the target population. This finding has great practical importance; females are generally difficult to locate, and may ovulate and sometimes expel eggs in transit, so it would be logistically challenging to move their gametes into a target population at scale. In contrast, calling males can be easily collected from a breeding chorus, and their gametes harvested and stored for up to two weeks16, as well as investment in genomic resources that could aid in targeting adaptive traits77. Our results demonstrate that the outcomes of TGF in P. guentheri depend on the particular combination of source and recipient population, although surprisingly in one reciprocal cross this depended on the origin of the specific gametes. Further work is needed to clarify whether this result was due to the introduction of pre-adapted alleles, heterosis, mitochondrial/genomic interactions, polyploidy, or as yet unrecognised processes. However, the strong hybrid vigour and increased desiccation tolerance in AB crosses is promising, particularly considering that the parental populations are genetically divergent (FST 0.196).There is growing interest in Australia for exploring TGF for threatened speciesPseudophryne genus include Critically Endangered species , where genetic augmentation to improve resistance to the chyrid fungus , is being considered78. Coincidentally, these species were crossed in a genetic study in the late 1980s79, and hybrids  had high embryonic survival (~85%)—as did the more successful crosses in our study—and better survival to hatching and metamorphosis than intra-population crosses, suggestive of genetic rescue26. These and several other threatened amphibian species are subject to captive breeding programmes and efforts to improve the capacity to generate, harvest and store gametes81. Given that application of TGF in these species will require both a reliable source of adults, and a supply of viable eggs and sperm, we are well positioned to initiate laboratory trials that target the expression of traits that could enhance survival of wild populations as climates become increasingly stressful.Notably, toadlets in the P. bibronii)30, while in another congener (P. coriacea) the observation that females preferentially mate with related males was explained as a mechanism to avoid outbreeding depression82. Consequently, as highlighted in a recent review15, empirical tests of TGF in model species (as was demonstrated here), and the monitoring of long-term consequences, are essential. Otherwise, managers have few tools at their disposal to enhance the adaptive potential of populations and species threatened by climate change.Our results also highlight risks of TGF, as evident by apparent outbreeding depression in some long-distance crosses at levels that suggest reproductive isolation. As the effects we observed depended on the direction of the cross, we expect the outcomes of TGF will be difficult to predict theoretically. This point is especially pertinent considering that strong outbreeding depression has been shown in population crosses of a congener  Animal Ethics Committee (permit number RA/3/100/1510), and the research was conducted under license 08-000560-1 from the Western Australian Department of Biodiversity, Conservation and Attractions.Pseudophryne guentheri deposit large clutches of eggs that are fertilised externally, and embryos develop in terrestrial locations  until suspending development at Gosner83 stages 26–2882. Larvae are cued to hatch by flooding, and develop aquatically thereafter, reaching metamorphosis in around three months84. The winter rainfalls that promote breeding in this species have declined markedly in recent decades (19% reduction since the 1970s)87, and are projected to further decline by up to 30% by 209085. Additionally, many P. guentheri populations are now isolated due to extensive clearing of habitat89, and significant declines in genetic diversity  associated with repeated drought have been revealed through genomic analysis39.Female 90 provided an opportunity to employ a highly tractable framework to examine the outcomes of TGF. We collected adult P. guentheri from four geographically separated breeding sites, situated at two latitudes, in May and June 2017 , which suggests low levels of contemporary dispersal. P. guentheri from sites A and B form distinct genetic clusters39, indicating low historical gene flow despite their close proximity (100 km), whereas P. guentheri from sites C and D show admixture, but are genetically distinct from populations A and B39.Reliable in vitro fertilisation techniques developed for this speciesIn total, 15–16 calling males from each population were collected by hand and in pit-fall traps. Gravid females were more difficult to collect due to their cryptic behaviours, and so sampling was restricted to 5–13 females from each of three sites  effects on offspring fitness2). This buffer allows storage of sperm for extended periods (days–weeks) without substantial declines in motility93. Sperm concentrations were measured using an improved Neubauer haemocytometer  and sperm suspensions were diluted with 1:1 SAR to 100 sperm per μL.Sperm was obtained from testes macerates after euthanizing males via ventral immersion in < 0.03% benzocaine solution, followed by double pithing. Sperm was stored on ice in 25–458 μL (adjusted according to the weight of the testes) standard amphibian ringer  attached to a light microscope (Leica MZ7.5) at 6.3 Χ magnification. These images were used to measure the ovum diameter of 50 randomly selected eggs from each female, using ImageJ software94. Fertilisation success was initially scored one h after mixing eggs and sperm by counting eggs that had rotated (Gosner83 Stage 1). However, eggs from populations A and B took substantially longer to show signs of fertilisation when mixed with sperm from populations C and D. We therefore scored fertilisation success a second time, six hours after sperm and eggs were mixed.Upon arrival at the laboratory, females were gently squeezed to determine whether ovulation had occurred. Approximately 35% of females had ovulated naturally while in transit and their eggs were gently stripped. For the remaining females, ovulation was induced via two subcutaneous injections of the hormone LHRHa over the course of two daysP. guentheri breeding site, and the soil water potentials represented a range found in natural nest sites38. Embryo incubation and soil preparation were performed as described in Rudin-Bitterli et al.37. Briefly, soil was oven-dried at 80 °C for 24 h, distributed into small containers and rewetted with an appropriate mass of deionised water using a water-retention curve previously determined for the soil sample. The water content of the soil (g/g/ of oven dry soil) was approximately 50% in the wet treatment, and 21% in the dry treatment, and containers were sealed with a lid after wetting. Fertilised eggs from each cross were selected at random and distributed onto soils within 7–9 h of fertilisation. Small plastic rings  were labelled and placed around eggs to identify individual crosses. Sealed containers were then placed in incubators set at 16 ± 0.5 °C, and embryos were monitored every two days. Any dead eggs were removed and discarded.Fertilised eggs from each cross were reared on sandy loam soil at two water potentials (ψ): a wet soil (ψ = −10 kPa) and dry soil (ψ = −400 kPa). The soil was previously collected from a separate 83 Stage 26), hatching was induced by placing embryos individually in small test tubes containing 2 mL of deionised water38. Embryos were then monitored at least every 30 min until hatching, defined as when an individual completely escaped their egg capsule. Embryonic survival was recorded for each family as the percentage of fertilised eggs that hatched.Putative fitness from within- and between-population crosses, reared in dry and wet rearing environments, was assessed at hatching. At 33 days after fertilisation . For this purpose, individual hatchlings were placed in a petri dish (diameter = 150 mm) containing water 10 mm deep. After an initial acclimation period of 1 min, the tail of each hatchling was nudged with a glass cannula to elicit a burst swimming response. A video camera  installed 300 mm above the petri dish was used to film three burst swimming responses for each hatchling, and their movement was later tracked and analysed using EthoVision v8.5 software95. EthoVision enabled the quantification of the following swimming parameters: maximum velocity (cm s−1), mean velocity (cm s−1) and total distance moved (cm). We also recorded mean meander (deg cm−1), a measure of the straightness of the swimming response, as dry rearing environments can lead to asymmetrically shaped hatchlings38 that swim in a more circular motion. A hatchling was considered to be moving when it exceeded 0.45 cm s−1. As each video recording contained three burst swimming responses with periods of no movement in between them, EthoVision only analysed frames in which a hatchling moved faster than 0.45 cm s−1 (consequently merging the three swimming responses for each hatchling). Immediately following the swimming performance trials, hatchlings were euthanized in <0.03% benzocaine and preserved in 10% neutral buffered formalin.Swimming performance was recorded 6–12 h after hatching on a subset of hatchlings  using a digital imaging camera (Leica DFC320) attached to a light microscope (Leica MZ7.5) at x 6.3 magnification. These images were used to score malformations for each hatchling and to determine their developmental stage96 to compare offspring traits from pure and hybrid crosses. In these models, treatment, female (population) origin, male (population) origin and all interactions  were considered as fixed factors. We checked for overdispersion in our models using the ‘overdisp_fun’ function proposed by Bolker et al.97. Only one trait, embryonic survival, was overdispersed, which if uncontrolled can lead to biased parameter estimates. To account for this overdispersion, we included an observation-level random effect  when analysing this trait98. As the eggs from each female were subjected to a split-clutch design, the term for dam  was added as a random effect to account for the use of individual females across multiple fertilisation events. We also included ovum size as a covariate in all analyses to control for possible maternal effects arising from different patterns of egg provisioning among females38. The significance of the fixed effects was evaluated using Wald chi-squared tests.For each trait measured, sample sizes varied between 630 and 2973 offspring created from pure and mixed sources Tables , S2. All38. The significance of the fixed effects was evaluated using Wald Z-tests.Embryonic survival and hatchling malformation data were binomial variables and thus a generalised-linear mixed-effects model (GLMM) with a logit-link function was used for the analysis of these traits. In these models we included treatment, female (population) origin, male (population) origin and all interactions (see above) as fixed effects. As above, dam ID was treated as a random effect and ovum size was added as a covariate to control for possible maternal effectsFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary