Delphinium montanum, Ranunculaceae) is endemic to the Eastern part of the Pyrenees (France and Spain). It is now only observable at a dozen of localities and some populations show signs of decline, such as a recurrent lack of flowering. Implementing population genomics approach  is particularly useful to understand genomic patterns of diversity and differentiation in order to provide recommendations in term of conservation. However, it remains challenging for species such as D. montanum that are autotetraploid with a large genome size  as most methods currently available were developed for diploid species. A Bayesian framework able to call genotypes with uncertainty allowed us to assess genetic diversity and population structure in this system. Our results show evidence for inbreeding (mean GIS = 0.361) within all the populations and substantial population structure (mean GST = 0.403) at the metapopulation level. In addition to a lack of connectivity between populations, spatial projections of Ecological Niche Modeling (ENM) analyses under different climatic scenarios predict a dramatic decrease of suitable habitat for D. montanum in the future. Based on these results, we discuss the relevance and feasibility of different conservation measures.Species endemic to restricted geographical ranges represent a particular conservation issue, be it for their heritage interest. In a context of global change, this is particularly the case for plants which belong to high‐mountain ecosystems and, because of their ecological requirements, are doomed to survive or disappear on their “sky islands”. The Pyrenean Larkspur . Populations display low levels of genetic diversity as well as strong population structure. ENM approach predict a dramatic decrease of the availability of the habitat of the species in the future. In thisDelphinium montanum DC, 1815 perfectly illustrates this situation. This plant (Family Ranunculaceae), endemic to the Eastern part of the Pyrenees (France and Spain), is now only observable at a dozen of localities  and its global population size was estimated to range between 7800 and 10,300 individuals in total  and are negatively impacted by trampling  was also shown to be responsible for the seed bank's decrease in some populations  and populations studied .The threatened Pyrenean larkspur D. montanum could be particularly fruitful to inform conservation strategies by providing a substantial increase in the resolution of the genetic marker dataset and genome complexity reduction protocols such as Reduction site‐associated DNA sequencing  corroborates previous knowledge and confirms the large genome size of D. montanum . A large genome size can be considered has a limitation as itself, be it because it requires higher sequencing efforts and costs to carry out conservation studies. The difficulty to infer genotype and allele frequencies on polyploid species with RAD‐seq techniques as well as the restricted set of methods available to analyze such data  that provided us with thousands of genetic markers (SNPs) from which we inferred patterns of diversity and differentiation with an unprecedented resolution in order to quantify inbreeding and level of connectivity between populations. We then used an Ecological Niche Modeling (ENM) approach to better understand the bioclimatic features that constraint the current geographic distribution of D. montanum. Based on several Global Climate Models (CGM), we also predicted the putative evolution of the geographic distribution of the species in the future, at different time periods  and under different, more or less pessimistic but still realistic scenarios of evolution of global warming (or RCPs for Representative Concentration Pathways).In this study, we first aimed at reevaluating the conservation status of 22.1Delphinium montanum) geographic distribution ranges from Serra del Cadí‐Pedraforca  between July and October 2020  on the Red List of threatened species of IUCN France  on an Illumina NovaSeq 6000 with an expectation of a minimum number of 1.5 million read pairs per sample.Genomic DNA extraction and genotyping were subcontracted to LGC Genomics GmbH . Genotyping‐By‐Sequencing (GBS) was performed following a specific protocol called nGBS for “normalised GBS”. This double digest Restriction site‐Associated DNA seq (or RAD‐seq) like protocol relies on two restriction enzymes  to reduce 2.3process_radtags to demultiplex and clean reads, denovo_map.pl to build loci within individuals, create a catalog and match all samples against it and populations to further filter the SNPs obtained at the population level and compute basic population genetic statistics. We first optimized several key parameters of the pipeline: ‐m , ‐M , and ‐n  on a subset of 12 individuals representative of the whole data set . As recommended by several authors, we varied M and n (fixing M = n) while keeping m = 3  to allow the number of alleles per site to reach the number of 5 instead or 3 by default. However, as the populations script is unable to deal with polyploid genotypes, we followed a different approach to call variants and complete genomic data processing.We used Stacks v.2.41  was considered to represent well‐behaved markers from which inbreeding (F) was estimated at 0.73. From this estimate of F, we then simulated the expected distribution of Hind/HE, if all markers were behaving in a Mendelian fashion. Based on the distribution, we defined minimum and maximum thresholds of 0 and 0.406 comprising 95% of the observations and filtered out markers for which Hind/HE were outside this interval. We then tested overdispersion  and adjusted its parameter to 6 (from a range of 2 to 14). This information was used to call genotypes while taking into account population genetic structure. The most likely genotypes of the remaining 5 095 loci were then exported in several formats such as genind objects or Structure (.str).We then used polyRAD  as well as the number of alleles (A) and the number of private alleles was computed with the R‐package “poppr” ) and ln‐geographical distances  based on a matrix of 106 individuals (as rows) and 5095 SNPs (as columns) coded as a genind object with the R‐package “adegenet” Jombart, . We thenK = 1 to 10, and analyses were run with 10 replicates at each value of K.To further investigate population structure and characterize putative migration/admixture event, we used sNMF  analyses based on known occurrences of the species. Based on 19 bioclimatic layers, we downloaded from CHELSA v.2.1 , 2041–2070 (“2055”), and 2071–2100 (“2085”).Future climates correspond to estimates from phase 3b of the Inter‐Sectoral Impact Model Intercomparison Project (ISIMIP3b) based on output of phase 6 of the Coupled Model Intercomparison Project (CMIP6). They consist of the output of five GCMs  and three climate scenario specifiers . Climate scenarios specifiers reflect different Representative Concentration Pathways (RCPs) associated with different radiative forcing values: 2.6, 7.0, and 8.5 W/mThe ENM analyses were conducted thanks to the R‐package “ENMwizard v.0.3.7” . We assembled a total of 210,756 loci including 22,911 SNPs with Stacks. Average read depth ranged from 27× to 53× based on the combination of parameters we used. After filtering the data with polyRAD, we finally obtained 32,728 loci including 5095 SNPs.We obtained a total of 159 million read pairs across the 106 individuals . However, GIS values appear as similar from one population to another  represented 14.85% and 9.21% of the total genetic variance, respectively Figure . This an and PC2 GST value = 0.403, p < .05) and pairwise  classes following the “AUC” optimality criteria. The first model had the best corrected Akaike Information Criterion (AICc) score (152.25) and an AICc weight of 0.33, whereas the second one had an AICc score of 174.63 and an AICc weight of 4.53 × 10−6. These two models had mean omission rates of 0.375 and 0.25 for the 10th percentile and the lowest presence training thresholds, respectively, as well as a mean test AUC of 0.988. Values of these diagnostic metrics are provided for all candidate models in Appendix D. montanum was found to be 2670 and 2365 km2 following “LowAIC” and “AUC” selection model criteria, respectively . All future consensus spatial projections predict a dramatic decrease of the suitable area  classes as the best‐performing parameters for calibrating the final ENM following the “LowAIC” and 44.1D. montanum  data with the method implemented in PolyRAD allowed us to successfully address the challenge of conducting a population genomics approach on a plant species that is both polyploid (autotetraploid) and has a large genome size (>10 Gbp). It confirms the convenience of the workflow we used, specifically calling genotypes in an R‐environment, as an alternative to the ones followed in the handful of studies recently published on similar systems . These results confirm previous findings of Simon et al. (HO < 0.17). Polyploidy is expected to be associated with increased genetic variation, a characteristic that provides more diversity to adapt  and allows buffering effect against deleterious mutations  to decreased levels in genetic diversity or evidence for deviation from panmixia. This suggests that the evidences of possible decrease in effective size may be too recent to be detectable at the genetic level  globally distinguishes populations from the Serra del Cadí  from those of the Puigmal range (1‐Orri de Baix and 6‐Cambre d’Aze) plus the satellite populations of 3‐Vallter, 2‐Nohèdes, 7‐Tosa d’Alp, and 9‐Torreta de Cadí . Only the locality of 4‐Pedraforca shows some ambiguity in population assignment that could indicate a mixed origin. At K = 2, the intermediate status of 7‐Tosa d’Alp (and to a lesser extent of 4‐Pedraforca) are also in agreement with its intermediate geographic position and may reflect an ancestral split between Northern and Southern populations. Altogether, these results support that the insect pollinators (mainly bumblebees) and the natural dispersal of seeds produced (barochoric) are not able to maintain gene flow over the geographic distances involved.The PCA, the clustering analyses, the 4.3D. montanum, at least in some particular populations. According to Simon et al. (Aconitum spp.) have thus been considered as more or less serious problems for D. montanum. The most recent study of López‐Pujol et al. , by 2040 whatever the scenario envisaged. By 2070, this area would again have decreased to about 150 km2 based on RCP 2.6 and up to <50 km2 based on the two other scenarios (RCP 7 and RCP 8.5). A Representative Concentration Pathway of 2.6 W/m2 has to be considered as a rather optimistic pathway . It would require that CO2 emissions start declining by 2020 and go to zero by 2100 . RCP 7 is usually considered as a baseline rather than a mitigation target. According to RCP 8.5, emissions would continue to rise throughout the 21st century and can be considered as a pessimistic scenario (with an increase in average temperature in the order of 4°C by 2100). The various output of these scenarios likely explains why the bioclimatic niche of D. montanum would have completely disappeared by 2100 for RCP 7 and 8.5 but could have persisted and even started to increase again to about 10% of its current suitable area following the RCP 2.6 scenario.The two previous studies of Simon et al.  and Lópen et al. , naturall et al.  also empD. montanum populations is also supported from field observations. The population of 2‐Nohèdes has not flowered since 2011. The individuals there are very small  and remain at a vegetative stage. On this site, we could not notice any evidence of avalanche or landslide that could have altered the habitat. In addition, other plants species present in the vicinity  seem to suffer from flowering default, further supporting that this phenomenon could be nonspecific to D. montanum and perhaps caused by bioclimatic or at least environmental modifications. There, output from Regional Climate Models (RCMs) predict an increase in mean annual temperature that could reach 3.1 to 4.5°C  associated with a decrease in precipitation of up to 25% and perhaps more compared to present‐day conditions . This confirms that if any, botanical garden would have to be set up at a relatively high elevation , where environmental conditions are similar to the ecological optimum of the species.in‐situ conservation that could be envisaged for D. montanum  from a population to another one with the same ecogeography, to increase genetic diversity  and thus increase the likelihood of population persistence in a changing climate  translocate some individuals to localities where D. montanum seem healthy  and see if flowering can be restored and/or (iii) experimentally modify temperature/water supply in the lab to better understand the conditions required for flowering and figure out the limiting factor in the wild. Alternatively, it may be due to the fact that that this locality is formed by immature individuals and that the local population itself may be recovering from an important mortality event. At the moment, we have no further arguments to support this scenario.For the very peculiar situation of 2‐Nohèdes's locality, which seems to be the population the most at risk of extinction at the moment, there is still the question of the lack of flowering. To test whether this problem may be associated with the absence of a specific environmental stimulus, we may propose to either (i) experimentally modify temperature/water supply, 6D. montanum show evidence of decreased genetic diversity and evidence of inbreeding at a genomic scale. The degree of genetic structure observable is consistent with a set of strongly isolated populations no longer able to maintain gene flow between their sky islands. In this context, in‐situ conservation methods are likely to have an effect on the status of the species. Reinforcement would help counteracting decrease in local population sizes, and translocation would help increasing genetic diversity, buffering inbreeding depression and perhaps, bring the variation required for D. montanum to adapt. This being said, ex‐situ crossings are first recommended to verify the absence of outbreeding depression as local genetic pools were found to differ substantially. As our results support that environmental change may represent the main risk of extinction for D. montanum all these measures may be however considered as of limited interest on the long term, if the ecological niche of the species disappears.Overall, our results confirm that the remaining populations of The authors declare no conflict of interest.Pascaline Salvado: Data curation ; Formal analysis (lead); Investigation (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing (lead). Pere Aymerich Boixader: Data curation ; Investigation ; Resources ; Writing – original draft (supporting). Josep Parera: Data curation ; Investigation ; Resources ; Writing – original draft (supporting). Albert Vila Bonfill: Data curation ; Investigation ; Resources ; Writing – original draft (supporting). Maria Martin: Conceptualization ; Data curation ; Funding acquisition (lead); Investigation ; Methodology ; Project administration (lead); Resources ; Writing – original draft (supporting). Céline Quélennec: Conceptualization ; Data curation ; Funding acquisition (lead); Investigation ; Methodology ; Project administration (lead); Resources ; Writing – original draft (supporting). Jean‐Marc Lewin: Data curation ; Investigation ; Resources ; Writing – original draft (supporting). Valérie Delorme‐Hinoux: Conceptualization (lead); Data curation ; Funding acquisition (supporting); Investigation (lead); Project administration (lead); Supervision (lead); Validation (lead); Writing – original draft (lead); Writing – review & editing (lead). Joris A. M. Bertrand: Conceptualization (lead); Data curation ; Formal analysis (lead); Funding acquisition (supporting); Investigation (lead); Methodology (lead); Project administration (supporting); Resources ; Supervision (lead); Validation (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing (lead).Appendix S1‐S4Click here for additional data file.


The most prolific duck genetic resource in the world is located in Southeast/South Asia but little is known about the domestication and complex histories of these duck populations.Anas platyrhynchos) and 31 published whole-genome duck sequences, we detected three geographic distinct genetic groups, including local Chinese, wild, and local Southeast/South Asian populations. We inferred the demographic history of these duck populations with different geographical distributions and found that the Chinese and Southeast/South Asian ducks shared similar demographic features. The Chinese domestic ducks experienced the strongest population bottleneck caused by domestication and the last glacial maximum (LGM) period, whereas the Chinese wild ducks experienced a relatively weak bottleneck caused by domestication only. Furthermore, the bottleneck was more severe in the local Southeast/South Asian populations than in the local Chinese populations, which resulted in a smaller effective population size for the former . We show that extensive gene flow has occurred between the Southeast/South Asian and Chinese populations, and between the Southeast Asian and South Asian populations. Prolonged gene flow was detected between the Guangxi population from China and its neighboring Southeast/South Asian populations. In addition, based on multiple statistical approaches, we identified a genomic region that included three genes  on duck chromosome 1 with a high probability of gene flow between the Guangxi and Southeast/South Asian populations. Finally, we detected strong signatures of selection in genes that are involved in signaling pathways of the nervous system development  and in genes that are associated with morphological traits such as cell growth .Based on whole-genome resequencing data of 78 ducks (Our findings provide valuable information for a better understanding of the domestication and demographic history of the duck, and of the gene flow between local duck populations from Southeast/South Asia and China.The online version contains supplementary material available at 10.1186/s12711-021-00627-0. Anas platyrhynchos) industry achieves great economic benefits from meat, eggs, and feathers  and the FST values [the top 5% level where Z(FST) was 1.86] were identified as regions with strong signatures of selection along the genome.We compared the genetic diversity among 44 domestic ducks (25 domestic and 19 highly selected duck), which were considered as one group, to that of 19 wild ducks, and used 40-kb sliding windows across the genome, with steps of 10 kb to detect signatures of selection. The polymorphism levels , population fixation statistics (F-0.1.15) . To deteAnas platyrhynchos reference genome. In total, 1049 genes were identified and submitted to the KEGG databases for enrichment analyses.Genes located within the 40-kb regions with significant signatures of selection were annotated based on the Anas platyrhynchos), representing eight geographically diverse domestic populations/breeds, two wild populations, and two Muscovy duck populations in China, three local populations in Southeast Asia, and two local populations in South Asia were selected for genome re-sequencing analysis  were close to those of the Guangxi and Chinese indigenous populations (0.052–0.135)  Fig. d and  years ago  were obtained only when the Guangdong, Guizhou, and wild ducks were the targeted populations    and identified 18 genomic regions with extreme fd peaks  and glycerophospholipid metabolism , showed the highest fd values in each Southeast/South Asian population  value for this region was significantly reduced in the Southeast/South Asian ducks but was highly increased in the Chinese indigenous populations  in the genomic region with the strongest introgression signal  contain nine SNPs, among which one induces a change in the amino acid sequence , codon , and the nucleotide sequence . This missense mutation is conserved among birds  gene and 10 exonic SNPs in the DNAJB9 (DnaJ heat shock protein family (Hsp40) member B9) gene, respectively, were detected only in the Laotian and Bangladeshi duck genomes. These genes may, therefore, have a role in the adaptation of ducks to local conditions in Laos and Bangladesh.We identified three genes . Compared to wild ducks, the domestic ducks and the lineage ducks had a higher level of LD among loci   was 1.86) as regions with strong signals of selection along the genome. In total, we identified 23.9 Mb of the domestic duck genome that contain strong signatures of selection, which represent 2.12% of the genome and 1048 genes  and log2(θπ ratio) values compared to the whole genome ]  and high FST and a high θπ-ratio. Several functional categories were highly enriched among these genes, including “Steroid hormone biosynthesis” , “Caffeine metabolism” , “VEGF signaling”  and the FoxO signaling pathway   > 1.86, log2(θπ ratio) > 0.69]  and one RNA transport-related gene (TPR) with strong signatures of selection in domestic ducks  = 8.55, log2(θπ ratio) = 3.25)] . Further verification revealed that this region is the product of gene flow rather than a shared ancestral polymorphism, in accordance with the theoretical expectation that gene flow reduces dxy in the target regions, while shared ancestry does not [The Guangxi population showed significant gene flow with Southeast/South Asian populations, which is highly consistent with the Admixture and D-statistics analyses. Furthermore, we identified a genomic region that has a high probability of gene flow between the Guangxi and Southeast/South Asian populations based on different statistical approaches . Therefore, combining allele frequency and haplotype methods to detect gene flow can help extend the results.However, we also observed some inconsistent results when using the above methods to detect gene flow. For example, strong genetic drift after population admixture may hamper the detection of negative c values  between Based on genomic data and the use of a comprehensive and efficient workflow, we found that frequent gene flows occurred between the Chinese and Southeast/South Asian duck populations and between the Southeast Asian and South Asian duck populations. We inferred the history of duck populations with different geographical distributions and showed that the Chinese and Southeast/South Asian ducks share similar demographic characteristics. An intriguing finding is that the Southeast/South Asian populations have experienced more severe bottlenecks than the Chinese populations, which has contributed to smaller effective population sizes. In addition, our analyses revealed that population structure is mostly correlated with geographic distribution. For example, the Guangxi population, which is geographically distinct from the Chinese indigenous populations and the Southeast/South Asian populations, has experienced prolonged gene flow with the Southeast/South Asian populations. Regarding the domestication of ducks, we detected strong signatures of selection in genes that are involved in signaling pathways of the nervous system development and in morphological traits such as cell growth. Our results lay the foundation for future population genetics studies in duck.Additional file 1: Table S1. Sample information for the 78 sequenced individuals and 31 individuals from NCBI.Additional file 2: Figure S1. Mean pairwise FST values between groups from 14 populations.Additional file 3: Figure S2. Population structure plots with K = 2 to 10. The y axis quantifies the proportion of an individual’s genome from inferred ancestral populations, and the x axis shows the different populations. Refer to Additional file Additional file 4: Table S2. Nucleotide polymorphism (θπ) from different populations.Additional file 5: Figure S3. Splitting times between Chinese wild ducks and either Chinese domestic populations or Southeast/South Asian populations with 20 independent replicate analyses.Additional file 6: Figure S4. The model-testing approach compared eight models using the ABC approach.Additional file 7: Figure S5. Maximum likelihood based phylogenetic tree with zero to ten migration. Scale bar shows 10 times the average standard error of the estimated entries in the sample covariance matrix. Populations are colored by their geographic locations.Additional file 8: Figure S6. Fraction of variance in relatedness between populations explained by phylogenetic models with zero to 12 migration events.Additional file 9: Table S3. Significant (Z-score ≤ − 2) negative f3-statistics values testing admixture of populations.Additional file 10: Table S4. Observed (HO) and expected heterozygosity (HE) values of duck samples. Refer to Additional file Additional file 11: Table S5. The D-statistics detect whether there was gene flow between Southeast Asia and South Asian populations.Additional file 12: Figure S7. Clustering of individuals based on the FineStructure algorithm. The color intensity indicates shared haplotypic segments based on the chunklength coancestry matrix generated by the ChromoPainter algorithm. Refer to Additional file Additional file 13: Figure S8. Inference of the ancestry proportion of different donor populations in the genome of Guangxi, Guangdong, Guizhou and Anhui populations.Additional file 14: Table S6. Eighteen extreme fd peaks (fd >  = 0.5) with the introgressed genomic regions between Guangxi and Southeast/South Asian populations using the modified f-statistic (fd). Introgressed genomic regions were not shorter than 200 kb.Additional file 15: Table S7. Functional gene categories enriched for the genes within the introgressed genomic regions.Additional file 16: Figure S9. Treemix analysis of these 18 introgressed genomic regions.Additional file 17: Figure S10. Introgressed genomic regions identified in each of the Southeast/South Asian population tested were inferred by the modified f-statistic (fd) values. The vertical shadow line corresponds to the strongest introgressed region on chromosome 1.Additional file 18: Figure S11. Mean pairwise sequence divergence (dxy) and population differentiation (FST) around the introgressed region between the Guangxi and Southeast/South Asian populations. The vertical shadow line corresponds to the strongest introgressed region on chromosome 1.Additional file 19: Figure S12. Multiple sequence alignment of the PNPLA8 gene in birds.Additional file 20: Figure S13. The decay of linkage disequilibrium in Chinese wild populations, Chinese domestic populations and Lineages (LC × BY) populations measured by r2.Additional file 21: Figure S14. A neighbor-joining phylogenetic tree is constructed to understand the phylogenetic relationship of wild ducks (n = 19) and domestic ducks (n = 44) using SNPs present in regions with strong selective sweep signals.Additional file 22: Figure S15. Two-way PCA plot of duck breeds to understand the relationship of wild ducks (n = 19) and domestic ducks (n = 44) using SNPs in regions with strong selective sweep signals.Additional file 23: Table S8. Functional gene categories enriched for the 1048 genes with strong selective sweep signals in domestic ducks.Additional file 24: Table S9. Prior distribution of the parameters used to generate the eight models in the approximate Bayesian computation (ABC) analysis.Additional file 25: Table S10. Summary statistics for the observed dataset computed by the program arlsumstat.Additional file 26: Table S11. Results of eight models for model choice using nine components obtained by transforming 37 summary statistics with the PLSDA method.


Plathymenia reticulata . Genetic structure analysis of P. reticulata was congruent with the recognition of two ecotypes, whose divergence captured the largest proportion of genetic variance in the data (FCT = 0.222 and FST = 0.307). The ecotonal areas between the Cerrado and the Atlantic Forest constitute a hybrid zone in which a diversity of hybrid classes was observed, most of them corresponding to second‐generation hybrids (F2) or backcrosses. Gene flow occurred mainly toward the forest ecotype. The genetic structure was congruent with isolation by environment, and environmental correlates of divergence were identified. The observed pattern of high genetic divergence between ecotypes may reflect an incipient speciation process in P. reticulata. The low genetic diversity of the P. reticulata forest ecotype indicate that it is threatened in areas with high habitat loss on Atlantic Forest. In addition, the high divergence from the savanna ecotype suggests it should be treated as a different unit of management. The high genetic diversity found in the ecotonal hybrid zone supports the view of ecotones as important areas for the origin and conservation of biodiversity in the Neotropics.The Cerrado, the largest Neotropical savanna, and the Brazilian Atlantic Forest form large ecotonal areas where savanna and forest habitats occupy adjacent patches with closely related species occurring side by side, providing opportunities for hybridization. Here, we investigated the evolutionary divergence between the savanna and forest ecotypes of the widely distributed tree  Plathymenia reticulata in the Cerrado, the largest Neotropical savanna, and the Brazilian Atlantic Forest. We found that the ecotonal areas between the Cerrado and the Atlantic Forest constitute a hybrid zone in which a diversity of hybrid classes were observed, most of them corresponding to second generation hybrids. The high genetic diversity found in the ecotonal hybrid zone supports the view of ecotones as important areas for the origin and conservation of biodiversity in the Neotropics.Here, we investigated the evolutionary divergence between the savanna and forest ecotypes of the tree  The speciation process occurs along a continuum of genetic divergence where the incipient lineages often undergo a period of partial reproductive isolation until they become fully isolated  pattern, in which the association between genetic and environmental distance is stronger than that between genetic and spatial distance between populations  What level of genetic diversity characterizes P. reticulata and how is genetic diversity structured geographically, between and within ecotypes? (2) How much gene flow occurs in ecotonal populations, and is gene flow asymmetric between ecotypes? (3) What is the genotypic composition of the hybrid populations, are they mostly F1s or later generation hybrids? (4) What is the effect of environmental variation on the genetic structure? We discuss the evolutionary consequences of divergence in P. reticulata and the implications for conservation of genetic diversity of the ecotonal populations.In this study, we sampled 22.1P. reticulata, comprising the savanna and forest ecotypes in 10 localities from three different habitats with three localities sampled in the Atlantic Forest  and the Cerrado , and four localities sampled in the ecotone between the two biomes   Figure .2.2P. reticulata: Pre05, Pre10, Pre11, Pre14, Pre15, Pre16, and Pre23  and the mean observed (HO) and expected (HE) heterozygosities. The allelic richness (AR) and the inbreeding coefficient (FIS) were estimated in FSTAT version 2.9.3.2   Nei,  with samP. reticulata using the following algorithms: (1) discriminant analysis of principal components (DAPC) implemented in the adegenet package version 2.1.4 in R 4.1.0  iterations. The most probable number of genetic clusters was determined by the ad hoc statistic ΔK , “MaxMeaK” (maximum of means), “MedMedK” (median of medians), and “MaxMedK” (maximum of medians) statistics in StructureSelector  Jombart, . DAPC haTo estimate rates of recent migration between the two ecotypes (based on phenotype classification), we used BayesAss 3.0 , which is the fraction of loci that combine the ancestry of the two parental taxa  P. reticulata individuals of the Atlantic Forest and the Cerrado areas, respectively. The simulated datasets comprised 100 individuals of each parental and 100 individuals of each hybrid class, F1, F2, and backcrosses of F1 with each parental (B1 and B2). We also simulated more advanced generation hybrids including F3 (F2 × F2) and F4 (F3 × F3), B3 = F2 × savanna and B4 = F2 × forest, B5 = savanna × (F1 × savanna) and B6 = forest × (F1 × forest), B7 = savanna × (F2 × savanna) and B8 = forest × (F2 × forest), based on Chhatre et al.  Mantel tests as a first approach to assess effects of isolation by distance and isolation by environment and (2) distance‐based redundancy analysis, dbRDA. For Mantel tests, we calculated the pairwise spatial distance between populations using the coordinates of the localities separated by ecotypes with the geodist package version 0.0.7 in R , soil pH (pH), soil organic carbon (SOC), and cation exchange capacity of the soil (CEC), which were downloaded from https://files.isric.org/soilgrids/latest/data/ and the 19 bioclimatic variables from the worldclim2 dataset https://www.worldclim.org/data/bioclim.html  using the function varpart of the vegan package   ranged from −0.005 to 0.330 with three localities from the Cerrado, one from the Atlantic Forest, and two in the ecotone showing significant positive FIS  . When the hierarchical AMOVA was performed considering localities of the Atlantic Forest and the Cerrado only, the part of total variation attributed to divergence between ecotypes was even higher (FCT = 0.337 and FST = 0.396)  were higher between localities from different biomes (FST = 0.327–0.402) than within biomes (FST = 0.072–0.099) and showed large variation in the ecotone  Table . PairwisThe neighbor‐joining (NJ) tree showed that the savanna and forest ecotypes are highly divergent from each other Figure . Both grP. reticulata dataset was K = 2 based on the sequential k‐means clustering method. The DAPC analysis based on this grouping indicated that most of the genetic divergence in P. reticulata occurs between forest and savanna ecotypes  as a threshold for a pure individual. The highest proportion of admixed individuals was found in f‐SUE  and f‐FEE  in the forest ecotype and in s‐COE  in the savanna ecotype.The number of clusters that best explained the genetic structure in the s Figure . Individs Figure . The Bayo et al.  clearly s Figure . Individs Figure , consideIn agreement with the STRUCTURE and DAPC, the recent migration rates between ecotypes estimated in BayesAss were high in the ecotonal region Table . The migK = 5 in the forest and K = 7 in the savanna ecotype. In the forest ecotype, all the localities showed a prevailing genetic cluster despite showing high proportions of shared ancestry mainly among the three populations of the Atlantic Forest and among the three ecotonal localities , a pure parental composition for most individuals in the areas of the Cerrado and Atlantic Forest  ranged from 0.00 to 0.818, with the ecotonal localities showing higher mean values (Q12 ranging from 0.261 to 0.403) than the localities of each biome (Q12 ranging from 0.071 to 0.104)  Figure . In addi) Figure . A popul3.3introgress to identify and classify hybrids based on simulated data is presented in Appendix The model evaluation using NewHybrids with six simulated genotypic classes showed an average proportion of correct assignments of pure parental individuals of almost 91% when setting a threshold of 0.85 Table . Only th3.4r = .381, p = .006) than with spatial distance  or soil distance . The partial Mantel test showed that the correlation of the bioclimatic distance with the genetic distance remained significant when controlling for geographical distance  with the first three axes showing significant divergence between the P. reticulata populations . The genetic divergence was associated with Cation Exchange Capacity (CEC) of the soil and with Precipitation of the Warmest Quarter, Precipitation of the Wettest Quarter, and Mean Diurnal Range Temperature (summarized in the synthetic variable CLIM4). We detected IBE using SSR markers that are considered neutral, congruent with a scenario of genome‐wide hitchhiking following long‐lasting selection that may have affected a small fraction of the genome, in combination with assortative mating . Therefore, the high genetic variation we observed across very close‐by sites in the ecotone could suggest high adaptive potential, assortative mating of adapted individuals to local soil, and selection against maladapted individuals into the alternative habitat, leading to a pattern of IBE.We found an association of edaphic and climatic variables with Qualea grandiflora and Annona coriacea, respectively, two of the most common tree species of the Cerrado. A similar association was also found in an annual herb, Brasilianthus carajensis, in a mountain savanna in Canga plateaus within the Amazon Forest  tree species from the Atlantic Forest and Cerrado accounted for 26% of the total genetic variance based on seven nuclear microsatellites ; investigation ; methodology ; writing – original draft (lead); writing – review and editing . Ricardo José Gonzaga Pimenta: Formal analysis (supporting); methodology . Mariana Vargas Cruz: Formal analysis (supporting); investigation ; methodology . Jacqueline Gomes Rodrigues: Investigation (supporting); methodology . Renata Santiago de Oliveira Buzatti: Investigation (supporting); methodology . Myriam Heuertz: Formal analysis (lead); investigation ; methodology ; writing – review and editing . Jose P. Lemos‐Filho: Conceptualization ; funding acquisition ; investigation ; resources ; writing – review and editing . Maria Bernadete Lovato: Conceptualization ; formal analysis ; funding acquisition (lead); investigation (lead); methodology ; project administration (lead); supervision (lead); writing – review and editing .Supplementary MaterialClick here for additional data file.


Since prehistoric times, southern Central Asia has been at the crossroads of the movement of people, culture, and goods. Today, the Central Asian populations are divided into two cultural and linguistic groups: the Indo-Iranian and the Turko-Mongolian groups. Previous genetic studies unveiled that migrations from East Asia contributed to the spread of Turko-Mongolian populations in Central Asia and the partial replacement of the Indo-Iranian populations. However, little is known about the origin of the latters. To shed light on this, we compare the genetic data on two current-day Indo-Iranian populations — Yaghnobis and Tajiks — with genome-wide data from published ancient individuals. The present Indo-Iranian populations from Central Asia display a strong genetic continuity with Iron Age samples from Turkmenistan and Tajikistan. We model Yaghnobis as a mixture of 93% Iron Age individual from Turkmenistan and 7% from Baikal. For the Tajiks, we observe a higher Baikal ancestry and an additional admixture event with a South Asian population. Our results, therefore, suggest that in addition to a complex history, Central Asia shows a remarkable genetic continuity since the Iron Age, with only limited gene flow. For illustration, agropastoral communities present since the Djeitun culture3 6000 years BCE were replaced during the Chalcolithic (4800–3000 BCE) by the emergence of denser villages and the premises of irrigated agriculture. During the Middle Bronze Age, the Bactrio Margian Archaeological Complex (BMAC) civilization flourished in southern Central Asia with characteristic proto-urban cities, powerful irrigation techniques, and a marked social hierarchy4. A pastoral nomadic lifestyle emerged later in northern Central Asia around 3000 BCE and gained importance in this region during the late Bronze Age (2400–2000 BCE). At the end of the Bronze Age, from about 1800 BCE, the Oxus civilization underwent during its final phase important transformations: while remaining in the same tradition, the material culture was impoverished with some ceramic forms and artifacts disappearing; some habitat sites were abandoned, monumental architecture disappeared, the level of technological development seemed to decrease5; international trade, which had been flourishing during the previous peak phase, slowed down considerably, or even came to a halt, except for contacts with the steppes of northern Central Asia6; funerary practices changed with the appearance of new modes of burial, before the total disappearance of burials during the Early Iron Age, that can be linked to an ideological evolution7. The period between 1800 and 1500 BCE saw Andronovo-like culture take over, until the rise of Yaz culture9. Then, Central Asia was the scene of the eastwards conquests of Achaemenids, Greeks, Partho-Sassanians and Arabic people and of the westward movement of various Asian peoples like the Huns, the Xiongnus, and the Mongols10, before being a trade centre along the Silk Road, particularly during the Sassanid Empire and after the Islamic invasion.Central Asia is a large region stretching from the Caspian Sea in the west to Lake Baikal in the east, encompassing Tajikistan, Kazakhstan, Turkmenistan, Uzbekistan, Kyrgyzstan and north Afghanistan. This region has found itself at the crossroads of migration routes since modern humans left Africa10 and show genetic affinities with Eastern Asian and Siberian populations; and a second group formed by Tajiks and Yaghnobis who live in southern Central Asia, speak Indo-Iranian languages, practice agriculture, are sedentary and who are genetically more similar to present-day western Eurasian populations11 and Iranians12. Moreover, Yaghnobis are known to have been isolated for a long time with no evidence of recent admixture12. Modern DNA studies suggested that the Indo-Iranian group was present in Central Asia before the Turko-Mongol group11, maybe as early as Neolithic times; the Turko-Mongol group emerged later from the admixture between a group related to local Indo-Iranian and a South-Siberian or Mongolian group14 with a high East-Asian ancestry (around 60%). Turkmens, however, genetically stand out from the Turko-Mongol group, being intermediate with the Indo-Iranian group15, which suggests a recent language and culture shift16, possibly through a mostly elite dominance-driven linguistic replacement.Today, the complex demographic history of Central Asia results in a composite genetic diversity, with modern Central Asian populations being divided into two culturally distinct groups: a first group composed of Turkic and Mongolic-speaking populations , who are semi-nomadic herders20. Although the settlement of Europe was extensively studied26, there have been only a few studies exploring the population history of Central Asia, and even fewer focusing on southern Central Asia. In northern Central Asia , genetic studies evidenced eastward and westward movement of populations since the late Neolithic period28, leading to a gradient of western steppe genetic ancestry. In southern Central Asia where most of the ancient genomes date back to the late Neolithic and the Bronze Age, it was shown that populations from the BMAC were strongly related to southern Iranian ancient populations with some individuals displaying additional steppe-ancestry18.Paleogenetic studies confirmed that multiple migration waves and admixture events, in which steppe populations played an important role, have occurred in Eurasia in the last 10,000 yearsHowever, the relation between modern Indo-Iranian speaking populations and ancient populations from southern Central Asia remains unclear: what are the genetic sources of modern Indo-Iranian speakers? Can they be traced back to the Iron or the Bronze Age? Is there one or several different population histories among a given linguistic group of populations? What is the role of the Turkmens in this story?30 and 3109 ancient published genomes from all Eurasia45  as well as 1501 present-day genomes from Eurasia and Africa45 Table , includiTo explore the relation between present-day Central Asian individuals and the Eurasian genomic diversity, ancient and modern, we first performed a Principal Component Analysis (PCA) Fig.  and S2 oWestern_Steppe individuals clustering on the bottom of the European cluster and Central_Steppe individuals spreading from the Western_Steppe cluster to the Okunevo_BA cluster close to Baikal and Siberian modern individuals. The ancient individuals of southern Central Asia  follow a cline stretching from Neolithic Iranian individuals (Iran_N) to present-day Iranians and Yaghnobis.For the ancient individuals, Bronze Age, Iron Age, and historical steppe individuals fall on a cline stretching up from European to East Asian groups, with Turkmenistan_IA and Ksirov_Kushan individuals) are located close to modern Indo-Iranian populations, although slightly negative values on the first axis and positive values on the third axis suggest an addition of Baikal ancestry in the present-day Indo-Iranians. Finally, it appears from this PCA , of another maximized in Eastern European Hunter-Gatherers (EEHG) and Western Scandinavian Hunter-Gatherers (WSHG)  and of a third component  that is not completely maximized in any population of our dataset, but is found in present-day Europeans and in Anatolian Neolithic farmers (Anatolia_N). In addition, a fourth component maximized in Baikal Hunter-Gatherers (BHG: Shamanka_EN) and largely present in all modern Turko-Mongol populations  is also inferred to a lower extent in the modern Indo-Iranian populations, with a significantly smaller proportion in Yaghnobis than in Tajiks . Finally, the Tajiks present a small proportion (4%) of modern East Asian ancestry , which is largely present in all Turko-Mongol populations from Central Asia , and around 8% of the component maximized in present-day South Asian populations (orange), which are both absent in Yaghnobis.To confirm our initial observations and identify genetic structures, we performed an unsupervised clustering analysis using ADMIXTURETurkmenistan_IA has a profile with about 25% of WSHG/EEHG component, 30% of Iran_N component and 35% of the Anatolian farmer ancestry component but missing BHG ancestry , 19 Tajiks (TJE) and 24 Turkmens (TUR)Ancient population ; Turkmenistan_IA, present day Indo-Iranian) for every ancient population in our dataset 45, has a significantly positive D-statistic (Z > 3). Tajik individuals (TJE) display a higher number of ancient populations (N = 41) for which D-statistic is positive; the common characteristic of these ancient populations is to exhibit a large amount of BHG ancestry, consistently with the ADMIXTURE analysis , that is expected to be negative (Z < -3) if the Indo-Iranian populations can be modeled as admixed between the two sources  and obtained several negative f3-statistics always with the same ancient populations implied in the positive D-statistic  implying around 17% ancestry from XiongNu, almost 75% ancestry from Turkmenistan_IA, and around 8% ancestry from a South Asian individual (Indian_GreatAndaman_100BP)50 representing a deep ancestry in South Asia  we obtained dates from ~ 546 ± 138 years ago (18.8 ± 4.7 generations) to ~ 907 ± 617 years ago (31.2 ± 21.3 generations) for the West/East admixture. We also obtained a date of ~ 944 ± 300 years ago for the admixture with the South Asian population.Finally, we used 18 have already shown Turkmenistan_IA can be modelled as an admixture between BMAC and some steppe populations, and on the PCA  depending on their amount of Eastern Asian ancestry. The ADMIXTURE analysis discriminates the Western and Central steppe ancestries by the presence of a red and mauve component (maximized respectively in East-Siberia and East Asia populations) in the latter, which is absent from Turkmenistan_IA, indicating an affinity with the Western steppe. Nevertheless, we noted that Andronovo or Sintashta individuals also lacked this component while being classified as Central_Steppe. Thus Central_Steppe group is highly heterogenous and gathers populations with some East-Asian ancestry like Karasuk or Central Saka and others more Western steppe-like as Andronovo and Sintashta. Furthermore, we obtained the higher f3-outgroup statistic of the form f3 for ancient populations from BMAC complex or West Eurasia, highlighting the double origin and affinity with the West. This affinity is further confirmed with D-statistics of form D  – HG1 and HG2 belonging to WEHG, EEHG, WSHG, and BHG populations – we evidenced that the steppe populations admixed with BMAC lacked East Asian or Baikal component  Fig. B that arsuk Fig. B. With Dent Fig. A. Indeedent Fig. A. Using Turkmenistan_IA with qpAdm. We first constituted a set with Poltavka, Srubnaya (Western_Steppe) and 4 individuals from Russia labelled as Andronovo 52, to estimate the affinity with Europe and Western steppe previously highlighted with D-statistics and f3-statistics. We only obtained one model with 2 sources that we could not exclude  suggesting that Andronovo individuals are the best proxy for the steppe population which admixed with BMAC to form the Iron Age southern Central Asia group. When testing for the best model between Andronovo and Karasuk  to estimate the affinity with Asia, we produced a single fitting/ relevant model implying Andronovo  with roughly the same proportions. Further tests explored the best model between Andronovo and Sintashta, two genetically close populations, and the single significantly outcome was the one with Andronovo and BMAC  in the same proportions. Eventually, we tested the best model between the individuals labelled Andronovo and two populations belonging to the Andronovo-complex: Fedorovo Shoindykol18 and Alakul Lisakovskiy18. Once again, the only valid model was the one with Andronovo and BMAC. Overall, we can say that the Iron Age population from southern Central Asia emerges from the admixture of BMAC with a Bronze Age population close to the Andronovo individuals, which presents a profile with an affinity with Western steppe rather than with a Central steppe with an affinity with East Asia (like Karasuk).Finally, we tested different steppe populations which admixed with BMAC to model de Table , and it 53, Turkmens are genetically closer to Indo-Iranian populations than to Turko-Mongols55.Despite speaking a Turko-Mongol language and having the same cultural practices as other Turko-Mongol ethnic groupsp-value < 2.10–16) high amount of Baikal  and East Asian ancestry . Turkmens, for their part, display a completely different pattern, with an amount of Baikal component  closer to the proportion in Tajiks  and almost no East Asian component. They do not show as much South Asia related ancestry as Tajiks, suggesting that the admixture with South Asian populations occurred or continued after Turkmens split from the remainder of the Indo-Iranian group.Indeed, Turkmens (TUR) fall into the Tajiks cluster and not in the Turko-Mongol cline in the PCA Fig.  and in tWe have established genetic affinity profiles with ancient populations for all Central Asia populations including Turkmens of the first dataset, based on f3-outgroup statistics of the form f3 Fig. . The f3-DevilsCave_N); f3 = −0.0025, Z = −5.266). qpAdm modelling for Turkmens produces a single nonrejected model  implying 6% of Golden Horde Asian and 94% of Tajiks (TAB)   and East Asian ancestry  Table . For thiThese results enlighten that Turkmens were an Indo-Iranian-like population not so long ago, who recently shifted language and culture without a substantial genetic change in population.11 and as supported by historical56 and archaeological evidence57, we found that Indo-Iranian speakers settled in Central Asia long before Turko-Mongol speakers11. The main event at the bottom of Indo-Iranian ancestry in southern Central Asia occurred at the end of the Bronze Age/Early Iron Age, through the admixture between local BMAC groups and Andronovo-related populations perhaps linked to the end of the Oxus Civilization. We note here that the steppe group who admixed with BMAC did not present East Asian ancestry, which is consistent with both the archeological58 and genetic38 findings of the East Asian ancestry arriving in the Central steppe core only at the end of the Iron Age.Our research provides insight into the history of Indo-Iranians by using evidence to trace modern populations back to the Iron Age in southern Central Asia. As proposed by former genetic studies59, which is eastward, but show a genetic profile very close to the Sintashta individuals, whose area expanded near the Caspian Sea. Individuals from other cultures belonging to the Andronovo complex have been sequenced18 but overall they form a moderately heterogenous genetic group. Moreover, some studies have shown that Steppe groups can be labelled similarly but be different genetically, such as, for instance, Srubnaya Alakulskaya individuals being closer to Andronovo individuals than to Srubnaya from the Samara region28. The nomadic populations from the end of the Bronze and Iron Age being very genetically heterogenous, we suspect that the source of the Western steppe ancestry found in Iron Age southern Central Asia may not be sampled yet. It is interesting to notice that the gene flow between the Steppe and southern Central Asia went two-ways60. A recent study60 has highlighted that a gene flow from BMAC contributed to the genetic formation of Scythians. Our findings combined with these studies strongly corroborate the hypothesis based on archaeological evidence that southern Central Asia civilizations since BMAC and Western steppe culture had a strong cultural connection64.The populations falling under the name Andronovo form a complex group. Indeed, when screening the individuals used under the label Andronovo in our dataset, we note that they all belong to one site, Kytmanovo. work65, the present study shows no impact of the Arab cultural expansion in Central Asia on the Indo-Iranian speaker’s genetic diversity, despite the first one leading to a shift in language for Tajiks. We also do not see a gene flow from Iran despite the Persian cultural expansion which led to a language shift from an east-Iranian language to a west-Iranian in Tajiks—when Yaghnobis kept their east-Iranian language66.Overall, we demonstrate here a remarkable example of genetic continuity since the Iron Age in Indo-Iranian populations from Central Asia despite the frenzy of population migrations in the area since the Bronze Age. Similar to Zhabagin et al67. Yaghnobis are indeed an isolated ethno-linguistic population historically present in the hardly accessible valley of the Yaghnob River. Evidence suggests that the separation between Yaghnobis and Tajiks occurred at least 1000 years ago, which explains the high genetic differentiation observed in Indo-Iranians by previous studies67. Interestingly, it implies that Yaghnobis could represent a good proxy for the ancestry present in Central Asia before the migration waves that led to the current genetic diversity, despite the strong drift that occurred.Yaghnobis, for their pair, are characterized by strong genetic stability over time , which can be linked back to their long-term isolation.2, who observed the light impact the westward invasions  had on Indo-Iranian groups in Central Asia. On the other hand, we have highlighted for Yaghnobis, Tajiks, and Turkmens a small amount of gene flow from BHG-ancestry dating to around 1000 years ago, suggesting a recent wave of westward migration from the Altai mountains, after the Iron Age. This recent wave can be linked to the origin of the Turko-Mongol in Central Asia which has been demonstrated by Martinez-Cruz et al.2 and Li et al.68 to be from an ancestral group of Turkic speakers from the Altai region. Our quite recent date of admixture differs significantly from the date obtained by Palstra et al.11 which placed the admixture event back to 8 ky BP for Tajiks and 2.3 ky BP for Kyrgyz. The more recent inferred dates of admixture for Tajiks compared to Yaghnobis could be explained by the fact that Tajiks received a more continuous gene flow from the eastward source, continuous gene flow that occurred after the first admixture event that formed the Yaghnobis genetic composition. Indeed, the qpAdm method cannot detect a continuous admixture which can be expected in this context. Furthermore, the search of their ancestry confirms a genetic homogeneity within Yaghnobis, Tajiks, and Turkmens, despite their cultural, notably linguistic differences, with some genetic differences emerging from various patterns of gene flow in Tajiks and Turkmens.The amount of East-Asian ancestry due to admixture with modern Turko-Mongol groups remains low even in Tajiks, consistent with the findings of Martinez-Cruz et al69. According to previous archaeological studies71, multidirectional cultural exchanges with South Asia are known to have taken place as early as the Chalcolithic period: notably from Sialk culture and other Iranian cultures towards Balochistan70 or from Geoksjur culture of Turkmenistan to southern Afghanistan. In the opposite direction, from south to north, Mundigak III type ceramics find parallels as far as Badakhshan in northeast Afghanistan, material from Balochistan and shells used in necklaces and bracelets from the Arabian Sea are found at the Sarazm site in Tajikistan, showing a long-distance commercial exchange. All these ancient populations were on the move with probably quite frequent exchanges and cultural blends between populations, Iron Age included71. Intriguingly, genetic proximity between southern Central Asian and South Asian groups has already been suggested for BMAC samples18 and raises the question of the timing of this gene flow. Two models can be considered: the first one assumes the formation of a homogeneous basal Indo-Iranian background (as observed today in Yaghnobis) and subsequent recent gene flow from South Asian populations; the second model acknowledges the presence of South Asian ancestry in some Bronze Age BMAC samples18 and suggests Tajiks and Yaghnobis could have derived from distinct BMAC populations, respectively with and without South Asian ancestry, who have both experienced independent admixture with Andronovo-like steppe populations during Iron Age, and eastern nomads with BHG ancestry afterwards. Because the date of the gene-flow from South Asian populations in Tajik genomes is relatively recent, the data favours the first hypothesis; however, uncertainties on the model of admixture  may be compatible with continuous gene-flow since the Bronze Age. Additionally, our recent date of admixture fits with the arrival of the South Asian ancestry at the same that the shift from east to west-Iranian language in Tajiks linked to the Persian expansion 1500 years ago66.Notably, we evidenced an admixture event from South Asia restricted to the Tajik population, undocumented before despite evidence in Iranian Turkmens72, which cover an area ranging from Siberia to Eastern Europe and the Middle East, through Central Asia and have been occurring during a wide period, the 5th–16th centuries14. In regions other than Central Asia, several studies have shown that Turkic-speaking peoples genetically resemble their geographic neighbours, with no clear genetic signal that would distinguish them72. This lends to support the model of a language replacement by elite-dominance rather than by demic diffusion for languages of the Turkish family expansion72. Turkmens fit in this global model but are an exception in their region. Indeed, the other Turkic-speaking populations, like Kyrgyz or Kazakhs, show a different genetic profile with a clear dominant East Asian and Baikal components, attesting to a more significant admixture with nomads from South-Siberia and Mongolia, which have been dated around the tenth-fourteenth centuries14. The small amount of East Asian ancestry in Turkmens has been linked to an admixture dated around the 15th century, so slightly after the first admixture in Central Asia, and may come from gene flow with these Turco-Mongol groups.Lastly, the case of Turkmens is a notable example of a population changing language and cultural practices without substantial changes in their genetic ancestry. Indeed, Turkic-speaking peoples found in all Eurasia are the result of several nomadic migrations75. Linguistic analyses point either to Anatolia74 or the Pontic Steppe75 as the region where the Indo-European languages originated. The expansion of Yamnaya related populations westward during the late Neolithic, and eastward during the Bronze Age, through the migration of Andronovo groups, suggests that they were speakers of such languages. Interestingly, the ancestry pattern found in Indo-Iranian speakers from Central Asia is not found in other Indo-Iranian speaking populations, namely, the Iranians Persians69. This ethnic group displays a genetic continuity since the Bronze Age with ancient individuals from Iran, with limited gene flow from the steppes 69. Furthermore, our study of the Turkmen population presents another example where language and genetics do not match, questioning the idea of inferring language displacement using population movement. Their genetic affiliation to modern western Eurasian populations, seen in earlier studies, is due to a common steppe ancestry.The question of the diffusion of Indo-European languages has been a hot topic in the last few yearsOur results bring to light that for Indo-Iranian speakers various patterns of genetic and linguistic continuity or discontinuity coexisted through time. In southern Central Asia, we show that the actual Indo-Iranians are the product of a long-term continuity since the Iron Age with only limited recent impulses from other Eurasian groups. Our results provide further evidence that the demography of this region is complex and needs small-scale studies like this one to be fully understood. From this perspective, the precise timing of these impulses cannot be solved until more genetic data from samples from the Iron Age and historical times, who do not belong to the Steppe cultural complex, have been obtained.https://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-data. We retained non-related individuals with more than 10,000 SNPs hit on the 1240k panel. We also added ancient individuals from two recent publications about Middle East37 and Mongolian Steppe38.We selected 3102 published ancient human genomes from Eurasia Table  from PalWestern_Steppe, Central_Steppe or Eastern_Stepppe respectively referring to a population with a close genetic affinity with Western European Hunter Gatherer  or Eastern European HG ; to individuals with a higher ancestry from Western Siberian HG , a strong affinity with Baikal HG  or with eastern non-Africans; to populations exhibiting an East Asian component, although not all of them do like for example Andronovo population.The ancient dataset was classified based on geographical, chronological, and ancestry criteria. The individuals from the steppe with known ancestry  were labelled as 30, the 1000 Genomes dataset29. Furthermore, we also used a Central Asian specific dataset53 obtained in our lab using capture including 527 individuals. We merged these modern data using mergeit from EIGENSOFT49 and we haploidized them by randomly selecting one allele per position. The final merge includes 237,644 SNPs for 5129 individuals. For the analysis requiring more SNPs, we used individuals sequenced by shotgun48 from only three populations in the Central Asia dataset: 3 Yaghnobis (TJY), 19 Tajiks (TJE) and 24 Turkmens (TUR), and we pseudo-haploidized and combined them with the 1240k panel to obtain a second dataset of 716 743 SNPs and 4648 individuals.We analysed ancient genotypes with 1388 Eurasian individuals, 109 Yoruba, and 3 Mbuti individuals from two modern publicly available datasets: the SGDP datasetsmartpca49 on 1915 Eurasian present-day individuals and we projected all the 3109 ancient samples on top of the 3 best PCs. We used default parameters with lsqproject: YES, and numoutlieriter: 0 settings.We performed PCA with 76. We generated a reference space using 1915 Eurasian present-day individuals , after filtering for a minimum allele frequency of 0.05. Then we downloaded the bam files for 635 relevant ancient individuals18 and projected independently every ancient individual into the reference space with 10 replicates. We obtained similar plots, with nearly perfect correlation between the first 3 PCs for the ancient individuals generated by smartpca and LASER . We run ADMIXTURE analyses for clustering with K between 2 and 15, with 20 replicates performed for each K. The best value of K clusters, 10, is the value with lowest cross-validation error .We computed the f3 outgroup statistics using the qpAdm analysis with ADMIXTOOLS package to model the ancestry of Central Asian modern populations. For Indo-Iranians, we used Mbuti, Han, Natufian, WEHG, Ust-Ishim, MA1, Kostenki14, EEHG as reference populations.We performed rotating BMAC and Iran_N.Prior to the analysis, we checked if the reference populations could well discriminate between the source populations by computing f4-stat of the form f4, for all the sources and all the combinations of outgroups possible. Then we plotted the pairwise f4 and calculated a correlation score. We observed that our dataset discriminates well Turkmenistan_IA, XiongNu, and GoldenHordeAsian to assert the best source of East-Asian ancestry in our model. To test different models, we used Iran_N, Europe_EN, BMAC, Turkmenistan_IA, XiongNu, Ukraine_Scythian and Germany_Corded_Ware as rotating source populations. And to model Tajiks, we add Indian_GreatAndaman_100BP to represent a deep ancestry from South Asia.We first tested a rotating group with Turkmenistan_IA, we used the same reference group with Iran_N and BHG added and we used Germany_CordedWare, Russia_Poltavka, XiongNu, EuropeEN, BMAC, Andronovo, Ukraine_Scythian as the rotating group. We also performed tests with smaller rotating groups: (1) Andronovo, Alakul_Lisakovskiy, BMAC (2) Andronovo, Fedorovo_Shoindykol, BMAC (3) Andronovo, Sintashta, BMAC (4) Andronovo, Karasuk, BMAC, (4) Andronovo, Afanasievo, BMAC.To model Turkmenistan_IA, and used TJY, Mongolia_XiongNu, Kazakhstan_GoldenHordeAsian, TAB, Turkmenistan_IA as the rotating group.To model Turkmens, we used the same reference group as for DATES v75318 to estimate the time of admixture events in Tajiks, Yaghnobis, and Turkmens. To convert the estimated admixture date in generations into years, we assumed 29 years per generation51. The standard errors of DATES estimates come from the weighted block jackknife with ‘‘binsize: 0.001,’’ ‘‘maxdis: 1,’’ ‘‘runmode: 1,’’ ‘‘mincount: 1,’’ ‘‘lovalfit: 0.45’’ as parameters as in the example file at https://github.com/priyamoorjani/DATES/blob/master/example/par.dates.We used Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Oreochromis niloticus  is among the economically most important freshwater fish species in East Africa, and a major source of protein for local consumption. Human induced translocations of non-native stocks for aquaculture and fisheries have been found as a potential threat to the genetic diversity and integrity of local populations. In the present study, we investigate the genetic structure of O. niloticus from 16 waterbodies across Ethiopia using 37 microsatellite loci with SSR-GBAS techniques.Nile tilapia, FST values  between populations indicate a high level of genetic differentiation and may suggest long term isolation even within the same drainage systems. Natural populations of the Omo-Turkana system and the lakes in the Southern Main Ethiopian Rift showed the highest genetic variability while low variability was found in stocked populations of lakes Hora, Hashenge and Hayq.The samples are structured into three main clusters shaped either by biogeographic factors or stocking activities. High The results presented herein, may provide an essential basis for the management and conservation of the unique genetic resources in northern East Africa, and advance our understanding of biodiversity, phylogeny, evolution and development towards phylogenetically more accurate taxonomic classifications.The online version contains supplementary material available at 10.1186/s12862-021-01829-2. Oreochromis niloticus , is native to East, Central and West Africa, as well as to the Middle East, particularly in the Jordan valley [Nile tilapia, n valley , 68. In n valley , 52. Duen valley , Nile tin valley  and is cn valley , 68.Its large natural distribution area in sub-Saharan Africa and its broad ecological tolerance makes Nile tilapia a successful species in a wide range of aquatic habitats . On a maLabeobarbus intermedius complex [While most previous molecular studies , 36, 53  complex . These sO. spilurus (Günther 1894)) is found [Tilapia cancellata Nichols, 1923 (presumably Awash); T. calciati Gianferrari, 1924 (Atbara-Tekeze); O. n. filoa [O. n. tana [O. niloticus might be a species complex awaiting taxonomic revision [Nile tilapia is native to most drainage systems of Ethiopia, but absent from the Wabe Shebelle and Genale-Dawa rivers, where the Sabaki tilapia  and Lake Tana (41). Populations in the MER (Main Ethiopian Rift) showed comparably few private alleles with highest numbers in the SMER (Table F = 0.46 for lake Tana and F = 0.36 for Kada Bada). These populations were also the ones showing more loci with proportion of null alleles above 0.1  among the 16 Nile tilapia populations investigated are given in Table FST values above 0.40 as high, between 0.20 and 0.39 as medium and below 0.20 as low. In general, the highest FST values were found in comparisons involving populations from Lake Tana (0.38–0.56) and Gilgel Gibe (0.30–0.47). Though, the Gilgel Gibe population showed relatively low FST values with Lake Turkana that is from the same drainage system. Populations from the SMER were distinct from Lake Turkana (0.19–0.22) while genetically closer to the CMER and the NMER populations. While the interconnected CMER lakes showed low FST values (0.02–0.04), a higher differentiation was found in the Awash drainage (0.05–0.13) with highest FST values between Lake Metahara and Lower Awash (Yardi).Pairwise genetic differentiation  indicated that 38% of variation was explained by differences among populations, and 53% among individuals within populations. The remaining 9% of variation were attributed to differences within individual.FST values (0.19–0.32). While populations from lakes in the CMER cluster together with samples from the Lower Awash River (Yardi and Kada Bada), they appear to be distinct from the geographically closely situated Koka and Metahara populations. Though bootstrap support is low, the latter were found to cluster together with stocked fish from crater Lake Hora and the Fincha Reservoir in the Blue Nile drainage.Genetic distances between populations varied from 0.012 (between Kada Bada and Lake Yardi in the Awash drainage) to 1.981 (between Hawassa and Lake Tana)  the best K was 2 and suboptimal was 8. The result obtained did not change from the analysis including all samples  values K = 2 was considered the best-fitted to the data. STRUCTURE HARVESTER results also suggested K = 11 and K = 13 as the second and third-best fit to the data. Plots for these K values are shown in Fig. les Fig. b. The reFST values exist in Ethiopia that correspond to the flowing groups from Tibihika et al. [Nile tilapia shows a very pronounced genetic structure in its native range that is on one hand strongly correlated with historical connectivity between waterbodies but also influenced by contemporary stocking activities. This underlines the potential of translocations causing genetic changes, loss of variation, loss of specific adaptations and changes in genetic structure in accordance to Laikre et al. . Nile tia et al. , 60: (1)Na) and heterozygosity, of Ethiopian Nile tilapia populations was slightly lower than in the analysis of East African populations [Genetic diversity, indicated by the mean number of alleles  Lake Tana, (2) Turkana and Gilge Gibe, and (3) Ethiopian Rift Valley populations. Tibihika et al. [Pairewise a et al.  had alreO. n. cancellatus, which was later described as O. n. tana by Seyoum and Kornfield [The taxonomic revision of Nile Tilapia has been suggested by other authors. Trewavas , Seyoum ornfield . Howeverornfield  suggestea, Turkana also exhibited the highest mean number of private alleles pre locus  and the Awash (Kadbada and Yardi) populations were genetically more similar to each other than to the SMER populations, Abaya and Chamo. This is also shown by Nei’s genetic distances , 6637, 6Based on the high genetic differentiation between some of these populations it is expected that uncontrolled translocations may lead to catastrophic consequences such as out breeding depression . This wiThe development of a sustainable aquaculture practice could offer alternative livelihoods and help fish stocks in natural ecosystems to recover, thereby easing overfishing pressures . SustainFST values between the populations indicate a high level of genetic differentiation among these 16 populations. It also suggested that many of the assessed populations are genetically different and this indicates that they may have evolved in isolation. Genetic clustering of the native populations reflect their geographic distribution pattern for the Main Ethiopian Rift, the Omo-Turkana system and Lake Tana. Moreover, the large genetic differentiation from Gilgel Gibe, Tana and Turkana, indicates greater diversity and the possible existence of multiple sub-species or even species. Nevertheless, the taxonomic position of Nile tilapia in these water bodies should be further investigated using mtDNA analysis. The results presented herein have important implications concerning anthropogenic activities, such as stocking programs and aquaculture practices, for securing the genetic resources in the country and advancing our understanding of biodiversity, phylogeny, evolution and development towards phylogenetically more accurate taxonomic classifications.In this study, we clearly showed that the genetic structure of Nile tilapia populations in Ethiopia is complex. The genetic structure patterns found here are likely a consequence of both biogeographic and anthropogenic factors. Our results indicate that the Abaya, Chamo, Gilgel Gibe and Turkana populations contain the highest genetic variability while the translocated populations of Hora, Hashenge and Hayq showed the least. High Abay/Blue Nile River: Lake Tana (TA) and Fincha Reservoir (FI) represent populations from the Nile drainage. Lake Tana, the largest lake in Ethiopia, is the source of the Blue Nile but largely separated from this river by the Blue Nile Falls. The Fincha Reservoir is located in the headwaters of the Fincha River, a left bank tributary of the Blue Nile. The Nile tilapia population in the reservoir is introduced , 61.Omo-Turkana system: Gilgel Gibe Reservoir (GG) and Lake Turkana (TU) represent two natural populations of Nile tilapia in the Omo-Turkana system. The Gilgel Gibe Dam and the associated reservoir are situated along the Omo River, forming one of the largest reservoirs in Ethiopia. Lake Turkana is a large alkaline lake in the northern Kenyan Rift with paleo-connectivity to the Nile . Less thSouthern Main Ethiopian Rift (SMER): Lakes Abaya (AB) and Chamo (CH), represent a system of interconnected lakes with a natural population of Nile tilapia and evidence of past connections to lakes Chew Bahir and Turkana .Central Main Ethiopian Rift (CMER): Lakes Hawassa (HW), Langano (LA) and Ziway (ZI) represent a system of partially interconnected lakes in the central part of the Main Ethiopian Rift with a natural population of Nile tilapia. Evidence for frequent lake level fluctuations and past connections to the Awash system are known .Northern Main Ethiopian Rift (NMER): The Awash River systems with sampling sites in the Koka Reservoir (KO), lakes Metahara (MT) and Yardi (YA) and the mainstem Awash River at Kada Bada (KA), represent presumably natural populations of Nile tilapia. Englmaier et al.  recentlyVolcanic crater lakes in the NMER: Lake Hora (HR), is one of five crater lakes near the town of Debre Zeyit (Bishoftu). A long history of stocking Nile tilapia from various sources is reported for all five lakes .Volcanic crater lakes in the Ethiopia Highlands: Lakes Hashenge (HS) and Hayq (HQ). These lakes are located near the north-western escarpment of the MER at altitudes between 2000 and 2500 m a.s.l. Both lakes were stocked with Nile tilapia of unknown origin .A total of 348 individuals of Nile tilapia were sampled from 16 localities across different drainage systems of Ethiopia between 2017 and 2019 Table . SamplesWhole genomic DNA was extracted using following the protocol from Tibihika et al.  using thhttps://github.com/mcurto/SSR-GBS-pipeline. For further analyses, all loci and samples with missing genotypes ≥ 50% were excluded [Sequences generated by Illumina were subsequently quality checked using criteria described in Curto et al. . Allele excluded  leaving Na), effective number of alleles (Ne), total/mean number of private alleles (Np), observed heterozygosity (Ho), expected heterozygosity (He), F-statistics (FST) and Shannon’s information index (I). Analysis of molecular variance (AMOVA) was applied to partition the total genetic variance into components explaining divergence between populations, among individuals within populations, and among individuals within each sampling site. Moreover, deviation from Hardy Weinberg Equilibrium (HWE) per loci were estimated. These analyses were performed using GenAlEx v6.503 [Genetic diversity and variability per population was estimated based on the average number of alleles per locus  was estimated based on the rate change in the log probability of data between successive K values according to the Delta K method [K values ranging from 1 to 20 with 10 replicates were performed with a burn-in length of 10,000 Markov chain Monte Carlo (MCMC) generations followed by 10,000 generations. This short burn-in length was chosen to save computational time. To evaluate if a longer burn-in was required, besides checking for convergence of FST and alpha parameters, we performed a run with 100,000 generation burn-in which the result was congruent with the short one. Since this was the case burn-in length was not increased. Both Ln (K) and delta K (∆K) statistics were used to select the most likely number of clusters using STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/structureHarvester/) [http://clumpak.tau.ac.il/) [Population structure was further examined by assigning individuals to populations based on the Bayesian clustering method using STRUCTURE v2.3.4 , 46. ThiK method . Indepenvester/)  which va.ac.il/) . By doinAdditional file 1: Table S1. Unbiased Nei’s genetic distance between 16 Nile tilapia populations revealed by 37 microsatellite loci.Additional file 2: Figure S1. Structure analysis (admixture model) for all Nile tilapia samples investigated for all K values up to 13.Additional file 3: Figure S2. Structure analysis (admixture model) excluding the most divergent populations from Omo-Turkana  and Lake Tana for all K values up to 8.Additional file 4: Table S2. Characteristics of microsatellite loci used to genotype Nile tilapia populations in Ethiopia.


Aethina tumida, are parasites of bee nests. Originally endemic to sub‐Saharan Africa, they are now found nearly globally. Here, we investigated the molecular bases of the adaptations to novel environments underlying their invasion routes. Genomes of historic and recent adults A. tumida from both the endemic and introduced ranges were compared. Analysis of gene–environment association identified 3049 candidate loci located in 874 genes. Functional annotation showed a significant bias toward genes linked to growth and reproduction. One of the genes from the apoptosis pathway encodes an “ecdysone‐related protein,” which is a crucial regulator in controlling body size in response to environmental cues for holometabolous insects during cell death and renewal. Genes whose proteins regulate organ size, ovary activation, and oviposition were also detected. Functions of these enriched pathways parallel behavioral differences between introduced and native A. tumida populations, which may reflect patterns of local adaptation. The results considerably improve our understanding of the underlying mechanisms and ecological factors driving adaptations of invasive species. Deep functional investigation of these identified loci will help clarify the mechanisms of local adaptation in A. tumida.Invasive species are a major driver of ecological and environmental changes that affect human health, food security, and natural biodiversity. The success and impact of biological invasions depend on adaptations to novel abiotic and biotic selective pressures. However, the molecular mechanisms underlying adaptations in invasive parasitic species are inadequately understood. Small hive beetles,  Small hive beetles, Aethina tumida, invasive parasites of pollinating bees have recently invaded multiple continents, across a wide range of climates. The molecular mechanisms underlying the adaptation of this invasive beetle remain poorly understood. Here, the identification of genetic basis, analyses of gene ontology, and pathway enrichment of identified genes provide a mechanistic understanding of a biological invasion. During pupation in the soil . To ensure taxonomic status, samples were first confirmed as SHB using morphometrics  using a phenol–chloroform extraction protocol  were prepared for whole‐genome sequencing with the Illumina NovaSeq S4 and NovaSeq 6000 (only for Tanzania samples) sequencers following standard procedures.Since previous studies suggested that Tanzania and/or South Africa were the most likely source populations of beetles introduced into the United States . Low‐quality bases and adaptors, as well as short reads, were removed and trimmed with fastp v0.12.5  were called following the recommended variant calling steps of the Genome Analysis Toolkit HaplotypeCaller v3.8  between SNP pairs  using software PLINK  tree was constructed using MEGA v7.0  from 2 to 7. Both analyses were conducted by using all non‐admixed samples of each group. Cross‐validation errors were used to estimate a reasonable value of K for model fitness. Based on the results of PCA, NJ, and structure analyses, we separated individuals that had apparent stratification within the same population into subpopulations for the subsequent analyses. SNPs with a missing call rate <0.9 were applied to compute the pairwise kinship coefficients as well as the population fixation statistic (FST) (20 kb windows sliding in 10 kb steps) using VCFtools v0.1.15  based on all bi‐allelic SNPs among the 68 SHB individuals were performed using the software PLINK v1.9  were downloaded from the WorldClim database  genotyped for 30 individuals from an introduced US (Maryland) population and its source (ancestor) populations in the native range—South Africa and Tanzania, according to the relationships of these populations. In this way, we can avoid cofounded mutations because of evolutionary history. SNPs with minor allele frequency <.05, missing data, and 4 Figure  were fil4 Figure . The pubK  was estimated using snmf function in LEA  was used to find the correlation with environmental gradient after the number of latent factors 2.6A. tumida  was used to determine the significant level of enriched pathways  were annotated by a homology‐based method with the GFF gene models of 2.7https://github.com/rambaut/figtree/releases/download). In R .The 64 recent samples generated an average number of 70.6 million paired‐end reads of 150 bp read length, and on average, 22.98 million such reads were obtained for the four historical specimens Table . Analyzi3.2K) disclosed genetically distinct clusters that mirrored both geographic proximity and gene flow. When K = 4 (determined as the best fit model), a similar structure was found between the Liberian and Burkinabe populations, as well as for the South African, Tanzanian, and American populations  were estimated as 2 Figure a–b, and 2 Figure . The maj2 Figure , which i3.4T. castaneum were predominantly involved in molecular function such as “binding,” “catalytic activity,” and “molecular function regulator”  with four displayed in Figure The mapped protein sequences of 737 unique homologs in ” Figure a, as wel” Figure b. The en4In this study, we characterized the relationships of seven representative SHB populations across native and introduced ranges and the genomic response to local adaptation. We verified the ancestor populations of the SHBs introduced into the United States. Our association analysis between loci and environmental gradients using LFMM led to insights into the evolutionary history and genetic basis of local adaptation between the introduced population and its ancestor populations. Analysis of gene–environment association identified 3049 genome sites located in 874 genes. Functional annotation showed that several top enriched pathways reflected by 49 genes were closely related to growth and reproduction, which seemed to parallel the behavioral differences between introduced and native SHB populations and may reflect their local adaptations. These results supported our hypothesis that genes associated with reproduction and body size have been involved in the adaptive evolution of invasive SHBs.FST outlier tests  that were non‐coding transcript variants located in intergenic and intron regions. However, these outlier variants by GEA analysis indicated that allele frequencies differed strikingly between the introduced and their ancestral populations. The designed comparison in combination with the LFMM method that considered confounding factors and genotype–environmental analyses allowed us to identify reliable polygenetic signatures of recent adaptation, because the random drifts and the cofounding factors influence the correlation with environment gradient and this method had more power than the An increasing number of studies have shown that phenotypic variability could be influenced by genetic variations outside of coding genes acting as regulatory elements such as enhancers and/or involving in transcriptional regulation ; Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Resources (supporting); Software (lead); Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Jan Henkel: Formal analysis (supporting); Writing‐review & editing (supporting). Alexis Beaurepaire: Writing‐review & editing . Jay Daniel Evans: Methodology ; Resources (supporting); Supervision ; Writing‐review & editing (supporting). Peter Neumann: Conceptualization ; Funding acquisition (lead); Investigation (supporting); Project administration (lead); Resources (lead); Supervision (supporting); Writing‐review & editing (supporting). Qiang Huang: Conceptualization ; Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Supervision ; Writing‐review & editing .Fig S1‐S4Click here for additional data file.Table S1, S3‐S4Click here for additional data file.Table S2Click here for additional data file.


Kandelia obovata (Rhizophoraceae) is a mangrove species with high cold tolerance in the Northern Hemisphere. We investigated the phenotypic responses of an artificial population of K. obovata, with plants transplanted from different source populations, to extremely low temperatures during winter of 2015–2016 in Yueqing County (28°20′N), Zhejiang Province of China. Using two binary traits, “with/without leaves alive on the branches” and “with/without alive buds on the tips of branches,” we classified plants in this artificial population into strong, moderate and poor cold resistance groups. We further assessed the genetic diversity, structure and differentiation of these three groups, as well as five natural populations along a latitudinal gradient using ten nuclear and six plastid microsatellite markers. Microsatellite data revealed genetic differentiation among the natural populations along the latitudinal gradient. Molecular data indicated that the cold tolerance of three groups in the artificial population was associated with their geographic origins, and that the most cold-tolerant group came from the northernmost natural population. Our study thus indicates that natural populations of K. obovata may have evolved divergent capacity of cold tolerance.Temperature is one of the climatic factors that shape the geographic distribution of plant populations. Mangroves are temperature-sensitive plants, and their distributions are severely limited by low temperatures. It is unknown, however, to what extent temperature contributes to their population differentiation and evolution.  Pseudotsuga menziesii (Pinaceae) and Cicer arietinum (Fabaceae), that investigated the relationships of population genetic structure with phenotypic traits and with cold tolerance  in New Zealand (38°45′S), Avicennia germinans (Acanthaceae) in Bermuda (32°20′N), and Kandelia obovata in Japan (31°N), which may move from mangrove-rich regions to their latitudinal limits by the aids of warm ocean currents (K. obovata and Aegiceras corniculatum (Myrsinaceae), and found that their cold-resistant ability increased with decreasing air temperature. In a previous study, where the cold-hardiness of leaves of 16 mangrove species was measured by conductivity method, Mangroves are temperature-sensitive plants. Both the annual coldest air temperature and the winter sea surface temperature can directly or indirectly affect mangroves . Therefocurrents . Accordicurrents . Since tcurrents . These sKandelia obovata is a widely distributed mangrove species, and its distribution range contains the Ganges Delta, Burma, Southeast Asia, the Ryukyu Islands, and even southern Japan  of Wenzhou City in Zhejiang Province for the first time  was one of the known exact source populations for the Yueqing (YQ) artificial population . Populat1. On June 17, 2016, we conducted the first survey and collected materials in YQ. Over a growing season approximately 10 months after the chilling event, our second “recovery” survey and materials sampling started on November 12, 2016 . From the five natural populations, 117 individuals were sampled with a distance of more than 10 m between each pair of individuals. In total, 277 individuals were sampled from six sites. Leaves were collected from each selected individual and then desiccated with silica gel, and then preserved at room temperature until DNA extraction. For the measurement of morphological traits, three mature leaves of each individual were used to measure leaf length (LL) and leaf width (LW), except for some individuals of the YQ population with a defoliation rate over 90%. In total, we obtained measurements of six morphological traits, e.g., plant height (PH), stem basal diameter (SBD), diameter at breast height (DBH), crown diameter (CD), leaf length (LL), and leaf width LW; . All of Total genomic DNA was extracted from dried leaf samples using the Plant Genomic DNA Kit (Tiangen) following the standard protocol provided by the manufacturer. DNA extractions were checked for concentration and purity using NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific), and subsequently diluted to a concentration of 5–20 ng/μL and stored at −20°C. For PCR amplification, 16 microsatellite markers (ten nSSR markers and six cpSSR markers) developed by previous studies were used , 2008, aHWE) and for linkage disequilibrium (LD). The nSSR loci under selection were detected with BayeScan 2.03. Both neutral loci and all loci were used to calculate genetic diversity, but only neutral loci were used to infer population structure. Genetic diversity parameters including the number of alleles (Na), number of effective alleles (Ne), Shannon diversity index (I), observed heterozygosity (Ho), expected heterozygosity (He), and unbiased expected heterozygosity (uHe) were calculated using GenAlEx 6.5 , 20 independent runs were performed with the Markov chain Monte Carlo (MCMC) run for 1,000,000 replications and a burn-in period of 100,000 interactions. The most likely number of K was determined following the method described by Although 160 samples were collected in YQ, there were 12 samples with missing data, and therefore a total of 148 individuals in YQ and 114 individuals in five natural populations (three samples with missing data were excluded) were included in the subsequent analysis. The simple sequence repeats (SSRs) data (both nSSR loci and plastid SSR loci) used for subsequent analyses is listed in AlEx 6.5 . Populate of FST . The anae of FST . Two AMOe of FST . For eacNa), number of effective alleles (Ne), Shannon diversity index (I), haplotype diversity (h), and unbiased diversity (uh)  and GST (depending on the frequencies of haplotypes) with 1,000 random permutations in Permut cpSSR 2.0  . AMOVA wity (uh) , and theulations ,b. We rapSSR 2.0 .Based on the phenotypic response of individuals to chilling event in the YQ population during the winter of 2015–2016, we could classify them into three categories of cold resistance: the group with strong resistance to cold (SRC), the group with moderate resistance to cold (MRC) and the group with poor resistance to cold (PRC) . The resRecent studies have shown that leaf size was often correlated with mean annual temperature . To testLD were found, while 10 out of 45 pairwise comparisons showed significant LD in YQ population (p < 0.01). Four nSSR loci  deviated significantly from HWE (p < 0.01). One locus (Kcan011) was identified by BayeScan as an outlier and the remaining nine loci were considered neutral (Na) ranged from 8 to 35, with a mean of 17.6 per locus. The genetic diversity parameters for each nSSR locus were shown in Of the ten loci, the mean frequency of null alleles was 10%, ranging from 2.2 to 11.9%. Across the five natural populations, no signs of  neutral . In totaNe, Ho, He, and I, were generally within the range of natural populations . Similarly, AMOVA based on nuclear microsatellites in YQ revealed that major genetic differences existed within different cold resistance groups (87%), and only 13% occurred among different groups (p < 0.001) (Genetic differentiation among six populations assessed by pairwise < 0.001) .K = 3 was the optimal parameter of their genetic structure  and the lowest was in SRC  (p < 0.001), consistent with the haplotype sharing between different groups (The genetic polymorphisms at the six plastid microsatellite loci were relatively low . Only 24ulations . Interes= 0.200) . AMOVA bt groups .A total of 15 plastid microsatellite haplotypes were found across three phenotypic groups of the artificial population . Only onK = 12 was the optimal parameter, log-likelihood values = −352.618)  . In detaK. obovata. The presence of phylogeographic structure was also confirmed by RST (0.308) being significantly higher than GST (0.217) (P < 0.001).The plastid haplotype network using the Median Joining methods indicated that the 36 haplotypes were grouped into two major clusters , separatK. obovata along latitude. The chilling event that occurred during the winter of 2015–2016 in Yueqing provided us with an ideal opportunity to obtain data of phenotypic response of K. obovata to low temperature and to compare the cold-resistant capability between populations of different geographic origins.Low temperature has been regarded as one of the primary factors affecting the latitudinal range limits of mangrove species . Yang anK. obovata in the Shacheng Bay  displayed leaf scorch, defoliation, and bud withering after frost , and thus, can be used for detecting ecological adaptation . EspeciaNevertheless, by using nuclear microsatellites we failed to detect a clear correlation between phenotypic and genetic patterns as revealed by the plastid markers. One reason could be that, concerning neutral genetic markers, plastid markers in this case may be more effective than nuclear markers in providing information of ecological adaptation due to the lack of DNA recombination in the plastome and thus stronger linkage to the selection-targeted loci. This assumes that at least some of the relevant loci are in the plastid genome, which requires further studies. A second reason may be the extent of gene flow. Previous studies reported that pollen dispersal distances are often long in tropical tree species, which depend on insects for pollination . In theoThe original contributions presented in the study are included in the article/W-XL and S-CY conceived the idea, designed the experiments, and collected the plant materials. W-XL and B-HZ conducted the molecular work. W-XL performed the analyses and drafted the manuscript. W-XL, S-CY and Y-YZ revised the manuscript. All authors contributed to the study and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.