Dianthus sylvestris coupled with integrated population genomic and gene-environment models, we reconstruct the past neutral and adaptive landscape of this species as it was shaped by the Quaternary glacial cycles. We show that adaptive responses emerged concomitantly with the post-glacial range shifts and expansions of this species in the last 20 thousand years. This was due to the heterogenous sieving of adaptive alleles across space and time, as populations expanded out of restrictive glacial refugia into the broader and more heterogeneous range of habitats available in the present-day inter-glacial. Our findings reveal a tightly-linked interplay of migration and adaptation under past climate-induced range shifts, which we show is key to understanding the spatial patterns of adaptive variation we see in species today.Quaternary climate fluctuations drove many species to shift their geographic ranges, in turn shaping their genetic structures. Recently, it has been argued that adaptation may have accompanied species range shifts via the “sieving” of genotypes during colonisation and establishment. However, this has not been directly demonstrated, and knowledge remains limited on how different evolutionary forces, which are typically investigated separately, interacted to jointly mediate species responses to past climatic change. Here, through whole-genome re-sequencing of over 1200 individuals of the carnation  The interplay of migration and adaptation was key in shaping species’ responses to Quaternary climate change. Illustrating this, Luqman et al. show that adaptive responses in a plant species emerged from climate-induced range shifts due to heterogenous sieving of adaptive alleles across space and time. Species responded to these changing conditions by shifting their range, adapting or going locally extinct, generating the distribution ranges and patterns of genetic structure that we see today4. By shaping the distribution of genetic variation potentially relevant for climate-related traits, past climate fluctuations may have played major roles in dictating the adaptive potential of species, that is, how able species are to adapt to subsequent bouts of climate-driven selection6. While studies on the biotic impacts of climatic change are rife9, few studies consider these genetic legacies of past climate, and fewer still incorporate both past and present, neutral and adaptive, evolutionary processes in their evaluations. Such an integrative approach, however, may be crucial to understand and predict species’ evolutionary responses to changing climate10.Present-day species have persisted through repeated periods of fluctuating climate, exemplified by the Quaternary ice ages (2.58 Mya – present) that occasioned major shifts in global sea levels, continental ice sheets and consequently in the habitats of species3), through reconstructions of past distributions based on fossil and contemporary occurrence records, and through inferences of past demography based on patterns of neutral genetic variation12. This focus was motivated by the long-held assumption that taxa are more likely to migrate and colonise adjacent habitat than evolve a new range of climate tolerances3. The rationale, in part, was that if species could effectively adapt to cope with past climatic change, they would have been able to persist in-situ without shifting their geographic distribution; in apparent contrast with the numerous evidence for range shifts across taxa14. This paradigm has been increasingly challenged in the last two decades15. Pre-eminently, Davis and Shaw (2001)3 argued that adaptation concomitant with range shifts may have been central to species responses during Quaternary climate fluctuations. They suggested that adaptation during range shifts can emerge due to selective “sieving”16 of genotypes intolerant to local conditions during colonisation and establishment3. In other words, local conditions can act as sieves that sort standing genetic variation unequally across the landscape16 during the course of species’ range shifts  genotypes can further be predicted;21 contingent that future or ancestral-like habitats are present and sampled today. While gene-environment models can incorporate adaptation into evaluations of species’ response to climatic change, they do not currently integrate other evolutionary processes such as migration and drift, i.e., as shaped by demography10. Integration of these demographic processes is, however, increasingly feasible with modern population genetic approaches15, and provides a promising avenue to reconstruct the past neutral and adaptive landscapes of species.The selective sieving of genotypes across space, and local adaptation in general, imply that populations inhabiting different areas of the species range may carry different genotypes and hence respond to changes in climate differentlyDianthus sylvestris. This perennial flowering plant native to the Alpine, Apennine and Dinaric mountain ranges of Europe inhabits an environmentally and topographically diverse landscape that was intimately affected by the Quaternary glacial cycles. By quantifying shifts in adaptive genomic composition from inferred ancestral to present-day populations through the novel metric “glacial genomic offset”, we show that adaptive responses emerged as a natural consequence of populations’ expansion from glacial refugia into the increasingly heterogeneous environmental landscape created with the post-glacial retreat of glaciers. We validate this result by testing glacial genomic offset predictions against observed population genetic signatures sensitive to past selection and demography, demonstrating that we can accurately predict where adaptive diversity is most constrained in contemporary populations. Our results corroborate theory13 that the interplay of adaptation and range shifts has been central in species response to past climatic fluctuations, and highlight the continued role that past fluctuations play in shaping evolutionary responses to climate-driven selection today.Here, we employ such an integrative approach to elucidate the interplay of adaptation and range shifts in response to Quaternary climatic fluctuations in D. sylvestris at low (mean ca. 2x) sequencing depth  between pairs of populations via Monmonier’s algorithm24  across the contemporary geographic range of pth Fig. , S2. Thi 23 Fig. . To visu 23 Fig. , S5. We m24 Fig. , S7, andris Fig. . In addiris Fig. , S5, S8.STRUCTURE-like algorithms27 can arise even in admixture-free demographies because of violations from model assumptions28. To tease apart alternative demographic scenarios, we assessed the goodness of fit of an admixture model to the underlying genetic data using patterns of allele sharing inferred by chromosome painting28. We observe systematic patterns in the residuals that are more consistent with recent bottlenecks in the western populations of the Alpine lineage and in the north-western populations of the Apennine lineage, than with recent between-lineage admixture scenarios , the PCA of genetic structure suggests deep divergence and consequently a prolonged history of isolation between clusters Fig. . This isios Fig. . In othehem Fig. ; a confo30 is observed along the Alpine arch from east to west . In contrast, refugial areas in the Apennines and Balkans were much larger and covered extensive areas across their respective peninsulas. The marked difference in size between predicted refugia for the three lineages is consistent with the lower genetic diversity observed in present-day Alpine populations compared to Apennine and Balkan populations ans Fig. , S15. Duans Fig. , consistons Fig. .Fig. 4Sh32. We applied a dispersal kernel to limit the rate of dispersal and enforced competitive exclusion among lineages, to address non-abiotic factors that may influence lineage distributions35. Under this forward-simulation, we tracked the advance of lineage-specific SDMs in time, and generated an expectation of present-day lineage distributions highly congruent with the genetic structure 36 to model SNP allele frequencies to contemporary environment, correcting for structure. We focus on the Alpine lineage leveraging on our reconstruction of its expansion dynamics, and apply GF on ca. 400,000 exon SNPs that segregate across Alpine populations  by performing comparisons between the predicted adaptive genomic composition of each current population and that of its  closest predicted refugial source during the LGM  than with genome-wide estimates .Non-random evolution of populations, through processes such as directional selection and demographic expansion-contraction, can be inferred from the genetic data of contemporary populations via perturbations in the site frequency spectrum (SFS) from random expectationsπ) for contemporary low- and high-elevation populations along the Alpine expansion axis. We find higher levels of diversity around environmentally-associated loci compared to genome-wide  in low-elevation compared to high-elevation populations (controlling for the effect of distance), both for genome-wide diversity (πGW) and for diversity centred around environmentally-associated loci (πGF)  increases from east to west along the expansion axis, suggesting that populations simultaneously at the expansion front and environmental margin of the lineage host lowest adaptive diversity  Fig. . This caity Fig. , C.13. The prevailing assumption has been that range shifts, rather than adaptation or an interplay of the two processes, were species’ principal response to past Quaternary climate fluctuations13. While this paradigm has been challenged in recent years, arguments supporting a joint response of adaptation and range shifts have remained largely conceptual13. Here, by modelling the response of climate-associated alleles in the plant D. sylvestris under post-glacial warming (20 kya - present), we show that adaptive processes concomitant with range shifts were central to this species’ evolutionary response to past climate shifts. Specifically, increasing regional temperatures and the extensive retreat of glaciers post-LGM led to the emergence of novel warm valley environments in the Alps, generating a heterogenous mosaic of alpine and warm habitats in the path of the colonising species. As D. sylvestris populations expanded out of alpine-like glacial refugia into this broader and heterogenous environmental space, warm-associated alleles increased in frequency due to climate-driven selection in the low-elevation habitats. In contrast, populations in alpine habitats retained genotypes closer to those of ancestral populations. This spatially-structured adaptive response supports theory that differential survival imposed by local conditions during migration and establishment selectively sieves out maladapted genotypes3. In this study, we capture this spatio-temporal sieving process via gene-environment models and the glacial genomic offset that explicitly integrate the effects of past adaptation, migration and expansion.The impact of past Quaternary climate shifts on species has typically been investigated through the lens of neutral genetic variation and species distribution models, with adaptive processes either ignored or treated separatelyD. sylvestris. Genetic structure and demographic inference reveal that D. sylvestris comprises three evolutionary lineages which diverged during the PGIP (ca. 200–115 kya). Post-divergence, these three lineages had relatively independent histories, as inferred from their survival in separate glacial refugia and from a lack of recent admixture between them. The Alpine lineage was particularly affected by the LGM, as evidenced by its highly constrained glacial refugial range relative to present, and the relatively low genetic diversity in contemporary populations. Our identification of glacial refugia and expansion routes in the Alps allow for the effects of IBD, expansion and adaptation to be jointly incorporated in our glacial genomic offset, a measure that quantifies shifts in adaptive genomic composition from inferred ancestral to present-day populations. Such an integrative approach addresses previously highlighted shortcomings of gene-environment models10 and enable us to assess both range shift and adaptation responses simultaneously.Our inference of adaptive shifts hinges on the reconstruction of the evolutionary history and post-glacial expansion dynamics of E and Fay & Wu’s H (population genetic statistics that are sensitive to past selective sweeps) with glacial genomic offset provide complementary validation, and indicate that our model assumptions were justified. Together, this evidence lends credence to our hypothesis that migration and adaptation acted jointly in D. sylvestris’ response to past climate shifts.Our retrospective approach provides a unique opportunity to validate evolutionary predictions based on the glacial genomic offset against realised biological outcomes. Population genetic signatures observed across high-low elevation population pairs substantiate glacial genomic offset predictions by showing that populations simultaneously furthest away from glacial refugia and inhabiting environments divergent from the ancestral habitat have lowest levels of adaptive genetic diversity, as a consequence of experiencing the most evolutionary change. Correlations of Zeng’s D. sylvestris in the Alps, the described decay of adaptive diversity implies that populations inhabiting the warm, low-elevation environments at the expansion front bear limited potential to adaptively respond to new bouts of climate-driven selection, though predictions can be nuanced. On one hand, low-elevation populations may be relatively well-adapted to tolerate further warming due to past selection in that direction. On the other hand, the consequences of climate change are not exclusive to increase in temperatures, but also novel biotic interactions including competition43 and shifts in other environmental parameters 44, where the general loss of diversity as observed in low-elevation populations may be detrimental. While the loss of genetic diversity at expansion margins has been well-described48, we show that in heterogenous landscapes, genetic diversity is also determined by the environmental distance between a population’s current habitat and that which the population was pre-adapted to . Thus, our historical perspective can complement future-oriented approaches19 by providing evolutionary context to observed and predicted patterns of adaptation.The characteristic imprint left by the concerted mechanism of migration and adaptation on patterns of adaptive genetic diversity has key relevance on how able populations are to respond to future climatic changes. For Analysing adaptive variation facilitates our understanding of species response to climatic change because it acts as a lens into the past different than and complementary to that given by stochastic neutral variation, which perceives the demographic past exclusively. Here, we jointly assess patterns of neutral and adaptive genetic variation in an Alpine plant and provide novel evidence for the interplay of migration, adaptation and expansion in the species’ Quaternary history. In doing so, we elucidate the manner in which climate acts to shape species’ adaptive variation, and highlight species’ reliance on the genetic legacies of past climate to respond and adapt to future changes.D. sylvestris individuals from 115 populations  under a modified protocol49 of the Illumina Nextera DNA library preparation kit (Supplementary Methods 50. Libraries were sequenced (150 bp paired-end sequencing) in four lanes of an Illumina NovaSeq 6000 machine at Novogene Co. This resulted in an average coverage of ca. 2x per individual. Sequenced individuals were trimmed for adapter sequences (Trimmomatic version 0.3551), mapped (BWA-MEM version 0.7.1753) against a reference assembly54 (ca. 440 Mb), had duplicates marked and removed , locally realigned around indels (GATK version 3.555), recalibrated for base quality scores (ATLAS version 0.956) and had overlapping read pairs clipped (bamUtil version 1.0.1457) (Supplementary Methods ANGSD version 0.93358 and ATLAS versions 0.9–1.056), to accommodate the propagation of uncertainty from the raw sequence data to population genetic inference.DNA libraries were prepared for 1261 PCAngsd version 0.9859, following conversion of the mapped sequence data to ANGSD genotype likelihoods in Beagle format (Supplementary Methods akima R package version 0.6.260) and had the first two principal components represented as green and blue colour channels. Given that uneven sampling can bias the inference of structure in PCA, PCA was also performed on a balanced dataset comprising a common, down-sampled size of 125 individuals per geographic region  for K = 2–6, using the balanced dataset to avoid potential biases related to imbalanced sampling23 and an automatic search for the optimal sparseness regularisation parameter  soft-capped to 10,000 28 and compared this to the PCAngsd-inferred palettes  and Julian Alps  were analysed under K = 2 in PCAngsd and badMIXTURE , Tuscany  and Julian Alps  were analysed under K = 2 in PCAngsd and badMIXTURE.To test if between-lineage admixture underlies admixture patterns inferred by tes Fig. . We refeURE Fig. . For assATLAS, employing a distance measure (weight) reflective of the number of alleles differing between the genotypes  model, as we noticed irregularities in the site frequency spectra (SFS) with the SAMtools (-GL 1) model similar to that reported in Ref. 58 with particular BAM files. All analyses described above were performed on the full genome.To construct a genetic distance tree , D. lusitanus, D. pungens, D. superbus alpestris, D. superbus superbus, and D. sylvestris (Alpine lineage). This tree topology was extracted from a detailed reconstruction of Dianthus phylogeny based on 30 taxa by Fior et al.  (Supplementary Methods Trimmomatic), mapped against the D. sylvestris reference assembly (BWA-MEM) and had overlapping read pairs clipped (bamUtil) , while masking  sites (i) with zero depth and (ii) that didn’t pass the applied variant filtering criteria  prequel65, using a tree model produced by PHAST phylofit under a REV substitution model and the specified tree topology 31, which defines ψ in terms of the two-population site frequency spectrum (2D-SFS)  - D. sylvestris (Alpine lineage) ancestral node. Correlation of pairwise ψ and (great-circle) distance matrices was tested via a Mantel test . To infer the geographic origin of the expansion  as embedded within the larger phylogeny of the Eurasian Dianthus clade  excludes Balkan representatives of D. sylvestris). Trees were inferred based on low-coverage whole-genome sequence data of 1–2 representatives from each D. sylvestris lineage, together with whole-genome sequence data of 7 other Dianthus species, namely D. carthusianorum, D. deltoides, D. glacialis, D. lusitanus, D. pungens, D. superbus alpestris and D. superbus superbus, that were used to root the D. sylvestris clade , ASTRAL-III quartet scores (>0.5) and bootstrap values (>99%). Topologies deeper in the tree were less well-resolved . Under the inferred D. sylvestris topology and a less-assumptive simultaneous trichotomous split topology, 18 models were formulated spanning from simple to complex  comprised of one representative population each per lineage via ANGSD and realSFS, using 20 individuals per population (Supplementary Methods D. lusitanus - D. sylvestris (Alpine lineage) ancestral node  with similar life history .To estimate the demographic parameters of these models, we used http://flore.silene.eu), Conservatoire Botanique National Alpin , GBIF , iNaturalist , Info Flora (https://www.infoflora.ch), Wikiplantbase #Italia , Sweden’s Virtual Herbarium (http://herbarium.emg.umu.se), Virtual Herbaria Austria (https://www.jacq.org) and personal collaborators. The environmental data comprised an initial set of 19 bioclimatic variables (CHELSA32) together with 3 topographic variables  , soil type and pH (at 5 cm depth) (SoilGrids74). Prior to running SDMs, a coherent set  of most important, least collinear and biologically relevant variables was selected. Variable selection followed an iterative process of model fitting via generalised linear , maximum entropy  and random forest  modelling to assess variable importance, combined with variance inflation factor  and correlation analyses. We retained the most important and least collinear variables (VIF < 10 and Pearson’s correlation r < 0.7), based on recommended cut-offs for this type of analysis80. This resulted in a final set of 10 variables: (1) isothermality, (2) temperature seasonality, (3) temperature maximum warmest month, (4) temperature mean wettest quarter, (5) temperature mean driest quarter, (6) precipitation seasonality, (7) precipitation warmest quarter, (8) precipitation coldest quarter, (9) soil pH at 5 cm and (10) topographic slope. Distribution models were generated for each lineage separately as well as for the pooled species. For each run, occurrences were randomly sampled from a larger set and disaggregated to balance sampling density in geographic space, resulting in ca. 420, 260, 170 and 530 retained occurrences for the Alpine, Apennine, Balkan and pooled species, respectively. Using these occurrence data and the final set of environmental predictors, we generated an SDM ensemble model built from the weighted average of four separate models: (1) a generalised linear model (GLM), (2) a general additive model (GAM), (3) a maximum entropy model (maxent) and (4) a random forest model (RF). Model weights reflected their classification performance  of the receiver operating characteristic (ROC) curve). For each model, 5-fold cross-validation and model evaluation was performed. The resultant ensemble SDM model was projected onto present-day climate as well as hindcasted to the LGM climate (Supplementary Methods 32: (1) NCAR-CCSM481, (2) MIROC-ESM82, (3) MRI-CGCM383, and (4) MPI-ESM-P;84 selecting models that are distinct and with low amount of interdependence between each other85. All environmental variables apart from soil variables were available for present-day and LGM predictor datasets. We thus assume in our models that soil pH remained constant through time. While this a strong assumption, the alternative strategy of excluding pH would similarly assume a constant effect in time, in addition to a constant effect across space. Here, we argue that it is better to have an informed constant over an uninformed constant; given that no paleo-model of global soil is currently available.To model species and lineage distributions in space and time, we acquired species occurrence data and environmental data from various sources. Species occurrence data was acquired from Conservatoire Botanique National Méditerranéen de Porquerolles , which coincided with, and were denominated according to, geographic regions . We then sequentially projected the lineage-specific niche models to climate rasters at 100-year time intervals for the period between the LGM to the present-day. At each (successive) time-point, new occurrences (in space) were assigned a lineage based on the k-nearest neighbour (k = 1) of the previous (time-point’s) set of lineage-assigned occurrences (FNN R package version 1.1.2.187), conditional that the new occurrences lie within a defined distance d away . In addition to this limit on dispersal rate, we enforced competitive exclusion between lineages such that only a single lineage can occupy a spatial cell at a given time. The 100-year time interval dataset was generated by linearly interpolating climatic variables between the current and ensemble LGM models. While it is known that climate did not change in a linear fashion between these time points, we considered this approach to be more informative than the alternative of assigning present-day occurrences to geographic refugia based on distance measures, as the former incorporates landscape heterogeneity in an explicit albeit approximative spatio-temporal manner. Recently, CHELSA-TraCE21k89 was published which explicitly models climate in 100-year time intervals from the LGM to the present-day. However, LGM hindcasts from this model were inconsistent with that of the other 4 PMIP3 models employed here, potentially as a result of being based on the older CCSM3 GCM90, and so we refrained from use of the TraCE dataset here.To reconstruct lineage-specific routes of expansion and assign present-day populations to their most likely ancestral refugia, we first projected lineage-specific niche models to the LGM climate. We identified distinct, contiguous refugia  via an unsupervised density-based spatial clustering (DBSCAN) algorithm (ggplot2 R package version 3.3.2), which allowed for the quantification of area at each point . The geographic extents in which the environmental data were taken from and constrained to are shown in Supplementary Fig. To visualise the shift in environmental space and habitat availability from LGM to present, we projected the environmental space of the LGM and present-day to a common, lower-dimensional space, via applying the PCA transformation  of the present-day environment to both present-day and LGM environments  functions that transform multidimensional environmental space into multidimensional genomic space36. Starting with the full set of environmental variables described above, environmental predictors were selected by quantifying variable importance via random forests (gradientForest R package 0.1.1836) and by assessing variable collinearity via variance inflation factor (VIF) and correlation analyses. We retained the most important and least collinear variables (VIF < 10 and Pearson’s correlation r < 0.7), resulting in a final set of 10 variables: (1) temperature diurnal range, (2) temperature seasonality, (3) temperature minimum coldest month, (4) temperature mean wettest quarter, (5) temperature mean driest quarter, (6) precipitation seasonality, (7) precipitation warmest quarter, (8) precipitation coldest quarter, (9) soil pH at 5 cm, and (10) topographic slope. To account for genetic structure in the Alps (Alpine lineage), we included longitude and latitude as co-variates in the model, in light that these were shown to correlate strongly with the main two principal components of genome-wide structure   explained the majority of the variance and was used as the spatial co-variate  (Supplementary Methods vcflib popStats (version 1.0.1.1)94 utilising genotype likelihoods. We filtered this dataset to retain only sites with depth ≥7 per population. GF was run on the resultant set of 390,262 exon SNPs in batches of 10,000 SNPs, using 500 decision trees. Batch runs were combined via combinedGradientForest {standardise = “before”, method = 2} in the gradientForest R package. When taking the ensemble of all exon genetic variation—weighting the contribution of each SNP by the coefficient of determination (R2) of its environment association  to biological variables of composition turnover 36—are applied to present-day and LGM climate rasters, to characterise adaptive genomic composition of populations in space and time. To visualise adaptive genomic composition in space, we plotted the first three principal components of the transformed environmental space excluding the transformed longitude and latitude variables, to visualise only the adaptive component. Note here that the PCA was centred but not scaled to retain GF-calculated importance of the transformed environmental variables19.To predict the sieving of adaptive genetic variation in space and time, we modelled the association of genetic variants (SNPs) with changes in environment, using gradient forest (GF)95, to include (model) the effect of distance in the glacial genomic offset. Such an interpolation approach is coarse and prone to artefacts; however, provides an alternative way of modelling distance effects when genetic structure is not well-represented by a geographic cline. Note that in our calculation of glacial genomic offsets, the geographic extent of glacial refugia relied on distribution models, which assumes niche conservatism. This may appear counter to the aim here of capturing adaptation. We alleviate this methodological constraint, however, by employing lineage-specific, rather than species, niches and by relying additionally on population genetic inferences (ψ) to reconstruct glacial refugia. Moreover, our inference of past distributions are conservative, such that inferred glacial refugia would have been smaller, not larger, had adaptation facilitated post-glacial expansion. Finally, we note that our prediction of adaptive variation during the LGM is highly homogenous across space, meaning that our predictions of glacial genomic offset remain robust under fluctuations in the inferred geographic extent of glacial refugia.To evaluate differences in genomic composition and quantify evolutionary change of populations between assessed time points, we evaluated (genomic) compositional differences (between time points) accounting for the location of the populations’ glacial refugia. Specifically, we calculated the multivariate Euclidean distance between the genomic composition of every population in the present-day and that of its  closest predicted refugial source at the time of the LGM , we partitioned populations  into low-elevation <1000 m) and high-elevation (>1500 m) categories. To avoid biases related to imbalanced sample sizes, categories were sub-sampled to a common sample size of 70 individuals (5 populations) each  of the SNP’s environmental association  for contemporary low- and high-elevation populations (ca. 1000 m elevation difference) in areas where they co-occur in close geographic proximity, to control for the effects of isolation by distance (IBD) and the spatial expansion  and high-elevation (>1500 m) bins, ordered along the expansion axis. π was calculated both genome-wide (πGW) and centred around environmentally-associated loci (πGF). The latter was calculated as the weighted mean π of exon SNPs, with weights given by the R2 of the SNP’s environmental association  or under the International Union for Conservation of Nature (IUCN) Red List of Threatened Species.Access, collection and import/export of samples were conducted in a responsible manner and in compliance with all relevant local, national and international laws. All necessary sampling permits were obtained prior to sample collection. This comprised of sampling permits from Comunità della Vallagarina for sampling in Monte Baldo, Italy ; from Parco Nazionale Gran Paradiso for sampling in Gran Paradiso National Park, Italy ; from Amt für Natur und Umwelt for sampling in Graubünden, Switzerland ; from Amt für Natur, Jagd und Fischerei for sampling in St. Gallen, Switzerland , and from Amt für Wald und Landschaft for sampling in Obwalden, Switzerland . Nagoya Protocol on Access and Benefit Sharing was followed in countries where it had been ratified at the time of sampling. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


In Ethiopia, livestock contributes 45% of agricultural GDP. Despite the economic role played by the sector, there have been little efforts to genetically improve the indigenous cattle. Morphological characterization of selected Ethiopian indigenous cattle has been made for  cattle types. But, the selected indigenous cattle were not characterized at molecular level (genetic diversity information). Hence, this work was initiated to detect and determine the genetic diversity and population structure of selected Ethiopian indigenous cattle ecotypes using microsatellite markers. F-statistics (Fst) (0.129 = 13%) indicated that there was moderate genetic differentiation among ecotypes. The (UPGMA) revealed, Bonga and Jimma clustered together while Kerayu cattle were relatively distinct, Principal coordinates analysis (PCOA) and structure analysis grouped the individual into different clusters confirming the presence of ecotype admixture due to geographical origins and uncontrolled mating.  Different alleles were identified (131) and thirty-three of these alleles were unique to specific ecotypes. All loci used were informative with PIC values ranging from 0.5 (TGLA126) to 0.84 (ETH10) with a mean of 0.70 per locus. The Shannon information index ranged from (I = 1.02) ILST006 to (I = 1.63) ETH10 with an average of 1.28 revealing there is genetic diversity. Moreover, analysis of molecular variance (AMOVA) revealed 84% genetic variation within a population and 13% variation among populations. The value of  In general, this study has successfully characterized the genetic diversity and population structure of Bonga, Jimma, and Kerayu cattle ecotypes using high polymorphic/informative microsatellite markers. According to this study, Kerayu cattle have high AR and PA when compared to Bonga and Jimma cattle populations. So, the Kerayu population is more diverse than others and it is the hotspot for genetic diversity study. The generated information is very relevant for breeder and genetic conservation. In several parts of developing countries including Ethiopia, livestock production is the major and achieving good living standards. From Africa, Ethiopia is ranked first and among the top 10 countries in the world in major farm animal populations . Po. Po15]. μl of the PCR product was loaded on 2% agarose gel prepared by dissolving 2 g of agarose in 100 ml 1XTAE buffer, staining with gel red. Electrophoresis was carried out at 80 V for 3 : 00 hrs. After completion of electrophoresis, the gel pictures were taken under UV Tran's illuminator by Biodoc analysis with a digital cannon camera  and expected (He) heterozygosities . The Polatistics , and theatistics  and Poweatistics  and the atistics .Arlequin 3.5 was used to estimate basic frequency-based population genetic parameters such as gene diversity, the total number of alleles (No), Ne, breed private alleles (PAs), allele sizes, and allele ranges (in base pairs) . PrincipHP-Rare 1.1software were used to calculate the rarified allelic richness (Ar) and private rarified allelic richness (Arp) .K value was identified based on the computed K value, and k = 3 was discovered to be the most likely number of clusters to partition the 72 genotypes into three , the population structure analysis was carried out (1–10 K). Its most appropriate to three  using thThe 16 loci yielded a total of 131 alleles, with an average of 8.18 alleles per locus. The No ranged from 5 (RM067) to 12 (ETH3 and TGLA122) and MAF ranged from 0.23 (ETH10) to 0.61 (TGLA126) with a mean of 0.39 per locus. Observed heterozygosity (Ho) varied between 0.00  and 0.297 (ETH3), while expected heterozygosity (He) ranged from 0.5 (RM67) to 0.75 (ETH10). Ho (0.023) was lower than He (0.66) and this showed that the populations are carrying out inbreeding .P < 0.001) deviation from the Hardy‒Weinberg equilibrium  was 4.57, ranging from 3.33 (TGLA126) to 6 (ETH3), and private alleles (PA) also ranged from 0.66 (BM1818) to 3.66 (INRA023) with an average of 2.1 per locus, respectively, out of 131 alleles, 33 (25%) of PA was unique to specific breeds. The gene diversity (GD) ranged from 0.55 (TGLA126) to 0.86 (ETH10), and the average gene diversity over all loci was 0.74. The 16 microsatellites showed a highly significant  to Na = 5.56 (Kerayu), and the medium mean number of alleles observed was in Jimma cattle populations. Secondly, Kerayu populations (Ne = 3.4) score the highest effective number of alleles than Jimma (Ne = 3.2), Bonga (Ne = 3.06) and with a total mean of Ne = 3.23. In Kerayu cattle populations, the highest number of private alleles were also observed (PA = 4.13), Jimma (PA = 1.13), and Bonga (PA = 1.0) with a total mean of PA = 2.1. Shannon's information index ranged from I = 1.26 (Bonga and Jimma) to I = 1.34 (Kerayu) with an average mean of I = 1.29. The other things listed above the table were Ho and He. The Ho ranged from 0.044 (Kerayu), 0.016 (Jimma), and 0.011 (Bonga), respectively .Allelic richness ranged from AR = 3.88 (Bonga) to (AR = 5.56) Kerayu. Expected heterozygosity (He) had the highest mean value of 0.658 in (Bonga) and the lowest in Jimma (0.63). Observed heterozygosity (Ho) had a mean value of 0.011 (Bonga) to 0.044 (Kerayu). In Kerayu cattle populations, the highest number of private alleles were also observed (PA = 4.13), Jimma (PA = 1.13), and Bonga (PA = 1.0). Shannon's information index ranged from I = 1.26 (Bonga and Jimma) to I = 1.34 (Kerayu) with an average mean of I = 1.29. The Ho ranged from 0.044 (Kerayu), 0.016 (Jimma), and 0.011 (Bonga), respectively. The average mean number of alleles was from Na = 4.94 (Bonga) to Na = 5.56 (Kerayu), and allelic richness ranged from AR = 3.88 (Bonga) to (AR = 5.56) Kerayu. Expected heterozygosity (He) had the highest mean value of 0.658 in (Bonga) and the lowest in Jimma (0.63). Observed heterozygosity (Ho) had a mean value of 0.011 (Bonga) to 0.044 (Kerayu) .Population variance could be classified based on AMOVA among individuals within populations variability (84%) and (13%) variation among populations/among individuals and 3% within the individual. The analysis also confirmed the presence of considerable gene flow (1.69) among subpopulations .The genetic differentiation between populations ranged from 0.100 between (Bonga and Jimma) to 0.120 (Kerayu and Bonga). The highest GD (0.120) was between Kerayu and Bonga populations, and the lowest genetic differentiation was observed between Jimma and Bonga populations . This miThe unweighted neighbor-joining cluster analysis (UPGMA) categorized the 72 genotypes into three major clusters . From Cluster-II consists of 33% and 15.2% from Jimma and Bonga, respectively. Cluster three consists of only genotype from Bonga (16%). Genotypes from Bonga are mainly found in all three clusters, especially found in cluster-II (Jimma) .Principal coordinate analysis was also used to look at the genetic relatedness of 72 genotypes (PCOA). The pattern of genotype distributions on a two-dimensional plot showed separate clustering of populations based on the geographic locations and revealed a high pattern of grouping. The PCoA analysis displayed in K)|/s[L(K)] published by Evano et al. [K values that reflected the proportion of change in the logarithmic probability Pr(X|K) of data between K values (28) , the population structure analysis was carried out (1–10 K). In the method  . Its mosto three .According to the classification of Botstein et al. , the higGororo et al.  found 11Previously, both Dadi et al.  and GoroDemir and Balcioğlu  reportedThe number of individuals sampled per population and the number of populations studied might be the main cause for the higher numbers of Ho and He in Dadi's et al.  study anIn this study, the mean number of alleles for each breed  was 4.94, 5.063, and 5.56, respectively. Demir and Balcioğlu  reportedThe other measure of gene diversity is the Shannon information index (I), if Shannon's information index value is close to one or above, it indicates that there is variation in the tested populations and that the markers are suitable for studying diversity . The valAccording to Zerabruk et al. , allelicAgung et al.  reportedTo quantify the genetic variability, in Ethiopian indigenous cattle breeds and 16 microsatellite markers were used resulting in a Fis value (0.966), showing that the selected Ethiopian indigenous cattle have undergone inbreeding. Jakaria et al.  reportedThe estimated FST value for 3 different cattle breeds was higher than Zimbabwean cattle breeds (0.084) ; this fiIn addition, AMOVA shown in genetic variation among breeds was 13%, and 84% within the population, 3% within an individual was observed. Significant genetic differentiation was observed among all 3 different Ethiopian indigenous cattle  estimated by FST = 0.129. Dadi et al.  also repIn clustering, a dendrogram of cluster analysis based on NJ algorism using UPGMA categorized the three ecotypes into three clusters based on the geographical locations  with different subgroups. Dadi et al.  obtainedThe difference might be due to the number of microsatellites/type of microsatellite used, some ecotypes/breeds, and sample numbers. The clustering model showed that there was a relationship between the patterns of genetic diversity and the geographical origins of the collection. Populations collected from Bonga and Jimma had a strong relationship. Bonga and Jimma cattle were close to each other and the existence of gene flow among the neighboring populations seems possible.K = 3) in three populations of Ethiopia. Previously, Jakaria et al. [k = 3 in four cattle populations and there were genetic admixtures. This could be likely due to the presence of gene flow between the ecotypes because of the movement of cattle and uncontrolled mating/exchange of breeding animals, migration from one region to another. Grouping of PCoA corresponds with the clustering dendrogram based, which showed conformity result obtained from UPGMA analysis Agung et al. [Moreover, this result is also clearly reflected in population structure showing weak admixture of a gene across populations. It revealed the existence of substructuring  accounted for within populations, likely due to gene flow, sexual recombination, and mutation. The populations showed moderate genetic differentiation, due to high gene flow. The highest genetic diversity indices were recorded for Kerayu cattle populations, suggesting that this area is a hotspot for genetic diversity studies, sources of important alleles for breeding purposes, and conservation strategies must be applied.In general, cluster analysis, PCoA, and population structure analysis exhibited moderate grouping of samples. Studying the genetic basis of locally adapted indigenous cattle populations is critical for developing appropriate breeding strategies and programs aimed at improving and conserving their genetic diversity. From this study, the generated information is valuable for the national animal breeding program and conservation purposes.


Allium ledebourianum is a narrow-lined endemic species, with natural populations located in the extreme climatic conditions of the Kazakh Altai. A. ledebourianum populations are decreasing everywhere due to anthropogenic impact, and therefore, this species requires preservation and protection. Conservation of this rare species is associated with monitoring studies to investigate the genetic diversity of natural populations. Fundamental components of eukaryote genome include multiple classes of interspersed repeats. Various PCR-based DNA fingerprinting methods are used to detect chromosomal changes related to recombination processes of these interspersed elements. These methods are based on interspersed repeat sequences and are an effective approach for assessing the biological diversity of plants and their variability. We applied DNA profiling approaches based on conservative sequences of interspersed repeats to assess the genetic diversity of natural A. ledebourianum populations located in the territory of Kazakhstan Altai. The analysis of natural A. ledebourianum populations, carried out using the DNA profiling approach, allowed the effective differentiation of the populations and assessment of their genetic diversity. We used conservative sequences of tRNA primer binding sites (PBS) of the long-terminal repeat (LTR) retrotransposons as PCR primers. Amplification using the three most effective PBS primers generated 628 PCR amplicons, with an average of 209 amplicons. The average polymorphism level varied from 34% to 40% for all studied samples. Resolution analysis of the PBS primers showed all of them to have high or medium polymorphism levels, which varied from 0.763 to 0.965. Results of the molecular analysis of variance showed that the general biodiversity of A. ledebourianum populations is due to interpopulation (67%) and intrapopulation (33%) differences. The revealed genetic diversity was higher in the most distant population of A. ledebourianum LD64, located on the Sarymsakty ridge of Southern Altai. This is the first genetic diversity study of the endemic species A. ledebourianum using DNA profiling approaches. This work allowed us to collect new genetic data on the structure of A. ledebourianum populations in the Altai for subsequent development of preservation strategies to enhance the reproduction of this relict species. The results will be useful for the conservation and exploitation of this species, serving as the basis for further studies of its evolution and ecology.Endemic species are especially vulnerable to biodiversity loss caused by isolation or habitat specificity, small population size, and anthropogenic factors. Endemic species biodiversity analysis has a critically important global value for the development of conservation strategies. The rare onion  Alliaceae families compose nearly 30% of the area’s endemics [Allium species, only approximately 20% out of 125 species have been partially studied, including ca. 30 endemics growing in the territory of Kazakhstan [Allium species growing in the Kazakhstan Altai territory have not been studied sufficiently, and certain species require protection measures. One of these narrow endemic species, found only in the Altai Mountains of Russia, Mongolia, China, and Kazakhstan, is a poorly studied relic of Allium ledebourianum Schult. Its narrow distribution area along with the influence of anthropogenic factors significantly reduces the number of populations of this wild species. The increasing detrimental impact of human activities is highly damaging to such endemic and poorly studied species [A. ledebourianum exists as small, isolated populations, which are characterized by a decrease in the level of genetic variability due to the negative consequences of genetic drift. The low level of genetic polymorphism typical of endemic species can be caused by the narrowness of the distribution range and the small number of populations [Allium species. These PCR-based molecular markers are quite effective for determining genetic diversity, but each method has limitations in terms of reproducibility, cost, or development method. Molecular microsatellite markers or SSRs (Single Sequence Repeats) would be a promising alternative if such markers were developed for all onion species. However, these PCR-based molecular markers are expensive at the initial stage of development and require the comparative genome sequencing of several genetically distant genotypes for each species. Subsequent comparative bioinformatics analyses of these genomes are also required to identify polymorphic SSR loci and select efficient primer pairs for PCR. Such work is complex and expensive and is usually only required for critical crops that require such markers for identification and genotyping of the breeding lines. To investigate intraspecific genetic polymorphisms of Allium wildlife relict species, using markers characterized by wide genome distribution would be ideal and, above all, markers accessible for any species, including those that have not been studied. Such DNA genetic markers include all PCR-based DNA fingerprinting method variants of the RAPD method [Allium species genome is also mostly represented by repeating sequences of LTR retrotransposons, satellite DNA, tandem repeats, and DNA transposons; more than 90% of the onion genome consists of repeated elements. Analysis of the sequences genomic composition of the repeatome, carried out on several species of Allium using Next Generation Sequencing (NGS), revealed that Gypsy and Copia elements were most common for the LTR retrotransposon classes [Allium species. This applies not only to cultivated species but also to wild species [Allium family is not well studied; in total, more than 400 genes are known that are expressed in response to stress [A. ledebourianum is novel and relevant. Ours study is devoted to examining the genetic diversity of the endemic species A. ledebourianum using iPBS (inter-Priming Binding Site) amplification DNA profiling markers. The genetic diversity analysis of A. ledebourianum populations in the territory of Kazakhstan Altai was carried out for the first time, enabling the clarification of the genetic structure of this narrow endemic species’ populations. Our research results are relevant in terms of developing a strategy for the conservation of natural genetic diversity and re-introduction of endemic species.Endemic plants constitute an integral part of flora and fauna, and the extinction of these species can lead to significant ecological changes. Anthropogenic transformation of habitats is the greatest threat to the ecological niches of rare and endemic plant species. This leads to the isolation and disappearance of endemic populations, which significantly changes the ecological structures of local flora and fauna. The number of weed species that replace balanced and stable populations increase concurrently, and consist of a large number of species and their forms . The negendemics ,7. Despizakhstan . The gen species ,10. Unco species . Like maulations ,14,15. Culations ,16,17. Mulations , Inter Sulations , and AmpD method , such asD method , includiD method ,22 and oD method . This liD method ,25. For D method ,27. Thes classes ,29,30. A species ,32. The  species . LTR ret species ,33. Vari species ,34,35. M species ,37. Thes species . Such ma species ,40,41. H species ,43,44,45o stress . The inso stress ,48. Trano stress . The proo stress ,51. In tAllium ledebourianum, collected in places of their natural growth in the Kazakhstan Altai territory, were used as study objects. Expeditions to collect samples were carried out after snowmelt, guided by weather conditions and temperature conditions. At least 10 plants were selected for each population. Populations were distant from each other and were growing relatively evenly over the entire occurrence area. For each population, the coordinates (latitude and longitude) and the absolute altitude of its location were determined using a Garmin GPS 72 H GPS navigator . The locations for the populations are shown in Samples of three Kazakhstani populations of A. ledebourianum were carried out on the test plots, each of which was 250 m2 in size. Ontogenetic age states of the plants and the spatial structure of A. ledebourianum populations were determined using standard centenopopulation methods [The geobotanical descriptions of communities with  methods ,53,54.Allium ledebourianum populations were assessed based on quantitative morphological characters, which were taken into account only in plants of a mature ontogenetic state. Generative age states were identified according to the recommendations of NI Fedorov [A. ledebourianum populations). Fedorov . Samples3EDTA, 100 mM HEPES, pH 5.3) in the presence of RNAse A [Total plant DNA was extracted from fresh plant leaves (50–100 mg) using a modified acidic-CTAB extraction buffer . PCR amplification was carried out in a SimpliAmp ™. Thermal Cycler  under the following conditions: an initial denaturation step at 98 °C for 1 min, followed by 30 amplifications at 98 °C for 5 s, at 50–57 °C (depending on primer sequence) for 20 s, and at 72 °C for 60 s, followed by a final extension of 72 °C for 3 min.The genetic variability of endemic r et al. . PCR reaA. ledebourianum for DNA profiling. There were no limitations in the number of PBS primers used in the previously published study [A. ledebourianum plants from different populations.All PBS primers were tested to assess the genetic diversity of ed study . HoweverA. ledebourianum, we only used clear bands that can be followed for all the samples. Even if the bands were deformed during electrophoresis, we monitored these bands for correspondence to a specific band in the other samples. Common bands, or bands characteristic of most of the samples, were an excellent source for tracking and controlling all the bands. Bands above 2 kb were difficult to separate by electrophoresis, and we therefore did not analyze them. Analyzing the short bands was most straightforward, as they were easily followed for all samples. A band of unique size corresponds to a unique locus, and heterozygotes on the band were not considered. To construct the binary matrix, the PCR fragments were scored as present (1) or absent (0). The GenAlex 6.5 program (operates within Microsoft Excel) [To study the genetic diversity of t Excel)  was usedt Excel) . Correlat Excel) .A. ledebourianum (2n = 16) is a unique endemic, whose populations are found only on the Altai ridges , and Sarymsakty (South Altai) ridges 1100–1900 m above sea level. Despite the significant spatial isolation of the populations, the species grows in approximately the same ecological conditions: all identified populations of A. ledebourianum have a narrow ecological confinement to places of increased moisture. The populations were located in places that allow a significant amount of moisture to accumulate during the entire growing season, e.g., mountain riverbanks or swampy meadows on organic-rich moist chernozem soils . Damage by fungal diseases was noted due to excess moisture. The aging population is mainly represented by generative individuals, and the proportion of young 1–2-year-old seedlings is insignificant, possibly due to seeds and young seedlings being washed out by melt water or dying due to deep soil freezing and ice crust formation. The LD64 population has the densest spatial structure. The species composition of the phytocenosis is very poor, which allows A. ledebourianum plants to form dense clumps consisting of several generative stems in places of excessive moisture. Humus-rich soil substrate and the presence of a well-developed above-ground cover creates favorable conditions for A. ledebourianum growth and development. Despite the high substrate moisture content, no fungal diseases were detected in the LD64 plants, as the habitats are open and well warmed by the sun. The population is full-lived, and the dominant position is occupied by vegetative and generative individuals, which indicates rather favorable conditions for the population’s existence. The characteristics of the A. ledebourianum samples, carried out on 20 measurements, are presented in A. ledebourianum predominantly reproduces via seeds, we mainly considered the characteristics of the generative organs. The results showed that the populations differ in almost all studied morphological characteristics. The maximum value of the generative shoot height varied from 108 cm in the LD20 population plants to 32 cm in the LD6 population plants. The maximum number of bolls per inflorescence (995 pieces) was also noted in the LD20 population, the minimum (565 pieces) in plants from the LD64 population. Our results show that the number of inflorescences, fruits (capsules), and seeds decreases, and so does the weight of 1000 seeds with an increase in habitation altitude in A. ledebourianum plants. Plants from the low-mountain population LD20, located in relatively favorable conditions, where optimal conditions are formed that contribute to an increase in organic components in the soil, were found to have plant habitats at higher altitudes. The average height of the generative shoot was also higher. We observed a significant decrease in these studied traits in plants from the other two high-altitude populations (1925 m above sea level). We noted the absence of formed bulbs in the plants of all populations. For the rest of the characteristics, we did not observe any regularities in the decrease in productivity indicators due to an increase in the height of the range.The vegetation that forms this phytocenosis is represented by species that are resistant to soil moisture. The above-soil layer is loose, formed mainly of leaf litter, which decomposes more slowly due to water stagnation during the spring–autumn periods, and high grass that does not allow sunlight to pass through. We assigned this population as a full member, i.e., a normal type where all age categories are represented. Single plants affected by rust, gray mold, and powdery mildew were identified, but this does not significantly affect the population size and structure. The other two populations (LD64 and LD6) are located at the same altitude of 1925 m above sea level. LD6 is in Western Altai, while population LD64 is located to the south, in the South Altai territory. However, differences in soil and climatic conditions affect the species composition of phytocenoses and, accordingly, the structure of A. ledebourianum populations, the most informative PBS primers  were selected out of 22 alternatives, and they were preliminarily analyzed in accordance with Kalendar et al. [To assess the genetic polymorphism of natural r et al. . The abiA. ledebourianum accession. Amplification profile data obtained using PBS primers was assessed with the DNA profiling method using informative parameters such as amplicon polymorphism (%) and polymorphic information content (PIC). These indicators reflect the primer resolutions for detecting differences between A. ledebourianum genotypes (both within and between populations). All data on DNA fingerprinting analysis are given in A. ledebourianum populations.Electropherograms of the amplification results demonstrate the presence of both common amplicons that are typical for samples of all populations, and unique ones that are typical for each population. To confirm the reproducibility of the analysis results, we carried out the studies with each DNA sample at least three times. This made it possible to identify reproducible specific iPBS profiles that are unique for each A. ledebourianum population was examined individually to study intrapopulation diversity. The main indicators of genetic variability were determined based on the iPBS profiles in each A. ledebourianum population (A. ledebourianum populations ranged from 0.108 to 0.152. Results of the molecular variance (AMOVA) demonstrated that most (67%) of the molecular variation in A. ledebourianum populations exists among individual populations and within populations (33%), respectively. Permutation tests (based on 999 permutations) suggest that the overall Φ PT was significant . The results of the cluster analysis showed that the results of the iPBS amplification profiling positively correlated with the geographic data of the collection sites and with the morphological analysis of the A. ledebourianum samples. Three clades were identified as a result of cluster analysis based on iPBS profiling data , or on species of breeding value [A. ledebourianum using the DNA profiling method. Our previous studies of molecular genetic analysis of the biodiversity of the relict species Allium altaicum showed a high level of detectable polymorphism in the study of natural populations. The research results presented in this work are similar to the results of our previous studies on A. altaicum. Using the iPBS amplification profiling method, we observed the influence of the population’s altitude on the level of population variability [A. altaicum populations located in favorable midland conditions showed a high level of genetic polymorphism, which was caused by the presence of several reproduction strategies, i.e., clonal and by seed germination. Also, we observed higher indicators of diversity for populations located in remote areas of Altai . In our studies, no significant dependence of the effect of height on the productivity of A. ledebourianum plants was revealed in the A. ledebourianum populations, or in the indicators of genetic diversity. Comparing the biodiversity indicators, we note that the values of the Shannon diversity index and the number of effective polymorphic amplicons were higher in the A. ledebourianum populations, but the values of heterozygosity were lower in A. ledebourianum populations (μHe = 0.136) compared to Altai onion populations (μHe = 0.145). Higher values of biodiversity indices in A. ledebourianum populations are possibly associated with the dominance of seed reproduction, which creates a higher level of diversity, in contrast to the clonal one, in which the daughter bulbs are genetically identical to the parent plant [Allium species, is a peculiar way of ensuring the genetic stability of populations and preserving the genetic diversity of populations, and reducing losses from genetic erosion [A. ledebourianum populations using the iPBS amplification profiling method. Estimating the biodiversity level of A. ledebourianum populations using iPBS amplification markers, we note the average level of genetic variability  associated with inbreeding. This phenomenon is typical for endemic species with small populations, for which even a slight change in allele frequencies can have a significant impact on the biodiversity level, as the number of homozygous individuals increases [A. ledebourianum populations may be due to their recent isolation, as well as to the absence of genetic drift that is caused by the ecological and biological characteristics of this species, which is narrowly adapted to places of increased moisture. The maximum adaptation to specific environmental conditions, acquired during the evolution of this species, is manifested by the presence of a short flowering period, which is possible even under snow cover, an abundant number of flowers on the shoot, and high potential seed production. All this allows them to maintain their genetic diversity in changing environmental conditions. Analysis of molecular variance showed that most of the molecular variability is due to interpopulation differences (67%), although individual populations (LD64 and LD6) living in favorable conditions show a relatively high level of intrapopulation polymorphism. Correlation analysis of quantitative characteristics, carried out using Spearman’s rank correlation coefficient, confirms our assumption that the existence of A. ledebourianum plants at different heights cannot be considered a factor determining the interpopulation diversity of this endemic species. Correlation analysis (A. ledebourianum (Shannon diversity index and population heterozygosity). Our studies have shown that the most distant population (LD64) has the lowest allelic diversity (Na = 0.971), which is associated with geographic isolation. In the UPGMA cluster analysis based on the iPBS amplification profiling data, the populations were distributed according to their geographic location. We observe that the populations LD6 and LD20, located relatively close to each other, demonstrate genealogical closeness on a dendrogram, which indicates the average level of their differentiation. In the evolutionary past it was likely one large population, divided by the fragmentation of the area. In this work, for the first time, we analyzed the molecular genetic diversity of samples from natural populations of A. ledebourianum, which are endangered due to anthropological factors. A. ledebourianum is a vegetatively sedentary species, which, along with the spatial isolation of populations, is one of the reasons for its endemism. Despite the genetic diversity of each isolated A. ledebourianum population being closed and not supported from the outside, the genetic diversity in general is preserved within different populations. Therefore, each of these populations untouched by anthropogenic factors must be protected to maintain a certain level of genetic variability. Attention should also be paid to the preservation of the habitats of this endemic species, to exclude grazing, mowing, and other human economic activities. Due to its limited distribution, A. ledebourianum is highly vulnerable in terms of genetic biodiversity conservation, the study of which is very important against the high level of ecological degradation faced by the species.The narrowing of the genetic diversity of species leads to a decrease in plasticity, creating the risk of species abundance decreasing under the negative impact of environmental factors. Urgent measures to preserve relict and endemic species are therefore necessary. The study of genetic diversity is a main element in the strategy for preserving natural populations of endemic species, as genetic diversity arises as a result of adaptations to certain environmental conditions and/or geographical isolation of populations ,63,64. Dr genome ,66. The r genome . The user genome , ISSR [1r genome , or AFLPr genome , is idear genome . When der genome ,70. Usinr genome ,71. In tr genome ,62,63,64r genome . Under this type ,73. Accong works ,72,74. S [Allium ,76,77. Mng value ,80,81,82iability . A. alta erosion ,84. The ncreases . The aveanalysis  revealedA. ledebourianum using the iPBS amplification profiling method and first reliable genetic data of its population structure. Both are essential for maintaining the genetic diversity of this endemic species. Therefore, high levels of intra- and interpopulation polymorphism were detected in natural populations of endemic A. ledebourianum using informative iPBS amplification markers, which was possible due to its simple, reproducible, and genome-wide distribution. Genetic diversity data, population structure, and genetic relationships between populations through iPBS amplification analysis will be helpful for A. ledebourianum germplasm management and for assisting conservation. For the first time, we present results of a biodiversity study using iPBS amplification profiling markers for the narrow endemic A. ledebourianum species. We investigated the genetic structure of populations that are important for the conservation of its genetic diversity. Our research improved understanding of the genetic diversity of natural populations of the relict A. ledebourianum species in the Altai territory. These findings may facilitate the development of a restoration strategy for the endangered species. Thus, highly informative iPBS amplification markers are effective in studying intra- and interpopulation polymorphisms of natural A. ledebourianum populations due to their simplicity, reproducibility, and wide distribution throughout the genome. This study revealed that the populations of A. ledebourianum are distinguished by an average level of genetic diversity, which is associated with adaptation to highly specific habitats. The iPBS amplification method may address the problem of species identification of the rare and endangered A. ledebourianum and the preservation of its biological diversity. The A. ledebourianum samples from the most distant populations can be used for the preservation and reproduction of the gene pool of this valuable plant species.In conclusion, this study presents a detailed analysis of narrowly endemic


Fusarium graminearum (FG), once ranked as a minor disease in wheat, is now emerging as one of the economically important diseases in India. The present study represents the first in-depth population genetic analysis of the FG from the northern wheat belt of India. In this study, multiple conserved gene sequences comprised of β-tubulin (TUB), translation elongation factor 1-α (TEF), and histone-3 (HIS) regions were used for multi-locus phylogenetic analysis of 123 geographically distinct F. graminearum isolates collected from four different states (Haryana (HR), Punjab (PB), Rajasthan (RJ) and West Bengal (WB)) of India. The phylogenetic and haplotype analysis showed the presence of thirty haplotypes in all the analyzed populations. The haplotypic diversity in the RJ population (Hd = 0.981) was higher than in the HR (Hd = 0.972), PB (Hd = 0.965) and WB population (Hd = 0.962). Recombination events (Rm = 12) and mutation events (485) were also detected. Analysis of molecular variance (AMOVA) indicated that genetic diversity was exclusively due to the differences within populations. The haplotype network was widely dispersed and not associated with specific populations, as a single common haplotype was not detected. The PB population contained both unique  and shared haplotypes (27 haplotypes) in a higher number in comparison to other geographical locations. Except for haplotype H22 (contains highly aggressive isolates), there was no specific linkage noticed between the isolate aggressiveness and haplotype. The concatenated sequences of all the three genes demonstrated a low level of genetic differentiation (Fst = −0.014 to 0.02) in the analyzed population. Positive values for the neutrality tests in PB, HR and RJ reveal a balancing selection mechanism behind the FG population structure. The WB population showed both positive and negative values of neutrality indices, indicating the role of both population expansion as well as balancing selection in structuring the FG population.Head blight or scab caused by  Fusarium head blight (FHB) incited by Fusarium graminearum (FG) fungus is ranked as the one of the prime annihilating fungal diseases of wheat (Triticum aestivum L.) globally [Fusarium head blight is liable to enhance under reduced tillage-based wheat cultivation and further aggravated with climate shift especially in the northern part of India, which is recognized as the main wheat basket of India [globally . The pubglobally ,3,4. It globally . In Indiglobally . Later oglobally  and Gurdglobally . Bagga eglobally  documentglobally ,11. In rFusarium toxins production, despite agro-ecological and resistance development problems [Fusarium spp. still exist as a daunting task because of the complex nature of FG and fungicide-resistant development in natural FG populations [FG showed a broad host range, and it has the ability to infect different plants such as maize, sorghum, millets, rye, triticale, oats, etc. . Variousproblems ,18,19,20problems . Moreoveulations . Therefoulations ,23. Diffulations ,25. MostFusarium diversity in agricultural crops in recent times. The molecular identification of fungi is primarily based on the internal transcribed spacer region (ITS) [Fusarium genus comprise orthologous regions and as a consequence, ITS region-based identification resulted in erratic and unreliable results. In this context, genomic regions representing translocation elongation factor 1-α (TEF), β-tubulin (TUB), histones (HIS) and calmodulin (CAL) have been widely used by different group of researchers to distinguish Fusarium spp. [Fusarium spp. Similarly, TEF-1α, β-tubulin and histone 3 regions have been explored by Webb et al. [F. oxysporum isolates. Recently, Fulton et al. [F. oxysporum f. sp. niveum isolates from the major watermelon-producing regions in north, central, and south Florida. A flooding of reports are available that highlight the significance of TUB, TEF and HIS in resolving boundaries of fungal species and further revealed species identification and discrimination based on the combined sequences of gene loci as a more valuable genomic resource than single gene loci [Molecular marker-driven technologies play a significant role in species identification because of their potential usage in exploring the population structure and genetic diversity within the fungal species and their isolates ,32,33,34on (ITS) ,36. Howeium spp. ,44,45,46ium spp.  demonstrb et al.  and Tahab et al.  for dissn et al.  employedene loci ,52. UnfoThe history of FG in wheat in India is not too old, and therefore, the northern plains, a major wheat-growing belt of India, offers a paragon site to pinpoint the prime evolutionary mechanism acting on a newly developing FG isolate as it dispersed topically and regionally within the Indo-gangetic plains of India. Therefore, the current research was planned with an intention to obtain the answers to the question of whether FG contains distinct evolutionary lineages in the northern plains of India. The other questions include: (i) Is the phylogenetic and evolutionary structure of FG indicative of recombination or mutation?; and (ii) Is there a biogeographic relationship with the evolutionary structure of FG? The answers to these questions are essential to improve the understanding of the ecology of the plant pathogen and devise a better management strategy for the management of FHB disease in wheat.−1). The Petri plates were incubated at 25 ± 2 °C. After five days of cultivation, the hyphal tip of fungus coming out from wheat tissue was placed onto other PDA-amended Petri plates and incubated at 25 ± 2 °C for conidia production. A single spore isolation methodology was adopted to raise the pure cultures of each FG isolate, which were stored at 4 °C as per the protocol of Kumar et al. [One hundred and twenty three isolates of FG were used in the present study . These ir et al. .−1 concentration) and each primer pair (1 μL of 10 μM concentraion). In addition, nuclear-free water was used to make the total PCR master mix volume at 25 µL. The detailed information of the temperature profile used in the PCR for amplification of target genes  has been depicted in F. graminareum isolates in National Center for Biotechnology Information ), and we obtained the gene accession numbers.Fungal isolates were grown for five days in shake cultures (200 rpm) at 25 ± 2 °C in 100 mL of potato dextrose broth . The resulting mycelium was filtered through Whatman filter paper and thoroughly washed with distilled water. About 25–30 mg of mycelium was ground with the help of liquid nitrogen and used to extract the total genomic DNA as per the methodology of Kumar et al. . The dethttp://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 11 June 2021)). Editing of gene sequences was conducted by using BioEdit 7.4.0.1 software [Nucleotide sequences of each gene loci  were matched with the gene sequences of respective loci available in the NCBI databank by basic local alignment search tool  with each isolate during 2021–2022 under a greenhouse. The mass production of FG isolates was completed by cultivating them on PDA media. After 15 days of incubation at 25 ± 2 °C, the inoculum was collected by rinsing the Petri plates with sterile distilled water containing Tween 20. Afterwards, the scraped mycelial mat with spores was passed through a double-layer sterile cheesecloth. Final spore inocolum concentration was adjusted at 5 × 106 spore ml−1 with the help of a hemocytometer. The cotton wool ball technique [Aggressiveness analysis of echnique  was usedThe aggressiveness of all the FG isolates was categorized into four classes as highly aggressive , moderately aggressive  and Least or weakly aggressive .F. graminareum. The sequences of all three genes of all 123 FG isolates reflected 99–100% similarity  confirmed their identity as milarity . The seqmilarity . The permilarity .The analysis of the combined gene loci reflected that both haplotype and nucleotide diversity were high (Hd = 0.974 and π = 0.083) . The hapThe haplotypes calculated on the basis of individual locus, i.e., TUB, TEF, and HIS, were 11, 15, and 17, respectively . There wp > 0.05). Negative Fst values were observed for PB and HR and WB and HR with p > 0.05 (The nucleotide diversity among the four FG populations was low and lies between 0.070 (WB population) and 0.098 (RJ) . The paip > 0.05 . The resp > 0.05 .The results  indicateThe results of neutrality tests  reflecteThe results of AMOVA analysis demonstrated a variance of 100.84 within the FG populations. However, a negative percentage of variations (−0.84%) was recorded among FG populations . The fixThe FG isolates were evaluated for their aggressiveness on wheat cultivar (cv. PBW343), and results in terms of HA, MA and LA are mentioned in Fusarium in the Asian and sub-Saharan Africa terrains [FG is one of the emerging and economically important diseases affecting wheat in India ,65. Diveterrains . As IndiTrichoderma and Fusarium) of the ascomycetes group [Fusarium spp. to the nearest neighbor fungal species was deduced on the basis of ITS regions, very low genetic distance values were obtained, which ultimately resulted in the inferior resolution and poor taxon placement in phylogenetic lineages [Previous published literature indicated that TEF is highly recommended for species delineation and rejected ITS sequences in offering superior taxa resolution for several of the fungal genera  have been present in the population for a long time and detected from PB, RJ and WB population dominantly. Contrarily, other seem to be less frequent haplotypes and indicate toward more recent mutation events. Moreover, the terminal haplotypes  located at the network tips in the present study indicate a holocene pedigree rather than old ancestry  as placed in the interiors of the haplotype network. Carbone and Kohn [Geography and climate are two of the most important epidemic linked factors and strongly influence the prevalence and establishment of f plants ,73. Usuaf plants . Similarf plants  also conand Kohn  reportedand Kohn ,78.Haplotype analysis provides important information on the existence of different types of haplotypes (h), their diversity (Hd) and frequency. Usually, Hd values varied between 0 and 1, which reflects zero to high level of haplotype diversity, respectively . In the F. graminearum in Canada and the USA has been documented [The population genetics analysis performed in the current study revealed that the FG population from RJ seems genetically similar with other populations of PB, HR and WB. This statement is clearly supported by the values of high gene flow (Nm = 11.89–88.51) and low genetic differentiation (Fst = 0.002−0.025). A similar observation of high gene flow among different subpopulations of cumented . Furthercumented .A high variation in the aggressiveness of FG isolates has been observed in FG isolates collections from different wheat-growing states. These results are in agreement with earlier workers’ findings in both within-field populations and crossing populations ,83,84. T


Haloxylon ammodendron. Based on the variation of two cpDNA regions (trnS-trnG and trnV) and one nDNA sequence (ITS1-ITS4) in 420 individuals from 36 populations, the spatial genetic structure and demographic history of H. ammodendron in arid China is examined. Median-joining network and Bayesian inference trees enabled the identification of three diverged lineages within H. ammodendron from 24 different haplotypes and 16 ribotypes, distributed across western (Xinjiang), eastern (Gansu and Inner Mongolia) and southern (Qinghai) regions. AMOVA analysis demonstrated that more than 80% of observed genetic variation related to lineage split was based on cpDNA and nDNA variation. Allopatric divergence among the three groups was mainly triggered by geographical isolation due to Xingxingxia rock and uplift of the Qilian Mountains during the Quaternary. Local adaptive differentiation among western, eastern and southern groups occurred due to gene flow obstruction resulting from arid landscape fragmentation accompanied by local environmental heterogeneity of different geographical populations. The southern margin of the Junggar Basin and the Tengger Desert possibly served as two independent glacial refugia for H. ammodendron. The distribution of genetic variation, coupled with SDMs and LCP results, indicated that H. ammodendron probably moved northward along the Junggar Basin and westward along Tengger Desert at the end of the last glacial maximum; postglacial re-colonization was probably westward and southward along the Hexi Corridor.The influence of aridification and climatic oscillations on the genetic diversity and evolutionary processes of organisms during the Quaternary in northwestern China is examined using Atraphaxis frutescens , carbon sequestration  to use cpDNA and nDNA sequence variations to resolve the spatial genetic structure, intraspecific differentiation and geographical and ecological driving forces of H. ammodendron; (2) to identify potential glacial refugia and infer demographic history under climatic fluctuations and desert expansion in the Quaternary; and (3) to identify local environmental adaptive differentiation of different geographical populations under the background of climate change and arid landscape fragmentation.Based on the combination of chloroplast DNA (cpDNA) and internal transcribed spacer (ITS) of nuclear ribosomal DNA (nrDNA) data, this study focuses on spatial genetic patterns and population dynamic history of H. ammodendron individuals across 36 natural populations were collected between 2017–2019 across different geographical, climatic and altitudinal ranges in northwestern China. Sample locations included 19 populations in the Xinjiang Autonomous Region, four populations from Gansu province, six populations from Inner Mongolia and seven populations from Qinghai  from Qaidam Haloxylon Forest National Nature Reserve. Young and healthy fresh assimilating shoots were collected from 10–15 individuals in each population which were spaced at least 10 m apart. Shoots were dried on silica gel and stored at 4 °C until DNA extraction. Due to their relatively close evolutionary relationship, Comulace alaschanica (Chenopodiaceae) (EF453406.1), Gimengohin appostriora (Chenopodiaceae) (EF453412.1) and Halogeton glamaranntus (Chenopodiaceae) (EF453431.1) were selected as outgroups in the phylogenetic analysis (http://www.gscloud.cn).Shoots from 420  Qinghai . Four poanalysis . The Alttrn S-trn G and trn V  mixture and amplification program followed the method used in our previous investigation . PurificHd, Rd), nucleotide diversity (π) and the total number of individuals for each genotype were calculated in DNASP 5.0 based on both cpDNA and nDNA datasets (HT), within-population genetic diversity (HS) and population differentiation (GST and NST) were calculated using Permut CpSSR 2.0 with 1000 permutation tests  between populations of H. ammodendron was also calculated in GenALEX 6.5; geographical distance was generated in ARLEQUIN 5.0  with ARLEQUIN 5.0 . Total gutations . GeneticQUIN 5.0 .Furthermore, Monmonier’s maximum-difference algorithm was used in BARRIER v2.2  to identH. ammodendron  analysis was performed for 20 million generations, with samples recorded every 1,000 generations for the Bayesian analysis. Bayes factor (BF) values were caculated by Tracer v1.5 to detect convergence of the MCMC, and the effective sample size (ESS) of each parameter above 200 after the first 10% of generations was discarded as burn-in.Beast 2.2.1 was used to estimate divergence time between different lineages of odendron . By usinodendron . The relH. ammodendron underwent recent range expansion, pairwise mismatch distributions were computed using DnaSP 5.0 , having a resolution of 30s. Soil data, including soil pH, soil-carbon density and soil moisture were obtained from the Harmonized World Soil Database. Pearson correlation analysis of environmental variables was carried out using the hmisc package in R 3.6.2; variables with high correlation were excluded  was performed to reconstruct the Last Glacial Maximum  and the present distributions for T v3.4.1 . NineteeH. ammodendron migration corridors during the LGM and today were visualized based on least-cost path (LCP) analysis using SDMtoolbox 2.0 in ArcGIS 10.5 . For this process, we initially inverted the species distribution model (1-SDM) for H. ammodendron during the LGM and the present period generated in MAXENT 3.4.1 to a “dispersal cost layer (resistance layer)”. Secondly, by calculating a cost distance raster for each sample locality using the resistance layer of H. ammodendron, corridor layers were established between every pair of localities. Finally, all pairwise corridor layers were summed as the eventual dispersal corridor for H. ammodendron , the absolute values of standardized PC1 scores from the present period (CPre) minus scores from the LGM period (CLGM) were calculated. After standardization, relatively stable climate since the LGM was indicated by values closer to 0, and significant climatic changes were indicated by values near 1  for the selected climate variables was performed using the ‘ggbiplot’ package in R. Estimated principal components summarized the overall pattern of variations in climate variables among populations during the present and LGM periods. To compare climate changes for populations since the LGM  analysis was performed in R to estimate the contributions of climatic variables used for the simulation of population genetic structures and to understand genetic diversity along a climate gradient . Here, Gterature . The sevtrn S-trn G and trn V were 852 and 516 bp in length, respectively, and 1, 368 bp for the combined data. A total of 26 polymorphic sites (18 substitutions and eight indels) and 24 different haplotypes (H1-H24) were identified. The aligned fragment ITS1-ITS4 was 573 bp in length, with 15 polymorphic sites and 16 ribotypes (R1-R16) being revealed .The cpDNA aligned sequences of revealed , Fig. 1.Based on cpDNA and nDNA datasets, similar phylogenetic networks were obtained  and 3, aIn the western group, the most widespread haplotype (H10) was carried by 25.33% of individuals. This haplotype was distributed in the northern Junggar Basin, and three unique haplotypes  were found in the southern Junggar Basin. The most widespread ribotype (7) was carried by 35.1% of individuals. This ribotype was found in each population in the northeast Junggar Basin, and one unique ribotype (8) was fixed in the northeastern Junggar Basin (XHS). The most widespread haplotype (14) and ribotype (9) in the eastern group were found in more than 90% of populations from western Gansu and central Inner Mongolia; one unique haplotype (17) was fixed in southwestern Ulan Buh Desert (MWS). In the southern group, haplotype 19 and ribotype 14 were the most prevalent, being found in more than 90% of the populations in Qaidam Basin; four unique haplotypes  were found in populations from eastern Qaidam Basin  , Fig. 1.H. ammodendron populations. Genotype diversity ranged from 0 to 0.866 and 0 to 0.812, and nucleotide diversity ranged from 0 to 0.00504 and 0 to 0.007782 for cpDNA and nDNA, respectively  and highest in the eastern group based on nDNA . Analysis of genetic landscape shapes indicated that high genetic diversity was identified in the population located in the eastern Junggar Basin from the western group (XFK), three populations located in the northern margin of the Qaidam Basin from the southern group , and six populations located in the Aksai Desert, Ulan Buhe and Tengger Deserts from the eastern group   .HT) was much higher than average gene diversity within populations (HS), indicating considerable population differentiation across the distribution range  indicated a significant phylogeographic structure in the species range. AMOVA showed that 77.81% and 84.20% of total variation primarily occurred among populations  were used to estimate the potential distribution of H. ammodendron. High AUC scores (AUC = 0.938 and 0.965) in the evaluation model indicated that the MAXENT models had a good level of performance. Predicted present distributions for H. ammodendron were widespread in the Junggar Basin, the northern rim of Tarim Basin, northernmost Gansu, Hexi Corridor, eastern Alxa of Inner Mongolia and in the northern Qaidam Basin. From the LGM to the present, the habitat distribution area ratio  was 0.065, indicating that H. ammodendron likely experienced significant range expansion with eastward shifts along the desert margins, or the Gobi Desert  and the present stage (CPre: −3.79–0.387), as well as the change values of the six climate factors since the LGM (Cchange: 0–1)  of observed climate variation of the occurrence points . PCA 1 sge: 0–1) .GF analyses indicated that among the seven tested environmental variables, precipitation of the wettest month was the most important predictor of species allele frequency variation . Other vH. ammodendron populations were identified , eastern (Gansu and Inner Mongolia) andsouthern (Qinghai) regions  showed adaptation to arid conditions  and divergent climatic conditions since the LGM  that have the most high contributions to the first two principal components, are shown in bold.Click here for additional data file.10.7717/peerj.14476/supp-5Supplemental Information 5The first two variables  that have the most high contributions to the first two principal components, are shown in bold.Click here for additional data file.10.7717/peerj.14476/supp-6Supplemental Information 6■, GTTA; , ACAA; ∘, TGG; , CC; , AT; * , CAC; △, ACA. ‘–’ represents indels with a length equal to 1 bpClick here for additional data file.10.7717/peerj.14476/supp-7Supplemental Information 7■, TTTTTTTATGAATCCG; , TCCGTTTTTTTTATGAA; ∘, GATTTGACTT; , TGACT; , TGAC; * , CT; △, AATA. ‘–’ represents indels with a length equal to 1 bpClick here for additional data file.


Neophocaena phocaenoides Cuvier, 1829) is distributed in the coastal waters of Asia, throughout Indonesia to the east, and as far north as the Taiwan Strait. The finless porpoise has been declared critically endangered by the WWF (World Wide Fund for Nature), and in 2017 was rated vulnerable on the IUCN Red Threatened Species List. Since this species is distributed near the coast and has many interactions with humans, effective conservation of the species requires further studies into their genetic diversity and population. In this study, 45 samples were obtained from bycatch or stranded individuals in the East, South, and West Seas, where Korean porpoises were mainly distributed from 2017–2021. We compared 473 bp mtDNA sequences from the control region. Pairwise fixation indices (FST) revealed that the two populations differed significantly . In contrast to high levels of genetic differentiation, gene flow was identified as medium levels (Nm of 0.04–0.71). Our data suggest that finless porpoises may have undergone a historic differentiation event, and that finless porpoises in the three regions could be divided into two populations: West and East/South.The finless porpoise ( N. phocaenoides, One of eight porpoise species is the finless porpoise . The thermal cycling included an initial denaturation at 95 °C for 10 min, 29 cycles of denaturation at 94 °C for 30 s, annealing at 50 °C for 30 s, an extension at 72 °C for 1 min, and a final denaturation at 72 °C for 5 min; the mixture was then kept at 4 °C. The PCR products were cleaned using EXOSAP-IT  prior to being sequenced on an ABI 3730XL DNA Analyzer using BigDye Terminator v3.1 Ready Reaction Cycle Sequencing Kits . Animal experiments were conducted under the guidance approved by the Animal Research and Ethics Committee of the National Institute of Fisheries Science with the authorization number: 2019-NIFS-11. The newfound mitochondrial haplotypes (control region) were deposited in GenBank, with accession numbers ON470395–ON470439.Genomic DNA was isolated from muscle samples using 10% Chelex 100 Resin . A 473 bp fragment of the CR was PCR amplified using the following primers: MT4-F  and DlpFs was used to test for evidence of population expansion [π; [h; [FST) were calculated using Arlequin. The median-joining network (MJN) technique [FST; that is, Nm = (1/FST − 1)/2 [The CR sequences generated were edited using BioEdit ver. 7.2.5  . The seqxpansion , while Txpansion , using Dxpansion . Nucleotsion [π; ), haplot [π; [h; ), and paechnique , using technique , was useechnique ,33. GeneT − 1)/2 .h) were 1.000 regardless of location; however, nucleotide diversities (π) varied by location: 0.003 in the East Sea, 0.004 in the South and West Seas. Analyses of CR variability revealed that the finless porpoise population possessed consistent haplotype and nucleotide diversities, indicating a significant number of distinct haplotypes, as indicated by prior research , and when analyzed with two groups, ES  and W , the index was confirmed at 0.456 (p = 0.000). The pairwise fixation index (FST) was higher in the two populations, W and ES, than in the respective populations of the East Sea, South Sea, and West Sea. This indicated a significant level of genetic differentiation between the two populations, W and ES (FST showed a significant genetic structure (FST data indicated that a historic differentiation event may have happened between two clusters. Gene flow varied from 0.597 to 1.753 between populations in our study. Following Wright [FST of 0.456) and medium levels of gene flow (Nm of 0.04–0.71) between the W and ES populations, based on CR sequences.In terms of differentiation index, the index of each group in the East, South, and West Seas was 0.374  and θ1 (6827.474) for W was larger than the difference between θ0 (0.000) and θ1 (2.246) for ES. The estimates of θ for CR showed a larger difference in the W  than in the ES . The W deviated significantly from the neutral constant population size anticipations with statistically significant negative values for Fu’s sF and Tajima’s D. This result reveals a significant excess of singleton mutations relative to the neutral expectation, which is comparable with a bottleneck or a selective sweep [sF values of ES showed a significant negative value, Tajima’s D showed no significant deviation despite a negative value display. The tau (τ) value of the W was 2.988 and the tau (τ) value of the ES was 1.832, indicating that the estimated time since the sudden population was 87,738 and 53,804 years ago, respectively (The difference between the estimates of ve sweep . Althougectively .Phocoena phocoena [π: 0.0042) [Neophocaena asiaeorientalis. Chen et al. [N. asiaeorientalis mtDNA CR from another Neophocaena species. The nucleotide diversity  . The mtDn et al.  and Zhenn et al.  reported 0.0011 ,44) and  and Phoc (0.0011 ) were lo (0.0011 . Recent FST) showed overall significant levels of genetic structuring . As a result of AMOVA analysis, molecular variation was 45.57% higher than in previous studies  of finless porpoises in the coastal waters of Japan. These researchers concluded that there was a distinct population in each of the five locations. Genetic studies of finless porpoises off the coast of Korea have been conducted previously , but no  41.38% ), which .38% , whThe population structure of finless porpoises was investigated by merging East and South Sea porpoises along the east coast of Korea. Previous research reveals a continuous distribution of porpoises in these waters; therefore, these data have been combined ,48. D value, suggesting a historical population growth, a finding which is similar to the results of other studies [Fs tends to be negative when there is an excess of recent mutations, indicating deviation caused by population expansion and selection [Our findings indicate that there is a new demographic structure of finless porpoise off Korean shores. We found a negative and highly significant Tajima’s  studies ,16. The election . Consistθ0 (0.234) and θ1 (6827.474) is evidence of population expansion in the West Sea, due to the influence of the last glacial age. During this time, there was large-scale iceberg discharge into the Pacific Ocean, and large volumes of freshwater would have been injected into the ocean [Using a rate of 3.6% per million years for mtDNA CR sequences divergence, the divergence between clusters W and ES was predicted to have occurred 87,738 and 53,804 years ago. During the glacial epoch, a lowered sea level secluded the East Sea from the Pacific Ocean ,50. The he ocean . Therefohe ocean . This obhe ocean  confirmeThere was a statistically significant disparity between the West and East populations. Therefore, these populations should be treated as separate management units. Samples from the whole span of the South Sea and the East Sea were not included in this study. Genetic samples of finless porpoises that were bycatch or stranded in the West Sea, the eastern portion of the South Sea, and the southern portion of the East Sea were collected and analyzed. The genetic examination of samples from three marine regions off the coast of Korea, however, provides crucial information for the study of finless porpoises in Asia. In future research, it will be necessary to examine a wide variety of genetic samples from the East, West, and South Seas off the coast of Korea, as well as to examine them in various ways  by adding samples from the western and northern parts of the South Sea. Our findings indicate the necessity of incorporating Korean population genetics into stock assessment models of finless porpoises in order to ensure their sustainable management. These findings may have substantial consequences for the conservation and management of finless porpoises along the Korean coast.