Ophiocomina nigra, covering most of its distribution range along the Atlantic-Mediterranean region. Interestingly, O. nigra is extremely variable in its coloration, with individuals ranging from black to yellow-orange, and different colour morphs inhabiting different depths and habitats. In this work, we used a fragment of the mitochondrial COI gene and 2,374 genome-wide ddRADseq-derived SNPs to explore: (a) whether the different colour morphs of O. nigra represent different evolutionary units; (b) the disruptive effects of major oceanographic fronts on its population structure; and (c) genomic signals of local adaptation to divergent environments. Our results revealed exceptional population homogeneity, barely affected by oceanographic fronts, with no signals of local adaptation nor genetic differentiation between colour morphs. This remarkable panmixia likely results from a long pelagic larval duration, a large effective population size and recent demographic expansions. Our study unveils an extraordinary phenotypic plasticity in O. nigra, opening further research questions on the ecological and molecular mechanisms underpinning coloration in Ophiuroidea.The Atlantic-Mediterranean marine transition is characterised by strong oceanographic barriers and steep environmental gradients that generally result in connectivity breaks between populations from both basins and may lead to local adaptation. Here, we performed a population genomic study of the black brittle star,  One of these relevant oceanographic transitions is the English Channel, which brings the southern Atlantic water mass into contact with the colder and shallower North Sea6. In the southern European area, the Strait of Gibraltar and the Almeria-Oran front define two oceanographic breaks, due to the strong and well-defined marine circulation system across this area, that separate the Atlantic and the Mediterranean basins7. These two barriers delimit the Alboran Sea, a mixing-water zone between the warmer and saltier Mediterranean Sea and the lower-salinity and colder Atlantic Ocean8. Depending on the species, these marine barriers have different effects on the connectivity and gene flow patterns between Atlantic and Mediterranean populations14, thus providing different disruptive effects of these two oceanographic barriers18. Genetic diversity patterns highlighted that large- and short-scale oceanographic circulation interplays with each species life history traits and its evolutionary history to determine its divergence level20. Therefore, it is mandatory to obtain genetic data from a wide range of species displaying a variety of biological traits and evolutionary histories, to extract general conclusions about the effects of oceanographic breaks on population connectivity, as well as the relative importance of each factor involved in the divergence patterns.The Atlantic-Mediterranean biogeographic marine region encompasses a wide range of subtropical, temperate and even subarctic marine areas that also includes several environmental transition zones and oceanographic fronts24, some animal groups, such as the ophiuroids  have been less studied. Ophiuroids, commonly known as brittle stars, are the most diverse echinoderm class, with important ecological roles in marine benthic food webs that occupy a large range of ecological and trophic niches in all oceans around the world27. To date, only a few studies on genetic connectivity and structure of ophiuroid populations along the European coast have been performed with nuclear markers30, whereas most studies were exclusively based on mitochondrial DNA31. These studies showed contrasting patterns of population divergence that were largely influenced by the reproductive strategy of the studied species (brooding versus non-brooding species), among other biological factors. Additionally, ophiuroids usually present cryptic species complexes35, and several cryptic species have been reported along the European Atlantic and Mediterranean coast for the genus Ophioderma . Cases of overlooked diversity have also been reported, where different species had been mistakenly considered colour phases of the same species32, due to the generally large morphological plasticity found in this marine invertebrate group37.Although an array of studies on population genetics, phylogeography and speciation have been performed along the Atlantic-Mediterranean coasthttps://www.ncbi.nlm.nih.gov/assembly, checked in February 2023). This general lack of genomic information prevents the implementation of genomic approaches that rely on reference genomes, which have remarkable applications on population genomics, evolution and conservation39. Nevertheless, reduced representation library techniques, such as restriction-site associated DNA sequencing (RADseq), enable the isolation of thousands of nuclear markers for population genomic analyses of non-model organisms40. Indeed, RADseq techniques have been successfully applied to a wide variety of non-model marine invertebrate species in absence of reference genomes: from sponges41 to molluscs42. In line with the lack of genomic resources for Ophiuroidea, there are only three studies using RADseq techniques on brittle stars, all of them focused on Antarctic species45.Despite their high diversity and ecological relevance, genomic research on brittle stars is still on its infancy compared to other echinoderm classes. For instance, only three out of the 45 echinoderm genome assemblies available on NCBI are from ophiuroid species . It is a slow-growing species that lives up to 14 years, and juveniles tend to settle far from adults51. O. nigra has a planktotrophic larva that remains in the water column for several months before its settlement, and therefore, with a potentially high dispersal ability47. Both field observations and laboratory experiments determined that O. nigra forms low-density and regular dispersed patches on the marine floor in the English Channel, where individuals avoid contact with each other, a social behaviour that largely differs from other co-occurring brittle stars such as Ophiothrix fragilis. However, O. nigra aggregations with densities over 100 individuals·m−2 might be induced by environmental disturbances or under presence of food47.In this work, we carry out a phylogeographic and population genomic study of the brittle star O. nigra presents an impressive colour variation, with individuals ranging from black to yellow-orange, and some organisms also presenting reddish hues52. In fact, dark and light colour morphs were once considered different species: the light-coloured morph was initially described as Ophiocoma raschi , but was later synonymised with O. nigra53. In the southern coast of Great Britain, the distribution of colour morphs has been related to depth, with darker colour morphs appearing more often at shallower areas and light-coloured individuals becoming more common in deeper waters52. Coloration of O. nigra depends on the oxidation state of a melanin pigment present in specialised melanin-containing cells of the integument, the melanocytes, which might be regulated by fluorescent pigments and an enzyme system controlling melanogenesis54. These differences in coloration, habitat preference and physiology could be due to phenotypic plasticity of a single species or, contrarily, they may indicate that they are in fact two different species, hypotheses that have not hitherto been contrasted from a genetic perspective.Interestingly, O. nigra are underpinned by genetic differences, and hence they should be considered different evolutionary units within a cryptic species complex; (b) assessing the genetic diversity, demographic history and population structure of O. nigra, focusing on the effects that multiple oceanographic barriers may have on its population connectivity; and c) looking for genomic signals of local adaptation to different environments. Our first hypothesis is that the two colour morphs of O. nigra may correspond to two cryptic species, as cryptic speciation is common in Ophiuroidea (e.g.34). Also, we anticipate high differentiation between distant localities due to the strong oceanographic barriers present in our study area. And finally, we expect to find genomic signals of local adaptation due to the contrasting habitats and sharp environmental gradients present in our sampling area.In the present study, we sequenced a mitochondrial marker and isolated thousands of nuclear markers using a RADseq approach, in order to achieve the following objectives: (a) testing whether the two colour morphs of Ophiocomina nigra individuals from 10 localities in the Northwestern Mediterranean Sea, the Alboran Sea, and the Northeastern Atlantic Ocean  was amplified and sequenced for all organisms.We collected 192 Single Nucleotide Polymorphisms (SNP) isolation using double digest restriction-site associated DNA sequencing (ddRADseq). After filtering the Illumina reads, we obtained a total of ca. 272 M reads, and retained 109 individuals with an average of ca. 1.9 M reads per individual. The final dataset included 2,374 neutral SNPs and 109 individuals, containing 30.46% of missing data and an average depth of 9.6 reads per SNP per sample.From these 192 individuals, we sub-sampled 144 organisms that were used for We detected a total of 124 outlier SNPs using Arlequin, as potential candidates under positive selection. However, no SNP was identified as under selection using Bayescan. Therefore, since we did not find common SNPs between the two methods, all SNPs were considered as neutral for subsequent population genomic analyses.COI dataset, haplotype number, nucleotide diversity (π) and haplotype diversity (Hd) values were high in all O. nigra localities  presented low values in all sampling sites, ranging from 0.079 in Cabo Negro (CNE) to 0.138 in Es Caials (CAI), with a total value of 0.099. All populations were deviated from the Hardy–Weinberg equilibrium as demonstrated by their significant values.Population genomic statistics for the SNP dataset are shown in Table COI haplotype network is shown in Fig. https://doi.org/10.17632/5m6v5sm5f6.1). It reveals deep diverse bush-like genealogies, with twenty shared haplotypes and a large number of singletons. The shared haplotypes suggest population homogeneity and high population connectivity, with haplotypes shared among individuals from the three different regions studied , but the bimodal distribution on the observed pairwise differences may correspond to two consecutive expansion events , including other brittle stars18. Such a pattern of panmixia, or low genetic divergence, between the Mediterranean and Atlantic basins, or within the Mediterranean sub-basins is relatively common in echinoderms with large dispersal potential59 and in those with a predominance of asexual reproduction17. Nevertheless, the absence of divergence found in O. nigra between the Northeast Atlantic and the Mediterranean is an exceptional pattern even for species with large dispersal potential59. Past oceanographic processes across the Atlantic-Mediterranean transition6, and the current disruptive effect of major oceanographic circulation across the Gibraltar Strait and the Almería-Oran front, have generated a large variety of genomic patterns in marine invertebrates, but most of them are characterised by a major divergence between these two main basins16, although exceptions also exist31. Interestingly, within the Ophiothrix species complex, Ophiothrix sp. II displayed a lack of genetic divergence similar to that obtained in O. nigra, with genetic homogeneity along the Atlantic-Mediterranean transition31. However, for Ophiothrix sp. II the sampling area was restricted to the Iberian Peninsula and only two mitochondrial markers were studied18.Despite this morphological variation within the species, and the large geographical range explored, we found general population homogeneity. We detected high levels of genetic diversity for both the mitochondrial O. nigra may result from the combination of different biological features, demographic and historical events, including a large dispersal potential of the larva, large effective population size, and past demographic expansions31. The planktotrophic larva of O. nigra remains in the water column for several months avoiding settlement near adults51, and hence can be dispersed hundreds of kilometres by marine currents47. A long dispersal potential during early life stages, which interacts with oceanographic circulation, significantly boosts population connectivity60. Additionally, the low-density and regularly dispersed patches formed by O. nigra across large spatial areas47 may favour connectivity along distant sites.The lack of significant differentiation between distant geographical areas in O. nigra could be also the result of the recent demographic expansions that we identified from both COI and SNPs datasets. Our demographic history analyses identified a recent effective population expansion consisting in two growth periods, likely related to expansions during Pleistocene interglacial periods (e.g.18). The ancient growth period was recovered from both the COI BSP and the ddRADseq Stairway Plot 2 results, and was dated between 50,000 and 60,000 years ago, similar to that dated for Ophiothrix spp18 and some other Mediterranean species61. The most recent one was also identified by both datasets, and pointed to a tenfold increase of O. nigra populations between 10,000 and 20,000 years ago . This recent demographic expansion could have partially erased previous population differentiation in O. nigra, favouring the current genetic homogeneity found in both datasets. Additionally, a large effective population size reduces genetic drift effects63, reducing genetic differentiation between populations and increasing genetic homogeneity.The population homogeneity found in see Fig. . This coO. nigra, the presence of sub-regional private haplotypes in the COI marker suggested a certain degree of genetic substructuring in major geographical areas. In agreement with these results, migration analyses based on ddRADseq-derived SNPs showed that gene flow is not symmetric and homogeneous over our study area. The migration network . See collection details in Table Among all samples, we identified the two colour morphs previously described for this species and likely associated with depth (see Table No endangered or protected species were involved in this study. Authors possessed the required permits to collect echinoderm samples for research.www.sigma.com). Specific primers for a fragment of the mitochondrial cytochrome c oxidase subunit I (COI) marker of O. nigra were designed using Primer v. 3. 0 (http://simgene.com/Primer3) from the available sequence in GenBank (Accession number FN562577.1). These newly designed primers were: Onig-F (5′-AGTCGGTGATTATTTTCTACAA-3′) and Onig-R (5′-AATTATCATTGTAGCAGCAGTG-3′). PCR conditions were as follows: initial denaturalization at 94 °C for 5 min, 40 cycles of 94 °C for 45 s, 50 °C for 45 s and 72 °C for 90 s, and a final extension of 72 °C for 5 min. The PCR-amplified products were bi-directionally sequenced at Macrogen Europe. Sequences were edited using BioEdit v. 7.065 and aligned with ClustalW as implemented in Mega v. 666.Genomic DNA was extracted from ethanol-preserved tube feet using the REDExtract-N-Amp Tissue extraction kit protocol  libraries following67. High molecular weight DNA was re-extracted from preserved samples following a modified cetyltrimethylammoniumbromide (CTAB) protocol68. Briefly, tube feet tissue samples were digested overnight at 57 °C in CTAB buffer , 2-mercaptoethanol and Proteinase K. DNA was subsequently isolated with 24:1 chloroform:isoamyl alcohol and precipitated with ice-cold ethanol. DNA integrity, purity, and quantity were checked in agarose electrophoresis gels, Nanodrop (Thermo Fisher), and using a Qubit DNA HS assay (Life Technologies), respectively. For ddRAD library preparation, we double digested 510 ng of DNA per sample using high-fidelity restriction enzymes EcoRI and MseI (New England Biolabs). Fragments per sample were purified with Agencourt AMPure beads (Beckham Coulter) before ligation with barcoded Illumina adapters. Samples with unique adapters were pooled, and each pool was size-selected (200 to 400 bp) using a Blue Pippin (Sage Science). Illumina multiplexing indices were added to each library during PCR amplification with a Phusion high-fidelity DNA polymerase kit (New England Biolabs), using a common forward primer (PCR1) and a reverse library-specific primer (indexed PCR2). Six libraries, with 24 individuals per library, were paired-end sequenced (150 bp) on an Illumina NovaSeq 6000 at the Novogene Core Facility, Cambridgeshire, UK.A subset of 144 specimens from eight representative 69. RAD-tags  were processed using process_radtags, where raw reads were quality-trimmed to remove low quality reads, reads with uncalled bases, and reads without a complete barcode or restriction cut site. After these filtering steps, we retained a total of 272,024,358 reads from the initial 307,074,382 raw reads, with an average of 1,889,058 reads per sample. Then, the core Stacks pipeline was run with optimised parameters following70 and71. Optimal parameter values were: m = 3, M = 4, and n = 4. The Stacks population module was used to conduct a first filtering of the data, retaining SNPs present in at least 50% of the individuals (r = 0.5) and with a minimum allele frequency higher than 0.05 (–min-maf 0.05). We also used the “–write-single-snp” option in order to retain only the first SNP from each RAD-tag to prevent the inclusion of physically linked loci in the dataset. We then filtered out individuals with less than 20% of the loci using the adegenet R package72. Then, we used the perl script “filter_hwe_by_pop.pl” from the dDocent pipeline73 with default parameters  to filter SNPs deviating from Hardy–Weinberg equilibrium within sampling stations.Quality filtering, locus assembly and SNP calling were conducted using the Stacks pipeline v. 2.5974 and Bayescan v. 2.175. Arlequin was run with default parameters using 100,000 coalescent simulations and 100 demes. Benjamini-Yekutieli FDR corrections were applied to 0.01 p-value. Bayescan was run with default parameters, using the -snp flag and a false discovery rate of 0.05 (FDR = 0.05). Only SNPs identified by both methods were considered as being under selection and removed from the neutral SNP dataset used for subsequent population genomic analyses.The identification of putative SNPs under selection was performed using Arlequin v. 3.5COI data, including haplotype diversity (Hd), nucleotide diversity (π), the number of haplotypes for the different populations (H), and number of private haplotypes per sampling site, colour morph and region using DnaSP v. 5.1076. The software PopART77 was used to construct an un-rooted haplotype network using the TCS statistical parsimony network approach78.We calculated diversity indices for the ST indices were estimated between sampling stations using Arlequin with 90,600 permutations to determine their associated p-values. We used a Benjamini-Yekutieli false discovery rate correction for multiple comparisons79 and an overall corrected 0.05 α-level. In order to test whether the groupings of samples by colour morph or by region explained a significant part of the total genetic variation we performed two analyses of molecular variance (AMOVA) in Arlequin.Pairwise FIS), and the Hardy–Weinberg Equilibrium were estimated by sampling site, colour morph and region using GenoDive v. 3.0580.Indices of genetic diversity including mean number of alleles, expected heterozygosity (He), observed heterozygosity (Ho), inbreeding index 56 as implemented in the adegenet R package72. STRUCTURE was run using the admixture model for 500,000 MCMC iterations, with a burn-in of 50,000 iterations, setting the number of clusters (K) from 1 to 9, with 10 replicates per K. We used STRUCTURE HARVESTER81 to inspect the most likely number of K, and CLUMPAK82 to average individual’s membership coefficients across replicates and graphically represent STRUCTURE results. The DAPC was performed estimating the number of clusters with the find.clusters function in adegenet, using the optim.a.score function to optimise the number of retained PCs. Additionally, in order to identify gene flow patterns, a relative migration network analysis was conducted with the divMigrate function in the diveRsity R package84 using Nei’s GST method.Pairwise Ophiocomina nigra and infer how it might have affected the current genetic diversity distribution and population structure, we ran demographic history analyses for both COI and ddRAD data.In order to unveil the evolutionary history of 85. In addition, Fu’s Fs and Fu and Li’s F neutrality tests were calculated for the ddRAD and the COI datasets, respectively, for each sampling site, colour morph and region, using DnaSP. We used the fasta file generated by the Stacks population module with the -fasta-samples option as the ddRAD data input file for DnaSP.For both datasets, Tajima’s D and Ramos-Onsins and Rozas’ R2 neutrality tests were calculated for each sampling station, colour morph and region, and for the whole datasets using DnaSP v. 6.12COI data, we calculated the pairwise mismatch distribution using DnaSP and performed a Bayesian Skyline Plot (BSP) using BEAUTi and BEAST v. 1.10.486. For the pairwise mismatch distribution, the effective population size parameter (θ) was set to follow the Rogers & Harpending model87. In order to determine if the empirical data fitted an expansion model we calculated the raggedness (r) index88 using 10,000 permutations. Our priors for the BSP model included the implementation of the substitution model previously defined by jModelTest v. 2.1.1089 (HKY + I + G), a strict clock model, and the constant skyline model. As no molecular clock has been calibrated for ophiuroids, a mutation rate of 1.01% per nucleotide per million years was used for the COI, following other studies in echinoderms and the most recent estimations for this group90. Analyses were run for 500 million generations, with a sampling frequency of 50,000 generations, and a burning of 25% of the MCMC. The software Tracer v.1.7.291 was used for assessing stationarity of the MCMC, effective sample sizes (ESSs > 200) and generating the evolution of effective population size under the skyline plot model, expressed as NeT (T = generation time) over time.For the 57 using the ddRADseq-derived SNP dataset. Firstly, we obtained a new vcf file using the Stacks populations module only applying the -r 0.5 option. From this vcf file we obtained the folded Site Frequency Spectrum (SFS) using the easySFS software (https://github.com/isaacovercast/easySFS) with 114 projections. We prepared the blueprint file for Stairway Plot 2 using the following parameters: nseq = 114; L = 3,415,039 (from the populations module output); whether_folded = true; smallest_size_of_SFS_bin_used_for_estimation = 1 ; nrand = 28, 56, 84, 112; ninput = 200; mu = 9.4e−9 (nuclear mutation rate for other echinoderms92); year_per_generation = 7. Results were plotted using the ggplot2 R package93.Additionally, we ran Stairway Plot 2Supplementary Information.


Saussurea medusa Maxim. is a typical “sky island” species and one with the highest altitude distributions among flowering plants. The present study aimed at analyzing the genetic diversity and population structure of 300 S. medusa accessions collected from 20 populations in the Qilian Mountains in the northeastern Qinghai–Tibet Plateau (QTP), using sequence-related amplified polymorphism (SRAP) markers. A total of 14 SRAP primer combinations were employed to analyze genetic diversity and population structure across all accessions. Out of 511 amplified bands, 496 (97.06%) were polymorphic. The populations in the eastern Qilian Mountains had significantly higher genetic diversity than those in the central and western groups. Population structure analysis revealed greater genetic differentiation among populations with a Gst of 0.4926. UPGMA-based clustering classified the 300 S. medusa accessions into 3 major clusters, while the Bayesian STRUCTURE analysis categorized them into 2 groups. Correlation analyses showed that the genetic affinity of the populations was based on differences in geographical distance, moisture conditions, and photothermal conditions between the habitats. This study represents the first comprehensive genetic assessment of S. medusa and provides important genetic baseline data for the conservation of the species. S. medusa, a rare monocarpic perennial herb belonging to the genus Saussurea, family Asteraceae, grows on rocky beaches, rock cracks, and gravel hillsides at a height of 3000–5600 m above sea level [S. medusa is widely used to treat rheumatoid arthritis and gynecological diseases. Modern pharmacological experiments have shown that S. medusa has anti-inflammatory, analgesic, anti-tumor, anti-oxidative, hypolipidemic, and uterine contraction stimulation effects [ea level . In Chinea level . In trad effects ,5,6,7. I effects ,12,13,14S. medusa has a small range of distribution and population size, which could have produced its loss of alleles and genetic variability due to genetic drift. As one of the three main forms of biodiversity, genetic diversity provides raw materials for the evolution of species, thereby providing potential for adaptation to constantly changing environments [S. medusa is a predominantly outcrossing plant, mainly pollinated by insects , despite a certain degree of self-pollination in the absence of pollinating agents [S. medusa, particularly in terms of genetic diversity and genetic structure, has been rarely studied owing to its small population size and sampling difficulties.In addition, its populations are relatively isolated by steep valleys and high mountain ridges, showing the typical distribution model of “sky island” ,16,17. Lronments . The matronments . S. medug agents . Howeverg agents . In addig agents ,24. Plang agents ,26,27,28Eryngium alpinum populations  in the European Alps. Pluess et al. [Geum reptans, an outcrossing clonal plant species in the Swiss Alps. Sequence-related amplified polymorphism (SRAP) is an effective and convenient molecular marker for assessing biological genetic diversity based on PCR methods. This technology presents several advantages over other marker technologies, including its high throughput rates, high commonality, easy band separation and sequencing, and its targeting of open reading frames (ORFs) [Cynara, which is a close relative of Saussurea [Saussurea in our literature search.Currently, molecular genetic markers are a reliable method for the population-level genetic analysis of alpine plants. Gaudeul et al.  used an s et al.  used a rs (ORFs) ,32,33,34aussurea ,36,37. HS. medusa accessions collected from the Qilian Mountains in the northeastern QTP. We also analyzed the objective factors that may contribute to population differentiation, considering geographical distance and climatic factors. To our knowledge, this is the first report on the genetic diversity and population structure of S. medusa.In this study, we used 14 pairs of SRAP markers to evaluate the genetic diversity and population structures of 300 S. medusa. The primer sequences are shown in PIC) was from 0.48 to 0.50. Examples of the PCR amplification results with various primer combinations are shown in A total of 88 pairs of SRAP primer combinations were preliminarily screened. The amplification bands of 14 pairs were clear and evenly distributed, which were used to estimate the genetic diversity of Na, Ne, He, and I values at the species level were 1.9706, 1.4598, 0.2757, and 0.4237, respectively. At the population level, assuming a Hardy–Weinberg equilibrium, pop SJC  had the highest genetic diversity, followed by pop SNK, whereas pop LMX  had the lowest genetic diversity. The mean Ht, Hs, and Gst values were 0.2752, 0.2396, and 0.4926, respectively. The Nm value was 0.5150, less than 1, indicating limited gene exchange among the populations. By AMOVA analysis, the genetic difference coefficient  was found to be 0.490 (p < 0.01) and about equal to the Gst value. The results demonstrated 49% variance among populations and 51% variance within populations (ulations .pop BYBC/RSDB) to 184.004 (pop MY/NQY). The resulting distance matrix was visualized using PCoA. The results of the population cluster  from the alpine area surrounding the Menyuan Basin, while Clade III included 15 populations divided into 2 sister clusters located in the central  and western  Qilian Mountains.A dendrogram constructed using the corresponding genetic similarity coefficients obtained from the UPGMA analysis was used to determine the genetic associations in and between the populations of 300 accessions. The generated UPGMA dendrogram categorized the 300 accessions into 3 main phylogenetic clades . Cluster II members were mostly from the central and western regions of the Qilian Mountains . For K = 3, of the 300 accessions, 76 appeared in Cluster I , 34 in Cluster II , 136 in Cluster III , and the remaining accessions were assigned to the mixed-lineage cluster (An admixture simulation model was used to assess the clustering of the 300 accessions. Log mean probability and change in log probability (ΔK) were determined using STRUCTURE HARVESTER. A 1–20 K cluster range was evaluated; the output showed a sharp peak with the highest ΔK at K = 2 . Subsequ cluster .ggd data of 20 populations, which ranged from 15.176 km (pop SNK/YNG) to 636.504 km (pop MY/DTYK). gd and ggd , gd and pre , gd and vapr , gd and sra , and gd and tmin . However, no correlation was found between gd and win , gd and tav , and gd and tmax  ( > 0.05) .S. medusa have not been reported. In the present study, 14 pairs of SRAP primers were used to analyze the genetic diversity and structure of 300 S. medusa accessions from 20 populations. A total of 511 loci were amplified, of which 496 (97.06%) were polymorphic; using this loci information, genetic diversity indices were calculated. Indices such as Na, Ne, He, and I exhibited marked variability among sample collections and revealed genetic diversity within and among the sampled populations. The results from a species perspective showed low genetic diversity of S. medusa with an He of 0.2757, lower than the average He of 0.65 for outcrossing plant species and the He of 0.55 for short-lived perennials observed in previous genetic studies [S. medusa populations located in the western Qilian Mountains  was significantly lower than that in the central and eastern regions. In particular, pop LMX from the west had the lowest genetic diversity, while pop SJC from the east–central regions had the highest genetic diversity. The low diversity in the western populations could be linked to low gene flow with central or eastern populations due to geographic or ecological isolation by the existence of valleys in the western Qilian Mountains. Simultaneously, there are no serious geographical barriers between central and eastern native populations, and the distribution of populations was relatively concentrated. Thus, the frequent gene flow among these populations reduced the genetic divergence among populations but enhanced the genetic diversity within them.The plant population in an isolated “sky island” distribution state usually has low genetic diversity because of the limited gene flow and colonization opportunities of new sites . To date studies . From a S. medusa, although outcrossing was dominant due to the mechanism of herkogamy and dichogamy [Gst represents the proportion of interpopulation variation in the total genetic variation of a species and is the most commonly used indicator to measure population genetic differentiation and structure. An increasing Gst indicates a greater degree of differentiation between species populations, and a Gst of >0.25 suggests significant genetic differentiation [Gst of approximately 0.5 exhibit certain selfing, which confirms the existence of selfing in S. medusa (Gst = 0.4926). Selfing in the absence of pollinators can promote the growth of individual populations to ensure species reproduction. This growth strategy is beneficial to gain a competitive advantage in this niche, as observed in other alpine plants such as Gentiana lawrencei var. Farreri, G. straminea [Pedicularis dunniana [S. medusa [Different breeding methods determine the genetic variation within a species, such as in plants that are mainly selfing or inbred, where the genetic variation mainly occurs among populations . A studyichogamy . Gst repntiation . Accordintiation  and Nybontiation , speciestraminea , and Peddunniana . However. medusa . This mapop MY), the 4 Menyuan Basin populations , and the remaining 11 populations from the central and western Qilian Mountains. In addition, two populations, such as pop SJC and JYL, without obvious geographical isolation in the same subgroup, also had greater genetic differentiation. Conversely, populations with geographical isolation had similar genetic backgrounds, for example pop MY showed mixed lineages from populations in the central and western Qilian Mountains when K = 3. This confirmed that geographical distance was not the only explanatory factor responsible for the genetic differentiation of S. medusa populations. Genetic differentiation may also result from adaptive allele frequency generated by natural selection [To clarify the genetic relationships of the 300 accessions from 20 populations, we used 3 different clustering methods. Using the two-dimensional PCoA distance matrix to visualize the relationship between samples made it difficult to subgroup and analyze them. In contrast, cluster analysis using UPGMA and STRUCTURE showed clear separation patterns among the populations. The results, taken as a whole, showed high genetic differentiation between the regions. Individuals were clustered in 3 major subgroups, i.e., representing the single population sampled from the easternmost Qilian Mountains , but the annual solar radiation is extremely high, exceeding 2800 h. Annual precipitation diminishes from east to west and increases with elevation [S. medusa populations between regions. A study on Qilian juniper  in the semiarid eastern Qaidam Basin found that precipitation, temperature, and growing-season direct solar radiation had potential impacts on its radial growth [S. medusa. Although the achenes had a dense pappus adapted to wind propagation, the seedlings were mostly distributed on either side of the water erosion line, showing that water had a certain auxiliary effect on the seed dispersal of S. medusa. During the vegetative growth stage, the leaves of S. medusa grow as a rosette. At the reproductive stage, S. medusa completes the rapid elongation of the aboveground shoot and development of floral organs in a few months (from May to September every year), during which it needs to absorb sufficient solar radiation. However, high temperatures caused by strong solar radiation can reduce pollen viability [S. medusa from the western end of the Qilian Mountains is also located in the northern part of the Qaidam Basin, which has an arid/semiarid climate, receiving more solar radiation during the reproductive stage than S. medusa growing in the central and eastern Qilian Mountains. Finally, in arctic or alpine environments, the low temperature slows the development of flowers and seeds [S. medusa can survive the coldest month. This may be possible through the development of spectacular pubescence or other thermal insulation systems that have been studied by Tsukaya [S. medusa deserves further research.We collected data on climate factors in different populations, which showed significant differences in the environment of different habitats. Based on the correlation analysis results, we conclude that differences in moisture conditions such as and vapr C,D and pand tmin E,G may elevation . The difl growth . Adequatiability . S. medund seeds ,16,49. T Tsukaya  and YangS. medusa seeds and seedlings being washed out by flowing water or piled up under flowing rocks. The limited seed dispersal method of this species as well as the decrease in the seed settling rate caused by selfing or inbreeding greatly increases the difficulty of population expansion. Although the Qilian Mountains have the densest distribution of S. medusa, frequent human activity and overgrazing have also contributed to its population reduction. We hope that these observations together with the present study’s findings can draw the attention of the International Union for Conservation of Nature (IUCN) to this species, and then the protected areas and a core germplasm resource bank of alpine plants can be established in the future. The present study also suggests that researchers should pay attention to the genetic variation differences caused by different habitat environments when evaluating the genetic diversity of a species.In the summer, flowing stones are prone to loosening and collapsing due to rainwater erosion. During the field sampling, we observed many S. medusa samples from all regions, covering the platform and edge areas of the QTP. We could not avoid estimation errors in the overall genetic diversity assessment. Second, due to the differences in the number and distribution uniformity of amplification sites, using a single molecular marker is often insufficient. Nowadays, genetic diversity analysis methods based on sequencing technology are used in plants, making it possible to obtain more differential site information at the whole-genome level. We look forward to more novel technologies being applied to evaluate the genetic characteristics of S. medusa in the future. Third, morphologic and geological contrasts were not taken into account when studying the influencing factors of population differentiation. Despite some of the limitations in the study, the results provide useful outcomes and knowledge that can guide government officials to develop strategies for in situ and ex situ conservation of S. medusa.This study has certain limitations. First, it is difficult to obtain enough S. medusa seedlings were collected from 20 natural populations in the Qilian Mountains from east to west. S. medusa and morphology of two growth stages of it. Individuals in each population were randomly sampled at an interval of ≥10 m. We recorded the geographical coordinates and altitude of each sample population using GPS, provided in A total of 300 leaf accessions of The genomic DNA of each individual was manually extracted using a modified CTAB method suitable for medicinal plant . The quaAfter amplification, the PCR products were separated by 8% polyacrylamide gel electrophoresis (PAGE) followed by dying in 1× Tris-Borate-EDTA buffer (TBE) with ethidium bromide for several seconds. Then, the gel was imaged using the ChemiDoc MP Imaging System .Amplified fragments were scored according to the presence (1) or absence (0) of homologous bands; only reproducible bands were considered. Then, band data were transformed into a binary matrix for each primer combination or population .PIC was calculated as PIC = 1 − p2 − q2,  [PPB), number of alleles (Na), effective number of alleles (Ne), Nei’s gene diversity (He), and Shannon’s information index (I) were calculated to estimate the genetic diversity within different populations of S. medusa. Total gene diversity (Ht), diversity within a population (Hs), and coefficient of genetic differentiation (Gst) were calculated as well. The gene flow (Nm) was calculated according to the following formula: Nm = 0.5(1 − Gst)/Gst.POPGENE 1.32  was usedo bands) . Geneticgd) matrix of 20 populations was calculated using the distance-based module in GenAlex 6 [http://taylor0.biology.ucla.edu/structureHarvester/, accessed on 5 September 2021) to identify the optimal cluster number (ΔK).The genetic distance . ArcMap 10.6  was used to obtain the average annual climate data of 20 sampling points from the WorldClim online database  with a 2.5 arc-minute resolution. According to the needs of the calculation, the above data were sorted into seven climate factors: annual average wind speed (win), total annual precipitation (pre), annual average water vapor pressure (vapr), daily average solar radiation during the growth phase from May to September (sra), annual average temperature (tav), annual minimum temperature (tmin), and annual maximum temperature (tmax). The Mantel test revealed the correlation between genetic differentiation and geographical distance, as well as genetic differentiation and the seven climatic factors.In GenAlex, the population coordinate information was input to calculate the tri-matrix of geographic distance (S. medusa in the Qilian Mountains, which can best be explained by its isolated habitats and limited gene flow. In addition, we observed genetic differentiation among populations and a clear west–east differentiation between population groups. Geographic distance and moisture–photothermal conditions may also play key roles in the population differentiation of S. medusa.A low population genetic diversity is reported for the rare monocarpic perennial


Lutra lutra). Previous work identified four genetically differentiated “stronghold” populations of otter in Britain, derived from regional populations that survived the population crash of the 1950s–1980s. Using whole genome resequencing data from 45 samples from across the British stronghold populations, we confirmed some aspects of population structure derived from previous marker-driven studies. Importantly, we showed that genomic signals of the population crash bottlenecks matched evidence from otter population surveys. Unexpectedly, two strongly divergent mitochondrial lineages were identified that were undetectable using CR fragments, and otters in the east of England were genetically distinct and surprisingly variable. We hypothesize that this previously unsuspected variability may derive from past releases of Eurasian otters from other, non-British source populations in England around the time of the population bottleneck. Our work highlights that even reasonably well-studied species may harbor genetic surprises, if studied using modern high-throughput sequencing methods.Conservation genetic analyses of many endangered species have been based on genotyping of microsatellite loci and sequencing of short fragments of mtDNA. The increase in power and resolution afforded by whole genome approaches may challenge conclusions made on limited numbers of loci and maternally inherited haploid markers. Here, we provide a matched comparison of whole genome sequencing versus microsatellite and control region (CR) genotyping for Eurasian otters ( As molecular ecology expands to include whole genome sequencing (WGS), the congruence between genetic and genomic methods has been called into question . Rather Ascertainment bias, the nonrandom analysis of loci resulting in parameter estimate biases , has the−3 to 10−4 mutations per locus per generation), whereas single nucleotide polymorphisms (SNPs) are primarily biallelic and have a lower mutation rate of 10−8–10−9 per nucleotide per generation  is unlikely to be accurately quantified through analysis of a few loci. This issue is less critical in analyses of divergent, small, and inbred populations  populations in Britain suffered a substantial population crash between 1950 and 1980 due to environmental chemical contaminants, such as persistent organic pollutants  will be identifiable from genomic signatures, such as runs of homozygosity (ROH), linkage disequilibrium (LD), and changes in effective population size over time.a) was dominated on both PC1 (12.8%) and PC2 (7.7%) by variation among samples within the East population. Removing samples from the East population and repeating the PCA of SNP data revealed geographic clustering of samples from the remaining three populations (a inset). In the PCA of the microsatellite data from the same specimens (b), PC1 accounted for 8.6% of variation in the data and PC2 8.4%, without clear separation of samples by population, except for the Southwest England population. Pairwise FST values among the four populations were significantly higher in the microsatellite than the whole genome data set, with microsatellite-derived values ranging from 0.08 to 0.28 and SNP-derived values from 0.05 to 0.07 and a mean difference in FST between methods of 0.11 .Principal component analysis (PCA) of SNPs from the whole genome resequencing data a was domulations a inset. pecimens b, PC1 acK to be 1; however, the delta K of STRUCTURE analyses of the microsatellite data indicated the most likely value of K to be 3 . No pairwise relatedness of third degree or closer was identified among the samples  error of ADMIXTURE analyses on the SNP data indicated the most likely value of  to be 3  online.  samples  online.K, fineSTRUCTURE analyses were run on the SNP data. The fineSTRUCTURE coancestry matrix . The realized inbreeding coefficient (FROH) based on the proportion of genomes in ROH ranged from 0.43 to 0.77 and was significantly lower in the East, with no significant differences among the remaining populations . The majority of ROH fell into the shortest, and therefore oldest class, indicating that extensive inbreeding occurred at 1,024 or more generations ago . Signals of longer ROH  were visible in a few individuals in the East and scarcer in the remaining populations.Based on whole genome SNP data, heterozygosity was higher in the East population than in other populations . Private SNPs showed the same trend, with more SNPs identified as private to the East  than were private to any other population or common across all populations  was significantly higher across genomic windows in the East and significantly different among all population pairs except Southwest England and Wales (LD decay over distance (up to 100 kb) showed varying trends among populations  online. FIS/FROH showed a more notable difference, with whole genome SNP results ranking Southwest England > Wales > North > East and microsatellites ranking East > North > Southwest England > Wales.Genetic diversity statistics based on microsatellites were within the range of values reported in previous microsatellite studies  despite Ne) was estimated using GONE for the recent past (up to 800 years ago) and pairwise sequentially Markovian coalescent (PSMC) for the ancient past  . The decline in Ne started earlier in Wales, during the 1800s, and showed an increase in the past 50 years. The North showed a gradual, continual decline through the past 800 years. For the most recent 50 generations, we compared Ne estimates using GONE (fig. 4b) to the survey data recording the number of sites showing positive signs of otters by region (fig. 4c). Visually, the trends matched reasonably well, with the differences in trends between regions and the extremely low proportion presence in the East coinciding with the low Ne estimates. At deeper timescales, PSMC analyses of 10,000 to 1,000,000 years ago showed a decline in Ne across all populations, albeit with some local variation.Historic effective population size . Due to the presence of both divergent lineages in the East, the number of segregating sites and nucleotide diversity were higher in the East than other British populations; however, haplotype diversity was highest in the North . Geographic distribution of these haplotypes was broadly consistent with previous findings. For example, Lut1 was found across all populations, whereas Lut3 was only identified in the East. Interestingly, however, Lut6, previously only found in Western Britain, was identified in eight samples from Wales and one sample from the East in our data set. Only the four samples belonging to lineage 1 corresponded to CR haplotype Lut3, separated from the remaining haplotypes by a single mutation.All five control region (CR) haplotypes which have previously been identified in Britain were found in our data set. Haplotypes Lut1, Lut3, Lut6 , Lut4 C, and Lutfig. 5L. lutra available in the short-read archive. These read sets were successfully assembled using NOVOPlasty and aligned with European and Asian L. lutra mitochondrial genome sequences from GenBank (n = 13) and the British samples generated in this work, yielding a total of 60 L. lutra sequences. We added a single hairy-nosed otter (Lutra sumatrana) as outgroup. From the 60 L. lutra mitochondrial genomes, 34 unique haplotypes were identified across 772 segregating sites. Phylogenetic analysis showed separation of samples into four main lineages, all with posterior probabilities of 1.00 , lineage 1, and lineage 2, and one new lineage was identified in this study (lineage 3). Geographic locations of these lineages are provided in fig. 5c, with the exception of Britain and Russia for which multiple lineages were identified. The British samples sequenced in this study identified two divergent lineages: lineage 1 and lineage 3. Sequences assigned to lineage 3 include 40 samples from Britain  and the SRA-derived sequences from Denmark, Russia, and Norway. Lineage 1 included four samples from the East of Britain, alongside GenBank samples from China, Laos, and Japan, and the reference genome sequence, from a British otter . Although the rooted phylogenetic tree indicated that lineage 5/L. l. nippon split first, followed by lineage 3 and then lineages 1 and 2, variation in sequences within lineage 3 appeared to be a recent diversification relative to the older branching within lineages 1 and 2. These results were reflected in the higher number of segregating sites and nucleotide diversity observed within lineages 1 and 2, relative to lineage 3.We also assembled mitochondrial genomes from three previously generated short-read data for European Our study provides a direct comparison of genetic and genomic methods in assessment of genetic diversity and population structure in a threatened wild carnivore, the Eurasian otter. We applied genomic methods to investigate the occurrence and effects of a recent, anthropogenically driven population decline and recovery of Eurasian otters in Britain. Our concurrent analysis of microsatellite and SNPs from the same sample set highlights the complexities inherent in interpreting results of these approaches.K = 3, the SNP data set grouped Southwest England and Wales, whereas the microsatellite data grouped East and North England. Across all samples, the proportion of admixture identified is higher in the microsatellite data set than the SNP data. However, for the purpose of population assignment based on a suitable K value, there is also some concordance across data sets, with the exception of some samples between the East and North in the microsatellite data. To summarize, similarities in the ADMIXTURE/STRUCTURE comparison broke down at higher values of K and in more complex scenarios such as the substructuring within the East. Similarly, SNPs showed more population structuring in PCA, and microsatellites showed significantly higher pairwise FST estimates. These results are not unexpected, as both similarities  and more complex scenarios, when compared with the genomic results. Since the complexity of any given study system is typically unknown, it is important to recognize that conclusions based on genetic methods may change following the application of genomic data. In particular, the measures which showed the most significant differences between methods  are likely to be inflated due to ascertainment bias of the microsatellite loci. Specifically, these microsatellite loci were identified and selected based on the variation they identified in Scotland and Wales (both sampled in this study), and therefore, they are likely to represent only a subset of the variation present across populations in Britain  using microsatellites relative to SNPs, where the microsatellites had been selected based on allele frequencies identified in dogs. Taken together, these findings highlight potential impacts of microsatellite ascertainment bias, leading to inflated admixture proportions  and greater proximity in the genome. Therefore, although linkage occurs in both data sets, it is likely to be affecting the genomic results more significantly than the genetic results in this study. At this point, it becomes difficult to disentangle the effects of number of loci, ascertainment bias, and linkage, as using too few loci or using nonrandomly selected loci could both underrepresent rare alleles and therefore lead to inflated FST over time) likely being much more informative. Importantly, the scale of the effect of ascertainment bias varies depending on the specific loci and samples, and therefore, comparisons between studies based on different loci, populations, samples, or species would be inappropriate.We are not suggesting microsatellite markers are redundant, and we emphasize the important role they continue to play as an affordable marker system for cluster assignment and other scenarios. Equally, long-term genetic data sets, such as Using whole mitochondrial genomes, we were able to identify two divergent lineages within Britain, which were not previously identified based on CR studies , and werNe occurring concurrently across southern England . In contrast, Wales showed a decline beginning around 1830s, coinciding with the “improvement of the sporting gun with percussion detonation,” and reduced otter hunt results in North Wales , which was unexpected but potentially due to the impact and changes of otter hunting around this time, despite legal protection only being obtained in 1978 in Wales and England , with a sharp decrease and subsequent increase in th Wales . Ne esti England . The Nor England . These f England . The accPuma concolor . Despite the length and severity of this bottleneck, especially in the East (from the lowest Ne estimate of around 100 to around 3), it does not appear to have led to a consistent burden of ROH across multiple individuals. Conversely, we observe many, short ROH across all samples. Theory would suggest this indicates very old inbreeding followed by generations of recombination, potentially from a bottleneck that is older than we are able to estimate using RzooROH or due to background relatedness (FROH) observed in the modern populations, provides cause for concern with respect to the species’ long-term viability, particularly given the current context of small populations and anthropogenic threats for a near threatened species . In line with previous studies . Although studbook records show no evidence of crossing between otters of Thai origin and British L. lutra , escapes or unknown mixing between pens cannot be excluded, however unlikely (P. Chanin pers. comm.). Although there are no whole mitochondrial genome sequences available from Eurasian otters from Thailand, we have included a sequence from the neighboring Laos in this study, which groups with samples from China and the east of England. Therefore, when combined with our genomic results, these findings indicate the possibility of individuals of Asian origin (likely Thailand) being either accidentally bred, released, or escaping into the south and east of England. Otter releases in this region occurred between 1983 and 1996, coinciding with the increase in our estimate of effective population size for the East. We therefore suggest that the introduction of a few Asian-origin or admixed otters to a very small existing population had a large impact on the genetic identity of the population, leading to the high proportion of unique genomic variation and divergent mitochondrial lineages. The captive B-line otters are no longer being bred in captivity ; however, there are indications that some of their descendants have been released in Europe . An orgn Europe . Our resL. l. barang, L. l. chinensis (found in Thailand), and other Asian lineages, alongside any other populations where B-line otters may have been reintroduced. This would also enable a thorough genomic assessment of subspecies classifications, clarifying the existing confusion around Asian subspecies, such as L. l. barang and L. l. chinensis. We note that population declines and local extinctions across the species’ range mean that it is possible that the source population could be missed in this sampling, highlighting the importance of historic samples , as well as B-line captive-bred otters, to compare these to the population we observe presently and assess the likelihood of a replacement event occurring through the reintroductions . Secondl samples .Our genetic to genomic comparison highlights the value of genomic methods in conservation to avoid misinterpretations of potentially biased, low-resolution markers. However, this does not make the Eurasian otter microsatellite panel or previous studies based on this redundant. Rather, this study identifies the limits of the interpretations of these results when compared with genomic data. A SNP array for capture-based SNP genotyping could be designed based on our findings, to provide cheaper genomic analyses of British otters. However, based on our findings of ascertainment bias, we would recommend a SNP array based on SNPs identified from a range-wide sampling of the species.Here, we provide the first clear evidence of a population bottleneck in Eurasian otters in southern England, highlighting an important contrast to the history of populations in northern England, Scotland, and Wales. We provide clear evidence of previously unexpected and unusual signatures in the east of England and assign these to a mitochondrial lineage only found, as yet, in Asia. Both of these findings have only been possible through the application of genomic methods, enabled by the prior publication of an extremely high-quality reference genome , illustrSamples from across the four known stronghold populations  were selected based on location from the Cardiff University Otter Project (CUOP) archive. The 45 individuals analyzed in this study were collected between 2016 and 2020 and comprised 35 adult males, 5 females (1–2 per stronghold), and 5 male juveniles see  online. Sequencing was conducted as part of the Darwin Tree of Life (DToL) program  by the Wn = 23) make up 0.34% of the total reference genome assembly and were discarded from all further analyses. Only biallelic SNPs from chromosome 1 to 18  were used in population genomic analyses, unless otherwise stated. Vcf subsetting, viewing, variant counting, indexing, and converting between file formats was conducted using BCFTools v1.14 . Four samples were replicated for all 3 multiplexes to ensure consistency, and 38 sample/multiplex combinations were rerun. One locus (Lut733) for one sample (mLutLut40) could not be genotyped reliably, providing inconsistent results across four replicates, resulting in an overall success rate of 99.85%.Genomic locations of microsatellite loci commonly used in studies of Eurasian otters were identified in the reference genome using Blast see  online. FST was calculated using VCFtools v0.1.16 . All 15 loci were therefore kept for downstream analyses. Calculation of means and standard errors by population, of observed and expected microsatellite heterozygosity  and allele number (A), alongside PCA was conducted using the R package adegenet version 2.1.5  and assuming allele frequencies to be correlated. The ten repeated runs for each K value were then assessed for alternative solutions and combined using the CLUMPAK web server  for each population  changes  rather than true mitochondrial sequences, therefore biasing phylogenetic interpretation . Despiteages see  online.n = 1), Norway (n = 1), and Denmark (n = 1) were downloaded from the Sequence Read Archive (SRA) and assembled using NOVOPlasty as described above  were also incorporated into the analyses, alongside a hairy-nosed otter  sequence as an outgroup , leaving a total alignment length of 16,392 bp. The R packages pegas (http://tree.bio.ed.ac.uk/software/figtree/).After aligning to previously published es pegas  and ape es pegas  were usees pegas  ModelFines pegas  was usedes pegas , allowines pegas  and visumsad207_Supplementary_DataClick here for additional data file.


Laelaps agilis C.L. Koch, 1836 is one the most abundant and widespread parasitic mite species in the Western Palearctic. It is a permanent ectoparasite associated with the Apodemus genus, which transmits Hepatozoon species via the host’s blood. Phylogenetic relationships, genealogy and host specificity of the mite are uncertain in the Western Palearctic. Here, we investigated the population genetic structure of 132 individual mites across Europe from their Apodemus and Clethrionomys hosts. Phylogenetic relationships and genetic variation of the populations were analyzed using cytochrome c oxidase subunit I (COI) gene sequences.L. agilis in the Western Palearctic, which differentiated between 1.02 and 1.79 million years ago during the Pleistocene period: (i) Lineage A, including structured populations from Western Europe and the Czech Republic, (ii) Lineage B, which included only a few individuals from Greece and the Czech Republic; and (iii) Lineage C, which comprised admixed populations from Western and Eastern Europe. Contrary to their population genetic differentiation, the lineages did not show signs of specificity to different hosts. Finally, we confirmed that the sympatric congener L. clethrionomydis is represented by a separated monophyletic lineage.We recovered three main mtDNA lineages within L. agilis Lineages A and C, corroborated by the neutrality tests and demographic history analyses, suggested a stable population size in the structured Lineage A and a rapid range expansion for the geographically admixed Lineage C. We hypothesized that the two lineages were associated with hosts experiencing different glaciation histories. The lack of host specificity in L. agilis lineages was in contrast to the co-occurring highly host-specific lineages of Polyplax serrata lice, sharing Apodemus hosts. The incongruence was attributed to the differences in mobility between the parasites, allowing mites to switch hosts more often.Differences in the depth of population structure between The online version contains supplementary material available at 10.1186/s40850-022-00115-y. Understanding the common patterns and processes of speciation is a major goal of evolutionary biology , 2. ParaApodemus mice and their Polyplax lice, studies of Štefka and Hypša ) R script , the BayR script .L. agilis populations were inferred using mismatch distributions in DnaSP V6.0. In addition, values of Tajima's D [Signatures of demographic or spatial population expansion in two major jima's D  and Fu'sjima's D  were detjima's D .Additional file 1. Additional file 2:Fig. S1. Plot of the total numbers of transitions (s) andtransversions (v) against corrected distances based on GTR model revealed notrend toward saturation for transversions. All positions were employed in thesubsequent analysis. Fig. S2. Genealogic relationships (Median Joining network) of the L.agilis and L. clethrionomydis,based on 1026 bp of COI sequence. Numbers above the connecting branches reflectthe number of mutational steps joining the haplotypes (denoted by circles). Thesize of each circle is proportional to the number of individuals. Table S2. Analysis of molecular variance (AMOVA) forpopulations within lineages A and C.


Montastraea cavernosa. Over 750 coral genets were collected across the Northwest and Southern Gulf of Mexico, Florida, Cuba, and Belize, and ~5000 SNP loci were generated to quantify high‐resolution genetic structure and connectivity among these populations. Generally, shallow and mesophotic populations demonstrated higher connectivity to distant populations within the same depth zone than to adjacent populations across depth zones. However, exceptions to this pattern include the Northwest Gulf of Mexico and the Florida Keys which exhibited relatively high vertical genetic connectivity. Furthermore, estimates of recent gene flow emphasize that mesophotic M. cavernosa populations are not significant sources for their local shallow counterparts, except for the Northwest Gulf of Mexico populations. Location‐based differences in vertical connectivity are likely a result of diverse oceanographic and environmental conditions that may drive variation in gene flow and depth‐dependent selection. These results highlight the need to evaluate connectivity dynamics and refugia potential of mesophotic coral species on a population‐by‐population basis and to identify stepping‐stone populations that warrant incorporation in future international management approaches.Despite general declines in coral reef ecosystems in the tropical western Atlantic, some reefs, including mesophotic reefs (30–150 m), are hypothesized to function as coral refugia due to their relative isolation from anthropogenic stressors. Understanding the connectivity dynamics among these putative refugia and more degraded reefs is critical to develop effective management strategies that promote coral metapopulation persistence and recovery. This study presents a geographically broad assessment of shallow (<30 m) and mesophotic (>30 m) connectivity dynamics of the depth‐generalist coral species  We assessed the population genetic connectivity of shallow and mesophotic Montastraea cavernosa corals using a 2bRAD sequencing approach across the Gulf of Mexico and western Caribbean. We found that there was generally significant genetic structuring between shallow and mesophotic depth zones and between the Gulf of Mexico and western Caribbean populations. While the deep reef refugia potential is limited to certain reefs, horizontal connectivity across international boundaries is fairly high in some cases. Mesophotic samples (30–75 m) were collected by either technical divers on open‐circuit trimix or closed‐circuit rebreathers using hammer and chisels, or by the ROV Mohawk employing a five‐function manipulator and suction sampler. Samples were preserved in either TRIzol reagent or 100% molecular‐grade ethanol and were frozen at −20°C until transported back to Harbor Branch Oceanographic Institute where they were kept at −80°C for long‐term storage. In cases where samples had been previously analyzed using microsatellites, care was taken to avoid using multiple samples from previously identified clonal groups to avoid redundant sequencing . Briefly, 100 ng of high‐quality  DNA was digested with the restriction endonuclease BcgI (except samples from Cuba where 200 ng of DNA was incorporated into the digestion reaction). Using the smaller amount of DNA template (100 ng) was still enough DNA to yield successful digestions, and when both amounts were compared, the 100‐ng reactions typically digested and ligated more successfully. This may be because the higher amount of DNA overwhelmed the enzyme or because further diluting the template may have reduced the amount of potential coextracted inhibitors in the digestion reaction. Following digestion, these 2bRAD sequences were prepared as detailed in Sturm et al.  and further quality‐filtered using the fastq_quality_filter in the FASTX‐Toolkit , Symbiodinium microadriacticum , and the package StAMPP was used to calculate pairwise FST between the populations and corresponding p‐values , mean percent cloud cover, mean light attenuation coefficient (m−1 at 490 nm), mean photosynthetically available radiation , mean carbon phytoplankton biomass (μmol m−3), mean chlorophyll concentration (mg m−3), mean current velocity (m s−1), mean dissolved oxygen concentration (μmol m−3), mean iron concentration (μmol m−3), mean nitrate concentration (μmol m−3), mean phosphate concentration (μmol m−3), mean primary production (g m−3 day−1), mean salinity (PSS), mean silicate concentration (μmol m−3), and mean sea surface temperature (°C).Ecologically relevant environmental parameters were extrapolated from marine raster layers accessed through BIO‐ORACLE using our sites' geographic coordinates  model based on adjusted R2 and p‐values  were generated using the program NGSAdmix  was estimated from likelihood values from each NGSAdmix run  which were imported into the program StructureSelector, which employs the Puechmaille method to generate four different estimators for the optimal value of K  = 0.0750, allele frequency (a) = 0.3250, and inbreeding coefficient (f) = 0.0188. Seven repeated runs using these mixing parameter values were run for 18 M iterations with 9 M burn‐in, and 100 sampling intervals. Individual trace files from independent runs were visualized in the program TRACER to ensure model convergence and that runs were consistent with one another. Bayesian deviances were calculated for each run in R, Bayesian deviances were similar across all runs but the run with the lowest deviation was used for further analysis. Estimates of migration rates are calculated from means of the posterior distributions of m and the associated 95% highest posterior density intervals are reported.Estimates of contemporary migration rates (within the last few generations) among 3Cladocopium genome. From the high‐quality M. cavernosa aligned sequences, a set of 5147 SNPs were identified that met the quality filtering parameters and were retained across a minimum of 75% of the samples.Following quality filtering steps and the removal of PCR duplicate sequences, technical replicate libraries, and natural genetic clones, ~1.5 B 2bRAD reads or on average 1.98 M reads per sample were retained. The average alignment rate to the Symbiodiniaceae‐concatenated reference was 8.35%. Of the reads that aligned to the Symbiodiniaceae reference, on average 98.9% aligned to the p < .01) to differences among sample populations. Pairwise FST values were significant for all comparisons except for the adjacent mesophotic Florida Keys and mesophotic Dry Tortugas populations , mean calcite concentration (mol m−3), mean sea surface current velocity (m s−1), and positive MEMs 1–7, 9, and 10 (Table p < .001) but the first two axes captured 48.1% and 18.0% of the fitted variation, respectively , mean light attenuation rate .When plotting samples dominated by each of the four genetic clusters, as well as admixed samples (samples that did not have >50% of a single genetic cluster), across depth, latitude, and longitude, some regional patterns occur Figure . For exaAnalysis of recent migration found that overall average migration rates were low  and that shallow populations on average were more important sources than mesophotic populations  and mesophotic (~40 m) depth zones across sites in Bermuda  populations found high levels of depth‐dependent genetic differentiation across Florida and low levels of differentiation between shallow and deep zones in Bermuda identified for both species, and Guadeloupe for P. astreoides  and mesophotic (30–90 m) depth clines across the Bahamas and Little Cayman Island, over 1000 km away, identified significant genetic differentiation between shallow and mesophotic populations in Little Cayman but not in the Bahamas  and mesophotic (30–70 m) reefs across southwestern Florida, the Flower Garden Banks, and Belize using microsatellite markers  also exhibited variable levels of vertical connectivity across depth based on location. The Flower Garden Banks exhibited low levels of genetic differentiation across depth, as mentioned previously and reaffirmed in this study, while shallow populations in Belize and southwestern Florida were genetically distinct from their mesophotic counterparts, similar to the patterns noted in this study  and much shallower depth ranges in the Dry Tortugas , to a shallow offshore backreef (~10 m), and a deep offshore site  also explained a significant amount of genetic variation  face increasingly compounding threats and severe declines, mesophotic populations cannot solely be depended on to reseed shallow populations. This genetic dataset showcases that, in certain areas, mesophotic populations host unique genetic lineages not found prevalently across their shallow counterparts ; data curation (lead); formal analysis (lead); funding acquisition (supporting); investigation (lead); methodology ; project administration (supporting); visualization ; writing – original draft (lead). Ryan J. Eckert: Conceptualization (supporting); data curation (supporting); formal analysis ; investigation (supporting); methodology (supporting); validation ; visualization ; writing – review and editing (supporting). Ashley M. Carreiro: Data curation (supporting); investigation (supporting); writing – review and editing . Allison M. Klein: Data curation (supporting); investigation (supporting); writing – review and editing . Michael S. Studivan: Conceptualization (supporting); data curation (supporting); investigation ; project administration (supporting); resources ; writing – review and editing (supporting). Danielle Dodge Farelli: Conceptualization (supporting); data curation (supporting); investigation ; writing – review and editing (supporting). Nuno Simões: Funding acquisition (supporting); project administration ; resources ; supervision ; writing – review and editing (supporting). Patricia González‐Díaz: Data curation (supporting); investigation ; project administration ; supervision (supporting); writing – review and editing (supporting). Juliett González Méndez: Data curation (supporting); investigation (supporting); writing – review and editing (supporting). Joshua D. Voss: Conceptualization ; data curation (supporting); funding acquisition (lead); investigation (supporting); methodology (supporting); project administration (lead); resources ; supervision (lead); writing – review and editing .The authors have no competing interests to disclose.This article has earned Open Data and Open Materials badges. Data and materials are available at [[insert provided URL(s) on the Open Research Disclosure Form]].https://github.com/lexiebsturm/regionalMcavConnectivity.Trimmed, deduplicated, and quality‐filtered 2bRAD sequences and sample metadata are uploaded to the National Center for Biotechnology Information Sequence Read Archive as part of following BioProjects: PRJNA1024506‐Southeast Florida, Pulley Ridge, Northwest Gulf of Mexico, and Belize; PRJNA742926‐Florida Keys and Dry Tortugas; PRJNA626681‐Cuba; and PRJNA789549‐Southern Gulf of Mexico. Associated data files including IBS matrix, BCF, NGSAdmix output files, analysis and figure generation scripts, and protocols are available through the Sturm et al. ‐archivedData S1.Click here for additional data file.


Euterpe edulis Martius, an endangered palm tree, in forest remnants located in landscapes presenting different forest cover amount and composed by distinct bird assemblage that disperse its seeds. We sampled 22 areas of the Brazilian Atlantic Forest in four regions using SNP markers inserted into transcribed regions of the genome of E. edulis, distinguishing neutral loci from those putatively under natural selection (outlier). We demonstrate that populations show patterns of structure and genetic variability that differ between regions, as a possible reflection of deforestation and biogeographic histories. Deforested landscapes still maintain high neutral genetic diversity due to gene flow over short distances. Overall, we not only support previous evidence with microsatellite markers, but also show that deforestation can influence the genetic variability outlier, in the scenario of selective pressures imposed by these stressful environments. Based on our findings, we suggest that, to protect genetic diversity in the long term, it is necessary to reforest and enrich deforested areas, using seeds from populations in the same management target region.Human activity has diminished forests in different terrestrial ecosystems. This is well illustrated in the Brazilian Atlantic Forest, which still hosts high levels of species richness and endemism, even with only 28% of its original extent remaining. The consequences of such forest loss in remaining populations can be investigated with several approaches, including the genomic perspective, which allows a broader understanding of how human disturbance influences the genetic variability in natural populations. In this context, our study investigated the genomic responses of  This species occurs throughout the Brazilian Atlantic Forest and has great ecological importance since its fruits are consumed by approximately 58 species of birds and 20 species of mammals. It has being considered a key species for maintaining fauna in periods of resource scarcity  nontimber products in the Brazilian Atlantic Forest. Its apical meristem , usually obtained by illegal extraction, is very desired for human consumption  genetic structure and variability of populations reflect regional characteristics, due to the different selection regimes imposed by the biogeographic regions and/or different deforestation histories; (ii) landscape forest loss as well as reduction in the richness and abundance of seed‐dispersing birds reduce the neutral genetic variability in populations due to genetic drift and a decrease in gene flow; and (iii) changes in environmental or ecological conditions  may impose selective pressures, favoring the detection of genes under selection  in E. edulis populations.In the present study, we aimed to investigate the patterns of variability and genetic structure in 22.1The study was performed in remnants at the Atlantic Forest located in the states of Bahia (BA) and São Paulo (SP), Brazil Figure .In Bahia, we sampled in 16 forest fragments located in the southern of this state, comprising one of the largest remnants of the Atlantic Forest in northeastern Brazil  within a landscape radius of 2 km from the center of each selected forest location was calculated using the Quantum GIS 3.20.3 program  that covered 3470 kmer Table ; nine ofer Table , all sitE. edulis , although it has self‐compatibility  and estimated the genetic divergence among populations based on all SNP loci. The PCA was carried out with the LEA package  estimates between populations  are those based on significant deviations from TX statistic) and two tests of environmental association analyses . Based on the results of PCA and FST between E. edulis populations , using Bayesian statistics. For the analyses using the XTX statistic, allelic frequency covariance estimates between populations for each locus, and regression coefficients with environmental variables were estimated from 20 pilot analyzes with 2000 iterations of MCMC, followed by a burn‐in period of 50,000 and 500,000 iterations of MCMC  were used to compare two alternative models for each SNP : model with association (βik ≠ 0) and model without association (βik = 0). We considered SNPs significantly associated with environmental variables those with BF.dB above the 95% percentile of the calibration estimates.For ByPass and LFMM analysis, we used as covariates the percentage of forest calculated in each landscape, the richness and abundance of seed dispersers . For the two regions in BA, we used richness and abundance data for these birds sampled in each study area  for each state , allelic frequencies are considered as response variables to environmental predictors. The LFMM considers that unobserved latent factors, such as demographic factors, are modeled as confounding effects. After that, associations between environmental variables and markers can be interpreted as hypothetical signatures of natural selection  if they were identified as outliers by at least two of these four tests simultaneously Figure . In tota2.7E) was estimated and was corrected for finite population sizes (each locus and each population) according to the formula: HE=2n2n−11−∑pi2 where HE is gene diversity, n is the number of individuals per population, and pi is the frequency of the I the allele.We considered as neutral SNPs those makers that were not identified as outliers by at least two of the methods described above. The estimates of the average number of neutral and outlier alleles per locus in each of the 22 populations was obtained with the adegenet package  was due to the isolation by distance (IBD) process, we performed FST/(1 − FST) regression against the logarithm of geographic distances in E. edulis populations within each state  of the LEA package  and Akaike weights to select the most plausible model , we evaluated whether the richness and abundance of bird seed dispersers and the amount of forest cover at the landscape scale affected the number of alleles or the gene diversity of the neutral and outlier loci. We assessed only the populations from BA due to the low number of sampled populations in SP (SP_SE and SP_RA), which could bias the models. To carry out this investigation, we used data from birds that regurgitate or defecate  Bolker,  and plot Bolker, . All the Bolker, .33.1E) of 0.238 (±0.01 SD) for neutral loci and 0.230 (±0.05 SD) for outlier loci  for neutral loci and 1.596 (±0.11 SD) for outlier loci, with an average gene diversity  and São Paulo , with the highest averages observed in SP Figure .FST with neutral loci, we recorded the formation of three groups, separating BA_NR from the other two groups formed by BA_SR populations, just as observed in PCA  within each state with the Mantel test, significant relationships were observed for neutral  and outlier  SNPs in BA, as well as for the neutral  and outlier  SNPs in SP. However, the regression between FST/(1 − FST) against the log of geographic distances did not provide a linear relationship for neutral SNPs or outlier, as would be expected by the IBD process , and the gene diversity (p = 4.4 × 10−6). Interestingly, a clear negative relationship between the percentage of forest in the landscape and the genetic estimators of E. edulis was observed in the BA_SR region, while no pattern was evidenced in the BA_NR region . In this way, our findings advance our understanding of key factors influencing genetic variability in an important palm species from one of the most biodiverse and endangered biomes on the planet. Based on our findings, we recommend increasing the amount of forest and specie enrichment in more deforested Atlantic Forest landscapes  was evidenced, regardless of the sets of markers used in the analysis  only genetic estimators accessed with outlier SNPs respond to deforestation and (ii) geographic location is crucial to explain the genetic variability under selection. Our results demonstrate that the negative impacts related to the current forest loss are not yet reflected in the neutral genetic variability of this species, as already evidenced in studies with microsatellite markers in the same forest fragments (Santos et al., E. edulis populations inserted in more deforested landscapes have to deal with more and greater environmental adversities, which could require different adaptations, potentially explaining the greater number of alleles and diversity under selection (Estravis‐Barcala et al., Interestingly, the results obtained for the outlier loci showed a negative effect of forest cover on number of alleles and gene diversity only in the BA_SR region, indicating a possible process of natural selection driven by deforestation. Thus, we believe that In general, the landscape genomics approach used in the present study has advanced the understanding of genetic responses of perennial species dispersed and pollinated by animals in a scenario of rapid anthropogenic environmental changes. Thus, our results can assist in decision‐making aimed at efficient strategies for the conservation of a target species as well as in predicting the responses of other perennial species with similar biology facing anthropogenic disturbances. In addition, our findings reveal the genomic effects caused by forest loss in tropical environments. They make it possible to predict the consequences of disturbances on genomic variability and, consequently, on the adaptive potential of species.The authors have no conflicts of interest to declare.Appendix S1.Click here for additional data file.


Y-STR polymorphisms are useful in tracing genealogy and understanding human origins and migration history. This study aimed to fill a knowledge gap in the genetic diversity, structure, and haplogroup distribution of the Han and Manchu populations from the three northeastern provinces in China .Fst) were calculated to compare the genetic relationships among Han and Manchu populations from Northeast China and other 23 populations using 27 Yfiler Plus loci set. Multi-dimensional scaling and phylogenetic analysis were employed to visualize the genetic relationships among the 27 populations. Moreover, haplogroups were predicted based on 27 Yfiler Plus loci set.A total of 1,048 blood samples were collected from unrelated males residing in Dalian. Genotyping was performed using the AGCU Y37 + 5 Amplification Kit, and the genotype data were analyzed to determine allele and haplotype frequencies, genetic and haplotype diversity, discrimination capacity, and haplotype match probability. Population pairwise genetic distances (The Han populations from Northeast China exhibited genetic affinities with both Han populations from the Central Plain and the Sichuan Qiang population, despite considerable geographical distances. Conversely, the Manchu population displayed a relatively large genetic distance from other populations. The haplogroup analysis revealed the prevalence of haplogroups E1b1b, O1b, O2, and Q in the studied populations, with variations observed among different ethnic groups.The study contributes to our understanding of genetic diversity and history of the Han and Manchu populations in Northeast China, the genetic relationships between populations, and the intricate processes of migration, intermarriage, and cultural integration that have shaped the region’s genetic landscape.The online version contains supplementary material available at 10.1186/s12864-023-09636-3. Northeast China usually refers to the three northeastern provinces in China including Liaoning, Jilin, and Heilongjiang. As one of the major port cities of Northeast China, Dalian has attracted a considerable population from the three northeastern provinces due to its advanced economic development and plentiful job opportunities  Fig. 1)1). At prThe Han Chinese was a small minority in Northeast China. However, in the late 19th century, the Qing government encouraged the development of Northeast China to prevent the Russian invasion, which led to a massive influx of Han people from the Central Plain  to Northeast China for seeking new economic opportunities  Today, about 97% of the Manchu population resides in Northeast China, Inner Mongolia, and Beijing, with the largest concentration in Liaoning Province, the birthplace of the Manchu people . DespiteY-chromosomal short tandem repeats (Y-STRs) are widely employed in forensic DNA analysis, serving as a valuable tool in defining paternal lineages in situations such as identifying male suspects in sexual assault cases . AdditioThis study aimed to fill a knowledge gap in the genetic diversity, structure, and haplogroup distribution of the Han and Manchu populations in Northeast China. We genotyped 1048 males residing in Dalian city using a 42 Y-STR system and investigated the genetic diversity and structure of this population. Among them, the genetic relationships of Han and Manchu populations from Northeast China were compared with various ethnic groups in different regions and countries based on the 27 Y-STR system. Next, we explored the Y-STR haplogroup distribution of Northeast Han and Man populations and several other populations throughout China.A total of 1048 unrelated males in Dalian were genotyped using AGCU Y37 + 5 Amplification Kit, which includes 27 Yfiler Plus loci. In order to estimate the genetic diversity for Y-STRs in 958 Han individuals  and 68 Manchu individuals in Dalian Table  and S2, To assess the Y-STR haplotype DC, haplotype diversity (HD), and haplotype match probability (HMP) of 958 Han individuals in Dalian, haplotype information was analyzed in different combinations of Y-STR loci, including minimal nine loci, PowerPlex Y12 loci, 17 Yfiler loci, PowerPlex Y23 loci, 27 Yfiler Plus loci and 29 Ymax loci (Table S5). As more Y-STR loci were included in the analysis, the percentage of unique haplotypes, HD, and DC increased, which means that the individuals had more diverse haplotypes. At the same time, HMP decreased, indicating a reduced likelihood of two individuals having matching haplotypes. Therefore, incorporating an increased number of Y-STR loci in genetic analysis enhances the precision and dependability of discerning genetic associations among individuals. However, the 27 Yfiler Plus loci set and the 29 Ymax loci set were shown to yield identical haplotype numbers, GD, HD, and HMP values, corroborating findings from previous studies , 11. ThiFst) and corresponding p-values were calculated among these four groups and the other 23 populations, utilizing a 27 Yfiler Plus loci set (Table S6 and S7). Out of the 27 populations included in this study, the Han population from Jilin and Shandong have the least genetic distance (Fst = 0.00080), whereas the Han population from Guangxi and the Kyrgyz population from Xinjiang exhibit the largest genetic distance (Fst = 0.15996). The heatmap provides a visual representation of the degree of genetic differentiation between populations , Jilin (n = 89), Heilongjiang (n = 158), and the Manchu population from Liaoning (n = 62) Table  and S2. Fst = 0.00167) and Heilongjiang subgroups (Fst = 0.00119). Conversely, the genetic distance between Liaoning and Heilongjiang subgroups was comparatively larger (Fst = 0.00306). Within the broader scope of China, the Northeast Han populations show the highest genetic affinity with Shandong Han (0.00080 ≤ Fst ≤ 0.00113), Henan Han (0.00137 ≤ Fst ≤ 0.00266), and Sichuan Qiang (0.00188 ≤ Fst ≤ 0.00414) populations. In particular, the Shandong Han population displays a closer genetic relationship to the Han populations from Jilin (Fst = 0.00080) and Heilongjiang (Fst = 0.00082) than to those from Liaoning (Fst = 0.00113). Conversely, the Henan Han population exhibits a more pronounced genetic similarity to the Liaoning Han population (Fst = 0.00137), as compared to the Jilin Han (Fst = 0.00236) and the Heilongjiang Han (Fst = 0.00266) populations. These findings suggest that during the migrations of the Han populations from the Central Plain to Northeast China, a larger number of individuals from Shandong chose to relocate to Jilin and Heilongjiang, whereas a considerable portion of individuals from Henan decided to settle in Liaoning.Among the Han population from the three northeast provinces in China, the Jilin subgroup exhibited a relatively smaller genetic distance with Liaoning (Fst = 0.00199), Jilin (Fst = 0.00188), Shandong (Fst = 0.00214), Henan (Fst = 0.00263), and Chongqing (Fst = 0.00150). Notably, Sichuan is geographically distant from all these regions, except for Chongqing. Although several studies have uncovered evidence of a close genetic relationship between the Qiang and Han groups, none have explored this genetic relationship considering potential gene flow and geographic location [Additionally, the Sichuan Qiang population demonstrates a genetic affinity with the Han populations from Liaoning . The genetic distance between the Manchu and Han populations increases as one moves northwards from Liaoning towards Heilongjiang. The Manchu population presents a relatively large genetic distance from all the other 26 populations examined in the study (Fst > 0.00500). The genetic differences between the Han and Manchu populations in Liaoning were also reported in the studies conducted by Adnan et al. [Furthermore, our study includes a Manchu population, about 91% of which is from Liaoning. Even though Han and Manchu people cohabit in Liaoning, they display a relatively substantial genetic difference  plot Fig. . The MDSFst values to further verify the genetic relationships among the 27 populations . This may be attributed to specific historical intermarriage or migration patterns. Nevertheless, the small sample size of the Northeast Manchu population may lead to misleading results.Subsequently, we constructed a neighbor-joining (N-J) phylogenetic tree based on the ons Fig. . GeneralA total of 818 Northeast Han  and 68 Manchu individuals as well as 13 other Chinese populations were assigned to haplogroups based on 27 Yfiler Plus loci using the Whit-Athey algorithm with the Haplogroup Predictor tool. Due to changes in some haplogroup names since 2009, when the version of the Haplogroup Predictor tool was available, the haplogroup names have been updated to ISOGG-2020 names in this study. The detailed changes in haplogroup names and the fitness probability for 17 populations are presented in Table S8.A total of 27 Asian haplogroups were identified, including C2, E1a, E1b1a1, E1b1b, G1, G2a, G2b1, H1a, I1, I2a1a, I2a1a1a, I2a1b, I2a1b1, J1, J2a1a1a2b2, J2a1a1b2, J2a1a, J2b, L, N, O1b, O2, Q, R1a, R1b, R2a and T (Table S8). The haplogroup distribution of 17 populations was visualized with varying ethnicities and geographical locations within China Fig. . The majOur findings slightly differ from previous studies, which have indicated that haplogroups O1b and O2 are the most prevalent haplogroups among East Asians –24. ThisAdditionally, in our study, the Kyrgyz population displays a higher prevalence of haplogroup O2 (64%) and a significantly lower percentage of E1b1b (4%) compared to other Chinese populations. This observation implies that the Kyrgyz people may have a unique genetic history that sets them apart from other populations in China. Furthermore, six haplogroups  were not observed in the Manchu population, in contrast to other Chinese populations. This may be indicative of limited intermarriage between the Manchu population and other groups. However, it is important to consider that the small sample size of the Manchu population could lead to less precise estimates of the population’s haplogroup diversity. Furthermore, our study is limited by the absence of sequencing for single nucleotide polymorphisms, which may offer a more accurate approach to determine haplogroups.In summary, this study investigated the genetic diversity, structure, and haplogroup distribution of the Han and Manchu populations from Northeast China. The results revealed several key findings. First, the Han populations from Northeast China exhibited genetic affinities with Han populations from the Central Plain and Qiang population from Sichuan, indicating historical migrations, substantial gene flow, and intermarriage among these groups. Second, the Manchu population displayed distinct genetic characteristics and maintained a relatively large genetic distance from other populations, possibly due to historical policies and limited intermarriage with the Han population. Finally, the haplogroup distribution analysis revealed the prevalence of haplogroups E1b1b, O1b, O2, and Q in the studied populations, with variations observed among different ethnic groups. Overall, this research contributes to our understanding of the genetic diversity and history of the Han and Manchu populations from Northeast China. The findings shed light on the genetic relationships among these populations and provide insights into the complex processes of migration, intermarriage, and cultural integration that have shaped the genetic landscape of the region.A total of 1048 blood samples from unrelated Chinese males were collected from Dalian, Liaoning Province, China. Their ethnic groups and birthplaces were confirmed through their ID cards and self-declarations . All participants signed informed consent. All the experimental procedures were performed following the standards of the Declaration of Helsinki. This study was approved by the Medical Ethics Committee of Dalian Blood Center .Human genomic DNA was directly amplified from FTA cards  by AGCU Y37 + 5 Amplification Kit  ‘in a single multiplex PCR reaction  using TC-96 PCR Thermal Cycler  . The AGCU Y37 + 5 Amplification Kit includes 27 Yfiler Plus loci  plus 10 highly polymorphic Y-STR loci  and 5 Y-indel loci . The amplified products were detected by capillary electrophoresis on the ABI-3730 Genetic Analyzer . The data were analyzed by the GeneMapper ID -X . The control DNA 9948 was genotyped for quality control purposes.Fst) and p values were estimated by analysis of molecular variance (AMOVA) using Arlequin v3.5 software. This allowed us to compare the genetic distances between the 4 populations under study and 23 other populations reported in the YHRD. Then a heatmap was generated using R-4.2.2 program (R Core Team), where each cell is colored corresponding to the magnitude of the pairwise Fst values. MDS was also employed to create a reduced dimensionality spatial representation of populations based on Fst values using ‘smacof’, ‘ggplot2’ and ‘ggrepel’packages by R-4.2.2 program (R Core Team). Additionally, phylogenetic relationships among 40 populations were depicted by an N-J phylogenetic tree using ‘ggplot2’ package by R-4.2.2 program (R Core Team). Furthermore, based on 27 Yfiler Plus loci, the haplogroups of 17 populations in China were identified with the 27-Haplogroup Program in Whit Athey’s Haplogroup Predictor (http://www.hprg.com/hapest5/). Then Tableau 2021.1 was employed to visualize the distribution of haplogroups across various populations.Allele and haplotype frequencies, GD, and HD were computed by GenAlEx 6.5 software. DC and HMP were calculated following the given formulae . Based on 27 Yfiler Plus loci, population pairwise genetic distance (Below is the link to the electronic supplementary material.Supplementary Material 1