Lethrus apterus that inhabits the western part of the Steppe belt through the study of population-level variance of mitochondrial cytochrome oxidase I sequences and nuclear microsatellites. We detected a concordant geographic structure of genetic data with a significant isolation-by-distance pattern. We found more genetic variation in the western part of the area and identified Northern Bulgaria and the Pannonian Basin as possible refugia. Genetic clusters were separated by main rivers in the eastern part of the area. This implies west-to-east colonisation and argues for an evolutionarily recent arrival of this species to its current main distribution area, the Pontic Steppes. This contradicts the classical biogeographical wisdom that assumed an east-to-west colonisation pattern.The Eurasian Steppe belt is one of the largest biomes in the Northern Hemisphere. We provide here a range-wide phylogeography of the flightless steppe beetle  The western Eurasiatic part of this vast grassland zone is called the Euro-Siberian Steppe Zone (ESSZ)3 that reaches its westernmost edge in Europe represented by the Pannonian Steppes 4. The core area of the ESSZ in Eastern Europe is the Pontic Steppes 4. The several dozens of animal and plant species showing a distribution pattern with presence in both the Pannonian and the Pontic Steppes are commonly called Pontic-Pannonian species and they represent a distinct distribution type in European biota6.The Eurasian steppe zone is one of the largest biomes on our planet covering approximately 13 million km3. As these grasslands produced one of the most fertile soils of our planet, the whole steppe region, by providing arable fields for grain production, has shrunken to habitat islands especially in the European part2. The remaining steppe patches, however, still host an outstanding biological diversity9. The vulnerability of the remaining steppe habitats, however, urgently calls for a better understanding of the structure, functional components and evolutionary history of this biome. Results obtained by phylogeographic studies can effectively contribute to the efficient management of threatened species10.Under natural conditions, this biome is characterised by the overwhelming prevalence of grasslands with Poaceae species playing a major role in vegetation architecture12, can inform us about important past processes that led to the formation of the species’ current distribution area. Past major geoclimatic events, for instance, had a strong influence on the phylogeographic structure of many species14. This especially holds for temperate European species strongly affected by the Pleistocene glaciations16 leading to genetic disparities within the species’ area. Describing the spatio-temporal genetic variations of such species, apart from its nature conservational value, is important for the general understanding of the biogeographic history of the temperate biota. While we have a thorough understanding of the phylogeography of several European temperate arboreal species including both plants and animals, similar information on steppe organisms is scarce3. Nevertheless, several steppe organisms reach the western edge of their distribution in Europe17, and this peripheral part of the area can also be important for the evolutionary history of even more easterly distributed species19.Phylogeography, the spatio-temporal genetic variation of populations of a species on its distribution area20. This usually leads to increased rate of diversification21, and hence the genetic structure of the populations of those species is likely to show strong geographic patterns22. Therefore, such species can provide a unique insight into the biogeographic history of territories like the ESSZ. Furthermore, spatial patterns in genetic variation can help us to identify those parts of the area that have conservation significance23. The flightless beetle, Lethrus apterus  (Coleoptera: Geotrupidae), is a good candidate as model species for such study as it inhabits steppe habitats  on the western part of the ESSZ. Apart from a few satellite occurrences to the east of River Volga, the distribution range spans from the Pannonian Basin on the west to the River Don and partly River Peskovatka on the east24  sequences of 192 individuals from 36 populations , whereas the western part of the area hosts two clusters that are less clearly defined spatially. Additionally, potential ancestral areas are indicated only at the western part of the distribution range: in Northern Bulgaria (that still belongs to the Pontic Steppes in biogeographic classifications) and in the Hungarian Middle Range.Lethrus schaumii were used as outgroup from our forerunning phylogeny (GenBank accession numbers: MK558533–MK558540)26. The maximum clade credibility tree divided the ingroup haplotypes into four monophyletic lineages. Only one of these received strong Bayesian posterior probability (Bpp) statistical support (Bpp = 0.95) and it grouped haplotypes found exclusively in the Hungarian Middle Range within the Pannonian Basin. A second lineage received moderate support (Bpp = 0.81) and grouped haplotypes from the easternmost part of the area. A third lineage, with low statistical support (Bpp = 0.67), grouped together haplotypes from a Bulgarian locality (population 21—“Ovcha Mogila”) and one haplotype from a Ukrainian population (population 29—“Staroluzovatka”). Finally, the fourth main lineage, that did not receive statistical support, contained haplotypes that originate from populations of the whole area except the Hungarian Middle Range. The phylogenetic relationship between these four main lineages remained unresolved as none of the branches received statistical support. Apparently, these four lineages have diversified approximately simultaneously.We further assessed the evolutionary history of these 43 haplotypes by using a Bayesian approximation of the phylogenetic tree of haplotypes in a BEAST analysis  and we did not observe the presence of null-alleles. Therefore, all loci were used in downstream analyses without correction. The Neighbour-Joining (NJ) tree based on Chord-distance of allelic frequencies showed genetic groups that mirror geography  lociLarge rivers were found to be important indicators of significant separations. The populations from the eastern shore of River Dnieper are separated significantly (bs = 99%) on the NJ-tree Fig.  from tho2 area), whereas only four were found on the vast area  of the Pontic Steppes east of River Dnieper , we decided to visualise individual assignment plots at hierarchical level for K = 2, 3, 4, and 6 , we found K = 6 to be informative  is remarkably higher than on the east . Also, the explicit ancestral area assessment of the BPEC analysis found these two western areas to be possible ancestral area of the species . Taking the frequently elevated evolutionary rate of flightless beetles20 into consideration, this finding can be a result of relatively recent, independent isolation from the common haplotype probably as a result of post-dispersal diversification.This finding is further supported by finer structures found in our analyses. The presence of a highly supported mitochondrial lineage Fig.  in the HLethrus apterus.Nuclear microsatellites (SSRs) show further evidence of the evolutionary importance of the western part of the area. Genetic distance Fig.  reflects33. Although several outlier populations can also be spotted on the figure, we interpret those as a result of recent bottlenecks these populations  went through. Such founder effects could happen when the pioneer population manages to surmount an important barrier; such as a large river which must be a difficult-to-surpass barrier for a flightless beetle. And indeed, populations groups on either side of River Dnieper or River Danube are usually separated by highly supported branches indicative of significant genetic distances on our NJ-tree of genetic distance  in the Pannonian Basin34.This evolutionarily recent arrival of populations to the easternmost part of the area is also clearly indicated by the genetic differentiation of populations Fig. .: populance Fig. . The rolnce Fig.  clearly 35, supposedly the crossing of Danube was more feasible than the much wider floodplain of River Tisza. Another remarkable exemption is River Don on the very eastern part of the area , but it could also have survived in the Bulgarian Plain. These two areas then became the core areas of colonisation of the current distribution area. The Hungarian population could colonise the Great Hungarian Plain where it could have mixed with the lineage surviving in the south. Clearly, the Bulgarian group of populations managed to colonise the Pontic Steppes. This marching eastwards must have been hindered by the large, north–south flowing rivers of Eastern Europe, especially Rivers Dnieper and Don, which resulted in reduced number of colonisation events to the east banks.Notwithstanding, BPEC analysis also indicated the possible presence of an ancestral population on the Bulgarian Plain. Whereas we do not have further evidence to support the presence of an ancestral area here, we cannot rule out the primary role of this territory in the colonisation of Lethrus populations on the east.An alternative scenario can also be drawn from these results. It is possible that the only surviving populations of the unfavourable period were those in the Hungarian Middle Range, and the species has simply marched eastwards from this ancestral area via the Lower Danube region (located on the border between Serbia and Romania). This scenario is supported by the strong isolation-by-distance we found, and the presence of the single, well-differentiated mitochondrial lineage that is confined to and trapped in the Hungarian Middle Range. Whatever was the actual history of the populations, both of the above alternatives of colonisation history share a common regularity: the Pontic Steppes was recently colonised from the west. Therefore, we can safely conclude the western origin of this Pontic-Pannonian steppe species with a rear edge at the western part, and a leading edge on the east. This is a somewhat surprising discovery given the higher density of current 40. Although numerous examples of phylogeographic analysis of Ponto-Pannonian steppe species are known in the literature45, most of these focus their sampling on a smaller part of the whole distribution area, which may undermine phylogeographic reconstruction of the analysed species46. Therefore, we must be cautious in comparing our phylogeographic results with similar results on other steppe species, but the general acceptance of the presence of a steppe refugium in the western part of the area for certain steppe species forecast similar colonisation patterns for—at least some—steppe species: from the west to the east, from the current periphery to the eastern zonal range (“centre”). Somewhat similar eastward colonisation from a Balkan (not a Pannonian) refugium has been demonstrated for two animals inhabiting dry grasslands: the butterfly Melitaea ornata47 and the land snail Caucasotachea vindobonensis48 reached the Pontic Steppes from the south-west.The importance of the Pannonian Basin as a refugium for certain steppe plants have already been suggested49 used in this study and could validate one of the two equally parsimonious hypotheses about western refugia for this species.If we suppose a more general west-to-east colonisation pattern of steppe species from the current periphery to the extant main area , this can also raise the question of the relative role of an Eastern Balkan and/or a Pannonian refugium in this process. To further decipher the detailed evolutionary history of populations on the western edge of the distribution area, both in general and in this specific species, a deeper phylogenetic resolution is needed. Reduced genomic representation sequencing  can easily outperform both marker systems23, the western populations deserve high conservation priority as rear edge populations were shown to possess more genetic resources and thus, they can source future population expansion. In contrast to the eastern part of its distribution area, where this species is rather common50, Lethrus apterus has recently experienced a steep decline in Hungary that qualified this species to be protected in the country51. In light of our above results this protection can be fully justified and not just as a declining species of the steppes but also as important core (“rear edge”) populations of the species. The remnant steppe patches and their characteristic steppe species can be important flagships of Pannonian conservation as these are not just interesting steppe relicts, but can also be important sources of the current genetic diversity. In this respect, the Pannonian Steppes (incl. the colline level of the Hungarian Middle Range) can play a similar role to what was found by Kirschner et al.19 for the steppe relicts at the range periphery of the European Alps.There is also a clear conservation message here. As postulated by the “rear edge—leading edge” hypothesis17, this example may serve as a general model for future, range-wide phylogeographies on steppe species with a similar distribution. These results also suggest the prominent role of areas on the western border of the steppe zone in the (re)colonisation of its more easterly parts.In summary, our study yielded an insight into the evolutionary history of this Pontic-Pannonian steppe species, and thus can help to better understand the evolutionary history of this part of the Euro-Siberian steppe zone. Given the generality of such distribution type of steppe organismsLethrus apterus . Raw electropherograms were checked by eye for obvious sequencing errors which were corrected manually. For some analyses the outgroup sequences of Lethrus schaumi were obtained from the dataset of Tóth et al.26 (GenBank accession numbers: MK558533–MK558540). Sequences were aligned using MUSCLE52, then the alignment was checked for erroneous internal stop codons.Amplification of the mitochondrial cytochrome oxidase I (COI) gene followed the protocol of Tóth et al.53.Individual sequences were collapsed into haplotypes and the most parsimonious relationship between them was inspected by calculating the 95% connection limit as implemented in TCS v.1.2154 package BPEC55. This method assumes that population substructure is a result of migration and defines clusters as geographically aggregated haplotype groups. BPEC was run for five million generations saving 10,000 iterations in total with a parsimony relaxation value of two and setting the maximum number of migrations to four.The variability discovered in the mitochondrial dataset was partitioned into phylogeographic clusters by a Bayesian clustering method used by the R56. For this analysis we picked one random representative of each haplotype and included Lethrus schaumi as outgroup species. BEAST was run twice for 100 million generations saving every 10 000th iteration. Usage of the optimal substitution model was ensured by running bModelTest57 simultaneously. In both runs we assumed a strict molecular clock with the Yule process of speciation. The two runs were checked for convergence using Tracer v.1.6.0 and combined with the LogCombiner module of BEAST. The maximum clade credibility (MCC) tree was assessed and drawn by the TreeAnnotator module of BEAST.The phylogenetic relationship of haplotypes was reconstructed using BEAST v.2.6.030. Allele length were scored by the same person (R. Rácz) using Peak Scanner v.1.0 (Applied Biosystems).To genotype SSR alleles of 16 nuclear loci we applied the protocol detailed in Rácz et al.58. Population genetic statistics including allele frequencies of populations (AFP), observed and expected heterozigosity (Ho and He), deviation from the Hardy–Weinberg equilibrium (HWe) and genetic differentiation (Gst) were calculated by using GenAlex v.6.5b359. The Neighbour-Joining (NJ) phylogram based on pairwise Chord-distance of raw allelic frequencies between populations was constructed and visualised with PAST v.2.1760 using 100 bootstrap replications to test the support of branches. Isolation by distance (IBD) was tested with the mantel.randtest function of the R package ade4 v.1.761 using 10 000 permutations on the pairwise unbiased Nei genetic distance calculated by GenAlEx and pairwise geographic distances calculated from the geographic coordinates by the R package fossil v.0.4.062.The presence of null alleles was checked by using Micro-Checker v.2.2.363 with K values ranging from one to 30 with a chain length of five 500,000 steps and a burn-in of 100,000 steps. To assess convergence between runs each K value was run five times using ParallelStructure v.1.064. Optimal clustering was checked by Evanno’s method as implemented in Structure Harvester (http://taylor0.biology.ucla.edu/structureHarvester/). As suggested by Janes et al.31, we checked multiple K values to assess the hierarchical clustering of populations. Our second approach used the spatial clustering algorithm of the R package GENELAND v.4.0.665, which is able to assign individuals to genetic clusters based on their multilocus genotype and spatial placement. For this analysis we used the correlated frequency model and set the length of the Markov Chain to two million with a thinning of 200 and maximum number of nuclei of 1593, three times the number of individuals. We assumed a minimum K value of one and a maximum of 30. Again, we checked convergence of runs by running the analysis five times, then evaluated the run that had the highest average posterior probability.Bayesian clustering based on SSR multilocus genotypes was achieved using two different approaches. First, we ran STRUCTURE v.2.3.5Supplementary Information.


Here we present genomic data from 5 individuals dating to around 3000–2800 bc from the Dzungarian Basin and 13 individuals dating to around 2100–1700 bc from the Tarim Basin, representing the earliest yet discovered human remains from North and South Xinjiang, respectively. We find that the Early Bronze Age Dzungarian individuals exhibit a predominantly Afanasievo ancestry with an additional local contribution, and the Early–Middle Bronze Age Tarim individuals contain only a local ancestry. The Tarim individuals from the site of Xiaohe further exhibit strong evidence of milk proteins in their dental calculus, indicating a reliance on dairy pastoralism at the site since its founding. Our results do not support previous hypotheses for the origin of the Tarim mummies, who were argued to be Proto-Tocharian-speaking pastoralists descended from the Afanasievo2 or to have originated among the Bactria–Margiana Archaeological Complex3 or Inner Asian Mountain Corridor cultures4. Instead, although Tocharian may have been plausibly introduced to the Dzungarian Basin by Afanasievo migrants during the Early Bronze Age, we find that the earliest Tarim Basin cultures appear to have arisen from a genetically isolated local population that adopted neighbouring pastoralist and agriculturalist practices, which allowed them to settle and thrive along the shifting riverine oases of the Taklamakan Desert.The identity of the earliest inhabitants of Xinjiang, in the heart of Inner Asia, and the languages that they spoke have long been debated and remain contentious A genomic analysis of human remains from the Bronze Age provides insights into the origin of the Tarim Basin mummies from the Xinjiang region. Bisected by the Tianshan mountains, Xinjiang can be divided into two subregions referred to as North Xinjiang, which contains the Dzungarian Basin, and South Xinjiang, which contains the Tarim Basin  has long served as a major crossroads for trans-Eurasian exchanges of people, cultures, agriculture and languagessin Fig. . The Dzubc) and Chemurchek (or Qiemu’erqieke) (2500–1700 bc)12 sites have been plausibly linked to the Afanasievo herders of the Altai–Sayan region in southern Siberia (3150–2750 bc), who in turn have close genetic ties with the Yamnaya (3500–2500 bc) of the Pontic–Caspian steppe located 3,000 km to the west15. Linguists have hypothesized that the Afanasievo dispersal brought the now extinct Tocharian branch of the Indo-European language family eastwards, separating it from other Indo-European languages by the third or fourth millennium bc (ref. 14). However, although Afanasievo-related ancestry has been confirmed among Iron Age Dzungarian populations (around 200–400 bc)7, and Tocharian is recorded in Buddhist texts from the Tarim Basin dating to ad 500–1000 (ref. 13), little is known about earlier Xinjiang populations and their possible genetic relationships with the Afanasievo or other groups.Within and around the Dzungarian Basin, pastoralist Early Bronze Age (EBA) Afanasievo , Xiaohe (1884–1736 bc) and Beifang (1785–1664 bc)  island biogeography hypothesis4. The Yamnaya/Afanasievo steppe hypothesis posits that the Afanasievo-related EBA populations in the Altai–Sayan mountains spread via the Dzungarian Basin into the Tarim Basin and subsequently founded the agropastoralist communities making up the Xiaohe horizon around 2000 bc (refs. 23). By contrast, the Bactrian oasis hypothesis posits that the Tarim Basin was initially colonized by migrating farmers of the Bactria–Margiana Archaeological Complex (BMAC) (around 2300–1800 bc) from the desert oases of Afghanistan, Turkmenistan and Uzbekistan via the mountains of Central Asia. Support for this hypothesis is largely based on similarities in the agricultural and irrigation systems between the two regions that reflect adaptations to a desert environment, as well as evidence for the ritual use of Ephedra at both locations21. The IAMC island biogeography hypothesis similarly posits a mountain Central Asian origin for the Xiaohe founder population, but one linked to the transhumance of agropastoralists in the IAMC to the west and north of the Tarim Basin25. In contrast to these three migration models, the greater IAMC, which spans the Hindu Kush to Altai mountains, may have alternatively functioned as a geographic arena through which cultural ideas, rather than populations, primarily moved25.Multiple contrasting hypotheses have been suggested by scholars to explain the origins and Western elements of the Xiaohe horizon, including the Yamnaya/Afanasievo steppe hypothesis26, and that these profiles are likewise also distinct from those of pre-agropastoralist hunter-gatherer populations in Inner Asia30. As such, an archaeogenomic investigation of Bronze Age Xinjiang populations presents a powerful approach for reconstructing the population histories of the Dzungarian and Tarim basins and the origins of the Bronze Age Xiaohe horizon. Examining the skeletal material of 33 Bronze Age individuals from sites in the Dzungarian  and Tarim  basins, we successfully retrieved ancient genome sequences from 5 EBA Dzungarian individuals (3000–2800 bc) culturally assigned as Afanasievo, and genome-wide data from 13 Early–Middle Bronze Age (EMBA) Tarim individuals (2100–1700 bc) belonging to the Xiaohe horizon  , sheep (Ovis) and goat (Capra) milk , the Tarim mummies have been at the centre of debates with regard to their origins, their relationship to other Bronze Age steppe (Afanasievo), oasis (BMAC) and mountain (IAMC and Chemurchek) groups, and their potential connection to the spread of Indo-European languages into this region40.Although human activities in Xinjiang can be traced back to around 40,000 years agoThe palaeogenomic and proteomic data we present here suggest a very different and more complex population history than previously proposed. Although the IAMC may have been a vector for transmitting cultural and economic factors into the Tarim Basin, the known sites from the IAMC do not provide a direct source of ancestry for the Xiaohe populations. Instead, the Tarim mummies belong to an isolated gene pool whose Asian origins can be traced to the early Holocene epoch. This gene pool is likely to have once had a much wider geographic distribution, and it left a substantial genetic footprint in the EMBA populations of the Dzungarian Basin, IAMC and southern Siberia. The Tarim mummies’ so-called Western physical features are probably due to their connection to the Pleistocene ANE gene pool, and their extreme genetic isolation differs from the EBA Dzungarian, IAMC and Chemurchek populations, who experienced substantial genetic interactions with the nearby populations mirroring their cultural links, pointing towards a role of extreme environments as a barrier to human migration.37, perhaps learned from descendants of the Afanasievo, and they cultivated wheat, barley and millet41, crops that were originally domesticated in the Near East and northern China and which were introduced into Xinjiang no earlier than 3500 bc (refs. 42), probably via their IAMC neighbours24. They buried their dead with Ephedra twigs in a style reminiscent of the BMAC oasis cultures of Central Asia, and they also developed distinctive cultural elements not found among other cultures in Xinjiang or elsewhere, such as boat-shaped wooden coffins covered with cattle hides and marked by timber poles or oars, as well as an apparent preference for woven baskets over pottery44. Considering these findings together, it appears that the tightknit population that founded the Xiaohe horizon were well aware of different technologies and cultures outside the Tarim Basin and that they developed their unique culture in response to the extreme challenges of the Taklamakan Desert and its lush and fertile riverine oases4.In contrast to their marked genetic isolation, however, the populations of the Xiaohe horizon were culturally cosmopolitan, incorporating diverse economic elements and technologies with far-flung origins. They made cheese from ruminant milk using a kefir-like fermentationbc (refs. 25). While the arrival and admixture of Afanasievo populations in the Dzungarian Basin of northern Xinjiang around 3000 bc may have plausibly introduced Indo-European languages to the region, the material culture and genetic profile of the Tarim mummies from around 2100 bc onwards call into question simplistic assumptions about the link between genetics, culture and language and leave unanswered the question of whether the Bronze Age Tarim populations spoke a form of proto-Tocharian. Future archaeological and palaeogenomic research on subsequent Tarim Basin populations—and most importantly, studies of the sites and periods where first millennium ad Tocharian texts have been recovered—are necessary to understand the later population history of the Tarim Basin. Finally, the palaeogenomic characterization of the Tarim mummies has unexpectedly revealed one of the few known Holocene-era genetic descendant populations of the once widespread Pleistocene ANE ancestry profile. The Tarim mummy genomes thus provide a critical reference point for genetically modelling Holocene-era populations and reconstructing the population history of Asia.This study illuminates in detail the origins of the Bronze Age human populations in the Dzungarian and Tarim basins of Xinjiang. Notably, our results support no hypothesis involving substantial human migration from steppe or mountain agropastoralists for the origin of the Bronze Age Tarim mummies, but rather we find that the Tarim mummies represent a culturally cosmopolitan but genetically isolated autochthonous population. This finding is consistent with earlier arguments that the IAMC served as a geographic corridor and vector for regional cultural interaction that connected disparate populations from the fourth to the second millennium The archaeological human remains studied in this manuscript were excavated by the Xinjiang Institute of Cultural Relics and Archaeology from 1979 to 2017. Scientific investigation of these remains was approved by the Xinjiang Cultural Relics and Archaeology Institute, which holds the custodianship of the studied remains, based on the written agreements.45 and using the OxCal v.4.4 program46. All of the samples were dated to time periods consistent with those estimated from archaeological stratigraphic layers and excavated grave goods.Of the 18 individuals reported in this study, 10 were directly dated using accelerator mass spectrometry (AMS) at Beta Analytic, Miami, USA, and/or at Lanzhou University, China. To confirm the reliability of our AMS dating results, 4 out of the 10 individuals were AMS-dated at both Beta Analytic and Lanzhou University. Consistent dates were obtained in all cases (Supplementary Data 47 with slight modifications (10.17504/protocols.io.baksicwe). A subset of DNA extracts (n = 16) was subjected to a partial uracil-specific excision reagent repair following the methods described in ref. 48 (Extended Data Table n = 26) were directly shotgun sequenced on an Illumina HiSeq X10 or HiSeq 4000 instrument using 2 × 150-base-pair (bp) chemistry, and those with endogenous human DNA higher than 10% (n = 12) were sent for deeper sequencing. One of the 12 individuals (XHBM1) was later excluded from this study owing to high modern human DNA contamination (Supplementary Data n = 7) were further enriched for approximately 1.2 million nuclear SNPs and then deeper sequenced on an Illumina HiSeq 4000 instrument using 2 × 150-bp chemistry. Together, a total of 18 individuals yielded sufficient high-quality ancient genomic data for downstream analyses , a pipeline specially designed for processing ancient DNA sequence data. Specifically, raw reads were trimmed for Illumina adaptor sequences, and overlapping pairs were collapsed into single reads using AdapterRemoval 2.2.0 (ref. 50). Merged reads were mapped to the human reference genome  using the aln/samse programs in BWA v.0.7.12 (ref. 51). PCR duplicates were removed using DeDup v.0.12.2 (ref. 49). To minimize the effect of postmortem DNA damage on genotyping, we trimmed BAM files generated from samples treated (n = 11) or not (n = 7) with uracil DNA glycosylase (UDG) by soft-masking up to 10 bp on both ends of each read using the trimbam function on bamUtils v.1.0.13 (ref. 52) on the basis of the DNA misincorporation pattern per library tabulated using mapDamage v.2.0.9 (ref. 53). For each SNP in the 1,240k panel, a single base from a high-quality read  was randomly sampled to represent a pseudo-diploid genotype using the pileupCaller v.1.4.0.5 downloaded from https://github.com/stschiff/sequenceTools under the random haploid calling mode (-randomHaploid). For the transition SNPs (C/T and G/A), trimmed BAM files were used. For the transversion SNPs, BAM files without trimming were used.Raw read data were processed with EAGER v.1.92.55  of the human mitochondrial genome (NC_012920.1) to preserve reads passing through the origin, and then wrapped up the alignment to the regular rCRS with the circularmapper v.1.1 (ref. 49). We successively ran the contDeam and schmutzi modules in the schmutzi program against the worldwide allele frequency database of 197 individuals to estimate the mitochondrial DNA contamination rate. Last, we estimated the nuclear contamination rate on men using ANGSD v.0.910 (ref. 55), on the basis of the principle that mens have only a single copy of the X chromosome, and thus contamination will introduce extra mismatches among reads in SNP sites but not in the flanking monomorphic sites.We assessed the authenticity of our ancient DNA data as follows. First, we computed the proportion of C-to-T deamination errors at both the 5′ and 3′ ends of the sequencing reads, and found that all samples exhibited postmortem damage patterns characteristic of ancient DNA 59. We augmented both datasets by adding the Simons Genome Diversity Panel60 and published ancient genomes 61 and lcMLkin v0.5.0 (ref. 62), to determine the genetic relatedness between ancient individuals. We calculated pmr for all pairs of ancient individuals in this study using the autosomal SNPs in the 1,240k panel and kept individual pairs with at least 8,000 SNPs covered by both to remove noisy estimates from low-coverage samples. We used lcMLkin to validate our observation in pmr analysis and to distinguish between parent–offspring and full sibling pairs.We used pairwise mismatch rate (pmr)NC_012920.1, and then generated the mitochondrial consensus sequence of each ancient individual using Geneious software v.11.1.3 . We assigned each consensus sequence into a specific haplogroup using HaploGrep2 (ref. 64). For the Y chromosome, we used lineage-informative SNPs from the International Society of Genetic Genealogy 2016 tree (https://isogg.org/tree/2016/index16.html). For these SNPs, we called each individual’s genotype using bcftools v.1.7 (ref. 51) mpileup and call modules, after removing reads with mapping quality score < 30 (-q 30) and bases with quality score < 30 (-Q 30). We subsequently removed all heterozygous genotype calls. Then we assigned each individual to a specific Y haplogroup by manually comparing the genotype calls with the International Society of Genetic Genealogy SNPs. Before variant calling, we filtered alignment data using the pysam library v.0.15.2 (https://pysam.readthedocs.io/en/latest/) to reduce false positive variants due to postmortem damage and modern human contamination. We kept an observed base only if it was from a read shorter than 100 bp and the base was more than 10 bp away from the read ends. For transition SNPs, we further removed aligned bases if they were from a read with no postmortem damage pattern . We determined each individual’s Y haplogroup primarily on the basis of the transversion SNPs and additionally considered transitions if transversions were insufficient.We aligned the adapter-trimmed reads to the rCRS 65) using a set of 2,077 present-day Eurasian individuals from the HumanOrigins dataset (Supplementary Data 66). For ADMIXTURE, we removed genetic markers with minor allele frequency lower than 1% and pruned for linkage disequilibrium using the -indep-pairwise 200 25 0.2 option in PLINK v.1.90 (ref. 67). We used outgroup f3 statistics68 to obtain a measurement of genetic relationship of the target population to a set of the Eurasian populations since their divergence from an African outgroup. We calculated f4 statistics with the ‘f4mode: YES’ function in the ADMIXTOOLS package58. f3 and f4 statistics were calculated using qp3Pop v.435 and qpDstat v.755 in the ADMIXTOOLS package.We performed principal component analysis as implemented in smartpca v.16000 . We reported the total sum of ROH longer than 4, 8, 12 and 20 cM, and visualized the results using DataGraph v.4.5.1.We characterized whether the Bronze Age Xinjiang individuals descended from genetically related parents by estimating the runs of homozygosity (ROH). ROH refers to segments of the genome where the two chromosomes in an individual are identical to each other owing to recent common ancestry. Therefore, the presence of long ROH segments strongly suggests that an individual’s parents are related. We applied the hapROH method70) and qpAdm v.810 (ref. 57)). We used the following eight populations in the 1,240k dataset as the base set of outgroups (base) unless explicitly stated otherwise: Mbuti (n = 5), Natufian (n = 6), Onge (n = 2), Iran_N (n = 5), Villabruna (n = 1), Mixe (n = 3), Ami (n = 2), Anatolia_N (n = 23). This set includes an African outgroup (Mbuti), early Holocene Levantine hunter-gatherers (Natufian), Andamanese islanders (Onge), early Neolithic Iranians from the Tepe Ganj Dareh site (Iran_N), late Pleistocene Western European hunter-gatherers (Villabruna), Central Native Americans (Mixe), an indigenous group native to Taiwan (Ami) and Neolithic farmers from Anatolia (Anatolia_N). To compare competing models, we also took a ‘rotating’ approach, where we reciprocally added a source from a model to outgroups for a competing model. We specified which outgroups are used for all qpAdm models.We modelled our ancient Xinjiang populations using the qpWave/qpAdm programs (qpWave v.410 (ref. 26) for the dating of admixture events of the ancient populations with the pseudo-haploid genotype data under the simplified assumption that gene flow occurred as a single event, and assuming a generation time of 29 years (ref. 58). The DATES software measures the decay of ancestry covariance to infer the admixture time and estimates jackknife standard errors. In the parameter file for running DATES, we used the options binsize: 0.001, maxdis: 0.5, runmode: 1, qbin: 10 and lovalfit: 0.45 in every run on the pseudo-haploid genotype data. For each target population, we chose a pair of reference populations that we identified as good sources in the qpAdm analysis. In cases in which the qpAdm source had limited sample size or SNP coverage, we chose an alternative that had a similar genetic profile to the qpAdm source but with better data quality to enhance the statistical power of the DATES analysis (Supplementary Data n = 20) and Baikal_EBA (n = 9) as the references. For Kumsay_EBA and Mereke_MBA, we used the Afanasievo (n = 20) and Baikal_EN (n = 15). For Dali_EBA, we used Tarim_EMBA1 (n = 12) and Baikal_EBA (n = 9). For Tarim_EMBA1, we used West_Siberia_N (n = 3) and DevilsCave_N (n = 4).We used DATES v.753 (ref. 71). Extracted peptides were analysed by liquid chromatography with tandem mass spectrometry (MS/MS) using a Q-Exactive mass spectrometer (Thermo Scientific) coupled to an ACQUITY UPLC M-Class system (Waters AG) according to previously described protocols28. Potential contamination and sample carryover were monitored through the use of extraction blanks as well as injection blanks between each sample.Total protein extractions were performed on dental calculus obtained from seven Xiaohe individuals excavated from layers 4 and 5 . Mascot was set up to search the SwissProt Release 2019_08 database  assuming the digestion enzyme trypsin. Mascot was searched with a fragment ion mass tolerance of 0.050 Da and a parent ion tolerance of 10.0 ppm. Carbamidomethylation of cysteine was specified in Mascot as a fixed modification. Deamidation of asparagine and glutamine and oxidation of methionine and proline were specified in Mascot as variable modifications. A subset of samples were analysed in duplicate , which resulted in the additional identification of α-lactalbumin. Proteins that contained similar peptides that could not be differentiated on the basis of MS/MS analysis alone were grouped to satisfy the principles of parsimony. All samples yielded proteomes typical of a dental calculus oral microbiome, and damage-associated modifications (N and Q deamidation) characteristic of ancient proteins were observed . Peptide identifications were accepted if they could be established at greater than 86.0% probability to achieve a false discovery rate (FDR) less than 1.0% by the Peptide Prophet algorithmFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-021-04052-7.Supplementary InformationThis PDF file includes five sections of Supplementary text. (1) Environmental setting of Xinjiang; (2) Archaeological sites and context; (3) Linguistic background of the population history in Xinjiang; (4) Detailed description of genetic isolation of the Tarim group; and (5) Tarim mummies and the pre-pastoralist Central Asian genetic substratum.Reporting SummaryPeer Review FileSupplementary Data 1Sample information, qpAdm modelling results and phenotypic traits of the studied individuals.Supplementary Data 2Ancient and modern populations analysed in this study.Supplementary Data 3Dairy peptides identified within the Xiaohe dental calculus samples.


Mustela lutreola, a critically endangered species. In order to address this problem, a meta-analysis aiming to examine the completeness of mtDNA haplotype sampling in recent studies of M. lutreola inter-population genetic diversity was conducted. The analysis was performed using the sample-size-based rarefaction and extrapolation sampling curve method for three populations—the Northeastern , the Western (France and Spain), and the Southeastern (Romania). The extrapolated values of the Shannon–Wiener index were determined, assuming full sample coverage. The gap between the measured and predicted inter-population genetic diversity was estimated, indicating that the identified level of sample coverage was the lowest for the NE population (87%), followed by the SE population (96%) and the W population (99%). A guide for sampling design and accounting for sampling uncertainty in future population genetic studies on European mink is provided. The relatively low sample coverage for the Russian population clearly indicates an urgent need to take conservation measures for European mink in this country.Although properly designed sampling in population genetic studies is of key importance for planning evidence-informed conservation measures, sampling strategies are rarely discussed. This is the case for the European mink  Sample size is a critical issue for measuring genetic variation, yet for population genetic studies, sampling strategies are rarely discussed ,2,3. CorSeveral strategies for determining the sample size appropriate for correctly estimating the true genetic diversity of wild populations have been proposed. An ex ante, empirical approach requires a pilot study and, therefore, previous knowledge of the genotype frequencies for a given population ,10. AnotDifferent authors have suggested different numbers of randomly sampled individuals—from four to six, in the case of high inter-population differentiation, to 20–30, for populations characterized by low or unknown levels of diversity or genetically depauperated, and even up to hundreds ,18,19,20Mustela lutreola, a critically endangered species, with sparse, vanishing populations, often found in difficult-to-access areas, scattered across Russia, Romania, France and Spain [Neovison vison [One of the greatest limitations in research into the population genetics of threatened and endangered species is the often-very-limited access to a sufficient number of samples to capture their real genetic diversity . This isnd Spain . The speon vison ,25. It ion vison . The samM. lutreola revealed the possible numbers of haplotypes (haplotype richness) present in the examined populations and to ex post quantify the potentially missing haplotypes in the samples, through a meta-analysis approach using the rarefaction method. By building on previous work, this study aimed not only to identify a possible gap between measured and predicted inter-population genetic diversity in preserved populations, but also to provide a guide for sampling design and accounting for sampling uncertainty in future population genetic studies on European mink.Since it is postulated that research on critically endangered populations should include measures of genetic diversity, even if the sample size is not optimal , our goacytb gene (GenBank acc. no. AF207720-AF207725) [In order to estimate the sampling completeness (the difference between the observed richness and the estimated asymptotic richness), a dataset consisting of results reported by Davison et al. , MichauxF207725) , and fraF207725) ,28, 525-F207725)  and 501-F207725) . In totaThe analyses were based on the number of haplotypes (haplotype richness) determined in these studies and assigned to three genetically distinguishable extant study populations , i.e., tFor each population, the number of detected haplotypes was determined . The valThe sample completeness curves were also drawn to determine the haplotype richness given the current sample sizes and then to determine how much the haplotype richness increased if the entire population was sampled. Additionally, the jackknife estimate of the haplotype richness  was calcThe largest amount of intra-population genetic diversity, expressed by the number of identified haplotypes, was found in the NE population. It was five times higher than that in the SE population and as much as ten times higher than that in the W population . For thep-values much lower than 0.01%  sample completeness would change the values of the Shannon–Wiener index for the haplotype richness of the studied populations, compared to the current one, are presented in M. lutreola genetics, a highly efficient approach to the results of previous population genetic studies, in terms of obtaining the full meta-analytical benefit from them, is very important [The effects of sample size on population genetic diversity estimates have been investigated for different species  is the greatest for the NE population, which is, in fact, a group of local populations inhabiting the Volga and Dvina drainage basins in the European parts of Russia, Belarus (extinct) and Estonia (extinct) ,28,30. Trotected ,38. Addirotected ,29.The obtained results demonstrate a simple and efficient predictive method for identifying the extent of uncaptured genetic diversity, defined by the number of haplotypes in the population and haplotype frequencies. This method allows determining the accuracy of genetic diversity tests, where the accuracy is defined as the difference between an estimate and the true value of a genetic differentiation indicator . The conThe validity of using a method based on the haplotype richness to estimate the completeness of the haplotype sampling, by analogy to the species richness, commonly applied in biodiversity studies, has been previously proven . AdditioM. lutreola being a rare and difficult-to-capture species of high conservation concern [Taken together, our results have several implications. Firstly, they demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in population genetic studies. As appropriate statistical advice should be sought at the research planning stage to ensure that the designed sampling scheme will enable the research questions to be properly and accurately answered, our results have implications for the design of future population genetic research on European mink. Specifically, further research on the NE population, with an increased sample size, has the greatest potential to detect new haplotypes and, thus, diagnose the actual intra-population genetic diversity, while this potential in the case of the SE population is low or moderately low, and increasing the sample size for the W population is expected not to generate new knowledge. Secondly, taking into account the serious challenges in obtaining large samples of individuals, due to  concern ,36, the  concern , Michaux concern ,28, Kora concern  and Cabr concern , accordi concern . ThirdlyQuestions about the effects of the number of markers, type of marker applied and possible multiple sampling on the detection of the real genetic diversity of wild populations of European mink have not been addressed in the present study and need to be further investigated. Similarly, the effect that different sample sizes will have on low-coverage genomic resequencing or reduced-representation sequencing, such as genotyping by sequencing (GBS) or restriction-site-associated DNA sequencing (RAD-Seq), needs to be addressed. Our results imply that, while moderate coverage for all populations is needed, a larger sample for the NE population should be recommended because, with a higher genetic diversity, more minor alleles are expected, which could be lost with filtering, resulting in an underestimate of the genetic diversity present.


Cystoseira sensu lato  forests play a central role in marine Mediterranean ecosystems. Over the last decades, Cystoseira s.l. suffered from a severe loss as a result of multiple anthropogenic stressors. In particular, Gongolaria barbata has faced multiple human-induced threats, and, despite its ecological importance in structuring rocky communities and hosting a large number of species, the natural recovery of G. barbata depleted populations is uncertain. Here, we used nine microsatellite loci specifically developed for G. barbata to assess the genetic diversity of this species and its genetic connectivity among fifteen sites located in the Ionian, the Adriatic and the Black Seas. In line with strong and significant heterozygosity deficiencies across loci, likely explained by Wahlund effect, high genetic structure was observed among the three seas , with an estimated dispersal distance per generation smaller than 600 m, both in the Adriatic and Black Sea. This strong genetic structure likely results from restricted gene flow driven by geographic distances and limited dispersal abilities, along with genetic drift within isolated populations. The presence of genetically disconnected populations at small spatial scales (< 10 km) has important implications for the identification of relevant conservation and management measures for G. barbata: each population should be considered as separated evolutionary units with dedicated conservation efforts. However, multiple anthropogenic stressors interacting at both local and global scales increasingly threaten these iconic ecosystems4. As a result, a severe decline of large seaweeds abundance has been observed across temperate coastal systems over the last decades8. In particular, Cystoseira sensu lato , which forms the most extensive macroalgae forest in the Mediterranean Sea10, have recently suffered from irreversible loss11, likely underestimated as much of it may have gone unnoticed13.Marine forests , EU Habitats Directive (The Council of the European Union 1992) and are listed in the European red list of habitats14. The resulting need of setting conservation actions for Cystoseira s.l. has recently fostered the development of ecological restoration programs: they aim to increase the effectiveness of conservation strategies of Cystoseira s.l., by notably including fostering natural recovery, reforesting lost areas, and/or enhancing the performance of existing human infrastructures as substrata for colonisation22. In addition, restoration actions also include local measures to control the drivers of canopy loss , such as improving water quality25 or restoring habitat locally12. Such actions intend to increase the natural recovery rate of disturbed populations of Cystoseira s.l., expected to be very low, notably due to their life cycle comparable to a one-phase life cycle. Indeed, although Cystoseira s.l. display a biphasic life cycle, alternating a haploid phase with a diploid phase, the gametophytic stage, i.e. the haploid phase, is highly reduced and retained in the conceptacles, so that plants superficially regenerate only from sporophytes26. In addition, relatively large and heavy eggs and zygotes tend to rapidly sink just after being released27. As a result, Cystoseira likely display very limited dispersal abilities30, hampering the natural recovery of the populations.Facing the dramatic decline of Cystoseira s.l. species distribution32 and ecology35 constantly improve scientific-based conservation plans. The diversity and phylogenetic relationships among the Cystoseira taxa from the eastern Atlantic and Mediterranean have also very recently been reappraised38. Nevertheless, knowledge of the genetic diversity and connectivity of natural populations further needs to be improved in order to assess the potential resilience of disturbed populations. Knowledge of the genetic variation in a study species could also help designing new restorations actions that are “future proofed” against rapidly changing conditions, such as assisted gene flow, or habitat restorations in sites according to the spatial pattern of genetic variation observed, i.e. prioritizing sites where genetic diversity would be seriously eroded.In parallel to encouraging restoration actions, expanded research efforts regarding 14. Beyond its significant effect on a number of ecological processes, including population recovery from disturbance and community structure43, genetic diversity is required for populations to evolve in response to ubiquitous, natural environmental change44. On the other hand, population connectivity, defined as the exchange of individuals among geographically separated subpopulations, plays a fundamental role in local and metapopulation dynamics, community dynamics and structure, and the resiliency of populations45. It ensures the genetic cohesion of a biological species over its distribution range46 because migration is the evolutionary force that spreads genetic diversity among populations, leading to relatively similar allele frequencies and counterbalancing the negative effect of genetic drift on genetic diversity47.On the one hand, genetic diversity, defined as the diversity of the basic units of hereditary information (genes) within a species, is one of the three biological diversity components, receiving increased consideration in IUCN planning and actions44. In species restricted to small and isolated remnant populations, notably due to the fragmentation or destruction of their habitat, these threats can be worsened, further reducing the ability of populations to cope with environmental changes and hence increasing their risk of local extinction48. Nonetheless, the risk of population extinction can be reversed by migration processes49, making population connectivity a key feature for the recovery of the demographic structure and genetic diversity of disturbed populations. To avoid the extirpation of disturbed populations, scientific-based genetic management measures are therefore warranted50. As such, inferring population connectivity is a first step as the degree of connectivity among geographic areas sets the scale at which conservation strategies for marine species need to be applied51.Severe and sudden reductions in population size are expected to result in a loss of genetic diversity and increased inbreeding52. Population genetics thus offer a conceptual framework that provides increasingly appropriate statistical analysis methods to determine intra-population genetic variation, assess genetic connectivity in marine environment and infer demography history of populations53. In Cystoseira s.l., high levels of genetic structure indicative of low population connectivity have been observed in several species58 in accordance with their expected poor dispersal abilities.Population connectivity can be inferred from the analysis of the genetic differentiation among populationsGongolaria barbata (Stackhouse) Kuntze, formerly known as Cystoseira barbata (Stackhouse) C. Agardh then Treptacantha barbata (Stackhouse)59, is one of the widely distributed Mediterranean Cystoseira s.l., thriving in shallow sheltered coasts and lagoons36. Local populations of G. barbata have recently suffered from spectacular regression in several areas of the Mediterranean Sea61, as well as in the Black Sea, where it was formerly a dominant species63. This species is severely threatened both by land-based and marine human impacts, such as the artificialization of the coastline which likely appears more important in sheltered bays than in exposed shores. The decrease in water quality, including pollution, eutrophication and increase in sediment loads, provides an additional threat whose effects are particularly important in enclosed bays with low water renewal. As a result, this species is now only found in few localities across its distribution range64. The combination of geographic isolation of remnant populations, recent decreases in abundance and expected restricted dispersal abilities in G. barbata call for an urgent exploration of its genetic status.In the Mediterranean Sea, G. barbata samples from several locations showed that Black Sea mitochondrial DNA (mtDNA) sequences of the cytochrome oxidase subunit 1 (COI) gene systematically differed by one nucleotide from Mediterranean sequences62, indicating restricted gene flow between these two seas. In addition, very low intra-specific diversity was found among the Adriatic Sea, Menorca, Sicily and Black Sea samples. However, as the mtDNA lacks the necessary resolution to provide efficient detection of genetic structure65, the use of nuclear makers with higher polymorphism level is warranted. Microsatellite markers would likely unravel the population genetic diversity and structure in G. barbata, thanks to their codominance and high level of polymorphism properties66.The only genetic work involving G. barbata depleted populations will strongly be compromised without enhanced restoration actions.In the present study, we investigated the genetic diversity and structure of 15 relict populations sampled in the Ionian, the Adriatic and the Black Seas based on highly polymorphic markers specifically developed for the study species. This will inform whether the natural recovery of A total of 503 individuals from 15 sampled populations  to 0.736 (Cysbar5). FIS values ranged from − 0.710 (Cysbar6) to 0.501 (Cysbar3), with significant deviations from Hardy–Weinberg Equilibrium observed for seven loci . Missing data were present in all subsamples and in ten out of the 12 microsatellite markers  for seven loci . However, using unilateral exact binomial tests, heterozygosity deficiencies were likely explained by the presence of null alleles in only Cysbar3 . MicroChecker did not detect any stuttering or short allele dominance (SAD), however, the regression approaches detected significant SAD in Cysbar5 . Stuttering was not detected following De Meeûs et al.67 approach.The quality and variability of the microsatellite markers were tested for population genetic studies of G. barbata individuals genotyped with twelve microsatellite markers showed significant linkage disequilibrium between two loci, cysbar6 and cysbar12, after Benjamini and Yekutieli adjustment. We removed three problematic microsatellite markers, Cysbar3 and Cysbar5 for which technical issues were observed, and Cysbar6, in linkage disequilibrium with Cysbar12, from the final dataset.The analysis of 375 IS and FST from one locus to the other following the determination key proposed by De Meeûs68. A strong and significant heterozygote deficiency was observed with f = 0.066 . The FIS variation across loci was moderate  = 0.257 p-value = 0.397) and the standard error of FIS (StrdErrFIS = 0.022) was two times lower than for FST (StrdErrFST = 0.046). A strong and significant correlation was however observed between the number of missing data and FIS values . According to the determination key proposed by De Meeûs68, null alleles may have persisted in the dataset, despite a careful attention paid to minimize them , which would explain these results. This hypothesis cannot be ruled out since null homozygotes  were still present in the final dataset. Heterozygote deficiencies may also be explained by demographic causes, such as a Wahlund effect.Once removing these three markers  ranged from 1.9 (site 14) to 3.5 (site 3), and expected heterozygosity (HS) from 0.272 (site 14) to 0.611 (site 11). Ar and HS did not differ significantly among subsamples .Estimates of allelic richness, gene diversity and departure from Hardy–Weinberg equilibrium, for each study site are presented in Table Departure from Hardy–Weinberg equilibrium was found in six study sites .p-values > 0.5) and no difference in MLH distribution was observed . Identity disequilibrium, i.e. a measure of departure from random associations of homozygosity between loci, was however identified in site 4, with the only significant g2 observed .Multi-Locus Heterozygosity (MLH) ranged from 0  to 8 . MLHs were normally distributed . Based on a hierarchical analysis with three levels , we observed that the three levels influenced genetic structure at a 5% level ; Flev2/lev1 = 0.279, IC = ); Flev3/lev2 = 0.142, IC = ); FInd/lev3 = 0.066, IC = ).High and significant genetic differentiation was observed among all subsamples across loci , but also by crosschecking these results with PCA and FST estimates. Further increases in K were provided in The Sles Fig. a, in linles Fig.  and the les Fig. . Note thThese two lineages, i.e. Mediterranean samples and Black Sea samples, required being analyzed separately.f = 0.103 . High and significant genetic differentiation was observed among all subsamples across loci . The FIS variation across loci was higher than the variation observed among all subsamples  = − 0.133 p-value = 0.733), and the standard error of FIS (StrdErrFIS = 0.066) was 1.5 higher than for FST (StrdErrFST = 0.043). No correlation was observed between the number of missing data and FIS values .A strong and significant heterozygote deficiency was observed with K=3 = 1551.4, ΔKK=4 = 112.6, ΔKK=2, 5–10 < 1, but also by crosschecking these results with PCA and FST estimates). Geographic groups comprised (i) sites 1–2, (ii) sites 3–4 and (iii) sites 5–6. A similar genetic structure was detected by PCAs, provided in Further, within the Ionian and Adriatic Seas, a strong structuration was revealed by the clustering analyses. Again, different values of K were explored . High and significant genetic differentiation was observed among all subsamples across loci . The FIS variation across loci was moderate  = 0.311, p-value = 0.300), and the standard error of FIS (StrdErrFIS = 0.040) was c.a. two times higher than for FST (StrdErrFST = 0.026). No correlation was observed between the number of missing data and FIS values .A moderate but significant heterozygote deficiency was observed with K=3 = 117. 44), (i) sites 14 and 15, (ii) sites 7–8 and 10, and (iii) site 13 showed clear differentiation among them, though some individuals were genetically assigned to the alternative genetic backgrounds  was estimated between 186 and 587 m, i.e. very restricted dispersal. In the Black Sea, based on the longest path that better explained the regressions, the slope b = 0.0238 was translated into a neighborhood size Nb = 42 individuals and an immigration from neighbors within the same sea of Nem = 6.69 individuals per generation and subpopulation. With an averaged Ne = 50.76, and based on an estimated number of populations between 100 and 1000 in the Black Sea, the estimates of effective population density ranged between 0.014 and 0.140 individuals/km2, i.e. very sparse populations, though higher than in the Adriatic, and the dispersal (δ) was estimated between 129 and 408 m, i.e. very restricted dispersal as estimated in the Adriatic.Significant correlations were observed between genetic differentiation and oceanographic distances among sites, either using the shortest path, i.e. the shortest distance between two sites, or the longest path, i.e. following the coastlines, in the Black Sea, but only using the shortest path in the Adriatic Sea Fig. . In the G. barbata sampled in three seas, the Ionian, the Adriatic and the Black Sea, aiming to evaluate whether the natural recovery of disturbed populations may occur without the help of restoration actions.In the present study, we investigated the genetic diversity and structure of fifteen remnant populations of 70. But a variety of potential pitfalls need to be carefully considered when using microsatellites, especially regarding null alleles72. The determination key68 applied on the microsatellite markers specifically developed for G. barbata allowed us interpreting data with heterozygote deficits and discriminating technical to demographic causes. Facing G. barbata population decline, low level of intra-population genetic variation would be expected due to recent demographic bottlenecks in wild populations. Interestingly, the polymorphism observed in G. barbata is on the same range than that observed not only in other Cystoseira species61, but also, in other Fucales75. Across the fifteen G. barbata sampled sites, we found significant heterozygote deficiency, which is common in Cystoseira58. However, in most previous published studies, markers with potential technical issues were not removed nor discussed, excluding the possibility of direct comparisons among species regarding the possible causes explaining these deviations.The use of microsatellites for conservation genetic purposes has undoubtedly be a major advance, providing several advantages over allozymes as nuclear markers78. Because all Cystoseira s.l. are considered to be self-compatible hermaphrodites with extremely restricted gamete dispersal, inbreeding may likely arise from the mating among relatives or selfing. In T. elegans, based on allele frequencies corrected considering the presence of null alleles, departures from HWE equilibrium in all three Catalan populations were interpreted as resulting from inbreeding79. Likewise, in E. amentacea, based on allele frequencies corrected considering the presence of null alleles, departures from HWE are likely explained by selfing or between neighboring individuals that are expected to be more related on average55. In the present study, however, departures from HWE in G. barbata cannot be explained solely by the presence of inbred individuals: inbreeding was detected in a single site located in the Adriatic Sea. In addition, we did not detect selfing from the analyses of the multilocus heterozygosity distributions. In Cystoseira s.l., fertilization is oogamous, with thousands of biflagellate male gametes meeting female gametes in the vicinity of hermaphroditic conceptacles grouped within terminal receptacles. This reproduction mode strongly suggests that selfing may occur in these species if gametes are self-compatible. But, we could not find solid evidence of self-compatibility in the literature, or direct citation of an experimental study specifically testing for it80, a common assumption that should therefore deserve further investigation. Given this, mating among relatives would be more likely, without solid evidence of self-compatibility in Cystoseira s.l., facilitated by the very low dispersal of the fertilized eggs29, leading to clustering of related individuals55. This said, deviations from HW expectations may also be attributed to a Wahlund effect: given the very limited dispersal abilities of the species30, mixing of populations that differ in allele frequencies would result in significant heterozygote deficiencies in the pooled population, a scenario that is very likely, especially if more related individuals settle close to each other so that populations are made of a sum of different families.Here, the technical issues of the microsatellite markers were ruled out and allele frequencies corrected to consider the presence of null alleles, leaving the origin of the significant deviations from HW expectations to be biological, such as inbreeding or related to a Wahlund effect. Most Fucales are monoecious, bearing hermaphroditic conceptacles so that self-fecundation may occurCystoseira s.l. species overall their distribution ranges54, but also in other canopy-forming seaweeds83. The genetic distinction of the Black Sea populations is very likely explained by a lack of exchange of migrants through the Bosphorus strait, consistent with previous finding based on mtDNA markers62. A very similar structuration pattern was found over the same sampled sites in Tritia neritea, a gastropod characterized by a direct cycle without a pelagic larval stage84. The restricted exchange of water masses and the difference in water density salinity and temperature between the water masses of the Mediterranean and the Black Seas across the Bosphorus strait has already been reported as a main driver of genetic structure in various species89. Besides the environmental gradients, transition zones are often zones of secondary contact of two lineages, i.e. a zone of contact between lineages previously isolated into glacial refugia90. Current data make it difficult to establish the historical demographic contexts of the observed genetic divergence, the identification of any barriers to gene flow that promote/maintain genetic isolation, as well as the exact spatial segregation of the two main genetic clusters in G. barbata. Given the observed differentiation between the Mediterranean and Black Seas populations, both using nuclear and mitochondrial markers62, and the inter-basins context, this pattern is likely resulting from divergence without gene flow. The extent to which these two genetic backgrounds are reproductively isolated deserves further attention which could be addressed, for instance, with a study covering the entire range distribution of the species, and especially covering the contact zones to identify barrier to gene flow91. The presence of reproductively isolated lineages would prevent the success of genetic rescue if the populations crossed were found too divergent to reproduce. Genome-wide data will likely help to delineate the presence of reproductively isolated lineages and test the hypothesis of one or several evolutionary species92. Similar and high genetic diversity was nevertheless observed in these two genetic backgrounds, supporting long-term regional persistence of large populations rather than recent expansions and isolation by distance, which are often associated with loss of diversity in fucoids93.One of the most expected results was certainly the strong genetic structure observed across the three seas, with the convergence of all methods towards nearly no gene flow between the Black Sea and the Ionian and Adriatic Seas. Strongly differentiated populations have been observed not only in other G. barbata, genetic analyses revealed high genetic structure at fine spatial scale, indicative of limited population connectivity, even between sampled sites separated by few kms. Such levels of genetic differentiation are in the range of that observed among populations of Cystoseira s.l. studied over the similar spatial scales (i.e. within seas) using microsatellite markers79, suggesting similar dispersal abilities of G. barbata to other Cystoseira s.l. The estimated dispersal distance per generation in G. barbata was extremely small, likely ranging from 129 to 587 m across the Adriatic and the Black Sea. Although these estimated dispersal distances should be carefully interpreted95, these estimations were nonetheless comparable to distances estimated from spatial autocorrelation analyses in E. amentacea55, and in agreement with estimated distances from field observations29. These estimated dispersal distance were likely smaller than the distance between two consecutive suitable habitat patches , enabling the migration to reverse the negative effect of genetic drift. Our results suggest that, so far, G. barbata isolated populations did not seem to suffer from inbreeding depression, and displayed intra-population genetic variation similar to other Fucales; genetic and demographic monitoring should be pursued to address the most appropriate conservation issues, e.g. assisted gene flow, minimizing non-genetic threats, or in case of small inbred populations, genetic rescue with highly divergent populations50.In addition to the strong differentiation between the Mediterranean and Black Seas observed in 96, and usually observed in Fucales99. However, in E. amentacea, genetic structure was not significantly related to geographic distances; this relationship was improved when dispersal probability across available rocky habitat was estimated by simulating stepping-stone directional transport mediated by ocean currents58. Considering the very low dispersal life history of E. amentacea, dispersal in this species would be facilitated by possible transport in floating rafts. Indeed, rafting is a relatively frequent and documented mechanism of marine dispersal allowing fragments of thalli to be transported entangled in floating rafts of other algae100. In G. barbata, relative long dispersal could also occur through drifting of fertile individuals or ramification, bearing fertile receptacles, thanks to their aerocysts, helping in preserving inter-population genetic variation. Greater connectivity was, for instance, observed in C. compressa compared to E. selaginoides and E. amentacea, which was ascribed to the presence of aerocysts, providing a buoyancy property that E. selaginoides and E. amantacea do not possess54. Dispersal as fertilized zygotes has also been observed in Cystoseira s.l. under strong hydrodynamic events100 but would not allow such long-distance dispersal. Such properties would enhance connectivity among suitable habitat patches in G. barbata, especially within the Black Sea as compared to the Adriatic Sea. Indeed, while differences in the range of geographic distances among sampled populations may partially explain this result, this finding could also reflect: (1) the presence of more free-living individuals within the Black Sea as compared to the Adriatic, (2) the higher homogeneity of water masses in the Black Sea; the oceanographic circulation is composed of a counter-rotating (cyclonic) Rim Current surrounded by many predominantly anticyclonic coastal eddies101 while the three cyclonic gyres  and the physical and biogeochemical features operating in the Adriatic Sea creates natural barriers for dispersion102 and (3) larger effective population size in the Black Sea, in line with higher abundances in the Black Sea than in the Mediterranean Sea, these three hypothesis being non-exclusive. Nevertheless, despite potential occasional long-distance gene flow through rafting or drifting, strong genetic differentiation among populations together with homozygotes deficiencies suggest very restricted connectivity among natural populations of G. barbata, a result coherent among Cystoseira s.l. studies58. G. barbata populations are likely connected via neighboring subpopulations in the form of stepping stones dispersal, as supported by significant IBD. Considering the limited dispersal estimates found here, our results call into question the natural recovery of disturbed populations if suitable patches of habitat are more than 1 km far apart.Pairwise genetic differentiation among sampled sites were significantly correlated with geographic distances in the Black Sea and Adriatic. Such isolation-by-distance (IBD) is common in macroalgae, whatever their algal division, habitat and life cycleG. barbata subpopulations suggests that each population represents a different evolutionary significant unit, giving the highest conservation priority to this threatened species103. Conservation should be designed to preserve the distinctive genetic diversity here observed. However, conservation efforts must be complemented by a reflection considering all the management units and their exchanges through gene flow, e.g. genetic rescue by assisted gene flow, to counteract genetic diversity erosion within each evolutionary significant unit. Other active strategies should be considered to restore the connectivity between these currently isolated populations, fostering the natural recovery of degraded populations (e.g. limiting the regression causes) or actively restoring lost populations20. Regarding the very restricted dispersal abilities, more genetic data on Cystoseria based on a sampling not only covering the large but also the very fine-scale distribution ranges and across time are needed to conserve the iconic Mediterranean and Black Seas marine forests and upscale reasoned ecological restoration programs.The restricted connectivity observed among Cystoseira sensu lato corresponds to three monophyletic clades recognized as three different genera104. The two clades outside the Cystoseira sensu stricto clade were recently better redefined, so that the formerly-known Cystoseira barbata was first renamed Treptancha barbata104 then Gongolaria barbata62. Gongolaria barbata samples were collected by snorkeling in 2013 in 15 sites along the coasts of the Ionian, Adriatic and Black Seas,  following manufacturer’s instructions.http://www.ecogenics.ch), commissioned to identify and develop a set of twelve Single Sequence Repeats (SSRs), i.e. microsatellite markers. Briefly, size selected fragments from genomic DNA were enriched for SSR content by using magnetic streptavidin beads and biotin-labeled CT and GT repeat oligonucleotides. The SSR-enriched library was analyzed on a Roche 454 platform using the GS FLX Titanium reagents. A total of 24,770 reads were sequenced with an average length of 474 base pairs. Of these, 3008 reads (12.2%) contained a microsatellite insert with a tetra- or a trinucleotide of at least six repeat units, or a dinucleotide of at least ten repeat units. Suitable primer design was possible in 1997 reads. Twelve markers (0.6%) showed a correct amplification and variability by polymerase chain reactions (PCR).Genomic DNA of 15 individuals from different locations in the Mediterranean and Black Seas was sent to the Ecogenics GmBH company , two multiplex reactions of six microsatellites with forward primers labeled with ABI fluorescent dyes  and 0.1 μM of each primer. DNA amplification started with a 5 min denaturation at 95 °C, followed by 30 cycles comprising a denaturation of 30 s at 95 °C, a hybridization of 90 s at 60 °C and an elongation of 30 s at 72 °C and finished with a last elongation step at 60 °C for 30 min. PCR products were genotyped using an ABI 3730XL capillary sequencer at Macrogen Europe Inc. . Alleles were scored using GeneMapper v. 4.0 (Applied Biosystems) using an internal size standard  and manually checked. Genotypes of every individual were checked twice with identical results.109. With a PI < 1.8 10–5 per population, any identical genotypes found in the dataset were presumed to be clones. These clones could either belong to a unique genet or be two representatives of the same clone that have spread. To discriminate between these alternative hypotheses, we carefully checked the sampling site(s) of the two individuals presumed as clones and their label numbering, used as a proxy of the distance between these individuals.Before further analysis, we checked for clonal propagation across all samples by estimating the probability of identity  using GenAlEx v6.502stat 2.9.4110, which was proven to be the most powerful approach to combine tests over all subsamples111. The 66 tests conducted (12loci × (12–1)/2) were not independent and p-values were corrected using the Benjamini and Yekuteli procedure112 to obtain FDR  corrected individual p-values.For each pair of loci, linkage disequilibrium (LD) was tested using the G-based test implemented in FicroChecker software113 was used to detect the presence of null alleles and eventual scoring errors . We reported the null allele frequencies estimated using the Bookfield's second method provided by MicroChecker, and computed (1) null alleles frequencies expected under Hardy–Weinberg Equilibrium, and (2) the number of null alleles expected based on these frequencies and the number of individuals genotyped. We then tested the adjustment between observed and expected numbers of missing data71 with a unilateral exact binomial (H1: there is less missing data observed than expected). Additionally, we tested for genotyping errors due to the occurrence of short allele dominance (SAD) using unilateral (ρ < 0) Spearman's rank correlation tests between allele size and FIS. These tests were also performed with FIT, aiming to increase the power of statistical tests71. Genotyping errors due to the occurrence of stuttering were tested following De Meeûs et al.67.Mall), allelic richness (Ar), gene diversity (HS) and the fixation index (FIS) were estimated for each study site using Fstat 2.9.4110. Briefly, Ar is the expected number of alleles in a random subsample of size g drawn from the population115. This rarefaction method allows evaluation of the expected number of different alleles among equal sized samples drawn from several different populations. Genetic diversities between two groups were compared using bilateral tests with 10,000 randomizations implemented in Fstat 2.9.4110. We tested for heterozygote deficiency across loci and subsamples using the U test112 implemented in Genepop 4.7.5116.Allele frequencies, the average number of alleles  − lnl(constrained)) and compared it to a chi-square with one degree of freedom118.To test for possible inbreeding occurring within each sample, individual Multi-Locus Heterozygosity (MLH) was calculated for each individual. In a population with variance in inbreeding, inbred individuals are less heterozygous (i.e. lower MLH) than non-inbred ones. Inbreeding variance generates Identity Disequilibria (ID)—i.e. correlations in homozygosity across loci, a measure of departure from random associations between loci68 based on the Wright's fixation indices119, FIS, which measures inbreeding of individuals relative to inbreeding of subsamples, FST, which measures inbreeding of subsamples relative to total inbreeding, and FIT, which measures inbreeding of individuals relative to total inbreeding. In other words, FIS measures the deviations form Hardy–Weinberg equilibrium , FST measures genetic differentiation between subsamples, and FIT reflects the combination of both. Briefly, this approach is based on the examination of the F-statistics stability across loci and subsamples to interpret data with heterozygosity deficiencies. Heterozygosity deficiencies can arise from null alleles, stuttering, short allele dominance, allele dropouts , Wahlund effect , and/or selfing and sib-mating. By examining the (1) missing data in the dataset (across loci and subsamples), (2) variations of FIS and FST across loci, (3) standard error of jackknife over loci for FIS (StrdErrFIS) and FST (StrdErrFST), and (4) the correlation between FIS and FST, De Meeûs68 proposed a determination key to help discriminating demographic from technical causes.First, we estimated population genetic structure through the approach developed in De Meeûs120, i.e. f, θ and F for FIS, FST and FIT estimators, respectively. We computed the 95% confidence intervals of F-statistics using the StrdErrFIS and StrdErrFST computed by jackknife over subsamples, for each locus, and we used the 95% confidence intervals computed by bootstrap over loci. Correlations between FIS and FST were measured using Pearson’s correlation coefficients in R107. All these analyses  were performed using Fstat 2.9.4110 and R107.Wright's F-statistics were estimated through Weir and Cockerham's unbiased estimatorsIS and the number of missing data71: the significance of correlations was tested with a unilateral (ρ > 0) Spearman's rank correlation test in R107.We further tested a correlation between FST estimates (2) Principal Component Analyses (PCA) computed on the matrix of genotypes, and (3) an individual-based Bayesian clustering method. Comparing results from analyses using different statistical approaches allows us to make solid assumptions about our data: it allowed us to cross check the outputs of a model-based approach with strong priors and hypotheses  as implemented in Structure 2.3.4122, with methods based either on distance with few (nearly no) assumptions on data (i.e. PCA) or on distance matrices among sets of individuals that fall within predefined population samples. Estimates of FST were calculated using FreeNA with the ENA correction72. To test for genetic isolation among subsamples, we used the G-based test over all loci. The PCAs were carried out using the R package adegenet 1.4–2123. The individual-based Bayesian clustering analysis was performed with the software Structure 2.3.4122. For each value of K (ranging from 1 to 18), 30 replicate chains of 200,000 Markov Chain Monte Carlo iterations were run after discarding 25,000 burn-in iterations. An admixture model with correlated allele frequencies was applied with a priori information on sample origin. For each value of K, 30 output files were produced by Structure 2.3.4 with slightly different ancestry proportions; using Clumpp124, we determined individual ancestry proportions  that best matched across all replicate runs for each K value. We visualized individuals’ assignment in the R software107.In addition to the aforementioned approach, genetic structure among sampling sites was depicted using more classical tools, aiming to look whether geographic differentiation exists. We used three different approaches: (1) F125 with three levels, the Mediterranean vs. the Black Seas subsamples (level 1), the Ionian, Adriatic and Black Seas (level 2), and all subsamples (level 3).We estimated and tested hierarchical F-statistics using the HierFstat package126 (Table 127. Pairwise oceanographic distance between sampled sites was estimated within each sea as the shortest oceanographic distance according to the ‘Vincenty (ellipsoid)’ method using the geosphere package128. However, as the shortest path does not reflect the sea hydrodynamics130, pairwise oceanographic distances between sampled sites were also estimated following the coastlines, calculated by summing all the straight lines connecting each cape on a map at 1:500,000 on QGis 3.18. For the Black Sea, which is circular, having the choice between following the movement against the clock or clockwise to calculate the coastline distance between two populations, only the shortest distance was kept.Finally, we tested for the presence of genetic Isolation-By-Distance (IBD) by investigating the relationship between using Cavalli-Sforza and Edward’s chord distance26 Table  and the stat110. With significant IBDs, the IBD slope allows estimating the neighborhood Nb = 1/b and the number of immigrants from neighbors Nem = 1/(2πb) = 4πDeσ2, with De the effective population density and σ2 the average of squared axial distances between adults and their parents132. To estimate De and σ2, we first estimated the effective population size Ne within each Sea (Adriatic and Black Seas): we measured the non-random association of alleles at different loci within F1 population133 using a bias correction135 implemented in NeEstimator v2 software136. The Pcrit was adjusted following recommendations135 and random mating was applied. Estimating Ne allows computing effective population densities De, as the averaged Ne multiplied by the number of subsamples within a sea divided by the surface of the sea. The number of populations in the Adriatic Sea was estimated as follow: (1) Gongolaria barbata stands for 10% of the Cystoseira s.l. observed in the Mediterranean Sea32, (2) in the Adriatic, ca. 100 Cystoseira s.l. populations were recorded, (3) we estimated that 10% would be Gongolaria barbata, so that the number of populations in the Adriatic would be close to 10. Despite this estimation, we thought very low, we decided to test different number of populations, ranging from 10 to 100. In the Black Sea, the number of populations was estimated to be 10063, but we also tested different number of populations, ranging from 10 to 100. Once De computed, a rough proxy of dispersal (δ), in order of magnitude, can be computed as δ ≈ 2 (1/(4πDe × b))−1/2 (i.e. Nem = 4πDeσ2 <  =  > δ ≈ 2 (1/(4πDe × b))-1/2126).Regressions between genetic and geographic distances as described above were investigated within the Adriatic on the one hand and within the Black Sea (excluding subsample 11) on the other hand, using FSupplementary Information.


Collichthys lucidus plays a vital role in the structure and functioning of marine ecosystem processes. C. lucidus has been shown to have an obvious population structure. Therefore, it is an ideal candidate for investigating population differentiation and local adaptation under heterogeneous environmental pressure.Understanding the genetic structure and local adaptive evolutionary mechanisms of marine organisms is crucial for the management of biological resources. As the ecologically and commercially important small-sized shallow-sea fish, C. lucidus populations. Population structure analysis based on all SNPs indicated that the northern group and southern group of C. lucidus have a strong differentiation. Moreover, 314 SNPs were found to be significantly associated with temperature variation, and annotations of genes containing temperature-related SNPs suggested that genes were involved in material  metabolism and immune responses.A total of 184,708 high-quality single nucleotide polymorphisms (SNPs) were identified and applied to elucidate the fine-scale genetic structure and local thermal adaptation of 8 C. lucidus populations may have been caused by long-term geographic isolation during the glacial period. Moreover, we suspected that variation in these genes associated with material  metabolism and immune responses was critical for adaptation to spatially heterogeneous temperatures in natural C. lucidus populations. In conclusion, this study could help us determine how C. lucidus populations will respond to future ocean temperature rising.The high genetic differentiation of 8 The online version contains supplementary material available at 10.1186/s12864-021-08045-8. Inferring the genetic diversity, population structure and changing patterns of marine species is critical to the successful management of exploited populations, allowing conservation units to be identified and individuals to be assigned to geographical areas , 2. HoweFST) is extremely obvious when it is applied to the genetics of large populations (even for species with weak migration abilities), which is especially obvious when single or limited genetic markers were used [Population genetics has a great advantage in correctly explaining the population genetic structure of marine species and exploring its influencing factors. Although previous studies have detected genetic differentiation patterns of marine species at a small spatial scale based on a small number of genetic markers , the defere used . TherefoCollichthys lucidus is a short-migratory shallow-sea fish that prefers brackish water in estuaries and has a life cycle including pelagic eggs [C. lucidus has become an important fresh seafood for residents in coastal areas. This species lives in warm water with low salinity and eats shrimp and crabs. As an ecologically and commercially important small-sized fish, C. lucidus is distributed along the inshore waters of China  for SNP calling , expected heterozygosity (HE), and polymorphism information (Pi) averages over the 184,708 SNPs varied across the eight C. lucidus populations . Among the eight C. lucidus populations, the TJ and ZH populations showed the highest and lowest HO, HE, and Pi values, indicating the highest and lowest population genetic diversity, respectively. All eight C. lucidus populations generally had a high percentage of polymorphic loci (73.4023 ~ 99.4640%). However, the genetic diversity in the XM and ZH populations was relatively low according to all indexes. We also found that the inbreeding coefficient (FIS) values of the eight C. lucidus populations were low (− 0.1477 ~ 0.0209), suggesting that each population contained a large number of individuals. Additionally, most of the pairwise fixation index (FST) values between the eight C. lucidus populations were significant .ADMIXTURE software was first used for clustering analysis of the eight d 3 Fig. A, the DLd 3 Fig. B. Meanwhd 3 Fig. C, which her Fig. D. AdditiIn the present study, we first calculated the average sea surface temperature (ASST), low sea surface temperature (LSST) and high sea surface temperature (HSST) of the eight sea areas  over 68 years  and southern (XM and ZH populations) groups . Additionally, we speculate that the pelagic eggs may be a key driver of the low pairwise FST values within C. lucidus clusters . We have suspected that those important candidate SNPs could be affected by temperature heterogeneity.With the advantages of identifying adaptive SNPs, RAD-seq can facilitate insights into the genetics of local adaptation in natural populations . The C. C. lucidus populations, ultimately leading to differences in physiological regulation. The KEGG annotation results showed that the sequences containing temperature-selected SNPs were mainly associated with material  metabolism and immune responses  gene was found to be associated with HSST  appears to stimulate insulin production and suppress glucagon production [C. lucidus populations to survive in low temperatures. Lower seawater temperatures may enhance oxidative stress of marine organisms. In the present study, we found that some genes were associated with oxidative stress . TLR9 gene is one of pattern recognition receptors (PRRs). As the sensory receptors of organisms to environmental stress, PRRs has been proved to play an important role in innate immune response [+ and Cl− account for a relatively major proportion of the composition of haemolymph in most marine organisms [SLC6A5; sodium- and chloride-dependent glycine transporter 2), and these genes may influence the sensitivity of different C. lucidus populations to Na+ and Cl− [C. lucidus populations have evolved different osmotic pressure regulation abilities. Interestingly, one gene  containing LSST-related SNP has been annotated to be involved in the coagulation process. Moderate hypothermia has been used as one of the international anti-coagulant methods [F9 gene may has reduces the coagulation rates of C. lucidus populations in the low temperatures.In our outlier study, only rocesses . Compareeratures . In our oduction . Thereforesponse . Previourganisms . Our res and Cl− . In fact methods , the mutC. lucidus populations adapt to local temperatures. However, it is undeniable that some temperature-selected SNPs could be false positives. Meanwhile, future studies will also need to conduct experiments  to investigate the exact function and expression levels of these candidate genes.In summary, the temperature-selected SNPs analysis revealed a number of potential genes that help different C. lucidus. All SNPs were further used to reveal the population genetic structure and genomic regions under temperature-driven selection in eight C. lucidus populations. Genetic structure analysis showed that there was significant genetic differentiation between the northerm  and southern (XM and ZH populations) groups. Combined with the temperature data of eight sea areas  over 68 years, 314 SNPs associated with temperature variables were identified and genes that contained temperature-selected SNPs were involved in material  metabolism and immune responses. The present research could help us determine how different C. lucidus populations respond to local temperatures.In the present study, RAD-seq was applied to develop the genome-wide SNPs of C. lucidus samples were collected from eight locations in China, namely, Dalian (DL), Tianjin (TJ), Lianyungang (LYG), Nantong (NT), Wenzhou (WZ), Zhoushan (ZS), Xiamen (XM), and Zhuhai (ZH)  Fig. . MusclesC. lucidus [All raw reads in FASTQ format were filtered using Trimmomatic software  based  lucidus  and used lucidus ;). The c lucidus ;) with dPi) and Tajima’s D at each SNP, were estimated using TASSEL software (version 5.2.31 [Pi), observed heterozygosity (HO), expected heterozygosity (HE), and the inbreeding coefficient (FIS) were calculated using the “populations” module in Stacks software (version 1.34 [FST) values between populations and their significance were calculated using Arlequin software . The rn 5.2.31 . To calcion 1.34 ;. Pairwision 3.5 ; with 10K) values ranging from 1 to 8. The optimal K value corresponded to the lowest cross-validation error. (II) We conducted a principal component analysis (PCA) using the “adegenet” package [K, we divided all the groups into two groups  and then performed an analysis of molecular variance (AMOVA) in Arlequin software (version 3.5 [FCT) and the differentiation among populations within groups (FSC).Population genetic structure based on all SNPs was analysed using five methods. (I) The Bayesian model-based clustering program ADMIXTURE  was us package  implemen package . The Net package ;) with a package . (IV) Onsion 3.5 ;) to esthttp://www.jma.go.jp/jma/index.html), Advanced Very High Resolution Radiometer  and Geostationary Operational Environmental Satellites . We first converted SNPs files and environmental data files into matrix files needed for software operation and the matrix was evaluated according to the software specification, the parameters were set as follows: -k 100,000 -r 63,479. Then, Bayenv tests were applied to identify putative SNPs correlated with temperature variations using “calc_bfs.sh” script in the Bayenv2 software, and a Bayes factor (BF) value higher than 10 was set as the filtering condition for putative SNPs. We repeated the Bayenv analysis four times to avoid false positives, and only the SNPs that were continuously screened were used for subsequent analysis. Thereafter, we used the union set of ASST-related, LSST-related, and HSST-related SNPs as the candidate temperature-selected SNPs. To determine the genetic mechanisms underlying temperature-related adaptive differentiation between C. lucidus populations, gene sequences containing these SNPs were then annotated using Blast2GO software along with KEGG database [C. lucidus (GCA_004119915.2).The genotype-environment association method implemented in Bayenv  was apdatabase . Meanwhidatabase  to annotAdditional file 1: Supplementary tables