We observed Dongxiang, Bonan, Yugur, Salar, and Hui people were admixed populations deriving ancestry from both East and West Eurasians, with the proportions of West Eurasian related contributions ranging from 9 to 15%. The genetic admixture was probably driven by male-biased migration- showing a higher frequency of West Eurasian related Y chromosomal lineages than that of mtDNA detected in Northwest China. ALDER-based admixture and haplotype-based GLOBETROTTER showed this observed West Eurasian admixture signal was introduced into East Eurasia approximately 700 ∼1,000 years ago. Generally, our findings provided supporting evidence that the flourish transcontinental communication between East and West Eurasia played a vital role in the genetic formation of northwest Chinese populations.Northwest China is a contacting region for East and West Eurasia and an important center for investigating the migration and admixture history of human populations. However, the comprehensive genetic structure and admixture history of the Altaic speaking populations and Hui group in Northwest China were still not fully characterized due to insufficient sampling and the lack of genome-wide data. Thus, We genotyped genome-wide SNPs for 140 individuals from five Chinese Mongolic, Turkic speaking groups including Dongxiang, Bonan, Yugur, and Salar, as well as the Hui group. Analysis based on allele-sharing and haplotype-sharing were used to elucidate the population history of Northwest Chinese populations, including PCA, ADMIXTURE, pairwise Fst genetic distance,  The human history of East Asia can be traced back to the Late Paleolithic Age. The anatomically modern humans permanently made an occupation in East Asia about 50,000 years ago . NumerouThe Eastern Steppe is characterized with grasslands, forest steppe, and desert steppe, connecting Russia, Mongolia, and China. The Eastern Eurasian Steppe is home to historic empires of nomadic pastoralists, including Xiongnu, Turkic Khaganate, and the Mongols. The East Steppe have also served as the important communication node between West and East Eurasia. The Central/East Steppe has witnessed intensive East and West communications and interactions in many aspects . HistoriNorthwest China locates in the west-east Eurasian interaction core region, populations in this region mainly belongs to Altaic language family which includes Mongolic, Turkic, and Tungusic language based on language classification. Modern populations in Northwest China were typical admixtures between populations all around the trans-Eurasia continent . Uyghur The procedures of the sample collection and the investigations were reviewed and approved by the Medical Ethics Committee of Xiamen University and Northwest Minzu University and were in accordance with the recommendations provided by the revised Helsinki Declaration of 2000. Moreover, our study stuff had already informed these potential participants about our purposes of this project, and every participant in our study had provided the informed consent.Our study focused on Gansu province in Northwest China. We collected 140 saliva samples from unrelated individuals of Altaic speaking populations and Hui group from Sunan, Linxia, Lanzhou, Dahejia, and Kangle, including 24 samples from Hui, 30 samples from Dongxiang, 30 samples from Bonan, 30 samples from Yugur, and 26 samples from Salar . All inchttps://reich.hms.harvard.edu/downloadablegenotypes-present-day-and-ancient-dna-data-compiled-published-papers) (We used PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific) to extract DNA and measure the concentration via the Nanodrop-2000 following the manufacturer’s instructions. All these qualified samples were genotyped using the Illumina WeGene Arrays covering about 700,000 Single nucleotide polymorphisms (SNPs) at the WeGene genotyping centre in Shenzhen. We first analyzed the biological relatedness of individuals using plink  softwere-papers) ; 2012.smartpca built in the EIGENSOFT package (http://www.r-project.org/).Principal component analysis (PCA) was carried out using the software called  package . The PCAWe carried out ADMIXTURE  analysisf statistics using ADMIXTOOLS with the default parameters, and calculated standard errors  using a block jackknife resampling across the genome  to measure the shared genetic drifts between population X and population Y since their separation from an outgroup population. We here used Mbuti as an outgroup population, a group who lived in the Congo basin in the middle region of Africa. We next used admixture-f3 statistics in the form of f3  for all pairs of references populations to make an evaluation of the possible admixture signals for the target populations. We conducted the heatmap visualization of the outgroup-f3 statistics values by the pheatmap package in the R software.We computed e genome . We carrqpAdm program as implemented in ADMIXTOOLS . The mtDNA haplogroups assignment was identified with mtDNA phylogenetic tree Build 16 (http://www.phylotree.org/).We assigned the Y chromosomal haplogroups by genotyping the most derived allele upstream and the most ancestral allele downstream in the phylogenetic tree by using an in-house script following the recommendations of the International Society of Genetic Genealogy  to quantify the population differentiation across East Asia. We showed the results in a heatmap plot  to explore the possible ancestral source populations for our studied populations in Gansu province. We observed the most significant negative signals when using the Neolithic Yellow River farming groups and the Bronze Age to Iron Age Steppe groups from West Eurasia and Central Asia as sources  and Iron Age Alan people in Russia (Russia_Alan) in admixture f3 showed the most significant admixture signal, we found that the Dongxiang group derived 14.9% western Eurasian ancestry from Russia_Alan related groups and the left from WLR_LN related groups. In general, the qpAdm model indicated the west-east admixture in our five studied populations, showing East Asian related ancestry dominantly made contribution to the genetic formation of Northwest Chinses Altaic speaking groups with different proportions of West Eurasian related ancestry.We next carried out ulations ; Table 2We assigned the haplogroups of Y chromosome and mtDNA for our newly genotyped samples . The hapWe next assigned the matrilineal mtDNA haplogroups for our studied populations. In the Hui group, we observed diverse mtDNA haplogroups, including D4, D5a2a1, F1, G3a1′2, M7, M8, Z3, and Z4. The maternal profile of Dongxiang group was similar to that in the Hui group, but the haplogroup A, B4, and F2 were more prevalent in Dongxiang. We found D4 was the most dominant lineage in Bonan group and we also detected B and G2a in Bonan group. Haplogroup D4 was also the most dominant haplogroup in Yugur group, following by A1, C4, F1g, and M9a1 haplogroups. Haplogroup A was the most prevailing haplogroup in the Salar group, following by F1, M9a1b1, and Z3. The main mtDNA haplogroups in our samples were also prevalent in East Asia, suggesting the local East Asians largely contributed to the maternal gene pool of Gansu Altaic speaking populations. The genetic influence from the West Eurasian human populations were more significant in the patrilineal lineages than in the matrilineal lineages. The details of the distribution of mtDNA haplogroups were listed in We estimated the admixture time between the East and West Euraisan related ancestry in Northwest Chinese populaton using the weighted linkage disequilibrium-based admixture inference implemented in ALDER . We usedWe further performed haplotype-based GLOBETROTTER to obtain the admixture landscaped of our studied northwestern Chinese populations . The easThe East Asia is a region with diverse culture communications, multiple language interactions, and complex population history. Many previous studies provided that the genetic substructure of populations in East Asia was consistent with the language affinities. The Hexi Corridor and its surrounding regions were known for the famous Majiayao civilization in the middle and late Neolithic Age and subsequently controlled by the Rong-Di tribes before the Han Dynasty. Moreover, the Northwest China witnessed the intersection of the eastward expansion of the barley and wheat agriculture and the westward expansion of the millet agriculture in the Neolithic to Bronze Age. Gansu province isone of the key regions in Northwest China where also connects the Hexi Corridor and Tibetan-Yi Corridor. The genetic diversity, fine-scale genetic substructure, and the western Eurasian admixture in the populations of Gansu are still needed to be fully explored. We collected 140 modern individuals from Hui, Dongxiang, Bonan, Yugur, and Salar groups from the Gansu province and genotyped with genome-wide SNPs. We reconstructed the population admixture history of the Altaic speaking populations in northwest China.Our studied populations of Northeast China showed similar genetic profile among those populations, suggesting the relatively genetic homogeneity in Northwest China, even though harboring subtle different proportions of East, and West Eurasian related ancestry. The close genetic affinity among Chinese Turkic speakers, Tungusic, and Mongolic populations indicated the probability of common ancestor of Altaic speakers. Our results showed that both West and East Eurasian contributed the genetic formation of Altaic populations in Northwest China, which coinciding with previous studies suggested the east-west admixture in Alatic populations and Hui population . The cloConsidering that the West Eurasian related ancestry proportions were limited in our studied populations (<15%), we noted that it was hard to determine the exact genetic source for the admixture. The sequencing of more ancient genomes from Northwest China may shed more light on determining the West Eurasian sources. We estimated the admixture event to have occurred in historic period based on ALDER and GLOBETROTTER . The ancient admixture we identified was roughly corresponding to the Song to Yuan Dynasty. But we noted if the admixture did not happen immediately after arrival or multiple times over an extended period, however, the true start of admixture would have been more ancient. Furthermore, the intensive and continuous contact between West and East Eurasian population started as early as the Bronze Age due to the advantage of horses, and the interaction became more frequent with the opening of Silk Road in the Han Dynasty. The establishment of Mongols empire and the Mongolian Conquests in the 13th and 14th centuries facilitated the west-east contacts. The true admixture history in Northwest China could be more complex than the simplified models as we presented in this study, the populations studied here, however, harbored prominent local East Eurasian related ancestry and limited West Eurasian related ancestry.Running through the ancient Silk Road, the human groups were all presented a west-east admixture structure. The Uyghur in Xinjiang was a typical one. Besides, the Altaic speaking populations in Central Asia all have the west-east interactions in genetic structure and culture. The east endpoint of the ancient Silk Road was near Chang’an City, and the Gansu pathway was the only route to it. The Altaic populations in this region lack of large-scale sampling and genome-wide genetic analysis. Our research answered this issue at a certain degree, but the more elaborate admixture history needed to be explored from the whole genome sequencing next.


Cerastoderma edule) is a marine bivalve distributed along the Northeast Atlantic coast of Europe and is an important resource from both commercial and ecological perspectives. We performed a population genomics screening using 2b‐RAD genotyping on 9309 SNPs localized in the cockle's genome on a sample of 536 specimens pertaining to 14 beds in the Northeast Atlantic Ocean to analyse the genetic structure with regard to environmental variables. Larval dispersal modelling considering species behaviour and interannual/interseasonal variation in ocean conditions was carried out as an essential background to which compare genetic information. Cockle populations in the Northeast Atlantic displayed low but significant geographical differentiation between populations , albeit not across generations. We identified 742 and 36 outlier SNPs related to divergent and balancing selection in all the geographical scenarios inspected, and sea temperature and salinity were the main environmental correlates suggested. Highly significant linkage disequilibrium was detected at specific genomic regions against the very low values observed across the whole genome. Two main genetic groups were identified, northwards and southwards of French Brittany. Larval dispersal modelling suggested a barrier for larval dispersal linked to the Ushant front that could explain these two genetic clusters. Further genetic subdivision was observed using outlier loci and considering larval advection. The northern group was divided into the Irish/Celtic Seas and the English Channel/North Sea, while the southern group was divided into three subgroups. This information represents the baseline for the management of cockles, designing conservation strategies, founding broodstock for depleted beds and producing suitable seed for aquaculture production.Knowledge on correlations between environmental factors and genome divergence between populations of marine species is crucial for sustainable management of fisheries and wild populations. The edible cockle ( Cerastoderma edule is dioecious and can live up to 10 years in the wild with a fast sexual maturation (reached in its first year) and high fecundity , mainly driven by salinity, larval dispersal by oceanographic currents and geographical distance. These results suggest that a finer structure can underlie cockle distributions in the Northeast Atlantic and that a genomic scan covering southern and northern beds combined with understanding of the dispersal of larvae in the area is necessary to understand how the species is structured for its appropriate management.Recently, Coscia et al.  analysedC. edule along the Northeast Atlantic coast considering environmental drivers and models of larval dispersal with the aim of providing essential information for the sustainable management of its resources. Major regions previously identified with microsatellites were confirmed, but refined information was obtained, mostly in agreement with the ocean dynamics and resulting larval dispersal patterns across the Northeast Atlantic.In this study, we applied a 2b‐RADseq genotyping by sequencing approach to assess the genetic structure of 22.1C. edule), when upwelling is a prominent feature along the Galician coast (NW Spain), the Portuguese coastal currents transport surface waters southward along the west coast of the Iberian Peninsula, whereas during the winter months the Iberian Poleward Current shoals and moves surface waters northwards  in more than 25% of samples were removed; and (iv) selection of the most polymorphic SNP in each RAD‐tag.Total DNA was extracted from gill tissue samples using the e.Z.N.A. E‐96 mollusc DNA kit (OMEGA Bio‐tech), following manufacturer recommendations. Single nucleotide polymorphism (SNP) identification and selection, as well as genotyping and validation protocols followed those described by Maroso et al. . Briefly2.4Ho) and expected (He) heterozygosities, proportion of polymorphic loci), departure from Hardy–Weinberg equilibrium (HWE) and intrapopulation fixation index (FIS) were estimated for each bed using GENEPOP v4.0 . Linkage disequilibrium between pairs of loci regarding physical distance (bp) was estimated with r2 across the cockle genome using PLINK 1.9 , and its significance calculated through exact tests over genotypic contingency tables using GENEPOP v4.0.Genetic diversity  between cockle beds were calculated with ARLEQUIN v3.5 using 10,000 permutations to test for significance. The number of genetically homogenous population units (K) was estimated using the variational Bayesian clustering method implemented in the package fastSTRUCTURE v2.3.4 . The most likely number of K was estimated using the ‘chooseK.py’ programme. This programme gives the best K value and K corresponding with weak population structure in the data using the heuristic scores. Summarized outputs were carried out using the software DISTRUCT 1.1 . Data were transformed using PCA , and an appropriate number of principal components (PC) and discriminant functions (DF) were retained to explain >90% of the variance. Analyses of molecular variance (AMOVA) to study the distribution of genetic diversity within (FSC) and among (FCT) bed groups obtained from fastSTRUCTURE analyses were carried out with the program ARLEQUIN v.3.5 and their significance tested with 10,000 permutations. Isolation by distance (IBD) was evaluated by the correlation between geographical (measured as the shortest ocean distance) and genetic  <0.05 were considered consistent outliers with this methodology. For ARLEQUIN analysis, loci at p < 0.01 were considered consistent outliers, while loci at p < 0.05 were just only suggestive  and used as landmarks for mining the genome to identify candidate genes related to selective drivers. The total genome size (794 Mb) was mostly assembled (95.2%) in the 19 mega‐scaffolds (chromosomes: C onwards) corresponding to the cockle's haploid karyotype  were averaged within 50 and 1 kb genomic windows within each bed and for the whole sampling collection respectively. Considering that LD was negligible above 50 kb in the whole collection  magnitude of the LD itself (average r2 between outlier pairs in the window). Genes included in those genomic windows were identified using the cockle's transcriptome assembled and annotated using RNAseq data from a study related to resistance to Marteilia cochillia within the COCKLES EAPA_458/2016 project  and the cockle's genome . The annotated cockle's transcriptome was used as reference to detect Gene Ontology (GO) functional enrichment of the genomic regions under selection (FDR 5%) using agriGO v2.0 ; and net primary productivity  (Table https://resources.marine.copernicus.eu/?option=com_csw&task=results?option=com_csw&view=details&product_id=IBI_REANALYSIS_PHYS_005_002") and the IBI_REANALYSIS_BIO_005_003 model (https://resources.marine.copernicus.eu/?option=com_csw&task=results?option=com_csw&view=details&product_id=IBI_REANALYSIS_BIO_005_003) for the period 2014–2018. The nearest model cell with water was taken to extract the data. Then, averages for the two seasonal extremes, winter (i.e. from January to March) and summer (i.e. from July to September), and for the spawning season  and abiotic factors (see below) was studied following a canonical redundancy analysis (RDA) using the VEGAN software Oksanen,  in R. Fo1) Table . MonthlyANOVA was performed to test the significance of the variance associated with the different variables using 1000 random permutations with VEGAN. The variance inflation factor (VIF) was estimated to explore collinearity (correlation) among landscape variables in the data set. VIF values higher than 10 would denote important collinearity problems, and values from 5 to 10 moderate problems, while values lower than 5 would indicate no collinearity problems . The underlying hydrodynamic model is based on the NEMO ocean model, version 3.6. The primitive equations are solved on a horizontal grid with a resolution of 1/36° and 50 unevenly spaced vertical levels with resolution decreasing from the surface into the deep. GEBCO08 was used for the bathymetry. Atmospheric forcing was extracted from ERA interim atmospheric fields. At the lateral open boundaries, ocean forcing fields were obtained from the global CMEMS GLOBAL eddy resolving system at 1/12°, and tidal forcing from the global FES2014 database . Freshwater fluxes from rivers were implemented for 33 rivers across the model domain. The model has been extensively validated and quality controlled  Ocean Analysis and Forecast system ; therefore, each particle's positions during this time were used for the analysis of larval connectivities between sites. Larval connectivities were calculated as the percentage of larvae released from the source location arriving within a square of 0.2° latitude/longitude distance of one of the other 50 sink locations or returning to the source location (self‐recruitment). Connectivities were averaged over the three vertical release levels, but also presented separately in the Supporting Information.33.1A total of ~2000 M raw reads were sequenced (~3.7 M reads per sample). After quality filtering and alignment to the cockle genome, 27.7% and 35.6% total reads, respectively, were removed, and ~750 M reads were fed into STACKS to yield 315,744 loci with 726,911 SNP positions. Filtering was mostly due to multiple matches in the cockle's genome. Nine individuals  and expected heterozygosity per locus (He). Ho ranged from 0.070 in Sylt—ASCE_17 and Burry—WBY_18 to 0.079 in Ría de Noia—SNO_17, Ría Formosa—PRF_17 and PRF_18 (mean ± SD = 0.074 ± 0.003), while He varied from 0.077 in Sylt—ASCE_17 and Burry—WBY_18 to 0.088 in SNO_17 (mean = 0.081 ± 0.003)  and He ranged from 0.153 in Lombos do Ulla—SLO_18 to 0.290 in Tejo Estuary—PTE_18 (mean ± SD = 0.177 ± 0.030). Similar to the FIS estimations considering all SNPs, FIS values using SNPs with MAF >0.01 were positive and nonsignificant in all cases . These figures greatly increased when suggestive outliers (i.e. p < 0.05) were considered, which suggests a considerable proportion of false positives with this approach as previously suggested . The total number of consistent balancing outliers across the different approaches was 36 , and thus suggestive of divergent or balancing selection, respectively. In the whole Northeast Atlantic region, BAYESCAN detected a total of 460 outlier loci potentially under selection, 19 under balancing selection (BS) and 441 under divergent selection (DS) (FDR <0.05). The two ARLEQUIN scenarios analysed in the same region (i.e. LOC and HIER) detected 308 (293 DS and15 BS) and 278 (274 DS and 4 BS) consistent outliers , and even when all data from the Northeast Atlantic was pooled , LD was mostly negligible and not significant above 50 kb on average . Despite the low LD detected on average on the whole genome, we decided to be cautious in establishing the size of the windows for mining (±250 kb), considering that specific regions under selection should display significant LD across wider regions due to sweeping. Using the whole population data, 56 genomic windows under divergent selection characterized by the presence of two or more consecutive outliers were identified, encompassing a total of 31.0 Mb (3.9% of the whole genome) (Table All the consistent outliers (36 BS and 746 DS) were mapped on the ly Table . Resultse) Table . A totalp < 0.001) and a r2 above the highest detected in the population analysis on average genomic windows , suggesting temporal genetic stability between consecutive cockle cohorts in the Northeast Atlantic (Table FSC) was nonsignificant and negligible, while the percentage among sampling sites (FCT) was highly significant (p < 0.001) and above 3%  to 0.0546 (Dee Estuary—WDE_17 vs. Sado Estuary—PSA_19), with a global FST value of 0.0240 (p < 0.001). FST values increased when only divergent outlier loci were considered , ranging from −0.0044 (Lombos do Ulla—SLO_17 vs. Ria de Aveiro—PRA_17) to 0.2196 (Dee Estuary—WDE_17 vs. Sado Estuary—PSA_19), and decreased, even below the whole data set, when only neutral loci were considered  to 0.0295 (IDC_18 vs. PRF_18); see Table r = 0.60214, p < 0.001; neutral data set: r = 0.42149, p < 0.001; outlier data set: r = 0.69568, p < 0.001; Table Pairwise ds Table A. FST fK = 2 as the most probable population structure  and southern group , indicating that the IBD pattern observed in the whole Northeast Atlantic region is not an artificial consequence of vicariance between two genetically differentiated genetic groups. AMOVA using these two groups showed that this structuring  captured close to the 80% of the total differentiation among beds (FST = 3.77%)  close to 75% of total differentiation among beds  identified six main well defined groups: (i) North Sea and English Channel beds up to the Bay of Somme ; (ii) the Dee bed in North Wales and Irish beds in the Irish Sea ; (iii) the Burry bed in South Wales in the Celtic Sea (WBY); (iv) the Arcachon bed in the Bay of Biscay (FAR_17); (v) the Northwest Iberian coast including Spanish and the northern Portuguese beds ; (vi) the Southwest Iberian beds   each bed constituted a single cluster  rendered a value of e Figure . One groe Figure . Signifi) Figure . AMOVAs r Figure . AlthougDiscriminant analyses of principal components plots confirmed the results found with fastSTRUCTURE regarding the main north‐south subdivision, but further clustering was suggested within groups. The analyses with the complete and neutral data sets showed an important scattering within each group Figure , while tThe population trees provided by TREEMIX without migration events also identified the aforementioned groups, clustering beds according to northern and southern groups and identifying the substructure suggested by outliers Figure . The bes3.4When all the seascape variables were included in the RDA analyses, latitude was suggested as the main driver for genetic differentiation, for the three different data sets  and seasons  analysed Table . Latitud3.5Connectivity pathways from the larval dispersal modelling, time‐averaged and averaged across all three depth scenarios are shown in Figure C. edule, the eastern and western (and northern and southern) areas of the channel are connected, albeit weakly, suggesting that sites in the channel may be grouped into one unit.Taken individually, the larval behaviour scenarios (i.e. particle cohorts released (and fixed) at 1, 15 and 30 m water depths) showed considerable differences in the simulated connectivity networks. The surface (1 m) release scenario Figure  simulateThe simulated interannual variations in connectivities were greatest for the surface release scenario and decreased the deeper the larvae were in the water column (results not shown). For the 15 and 30 m depth scenarios, simulated connectives over different years tended to vary in strength but much less so in overall patterns. Overall, the larval dispersal modelling suggested that the sites may be grouped into three distinct cohorts: (i) the Iberian and Bay of Biscay Atlantic coast sites; (ii) the English Channel sites; and (iii) and the Celtic and Irish Sea sites.44.1C. edule, to support breeding programmes for selecting high‐value seed for intensive production in particular areas . The genetic diversity range widened (0.153–0.290), when only polymorphic markers were considered , but mostly due to the Tejo Estuary representing an outlier population with much higher He than the rest (maximum He = 0.193 without Tejo Estuary). All samples showed a consistent slight heterozygote deficit (FIS > 0), although nonsignificant, likely related to a technical genotyping issue associated with the presence of null alleles as previously reported in molluscs , who applied the same MAF filter to characterize genetic diversity in C. edule beds from the Celtic and Irish Seas  using a classical RADseq technique  and the proportion of polymorphic loci (MAC ≥2) were slightly higher but significant or marginally significant in the southern group . Previous studies with microsatellite loci did not detect differences between both groups  and r2 > 0.05, which represents the highest average LD in genomic windows across the whole genome estimated for Northeast Atlantic. Two of these regions under divergent selection in C1 and C4 included 8 and 12 outliers, respectively, r2 > 0.20 and encompassed more than 1 Mb, suggesting strong selective sweeps. Gene mining in the most consistent genomic regions identified 105 candidate genes using the incomplete transcriptome annotation carried out within the COCKLES project . Groups of these genes and families were associated with relevant functions, but no functional enrichment GO terms were identified likely due to the small number of genes, poor functional annotation and the putative diverse drivers acting on the different genomic regions. More refined information should be obtained when the annotation of the cockle's genome is improved.We carried out detailed gene mining on the most consistent genomic regions putatively under divergent selection. They were targeted using different criteria to avoid false positives (two or more consecutive outliers criterion) and to confirm characteristics compatible with selective sweeps (significance and magnitude of linkage disequilibrium). Signals of balancing selection were very weak, as expected in a low structure scenario, and the few markers detected did not meet the criteria established. The percentage of divergent outlier loci detected considering all scenarios (8.7%) was in the range reported in other studies  but, as in the present study, beds from the southern Bay of Biscay were not included. Holey sampling in this region prevents sound seascape conclusions about the putative break responsible for the observed differentiation , and the English Channel and North Sea beds in the east, also interconnected. Concurrently, this northern subdivision was also detected with the larval dispersal modelling performed here. Within the southern group, three subgroups would also be differentiated: southern Iberian Shelf , northern Iberian Shelf  and Arcachon in the central Bay of Biscay (FAR_17). In the larval dispersal modelling, weaker connectivity was also observed between southern and northern Portuguese subgroups during June and July at depths of 15 and 30 m Figures , in contC. edule with regard to swimming behaviour remains to be studied in detail. While the surface releases showed a higher degree of interannual variability, the deeper releases had stable connectivity patterns between years, thus tying in with the genetic structure which seems stable interannually.As outlined above, the genetic structure of the edible cockle in the Northeast Atlantic shows a notable correspondence with the larval dispersal modelling performed here, although some discordances are suggested depending on the set of markers used . However, both data sets might be fully compatible if other variables such as larval depth in the water column is considered in larval dispersal modelling. The simulations carried out with larvae located at three different depths suggest that deeper depths (15 and 30 m) correlate better with the major subdivision observed with neutral loci (north vs. south) and are in accordance with other studies on bivalve larval behaviour, which show that larvae are preferably located below the surface of the water column rather than close to it , our results provide useful information for the management of cockle beds in the Northeast Atlantic and could be valuable for obtaining suitable seed either for restocking of depleted populations or for finding broodstock to enhance cockle production. An improved definition of management units considering both demography and adaptation to environmental variation along the Northeast Atlantic could be initially delineated, allowing the possible future definition of adaptive management units (AMU, Bernatchez et al., Authors have no conflict of interest to declare.Fig S1‐S10Click here for additional data file.Table S1‐S7Click here for additional data file.


The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks , increase the accuracy of association analyses and inform developmental efforts.The online version contains supplementary material available at 10.1186/s12864-021-07618-x. Association studies aim to detect whether genetic variants found in different individuals are associated with a trait or disease of interest, by comparing the DNA of individuals that vary in relation to the phenotypes . For exaThe completion of the human genome allowed genomic biobanks to be envisioned. The International HapMap Project, practically the first international biobank , facilitToday, biobanks are known as massive scale datasets containing many hundreds of thousands of participants from specified populations. Biobanks have brought enormous power to association studies. Although it was unclear whether these new databases would deliver their most ambitious promises, the potential of biobanks in enabling personalised treatment was noted before the technology matured. It was initially expected that these databases would lead to the rapid discovery of a better genetic understanding of complex disorders, allowing for personalised treatments . HoweverBack then, both private and government-sponsored banks began amassing tissues and data. For example, Generation Scotland  includesEven past the formation of biobanks, many associations results failed to replicate  or showWe argue that understanding population structure is a key component to answering these questions and contributing to the usefulness of biobanks and their ability to serve the general population –30. In tHuman genetic variation is a significant contributor to phenotypic variation among individuals and populations, with single-nucleotide polymorphisms (SNPs) being the most common form of genetic variation. Of the entire human genomic variation, only a paucity (12%) is between continental populations and even less genetic variation (1%) is between intra-continental populations . In othepopulation stratification bias, i.e., systematic ancestry differences due to different allele frequencies in the studied cohorts that produced false positives [Unfortunately, worldwide diversity is widely misrepresented in GWAS studies . By 2009ositives . Consequositives  and furtositives . Howeverositives .stratification bias; and thirdly, the unequal representation of diversity within major genetic datasets increases the risk for false positives, due to chance or undetected population structure, and current methods to attempt to correct this underlying population structure are inadequate [Not all major genetic datasets are equally diverse, and most are skewed towards individuals of European ancestry Fig. . For exaadequate . These ladequate  to improPopulation stratification may bias GWAS through two routes: the choice of the cohort and association analysis. Currently, individuals are matched and grouped mainly using self-reported “race” rather than genomic ancestry. This criterion is believed to account for the participants’ genetic background and supposedly allow controlling for population genetic structure . A . A Popule  HapMap populations  or 26 ofMany association studies ignore population stratification or implicitly assume its redundancy if the data were collected from continental groups . GroupsCurrently, “global correction” of such populations using either Principal Components Analysis . Meta-aDetecting associations between genotypes and phenotypes is only the beginning of the process. Different applications are, to various degrees, affected by a bias in the estimates of an effect, which is typically subjected to the very large variance for all but the strongest associations.A and C, exist at this polymorphic site, with those carrying the A allele exhibiting a tendency to smoke more cigarettes. Europeans without cryptic African/East Asian ancestry are unlikely to have the A allele regardless of their smoking practices, which may bias the MR study if ancestry is not properly accounted for in the study design. Within single studies where researchers have access to individual-level data, ancestry may be accounted for, to some extent, by adjusting for principal components. However, MR requires very large sample sizes, which necessitates collaboration across studies and meta-analysis, which may introduce genetic heterogeneity. MR’s susceptibility to population stratification is a well-recognised bias [First outlined by Katan  and furtsed bias , 87 in csed bias .Two-sample Mendelian Randomisation (MR), in which the SNP-exposure association is estimated in one study and the SNP-outcome association is estimated in another, is important because it allows sharable summary statistics to be used for causal inference. Often one or both associations are determined using summary statistics and the researcher does not access the primary data . ImportaRecently, MR studies using a two-sample approach  have beeSimilar concerns were raised by multiple groups concerning polygenic scores. Sohail et al.  reportedGWAS are also used to identify druggable target genes . Whilst There are two cases to consider for modelling population structure: when the individual data for all populations are available and when they are not. With access to the individual data, a wide range of options exist, which can be broadly split again to within-dataset and cross-dataset analysis. Within-dataset analysis for biobanks must scale to hundreds of thousands of samples though need not naturally be comparable. Cross-dataset analyses would typically reference standard datasets, creating a comparable statistic for each individual. Depending on the usage, these references may not themselves be biobank scales. Meta-analysis using summary statistics resembles a cross-dataset analysis, with the further requirement of the creation of sharable summary statistics that remains meaningful without individual-level data.This section summarises the current state of these methods, whilst the Usage section describes the challenges and benefits of the various tools that are available for each function.Genomic ancestry inference may employ specialized markers, such as ancestry informative markers (AIMs), which have significant differences in allele frequencies between populations. For instance, the T allele of the SNP rs316598 is very rare in Africans (3.3%) but common elsewhere and can, thereby, be used to differentiate Africans from non-Africans. AIMs, combined with other methods, can thereby be used to identify the origins of samples, provided that the genomes of worldwide reference populations are available , 58, 109One key advantage in using such markers to intensify the ancestry information, which can lead to its identification using downstream tools, is that frequencies of a particular dataset are sometimes already available as summary statistics. If frequency information has been released, then useful ancestry summaries can be extracted. However, to perform such an analysis in practice requires a global model to combine data together and form a meaningful and comparable report on ancestry for each dataset. This will typically require an examination of the methods to follow. Spatial admixture analysis (SPA) [PCA aims to reduce the dimensionality of the SNP dataset by reducing the genetic markers into principal components (PCs) , 110. Fois (SPA) , for exais (SPA) . These mis (SPA) .Unfortunately, PCA results may not be reliable, robust, or replicable as the field assumes . Given eN by N matrix of genetic similarity. Similarly, the association literature uses the “Genetic Relatedness Matrix” (GRM) [GCTA [GTAK [Bolt-LMM [LMM-OPS [Identity-by-state (IBS) is often used to represent population structure and further represents relatedness (see below). IBS is the proportion of SNPs that are shared between each pair of individuals and therefore forms an x” (GRM) , in whicM) [GCTA  and GTAKTA [GTAK . The GRMTA [GTAK  supplemeBolt-LMM , but the[LMM-OPS  attempt STRUCTURE [K admixture components, which are learned automatically, and represent “historical populations” in the model. Whilst computation was historically a concern, fast enough implementations now exist . Howevetructure , since s unclear , 124 and unclear  and henc unclear .Since related individuals do not represent statistically independent samples and may lead to false-positive associations, association analyses containing related individuals require special care , 126. ThThe difficulty in estimating relatedness increases as the relationship becomes more distant because the IBD DNA segments between individuals are shorter and more difficult to distinguish from DNA segments that are IBS. Typically, all the IBD segments that are more recent than a chosen (average) age of the pairwise relationship are sought by thresholding the lengths of segments.N by N analyses), which is time-consuming. One possibility is to create an unbiased random sample of genes and traits by sampling only one version of each IBD tract since the two copies are clearly dependent. Other possibilities are to treat long IBD as a sparse property, reducing the need to generate a full pairwise matrix.Alone, IBD is not a measure of ancestry, though its results can be summarised into a Kinship matrix analogously to the GRM. Kinship is the probability that two homologous alleles drawn from each of two individuals are IBD , 128. ThN by N matrix. Considering large matrices of pairwise haplotype information (throughout the genome) is not trivial and remains a challenge for biobanks.IBD matches may overlap and may ignore some parts of the genome entirely. An alternative approach is to identify the closest relative for every individual at each position on the genome. This is the approach taken in Chromosome Painting , which aThe purpose of Local Ancestry Inference (LAI) is to analyse individual segments of DNA to establish changes in ancestral origin. Being able to assign an SNP as having originated in a particular ancestry, association testing can, in principle, be carried out in each ancestry as if it were a single sample population.Conceptually, such methods examine a stretch of DNA and use a model related to the mixture approaches to identify the source population. The approaches vary in how appropriate stretches of DNA are defined and how they are matched to the sources. Many approaches use a Hidden Markov Model (HMM), which is strongly related to Chromosome Painting to assign genomic segments to specified reference populations by exploiting LDCurrent implementations may scale to the thousands (see Usage) but are limited in scale for learning population structure and are likely to only form a part of a biobank population model when describing external populations. Additionally, the biological parameters needed  may be unknown and are difficult to learn . A consiThe use of AIMs to represent genetic diversity within a biobank is not well developed. Because AIMs themselves are indicative, but not diagnostic, of a particular population and are a biased sample of the genome (towards ancestry), it is hard to arrive at an ancestry mixture or other measure of structure. However, with efforts in calibration for external datasets, the information required to assess large-scale structures is clearly present in AIMs, which are standard in all commercial microarrays .It is straightforward to project an individual into the genetic variation of a reference dataset when the reference is described by Principal Components. Associated with each SNP and PC is a weighting, and these must simply be summed. This approach is common in the study of ancient populations, which, due to the high missingness of their data, are often described in terms of modern variation .This has not been performed for biobanks because they contain large variations. However, as discussed above, a meta-analysis of many small populations leads to incomplete correction for stratification. Since there is no standard reference, the results of the projection would also be dependent on the choice of the reference populations. Thereby, they can be easily manipulated and are not comparable across studies .ADMIXTURE [The ancestry models described above can all be structured to allow comparison of a sample dataset with respect to a reference dataset. DMIXTURE  is the mSOURCEFIND [When an individual receives ancestry from different sources, they inherit SNPs and haplotypes in proportion to their ancestry from each source. Therefore, significant power can be obtained by considering not only SNPs but also haplotypes, quantified either by IBD, Chromosome Painting, or some other technique. These methods describe kinship or haplotype sharing with the reference. This, in turn, can be used to learn an individual’s ancestry mixture, which is routinely done, for example, via Non-Negative Least Squares (NNLS) , 136 or URCEFIND . BecauseK splits, produced by ADMIXTURE or alike program. These “populations” correspond to the putative ancestral populations of all individuals in the dataset. The advantage of creating these populations from a diverse panel of global individuals is that they can be used as a reference to infer the admixture components  of other individuals without changing the model. The admixture components can be used to correct for population stratification [Frequently, we do not have samples from the underlying ancestral components that led to modern populations. “Gene pool” models allow inferred putative ancestral populations to be used in place of fixed reference populations. Ancestral populations are first generated from the allele frequencies of a worldwide panel of individuals that correspond to chosen fication  in the sfication , has beefication , 139. DeA local ancestry model can be defined by constructing a reference dataset and applying the local ancestry models to identify ancestry structures within the reference. These approaches have not been widely applied to biobanks in the past due to issues of scale. However, as with the genome-wide haplotype approaches, local ancestry can be learned at scale – efficient approaches scale linearly in the biobank size.Local genomic ancestry tools are typically used to investigate ancestry on a granular scale, which is necessary when analysing highly admixed individuals, such as African Americans, Latinos or Ashkenazic Jews , 42. TheHowever, the promise of comparing to a standard reference simultaneously allows the methods to scale sufficiently and allows comparison across datasets. The key unsolved questions, above those for unlinked methods, are around value. This approach generates extremely large datasets of ancestry information potentially at each SNP. Storing and exploiting such information is a considerable ongoing challenge. Would a fine-scale representation of ancestry help understand the distribution of traits? Does it replace, or complement, the simpler approach of representing ancestry as a proportion of the genome?AIMs are identified by finding SNPs that are associated with particular populations or geographic regions. Although many sets of AIMs have been published , they weSTRUCTURE was initially the most popular approach, it suffered from several disadvantages. First, its accuracy and reliability have been a source of concern [STRUCTURE was shown to produce particularly misleading results [STRUCTURE is a notoriously slow tool, which was soon replaced by dramatically faster implementations.Global genomic ancestry tools can be categorised as shown in Figure  Table S. Whilst  concern , 142. WhFRAPPE and ADMIXTURE are based on a similar approach to STRUCTURE, but both use a maximum likelihood estimation approach to optimise the likelihood for allele frequencies and group memberships, using slightly different algorithms. By default, ADMIXTURE uses a block relaxation algorithm that allows for fast convergence and highly accurate parameter estimates [FRAPPE uses solely the EM algorithm [FRAPPE has been demonstrated to not only be much more computationally efficient than STRUCTURE but also to produce significantly fewer biased estimates [ADMIXTURE [STRUCTURE and FRAPPE.stimates  and has lgorithm , which olgorithm . FRAPPE stimates . HoweverDMIXTURE , which wGENELAND [TESS [BAPS [STRUCTURE but consider geographical coordinates in their prior distributions, allowing identification of the spatial location of genetic variants between populations. Therefore, these software not only group individuals genetically into clusters but are also able to estimate the spatial distribution of these clusters [Spatial approaches, exemplified by GENELAND , 146, TEND [TESS  and BAPSSS [BAPS , are conclusters –148. MitGENELAND [TESS and BAPS were shown to outperform GENELAND and each other under some scenarios but not in others [Bayesian clustering models have been known to have different strengths and weaknesses that depend on the spatial genetic patterns present and on factors such as gene flow, dispersal distance and demography. GENELAND , 146 hasGENELAND –151; howGENELAND , 153, anGENELAND , 154 andGENELAND , 146 wern others , 154. Inn others , which hn others .HAPMIX, ChromoPainter, LAMP and LAMP-LD  are typically unknown [MOSAIC [ChromoPainter to learn how frequently haplotypes from different ancestries appear in unadmixed ancestries. It is, therefore, plausible to run at biobank scales in principle, though considerable effort would be required to report and use the per-SNP ancestry information returned.HAPMIX  is a pop unknown . MOSAIC  [MOSAIC  places aLAMP and LAMP-LD work effectively with three-way admixture and gain a computational advantage by ascribing ancestry to pre-defined windows, though neither scales beyond hundreds of samples or tens of thousands of SNPs [ of SNPs  and are  of SNPs .ChromoPainter is part of the fineSTRUCTURE pipeline [PBWT package [pipeline , which apipeline . Chromospipeline  to exami package  that canThese methods allow characterisation of LAI and gain power and resolution through analysis of haplotypes. One typical assumption is that admixture tract lengths are independent and exponentially differentiated; therefore, they are less effective when the admixture is strong because the admixture tracts are longer than expected under an exponential distribution . FurtherOverall, the popular local ancestry tools are positioned along the extreme ends of limited models. At one end are mostly HMM-based tools, that either do not consider LD or are limited to two or three reference populations. At the other end are more robust tools that aim to identify haplotypes, but their high memory consumption limits their usage. An additional limitation of the local ancestry approach is the challenging evaluation of the results in follow-up analyses. The local ancestry approach should be preferred when the loci or region of interest are known; however, in an exploratory GWA or MR analyses, it is unclear how to analyse a large number of segments associated with various ancestral populations. In this case, grouping the ancestral populations into geographical regions may be an appropriate compromise between accuracy and power considerations.PLINK, KING, fastIBD, GERMLINE, PC-Relate and REAP ; KING, REAP and PC-Relate, use the statistical method of moments to estimate the realised k coefficients [KING can produce reliable inferences for large sample sizes (millions of unrelated and thousands of relative pairs) [KING is prone to biased estimates in admixed populations and in the presence of population structure due to the violation of simplifying assumptions that do not hold in the presence of population structure and/or ancestry admixture [REAP [PC-Relate [ADMIXTURE). Bias in these allele frequencies can lead to significantly biased relatedness estimates [PC-Relate has an advantage over KING and REAP, because they, unlike PC-Relate, have difficulty separating unrelated individuals from more distantly related ones [KING and REAP due to its ability to work effectively with admixed populations whilst also being able to distinguish between unrelated individuals and more distantly related ones.ML estimators mostly use an EM algorithm to estimate the ficients . KING cae pairs) . Howeverdmixture . Conversre [REAP  and PC-RC-Relate  are ablestimates . Despiteted ones . Both toted ones . OverallPLINK [PLINK was criticised for producing a high level of false positives  for second-degree relationships [fastIBD [GERMLINE [fastIBD can be applied to large sample sizes across genome-wide SNP data; however, it is obliged to carry out haplotype phasing and is therefore susceptible to phasing errors, particularly if the SNP set is small. Computer memory capacities may also limit the number of individuals that can be phased at one time; therefore, in practicality, it is computationally unfeasible to analyse over 100,000 individuals. Whereas fastIBD is based on shared haplotype frequency, GERMLINE is based on shared haplotype length.Methods for IBD detection identify the similarity between haplotypes that are statistically unlikely to occur in the absence of IBD sharing . PLINK [PLINK  incorporionships . fastIBD[fastIBD  and GERMGERMLINE  detect “Refined IBD [Ramstetter et al.  tested tined IBD  and recoined IBD . Concurrined IBD . Since ained IBD , furtherThe rise of genomic biobanks and biological and computational biotechnology advancements have allowed for significant developments in the field of personalised medicine, making the vision of targeted therapies, accelerated diagnosis and early disease detection become more of a reality. However, the geographic differentiation of human genetic variation (population genetic structure) suggests that the frequencies of certain disease-causing genetic variants, and variants in drug-metabolising genes may differ depending on geographic location, leading to geographic disparities in the susceptibility of an individual to a disease and/or specific drug treatment. Therefore, the lack of representation of diversity in genomic studies poses a limitation in the current global understanding of disease risk and intervention efficacy.It is widely accepted that increased samples from a much more diverse range of populations is required. However, diversity needs to be quantified, compared and annotated within and between biobanks in order to lead to insight. Biobanks must therefore contain genetic annotations that are comparable, computable and compatible across datasets. Whereas previous studies explored the applicability of bioinformatics tools for association studies , this rGenomic ancestry inference encompasses tools that are able to identify the ancestry of an individual by utilising specialised markers to compare the genetic similarity of an individual’s DNA to other individuals sampled from a variety of populations or geographic regions. Global genetic ancestry tools assess the average proportion contributed by each ancestral population across the whole of the individual’s genome, whilst local ancestry inference tools identify the ancestry of distinct segments within chromosomes.Simple descriptions such as Ancestry Informative Markers (AIMs) and Low Dimensional Representation with PCA are useful but insufficient. Current best-practice includes correcting for kinship using the Genetic Relatedness Matrix (GRM) which may be valuable but does not provide a framework for interpreting external datasets.For global genetic ancestry inference, some tools do scale well enough to be considered for biobanks. The limitations include unrealistic assumptions, a tendency to mistake cryptic relatedness for genetic structure, conceptual issues in the interpretation of admixture, and a lack of prior research into how global ancestry can be usefully applied for association studies.GRM approaches can jointly represent population genetic structure and cryptic relatedness, which can avoid consequent false-positive associations in GWAS within a single dataset. More fine-scaled representations exist in the form of kinship (measuring IBD) and haplotype similarity (Chromosome Painting) matrices, which are scalable. In all cases, these capture an inherently noisy and hence statistical property. Consequently, further efforts should be made towards the development of more robust tools for remote degrees of relatedness (over 3rd degree) in diverse samples, especially in the case of cross-dataset comparison. Studies are needed in order to explore the value of these fine-scale approaches for biobanks.Local ancestry inference tools are still slower, though they can be deployed similarly to phasing and imputation should a compelling use case be found. Efforts should be made to develop a new approach that addresses the common limitations, including a requirement of phased data and consequent susceptibility to phasing errors, ability to model LD, and restrictions in terms of the number of populations. The local ancestry approach is clearly deployable when the region of interest is known. It may be useful to group the ancestral populations by geographic region as a way of compromising between accuracy and power considerations. Correctly deployed, local ancestry could correct for local genetic correlations in a way that is much more powerful than simple correlations as captured by LD, though the value for association studies is yet to be determined.Furthermore, the rise of paleogenomic medicine and rapid accumulation of ancient genomes have already shed light on several conditions  of the different GAI software, separated into model-based (red) and non-parametric (blue) tools. The tools were found using existing review papers and free search, using the search engines ‘Google,’ ‘Google Scholar’ and the journal ‘Bioinformatics’ to search for keywords including: ‘software,’ ‘tools,’ ‘inference,’ ‘biogeographic,’ ‘ancestry,’ ‘kinship’ and ‘haplotype.’ The number of citations for the paper proposing the tool, taken from ‘Google Scholar,’ were compared for each tool within each domain. To account for the differences in the number of years since publication, the number of citations was normalised by dividing the number of citations by the number of years since publication. Figure S2. The popularity  of the different LAI software, separated into their technologies: Hidden Markov Model (HMM) (green), Chromosome Painting (red) and Statistical Learning Algorithm (SLA) (blue). Finding the tools and calculating the normalised citation number was done as in Figure S1. Figure S3. The popularity  of the different kinship inference software, separated into their software strategies: Identity-By-Descent (IBD) detection (red) and Kinship Coefficient Estimation (blue). Finding the tools and calculating the normalised citation number was done as in Figure S1


Geranium robertianum, a widespread biennial herbaceous species showing high morphological variation and wide ecological amplitude, can become invasive outside its distribution range. A mixed‐mating system may be expected given the species’ floral traits. However, autonomous selfing is considered as a common feature. Genetic variation and structure, and so population mating processes, have not been investigated in wild populations. We developed 15 polymorphic microsatellite markers to quantify genetic variation and structure in G. robertianum. To investigate whether selfing might be the main mating process in natural conditions, we sampled three generations of plants  for populations from the UK, Spain, Belgium, Germany, and Sweden, and compared open‐pollinated with outcrossed hand‐pollinated F2 progeny. The highly positive Wright's inbreeding coefficient (FIS) values in adults, F1, and open‐pollinated F2 progeny and the low FIS values in outcross F2 progeny supported autonomous selfing as the main mating process for G. robertianum in wild conditions, despite the presence of attractive signals for insect pollination. Genetic differentiation among samples was found, showing some western–eastern longitudinal trend. Long‐distance seed dispersal might have contributed to the low geographic structure. Local genetic differentiation may have resulted not only from genetic drift effects favored by spontaneous selfing, but also from ecological adaptation. The presence of duplicate loci with disomic inheritance is consistent with the hypothesis of allotetraploid origin of G. robertianum. The fact that most microsatellite markers behave as diploid loci with no evidence of duplication supports the hypothesis of ancient polyploidization. The differences in locus duplication and the relatively high genetic diversity across G. robertianum range despite spontaneous autonomous selfing suggest multiple events of polyploidization.Knowing species’ breeding system and mating processes occurring in populations is important not only for understanding population dynamics, gene flow processes, and species' response to climate change, but also for designing control plans of invasive species.  Geranium robertianum in wild populations, despite the presence of attractive signals for insect pollination. Genetic differentiation showed some longitudinal trend. Long‐distance seed dispersal might have contributed to the low geographic structure. Local genetic differentiation may have resulted not only from genetic drift effects favored by spontaneous selfing, but also from ecological adaptation. The presence of duplicate loci with disomic inheritance is consistent with the hypothesis of allotetraploid origin of G. robertianum.Genetic analyses based on 15 polymorphic microsatellite markers supported autonomous selfing as the main mating process for  Strict allogamy may also evolve into a heteromorphic self‐incompatibility system preventing selfing or into dioecy  is a common, biennial, ruderal herb and is highly variable morphologically. The species shows a wide ecological amplitude, mainly occurring in woodlands and hedge banks, but also in various open habitats, such as grasslands, wastelands, railway banks, skeletal soils, and walls, on calcareous and acidic soils , which is similar to other Geranium species  for populations from the UK, Spain, Belgium, Germany, and Sweden, and progeny obtained from outcrossed hand‐pollinated were compared with progeny in open‐pollinated conditions.s Tofts, . It is ws Tofts, . Individs Tofts, . The dehs Tofts, . Flowersy Tofts, . As a rey Tofts, . Moreove22.1G. robertianum, 43 populations were selected from various calcareous or acidic habitats , from the UK, Spain, Belgium, Germany, and Sweden . Leaves were collected from a total of 191 individuals  and dried in silica gel. Sampling occurred during the peak of flowering and pollinator activity, between mid‐June and mid‐August. F1 progeny was obtained from germination of seeds collected in wild populations on different maternal plants separated at least 1 meter from each other. F1 plants from eight populations from UK and Germany were grown (1) in the Botanical Garden of the Phillips‐Universität Marburg (Germany) in 2013 until flowering and fruiting, with plants randomly distributed ; F2 seed progeny was obtained from germination of seeds collected on the F1 plants after open pollination (F2o); (2) in a nonheated greenhouse at Meise Botanic Garden (Belgium) in late May‐June 2019, where F2 seed progeny was obtained after outcrosses (F2c) between F1 plants  as described in Van Rossum, Destombes et al. . GenomicTM Formamide  and GeneScanTM 500 LIZ® Dye Size Standard (Applied Biosystems). Fragments were migrated on a 3730XL DNA capillary sequencer (Applied Biosystems). Alleles were scored using the microsatellite plugin in Geneious 11.1.2 (Biomatters). Finally, 15 polymorphic and interpretable markers  and covering a wide range of PCR product sizes (from 101 to 316 bp) were tested for amplification on eight DNA samples. PCR amplifications were carried out in 10 μl reactions containing 20 ng of template DNA, 1× reaction buffer, 37.5 pmol MgCl2, 6 pmol dNTP, 10 pmol of each primer, and 0.5 U Taq polymerase (FastStart—Roche Diagnostics). The PCR cycling consisted of an initial denaturation at 95°C for 10 min, followed by 40 cycles: denaturation at 95°C for 30 s, annealing at 55°C for 30 s and extension at 72°C for 1 min, and a final extension at 72°C for 10 min. Primer pairs were discarded after migration of PCR products on 2% agarose gel electrophoresis when they did not amplify or gave multiple fragments. As a result, 39 primer pairs were validated from which 24 microsatellite loci that showed good amplification for all individuals and still covered a wide range of PCR product sizes (from 101 to 316 bp) were selected for polymorphism study on 15 DNA samples. PCR amplifications were performed with the same conditions than previously but with labeled primers (Di‐repeat +tail Applied Biosystems). Each PCR product (diluted at 1:50 with dH2O) was mixed with Hi‐Dirs Table  and subs2.32.3.16 Markov Chain Monte Carlo iterations, of which the first 105 were discarded as burn‐in phase to test two models: a full model  and a model (nb) where there was no inbreeding. The best fitting model corresponded to the lowest value of the deviation information criterion (DIC). INEST also estimated null allele frequencies for each locus with their 95% highest posterior density intervals (HPDI). To assess the independence of the loci, a test for genotypic disequilibrium was performed between pairs of loci with sequential Bonferroni‐type correction  and Wright's inbreeding coefficient (FIS), corrected for small sample size, were calculated for each locus and for each generation  using FSTAT. We tested the significance of the FIS values  by randomization tests and sequential Bonferroni‐type correction. Differences in He and FIS between F1, open‐pollinated, and outcross F2 progeny were tested for the eight populations used for the crossing experiment  values of the 15 independent runs computed with CLUMPP version 1.1.2  based on a standardized distance matrix using GenAlEx 6.5  showed two to four peaks ascribed to different alleles Figure . From thp < .05) for adults and F1 progeny, respectively.We scored two to 16 alleles in the 20 loci for a total of 133 alleles (Table 3.2He) values per locus ranged from 0.037 to 0.901 with a mean of 0.550 for adults and F1 progeny collected in wild populations . Wright's inbreeding coefficient (FIS) values significantly (p < .05) differed from the Hardy–Weinberg expectations, with a deficit of heterozygotes for almost all loci, except for (F2c) progeny, for which FIS values were not significant or significantly negative . FIS values were slightly but significantly lower  for the F2o progeny obtained from open pollination of randomly distributed plants from eight populations, compared with F1 progeny collected in the same eight wild populations , suggesting the occurrence of some, however, limited, outcrossing. The IIM analysis indicated that inbreeding contributed to the positive FIS values (lowest DIC for the nfb model). Population DI2, for which 21 adults were sampled across the whole population, showed 11 different multilocus genotypes, with 1–9 individuals sharing the same multilocus genotype, He = 0.318 and FIS = 0.656 .Expected heterozygosity  values for cluster 1  and clustered together with a few German and Belgian samples   and some longitudinal trend for the continental populations  Figure . A secons Figure . The clu4FIS) values found for adults, F1, and F2o progeny supported the former hypothesis . Local genetic differentiation between populations may have resulted not only from genetic drift effects promoted by spontaneous selfing, but also possibly from local ecological adaptation  samples and some western–eastern longitudinal trend. Long‐distance seed dispersal Tofts,  might haG. robertianum is an allotetraploid resulting from hybridization between G. purpureum and another unknown parental species, based on chromosome numbers, morphological similarities, cytological observations, and nectar composition ; Formal analysis (lead); Methodology (supporting); Visualization ; Writing‐original draft (lead). Olivier Raspé: Conceptualization (supporting); Formal analysis (supporting); Methodology (supporting); Validation (supporting); Writing‐original draft (supporting); Writing‐review & editing . Filip Vandelook: Conceptualization (lead); Formal analysis (supporting); Methodology (lead); Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing .Fig S1Click here for additional data file.


Pinna nobilis, endemic to the Mediterranean Sea, is a critically endangered species facing mass mortality events in almost all of its populations, following the introduction of the parasite Haplosporidium pinnae. Such a unique pandemic in a marine organism, which spreads rapidly and with mortality rates reaching up to 100%, could lead to the potential extinction of the species. Only few regions, involving lagoon habitats, remain healthy throughout the entire Mediterranean Sea. This study describes the genetic structure of P. nobilis across the Gulf of Lion, including confined locations such as lagoons and ports. A total of 960 samples were collected among 16 sites distributed at 8 localities, and then genotyped using 22 microsatellite markers. Genetic diversity was high in all sites with mean allele numbers ranging between 10 and 14.6 and with observed heterozygosities (Ho) between 0.679 and 0.704. No genetic differentiation could be identified (FST ranging from 0.0018 to 0.0159) and the percentages of related individuals were low and similar among locations (from 1.6 to 6.5%). Consequently, all fan mussels, over the entire coastline surveyed, including those in the most geographically isolated areas, belong to a large genetically homogeneous population across the Gulf of Lion. Considering the ongoing mass mortality context, this result demonstrates that almost all of the genetic diversity of P. nobilis populations is still preserved even in isolated lagoons, which might represent a refuge habitat for the future of the species.The fan mussel,  These extinctions are mainly caused by anthropic activities such as pollution, artificialization, overexploitation, habitat loss and global change, which can lead to the isolation of populations, reduction in their size, and thus a loss of genetic diversity2. The conservation genetics concept has been pushed forward in the context of population genetics with the goal of better understanding the dynamics of genes in populations to avoid extinctions. The idea is to apply genetic methods to the conservation and restoration of biodiversity, with genetic diversity being the proxy for the level of extinction risk. Genetic diversity enhances the stability and reliability of ecosystems by providing biological insurance against environmental change3. Hence, maintaining a high level of genetic diversity is one of the main targets of conservation biology. Knowledge concerning the level and the distribution of genetic diversity within and among populations of a given species, and on the processes that ensure its maintenance are now more than ever, necessary in order to design conservation strategies.Recent extinction rates have been shown to be 100 to 1000 times their pre-human levels in taxonomically diverse groups through a wide variety of different habitat types, and they continue to accelerate4. This dispersal phase allows connectivity between distant areas and is thus a critical feature of the ecology of marine organisms and evolution, as it drives the genetic structure and composition of populations5. The degree of connectivity between populations has an impact on local population survival through replenishment processes, the maintenance of high levels of diversity and by determining their persistence and resilience in case of local disturbance7. Enhancing dispersal will certainly favor the long-term persistence of species, in a framework where dispersal will be affected by parameters such as habitat fragmentation, effective population size, anthropic activities and climate change9.In marine systems, coastal environments are generally viewed as discontinuous and most organisms are sessile or have very limited movement abilities. There is thus a strong interaction between habitat fragmentation of coastal environments and species life-history traits that can affect dispersal and population connectivity, as found for the pelagic early life stage which represents the main opportunity for movementPinna nobilis , is a bivalve endemic to the Mediterranean Sea, that lives half-buried in soft-bottom habitats, generally covered by seagrass meadows. Because it is associated with seagrass meadows, P. nobilis is restricted to subtidal areas, up to 30 m deep, as beyond the meadow development is limited by light intensity and water transparency. As a consequence, P. nobilis distribution is usually aggregative and fragmented. Even if this association is generally accepted, very dense aggregations of the species were recently reported in lagoons or in artificial and degraded habitats such as ports, which puts into question the species’ habitat requirements10.The fan mussel, P. nobilis is a successive hermaphrodite organism with asynchronous gamete maturation, thus preventing self-fertilization11. Several consecutive spawnings occur during summer, producing millions of veliger larvae. For P. nobilis, larval duration has been estimated to be around 5 to 10 days during which larvae are spread by currents12. However, there remains a significant lack of knowledge concerning the P. nobilis life cycle, as a recent study has shown that the larval stage could last up to 20 days under controlled conditions13, a much longer larval duration which would certainly enhance dispersal capabilities.P. nobilis populations under high pressure which has led to population decay14. Since 1992, the fan mussel has been under strict protection, according to the European Council Directive 92/43/EEC (Annex IV) and the national laws of most Mediterranean countries. In recent years, P. nobilis abundance has increased and dense populations have been observed in anthropized habitats, such as lagoons and ports, which were previously thought to be unfavorable to their settlement as the species is generally considered as a bioindicator of the quality of coastal waters15. However, despite its endangered status, only few studies have investigated population genetics and connectivity patterns in P. nobilis21. Furthermore, information on the contemporary dynamics is limited, as none of the previous studies integrated the large populations recently described in lagoons.The species has been overexploited for years, for consumption or for jewelry fabrication. Over the last decades, human activities  have kept Haplosporidium pinnae22. However, the causes of the outbreak are far from being completely understood as one recent study supported the occurrence of a multifactorial disease with non-species-specific pathogens that could have triggered mass mortalities in P. nobilis populations23 whereas another study stated that H. pinnae is the only pathological agent considered as essential for the onset of the mortality24. The very first signs of mortality were observed in southeast Spain25 and the epidemic has now spread throughout the entire Mediterranean Sea26. To date, most of the infected populations have been devastated. This is an unprecedented situation for which neither the mortality rates (around 100%) nor the speed of propagation27 have ever been recorded for a marine species, and it could lead to the potential extinction of P. nobilis. Following this critical situation, the status of the species has been reevaluated and has been recently moved to “critically endangered” on the IUCN red list28. However, a couple of populations in localities along the early infected Spanish coastline remain less affected by the parasite mortality . Even if the reasons are not yet well understood, greater survival rates might be linked to salinity ranges in those areas compared to the open sea26: Alfacs Bay is subjected to high freshwater inflow of the Ebro River whereas Mar Menor is a confined coastal lagoon where salinity can reach up to 40 PSU. Even if observations are limited, lagoons are now a priority habitat in Spanish conservation plans, as large populations have recently been described in this habitat type throughout the Mediterranean Sea.Today, the species is facing a major ongoing pandemic that threatens its survival. Starting in October 2016, mass mortalities have been observed in fan mussel populations, probably caused by the protozoan parasite, 29. Recent surveys in these habitats revealed the presence of P. nobilis in high densities, even much higher than those observed in the usual seagrass meadows . Recent monitoring data demonstrated that almost 90% of the population of P. nobilis is found in the lagoons of Thau and Salses-Leucate along the Gulf of Lion10 and, to date, these areas are still free from parasite contamination. Considering the current pandemic situation for P. nobilis, these lagoons may represent a key habitat in a conservation strategy and may provide hope for the persistence of the species. In the meantime, as these lagoons are unstable environments, facing variations in nutrient concentrations or fresh water inflows29, the long-term survival of the species may be uncertain. Lastly, these lagoons are rendering the distribution of P. nobilis highly fragmented and isolated in small patches, and require better knowledge about the population structure and dispersal patterns of the species. Here, we used highly polymorphic markers to (i) assess the genetic diversity of fan mussel aggregations in the different types of habitats, (ii) estimate whether the habitat fragmentation tends to create genetic differentiation and spatial genetic structuring between aggregations and (iii) assess the regional patterns of dispersal throughout the Gulf of Lion. This work is the first genetic study of P. nobilis populations in the highly fragmented environment of the Gulf of Lion where lagoon habitats were also considered, before the spread of the pandemic along the French Mediterranean coast.While accounting for about 250 km of coastline in the Mediterranean Sea, the Gulf of Lion is characterized by numerous lagoons with various hydromorphological characteristicsP. nobilis is an endangered species, we received necessary permissions for sampling from the DREAL  of Occitanie . All Methods were carried out in accordance with relevant guidelines and regulations. Samples were collected from all sites where aggregations of P. nobilis were observed following a year of extensive and systematic searching in all habitats along the coast of the Gulf of Lion, from the Spanish border to the Rhône estuary. A total of 960 tissue samples were collected from 16 sites along the coast during the summer of 2018  or population density for further genetic analyses. Sites were structured in 8 localities which refer to the geomorphological entities where they were sampled . As a preventive measure, the smallest individuals were not sampled to avoid lethal biopsies. Each tissue sample was stored in absolute ethanol at room temperature.As P. nobilis lives half-buried in the sediment, it is impossible to directly measure the total length of the shell and thus the size of each sampled individual had to be estimated from three morphometric measurements: the maximum width, the minimum width and the unburied length, as described in Garcia-March et al.30. The following equation, described in De Gaulejac31, is used to determine the total length:Ht is the total length; H the unburied length; l the minimum width and L the maximum width.The size of each individual sampled was calculated to identify different size groups that will correspond to fan mussels that settled over successive years and which could therefore represent different clouds of larvae. Because The size estimates were used to further differentiate size groups and to investigate genetic structure in relation to size structure in order to search for genetic heterogeneity of recruitment.32 and González-Wangüemert et al.33.Extraction of genomic DNA from the 960 samples was performed using the QIAcube HT robot following the manufacturer’s instructions . All samples were genotyped using 30 microsatellite markers from Peyran et al.32. Microsatellite markers were amplified using Type-it Microsatellite PCR kit  in a final volume of 12.5 µL including 4 µL Type-it Multiplex PCR Master Mix (2X), 6 µL RNase-free water, 1 µL of each primer (2 µM forward and reverse primers diluted in TE pH 8 buffer) and 1.5 µL of DNA template with a concentration of around 50 ng/µL. PCR programs consisted of an initial denaturing step of 5 min at 95 °C, followed by 40 cycles of 30 s at 95 °C, 1 min 30 s at annealing temperature , 30 s at 72 °C and a final elongation step at 60 °C for 30 min. Loci were combined in multiplex panels according to their allele size and primer annealing temperature. Individuals were genotyped by assessing the allele size. Forward primers were labelled with fluorescent dyes  and PCR products were sent to a private company, GenoScreen , for fragment genotyping where they were visualized using an Applied Biosystems 3730 Sequencer. GeneScan 500 LIZ (Applied Biosystems) was used for accurate sizing. Allele sizes were scored and checked manually using GENEMAPPER v.5 (Applied Biosystems). Samples presenting ambiguous peak profiles were re-amplified, genotyped and re-scored and all peak profiles that were still unclear were treated as missing data.PCRs were performed following the same method as described in Peyran et al.34 to search for the presence of null alleles, scoring errors and large allele dropout. On the original panel of 30 markers, eight loci presented evidence of null allele high frequencies and were removed from the study to avoid any bias in further analyses. The final set of markers contained 22 polymorphic loci , number of private alleles (Ap), and expected (He) and observed (Ho) heterozygosities. As sample size ranged from 35 to 262 across localities, standardized allelic richness (Ar) and standardized private allelic richness (Apr) were estimated using ADZE software36, based on a standardized sample size of 35 . Hardy–Weinberg exact test was also performed for each loci using GENEPOP software37. The p-value of the exact test of Hardy–Weinberg was estimated based on a Markov Chain algorithm . Values for inbreeding coefficient (FIS) were calculated for all loci and for each localities using the method of Weir and Cockerham38, implemented in Genetix39 and significance of values was established by permutations (1000 permutations per population). Differentiation index (FST) was calculated between locality pairs and between size groups by using the Robertson and Hill estimator for FST40 corrected by Raufaste and Bonhomme41, RH’, implemented in Genetix, as this estimator is unbiased and shows a lower variance when values of FST are low (< 0.05). The Bonferroni sequential correction for multiple tests was then applied to correct the p-values42.Genetic diversity was investigated using stats package implemented in R software v. 1.1.45343 and based on the matrix of Nei’s genetic distances between localities which was calculated using GenAlex. The molecular genetic variance was analyzed between and within localities using the AMOVA framework in Arlequin suite v. 3.544.An exploration for any genetic structuring was also performed with a MultiDimensional Scaling (MDS), using the 45. An admixture model with no prior information about sampling location was used with a burn-in period of 50 000 iterations, followed by a 100,000 Monte Carlo Markov Chain (MCMC) replicates for K = 1 to K = 8 clusters and 10 iterations for each K. The choice of the most likely number of clusters (K) was made by calculating an ad hoc statistic (ΔK) using Structure Harvester online46 and based on the rate of change in the log probability of data between successive K as described by Evanno et al.47. Several authors pointed out that uneven subpopulations sampling may bias STRUCTURE results and lead to overestimate the number of clusters48. StructureSelector49, a web based software, was thus also used to estimate the number of clusters (K) based on four estimators, MedMedK, MedMeaK, MaxMedK and MaxMeaK, developed by Puechmaille48 and that were shown to be less sensitive to uneven sampling.Genetic structure was also explored using a model-based clustering method implemented in STRUCTURE 2.3.4ape50.A Mantel test was performed to test the isolation by distance hypothesis, based on geographic (distances with straight lines or those which followed the coastline were both tested) and Nei’s genetic distances between locations. The analysis was conducted using the R package rxy coefficient51 using the R package Demerelate52 to investigate family structure within localities and within sites. The relatedness coefficient indicates the proportion of shared alleles between two individuals. Thus, with a sufficient number of highly polymorphic loci, a rxy > 0.25 between two individuals assumes that they have at least one parent in common. The percentage of individuals sharing at least one parent (Prxy) was calculated within each locality (i.e. the percentage of relations with rxy > 0.25) and for each size group that could be identified in some localities. This percentage gives a proxy of the admixture in each locality as relatedness indexes are calculated between individuals of different size classes. Because the percentage of individuals biopsied in relation to the estimated abundance is different among localities and very low in some cases  in the existing database . For each locality, we thus had new datasets with 10, 13, 16, etc. or 10, 15, 20, etc. individuals until approaching the sample size. The procedure was repeated five times to increase the number of new datasets. Relatedness coefficients were calculated between all pairs of individuals within each of these new datasets and Prxy was estimated. A non-linear regression was then performed to model the relation between the number of individuals sampled and Prxy in each locality in order to estimate the threshold of sampling effort where adding more individuals does not increase the percentage of related individuals found. The non-linear regression was also used to extrapolate the maximum Prxy in localities where sample size was too small.Lastly, the genetic relatedness was calculated between all pairs of individuals by the The sampling was non-lethal and approved by the DREAL  of Occitanie .FIS values, indicating a significant heterozygote deficiency and departure from Hardy–Weinberg equilibrium. The analyses revealed a high level of genetic diversity as the mean allele number per locality (Na) ranged from 10 to 14.6 (Table Ar) was very similar between localities as it ranged from 8.1 to 8.5. The number of private alleles (Ap) was higher in Leucate and Thau, which are the two most isolated localities. However, the standardized private allelic richness (Apr) ranged from 0.26 to 0.37 and is thus very similar within localities. The observed (Ho) and expected (He) heterozygosities ranged from 0.679 to 0.704 and from 0.705 to 0.726, respectively. Overall, the number of alleles and the heterozygosity values were very homogeneous among localities and there was no decrease in diversity in isolated lagoon areas.13 out of 22 markers  and only two were significant after Bonferroni sequential correction: between Thau and Leucate and between Thau and Cap d’Agde  except for Frontignan (1.6%) and Port Ambonne (6.5%). Similar results were found when calculating Prxy within sites .The non-linear regression between xed Fig. . Prxy waFST values were non-significant .The mean sizes ranged between 30 and 45 cm except in Thau and Ayrolle where there was a larger number of small individuals  as allelic richness was similar among localities when standardizing sampling effort. The levels of diversity and heterozygosity of 68 studied the relatedness of red coral individuals and found that 25.98% of pairwise comparisons involved related individuals which is much higher than what we are reporting for P. nobilis.The percentage of related individuals was not larger in a given locality than considering all individuals mixed in a single sample, even for the most isolated localities such as lagoons. The two extreme values (Frontignan: 1.6% and Port Ambonne: 6.5%) were found in the smallest harbors with the smallest abundances. Low abundances increase the effects of the heterogeneity of larval supply, during recruitment, in both directions, thus leading randomly to more or less related individuals. Overall, only a small proportion of individuals showed relatedness values > 0.25 (mean of 3.3%) emphasizing evidence of large effective population size and substantial admixture during reproductive events. This result also correlates with similarity of the diversity indices among localities. Moreover, in the three sites where two size classes could be identified, individuals did not show more relatedness within a given size group. Only few studies investigated relatedness in wild marine species and to date, none of them have studied relatedness in the Gulf of Lion and within lagoons. However, Costantini et al.P. nobilis originate from large effective population sizes. This is mainly linked to the reproductive mechanism that consists of the synchronic release of gametes by all individuals to ensure success in the fecundation and an increase in admixture. In this framework, the question of connectivity between aggregations remains, since the large population size in lagoons could maintain high genetic diversity indexes while, at the same time, this high diversity in small isolated harbors could indicate a supply from outside populations.Both diversity indices and relatedness estimates demonstrate that even if patchy and isolated, aggregations of P. nobilis located in lagoons, the potential for a high self-recruitment rate remains likely as these lagoons have limited water inflow with the open sea which could limit the exportation of pelagic larvae. Then, as we are dealing with a species characterized by large effective population size, in relation to its reproductive mode, only a small amount of larval supply from other localities should be necessary to maintain low FST values, limited relatedness and the overall genetic homogeneity. In the meantime, due to their properties, larvae from all localities, even enclosed lagoons, have the potential to be exported to the open sea and spread to all localities, drifted by currents, which would also maintain the overall genetic homogeneity. Both scenarios would explain the low proportion of related individuals, as recruitment is the result of a large reproductive event, involving a large number of spawners, and relatives will not necessarily remain together until settlement (i.e. no kin aggregation).None of the analyses performed in this study showed genetic differentiation among localities when considering genetic diversity levels, allele frequencies or relatedness index . All fan mussel aggregations belong to a large homogeneous, highly connected, population across the entire Gulf of Lion. Even small aggregations located in isolated areas (lagoons or ports) were not genetically segregated. The absence of spatial genetic differentiation is often interpreted as a signal for significant connectivity but this is primarily an equilibrium between genetic drift and migration. In the context of large effective population size, the genetic drift will be limited and only a small amount of migration is required to maintain genetic homogeneity. Considering the isolated aggregations of Concholepas concholepas69, Crepidula fornicata57 and Pecten maximus70). Population genetics studies were performed for P. nobilis in Balearic Islands and along the Spanish coast and authors reported no, or very weak, genetic differentiation between sites20. Populations were situated in open sea locations and authors associated the lack of genetic structure to a high connectivity in their study area. Simulations performed by Wesselmann et al.20, highlight the high dispersal potential of fan mussels during the pelagic larval state. In the present study, considering the size of the study area and the dispersal potential of larvae, the hypothesis of high connectivity could thus be consistent, at least between the most exposed locations, and even between the most distant ones such as Côte des Albères and Port-St-Louis.The absence of genetic differentiation is common in mollusks, particularly at this spatial scale, as species are known to have large effective population sizes and long larval durations  that implies some connectivity among these localities that maintain genetic diversity. The question concerning the contribution of this connectivity within the context of repopulating localities remains, as we could oppose two scenarios involving a dominance of migration vs one with a negligible migration and dominance of self-recruitment from large local populations. In the case of large lagoons such as Thau and Leucate, it is likely that self-recruitment dominates the local recruitment since all size classes and more regular settlement patterns are present. Conversely, in the case of small harbors, repopulation is highly dependent on migration that is typically more variable, leading to more variability in size classes.Results from the present study on P. nobilis is a sedentary endemic species with a highly fragmented distribution. This study highlights a large genetically homogenous population with high genetic diversity across the entire coast of the Gulf of Lion. This high genetic diversity may involve a large number of spawners and thus the population may have a large effective size. Maintaining a high level of genetic diversity in populations is one of the fundamentals of genetic conservation as it preserve the species’ abilities to adapt to environmental changes and increase resilience75. Considering the results of the present study and regarding the pandemic occurring throughout the Mediterranean Sea, preserving fan mussels in a single locality, for example, might allow for the entire genetic variability of fan mussels in the Gulf of Lion to be preserved. Then, as migration between localities, even small, might exist, it could help to repopulate other areas. In a context of developing a species rescue plan, conserving a relatively small number of individuals in aquariums, under controlled conditions to protect them from being infested by the parasite, might be enough to provide a backup of almost the entire genetic diversity of the species.H. pinnae and leading to mass mortality, has now spread throughout almost the entire Mediterranean Sea and has already reached the coast of the Gulf of Lion where all of the infected populations have been devastated. To date, only individuals settled in lagoons remain less or unaffected and the survival of the species is likely to rest on them, as there is no longer dense population in the open sea. The present study thus has important implications for conservation as it highlights the opportunity to find a habitat that is suitable for fan mussels but which is unfavorable to the development of the parasite. Lagoons, thanks to their particular physico-chemicals conditions, might thus represent a refuge habitat for fan mussels, which could be the key for the persistence of the species in the wild.This study also showed the wide diversity of habitat types that the fan mussels can colonize. The epidemic, caused by the parasite Supplementary Information.