Spodoptera frugiperda  was reported in West Africa in 2016, followed by its chronological detection across the Old World and the hypothesis of an eastward Asia expansion. We explored population genomic signatures of American and Old World FAW and identified 12 maternal mitochondrial DNA genome lineages across the invasive range. 870 high-quality nuclear single nucleotide polymorphic DNA markers identified five distinct New World population clusters, broadly reflecting FAW native geographical ranges and the absence of host-plant preferences. We identified unique admixed Old World populations, and admixed and non-admixed Asian FAW individuals, all of which suggested multiple introductions underpinning the pest’s global spread. Directional gene flow from the East into eastern Africa was also detected, in contrast to the west-to-east spread hypothesis. Our study demonstrated the potential of population genomic approaches via international partnership to address global emerging pest threats and biosecurity challenges.Native to the Americas, the invasive  This population genomics study identifies the complex multiple introduction history of Spodoptera frugiperda  from the Americas, into Africa and Asia. This provides new insight into the ‘east-to-west’ directionality of gene flow, and suggests ample genomic exchange at the nuclear level. Spodoptera frugiperda was first reported in West Africa  in early 20161, followed by confirmation across central  sub-Saharan Africa between 2017/20186; the Middle East (Yemen7), India9 and surrounding nations, Myanmar10 and Thailand11 (August and December 2018), followed by Southern China (Yunnan Province) in early January 201914. Detections of FAW since January 2019 have gathered speed: south-ward to Malaysia (March 2019) and Indonesia ; Hong Kong (April 2019), Taiwan (May/June 2019); Laos and Vietnam (April 201915), the Philippines (June 201917), South Korea (June 2019), and Japan (June 2019)18. Within China, the FAW has been reported in a northward expansion pattern from Yunnan to 18 provinces by July 201921. As of September 2021, over 70 African and Asian nations have reported FAW22. In January 2020, FAW was trapped in Australia’s special biosecurity zone in the Torres Strait islands of Saibai and Erub, and confirmed on 3 February 2020, and on mainland Australia in Bamaga on 18 February 202024.Long an important pest of agriculture in its native New World range, the fall armyworm (FAW) 25 that the FAW was actually spreading west-to-east across and then from Africa. Based on the detection timeline, predictive simulations that assumed human-assisted spread, in particular, eggs on aircraft carrier surfaces26 and from agricultural trade  associated with egg and larval stages, have modelled this very vagile pest’s movement from the east coast of America/the Greater Antilles to West Africa3 between Central and Southern America and Africa, and between Africa and Asia28). Movements of soil from countries known to have FAW into the EU are prohibited and were put in place to limit accidental introductions of pupae27. The human-assisted spread model28 was also used to warn China and South East Asian nations of imminent impact by FAW following confirmation of the pest in India29. This model further forms the basis of international research efforts to track the movement, including using molecular tools to examine invasion biology , and simulations to model long-distance dispersal . Indeed a meteorological data-based simulation study concluded the Yunnan FAW populations originated from Myanmar, consistent with FAW being officially reported earlier in Myanmar35 than in China19. Other work has examined the impact and implications for global plant health and agricultural biosecurity , integrated pest management (IPM) and bioeconomics39, and insecticide resistance40.This chronologically ordered eastward spread of detections led to a widely adopted assumptionS. frugiperda , that have also been considered as potential sister species42 that exhibited low but significant genomic variation (~1.9%)30. These two host races are supported by phylogenetic analyses based on nuclear and mitochondrial DNA genomes30, and partial mitochondrial DNA genes . The distribution of these Sfr and Sfc populations in their New World range has only recently been investigated based on partial mitochondrial and nuclear genes43, while at the whole genome level they are less well-understood. Genotypes from both host races/sister species are present in the invasive populations . Since 201048 and especially in recent times during the FAW range expansion49, the partial Triose Phosphate Isomerase (Tpi) gene on the Z-chromosome has been adopted to clarify the Sfc/Sfr host race status. The Tpi marker relies on the presence of a single nucleotide polymorphic (SNP) marker at position 18349 to differentiate between corn- or rice-preferred FAW. Similarly, inconclusive host preferences based on the mtCOI gene marker also detected Sfc and Sfr on corn-host plants . Contrary to the introduction patterns of the noctuid H. armigera in South America50 which showed high genetic diversity53 similar to that reported for global H. armigera populations56, the current global partial mtCOI signatures of both Sfc and Sfr have each been consistent with a single ‘bridgehead effect’57 introduction, which, when considered together with the Tpi locus, was suggested to likely have a Florida/Greater Antilles source population3.Genetic studies on the spread of FAW have focussed on single genes on the mitochondrial genome, occasionally supported by a single partial nuclear gene marker. These markers have been widely used because, throughout much of the native range, FAW populations consist of two morphologically identical host races, the rice-preferred and corn-preferred 58 and eliminate candidate populations59 as well as to elucidate hybrid signatures60. Furthermore, under the current assumption , subsequent invasive populations would share the founding population’s genomic signatures, since as the more evolutionarily parsimonious scenario, only a single genetic change to match between the environment and the bridgehead invasive individuals would be needed57.What is missing from current research into the spread of FAW is an analysis of broader genomic evidence. Genome-wide SNP markers aligned to well-annotated genomes can provide powerful genomic evidence for understanding introduction pathwaysIn this study, we provide an assessment of global FAW movement history based on genomic data that incorporates populations from Northern, Central, and Southern Americas, and the Caribbean , Western and Eastern Africa, and Western and Eastern Asia, representing the pest’s Old World expansion. Here we reveal a multi-locus invasion that is likely independent of the reported detection patterns and their timelines, and provide genomic-based evidence to support multiple introductions of the FAW into the Old World, with movements of FAW detected between Asia and Africa. We also re-evaluated the pest’s global spread directionality to highlight implications in the future management of FAW, and the need for ongoing global agricultural biosecurity research and cooperation to improve preparedness for emerging invasive agricultural pest issues.TPI partial gene from the z-chromosome. Due to the non-recombinant and maternally inherited mode of the mitochondrial DNA genome , it is also possible to infer the minimum number of unique female founders responsible for establishing the invasive populations, as well as their likely native population origin/s through matching between mitochondrial genomes. We first examined the mitochondrial genome diversity in the invasive range to determine minimum maternal lineages, and an overview of the strain composition in both native and invasive populations. Of the 197 FAW individuals sequenced  mitochondrial DNA genomes of all individuals identified two distinct clades that corresponded to the ‘rice-preferred’ and ‘corn-preferred’ clusters Fig. . Based oThe ML phylogeny based on 870 unlinked and neutral SNPs revealed four distinct clades , Uganda, Benin and India are also present, indicative of likely separate introductions of FAW from the population(s) with genetic similarity to the Mississippi population into the Old World. Clade III is represented by a separate Brazilian  FAW population and the Peru FAW individuals. Invasive populations clustered within clade III were the Malawi FAW population, a single Tanzania and three Ugandan individuals, suggesting that these African FAW shared a similar origin that is different from other African  and Asian populations. The Ugandan population, in particular, appears genetically highly heterogeneous, indicating it too has mixed introduction backgrounds.Clade IV is dominated by the Florida population and other Caribbean islands/Greater Antilles /Lesser Antilles / Central American , and parts of the northern region of South America  FAW populations. Clade IV contained a single invasive Chinese FAW . Taken as a whole, the nuclear SNP phylogeny provides clear evidence for multiple introductions of FAW to the Old World, while identifying populations associated with the Mississippi and the Brazilian ‘BR’ populations as likely sources of invasive populations into the Old World. The source population for Malawi’s FAW was likely population(s) from South America, currently represented by Peru/Brazil (BC) populations. Based on interception data, with the exception of a single unique FAW, Florida and the Greater Antilles do not appear to be likely sources for the current invasive populations in the Old World.61 and between North and South America. Finally, an important overall finding was that our panel of neutral SNPs selected from whole-genome sequencing did not separate individuals based on ‘corn’ or ‘rice’ mitochondrial DNA genome signatures, nor did they support the host strain characterisation based on the Tpi partial gene marker.Our nuclear SNP phylogeny therefore clearly showed that the native range FAW populations could be classified based on their geographic origins. The unexpected direct phylogenetic relationship between the US Mississippi and Brazil ‘BR’ population, suggested potential movements of populations within North America  than the average expected heterozygosities (Hexp), both in the native and invasive ranges, with the highest Hobs seen in the Malawi population. Negative FIS values for all populations were consistent with Hobs being higher than Hexp, and suggested systematic avoidance of consanguineous mating62 within FAW subpopulations as a whole. The lower expected heterozygosity in all these populations  is most likely indicative of the recent mixing of previously distinct populations and does not support that these invasive populations originated from a single introduction  or had undergone a recent bottleneck from the widely suggested recent western Africa arrival. It is more likely that they represent the result of multiple introductions into the invasive range, as already suggested by mitochondrial and nuclear SNP phylogenies . Migratory patterns in hybrid populations across Africa, Asia, Southeast Asia, and Oceania, have remained largely unknown, and unlikely to represent panmictic populations due to multiple origins of founding populations, at least for the African, Indian, and Chinese FAW populations analysed.Basic population diversity statistics for each population are listed in Table es Figs. . The obsMultivariate Principal Component Analysis (PCA) of the 197 individuals in the native and invasive populations based on the 870 neutral and unlinked SNP loci showed the individuals to largely cluster according to their populations, as observed in the phylogenetic analyses (above). The native FAW populations formed five clusters Fig. , while nFST) between populations varied significantly , thereby suggesting some level of movements within African populations.Pairwise genetic differentiation estimates . Similarly, at K = 4, three of these six FAW individuals remained non-admixed as also indicated (red dots). However, at K = 5, the number of non-admixed individuals nearly doubled compared with K = 3. No other FAW individuals from the invasive range otherwise showed non-admixed genomic signatures irrespective of the K-values of 3, 4 or 5. The Malawi FAW individuals share very similar admixture patterns as FAW individuals from Peru and Brazil-CC  populations. This shared admixed profile between Malawi and Peru/BC populations is especially clear at K = 5, which also enable clearer visualisation of the Tanzanian individual and selected Ugandan individuals  as also having similar admixture profiles as Malawi individuals  also possessed the rice mitogenome haplotypes , and Guadeloupe  at predominantly pes Fig. . As withTo explore the population substructure revealed by the admixture analysis in relation to the ML clusters obtained from phylogeny and PCA, we performed network analysis using the plotAdmixture function in the NetView R package. The ML network of individuals belonging to each of the specified populations is shown in Fig. K = 5 on this network showed the different populations from China that predominantly comprise Cluster I each have distinct admixture profiles that are shared with those of individuals from Uganda and India. In cluster II, China-XP (CX), India (IN), Benin (BE), and Uganda (UG) formed networks with USA-Mississippi (UM) individuals. In Cluster III, all Malawi (MW) individuals and various Ugandan (UG) individuals and the single Tanzanian (TZ) individual formed a network cluster with Peru (PE), Brazil-CC (BC), and some USA Florida (UF) individuals. In cluster IV, only one Chinese FAW (CH) was found to group to this predominantly Caribbean/Central America FAW group , French Guiana (GF), Guadeloupe (GP), and Mexico (MX) FAW individuals).Plotting admixture proportions at Analysis of the directionality of gene flow  between populations using divMigrate enabled investigation of possible introduction pathways leading to the complex population substructure patterns seen in the above analyses. The most significant directional gene flow signatures seen were from all three Chinese populations  into Malawi and from the Cangyuan (CC) population into Uganda Fig. . SignifiCOI and CYTB46); and the nuclear Tpi partial gene  of various African, Asian and South East Asian invasive FAW populations, with comparisons to native New World FAW populations. These studies inferred the directionality of spread from the timing of official reporting to the FAO/IPPC, and described a single introduction of FAW to the Old World from an eastern American/Greater Antilles population, that spread rapidly across the sub-Saharan African nations, before moving to the Indian sub-continent via the Middle East, and then to South East Asia, and China45.The genomic analysis of FAW from native and invasive ranges contradicts recently published theories on the pathway, origin, and direction of the spread of this pest across the Old World. Neutral and unlinked genome-wide SNPs obtained from early stages of the FAW invasion showed, through population admixture analysis, ML distance network, and gene flow directionality analyses, that there were likely multiple introductions to both Africa and Asia. Studies to date have relied on analyses of limited partial mitochondrial DNA , one should expect all populations to share the same genomic signatures as the bridgehead  populations57. Genome-wide SNP analyses in this present study, however, showed the populations in China and Africa to be genetically diverse and demonstrates strong evidence for a complex spread pattern across the Old World, including a substantial proportion of east-to-west movement, with populations from Asia as a potential source of invasive FAW populations in Africa , although our study lacked other populations  that may be alternative sources of invasive FAW populations to Africa. The confirmation of FAW after reports by farmers of crop damage, i.e., in Nigeria and Sāo Tomé and Príncipe in early 20161 and in northern and eastern Uganda since 201422, suggested that S. frugiperda was present in the African continent earlier, and given the genomic evidence reported here would suggest that the FAW was present in Asia and/or Southeast Asia prior to 2016.Under the bridgehead effect of invasion scenario where subsequent successful invasive populations ; Asia ) originated via an intermediate and successful population  that originated from countries outside of the Americas since prior to 201471 , the early detections and report of FAW in Asia/S.E. Asia ; 2008 Hanoi outbreaks69), and the complex pattern of multiple introductions including potential North American origins for various Chinese FAW populations , are consistent with the perceived rapid spread experienced across the African73 and Asian continents74.Incidences of FAW attacking stem/leaf parts were reported from a farm producing turf grass for parks in Hanoi, Vietnam, between March and June (Spring/Summer seasons) of 2008, with heavy infestations reported between the months of April and May in 2008Sfc) and rice (Sfr), the neutral nuclear SNP analyses showed a more complex population and genomic structure. FAW populations in the New World could be differentiated into at least five distinct groups that broadly followed the species’ geographic distributions, and with no obvious pattern related to host race determination by mitochondrial or Tpi markers, providing the first genome-wide support for suggestions that these mitochondrial genomes (and the often associated Tpi marker) do not define any real population structure across the native range of FAW75, while a lack of consistent correlation between host plant and mitochondrial genome in native range populations were observed76. Frequent hybridisation has been known to occur in the field , and would also account for the observed pattern. Furthermore, African populations contained hybrids of F2 or even later generations, and mating time differences within the African populations were likely related to the differences in circadian gene expression previously identified in Sfc or Sfr populations in their native range78. Differences in mitochondria function could be directly related to host preferences79, which could explain the absence of a correlation between the mitochondrial and nuclear genotypes, but this lack of genomic correlation that was also in part due to the non-recombination nature for mitochondrial genome cf. nuclear genome, points clearly to the need of genome-wide studies in field populations, and that persistence to classify the invasive FAW populations into either ‘rice’ or ‘corn’ strains would contribute little in predicting crop damage, and may even hinder the management of these invasive FAW populations.Despite being one of the worst agricultural pests in the New World, there has been limited population genomic work on the FAW in their native ranges. Through our genome-wide SNP analyses, we have identified unexpected complexity in the FAW population structure in the New World. While the mitochondrial genome analysis confirmed the two canonical clades that have long been suggested to define two strains with different host preferences, i.e., corn  and the north-eastern region of South America . Mexican FAW formed a separate sub-clade within the Florida/Greater Antilles/Lesser Antilles FAW group. Significant pairwise FST estimates between Mexico and all native and invasive FAW populations and suggested population genetic differentiation that indirectly indicated limited gene flow. Northern Mexican populations have been shown to be similar to the Southern Texas overwintering population61, and this is reflected by our finding that the Mexican population sits within the broader Caribbean clade that includes Florida. Across the native range, evidence of population substructure has been reported , and a population genomic approach could help identify biological/ecological factors that underpinned patterns of population differentiation, gene flow directionality, and prevalence of admixture to further assist with their management.We detected directional migration from Florida and the Puerto Rican populations to the genetically distinct Mississippi one. This is consistent with findings based on mtCOI sequences that the Mississippi populations were established through seasonal migration from Texas and FloridaABCC2 gene40. The implications of the close phylogenetic relationship between the BR and Mississippi populations are great given that FAW is regarded as a major agricultural pest in Brazil40, and the possible movements of alleles that could potentially underpin resistance, especially to Cry1F and Cry1A toxins, would add to the challenge of managing this pest in the Americas.Our PCA on genome-wide SNPs identified the Brazilian FAW as two genetically distinct populations, with one population (‘BC’) being phylogenetically more closely related to the Peruvian FAW population, and the BR population which is phylogenetically more closely related to the Mississippi population. The Brazilian ‘BR’ population included individuals that had been found to have a novel 12 bp deletion mutation in the 57 from other invasive FAW populations not yet included in the current analysis, such as other Asian/East Asian/South East Asian populations. Global movements of invasive pests, exemplified by the spread of FAW, and other agriculturally important pests  are timely reminders of the need for global coordination of enhanced biosecurity preparedness strategies that build on advancement in genomic research. The potential negative impacts of introductions of alien species include the introgression of genetic traits to local species through hybridisation86. Development of new trans-continental trade routes to increase economic growth between trading partners must therefore recognise these potential risks and take into consideration the biosecurity implications associated with the rapid spreading of highly invasive pests and pathogens of plants, animals and humans87 that could instead undermine the aim to grow the global economy.Genomic analyses in the present study support multiple introductions of FAW from different sources into Africa, rather than via a single western Africa introduction. Phylogenetic inference and PCA clearly identified the South American FAW population, as represented by the Peru/Brazil (BC) samples, as the likely source for the Malawi population, although this could also represent the ‘bridgehead effect’Spodoptera frugiperda populations sampled and analysed in this study were sourced from Florida (n = 24)79, Mississippi 30, Puerto Rico 88, Peru (n = 16), Brazil , Mexico , Guadeloupe (n = 4), French Guiana (n = 3), Benin (n = 4), India (n = 12)89; Tanzania (n = 1), Uganda (n = 15), Malawi (n = 16), and three populations from Yunnan Province, China 40, and one individual (CH06) from Australia’s pre-border interception program overseen by the Department of Agriculture, Water and the Environment (DAWE), also from Yunnan, China (Supplementary Data 41 and a polymorphism within the Triose Phosphate Isomerase (Tpi) gene48.Sfr and Sfc have been sequenced and annotated30, allowing higher resolution analysis of genetic structure, migration patterns and sub-species status based on a high number of genome-wide SNPs to enable identification of the potential New World origins, and the species and admixture status of the invasive Sfc and Sfr populations. Extraction of total genomic DNA was carried out at the CSIRO Black Mountain Laboratories site in Canberra Australia for the Brazil, Tanzania, Malawi and Uganda populations, as well as the pre-border intercepted FAW sample from Peru and China, using the Qiagen Blood and Tissue DNA extraction kit following instructions as provided, with genomic DNA eluted in 200 µL EB. Total genomic DNA for the other three Chinese populations were extracted at Nanjing Agricultural University as detailed in Guan et al.40. Total genomic DNA from Mississippi, Florida, Puerto Rico, Guadeloupe, Mexico, and French Guiana, and Indian populations was carried out at INRAE DGIMI  as reported in Yainna et al.89.The genomes of both 90. Sequencing coverage ranged from 2–56× with a mean coverage of 19×.Genomic libraries prepared by CSIRO were constructed using an Illumina Nextera Flex DNA Library Prep Kit following manufacturer’s instructions and sequenced by Illumina NovaSeq6000 S4 300 sequencing system at the Australian Genome Research Facility (AGRF). Sequencing efforts were shared between three research institutions: 61 samples were prepared at CSIRO , 46 samples were prepared by NJAU for populations from China Yunnan Province , and 89 samples were prepared by DGIMI, France . The Peru FAW samples and the single FAW sample CH06 from Yunnan China were intercepted at Australia’s pre-border inspections of imported agricultural and horticultural commodities by the Department of Agriculture, Water and the Environment (DAWE) on fresh vegetables and cut flowers, respectively. The FAW CH06 was sequenced using the Illumina MiSeq high throughput sequencing (HTS) platform following the methods of Tay et al.Helicoverpa species as outlined in Walsh et al.91. Assembled mitogenomes were annotated using MITOS92 selecting invertebrate mitochondrial genetic code. All annotated protein-coding genes/coding sequences (PCGs/CDS) were re-annotated visually to identify putative stop codons and to align start codon positions. Four regions of low complexity  were trimmed due to alignment difficulties and low genome assembly confidence associated with simple repeat units, resulting in all samples having final mitochondrial DNA genome length of 15,059 bp. We identified unique mitogenome haplotypes using the DNAcollapser in FaBox (1.5) <https://users-birc.au.dk/~palle/php/fabox/dnacollapser.php>93 after alignment using MAFFT Alignment v7.45095 within Geneious 11.1.5 and selecting the Auto option for Algorithm and 200PAM / K = 2 for Scoring matrix, Gap open penalty of 1.53, and offset value of 0.123. GenBank accession numbers for full mitochondrial genomes from all individuals are listed in Supplementary Data The mitochondrial DNA genome for all samples were assembled using Geneious 11.1.5 based on strategies previously used for assembly of 30 for our raw data processing. While the nuclear genomes of the two strains were found to be ~1.9% different30, however, invasive populations analysed to-date have consisted predominantly of hybrids89. We used the native rice reference genome SfR from Florida  to map as it was found to be superior to the corn genome in terms of assembly statistics . Genomic raw data was cleaned and trimmed using Trimmomatic (v0.39)99 and aligned to the S. frugiperda30 (rice v1) genome using BWA-MEM (v2.1)100. Variants were predicted using BBMap (v38.81)101 using the following parameters: bgzip = t maxcov = 300 ploidy = 2 multisample = t; followed by indel normalisation using BCFtools (v1.10)102 to obtain a whole-genome SNP panel. Variants were filtered to remove SNPs with minimum allele frequency of 0.01, any missing data and linkage disequilibrium (LD) pruned with stringent parameters (–indep-pairwise 50 kb 10.000001) using Plink2.0103 < http://pngu.mgh.harvard.edu/purcell/plink/> to obtain 870 unlinked SNPs across all individuals.In this study, we used the originally assembled genomehttp://iqtree.cibiv.univie.ac.at>104. For the nuclear SNPs, the panel of 870 SNPs from each individual in fasta format was uploaded to the IQ-Tree web server and selecting the automatic substitution model option with ascertainment bias correction (+ASC) model105. For the mitochondrial DNA genome maximum likelihood (ML) phylogeny was inferred with edge-linked partition for the 13 protein-coding genes and excluding all four regions of low complexity , HKY + F + I (COII), TPM3u + F (ATP8),F81 + F (ATP6), HKY + F (COIII), HKY + F + I (ND3), HKY + F (ND5), HKY + F + I (ND4), HKY + F + I (ND4L), HKY + F + I (ND6), HKY + F (CYTB), HKY + F + I (ND1), HKY + F + I (ND2); ML phylogeny Log-likelihood: −17149.5022 ± 187.2074 s.e.). We used the Ultrafast bootstrap (UFBoot) analysis106 with 1000 bootstrap alignments to assess branch support for both mitochondrial DNA genome and nuclear SNPs phylogenies. We implemented the default IQ-TREE settings by specifying 1000 maximum iterations and 0.99 minimum correlation coefficient, single branch tests by SH-aLRT with 1000 replications, and default IQ-TREE search parameters . Output consensus tree files in Newick format were visualised and manipulated using Dendroscope version 3.5.7107. We did not include an outgroup as the study was to understand inter-strain differences and not to test hypothesis relating to speciation. Due to the close evolutionary relationship between the Sfc and the Sfr  the inclusion of an outgroup could obscure the recent migration signals .Unrooted phylogenies based on trimmed partial mitochondrial DNA genomes of 15,059 bp and from genome-wide SNPs were individually inferred using IQ-Tree <Hobs) and expected (Hexp) heterozygosity were calculated for each population using the populations program in Stacks109 and the Adegenet package in R111. The number of loci departing significantly from Hardy-Weinberg equilibrium (HWE) in the global population and individual populations was assessed using PLINK 2.0112 and VCFtools113. To test for neutrality, Tajima’s D114 and Fu and Li’s D*115 were calculated for each population using the PopGenome package in R116. Nucleotide diversity (π) and Wright’s inbreeding coefficient, FIS117, were calculated using the populations program in Stacks. Pairwise comparisons of weighted FST values between populations were calculated using Genepop (v4.7.5)118 and differentiation between populations tested for significance using the exact G test.Observed  distance matrix calculated from the IQ-Tree phylogeny, using the R package ‘ape’122.Principal component analysis (PCA) was performed using PLINK v1.9123 online version was used <https://popgen.shinyapps.io/divMigrate-online/>124. Gene flows between all sites were calculated and then normalized to obtain relative migration rates (between 0 and 1). The program divMigrate searches for gene flow directionality between each pair of populations by identifying significant asymmetry based on allele frequency, and against a hypothetically defined pool of migrants to estimate genetic differentiation between each population pair and the hypothetical pool. The relative levels of migration between the two populations are then estimated based on the resulting directional genetic differentiation124. To evaluate the significance of asymmetric migration, 1000 bootstraps were performed. Resulting migration matrices were then plotted using Gephi <https://gephi.org/>125 to generate network graphs. These show directional gene flows between populations (located at the nodes), with the thickness of the lines showing the relative strength of gene flow.To estimate directional gene flow between the populations, as well as the relative magnitudes of these flows, the function divMigrate in the R package diveRsityExperimental design and statistical details performed in this study are provided in the respective “Results” and “Methods” sections. Sample size of populations are provided in the “Methods” section. All programs were run with default parameters and statistics are absolute estimates with no replicates or corrections required for either.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplemental Data 1Supplemental Data 2Reporting Summary


Echinococcus granulosussensu stricto (s.s.) in common hosts of this parasite. Molecular surveillance included the fragment amplification of a partial mitochondrial gene, cox1 (750 bp), for a total of 69 isolates derived from sheep (n = 52), cattle (n = 11), pigs (n = 4), and goats (n = 2). It was ascertained that E. granulosus s.s. was the primary agent of infection among these ungulates and G1 genotype was highly prevalent (79.71%). Considerable intraspecific variation was found, revealing the existence of 22 haplotypes with relatively high haplotype (0.8555 ± 0.033) and low nucleotide diversities (0.00281 ± 0.00030). Population demographics indicated an expanding parasitic population signifying negative deviation from neutrality indices. Little genetic differentiation was found between the subpopulations of E. granulosus s.s. in the island. Moreover, the geographic dispersal of genotypes G1 and G3 also indicated similarity between Sardinian and mainland Echinococcus granulosus s.s. populations reaffirming the sympatric occurrence and efficient transmission of G1 and G3 genotypes. Molecular survey of CE has the potential to yield baseline information on the infective genotypes among the intermediate hosts and helps in devising suitable control strategies for curtailing the disease.Cystic echinococcosis (CE), a parasitic zoonosis of public health and economic concern, is highly endemic in Sardinia, Italy. The study involved examining the intraspecific variability and demographic structure of The online version contains supplementary material available at 10.1007/s00436-021-07186-9. Echinococcus granulosussensu lato (s.l.). It is listed among WHO-neglected diseases for which control strategies are suggested  is a globally widespread zoonosis caused by the larval stages of a tapeworm Echinococcus has remained a challenging issue for decades due to striking intraspecific genetic diversity, morphology, life cycle, and host range differences , Echinococcus equinus (G4 genotype), Echinococcus ortleppi (G5 genotype), Echinococcus canadensis (G6–G10 genotypes), and Echinococcus felidis (lion strain). The taxonomic status of genotypes G6/G7 and G8/G10 is still under dispute  have been reported from the horses and pigs in Sardinia  for appropriately estimating the prevalence of infective genotypes and their correct allocation on the basis of partial mitochondrial cox1 marker. Moreover, the population structure analysis of E. granulosus s.s. genotypes circulating among animal hosts in Sardinia was also undertaken to highlight genetic and demographic patterns.In Italy, CE is widespread and present in Sardinia , cattle (n = 11), pigs (n = 4), and goats (n = 3) during routine meat inspection in different municipalities of Sardinia from 2012 to 2018. Cyst presence in the visceral organs was analyzed by visual inspection and palpation. Infested organs (liver/lungs) were transported to the laboratory of Parasitology and Parasitic Diseases, Veterinary Teaching Hospital, University of Sassari, for further processing.A total of 70 hydatid cyst specimens were collected from the sheep  following the instruction manual. DNA concentration was assessed by a NanoDrop™ Lite spectrophotometer . PCR amplification for the partial mitochondrial gene, o et al. . PurifieE. granulosus s.s. populations from Sardinia and the other Mediterannean countries  was computed to understand the degree of gene flow among these countries , Nakao et al. , and Bonelli et al. (E. granulosus s.s. (G3).All sequenced chromatograms were checked individually for base-calling errors on FinchTV viewer . Obtained sequences (750 bp) were subjected to multiple sequence alignment along the reference sequence , cattle , pigs , and goats . G3 genotype was detected in 13 isolates originating from sheep , pigs , and cattle   than the transversions (n = 3). A maximum likelihood (ML) tree was constructed for phylogenetic resolution of the obtained sequences which clearly positioned the obtained sequences among E. granulosus s.s. (G1 and G3 strains) reference sequences  were parsimony informative. Among the total 22 nucleotide substitutions, 9 were non-synonymous (40.90%) and 13 were synonymous (59.10%). No indels or gaps were detected  grouped with G1 genotype whereas 4 microvariants (18.18%) were ascribed to G3 strain. A statistical parsimony network was constructed to discern genealogical relationship among the haplotypes which exhibited a star like configuration . None of the goat isolates harbored the dominant G1 haplotype. The second most common haplotype, EgSar7 (15.94%), shared 100% similarity with microvariant reported earlier in Tunisia  was observed along with low nucleotide diversity (0.00281 ± 0.00030), a feature characteristic of expanding populations. High haplotype diversity was demonstrated for cattle (0.8727 ± 0.0891), sheep (0.8620 ± 0.0363), and pig (0.8333 ± 0.2224) isolates. Nucleotide diversity values were quite low ranging from 0.002424 ± 0.001717 (cattle) to 0.002958 ± 0.001856 (sheep). Both haplotype and nucleotide diversities were zero for the goat isolates because of occurrence of a single haplotype and limited sample size (n = 2). Tests of neutrality, computed to delineate the demographic characteristics of populations, indicated a negative bias. Neutrality indices were negative  for the whole population. Tajima’s D value was only significantly negative for sheep population  whereas Fu’s Fs statistic was significantly negative for sheep  and cattle  populations  whereas highest Fst value was observed for Sardinia and Tunisia   occupy similar ecological niches around the globe but marked differences in prevalence of these genotypes could be linked to paleo-zoogeographic events and parasite’s life history. Phylogeographic routes based on Bayesian model point towards probable transmission of G3 from Asia into Europe , most probably because 75.36% isolates were derived from sheep. Alternatively, it could also be argued that sheep were the key hosts in shaping epidemiologic patterns for E. granulosus s.s. at Sardinia. High haplotype diversity (0.8555 ± 0.033) in congruence with low nucleotide diversity (0.00281 ± 0.0030) was manifested for Sardinia, an observation quite similar to other studies displaying genetic diversity and population expansion  haplotypes of G1–G3 complex were obtained in current molecular analysis forming two haplogroups. G1 was found as the principal strain (79.71%) infecting all host types. A multiple star-like configuration was observed among the E. granulosus s.s. exhibited an overall negative deviation from neutrality (D and Fu’s Fs). Sheep exhibited significantly negative values for these indices whereas cattle had negative values for both components out of which Fu’s Fs value was significant. Occurrence of rare polymorphic alleles was suggested by negative statistics and significant values alluded to past bottleneck events as a result of purifying selection and recent demographic expansion. Positive and non-significant bias from neutrality indicates low genetic polymorphism among populations that have undergone bottleneck as evident from lower number of haplotypes in pigs (n = 3) which demonstrated positive value (0.13353) for Fu’s Fs. It is important to mention that this positive outcome could be due to a small sample size which may partially account for detecting low polymorphism in pig specimens. Genetic differentiation estimates between different E. granulosus s.s. populations originating from the Mediterranean countries yielded very low Fst value among Sardinia and Italy  indicating higher gene flow. While comparing Sardinia with other Mediterranean countries, it was observed that none of the Fst value was negative; however, low Fst value for Spain and Sardinia implied sharing of alleles. Free gene flow because of geographical connectivity and absence of forces that lead to structuring of the populations is evident from the low Fst values.All subpopulations of Echinococcus granulosus s.s. does not primarily target pigs  within the sheep and buffalo strains by reaffirming their expansion within the host populations. A G3 haplotype was also identified from pig indicating its incorporation into pig-dog cycle; however, data on cyst fertility is to be correlated with such observations to reach some concrete conclusion. This study also emphasizes the need for refining transmission dynamics for goats and pigs; further studies involving more isolates from goats and pigs must be carried out for identifying their role in disease epidemiology. This study has provided an insight into infective and prominent genotypes cycling within the intermediate hosts and would enable the authorities to devise suitable control strategies for this disease in this hyperendemic region for CE.The present study provides a compelling evidence on predominant involvement of Supplementary file1 (DOCX 28 KB)Below is the link to the electronic supplementary material.


The UK Biobank is a large prospective cohort, based in the UK, that has deep phenotypic and genomic data on roughly a half a million individuals. Included in this resource are data on approximately 78,000 individuals with “non-white British ancestry.” While most epidemiology studies have focused predominantly on populations of European ancestry, there is an opportunity to contribute to the study of health and disease for a broader segment of the population by making use of the UK Biobank’s “non-white British ancestry” samples. Here, we present an empirical description of the continental ancestry and population structure among the individuals in this UK Biobank subset.N = 62,484). Principal component and K-means clustering analyses were used to identify and characterize population structure within each ancestry group. Of the approximately 78,000 individuals in the UK Biobank that are of “non-white British” ancestry, 50,685, 6653, 2782, and 2364 individuals were associated to the European, African, South Asian, and East Asian continental ancestry groups, respectively. Each continental ancestry group exhibits prominent population structure that is consistent with self-reported country of birth data and geography.Reference populations from the 1000 Genomes Project for Africa, Europe, East Asia, and South Asia were used to estimate ancestry for each individual. Those with at least 80% ancestry in one of these four continental ancestry groups were taken forward (Methods outlined here provide an avenue to leverage UK Biobank’s deeply phenotyped data allowing researchers to maximize its potential in the study of health and disease in individuals of non-white British ancestry.The online version contains supplementary material available at 10.1186/s40246-022-00380-5. As the research community strives to understand the genetic architecture of disease , it has The UK Biobank project (UKBB) has phenotypic and genomic data from a prospective cohort of approximately 500,000 individuals from across the UK , 12. It https://pan.ukbb.broadinstitute.org). The genetic “ancestry” groups identified by Pan-UK Biobank and within our study refer to groups of individuals with a shared genetic ancestry and demographic history. Studies and public resources like Pan-UK Biobank are vital to the goal of increasing under-represented populations and the larger goal of describing and understanding the genetic architecture of phenotypic traits and disease. However, the limited information on intra-population structure and non-specific use of covariates in Pan-UK Biobank GWAS models may influence association effect estimates. A description of the continental diversity and population structure present in the UKBB will aid future study design and methodological choice(s) and ultimately improve our understanding of how genotype influences phenotype.The Pan-UK Biobank, or the Pan-ancestry genetic analysis of the UKBB, has leveraged the diversity present in UKBB and is freely providing GWAS summary statistics for over seven thousand phenotypes in six continental ancestry groups  affinity or likeness . The aimEach of the 78,296 UKBB “non-white British” was included in a supervised ADMIXTURE analysis to estimate a proportion of ancestry to each of African (AFR), European (EUR), South Asian (SAS), and East Asian (EAS) continental ancestry groups Fig. . The proTo evaluate the level of population structure among the UKBB CAGs, we first re-estimated principal components for each, while also projecting individuals from 1KG populations from each super-population, respectively, onto the newly derived PCs Fig. A. In theK = 6) for the remaining analyses to improve our ability to investigate the utility of this analytical method to discriminate population structure  geographic regions onto the PCs Fig. . For exaAn evaluation of the degree of population differentiation within each CAG was performed by estimating Fst, or the fixation index between each pair of K-cluster groups and 1KG populations. All single-nucleotide polymorphisms (SNPs) that were included in each CAG’s principal component analysis were used here. An average, minimum, and maximum estimate was used to summarize the distribution of estimates between pairs Fig. . RelativHere, we present an analytical pipeline to identify individual participants of the UKBB study with diverse and under-represented ancestries to be used in genomic epidemiology studies. While cohort studies centered in diverse geographic locations are essential for elucidating the effect of environment and genotype on disease, the diversity present in deeply phenotyped studies such as the UKBB should be utilized where possible. This study presents a description of some of the diversity present in the UKBB. Further, the methods presented here provide an approach to identify subsets of individuals to help broaden, inform, and improve the relevance of genetic epidemiological studies and their findings for those of, in this specific instance, a non-white British ancestry Fig. .Fig. 8GrThroughout the paper, when we speak of ancestry, we are referring to “genetic ancestry,” or individuals who share a demographic history , 21, 22.The need to perform analyses like association studies, separately in unique ancestral populations, largely comes from the need to avoid correlations between phenotype and genetic ancestry, or differences in allele frequencies among populations—i.e., population structure or population stratification , 27, 28.In genome-wide association studies, the aim is to derive accurate unbiased effect estimates for a genetic variant on a trait. However, the task becomes increasingly challenging, as variation in genetic ancestry comes with different allele frequencies, genetic backgrounds, and environments . MethodsThe problems introduced by population stratification persist even in populations like the “white British” subset of the UKBB, where individual genetic variants and polygenic scores for individual traits can retain correlations with geography, even after correcting for population structure , 37. MorThe complications of population stratification and opportunities for improving health outcomes for more people, even at the continental level, are precisely why a description of the structure within each continental ancestry group was provided here. Namely, the structure present within a CAG, as identified here, may also be too great to be properly accounted for with common methodologies and may thus need to be resolved into smaller more homogenous groups. At the very least, careful consideration is warranted when interpreting results where CAGs are used—because structure matters . The unsIn summary, we assigned individuals to continental ancestry groups Figs.  and 2; iThe methods employed here do have several limitations: First, a single 1KG population was used to represent each of four continental ancestry groups evaluated—Africa, Europe, South Asia, and East Asia. One population is a poor proxy for all of the variation present in any one (sub)-continent. However, as the 1KG project does not have optimal population coverage, including more or all the 1KG populations of a CAG would still poorly represent all the variation present in a (sub)-continent and would complicate the assignment of individuals to a single ancestry group. Second, our analysis was limited to four (sub-)continental ancestry groups, to the exclusion of the Americas . Populations from the Americas often have a large and varying amount of recent admixture from various European and African populations , 48–52. Third, the UKBB Axiom array used to genotype all UKBB participants was designed to optimize imputation of a European population while also including genetic variants previously associated with disease and other phenotypic traits derived from studies primarily conducted in European populations , 12. As Fourth, the principal components illustrated and used in the unsupervised K-means clustering analyses were derived from the UKBB participants only and resultantly represent the diversity (point three) and genetic ancestry found in that data set. The inclusion or use of other public data sets with more numerous sample populations, that better represent regional, or continental diversity will provide alternative patterns of structure. Fifth, we are limited by the reference population used in the analyses. While the 1KG data set shall remain an essential reference panel for broad analyses like those conducted here, researchers with specific continental or geographically specific research questions could strengthen and refine the observations made here by including other geographically specific data sets. Finally, the unsupervised K-means clustering analysis is dependent upon the number of PCs included in it. Here, the number of PCs chosen did have an element of subjectivity  using the following publicly available software packages: PLINK v1.9 and v2.0 , 60, ADMN = 784,256 SNPs) were made available. It includes data for a total of 78,296 individuals identified by UKBB as “non-white British” participants—our analyses were restricted to this subset. In addition to genotypic data, we also acquired several variables of interest  data for this subset of individuals. 365 exclusions were made when filtering those with sex chromosome mismatch and/or aneuploidy, and outliers with high genetic heterozygosity and missing rates , were used to provide reference populations for admixture analyses and population structure inferences ( http://fThe directly genotyped data from UKBB were used to identify SNPs with the same SNP identifier (RefSNP ID) present in the 1KG data set. A total of 718,711 SNPs were identified with the same ID and extracted from both data sets using PLINK v2.0. The two data sets were then merged using the -bmerge function in PLINK v2.0. After removing problematic SNPs  in the merge step, a total of 718,487 SNPs remained.2 threshold of 0.025, a window size of 50 kilobases and a window step size of 10 kilobases. In addition, 24 previously identified genomic regions with extensive linkage disequilibrium were also excluded [Prior to ancestry estimation, the merged data set was reduced to a set of independent SNPs based on linkage disequilibrium (LD) estimates using the PLINK v2.0 function and parameters “–indep-pairwise 50 10 0.025,” indicating an rexcluded , 67. LD Four 1KG populations were included as reference populations in a supervised ADMIXTURE (v1.3.0) analysis. They were (1) British in England and Scotland (GBR), of the European ancestry (EUR) super-population, (2) Yoruba in Ibadan, Nigeria (YRI), of the African ancestry (AFR) super-population, (3) Indian Telugu in the UK (ITU), of the South Asian ancestry (SAS) super-population, and (4) Han Chinese South (CHS), of the East Asian ancestry (EAS) super-population. These singular population samples were chosen to broadly represent each of their four respective continental (super-population) ancestry groups, with an average population differentiation  value of 0.1055 among them, as estimated by ADMIXTURE. The supervised ADMIXTURE analysis provides, for each UKBB sample, a proportion of ancestry for each of the four reference populations. Those individuals with at least 80% of their ancestry attributed to one continental ancestry group, or 1KG defined super-population, were carried forward into further analyses.https://github.com/DReichLab/EIG) package was used to generate a new list of related individual pairs, along with our script “greedy_unrelated_selection.R” to identify a list of related individuals to exclude from principal component derivation [Unrelated individuals in each CAG including both 1KG and UKBB samples with >  = 80% ancestry to that CAG were identified  function –rel-cutoff and a minor allele frequency (MAF) filter of 0.05 (–maf 0.05)). Then for each CAG and using all (1KG + UKBB) unrelated individuals assigned to the CAG, a list of approximately 40 thousand LD-independent SNPs were identified (using the PLINK (v2.0) function –indep-pairwise 50 10 0.025 (–indep-pairwise 50 10 0.02 for AFR and –indep-pairwise 50 10 0.05 for SAS) along with a MAF filter of 0.01, and the exclusion of the 24 previously identified genomic regions with extensive linkage disequilibrium , 67). Nerivation , 32. An For each CAG, we estimated the variance explained by each principal component (PC) by dividing the eigenvalue of each PC by the sum of all eigenvalues. To identify the number of top PCs, we generated a scree plot, using the variance explained estimates, and identified the elbow or valley in each plot  for each pair of populations in the data set, including 1KG populations and UKBB K-means clusters. This was done with the inclusion of the parameters “fstonly” and “phylipoutname” , the latAdditional file 1. Table S1. Study genetic processing steps with relevant numbers. Table S2. Eigenvalue and eigenvector for each continental ancestry group. Table S3. United Nations geoscheme for each country and continent present in the study.Additional file 2. Figure S1. Continental ancestry PCA scree plots. Figure S2. K-means k selection with silhouette analysis. Figure S3. UK Biobank continental ancestry group PCs with K-means clusters. Figure S4. Population structure by UN defined geographic region. Figure S5. Population structure centers, as defined by UN geographic region. Figure S6. Population structure by country of birth in Africa by region. Figure S7. Population structure by country of birth in Europe by region. Figure S8. Population structure by country of birth in South Asia and East Asia. Figure S9. Population structure centers by country of birth. Figure S10. Population structure centers by country of birth.


Chthamalus stellatus Poli, populates the Mediterranean Sea, the North-Eastern Atlantic coasts, and the offshore Eastern Atlantic islands. Previous studies have found apparent genetic differences between the Atlantic and the Mediterranean populations of C. stellatus, suggesting possible geological and oceanographic explanations for these differences. We have studied the genetic diversity of 14 populations spanning from the Eastern Atlantic to the Eastern Mediterranean, using two nuclear genes sequences revealing a total of 63 polymorphic sites. Both genotype-based, haplotype-based and the novel SNP distribution population-based methods have found that these populations represent a geographic cline along the west to east localities. The differences in SNP distribution among populations further separates a major western cluster into two smaller clusters, the Eastern Atlantic and the Western Mediterranean. It also separates the major eastern cluster into two smaller clusters, the Mid-Mediterranean and Eastern Mediterranean. We suggested here environmental conditions like surface currents, water salinity and temperature as probable factors that have formed the population structure. We demonstrate that C. stellatus is a suitable model organism for studying how geological events and hydrographic conditions shape the fauna in the Mediterranean Sea.Poli’s stellate barnacle,  Chthamalus are a major worldwide component of the rocky intertidal zone of tropical and sub-tropical shores, with few species penetrating into temperate latitudes. Poli’s Stellate barnacle, Chthamalus stellatus Poli, is a species with wide geographical range, covering the Mediterranean Sea, the North-Eastern Atlantic coasts, and the offshore Eastern Atlantic islands: Madeira, Azores, and Cape Verde  was studied by C. stellatus, C. montagui and Euraphia depressa, in the Eastern Atlantic, the Mediterranean Sea and the Black Sea. Their wide selection of sampling sites revealed a significant genetic structure among the populations of these Chthamalids. However, for C. stellatus, the structure was based only on two markers (COI and EF1), each having a small number of sampled individuals , which is not sufficient for an adequate population study. In a geographical study of the barnacle Hexechamaesipho pilsbryi in Asia, Chthamalus malayensis four genetically differentiated allopatric clades, three of these were already regarded by Barnacles of the genus pe Verde . Recentltellatus . C. stelnditions . Severaloran Sea  is a majmontagui . Based oC. stellatus, but rather a description of the genetic structure of the population of this species in the Mediterranean and the adjacent region of the North-Eastern Atlantic. For that purpose, we used two nuclear markers, EF1 and NaKA. While the number of collecting sites (14) is slightly smaller than in the abovementioned studies, they adequately represent both the Eastern and the Western basins of the Mediterranean, as well as a few locations in the Eastern Atlantic. Sample sizes from each location, nevertheless, are quite large, enabling us to perform population genetic analyses and to construct dendrograms which are based on population samples, and not on just a single or a very few individuals from each location. Thus, we can describe and analyze the differences, if they exist, between the various sub-populations, and get a more accurate and more reliable picture of the population structure than that depicted by previous studies, which used only a single or a very few individuals to represent an entire local population.Our present study is not supposed to be an evolutionary or a phylogenetic analysis of Chthamalus has a wide distribution and intragroup morphological similarity. It has already been used as a model organism for taxonomic, ecological and phylogeographic studies .DNA was extracted from soft tissue of specimens of α (EF1) (2), 0.2 mM of each dNTP, 1 µM of each primer, 1 unit of Super-Term Taq polymerase (Hoffmann-La Roche), and about 100 ŋg template DNA. PCR reactions were processed in an MJ Research thermal cycler with the following thermal regime: an initial step of 2 min at 95 °C followed by 35 cycles of 0.5 min at 94 °C, 0.5 min at 57 °C, and 1 min at 72 °C, followed by 3 min at 72 °C and then held at 15 °C. PCR products were visualized on 1.5% agarose gels and sequenced bidirectionally using the PCR primers on an ABI 377 DNA Sequencer  following the manufacturer’s instructions.Two gene segments were amplified using previously described primers for the nuclear genes Na-K-ATPase (NaKA) and elongation factor 1α (EF1) , yieldinForward and reverse sequences obtained for each reaction were aligned. After alignment of all the sequences using BioEdit (Copyright 1986–1993 by Joseph Felsenstein and by the University of Washington), we were left with 425 positions for EF1 and 239 positions for NaKA.Most genetic studies of barnacles use haploid mitochondrial markers  for inferring relationships among populations. Here we used two nuclear, diploid markers (EF1 and NaKA), and employed three different approaches in our diploid analysis.The first approach (the ‘genotype-based’ method) was simply to calculate pairwise distances between genotypes using the F84 model of nucleotide substitution , which dThe second approach (the ‘haplotype-based’ method) tries to divide each diploid sequence into two haplotypes, using the DnaSP v6 software . As a reThe third approach (the ‘population-based SNP-distribution’ method) compares the nucleotide frequency among the various populations, as described in x1, x2, x3 and x4 are the proportions of A, C, G and T in population 1, and y1, y2, y3 and y4 are these proportions in population 2, then: the squared Euclidean distance = For the SNP-distribution method we used three different distance measures, the squared Euclidean distance, a modified squared chord distance and the Manhattan (or city block) distance. If xk1,, xk2,, xk3, and xk4, are the proportions of A, C, G and T in position k . We then averaged the measures of the two markers, to obtain the expected heterozygosity at the relevant population. Similarly, for each population, we calculated the percentage of polymorphic positions in each marker, and then averaged the percentages of the two markers, to obtain the polymorphism measure of the relevant population. Since distances are non-independent, comparing mean distance within groups to the mean distance among groups was performed by computer simulations . Other statistical tests (Kolmogorov–Smirnov and one-way ANOVA) were carried out using IBM SPSS Statistics 26. All p-values are given for a two-tailed alternative.For each population we calculated, separately for each marker, the mean number of different alleles per position. We then averaged over the two markers to obtain the overall mean number of different alleles per position in this population. In addition, we calculated for each population the mean expected heterozygosity of a marker, defined as The various between-population distance matrices are presented in The genotype-based method displays a geographic structure, dividing the populations between west and east . The wesA less distinctive geographic structure is displayed by the haplotype-based method . NeverthThe SNP population-based method displays a clear-cut division into two major clusters, western and eastern. Moreover, each of these two major clusters is distinctly divided into two smaller clusters: The Eastern Atlantic cluster  and the Western Mediterranean cluster  are the two clusters within the western major cluster, whereas the Mid-Mediterranean cluster  and the Eastern Mediterranean cluster  are the two clusters within the eastern major cluster –6.n haplotypes. Suppose that in this position, all n–1 haplotypes are identical, but one is different. This produces a variation of size Note that the population-based SNP-distribution method is less sensitive to the inclusion of some rare individuals  than the individual-based (genotype or haplotype) methods. To illustrate this, let us focus on a single position in a population of p < 0.001 in each case). The results are presented in In accordance with the results of the multi-variate analyses, as presented by the population dendrograms –6, we diThe mean number of different alleles per position, the mean expected heterozygosity measures and the mean percentage of polymorphic positions in each population are presented in p = 0.364). The means (± se) of each cluster are: 1.361 ± 0.030, 1.782 ± 0.042, 1.857 ± 0.039 and 1.942 ± 0.020, for EA, WM, MM and EM, respectively . The means (±  se) of each cluster are: 0.069 ± 0.008, 0.154 ± 0.007, 0.205 ± 0.005 and 0.230 ± 0.003, for EA, WM, MM and EM, respectively . The means (±  se) of each cluster are: 33.9% ± 2.7%, 67.2% ±3.0%, 72.6% ± 3.4% and 77.5% ± 1.62%, for EA, WM, MM and EM, respectively C. stella cluster .Chthamalus from the Cape Verde Islands Fig. 8) eC. stellatus is a result of the geological history of the Mediterranean. One of the most conspicuous events that shaped the composition of flora and fauna of the Mediterranean is the Messinian Salinity Crisis (MSC) that started at the end of the Miocene, about 6 million years ago (C. stellatus are a relic of the MSC lakes while the WM populations resemble the Atlantic “invaders” arriving like many other organisms in the Zanclean flood.We may also speculate that the separation between the Western and Eastern Mediterranean populations of ears ago  when theears ago . Some ofC. stellatus was found to be better adapted to higher temperatures while less inhabiting lower salinity niches. Low salinity has been correlated with reduced number of eggs per brood in C. stellatus (C. stellatus in the lower salinity and temperature of the East Atlantic Ocean. The Atlantic current influence on the Mediterranean is also evident in the low salinity region stretching from the Strait of Gibraltar along the Northern Africa shores to Bizerte (C. stellatus in the western part of the Mediterranean. Mean surface salinity and temperature of the Northern Tyrrhenian and the Ligurian Seas are the highest in the Western Mediterranean Basin (In order to better understand how these populations were shaped, the selective pressures of the habitats, like salinity and temperature, must be considered. When compared to other species of barnacles , C. steltellatus . These atellatus  & 7B. Thtellatus –7C, pres Bizerte . As expean Basin , p 23. TC. stellatus does not spread further south to the Western Africa shore and even the Cape Verde Chthamalus was shown to be a different species ( species .Yet another explanation may be the advantage of variability within the eastern basin populations in overcoming the higher temperature changes as well as other rapid condition changes and processes typical to this part of the Mediterranean, especially since the opening of the Suez Canal.Our study presents a unique opportunity to study processes of population settlements in sessile animals, the influence of oceanographic conditions and processes including selection and genetic variation. In order to better understand and solidify some of our speculations, more studies of this range of geographic distribution, population size and genetic polymorphism are needed.10.7717/peerj.11826/supp-1Supplemental Information 1Deposited in the Hebrew University of Jerusalem collection.Click here for additional data file.10.7717/peerj.11826/supp-2Supplemental Information 2Click here for additional data file.