Oreochromis niloticus) is one of the major factors in this respect. Using 40 microsatellite loci with SSR-GBS techniques, we amplified a total of 664 individuals to investigate the genetic structure of O. niloticus from East Africa in comparison to Ethiopian and Burkina Faso populations.The need for enhancing the productivity of fisheries in Africa triggered the introduction of non-native fish, causing dramatic changes to local species. In East Africa, the extensive translocation of Nile tilapia (st = 2.1) than expected suggesting that it might be a different sub-species. In East Africa, the genetic structure was congruent with both geographical location and anthropogenic activities . O. niloticus from Lake Turkana (Kenya) was isolated, while in Uganda, despite populations being rather similar to each other, two main natural catchments were able to be defined. We show that these two groups contributed to the gene-pool of different non-native populations. Moreover, admixture and possible hybridization with other tilapiine species may have contributed to the genetic divergence found in some populations such as Lake Victoria. We detected other factors that might be affecting Nile tilapia genetic variation. For example, most of the populations have gone through a reduction in genetic diversity, which can be a consequence of bottleneck  caused by overfishing, genetic erosion due to fragmentation or founder effect resulting from stocking activities.All three African regions were characterized by independent gene-pools, however, the Ethiopian population from Lake Tana was genetically more divergent (FO. niloticus translocations, promoted artificial admixture among Nile Tilapia populations. Translocations may also have triggered hybridization with the native congenerics, which needs to be further studied. These events may contribute to outbreeding depression and hence compromising the sustainability of the species in the region.The anthropogenic activities particularly in the East African  Oreochromis niloticus, is native to the Levant and African freshwater systems e.g., in the Western part of the continent  as well as to many of the East African rivers (e.g. R. Nile) and Rift Valley Lakes like, Albert, Turkana, George, Edward, Tanganyika, Kivu, etc. [O. niloticus is native to Africa, the cichlid is naturally absent in the world’s largest tropical freshwater body, Lake Victoria and the neighboring Lakes Kyoga and Nabugabo as well as many of the East African satellite lakes [O. variabilis  and O. esculentus (Ngege) [O. niloticus has been intentionally dispersed worldwide, in particular for aquaculture and restocking programs [O. niloticus, and other tilapiines e.g. Athi River Tilapia  as well as Black bass , being initially translocated for enhancing fisheries productivity in water bodies naturally considered as unproductive like the southwestern Uganda high-altitude lakes [O. niloticus from Lake Edward [O. variabilis and O. esculentus) [O. niloticus, O. leucosticus (Blue-spotted tilapia), Coptodon zillii (Red-belly tilapia) and O. melanopleura, were all suspected to originate from Lake Albert [O. niloticus [O. variabilis and O. esculentus) were suspected to a combination of factors including; competition, over fishing, as well as predation pressures from another introduced species, the Nile perch (Lates niloticus) [O. variabilis and O. esculentus) and the introduced tilapiines, particularly O. niloticus [O. niloticus in East Africa complicates the differentiation and identification of genetic units for management and conservation. For example, the population considered as non-native O. niloticus in Lakes Victoria and Kyoga might have genetically diverged via admixture and hybridization with the indigenous species [O. mossambicus due to hybridization with the introduced O. niloticus has been reported in South Africa [Nile tilapia, vu, etc. , 2. Althte lakes . These l (Ngege) . For morprograms . In Eastde lakes , 8. A cae Edward . Similarulentus) –5, 9. The Albert . Howeveriloticus , 4, 11. loticus) , 5. But  species . The losh Africa . The sith Africa . In thisO. niloticus, as the species were potentially affected by various anthropogenic activities, a thorough characterization of the populations at the molecular level might be needed.In nearly the last two decades, the East African countries have been developing measures for fisheries sustainable exploitation through the implementation of co-management strategies . NonetheO. niloticus diversity has been studied using both traditional morphometric methods and molecular markers, which led to contradictory patterns in the species description. For example, using biometrics and counts, seven O. niloticus subspecies from African different regions or lakes were described [O. niloticus strain from Lake Edward is closely related to that of the lower Nile (Egypt) [O. niloticus from Lake Tana is distinct, contrary to the earlier traditional morphometric and meristic analyses [O. niloticus populations in East Africa, shed some additional light to these incongruences [O. niloticus populations from Albert Nile (the Egyptian stretch of River Nile) are distinct from the West African populations, also contrary to earlier morphometric studies [O. niloticus in from Lake Tana is clustered with Lake Edward and the Kenyan Lake Turkana system, which differs from the findings based on restriction endonuclease analysis of mtDNA [O. niloticus are insufficiently documented.Based on the earliest studies, East African escribed . Neverth (Egypt) . Also, oanalyses . Other egruences , 19, 20.of mtDNA . These rof mtDNA –26. Addiof mtDNA . The lacO. niloticus in East Africa using next-generation sequencing. SSR loci have been proven robust when investigating the genetic structure of O. niloticus, particularly, using SSR genotyping by sequencing (SSR-GBS) [O. niloticus were limited to few water bodies in Kenya, with the broader scope of the African Great Lakes missing [O. niloticus is present (native and non-native with possible admixture). Such research would provide information on the genetic structure and diversity which would establish a firm base for management and conservation of these cichlids [In the present study, we utilize nuclear microsatellite markers, simple sequence repeats (SSRs), to typify the SSR-GBS) . SSR-GBSSSR-GBS) , 29. HowSSR-GBS) . For exa missing –33. It icichlids .O. niloticus, in East Africa including some populations from Ethiopia and West Africa (Burkina Faso), representing the Sub-Saharan African Great Lakes. We compare natural/native with introduced/non-native O. niloticus populations, including other populations from aquaculture systems. With this approach, we investigate the impact of anthropogenic activities, particularly the translocations, on the O. niloticus’ gene pool. This is especially important to evaluate the genetic integrity of native stocks. We hypothesized that anthropogenic activities have affected the genetic divergence of O. niloticus populations, particularly in environments where the species was introduced. We also predict that the geographical context exhibited by aquatic interconnectivity may influence the genetic homogeneity of cichlid in such environments. We test these hypotheses by answering the following research questions: 1) Does the genetic structure of the East African O. niloticus populations differ from those outside the region? 2) To what extent does the genetic structure of the East African O. niloticus populations reflect the geography and anthropogenic activities associated with the pathways of the translocation?Here, we explicitly investigate the genetic structure of In total, 13,530,228 paired reads were produced for genotyping, from which 9,579,578 passed the quality control steps, which were later used for allele calling. Genetic variation results for the 40 SSR loci are presented in the Additional file The UPGMA dendrogram showed that all East African populations were more similar to each other than to the other regions Fig. . In thisNeighbor network results showed a similar pattern to the UPGMA dendrogram both at regional and local levels Fig. . In thisGenetic distance between individuals which was visualized through principal coordinates analysis (PCoA), analysis showed a separation of population groups based on geographic regions Fig. a. SampleO. niloticus populations from each African region were assigned to different groups , generally indicated that nearly all of the studied populations went through a bottleneck, apart from the Ethiopian Lake Tana Fig. a. In thest values, and consistently exhibited higher genetic diversity indices   Fig. .2 = 0.30)  between the populations was only found when Burkina Faso was excluded from the analysis  across all samples showed a positive correlation between geographical and genetic distance (R30) Fig. a. Howevesis Fig. b. The gesis Fig. c, which sis Fig. d.O. niloticus, towards the alignment of management and conservation strategies. In this study, we investigated the phylogeographical patterns and we found large differences between lakes (e. g. Lake Tana) and also differences between natural water catchments that allow populations to be identified. Here, we discuss the current state of O. niloticus in reference to phylogeographical patterns and anthropogenic activities.Fisheries and fishery products are vital in the developing world but heavily threatened through various anthropogenic activities which may compromise the continuity of the resources . One aspIn all analyses, we found a clear differentiation among all three African regions included in this study , indicating a low degree of connectivity amidst them and highlighting the high level of differentiation between regions. Lake Tana was completely distinct from the remaining populations. This applies not only to the Ethiopian populations but also to the East African ones. So, the genetic distance in Ethiopia is higher than between the East African and West Africa populations, indicating a divergence higher than we would expect within a species. These results are consistent with previous genetic reports , but notO. niloticus following mass mortalities of the native species [O. niloticus that originated from the Rift Valley Lakes. Besides that, we see a slight differentiation in PCoA between Lake Hashenge and the Rift Valley Lakes in Ethiopia, which may reflect an unsampled source of stocking or differentiation accumulated because of the high degree of isolation of the lake.Lake Tana lies in the Ethiopian mountains and is isolated from the Lakes in the Rift valley . This mi species . The natO. vulcani) [In East Africa, genetic structure reflected different catchments. The population from Lake Turkana was genetically distinct from the Ugandan populations which is expected given its high geographical isolation . Our finvulcani) . The higO. niloticus populations were clearly structured. These reflected three main groups: 1)  Lake Albert, River Nile, and Kyoga and 3) Lake Victoria system. The 2nd and 3rd groups are discussed in more detail under anthropogenic activities subsection. The 1st group, Lakes George and Edward are connected via the Kazinga channel which also explains the high natural migration rates between these populations. The different genetic structure between the western Rift Valley Lakes (Edward-George-Kazinga Channel and Albert) was conserved despite being connected through River Semliki that flows from Lake Edward and Albert [O. niloticus geometric morphometrics [O. niloticus from the Edward-George system and Albert was treated as one subspecies; O. niloticus eduardianus [In Uganda, despite the high degree of connectivity and proximity between the water bodies, d Albert . The strd Albert , 42, migometrics  but do nometrics . This inardianus . Similarardianus  employedardianus .O. niloticus was introduced into several water bodies through stocking activities. We were able to genetically track these translocation events to both non-native water bodies and fish farms. All genetic structure analyses and migration rates showed that the two Ugandan groups (the George-Edward complex and Lake Albert) contributed to the stocking of different water bodies. O. niloticus from the southwestern Ugandan high-altitude Lakes; Mulehe and Kayumbu, originated from the Western Rift Valley Lakes – Edward and George. For the 2nd group, Lake Kyoga and River Nile (Victoria Nile) are genetically similar to Lake Albert, suggesting that, the latter population might have contributed genes to the gene-pool of the former systems. Although Lake Kyoga is connected to Lake Albert via River Nile, their genetic similarity is unlikely related to the consequence of natural migration via water flow. The main reason here is the natural occurrence of Murchison Falls on the River Nile that acts as a barrier between the systems [In East Africa, we know that  systems , 41. For systems .O. niloticus populations may stem from three main processes: 1) translocation from multiple sources into the non-native water bodies, 2) back translocation from non-native to native populations, and 3) hybridization of O. niloticus with congeneric species promoted by translocations.Fish farms seem to have sourced fish seed from multiple populations, resulting in admixed stocks. Our results show that Lakes Albert, and Kyoga, as well as River Nile, contributed to the gene pool of the farmed populations Figs. , 8 & 7c.O. niloticus in Lake Victoria is generally isolated, based on the distance neighbor Network tree . Although O. niloticus with the indigenous relative species (O. variabilis and O. esculentus) in Lake Victoria may explain some of the genetic variation patterns found in this lake. First, this lake together with Turkana showed values of private alleles up to four times higher than the remain populations. This genetic variation could have originated from introgression by species that have not been included in the analysis. Similarly, the probable hybridization may explain the high genetic diversity and divergent gene-pool detected in the system. Within Lake Victoria, the Sango Bay subpopulation appears to be an extreme case from this by showing the highest degree of genetic divergence. Remarkable genetic differentiation in Sango Bay was noticed only when compared with the remaining subpopulations within the lake, but also with the other East African populations. In this case, during the boom of the O. niloticus population in Lake Victoria [O. niloticus gene-pool. This is just a hypothesis since, in this study, we cannot directly test for hybridization because we did not include samples of O. niloticus congenerics. However, hybridization involving O. niloticus and other tilapiines has been reported to be relatively frequent and it needs to be considered [On the other hand, possible hybridization of the introduced Victoria –5, 45, ansidered , 46, 47.O. niloticus. Although hybridization may lead to heterosis/hybrid vigor [O. niloticus populations in Lake Victoria was unexpected and has not been reported before, which calls for further investigations for taxonomic recognition.If admixture/hybridization shaped the gene-pool of Lake Victoria, it may have adaptive consequences and compromise the sustainability of id vigor , 49, admid vigor , 50. Intid vigor . In moreid vigor . Alternaid vigor . The obsEvidence for the second process of admixture was only found in Lake Albert. In the structure analysis, this population showed admixture with Lake Kyoga. We also found significant migrations from Lake Kyoga to Lake Albert. These results indicated that admixture with respect to translocations not only contributes to non-native populations but also to native ones. The sequence of gene flow from Lake Kyoga to Albert is not clear as none of the previous reports have indicated this. However, it is likely that aquaculture activities might be contributing to the observed gene flow between Lakes Kyoga and Albert.O. niloticus boosted the capture fisheries in the Lake Victoria basin (Lakes Victoria and Kyoga) following introductions, the species was subsequently overexploited between the 1970s and 80s [Some water bodies, especially Lake Kyoga and River Nile showed low genetic variability and evidence of bottleneck with respect to G-W estimations. Given the recent stocking of these water bodies, this pattern may be explained by the founder effects. Nevertheless other anthropogenic activities need to be considered as well. High loss of genetic diversity among populations, particularly, in fishes has been attributed to over-exploitation . This mi and 80s , 54. ThiO. niloticus. The main consequence might have been admixture and potentially hybridization between different stocks and species respectively. In the long term, this may have negative effects on population fitness due to outbreeding depression and genetic swamping. Thus, management measures should inhibit any form of unauthorized spread of fish in the aquatic ecosystems. The Western or Albertine Rift Valley lakes (Edward-George) may be ideal broodstock sources for subsequent breeding programs and aquaculture, as these systems seem not to be admixed. To avoid an influx of feral populations, a proper environmental impact assessment should be prioritized before implementation. Genetic diversity might also have been affected by overfishing and the construction of hydropower dams, which should also be taken into consideration in future management options.Overall, we found evidence that anthropogenic activities affected the gene-pool of the East African O. niloticus populations in East Africa. The genetic variation of some populations, especially from Lake Victoria, corresponded with possible hybridization of O. niloticus with native congeneric species, which may have been mediated by anthropogenic activities. This study also contributed to the knowledge of O. niloticus phylogeography in East Africa. In this case, we found several new genetic groups such as the populations from Lake Tana, Victoria and the two natural catchments in Uganda. Some of these may require further taxonomic exploration. Additionally, we show that gene-flow among the East African O. niloticus populations was not entirely from native to non-native environments, but also from non-native to native environments likely through aquaculture and restocking programs. Moreover, this study shows the importance of molecular markers, in particular, the use of SSR-GBS in cataloging populations. Further studies should include O. niloticus samples from other regions such as the lower Nile , Lake Kivu (Rwanda), Tanganyika and Baringo as well as the congenerics for a more comprehensive picture.Our results were congruent with the hypothesis that anthropogenic activities affected the genetic structure of O. niloticus specimens from three water body types: a) those where O. niloticus is native, b) where introduced, and c) from fish farms . The resulting codominant matrix and information for which sequences correspond to each allele can be found in the Additional file Genomic DNA extraction was conducted using magnetic beads based on the MagSi-DNA Vegetal kit  and a magnetic separator, SL-MagSep96  , 30. We http://clumpak.tau.ac.il/. Similar analyses were performed for Lake Victoria within populations.Genetic structure was first assessed by calculating the genetic distance between individuals and then visualized through Principal Coordinate Analysis (PCoA), all conducted in GenAlex Version 6.5 . GeneticO. niloticus populations. However, recent migratory rates were only estimated for the East African populations, since the corresponding water bodies are the most affected by anthropogenic activities such as fish translocations. Pairwise recent migration rates were estimated using BayesAss Version 3.0 [st) values.Recent migratory rates and the number of migrants per generation were determined as proxy estimates of gene flow among the sion 3.0 . Here, tsion 3.0 . Only reO. niloticus populations throughout East Africa and beyond were examined using the following indices: expected heterozygosity (He), observed heterozygosity (Ho), number of alleles (Na), allelic richness (Ar), fixation index (Fst), private alleles, and Garza-Williamson index (G-W). Na, Fst, G-W and He per population were analyzed using the program Arlequin Version 3.5 [O. niloticus populations conform to isolation by distance (IBD), we plotted genetic distance (Fst) against the geographical distance (GGD in kilometers) and conducted correlation analyses using Mantel test (999 permutations) implemented in GenAlex Version 6.5 [Genetic diversity and differentiation indices between sion 3.5 . Ho, He,sion 3.5 . Ar was sion 3.5 . G-W wassion 3.5 . To testsion 6.5 .Additional file 1: Table S1. Final List of 40 SSR primers utilized in PCR reactions. Table S2. Variation of the final set of 40 SSR loci. Loci Ti1–Ti35 were taken from Tibihika et al. [Table S3. List of the linkage group and location/position for each of the primer pairs used. These primers were BLAST against the genome assembly GCF_001858045.2 available in GenBank. The field reference refers to the accession number of the matching Linkage group. Figure S1. Genetic structure based on PCoA within the East African Western Rift Valley lakes, Albert (a) and Edward (b) metapopulations. Figure S2. STRUCTURE HARVESTER analyses for depicting the optimal K values, which is derived from STRUCTURE results. a represents the best K for all populations (K = 10), b best K for all native populations (K = 7), c best K for East African native populations (K = 2), d best K for only Ugandan native populations (K = 2), e best K for all Ugandan populations including farms (K = 4) and f best K for Lake Victoria within populations (K = 2). Figure S3. Bayesian clustering for genetic assignments of O. niloticus populations. a represents East African native, b Only Uganda native populations and c Lake Victoria subpopulations. K values with a superscript symbol (¶) indicate the optimal K value based on STRUCTURE HARVESTER analyses. Table S3. Matrix for the number of migrants per generation between population pairs as evidenced by Fst values. Bold values (> 10) indicate the vital number of migrants explained by Fst values: Note the variations in boldness which explains the level of important migrations between population pairs. Figure S4. Within-population genetic diversity of Albert, Edward, Kyoga, and Victoria populations. Vic = Victoria, Na = mean number of alleles, No. = number, Pa = mean number of private alleles and He = mean expected heterozygosity.a et al.  while loAdditional file 2. Allele list.


Molossus molossus, M. coibensis, and M. milleri), which represent different ecological histories and geographical distributions in the genus. We tested the hypotheses that oceanic straits serve as barriers to dispersal in Caribbean bats and that isolated island populations are more likely to experience genetic drift and bottlenecks in comparison with highly connected ones, thus leading to different phylogeographic patterns. We show that population structures vary according to general habitat preferences, levels of population isolation, and historical fluctuations in climate. In our dataset, mainland geographic barriers played only a small role in isolation of lineages. However, oceanic straits posed a partial barrier to the dispersal for some populations within some species (M. milleri), but do not seem to disrupt gene flow in others (M. molossus). Lineages on distant islands undergo genetic bottlenecks more frequently than island lineages closer to the mainland, which have a greater exchange of haplotypes.Historical events, habitat preferences, and geographic barriers might result in distinct genetic patterns in insular versus mainland populations. Comparison between these two biogeographic systems provides an opportunity to investigate the relative role of isolation in phylogeographic patterns and to elucidate the importance of evolution and demographic history in population structure. Herein, we use a genotype‐by‐sequencing approach (GBS) to explore population structure within three species of mastiff bats ( In this manuscript, we use the genotype‐by‐sequencing approach on three species of Mastiff bats with different ecological histories and geographical distributions to explore population genetics parameters and better understand the role of geographic barriers in the dispersal and gene flow in bats. Our analyses show that the population structure of each species varies according to environmental factors, levels of population isolation, and demographic histories. We show that oceanic straights seem to pose a partial barrier for some species and that more isolated lineages on islands tend to undergo genetic bottlenecks more than connected lineages closer to the mainland. SeveralMolossus are uncertain, 14 species are currently recognized  with different ecological histories and geographical distributions to explore population genetic parameters and better understand the role of geographic barriers in dispersal and gene flow in bats. We tested the hypothesis proposed by Koopman  pipeline, available on TASSEL 3.0 software  in each species was estimated. To rule out population substructuring within samples of individuals for each species, we allowed K to range from 1 to a number in excess of the geographic locations. For example, we obtained samples of M. milleri from four different Caribbean islands, but we allowed K to vary between 1 and 10  plots by interaction and ln L (K) by interaction. We used Structure Harvest v0.694  of pairwise individual genetic distances among populations and discriminant analysis of principal components (DAPC). We conducted these analyses converting the observed SNP data into principal components that summarize the variation between samples using the R package poppr   for each population within a species using Arlequin 3.5  for seven pairs of loci in M. molossus, one pair of loci in M. coibensis, and no loci in M. milleri. One locus of each linked pair was removed to avoid bias in the results due to linked SNPs. Linkage disequilibrium is not common within subpopulations, except between very close or adjacent sites. In addition, isolated populations are less likely to exhibit strong correlations among loci   has almost 100% of its genotypes shared with the continental population and not with the Lesser Antilles  with distinct geographic and ecological distribution patterns and found that their population structures have a low admixture of haplotypes and vary with habitat preferences, level of population isolation, and historical fluctuations in climate, resulting in mainland and insular species having different phylogeographic patterns. Mainland geographic barriers such as the Andes did not correspond to lineage breaks, except for the Amazon River, which acts as a filter barrier for M. molossus and also correlates with differences between rainforest and savanna habitats. Oceanic straits pose a partial barrier for some bats but not others, isolating populations of M. milleri between islands in the Greater Antilles, but with gene flow occurring among island populations of M. molossus within the Lesser Antilles. M. molossus populations from the mainland are distinct from those in the Lesser Antilles, indicating a degree of isolation. However, migrants from the continent found in the archipelago (such as Grenada) demonstrate ongoing gene flow between these two regions. For this group of bats, oceanic straits appear to act as a partial, filter barrier to dispersal. Our dataset also is consistent with the expectation that more isolated lineages on islands undergo genetic bottlenecks more frequently than lineages closer to the mainland.We examined three species of the mastiff bat  tropical forests in Panama; (b) tropical forests of northern South America; and (c) savannas of Venezuela and Guyana. The Amazonian region of South America is composed primarily of rainforest, but patches of savanna are distributed from Venezuela to Suriname and southern Brazil  and by the Guiana Highland plateau  had higher genetic similarity with the Middle America–northern South America group than to the Caribbean population. Bonaire is located approximately 80 km off the coast of Venezuela, and geologically considered a part of the continent, and Grenada is located about 160 km north of Trinidad, but geologically considered part of the Lesser Antilles. Due to the proximity of both islands to South America, they share similar fauna and flora with the mainland , and in some islands, 4.3All mainland populations showed a constant or expanding effective population size through time, whereas the isolated Caribbean populations showed a higher probability of experiencing bottlenecks. These results support the hypothesis that island populations are more susceptible to bottlenecks than their continental relatives  closely fit the bottleneck model, and the population of M. molossus from the Lesser Antilles showed a higher likelihood for the constant population size model. Considering the same generation length of 3.9 years estimated for M. molossus and for other species of the genus  in small numbers of sampled individuals. In our data, when the models that included changes in population size had a higher likelihood, all the events (bottlenecks or expansions) occurred more than 0.5Ne generations ago, corroborating with Robison et al.  found that the Amazon River and ecological habitats (rainforest and savanna), but not the Andes Mountains, have an effect on the genetic structuring of M. molossus in South America. By contrast, oceanic barriers in the Greater Antilles play a role in isolation of some species (M. milleri), and that these isolated populations are more subject to bottlenecks and therefore vulnerable to environmental change. We expect these patterns to be even more pronounced in populations of bats with lower dispersal abilities, such as fruit and nectar feeding phyllostomids. Demographic research on the bat fauna as a whole would provide important information relevant to biological conservation in the Caribbean as climate change and environment vulnerability accelerate.Our study of phylogeographic patterns in mainland and island populations in a group of highly mobile bats (None declared.L. Loureiro, M. Engstrom, and B. Lim collected the data through field trips in several Neotropics countries. L. Loureiro conducted sequencing and analyses. M. Engstrom and B. Lim verified the analytical methods and supervised the findings of this work. All authors contributed to the interpretation of the results. L. Loureiro wrote the manuscript with support from M. E. and B. Lim. Click here for additional data file.


Numida meleagris belongs to the order Galliformes. Its natural range includes a large part of sub‐Saharan Africa, from Senegal to Eritrea and from Chad to South Africa. Archaeozoological and artistic evidence suggest domestication of this species may have occurred about 2,000 years BP in Mali and Sudan primarily as a food resource, although villagers also benefit from its capacity to give loud alarm calls in case of danger, of its ability to consume parasites such as ticks and to hunt snakes, thus suggesting its domestication may have resulted from a commensal association process. Today, it is still farmed in Africa, mainly as a traditional village poultry, and is also bred more intensively in other countries, mainly France and Italy. The lack of available molecular genetic markers has limited the genetic studies conducted to date on guinea fowl. We present here a first‐generation whole‐genome sequence draft assembly used as a reference for a study by a Pool‐seq approach of wild and domestic populations from Europe and Africa. We show that the domestic populations share a higher genetic similarity between each other than they do to wild populations living in the same geographical area. Several genomic regions showing selection signatures putatively related to domestication or importation to Europe were detected, containing candidate genes, most notably EDNRB2, possibly explaining losses in plumage coloration phenotypes in domesticated populations.The helmeted guinea fowl  Its natural range includes large parts of sub‐Saharan Africa, from Senegal to Eritrea and from Chad to South Africa, where eight subspecies have been identified , probably extinct today, was present in Morocco . In these countries, local domestic populations, freely raised around the villages, coexist with the wild populations, providing opportunities for random admixture events. More intensive livestock farming has been developed in some countries since the 1960s, especially with a view towards diversifying meat production. France is the leading producer of guinea fowl, with 75% of European production and 66% of world production in 2010  were selected, from which blood was sampled and genomic DNA extracted using a high‐salt extraction method . Eleven 21‐day‐old male allpaths2 assembler , the Gallus gallus model RefSeq proteins (with the XP_ prefix), and the bird and Xenopus GenBank proteins available in the NCBI Entrez Protein database on the day the annotation started (5 June 2017). The gene models’ structures and boundaries were derived from the alignments and complemented with HMM‐based ab initio spans by Gnomons, where the alignments only partially covered open‐reading frames with high enough coding propensity (score of 40). Coding genes were assigned a function based on orthology to human and homology to SwissProt proteins. The final annotation is named N. meleagris Annotation Release 100, and its results are summarized in the web report https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Numida_meleagris/100/. A full description of the NCBI gene annotation pipeline was previously published  with the 2.3In order to evaluate genetic diversity in guinea fowl, we sampled individuals from 12 different African and European populations  and sequenced a single DNA pool for each population Table . Hungariwww.ncbi.nlm.nih.gov/sra under the project accession PRJNA496587.Paired‐end sequencing with 100 bp reads was performed at the Genotoul Get‐platform in Toulouse, France, on Illumina HiSeq 2000 and 2500 instruments, following the manufacturer's protocols for library preparations. Each library from one DNA pool was sequenced in three different runs to eliminate possible run effects. Sequences for this project have been deposited in the Sequence Read Archive (SRA) at picardtools version 1.88 SortSam.jar and MarkDuplicated.jar (http://broadinstitute.github.io/picard/); BAM files corresponding to the same population were merged with samtools version 1.3 merge  the variance of sequencing depth and of individual contributions to the pool along the genome, and (ii) the sequencing error probabilities. Based on these likelihoods, the genome‐wide allele frequency spectrum is computed using a random sample of genomic positions (option only‐spectrum), and the software returns for each genomic position the allele frequency with the highest posterior probability (option estim). In our analysis, the proportion of genomic positions used to compute the allele frequency spectrum was set to 0.001 (option −R) according to the author's recommendations, and the starting value for the population mutation rate (option −t) was the default value 0.005.Within each pool, the frequency of the minor allele was estimated for all genomic positions that were covered by at least five reads, using pool‐hmm webpage. This provided 80,779,963 chromosomal variants with at most two alleles . Monomorphic variants and variants with missing data in at least one of the pools were then removed using r , the three domestic populations from Burkina Faso were considered jointly, providing a population named BUR‐t. For this purpose, we applied the procedure described above after having merged the three pileup files corresponding to these pools.2.5p(1−p) over the 10,205,115 filtered variants, where p is the minor allele frequency. A correction factor (1−1/(2n)) was applied in order to account for the number of individuals n within each pool. For each population, the Watterson θ was computed directly from the pileup file using popoolation . In order to evaluate the influence of unequal genome coverage between pools, we compared the estimates obtained from raw data with those obtained after subsampling all populations and positions at a uniform 10 × coverage, focusing on a small part of the genome (Chromosome 8). Principal component analysis was performed with the pca function of the r mixOmics library  approach originally proposed by Boitard, Schlotterer, and Futschik (k) was set to 0.000001.Selective sweeps within each population were detected using the Futschik  and adapFutschik . The objFutschik  includespool‐hmm within each pool, we focused on three specific types of sweep regions: (a) potential domestication sweeps, which were detected in at least six (out of seven) domestic populations while showing no significant signal in any of the three wild populations; (b) potential European sweeps, which were detected in at least four (out of five) European populations while showing no significant signal in any of the five wild and domesticated African populations; and (c) potential ‘production’ sweeps, which were detected in the three populations managed with strong artificial selection criteria, while showing no significant signal in any of the seven other populations.After running 2.7p‐value measuring the evidence for selection is computed independently for all observed variants. Second, regions with an excess of low p‐values are detected using the statistical local score theory. More precisely, (a) each p‐value is converted into the score −log10(p)−ε, where ε is a threshold to be set by the user, (b) a cumulated score called the Lindley process is computed for each genomic position, and (c) local maxima of this cumulated process are considered as candidate regions, because they correspond to regions with an excess of low p‐values. Compared to sliding window approaches, the local score approach avoids defining arbitrary sliding windows and allows quantifying the statistical evidence of detected regions, that is the significance level of local maxima of the Lindley process.Genomic regions showing an excess of genetic differentiation between populations, compared to what is expected under neutral evolution, were detected using the local score approach of Fariello et al. . This app‐values using the FLK test . This neutral tree is first estimated using genome‐wide data, and the deviation from this tree is then tested for each SNP by the FLK statistic. If migration between populations and genetic drift is not too high, the expected distribution of FLK under neutral evolution is a chi‐square with n − 1 degrees of freedom, where n is the number of observed populations. Thus, computing this statistic and the associated p‐values for all variants in a sequence is straightforward. We computed FLK for a set of nine populations: because of small sample sizes, the three domestic populations from Burkina Faso were merged into a single one named BUR‐t, and AFS‐w was removed, although it was still used to root the population tree. For this analysis, we focused on SNPs with a minor allele frequency >10% in at least one the nine analysed populations.Following Fariello et al. , we compp‐values using a score function with ε = 2, which means that we cumulated p‐values below 0.01. Fariello et al.  is nonuniform. Indeed, this was the case in our analysis, because the assumption of a chi‐square distribution for FLK did not exactly hold, due to the high genetic drift observed between populations , we only considered variants having one of the two alleles at high frequency (0.75 or more) in all selected populations and at low frequency in all nonselected populations (0.25 or less). AFS‐w was not considered in this analysis, because allele frequencies in this population could not be estimated with sufficient precision, due to the very small sample size. The functional analysis of candidate variants was performed using snpeff, version 4.2 (http://snpeff.sourceforge.net/index.html). Enrichment in specific Gene Ontology categories was tested using g:profiler , estimated using a genome size of 1.04 Gb Table . The assN. meleagris chromosomes was done by taking into account the cytogenetic rearrangements described by Shibusawa et al.  and a much larger set of smaller chromosomes (microchromosomes). The chicken karyotype is composed of 38 pairs of autosomes plus the Z and W gonosomes, and only the ten largest autosomes can be identified by classical cytogenetics methods and are usually referred to as the macrochromosomes  of population allele frequencies Figure . The firθ) and the other on expected heterozygosity  between markers when detecting selection, despite the fact that individual genotypes cannot be observed from Pool‐seq experiments. Eight regions were detected  of the genome Figure . To refi3.4MYO3A. The 58 genes were grouped together according to their location on the genome, based on the first 103 regions detected, giving a total of 22 candidate regions. A few of these clearly stand out, as they present a high density of SNPs in several genes, such as the region around 15.5–15.7 Mb on chromosome 2 containing six genes including the annotated genes MYO3A, GAD2 and APBB1IP; around 11.1–11.5 Mb on chromosome 8 with 15 genes including TMLHE, SPRY3 and VAMP7 and around 10.6–11.1 Mb on chromosome 11 with seven genes including UROC1, CHCHD4, SLC6A6 and GRIP2  in all selected populations, while having a low frequency (0.25 or less) in all nonselected populations, were finally kept. All SNPs discussed in the text further down refer to these specific ones. A final list of such 58 genes could be defined, 25 of which have a gene name in the genome annotation  with a polymorphism referred to as having a moderate effect (missense or splice variant) by SNPeff. The first missense polymorphism at position chr7:7,353,449 bp in PAPPA2 is Met1350Thr and the allele frequencies are as follows: KOF‐w = 0.0; YAB‐w = 0.0 and AFS‐w = 0.17 for the three wild populations and 1.0 for all other populations, except BEN‐t, for which the data are missing. The second missense polymorphism at position chr2:15782251 bp in MYO3A is Ser1264Ala, and the allele frequencies are 0.0 in all three wild populations, 1.0 in all five European populations, 0.67 in BEN‐t and 0.87 in BUR‐t. Eight polymorphisms having low effect according to SNPeff (mostly synonymous variants) were also detected in this candidate region on chromosome 2, including three in MYO3A  and N. m. coronata (South Africa), which appear clearly differentiated , allowing contact and reproduction between wild and feral animals. Such a situation is neither reported for Burkina Faso nor Sudan.Because guinea fowl are roaming freely around farms, they are likely to meet and mate with wild relatives as has been demonstrated for chicken in Vietnam . PAPPA2SLC41A2, a magnesium transporter  and contains 12 genes in total, three of which  have gene names in the annotation and a fourth, LOC110403465, has a missense polymorphism and the description ‘EDNRB‐like’ , the three of them possibly important drivers of the domestication signature through different biological mechanisms, without excluding, at this stage, the neural crest theory.Close to nd Fitch , who prond Fitch , whereasnd Fitch  who poina et al. . The propool‐hmm or popoolation). These methods account for the effects of sample size, coverage or sequencing errors and have been extensively validated using simulations. For the detection of positive selection, we looked for signatures at the level of genomic regions , which both limits the influence of allele frequency estimation accuracy at each single SNP and exploits linkage disequilibrium information. However, we cannot exclude that our Pool‐seq approach, with limited sample sizes and coverage in most pools, had an impact on the results. For instance, focusing on a small part of the genome (Chromosome 8), we compared the estimations of Watterson θ obtained with raw data with those obtained after subsampling all populations and positions at a uniform coverage of 10× (Figure popoolation, which includes a correction term accounting for sample size and coverage, is slightly biased. This is not expected to occur, but we note that the simulations provided by the authors of this software focused on much higher values of these parameters than the ones considered here (Kofler, Orozco‐terWengel et al., pool‐hmm is expected to be unbiased, the variance of this estimation is increased by the stochastic contribution of one given individual to the pool at each position, compared to individual sequencing. This leads FLK to overestimate the amount of drift, thus reducing its capacity to detect selection events with increased allele frequency variance between populations. The pool‐hmm approach, on the other hand, has been shown by the authors to be as powerful as the equivalent HMM approach based on ideal true allele frequency data, for sample sizes as low as 25 alleles. Overall, we anticipate that further studies based on individual sequencing would refine our conclusions concerning guinea fowl domestication history, but not change them fundamentally.This first study of guinea fowl genetic diversity was based on Pool‐seq data, which is a very cost effective approach for estimating population allele frequencies genome‐wide (Schlötterer, Tobler, Kofler, & Nolte, × Figure . AlthougIn conclusion, we present here the first genome assembly of the guinea fowl and its utility in a selection signature study of domestication. Using the Galgal5 chicken genome as a reference and published comparative cytogenetic data has proven an efficient method for working at the chromosome level. The pooled whole‐genome sequencing approach has revealed the main features of domestication and selection in guinea fowl, for which the domestication scenario could be refined. We propose that the ancestors of the guinea fowls bred in Europe come from the traditional populations of guinea fowls of Western Africa, and furthermore, we show that body size, behaviour and plumage colour are likely to be the main motivations for domestication of this species. We also confirm that the gene pool of European guinea fowls is a limited subset of the genetic variation present in domestic guinea fowls of Africa. While some genes detected here were also found in similar studies performed in other species, a convergent role in their suspected involvement in the domestication process will require more comparative studies. We plan to sample more wild and traditional domestic populations from Africa, as well as domestic guinea fowls from other continents, to strengthen and refine the selection signatures discovered thus far.X.R., A.V. and M.T.B. conceived the project. A.V., M.T.B., G.‐K.D., V.Y.‐G., I.Y., J.B., D.G., C.B.‐S. and X.R. planned the selection of samples and data collection. A.V. coordinated the sequencing of the reference genome. W.W. supervised the sequencing and scaffold assembly and F.T.‐N. the annotation. A.V. and N.T. assembled the scaffolds into chromosomes. SB performed the genetic diversity and selection signatures analyses. S.B., A.V., M.T.B. and X.R. wrote the manuscript, and all the authors participated in the discussion. All authors read and approved the final manuscript. Click here for additional data file.


Betula platyphylla and B. ermanii, in NEC. Rare haplotypes were identified in the chloroplast genome of these species, and both exhibited high levels of nucleotide diversity based on a fragment of the nuclear gene G3PDH and microsatellites. Moreover, significant phylogeographic structure was detected for B. platyphylla, suggesting that these populations had recolonized from independent glacial refuges, whereas no genetic structure was found for B. ermanii.Climatic oscillations during the last glacial maximum (LGM) significantly affected the distribution patterns and genetic structure of extant plants. Northeast China (NEC) is a major biodiversity center in East Asia, and the influence of historical climate change on NEC populations is critical for understanding species responses to future climate change. However, only a few phylogeographic studies of cool temperate deciduous tree species have been conducted in the area, and results are inconsistent for species with different niches or distribution areas. We employed multiple chloroplast and nuclear markers to investigate the genetic structure of two ecologically contrasting species, https://doi.org/10.5061/dryad.230d176). Sequences generated from this study were deposited in GenBank under Accession nos. KY199568–KY200162 and MK819541–MK819970.The nSSR datasets used in the current study and the table of pairwise FST  and its standardized F'ST  among 25 populations based on seven SSRs are available from the Dryad (DOI:  B. platyphylla in NEC were recolonized from two independent glacial refuges. More cold‐tolerant species was probably able to survive at higher latitudes, leading high levels of gene flow between the geographically separated populations during LGM, so no geographic structure was detected in B. ermanii.Climatic oscillations during the Quaternary have led to the establishment of current terrestrial ecosystems. The high species richness and endemic species richness in the Changbai and Greater Khingan mountains suggest that these two regions may have different demographic histories. Here, phylogenetic analyses based on multiple chloroplast and nuclear genome markers demonstrated that populations of  B. platyphylla is a pioneer species that is widely distributed in low‐elevation regions and is usually a dominant species in forest systems. B. ermanii and B. platyphylla are all distributed from Siberia to Far East Asia, and NEC comprises the main and central range of the two species according Tsuda et al.  and 10 nSSRs. The aims of the study were to (a) evaluate the population genetic structure of the two birch species and (b) assess whether these species in NEC survived in multiple refuges during the LGM. The findings of our study may provide insight into the demographic history of ecological systems in NEC.To obtain the genetic structures of the two congeneric species, we surveyed nucleotide variation patterns of chloroplast genomes for both species. As the chloroplast genome is maternally inherited and usually shows a low mutation rate, we also sequenced a fragment of the single‐copy nuclear gene glycerol‐3‐phosphate dehydrogenase .Leaf material of y Figure . In addins Table . We samply Table . To avoiB. platyphylla and NENU4102‐NENU4107 for B. ermanii). Total genomic DNA was extracted from dried leaves for each individual of B. ermanii using the Plant Genomic DNA kit (TianGen). In contrast, as the quality of B. platyphylla DNA extracted by the Plant Genomic DNA kit was poor, we used a modified CTAB method to extract genomic DNA from this species  were employed to analyze the genotypes of both species. Of the remaining three microsatellites, one (BE8) was scored for B. platyphylla, and two (BP10 and BE7) were genotyped for B. ermanii. PCR amplifications using these nSSR primers were performed in a 20‐μL reaction volume including 50 ng of genomic DNA, 1 × PCR buffer (plus Mg2+), 0.2 mM of each dNTP, and 0.5 μM of each primer, with each forward primer labeled with fluorescent dye  (Invitrogen) and 1 unit (U) of Taq polymerase (Takara). The PCR amplification reactions were carried out using an ABI2720 Thermocycler (Applied Biosystems) according to the following procedure: an initial denaturation step at 95°C for 5 min, followed by 35 cycles of 30 s at 94°C, 30 s at an optimal annealing temperature, and 30 s at 72°C, and a final elongation step at 72°C for 8 min. The amplified fragments were mixed and resolved using an ABI 3730 DNA sequence (Applied Biosystems) capillary electrophoresis instrument. We then determined genotypes based on the dosage of PCR product peaks  for B. ermanii. The three cpDNA regions were then sequenced in subsets of 98 and 64 individuals of B. platyphylla and B. ermanii, respectively. Each individual of three species, B. dahurica Pallas, B. costata Trautvetter, and B. ovalifolia Ruprecht, was also sequenced as outgroups. Polymerase chain reaction (PCR) for the three chloroplast regions was performed using a 30‐μl reaction containing 1 × PCR buffer (plus Mg2+), 0.2 mM of each dNTP, 0.5 μM of forward and reverse primers, 1 U of Taq DNA polymerase (Takara), and 50 ng of genomic DNA. The PCR protocol consisted of 4 min of initial denaturation at 94°C, 35 cycles of denaturation for 45 s at 94°C, annealing at Tm for 30 s . The selected nuclear gene was successfully amplified across all populations of B. platyphylla and B. ermanii in a 20‐μl reaction using an ABI2720 Thermocycler (Applied Biosystems) under the following PCR protocol: 4 min of initial denaturation at 94°C, followed by 35 cycles of denaturation for 30 s at 94°C, annealing at 68°C for 30 s, and extension for 90 s at 72°C, ending with a final extension step at 72°C for 8 min. The PCR products were ligated to a pMD‐18 vector (Takara) and transformed into the competent Escherichia coli DH5α cells (Takara) according to the manufacturer's instructions. Positive clones were validated using M13 primers . An average of one and four clones for each individual of B. platyphylla and B. ermanii were sequenced, and a total of 179 and 351 positive clones were sequenced for B. platyphylla and B. ermanii using an ABI 3730 DNA analyzer (ABI), respectively , genetic diversity (h) , allelic richness (Ar) , tetraploid individuals were regarded as two diploid individuals using FSTAT software. This approach was employed in previous studies , and principal component analysis (PCA) were performed using GenoDive  Nei, , proportK) within each species without consideration of sampling location. In genetic structure analysis of total samples, the genotypes of the B. platyphylla individuals were duplicated to show tetraploid‐like genotypes. As ambiguous locus was found for a few individuals of B. ermani, we created another dataset of B. ermanii for STRUCTURE analysis according Hu et al. , and Delta K (ΔK) was calculated using STRUCTURE HARVESTER   , and number of haplotypes (h), with p values based on the chi‐squared text or t test (two‐tailed). In addition, the neutrality test statistics Tajima  Nei,  and (θ) s Tajima  and Fu as Tajima  were ests Tajima . Relatios Tajima , with a s Tajima  with 1,02.6B. platyphylla and B. ermanii at present and during the LGM and last interglacial (LIG). In addition to our sample locations, the geographic locations of all B. platyphylla and B. ermanii populations were retrieved from Chinese Virtual Herbarium (http://www.cvh.org.cn/) and Global Biodiversity Information Facility (GBIF) (http://www.gbif.org). We also removed duplicate records within 5 km  of one another to reduce the effect of spatial autocorrelation in climate variables, resulting in a total of 436 and 228 occurrence records covering the current distribution ranges of B. platyphylla and B. ermanii, respectively. For ecological niche modeling, all 19 climatic variables were downloaded from the WorldClim database (http://www.worldclim.com/) at 2.5‐arc‐min grids for both the present and LGM (approx. 21 Ka BP) and LIG (approx. 130 Ka BP) at 30‐s arc‐sec.We used the maximum entropy method in MAXENT v3.3.3k   for B. ermanii. We then ran 5,000 iterations for the data with 15 replicates, of which 40% of the data were used to simulate the model and the remaining 60% to test the model. The Climate System Model (CCSM) . Higher differentiation was detected between populations of B. platyphylla compared with B. ermanii, as revealed by pairwise FST and F'ST (online data).Both individual‐based and population‐based PCA revealed a separate pattern of genetic structure between the two species Figure a, thoughB. platyphylla  increased constantly from K = 1 to K = 7  was found across B. ermanii populations, and NST was not significantly different from GST. However, NST (NST = 0.308) was significantly higher than GST (GST = 0.224) in B. platyphylla populations, indicating the presence of phylogeographical structure according to Pons and Petit  sampled in NEC presented almost the same number of alleles as B. ermanii (NA = 100), the values of NA for each B. platyphylla population were much lower than those for B. ermanii, as were the values of Ar and Ht . However, Ho values for B. ermanii populations were not different from those for B. platyphylla populations. Additionally, allelic diversity was highly variable across loci, excluding BP12, and B. ermanii populations exhibited higher genetic diversity at the remaining five loci  at the species level. Similar genetic diversity was also observed in the six populations of B. ermanii. A total of 351 sequences, corresponding to 258 haplotypes, generated a 904‐bp alignment with 296 polymorphic sites. There were no significant differences in the average nucleotide diversity of the populations from the three different geographic areas of both species . For B. platyphylla, the LGM model  because new alleles would derive by allopolyploidization. However, we cannot rule out the possibility of combined effects of autopolyploidy and admixture. We also suggest that the relatively high Ar and Ht of B. ermanii may be attributed to relatively broad LGM distributions and greater population connectivity in glacial and postglacial landscapes. Both gene diversity and allelic richness are generally higher in tetraploid birch species than in diploids  per population we used . Geographically separated populations of B. platyphylla exhibited high levels of genetic differentiation (online data), which may have been caused by current and historical isolation . Different genetic variation patterns were found between the two congeneric species B. platyphylla and B. ermanii, and a possible reason is that the two species inhabit two contrasting niches that led to a distinct history during and after the LGM. More cold‐tolerant B. ermanii was likely better able than B. platyphylla to survive at higher latitudes. It has been proposed that according to the pollen record, East Asia was not covered by an extensive terrestrial ice cap but was most likely covered by tundra and taiga forest during the LGM (Cao, Herzschuh, Ni, Zhao, & Böhmer, B. ermanii in NEC may have resulted from high levels of gene flow between the geographically separated populations in the LGM. A similar result was observed in the phylogeographic study of four forest‐dependent bat species (Kuo, Chen, Fang, Flanders, & Rossiter, B. ermanii from other regions (Tsuda et al., In contrast, no geographic separation of the nSSR variation in 4.3In NEC, a total of 2,621 species were recorded, belonging to 128 families and 74 genera Fu, . In our B. platyphylla and B. ermanii, based on nSSRs, nuclear, and chloroplast markers. As expected, populations of B. platyphylla collected from the Daxing'anling Mountains might have migrated from a cryptic refuge distinct from the Changbai Mountain populations. Theoretically, populations from different refuge would be expected to carry different private alleles, mainly due to long‐term isolation (Provan & Bennett, To confirm this hypothesis, we performed population genetic analyses for two woody species, Betula species. Our results based on both CCSM and MIROC models predicted the existence of multiple glacial refuges in the Changbai and Greater Khingan mountains, indicating that B. platyphylla survived among both floras during the LGM. Our findings support the view that multiple LGM refuges may have allowed cool temperate species to persist in NEC (Bai et al., We also simulated the historical and current distribution ranges of the two 5B. platyphylla in NEC had recolonized from two independent glacial refuges. More cold‐tolerant species were likely able to survive at higher latitudes, leading to high levels of gene flow between geographically separated populations during the LGM, with no geographic structure detected for B. ermanii.Climatic oscillations during the Quaternary have led to the establishment of current terrestrial ecosystems. The high species richness and endemic species richness in the Changbai and Greater Khingan mountains suggest that these two regions may have different demographic histories. Here, phylogenetic analyses based on multiple chloroplast and nuclear genome markers demonstrate that populations of The authors declare that they have no competing interests.HYW, XY, DXY, and LL conducted the experimental work and performed the analyses. HYW and XY drafted the manuscript. HXX participated in the design and coordination of this study, collected the samples, and helped draft the manuscript. All authors read and approved the final manuscript. Click here for additional data file.


Formica fusca and the relative contribution of the dispersing males and females to this. We sampled 12 localities across a ∼35 km2 study area in Finland and generated mitochondrial DNA (mtDNA) haplotype data and microsatellite data. First, we assessed queen dispersal by estimating population differentiation from mtDNA haplotype data. Second, we analysed nuclear DNA microsatellite data to determine overall population genetic substructure in the study area with principal components analysis, Bayesian clustering, hierarchical F statistics and testing for evidence of isolation-by-distance. Third, we directly compared genetic differentiation estimates from maternally inherited mtDNA and bi-parentally inherited DNA microsatellites to test for sex-bias in dispersal. Our results showed no significant spatial structure or isolation by distance in neither mtDNA nor DNA microsatellite data, suggesting high dispersal of both sexes across the study area. However, mitochondrial differentiation was weaker (Fst-mt = 0.0047) than nuclear differentiation (Fst-nuc = 0.027), which translates into a sixfold larger female migration rate compared to that of males. We conclude that the weak population substructure reflects high dispersal in both sexes, and it is consistent with F. fusca as a pioneer species exploiting unstable habitats in successional boreal forests.Dispersal is a fundamental trait of a species’ biology. High dispersal results in weakly structured or even panmictic populations over large areas, whereas weak dispersal enables population differentiation and strong spatial structuring. We report on the genetic population structure in the polygyne ant  Dispersal serves to move genes between natural populations, maintaining gene flow and connectivity, thereby reducing the risks of local population extinction and genetic drift . High geEusocial insects  form societies in which only specialized sexual castes  disperse and reproduce, whereas workers forgo their own reproduction to help raise the offspring of the resident queen(s). In ants, queens and males are commonly winged  and fligQueen founding strategy has consequences for hierarchical genetic substructure, i.e. partitioning of genetic variation from nests to populations or species. With independently founding queens, the resulting population substructure is expected to show strong structure at the nest level, yet sub-structuring at higher hierarchical levels such as habitat patch or population may be absent if queen dispersal is strong. With dependently founding queens short-distance dispersal is expected to lead to genetic viscosity (isolation-by-distance (IBD) over small spatial scales) and discontinuous population substructure . DependeThe population level consequences of the queens’ strategy for nest founding can be elucidated from patterns of population differentiation based on maternally inherited mitochondrial DNA (mtDNA). Furthermore, by contrasting mtDNA differentiation with that estimated from nuclear markers (e.g. DNA microsatellites) relative levels of female and male dispersal can be estimated. Such research on ants has revealed that dispersal is usually but not overtly male biased, with greatest bias in species with wingless queens, regardless of gyny level . To our Formica exsecta  to mature conifer forest (F. rufa group)  . Habitat species , yet no lugubris , F. exse exsecta  and F. llugubris  and thouF. fusca (2) using DNA microsatellites and allozymes suggest weak population substructure, no dispersal limitation, monodomy (nests do not form interrelated networks), and no genetic differentiation between social forms  and mtDNA  by estimating and comparing population differentiation in these two markers.http://www.worldclim.org/bioclim, A total of 12 localities were sampled in April–June 2005 in an area spanning ca. 10 × 35 km in Southern Finland ; Table 1All workers were genotyped at nine DNA microsatellite loci: FL12, FL20 , FE13, FFor one queen  or one worker from each nest, a ca. 600 bp segment of the mitochondrial COI gene was amplified with primers: coi_bar_F: 5′-ACTAGGATCTCCAGACATAGC-3′ and coi_bar_R: 5′-GCTCGTGTATCAACATCTAA-3′, following protocols in rlequin (IS), gene diversity (HS) and allelic richness (AR) were calculated for each locality using Fstat2.9.3.2. (.For the DNA microsatellite data we tested for population deviations from Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) using Arlequin . Allele 2.9.3.2. .martpca as implemented in Eigensoft 4.2.  using Soft 4.2. . Principst’s  in a fully random hierarchical design. IBD patterns were tested with Mantel tests , with the package cultevo for R  was estimated by running the analysis with K ranging from 1 to 12, using the admixture model with locprior and correlated allele frequencies. For each K value, the analysis was run ten times with a burn-in of 100,000 steps for a total run length of 300,000 steps. Structure Harvester (K) for each run, and to apply the deltaK method . To visualize haplotype divergence, a Neighbour-joining tree (rooted at the centre) using Jukes and Cantor distance and 1,000 bootstraps was constructed in SeaView 4.2.12. (ST (FST-mt) using haplotype frequency data in Arlequin v. 3.5.1.2 . Most loci were in HWE in most localities after Bonferroni correction, and only a few significant deviations appeared randomly: FY13 in BAC, FY7 in FOR and FL12 in MOS. Significant LD found appeared also random; locus FE19 was found to deviate from LD in six locations, but always with different loci, and some other locus combinations were sporadically significant in different locations. We hence kept eight loci for further analyses.FIS) was estimated at −0.003, suggesting random mating at the level of the total population. FIS estimated by locality ranged from −0.172 to 0.196, with small deviations from zero in eight of the 12 populations . A plot of the two first principal components clearly shows the absence of a spatial pattern .FST = 0.048; 95% CI [0.030–0.074]). When nest structure was taken into account in a hierarchical F analysis, differentiation among localities within the total population was weaker but still significant (FCT = 0.015). A negative inbreeding value for individuals within nests (FIS = −0.38) was most likely a consequence of the non-independence of individuals sampled from the same nest, i.e. nestmates are related. Finally, the large and similar inbreeding values for nests in the total populations (FIT = 0.31) and for nests in localities (FIC = 0.30) are consistent with a strong Wahlund effect, i.e. that nestmate workers are related and that nests act as if they are randomly breeding subunits within the localities. These results hence suggest that nests are highly differentiated within localities and in the total population, and that there is only very weak substructure corresponding to geographic location, results which are in line with those obtained from the principal components analyses.The overall genetic differentiation among localities was significantly greater than zero in nuclear markers , nor among colonies . However, several pairwise FST’s between localities were significant  was not significantly larger than zero, suggesting panmixia. The test for isolation by distance was not significant .A total of 555 bp of COII sequence data was recovered for mtDNA analysis. There were 25 informative differences in the dataset (position: 60 (C/T) 69: (C/T), 78: (A/C), 123: (T/C) 144 (C/T), 156 (A/G), 228(C/A), 319 (T/C), 351 (G/A), 354 (C/A) 537(T/C)), which resulted in eleven different haplotypes. The NJ tree resolved two fairly well separated clades, joined by an intermediate haplotype . The mosFST = 0.040; 95%, CI [0.011–0.076]) than for the mtDNA data (see above), and only the former was significantly greater than zero. After adjustment (FST = 0.027) is six times larger than that indicating female gene flow (adjusted FST-mt = 0.0047).The overall genetic differentiation among localities estimated from a single individual per nest was larger for the microsatellite genotype data  were related in an older habitat, but unrelated in a younger habitat  to ensure good resources for mating flights . Following F. fusca flight altitude is not known, but the similar-sized and closely related F. lemani . Travelling the shortest distance between the localities sampled for this study (1.4 km) would take 4–6 min on such wind currents, and the farthest distance (57.2 km) 170–250 min. This is a simplification of insect flight and wind dynamics, but in short, dispersing F. fusca sexuals could potentially reach considerable dispersal distances in our study area given a flying altitude of ca 40 m and normal wind conditions. flights . The ave. lemani  flies at. lemani . Since Fispersal . At an aFST estimates, was weak, but significantly greater than zero. Thus, the study area was not genetically entirely homogenous, but there was no pattern of IBD, neither among localities, nor among nests across localities, in neither of the markers. Such a pattern is expected to arise when dispersal is high in a spatially and temporally unstable habitat  who arrive and outcompete them (F. fusca such as Vaccinium plants (Anoplolepis gracilipes (The PCA analysis and the hierarchical F-statistics revealed nuclear genetic substructure only at the level of the nest. This pronounced colony-level structure is consistent with monodomy and significant within-colony relatedness, as reported from previous studies . Finally habitat , such tholonized . These hal years . As the ete them . Similarm plants , the invm plants  and the acilipes .F. fusca. Our results are consistent with independent founding and the winged status of both sexes, and with species’ ecology. Temporally and spatially variable habitat necessitates the maintenance of wings and independent founding for rapid colonization and habitat escape, in both queens and males. Polygyny in F. fusca may nevertheless be a dispersal adaptation. Re-adopted queens face no dispersal risks, and if unrelated queens can also either be adopted into nests, or if queens can found nest through pleometrosis, dispersal risks are reduced also for dispersing queens. The results in this study contrast with findings from other polygyne European Formica species, for which stable habitat and weak dispersal are thought to promote polygyny and polydomy (F. fusca’s habitat is intermediate in stability, and perfectly exploited by queen dispersal strategies.This study shows high male and queen dispersal, and as a consequence, near panmictic population genetic structure in polydomy . They alpolydomy . Relativ10.7717/peerj.5024/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.5024/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.5024/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.5024/supp-4Supplemental Information 4Click here for additional data file.


In order to shed light on the genetic origin of the Conquerors we sequenced 102 mitogenomes from early Conqueror cemeteries and compared them to sequences of all available databases. We applied novel population genetic algorithms, named Shared Haplogroup Distance and MITOMIX, to reveal past admixture of maternal lineages. Our results show that the Conquerors assembled from various nomadic groups of the Eurasian steppe. Population genetic results indicate that they had closest connection to the Onogur-Bulgar ancestors of Volga Tatars. Phylogenetic results reveal that more than one third of the Conqueror maternal lineages were derived from Central-Inner Asia and their most probable ultimate sources were the Asian Scythians and Asian Huns, giving support to the Hungarian Hun tradition. The rest of the lineages most likely originated from the Bronze Age Potapovka-Poltavka-Srubnaya cultures of the Pontic-Caspian steppe. Available data imply that the Conquerors did not have a major contribution to the gene pool of the Carpathian Basin.It has been widely accepted that the Finno-Ugric Hungarian language, originated from proto Uralic people, was brought into the Carpathian Basin by the conquering Hungarians. From the middle of the 19 Hun-Hungarian affinity was declared in Hungarian and foreign written sources and has been maintained in Hungarian folk memory [Foundation of the Hungarian state is connected to the conquering Hungarians, which arrived from the Pontic steppes and occupied the Carpathian Basin at 895–905 AD as a confederation of seven tribes under the leadership of prince Árpád. Modern Hungarians are generally identified as successors of the conquering Hungarians (hence shortened as Conquerors). Until the middle of the 19k memory . In the k memory . PhiloloN-L1034) is shared between 4% of the Hungarian Seklers (Hungarian-speaking ethnic group living in Transylvania) and 15% of the closest language relatives the Mansis, though the same marker is also present in Central Asian Uzbeks and has been detected just in one Hungarian [The possible genetic relation of modern Hungarians to Finno-Ugric groups was tested in several studies –8, howevN-Tat (M46) Y-chromosome marker (a major clade of the above mentioned N-L1034) in two of the Conqueror samples and one living Sekler, which was interpreted as a Finno-Ugric link. It is notable that in the latest studies [The genetic composition of the Conquerors was also analyzed in several ancient DNA (aDNA) studies –13 and i studies ,13 popul studies  have not studies .In order to elicit the genetic origin and relationships of the Conquerors, we set out to assemble a full length mtDNA sequence database from the earliest Conqueror cemeteries. Full length mitogenomes are the most informative source of maternal population histories, as some of the subclades have very distinctive geographic distribution ,18, revith century a uniform well distinguishable new archaeological culture appeared in the Carpathian Basin which can be connected to the historical record of the conquering Hungarians. We extracted ancient DNA from 102 Conqueror individuals, derived from 8 different cemeteries  under accession number PRJEB21279.Details of NGS data are shown in We have downloaded all available modern n = 32683) and ancient n = 564,  complete683 and aWe have created an Eurasian population database by grouping those mtDNA genomes according to their geographic origin, for which this information was available . Our popWe compared the genetic similarity of populations with two independent methods. We applied the traditional sequence based method calculating pair-wise population differentiation values (Fst) with Arlequin 3.5.2.2  from entMultidimensional scaling (MDS) was applied on the matrix of linearized Slatkin Fst values  and visuIn a second novel approach we also calculated so called Shared Haplogroup Distance (SHD) values between populations . This meK populations to find the best fitting admixtures with the smallest SHD values from a test population [K values greater than 6 do not significantly improve the result. Theoretically MITOMIX can accurately reconstruct past population admixtures if representative data are available from all periods and locations, but it allows meaningful insights even from limited data [As an additional benefit, SHD enables a hypothesis independent computation to reveal plausible past admixture events. Thus we have also introduced another novel algorithm called MITOMIX, which computes all possible combinations and proportions of pulation . In our ted data . With thThe sculpting craniofacial reconstructions of three skulls from the Karos cemeteries  were carUsing the NGS sequencing method combined with target enrichment, we could obtain 102 ancient mitogenome sequences, 78 of which are first reported in this paper, while 24 had been reported in . The 102Phylogenetic trees revealed that the Conqueror maternal lineages originated from two distant geographical regions; 31 were unequivocally derived from East Eurasia, while 60 from West Eurasia. The remaining 11 Conqueror Hg-s are ubiquitous in Eurasia. Out of the 60 west Eurasian lineages 13 are characteristic for modern Northwestern Europeans, while 7 have primarily Caucasus-Middle-East distribution.N1a1a1a1a, represented by 7 Conqueror samples, while two more samples belonged to the progenitor N1a1a1a1 lineage , Srubnaya (Sru) and Poltavka-Potapovka (PoBA) populations . This isWhen only ancient populations are considered as a source, the best admix includes 36–44% Poltavka-Potapovka, 18–20% Baltic Bronze Age, 11–29% Combed Ware, 14–18% Sintashta and 14% Srubnaya components , all of The most plausible interpretation of the phylogenetic and population genetic results is that the majority of eastern lineages were ultimately derived from Inner Asia which then migrated to Central Asia where they admixed with Eurasian lineages before moving to Europe, where they in turn incorporated west Eurasian elements. As the Conquer population was apparently assembled from multiple sources this raises the questions as to when did the admixtures happen, which ancient populations could have been the source and how can our results be reconciled with historical, archaeological, anthropological and other genetic data.th century, and intermingled with local Scythian and Finno-Ugric populations, then in the 13th century Kipchak Tatars of the Golden Horde brought a final Central-Inner Asian genetic layer and their language to the region. MITOMIX seems to identify these historical components, as Finno-Ugric Mansis and Khantys (Yug) comprise a major component of Volga Tatars besides Russians, while Scythians also appear among their potential sources  another grouping of Turkic tribes, the Ogurs appeared on the Pontic steppe from Central Asia. The Onogurs are the first nomadic groups from the east, which are reliably connected by historical sources to the later appearing Bulgars, and less reliably to the Conquerors . OnogursThe succeeding group arriving from East Eurasia to the Pontic steppes in the middle of 6th century were the Avars, who established an empire in the Carpathian Basin lasting for three centuries . It is rth century, moreover most Turkic loanwords in Hungarian originate from West Old Turkic [Subsequent east-west migrations are connected to Göktürk, Kipchak and Mongolian groups, but these could have minor effect on the Conquerors as mostly arrived after the 10d Turkic , the OghTaken together genetic and historical data refer to four major groups delivering significant east Eurasian lineages to Europe which could be connected to the Conquerors; Asian Scythians, Huns, Onogurs and Avars. Of these groups we have mitogenome sequences just from European Scythians . DespiteR1b-M269 and I2a in the Conquerors, reported in our previous study [According to our data the best fitting sources of the west Eurasian lineages are the Late Bronze Age Srubnaya (Timber-grave) culture  and its ancestors the Potapovka  and Poltavka  cultures . The Sruus study .The Armenian Bronze Age (ArmBA) population also appears very close to the Conquerors , Fig 5, N-tat, (new name N1a1) which is typical for Uralic speaking groups, thus Estonians must have received their east Asian-Siberian components after the BalBA period, from a different source [Surprisingly we did not find significant genetic relations to Finno-Ugric groups. Though population genetic analysis indicates some connection of the European Conqueror component to modern Finnish (Fin) and Baltic  people, but no relation to Saamis (Sam), Mansis and Kanthys (Yug)  Tables. t source . AccordiY, B and N1a1a1a1a Hg-s have not been detected in Finno-Ugric populations [Moreover, ulations –84, implulations , which rulations ,87 and iulations .It must be emphasized that Finno-Ugric groups are underrepresented in our population database, as we have no mitogenomic data from Komis, Maris, Mordvins and Udmurts and only limited samples from Mansis, Kanthys, Saamis and Estonians. Therefore appearance of Finno-Ugric matches from a more representative dataset cannot be excluded, but our data imply that incidental Finno-Ugric link is rather expected in the European component if any.Archaeologist presume that the rich 10th century cemeteries of Karos and Kenézlő comprise the Conqueror military elite, raising the question as to what extent can our findings be generalized to the entire Conqueror population. Our fragmentary data from other cemeteries indicate the presence of the same eastern and western genetic components , which sth century immigrant military elite characterized with partial horse burials, though further mitogenomic and genomic data are required for the accurate answer.Thus our conclusions probably apply to the entire Conqueror population, but definitely to the 10X2f maternal haplotypes and I2a1 Y chromosomal haplotypes (data not shown), so were probably brothers. This indicates that these neighboring communities did not intermarry perhaps because of different group-identity. Furthermore the east Eurasian haplogroup lineages from the three Karos cemeteries indicate a discernible structuring Click here for additional data file.S1 FigPhylogenetic trees are arranged in alphabetic order according to haplogroups. The 67 sub-haplogroups are depicted on 58 Networks. Samples falling into the same sub-haplogroup with the studied sample are encircled. The smallest colored circles represent one individual; circle sizes are proportional to the number of individuals with identical sequences.  Green circles identify Hungarian conqueror samples, red circles represent modern samples, and violet circles correspond to ancient samples. A few ancient samples belonging to the shown haplogroup could not be properly aligned due to incomplete sequences, and these were connected with dashed line to the tree. Number of crosslines between neighboring circles denotes mutation distances. Length of connecting lines is irrelevant, as they were modified in order to fit page. Genebank accesion number and origin of samples closest to the studied conquerors are listed next to the circles. Known Conqueror Y-chromosome haplogroups were added in blue color. We summarized the probable origin of the samples’ Hg lineage in colored framed text. In some cases comments are given next to the trees.(PDF)Click here for additional data file.S1 TableTable a. Description of samples including anthropological and archaeological details. Haplogroups and closest matching sequences are also summarized from (XLSX)Click here for additional data file.S2 TableTable a. Details of NGS data for each samples. Samples highlighted with blue were published in . Lowest (XLSX)Click here for additional data file.S3 TableTable a. Modern population database with NCBI GenBank accession numbers and haplogroups given next to population abbreviations (provided in S3B Table). 314 newly deposited mitogenomes from  includin(XLSX)Click here for additional data file.S4 TableTable a. Pairwise Fst (top) and linearized Slatkin Fst (below) matrix of population distances between all combinations of modern and ancient population. In the upper right part of the table probability values are depicted, + correspond to significant P values (<0.05), while—means not significant P values. Color code (provided below the table) highlights the best similarity levels. Abbreviations of population names are given in (XLSX)Click here for additional data file.S5 TableTable a. Best MITOMIX results for the entire Conqueror population from available population Hg frequency data . Data we(XLSX)Click here for additional data file.