Balaenoptera musculus intermedia) – a critically endangered subspecies and the largest extant animal – by capitalizing on the largest genetic dataset to date for Antarctic blue whales. We found evidence of three populations that are sympatric in the Antarctic feeding grounds and likely occupy separate breeding grounds. Our study adds to knowledge of population structure in the Antarctic blue whale. Future research should invest in locating the breeding grounds and migratory routes of Antarctic blue whales through satellite telemetry to confirm their population structure and allow population-level conservation.Population-level conservation is required to prevent biodiversity loss within a species, but it first necessitates determining the number and distribution of populations. Many whale populations are still depleted due to 20th century whaling. Whales are one of the most logistically difficult and expensive animals to study because of their mobility, pelagic lifestyle and often remote habitat. We tackle the question of population structure in the Antarctic blue whale ( Population-level conservation is increasingly being recognized as a requirement for preventing biodiversity loss23a priori boundaries of populations for analysis. Also, Antarctic baleen whales are highly expensive and logistically difficult to study. This is because of the remote and extreme weather conditions of the Antarctic, the high level of expertise required for expeditions, and the effort it takes to locate rare animals in an area encompassing millions of square kilometres.Baleen whales were killed in the hundreds of thousands during 20th century whaling. It has long been recognized that their conservation should operate on a population levelResearch methods have and continue to be developed to mitigate the intrinsic difficulties in studying the population structure of baleen whales. A method used during whaling was Discovery marking, where a whale was marked with a uniquely numbered Discovery tag and the tag recovered later when the whale was killed911121415171819Balaenoptera musculus), and the largest subspecies of blue whale is the Antarctic blue whale (B. m. intermedia)24The largest animal is a species of baleen whale, the blue whale , Antarctic blue whales are currently protected from commercial whaling and have not been killed under Special Permit (scientific) whaling. However, other anthropogenic activities may impede their recovery in numbers. Climate change may have consequences throughout the distribution of all populationsa priori putative populations, which are often geographic-based, and those that have been used (the IWC management Areas) as well as any other potential a priori geographic groupings are not necessarily biologically relevant given the continuous distribution of blue whales around the Antarctic. There has been no evidence of multiple populations based on Bayesian clustering assignment analyses4042a priori geographic groupings. The lack of evidence could be due to insufficient sample sizes (n = 4740B. m. brevicauda)42The population structure of Antarctic blue whales can currently only be assessed where they feed, in the Antarctic, due to very limited knowledge of the locations of their breeding grounds. Movement of Antarctic blue whales studied using Discovery marks, photo-identification, and satellite tagging show large-scale movements as well as a degree of site fidelity to areas off Antarctica3334353637Genetic samples of Antarctic blue whales have been collected since 1990 through highly costly and logistically difficult boat-based surveys conducted through the IWC. Genetic data collection from these samples should be maximized to provide high quantity and quality data that will inform conservation of this critically endangered animal. Here we examine the poorly defined population structure of Antarctic blue whales at their feeding grounds using the largest genetic dataset to date – 142 individuals of the Antarctic subspecies and information from 20 microsatellite markers and the mtDNA control region. We report novel findings that should be used to direct future research on the population structure of Antarctic blue whales.K showed a peak at a K of three (−10406.48 (SD 39.99)) and one (−10385.12 (SD 0.56))  and equal membership of individuals to each genetic cluster when K was set to more than one.The Bayesian analysis of STRUCTURE inferred three genetic clusters that occur in sympatry off Antarctica. First, the likelihood in STRUCTURE across different values of D 0.56)) . Second, cluster . Fourth, cluster . AdditioK value of three, CLUMPAK identified one major clustering solution  (Genetic variation was similar between each inferred cluster detected by STRUCTURE. This was regardless of whether variation was based on microsatellites or the mtDNA control region . There w6–0.029) . There w6–0.029) .B. m. intermedia. A Bayesian clustering assignment method based on microsatellite DNA provided evidence that three genetically differentiated populations occur sympatrically off Antarctica during the austral summer feeding season. For simplicity, these are henceforth referred to as populations. The levels of differentiation were low and similar between the populations, and consistent between analyses of microsatellites and the mtDNA control region. Genetic variation based on microsatellites and the mtDNA control region was similar in the three populations.Our study helps refine knowledge of population structure in the Antarctic blue whale, a priori putative populations, have previouslya priori groupings, this genetic differentiation between Areas could be due to different proportions of the populations in different management Areas.Previous studies were unable to detect the multiple, sympatric populations of Antarctic blue whales, even though they also used the same standard Bayesian clustering method4042The only longitudinal range where the proportion of different populations can be accurately assessed is between 0° and 20°E since 57% of samples were from this area. Here, there is an almost equal ratio of individuals from each population . In contrast, in the Pacific Ocean basin sector there is perhaps greater proportions of populations 1 and 2 compared with population 3 as only three of the 26 individuals there were identified as belonging to population 3, and these had low memberships (estimated in STRUCTURE) to population 3 of 0.547 to 0.597 inclusive. These individuals and others with relatively low membership to their assigned population may have admixed ancestry to multiple populationsThe movement data available for Antarctic blue whales corroborate the current study’s findings. They show large-scale movements off Antarctica – including movements that cross IWC management Areas and ocean basins – as well as small-scale movements. The data are from a wide range of methodologies and are across a wide time span: Discovery marks deployed from the 1934/35 to 1962/63 season and recaptured until the 1966/67 season34353633Euphausia superba)5051533355The individual whales likely move depending on the densities of their prey due to their high energetic requirements as the largest extant animalMegaptera novaeangliae) – arguably the most well-understood Antarctic baleen whale – have discrete distributions in the Antarctic, with overlap in the distribution of some populationsB. bonaerensis) may also have a similar pattern of discrete population distributions in the Antarctic with some overlapB. acutorostrata) appears to have complete sympatry on feeding grounds in the North AtlanticThe exact breeding ground locations of Antarctic blue whales are unknown. One possibility is that populations breed at different ocean basins. This means that populations would be physically separated by the continental land masses of South America, Africa and Australia during the austral winter breeding season. It has long been suggested that at least one Antarctic population breeds in each of these physically separated regions5824It is also possible that at least one population of Antarctic blue whales is resident in the Antarctic throughout the year. A resident population may follow the ice edge as it expands northwards in the austral winter and recedes in the austral summer. This is suggested by year-round acoustic detections of the Antarctic subspecies off the western coast of the Western Antarctic Peninsula635959656667697072737472Another explanation for year-round presence at a feeding ground is overlapping timing of departures and arrivals. This overlap could be mediated by temporal segregation of migration based on age, sex, reproductive state, or migratory destination, as is thought for humpback whales767980801763It is possible that whaling has influenced the population structure detected here. Exploitation can lead to the formation or loss of populations, or for the level of genetic differentiation between populations to increase or decrease243358343536The current level of gene flow between populations may be greater than that prior to whaling, which could account for the low level of genetic differentiation and evidence of admixture found in the current study. Increased gene flow may occur because individuals need to travel further afield to find mates given that the number of Antarctic blue whales reduced from 239,000 to 360 individuals due to whalinga priori groupings and a baseline for comparative genetic analyses of samples collected off Antarctica. Monitoring each population’s abundance would then need to be performed at their breeding grounds. Monitoring is needed because the populations may differ in pre- and post-whaling abundance and recovery trends, especially because they occupy different migratory routes and breeding grounds with likely different carrying capacities and anthropogenic threats.Locating the breeding grounds of blue whales feeding off Antarctica is needed to confirm their current population structure. The biopsy samples used in the current study and the raw data for much of the discussed post-whaling Antarctic blue whale research, including photo-identifications3435362640428712et al.® Blood and Tissue Kit (Qiagen).Genetic samples from the 142 Antarctic blue whale individuals used in this study were collected off Antarctica using non-lethal biopsy darts. These samples exclude resamples, migrant pygmy blue whales, and subspecies hybrids found off Antarctica by Attard et al.et al.et al.et al.Genetic data were collected from the samples at 20 microsatellites and a 414 bp fragment of the mtDNA control region. The data used in the current study from the 20 microsatellites were obtained by Attard Analyses confirming microsatellite data adhered to the assumptions of subsequent analyses were conducted for the Antarctic subspecies and each genetic cluster identified by Bayesian analyses. This included checking for homozygote excess, and genotyping errors from stuttering and short allele dominance, using MICROCHECKER 2.2.36). Thirty independent runs were conducted for each value of K from one to 10, where K is the number of inferred genetic clusters. Runs were summarized using CLUMPAKPopulation structure was assessed using a standard strategy that is suitable for continuously distributed and potentially sympatric populations. The Bayesian clustering assignment method of STRUCTURE 2.3.4K) detected by STRUCTURE. First, the probability of the data for each tested value of K was taken into account, with the highest probabilities expected at meaningful values of K. Second, the ΔK methodK, it can only detect a true K that is greater than one. Third, the distribution of estimated fraction of membership to each cluster for each individual at a given K was taken into account. Approximately 1/K fractions are expected for each individual at K > 1 when there is no genetic structure. Fourth, spurious genetic clustering was considered a possibility if the geographic distribution of inferred genetic clusters was even or if STRUCTURE detected two or more clusters for any of ten simulated panmictic populations.Multiple measures were used to infer the most meaningful number of populations . These were analyzed in STRUCTURE using the models, burn-in and run length implemented for the empirical dataset. Ten independent runs were conducted for each value of K from one to five. The most meaningful K was determined based on the probability of the data for each tested value of K and the estimated fraction of membership to each cluster for each individual at a given K. The ΔK method was not implemented as it can only detect a true K that is greater than one.The panmictic populations were simulated and analyzed following Quintela FST) was calculated based on microsatellites and the mtDNA control region using ARLEQUIN  between the clusters, as well as between a priori geographic groupings of IWC management Areas. Genetic differentiation was calculated to allow comparisons with previous a priori genetic differentiation analyses of Antarctic blue whales105Pairwise genetic differentiation (O) and unbiased expected heterozygosity (HE) using GENALEX 6.501107h) using ARLEQUIN, and haplotype richness using CONTRIB 1.02Genetic variation was determined for the Antarctic subspecies and each genetic cluster identified by Bayesian analyses. Variation based on microsatellites was determined by calculating the number of alleles, observed heterozygosity .


Among the various aquatic plant life forms, the evolutionary processes of freshwater submerged species are most likely distinct due to their exclusive occurrence in the discrete and patchy aquatic habitats. Using the chloroplast ST > GST, p < 0.01), in which four distinct lineages occurred in different areas. A high level of genetic differentiation among populations  was detected. The divergence of Hydrilla was estimated to have occurred in the late Miocene, and the diversification of various clades was dated to the Pleistocene epoch. Biogeographic analyses suggested an East Asian origin of Hydrilla and its subsequent dispersal throughout the world.We sequenced 681 individuals from 123 sampling locations throughout China and identified a significant phylogeographic structure  contains supplementary material, which is available to authorized users. Aquatic vascular plants are a distinctive group, differing from terrestrial plants in their growth forms and habitats. They have multiple evolutionary origins from terrestrial environments and show a complex evolutionary history ,2. Many An exponential growth of plant phylogeographic studies has been observed in Europe and North America in the past two decades, and a similar trend has recently been found in China and adjacent regions ,11. CommHydrilla, a monotypic genus of the family Hydrocharitaceae, which is distributed worldwide. The single Hydrilla species H. verticillata (L.f.) Royle (hydrilla) is found on all continents except Antarctica [Here, we focus on the submerged plant genus tarctica ,33. Thistarctica ,32,34. Starctica . Hydrilltarctica –40. Monotarctica . Furthertarctica ,38, randtarctica ,42, and tarctica . HowevertrnL-F region. We then inferred the biogeographic history of hydrilla by combining the trnL-F sequences from previous studies conducted worldwide. Our objectives were (1) to examine the genealogical patterns of hydrilla in China, and (2) to infer the original area and dispersal route of hydrilla. This study will provide a good example for us to understand the evolutionary processes that occur in submerged macrophytes.In this study, we first examined the phylogeographic structure of an extensive sample population of hydrilla from China using sequences of the chloroplast A total of 681 sequences were obtained, with lengths ranging from 1,066 to 1,105 bp. The length of the aligned sequences was 1,130 bp, and 32 polymorphic sites were observed, including 18 indels and 14 base substitutions. The sequences were collapsed into 9 haplotypes . The two most common haplotypes were C1  and B1 , which were present in 78 populations and 49 populations, respectively. Haplotypes D1, B4, A1, and B3 were detected in 11, 6, 5 and 2 populations, respectively. The remaining three haplotypes, A2, B2, and C2, were each present in a single population. Of the 123 populations we surveyed, 93 populations were monomorphic and consisted of a single haplotype. In the nine groups we defined based on river basins, the highest diversity was present in the Yangtze River Basin  and the river basins in Southeast China   was significantly greater than GST , indicating that closely related haplotypes tended to occur in the same area. An AMOVA revealed that 17.97 % of the total variation occurred within populations, and 82.03 % occurred between populations. The global FST value (0.820) indicated a significant genetic structure among the 123 hydrilla populations. When we grouped the populations into basins, an AMOVA showed that large amounts of variation occurred both among the basins (40.09 %) and among the populations within basins (43.14 %) and that 16.77 % of the variation occurred within populations. The mismatch distribution of the overall populations was multimodal (not shown), and a sudden expansion model for hydrilla was therefore rejected.A haplotype network was constructed with four distinct groups based on all haplotypes from worldwide samples (see below for the definition of haplotypes) Fig. . The firet al. [The accessions collected from worldwide by Madeira et al.  were coltrnL-F sequences were used to estimate interior divergence times. The crown age of clade A + B was estimated to be 4.57 Ma (95 % HPD = 2.06–7.15 Ma) based on the trnL-F sequence data  and 6.71 Ma 0.12–22.42 Ma), respectively, based on the combined data , indicating that most genetic variation is found among populations. The results of an AMOVA suggested that half of the detected genetic variations should be ascribed to genetic differentiation among the basins. This finding was supported by the significant phylogeographic structure revealed, in which the haplotypes of lineage C mostly occurred in the northern part of China, lineage B occurred in the southern part of China, lineage D haplotype was restricted to the Huai River and the middle and lower reaches of the Yangtze River, and lineage A was restricted to the southeast corner of China . SimilaST 0.80, indicaNajas, Ottelia, and Blyxa [trnL-F sequences despite its high polymorphism  inferred vicariance at nodes 17, 22 and 25, of which node 17 is the crown node of clade A and 22 is the crown node of clade B with robust support  have been recognised in the United States . These bThe northernmost monoecious hydrilla population occurs in the Lucerne/Pipe Lakes complex in Washington, at 47.37° north latitude . The norHydrilla, and our findings point to an East Asian origin of Hydrilla. The study provides empirical evidence, based on a phylogeographic analysis, that reveals the complex biogeographic history of diversification and colonisation in worldwide species of submerged macrophytes. Our results will be more persuasive once more extensive samples from other countries are included. Comparative studies on other submerged macrophytes that are distributed worldwide would be valuable in better understanding the diversification and colonisation of this distinct group of plants.Our study reveals that China is most likely the centre of genetic diversity in A total of 681 individuals of hydrilla were collected at 123 sites throughout its distribution range in China, from the northeast to the southwest  was performed using 10–30 ng of genomic DNA, 0.1 μM each primer, 0.2 mM each dNTP, 2 mM MgCl2, and 0.6 U of ExTaq DNA polymerase (TaKaRa) in a volume of 25 μL under the following conditions: 3 min at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 55 °C, and 90 s at 72 °C, and then a final 5 min extension at 72 °C. Amplifications were conducted in a Veriti 96-Well Thermal Cycler . The PCR products were purified and sequenced in both directions by the Beijing Genomic Institute in Wuhan, China. All sequences of different haplotypes were deposited in GenBank (Accession Nos. KM982392–KM982400).Total genomic DNA was extracted from silica-dried plant fragments using the DNA Secure Plant Kit . Primers “c” and “f” reported by Taberlet et al.  were usehttp://www.fluxus-engineering.com). In the network analysis, gaps with two or more base pairs were coded as single mutation events. When overlapping indels occurred, the overlap portion was considered a single event [Sequences were aligned using the program Mafft 6.7 , and manle event .ST and NST, based on 1,000 permutations in PERMUT.We defined groups of 123 populations of hydrilla based on basin boundaries. Nine groups were defined from the northeast to the southwest, corresponding to nine main basins: the Amur-Heilong River Basin (RB1), the Liao River and Hai River Basin (RB2), the Yellow River Basin (RB3), the Huai River Basin (RB4), the Yangtze River Basin (RB5), river basins in Southeast China (RB6), the Pearl River Basin (RB7), river basins in South China (RB8), and river basins in Southwest China (RB9) Fig. . An analWe examined pairwise mismatch distributions to detect historical demographic expansions using DNASP. Populations at demographic equilibrium should present a multimodal or random and rough distribution of pairwise differences, whereas populations experiencing a sudden demographic expansion are expected to display a unimodal and smooth distribution ,76.Vallisneria natans, V. spinulosa and Najas marina, were included as outgroups. We conducted maximum likelihood (ML) analysis in the program GARLI [We combined the sequences of 30 samples collected worldwide  into ouram GARLI , beginniam GARLI . Two indam GARLI  was emplam GARLI .rbcL + matK + trnK 5’ intron + rpoB + rpoC1 + cob + atp1 sequence data from 12 genera within Hydrocharitaceae and three outgroups from Chen et al. [p > 0.05). The divergence time estimate was conducted in BEAST 1.7.4 [et al. [trnL-F sequences to infer the divergence time of internal nodes in hydrilla. Second, we employed the age of the stem and crown nodes of hydrilla to estimate interior divergence times in hydrilla based on the trnL-F sequences. We applied the GTR model of nucleotide substitution with Gamma Categories set to six under an uncorrelated lognormal relaxed clock model [The divergence time between clades in hydrilla was estimated in two steps. First, we estimated the age of the stem and crown nodes of hydrilla based on combined 18S + n et al. . We ampln et al.   and a Bayesian binary MCMC (BBM) were implemented in the program RASP . Anotheen et al..The invahttp://www.ncbi.nlm.nih.gov/Genbank/.The data set supporting the results of this article is included within the article and its additional files. The 41 cpDNA sequences supporting the results of this article are available in the National Center for Biotechnology Information (GenBank) under accession numbers KM982360–KM982400,


The movement of people into the Americas has brought different populations into contact, and contemporary American genomes are the product of a range of complex admixture events. Here we apply a haplotype-based ancestry identification approach to a large set of genome-wide SNP data from a variety of American, European and African populations to determine the contributions of different ancestral populations to the Americas. Our results provide a fine-scale characterization of the source populations, identify a series of novel, previously unreported contributions from Africa and Europe and highlight geohistorical structure in the ancestry of American admixed populations. The genetic make-up of people living in the Americas has been shaped heavily by migration. Here the authors use a haplotype-based approach to reconstruct American genomic ancestry using genotype data from 2,500 individuals, revealing a previously unrecognized genetic contribution from European and African populations. The genetic make-up of the Americas has been significantly shaped by the Colonial Era and the Atlantic slave trade. Given its historical and epidemiological implications, the estimation of the genetic ancestry of admixed American populations has been the subject of much attention12344In order to obtain a finer characterization of the ancestry landscape of admixed American populations, we implemented a novel inference method that reconstructs local genomic ancestry using a haplotype-based approach667In order to minimize the impact of within-source genetic heterogeneity in the ancestry characterization process, we partitioned the 1,414 individuals from 42 population-label donors into genetically homogeneous clusters using a CHROMOPAINTER and fineSTRUCTURE analysis as described in the Methods section. This identified 78 clusters  related African individuals are divided within 33 clusters. Populations from West Africa showed a high degree of homogeneity, with all the Yoruba individuals from Nigeria forming a single cluster and the Mandenka from Senegal grouped into two. Individuals from Eastern and Southern Africa were distributed across 20 different clusters from three different regions , perhaps because of the complex demographic histories of populations from these areas1011Pygmies, Sandawe and San  contains 10 clusters composed of individuals from Romania, Cyprus, Italy (excluding Sardinia), Bulgaria, Greece and France . Notably, Italian individuals are distributed into four different clusters according to their geographic origin .A North-Western Europe group (‘NW Europe’) consists of eight clusters comprising individuals from British Isles, Orkney Islands, Norway, France, Germany and Austria. Similarly to the Basque populations, our approach clusters 23 individuals in a clade containing members of the Orcadian sample from the HGDPThe North-Eastern Europe group (‘NE-Europe’) is composed of eight clusters including individuals from Lithuania, Poland, Belarus, Hungary, Russia, Germany, Austria, Finland and Norway. Native American and East Asian (China) individuals are grouped into eight clusters, each exclusively containing individuals from the same labelled sample. These results confirm the extent of genetic structure in Africa and Europe, and provide a number of potential donor groups to the present-day American populations.K=3  between all three approaches  to 0.87 (Barbados) in all the analysed populations.Caribbean populations show a higher African component than Southern American ones, consistent with historical records that documented a larger number of slaves in the Caribbean Islands22Although our sampling of Africans is incomplete, we see variation among groups in similarity to present-day populations from different parts of Africa. In all groups, the Yorubans from West Africa are the largest contributor, confirming this region as the major component of African slaves12The degree of resolution in the identification of the sources provided by our approach is also evident in the fine characterization of the European component, which ranges between 0.078 (Barbados) and 0.79 (Puerto Rico). We specifically identify Spaniards among other available Southern European populations as the most represented European source for all nine Hispanic/Latino populations. In contrast, the most represented European sources in the Afro-Americans and Barbadians were Great Britain clusters  and 4a, 24Among smaller genetic contributions, we identify for the first time a genetic signature of Basque ancestry in five (out of six) of the Continental South American populations, ranging between 0.015 in the Maya population to 0.07 in Colombia. It has been documented that Basque individuals were a considerable fraction of Spanish immigrants in the XVI and XVII centuries, especially to Mexico, Cuba, Chile, Peru and ColombiaAmong the remaining European clusters the most represented, contributing to five of the analysed populations, is composed of individuals from South Italy and Sicily. This might indicate a minor contribution from the Italian peninsula as documented in historical records28At the individual level, the analysis highlights a high heterogeneity in several analysed populations , as expeWe explored the variation in ancestry determination when using a population-label-based approach instead of a clustering-based one by comparing estimates obtained using the same set of source individuals but grouped in different ways . Populat71330We used a hierarchical clustering algorithm on the basis of the Euclidean distances between relative ancestry proportions to explore the dissimilarities in source composition across admixed populations  and conshttp://www.slavevoyages.org/tast/assessment/estimates.faces) as suggested by their different European source composition  and the inclusion of more sources, particularly from Africa and the Americas, are expected to further clarify the complexity of the ancestry composition of the American continent.We assembled from literature a data set composed of 4,139 individuals from 64 populations sampled from Europe, Africa, East Asia (represented by a single sample from China) and the Americas, genotyped with different Illumina platforms . The datWe screened the pruned data set using KINGhttps://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html#gmap).The data set was phased using the Segmented Haplotype Estimation and Imputation tool ver. 2 (ShapeIT) softwareAs a first step, we clustered the individuals belonging to ‘donor’ populations into homogenous groups. This approach allows a more detailed reconstruction of the ancestry of a given population, taking into account the genetic structure of the donor demes.First, we used a novel inferential algorithm implemented in CHROMOPAINTERSecond, we analysed the painted data set using fineSTRUCTUREA Posteriori state characterized by the higher likelihood.We analysed CHROMOPAINTER's output performing two different MCMC runs, each composed by 5 million iterations, and extracted the Maximum We painted each individual belonging to recipient populations as a combination of genomic fragments inherited by ‘donor individuals’ pooled using the clustering affiliation obtained as previously described and summarized in CHROMOPAINTER provides a digested output of the reconstructed individual’s chromosomes in the form of a ‘copying vector’, which is a summary of the amount of DNA copied genome-wide from each donor population. By normalizing this vector to sum to 1, it is possible to obtain a representation of the proportion of genome copied from each donor population by each recipient individual. We identified the most closely ancestrally related donor population for each Afro-American and Latino/Hispanic population by comparing their copying vectors to copying vectors inferred in the same way for each of the donor clusters, using the non-negative least square functionIn order to avoid any possible distortion in the assignment, we removed all the clusters composed only by a single individual.In addition, given prior knowledge of strong genetic bottlenecks that have shaped the gene pool of modern Native American populations, we anticipate extremely strong genetic drift of these specific admixing groups, relative to East Asian groups with whom they still share ancestral haplotypes. Because the mixture decomposition does not model such drift, which is expected to be reflected in inaccurately modelled  copying from the ‘East Asia’ group in particular, we re-performed the mixture analysis, removing the contribution that each population copied from China, in order to ameliorate the impact of such recent drift.The ancestry composition of each individual within the recipient populations was estimated using the same approach as described above but comparing the individual’s copying vector to the source population-copying vector. Results are reported in The uncertainty in the ancestry estimation at the population level was assessed by applying a jack-knife approach, and estimating the s.e. as in ref. In addition, for comparative purposes we performed the same analysis using generally coarser population labels, instead of clusters inferred by fineSTRUCTURE .The relative African and European ancestry composition was calculated using the results described above and reported in n individuals, where n is the size of each analysed sample. Each individual was generated by combining 22 ‘painted’ chromosomes randomly selected from the analysed population.We built a consensus tree (retaining only branches with >80% support) based on 1,000 bootstrapped simulated samples, using the ‘ape’ R packageHow to cite this article: Montinaro, F. et al. Unravelling the hidden ancestry of American admixed populations. Nat. Commun. 6:6596 doi: 10.1038/ncomms7596 (2015).Supplementary Figures 1-5, Supplementary Tables 1-2 and Supplementary ReferencesAncestry composition for recipient populations; standard error estimated by 22 jack-knife resampling indicated in brackets.


Nothobranchius furzeri is an annual fish from south-eastern Africa, inhabiting discrete temporary savannah pools outside main river alluvia. Their dispersal is limited and population processes affecting its genetic structure are likely a combination of those affecting terrestrial and aquatic taxa. N. furzeri is a model taxon in ageing research and several populations of known geographical origin are used in laboratory studies. Here, we analysed the genetic structure, diversity, historical demography and temporal patterns of divergence in natural populations of N. furzeri across its entire distribution range.Intraspecific genetic variation of African fauna has been significantly affected by pronounced climatic fluctuations in Plio-Pleistocene, but, with the exception of large mammals, very limited empirical data on diversity of natural populations are available for savanna-dwelling animals. N. furzeri were analysed using a combination of mitochondrial  and nuclear (13 microsatellites) markers in 693 fish from 36 populations. Genetic markers consistently demonstrated strong population structuring and suggested two main genetic groups associated with river basins. The split was dated to the Pliocene (>2 Mya). The northern group inhabits savannah pools across the basin of the intermittent river Chefu in south-western Mozambique and eastern Zimbabwe. The southern group (from southernmost Mozambique) is subdivided, with the River Limpopo forming a barrier (maximum divergence time 1 Mya). A strong habitat fragmentation (isolated temporary pools) is reflected in significant genetic structuring even between adjacent pools, with a major influence of genetic drift and significant isolation-by-distance. Analysis of historical demography revealed that the expansion of both groups is ongoing, supported by frequent founder effects in marginal parts of the range and evidence of secondary contact between Chefu and Limpopo populations.Genetic structure and historical demography of N. furzeri lineages, their recent secondary contact and lack of reproductive isolation; (2) important genetic structuring attributed to the fragmented nature of their environment and isolation-by-distance, suggesting that dispersal is limited, occurs over short distances and is not directly associated with river routes; (3) an apparent role of the River Limpopo as a barrier to dispersal and gene flow.We demonstrated: (1) ancient (pre-Pleistocene) divergence between the two main  Phylogeography and historical demography play crucial roles in explaining species distributions and are instrumental in revealing episodes of fragmentation and subsequent expansions connected to environmental and climatic fluctuations ,2. Most In Africa, Pleistocene climatic fluctuations have resulted in more arid conditions during glacial periods, as large masses of water were deposited in continental glaciers, alternating with more mesic conditions during interglacials . Thus African ecosystems have undergone a series of aridification events, accompanied by spreading savannah habitats, since the Pliocene, with relatively wetter periods between 2.7-2.5 Mya, 1.9-1.7 Mya and 1.1-0.9 Mya . StudiesNothobranchius . The pools are strictly seasonal and filled with rainwater at the beginning of the rainy season. Pools desiccate during the dry season, with the duration of pools depending on local climate and ranging from 3–11 months [Nothobranchius [Nothobranchius across the savannah to specific habitat patches. Therefore, population processes affecting genetic structure of Nothobranchius are predicted to be a combination of processes that are unique to Nothobranchius and these affecting terrestrial and aquatic taxa.Killifishes (Cyprinodontiformes) are an ancient fish clade with a predominantly Gondwanan distribution and high species richness in Africa . Africanspecies; ) from Eaah pools . The poo1 months ,17,18. Iranchius , limitinNothobranchius species are annual. Fish hatch soon after a pool fills with water [Nothobranchius seldom co-occur with other teleost fish species [Nothobranchius may be limited to occasional large-scale floods in years with unusually high rainfall, which may transport live fish among savannah pools. Alternatively, eggs encased in mud may be carried attached to the bodies of large herbivores, as reported for aquatic invertebrates and macrophytes [All th water ,20, growth water , and repth water . Adult lth water . Populat species . Dispersrophytes , or possNothobranchius furzeri Jubb to investigate phylogeographic patterns and demographic processes in this group of annual, savannah-adapted fish. Its rapid development, short lifespan and age-dependent deterioration of physiological functions have made it a valuable vertebrate model organism in ageing research [Nothobranchius furzeri is distributed in southern Mozambique [Nothobranchius included samples from several N. furzeri populations [COI (cytochrome oxidase I) suggested high spatial structuring within the range of all species investigated, but the three nuclear sequences used in the study  had insufficient power to resolve intraspecific relationships.Here we use research . In contresearch . Nothobrzambique , with a zambique . Within zambique ,19. Prevulations . The mitN. furzeri, covering the entire distribution range of the species using a combination of mitochondrial gene CYTB (cytochrome b) and 13 nuclear microsatellite markers. Specifically, we tested following hypotheses: (1) N. furzeri is composed of three reproductively isolated clades  Genetic diversity will be lower and genetic structure higher on the range periphery, where populations are exposed to more frequent extinction-recolonization processes associated with strong genetic drift.Here, we provide the first detailed study of the phylogeographic patterns and demographic processes in s (sensu ), with nNothobranchius spp., 73 pools were inhabited by N. furzeri, all of them south of the River Save  were selected which cover the entire known species range  and REV1019 (CCT CCA ATT CAT GTT AGG GTG) designed on the basis of the mtDNA sequence of N. furzeri (GenBank Accession NC_011814). PCR products were sequenced directly using BigDye Terminators v. 3.1 chemistry (Applied Biosystems) .Sequence variation in FST and STRUCTURE - microsatellite data• NA) at each locus and population in FREENA [FST according to [FST), two genotyped individuals from captive population GRZ were removed.We estimated the proportion of null alleles  populations with different allele frequencies at a set of independent loci. The program was run with 20 independent simulations for each of K from 1 to 15, each of 106 iterations, following a burn-in period of 105 iterations. In all simulations an admixture ancestry model and correlated allele frequency model (with λ = 1) was used. The likelihood of K; i.e. Ln Pr(X|K), was used to infer the best number of real populations in the datasets using the method of Evanno et al. [K were combined using the Greedy algorithm of CLUMPP 1.1.1 [K were displayed graphically using DISTRUCT v. 1.1 [An individual-based Bayesian clustering procedure, implemented in STRUCTURE 2.3.3 , was useo et al. . We alsoo et al. ). The rePP 1.1.1  and summT v. 1.1 .• Spatial genetics - microsatellite dataK spatial clustering of groups of individuals).An individual-based spatial approach of Bayesian clustering was used to analyze the spatial structure of the genetic data. We employed the methods implemented in BAPS version 4.1 . BAPS esFST/(1- FST) against ln-distance between sample sites [Isolation by distance was analysed by regressing pairwise estimates of le sites . Mantel FST/- FST agan-island migration model. In particular, we considered two migration models. First, we assumed that migration only occurs between neighbouring populations along the axis following the course of the main rivers (stepping-stone migration model). Second, we assumed that migration only occurs downstream, i.e. from a higher altitude sites to the lower altitude sites (mediated by floods).To test particular hypotheses of the genetic connectivity among populations, we used the maximum likelihood approach to estimate the effective population numbers and immigration rates as implemented in Migrate v. 3.5.1 . We perfDue to high number of sampling sites in the Chefu region, which would make the model computationally too complex, we performed the Migrate analysis only in the LimpS and LimpN clades Table . We were• Genetic variation and bottleneck - microsatellitesA), observed (HO) and expected  heterozygosities were calculated in GENETIX. Allelic richness (AR) corrected for sample size by the rarefaction method  were calculated for each population in FSTAT 2.9.3.2 [Deviations from Hardy-Weinberg equilibrium (HWE) were tested for each locus and population using the Markov chain method in GENEPOP. The number of alleles , number of haplotypes (Nh), haplotype diversity (Hd), nucleotide diversity , the average number of nucleotide differences (k) and Watterson’s estimate of θ (θ = 4Ne*μ) were calculated using DnaSP v. 5.10.01 [Demographic analyses assume more or less homogeneous populations. Based on preliminary analyses of microsatellites in STRUCTURE and BAPS as well as the haplotype network of mtDNA and distribution of haplotypes, we defined three genetic groups  or small (R2) values in the case of recent population expansion, while population decline and/or structure tend to return positive or high values.Demographic histories of the three groups were evaluated using several approaches. We first estimated the neutrality indices Tajima’s tatistic  in DnaSPSecond, we used the DnaSP and ARLEQUIN 3.11  to reconThird, we reconstructed demographic history of the main lineages backward in time using the coalescent-based Bayesian skyline plot (BSP)  in BEASTN. kadleci, N. orthonotus and N. rachovii. The tree was rooted with Aphyosemion herzogi (GenBank: EU885235.1). An appropriate model of sequence evolution  was selected with the jModelTest [7 generations were conducted with sampling every 1000th generation. The results were checked for convergence and stationarity using Tracer and combined in LogCombiner after a burn-in of the first 10% of generations. Finally, the molecular clock tree was summarised using the TreeAnnotator 1.4.8 module, using the medians as node heights. Given the lack of an internal calibration point, divergence time estimates are likely overestimates of the real time due to the tendency of τ to artificially antedate recent events [To estimate the divergence times of the major phylogroups we reconstructed the time-calibrated tree in BEAST using the dominant  haplotypes from each clade and single sequence of closely related species odelTest  and implodelTest . A normaodelTest . The seat events .p < 0.05. Most pairs of loci were significantly linked only in one or two populations. Only two pairs showed significant linkage in four (out of 30) populations (Nfu_0023_FLI - Nfu_0038_FLI and Nfu_0010_FLI - Nfu_0041_FLI), which is consistent with their position in the same linkage group  the mean frequency of null alleles was low (5.25 ± 5.24%) and we did not consider null alleles as a problem in subsequent population genetic analyses (but see further for their possible effect in tests of the HWE). The microsatellite loci used in our study can be considered to be unlinked. From 1841 results of genotypic linkage disequilibrium tests (i.e. with adequate data), only 50 were significant at 40_FLI thZ strain .Most populations (25 out of 30) showed deviance from HWE, when calculated over all loci. In most cases deviations from HWE were caused by null alleles present at an increased frequency at some loci and populations. The mean frequency of null alleles for 21 locus-population tests significant after FDR correction (from 13 × 30 = 390 locus-population pairs) was 13.1 ± 5.45% (N = 21), while for the whole dataset the mean frequency of null alleles was only 2.43 ± 4.14% (N = 390). In most populations, the HW disequilibrium was caused by just one or two loci. The only exceptions were peripheral populations Pop124 and Pop323, which showed a deficit of heterozygotes at four loci, suggesting an increased level of inbreeding . The most variable populations were usually located in the centres of the distribution of the three main genetic groups. The lowest estimates of AR were encountered in sites at the periphery of their distribution  revealed 49 different haplotypes from two main genetic groups, likely associated with discrete river basins  revealed the same proportion of Chefu and LimpN haplotypes; six individuals possessed Chefu haplotypes and three individuals the LimpN haplotypes.Median-joining network analyses of 149  Figures . The firn Figure . The secn Figure . SequencN. furzeri in the study area is genetically structured (mean pairwise FST = 0.077 ± 0.0451). Only five of 420 pairwise FST were not significant  suggesting important barriers to gene flow over short distances and/or strong effect of genetic drift.Based on microsatellite data, K = 2, but suitable models according to ΔK[K = 5, 7, 9, and 13  and, similarly, for K = 3 the populations around the River Limpopo roughly diverged to LimpS and LimpN . Population 406, with individuals belonging to the two mtDNA haplogroups (Chefu and LimpN), is genetically homogenous on nuclear microsatellites . Further increase in the number of clusters led to separation of peripheral populations in all three main groups and separation of left and right banks of the River Limpopo (LimpN and LimpS) , despite the same trend observed in both LimpN (p = 0.062) and LimpS (p = 0.085). This pattern was strongly influenced by the genetic distinctiveness of peripheral populations in all three groups, mainly populations 323, 13, and 124 in Chefu, LimpN, and LimpS, respectively . When populations were separated into the three main genetic groups, the IBD pattern remained significant only in the Chefu populations ( p = 0.06 and Limpp < 0.001). This demonstrates that genetic connectivity in both clades strongly deviate from a simple stepping-stone migration model and from a model assuming strictly downstream migration. A further support comes from the fact that the full models indicated that all localities received non-negligible immigration rates from non-neighbouring sites and that several localities in both clades received considerably higher immigration rates from sites located at lower altitude than from any of the upstream sites , while all tests indicated highly significant deviations from neutrality in the Chefu clade  was estimated at around 6.72 Mya (1.58 – 31.99). Note that the estimated divergence time likely represents the upper limits of the actual timing [Assuming 0.007 substitutions per site per Mya, the clade LimpS diverged from LimpN approximately 0.99 Mya (0.02-8.39), while the Chefu clade diverged from the Limpopo around 3.64 Mya (0.43-19.95). The age of the whole F clade sensu . Finer subdivision of the southern clade is facilitated by the River Limpopo, which apparently serves as a barrier to gene flow between the two subclades. Within clades, high genetic structuring of populations inhabiting individual pools likely stems from limited dispersal between pools and frequent founder effects and bottlenecks. Significant isolation by distance within main genetic groups suggests that dispersal occurs in small steps. Genetic connectivity analysis further demonstrated frequent migration to the higher altitude, discounting hypothesis that dispersal is strictly related to major floods associated with river routes. Demographic analyses revealed ongoing expansion of the main clades from their putative refugia.We demonstrated a deep and ancient division of N. furzeri was unambiguously confirmed by both mitochondrial and nuclear markers. This division suggests a historical split of N. furzeri populations between two refugia. Based on current genetic diversity at microsatellite markers and haplotype distribution, the first refugium was probably located in the central Chefu basin. The second refugium is hypothesised to have been located in the left bank of the Limpopo, in the region between the confluences of the Limpopo with the Rivers Olifantes and Chefu  is at present a relatively small river, with a large portion of the river bed dry for much of the year, including an extensive period of the rainy season (notably in its upper and middle reaches). The area forms a large pan confined by hills in the south-west and the River Save basin in the north Figure . To the Aethomys[Nothobranchius and dispersal across the main channel may be an extremely rare event. An analogous pattern has been described for riverine cichlid fish from the lower Congo basin, where powerful rapids in the main river form a strong barrier to dispersal between shallow-water cichlids species, effectively isolating populations on opposite banks of the river [The distribution of genetic variation suggests further splitting of southern lineage at an estimated time of 1 Mya. Dating of this event corresponds with the last wet period in the African climate record (dated as 1.1-0.9 Mya), followed by a further aridification in late Pleistocene [Aethomys. Thus, uhe river .sensu[We identified a single population with secondary contact between the Chefu and Limpopo groups Figure , where msensu) needs tsensu,62 and hsensu. It is nsensu,64), andsensu).N. furzeri populations are subject to strong fluctuations in size, extinctions, and recolonizations, suggesting a strong metapopulation structure over an evolutionary time scale. These conclusions are based on evidence for recent genetic bottlenecks, low allelic richness and Hardy-Weinberg disequilibria, particularly in populations at the periphery of the range. Strong geographical structuring, with significant FST values, even between adjacent populations that are separated by 1.5 km of flat woodland savannah habitat, suggests that dispersal between pools is a rare event on an ecological time scale.Our data indicate that individual N. furzeri populations across savannah is slow and likely to be accomplished through longer-term geological events at the larger scale. Propagules of a large number of aquatic invertebrates and macrophytes have been reported to be effectively dispersed by large herbivores [Nothobranchius eggs encased in mud over longer distances appears unlikely given the limited contact between the clades and populations. Similarly, dispersal aided by waterbirds would result in weaker geographical structuring. On the other hand, genetic connectivity analysis clearly demonstrated that dispersal is not unidirectional and propagules (eggs or adults) from the lower altitude sites sometimes colonize higher altitude pools. This may indicate animal-mediated dispersal on short distances. At present, these ambiguous outcomes make Nothobranchius dispersal mode elusive, but a further insight may be provided by ongoing analysis of sympatric congeners.Dispersal of rbivores  or waterThe Chefu group, with strong isolation by distance, a star-like haplotype network, and decreasing genetic diversity from the centre to the periphery of its range has all the attributes of an expanding population, supported by analysis of its historical demography. This expansion is dated to begin approximately 160–250 kya, corresponding to a very dry phase in the African climate , and predating a series of extreme droughts reported from eastern Africa , which rNothobranchius furzeri represents an emerging model for ageing research and different captive strains of N. furzeri of different geographic origin show large differences in captive lifespan and senescence rates [N. furzeri are strongly structured and suggests that two large groups represent ancient clades. This finding contrasts with other fish genomic models  [N. furzeri spans a strong cline in environmental conditions. More arid conditions are found in the Chefu river basin where savannah pools are filled with water for a shorter period, while relatively more mesic conditions are found in the southern part of its range [N. furzeri clades have ranges encompassing a substantial variation in environmental conditions, making comparative studies possible at two levels of divergence. As genome sequencing of N. furzeri is underway [ce rates . Our stuoations) ,68 whichts range ,67. We dunderway  and genounderway , an intrunderway ,68.N. furzeri were submitted to GenBank (Accession numbers KC777039-KC777187), and microsatellite dataset was deposited to the database DRYAD (doi:10.5061/dryad.30sq1).Sequences of CYTB of 149 individuals of N. furzeri, a newly emerging model in aging research. By using the comprehensive sampling covering the entire distribution range of the species and by a combination of mitochondrial and nuclear genetic markers we demonstrated: (1) ancient (pre-Pleistocene) divergence between the two main N. furzeri lineages, their recent secondary contact and lack of reproductive isolation; (2) important genetic structuring attributed to the fragmented nature of their environment and isolation-by distance, suggesting that dispersal is limited, occurs over short distances and is not directly associated with river routes; (3) an apparent role of the River Limpopo as a barrier to dispersal and gene flow.In this study we provide the first detailed analysis of the phylogeographic patterns and demographic processes in a short-living fish The authors declare that they have no competing interests.MR and JB conceived and designed the study, MR, MP, RB, VB and JB collected samples, VB conducted genotyping, VB, JB and KJ analysed the data, AC and KR contributed primers and samples of the captive GRZ strain, and JB, MR and VB wrote the paper. All authors read and approve the final manuscript.Nothobranchius fishes, the research of AC and KR is focused on aging processes using killifishes as one of the models.This is a part of master thesis of VB, supervised by JB and MR. They are all interested in evolutionary processes influencing African biodiversity using fish and mammals as model animal groups. MP and RB are working in MR’s lab on the factors affecting life histories of Protocols for genotyping microsatellites and mtDNA. (a) Genotyping of microsatellites. (b) Genotyping of mtDNA.Click here for filePairwise FST and isolation by distance. (a) Matrix of pairwise FST values calculated in Genetix. Highlighted values indicate pairs with non-significant FST values (i.e. p > 0.01). (b) Correlation between Ln(distance) and linearized pairwise FST values (FST /(1- FST)) tested with Mantel tests (1000 permutations). Isolation by distance was calculated separately for the three population groups and for the whole dataset. Red points indicate pairwise FST including most isolated localities in all three main groups; i.e. Pop323 in Chefu, Pop13 in LimpN, and Pop124 in LimpS.Click here for fileAdditions to analysis of population genetic structure. (a) Evaluation of 20 runs in STRUCTURE for each number of presumable clusters from K = 2 to K = 15. (i) Likelihood of models in STRUCTURE for increasing number of populations (K); (ii) Estimation of the best K division according to Evanno et al. (2005). The most supported division is for K = 2, but suitable models are represented also by K = 5, 7, 9, and 13. (b) Assignment of individuals to particular populations using models for K = 2 to 15 in STRUCTURE (based on 13 microsatellite loci). Codes for localities correspond to Figures Click here for file


Thaumetopoea pityocampa) is an important pest of coniferous forests at the southern edge of its range in Maghreb. Based on mitochondrial markers, a strong genetic differentiation was previously found in this species between western (pityocampa clade) and eastern Maghreb populations (ENA clade), with the contact zone between the clades located in Algeria. We focused on the moth range in Algeria, using both mitochondrial (a 648 bp fragment of the tRNA‐cox2) and nuclear (11 microsatellite loci) markers. A further analysis using a shorter mtDNA fragment and the same microsatellite loci was carried out on a transect in the contact zone between the mitochondrial clades. Mitochondrial diversity showed a strong geographical structure and a well‐defined contact zone between the two clades. In particular, in the pityocampa clade, two inner subclades were found whereas ENA did not show any further structure. Microsatellite analysis outlined a different pattern of differentiation, with two main groups not overlapping with the mitochondrial clades. The inconsistency between mitochondrial and nuclear markers is probably explained by sex‐biased dispersal and recent afforestation efforts that have bridged isolated populations.The pine processionary moth ( Determining species boundaries in a set of closely related species is essential for biodiversity and evolutionary studies as well as for pest management and other areas of applied biology. This task is often complicated by the presence of cryptic species, morphologically not distinguishable , and Thaumetopoea wilkinsoni (Tams), with T. pityocampa found in southern Europe and northern Africa, and T. wilkinsoni in the Near East. They diverged at the end of the Miocene   whereas the eastern Maghreb populations (“ENA clade”) were limited to eastern North Africa  , the maritime pine (Pinus pinaster) localized along the north‐eastern coast, and the Atlas cedar (Cedrus atlantica) fragmented on mountain tops across the country. The major aims were: (1) to identify the potential contact zone between the two mitochondrial clades, and the underlying genetic structure; (2) to understand if the ENA clade can be supported as an independent taxon, as hypothesized by Simonato et al. P. halepensis; 11 sites), the high elevation stands of Atlas cedar , and the coastal stands of maritime pine . Sites were chosen based on accessibility and no major areas where potential host‐plant species grow were excluded from the sampling , 1 μl of 2 mmol/L dNTPs (Promega), 0.5 U of Taq DNA polymerase (Promega), 1.0 μL of each primer (10 μmol/L), and 2 μL of template DNA. Cycling conditions started with an initial step of 96°C for 5 min followed by 35 cycles of 96°C for 1 min, 55°C for 1 min, 72°C for 1 min, and a final extension of 72°C for 5 min. PCR products were then sequenced with the primer TKN3772 at the BMR Genomics sequencing service . The sequences were aligned using MEGA version 6 , was amplified using the universal primers C1J2183 and TKN3772 , namely hz‐tp (5′‐GAACATTGTCCATAGAAAG‐3′) and hz‐te (5′‐GGCTATTTAGTTCATCCAG‐3′), amplifying only the haplotypes belonging to either the pityocampa or the ENA clade, respectively .For the fine‐scale sampling, a clade‐specific PCR was developed and used in order to selectively amplify haplotypes belonging to one of the two mitochondrial clades found, that is the pityocampa and ENA clade, respectively, encompassing a part of the Cytochrome Oxidase I (cox1) gene, the tRNA‐Leu and cox2. Each sample was amplified with both primer combinations. PCR conditions were the same as above except for the extension step, performed at 72°C for 1 min and 30 sec. PCR products were separated by gel electrophoresis using 1% agarose gel and visualized with SYBR Safe . A subset of doubtful samples  was sequenced in order to confirm the identity of the PCR bands, as amplifications were successful using both the pityocampa and the ENA‐specific primers.The designed primer pairs amplified fragments of 1467 and 1155 bp for the For the microsatellite analysis, eleven loci were used to genotype the samples, namely Thpit7 – Thpit13 and Thpit15 – Thpit18, as described by Burban et al. . Fluorespityocampa clade , the ENA clade  and the sister species T. wilkinsoni, were retrieved from GenBank  and the effective number of alleles (Ae) for each population were calculated using Genetix 4.05.2  was estimated according to the EM algorithm  represented by the data was determined with the Delta K method described in Evanno et al.  convergence, testing genetic groups from K = 1 to 10 for each level of the analysis. Each run consisted of a burn‐in period of 20,000 MCMC steps followed by 80,000 iterations. The method was repeated until each cluster could not be divided further. For this reason, the posterior probability of the data [lnP(D)] for each value of K was checked to determine if the lnP(D) value was maximum for K = 1. As in Coulon et al. (P(D) for a given K were chosen and displayed using DISTRUCT 1.1 , and by the host plant (grouping populations collected on cedar or pine). AMOVA was performed with Arlequin using Fst as genetic distances with 10,000 permutations per run.Finally, we carried out an analysis of the molecular variance  were found in the northern population of OD (π = 0.0233) and in the south‐western population of SE (π = 0.0017). In the south‐eastern region of the country, population CL was the only one showing a π value different from zero  mainly in the east part of Algeria, whereas the highest values were found in populations both in the northern part (OD and CR) and in the south‐western part of Algeria (SE and MO) (Table pityocampa clade (P1–P14) and 10 haplotypes (E1–E10) belonging to the ENA clade. The two clades showed a disjoint geographical distribution, with the pityocampa haplotypes found in the western part of the country, whereas the ENA haplotypes were found only in the east . It contained two main haplotypes (P10 and P11) and three rare ones diverging from the two main haplotypes by just one mutational step. The other sub‐clade corresponded to the south‐western populations TL, EB, SE, and MO. It showed a star‐shaped structure, with one frequent haplotype (P1) and 8 low‐frequency haplotypes differing by a few (1–3) mutational steps. The two pityocampa sub‐clades were separated by seven mutational steps and were found in different regions   and retained as the best option to group populations (Table S1). For each of these homogeneous groups, Tajima's D and Fu's Fs were estimated and tested in order to check for past demographic events  belonged to the pityocampa clade, central populations  were composed of individuals belonging to both clades, while the easternmost population (bf) belonged to the ENA clade.Based on clade specific primers amplifying a fragment of cox1‐tRNALeu‐cox2 genes, individuals along the 40 km transect between OD and CR were assigned either to the Two loci (Thapit09 and Thapit17) showed a high frequency of null alleles in all the populations analyzed (Table S2) and they were therefore excluded from the subsequent analyses. The other nine loci showed a percentage of null alleles across populations between 0.68% and 5.65%.Fst obtained with the ENA correction for the presence of null alleles .The average number of alleles per locus was found to be quite similar among sites, ranging between 3.3 and 4.2 . Population TL was thus not included in the following step. The second and final round of the hierarchical analysis divided each group into two further sub‐groups. Individuals belonging to the north‐western group were split between a sub‐group including populations OB, OD, and all the transect populations, and another sub‐group including populations TE, CR, TK, and MOS  and the population KA from the easternmost part of Algeria, and another sub‐group comprising individuals from populations SAM, SAE, EH, BP, and CL, with individuals from BP and CL showing a mixed assignment with the first sub‐group  explained by the main mitochondrial clades (pityocampa and ENA) that significantly increased when considering the clustering based on the SAMOVA population assignment . When considering groupings based on host plant the percentage of variance explained was again low although significant   and the late Tertiary climatic change has been suggested as a possible explanation  and Algeria (Saharian Atlas) for the é et al. . The mitpityocampa clade in Algeria  show that these sub‐groups are still well differentiated, with Fst values similar to those observed between geographically distant  and eastern  populations seem to cluster in two genetically homogeneous groups. The subdivision between the north‐western and the eastern groups contrasts with the less clear differentiation in the south‐west , showing admixed assignments in the Structure analysis. The hierarchical analysis within each group showed a further genetic structure. In the north‐western group, populations along the coast  are separated from those at higher elevations , although MOS, along the coast, joins the latter. This pattern could suggest a prime role of elevation or host plant in population differentiation, possibly through assortative mating. The relative effect of elevation or association to cedar cannot be disentangled as both factors are tightly linked, although a low but still significant association of the genetic variance to host plant was found. A more complex pattern has been found in the eastern group that also shows a closer similarity to the south‐western populations. These latter populations  together with the easternmost population KA form a distinct cluster, differentiated from the north‐eastern populations . This pattern could be due to a partial connection of the host plant range between the Saharan Atlas and the eastern mountains (Aurès) in the south, possibly favored by the recent, large afforestation efforts in the Barrage Vert bridging islands of native stands Sahraoui ; Fig. 2.pityocampa/ENA mitochondrial clades underlines the lack of a strict concordance between mitochondrial and nuclear markers. This holds true both at higher geographical scale and along the transect corresponding to the mitochondrial contact zone. A conflicting pattern between mitochondrial and nuclear markers, found also in several other animal species, is usually ascribed to causes ranging from adaptive introgression to demographic disparities and sex‐biased asymmetric gene flow  .Click here for additional data file.Table S1. Fct values for the different number of groups (K) of population inferred by SAMOVA algorithm on the whole mtDNA dataset.Table S2. Total number of alleles per locus and average null alleles percentage (%NA) per locus.Click here for additional data file.


Pinus strobus) to test the hypothesis that it is the inter-genic effects that primarily drive climate-induced local adaptation. The genetic structure of 29 range-wide natural populations of eastern white pine was determined in relation to local climatic factors using both a reference set of SSR markers, and SNPs located in candidate genes putatively involved in adaptive response to climate. Comparisons were made between marker sets using standard single-locus outlier analysis, single-locus and multilocus environment association analyses and a novel implementation of Population Graphs. Magnitudes of population structure were similar between the two marker sets. Outlier loci consistent with diversifying selection were rare for both SNPs and SSRs. However, genetic distances based on the multilocus among population covariances (cGD) were significantly more correlated to climate, even after correcting for spatial effects, for SNPs as compared to SSRs. Coalescent simulations confirmed that the differences in mutation rates between SSRs and SNPs did not affect the topologies of the Population Graphs, and hence values of cGD and their correlations with associated climate variables. We conclude that the multilocus covariances among populations primarily reflect adaptation to local climate and environment in eastern white pine. This result highlights the complexity of the genetic architecture of adaptive traits, as well as the need to consider multilocus effects in studies of local adaptation.Natural plant populations are often adapted to their local climate and environmental conditions, and populations of forest trees offer some of the best examples of this pattern. However, little empirical work has focused on the relative contribution of single-locus versus multilocus effects to the genetic architecture of local adaptation in plants/forest trees. Here, we employ eastern white pine ( Natural plant populations are often adapted to their local climate and environmental conditions ,2. Popule.g.  ,53 41,53Arabidopsis protein database using BLASTx. Putative homologs of the candidate genes have been previously demonstrated to be functionally involved in the adaptive responses to climate factors in the model plant, Arabidopsis  using standard statistics , and Wright’s fixation index (FIS) was calculated from these values. These parameters were estimated for each locus in each population. Averages across loci within populations were used to represent the overall multilocus patterns. Correlations involving multilocus averages within populations were assessed using Pearson correlations, the significance of which were assessed via permutation of population identifiers  at α = 0.05.Genetic diversity of the sampled populations was quantified for each dataset (i.e. SSRs) and putatively functional candidate gene (i.e. SNPs) marker sets. We focused specifically on both aspects of genetic structure: its magnitude [e.g. FST) and (2) Population Graphs to assess its shape.Genetic structure among the sampled populations was estimated to test the hypothesis that it differs between reference  was used to construct 95% confidence intervals for each estimate. We tested a null model of no differences in the magnitude of genetic structure between marker sets using the 95% bootstrap confidence intervals for the multilocus estimates for each marker type. For comparison, we also estimated GST’ using the gstudio library in R. This approach accounts for differences in heterozygosities between marker types [We used the  dataset . Bootstrer types .FST were identified using BayeScan ver. 2.1. Separate analyses were conducted for each marker type. This approach partitions the observed values of FST into effects of populations shared across all loci and effects of loci shared across all populations using logistic regression [i.e. FDR q-value < 0.10), the locus is labeled as an outlier. BayeScan implements the logistic regression model in a Bayesian framework, with model parameters estimated via Markov Chain Monte Carlo (MCMC). Point estimates for parameters are given as the mean of their posterior distributions. We used the default MCMC settings for BayeScan . Multiple MCMC analyses (n = 3) were conducted for four different prior weights on the null model  to check for convergence. The proportion of outliers for each marker type at each prior weight was compared using a Z-test and α = 0.05.Outliers with respect to the magnitude of gression . When thi.e. pairwise partial correlations; see [i.e. as Type III sums of squares are for linear models), so that they highlight edges of the graph that cannot be explained using the remaining edges. Simulation and empirical work have shown that the topology of a Population Graph is sensitive to standard demographic processes, such as gene flow and range expansions [The Population Graph approach begins by partitioning multilocus genetic variation into components of within- and among-population genetic variance using standard methods ,65. The ons; see ) to defions; see . Values pansions . This istest_congruence function in the popgraph library of R, which represents a Mantel test between a matrix of cGD values derived from each Population Graph. If a significant correlation  is detected then the two Population Graphs share more similarities than by chance.A separate Population Graph was estimated for each dataset. Differences in the topology of genetic structure between datasets was tested using distance congruence as employed in the For comparison, we also conducted a simple, single-locus environmental association analysis. We used square root arcsine transformed allele frequencies within the 29 populations to correlate with the 19 bioclimatic variables. Specifically, for each SNP, each of the top three principal components (PCs) from the principal components analysis (PCA) of the 19 bioclimatic variables were correlated with the residuals of the transformed population allele frequencies. The top three PCs were chosen because they had eigenvalues greater than 1.0 and collectively explained 88.9% of the total variance. Correlation was assessed using Spearman rank correlations, the significance of which was assessed using large-sample approximations as employed in the cor.test function of R. Latitude and longitude and their nonlinear transformations were used to control for spatial autocorrelation and demographic history, as done similarly for the multilocus analyses, with the residuals of the population allele frequencies produced via regressing these quantities on the square root arcsine transformed allele frequencies and taking the residuals. Multiple tests were accounted for using the false discovery rate of , with a ordistep function with the default settings in the vegan library of R, which is a stepwise model-building algorithm suitable for constrained ordination. Both unconditional and conditional RDA models were fit to each type of molecular data and variance partitioning of the effects of climate, geography, and the interaction of climate and geography was conducted using these results  analysis of variance (ANOVA) following [vegan package in R.We used Redundancy Analysis (RDA) to explore the linear relationship between allele frequencies and climate for each dataset, as well as to partition the overall variance in allele frequencies across loci to effects explained by climate, geography, and their interaction . In thislts (see ). Statisollowing . All anaecodist library of R. Pairwise Euclidean distances were used to represent climate variation and were calculated using the nine centered and standardized bioclimatic variables. Geography was represented as either distance (km) as calculated using the rdist function in the fields library in R or as Euclidean distances of the geographical variables used in the RDA. The same models as those used for the RDA were fit using MRM, with the conditional models based on the residuals in the response matrices calculated using a custom R script. Separate analyses were performed for each dataset.We also tested the relationship of cGD to climate and geography using multiple regression on distance matrices (MRM)  as carrims [Nem = 2.5. All other connections among populations were set to 2Nem = 0.0. From each population, 10 diploid individuals were sampled. The global effective population size (Ne) was set to 10,000 for each marker type, with SNP data simulated assuming a mutation rate of 1.0 x 10−9 per site/generation and SSR data simulated assuming a mutation rate of 1.0 x 10−3 or 1.0 x 10−5 per site/generation using the infinite allele model. Simulation of SSR data followed the algorithm of [test_congruence function in the popgraph library of R. The set of Mantel correlation coefficients describing congruence in each case was used as the null distribution against which the observed value was compared. If mutation rate explained the difference in the observed data then we would expect the observed Mantel correlation coefficient to be explained as a likely outcome of simulated null distribution. We discarded simulated Population Graphs containing disjunct groups of populations, which produce matrices of cGD values, upon which the congruence test is based, containing undefined values. This was sensible because the observed Population Graphs did not contain disjunct groups of populations.We used a coalescent simulation approach as employed in the program ms  to assesrithm of . To testFIS), were different between the marker types (HO and HE), higher AE values, and a larger deficit of observed heterozygotes relative to SNPs (i.e. FIS > 0). Differences between marker types were significant for averages taken across loci and populations . Genetic diversity illustrated geographical trends for each marker type, with HE and AE inversely correlated with latitude  and positively correlated with longitude   and longitude , although the correlation of FIS with longitude was not significantly different from 0 for SNPs. Given that FIS was positive on average within populations for SSRs, this translated into FIS decreasing towards 0 from south to north for SSRs. Given that FIS was negative on average within populations for SNPs, FIS increased towards 0 from south to north for SNPs.Measures of genetic diversity were correlated across populations between marker types ( 0.2766) . As suchFST were significantly greater than 0 for both SSRs (FST = 0.113) and SNPs (FST = 0.136). The 95% bootstrap confidence intervals for each estimate, however, revealed that the magnitude of this statistic did not differ between the marker types . When differences in heterozygosities were accounted for between marker types, however, SSRs  were significantly more structured than SNPs .Allele frequencies across the range of eastern white pine were structured for both marker types  Tables. FST that was approximately two-fold larger than the average, whereas the outlier SNP (RPSS61_05) which was consistent with stabilizing selection had an observed FST that was approximately four-fold smaller than the average. These SNPs, however, only exhibited significant estimates for locus effects when the prior odds of the neutral model were set to 1000:1 or less , but not when considering only outliers consistent with diversifying selection .The BayeScan analysis resulted in two and five of the loci being identified as outliers for SNPs, and SSRs, respectively . In the  or less . All fivr = 0.132, P = 0.0070), although summary statistics of Population Graph topologies for each marker type revealed many differences  and SNP RPSS62_01 and climate PC3 . Climate PC1 explained 53.2% of the variance in the original climate data, whereas climate PC3 explained 9.5% of this variance. The putative homolog of the contig containing RPSS28_05 is an elongation factor like protein, whereas that for the contig containing RPSS62_01 is cinnamate 4-hydroxylase. Inclusion of nonlinear transformations and cross products of geographical variables, however, removed these 2 significant correlations [Correlations between transformed allele frequencies within populations and climate were apparent. A total of 21 SNP-climate PC combinations survived multiple test corrections when geography was ignored. When the effect of geography was removed, this dropped to a total of 2 SNP-climate PC combinations. These correlations were between SNP RPSS28_05 and climate PC1 . After accounting for the influence on climate by geographical variables, this effect disappeared, with the pRDA model describing the effects of climate conditional on geography not being statistically significant for SNPs  for SSRs versus that for SNPs, however, was similar to that observed for RDA. For example, the R2 derived from a MRM model of climate predicting cGD for SNPs was approximately 14-fold larger than the same model for SSRs, whereas the same comparison for RDA models resulted in an approximately 11-fold increase in explanatory power.We further explored these patterns using MRM where the response matrix was constructed from values of cGD and the predictor matrices were constructed from Euclidean distances based on climate variables and those geographical variables used in the RDA. In this case, none of the MRM models were statistically significant for SSRs, while four of the five models were statistically significant for SNPs . The relPartitioning the variance among effects due to climate, geography, and the confounded effects of climate and geography revealed large differences relative to the analysis based on RDA . In thisr = 0.132) was not a likely outcome in simulations along a one-dimensional stepping stone model for a difference in mutation rates between marker types of 1.0 x 104 (P < 0.001) or 1 x 106 (P = 0.001). This value was too small compared to those simulated under a neutral, one-dimensional stepping stone model. Under a null model of no correlation between population structure and climate, both marker sets yielded slightly positive correlations on average (Mantel r: 0.024 to 0.028). The distribution of differences between marker types, however, was centered on zero  decreased from south to north and increased from east to west for both marker types. This geographical pattern is consistent with the postglacial migration of eastern white pine from a southern refugium [FIS, which was due to the differing strength of geographical influence on HO versus HE (see e.g. migration-drift-selection equilibrium), although many of the commonly employed methods to identify components of the genetic architecture underlying local adaptation assume some form of equilibrium.Genetic diversity  between loci may reflect the evidence of selection [FIS may reflect the differences in the intensity of selection. In our study, we consistently observed significantly higher FIS values for SSR than for SNP markers for the studied populations with most populations showing deficiency of heterozygotes for SSR markers and excess of heterozygotes for SNP markers , and non-significant negative correlation with longitude . These results may reflect the geographic pattern of differences in the intensity of selection along the climate gradient. However, these inferences need to be confirmed through experimentation.It is well established that the mating system, such as inbreeding, normally affects all loci and alleles in the same way, whereas selection does not affect all loci and alleles. Thus, significant heterogeneity in inbreeding coefficients  analyses, but not necessarily in single-locus analyses. Analysis of FST outliers identified only a single marginal case  consistent with diversifying selection for SNPs. Since climate and genetic variation are structured spatially, FST outliers are a reasonable approximation to single-locus effects, so that these effects are indeed rare within our data. This SNP was located in a putative ABC-transporter , which is a member of a gene family that has been implicated in a variety of stress responses in model plants [FST outliers were not the major signal in our data despite the genes from which SNPs were derived being candidates for functional responses to climate-related factors. Even if they were, there are numerous ways with which to be an outlier, not all of which are consistent with climate-mediated selection pressures [Drosophila, this is also consistent with interactions among quantitative trait loci without strong, single-locus effects [Eastern white pine established its current range recently, at least in terms of 4erations . As sucherations –10. Thisl plants . The poiressures . It was  effects ,80, alth effects ,81.FST. As such, these edges often are most sensitive to demographic processes underlying the observed data [Climate and geography were strongly confounded for the RDA and MRM analyses, although the magnitude of this confounding varied by marker type. For the SNP data, the strongest signals of correlation between genetic variation and climate came from the analysis using cGD. Values for cGD are estimated conditional on the Population Graph topology, so that they highlight edges of the graph that cannot be explained using the remaining edges. This is why the set of cGD values can be considered as the shape of genetic variation underlying magnitude-based statistics such as ved data . In the e.g. [e.g. trait architecture, patterns of linkage disequilibrium) and statistical  attributes of the study system. Some of this disconnect may be remedied by using genomic prediction methods [cf. [e.g. [Our results are consistent with theoretical predictions that the loci contributing to local adaptation are remarkable because of correlated and subtle allele frequency differences among populations  and not large, single-locus effects –11, yet e.g. ,82,83, be.g. , 84–86 f methods , cf. [84ods [cf.  to rank f. [e.g. . Foundatf. [e.g. , althougf. [e.g. .6 was r = 0.864 (95% CI: 0.324–0.991). The observed value for the SNP and SSR data was r = 0.132. Although additional models of population structure and demographic processes need to be examined, it is clear that mutation rate difference is not likely the driver of the observed patterns . Use ofsee also  for a sir2 and D’ . The green line in each plot gives the average across the chain. Note that the scale on the x-axis is the generation divided by 10, which was the thinning interval.(PDF)Click here for additional data file.S2 Fig(A) SNPs. (B) SSRs. Note that the uncorrected sample size from the posterior distribution was n = 5,000. The difference between the ESS pictured 5,000 is the correction due to autocorrelation along the Markov chain.(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 FigP-value in the right panel. Only P-values for tests involving comparisons between runs with different prior odds for the null model were significant (α = 0.05).The lower triangular is pictured in all plots, with the three runs for a prior odds of the null model of 10:1 at the bottom and the three runs for a prior odds of the null model of 10000:1 at the top. The diagonal has been omitted in all plots. Note that the test statistic is illustrated in the left panel and the corresponding (PDF)Click here for additional data file.S5 FigRed color denotes estimates for observed heterozygosity, while black color denotes estimates for expected heterozygosity.(PDF)Click here for additional data file.S6 Fig(A)r2. (B) |D’|.(PDF)Click here for additional data file.S1 Tableet al. (1996). The column entitled BAC gives the GenBank accession for the loblolly pine Bacterial Artificial Chromosome (BAC) to which the reference sequence for the SSR was assigned using results from BLASTn.Shown is the reference sequence for each marker. Primers are listed in (DOCX)Click here for additional data file.S2 Tableet al., 2008), and published articles . Numbers after SNP names denote different SNPs derived from the same amplicon. Biological functions of the candidate genes (ESTs) listed are based on the functions reported from functional analysis of homologues in model plant Arabidopsis or other plants. Also SNP in the homolog of RPSS96 was reported to be co-located with the QTL for cold hardiness in Douglas-fir .EST sequences were obtained from GenBank, the TreeGenes database (Wegrzyn (DOCX)Click here for additional data file.S3 TableThe two alleles at each focal SNP are located between parentheses.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TableNegative values are effectively zero. The fraction of FST due to climate group (CG) was estimated using the variance components obtained from hierfstat (i.e. σCG2/(σCG2 + σPOP2)).(DOCX)Click here for additional data file.S7 TableNegative values are effectively zero. The fraction of FST due to climate group (CG) was estimated using the variance components (σ2) obtained from hierfstat (i.e. σCG2/(σCG2 + σPOP2)).(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.