Falco peregrinus) have been long debated because of the polytypic nature of melanin-based plumage characteristics used in subspecies designations and potential differentiation of local subpopulations due to philopatry. In North America, understanding the evolutionary relationships among subspecies may have been further complicated by the introduction of captive bred peregrines originating from non-native stock, as part of recovery efforts associated with mid 20th century population declines resulting from organochloride pollution. Alaska hosts all three nominal subspecies of North American peregrine falcons–F. p. tundrius, anatum, and pealei–for which distributions in Alaska are broadly associated with nesting locales within Arctic, boreal, and south coastal maritime habitats, respectively. Unlike elsewhere, populations of peregrine falcon in Alaska were not augmented by captive-bred birds during the late 20th century recovery efforts. Population genetic differentiation analyses of peregrine populations in Alaska, based on sequence data from the mitochondrial DNA control region and fragment data from microsatellite loci, failed to uncover genetic distinction between populations of peregrines occupying Arctic and boreal Alaskan locales. However, the maritime subspecies, pealei, was genetically differentiated from Arctic and boreal populations, and substructured into eastern and western populations. Levels of interpopulational gene flow between anatum and tundrius were generally higher than between pealei and either anatum or tundrius. Estimates based on both marker types revealed gene flow between augmented Canadian populations and unaugmented Alaskan populations. While we make no attempt at formal taxonomic revision, our data suggest that peregrine falcons occupying habitats in Alaska and the North Pacific coast of North America belong to two distinct regional groupings–a coastal grouping  and a boreal/Arctic grouping (currently anatum and tundrius)–each comprised of discrete populations that are variously intra-regionally connected.Subspecies relationships within the peregrine falcon ( Clarification of the relationship between phenotypic and molecular characters is particularly relevant to studies involving northern high-latitude avian species, as many vertebrate populations there represent the leading edge of post-Pleistocene expansions from temperate glacial refugia . Such poMC1R), which codes for primary plumage color in avian species, is perfectly associated with the presence of melanism in two Arctic species, the snow goose (Chen caerulescens), and Arctic skua (Stercorarius parasiticus), in which polymorphisms in color apparently influence mate choice [MC1R genotype-phenotype association in snow geese, as well as skuas, is specific to the MC1R locus and does not reflect shared demographic history of populations containing different morphs [MC1R gene is a determinant of leucistic captive [Falco rusticolus), a raptor species that occupies Arctic habitats, but for which variation in plumage color  is not currently associated with subspecies designations [Buteo jamaicensis) in western North America are based in part on the presence of certain melanin-based plumage characteristics, there is no apparent correlation between specific variants of the MC1R gene and variation in plumage color [Differences in levels of melanism, which can be due to variation at one or a number of genes, can also emerge from both neutral and adaptive evolutionary processes, such as genetic drift  for the e choice . Althouge choice , the MC1t morphs . Similar captive  and wild captive  gyrfalcognations –18. In ggnations . In contge color .F. peregrinus) in North America are based in part on morphological characteristics, including plumage traits associated with melanism [th century augmentation or reestablishment of extirpated peregrine falcon populations using non-native stock [Similar to red-tailed hawks, designations among the named subspecies of peregrine falcons , the largest subspecies, is distributed in maritime habitats coastally from Washington State through the Aleutian Islands, Alaska, to the Commander Islands, Russia  have grayed tones and show a greater range of variation [In North America, peregrines breed from Mexico to the middle Arctic and from the Pacific to the Atlantic coast. Morphological variation among peregrine falcons is sufficient that 19 subspecies have been described worldwide , three o, Russia . Peregriariation .F. p. tundrius [F. p. tundrius is in the Nearctic tundra region , from about 77° N south to treeline, east to Greenland and west to Cape Prince of Wales, Alaska, and individuals within the range of F. p. tundrius winter from Florida south to Argentina (38° S) and Chile (41° S). The third subspecies, F. p. anatum (Bonaparte 1838), has a continental distribution . However, there is overlap in plumage characteristics and morphological measurements in Alaskan F. p. tundrius and F. p. anatum [The Nearctic subspecies , F. p. ttundrius , is generibution , and is . anatum .th century suggest the peregrine falcon was never abundant in North America [p-chlorophenyl) ethane (DDT). Insular, non-migratory populations, including F. p. pealei along the Pacific Coast of Alaska and British Columbia (with the exception of the population on Haida Gwaii) and along the Aleutian Island chain, were considered least affected [F. p. anatum) and Arctic (F. p. tundrius) habitats were listed as endangered in 1970 under the Endangered Species Conservation Act of 1969 , a law that preceded the Endangered Species Act of 1973. Among the three subspecies, F. p. anatum was the focus of the most intense recovery efforts.Studies conducted in the early part of the 20 America . Reprodu America . This we America , includi America . In some America ), locali America ,27,28, maffected . By 1975affected  althoughp-chlorophenyl) ethylene, DDE, in North American food chains. Recovery efforts in Canada and much of the contiguous United States that occurred between 1974 and 1999 included the introduction of captive-bred peregrine falcons to bolster remnant or replace extirpated populations [F. p. anatum to augment eastern Canadian populations [F. p. brookei, F. p. peregrinus; South America: F. p. cassini; and Australia: F. p. macropus [F. p. tundrius populations had declined by approximately 80%, this subspecies was downlisted from endangered to threatened in 1984  and delisted in October 1994 . Populations of F. p. anatum were delisted in the United States on August 25, 1999 (64 FR 46541–46558).Recovery of the peregrine falcon in North America proceeded after the ban of DDT for agricultural use in Canada in 1970, and the United States in 1972, and the subsequent, although gradual, decrease of the breakdown product 1,1-dichloro-2,2-bis . th century recovery effort dispersed into Alaska. As well, previous inferences associated with levels and distribution of genetic diversity within North American F. p. pealei [In Alaska, which like Canada hosts all three nominal North American subspecies, recovery of peregrines occupying boreal and Arctic habitats commenced in the late 1970s, following the prohibition of DDT for agricultural purposes in 1972 ,32. UnliF. p. pealei), as well as boreal (F. p. anatum) and Arctic habitats (F. p. tundrius) of Alaska. We also include comparative data from a population of falcons nesting on the San Juan Archipelago in western Washington State, a locale thought to comprise a contact zone between southeastern populations of F. p. pealei, and southern populations of F. p. anatum [th century decline in two of the three North American subspecies, to test for genetic signatures of recent demographic change against a background of more historical population change, we include data from falcons of western Canadian Arctic and boreal populations.To provide a phylogeographic framework within which to assess the relationships among the named subspecies, we used genetic markers from the mitochondrial and nuclear genomes to investigate taxonomic relationships among the three North American subspecies occupying non-augmented northwestern-most locales. We focus on two central questions: 1) is the current subspecies taxonomy, which is based largely on plumage characteristics, predictive of the distribution of neutral gene variation within Alaskan peregrine falcons; and 2) what are the relationships among populations within named subspecies in western high latitude North America? We use sequence data from the control region of the mitochondrial DNA and fragment data from 11 polymorphic microsatellite loci to investigate relationships among maritime populations occupying the Aleutian Island chain, including the Commander Islands, Russia, and coastal North Pacific locales (. anatum . To infeSamples included in this study were obtained from: A) specimens in museum collections  and the Museum of the Zoological Institute in St. Petersburg, Russia (n = 7); B) a single blood sample from the Bird Treatment and Learning Center, collected by veterinarians or other clinical staff with permission of State and Federal wildlife authorities; and C) blood, eggs, epithelial swabs, or feather samples collected as part of past and ongoing field research ,32,36–40F. p. tundrius, is represented in Alaska by samples taken from the Sagavanirktok River (n = 1) and the Colville River (n = 46), pooled and designated as COL, and in western Canada by samples collected from Hope Bay, Northwest Territories , Tanana  and Yukon  rivers, and in western Canada by samples collected from Alberta and Saskatchewan [F. p. pealei is represented by samples collected from 1) the North Pacific coast of North America, including the Alexander Archipelago in southeast Alaska, and Haida Gwaii Archipelago of British Columbia , and south central coastal Alaska, including locales near Yakutat, Kenai Fjords, and Kodiak Island . The western distribution of F. p. pealei is represented by samples (n = 44) collected from across four island groups along the Aleutian island chain; these include the Rat , Andreanof , Near  and Commander  island groups. Peregrines sampled from the San Juan Islands , thought to represent a contact zone between continental F. p. anatum and the North Pacific segment of F. p. pealei [We collected whole blood, buccal swabs, and/or feathers from adult nesting female and male peregrine falcons, or young in nests, from sites within the distribution of each subspecies . The Arc n = 18, ). F. p. atchewan ,40, mostver n = 4, pooled . pealei .Blood samples were obtained from either nestlings or falcons trapped using standard techniques (see ). Blood See An unrooted phylogenetic network was constructed for mtDNA control region haplotypes using NETWORK 4.610  and the reduced median method .Fs [D [Details of analyses assessing levels of genetic diversity and neutrality at microsatellite loci and the mtDNA control region are provided in Fs  and TajiFs [D ) can alsST and RST) and their analogs ; Critical α-values were adjusted using Bonferroni corrections. Because populations are represented by unequal sample sizes [ST  and tesST , using ARLEQUIN 3.1 [a priori groups of testable subspecies hypotheses applicable to peregrine falcons of North America, we experimented with various a posteriori groups in AMOVA analyses for both marker types that tested relationships of certain populations to named subspecies . We assumed that the best geographic subdivisions were significantly different from random distributions and had maximum among group variance . We also examined regional interrelationships by constructing and visualizing a network based on Cavalli-Sforza and Edwards [SE) among microsatellite loci and neighbor-joining methods [F. p. pealei, which was partitioned into the Aleutian group and the eastern group, based on results of population differentiation analyses.We used hierarchical analyses of molecular variance  to testQUIN 3.1 . Calcula Edwards  genetic  methods –65. Two em) and number of female migrants per generation (Nfm) for nuclear microsatellites and mtDNA sequences, respectively. Significance of asymmetry in gene flow was based on non-overlapping 95% confidence intervals generated from full models, in which composite measures θ (4Neμ or Nfμ) and M (m/μ) were estimated individually from the data and allowed to vary among populations. For this analysis, HB and MCV, which showed no significant interpopulational structuring even though they have traditionally been ascribed to different subspecies, were pooled to represent Arctic Canada; similarly, POR, TAN and YUK were pooled to represent interior boreal Alaska and the different Aleutian Archipelago islands were pooled to represent the Aleutian Islands as a single group. Arctic Alaska is represented by COL.We estimated the magnitude and polarity of evolutionary dispersal (gene flow) among populations and subspecies, using the maximum likelihood approach implemented in MIGRATE 3.0.3 ,67; detaGenetic evidence for recent demographic fluctuations was evaluated for the microsatellite loci using BOTTLENECK 1.2.02 , applying, incorporating coalescence theory. Positive values of g suggest population growth over deeper time frames; negative values suggest population decline. Because this method may be upwardly biased [g to indicate population growth if g > 3 SD(g). Fluctuations in historical population size were also inferred from Tajima’s D and Fu’s FS, generated using ARLEQUIN to test for locus neutrality. Additional details of these analyses are provided in Evidence of more historical population change trends was evaluated using mtDNA sequence data, employing FLUCTUATE , which ey biased , we usedP = 0.002) and NVHfp92-1 , which differed significantly from HWE expectations due to deviations in 2 and 6 of the 14 populations, respectively. Analyses using MICRO-CHECKER 2.2.3 [P = 0.014–0.048). Inbreeding coefficients for all populations were not significantly different from zero . All populations comprising F. p. pealei were significantly differentiated from those comprising both F. p. anatum and F. p. tundrius  were significantly different from zero .Overall, significant differentiation at microsatellite loci was observed across traditional metrics  and western segments  between nodes separating the Aleutian and southeast and southcentral populations comprising F. p. pealei, and pooled populations comprising of F p. anatum and tundrius  and Porcupine River (F. p. anatum) were characterized by a single haplotype, resulting in haplotype and nucleotide diversity values of zero  were observed for the mtDNA relative to the microsatellite data, we nevertheless observed a greater percentage of significant pairwise population comparisons between populations representing F. p. pealei and populations representing F. p. anatum and tundrius, versus within . Similar to microsatellite data, variance in the distribution of haplotypes was not significantly partitioned at the regional level in hierarchical AMOVA analyses based on the current subspecies hypotheses , respectively, and suggested levels of asymmetry in gene flow varied among populations  populations  receive the fewest immigrants relative to the other Alaskan population segments assayed. Gene flow estimates based on the microsatellite data suggest that the western segment of F. p. pealei (Aleutians) receives, on average, the lowest immigration levels, generally <1 per generation per pairwise population comparison among all populations assayed (em) into the Aleutians originated from the North Pacific populations (NPAC), and effective female gene flow estimates (Nfm) also suggest substantial gene flow originating from populations in the eastern portion of the distribution of F. p. pealei , though confidence limits overlap (em) from SJI into populations within the range of all three subspecies  from SJI into interior Alaska  and only under the IAM. Analysis under the SMM and TPM also gave evidence of significant heterozygosity deficit expected in the case of recent population growth due to the influx of alleles, in Alaskan F. p. anatum (P < 0.003), represented by pooling data from POR, YUK, and TAN. After the application of Bonferroni correction, no significant heterozygosity excess or deficit was observed in falcons sampled from Canada , Arctic Alaska , or the eastern or Aleutian components of F. p. pealei .There was evidence of significant heterozygosity excess, expected in the case of a recent bottleneck, only in SJI (viz significantly negative Fu’s FS (P < 0.02) and Tajima’s D (P < 0.05). SJI also showed a significantly negative Fu’s FS. FLUCTUATE analyses of mtDNA sequence  and in peregrines in two locales within the range of anatum in Alaska (YUK and TAN).Significant fluctuations in historical population demography, inferred from tests of neutrality of the mtDNA data , were obsequence  uncovereF. p. anatum and tundrius in high latitude habitats in western North America. A close relationship among populations representing F. p. anatum and tundrius is supported by lack of regionally significant variance in allele and haplotype frequencies and estimates of substantial levels of gene flow between populations ascribed to one or the other subspecies, the high percentage of shared common haplotypes, and general lack of diagnostic genetic markers. These results highlight a signal of both historical and contemporary gene flow between F. p. anatum and F. p. tundrius, even in Alaskan populations, which were not augmented subsequent to the mid 20th century population decline.Although inference of relationships among North American subspecies made by Brown et al.  were basF. p. pealei is differentiated at the regional level from populations currently ascribed to the other two subspecies. Traditional population differentiation metrics based on both marker types showed significant structuring among most populations comprising F. p. pealei and those comprising the mainland and Arctic Alaskan and Canadian populations, supporting a distinction of peregrines within the distribution of F. p. pealei. As well, Bayesian analyses of population structure suggested peregrine falcons in populations belonging to F. p. pealei cluster away from populations ascribed to the other two subspecies.By contrast, and also like Brown et al. , we founF. p. pealei; the Aleutian segment is genetically differentiated from the North Pacific segment (the Alexander and Haida Gwaii archipelagos) as well as from populations along south central coastal Alaska , for which subspecies attribution is unclear [F. p. pealei, and from mainland Alaskan and Canadian populations currently ascribed to F. p. anatum; nevertheless, falcons occupying the San Juan Islands consistently cluster with F. p. pealei in AMOVA tests. Coastal Alaskan populations comprising the eastern arm of F. p. pealei, including locales along the northern Gulf of Alaska’s Lost Coast (SCCOA: Yakutat and Kenai Fjords) that were recently attributed to F. p. anatum based on morphology and migratory behavior [F. p. pealei , we suggest a comprehensive survey of North American populations be conducted to better understand the relationships among contemporary North American peregrine falcons.A novel finding is that of substructuring within  unclear . Peregribehavior , appear th century, analyses using the approach of Cornuet and Luikart [F. p. anatum and tundrius, respectively) that were listed for several decades as endangered or threatened under two Acts of the U. S. Congress. However, heterozygosity deficit, a genetic signal of demographic increase attributed to the influx of alleles [F. p. anatum) peregrines in Alaska. This is consistent with data emerging from long-term field surveys that have documented an increase in the number of occupied territories along the upper Yukon River from 1970 to 2012 [g) consistent with population expansion over more historical time periods   . CombineFS and D ,81.F. p. pealei is notable for its isolation: although they provide immigrants into the eastern segment of pealei, across generations and, on average, Aleutian Island populations appear to receive the fewest effective immigrants (Nem) among assayed populations. Augmentation of Aleutian populations appears to be largely via female gene flow from other southcentral and southeast Alaskan maritime populations. Other populations of F. p. pealei occupying habitats along the coast of Alaska, British Columbia, and Washington are connected via varying levels of evolutionary dispersal with boreal and Arctic Canadian and Alaskan populations. In particular, the population on the San Juan Islands represents a source for evolutionary dispersal into interior boreal and Arctic Canada and Alaska.Although we uncovered moderate population-level structuring overall, gene flow rate estimates among subspecies and populations, based on both marker types, suggest substantial gene flow within and among regions. The Aleutian segment of em) from Arctic Canada into Arctic Alaska, based on microsatellite data. However, significant signals of asymmetry in gene flow between augmented Canadian populations and non-augmented Alaskan populations have been in the form of effective female gene flow (Nfm) from interior boreal Alaska into Arctic Canada. We note that the female peregrines typically disperse farther than males from natal areas to breed [Of considerable interest is the finding of gene flow between the Canadian populations, presumably augmented, and the non-augmented populations in interior boreal and Arctic Alaska, including estimates of asymmetrical gene flow  occupy island archipelagos  and unlike the boreal and Arctic peregrines tend to show little to no migratory behavior, a characteristic that may contribute to greater population and regional structuring, as seen in other avian species [Our analyses, similar to those of Brown et al. , fail toithology ,3,93–96.ithology , howeverithology  criterioithology  criteriauishable , would e species ,99 and p species ,101.F. p. pealei) and a boreal/Arctic grouping (currently F. p. anatum and tundrius), each comprised of discrete populations that are variously intra-regionally connected. Gene flow estimates indicating that distant high-latitude populations in Canada provide immigrants into and receive immigrants from mainland Alaskan populations ascribed to both previously listed subspecies. This suggests that management prescriptions applied at a distance may have unforeseen consequences in locally-managed populations of species with high gene flow potential, even in species characterized by reasonably strong philopatry. This highlights the importance of inter-regional and international cooperation in management and conservation in such species [F. p. pealei is stronger than among boreal/Arctic populations, possibly due to decreased or absent migratory tendencies that restrict gene flow into the Aleutian Island segment of F. p. pealei. Thus, peregrines residing on islands in the Aleutian Archipelago likely remain the least genetically impacted by DDT-induced population fluctuations that fostered the mid 20th century decline of peregrines in North America and subsequent augmentation by non-native subspecies.Our genetic survey provides insights into the status and delineation of natural populations as well as a better understanding of gene flow among unaugmented high latitude peregrine falcons. While we make no attempt at formal taxonomic revision, our data suggest that peregrine falcons occupying habitats in Alaska and the North Pacific coast of North America belong to two distinct regional groupings–a coastal grouping ( species . DiffereS1 Methods(DOCX)Click here for additional data file.S1 FigF. p. anatum, F. p. tundrius, and F. p. pealei). Populations within the distribution of F. p. pealei are designated with red branches, within the distribution of F. p. anatum with yellow branches, and within the distribution of F. p. tundrius with blue branches. Values in nodes designate bootstrap values, based on 1000 permutations .Illustrated are relationships among A) 14 populations of peregrine falcon in high latitudes habitats in Canada, Alaska and the Commander Islands, Russia; and B) three subspecies ((TIF)Click here for additional data file.S1 TableST  comparisons among populations of F. peregrinus within Alaska, based on data from 11 microsatellite loci. See manuscript for locales associated with acronyms. Values in bold, and values listed as ∞ (= infinity) indicate significant differences in the distribution of alleles/haplotypes or variance in allelic/haplotypic frequency, respectively, following Bonferroni corrections (α = 0.0045). The population on the San Juan Islands (SJI) is considered a contact zone between continental populations (F. p. anatum) and maritime populations .Shown are values for pairwise χ2  and F(DOCX)Click here for additional data file.S2 TablePopulations augmented with non-native subspecies following the population decline are shaded.(DOCX)Click here for additional data file.S3 TableST  comparisons among populations of F. peregrinus within Alaska, based on sequence data from the mitochondrial DNA control region. See manuscript for locales associated with acronyms. Values in bold or (for χ2) designated as ∞ (infinity) indicate significant differences in the distribution of haplotypes or variance in haplotype frequency (P < 0.05), respectively. The population on the San Juan Islands (SJI) is considered a contact zone between continental populations (F. p. anatum) and coastal populations .Shown are values for pairwise χ2  and ϕ(DOCX)Click here for additional data file.


Aedes fluviatilis is an anthropophilic mosquito found abundantly in urban environments, its biology, epidemiological potential and genetic characteristics are poorly understood. Climate change and urbanization processes that result in environmental modifications benefit certain anthropophilic mosquito species such as Ae. fluviatilis, greatly increasing their abundance in urban areas. To gain a better understanding of whether urbanization processes modulate the genetic structure of this species in the city of São Paulo, we used eight microsatellite loci to genetically characterize Ae. fluviatilis populations collected in nine urban parks in the city of São Paulo. Our results show that there is high gene flow among the populations of this species, heterozygosity deficiency and low genetic structure and that the species may have undergone a recent population expansion. There are two main hypotheses to explain these findings: (i) Ae. fluviatilis populations have undergone a population expansion as a result of urbanization; and (ii) as urbanization of the city of São Paulo occurred recently and was quite intense, the structuring of these populations cannot be observed yet, apart from in the populations of Ibirapuera and Piqueri parks, where the first signs of structuring have appeared. We believe that the expansion found in Ae. fluviatilis populations is probably correlated with the unplanned urbanization of the city of São Paulo, which transformed green areas into urbanized areas, as well as the increasing population density in the city.Although  Aedes aegypti, Aedes albopictus, Culex quinquefasciatus, Culex nigripalpus, Aedes scapularis and Aedes fluviatilis) [Urbanization is often a chaotic process that causes environmental stress, leading to the domiciliation of insects that have adapted to man-made changes –3. Culiciatilis) ,2,4–9.Ae. fluviatilis  is a mosquito species native of Brazil found abundantly in urban areas. It is distributed in the Neotropics and can be found from Mexico to Argentina. The species is able to survive in wild, semi-wild, suburban and urban areas [Ae. fluviatilis in the literature, it is considered a neglected species.an areas ,11. Indean areas ,12. Femaan areas . HoweverAe. fluviatilis is considered a potential vector of yellow fever virus [Plasmodium gallinaceum and Dirofilaria immitis [Wolbachia (wFlu), this causes only incomplete cytoplasmic incompatibility and has no effect on its infection by P. gallinaceum [er virus , Plasmod immitis –17, and linaceum ,19.Ae. fluviatilis populations are modulated by selective pressures in the environment. Microsatellites can be used in genetic population studies as they are highly polymorphic and assumed to be neutral markers. They flank conserved regions of the genome and amplify polymorphic regions [The success of some species of mosquitoes in inhabiting urban environments and the consequent increase in their abundance can be attributed to two factors: the availability of breeding sites and the availability of blood-meal sources . Along w regions . Hence,  regions ,25,26 an regions ,28 scaleTriatoma infestans populations in Argentina [Ae. aegypti populations in villages no more than 10 km apart in Thailand. In the latter case, the structuring may have been caused by genetic drift due to adult mosquito oviposition patterns. These studies proved the effectiveness of microsatellite markers in identifying fine-scale genetic differentiation.Genetic structure studies of vector insect species conducted on a microgeographic scale found structuring in sympatric rgentina , and Olargentina  found geAe. fluviatilis populations are genetically structured in the city of São Paulo and whether urbanization processes can modulate the genetic structure of this culicid.In light of the above, this study used microsatellite markers to investigate how Ae. fluviatilis mosquitoes were collected from nine urban parks  in different areas of the city of São Paulo, Brazil  and examined under UV light.DNAs were extracted using the DNEasy Blood and Tissue Kit  following the manufacturer’s protocol. PCR reactions were carried out with eight microsatellite primers originally designed for i et al. . The prii et al. , Chamberi et al.  and Beebi et al.  in an E6PCR products were diluted 1:7 by mixing 3 μL of each product labeled with a different dye with 21 μL of Ultra-Pure Water  to a final volume of 30 μL. A second dilution was performed with 2 μL of the previous dilution suspended in 8.925 μL of Hi-Di formamide  and 0.075 μL of GeneScan 500 ROX size standard  to a final volume of 11 μL. The samples were then sent to the University of São Paulo Center for Human Genome Studies and size-sorted in an ABI 3730 automatic sequencer . Fragment analysis was performed with Gene Marker .O), expected heterozygosity (HE), deviations from Hardy-Weinberg equilibrium , inbreeding coefficient (FIS), linkage disequilibrium and gene flow were calculated using Genepop (v4.2 http://genepop.curtin.edu.au/) and Arlequin (v3.5) [Allele frequency, observed heterozygosity (Hn (v3.5) ,36. Allen (v3.5) .ST) and Cavalli-Sforza and Edwards chord distance taking into account the presence of null alleles. To compute pairwise FST values and their significance for all the populations, Arlequin (v3.5) [The probability of null alleles occurring was calculated for each locus for each population using FreeNa . The samn (v3.5)  with 50,ST/(1-FST) and geographic distance (km) and between FST/(1-FST) and environmental variables such as elevation, slope (variation in elevation vs. area), perimeter-to-area ratio (Patton index) [2) in the vicinity of the park, monthly accumulated rainfall, mean annual temperature and area of the park (km2) [Linear correlation analyses between Fn index) , green ark (km2) ,40 were rk (km2) . A dendrrk (km2) .The amplified alleles were subjected to Bayesian model-based clustering analysis using Structure (v2.3.3) . The estO was higher than HE in 37 of the 64 tests, and the average FIS was 0.142 , for the OchcB5 locus in four populations , for the OchcB9 locus in two (Burle Marx and Chico Mendes) and for the Albtri33 locus in one (Ibirapuera). The probability of null alleles in the populations studied ranged from 0 to 0.19 for the Albtri3 locus, 0 to 0.28 for OchcB5, 0 to 0.18 for OchcB9 and 0 to 0.16 for Albtri33. The OchcD11, Albtri20, Albtri44 and AEDC loci had low (<0.07) or zero probabilities of null alleles.ST values ranged from 0 to 0.02, indicating low genetic structure among the populations; 67% of these values were statistically significant. The FST values calculated by FreeNA  ranged from 0.0002 to 0.05, indicating low to moderate genetic structure. There was therefore no statistically significant difference between the corrected and uncorrected values of FST  containing the populations from Ibirapuera and Piqueri, which segregated together and are fairly different from the other populations, and another (b) with the remaining populations. Cluster (b) segregated into two subclusters, one containing similar populations with no great differences between them , and the other formed by the population from Alfredo Volpi .2 = 0.058614; P = 0.15485), and a similar analysis of genetic distance and environment variables indicated that the former does not correlate with any of the variables tested apart from slope   .The results of the Bayesian analysis and application of Evanno’s method were used to identify the k value that represents the number of groups that best explains the results , which wP-values were only significant (<0.05) for the Piqueri and Carmo populations and were borderline for the Burle Marx and Alfredo Volpi populations (Ae. fluviatilis populations have probably suffered a population expansion (He < Heq).While the heterozygosity tests conducted in Bottleneck under the SMM (Stepwise Mutation Model) showed more loci with heterozygosity deficiency in eight of the nine populations, ulations . These rHe< Heq) and a significant P-value (P = 0.01367), lending support to our finding that the populations in this study have probably suffered a recent population expansion.In a subsequent analysis in which all 270 specimens were considered one population, the results showed six loci with heterozygosity deficiency . In the case of these mosquitoes, the same phenomena were observed, namely, recent population expansion, high gene flow and an absence of isolation by distance (IBD) [Our findings suggest that ce (IBD) –50, suggAe. fluviatilis populations studied here: (i) Ae. fluviatilis population expansion happened because this species is well adapted to the urban environment and is able to complete its entire life cycle within the city. Therefore, urbanization may have been beneficial to this species and a major factor contributing to population expansion, a hypothesis supported by the finding of high gene flow in these populations, which indicates a homogenizing genetic effect [Ae. taeniorhynchus mosquito populations from Colombia [There are two main hypotheses to explain the low genetic structure in the c effect . This phColombia ,53.Bayesian analysis revealed low genetic structure among the populations studied, although the Ibirapuera and Piqueri populations, which are quite similar to each other, are different from the other populations. This segregation can also be observed in the dendrogram. These parks, which are located in densely populated areas, have undergone major environmental changes as a result of human activities and urbanization. More than 1 million people visit Ibirapuera park every month, and Piqueri park is located next to a major highway, where more than 400 thousand vehicles circulate every day ,12.Ae. fluviatilis population was actually a large population that became fragmented as the city grew. As this urbanization occurred recently and was very intense, structuring in these populations cannot be observed yet apart from in the Ibirapuera and Piqueri populations, where the first signs of population structuring are apparent.These results indicate that genetic structuring may be occurring in the Ibirapuera and Piqueri populations, which leads to the second hypothesis: (ii) the city of São Paulo’s recent history of intense, haphazard urbanization starting in the 1960s  suggestsAedes species , in which the authors did not observe IBD on either a microgeographic or macrogeographic scale [Ae. fluviatilis, including environmental changes caused by humans in urban areas [The absence of IBD indicates that urbanization has a greater influence on population structure than distance does. Similar results were also found for populations of other ic scale ,55,56, sic scale ,58. OtheAe. fluviatilis populations studied here show low genetic structure, a common trait in Aedes species [The Hardy-Weinberg equilibrium tests for each locus and each population indicating heterozygosity excess in the present study may be due to binomial sampling error , as when species ,60.Ae. fluviatilis in this study was similar to that of other Aedes species [Aedes, which could also explain the presence of null alleles [The allelic richness found in  species ,61, indi alleles ,62,63.Ae. fluviatilis is not directly implicated in the transmission of pathogens to humans, it can be found in large numbers in urban areas. Its epidemiological role has yet to be elucidated, and infectivity studies on this mosquito are dated; the yellow fever virus infectivity test was carried out in 1931 [Ae. fluviatilis by the dengue, chikungunya and Zika viruses. In addition, there is an inherent risk of new pathogens being introduced, especially in large, populous cities [Cx. tarsalis in the United States and the arrival of West Nile virus [Ae. ochraceus, which acted as an important vector in the epidemic caused by Rift Valley fever virus in Kenya in 2007 [Secondary mosquito vectors are commonly neglected in genetic population studies. However, although  in 1931  and, to s cities ,65. Thisle virus  and also in 2007 .Ae. fluviatilis population structure patterns found in this study revealed a significant population expansion that we believe to be associated not only with the transformation of green areas into urbanized areas, but also with the increasing human population density in large cities, representing a scenario in which there is an ever-greater risk of disease transmission and epidemics.The S1 Fig(TIF)Click here for additional data file.S1 TableP-values in bold.Significant (DOCX)Click here for additional data file.S2 TableAllelic richness (Na) and private allelic richness (Np).(DOCX)Click here for additional data file.S3 TableP-value.*Significant (DOCX)Click here for additional data file.


Rhodnius ecuadoriensis is the main triatomine vector of Chagas disease, American trypanosomiasis, in Southern Ecuador and Northern Peru. Genomic approaches and next generation sequencing technologies have become powerful tools for investigating population diversity and structure which is a key consideration for vector control. Here we assess the effectiveness of three different 2b restriction site-associated DNA (2b-RAD) genotyping strategies in R. ecuadoriensis to provide sufficient genomic resolution to tease apart microevolutionary processes and undertake some pilot population genomic analyses.R. ecuadoriensis adults collected from the central coast and southern Andean region of Ecuador, from June 2006 to July 2013. 2b-RAD sequencing data was performed on an Illumina MiSeq instrument and analyzed with the STACKS de novo pipeline for loci assembly and Single Nucleotide Polymorphism (SNP) discovery. Preliminary population genomic analyses  were implemented. Our results showed that the 2b-RAD genotyping protocol is effective for R. ecuadoriensis and likely for other triatomine species. However, only BcgI and CspCI restriction enzymes provided a number of markers suitable for population genomic analysis at the read depth we generated. Our preliminary genomic analyses detected a signal of genetic structuring across the study area.The 2b-RAD protocol was carried out in-house at a non-specialized laboratory using 20 Our findings suggest that 2b-RAD genotyping is both a cost effective and methodologically simple approach for generating high resolution genomic data for Chagas disease vectors with the power to distinguish between different vector populations at epidemiologically relevant scales. As such, 2b-RAD represents a powerful tool in the hands of medical entomologists with limited access to specialized molecular biological equipment. Rhodnius ecuadoriensis is a cause of concern for Chagas disease transmission, since it is widely distributed from the central coast to southern Ecuador. Here, a genome-wide sequencing (2b-RAD) approach was performed in 20 specimens from four communities from Manabí  and Loja (southern) provinces of Ecuador, and the effectiveness of three type IIB restriction enzymes was assessed. The findings of this study show that this genotyping methodology is cost effective in R. ecuadoriensis and likely in other triatomine species. In addition, preliminary population genomic analysis results detected a signal of population structure among geographically distinct communities and genetic variability within communities. As such, 2b-RAD shows significant promise as a relatively low-tech solution for determination of vector population genomics, dynamics, and spread.Understanding Chagas disease vector (triatomine) population dispersal is key for the design of control measures tailored for the epidemiological situation of a particular region. In Ecuador,  Vector control has been the mainstay of Chagas disease control strategies in Latin America. Several Latin American countries implemented nation-wide insecticide-spraying programs to eradicate Chagas disease vector populations in human dwellings over the last 30 years. These campaigns resulted in a dramatic reduction in vectorial transmission –3. DespiTriatoma dimidiata and Rhodnius ecuadoriensis are main vectors of Chagas disease, with the latter widely distributed from coastal and southern Ecuador to northern Peru [Triatomines, members of the arthropod family Reduviidae, subfamily Triatominae, commonly known as kissing bugs, are distributed from the southern United States to central Argentina . Over 13ern Peru ,12.et al. [et al. confirmed that gene flow  occurs between sylvatic, domicile and peridomicile ecotopes in Venezuelan Rhodnius prolixus based on pairwise FST values derived from both cytochrome b (cytb) and nine microsatellite loci. R. prolixus is the major vector species in Venezuela and Colombia, as well as Andean and Central American countries. Fitzpatrick et al.’s data suggested that colonization of domestic locales by wild triatomines is indeed possible in the region, and these findings had major implications for control. Other species have also been the subject of study. Population genetic data from Triatoma infestans based on ten microsatellite loci showed fine-scale genetic structure in domestic populations several years after the spraying of insecticides [cytb and 8 microsatellite loci in Triatoma brasiliensis to investigate its genetic structure and to assess gene flow among sylvatic and domestic/peridomestic populations. As with Fitzpatrick et al. [ST values obtained from microsatellite loci analysis also demonstrated connectivity between locales.Multiple molecular genetic studies exist which attempt to explain genetic structure and gene flow in triatomine populations ,13–20. Acticides . In thisTrypanosoma cruzi typing [R. prolixus genome in 2015, as compared to other vector species, represents a step in the right direction and has revealed much about the core adaptations that underpin the biological success of triatomines [T. infestans [Given that vector control remains the mainstay of Chagas disease intervention strategies, greater understanding of vector genetics and dispersal is urgently required. Of particular importance are genotyping approaches that provide very high resolution at local, epidemiologically relevant scales, as well as the ability to share and combine datasets across different studies and research groups. Microsatellite loci offer little flexibility in terms of shareability as data standardization guidelines for amplicon size estimation and allele nomenclature between laboratories, although possible , are rari typing . Likely atomines . A numbenfestans ,25. HoweIn tandem with the emergence of high throughput next generation sequencing (NGS) approaches, several groups have pioneered the use of restriction enzymes (REases) on restriction site-associated DNA sequencing (RADseq) protocols to allow a small fraction of the genome to be sequenced across multiple samples –34. SeveThe 2b-RAD genotyping strategy specifically uses Type IIB restriction enzymes (IIB-REases) for genomic DNA (gDNA) digestion . AdvantaR. ecuadoriensis allowing for resolution of regional population genetic structure. Furthermore, by comparing the performance among the three IIB-REases, we were able to recommend the appropriate IIB-REase and read depth to employ in order to yield a given number of SNP markers for R. ecuadoriensis, and presumably for other members of the Rhodnius genus.In our study, we were able to rapidly and cost-effectively generate several hundred Single Nucleotide Polymorphism (SNP) markers for R. ecuadoriensis were selected from the communities of La Extensa, Chaquizhca, and Coamine in Loja Province (southern Andean region), and from the community of Bejuco in Manabí  in Ecuador . The modified protocol involved an additional overnight chitinase digestion step, as well as one overnight 75% ethanol wash to ensure purity . For this purpose, 7 REases  were screened. Three IIB-REases, namely AlfI, BcgI and CspCI were chosen based on the total number of restriction fragments produced in silico for the draft R. prolixus genome (www.vectorbase.org), financial resources, known efficiency in previous studies [in silico restriction sites to show larger coverage variability among samples, at lower read depths. On the contrary, REases with less abundant restriction sites in silico could provide more exploitable markers at lower read depths.Initial selection of potential IIB-REases for our 2b-RAD protocol involved an  studies –33,38 an studies . We expeet al. [Libraries were prepared using the 2b-RAD protocol proposed by Wang et al.   approach ,53 was uY is the expected number of polymorphic loci at reads yield X; a is the estimate starting amount of Y when X is close to 0; b is the estimate of the relative change of Y in relation to a unit change in X (slope). A detailed description of the equations used for each dataset is provided in Here, in silico assembly of loci and individual genotyping was performed by running the DENOVO_MAP.PL pipeline  to 5. We kept the number of mismatches allowed between loci when building a locus in a single individual (-M) and when comparing across all individuals to build the population catalogue (-n) at default values. The bounded SNP calling model for identifying a SNP and estimating the sequencing error rate for calling at that SNP (—bound) ranged from 0 to 0.05. Finally, the significance level required to call a heterozygote or homozygote  was set to 0.01. The EXPORT_SQL.PL utility was used to export loci shared by at least the 80% and the 90% of samples with the same polymorphism level (loci with up to 2 SNPs) from the MySQL database for all datasets analyzed in STACKS for each IIB-REase , we decided to discard this sample from the other two datasets to avoid biased results in the IB-REase .R. ecuadoriensis in the study area. We retained polymorphic loci shared by at least 90% of the samples, characterized by the presence of 1 and 2 SNPs and with a minor allele frequency of 0.01. We performed preliminary genomic analysis using two different datasets: i) one dataset contained 361 polymorphic loci obtained from 18 samples processed with the BcgI IIB-REase  and ii) the second dataset contained 1225 polymorphic loci obtained from 19 samples processed with the CspCI IIB-REase. The number of markers obtained for the AlfI dataset derived from digestion with AlfI was too low to be used for the preliminary assessment of genomic structure of this particular sample. During the genotype calling, it is possible for more than one SNP to appear within the same region. When two SNPs were recovered at a single locus, a conservative approach was used to retain the first SNP for analysis, thereby excluding tightly linked SNP variation.Although both total number of samples (N = 19) and sample size per community (N = 4–5) were low, we conducted pilot explorations of the population structure of Rhodnius sp.-related, we also aligned the total polymorphic loci shared by at least the 90% of samples obtained from BcgI and CspCI datasets to the reference R. prolixus genome using BOWTIE 1 [—best) was chosen and no more than 3 mismatches (-v) were allowed.ARLEQUIN version 3.5  was usedBOWTIE 1 . The higin silico digestion on R. prolixus genome sequence by AlfI, BcgI and CspCI IIB-REases produced 204895, 103268 and 69984 putative cut sites, respectively.The extraction method allowed us to obtain RNA-free genomic DNA from all twenty samples with an average DNA concentration (ng/μL) of 62.77 ± 33.75 (s.d.) with average DNA purity ratios of 1.81 ± 0.05 (s.d.) and 1.81 ± 0.62 (s.d.) for absorbance at 280/260 and at 260/230, respectively  for the reads of all samples processed with each of the three IIB-REases. After trimming the adaptors and filtering the IIB-REase-specific recognition site, 2.9, 5.8 and 4.8 million reads for AlfI, BcgI, and CspCI (respectively) were retained . The avey and predicted values, and Akaike Information Criterion (AIC). In the first dataset (y∼a+bln(x)), geometric (y∼axbx) and exponential (y∼ae(bx)) NLS equations best fit to the AlfI, BcgI and CspCI datasets, respectively, allowing the estimation of growth rate parameters α and b (y∼axbx) and Power-law (y∼axb) equations converged the best fit and parameters estimation for AlfI and BcgI, and CspCI, respectively  and 0.39327 (CspCI). The most likely number of genetic clusters (K) identified by STRUCTURE was 2 for both datasets: on one side, the 3 samples from Loja region  were grouped together, and on the other, the sample from Manabí (BJ) was considered as a distinct cluster  detected a strong signal of genetic structuring across the study area, with highly significant (P< 0.0001) global F cluster . The aliet al. [Our data demonstrate the power of 2b-RAD as a valid genotyping approach that can be applied to Chagas disease vectors for which either no reference genome exists or, as in our case, a reference genome exists for a species within the same genus. Our data broadly support the assertion of Wang et al.  that theet al. . In factet al. . Moreoveet al. ). Howeveet al. , somewhain silico for their performance in terms of marker density against the Rhodnius prolixus genome [in silico cutter and to provide recommendations for suitable read depths, marker numbers and sample sizes for studies involving Rhodnius sp. vectors. We expected that an abundant in silico enzyme cutter would provide less usable molecular markers at lower read depths . Additionally, we were able to fit nonlinear regression models to the data and estimate growth rate parameters for each enzyme  of our datasets revealed distinct relationships between read depth and marker (polymorphic locus) number between the different enzymes, CspCI, BcgI and AlfI  broadly h enzyme . Although enzyme  did not R. ecuadoriensis DNA samples, we generated 1244 markers for CspCI, 367 for BcgI and 68 for AlfI. Even the lowest of these values eclipses the size of marker panels currently in use to explore Triatomine population genetics [et al. [At the read depth we achieved on one Illumina MiSeq single-ended run across 20 genetics –20. Howe [et al.  assessed [et al.  on the MR. ecuadoriensis in Ecuador would also predict a similar pattern of diversification [As well as ‘range finding’ for the application of 2b-RAD sequencing to triatomine populations, our second aim was to undertake preliminary population genomic analysis to explore genetic structuring in our study region. To this end, we focused on datasets generated with BcgI and CspCI since they presented higher numbers of polymorphic loci. An AMOVA indicated a significant proportion of variation was explained by between-population differences for both datasets. Moreover, we demonstrated the feasibility of our markers to distinguish structuring among populations in both BcgI and CspCI datasets. By using a Bayesian clustering framework our markers from both data sets detected two distinct clusters without previous location information, one of them was Bejuco, the clear geographic outlier with respect to Loja populations. Morphometric and genetic studies of fication ,74. Howefication .et al. [et al. [Earlier in the manuscript we presented the notion that, fewer steps, simplicity, cost-effectiveness, fragment size and strand bias absence are advantages of using a 2b-RAD protocol compared to other RADseq methods. Nevertheless, researchers must be aware of potential pitfalls and sources of bias accompanying all RADseq protocols, as well as most NGS-based methods. However, development of sophisticated analysis and more powerful software tools to deal with the types of issues produced by most NGS platforms is an active and evolving field of research . During et al.  to ident [et al.  highligh [et al. ,59.R. ecuadoriensis and not to microorganisms such as bacteria and fungi. Furthermore, it may be important to differentiate between mitochondrial and autosomal loci or sex-specific chromosomes that might have an effect in population divergence analysis. To overcome this difficulty, we adopted a stringent approach during raw data trimming and genotype calling. We focused analyses to loci shared by a high proportion of individuals and removed loci and samples with high amounts of missing data.In our study, most of the above issues encountered in RADseq have been circumvented either during the library preparation or the raw data filtering steps. Nevertheless, our main challenge is the absence of a reference genome to map short reads in order to ensure that all markers do indeed belong to R. ecuadoriensis genetic structuring and dispersal in Ecuador. In our study, the main limitation to carry out a wide range of conventional between and within-population analysis was the sample size per population. Low sample size required our analyses to consider an extended area to resist exploration of processes at finer geographic scales.Landscape genetics/genomics is a powerful and relatively new approach to explore the underlying spatial processes that affect genetic diversity in biological organisms . Next toAedes albopictus [Anopheles sinensis has been tracked in China using landscape genetics approaches, demonstrating multiple origins and the importance of long term agricultural insecticide use [Aedes aegypti [The high-resolution genotyping approach we have developed in this study now paves the way for landscape genetic/genomics analysis in vector-parasite systems , with gebopictus . Similarcide use . More wi aegypti ,82).2 or backcrosses of R. ecuadoriensis or other triatomine species.2b-RAD typing not only promises a potential applicability for population genetic studies but also for linkage and quantitative loci mapping given that marker density can be controlled using selective adaptors . In factIn conclusion, the decreasing cost and increasingly simplicity of approaches to generate high resolution SNP data puts such tools increasingly in the hands of researchers in endemic countries working on non-model organisms that act as vectors of Neglected Tropical Diseases. An analytical framework to incorporate detailed spatial and environmental variation into genetic analyses is now in place to facilitate a better understanding of the biology and dispersal of disease vectors.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S1 Code(PDF)Click here for additional data file.


While the series of events that shaped the transition between foraging societies and food producers are well described for Central and Southern Europe, genetic evidence from Northern Europe surrounding the Baltic Sea is still sparse. Here, we report genome-wide DNA data from 38 ancient North Europeans ranging from ~9500 to 2200 years before present. Our analysis provides genetic evidence that hunter-gatherers settled Scandinavia via two routes. We reveal that the first Scandinavian farmers derive their ancestry from Anatolia 1000 years earlier than previously demonstrated. The range of Mesolithic Western hunter-gatherers extended to the east of the Baltic Sea, where these populations persisted without gene-flow from Central European farmers during the Early and Middle Neolithic. The arrival of steppe pastoralists in the Late Neolithic introduced a major shift in economy and mediated the spread of a new ancestry associated with the Corded Ware Complex in Northern Europe. The population history of Europe is complex and its very north has not yet been comprehensively studied at a genetic level. Here, Mittnik et al. report genome-wide data from 38 ancient individuals from the Eastern Baltic, Russia and Scandinavia to analyse gene flow throughout the Mesolithic and Bronze Age. However, the archaeological record of the very north of the European subcontinent surrounding today’s Baltic Sea shows a history distinct to that of Central and Southern Europe which has not yet been comprehensively studied on a genomic level.Recent studies of ancient human genomes have revealed a complex population history of modern Europeans involving at least three major prehistoric migrations9. To the west and south, hunter-gatherers sharing a common genetic signature 4. They derived part of their ancestry, referred to as Ancient North Eurasian (ANE) ancestry, from a population related to the Upper Palaeolithic Mal’ta boy found in Siberia (MA1)12. Late Mesolithic foragers excavated in central Sweden, which have been called Scandinavian Hunter-Gatherers (SHG)2, were modelled as admixed between WHG and EHG6. Archaeological evidence for the settlement of Scandinavia suggests both a route through southern Scandinavia and a route along the northern coast of Fennoscandia13. Foraging groups that inhabited the eastern coast and larger islands of the Baltic Sea as well as the Eastern Baltic inland during the 8th and 7th millennium calibrated radiocarbon years before Common Era  developed a dual habitation system, establishing more permanent settlements than their surrounding contemporaries while remaining partially mobile15.Settlement of the Eastern Baltic and Scandinavia by mobile foragers started after the retreat of the glacial ice sheets around 11,000 years before present17. This development reached southern Scandinavia at around 4000 calBCE with farmers of the so-called Early Neolithic Funnel Beaker Culture  who gradually introduced cultivation of cereals and cattle rearing. At the transition to the northern Middle Neolithic, around 3300 calBCE, an intensification of agriculture occurred in Denmark and in western central Sweden accompanied by the erection of megaliths. Settlements in eastern central Sweden increasingly concentrated along the coast, where the economy shifted towards the marine resources. Early pottery of these coastal hunter-gatherers, known as the Pitted Ware Culture (PWC), resembles the Funnel beakers in shape. Analysis of ancient genomes from PWC and megalithic Middle Neolithic TRB (MN TRB) context in central Sweden has shown that the PWC individuals retain the genetic signature of Mesolithic hunter-gatherers while the TRB farmers’ ancestry can mainly be traced back to Central European farmers, albeit with substantial admixture from European hunter-gatherers20. As these TRB individuals date to a period one millennium after the initial Neolithization in southern Scandinavia, the question remains whether the first introduction of farming around 4000 BCE was driven by newcomers or by local groups involving later gene-flow from Central European farmers.The following Early Neolithic period, starting around 6000 calBCE, saw the transition from foraging to a sedentary agricultural lifestyle with the expansion of farmers out of Anatolia into Central and Southern Europe15, ceramics technology was adopted before agriculture, as seen in the Narva Culture and Combed Ceramic Culture (CCC). Recent genome-wide data of Baltic pottery-producing hunter-gatherers revealed genetic continuity with the preceding Mesolithic inhabitants of the same region as well as influence from the more northern EHG22, but did not reveal conclusively whether there was a temporal, geographical or cultural correlation with the affinity to either WHG or EHG.The production and use of pottery, in Central and Southern Europe often seen as part of the ‘Neolithic package’, was already common among foragers in Scandinavia during the preceding Mesolithic Ertebølle phase. Similarly, in the Eastern Baltic, where foraging continued to be the main form of subsistence until at least 4000 calBCE2 and north-eastern Europe21. Studies of ancient genomes have shown that those associated with the CWC were closely related to the pastoralists of the Yamnaya Culture from the Pontic-Caspian steppe, introducing a genetic component that was not present in Europe previously3. This genetic component is hypothesized to have spread in the subsequent millennia throughout Europe and can be seen in today’s European populations in a decreasing north-east to south-west gradient.The transition from the Late  Neolithic to the Early Bronze Age (LNBA) is seen as a major transformative period in European prehistory, accompanied by changes in burial customs, technology and mode of subsistence as well as the creation of new cross-continental networks of contact seen in the emergence of the pan-European Corded Ware Complex  in Central1, supporting the theory that the hunter-gatherer population of this region left a lasting genetic impact on subsequent populations23.Intriguingly, modern Eastern Baltic populations carry the highest proportion of WHG ancestry of all EuropeansHere, we investigate the modes of cultural and economic transitions experienced by the prehistoric populations surrounding the Baltic Sea. Were the changes seen in the Eastern Baltic Neolithic, which did not involve the introduction of agriculture, driven by contact with neighbouring groups and if so can we identify these? Was the earliest practice of farming in southern Scandinavia a development by a local population or did it involve migration from the South? How did the unique genetic signature of modern Eastern Baltic populations come to be?We present novel genome-wide data from 38 ancient individuals from the Eastern Baltic, Russia and Sweden spanning 7000 years of prehistory, covering the transition from a mobile hunter-gatherer to a sedentary agricultural lifestyle, as well as the adoption of bronze metallurgy. We show that the settlement of Scandinavia by hunter-gatherers likely took place via at least two routes, and that the first introduction of farming was brought about by the movement of the Central European farmers into the region at around 4000 calBCE. In the Eastern Baltics, foraging remained the dominant economy among interconnected north-eastern hunter-gatherer groups that did not experience admixture from European farmers until around 3000 calBCE, when a shift towards agro-pastoralism came about through migrations from the Pontic-Caspian steppe.24. After quality control, genome-wide data from 38 individuals, with an average coverage of 0.02–8.8-fold on targeted SNPs, were included in further analysis.The skeletal remains studied here were recovered from 25 archaeological sites in the territory of modern Lithuania, Latvia, Estonia, Archangelsk Oblast and Karelia (north-western Russia) and Sweden dating from around 7500 to 200 calBCE Fig. . In tota4; two Mesolithic hunter-gatherers from Lithuania  associated with the Kunda Culture , whose archaeological assemblages found in Lithuania, Latvia, Estonia and adjacent parts of Russia.The 38 final samples fall into nine broad groups Table : first, Twelve individuals were associated with pottery-producing forager cultures. Ten of them from Lithuania and Estonia  were assigned to the Narva Culture that occupied the Eastern Baltic region from the Late Mesolithic to the Middle Neolithic  and two individuals from Estonia were associated with the CCC that was spread across the northern part of the Eastern Baltic by the Middle Neolithic .Five individuals from Lithuania and Estonia were dated to the Late Neolithic . For the individual Gyvakarai1, we present genome-wide data at 7.6-fold average coverage from shotgun sequencing.Fourteen samples from Latvia and Lithuania were attributed to the Baltic Bronze Age  and date to ca. 1230–230 calBCE.21 and Estonia22, and merged data from identical individuals where they overlapped with the latter. We present the first data from the southernmost region of the Eastern Baltic (the territory of modern-day Lithuania), the Early Neolithic of Estonia and the Eastern Baltic Bronze Age.We supplemented our dataset of ancient Eastern Baltic samples with recently published data from 13 individuals spanning the Mesolithic to Late Neolithic in Latvia19 as well as other ancient and modern genome-wide datasets described below.From southern Sweden, we analyzed one farmer  from the EN TRB, the earliest agricultural population in Scandinavia for which there exists no genetic data to date. One sample from northern Sweden  dates to the Late Neolithic but was found without associated archaeological assemblages. The data were analyzed in context with published data from the Scandinavian Middle Neolithic to Bronze Age22, using a principal component analysis (PCA), constructed from 1007 modern individuals from a diverse set of West Eurasian contemporary populations, and used the same individuals to investigate model-based clustering using ADMIXTURE. We see that the Mesolithic foragers of Northern Europe fall into three distinct clusters, associated with EHG, SHG and WHG, respectively, as evidenced by their position on the PCA  for populations X with ANE ancestry, which are significant and among the highest in EHG  is strongly significant for excess allele sharing of SHG and EHG  was also found in the Palaeolithic Siberian AG35 and a haplogroup within the C1 clade, which appears today in highest frequencies in north-east Asia and the Americas, was described in several samples from Yuzhnyy Oleni Ostrov26. It was shown that some SHG carry the derived variant of the EDAR allele, which affects hair thickness and tooth morphology among other things, and which is found today in high frequency in East Asians and Native Americans4.SHG appear intermediate between WHG/Baltic Mesolithic and EHG in PCA space, have increased shared genetic drift with both shown in outgroup D-statistics of the form D do not show evidence of admixture with the contemporaneous European farming populations that were related to the Anatolian Neolithic  and WHG 54–100%) ancestry Fig. . In prin4–100% anHERC2 allele which codes for light iris colour, and like SHG and EHG they already possess an increased frequency of the derived alleles for SLC45A2 and SLC24A5, coding for lighter skin colour 29, as well as haplogroup U4 which has been found in high frequency in Mesolithic foragers from Russia25. We also find mtDNA branch K1, a subclade of U8, in one Baltic Mesolithic forager, adding to the mounting evidence that this lineage was present at low frequency among European hunter-gatherers before the arrival of agriculturalists27.One Narva individual, Spiginas1ps Table . In addiD-statistics show increased allele sharing of EN TRB with hunter-gatherer populations in comparison to the preceding farmers of the Linearbandkeramik (LBK) culture and no difference to the contemporaneous Middle Neolithic and Chalcolithic (MNChL) farmers of Central Europe  (Supplementary Table The previously published succeeding farmers of the Middle Neolithic (MN) TRB culture in West SwedenD reaches weak significance when X is MN TRB (Z = 2.94) and a two-way admixture model for PWC involving SHG and TRB farmers is not rejected by qpWave/qpAdm  fall in PCA space in the diffuse European LNBA cluster formed by individuals admixed between Early and Middle Bronze Age pastoralists from the Yamnaya culture of the eastern Pontic Steppe and Middle Neolithic European farmers , we find that the two individuals from the early phase of the LN  form a clade with Yamnaya , stands out in that it shares an excess of alleles with European forager groups when compared to the Yamnaya populations, with the top hits being Switzerland_HG, WHG, Baltic Mesolithic and Baltic EMN Narva  is strongly significant  when X was replaced by various agricultural populations of Europe and the Near East  that cluster together on the PCA in the same space occupied by modern Lithuanians and Estonians, shifted from other Europeans to WHG and Baltic hunter-gatherers Fig. . The sta32.The individual from Olsund in north-eastern Sweden was dated to the Late Neolithic, when agriculture had been introduced to the coastal areas of northern Sweden with the Battle Axe Culture, the regional variant of the CWC, while foraging persisted as an important form of subsistence. The remains were found without any associated artefacts, but in close proximity to a site where the assemblage showed a mix between local hunter-gatherer traditions and CWC influence33. Assemblages similar to the early Battle Axe Culture of Sweden have been found in south-western Finland, across the Bothnian Sea35 which could be considered a geographically closer source than southern Scandinavia.On the PCA plot, this sample falls within the European LNBA cluster Fig.  and simiD reveals significantly positive results for many modern Near Eastern and Southern European populations  gives significant positive hits for East Asian and Siberian populations  from the Eastern Baltic region, ranging from the Mesolithic Kunda culture to the Late Bronze Age  light from all sides for 10 min to reduce surface DNA contamination. Teeth were sawed transversally at the border of root and crown before sampling dentine powder from the inside of the crown with a sterile dentistry drill. Bone powder was taken from the inner parts of the bones with a sterile dentistry drill after removing the surface layer of the bone.48 with use of the High Pure Viral Nucleic Acid Large Volume Kit (Roche). Extraction buffer , 0.25 mg/ml Proteinase K (Sigma-Aldrich)) was added to the bone powder aliquot, and rotated overnight at 37 °C. The powder was then pelleted by centrifugation at 14,000 rpm. The supernatant was added to 10 ml binding buffer (5 M GuHCl (Sigma-Aldrich), 40% Isopropanol (Merck)) with 400 μg sodium acetate, pH 5.5 (Sigma-Aldrich) and mixed. The mixture was then transferred to the High Pure Extender Assembly funnel with a purification column attached and contained in a 50-ml Falcon tube. The tube was then spun at 1500 rpm for at least 8 min with slow acceleration until the binding buffer had mostly passed the purification column. Then the column was transferred into a new collection tube and the liquid remaining in the funnel was transferred to the column that was then centrifuged at 14,000 rpm. This was followed by a wash step consisting of adding 450 μl of wash buffer  to the column and spinning it at 8000 rpm for 1 min, the wash step is repeated and then followed by two dry spins at 14,000 rpm for 1 min. The DNA was eluted in a fresh siliconized Eppendorf tube in two elution steps of 50 μl TET , 0.05% Tween-20 (Sigma-Aldrich)) centrifuged for 1 min at 14,000 rpm, resulting in 100 µl of DNA extract for each sample. Negative controls were taken along for each extraction setup.Between 30 and 200 mg of powder was used for each DNA extraction , 100 µM dNTP mix (Thermo Scientific), 0.8 mg/ml BSA (NEB), 0.4 U/µl T4 Polynucleotide Kinase (NEB), 0.024 U/µl T4 Polymerase (NEB) and 1 mM ATP (NEB) and incubating at 15 °C for 15 min, then for 15 min at 25 °C, followed by purification with the MinElute kit (QIAGEN) and elution in 18 µl of TET. The following adapter ligation was performed by adding 1× Quick Ligase Buffer (NEB), 250 nM Illumina Adapters (Sigma-Aldrich) and 0.125 U/µl Quick Ligase (NEB) for a final reaction volume of 40 µl. The mix was incubated for 20 min at room temperature after which another MinElute purification was performed and the DNA is eluted in 20 µl TET. The following fill-in step consisted of adding the 20 µl DNA to 1× Isothermal Buffer (NEB), 125 nM dNTP mix (Thermo Scientific) and 0.4 U/µl Bst Polymerase 2.0 (NEB) for a final reaction volume of 40. The mix was incubated for 20 min at 37 °C and for 20 min at 80 °C. After the fill-in step, libraries were quantified via qPCR to ensure that the reactions were efficient. Some DNA extracts showed evidence of inhibition of enzymatic reactions, possibly due to the presence of humic acids or chemicals (glue or hardener) used for bone treatment50. To overcome inhibition, library preparation was repeated for samples that had a low DNA yield after initial library preparation or had abnormal extracts  using 10-fold less extract as template to dilute the potential inhibiting factors.Double-stranded next-generation sequencing libraries were prepared from a 20-µl aliquot of extract following a protocol established for ancient DNA51 and limiting the amount of template molecules to 2e+10 per reaction , 0.25 mM dNTP mix, 0.3 mg/µl BSA, 0.025 U/µl Pfu Turbo (Agilent) for a total reaction volume of 100 µl). The amplification took place in a modern DNA lab with an initial denaturation of 2 min at 95 °C, then 10 cycles of: 30 s at 95 °C, 30 s at 58 °C, 1 min at 72 °C; followed by elongation for 10 min at 72 °C.Libraries were then barcoded in a PCR-reaction using primers containing sample-specific index sequence combinations52, pooling at most five different sample libraries into one capture pool.Libraries were enriched for human mitochondrial DNA using a bead-based hybridization protocol53. Libraries enriched for mtDNA were sequenced separately to allow for reconstruction of the mitochondrial genome of each individual and estimation of modern mitochondrial contamination. Library pools were sequenced according to the manufacturer’s protocols on an Illumina HiSeq2500 at the department of Medical Genetics at the University of Tübingen for 2 × 100 cycles to a depth of ~1.5 million reads per sample.Libraries and mtDNA-enriched library pools were quantified on an Agilent 2100 Bioanalyzer DNA 1000 chip and pooled at equimolar concentrations. Libraries not enriched for human mtDNA were shotgun sequenced to determine the percentage of endogenous human DNA in every DNA library and assign the genetic sex of the individuals54 that merges paired-end reads (default parameters) and mapping of reads against a user-specified reference genome. Between 326 and 10,039,616 shotgun sequenced reads  could be reconstructed for 61 individuals and less than 5% mitochondrial contamination  were chosen for nuclear capture or deep shotgun sequencing. Uracil–DNA–glycosylase treated  libraries59 were prepared out of the DNA extracts of these samples by adding the extract to a reaction of total volume 60 µl with 1× Buffer Tango (Thermo Fisher Scientific), 100 µM dNTPs, 1 mM ATP and 0.06 U/µl USER enzyme (NEB) and incubating for 30 min at 37 °C. The reaction was then inhibited by adding 0.12 U/µl UGI (NEB).Forty-one samples  and enriched using an in-solution hybridization protocol24 for a targeted set of ~1.2 million nuclear SNPs (1240k SNP set)4.These libraries were then barcoded with sample-specific index sequence combinationsEnriched libraries from the Eastern Baltic and Swedish samples were paired-end sequenced on a NextSeq500 at the department of Medical Genetics at the University of Tübingen using 2 × 75 bp cycles and a HiSeq4000 at the IKMB in Kiel, using 2 × 150 bp cycles, and single-end sequenced on a HiSeq4000 for 75 cycles at the Max Planck Institute for the Science of Human History in Jena. The UDG-treated library of UzOO77 was processed at the Max Planck Institute for the Science of Human History in Jena, Germany, and was sequenced there on a HiSeq4000 for 2 × 75 cycles, and the UDG-treated library for Popovo2 was processed at Harvard Medical School, Boston, USA, and sequenced here on a NextSeq500 for 2 × 75 cycles.Additionally, the non-UDG-treated screening library of Gyvakarai1 was paired-end sequenced on two lanes of a HiSeq4000 for 2 × 75 cycles, and on a full run of a NextSeq500 for 2 × 75 cycles. The screening library for Kunila2 was paired-end sequenced deeper on 80% of one lane of a HiSeq4000 for 2 × 100 cycles. Additionally, 40 μl of DNA extract of Kunila2 was converted into a UDG-treated library, and pair-end sequenced on one lane of a HiSeq4000 for 2 × 75 cycles. The three UDG-half libraries for Olsund were single-end sequenced on a HiSeq4000 for 75 cycles.Furthermore, DNA was extracted from the dense petrous portion of individual MotalaAA and converted into a UDG-half library which was shotgun single-end sequenced on a HiSeq4000 for 75 cycles. Sequencing strategies and facilities are summarized in Supplementary Data 54. This included mapping with BWA (v0.6.1)55 against UCSC genome browser’s human genome reference GRCh37/hg19, and removing duplicate reads with same orientation and start and end positions. To avoid an excess of remaining C-to-T and G-to-A transitions at the ends of the reads, two bases of the ends of each read were clipped for each sample except for the non-UDG-treated data for Gyvakarai1, where 10 bases from each end were clipped.After demultiplexing, resulting sequence data were further processed using EAGERpileupcaller (https://github.com/stschiff/sequenceTools).For each of the targeted 1240k SNP positions, a read was chosen at random to represent this position using the genotype caller 58 and heterozygosity on the X chromosome in male samples with ANGSD61 . We do not include the lower coverage sample Kretuonas5 in ADMIXTURE analysis and other analyses that cluster the individuals into one population, thereby mitigating bias resulting from a defined population consisting of closely related individuals.Using the software READ22 after confirming the identity with outgroup f3 and READ.We merged our data of Kunila2 with previously published data of the same individual5.Genetic sex of the 41 selected samples was assigned using shotgun and SNP capture data by calculating the ratio of average X chromosomal and Y chromosomal coverage to average autosomal coverage at the targeted SNPs . Samples with an X rate between 0.65 and 1 and a Y rate between 0 and 0.15 were assigned female and those with an X rate between 0.35 and 0.55 and a Y rate between 0.4 and 0.7 were assigned male, supporting the informative value of the Y rate over the X rate using this method , as well as genotyping data from worldwide modern populations (Human Origins or HO dataset) published in the same publications and provided by the David Reich lab6. When analyzing only ancient samples, we make use of the 1,196,358 SNPs targeted by the 1240k SNP capture, using the genotypes of deep shotgun sequenced modern Mbuti as the outgroup. For analyses involving modern populations, we restrict to the intersection of 597,503 SNPs between the 1240k SNP set and the HO dataset.Reference datasets for ancient populations are taken from the publicly available dataset used in refs. smartpca in the EIGENSOFT package64 by constructing the eigenvectors from modern West Eurasian populations  error and report in Fig. k = 11, where the CV error levels out at a minimum.Admixture analysis Fig.  was carrf-statistics using the programs qp3Pop and qpDstat in the ADMIXTOOLS suite (https://github.com/DReichLab) for f3- and f4-statistics, respectively. f3-statistics of the form f3 measure the amount of shared genetic drift of populations X and Y after their divergence from an outgroup. Admixture f3-statistics of the form f3 indicate when significantly negative that population Test is intermediate in allele frequencies between populations X and Y and could be considered an admixed population. This test was performed with parameter inbreeding:YES and cannot be done for populations with less than two individuals. D-statistics of the form D show if population Test is symmetrically related to X and Y or shares an excess of alleles with either of the two. Results are only reported for statistics based on more than 10,000 SNPs.To quantify population affinities and admixtures suggested in the PCA and ADMIXTURE analysis, we carried out qpWave and qpAdm programs from ADMIXTOOLS. These programs implement the methodology of using regression of f4-statistics of a Reference population with various outgroups to relate its ancestry to a Test population6. With qpWave, we identified potential source populations for our population under study by testing if a set of Left populations  is consistent with being descended from n waves of admixture which have unique relationships to the Right outgroup populations . This is given when rank n−1 cannot be rejected (p > 0.05), and rejected (i.e. more than n waves of admixture are needed to explain the ancestry of Test and Reference) if rank n−1 can be rejected (p < 0.05).To formally test for the number of source populations and the proportion of ancestry these contributed to our studied populations, we used the qpAdm to model the Test population as a mixture of various source populations postulated from the qpWave test, setting as Left populations the Test and source populations and as the Right populations the various outgroups named above.To estimate admixture proportions, we used The sequence data reported in this paper are deposited in the Sequence Read Archive (Accession numbers: SAMN08139261–SAMN08139301) and complete mitochondrial consensus sequences are deposited in GenBank (Accession numbers MG428993–MG429049).Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1