Poecile gambeli) a resident bird species in western North America: (1) Do populations exhibit similar levels of genetic diversity across the range? (2) What is the genetic affinity of western populations in Oregon and Washington? (3) Do genetic patterns exhibit isolation by distance, or are genetic patterns more heavily influenced by habitat discontinuity? We tested the effects of isolation by distance and habitat distribution on genetic structure by analyzing 266 samples from 17 sites across western Canada and the United States. We found a near significant relationship between genetic diversity and latitude, however, our results indicate that overall, latitude is not a strong predictor of genetic diversity. Our analyses of populations in Oregon and Washington revealed a mismatch between patterns detected with mtDNA and microsatellite data. In particular, Washington clustered with the Coast Range/Cascades/Rocky Mountain mtDNA group, but with populations in southern Oregon/California based on microsatellite data. These results suggest the presence of a contact zone in Washington between the two mtDNA clades Coast Range/Cascades/Rocky Mountain and southern Oregon/California clades. Finally, our study revealed a greater effect of isolation by distance than isolation by habitat for both mtDNA and microsatellite data. Overall the isolation by distance signal was greater for mtDNA than microsatellite patterns. The greater signal of isolation by distance on mtDNA patterns likely reflects the strong effects of Pleistocene glaciations in shaping genetic patterns in western North America.Examining the factors that influence contemporary genetic patterns is important given the alarming rate at which natural environments are changing. In particular habitat fragmentation and climate change are expected to influence the distribution and diversity of natural populations. In this study we used both mitochondrial control region (mtDNA) and microsatellite data to answer the following questions about genetic diversity and divergence in mountain chickadees ( Contemporary processes are especially important given the alarming rate that environments are changing and the effects of these changes on populations6. One only has to look at the Pleistocene glaciations to see the historical impact of climate change on population genetic structure. The fragmentation of populations due to repeated habitat contractions and expansions led to many recent speciation events7 and fragmented habitat including the separation of coniferous forests in western North America8. In the Pacific Northwest, the legacy of the last glacial maximum (LGM) combined with contemporary landscape features on population genetic structure is especially evident. Within this region, many subalpine species exhibit concordant genetic breaks associated with habitat fragmentation and isolation across arid, low elevation barriers, emphasizing the role of habitat discontinuity on genetic diversity and population differentiation14.Although life history and historical processes are the primary factors shaping an organism’s evolutionary history, one must consider the role of contemporary processes on genetic variation16. By comparison organisms with seasonal migration have higher dispersal potential and lower levels of population genetic structure18 . The higher genetic homogeneity may mean individuals are not as highly adapted to local conditions and may be able to survive in a variety of areas.Environmental change (i.e. habitat fragmentation) impacts not only the landscape, but also behaviour and demographic factors. Species with strong year-round philopatry and infrequent dispersal will be heavily impacted, especially species with highly structured populations20. Despite being capable of dispersing long distances, geographic and ecological barriers can impede dispersal between populations. Rivers, mountain ranges, and habitat discontinuities have all been shown to limit dispersal and promote genetic differentiation between populations23.Birds are a good model group for examining the influence of landscape features on dispersal because of their exceptional dispersal capabilities and ability to move large distances21 including the mountain chickadee (Poecile gambeli). Previous work on mountain chickadees demonstrated two well-supported mitochondrial DNA (mtDNA) groups, an eastern (Rocky Mountains and Great Basin) and a western (Sierra Nevada and Cascades) clade24, although a recent study suggested contemporary gene flow may be occurring between these two clades25. A common year-round resident of dry, montane, coniferous forests in western North America, the mountain chickadee is thought to have limited natal dispersal, exhibit strong philopatry, and limited altitudinal migration during winter26. Given that this species is thought to have limited dispersal capabilities, it is well suited for studies examining the effects of dispersal and biogeographic barriers on population genetic structure and phylogeographic structure28.Given the presence of geographic and ecological barriers and the glacial history of the region, western North America is an ideal area to examine the effect of landscape and dispersal barriers on population genetic structure. Many sedentary species exhibit restricted dispersal across these barriers31; (3) Do genetic patterns exhibit isolation by distance, or are genetic patterns more heavily influenced by breaks in habitat? Mountain chickadees display strong philopatry and limited altitudinal migration, which suggests that distance between sites may reduce gene flow. By comparison mountain chickadees are primarily found in montane coniferous forests, and therefore habitat isolation and not geographic distance may influence genetic differentiation. Therefore, comparing these two variables, geographic distance and habitat isolation, will allow us to examine the roles of each variable on genetic differentiation in this species.Here we examine phylogeographic and population genetic structure in mountain chickadees using the rapidly evolving mtDNA control region and microsatellite markers. In addition to exploring genetic structure, we sought to answer the following questions: (1) Do populations exhibit similar levels of genetic diversity across the range? Given that their range includes areas that were glaciated during the LGM, it allows us to test if younger populations (i.e. those populations in previously glaciated regions) exhibit similar levels of genetic diversity as older populations (i.e. those populations found in areas that were free of ice during the LGM); (2) What is the genetic affinity of western populations in Oregon and Washington? Previous research found eastern and western clades, but did not sample the area separating these two groups. The Pacific Northwest is a well-documented contact zone for many other species13,84 = 1.20, p = 0.30) observed heterozygosity: F13,84 = 0.41, p = 0.96).We observed high levels of genetic diversity in our analysis of mountain chickadee control region sequences and microsatellite markers Table . For pop2 = 0.15, t = 1.83, p = 0.09). By comparison, we observed a negative non-significant relationship between latitude and microsatellite genetic diversity . For our RDA, latitude was a near-significant predictor of mtDNA genetic diversity , and accounted for 21.86% of the observed variation. Similarly latitude was a near significant predictor of microsatellite genetic diversity , and accounted for <1% of the observed variation.Our analysis of genetic diversity along a north-south transect Fig.  revealedet al.24, northern populations in the Cascades and Coast Range  did not group with southern Oregon and California populations. Instead these populations grouped with populations in the Great Basin, Rocky Mountains and Canada.We identified a total of 80 control region haplotypes. Our statistical parsimony network revealed three distinct groups: (1) Coast Range/Cascades/Rocky Mountain; (2) southern Oregon/central California; and 3) southern California Fig. . Althoug southernST and FST values revealed significant differentiation in mountain chickadees  accounting for all but one of the significant ΦST values. Among Rocky Mountain populations 24 of 45 FST comparisons were significant, Idaho was significantly different from all populations, and northeast Oregon from all populations except southern Alberta and Utah, Central Oregon from all populations except southern Alberta, eastern and western Montana, and Colorado, and central British Columbia from all but three populations .Pairwise Φes Table ; 71 of 9CT = 0.85; p < 0.0001). The majority of variation was explained by differences among groups , although there was significant variation among populations within groups  and within populations . In contrast to these previous methods, BAPS identified four distinct clusters; it split the Coast Range/Cascades/Rocky Mountains into two different groups. Every population with exception of central Oregon and Colorado had at least one individual with a haplotype from each of the two Coast Range/Cascades/Rocky Mountains groups identified with this analysis.Similar to our statistical parsimony network, PCoA Fig.  showed tStructure revealed four distinct clusters  = −7338.60; Fig. Comparisons of microsatellite and mtDNA patterns of differentiation revealed contrasting patterns. Whereas four explanatory variables  explained relatively little of the observed differentiation  Do populations exhibit similar levels of genetic diversity across the range? (2) What is the genetic affinity of western populations in Oregon and Washington? (3) Do genetic patterns exhibit isolation by distance, or are genetic patterns more heavily influenced by breaks in habitat? Across the range, we found a near-significant positive correlation between latitude and mtDNA genetic diversity using both a linear model and RDA approach, and microsatellite genetic diversity using an RDA approach only. Although these results indicate that latitude may predict genetic diversity, latitude may be reflective of other processes including isolation and post-glacial colonization patterns for this species. Our inclusion of populations from Washington and Oregon provided critical insights into genetic patterns; in particular our results revealed a contact zone in Washington between southern Oregon/California and Coast Range/Cascades/Rocky Mountain mtDNA clades given that Washington grouped with southern Oregon/central California populations based on microsatellite patterns. This area has been reported as an area of contact in several other avian speciesRana latastei) found that the distance from glacial refugium and isolation were strong predictors of genetic diversity in this species27. In our own study, isolation may drive patterns of genetic diversity, given that our most isolated population in southern California exhibited some of the lowest levels of genetic diversity for both mtDNA and microsatellite markers. Additionally Idaho, a distinct population based on microsatellite markers also exhibits lower microsatellite genetic diversity and appears to be isolated from other populations in the Coast Range/Cascades/Rocky Mountain mtDNA group (see below). Further our analyses of population structure indicate limited gene flow is occurring between populations in the Rocky Mountains and central Oregon, and along the Pacific Coast among Washington, southern Oregon and California populations.Our analyses of genetic diversity across the range suggest the potential for a correlation between genetic diversity and latitude. In particular mtDNA genetic diversity showed a stronger correlation with latitude than microsatellite genetic diversity, indicating that genetic diversity was higher in previously glaciated areas than ice-free areas. It is important to note though that this relationship may reflect both past and present processes, including isolation and post-glacial colonization patterns. For example work on post-glacial patterns of genetic diversity in Italian agile frogs 32 and black-capped chickadees (Poecile atricapillus)23. Perhaps contemporary mountain chickadee habitat is not as isolated as that of these two sister species, however, this seems unlikely. Mountain chickadees are specialists inhabiting dry coniferous forests at high elevation, so their distribution range may be more disjunct than it appears. Therefore we must also consider that although our sampling is adequate enough to detect isolation by distance, it may be too coarse of a scale to detect isolation by habitat resistance. Thus a more appropriate sampling scale at a regional scale may be required to detect a pattern of isolation by habitat resistance.Our combined analyses indicate geographic distance has a greater influence on genetic differentiation than habitat discontinuities, although both of these factors explained a relatively low portion of variation for microsatellite loci. This result is surprising as habitat discontinuities correspond to genetic differentiation in two closely related species with similar dispersal potential: chestnut-backed chickadees  in our study than the ND2 marker used by Spellman et al.24.MtDNA patterns indicate historical isolation of mountain chickadee populations and continued isolation of maternal lineages, as we found no shared haplotypes between western and eastern populations. Further, we found clear north/south divisions within the western clade, as indicated by pairwise ΦStructure and pairwise FST comparisons despite being with all Coast Range/Cascades/Rocky Mountain populations based on mtDNA data. Adams and Burg23 found that black-capped chickadee populations in Idaho were genetically distinct from northern population in British Columbia and Alberta, as well as populations in Montana and southern Alberta. They attributed this pattern to reduced connectivity between forested areas as a result of mountain ranges and large canyons, restricting gene flow between populations. Therefore biogeographic features in this region may explain the isolation of this population.In addition to genetic patterns indicating historical isolation, our results also suggest evidence for recent isolation in mountain chickadees. For example, a fourth genetically distinct group was detected with microsatellite data in Idaho. Idaho was completely distinct from other populations based on et al.25. Sex-biased dispersal may explain differences between mtDNA and microsatellites40, although female-biased dispersal is more common in avian species20.The reduced genetic differentiation between Colorado, Utah, northeast Oregon, and central Oregon may be indicative of increased contemporary gene flow in mountain chickadees as suggested by Manthey 28. Further work is necessary to determine the variables that shape contemporary genetic patterns within this diverse region.The mountain chickadee exhibits strong phylogeographic and population genetic structure. Our inclusion of Pacific Northwest populations revealed a contact zone between southern Oregon and California populations and Coast Range/Cascades/Rocky Mountain populations based on contrasting patterns for mtDNA and microsatellite data. MtDNA and microsatellite patterns were primarily concordant, indicating that both historical and contemporary gene flow are restricted between populations. Although isolation by distance exhibited a greater effect than habitat resistance in our analyses, we cannot discount the effect of habitat on genetic patterns. Overall our study indicates the important effects that Pleistocene glaciations have had on genetic patterns, especially in resident species like the mountain chickadee. Further our study highlights the complex biogeographic history of northwestern North America where the interaction between historical process, barriers to dispersal, and habitat configuration have influenced genetic diversity and divergence40. All protocols were approved by the University of Lethbridge animal care board, and all methods in this study were performed in accordance with the relevant guidelines and regulations.A total of 266 blood and tissue samples from 17 sampling sites were collected across the contemporary mountain chickadee range  and PCR buffer (Crimson or Promega) in a final volume of 25 μl. Amplification consisted of one cycle at 95 °C for 2 min, 54 °C for 45 s, and 72 °C for 60 s; 37 cycles of 94 °C for 30 s, 54 °C for 45 s, and 72 °C for 60 s; and one final cycle at 72 °C for 5 min. The PCR products were sequenced using an Applied Biosystems 3130 Genetic Analyzer or sent to Genome Quebec for sequencing . MtDNA chromatograms were checked and sequences manually aligned in MEGA v5.039.Two PCR primers, H1015 (5′-CGCGGGTTTAACGAATGTGG-3′) and LmochCR1 , were used to amplify a 765 bp product within Domains I and II of the control region for 190 samples. The polymerase chain reaction (PCR) was carried out in an Eppendorf Mastercycler. PCR consisted of approximately 100 ng of template DNA, 1 μM of each primer, 200 μM dNTP, 2.5 mM MgCl45. All forward primers were modified with the addition of M13 sequence to the 5′ end to allow for direct incorporation of a fluorescently labeled M13 primer. PCR reactions consisted of approximately 100 ng of template DNA, 0.5 μM of each microsatellite primer and 0.05 μM M13 tag, 200 μM dNTP, 1–2 mM MgCl2 (see below), 0.5 units of Crimson Taq DNA polymerase (New England BioLabs) and PCR buffer in a final volume of 10 μl. MgCl2 concentration varied depending on the locus  and 1% formamide was added to the PCR for Escu4 and Ppi2. All loci were amplified using a two-step annealing procedure: one cycle for 2 min at 94 °C, and 45 s at TA1, 1 min at 72 °C; 7 cycles of 1 min at 94 °C, 30 s at TA1, 45 s at 72 °C; 31 cycles of 30 s at 94 °C, 30 s at TA2, 45 s at 72 °C; and one final extension of 5 min at 72 °C. For loci Escu4 and Pdo5 TA1 = 45 °C and TA2 = 48 °C, and for the other five loci TA1 = 50 °C and TA2 = 52 °C. The PCR was carried out in an Eppendorf Mastercycler and PCR products were run on a 6% acrylamide gel using a Li-COR 4300 (Li-COR Inc.) with appropriate controls and size standards. All microsatellite genotypes were visually scored independently by two people (JAH and TMB). Finally we re-amplified and ran a subset of individuals to ensure that we accurately scoring across gels.Seven microsatellite primer pairs isolated from other passerine species  were used for genotypingHd), and nucleotide diversity (π) using DnaSP v5.1046.To measure genetic diversity for mtDNA, we calculated the number of haplotypes (H), haplotype diversity (48. We found no significant linkage disequilibrium between loci (p > 0.77), but following corrections for multiple tests six of 119 population x locus comparisons deviated from HWE: western Montana at locus Pdo5 (p < 0.001), southern Oregon at Ppi2 (p < 0.001), Washington at Titgata02 (p = 0.030), south-central California at Pat14 (p = 0.003) and southern California for Pdo5 (p < 0.001) and Titgata39 (p = 0.017). As no locus or population showed consistent deviations from HWE, we used all seven loci for subsequent analyses. We calculated observed heterozygosity, expected heterozygosity, and allelic richness for each population using Fstat 1.249. Finally, we compared microsatellite genetic diversity between populations using an Analysis of Variance in PAST version 350.We tested all microsatellite loci x population combinations for deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) using GENEPOP 4.0.1051, we followed the methodology of Ficetola et al.27 and performed a principal component analysis (PCA) to estimate genetic diversity at each population for both our mtDNA and microsatellite datasets. For our mtDNA dataset we included nucleotide diversity and haplotype diversity in our PCA; the first principal component  explained 84.43%. For our microsatellite dataset, we included allelic richness, observed heterozygosity, and the number of private alleles; again we retained only a single principal component , which explained 48.88% of the variance.Rather than using single genetic metric (e.g. observed heterozygosity) to arbitrarily measure genetic diversity52. For these analyses we only included populations with more than eight samples and therefore we excluded northwest British Columbia, British Columbia Revelstoke, and Arizona.To compare patterns of genetic variation between new populations (populations in previously glaciated areas) and old populations , and a maximum likelihood (ML) tree was constructed in MEGA using the same substitution model (discrete gamma categories n = 4) and nearest neighbor interchange heuristic model, with 1000 bootstrap replicates to evaluate robustness.Two different phylogenetic approaches, statistical parsimony and maximum likelihood, were used to determine the phylogeographic relationship among the 190 chickadee mtDNA sequences. A statistical parsimony network was constructed using the program TCSST (mtDNA) and FST (microsatellite) values for all populations with at least eight samples 57, while pairwise FST was calculated from microsatellite data using GENODIVE v2.0b20 58. All p-values were corrected for multiple tests using the Benjamini-Hochberg false discovery rate (FDR) correction60. We used FDR corrections, given that previous studies have indicated that Bonferroni corrections often result in an increase of Type II errors, where the number of significant pairwise comparisons is underestimated60.We calculated pairwise Φes Table . PairwisST values and then performed a PCoA using GenAlEx 6.5 61. Next we used the Bayesian clustering program BAPS 5.362 to estimate the number of clusters. We conducted 10 runs with the maximum number of possible clusters set at 17; we did not use sampling location as a prior. Individuals were assigned to the cluster with the highest average Q value (ancestry coefficient) from the 10 runs at the optimal K. Finally, we conducted a spatial analysis of molecular variance using SAMOVA63 to identify groups of sampling sites that are geographically homogenous and maximally differentiated from each other. SAMOVA uses a simulated process to define groups maximizing the proportion of total genetic variance due to differences between groups and, unlike AMOVA and SAMOVA, does not require groups to be defined a priori.We used three different approaches to examine population structure based on our mtDNA dataset. First we used a Principal Coordinate Analysis (PCoA) to examine if haplotypes clustered geographically. For this analysis we created a distance matrix using our pairwise ΦStructure v2.364. To estimate the most likely number of clusters, we ran each K (1–10) for 10 iterations, using a burn-in of 50,000 and a run of 100,000 steps65. We ran this analysis for all 17 populations using the admixture model, correlated alleles, and sampling location as priors. We estimated K by calculating ΔK65 in Structure Harvester66 and examining posterior probabilities as suggested by Pritchard et al.64. Following our initial STRUCTURE analyses, we explored the potential for hierarchical structure by performing subsequent runs on each of the identified clusters. We found no evidence of further population substructure in any of the subsequent runs.We examined genetic differentiation for microsatellites using the Bayesian clustering program 67. It uses a raster  that represent a property of the landscape, in this case, tree-habitat. We created a least cost resistance matrix using latitude and longitude of all sampled mountain chickadees, and we used a global map of tree diversity68 to extract habitat tree density (habitat) values for each coordinate point. The global map of tree diversity was restricted to the distribution of the mountain chickadee in the USA and Canada. The resistance values calculated for each pair of individuals was used to explain genetic variation for both microsatellites and mtDNA. All geographic analyses were done in R statistical program.To get a measure of habitat connectivity and resistance between sampled individuals of mountain chickadee, we used graph theory to calculate distance and routes as implemented in the package gdistance in R53. For these analyses, we specifically used distance based-Redundancy Analyses (dbRDA) to examine the effects of geographic distance, habitat resistance, latitude, and longitude on individual genetic variation for both microsatellite and mtDNA data. We analyzed each dataset individually and used the capscale function in the R-package VEGAN to perform all analyses52. We calculated the Cavalli-Sforza chord distance in GENODIVE for our microsatellite data and for our mtDNA data set we calculated Nei’s genetic distance using GenAlEx for our response variables. For our explanatory variables we used the decimal degree geographic coordinates (longitude and latitude) of each individual to test the effect of geographic distance. Next, we reduced the habitat distance matrix (as described above) to a single continuous variable using a Principal Coordinate Analysis in GenAlEx, retaining the first significant Principal Coordinate which explained 57% of the variation; p < 0.05 as our explanatory variable. We tested the effect of latitude and longitude separately to determine if north-south or east-west differences influenced genetic variation. In addition to testing the explanatory variables separately, we performed conditional tests where we controlled for the effect of geographic distance on habitat resistance, as well as the reciprocal test to determine which factors primarily influence genetic variation.Similar to our analyses of genetic diversity, we used Redundancy analysis Models to examine the effect of distance and habitat resistance on genetic differentiationAll microsatellite genotyping will be archived in Dryad and all mtDNA sequences will be archived in GenBank.


Population demography and gene flow among African groups, as well as the putative archaic introgression of ancient hominins, have been poorly explored at the genome level.Here, we examine 15 African populations covering all major continental linguistic groups, ecosystems, and lifestyles within Africa through analysis of whole-genome sequence data of 21 individuals sequenced at deep coverage. We observe a remarkable correlation among genetic diversity and geographic distance, with the hunter-gatherer groups being more genetically differentiated and having larger effective population sizes throughout most modern-human history. Admixture signals are found between neighbor populations from both hunter-gatherer and agriculturalists groups, whereas North African individuals are closely related to Eurasian populations. Regarding archaic gene flow, we test six complex demographic models that consider recent admixture as well as archaic introgression. We identify the fingerprint of an archaic introgression event in the sub-Saharan populations included in the models  from an early divergent and currently extinct ghost modern human lineage.The present study represents an in-depth genomic analysis of a Pan African set of individuals, which emphasizes their complex relationships and demographic history at population level.The online version of this article (10.1186/s13059-019-1684-5) contains supplementary material, which is available to authorized users. Paleontological and genetic evidence points towards a recent African origin of anatomically modern humans (AMHs) around 150–300 thousand years ago (kya) and a posterior Out-of-Africa expansion 50–100 kya . The speKhoisan languages, defined by their use of click consonants as phonemes and by exclusion of the Niger-Kordofanian linguistic family, are spoken by several Khoisan populations who currently reside in the Kalahari regions of Namibia and Botswana in southern Africa, as well as by two other populations in Tanzania, the Hadza and the Sandawe ethnic groups. The basal split of Khoisan people from any other extant human populations has been consistently inferred using uniparental markers , 16, micOn the other hand, African Pygmies, broadly characterized by their short statures, include a group of more than 20 culturally heterogeneous populations , 20. As The “Bantu expansion”, which is the migration of Bantu-speaking people from present day Cameroonian Grassfields region close to Nigeria, began around 5–3 kya ago and has been associated with the spread of Late Iron Age culture over most of sub-equatorial Africa , 21, 26.MUC7 gene, a protein abundantly expressed in saliva and associated with the composition of oral microbiome [Archaic hominins could have also left a footprint in the gene pool of extant populations, which would represent another confounding parameter when analyzing the genetic diversity within the African continent. Initial studies carried out on archaic genomes reported that Neanderthal or Denisovan signatures were found in non-African groups but not in the genomes of sub-Saharan populations , 31. Reccrobiome , or 265 crobiome . Moreovecrobiome . An altecrobiome , 45 or bcrobiome .A feasible approach to model the complex demographic process that has produced the genetic variation present in current human African populations (including the role of putative archaic introgression from archaic ghost populations) and estimating each of the demographic parameters would be to analyze the data within an Approximate Bayesian computation (ABC) framework. ABC is a statistical framework for inferring the posterior distribution of parameters when the likelihood of the data given the parameters is unknown but there is a way to generate simulated data , 47. TheFinally, it is worth mentioning that in the scenario described above of admixed societies with complex relationships between themselves throughout their history, pioneering whole-genome sequence studies in African individuals have highlighted the need for a broader geographic sampling coverage across the continent to elucidate the evolutionary history of African populations , 52, 53.We collected 21 samples from the four major continental African linguistic groups that belong to 15 different African populations which are either agriculturalists or hunter-gatherers , of at least 0.5, 1, and 1.5 Mbp of callable genome in each sample . This correlation suggests that genetics tends to fit the geographic location of the sampled individuals. In fact, we observed that genetic differentiation tends to increase monotonically with geographic distance between individuals .We explored the correspondence between genetic and geographic diversity in our African samples  and ran ADMIXTURE . For ADMOverall, results from both analyses suggest that African populations can be clustered in four major genetic groups: Khoisan, Pygmy, sub-Saharan agriculturalist, and North Africa Fig. . Consist4-ratio estimates [To formally test admixture, we applied the D-statistics test  addressistimates , which pWe found clear evidence of admixture between Khoisan populations and the South African Bantu individual, as well as between Dinka and Mbuti Pygmies, as this was consistently observed in several comparisons made using different African populations  and North African populations was formally identified with the D-statistics test .To unravel the ancient demographic history of the African populations that are present in our data set, we used the Pairwise Sequentially Markovian Coalescent (PSMC) model that analyzes the dynamics of the effective population size over time . We incl−8  and the analyses , 63. Thee, maintained through all time periods, as recently reported [e reduction. Interestingly, the Baka Pygmy sample showed a sharp increase in Ne around 30 kya. In order to discard a possible spurious increase occurring in one specific time period, we changed time parameters of PSMC to obtain a finer scale. The new estimates revealed a bit more gradual increase spanning three different time intervals  analysis coupled to a Deep Learning (DL) framework  (Additioi) presence of archaic populations out of the African continent, represented by the Neanderthal and Denisovans lineages, (ii) introgression from early anatomically modern humans into Neanderthal [iii) introgression from an extremely archaic population into Denisovans [iv) Khoisans at the root of mankind [v) Out-of-Africa event of AMHs [vi) archaic introgression of a Neanderthal-like population after the Out-of-Africa event in Eurasian populations [vii) archaic introgression from a Denisovan-like population in East Asians [We implemented six demographic models  Kh mankind , 14–18,  of AMHs , (vi) arulations , and  than usually reported. Since sub-Saharan populations are traditionally used as outgroup for detecting archaic introgression out of Africa, we wondered whether these estimated values of archaic introgression in Eurasia could be higher than previously by the fact that we were considering in model B archaic introgression within Africa. We conducted the ABC-DL analysis using the model A, the basic model that does not consider XAf  of African samples compared to non-African samples. All hunter-gatherers, not only Khoisan, present higher Ne along modern-human history than any other population. It is noteworthy that we observed by PSMC a sudden Ne increase in Baka Pygmy around 30 kya. A similar increase was observed in another study that analyzed several Baka and Biaka samples [Ne increase can be attributed to either a population expansion or episodes of separation and admixture [In agreement with the origin of humans in the African continent and further founder bottlenecks events out of Africa, our PSMC estimates a larger effective population size  reveals four major genetic human groups in Africa, associated to geographic and cultural/linguistic groups and comprising Khoisan, Pygmies, sub-Saharan non-hunter-gatherers, and North African populations. While different hunter-gatherer groups show more differentiation compared to the rest of samples, agriculturalist sub-Saharan individuals are genetically more homogeneous, most likely due to the Bantu expansion. Northern African individuals are closely related to non-African populations, in agreement with a recent split of both groups and continuous gene flow, as clearly determined with D and Fn Africa . These fCompelling evidence accumulates in favor of interbreeding between early hominin species being common instead of exceptional. Neanderthal and Denisovan introgression in Asia, Europe, and North Africa has been well established in previous studies –32 and cOur results suggest interbreeding of AMHs with an archaic ghost population that diverged from the AMH lineage at a temporal scale similar to the one between the Neanderthals and Denisovans. This observation would indicate the presence of a deep archaic population substructure also in the African continent and contrasts with previous studies that suggested that a basal lineage had a major impact only on particular western African populations . FurtherWe have comprehensively analyzed the genetic relationships among a Pan African set of human genomes sequenced at high coverage. By implementing novel methodologies when necessary, we have assessed demographic population changes and recent admixture between their populations, as well as, archaic interbreeding with other hominins. Our data point to a complex demographic scenario within Africa related to the complex history of AMHs.http://www.ncbi.nlm.nih.gov/sra)  and from cdna.eva.mpg.de/neandertal/altai/ModernHumans/bam. All sequences were sequenced at deep coverage (21–47x) ; realigned regions around indels, recalibrated base qualities, called genotypes, and filtered variants by quality using GATK [Single-nucleotide polymorphism (SNP) genotyping calling of each sequenced sample in autosomal and sexual chromosomes was performed by means of a stringent procedure. Briefly, we mapped the paired-end reads of each sample against the human assembly GRCh37 using the BWA aligner ; removeding GATK  and VQSRing GATK . Furthernumts. On the other hand, to improve the sequence reconstruction at the extremes of the reference assembly, we repeated the same procedure but mapping against a reference genome with a modified origin (8 kbp from the reference origin). Consensus mitochondrial sequence for each individual was constructed from the de novo assembled 40 mitochondrial assemblies. Mitochondrial haplogroups were then determined by locating sample variants in the updated mitochondrial phylogenetic tree available in www.phylotree.org  were considered for comparison. Genotypes generated on Affymetrix’s Genome-Wide Human SNP array 6.0 were compared with our calls for additional nine samples. After stringent data management, we retained a shared set of 734,734 SNPs for validation, of which 19,472 SNPs belong to the X chromosome  and 110 SNPs to the Y chromosome . Genotype sensitivity was assessed as the proportion of alleles having the same genotype in both sets over the total set of alleles under evaluation  were computed by counting the number of heterozygous genotypes present in 1 kbp of callable genome and identifying continuous windows with less than 10% of the expected heterozygosity and spanning more than 0.5, 1, and 1.5 Mbp. We assumed an average heterozygosity of 1 per kbp to calculate the expected heterozygosity in a region and imposed that at least 67% of the total length of the ROH had to belong to the callable genome  using prcomp function in R and considering all autosomal SNPs that were not fixed for the alternative allele. To run ADMIXTURE , we incrsoftware , 59. Stasoftware   model t4-ratio statistics was calculated as f4/f4 to estimate the proportion of Neanderthal ancestry present in the X sample. The computation was also performed through the ADMIXTOOLS 4.1 software [We tested for Neanderthal and Denisovan introgression into our whole set of African populations by means of D-statistics, using the ADMIXTOOLS 4.1 software . Additiosoftware


Ochotona roylei), a herbivorous talus‐dwelling species in alpine ecosystem, which forms a major prey base for many carnivores in the Himalayan arc. In this study, we used seven polymorphic microsatellite loci to detect evidence for recent changes in genetic diversity and population structure in Royle's pika across five locations sampled between 8 and 160 km apart in the western Himalaya. Using four clustering approaches, we found the presence of significant contemporary genetic structure in Royle's pika populations. The detected genetic structure could be primarily attributed to the landscape features in alpine habitat  that may act as semipermeable barriers to gene flow and distribution of food plants, which are key determinants in spatial distribution of herbivores. Pika showed low inbreeding coefficients (FIS) and a high level of pairwise relatedness for individuals within 1 km suggesting low dispersal abilities of talus‐dwelling pikas. We have found evidence of a recent population bottleneck, possibly due to effects of environmental disturbances . Our results reveal significant evidence of isolation by distance in genetic differentiation (FST range = 0.04–0.19). This is the first population genetics study on Royle's pika, which helps to address evolutionary consequences of climate change which are expected to significantly affect the distribution and population dynamics in this talus‐dwelling species.Understanding population genetic structure of climate‐sensitive herbivore species is important as it provides useful insights on how shifts in environmental conditions can alter their distribution and abundance. Herbivore responses to the environment can have a strong indirect cascading effect on community structure. This is particularly important for Royle's pika (Lagomorpha:  We analyzed spatial data using the R package “geosphere” , and premonsoon (late May–June 2015). The talus size varies between 10 and 200 m2.2The Royle's pika often feeds on plants with high levels of secondary metabolites .We used 64 nuclear microsatellite loci which have previously been used in population genetic studies of other lagomorph species , observed heterozygosity (HO), and expected heterozygosity (HE) were determined using CERVUS version 2.0  that indicate microsatellite primer's informativeness, mean number of alleles per locus    from 0 suggests that Royle's pika individuals within a specific distance class are significantly related  and not random.We examined the relationship between genetic relatedness and geographic distance using Wang's estimator “r” Wang,  in SPAGer” Wang, . This esr” Wang, . The pror” Wang, . The star” Wang, , and dev2.3.3FST estimations, the analysis of the molecular variance (AMOVA), and isolation by distance (IBD). We included one locus that showed HWE deviation as the deviation was not observed in all populations.We used five different methods to assess the population structure and differentiation in Royle's pika. The first two methods used a clustering approach: STRUCTURE and the discriminant analysis of principal components (DAPC) and three methods were based on distances: K) based on allele frequencies, using Bayesian clustering methodology in STRUCTURE version 2.3.4  were allowed to vary from one to 10 with 50,000 burn‐in iterations and 500,000 Markov chain Monte Carlo (MCMC) iterations  to visualize the STRUCTURE results . STRUCTURE results often fail to detect fine‐scale hierarchical population structure . The clustering identifies groups of populations with similar genotypes by specifying the actual number of clusters (K = 5) as a priori information  obtained for seven microsatellite loci were correlated against log‐transformed geographic distances between talus using a Mantel test with 1,000 permutations in ARLEQUIN.To determine spatial patterns driving genetic differentiation among locations, linearized pairwise 2.3.4The detection of evidence for any population bottleneck was primarily based on Wilcoxon's signed rank test and shift in allelic distribution  and locations . The mean inbreeding coefficient (FIS) was 0.13 and varied from 0.01 to 0.50 across populations  and false allele rate (0.00–0.07) were not significant for any locus. Null alleles were not detected in any population. The mean number of alleles per locus across all populations was 8.42. The mean polymorphism information content (PIC) was high (0.72) and varied from 0.40 to 0.85 indicating that loci were informative in detecting high levels of allelic polymorphism among genotyped individuals. Table . The ave3.2b = −0.22 ± 0.005, p < 0.05) between Wang's estimator (r) and geographic distance suggested neighboring individuals within 1 km distance are more genetically related than any random pair of individuals  at K = 2  to specific clusters. In K = 2, individuals in NAN (97%) and HAK (83.34%) were assigned in genetic cluster “A” and MAD (38.46%), TUN (69%), and RUD (57.14%) were assigned in cluster “B” , followed by 6.05% of the variation among locations, and 6.84% variation among talus within each location Table . TUN and.45%, folr = 0.033, p < 0.001; Figure The Mantel test showed significant evidence for a decrease in genetic differentiation across sampled individuals with an increase in geographic distance (3.4p < 0.05) heterozygosity excess across three locations suggesting that demographic change leads to a recent bottleneck  and high level of pairwise relatedness in populations within 1 km. For a mainland mammal with broad geographic distribution, American pika showed similar patterns with low estimates of heterozygosity, low population density  with high philopatry that suggest life‐history characteristics result in high within‐site inbreeding coefficients  indicated comparatively low genetic differentiation, suggesting more gene flow in recent times. This is in contrast to the pattern observed in the two geographically distant populations (HAK and NAN: 42–160 km), which showed moderate to very high genetic differentiation—so suggesting low gene flow (pairwise FST = 0.04–0.19). Furthermore, isolated mountain valley (cradle shaped) and absence of potential talus habitat near HAK location, which possibly reduces the gene flow from other pika populations, lead to high inbreeding coefficient. The genetic similarities within Royle's pika populations could be attributed to the following factors: (i) patchy distribution of talus habitat with suitable crevices  to those observed in other studies on talus‐dwelling FST estimates due to limitation of STRUCTURE for detecting fine‐scale genetic structure , and microclimate, causing significant variation in snow cover, even across small geographical distances .The GPS locations of all sample collection points have already been added in  Click here for additional data file.


Operophtera brumata), an important defoliator of oak forests across Europe and northern Africa, we previously determined that contemporary populations correspond to genetic diversity obtained during the last glacial maximum (LGM) through the use of refugia in the Iberian and Aegean peninsulas, and to a lesser extent the Caucasus region. Missing from this sampling were populations from the Italian peninsula and from North Africa, both regions known to have played important roles as glacial refugia for other species. Therefore, we genotyped field‐collected winter moth individuals from southern Italy and northwestern Tunisia—the latter a region where severe oak forest defoliation by winter moth has recently been reported—using polymorphic microsatellite. We reconstructed the genetic relationships of these populations in comparison to moths previously sampled from the Iberian and Aegean peninsulas, the Caucasus region, and western Europe using genetic distance, Bayesian clustering, and approximate Bayesian computation (ABC) methods. Our results indicate that both the southern Italian and the Tunisian populations are genetically distinct from other sampled populations, and likely originated in their respective refugium during the LGM after diverging from a population that eventually settled in the Iberian refugium. These suggest that winter moth populations persisted in at least five Mediterranean LGM refugia. Finally, we comment that outbreaks by winter moth in northwestern Tunisia are not the result of a recent introduction of a nonnative species, but rather are most likely due to land use or environmental changes.Numerous studies have shown that the genetic diversity of species inhabiting temperate regions has been shaped by changes in their distributions during the Quaternary climatic oscillations. For some species, the genetic distinctness of isolated populations is maintained during secondary contact, while for others, admixture is frequently observed. For the winter moth ( Here, we find the use of last glacial maximum refugia in southern Italy and North Africa by the forest defoliator winter moth. This suggests that winter moth populations persisted in at least five Mediterranean LGM refugia. Finally, we comment that outbreaks by winter moth in northwestern Tunisia are not the result of a recent introduction of a nonnative species, but rather are most likely due to land use or environmental changes. For exaOperophtera brumata (Lepidoptera: Geometridae). Throughout its distribution, winter moth is a serious pest of many broadleaf tree and shrub species, including oak (Quercus spp.), maple (Acer spp.), and birch (Betula spp.) trees  in northwestern Tunisia beginning in 2009  and pure Q. canariensis oak  forests, with lower densities being found on Q. afares and Q. suber compared with Q. canariensis in Aïn Zena. These differences may be the result of differences in budburst timing of different oak species  by winter moth . Once at the laboratory, larvae were placed in 96% ethanol, and then stored at −80°C prior to molecular analyses. See Table Adult male winter moths were collected using pheromone‐baited traps  following the manufacturer's instructions. For adult males, prior to DNA extraction, the uncus and wings were removed and stored as morphological vouchers. Vouchers were deposited at the Yale Peabody Museum of Natural History, New Haven, Connecticut, USA. Specimen was then homogenized using 3/16″ stainless steel beads (GlenMills Inc.) using a FastPrep‐24 Sample Homogenizer . Twenty‐four polymorphic microsatellite loci were amplified following protocols presented in Havill et al. (geneious v. R11 (https://www.geneious.com). To address potential variation in allele fragment lengths generated during different genotyping runs  of each individual to distinct genetic clusters (K) were estimated using the Bayesian genetic clustering software structure v.2.3.2 , the average number of alleles per locus (Na), the effective number of alleles (Eff_Na), the average observed heterozygosity across loci (Ho), the average heterozygosity within each population (Hs), the total heterozygosity (Ht), the average inbreeding coefficient (GIS), and the presence of deviation from Hardy–Weinberg Equilibrium using genodive v.2.0b27  among populations from each country was estimated were estimated using FreeNA  four loci with especially large allelic ranges  were removed to improve the shape of the cloud of simulated datasets. For both sets of simulations, the following summary statistics were used: one sample; mean number of alleles, mean genic diversity, mean size variance: two sample; FST, classification index, and (dμ)2 distance. Distributions for parameter priors are presented in Table iyABC based on comparisons of 1% of simulated datasets closest to the observed data  multiple population size parameters to allow for changes in population sizes following splitting/merging events; (b) default mutation model parameters, except that the minimum mean mutation rate to 1 × 1033.1After filtering individuals from which less than 20 of 24 polymorphic microsatellites were amplified, ten winter moth individuals collected in Barcelona, Spain, 26 individuals from Ortì, Italy, and 17 individuals from Mzara Forest, Tunisia were included in subsequent analyses.3.2Q) of individuals to major genetic clusters (K) for K = 1 through K = 8 after averaging across independent Structure runs is presented in Figure lumpak are presented in Figure structureharvester indicated that the optimal number of genetic clusters (K) present in the dataset was K = 6  as well as the highest level of observed heterozygosity (Ho = 0.670) while the Mzara Forest, Tunisia population had the lowest number of effective alleles (Eff_Na = 2.817) and the lowest level of observed heterozygosity (Ho = 0.450). As per Andersen et al. . The average value ± standard error (SE) for the null allele frequency was 0.053 ± 0.024. The average ENA‐corrected measure of FST pairwise differences among population pairs was 0.185 ± 0.02. Values for all pairwise comparisons are presented in Table Population genetic statistics for each population are presented in Table n et al. , all sam3.4diyabc analyses indicated that the Ortì, Italy population was most closely related to the Spanish population  during the LGM . Lymantria dispar has been reported at outbreak densities in Tunisian oak forests , Erannis defoliaria (Clerck) (Lepidoptera: Geometridae), and possibly winter moth to grow to outbreak densities (Mannai, Our results provide evidence that the winter moth population sampled in northwestern Tunisia arrived in North Africa during the LGM. Therefore, factors other than a recent biological invasion are likely responsible for the current outbreak. These factors might include reduced interspecific competition, particularly from other oak defoliators such as  Mannai, . In addi Mannai, . Indeed, Mannai, . Environ Mannai, . It is t5Here, we find the use of two additional glacial refugia, North Africa and southern Italy, by winter moth during the LGM. Our results indicate that populations that utilized both refugia during the LGM diverged from a population that migrated into the Iberian Peninsula, and that subsequently each population became genetically distinct. Curiously, our findings continue to support the results from Andersen et al.  that souNone declared.All authors contributed equally to the study design and manuscript preparation. YM, OE, SD, and MLBJ oversaw sample collection. JCA and NPH conducted analyses. AC and JSE oversaw laboratory work. JSE coordinated the research team.https://openscience.com Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://osf.io/yfzh3/?view_only=11a9e5ca6c2743b38abf5aa5fd2bec5f.This article has earned an  Click here for additional data file. Click here for additional data file.


We used a Bayesian approach to determine population groupings without a priori assumptions and performed analysis of molecular variance (AMOVA) and exact tests of population differentiation (ETPD) to examine their validity. Molecular dating was used to establish divergence times.Pleistocene glaciations have had an important impact on the species distribution and community composition of the North American biota. Species survived these glacial cycles south of the ice sheets and/or in other refugia, such as Beringia. In this study, we assessed, using mitochondrial DNA from three Diptera species, whether flies currently found in Beringian grasslands (1) survived glaciation as disjunct populations in Beringia and in the southern refugium; (2) dispersed northward postglacially from the southern refugium; or (3) arose by a combination of the two. Samples were collected in grasslands in western Canada: Prairies in Alberta and Manitoba; the Peace River region (Alberta); and the southern Yukon Territory. We sequenced two gene regions (658 bp of cytochrome Two geographically structured populations were found for all species: a southern Prairie and Peace River population, and a Yukon population. Although AMOVA did not show significant differentiation between populations, ETPD did. Divergence time between Yukon and southern populations predated the Holocene for two species; the species with an ambiguous divergence time had high haplotype diversity, which could suggest survival in a Beringian refugium.Populations of Diptera in Yukon grasslands could have persisted in steppe habitats in Beringia through Pleistocene glaciations. Current populations in the region appear to be a mix of Beringian relict populations and, to a lesser extent, postglacial dispersal northward from southern prairie grasslands. Pleistocene glaciations have left their mark on North America, affecting topography and drainage systems, as well as biota. Changes in climate altered species distributions and community compositions, forcing species southward or into other refugia .Although the ice receded approximately 10,000 years ago, the effects of the last glaciation are still felt: the continent is still rebounding , many spMost species survived past glaciations south of the ice sheets , 8. HoweThe biogeographic patterns of many Beringian taxa have been explored to infer the glacial history of the region e.g., , 5). How. How5]).Artemisia frigida Willd.), bunch-grasses and forbs, these isolated communities are associated with arid, exposed, south-facing slopes and have a unique insect fauna. Although located mostly in the southern Yukon, there are northern outliers on steep slopes near Old Crow and along the Yukon and Firth Rivers [Xeric grasslands in the Yukon Territory and Alaska are rare and distinctive environments. Characterised by prairie sage , 1615, 1Some plant and insect distributions suggest that some species in these grasslands may have a southern origin , 16 whenSolecki et al.  analysedThe objective of this study was to (1) obtain molecular data on population structure of flies in western grasslands  and (2) evaluate these population patterns in the context of glacial and postglacial history. Species associated with these habitats may exhibit population patterns that mirror the postglacial history of the habitat. The Yukon grasslands have been hypothesized to be primarily composed of species that persisted in the Pleistocene Beringian steppe; or species that dispersed northward postglacially due to expanding prairie grasslands which later became disjunct because of mid-Holocene climate change.Diptera populations found in all three grassland regions may have (1) survived glaciation as disjunct populations in Beringia and in the southern refugium; (2) dispersed northward postglacially from the southern refugium; or (3) arisen by a combination of the two. In terms of genetic expectations at the population level, each alternative would have different consequences: (1) would show distinct Yukon haplotypes in a network clearly separated from southern haplotypes; (2) would show haplotypes in the Yukon that are also found in the south, or are linked by short branches to clusters of southern haplotypes; (3) would show a combination of distinct clusters of Yukon haplotypes , as well as other Yukon haplotypes that are linked to clusters of southern haplotypes .Incertella incerta (Becker) (Chloropidae)) has a continuous distribution in multiple habitats, and two (Trixoscelis fumipennis (Melander) (Heleomyzidae) and Meromyza columbi Fedoseeva (Chloropidae)) are disjunct between the southern grasslands and the Yukon. By using species with different distribution patterns, we hoped to characterize the isolation which may have been due to range disjunction to ensure patterns were due to historical rather than landscape factors [Phylogeographic patterns for single species can be difficult to decipher due to factors such as mutation rates, gene flow within populations or variable genetic diversity through time . Therefo factors .c oxidase subunit I (COI) and cytochrome b (Cyt b)) per region, except T. fumipennis in the Prairie region  , and similar values of haplotype and nucleotide diversity  in both the Prairies and Peace River, there were more T. fumipennis haplotypes in the Peace River region (18 versus 11).Haplotype and nucleotide diversity were calculated for each species. Both ty Table . AlthougI. incerta nucleotide diversity decreased from north to south, but haplotype diversity was similar in the two southern regions and decreased in the north. For T. fumipennis, nucleotide and haplotype diversity were highest in the Peace River region and lowest in the Yukon. The overall nucleotide diversity of M. columbi was much lower than that of the other species and nucleotide and haplotype diversity were highest in the north.Overall, M. columbi had a haplotype shared among all three regions  and the Yukon.Haplotype networks were built using statistical parsimony to characterize population patterns and examine them visually. Only ons Fig. b. No hapI. incerta had one shared haplotype between the Prairies and Peace River region , implying that population disjunction also plays a role in forming this pattern. The two other species overall did not show isolation by distance (results not shown).We estimated the number of populations in our dataset without a priori assumptions using a Bayesian approach in the program Geneland . Multiplumptions . Guillotumptions  recommenumptions  suggesteCT)  to test for differentiation among the populations defined by Geneland. The AMOVA showed no significant differentiation between the southern population grouping of the Peace River and Prairie regions, and a Yukon population, for any species  Table .Table 2RAn exact test of population differentiation (ETPD) was performed on this same population structure. The ETPD results were not concordant with AMOVA; there was a significant difference between the southern population assemblage (Peace River + Prairies) and the Yukon population Table . In addiT. fumipennis whose M value was estimated to be infinity . The M values showed that migration between the Peace River region and Prairies was high for all species, especially ty Table . AlthougI. incerta tree)   Fig. .M. columbi tree, Fig. M. columbi tree, Fig. Incertalla incerta   had an HPD interval between 0.16 and 0.7 Mya (mean = 0.67), and the Yukon 2 grouping 0.21–1.29 Mya (mean = 0.98) , with an HPD interval between 0 and 0.54 Mya (mean = 0.38)  Fig. a.Phylogeographic studies have shown that Beringia has been a refugium for species in multiple taxa, including plants, mammals and fish . Our resTrixoscelis fumipennis showed the most distinct division between Yukon and southern populations, with all Yukon haplotypes, except one, in a single group  at least four clades whose divergences can be dated back to glaciation events have persisted through several glacials [Dendroctonus rufipennis Kirby (Coleoptera: Curculionidae), also exhibits two distinct clades in Beringia separated by a more southern clade which suggests secondary contact between both northern clades following glacial cycles [Some phylogeographic studies have found population substructure within Beringia , as we dglacials . The sprl cycles .I. incerta Yukon groups is unclear. The geographic extent of our sampling was limited and does not allow us to determine whether this substructure could be due to populations residing in different refugia, for instance. Many patterns of Beringian population substructure have been detected in different taxa [In the case of our study, the precise reason for a population substructure in the ent taxa  and our M. columbi are also difficult to interpret  and ETPD (significant difference in population structure). When examining disjunct and continuous butterfly species, Bromilow and Sperling  found thBromilow and Sperling  evaluateDivergence time estimates are critical in testing phylogeographic hypotheses , but thiBecause of the Pleistocene-Holocene time frame of our study, recent calibration points would have provided more accurate rate estimates for divergence time . HoweverTo mitigate issues regarding time-dependency due to calibration constraints, we used multiple demographic models depending on whether the data was interspecific or intraspecific . Other mWhile our analyses are concordant with one another, this study was based on two mitochondrial genes, which represent only the matrilineal side and may not fully reflect population history . AdditioOur analyses support the conclusion that populations of Diptera in Yukon grasslands could have persisted in steppe habitats in Beringia through Pleistocene glaciations. Current populations in the region appear to be a mix of Beringian relict populations and, to a lesser extent, postglacial dispersal northward from southern prairie grasslands.Given the limited current extant and potential glacial history of xeric Yukon grasslands, they have been surprisingly understudied. Most research to date has been focused on paleoecological data and current species assemblages, and not on phylogeography or genetic patterns e.g., , 44–46).–46.44–46Connatichela artemisiae Anderson (Coleoptera: Curculionidae), are endemic to eastern Beringian grasslands [Most phylogeographic work with a Beringian scope has been on arctic and alpine organisms . Howeveasslands , and somDiptera were collected from grasslands in three regions in Canada: Prairies , the Peace River region , and southern Yukon Territory 4 sites) (Table  sites (TArtemisia), and other forbs [Artemisia frigida [Vegetation in the Prairies is broadly characterized by grasses (Poaceae), sedges (Cyperaceae), Asteraceae, especially sages  (Chloropidae), a widespread Nearctic generalist saprophagous species, present in habitats between the study regions , a phytophagous western Nearctic grassland species disjunct between the three study regions [Trixoscelis fumipennis (Melander) (Heleomyzidae), an apparently saprophagous species widespread in the southern Nearctic, south of the Peace River region, but disjunct between Peace River and the Yukon Incertellkon Fig. . Althougrn range , in the DNA extraction, amplification and sequencing protocols follow Gibson et al. . Total gc oxidase subunit I (COI) (the DNA barcode) using the forward primer LC01490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) [b using the forward primer CytB-Dipt-11035F (5′-GGNTTYKCNGTNGAYAAYGC-3′) [2O, 2.5 μL 10X Ex-Taq PCR buffer (containing 20 mM MgCl2), 0.625 μL 25 mM MgCl2, 1 μL of each 10 μM primer, 2 μL 10 μM dNTPs, 0.125 μL ExTaq HS DNA polymerase , and 1 μL genomic DNA template. Amplification cycles were performed on an Eppendorf ep Gradient S Mastercycler  as follows: 94 °C for 3 min; 30 amplification cycles of 94 °C for 45 s, 45 °C for 45 s, 72 °C for 1 min; and a final step for 5 min at 72 °C.Two mitochondrial gene regions were targeted and amplified: (1) a 658 bp fragment of cytochrome TTGG-3′)  and the TTGG-3′) ; (2) a 5AYGC-3′)  and the AYGC-3′) . Amplifi® system  as outlined in Gibson et al. [® Terminator v3.1 Cycle Sequencing kit .Amplification products were visualised on 1% agarose electrophoresis gels and target genes were isolated and purified using the E-Geln et al. . PurifieChromatograms for sequences LEM0276023–0276140 were edited and visualised using Sequencher 4.7 . Other chromatograms  were edited and visualised using ChromasPro .Sequences were aligned using Clustal X v.2.0 with default parameters . Overhanh) and nucleotide diversity (π) were calculated for the entire dataset and for each region using the program DnaSP v.5 [All analyses were performed on the concatenated dataset. Mitochondrial haplotype diversity  were converted to UTM. Coordinate uncertainty did not affect results and thus, for the final runs, none was assigned. The analysis was executed for five runs, with 1,000,000 Markov chain Monte Carlo (MCMC) iterations each. Thinning was set at 400. The minimum and maximum value of K (number of populations) was set to 1 and 10, respectively. Burn-in was set to 2000.ST and Kimura’s Two-Parameter Model which accounts for different rates of transition and transversion [Because Bayesian methods can overestimate population structure when there is isolation by distance , we examsversion .ST—within region , ΦSC—within regions among populations and ΦCT—among populations as defined by Geneland. Arlequin was also used to calculate M values using the formula M = (1 − FST)/2FST [Population structures defined by Geneland were tested within an AMOVA framework and with ETPD. Both were implemented in Arlequin v.3.5 . The AMOST)/2FST .Divergence times were calculated with BEAST v.1.7.5  and the The Diptera tree was built using sequences of Cyclorrhapha, Schizophora and Acalyptratae obtained from GenBank  . NucleotDemographic analyses were run using BEAST, for each species separately. All sequences available for the study taxa were used. Tree shape was defined by a Bayesian skyline prior, a variable population size coalescent model . The TmrAdditional file 1. Number of sequences per species per location. Abbreviations as in Table Additional file 2. Coordinates for distribution maps of all 3 species. Each excel sheet contains the coordinates used to create the distribution map of one of the species along with the locality information and source.Additional file 3. GenBank accession numbers for Diptera phylogeny. Additional taxa (with GenBank accession numbers) used for calculating divergence times.


Daphnia magna to different photoperiod lengths co-occurring with warm temperature thereby assessing the impact of earlier spring warming on its fitness. Daphnia uses temperature and photoperiod cues to time dormancy, and to switch between sexual and asexual reproduction. Daphnia life cycle offers the opportunity to measure the relative contribution of plastic and genetic responses to environmental change across generations and over evolutionary time. We use transgenerational common garden experiments on three populations ‘resurrected’ from a biological archive experiencing temperature increase over five decades. Our results suggest that response to early spring warming evolved underpinned by a complex interaction between plastic and genetic mechanisms while a positive maternal contribution at matching environments between parental and offspring generation was also observed.Temperature and photoperiod regulate key fitness traits in plants and animals. However, with temperature increase due to global warming, temperature cue thresholds are experienced at shorter photoperiods, disrupting the optimal seasonal timing of physiological, developmental and reproductive events in many species. Understanding the mechanisms of adaptation to the asynchrony between temperature and photoperiod is key to inform our understanding of how species will respond to global warming. Here, we studied the transgenerational mechanisms of responses of the cyclical parthenogen  Plasticity is expected to play a key role in rapidly changing environments, especially when genetic adaptation may be constrained under predicted global change3. So far, within-generation plasticity (WGP) is advocated as the main mechanism of response to environmental change, allowing for rapid adjustments to novel environmental conditions6. Yet this consensus is subject to a scarcity of studies on the mechanisms of genetic adaptation to environmental change, because of the significant challenges at identifying the genetic elements underpinning adaptive phenotypic trait variation in nature7. Moreover, it is unclear how plasticity impacts long-term evolutionary responses to environmental change, as plasticity can either help9 or hinder10 evolutionary responses. Obtaining evidence that identifies the relative contribution of genetic adaptation and plasticity in the wild represents an ongoing challenge6.Organisms respond to changing environments by shifting their distribution, via genetic adaptation or through plasticity of fitness related traits13. TGP is especially relevant to understand the impact of environmental change that persists across generations. Currently, the term TGP is loosely used to include any non-genetic effects that are observed in the offspring generation, which is associated with exposure of a previous generation to a new environmental condition14. However, TGP may also be adaptive, enhancing offspring performance, by buffering populations against the immediate effects of environmental change and providing time for genetic adaptation to happen in the longer term15. Adaptive transgenerational plasticity, mediated by parental effects, maximizes offspring fitness when the parental and the offspring environments are matched13. Examples of adaptive transgenerational plasticity include the effect of maternal environment on the timing of seed germination16 and shade-avoidance in plants17. Understanding the role of adaptive transgenerational plasticity is key to predict the consequences of parental effects on population dynamics, and to inform our understanding of how species will respond to rapid environmental change18. Presently, the prevalence and strength of TGP in natural systems remains controversial13.Plasticity experienced within a generation may influence the response of following generations via non-genetic or epigenetic processes. This form of plasticity is known as transgenerational plasticity (TGP) and occurs when the environment that individuals experience influences the expression of traits in their offspring20. The asynchronous change of temperature and photoperiod cues has disrupted the optimal seasonal timing of physiological, developmental and reproductive events in many species23. Therefore, many organisms have experienced a decline in fitness when preparing to develop, reproduce, hibernate, enter dormancy, or migrate at seasonally inappropriate times22. Parthenogenetic zooplankton species  use both temperature and photoperiod cues to time life history events such as male production and dormancy24. Generally, short photoperiod and low temperatures induce a switch from the production of clonal (parthenogenetic) females to the parthenogenetic generation of males and to initiating sexual reproduction, which culminates in dormant embryos25. Conversely, long photoperiod and high temperatures are cues for the termination of dormancy in spring22. Dormant embryos are early stage embryos that arrest their development to overwinter; dormant embryos at the water-sediment interface hatch during the following growing season contributing to the genetic diversity of the local population28. A proportion of these embryos becomes buried in the sediment missing the opportunity to hatch thereby creating biological archives, which represent an excellent resource to investigate population dynamics in natural systems over evolutionary time31. Because of the changes in water temperature due to global warming, the temperature cue thresholds are experienced by some parthenogenetic zooplankers at shorter photoperiods altering population-level investment in dormancy and male formation33. However, the impact of persistent asynchrony between temperature and photoperiod across generations is poorly understood. Understanding the transgenerational impact of this asynchrony is helpful to predict how rapidly these species, and the community which they sustain, may track climatic change.Temperature and photoperiod are two pivotal cues that regulate key fitness traits in plants and animals (e.g. flowering time and dormancy). However, whereas climate is changing, photoperiod is stable across latitudesDaphnia magna. Daphnia are keystone species central to the food-web of lentic freshwater ecosystems worldwide35. We study within-generation plastic and genetic responses, as well as the cross-generational responses of fitness-linked life history traits to environmental conditions that mimic early spring warming (warm temperature and short photoperiod) as compared to a typical spring environment (warm temperature and long photoperiod).Here, we study the impact of the asynchrony between temperature and photoperiod on the fitness of the freshwater crustacean Daphnia’s life cycle overcomes these limitations to reveal the relative contribution of plastic and genetic responses to environmental change across generations and over extended time periods37. Daphnia alternates sexual recombination with asexual  reproduction. Sexual recombination results in early stage embryos that can be ‘resurrected’ and propagated indefinitely as clonal lineages in the laboratory38. These properties and a short life cycle provide the advantages of clonal species while retaining the natural genetic diversity. Taking full advantage of these properties, we measured transgenerational fitness changes in three populations of dormant embryos previously resurrected from the biological archive of Lake Ring and spanning 50 years39. We quantified WGP and TGP as changes in trait values of fitness-linked life history traits between environments and across generations. We quantified genetic responses to photoperiod as mean trait differences among populations and as genetic divergence at 15 candidate genes, previously linked to environmentally driven local adaptation41. We also performed a gene-traits association analysis between the life history traits measured in the parental generation and the 15 candidate genes in an effort to identify the genetic basis of fitness-linked life history traits responding to photoperiod and temperature cues. Our study provides important insights into the transgenerational mechanisms of response to early spring warming in a freshwater keystone species.Studies that investigate mechanisms of response to environmental change face two main limitations. If genetically diverse species are studied, the same genotype or a diversity of genotypes cannot be replicated across environmental exposures and transgenerational responses to environmental change are confounded by genetic variation. Conversely, if clonal species are used and genotypes are replicated across experiments, genetic diversity is not representative of sexually reproducing species. D. magna separated in time and previously revived from a biological archive of Lake Ring, a shallow mixed lake (maximum depth is 5 m) in Jutland, Denmark 42. According to historical records43 and paleolimnological analyses38, the lake experienced a steadily, even if modest (∼1 °C), increase in temperature over the last five decades36. Further, changes in water chemistry and transparency occurred over time because of eutrophication and agricultural land use. These changes are described in detail elsewhere39 and summarized in Supporting Information Data We investigate the impact of temperature and photoperiod cues on three populations of Daphnia reproduces parthenogenetically in the laboratory, allowing experiments across generations. Several parthenogenetic broods are produced in each generation (up to eight) allowing the same genotype to be used in parallel experiments. Between nine and eleven genotypes per population  to reduce potentially confounding maternal and grandmaternal effects : short photoperiod  and long photoperiod  .The following fitness-linked life history traits were measured in G1 and G2: size at maturity (the distance between the head and the base of the tail spine), age at maturity (first time parthenogenetic eggs are released in the brood pouch), fecundity (number of juveniles across 8 broods), interval between broods , proportion of male offspring across 8 broods, and mortality. For size at maturity, all animals were measured after releasing their first brood into the brood pouch using imageJ software , all life history trait measurements followed a Gaussian distribution. Because the populations separated in time originate from the same genetic pool and because genetic drift is negligible30, a significant population term indicates genetic differences among populations. Differences in mean trait values between photoperiods, after controlling for maternal effects, are the expression of WGP, an environmental effect. Differences in mean trait values between generations are the expression of TGP. If the effect of the treatment (photoperiod) differed significantly among populations (genetic effect), we would have evidence of a Pop (population) × Pht (photoperiod) interaction. Similarly, if the effect of the treatment (photoperiod) differed significantly among generations, we would have evidence of Gen (generation) × Pht (photoperiod) interaction. If population means varied by generation, we would have evidence of a Gen (generation) × Pop (population) interaction. We also measured the three-way interaction term (Gen × Pop × Pht). All evidence of interactions or main effects were assessed via Type II analysis of deviance tables using the Anova function in the “car” package (R v.3.3.3)45. We visualized the main effects of population , treatment (SP and LP) and generation (G1 and G2) plus their interaction terms on individual life history traits through reaction norms, which describe the pattern of phenotypic expression of each population across treatments and generations46.We assessed evolutionary mechanisms  of response to early spring warming on life history traits using linear mixed models (LMMs) in the “lme4” package in R v.3.3.344). Prior to the PCA analysis, the life history traits variables were log transformed.We performed a principal component analysis (PCA) to quantify the principal modes of variation and covariation among life history traits within generation using the ‘prcomp’ function in the R “stats” package. The PCA plots were obtained using the “ggbiplot”  and population was treated as fixed effect. All mortality curves were plotted using the “survplot” function form “rms” package in R v.3.3.344.Mortality rates per population were calculated with a survival model fit via the “psm” function in the rms R package V.3.3.341 by mapping the sequences of the candidate genes against the reference genome of D. magna 2.4 (NBCI: LRGB00000000.1). The panel of SNPs at the 15 candidate genes is shown in Table The genome of the 30 genotypes used in this study has been sequenced and will be published elsewhere. Here, we use the sequenced genome to identify SNP polymorphisms at 15 candidate genes previously associated with environmentally driven local adaptation, including male formation and the induction of sexual reproductionST) and neutrality tests. Previous results identified a small yet significant proportion of neutral genetic divergence among the D. magna populations studied here (1%)30. Larger divergence among populations at the 15 candidate genes would identify genes under divergent selection, whereas a smaller divergence at the candidate loci would suggest balancing selection. In the former, different polymorphic sites at the same genes are found in different populations, resulting in higher divergence than at neutral loci. Conversely, balancing selection reduces population differentiation, because of moderate to intermediate frequencies of polymorphic sites47.To assess whether the candidate genes were under selection in the populations studied here, we used measures of genetic differentiation  using Arlequin49. The two hierarchical levels used in the analysis are (i) among populations and (ii) within populations. Statistically significant values (P < 0.001) were calculated with permutation tests . Further, we performed neutrality tests on the candidate genes to quantify departure from neutrality using DnaSP50. Typically, these tests are affected by demography. In our study, the three populations are snapshots of the same genetic pool at different time points across five decades. Therefore, demographic confounding factors are negligible.We quantified pairwise population differentiation as F51 with the following criteria: minor allele frequency (MAF) > 0.05 and Hardy-Weinberg Equilibrium (HWE) > 0.001, applying a false discovery rate of FDR = 0.05 to correct for multiple testing. We investigated significant association between gene polymorphisms at the 15 candidate genes and the life history traits in long and short photoperiod, separately. We also quantified association between the gene polymorphisms and the plastic change in life history traits as delta values between long and short photoperiod within G1.To assess whether the candidate genes were associated with life history traits showing responses to photoperiod, we performed a gene-trait association analysis between the candidate gene polymorphisms and life history traits measured in G1, using Mixed Linear Models in TASSELD. magna. Short photoperiod at 20 °C mimicked early spring warming, whereas long photoperiod at 20 °C mimicked a typical spring environment, acting as reference.We investigated the impact of photoperiod and temperature cues on fitness-linked life history traits in We quantified the effects of Generation, Population, Treatment and their interactions on individual life history traits using an analysis of variance. The three-way interaction term (Generation × Population × Photoperiod) was significant for the proportion of male offspring Table . The effShort photoperiod caused a decrease in size and in the time elapsing between parthenogenetic broods in the first experimental generation Fig. ; G1. In age: −53%; PC1size: −62%). PC2 was positively correlated with fecundity (PC2: 48%) and negatively with the proportion of male offspring (PC2: −71%). PC3 was positively correlated with fecundity (PC3: 87%) and negatively with size and age at maturity  , whereas PC2 negatively correlated with fecundity, the proportion of male offspring and the time elapsing between broods . In this generation, PC3 was negatively correlated with the proportion of male offspring (PC3: 89%) and positively correlated with fecundity and average time elapsing between broods   Fig. . In G2, 9%) Fig. .Figure 3The genetic divergence among populations was higher at neutral loci than at the candidate genes in presence of negligible drift Table . The neu54. We used transgenerational common garden experiments on resurrected D. magna populations separated in time to study the mechanisms of response – genetic, plastic  or a combination thereof - to earlier spring warming. Resurrection ecology provided us with the unique advantage of following evolution in situ across decades by studying genetic differences among populations that evolved from an initial genetic pool in response to environmental selection56. The use of genetically distinct clonal lineages across generations enabled us to perform the concurrent analysis of plastic and genetic mechanisms of adaptation to environmental change within and across generations minimizing confounding factors.Investigating both plasticity and genetic adaptation across generations enables to establish a link between evolutionary potential (either additive genetic variation or genotype by environment interactions) and TGPWe observed significant difference in the plastic response to spring warming between generations, made evident from a significant generation term and a significant interaction term Generation × Photoperiod in the ANOVA. Trade-offs among life history traits clearly differed between generations. In G1 the time elapsing between parthenogenetic reproductions showed trade-offs with age and size at maturity, and fecundity showed trade-offs with the proportion of male offspring. In G2, size and age at maturation showed trade-offs with all other life history traits; moreover, the investment in male offspring came at a cost of fecundity and time elapsing between parthenogenetic reproductions.57. For example, elytron length in seed beetles, a highly plastic trait, is strongly influenced by parental effects and has no detectable genetic variance58; body and offspring size in stickleback is strongly influenced by the parental environment but does not show high genetic variance59. Our results suggest a positive maternal effect on earlier spring warming when the offspring environment perfectly matches the maternal one. This effect is evident from fecundity, which is higher in G2 than G1, and from size at maturity that does not change significantly in G2 but significantly decreases in G1 in short photoperiod. However, a full factorial design measuring the anticipatory maternal effect in matching and not matching environments is needed to confirm positive maternal effect in matching environments13. In addition, the photoperiod cue experienced by the second generation during fertilization may have influenced the phenotypic response of the offspring generation, result of developmental plasticity. The timing at which the second generation directly experience the parental environment has been shown to influence offspring response in matching environments61. In particular, exposure during fertilization and early juvenile development may strongly affect offspring response to parental environments2. Follow up experiments, in which the parental generation is exposed for different lengths of the life cycle to early spring warming, is needed to exclude whether exposure during fertilization influenced offspring response to early spring warming.Theory predicts that highly plastic traits show strong maternal effect variance and little to no genetic variance, because highly plastic traits can be influenced by the environment, including parental environment, and because additive genetic variance may be masked by high environmental variationWe observe a strong role of WGP in response to earlier spring warming, made evident from significant photoperiod effect on the majority of life history traits. The effect of photoperiod resulted in smaller size at maturation, delayed maturation and a decrease in the time lapse between parthenogenetic reproductions in G1, whereas it resulted in higher fecundity, and an increase in the time between parthenogenetic reproductions in G2.Daphnia populations were sampled has a documented increase in average ambient temperature and recurrence of heat waves over time36. Previous studies on these populations showed evidence for temporal evolution of the critical thermal maximum (CTmax), the upper temperature at which animals lose motor function62, in presence of warming as a single stress63. Here, we observe significant evolutionary differences among populations to early spring warming cues (G1). These evolutionary differences indicate that the most recent population evolved to be less fecund than the historical populations. This may be the result of maladaptation or of evolutionary responses to multiple stressors. It is possible that multiple stressors documented in Lake Ring (e.g changes in water transparency and lake chemistry39) have influenced adaptive responses of the population over time64. Indeed, in a previous study on the populations of Lake Ring, higher CTmax in the most recent population was observed in presence of warming alone, whereas it was no longer observed in presence of multiple stressors63.We observe genetic responses to early spring warming quantified as genetic differences among populations in fecundity. Moreover, we observe a lower than neutral genetic divergence at the candidate loci, which were significantly departing from neutrality expectations. The lake from which the 30, the candidate genes show a departure from neutrality and a lower divergence among populations than neutral markers. These patterns are expected when balancing selection affects the frequency of gene polymorphism across populations47. The candidate genes putatively under selection in the studied populations have been previously linked to functions in crustaceans and insects relevant in the context of dormancy, sexual reproduction, male formation and general stress responses: Rhodopsin has been associated with dormancy in Daphnia41; SAP18 has been associated with embryonic development and environmental stress response in insects66; PTB  has been linked to sexual differentiation in insects and crustaceans68; MF, a catalyser for the methyl farnesoate and a putative juvenile hormone in daphniids, and AKR  have been linked to the propensity to form male offspring70. In the MF pathway, the AKR family catalyses the conversion of farnesal to farnesol, and mutations in the AKR gene have been shown to reduce MF production in favour of the juvenile hormone, resulting in higher male production71. The function of these genes and their significant association with life history traits in the population studied here suggest their potential role in the response to photoperiod and temperature cues. However, it is possible that other environmental stressors, which have been documented in Lake Ring, may have contributed to the observed response at the candidate genes and/or that additional genes not investigated here are contributing to modulate the life history traits analysed. Indeed, all traits showed significant association with multiple candidate genes suggesting epistasis among genes underpinning the life history traits. Furthermore, single genes showed association with multiple traits, suggesting extensive pleiotropy. It is expected that multiple genes regulate complex fitness-linked life history traits. In a follow up experiment, we generated further evidence supporting this expectation (Supporting Data 72. We focused on the propensity to form males because some of the candidate genes studied here have been previously associated with this trait70. Fixed polymorphisms among the strains with divergent propensity to form male offspring would suggest that the candidate genes are underpinning this trait. Lack of evidence for fixed polymorphisms at the candidate genes would suggest that other genes underpin male offspring formation. We found that the divergent patterns in propensity to form male offspring in the four genotypes did not correspond to fixed polymorphisms at the 15 candidate genes, confirming pleiotropy and the need to investigate genome-wide patterns to identify the genes underpinning complex fitness traits (Supporting Data In the presence of stable neutral genetic diversity over timeDaphnia to evolve in response to early spring warming mediated by a complex interaction between plastic and genetic mechanisms. The results also suggest a positive maternal effect in presence of matching environments between parental and offspring generations. A positive maternal effect at a life history trait indicates accelerated rates of microevolution that can facilitate adaptation73. However, a full factorial design is needed to assess whether non-matching environments provide different responses.Overall, our results point to an adaptive potential for a keystone zooplankter 76. This finding supports previous experimental results showing that populations in complex environments can overcome fitness cost exhibiting synergistic pleiotropy74.We identified extensive pleiotropy in candidate genes underpinning life history traits responsive to early spring warming. Pleiotropy is commonly observed in complex environments with multifarious selection pressures acting on multiple aspects of the phenotypes, resulting in trade-offs among competing functionsSupporting Tables and FiguresTable S2Table S4SUPPORTING DATA 1