Indigenous populations of the Tibetan plateau have attracted much attention for their good performance at extreme high altitude. Most genetic studies of Tibetan adaptations have used genetic variation data at the genome scale, while genetic inferences about their demography and population structure are largely based on uniparental markers. To provide genome-wide information on population structure, we analyzed new and published data of 338 individuals from indigenous populations across the plateau in conjunction with worldwide genetic variation data. We found a clear signal of genetic stratification across the east-west axis within Tibetan samples. Samples from more eastern locations tend to have higher genetic affinity with lowland East Asians, which can be explained by more gene flow from lowland East Asia onto the plateau. Our findings corroborate a previous report of admixture signals in Tibetans, which were based on a subset of the samples analyzed here, but add evidence for isolation by distance in a broader geospatial context. EGLN1 (egl-9 family hypoxia-inducible factor 1) and EPAS1  [The Tibetan plateau covers a vast geographic area stretching roughly 2,500 km in east-west direction and 1,000 km in north-south direction, corresponding to a quarter of the size of the United States. It is home to several million ethnic Tibetans as well as other ethnic groups. Recent genomic studies of ethnic Tibetans have focused on their adaptations to high-altitude hypoxia, and have discovered oxygen homeostasis genes harboring strong signatures of recent positive selection, such as otein 1) . The genotein 1) ,5,6,7. Ootein 1) ,8. Whileotein 1) , the othotein 1) . The resotein 1) ,12,13,14We generated novel genome-wide variation data for 53 Tibetans from highland Nepal and compiled these new data with published genome-wide variation data from 285 Tibetans (including Sherpa) and with world-wide population data from the Human Genome Diversity Panel  and the ADMIXTURE [f3 statistic which is negative when the target population can only be modeled as a mixture of groups related to the two reference samples [f3 statistic with another Tibetan and non-Tibetan groups as references  separated the Tibetan samples from other East Asian samples across PC1 . PopulatDMIXTURE  shows thDMIXTURE  in the f samples . All Tibr = 0.73, Mantel test p < 0.001; r = 0.68–0.83, Mantel test p < 0.001), confirming that this pattern is not driven by outlier samples. In contrast, there was no such correlation with latitude . Allowing for long-range gene flow in the model did not detect substantial long-range gene flow into any of the Tibetan samples  and found nearly identical geogenetic locations for all populations (r2 ≥ 0.99). Mirroring geography, geogenetic longitudes showed strong correlations with D statistics , which infers an “effective migration surface” across a spatial grid from a pairwise genetic distance matrix . As in tTo summarize, our results clearly show that a longitudinal cline is a major feature of Tibetan population structure and that this cline is likely to be an outcome of gene flow with lowland East Asians, the magnitude of which increases as one moves eastwardly . Our finPrevious studies of the genetic history of Tibetans focused on the presence of the Y chromosome haplogroup D, which is shared with Ainu of Japan, but is otherwise absent in East Asia . More reIn this study, we used newly generated genome-wide genotype data of 53 unrelated ethnic Tibetan individuals from high altitude regions in the Himalayas, Nepal. Tibetan participants were recruited from two districts during spring and summer of year 2012: 23 individuals are from Tsum region in Gorkha district and 30 individuals are from Upper Mustang region in Mustang district. All participants were born and raised in high altitude regions . These 53 individuals are a subset of a bigger cohort recruited at the same time, and selected for this study based on harboring negligible level of South Asian ancestry. Saliva samples were collected using OG-500 Oragene saliva collection kits  and genomic DNA was extracted using PT-L2P reagents  following manufacturer’s protocol. Genome-wide genotyping experiments were performed at the Genomics facility at the University of Chicago, using both Illumina HumanCore v1-0  and HumanOmniExpress-24 v1.0  arrays. Illumina GenomeStudio genotyping module was used for calling genotypes from intensity data, using default parameters  and cluster files provided by the manufacturer. All study participants provided written informed consent. This study was approved by the IRBs at Case Western Reserve University and University of Chicago, by the Oxford Tropical Research Ethics Committee and by the Nepal Health Research Council.n = 938) genotyped on Illumina 650Y array [IMPUTE2 imputation reference format , and published Tibetan and Sherpa data (n = 285), genotyped on various Illumina and Affymetrix genotyping arrays [http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/liftOver. Autosomal biallelic SNPs were used in the analysis, after removing A/T and G/C SNPs for strand ambiguity. For HGDP data, we included SNPs only if all populations have less than 2 (for populations with ≤ 30 samples) or 3 (for populations with > 30 samples) missing genotypes. For Tibetan data, we removed individuals and SNPs with > 5% missing genotypes and SNPs with Hardy-Weinberg p-value < 0.000001 for each study. We randomly removed one individual from three pairs of genetic relatives in Tibetans closer than first cousins. Data filtering and relatedness estimation were performed using PLINK v1.90b3j [We compiled published genome-wide variation data of world-wide populations, focusing on Tibetan samples. Specifically, the following data sets were combined: Human Genome Diversity Panel (HGDP) samples  from 2 to 11, we ran 50 replicates with different random seeds and chose one for each K with the highest log likelihoods (LL) as the best run. For all K values, top 10 runs have similar LL values within difference of 1, showing convergence to global optimum. K = 9 was chosen as the best K values based on the smallest value of five-fold cross validation error. Three Tibetan samples  showed genetic affinity with lowland East Asians much higher than the rest of their cohorts and therefore excluded from further population-based analyses. One Papuan and two Yi individuals from HGDP were also excluded as outliers.Principal component analysis (PCA) was performed using the smartpca program version 10210 in EIGENSOFT 5.0.1 package . 62,691 RE v1.22 . For thif3 statistics were calculated for all population sets using qpDstat and qp3Pop programs in the ADMIXtools v2 package [6 generations and a long run of 107 generations was followed using estimates from the last generation of the best fast run as initial values. A sample was taken in every 104 generations, resulting in 1,000 samples for estimating posterior distribution of each parameter. For models inferring geogenetic location of populations, geographic location was used as a prior. We also applied the EEMS program [m below) and local diversity . In effect, this is done by comparing the expected distances induced by the migration rates with an observed genetic distance matrix, and a posterior distribution is inferred using Markov Chain Monte Carlo (MCMC). For all analyses, we performed 10 replicate runs, each of which consisted of a burn-in of 1,000,000 iterations, and recorded 250 iterations each at a thinning proportion of 0.1%. We then collated the samples from all MCMC-chains and produced contour plots of the posterior means of each parameter over space.Patterson’s D and  package . Standar package . The Spa package  was used program  to our dS1 FigPC1 and PC3 are plotted. PC3 shows divergence of Sherpa individuals away from the rest of Tibetans, due to strong genetic drift they experienced. Colored circles mark mean positions of populations. Numbers in parenthesis represent proportion of total variation explained by each PC.(TIF)Click here for additional data file.S2 FigFive-fold cross validation was lowest at K = 9. At this K value, Tibetan individuals harbor distinct components as their major ancestry, i.e. orange in Sherpa and green in other Tibetans. However, variable proportions of their ancestry are from components most concentrated in non-Tibetan East Asians, such as navy and blue ones, which are most concentrated in Dai and Yakut, respectively.(TIFF)Click here for additional data file.S3 FigNo substantial deviation from zero was observed when populations with no East Asian ancestry were used as an outgroup, strongly supporting Tibetan cladeness against them. In contrast, D statistics significantly deviated from zero when any of East Asian populations were used as an outgroup.(TIF)Click here for additional data file.S4 FigIn contrast to longitude, there was no correlation between latitudinal distance and genetic affinity with lowland East Asians. The grey line shows a least square fit and vertical bars represent ± 1 SE.(TIF)Click here for additional data file.S5 Fig(A) A SpaceMix model using fixed geographic locations does not explain the observed pattern of genetic covariance. (B) A SpaceMix model with inferred “geogenetic” location well fits the observed pattern of genetic covariance decay.(TIF)Click here for additional data file.S6 FigSimilar to geographic longitude, geogenetic longitude was strongly correlated with genetic affinity with lowland East Asians. Geogenetic latitude was also correlated with D statistics, but a cohort from southeastern margin of the plateau (“Yunnan”) mainly drove this signal. Pairs including Yunnan Tibetan are marked with grey square dot. The grey line shows a least square fit and vertical bars represent ± 1 SE.(TIF)Click here for additional data file.S7 Fig(A) All East Asian and Tibetan samples and (B) all Tibetan samples and Naxi, Yi and Tu. The model estimates show a good fit with the observed data.(TIFF)Click here for additional data file.S8 Fig(A) All East Asian and Tibetan samples and (B) all Tibetan samples and Naxi, Yi and Tu. Brown and blue colors represent areas of low and high gene flow, respectively. Barriers to gene flow were estimated around the Himalayas and, to a lesser extent, between central and eastern Tibet. Lhasa, the capitol of Tibet Autonomous Region, shows an increased connection in comparison to the surrounding area.(TIFF)Click here for additional data file.S9 Fig(A) 32 East Asian cohorts with minimum sample size of 8. (B) 12 East Asian cohorts with minimum sample size of 19. For each, we randomly sampled the corresponding number of samples (either 8 or 19) to match sample size across all populations. Tibetan cohorts from Nepal , together with Yakut from southern Siberia, show elevated LD, reflecting strong genetic drift they experienced.(TIF)Click here for additional data file.S10 FigPairwise distance and D statistic were calculated between all pairs Tibetan cohorts and nearby populations . (A) A correlation between longitudinal distance and D statistic was well maintained after including three non-Tibetan populations. (B) No latitudinal correlation was found even after including three non-Tibetan populations. Vertical bars represent ± 1 SE. Grey dots represent pairs including non-Tibetan populations, with squares, triangles and circles representing Naxi, Yi and Tu, respectively.(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


The GV with the lowest variability was rs36068923 (VI=1.07). The minor allele frequencies (MAFs) were 0.189, 0.192, 0.256, 0.183 and 0.194 for EAS, EUR, AFR, AMR and SAS, respectively. The GV with the highest variability was rs7432375 (VI=9.46). The MAFs were 0.791, 0.435, 0.041, 0.594 and 0.508 for EAS, EUR, AFR, AMR and SAS, respectively. When we focused on the EAS and EUR population, the allele frequencies of 86 GVs significantly differed between the EAS and EUR (P<3.91 × 10−4). The GV with the highest variability was rs4330281 (P=1.55 × 10−138). The MAFs were 0.023 and 0.519 for the EAS and EUR, respectively. The GV with the lowest variability was rs2332700 (P=9.80 × 10−1). The MAFs were similar between these populations . Interestingly, the mean allele frequencies of the GVs did not significantly differ between these populations (P>0.05). Although genetic heterogeneities were observed in the schizophrenia-associated GVs across ethnic groups, the combination of these GVs might increase the risk of schizophrenia.Schizophrenia is a common polygenetic disease affecting 0.5–1% of individuals across distinct ethnic populations. PGC-II, the largest genome-wide association study investigating genetic risk factors for schizophrenia, previously identified 128 independent schizophrenia-associated genetic variants (GVs). The current study examined the genetic variability of GVs across ethnic populations. To assess the genetic variability across populations, the 'variability indices' (VIs) of the 128 schizophrenia-associated GVs were calculated. We used 2504 genomes from the 1000 Genomes Project taken from 26 worldwide healthy samples comprising five major ethnicities: East Asian (EAS:  The largest GWAS in the Schizophrenia Working Group of the Psychiatric Genomics Consortium (GWAS PGC-II), including 36 989 patients with schizophrenia and 113 075 controls, has identified 128 linkage disequilibrium (LD)-independent variants across 108 genomic loci.7 However, most of these participants were of European (EUR) ancestry. The second most common ethnic population included case–control samples from East Asia (1866 cases and 3418 controls).Schizophrenia is a common, complex psychiatric disease with a lifetime prevalence of ~0.5–1%ZNF804A, NRGN, VRK2 and ITIH3/4 genes7 are found in both EUR9 and Asian12 patients with schizophrenia; however, the significance levels of these associations in Asian populations are marginal but not significant across the genome. In contrast, GV rs115329265 in the major histocompatibility complex region on chromosome 6, the most significantly associated GV in schizophrenia,7 is not polymorphic in the Japanese population, according to the 1000 Genomes Project (1000GP: http://browser.1000genomes.org/index.html). This finding appears to contradict the evidence, suggesting that schizophrenia affects ~0.5–1% of individuals across distinct populations. We hypothesized that the sum of the allele frequencies of the 128 GVs would not differ across ethnic populations; however, the frequencies of each GV would differ across populations.These 128 LD-independent schizophrenia-associated GVs contribute to the risk of schizophrenia across distinct populations. For example, the schizophrenia-associated GVs in the 13 The 1000GP, which was conducted between 2008 and 2015, sought to study these variations in many people; in doing so, it has provided a solid foundation upon which to build understanding of the genetic variation in humans.15 The 1000GP consortium has analyzed 2504 genomes across 26 populations from five continental regions (East Asians (EAS), EUR, Africans (AFR), Americans (AMR) and South Asians (SAS)), by using a combination of low-coverage whole-genome sequencing, deep exome sequencing and dense microarray genotyping. The 1000GP has demonstrated that a typical genome differs from the reference human genome at between 4.1 million and 5.0 million sites.15 The total number of observed non-reference sites differs greatly across populations.15 Individuals of AFR ancestry harbor the greatest number of variant sites among the five ethnic populations. In addition, individuals from recently admixed populations show great variability in the number of variants. The present study tested the genetic variability of the 128 LD-independent schizophrenia-associated GVs, including SNPs and indels, detected by using the most recently available data from the GWAS PGC-II7 with regard to the five ethnic populations, particularly EAS and EUR, studied in the 1000GP.The human genome consists of three billion bases and over 88 million GVs (including 84.7 million single-nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels) and 60 000 structural variants), which can differ between any two genomes in different people.15 The healthy individual genomes are divided into five major ethnic populations, EAS (n=504), EUR (n=503), AFR (n=661), AMR (n=347) and SAS (n=489), which were included in the current study and accessed via the 1000GP Phase 3 Browser (http://browser.1000genomes.org/index.html). EAS consists of Chinese Dai in Xishuangbanna, China (CDX); Han Chinese in Bejing, China (CHB); Southern Han Chinese (CHS); Japanese in Tokyo, Japan (JPT); and Kinh in Ho Chi Minh City, Vietnam (KHV). EUR consists of Utah residents with northern and western European Ancestry (CEU), Finnish in Finland (FIN), British in England and Scotland (GBR), Iberians in Spain (IBS); and Toscani in Italy (TSI). AFR consists of African Caribbeans in Barbados (ACB); Americans of African Ancestry in Southwest USA (ASW); Esan in Nigeria (ESN); Luhya in Webuye, Kenya (LWK); Mandinka in the Gambia (MAG); Mende in Sierra Leone (MSL); and Yoruba in Ibadan, Nigeria (YRI). AMR consists of Colombians from Medellin, Colombia (CLM); Mexicans from Los Angeles USA (MXL); Peruvians from Lima, Peru (PEL); and Puerto Ricans from Puerto Rico (PUR). SAS consists of Bengali from Bangladesh (BEB); Gujarati Indians from Houston, TX (GIH); Indian Telugu from the UK (ITU); Punjabis from Lahore, Pakistan (PJL); and Sri Lankan Tamils from the UK (STU). Demographic information for the participants is shown in 7 the 128 LD-independent schizophrenia-associated GVs were extracted from these populations using the 1000GP Phase 3 Browser.The 1000GP is the largest public catalog of human variation and genotype data, comprising 2504 human genomes from 26 ethnic populations.http://www.r-project.org/). We defined a variability index (VI) to investigate the genetic variability of the 128 LD-independent schizophrenia-associated GVs among the five ethnic populations by using the following formula:All statistical analyses were performed using SPSS 21.0  and R 3.1.1  weighted for the sample size ) in each ethnic population and χ2 or Fisher’s exact tests. The mean allele frequencies of the GVs between these populations were analyzed using non-parametric Mann–Whitney U-tests. To control for type I error , P-values less than 3.91 × 10−4 were considered to be significant (α=0.05/128 GVs).where X20, 101=0.50, P=0.96). The mean VIs of the total GVs and highest and lowest chromosomes were 4.34±1.66 (n=122); the variability was 5.19±1.89 on chromosome X (n=3) and 2.84±1.20 on chromosome 20 (n=2). In addition, according to the GWAS rank of each GV , suggesting that the VI of each GV is not associated with its significance with schizophrenia in GWAS PGC-II.First, we investigated the genetic variability of 128 independent schizophrenia-associated variants among five major ethnic populations by using the VI. Exactly 122 of the 128 GVs were found in the 1000GP Phase 3 Browser. As shown in  each GV , the sch7 The allele frequencies of 86 GVs significantly differed between the EAS and EUR populations . The T-allele frequencies were 0.023 and 0.519 in the EAS and EUR populations, respectively. The GV with the lowest variability was rs2332700 on chromosome 14 . The C-allele frequency was similar between the two populations, 0.246 and 0.247 in the EAS and EUR populations, respectively. Compared with randomly selected 128 GVs  from genomic regions 1 Mb away from the schizophrenia-associated 128 GVs , the schizophrenia-associated GVs showed marginally higher genetic variability between the EAS and EUR populations . Interestingly, the mean allele frequencies of 122 GVs did not statistically differed between these populations . As each GV does not contribute to pathogenesis of schizophrenia with same effect sizes, the mean allele frequencies were calculated by weighing the allele frequency by the logarithm of the OR (log OR) of each variant in the original GWAS.7 This finding did not change after modifying the log OR of each variant .Next, we focused on the genetic variability of the schizophrenia-associated variants between the EAS and EUR populations utilized in our first analysis because these groups represented the major ethnicities that participated in a previous GWAS.7 among five major ethnic populations: EAS, EUR, AMR, AFR and SAS. To compare genetic variability among ethnic populations, we calculated a VI. The VIs of the GVs ranged from 1.07 to 9.46. We successfully detected GVs with high or low genetic variability by using the VI. When we focused on the genetic variability between the EAS and EUR populations (the major ethnic groups included in the previous GWAS),7 ~70% of the allele frequencies of the schizophrenia-associated GVs significantly differed between these populations. However, the mean allele frequency of the GVs did not differ between these populations. Consistently with the findings of polygenic risk score studies,17 our results suggest that the sum of the GVs contributes to the pathogenesis of schizophrenia across ethnic populations.To the best of our knowledge, this study is the first to examine the genetic variability of the 128 LD-independent schizophrenia-associated GVs detected by the GWAS PGC-IIn=4), AFR (n=14) and SAS (n=4) populations. Four GVs with MAFs<0.01 were shared by these ethnic groups. Given that the genetic risk for schizophrenia is due to many GVs with small effects, a cumulative GV effect might be associated with the pathogenesis of schizophrenia, rather than each genetic effect individually. However, as shown in Several GVs showed genetic heterogeneity across ethnic populations. GVs with MAFs<0.01 were identified in the EAS , genetic heterogeneities were nevertheless observed. Some of these risk GVs may exert as the onset of schizophrenia in a specific environmental backgrounds, such as climate and infection exposures. Given that environmental exposures as well as individual common genetic risk variants confer risk of schizophrenia, gene–environment interactions (G × E) could have an important role in the etiology of schizophrenia.19 Further studies are needed to reveal G × E involving these GVs detected in the GWAS PGC-II.As predicted by the out-of-Africa model of human origin,9 Numerous genome-wide significant variants within the major histocompatibility complex region have been identified. However, it is difficult to analyze this region because of its high LD and ethnic heterogeneity.21 We hypothesized that the rs115329265 GV in this region would show high genetic variability among the ethnic populations, and GVs on chromosome 6 would have higher genetic variability than those on other chromosomes. As expected, the rs115329265 GV showed high genetic heterogeneity (VI=5.16). The MAFs of this variant were 0.022, 0.151, 0.445, 0.146 and 0.088 in the EAS, EUR, AFR, AMR and SAS populations, respectively. The VI of this variant was the 36th highest of 122 GVs. In contrast, the GVs on chromosome 6 (VI=4.42) did not show significantly higher genetic variability than those on other chromosomes (VI=4.34). Furthermore, no specific chromosome with high genetic variability was identified among the ethnic populations.The major histocompatibility complex on chromosome 6 is one of the strongest and most persistently well-replicated regions associated with schizophrenia according to previous GWASs.7 most associated variants were not in LD with known protein-coding variants, splice sites or 3’/5’ untranslated regions. In general, SNPs associated with common diseases and phenotypes identified by previous GWASs are enriched in regulatory regions of the genome.23 These findings suggest that most GWAS-detected SNPs contribute to disease susceptibility by altering gene expression rather than the protein structure. Therefore, careful examinations of gene expression and its relationship to GVs have become a critical step in elucidating the genetic basis of schizophrenia.28For the majority of these 108 loci, the molecular mechanisms that underlie susceptibility to schizophrenia are unknown. Although 75% of the 108 loci harbor protein-coding genes and 40% harbor a single gene,The current study sought to identify genetic variability in schizophrenia-associated GVs detected by a previous GWAS (PGC-II) among five major ethnic populations. As expected, numerous GVs showed genetic heterogeneities among these populations. In particular, 86 of 122 GVs showed significant genetic heterogeneities between the EAS and EUR populations. However, a composite of these GVs did not differ between these populations. Our findings suggest that the cumulative effect of GVs contributes to the risk of schizophrenia across ethnic populations.


Streptopelia turtur, is a warm-temperature adapted species and a long distance migrant that uses multiple flyways to move between Europe and Africa. Despite being abundant, it is categorized as vulnerable because of a long-term demographic decline. We studied the demographic history and population genetic structure of the European turtle dove using genomic data and mitochondrial DNA sequences from individuals sampled across Europe, and performing paleoclimatic niche modelling simulations.Understanding how past climatic oscillations have affected organismic evolution will help predict the impact that current climate change has on living organisms. The European turtle dove, Ne) expansion that occurred between the late Pleistocene and early Holocene, followed by a decrease in the Ne that started between the mid Holocene and the present. Our niche modelling analyses suggest that the variations in the Ne are coincident with recent changes in the availability of suitable habitat.Overall our data suggest that this species is panmictic across Europe, and is not genetically structured across flyways. We found the genetic signatures of demographic fluctuations, inferring an effective population size  contains supplementary material, which is available to authorized users. Climate oscillations during the Quaternary strongly affected organismic evolution worldwide e.g. –3). The . The 3])Ciconia ciconia, [Circus pygargus, [Catharus ustulatus, [Phylloscopus trochiloides, [Around 2 billion birds migrate between Europe and Africa each year , crossinciconia, ; Montaguygargus, . Similartulatus, ; the Asiiloides, ). In somiloides, . The preiloides, .Streptopelia turtur (hereafter turtle dove), is the only columbid that takes part in the African-European migration system [The European turtle dove, n system . The spen system . The turn system . Mark-ren system . Birds tn system . Both thHere we studied the demographic history and population genetic structure of the turtle dove across Europe. We performed a genome wide scan using double digest restriction-site associated DNA sequencing (ddRADseq) and obtained mitochondrial DNA sequences (cytochrome b). Demographic history analyses were complemented with niche modelling analysis to test if variations in the effective population size are correlated with past changes in the availability of suitable habitat. Based on the ecological requirements of our focal species, we expected to find evidence of large effective population sizes during warm interglacial periods, followed by small effective sizes during cold glacial periods. We would expect to observe a similar trend in the availability of suitable habitat. We also hypothesized that the current demographic decline in the turtle dove would be strong enough to show the genetic signature of a decrease in its effective population size. Finally, we tested whether the alternative flyways described for the turtle dove actually correspond to migratory divides promoting intraspecific genetic diversification. In that case we expect to find three genetically differentiated groups associated to each flyway .n = 18, breeding season), Spain , Germany  and France ; the Central flyway (CF) with samples from Italy  and Malta ; and the Eastern flyway (EF) with samples from Bulgaria  and Greece . Sampling took place between 2011 and 2014. Tissue samples were taken from wings of hunted individuals, and blood samples were taken from the brachial vein of individuals either caught at their breeding sites or during migration while on a flyway. Tissue samples were preserved in ethanol, and blood samples were preserved either in ethanol or on FTA cards . A table showing more details of the analysed samples can be found in the additional files section . Primers used were specifically designed for this study: F_cytb_St: 5′-TGATAACTCAAATCCTAACTGGTC-3′ and R_cytb_St: 5′-TTGTTTTCTAGGGCTCCGAT-3′. Further details on the ddRADtags and cytochrome b amplification laboratory methods are provided in the additional files section  and quantified with a Qubit fluorometer . Double-digest restriction-site associated DNA markers (ddRADtags) were generated following Peterson et al., . We alsohttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/), we trimmed the final 29 bp to discard lower-quality base calls using FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). We filtered reads with at least a single base call with a Phred quality score below 10 , and/or more than 5 % below a Phred score of 20 . We demultiplexed reads with the process_radtags program from the STACKS v1.20 [The samples were sequenced on two Illumina HiSeq 2500 lanes that produced a total of ~304 million, 151 bp single-end reads. After assessing quality using FASTQC (Columba livia genome (assembly 1.0) with BOWTIE2 v2.2.2 [populations, retaining loci that were present in every sampling locality, had less than 20 % missing data and a coverage of at least 20x. These settings produced 3,012 RAD loci, 2,934 of which had at least one SNP. When a RAD locus had more than one SNP, we selected the first one for downstream analyses to avoid using tightly linked SNPs.We obtained 1.03 ± 0.46 million, 115 bp quality-filtered sequences per individual, which were aligned to the 2 v2.2.2 . The aveSummary statistics of levels of polymorphism (i.e. haplotype and nucleotide diversity) were obtained with DNASP v5.10.1 . We obtaK) that better explained our data. Variational Bayesian inference aims to solve the problem of inference as an optimization rather than a sampling problem, a key advantage of this method is that it runs faster while achieving similar levels of accuracy than other available algorithms [simple prior testing from K = 1 to K = 10. Afterwards, three independent runs with different starting random seeds were performed with the logistic prior, testing from K = 1 to K = 3; the usage of the latter prior is advised when genetic structure is difficult to determine or weak [Q-values) for the value of K that better explained our data were graphed with DISTRUCT v1.1 [dudi.pca, from the ADE4 R package [Genetic structure was investigated using two kinds of approaches; model and non-model based methods. For a model-based approach we employed the variational Bayesian inference method implemented in fastSTRUCTURE , to invegorithms . We perf or weak . During UCT v1.1 . For non package . An AMOV package  followin package  included package .Ne). The Ne is defined as the population size that is evolutionary relevant  [Fs and Tajima’s D; the significance for each test was estimated after 1,000 permutations.We searched for signatures of past population expansion or contraction in the turtle dove effective population size  . We usedviduals)  to invesb data and sequences from 29 randomly selected nuclear loci. All partitions were unlinked and we implemented a strict clock model, with a substitutions rate of 2.1 % per million years as estimated by Weir and Schluter [b gene in birds. We ran the analysis using the “Coalescent: Extended Bayesian Skyline” tree prior for 100 million generations, sampling every 4,000. We inspected the results in Tracer version 1.6 [b sequences; all three runs produced similar results.We also explored the demographic history of the turtle dove by performing an Extended Bayesian Skyline plot (EBSP), as implemented in BEAST version 1.8.2 . We did Schluter  for the sion 1.6 , checkinNe variation in time (t), see Fig. 6 simulations (1 million simulations per tested scenario). The PCA function embedded in DIYABC was used to test the goodness-of-fit of the observed data against that generated by the simulated scenarios. The relative posterior probability was then estimated for each scenario with the direct and logistic regression approaches, on 0.1 and 1 % respectively, of the simulations closest to the observed data set. Finally, we estimated the posterior distribution of parameters for the most likely scenario; we assumed a generation time of one year for the turtle dove [An Approximate Bayesian Computation (ABC) analysis was performed using DIYABC v2 . ABC mettle dove  to scaleMaxEnt 3.3.3 k , with the Bioclim data sets (http://worldclim.org/) for the present, the Mid-Holocene , the Last Glacial Maximum , and the last interglacial . MaxEnt t is a program for modelling ecological niches from presence-only species records . We commum LGM, 2,000 ybpb sequences were highly polymorphic, with 73 segregating sites and 40 haplotypes found among 95 turtle dove individuals (haplotype diversity (Hd): 0.92; nucleotide diversity (π): 0.0075). Our median joining network suggested two main haplogroups, differentiated by six mutational steps  and the model complexity that maximizes the marginal likelihood (K*ε) were both 1. When the logistic prior was used, K*Øc was still 1, however K*ε = 3. Even though K = 3 was suggested with the latter prior, the greatest proportion of the ancestry of all individuals was explained by only one cluster (mean Q = 0.999) .SNP-based analyses with fastSTRUCTURE -using the Fs = −20.95 (p = 0.001) and Tajima’s D = −1.70 (p = 0.02). The EBSP analysis that combined mtDNA sequences with 29 randomly selected nuDNA loci also supported a population expansion. Based on our assumption of molecular clock calibration for cytochrome b (2.1 per million years), this expansion started ~14,000 years before present  with the logistic regression method  was estimated to be ~84,400 individuals. Following a demographic expansion during the early-mid Holocene , the population reached an estimated Ne of ~3,160,000 doves. Finally, we found evidence of a recent contraction that started between the mid-Holocene and the present, resulting in a current Ne estimate of ~172,000 individuals . None of our analyses recovered evidence of a large Ne during the last interglacial .Our DIYABC analysis supported a compatible, yet more complex demographic scenario than that suggested by the EBSP. From the five simulated scenarios, scenario four Fig.  showed tMaxEnt models achieved a good fit to the data of the present distribution . Bio1  was identified as the most important parameter , and also achieved the highest importance in the permutation test . Other key parameters were Bio18  and Bio12 . Bio8 (Mean Temperature of Wettest Quarter) was of minor importance . The 10th percentile training presence logistic threshold was 0.36 and this value was applied to map and calculate the suitable habitat. The resulting fits were extrapolated to the past climate. According to the global climate model MIROC-ESM, the potentially suitable habitat during the breeding season was found to be largest during the mid Holocene  and smallest during the Last Glacial Maximum (19 %)  was a warm period, with climate conditions that were similar to those of the Holocene ; therefo0,000 ybpD and Fs analysis suggest a scenario of a recent demographic expansion, which, according to the EBSP analysis started during the early Holocene and reached the highest Ne in the present , 5221, 5We propose two possible explanations for not finding genetic structure associated to the migratory flyways. 1) The described flyways for the turtle dove are coupled with a weak migratory connectivity (as defined in ), meaninhttp://www.iucnredlist.org/), as a consequence of a global abundance decline , this is still relevant to turtle dove’s conservation. Many bird species that also suffered recent declines in abundance and are categorized as endangered (sensu IUCN Red List) show genomic signatures of long-term demographic declines that predate the appearance of modern anthropogenic threats [Ne/Nc) is around 0.1 [Nc for the turtle dove in Europe is between 6 and 12 million individuals [Ne estimate for the present is ~172,000 individuals . This t5 years, ). The re5 years,  and wint5 years, , also ga5 years, , 58. Eve threats . We alsoound 0.1 , howeveround 0.1 . The estividuals  and our tuations , 63. Bectuations ), we sugatorius, , and Rocspretus, ).Ne that coincided with the appearance of human threats were the main causes of its sudden extinction [The described demographic scenario (i.e. long-term decline and drastic fluctuations) could increase species’ vulnerability to the impacts of macroecological changes resulting from natural earth cycles, which are currently enhanced by anthropogenic activities , 68. To tinction .Our finding that the turtle dove shows the genomic signatures of a fluctuating demographic history coupled with a recent effective population size contraction, provides support for its vulnerable conservation status. Finally, because we found no evidence of population genetic structure, we prompt that all turtle dove populations across Europe (the portion of the range that was sampled) are equally relevant for conservation.


Brachionus plicatilis by following populations established in new temporary ponds during the first three hydroperiods. Rotifer populations established rapidly after first flooding, although colonisation was ongoing throughout the study. Multilocus genotypes from 7 microsatellite loci suggested that most populations (10 of 14) were founded by few clones. The exception was one of the four populations that persisted throughout the studied hydroperiods, where high genetic diversity in the first hydroperiod suggested colonisation from a historical egg bank, and no increase in allelic diversity was detected with time. In contrast, in another of these four populations, we observed a progressive increase of allelic diversity. This population became less differentiated from the other populations suggesting effective gene flow soon after its foundation. Allelic diversity and richness remained low in the remaining two, more isolated, populations, suggesting little gene flow. Our results highlight the complexity of colonisation dynamics, with evidence for persistent founder effects in some ponds, but not in others, and with early immigration both from external source populations, and from residual, historical diapausing egg banks.Understanding the colonisation process in zooplankton is crucial for successful restoration of aquatic ecosystems. Here, we analyzed the clonal and genetic structure of the cyclical parthenogenetic rotifer  During the early stages of population establishment, monopolization by the first colonists may be counteracted by other factors favouring ongoing migration1913Daphnia16The relative contribution and interaction of such evolutionary forces on the genetic makeup of colonising populations is currently much debated. In order to better understand the underlying processes, populations in newly colonised habitats must ideally be tracked over several years. However, evolutionary studies of colonisation in the wild are difficult and labour intensive, and very rarely have natural populations been tracked from the first stages of colonisation, partly due to the fleeting nature of the colonisation process on an ecological scale18255182723Brachionus plicatilis, during the first three hydroperiods after pond construction. According to the Monopolisation hypothesis, and assuming an absence of historical egg banks due to either aging or pond excavation, we aimed at testing the following specific hypotheses: (i) new populations are founded by few diapausing eggs dispersing from external sources, resulting in low clonal richness and no linkage or HW equilibrium for the new populations in the first hydroperiod; (ii) limited or no increase of allelic diversity over subsequent hydroperiods would be observed when persistent strong founder effects prevent the successful establishment of immigrant genotypes; (iii) genetic drift should result in increasing population genetic differentiation with time.In the present study, we took advantage of the construction of temporary ponds in a restored marshland in one of the most important European wetlands B. plicatilis species complex was found in 36 out of the 58 sampled new ponds and in one of eight reference sites in the surrounding area (−1). Four populations detected for the first time in the 3rd hydroperiod  were not analysed as these ponds had not been sampled in previous hydroperiods , according to positive species-specific Bp1b PCR amplificationB. plicatilis was found in any of the reference sites . In the 3rd hydroperiod, populations showed high densities and the species became widespread. We cannot exclude the presence of populations in the southern block of ponds during the 2nd hydroperiod since sampling was prevented during most of the hydroperiod (see Methods).Only four of the 14 ponds studied held ysis see . Eight aBp1b, 6 alleles in Bp2, 5 alleles in Bp3c, 14 alleles in Bp4a, 4 alleles in Bp5d and 3 alleles in Bp6b, which were used to construct the multi-locus genotype  for each individual. Despite previous successful amplification in a control population Bp3 could not be used in our populations due to inconsistent amplification. Due to the low  frequency of null alleles across microsatellite loci and populations, correction for null alleles was not performed. The least polymorphic locus Bp6b was fixed in 14 of the 27 samples for one allele, which in the remaining samples showed a frequency ≥67%. Private alleles were found only in the most polymorphic locus Bp4a: . The number of polymorphic loci per sample ranged from 3 to 6, and the total number of alleles per sample from 11 to 30. The allelic richness per sample, averaged over loci and adjusted for the minimum sample size , ranged from 1.50 to 4.85 (B. plicatilis populations (i.e. (A) inbreeding coefficient (FIS), (B) allelic richness, and (C) average pairwise genetic differentiation (Dest)).Six loci were polymorphic in the populations studied with 6 alleles in  to 4.85 . No linkrd hydroperiod, with its allelic richness increasing from 1.50 to 4.07 , which could be regarded as an accurate estimate of the number of clones according to GenClone 2.0 (see Methods). In the most extreme case, all individuals of population 6N2 (N = 63) shared the same MLG, and thus this was considered a monoclonal population in the 1st hydroperiod  and low clonal diversities  in the 1st hydroperiod to Dest   = 0.183 in the 2nd (p = 0.001) and it decreased to Dest = 0.150 (p = 0.001) in the 3rd .When considering all populations studied within each of the three hydroperiods, the overall genetic differentiation was very similar in each hydroperiod. It increased slightly from  the 3rd . A spatind and 3rd hydroperiod . Although differentiation increased and became significant in the 2nd hydroperiod , it decreased again in the 3rd hydroperiod .The four populations that persisted throughout all three hydroperiods became more similar through time, converging on the highly diverse 3N3. The decrease in the average pairwise genetic differentiation observed between the 2roperiod  was mainroperiod , which sroperiod  indicateBrachionus plicatilis populations throughout the first three hydroperiods of a subset of these ponds. Our results did not indicate a uniform colonisation pattern among the populations, highlighting the complexity of the colonisation process during these initial stages, with evidence for persistent founder effects in some populations, but also successful migration and subsequent homogenisation in other populations.The understanding of the early stages of habitat colonization is essential for a successful restoration of aquatic ecosystems. The construction of new, temporary ponds as part of a marshland restoration project, together with the high dispersal ability of rotifers34B. plicatilis species complex, which rapidly established large populations within three years of pond construction. Six out of the 14 populations analysed were detected during the 1st hydroperiod and attained considerable densities within weeks of the first flooding. Five populations were first detected in the 3rd hydroperiod, indicating that additional ponds continued to be colonised throughout the study period.Over 60% of the new ponds monitored were colonised by the st hydroperiod had low values of genetic and clonal diversity of one to 11 clones (Daphnia populations171636Brachionus plicatilisDaphniard hydroperiod is more difficult to explain. We may have failed to detect these populations in previous hydroperiods because abundance was below our detection limit or because the area where these ponds were located (southern block) was inaccessible for sampling due to flooding. All these ponds became part of a larger flooded area connecting several ponds in which B. plicatilis may have been present but that were not sampled (see Methods).In support of the first hypothesis, we found that five of the six populations detected in the 11 clones , indicatB. plicatilis sensu stricto was not recorded in any of the reference sites, the sources of colonists for populations founded by a small number of clones remain uncertain. However, the lack of significant genetic structure at the onset of colonisation . When persistent founder effects are present, an increase in genetic differentiation between populations over time can be expectedThe decrease in genetic differentiation observed in the 3rd hydroperiod was driven by two adjacent populations (3N3 and 6N2), suggesting gene flow. The absence of persistent founder effects in recently founded populations, like 6N2, may result from a small egg bank with little buffering capacity against migration. The size of the egg banks produced during the early stages of colonisation is likely to be smaller than those of mature habitats, and may have made a limited contribution to population dynamicsThe four populations followed throughout three hydroperiods presented diverse patterns: the population presumably re-established from a historical egg bank (3N3) showed high and stable allelic and clonal diversity throughout the study. In the two spatially isolated ponds (AC3 and AE6), which were never connected to other ponds by flooding, allelic diversity was low and increased only slightly over time. The clonal diversity in these ponds increased exclusively by recombination during the sexual phase, given the absence of new alleles (Appendix 3). The significant spatial pattern found in the 3rd hydroperiod may suggest low immigration rates in the isolated populations compared to those in within-block ponds. If the number of immigrant genes is limited, and they do not disappear by drift, their frequencies in populations are expected to be low and to increase very slowly181926st hydroperiod in population 6N2 that showed a continuous increase of allelic richness and clonal diversity. This population was founded by only one clone, presumably resulting in selfing for the production of dormant eggs at the end of the 1st hydroperiod. This population is likely to have suffered from strong inbreeding depression at the onset of the 2nd hydroperiod, providing a competitive advantage to immigrant genotypes, as observed in Daphnia metapopulations1922st hydroperiod, and strong selection for heterozygous individuals during the following hydroperiods17Hardy-Weinberg equilibrium was established after the 1et al.Daphnia colonisation. Our results suggested that most of the colonising propagules came from external sources, but they also highlight the role of historical egg banks, as a local “genetic archive”, in facilitating re-colonisation and the build-up of new populations in restored habitatsOverall, our study, although admittedly based on a small number of ponds, revealed that the colonisation of a cyclical parthenogenetic rotifer in newly created habitats was subject to a high level of stochasticity, at least during the early stages of the process. Founder effects were not expected in the population re-established from pre-existing egg banks, but were not apparent in the pond founded by a single clone, which showed that immigration might be important when bottlenecks are very strong. Even in those populations founded by few genotypes, the persistent founder effects were difficult to detect. Spatial isolation of these ponds may mask the founder effects, lowering the immigration rates in comparison to the ponds located within blocks. Whether persistent founder effects may establish during later years remains to be tested, similar to a study by Ortells Brachionus plicatilis sensu stricto is a cosmopolitan rotifer that inhabits salt lakes and brackish coastal lagoons482 turned into arable farmland in the 1960 s after drainage. Between summer 2004 and spring 2005, 96 experimental ponds were dug in the estate as part of a restoration project, ‘The Doñana 2005 restoration plan’The studied ponds are located within Caracoles estate, in Doñana National Park , and another for genetic analysis (preserved in absolute ethanol). No genetic samples were available in May 2006. Samples were kept in the dark at 4 °C until DNA was extracted. Each sample was obtained by filtering a minimum of 20 L of water through a 64 μm net. To avoid dispersing zooplankton and sediment transfer among ponds, the sampling equipment was rinsed with tap water and 70% ethanol between ponds, and boots were covered with plastic bags  were screened (see 4 PCR buffer (BIOLINE), 0.2 mM dNTPs, 0.5 μM of each primer, 1.5 mM MgCl2 and 0.025 U of Taq DNA polymerase (BIOLINE). PCR reactions were performed in a Veriti® Fast Thermal Cycler (Applied Biosystems) as described in ref. B. plicatilis individuals were selected for further study.For samples containing individuals of the ened see . DNA wasBp1b, Bp2, Bp3, Bp3c, Bp4a, Bp5d, Bp6b)® Multiplex PCR Master Mix  and 2 μL of RNAse-free water. Each reverse primer was labelled in 5′ with a fluorescent dye  for their detection in capillary electrophoresis. Primer concentration was optimized to improve amplification performance given the different strength of the fluorescent dyes to 0.1 μM for the Cy5-labelled Bp4a and Bp6b, 0.2 μM for the Cy5.5-labelled Bp2, Bp3c and Bp5d, 0.3 μM for D2-PA-labelled Bp1b and 0.4 μM for Cy5-Bp3. The multiplex PCR programme used was following QIAGEN recommendations. Diluted PCR products (1:20) were combined with a 400 bp-size standard and separated on a Beckman-Coulter CEQTM 8000. Alleles were scored using the CEQ Fragment Analysis software (Beckman-CoulterTM) and then checked manually.Between 41 and 66 individuals per sample (depending on availability) were genotyped for seven trinucleotide microsatellite loci , which combines richness and evennessA multi-locus genotype (MLG) was constructed for each individual based on its genotype at each individual locus to infer the clonal structure of the populationsST values were calculated with FreeNAThe frequency of null alleles and pairwise FFST in each hydroperiod separately, using GenAlEx 6.4DestDest values, 95% confidence intervals and p-values (null hypothesis of zero differentiation) by means of 1000 bootstrapping iterations. All p-values were adjusted using Bonferroni correction for multiple tests. Pairwise measures of Dest were used as a distance matrix in a nonmetric multidimensional scaling ordination (NMDS) to provide a visual representation of the genetic variability among populations. Isolation by distance was assessed by Mantel test (9999 permutations), testing for relationships between genetic and geographic distances.To test for temporal changes in the amount of genetic variance between and among populations, we performed analyses of molecular variance (AMOVA) based on How to cite this article: Badosa, A. et al. Isolation mediates persistent founder effects on zooplankton colonization in new temporary ponds. Sci. Rep.7, 43983; doi: 10.1038/srep43983 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Salminus brasiliensis. We specifically investigated whether Pleistocene climate changes affected the demography of this species. If this was the case, we expected to find changes in population size over time. Thus, we assessed the genetic diversity of S. brasiliensis to trace the demographic history of nine populations from the Upper Paraguay basin, which includes the Pantanal floodplain, that form a single genetic group, employing approximate Bayesian computation (ABC) to test five scenarios: constant population, old expansion, old decline, old bottleneck following by recent expansion, and old expansion following by recent decline. Based on two mitochondrial DNA markers, our inferences from ABC analysis, the results of Bayesian skyline plot, the implications of star-like networks, and the patterns of genetic diversity (high haplotype diversity and low-to-moderate nucleotide diversity) indicated a sudden population expansion. ABC allowed us to make strong quantitative inferences about the demographic history of S. brasiliensis. We estimated a small ancestral population size that underwent a drastic fivefold expansion, probably associated with the colonization of newly formed habitats. The estimated time of this expansion was consistent with a humid and warm phase as inferred by speleothem growth phases and travertine records during Pleistocene interglacial periods. The strong concordance between our genetic inferences and this historical data could represent the first genetic record of a humid and warm phase in the Pantanal in the period since the Last Interglacial to 40 ka.Pleistocene climate changes were major historical events that impacted South American biodiversity. Although the effects of such changes are well-documented for several biomes, it is poorly known how these climate shifts affected the biodiversity of the Pantanal floodplain. Fish are one of the most diverse groups in the Pantanal floodplains and can be taken as a suitable biological model for reconstructing paleoenvironmental scenarios. To identify the effects of Pleistocene climate changes on Pantanal’s ichthyofauna, we used genetic data from multiple populations of a top-predator long-distance migratory fish,  The genetic diversity and population structure of species are largely determined by intrinsic traits, contemporary factors that include anthropogenic activities and historical events that have affected species over geological time . PhylogeThe Pleistocene (2.58–0.01 million years ago) was marked by worldwide climatic changes involving multiple successive cycles of glacial and interglacial events, associated with abrupt and dramatic changes in temperature and precipitation . OverallAlthough there are no records of large ice sheets covering the landscape, as happened in the Northern Hemisphere, climatic changes during the Pleistocene also drastically affected Southern Hemispheric biomes . SeveralThe Pantanal is a large seasonal floodplain located in the center of South America. This region is entirely contained in the Upper Paraguay basin, which is a complex drainage network due to its geological history . PantanaSalminus brasiliensis. As a top predator distributed throughout the La Plata basin , old expansion, old decline, old bottleneck followed by recent expansion, and old expansion followed by recent decline. We specifically investigated whether Pleistocene climate changes affected past S. brasiliensis demography. If so, we expected to find a population reduction signature during glacial periods due to climatic conditions that reduced the habitat, when the Pantanal region was almost dry. Further, a population expansion signature was expected in the interglacial periods, when the wetlands were re-established, increasing fluvial discharge and suitable habitats for the biological model. After reconstructing the most probable demographic history for S. brasiliensis based on two mitochondrial markers (CytB and Dloop), we associated changes in population size over time with known regional climatic fluctuations. Our findings might contribute to the conservation and management of natural populations of S. brasiliensis from the Upper Paraguay basin by being used to predict whether these populations will be able to persist in future scenarios of environmental change.Here, we focused on assessing the genetic diversity and pattern of S. brasiliensis individuals were sampled in nine rivers from the Upper Paraguay basin , and carried out under a temporary scientific collection license number 32217-4 provided by ICMBio-SISBIO. A total of 52 2 (2 mM), 0.5 μL of each primer (0.2 mM), 0.1 μL of Platinum®Taq Polymerase (0.5 unit) and 1 μL of DNA template (50 ng/μL). The annealing temperature of PCR thermal profile was described in Supplementary Table We amplified partial sequences of two mitochondrial DNA markers: Cytochrome B (CytB) and Dloop  v. 6.0 . BAPS idS. brasiliensis using BAPS , using the relaxed lognormal model: 6.31 × 10-3 (CytB) and 1.935 × 10-2 (Dloop) mutations per site per million years. This rate was based on a previous topology calibrated from the genus rooted with Brycon specimens . This topology was reconstructed using two mitochondrial and two nuclear markers and calibrated using a biogeographic event, the uplift of the Eastern Andes Cordillera  using an approximate Bayesian computation (ABC) framework in DIYABC v2.1.0 . ABC approach allows a quantitative evaluation of the demographic and evolutionary history by strictly contrasting realistic models defined a priori and estimating relevant parameters . The prior distribution of demographic parameters and the mutation rate are listed in Supplementary Table All scenarios assumed an initial ancestral population (Na), demographic changes occurring during the Late Pleistocene , and recent populations represented in t0  were observed for either marker.Fragments of 710 base pairs (bp) of CytB and 571 bp of Dloop were obtained from 52 individuals of We identified 21 and 33 haplotypes for CytB and Dloop, respectively . The greatest expansion was underway through around 40 ka and continued gradually until 20 ka, when the effective population size became stable.The BAPS approach clustered all individuals into only one mitochondrial genetic group  expanding at 31000 generations ago . At present, the median effective population size is estimated at N2 = 811000 (Table 1). Large confidence intervals are common in such demographic inferences. All our RMAE values are <0.2, except by current effective population size (RMAE = 0.299), indicating the parameters estimated by ABC analysis were reliable values. This was expected because mitochondrial is reliable to detect past demographic parameter estimates (Na and t2)  . When we and t2) . This inS. brasiliensis were here observed. The results also revealed that its current genetic diversity was strongly influenced by an expansion event that happened during the Pleistocene, likely associated with warm and humid weather conditions during interglacial periods. All of these findings are discussed in detail below.High haplotype of genetic diversity and no spatial genetic structuring among populations of S. brasiliensis . Our inference from ABC analysis, the results of BSP, the implications of star-like networks, and the patterns of genetic diversity (high haplotype diversity and low to moderate nucleotide diversity) indicated a sudden population expansion.The demographic history of S. brasiliensis, beyond making strong quantitative inferences about its demographic history. We estimated a small ancestral population size that underwent a drastic fivefold expansion, probably associated with the colonization of newly formed habitats. The time estimated for this expansion was consistent with the humid and warm phase inferred from speleothem growth phases and travertine records during Pleistocene interglacial periods (The ABC approach was an efficient tool for reconstructing and testing alternative demographic scenarios; this allowed us to determine the most plausible evolutionary history of  periods .Pleistocene climatic oscillations in South America drastically altered freshwater systems by changing hydrological flow and lake distributions as well as rerouting rivers . As an aS. brasiliensis was dated to 62 ka. Although there is a lack of regional paleoenvironmental data extending further than 40 ka, speleothem data from the Brazilian Northeast region reveal a pluvial maxima phase at about 61.2–59.1 ka (S. brasiliensis. The strong concordance between our genetic inferences and this historical data could represent the first genetic record of a humid and warm phase in the Pantanal in the period since the Last Interglacial to 40 ka.The pulses of the reduction and expansion of the landscape left a phylogenetic signature in regional fauna, leading to a population expansion in bird  and mamm–59.1 ka , reinforS. brasiliensis from the Upper Paraguay basin. Our findings suggest that Pleistocene climate fluctuations fundamentally shaped the genetic diversity and pattern of this species in this region. Coalescent-based analyses, particularly the statistical framework provided by the ABC method, supported an ancient expansion event before the LGM as the best scenario to explain the current genetic diversity of S. brasiliensis based on two mitochondrial markers.In summary, we focused to identify the effect of Pleistocene climatic changes on demographic history of S. brasiliensis from the study area show high levels of genetic diversity, our concern over its conservation status cannot wane, because its genetic pool is unique and completely different from the S. brasiliensis found in the Upper Paraná basin (Understanding how historical events impact genetic diversity is important for predicting whether populations will persist under future environmental changes . High gená basin . We suggLCM, CM, ER, DM, and PG conceived the ideas and designed the experiments. LCM, ER, and DM collected the samples. CM and PG performed the bioinformatics analyses. LCM, CM, and PG led the writing, with assistance from ER and DM. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Aedes albopictus, the Asian tiger mosquito. This species is a vector of medically important arboviruses, notably chikungunya (CHIKV), dengue (DENV) and Zika (ZIKV). The reconstruction of the complex colonization pattern of this mosquito has great potential for mitigating its spread and, consequently, disease risks.Invasive species represent a global concern for their rapid spread and the possibility of infectious disease transmission. This is the case of the global invader Aedes albopictus populations from representative countries in the Southeast Asian native range and in the recent and more recently colonized areas. In Southeast Asia, the low differentiation and the high co-ancestry values identified among China, Thailand and Japan indicate that, in the native range, these populations maintain high genetic connectivity, revealing their ancestral common origin. China appears to be the oldest population. Outside Southeast Asia, the invasion process in La Réunion, America and the Mediterranean Basin is primarily supported by a chaotic propagule distribution, which cooperates in maintaining a relatively high genetic diversity within the adventive populations.Classical population genetics analyses and Approximate Bayesian Computation (ABC) approaches were combined to disentangle the demographic history of Ae. albopictus may have facilitated the rapid establishment of adventive populations through admixture of unrelated genomes. As a consequence, a great amount of intra-population variability has been detected, and it is likely that this variability may extend to the genetic mechanisms controlling vector competence. Thus, in the context of the invasion process of this mosquito, it is possible that both population ancestry and admixture contribute to create the conditions for the efficient transmission of arboviruses and for outbreak establishment.From our data, it appears that independent and also trans-continental introductions of  Aedes albopictus, the Asian tiger mosquito is the most invasive species in the world. Over the past 30 years, from its native tropical Southeast Asia it rapidly spread across the world, and it is currently present in several tropical, subtropical and temperate countries due to its high ecological plasticity. Its dramatic global expansion has resulted in public health concerns as a consequence of its vector competence for several arboviruses, such as chikungunya, dengue and Zika, which are the most prevalent pathogens of humans. The identification of the migration routes and, consequently, the demographic origin of mosquito adventive populations is a priority to prevent local outbreaks of viral infection. Using a combined population genetics and Bayesian approach, we found that the worldwide invasion of this mosquito has not occurred through a step-by-step expansion process from the native Southeast Asia. A recent and chaotic human-mediated propagule distribution allowed adventive populations to be established in the Indian Ocean, Mediterranean Basin, and North America regions. We have detected high intra-population variability, which may co-operate with the genetic mechanisms controlling vector competence. The term ‘successful biological invasion’ includes the detrimental impact on human health, economy and native biodiversity –3. IndeeAedes mosquito species [Ae. aegypti, Ae. albopictus and Ae. japonicus japonicus [Ae. albopictus , the Asian tiger mosquito, is an example of human-aided transport [Ae. albopictus is creating public concern due to its vector competence for at least 20 arboviruses, notably chikungunya (CHIKV), dengue (DENV) and Zika (ZIKV) viruses [Ae. albopictus at the global scale have been inferred from historical and observational data [ species  offer a  species , and pro species , 12, 13.aponicus . Their eaponicus –17. On ts Skuse, 94, the Aransport . Originaransport , 21, sprransport  and, durransport , 23–26. ransport –29. One ransport –32. The  viruses , 33–42.  viruses , 44. In  viruses , 46. Lat viruses  and Croa viruses . Therefo viruses . Plausibnal data . These dnal data –58. Howenal data .Ae. albopictus was primarily supported by a chaotic propagule distribution mediated by human activity [Previously, we suggested that the global expansion of activity . In ordei.e., China, Thailand, Japan), the Indian Ocean, the Mediterranean Basin, the Pacific Ocean islands and North America. Since tiger mosquito populations display differences in vectorial capacity for different arboviruses [Specifically, we provide information on the origins of populations from Southeast Asia  and seven from invaded areas: Indian Ocean (La Réunion Island), Mediterranean Basin , Pacific Ocean (Hawaii) and North America (U.S.A.) . These le period . A stande period , 64. In e period . Three ii.e., from Aealbmic1 to Aealbmic17. These loci, which are spread across the genome, ensure sufficient power to detect genetic variability even in relatively small samples [Each mosquito was screened for seventeen previously characterized microsatellite loci , i.e., f samples . PCR rea samples . The TAN samples  for the na), number of private alleles (np) and frequency of private alleles (Ap) using GenAlEx 6.5 [a/n and np/n, respectively. The inbreeding index (FIS) was obtained using FSTAT V.2.9.3.2 [FST [FST value was assessed by comparison of the observed value with the values obtained in 10,000 matrix permutations and Bonferroni corrections were applied. Linkage disequilibrium between pairs of loci in each sample  and deviations from Hardy-Weinberg equilibrium (HWE) at each locus/sample combination were examined with GENEPOP V. 4.2 [An) for each locus were estimated using CERVUS [Genetic variation within each locality was estimated in terms of average numbers of alleles , set between 1 and 10 . The appropriate number of genetic clusters was determined by plotting the log probability (L(K)) and ΔK across multiple runs [The relationships among populations were assessed using Principal Coordinate Analysis in GenAlEx V.6.4 . Bayesiaple runs  as impleple runs . Finallyple runs  and DISTple runs  were useAe. albopictus, competing hypotheses regarding population divergence at the global scale were compared using an Approximate Bayesian Computation (ABC) method, as implemented in DIYABC v2.0 [i.e., population structure and ancestry). The competing scenarios were set using prior definitions and distribution of demographic parameters, as described in To infer the invasion routes followed by ABC v2.0 . BecauseAe. albopictus has great potential for rapid population growth [NF and its size was drawn from a uniform distribution bounded between 1 and 100 individuals. If necessary, the rate of admixture between two populations was drawn in a uniform distribution and set from 0.001 to 0.999. For the genetic parameters, the microsatellite Generalized Stepwise Mutation model was considered, and a mean microsatellite mutation rate across loci was set between 10−5 and 10−3. The parameter of geometric distribution was specified with a uniform prior distribution bounded between 0.1–0.3. For each locus the above considered mutation parameters were allowed to vary using Gamma distributions. Finally, the possibility of single nucleotide insertion/deletion mutations were considered with a mean frequency of 10−8–10−5 [The genetic variation within and among the populations was summarized using sets of statistics conventionally used in ABC analyses: the mean number of alleles per locus, the mean expected heterozygosity, the mean allelic size variance, the overall pairwise Fele size). This inele size). Confideele size). Once th10.17605/OSF.IO/U4JRP). Overall these loci display a mean Polymorphic Information Content of 0.54 suggesting that they are sufficiently informative for assessing the degree of population variability and structuring of this mosquito. Results of Fisher’s exact tests, after sequential Bonferroni correction [The considered seventeen microsatellite loci, scored in 252 mosquitoes across the 10 localities from Asia to America, displayed different levels of polymorphism in terms of number of alleles, ranging from 2 alleles  to 24 alleles . The number of alleles per locus in each population is provided in rrection , revealea/n). While private alleles were detected in all populations, the highest proportion was estimated in the Asian region and in La Réunion island. An estimate of variability distribution in and among the populations (AMOVA) indicated that most variation (85%) occurred within individuals, whereas only about 15% of total variation was detected among populations.Variability at these loci appeared relatively high across the ten populations  but in tST values were significantly different from zero in all the comparisons , but Greece displayed a certain genetic affinity with Thailand (FST = 0.083), and Albania with China (FST = 0.075). In America, Virginia shared greatest genetic affinity with the Asian populations  but the lowest FST value was with the IT2 (Brescia) Italian population (FST = 0.057). La Réunion, besides being linked to the Asian region, was genetically related to the Mediterranean area, with the exclusion of Greece (FST = 0.148). The overall absence of correlation between genetic differentiation (FST) and geographical distance was confirmed by the R2 value = 0.009, P = 0.649  were partially overlapping.Five separate sequential ABC analyses  were perIn the subsequent four scenarios the origins of the adventive populations were considered.Analysis 2 focused on the invasion of La Réunion Island. To determine which Asiatic population mostly contributed to the invasion of La Réunion, 5 alternative hypotheses were simulated . ScenariAnalysis 3 focused on the colonization history of Hawaii, North America and North Italy (IT2-Brescia). A total of 18 scenarios were designed to test different hypotheses of introduction. The results unambiguously indicated scenario 17 ; Fig 3, In analysis 4, the relationships among the nearby Mediterranean populations of Albania and Greece were considered together with their putative source, China and Thailand respectively, as suggested by the STRUCTURE data. The introduction of mosquitoes from China to Albania and from Thailand to Greece was supported by the highest posterior probability  never overlapping those of the other competing scenarios ; Fig 3.Finally, we tried to disentangle the relationship between the two North Italian samples, IT1-Cesena and IT2-Brescia. Out of the three considered scenarios , that wiInterestingly, the computations of the bottleneck severity parameter from posterior distributions provided overall support for minimal to moderate bottleneck severity during the invasions with the duration of the bottlenecks varing from 4  to 27 generations .Ae. albopictus, 2) the invasion process is supported by a chaotic propagule distribution, which may play an important role in maintaining the genetic diversity and in the establishment of the adventive populations, and 3) the complex invasion pathways inferred from genetic data are congruent with the historical documentation concerning the presence of this species. All these outcomes are summarized in Our results clearly confirm that: 1) Southeast Asia, and especially China, is the native range of It has been suggested that, before the global invasion, the presence of climatically suitable areas across East Asia enabled rapid population growth and expansion allowing the persistence of this mosquito in widely interconnected populations from Southeast China to the Indochinese Peninsula , 54. Recth century this mosquito is believed to have spread, along with humans, from Southeast Asia to Madagascar and the Indian Ocean islands [Outside East Asia, our data depict a picture of multiple migration patterns, with some erosion of variability. Historically, since the 18 islands . As a co islands . Our La Ae. albopictus began a chaotic global expansion that continues to the present [th century, probably from Japan [After its spread to islands in the Indian Ocean, the reported invasion of this species to new areas started around 1900, when  present . In the om Japan , 106 andom Japan . In contom Japan . The mosom Japan . These hAe. albopictus in North Italy, our data clearly show that, although North America acted as an important ‘bridgehead’ [In the Mediterranean region the outcomes of cluster analysis and scenario-testing portray a complex invasion pattern with most of the established populations being admixtures resulting from independent introductions scattered across time. The genetic discontinuity between the populations from geographically related countries, such as Albania, Greece and Italy, is a clear example. The Albanian population displays high co-ancestry with China from where it was introduced in 1979, when China was one of Albania’s few trading partners , 109. Thdgehead’ , differedgehead’ . We cannAe. albopictus may have facilitated the rapid establishment of adventive populations through admixture of unrelated genomes. As a consequence, a great amount of intra-population variability has been detected, and it is likely that this variability may extend to the genetic mechanisms controlling vector competence [Ae. albopictus autochthonous transmission of the CHIKV strain carrying the A226V mutation [It is likely that independent and also trans-continental introductions of mpetence . Intrigumpetence , 63, 115mpetence , 62. Thumpetence , 116. Thmutation , 117 premutation , 119. ThAe. albopictus has become established.These observations stimulate the need to explore how the dynamics of different mosquito genetic backgrounds, during the invasion processes, may have impacted variation in competence within and among the adventive populations. Moreover, we must consider that the spread of this mosquito occurred across different environmental conditions. Thus, the variation in competence between the eco-geographic populations may be the result of interactions between unexplored genetic factors and the complex environmental landscapes , 120. KnS1 Fig(DOC)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOC)Click here for additional data file.


Encholirium horridum populations located on granitic inselbergs in an Ocbil landscape within the Brazilian Atlantic Forest, using both nuclear and chloroplast microsatellite markers. Beyond to assess the E. horridum population genetic structure, we built species distribution models across four periods  and inferred putative dispersal corridors using a least‐cost path analysis to elucidate biogeographic patterns. Overall, high and significant genetic divergence was estimated among populations for both nuclear and plastid DNA (ΦSTn)( = 0.463 and ΦST(plastid) = 0.961, respectively, p < .001). For nuclear genome, almost total absence of genetic admixture among populations and very low migration rates were evident, corroborating with the very low estimates of immigration and emigration rates observed among E. horridum populations. Based on the cpDNA results, putative dispersal routes in Sugar Loaf Land across cycles of climatic fluctuations in the Quaternary period revealed that the populations’ connectivity changed little during those events. Genetic analyses highlighted the low genetic connectivity and long‐term persistence of populations, and the founder effect and genetic drift seemed to have been very important processes that shaped the current diversity and genetic structure observed in both genomes. The genetic singularity of each population clearly shows the need for in situ conservation of all of them.Here, we explore the historical and contemporaneous patterns of connectivity among  The climate is characterized by distinct seasons, with most of the precipitation occurring in “summer” while the driest months are in “winter,” which is when the species flowers.E. horridum population revealed weak SGS and restricted gene flow with pollination events that did not go beyond 45.5 m  and paternity analysis in one 2.2E. horridum  extraction kit.We collected leaf samples from 526 reproductive individuals of 11 natural populations covering the entire geographic range of m Figure  so indiv2.3E. horridum . Fluorescent‐labeled primers were used for genotyping reactions, following protocols described by Hmeljevski et al. (Eight nuclear microsatellite (nSSR) loci developed for i et al. . Both nS2.4E. horridum population with the program FSTAT  obtained were adjusted applying a sequential Bonferroni correction for multiple comparisons , the allelic richness (R), the number of private alleles (AP), the number of rare alleles , and expected heterozygosity (He) in Hardy–Weinberg equilibrium (HWE). Analyses were run using the program FSTAT  was estimated considering the presence and absence of null alleles, and its significance  was tested using the program SPAGeDi 1.5 , calculated with the software CONTRIB .For cpDNA data, alleles at cpSSR were combined into haplotypes for each individual. From this, we characterized each population by the number of haplotypes detected and haplotypic diversity . Conversely, if populations were isolated for a relatively short period of time (in comparison with the mutation rate) or if gene flow has been important, RST would not be significantly different from FST  on average than distinct haplotypes sampled in different populations  and to estimate admixture proportions (Q) for each individual .For the nSSRs and cpSSRs, we estimated the pairwise genetic distance and verified the isolation by distance among populations using SPAGeDi, and Mantel's test with 30,000 randomizations was performed using the program IBDWS 3.23  was estimated to examine the connectivity patterns of the population. A Bayesian approach was used to determine recent migration rates (m), which was implemented in BAYESASS 1.3  as well as the effective population sizes using Markov chain Monte Carlo techniques and coalescence theory. In this analysis, we used a Brownian motion model of mutation under the maximum‐likelihood framework. We used five independent replicates of 10 short chains 50,000 iterations in length, three long chains 500,000 iterations in length, and four heated static chains at temperatures 1.0, 1.5, 3.0, and 10,000 to increase the efficiency of the searches. The long‐term estimate of gene flow (M) was converted to the proportion of migrants (m) from population i to population j, using the formula mij = Mijμ  and long‐term gene flow (average over the past 2.8Encholirium horridum species distribution models (SDMs) were built for current conditions, mid‐Holocene , LGM , and Last Interglacial  periods.r > .9) were eliminated, which left 14 for subsequent analysis (Table http://www.jbrj.gov.br/jabot) and field surveys. SDMs were modeled using Maxent v.3.3  were obtained for all geographical coordinates, at 1 km spatial resolution, from the WorldClim database . The presence of null alleles was detected for all nuclear loci and populations ranging from 0 to 4 and 0 to 6, when considering the number of loci per population and the number of populations per locus, respectively , the mean haplotypic diversity was low (h = 0.064) due to the high frequency of fixed haplotypes in populations ST( = 0.463 and ΦST(plastid) = 0.961, respectively, p < .001). The permutation RST test revealed that genetic divergence between distinct alleles of nuclear genome is related to geographical separation, as the RST value was larger than both FST and pRST . However, for the plastid genome, it was not possible to identify a clear phylogeographic signal because NST was very similar to FST (0.975 ≅ 0.960).High and significant genetic divergences were estimated among populations for both nuclear and plastid DNA by AMOVA . Bayesian clustering of groups of individuals revealed 12 and 11 genetic clusters , considering no a priori population localities and with posterior information of the identity of populations, respectively . Estimates of long‐term gene flow were more variable, ranging from 0.0254 to 0.0001 (±0.047), and only four population pairs showed asymmetric estimates (Table p = .95).Values of immigration and emigration rates among populations based on the nSSRs were very low Table , corroboes Table . Short‐ 3.4E. horridum population connectivity changed little during climatic fluctuations ST = 0.961; p < .001). Hamilton and Miller (FST(plastid) ~ 2 Fn)ST. This demonstrates that, in E. horridum, seed gene flow can be a more important determinant of deme size and subdivision among populations than pollen gene flow. In fact, a strong genetic structure of cpDNA has been verified in other species endemic to granitic inselbergs  during periods of unfavorable conditions, with subsequent expansion if conditions improved , a reverse biogeographic pattern of colonization has been suggested: Occupation of the rocky savannah‐like vegetation has occurred more than once from Atlantic Forests ancestors , with most lineages being <4 Myr old (Simon, Grether, Queiroz, Skema, & Pennington, stafsson  suggest stafsson  identifi4.3E. horridum (Hmeljevski et al., E. horridum, individuals are perennial and have slow growth (Hmeljevski et al., E. horridum is an Ocbil organism.Ocbils are defined across three continuously varying axes of landscape age, disturbance regimes, especially relative climatic buffering, and nutrient impoverishment of soils Hopper, . ConsideE. horridum populations, reinforcing the geographic isolation of species restricted to inselbergs. Phenomena such as the founder effect and genetic drift appear to have been very important in shaping the current diversity and genetic structure observed in both genomes. The conjoint analysis of results obtained from the species distribution models and genetic markers point to the likely stability of the species in its core distribution area during Quaternary climatic oscillations. Finally, the set of results obtained from this and previous studies of E. horridum strongly suggests this species as an Ocbil organism. Although numerous and dense populations can be found in nature, the set of results enhance the genetic singularity of each population and the clear need of the in situ conservation of all of them. Nearly one‐third of the terrestrial biodiversity hotspots contain large or small Ocbil landscapes that deserve special treatment in conservation and restoration efforts (Hopper et al., In the current scenario, genetic analyses highlighted the low genetic connectivity and long‐term persistence of None declared. Click here for additional data file.


Leuctra hippopus at a site in central Norway, the Isterfoss rapids, in relation to three nearby and two remote conspecific populations. Adults of this population emerge about a month earlier than those of nearby populations, live on large boulders emerging from the rapids, and are short‐lived. This population also has distinct morphological features and was studied earlier during the period 1975–1990. We reassessed morphological distinctness with new measurements and added several analyses of genetic distinctness based on mitochondrial and nuclear sequence markers, as well as AFLP fingerprinting and SNPs mined from RAD sequences. The Isterfoss population is shown to be most closely related to its geographical neighbors, yet clearly morphologically and genetically distinct and homogeneous. We conclude that this population is in the process of sympatric speciation, with temporal isolation being the most important direct barrier to gene flow. The shift in reproductive season results from the particular temperature and water level regime in the Isterfoss rapids. The distinct adult body shape and loss of flight are hypothesized to be an adaptation to the unusual habitat. Ecological diversification on small spatial and temporal scales is one of the likely causes of the high diversity of aquatic insects.Stream dwelling invertebrates are ideal candidates for the study of ecological speciation as they are often adapted to particular environmental conditions within a stream and inhabit only certain reaches of a drainage basin, separated by unsuitable habitat. We studied an atypical population of the stonefly  With these analyses, we tested the hypothesis that Isterfoss is genetically homogeneous and distinct from its geographical neighbors. If the hypothesis of a distinct historical origin can be refuted, a low degree of admixture is considered indicative of (ongoing) local diversification and incipient speciation.In order to test the temporal isolation hypothesis, we reassessed the morphological distinctness of the 22.1Leuctra hippopus  and boulders (50–200 cm) of moraine origin, flanked on both sides by a broad floodplain. The central stream and the floodplains are separated by two rows of large boulders that rise above the water surface and are covered with lichens and mosses . Adults of a Ørmen, : 13. Als, L. hippopus does not occur.) Temperature data were also available from the Atna River, c. 5 km upstream from our additional sample collecting site on the Atna River . The temperature regimes of these two sites are typical for Norwegian lake outflows and mountain rivers, respectively  from 55 The nuclear markers 28S and ITS were sequenced for a subset of these. Specimens used in molecular research are preserved in 96% ethanol; all specimens are deposited in the DNA bank of the Natural History Museum, University of Oslo (NHMO). An overview of the specimens is presented in Table S1.For the characterization of the Isterfoss population by means of morphometry, AFLP fingerprinting, and SNP analysis, we used adult specimens collected in Isterfoss on 23 April 2011 and 8 May 2012. These were analyzed together with specimens from five additional collecting sites .Morphometrical measurements were used to reassess the morphological distinctness of the Isterfoss population. Lillehammer  measuredTable 2.4We extracted DNA from the head plus prothorax or, in the case of samples used for restriction site‐associated DNA (RAD) sequencing, the entire body except the abdomen. The GeneMole extraction robot was used for all extractions used in AFLP or RAD tag protocols; for specimens used for sequence data, we also used the Qiagen DNeasy Blood and Tissue Kit. In both cases, we followed the manufacturers' protocols. Skeleton parts were not crushed but retrieved after DNA extraction and stored with the remainder of the specimen.L. hippopus specimens and outgroup taxa was amplified with the protocol described in Boumans and Baumann  rRNA gene was amplified and sequenced with the primers and methods described in Boumans and Baumann  and maximum‐parsimony (MP) methods implemented in Paup*, and with Bayesian inference (BI) in MrBayes version 3.2 . We reduced the number of duplicates using the program clone_filter in Stacks v1.29. The output file of clone_filter was then used as input for the process_radtags program in Stacks .denovo_map.pl pipeline of programs in Stacks v1.29 . In order to avoid an artifactual surplus of real and erroneous homozygotes, we set the minimum read depth in populations to twice its value in denovo .We used the The amount of missing data is proportional to the genetic distance between taxa , or 1–3 SNPs and no more than six alleles Matrix 2) with the  with thepopulations program, we used whitelists of loci containing only a single SNP and two alleles, avoiding the inclusion of closely linked SNPs, for both all six populations and the set without Vardø.  With the filter settings as above, this resulted in a much reduced number of loci in matrices 1a and 2a  and clustering by identity by state (IBS) as implemented in the R package SNPRelate v. 1.2.0 . Our measurements show no difference between the specimens from Vardø and those from the “other Hedmark” group.On average, females are larger than males, and they also have longer forewings relative to the width of the head , but the ITS sequences, displaying much variation in the form of indels, support the monophyly of the Norwegian population. In particular, the two ITS sequences from Isterfoss have the same ITS allele as the single sequence from Folldal .A COI phylogenetic tree was inferred from the 636‐bp‐long alignment of 67 specimens Figure . Three m3.3K = 4, distinguishing (1) Vardø, (2) Isterfoss, (3) Folldal plus Femundsenden, and (4) Rekem  Vardø, (2) Isterfoss, and (3) Folldal plus Femundsenden plus Rekem, corresponding to the Structure analysis with K = 3  visualizes that the Isterfoss population differs from the neighboring populations.Clustering specimens or populations with the same data matrix in BAPS yielded three clusters, both with and without spatial information priors, if the maximum number of clusters 3 Figure a. With K3.4populations program is available as supporting data. While the number of specimens included in the RAD library varies from three to five per population, the effective number scored per locus lies between 1.2 and 3.1. Measures of within‐population genetic variation  all show that Isterfoss and Vardø are more homogeneous than the other populations. The variation in Vardø may be underestimated due to its smaller sample size; Isterfoss, on the other hand, has the highest number of specimens scored per SNP of all six populations. The number of private alleles shows that Vardø is most distinct from the other populations, followed by Rekem. The four populations in Hedmark have similar numbers of private alleles.Table K = 3 . The populations in Vardø and Belgium are distinguished first and the one in Isterfoss at K = 4 . For Matrix 2 (without Vardø), with each locus being represented in at least two of the five West European populations, the highest value of ΔK corresponds to K = 3 , the genetic homogeneity of this coastal population is plausible. The collection site in Vardø is situated along a c. 10‐km‐long river on a peninsula on the northernmost edge of the continent . Scientifically, our interest is in demonstrating speciation by temporal isolation as an evolutionary process. Adaptation to the Isterfoss rapids is both morphological and phenological. The Isterfoss population is morphologically distinct from the other Norwegian populations in having wider heads and relatively short wings. The wider heads can be an expression of overall larger size, a more compact build or—most likely—a combination of both. The whole body is “fairly long” , which emerge at the same time of the year. In addition to a number of infertile matings, he reports a single female producing only 20 eggs of which nine hatched; the first‐instar nymphs died for unknown reasons .As weather conditions vary from year to year, it cannot be excluded that the presence of adult s Ørmen, : 60. TypElliott, . Ørmen'sThe Isterfoss population must have diverged locally from other south Norwegian populations in the 10,000 years after the last glaciation. The four collecting sites in Hedmark  have similar numbers of private alleles in the RAD‐based SNPs. That only the specimens from Isterfoss form a distinct cluster appears to be due to their similarity, that is, more private and nonprivate alleles being shared among the specimens, rather than to their strong genetic divergence from the other Hedmark populations. Our interpretation is that genomewide allelic divergence is primarily a function of time and that the different south Norwegian populations are of similar age. The divergence of the Isterfoss population in morphology and life cycle characteristics does imply genetic changes, but these are likely to be few relative to the wide sampling of RAD loci.L. hippopus, it has not in recent times been recolonized by the typical form, even though this stonefly is common in all suitable habitats in Scandinavia. The habitat characteristics of the rapids are so different from the species' typical habitat with riparian vegetation that it may not be a suitable habitat for the typical L. hippopus. So how did this species colonize the Isterfoss rapids in the first place? A possible explanation is gradual habitat isolation, similar to cospeciation of parasites or pollinators and their hosts . Appendix S1 provides methodological details. Morphometric data are provided as Table S2. RAD tag reads filtered for duplications by means of the clone_filter program in Stacks are archived in SRA , while raw data matrices of both AFLP and RAD‐based SNPs used in our analyses, and the “Sumstats” statistics output of the Stacks program are archived in the Dryad Digital Repository (http://dx.doi.org/10.5061/dryad.231b9).Main characteristics of the samples included in this study are provided in Table S1, together with GenBank accession numbers for the associated DNA sequences; additional details are available from the BOLD online database ( Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.