Pseudoroegneria spicata  is an important component of grassland and shrubland plant communities and commonly used for restoration due to its drought resistance and competitiveness with exotic weeds. We used next‐generation sequencing data to investigate the processes that shaped P. spicata's geographic pattern of genetic variation across the Intermountain West. Pseudoroegneria spicata's genetic diversity is partitioned into populations that likely differentiated since the Last Glacial Maximum. Adjacent populations display varying magnitudes of historical gene flow, with migration rates ranging from multiple migrants per generation to multiple generations per migrant. When considering the commercial germplasm sources available for restoration, genetic identities remain representative of the wildland localities from which germplasm sources were originally developed, and they maintain high levels of heterozygosity and nucleotide diversity. However, the commercial germplasm sources represent a small fraction of the overall genetic diversity of P. spicata in the Intermountain West. Given the low migration rates and long divergence times between some pairs of P. spicata populations, using commercial germplasm sources could facilitate undesirable restoration outcomes when used in certain geographic areas, even if the environment in which the commercial materials thrive is similar to that of the restoration site. As such, population structure and history can be used to provide guidance on what geographic areas may need additional native plant materials so that restoration efforts support species and community resilience and improve outcomes.A species’ population structure and history are critical pieces of information that can help guide the use of available native plant materials in restoration treatments and decide what new native plant materials should be developed to meet future restoration needs. In the western United States,  At a coarse scale, Bower, St. Clair, and Erickson  created Originating from mutation, genetic variation becomes structured by gene flow, recombination, random genetic drift, and natural selection  Á. Löve  Gould). This drought resistant, perennial bunchgrass competes well with exotic weeds  steppe  have been developed from the Palouse region of Washington . The final two commercial germplasm sources, Secar and Discovery, represent Elymus wawawaiensis J.R. Carlson & Barkworth, which is an allotetraploid containing copies of the St (Pseudoroegneria) and H (Hordeum) genomes . Six of the commercial germplasm sources represent Atriplex canescens (Pursh) Nutt. internal standard. The sample and standard were finely chopped, and nuclei extraction and staining followed the Cystain UV Precise P assay procedure (Sysmex). Sampling localities were screened for polyploidy by randomly choosing three individuals per locality. If polyploid individuals were identified, an additional three samples were assessed to estimate the proportion of polyploids at the sampling locality. While using only three individuals per sampling locality for the primary screening may have caused us to miss polyploids when present in low frequencies, we note that this method correctly identified all sampling localities containing allotetraploids , regardless of their frequency in the population (as assessed using the genomic data—see Results).Approximately 15 mg of leaf tissue was ground using a bead mill, and DNA was extracted following the DNeasy plant extraction kit protocol . Genome size and ploidy were determined for wildland‐collected individuals using a Partec Cyflow Space flow cytometer under UV fluorescence with an 2.2PstI and MspI, followed by the ligation of Illumina adaptor sequences; each individual was barcoded four times using unique, 5–10 base pair barcodes. Ligation products were pooled, purified using QIAquick PCR kits (Qiagen), and amplified using 16 cycles of PCR with eight replicates. A Pippin Prep  was used to size select amplicons from 400 to 500 base pairs. Nine 96‐sample genotyping‐by‐sequencing libraries, with four barcodes for each sample , were sequenced on a HiSeq 2500  to generate 1 × 125 base pair reads.A total of 967 individual plants  were selected for genotyping‐by‐sequencing  with the ustacks program using a minimum stack depth (m) of 3 and a distance between stacks (M) of 2  was ≤2. The individuals chosen to construct the catalog represent wildland sampling localities across the geographic distribution of our sampling scheme; we limited the sample size to minimize error and restrict the accumulation of loci found in only a few individuals  to generate a Variant Call Format (vcf) file. We utilized a custom script to read the vcf file and calculate θ based on the number of segregating sites across loci in r . After filtering, the script wrote a structure formatted file, which was read into r using the adegenet . By iteratively using PCA and our filtering script, we were able to identify outlier individuals  and assess the impact of various amounts of missing data, thereby maximizing the number of SNPs while minimizing the loss of information content . We assessed the impact of variation in the number of individuals per sampling locality/commercial germplasm source by including all individuals as well as subsampling down to four individuals per sampling locality/commercial germplasm source, which represented the smallest number of sampled individuals; subsampling and statistic calculation were performed 20 independent times. A final execution of populations only included sampling localities pertaining to fastsimcoal2 modeling (see below) and generated a phylip file, which was analyzed with PhyML  statistics; together, these data informed the fastsimcoal2 modeling scenarios.Population genetics statistics were calculated for diploid 2.3P. spicata: (a) Bayesian clustering implemented in structure version 2.3.4 , population size (Ne), and gene flow (2Nm) were estimated from the SNP data using an allele frequency spectrum method  and only loci found in at least 13, 13, 24, and 22 individuals in the Wasatch, East Great Basin (EGB), Palouse/Wallowa, and West Great Basin (WGB) populations (see Results for population definitions), respectively, were retained to minimize errors with allele frequency estimates. Numbers of individuals per population were determined based on the number of individuals and number of loci in each of the groups.A folded joint site frequency spectrum  was calculated for each population pair based on polymorphic loci recorded in the whitelist‐filtered vcf file containing only T) were estimated accounting for the possibility of migration  and variation in effective population sizes   was fixed, whereas the other parameters were estimated based on the site frequency spectrum , assuming a genomewide SNP mutation rate similar to Arabidopsis thaliana  Figure . To impr) Figure , one pop33.16 per individual), of which >940 million passed quality control ; a PCA dataset including only P. spicata , which was filtered down to 776 individuals  and used in structure and sPCA. The vcf file from which the fastsimcoal2 site frequency spectrum was calculated included 295 individuals and 113 310 genotyping‐by‐sequencing loci; this dataset was also used to calculate corrected AMOVA FST values and generate a phylip file with 213,746 SNPs for the four populations resolved by structure and sPCA. Diversity statistics were calculated in populations from a vcf file containing 834 individuals and 113,310 loci. Data sets are archived in the Dryad Digital Repository (https://doi.org/10.5061/dryad.rc1jr0v).Nine lanes of Illumina sequencing produced >2.5 billion reads across 967 individuals  from P. spicata individuals along principal component 1, while variation within P. spicata was described by principal component 2 . We present illustrations of K = 2–5, as they are all helpful in unpacking the major, hierarchical axes of genetic variation within P. spicata  and southeastern (red) genetic clusters. An orange genetic cluster centered in southeastern Washington to northeastern Oregon is separated from the blue cluster at K = 3, and a yellow genetic cluster located primarily in the eastern Snake River Plain and eastern Nevada splits from the red cluster at K = 4. Finally, the blue genetic cluster decomposes again at K = 5, resulting in a southern gray cluster. In general, admixture is more common where genetic clusters meet, compared to the “cores” of their respective geographic distributions. These structure results contrast with the 21 clusters of variation reported by Larson, Jones, and Jensen . Unlike structure analyses, where the blue genetic cluster breaks down from K = 4 to K = 5, sPCA does not resolve this as a significant genetic axis. Because sPCA accounts for geographic distance among sampling localities when identifying global and local structures, we hypothesize that K = 5 represents isolation by distance along a latitudinal cline of the blue genetic cluster identified at K = 4  group WAS and EGB sampling localities together, these results justify our choice of alternative models used for fastsimcoal2 model selection and parameter estimation, which include the relationships proposed by the maximum likelihood tree (Model 1) and two isolation by distance scenarios , nucleotide diversities (π), and inbreeding coefficients (FIS) , and both have a genetic cluster that has, on average, lower diversity , and a cluster with a greater range of diversity . The EGB genetic cluster in Figure Genetic diversity statistics were similar across the s Figure . The eas3.3P. spicata population history represented by Model 3 has the lowest Akaike information criterion  compared to Models 1 and 2 ; in other words, Models 1 and 2 minimize information loss negligibly compared to Model 3  are similar among all three models, including in relative differences among populations, with the WAS (NWAS) and WGB (NWGB) populations inferred as having the largest and smallest effective population sizes, respectively. Migration rates  are also similar (relative to one another and in magnitude), with the highest rates inferred between WGB and P/W, and the lowest rates occurring between WAS and the EGB (Table T3)—in Models 1 and 3, divergence coincides roughly with the end of the Last Glacial Maximum, while in Model 2 the divergence of the ancestral population is near the height of the last glacial period.The 4Pseudoroegneria spicata genetic diversity is partitioned across western North America into populations that are robust and repeatable across analyses. Modeling results suggest that these populations have differentiated since the Last Glacial Maximum, and that adjacent populations exchanged migrants at different rates following divergence. This information may significantly contribute to ongoing use of P. spicata plant materials in restoration projects, as well as inform the development of new materials.4.1P. spicata distributed across the landscape was highly affected by glaciations, as genetic variation coalesces to a single, panmictic population roughly concordant with the Last Glacial Maximum , and we hypothesize that a cooler climate during the last glacial period may have supported a cohesive P. spicata population at lower elevations (compared to its present Great Basin distribution) somewhere in the vicinity of the Central Basin and Range ecoregion —as the climate warmed and P. spicata spread north (and/or east and west), the Wasatch Mountains were colonized early on. As the climate continued to warm, this population became more isolated as lower elevation habitat became inhospitable, while the geographic distribution of the species continued to expand northward. Sampling P. spicata throughout its remaining , US distribution would help determine how many refugia persisted during the Last Glacial Maximum and may facilitate better predictions on where the refugia were located.Determining the location of a refugium is an inherently difficult task . For example, the WAS population is highly differentiated from localities to the west in northwest Utah and eastern Nevada representing the EGB population , it is likely that isolated P. spicata localities throughout the Intermountain West function to maintain some level of cohesiveness for the species  have been exchanged between P/W and WGB since their divergence, it has taken multiple generations, on average, for one effective migrant to be exchanged between WAS and EGB Table . HabitatArabidopsis thaliana  in natural communities. However, our preliminary modeling trials using a range of generation times, as well as independent research  have the capacity to influence parameter estimations. In general, it is assumed that more data, such as is generated using next‐generation sequencing technologies, should lead to more precise estimates of parameters such as population divergence  may be lowest when the commercial germplasm sources are used in restoration treatments across the geographic area covered by these populations. Risks may increase when commercial germplasm sources are used in restoration treatments located within the distribution of the WAS or EGB genetic clusters due to the low levels of gene flow and/or long divergence times between these populations and the P/W and WGB populations from which commercial germplasm sources were developed. As such, future restoration treatments outside of the geographic area covered by P/W and WGB may benefit from the development of plant materials representing the EGB and/or WAS genetic identity. We note that the ultimate test of negative consequences that may be realized by using restoration materials outside of their optimal geographic range  would be a crossing experiment between the commercial germplasm source and the local plants that would track seed production and the subsequent germination/survival rate of the seedlings.Genetic analyses of genomewide SNP variation yielded information pertinent to restoration efforts. With respect to native plant materials available for restoration, the commercial germplasm sources remain genetically representative of the wildland localities from which plant materials were originally collected Figure . HoweverThe collection, development, and deployment of plant materials as suggested by the seed zones of St. Clair et al.  may also4.2.2P. spicata. Genetic diversity within the commercial germplasm sources is, on average, similar to or greater than the diversity represented by the four P. spicata populations (Figure Including commercial germplasm sources in our study design allowed us to assess their genetic diversity in relation to each other and wildland s Figure . Howevers Figure . Furthers Figure , this va4.3Investigating a species’ genetic variation can play a foundational role in the use and development of native plant restoration materials. Characterizing the genetic diversity and geographic distribution of populations can guide the development of diverse and representative plant materials. Furthermore, generating estimates of divergence times and migration rates among populations can provide restoration professionals with knowledge to deploy appropriate materials to project sites, with the goal of supporting species and community resilience and improving restoration outcomes.https://doi.org/10.5061/dryad.rc1jr0v.Data for this study will be available at the Dryad Digital Repository: None declared. Click here for additional data file.


We analyzed variations in 90 mitochondrial DNA (mtDNA) D-loop and heat shock protein 70 (HSP70) gene sequences from four populations of domesticated helmeted Guinea fowls  and 1 population of wild helmeted Guinea fowls  in Kenya in order to get information about their origin, genetic diversity, and traits associated with heat stress. 90 sequences were assigned to 25 distinct mtDNA and 4 HSP70 haplotypes. Most mtDNA haplotypes of the domesticated helmeted Guinea fowls were grouped into two main haplogroups, HgA and HgB. The wild population grouped into distinct mtDNA haplogroups. Two mtDNA haplotypes dominated across all populations of domesticated helmeted Guinea fowls: Hap2 and Hap4, while the dominant HSP70 haplotype found in all populations was CGC. Higher haplotype diversities were generally observed. The HSP70 haplotype diversities were low across all populations. The nucleotide diversity values for both mtDNA and HSP70 were generally low. Most mtDNA genetic variations occurred among populations for the three hierarchical categories considered while most variations in the HSP70 gene occurred among individuals within population. The lack of population structure among the domestic populations could suggest intensive genetic intermixing. The differentiation of the wild population may be due to a clearly distinct demographic history that shaped its genetic profile. Analysis of the Kenyan Guinea fowl population structure and history based on mtDNA D-loop variations and HSP70 gene functional polymorphisms complimented by archaeological and linguistic insight supports the hypothesis that most domesticated helmeted Guinea fowls in Kenya are related to the West African domesticated helmeted Guinea fowls. We recommend more molecular studies on this emerging poultry species with potential for poverty alleviation and food security against a backdrop of climate change in Africa. Numida meleagris) is a terrestrial game bird that is widespread and abundant in Africa. It is found in a wide range of sub-Saharan, open country vegetation type [The helmeted Guinea fowl  is a circular molecule that is 16,726 base pairs in size in Guinea fowls . It is r Kimball  showed t Kimball .NmHSPA2 in Guinea fowl) is promoted by heat shock [Heat stress in birds is one of the main concerns in poultry farming since it causes high mortality and low productivity especially during the hottest seasons , 23. In at shock , 25. OthThis study aimed to analyze variations in the mtDNA D-loop and HSP70 gene of the helmeted Guinea fowl in Kenya for the first time, in order to get some initial information about their genetic diversity, possible genetic structure, and functional polymorphisms in HSP70 that could be associated with heat stress.This study received ethical clearance from the Kenya Wildlife Service under permit number KWS/BRM/5001 to sample wild Guinea fowls and a “no objection for the research” from the Directorate of Veterinary Services, Ministry of Agriculture, Livestock and Fisheries in Kenya under permit number RES/POL/VOL.XXVII/162 to sample domestic Guinea fowls. Blood samples were collected from 90 unrelated adult individuals from five populations in Kenya . Four ofμl reaction volumes containing 20 ng genomic DNA, 1 X PCR buffer , 2.5 mM of each dNTP, 10 pM of each primer, and 1 unit of Taq DNA polymerase . Thermocycling conditions were as follows: 94°C (3 min), 35 cycles of 94°C (1 minute), 58°C (1 min), 72°C (2 min), and a final extension step at 72°C (10 min). The Gallus gallus HSP70 ortholog in Numida meleagris, that is NmHSPA2, was amplified via PCR using the forward primer HSP70-F 5′-ATCATCGCCAATGACCAGGG-3′ (20) and reverse primer HSP70-R 5′-CATTCTTCTCTCCAGCCCGG-3′ (20). PCR amplifications were performed in a 10 μl reaction volume containing 3.8 μl of double distilled water, 1 μl of template genomic DNA, 5 μl of Thermo Scientific™ DreamTaq™ Green Master Mix (2X), and 0.2 μl of 20pM/ μl primer (forward and reverse). The PCR was run under the following conditions: One cycle of initial denaturation at 94°C (3 min), 30 cycles of 94°C for 30 seconds, 55°C for 30 seconds annealing, 72°C for 30 seconds for primer extension, and a final extension step at 72°C (7 min).The first 700 bp of the mtDNA D-loop region was amplified via PCR using the forward primer AVIF2 and reverse primer CR1b . PCR ampPCR products were purified using the Wizard® SV gel and PCR Clean-Up Kit . Purified products were sequenced directly using the Big Dye® Terminator v3.1  on an ABI prism 3730 Avant DNA analyzer. The relevant PCR primers were used for the sequencing reactions.The sequences generated were edited manually using Chromas Lite version 2.1.1 . The cons [2 [Construction of the haplotypes was done both manually and by use of DnaSP v5.10 . Genetics , and R2 s [2  statistiThe number of haplogroups was determined by constructing a median joining (MJ) network  using NEST) and geographic distances between populations, a Mantel test was used to plot the regression graph of the genetic and geographic distances using GenAlEx v6.501 software [To infer the mtDNA maternal genetic structure of helmeted Guinea fowls across Kenya, analysis of molecular variance (AMOVA) was performed using ARLEQUIN. To assess the nonrandom association between genetic differentiation  defined by 41 polymorphic sites. For this study, the individual haplotypes were abbreviated Hap followed by a Hindu-Arabic numeral. These have been deposited into Genbank under accession numbers MH703540- MH703564. We observed an insertion in all individuals in the wild population and a second insertion in two wild individuals. The frequencies of the observed haplotypes in the various populations are represented in a pie diagram  and also galeata . We also galeata . Using mThe extent of haplotype sharing indicates the absence of a population structure in Kenya's domesticated helmeted Guinea fowls. Muchadeyi  and Mtilπ) values are generally low, with the wild population showing highest nucleotide diversity. Avise [We calculated several diversity indices for the five populations as shown in y. Avise  suggestey. Avise  where thy. Avise . The lowy. Avise . Insuffis). Our results show that all the sampled populations except Bungoma West have insignificant SSD values (P>0.05). Tajima's D  and Fu's Fs  were not significant (P>0.05). This could be due to the small number of samples studied since Tajima's D has low power in detecting population expansion when the sample size is small [s statistics do not support demographic and spatial expansion for mtDNA haplotypes across the other populations as previously suggested by Mwacharo [To reveal the historical population dynamics of the studied helmeted Guinea fowl populations across Kenya, we calculated observed and expected distributions of mismatches under the model of growing-declining populations  as shownis small . Demograis small  of the mis small , these ris small , 49 and Mwacharo .To infer the maternal genetic structure of helmeted Guinea fowls across Kenya, we performed analysis of molecular variance (AMOVA) as shown in Tables ST) and geographic distances between sampled regions was assessed using a Mantel test  is observed between genetic variations and the geographic location in helmeted Guinea fowls in Kenya just like previously described by Mwacharo . This coWe obtained partial sequences of the helmeted Guinea fowl HSP70 gene of 508 bp in length from samples of 90 individuals captured in Kenya. Three of these sequences were of poor quality and were not considered for further analysis. The sequences are compared with HSP70 sequences of other related avian species downloaded from Genbank. We observed four haplotypes, TGC, TAC, TGT, and CGC, with three polymorphic sites  shown in  Gallus gallus HSP70 genotype and heat shock resistance (heat tolerance) has been proposed [The relative frequencies of the observed haplotypes in the various populations are shown in proposed . Individproposed , 52.π). The wild population has the highest diversity indices.We calculated several diversity indices for the five populations as shown in Haplotype and nucleotide diversity values are generally low. These lower values reinforce our hypothesis that the observed haplotypes were closely related  and coul Galliformes.The phylogenetic relationship of the various helmeted Guinea fowl haplotypes in Kenya is compared with HSP70 sequences of other avian species and shown in The phylogenetic network diagrams produced using SplitsTree were used to validate the haplotypes. The splits decomposition network of the HSP70 haplotypes in Guinea fowls and related avian species is shown in The splits decomposition network also reveals that all the haplotypes clustered together with haplotype TAC observed to be genetically distant in relation to the other haplotypes. The other avian species were also relatively distant from the four Guinea fowl HSP70 haplotypes.To infer the population genetic structure of HSP70 haplotypes of helmeted Guinea fowls across Kenya, analysis of molecular variance (AMOVA) was performed (Tables ST) and geographic distances between populations by plotting the regression graph of the genetic and geographic distances  and strong positive correlation is observed between genetic variation and the geographic location in helmeted Guinea fowl populations in Kenya as previously described by Mwacharo . Again,  Numididae divergence from the Phasianidae lineage some 38 million years ago [th millennium bp and they do not seem to differ from modern wild specimens [Previous analysis of Guinea fowl DNA indicates a possibleears ago . Martineears ago  went on ears ago  agrees tears ago . The birpecimens –58. N.m. galeata [ N. meleagris readily become commensals of man, increasing in numbers and distribution because of the water, roosting, and feed resources resulting from human activity. The process of domestication probably continues even now.Studies show that appearance of Guinea fowls in the history of man's activities is traced to the fifth Egyptian dynasty about 2,400 B.C. when its figure was drawn in a mural . Early d galeata  which wa galeata –61. Acco galeata , wild poShillington  proposedAgain, it is also imperative to note that the Lugbara, a Nilo-Saharan people of north-western Uganda, have traditionally reared Guinea fowls as one of their main economic activity , althougAlso based on Western Bantu folklore, many Bantu communities of Uganda and Western Kenya claim that their origin is traced to Misri (the present day Egypt), through Congo and Uganda . On the Just like in Nigeria, most domesticated helmeted Guinea fowls in Kenya clustered into two mtDNA haplogroups: HgA and HgB, indicating a genetic relationship between Kenyan and West African Guinea fowls. The wild helmeted Guinea fowls which belong to a different subspecies, are grouped into distinct haplogroups. The lack of a population structure in domesticated helmeted Guinea fowls could suggest intensive genetic intermixing between the domestic populations. The differentiation of the wild Guinea fowls may be due to a clearly distinct demographic history that shaped its genetic profile. All helmeted Guinea fowls in Kenya group into 4 HSP70 haplotypes with two of the haplotypes unique to the wild Guinea fowl. Probably, some of these polymorphisms may be associated with certain environmental adaptations, such as heat tolerance. Analysis of the Kenyan helmeted Guinea fowl population structure and history based on mtDNA variations complimented by archaeological and linguistic insight clearly supports the hypothesis that majority of domesticated helmeted Guinea fowls are related to West African domesticated helmeted Guinea fowls. This study provides initial information on genetic variation across populations of the domesticated and wild helmeted Guinea fowls in Kenya. This is expected to help support the conservation efforts for helmeted Guinea fowls and also develop breeding programs aimed at mitigating the effects of climate change and improving food security.


Impatiens glandulifera across Europe. We resampled 13 populations in 6 regions along a 1600 km long latitudinal gradient from northern France to central Norway after 5 years, and assessed population genetic diversity with 9 microsatellite markers.The observation that many alien species become invasive despite low genetic diversity has long been considered the ‘genetic paradox’ in invasion biology. This paradox is often resolved through the temporal buildup genetic diversity through multiple introduction events. These temporal dynamics in genetic diversity are especially important for annual invasive plants that lack a persistent seed bank, for which population persistence is strongly dependent on consecutive seed ‘re-establishment’ in each growing season. Theory predicts that the number of seeds during re-establishment, and the levels of among-population gene flow can strongly affect recolonization dynamics, resulting in either an erosion or build-up of population genetic diversity through time. This study focuses on temporal changes in the population genetic structure of the annual invasive plant I. glandulifera experiences significant among-population gene flow, gradually resulting in higher genetic diversity and lower overall genetic differentiation through time. Nonetheless, moderate founder effects concerning population genetic composition  were evident, especially for smaller populations.Our study suggests sufficiently high numbers of genetically diverse founders during population re-establishment, which prevent the erosion of local genetic diversity. We furthermore observe that Despite the initially low genetic diversity, this species seems to be successful at persisting across its invaded range, and will likely continue to build up higher genetic diversity at the local scale.The online version of this article (10.1186/s12863-019-0721-4) contains supplementary material, which is available to authorized users. The number of invasive alien species continues to increase across the globe , 2. ConsRange expansion of the invasive species into new areas can result in additional sequential founder effects, bottlenecks and increased genetic drift, further eroding genetic diversity , 10. SevThese temporal dynamics in genetic diversity are particularly important for annual alien invasive (plant) species, where long-term population persistence is strongly dependent on successful seed establishment in each growing season. Depending on the seedling recruitment success (colonists) and the efficiency of long-distance seed dispersal (i.e. an influx of migrants), these re-establishment dynamics could result in sequential genetic founder events and genetic bottlenecks , 22. IndImpatiens glandulifera Royle  (2n = 18 or 20). This species was originally introduced to Europe in the 1800s as an ornamental plant from the western Himalayas [I. glandulifera can form large populations, the species has strongly fluctuating annual population sizes [I. glandulifera populations across several parts of its invaded range [I. glandulifera populations both within and across river catchments in the UK, suggesting founder/drift dynamics due to sequential population re-establishment under limited gene flow [Here we focus on the annual invasive alien plant imalayas  and subsimalayas , 26. Theimalayas , 28. Alton sizes , 29. Theon sizes . Previouon sizes , suggeston sizes ). This aon sizes , 33 and on sizes . Other sed range , 35. Simene flow . These cI. glandulifera populations, ten of which were studied in [In this study, we resampled 13 udied in , to asseO) varied between 0.09 and 0.26 (average: 0.17) (Table O varied between 0.07 and 0.25 (average: 0.18) (Table IS) was significant for 7 out of 13 populations for the 2011 sampling year, and for 5 out of 13 populations for the 2016 sampling year (Table O and the expected heterozygosity (HE) were not significantly different between the two sampling years, nor did they correlate with population size or population size change between 2011 and 2016. FIS, however, was significantly lower in 2016 for small populations, but not for large populations according to the repeated measures ANOVA . Similarly, the model results for %P showed a slight increase in polymorphism for the large populations but a decrease in the smallest population . Note that these interaction effects for FIS and %P are largely caused by the smallest population ‘Trondheim 3’  and Amiens 1 (Wilcoxon p = 0.031) for 2011. For the 2016 data, however, no significant recent bottlenecks were detected. As expected considering the low overall genetic diversity of the species, the assessed effective population sizes were relatively small, and much smaller than the actual population sizes   and for 10 out of 13 populations based on G’ST  and Jost’s D  .Genetic differentiation was significant among all pairwise populations for each sampling year, except between population Ghent 1 and population Ghent 2   indicated that molecular variance was significantly partitioned across all tested hierarchic levels for both sampling years (1) Table . Althoug1) Table .Table 3Rp = 0.003; sampling year effect: F = 17.3, p = 0.002; 2011 population size effect: F = 0.3, p = 0.591). Note that this pattern was mainly caused by the smallest population ‘Trondheim 3’ .Results of the principle coordinate analysis showed relatively large changes in genetic makeup for several populations between the 2011 and 2016 sampling year Fig. . The rep 3’ Fig. . ChangesI. glandulifera seems to be successful at persisting across its invaded range seemingly due to surprisingly high levels of gene flow. Our results thus support the theoretical ‘migrant pool’ colonization model, rather than the ‘propagule pool’ colonization model of annual population re-establishment between 2011 and 2015 [The overall constant genetic diversity in both sampling years, slightly increased polymorphism and reduced among-population genetic differentiation, strongly suggest that population re-establishment is effectuated by relatively high numbers of colonists and migrants, thus stabilizing both genetic diversity and among-population genetic differentiation across generations. These results are in agreement with observed temporal genetic patterns in two short-lived invasive alien insects after tens of generations , 19. In and 2015 , 24.I. glandulifera populations after two generations in the UK [Although no evidence was found for genetic bottlenecks in 2016 and slight increases in genetic diversity were evident, substantial shifts in the genetic makeup did occur for most populations. This is in agreement with the observed temporal genetic differentiation for two n the UK . These rn the UK .I. glandulifera in 2011 [IS between both studies are likely caused by the different number of studied individuals per population  and increased polymorphism/within-individual molecular variance (FIS) all suggests that gene flow among  populations is significantly shaping genetic patterns of these populations. This illustrates how I. glandulifera’s potential for within-study region, long-distance (mainly hydrochorous) seed dispersal can contribute to this species’ temporal genetic patterns.Interestingly, the retention of genetic diversity during sequential population re-establishment is not solely driven by high local seed colonization. Indeed, the significant decrease in overall genetic differentiation, decreased among-population molecular variance (FRT) has remained constant at the relatively high 26% level between both sampling years, also observed in the relatively high, average (among-region) genetic differentiation levels (FST = 0.218). This furthermore helps to explain why local (within-region) population genetic diversity has remained so low, despite the presence of among-population gene flow and strong differences in allele frequencies and identities among study regions [I. glandulifera’s invaded range, such as Finland [Not surprisingly considering the geographical distances, no indication for among-region gene flow was observed. The among-region molecular variance . Our results showed that initially small populations were characterized by the largest shifts, both in genetic composition and in F species , 38. The species , 29. Add species . Consequa plants .I. glandulifera populations across Europe, despite a seemingly overall decrease in their population sizes. These results suggest that annual population re-establishment is following the ‘migrant pool’ colonization model [In sum, we observed a small temporal increase in genetic diversity and decrease in among-population genetic differentiation between 2011 and 2016, for several on model , thus pron model , 24. Ouron model .I. glandulifera experiences significant among-population gene flow, gradually resulting in higher genetic diversity and lower overall genetic differentiation. Despite the initially low genetic diversity and associated low effective population sizes, this species seems to be successful at persisting across its invaded range, and will likely continue to build up higher genetic diversity at the local scale, potentially further enhancing its success. These results suggest that it is very unlikely that this species will show boom-bust dynamics on the longer run, despite its tendency for strong population size fluctuations [Our study furthermore suggests that tuations . In otheImpatiens glandulifera populations with at least 30 flowering individuals were selected for each of six study regions along a 1600 km latitudinal European gradient, ranging from Amiens (France) in the south to Trondheim (Norway) in the north  . We amplified nine microsatellites previously used for the rom 2011 . Six of rom 2011  , expected heterozygosity (HE) and polymorphism  for each population using GenAlEx 6.503 [IS) was estimated for all populations based on Weir & Cockerham’s F-statistics [We calculated the mean number of alleles (further referred to as “A”), observed heterozygosity . Final models were obtained using backward model selection on the covariates based on p-values.To test for changes in genetic diversity between the two sampling years we used repeated measures ANOVA (Pillai’s Trace test) with sampling year as repeated measure factor and 2011 population size, change in population size between 2011 and 2016 and their interaction as covariates using A, He) was assessed using the temporal method, for which the genetic composition of each population across the two sampling years (five generations) is used to estimate Ne. More specifically we used the method of Jorde and Ryman [e. Ne estimations were furthermore based on plan I sampling (non-destructive sampling), with a 0.02 critical value (frequency) for rare allele exclusion, using the NeEstimator v2 software [We used the Bottleneck software to test for potential recent bottleneck events in each population at both sampling years, using the two-phase model of mutation (TPM) with a 90% stepwise component . This tend Ryman , which isoftware .F-statistics (FST). Additionally, genetic differentiation was assessed between the two sampling years for each population. We additionally calculated Hedrick’s G’ST and Jost’s D as measures of genetic differentiation, since, unlike FST, these measures are not affected by marker variability [ST is the original GST as defined by Nei [ST values corrected for null-alleles using the ENA  correction method with the FreeNA software [We calculated pairwise genetic differentiation among populations for both sampling years separately, based on Wright’s iability . G’ST isd by Nei  standardd by Nei . Jost’s d by Nei . We usedd by Nei . Since tsoftware .ST, G’ST, Jost’s D and null-allele corrected FST) between the 2011 and 2016 populations. Significance of these paired t-tests was assessed based on 9999 bootstraps, to overcome issues with the pairwise dependency of the data (SPSS Statistics 21.0). We performed a hierarchical analysis of molecular variance (AMOVA) on pairwise FST values (9999 permutations) with GenAlEx 6.503 [We used paired t-tests to compare pairwise among population genetic differentiation . In these models, sampling year was included as a repeated measure factor and 2011 population size and change in population size between 2011 and 2016 as covariates. A similar PCoA and subsequent repeated measures ANOVA was subsequently performed on the null-allele corrected FST values.Genetic differentiation between populations was furthermore visualized using a covariance-based principal coordinates analysis (PCoA) based on the standardized FAdditional file 1:Impatiens glandulifera populations (FST). Lower left triangle, FST estimates for 2011; Upper right triangle, FST estimates for 2016; values on the main diagonal (grey), FST estimates between 2011 and 2016 populations along a gradient from Amiens to Trondheim. A = Amiens, G = Ghent, B=Bremen, L = Lund, S=Stockholm, T = Trondheim. Significance: NS: not significant; *: 0.05 ≥ P-value > 0.01; **: 0.01 ≥ P-value > 0.001; ***: 0.001 ≥ P-value. (DOCX 15 kb)Pairwise genetic differentiation among Additional file 2:Impatiens glandulifera populations (G’ST). Lower left triangle, G’ST estimates for 2011; Upper right triangle, G’ST estimates for 2016; values on the main diagonal (grey), G’ST estimates between 2011 and 2016 populations along a gradient from Amiens to Trondheim. A = Amiens, G = Ghent, B=Bremen, L = Lund, S=Stockholm, T = Trondheim. Significance: NS: not significant; *: 0.05 ≥ P-value > 0.01; **: 0.01 ≥ P-value > 0.001; ***: 0.001 ≥ P-value. (DOCX 15 kb)Pairwise genetic differentiation among Additional file 3:Impatiens glandulifera populations (Jost’s D). Lower left triangle, Jost’s D estimates for 2011; Upper right triangle, Jost’s D estimates for 2016; values on the main diagonal (grey), Jost’s D estimates between 2011 and 2016 populations along a gradient from Amiens to Trondheim. A = Amiens, G = Ghent, B=Bremen, L = Lund, S=Stockholm, T = Trondheim. Significance: NS: not significant; *: 0.05 ≥ P-value > 0.01; **: 0.01 ≥ P-value > 0.001; ***: 0.001 ≥ P-value. (DOCX 15 kb)Pairwise genetic differentiation among Additional file 4:Impatiens glandulifera populations . Lower left triangle, null-allele corrected FST estimates for 2011; Upper right triangle, null-allele corrected FST estimates for 2016; values on the main diagonal (grey), null-allele corrected FST estimates between 2011 and 2016 populations along a gradient from Amiens to Trondheim. A = Amiens, G = Ghent, B=Bremen, L = Lund, S=Stockholm, T = Trondheim. (DOCX 14 kb)Pairwise genetic differentiation among


Anopheles gambiae populations in north-western Lake Victoria is explored to assess their suitability as candidates for a pilot field study release of GM mosquitoes.Malaria is the leading cause of global paediatric mortality in children below 5 years of age. The number of fatalities has reduced significantly due to an expansion of control interventions but the development of new technologies remains necessary in order to achieve elimination. Recent attention has been focused on the release of genetically modified (GM) mosquitoes into natural vector populations as a mechanism of interrupting parasite transmission but despite successful in vivo laboratory studies, a detailed population genetic assessment, which must first precede any proposed field trial, has yet to be undertaken systematically. Here, the genetic structure of Anopheles gambiae mosquitoes were collected from six locations and a subset (N = 96) was selected for restriction site-associated DNA sequencing (RADseq). The resulting single nucleotide polymorphism (SNP) marker set was analysed for effective size (Ne), connectivity and population structure .478 ST 0.0480–0.0846). Ne estimates were generally small (124.2–1920.3).5175 high-quality genome-wide SNPs were identified. A principal components analysis (PCA) of the collinear genomic regions illustrated that individuals clustered in concordance with geographic origin with some overlap between sites. Genetic differentiation between populations was varied with inter-island comparisons having the highest values  contains supplementary material, which is available to authorized users. Plasmodium parasite control technologies [The World Health Organization (WHO) estimated that 216 million cases of malaria occurred globally in 2016 resulting in approximately 445,000 deaths [nologies , 4 will Anopheles vectors [Although challenging, and technically complex to construct, genetically modified (GM) mosquitoes as an alternative vector control tool have increasingly gained attention over recent years in parallel with the genome sequencing advancement of the major  vectors –8.Anopheles mosquitoes is to decrease their vectorial capacity to transmit Plasmodium parasites, either through population suppression or replacement [Anopheles vector populations are to be established as part of a malaria control intervention then these achievements must be successfully translated from bench to field. The first step in realising this strategy is to obtain a detailed understanding of the genetic structure of the natural populations into which the transgenic construct and gene drive system will be introduced as identifying levels of gene flow (genetic exchange) and the effective population size (Ne) will be critical to predicting the dispersal and maintenance of a transgene.The principal goal of creating GM lacement , by rendlacement , 11 and lacement –14. If tAnopheles gambiae is an important mosquito vector of the Plasmodium malaria parasite species, which infect humans. Its population structure across the African continent has been extensively studied and was unexpectedly shallow [ST 0.036) and microsatellites (mean FST 0.016) revealed extensive inter-population gene flow over a 6000 km distance [ST 0.104: mean mitochondrial DNA FST 0.176), a much shorter distance of 700 km, which was attributed to the KRVC acting as a physical barrier to gene flow [An. gambiae population structure have also demonstrated varying degrees of differentiation that range from considerable genetic exchange in the Bijagós archipelago of Guinea-Bissau (FST 0–0.019) to restricted gene flow between the Comoros Islands (FST 0.093–0.126) [An. gambiae populations of the Comoros, they are not well suited to the frequent monitoring that transgenic field studies require being nearly 1000 km offshore. A comparably appropriate alternative would be a lacustrine setting with multiple islands in a malarious region: Lake Victoria.In sub-Saharan Africa,  shallow –18. Compdistance  that conFST 0–0.0 to restrAn. gambiae population genetic studies in Lake Victoria. Chen et al. [ST 0.0010–0.019, p < 0.001), which also supported a significant correlation between geographic distance and genetic differentiation (Mantel: p < 0.001). Kayondo et al. [An. gambiae populations in the Ssese Islands, the focus of the present study, using microsatellite markers with temporal sampling that also demonstrated low but statistically significant genetic differentiation . In contrast with Chen et al. [e, and (iii) temporal instability, which, combined, had provided these mosquitoes with the opportunity to differentiate genetically.There have been two previous n et al.  developeo et al.  examinedn et al. ; howeverAn. gambiae populations using recent advances in next-generation sequencing technologies. Single nucleotide polymorphism (SNP) markers were selected to capture high-density sequence variation due to their: abundance in the An. gambiae genome [This study aimed to follow up that of Kayondo et al.  by detere genome , lower me genome , 24 was 2 while some are merely islets of rock, creating a total land coverage area of 454.8 km2. The islands share a general topographical characteristic in that they rise as gentle slopes from lake level (1220 m above sea level (ASL)) to central flat-topped ridges at a maximum elevation of 1260 m ASL  [The study area is located in the Ssese Islands, an archipelago in the north-western division of Lake Victoria, southern Uganda Fig. . Each of Bugala) , 26. The Bugala) –27. AnnuThe Ssese Islands fall under the administrative jurisdiction of the Kalangala District  local government. The most recent census  lists thAnopheles gambiae sensu lato (s.l.) mosquitoes were sampled from seven sites: (1) Kansambwe, Nsadzi (NZ); (2) Lutoboka, Bugala (BL); (3) Kafuna, Sserinya (SYK); (4) Bbosa, Sserinya (SYB), and (5) Nakibanga, Bukasa (BK) from the islands, and (6) Lunnyo, Entebbe (EB), and (7) Naama, Wamala (WL) from the mainland, reflecting the microsatellite populations analysed by Kayondo et al. [An. gambiae s.l. [o et al. . The vilo et al.  and urbaiae s.l. , meaningn sites:  KansambwCollections were made at random intervals between July and October 2012. Indoor-resting (IR) adult females were collected from houses or common buildings within a 3 km radius of the boat-landing site for each island and the Entebbe locations. Sampling at Wamala was conducted with the same criteria but used Naama village as a centre point. Buildings were constructed from a combination of mud or wooden walls and thatched or corrugated sheet metal roofs. IR adult females were captured between 06:00 and 10:00 a.m. via battery-powered mechanical aspirators. If insufficient IR adult females were collected then aquatic larval samples were sourced from 5 to 10 surrounding breeding sites , taken back to the laboratory and reared into adults in the water that they were collected in. This water was supplemented with mice feed pellets as required.Anopheles gambiae s.l. mosquitoes were morphologically identified from other anopheline species based on the identification keys of Gillies and de Meillon [An. gambiae and Anopheles arabiensis—the other important malaria vector in the An. gambiae s.l. complex—was determined by Scott et al. [An. gambiae were processed further. Meillon . Female t et al.  using leGenomic DNA was extracted from individual mosquitoes using a laboratory stock solution of 2% cetyltrimethyl ammonium bromide (CTAB). Each specimen was placed in an Eppendorf tube containing 200 μl of CTAB and electrically homogenized with a sterile conical Teflon pestle. RNA was removed from the homogenate by adding 20 μl RNAse A (10 mg/ml) (laboratory stock) and leaving it to incubate at room temperature (RT) for 5 min. Proteins were removed with the addition of 20 μl of Proteinase K (20 mg/ml) . The solution was briefly vortexed (1–2 s) on a low setting (3–4) to encourage maximum digestion and incubated at 56 °C for 1 h. Exoskeleton and other cellular detritus were pelleted by RT centrifugation at 14,000 rpm for 5 min. The supernatant was transferred to a Phase Lock Gel tube  with 250 μl of UltraPure™ Phenol:Chloroform:Isoamyl alcohol   for extraction via the standard Phenol:Choloroform method . SamplesEcoRI and MseI restriction enzymes  and incubated at 37 °C for 2 h, then 65 °C for 20 min with the heated thermal cycler lid (Eppendorf AG) at 105 °C followed by a 4 °C hold without the lid. The digested DNA fragments were then ligated to the EcoRI and MseI adapters with T4 DNA Ligase .RADseq libraries were prepared as per Parchman et al. , which wEcoRI adapter sequences consisted of Illumina adapters and primer sequences, a unique 8–10 nucleotide (nt) barcode created by a Python script [EcoRI adapter sequences: 5′-CTCTTTCCCTACACGACGCTCTTCCGATCT + 8–10 nt barcode + C-3′ and 3′-TGTGAGAAAGGGATGTGCTGCGAGAAGGCTAGA + 8–10 nt barcode + G-5′). The MseI adapter sequences were modified from the original protocol to facilitate PE sequencing strategy and also consisted of Illumina adapters and primer sequences, a protector base, and additional sticky end-matching bases: (JT-MseI1: 5′-GCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATC-3′ and JT-MseI2: 5′-TAGATCGGAAGAGCACACGTCTGAACTCCAGTCACATCACGATCTCGTATGCCGTCTTCTGCTTG-3′). The DNA plate was then incubated in a thermal cycler at 16 °C for 2 h with the heated lid at 20 °C followed by a 4 °C hold without the lid.The n script  that perEcoRI- and MseI-ligated adapters. Modifying the MseI adapter to facilitate PE sequencing necessitated modification of the reverse Illumina PCR primer, accordingly: . This step was performed running two separate 20 μl PCR amplification reactions for each adapter-ligated DNA sequence to ameliorate stochastic differences in the resulting reaction products. Both PCR plates were incubated in a thermal cycler using the following profile: 98 °C for 30 s; 30 cycles of 98 °C for 20 s; 60 °C for 30 s; 72 °C for 40 s, and a final extension at 72 °C for 10 min with the heated lid at 105 °C followed by a 4 °C hold without the heated lid.Adapter-ligated fragments were amplified using Illumina PCR primers, which were designed to amplify only those DNA sequences with the Reaction products were pooled and purified with Agencourt AMPure XP  magnetic beads and size-selected using the automated BluePippin  system, which recovered eluted DNA fractions between 400 and 500 base pairs (bp). Sequencing was accomplished in a single lane run on an Illumina HiSeq 2000 (v.1.5) machine at the University of California-Davis, Sacramento, USA via the Beijing Genomics Institute.Molecular karyotyping of the 2La inversion was conducted as per White et al.  with a mAfter quality checking of the sequence data in FastQC v.0.10.1 , IlluminIndividuals were pruned from the dataset on the basis of kinship and/or missing data. Familial relationships were assessed by pairwise comparison of kinship coefficients estimated using the (--relatedness) , and (--K) with which to run the model was chosen by a cross validation (CV) procedure that identified the lowest error value for which the model had the best predictive accuracy.Population structure was visualized by two methods: (1) principal component analysis (PCA) using the software packages PLINK v.1.9  and R v.ST) [ST distribution [ST/(1 − FST)) against logarithmic transformed geographical distances [e were obtained using the linkage disequilibrium (LD)-based method LDNe [Genetic differentiation between populations was quantified by Wright’s fixation indices (pairwise FST)  using WeST)  in VCFtoST) . Individribution . To testribution , a lineaistances  was creaistances  using a istances . Estimathod LDNe  of NeEsthod LDNe  with a mAn. gambiae and one as An. arabiensis (from the Entebbe collection site). A total of 373,099,980 reads were generated by the Illumina HiSeq 2000 platform. After demultiplexing the raw data of sequencing adapters, barcodes, EcoRI and MseI restriction cut sites and protector bases, a total of 172 million reads averaging 1.6 million per mosquito (N = 96) were retained for genomic alignment. 103 million forward reads (86.2%), approximately 83 bases in length, successfully mapped to the AgamP4 reference genome [479 individuals were molecularly identified as e genome , which wExamination of kinship identified a large number of familial relationships between individuals in the Sserinya (SY) and Bugala (BL) populations. Eleven individuals from SY and three individuals from BL were excluded from further analysis on the basis that their full and half kinship could confound the data at each site and in comparison with others. Three individuals from the Bukasa (BK) population with > 80% missing genotype information were also removed from the dataset (N = 79). Kinship coefficient estimates and percentage missing data values per individual are listed in Additional file After high quality SNP calling, application of hard filters to, and pruning from, the dataset, a total of 5175 SNPs were identified and mapped to the AgamP4 chromosomes  as folloVisualization of population structure by PCA illustrated how the SNPs genetically clustered within and between collection sites. Genome-wide analysis n = 5175) showed individuals clustering into three discrete groups on the first principal component (PC1) in a non-geographical configuration Fig. a, which 175 showeK = 2 was the most likely number of fractions  rather than geographic locations for this region of the genome  for the 4097 SNPs across the collinear genome after removal of the SNPs on chromosome 2L (Table Pairwise Fockerham  weighted2L Table .Table 1GST > 0.08).Generally, inter-island comparisons generated the greatest differences between populations with the strongest signals of genetic differentiation being observed in the comparisons with Sserinya (median FST/(1-median FST) against a natural logarithm transformation of geographic distance [y = 0.104975 − 0.012853x; R2 = 0.05; Mantel: p = 0.2) meaning that geographic distance alone could not explain the variation observed between the populations  [Estimations of Ne method  in NeEste method  on the be method , 62, 63. (Kenya) , 13,200  (Kenya) , 2 milli (Kenya) .Table 2Ee. The lower estimates of 0.001 observed for Bugala and Wamala indicated that these populations would be more resistant to the effects of genetic drift compared to the higher coefficients of variation recorded for Bukasa (0.005), Entebbe (0.005) and Nsadzi (0.008), which implied vulnerability to allelic dropout or fixation.The largest estimates were seen in Bugala (1098.3) and Wamala (1920.3), which were substantially higher than the other populations. The infinity estimates recorded for the Sserinya populations were derived from negative points, which implied that variation was due to sampling error alone and not genetic drift . This was unsurprising given the high levels of kinship observed in both populations as a result of the unrepresentative entomological sampling that necessitated the removal of eleven individuals from the dataset. The coefficient of variation is a measurement of genetic drift specific to the LDNe method of NeEstimator v2.01  and is cAn. gambiae mosquitoes. Only 1 of 480 female anophelines was molecularly identified as Anopheles arabiensis, and this was from the mainland (Entebbe) population. Kayondo et al. [An. arabiensis, which was attributed to asynchronous entomological sampling. This observation highlights the importance of systematic population sampling to establish changes in (a) species composition, (b) vector abundance, and (c) seasonality, which are some of the factors that can influence the genetic structure, and effective size of a population.The majority of the dataset comprised of o et al.  reportedPrevious studies have demonstrated a strong association between the frequency of the 2La inversion and aridity, which shifts seasonally and geographically according to climate , 67, butST 0.0480–0.0846) but significantly greater than the very low differentiation between An. gambiae populations observed across opposite sides of continental Africa (mean FST 0.016) [ST 0.104), which acts as a physical barrier to gene flow [An. gambiae in the Comoros (mean FST 0.199–0.250) [An. arabiensis in Madagascar, Reunion, and Mauritius (mean FST 0.169) [ST values of 0.014–0.105 in the same An. gambiae populations sampled here, which are of a lower magnitude than those in the oceanic island studies, but not unexpected given the smaller distances involved that allowed for frequent human-marine transportation routes or even wind-borne dispersal, which might have passively dispersed mosquitoes. This would suggest that water is not an absolute physical barrier to gene flow in this region as supported here by the evidence indicating limited migration between populations (Table ST 0.0342)  , and comene flow . It woulT 0.169) . KayondoT 0.169)  reportedand SNPs , 69. Thaand SNPs  and Lukiand SNPs  using mie that were obtained through the LD method of NeEstimator v.2.01 [e determines how random genetic drift affects the stability of allele frequencies in a population, which are more variable in smaller populations. Kayondo et al. [An. gambiae is usually found in close proximity to human habitation [An. gambiae.Higher levels of genetic differentiation could also be explained to some extent by the small estimates of Nr v.2.01 . Ne deteo et al.  estimatebitation , and thebitation  (< 397),bitation  and toure, which can fluctuate in accordance with climatic changes. Future research should; therefore, focus on monthly entomological sampling to account for variances in seasonal mosquito abundance [One of the key components of a population genetic analysis is the temporal stability of the population. A limitation of this study is that there is only one time point to estimate Nbundance , 73.An. gambiae population connectivity, and effective size in the Lake Victoria region. The island populations are comprised of a dominant malaria vector species (An. gambiae) with low to moderate genetic differentiation, and greater structure suggesting some limitation to migration between them. Smaller estimates of effective population size indicate that an introduced effector transgene should be susceptible to genetic drift but to ensure that it is driven to fixation instead of loss the construct would have to be paired with a robust gene drive mechanism.This is the first genome-wide SNP-based study of Taking these findings into consideration, together with their favourable location and suitability for frequent monitoring, the Ssese Islands contain several candidate field locations, which merit further evaluation in regard of a potential GM mosquito pilot release.Additional file 1: Table S1. GPS co-ordinates and distances between entomological sampling sites.Additional file 2: Table S2. Bioinformatics pipeline detailing software programs and parameters used in the analysis of RADseq genomic data.Additional file 3: Table S3. Kinship coefficient estimates and percentage missing data values per individual.Additional file 4: Table S4. Chromosome 2L inversion molecular karyotype results.Additional file 5: Figure S1. (a–d) PCA plots of An. gambiae chromosomes not shown in the main text.Additional file 6: Figure S2. ADMIXTURE bar plot of chromosome 2L SNPs (K = 2) showing probable ancestry fractions of the six An. gambiae populations (N = 79) based on the 2La inversion.Additional file 7: Figure S3. Generalized linear model plot of genetic differentiation against log geographic distance based on median FST.


Heterodera schachtii, a global invader that parasitizes over 200 plant species, represents a model for addressing important questions about the invasion genetics of PPNs. Here, a phylogeographic study using 15 microsatellite markers was conducted on 231 H. schachtii individuals sampled from four continents, and invasion history was reconstructed through an approximate Bayesian computation approach, with emphasis on the origin of newly discovered populations in Korea. Multiple analyses confirmed the existence of cryptic lineages within this species, with the Korean populations comprising one group (group 1) and the populations from Europe, Australia, North America, and western Asia comprising another (group 2). No multilocus genotypes were shared between the two groups, and large genetic distance was inferred between them. Population subdivision was also revealed among the populations of group 2 in both population comparison and STRUCTURE analyses, mostly due to different divergent times between invasive and source populations. The Korean populations showed substantial genetic homogeneity and likely originated from a single invasion event. However, none of the other studied populations were implicated as the source. Further studies with additional populations are needed to better describe the distribution of the potential source population for the East Asian lineage.Plant‐parasitic nematodes (PPNs) threaten crop production worldwide. Yet few studies have examined their intraspecific genetic diversity or patterns of invasion, critical data for managing the spread of these cryptic pests. The sugar beet nematode  The final elongation step was done at 72°C for 30 min. Negative controls (no template) were run in all PCR amplifications to check for contamination. PCR was repeated at least twice with modified conditions to confirm the results of reactions without target bands. Genotyping was performed using an ABI 3730 XL DNA Genetic Analyzer  and analyzed by GeneMapper 3.7 , number of alleles per population, number of multilocus genotypes (MLG), expected number of MLGs (eMLG) at the lowest common sample size (=10 in this study), and Simpson's diversity index (Lambda) were calculated for each H. schachtii population using poppr. For each locus, the number of alleles, observed heterozygosity, expected heterozygosity, and evenness were also calculated.Hardy–Weinberg equilibrium (HWE) was examined for each locus using the function FST  method, suggested by Chapuis and Estoup  and then visualized as a heatmap in R.Genetic differentiation between populations was estimated by calculating pairwise estimators of ST Weir, . The ENAd Estoup , was appH. schachtii, we repeated analyses again, excluding the two loci that had a null allele frequency >0.19. This value is a threshold over which underestimation of expected heterozygosity due to null alleles becomes significant  tree. A minimal spanning network (MSN) was built using Bruvo's distance (utilizing a stepwise mutation model) among all MLGs in the same package. Genetic distance within and between defined genotype groups was calculated and visualized as boxplots.Phylogenetic relationships were inferred at both the individual and population level. We calculated Provesti's distance (which can handle missing data) among all individuals in https://bioinformatics.org/populations/) to generate a NJ tree among populations based on the chord distance  with 1,000 permutations. This genetic distance was selected because it is considered the most efficient way for generating an accurate phylogeny  was used to view both NJ trees as radial dendrograms.We then used POPULATION software v. 1.2 , ten replicates were run using the admixture model, correlated allele frequencies and the prior population information with 100,000 Markov chain Monte Carlo (MCMC) iterations after a burn‐in of 10,000 steps. Structure Harvester version 0.6.92 , and CORE . Pooling populations with shared history is an efficient way to reduce the complexity of candidate scenarios and has been used in numerous other studies 2 distance for pairs of populations. A pre‐evaluation step based on a principal components analysis (PCA) was performed to ensure that at least one combination of scenarios and priors can produce simulated data sets that are close enough to the observed data set.For both steps, one million simulated data sets were run for each scenario to build a reference table under a generalized stepwise mutation model with mean mutation rate ranging from 1.00E‐004 to 1.00E‐3 and uniform prior distribution. We measured the similarity between real and simulated data sets using summary statistics both within and between populations as suggested in the DIYABC manual, including the mean number of alleles, mean genetic diversity and mean size variance for single population, and pairwise The relative posterior probabilities of the competing scenarios were estimated by a logistic regression analyses on the 1% of simulated data sets closest to the observed data set  and two Turkey populations  . The three Korean populations (hereafter referred to as group 1) showed very great differentiation from the other nine populations (hereafter referred to as group 2), with large FST[ENA] values ranging from 0.280 to 0.400  to great , with most comparisons displaying moderate differentiation  separated from the other populations; and when K = 4, NE became separated; this trend continued until K = 10  that explained 89.5% of the total variance. Two completely separate genetic clusters appeared, corresponding to groups 1 and 2 Figure a. The fip < 0.0001), followed by variation among groups  and variation among populations within groups  (Table Based on the population clustering suggested above (groups 1 and 2), AMOVA analysis revealed that most genetic variation came from variation within populations  and no overlap with the other scenarios  showed a higher value than the FST excluding null alleles (FST[ENA] = 0.244 [95% CI: 0.188–0.307] versus FST[ENA] = 0.235 [95% CI: 0.185–0.293]); and the CD distance heatmaps both with and without the “INA correction”  from the other populations worldwide (group 2) in the phylogenetic and genetic clustering analyses. Large genetic distance was estimated between groups 1 and 2: The median Bruvo's distance of MLGs between groups was nearly 3‐ and 1.5‐fold larger than the intra‐group distances Figure b. All th4.3FST[ENA] > 0.15] according to Hartl and Clark  in northern France based on five microsatellite loci, and one study of Caenorhabditis elegans, a free‐living soil nematode, also found weak differentiation between populations in Australia, Germany, and the United States (Koch, Luenen, Horst, Thijssen, & Plasterk, H. schachtii, human‐mediated transport may have played an important role in its broad dispersal among different continents (Subbotin et al., H. schachtii may be attributed to different divergence times between invasive and source populations, since populations that broke off earlier would have had more time to accumulate mutations (Estoup & Guillemaud, Passive dispersal of cysts by human activities, water, or wind is assumed to enhance PPN gene flow among distant regions (Gavassoni, Tylka, & Munkvold, nd Porte  found we4.4H. schachtii is considered native to Europe with observational data from most beet‐farming areas, but it has now attained a global distribution (Cooke, H. schachtii lineage that so successfully expanded out of Europe and around the globe is not related to the recent Korean invasion. Evolutionary scenario comparison indicates that a ghost population diverged early from the CORE population, accumulated large genetic differences, and subsequently was the source of the Korean invasion. Considering the substantial homogeneity among the Korean populations, they probably originated from the ghost population during a single introduction event and then subsequently spread in the Korean Peninsula. However, due to H. schachtii's wide distribution and the lack of precise knowledge of the relationships and historical movements among the studied populations, it is not currently feasible to pinpoint the location of the ghost population. Note that to simplify possible candidate scenarios, we did not resolve relationships among the CORE populations. In fact, the populations of the CORE have relatively low levels of differentiation, making it unsafe to reconstruct a “true” population tree to estimate their introduction routes (Estoup & Guillemaud, H. schachtii. Nevertheless, this study provides an important phylogeographic perspective on H. schachtii that can help to monitor its spread and track further invasion events in East Asia.n Cooke, . Our gron Cooke, . However5H. schachtii from four continents and discussed the most likely evolutionary scenarios behind these changes. These data are an essential foundation for creating strategies for better controlling and preventing the spread of H. schachtii and also represent a good model system for studying important questions in the invasion genetics of nematodes (Subbotin et al., Biological invasions and their underlying mechanisms have become increasingly better understood by the application of an evolutionary perspective (Chown et al., 6Heterodera schachtii are available in the Supporting information Appendix Genbank accessions of the fifteen microsatellite loci used in this study are KT716061‐KT716075, and the microsatellite genotyping data for The authors declare no competing commercial interests. Click here for additional data file. Click here for additional data file.


Coregonus is complex, and includes many endemic species of high conservation concern. However, because of commercial importance of whitefish fisheries, stockings and translocations have occurred repeatedly, which challenges the identification of local populations as conservation units. This study analyses the phylogenetic relationships of 15 contemporary and two historical populations of lake-resident and anadromous whitefish (Coregonus spp.) from the southern Baltic and North Sea basins. We elucidated the complex history of Lake Schaal (northern Germany) whitefish, for which a local tale suggests that the devil threw whitefish from the Central European Lake Constance into this lake. Studies from the early twentieth century indeed suggested numerous stocking events for Lake Schaal from Lake Constance, from Estonian/Russian Lake Peipsi and from the anadromous whitefish of the Baltic Sea.The diversity and phylogeny of whitefish of the genus C. holsatus) vanished from the lake, but has survived as a non-native population in the north German Lake Drewitz. The whitefish currently occurring in Lake Schaal and three adjacent lakes are identified as C. maraenoides, introduced from Lake Peipsi. The contemporary anadromous whitefish populations from the Baltic (German and Finnish coast) and the German River Treene  grouped together, but showed significant genetic differentiation. The 14 historical specimens of C. oxyrinchus from Rivers Rhine and Schelde were assigned to several contemporary whitefish populations, but among them only one specimen was assigned to the contemporary River Treene population. Therefore, we do not support the view that the whitefish from River Vida/Treene are identical with the historical C. oxyrinchus.Analyses of 13 microsatellite markers showed that Lake Constance whitefish are unrelated to any northern Germany whitefish population, including the contemporary whitefish population from Lake Schaal. Comparison with four historical specimens further showed that the native Lake Schaal whitefish  contains supplementary material, which is available to authorized users. Coregonus represent a large group of medium to large-sized resident or migratory freshwater fishes. Their diversity and phylogeny are still largely unresolved and traditionally considered a nightmare when it comes to the recognition of species and conservation units [Coregonus species are notorious for postglacial (< 12.000 years) adaptive radiations [Coregonus since the fourteenth century [Whitefish of the genus on units –8. Underdiations , 10 and diations  leading diations . Secondldiations , and the century –18, but  century .2, maximum depth 72 m, weakly eutrophic). At about 1920, a native origin was assumed by Thienemann [Coregonus holsatus. In 1997, Kottelat [C. holsatus from Lake Schaal might be conspecific with Coregonus widegreni, known as the valaamka whitefish, a large deepwater species from Russian Lake Ladoga, and the Scandinavian sandsik or sea-spawning whitefish [A typical example of a complex and unresolved genetic history of whitefish is the locally famous case of Lake Schaal in northern Germany  and a predominantly benthic foraging behaviour [C. holsatus is not of native origin, but has been introduced from Lake Constance already during the period of operation of the monastery on Lake Schaal (between 1246 and 1553). Subsequently, we refer to this tale as the “devil hypothesis”.However, there are numerous reports, which indicate potential stocking activities for Lake Schaal from other sources. Documentations from the 19th and early 20th centuries report the introduction of e Schaal , 25. Surhthalmus , 17. Howienemann  morpholoCoregonus maraenoides from Lake Peipsi (Estonia/Russia) have been introduced into Lake Schaal during the 1930s [C. holsatus from Lake Schaal by Thienemann [C. maraenoides  [C. holsatus [C. holsatus material deposited at the Zoological Museum of Berlin, Germany (ZMB). In contrast, a large whitefish population, which matches all morphological characters with Thienemann’s [C. holsatus and the corresponding material at ZMB, exists at present in the north-east German Lake Drewitz (synonymous name Alt-Schweriner See). According to unpublished reports, whitefish, presumably C. holsatus, were translocated from Lake Schaal into Lake Drewitz in 1931, before stocking of C. maraenoides into Lake Schaal commenced [To further complicate the situation, the lake-resident he 1930s , 27, aftienemann , 21. Pretruding) , and do holsatus , 21 or tnemann’s , 21 origommenced .Coregonus oxyrinchus, an anadromous whitefish from the North Sea is another potential source, which may have contributed to the stocking history of Lake Schaal. Historically, C. oxyrinchus has occurred in Rivers Rhine and Schelde, but meanwhile there is evidence that it is globally extinct [C. oxyrinchus [Finally,  extinct . Currentyrinchus , 31, altyrinchus .Coregonus populations are of special importance, because they often represent unique species flocks [C. holsatus) is still unresolved, but molecular methods may identify potential source populations to evaluate the conservation status of whitefish in this lake.For nature conservation, s flocks , 13, 32 s flocks , 14, 33.s flocks . AccordiC. wartmanni, C. arenicolus, or C. macrophthalmus, the “devil hypothesis”), or is more similar to and thus may originate from other European whitefish populations . In contrast, a native origin would be supported by genetic similarity with the presumably translocated contemporary C. holsatus population in Lake Drewitz and by correspondence with historical C. holsatus specimens as found in ZMB. To resolve the genetic uncertainty of the River Vida whitefish population, we also included historical museum specimens of C. oxyrinchus from the Rivers Rhine and Schelde populations. By including this broad variety of potential origins and the mix of contemporary populations and historical individuals, our study is the first opportunity to review the diversity of whitefish populations from the south-western Baltic and eastern North Sea basins for conservation purposes.In this study, we elucidate the phylogeographic history of the Lake Schaal whitefish population in comparison with other whitefish populations by molecular markers. We test specifically whether the present Lake Schaal population is genetically most similar to species from Lake Constance , and therefore we obtained specimens of the contemporary populations from these two lakes. There was no distinct information about the presence of C. holsatus in other nearby lakes in north-west Germany, but we included samples from whitefish populations in Lakes Poenitzer and Keller, both adjacent to Lake Selenter. These four lakes form the first group of potential origin  and the Rivers Schlei and Trave, both draining into the Baltic Sea  . From Lake Peipsi we obtained tissue from eight fish only, but successfully extracted DNA from dried scales of another 10 individuals. Historical tissue material from  Schöter .− 1 . All individuals were typed at 11 polymorphic microsatellites following the protocol developed by Bittner et al. [Total DNA was extracted from fin tissue using the QIAGEN® DNeasy kit following the protocol of the supplier. For historical samples, the tissue was soaked in repeatedly changed TE buffer for 48 h prior to extraction and the concentration of proteinase K and the time of tissue digestion was tripled for extraction . DNA conr et al. , supplemr et al. , CoCl6, r et al. , and BWFr et al. ; set 2 cr et al. , BWF2 [4r et al. , and CoCr et al. . The for2O (PCR grade), 1 μl DNA (~ 20 ng μl− 1) and 1 μl Primermix (~ 2 pmol μl− 1 each primer). The thermocycling profile started with an initial denaturation step at 95 °C for 15 min, followed by 30 cycles of 30 s at 94 °C, 90 s at 57 °C, 90 s at 72 °C and ended with a final extension step of 10 min at 72 °C. A quantity of 1.5 μl of a 1:2 dilution of the PCR reaction were added to a mix of 19 μl Hi-Di Formamide (ABI) and 1 μl LIZ™ size standard (ABI), and denatured fragments were resolved on an automated DNA sequencer (ABI 3100). Genotypes were determined with the software Gene Mapper™ version 4.0 (Applied Biosystems Inc). As a quality control for PCR and fragment analyses, DNA of two already previously genotyped individuals was amplified and analysed with any batch of 94 individuals. If loci or alleles of these two control samples were missing or differed from previous results, the fragment analysis was repeated for all samples per batch. If still alleles were missing, the PCR and fragment analysis were repeated. Analyses of individuals with missing genotypes  were also repeated until completion in a similar fashion. To estimate repeatability in addition, 9.7%  were analysed twice at all 13 loci and 10 mismatches were found, which relates to an overall genotyping error rate of 2.2%, not accumulated on specific loci.PCR amplification was carried out in 10 μl reaction volumes, containing 5 μl QIAGEN® Multiplex PCR mastermix, 3 μl HN and allelic richness AR, i.e. the allele number corrected for the smallest sample size among contemporary populations (N = 7) [IS) and population-wide deviations from Hardy–Weinberg equilibrium (HWE) were tested by the exact (probability) test using genepop_in_R based on Genepop 4.7.0 [All analysis and graphical output were performed in R version 3.4.2 . Genetic (N = 7) , by PopG (N = 7) . Allele op 4.7.0 , 45. Theop 4.7.0  per popuop 4.7.0 , completop 4.7.0  was run op 4.7.0 , 50.To infer population structure by determining the number of clusters observed without prior knowledge, we applied Discriminant Analysis of Principal Components (DAPC) , 52, a mTo account for potential bias in individual assignment to DAPC-clusters from locus-specific deviations from HWE, we re-run the DAPC without the four loci showing the most frequent deviations from HWE (see results). Subsequently, we estimated the probability of assignment to the population of origin for each individual, and calculated the mean self-assignment for each population. The mean self-assignment as based on all 13 loci was compared with the mean self-assignment as based on the 9 loci excluding those with substantial deviations from HWE by Pearson’s correlation.Population differentiation was estimated by F-statistics  between Furthermore, using the allele loadings, the individuals from historical populations  could be plotted onto the factorial DAPC plane, and we could derive their membership probabilities to the defined clusters. To support the DAPC analyses on historical individuals, we ran an alternative likelihood-based assignment procedure in Geneclass2 , as baseA) [Finally, to detect genetic relationships among all 15 contemporary and two historical populations, Nei’s genetic distances (DA)  were depA) . To infeR, the mean number of alleles per locus and population adjusted to the minimum N = 7, varied little among populations . In all but one sample, the expected heterozygosity across all loci was higher than the observed heterozygosity  and C. c.f. maraena from River Treene did not show any deviation from HWE , BWF2 (N = 6), C2–157 (N = 4) and CoCl224 (N = 3) . The strongest associations were found between the loci CoCl18 and CoCl4 (rD = 0.092), BWF1 and CoCl61 (rD = 0.086) and between CoCl4 and CoCl61 (rD = 0.082).The number of deviations from HWE tested individually for all 13 analyzed microsatellite loci and corrected by the false discovery rate for multiple tests varied between populations  and C. cf. maraena from River Treene were placed in between C. maraena and northern lake whitefish populations  was greater than the among-individuals within-populations variance .Discriminant Analysis of Principal Components (DAPC) was run with the cross-validated optimum number of 80 principal components (cumulated variance 93.2%) and six discriminant functions and differentiated the 343 individuals from the 15 contemporary populations along the first two discriminant axes  and Lake Peipsi Fig. . Mean inals Fig. .Fig. 3Inr = 0.92, P < 0.0001). For most of the populations, the mean self-assignment rate slightly declined by excluding the four loci, but remained > 90% for whitefish from Lake Drewitz and River Treene  or Achterwasser (1), N = 1 to C. c.f. maraena from River Treene, N = 1 to C. widegreni from Oulu)  were only weakly segregated from the north German Lakes Keller, Schaal, Poenitzer, Selenter and Russian/Estonian Lake Peipsi (31.3%), but more strongly segregated (69.4%) from Lake Constance whitefish and from the anadromous whitefish populations . River Treene C.cf. maraena grouped with Lake Constance whitefish populations (61.5%). However, the overall segregation between all anadromous populations and Lake Constance whitefish was weak , from which whitefish was stocked into Lake Schaal. A historical translocation of whitefish from Lake Constance into Lake Schaal (the “devil hypothesis”) could not be confirmed, because the molecular markers of both contemporary (C. maraenoides) and historical (C. holsatus) Lake Schaal whitefish were distinct from those of all four contemporary Lake Constance populations. Furthermore, there is no genetic evidence that anadromous whitefish populations of the Baltic and North Sea basins (= C. maraena) contributed to stocking in Lake Schaal. These studied anadromous whitefish are genetically closely related, but none of them was genetically truly identical with C. oxyrinchus historically occurring in the Rivers Rhine and Schelde, supporting the view of Freyhof & Schöter [C. oxyrinchus is extinct. Accordingly, our application of genetic markers to a mixture of contemporary and historical samples may guide future conservation practice to protect evolutionarily significant whitefish populations.Our results demonstrate that the contemporary whitefish population of Lake Schaal is non-native, and does no longer correspond to the originally described  Schöter  that C. C. maraenoides), and C. maraena. Accordingly, it is unlikely that the limitations of the dataset with in part small sample sizes and strong deviations from HWE have biased the results. This conclusion is supported by the additional analyses, which excluded the four loci most frequently deviating from HWE. Both the mean proportion of self-assignment in the DAPC, reflecting the individual level, and the NJ tree of genetic distances, reflecting the population level, remained almost unchanged when the analyses were repeated with nine instead of 13 loci.The population structure found by all applied statistical methods was fairly similar. Both the dendrogram as based on Nei’s genetic distances between populations and the individual-based DAPC showed strong allelic differences in particular between Lake Constance whitefishes, the whitefishes from north German lakes . Heterozygote deficits may be caused by population substructuring (Wahlund effect) , 63, andC. maraena populations and the five combined north German lake and Lake Peipsi populations (= C. maraenoides). These three groups separated clearly on the DAPC plane. In addition, whitefish from Lake Drewitz (= C. holsatus), Oulu (= C. widegreni) and River Treene (= C. cf. maraena) each were depicted as different populations by significant θ in pairwise comparisons and the highest proportions of individual self-assignment, although these populations grouped closely together on the DAPC plane. Most importantly in the context of this study, all Lake Constance whitefish were clearly distinguished from the contemporary Lake Drewitz whitefish and the historical C. holsatus from Lakes Schaal and Selenter. This unambiguously falsifies the assumption of Winkler et al. [C. holsatus from Lakes Schaal and Selenter originate from stocking of whitefish from Lake Constance. Therefore, we have to reject the “devil hypothesis” finding no support that C. holsatus was based on introduced Lake Constance whitefish.We found evidence for the existence of six genetically distinct groups of whitefish populations. The first three groups consisted of the four Lake Constance populations, the three r et al.  that hisC. holsatus was accordingly preliminarily treated as a unique and endemic conservation unit classified as “extinct in the wild” [C. holsatus has survived by translocation. Due to the clear morphological and genetic similarities of contemporary Lake Drewitz whitefish with the four historical C. holsatus specimens we classify contemporary Lake Drewitz whitefish as C. holsatus and confirm that C. holsatus from Lake Schaal was the initial source for the introduced and presently established whitefish in Lake Drewitz [C. holsatus from Lakes Schaal and Selenter to contemporary C. holsatus from Lake Drewitz was independently found by two statistical approaches (DAPC and Geneclass2), and hence is considered reliable. The whitefish in Lake Drewitz have to be classified as non-native since this species did not inhabit the lake historically. Nevertheless, Lake Drewitz whitefish have to be protected from any kind of stocking or other management endangering the genetic integrity of the species.For conservation purposes, he wild”  as it di Drewitz . The genC. maraenoides, in contrast to the common designation as European lake whitefish C. lavaretus  can be identified as Lake Peipsi whitefish s . C. marhe 1930s , 27, and century  before or et al. , it can C. holsatus from Lakes Schaal and Selenter were projected close to the whitefish from Lake Drewitz but also close to the contemporary north German lake populations  onto the DAPC plane may point to the presence of some remnant hybridized genetic material in the contemporary Lakes Schaal and Selenter populations. Admixture with stocked material was also suggested by the membership probabilities as calculated from the DAPC, and by the weak bootstrap support of branching between C. maraenoides and C. holsatus. Similarly, supplementary stocking in a south-eastern Finnish watercourse has contributed to a high degree of genetic admixture between originally distinct whitefish [Coregonus peled into local lake whitefish populations was found in Poland, despite artificial fertilization in hatcheries [The fact that the four historical individuals of hitefish , and trahitefish . In conttcheries . AccordiC. maraenoides is better adapted to eutrophic lakes and that stocking of artificially reared C. maraenoides gained higher yields than stocking of C. holsatus. Thus it is possible that C. maraenoides was preferred for artificial reproduction by commercial fishermen, since both species are easy to distinguish by mouth and body shape. When both lakes became strongly eutrophic preventing ultimately the natural reproduction of all whitefish species [C. holsatus may have vanished while the introduced C. maraenoides was kept by artificial reproduction. Stocking measures in combination with anthropogenically induced environmental changes have been presumed responsible for the extinction of several other indigenous populations of Salmoniformes in Europe [C. holsatus may have proliferated because in this lake the only other coregonid species occurring is the much smaller vendace C. albula [C. maraenoides was never stocked.In 1948, Lenz  reported species , the natn Europe , 75, 76.C. holsatus is an independent species we compared the historical C. holsatus specimens with contemporary whitefish species in North and Central Europe. Our limited dataset does not allow resolving the diversity of all northern whitefish, but we can unambiguously refute that C. holsatus is the same evolutionary unit as any other contemporary whitefish studied here. We could not confirm that C. holsatus and C. widegreni are conspecific [C. widegreni included into this study was genetically significantly differentiated from the contemporary Lake Drewitz whitefish . Furthermore, in the NJ tree, the Oulu sea-spawning whitefish grouped with contemporary anadromous whitefish from the Baltic , the North Sea (River Treene) and the Lake Constance whitefish populations. More studies are needed to identify the phylogeography of C. widegreni in the context of other Scandinavian and Russian anadromous whitefish species.To test Thienemann’s assumption that specific . The FinC. maraena [C. widegreni from Oulu and C. holsatus from Lake Drewitz and next to River Trave C. maraena, but the proportion of individual self-assignment to River Treene in the DAPC was almost 100%. In the NJ tree, River Treene whitefish primarily grouped with Lake Constance whitefish populations, whereas the next branch consisted of the anadromous whitefish C. maraena from the German Baltic Sea coast . Previously, the River Treene whitefish have likewise preliminarily been identified as C. maraena [C. maraena. This conclusion was supported by Hansen et al. [As a consequence of the broad variety of whitefish populations studied, we could also explore the genetic relationships of anadromous whitefish from the south-western Baltic and eastern North Sea basins for conservation purposes. In the south-western Baltic Sea basin, only one population of anadromous whitefish has survived in the lower Peene/Odra drainage . This sp maraena . The whi maraena , and the maraena , 79. Sin maraena . River V maraena , 80. In  maraena , and then et al. , 81 who C. maraena from River Peene/Odra  and whitefish from north German lakes, including Lake Poenitzer, potentially caused by erroneous stocking with non-native genotypes [C. maraenoides in our study) and C. maraena from River Trave. However, by sequencing the whole mitochondrial genome, recent strong reproductive isolation between Danish North Sea populations from River Vida and the Baltic lake whitefish C. lavaretus complex  has been demonstrated [C. c.f. maraena from the River Vida and C. maraena from the three Baltic Sea populations , although the bootstrap support was low (about 30%). Overall, the genetic similarity was higher between River Treene whitefish and C. maraena from River Trave, than between River Treene whitefish and C. maraena from Achterwasser, the original source of stocked whitefish in River Trave.A recent admixture analysis suggested a hybrid zone between River Vida whitefish, enotypes . Admixtuenotypes . The assnstrated . This stnstrated  correspoC. oxyrinchus individuals were assigned primarily to the contemporary C. maraena populations , whereas only one individual was assigned to the contemporary River Treene population, which is usually identified as C. oxyrinchus. Similarly, the historical C. oxyrinchus individuals were grouped next to the three C. maraena populations in the NJ tree. This suggests a weaker genetic differentiation between contemporary C. maraena and historical C. oxyrinchus than between contemporary anadromous River Vida/Treene and historical C. oxyrinchus populations. This has clear implications for the current stocking programs in the North Sea tributaries. According to our data, the fish currently stocked into the River Rhine [C. oxyrinchus [C. oxyrinchus are genetically fairly distinct from contemporary River Treene/Vida whitefish. However, it has to be stressed that the bootstrap support for the major branching in the NJ tree among all anadromous populations including the historical C. oxyrinchus was very low.The 14 historical er Rhine  from Rivyrinchus , as the C. oxyrinchus in England and in the Rivers Rhine, Meuse and Schelde, and C. maraena migrating along the rivers east of the Rhine . The scattered assignment of the 14 historical western houting individuals (named as C. oxyrinchus) to contemporary anadromous and lake-resident whitefish suggests that this distinction between C. maraena and C. oxyrinchus within the North Sea basins is less clear than assumed. We can only speculate about the background of these findings. For the nineteenth century when the historical material of C. oxyrinchus had been sampled, there is no reported stocking in the geographical range where C. oxyrinchus has occurred. However, we cannot exclude that C. oxyrinchus has experienced introgressive hybridisation involving genes from Lake Constance whitefish (as suggested by the assignment by Geneclass2), which have always also occurred in the lower Rhine, as well as from C. maraena straying from the east (as suggested by the assignment from both DAPC and Geneclass2). We encourage future in-depth studies to test for introgression of adjacent species into C. oxyrinchus. A broader spatial scale and exactly defined geographical origin of historical and contemporary samples from anadromous whitefishes of the Baltic and North Sea tributaries would help resolving the complex phylogeography of these northern and middle European whitefish species and populations.Based on morphological data, Freyhof & Schöter  have supStocking and translocations which have begun at least 100 years ago have modified the recent genetic signature of many whitefish species and populations. In some cases, local populations vanished and were replaced by stocked whitefish species originating from other sources. The often poorly documented stocking history and the unpredictability of stocking success contribute to the unresolved conservation status for many whitefish species and populations. To facilitate informed decisions on the conservation of evolutionarily significant units within the whitefish complex, further comparative genetic studies of contemporary and historical material are urgently needed.Additional file 1:Table S1. Geographical coordinates (degrees), catchment, basin and mean depth and surface area (for lakes only) of aquatic systems, from which the Coregonus populations were sampled. Table S2.P-values of deviations from Hardy-Weinberg equilibrium per locus and population, as controlled by the false discovery rate for multiple tests. Table S3. Matrix of pairwise θ between the 15 contemporary whitefish populations , and their lower 95% confidence intervals . Strong structure between populations is indicated in bold, as evidenced by the lower CI not including zero. For population origin, see Table