Pungitius pungitius) populations from four  different habitat types. In general, high magnitude (D’ > 0.5) of LD was found both in freshwater and marine populations, and the magnitude of LD was significantly greater in inland freshwater than in marine populations. Interestingly, three coastal freshwater populations located in close geographic proximity to the marine populations exhibited similar LD patterns and genetic diversity as their marine neighbors. The greater levels of LD in inland freshwater compared with marine and costal freshwater populations can be explained in terms of their contrasting demographic histories: founder events, long-term isolation, small effective sizes, and population bottlenecks are factors likely to have contributed to the high levels of LD in the inland freshwater populations. In general, these findings shed new light on the patterns and extent of variation in genome-wide LD, as well as the ecological and evolutionary factors driving them.Variation in the extent and magnitude of genome-wide linkage disequilibrium (LD) among populations residing in different habitats has seldom been studied in wild vertebrates. We used a total of 109 microsatellite markers to quantify the level and patterns of genome-wide LD in 13 Fennoscandian nine-spined stickleback ( Ultimately, this influences the extent and strength of associations among different parts of the genome. Such genetic associations are reflected in nonrandom coinheritance of alleles at different loci, a phenomenon known as linkage disequilibrium LD; . Interese.g., e.g., e.g., e.g., e.g., e.g., e.g., e.g., e.g., e.g., e.g., During the past few years, molecular markers across the whole genome have become available in many species, facilitating progress in quantifying the magnitude and patterns of genome-wide LD, for example in human (Pungitius pungitius) is a small cold-water adapted fish with a circumpolar distribution in the northern hemisphere . To this end, we used genotypic data on 109 microsatellite loci from 13 different nine-spined stickleback populations. Because isolated freshwater populations have very low levels of genetic variability  from three marine and 10 freshwater populations were included in the analyses. The sampling sites covered a large part of the Fennoscandian area and encompassed a diverse array of habitats . Polymerase chain reactions (PCRs) were carried out using the QIAGEN multiplex PCR Kit (QIAGEN) in a reaction volume of 10 μL containing 1× QIAGEN multiplex PCR Master Mix, 0.5× Q-Solution, 2 pmol of each primer, and 10–20 ng of genomic DNA. The PCR amplifications were performed using the following cycle: initial activation at 95° for 15 min, followed by 30 s at 94°, 90 s at 53 or 55°, and 60 s at 72° for 30 cycles, ending with a final extension at 60° for 5 min. PCR products were resolved on a MegaBACE 1000 automated sequencer (Amersham Biosciences), and their sizes were determined with ET-ROX 550 size standard (Amersham Biosciences). Alleles were scored using FRAGMENT PROFILER 1.2 (Amersham Biosciences) with visual inspection and manual corrections.Total genomic DNA for the samples was extracted from fin clips using the phenol–chloroform method (HO), expected heterozygosities (HE), inbreeding coefficient (FIS), and allele frequencies were calculated with FSTAT v2.9.3.2  were due to within-population substructuring. We conducted three independent runs for each K-value ranging from 1 to 20. The admixture model and correlated allele frequencies model  and Mc  were estimated using the programs M_P_VAL and CRITICAL_M (μ) of 1.5 × 10−4 per generation (θ = 4Neμ) that equate to a prebottleneck effective population size (Ne) of 1000, 5000, and 10,000 for the three marine and three coastal freshwater populations, and a prebottleneck Ne of 100, 500, and 1000 for the seven inland freshwater populations. The observed value of M was compared with the corresponding Mc, and a lower value of M relative to Mc indicated a historical population bottleneck  have the same number (n = 21) of chromosomes (http://www.ensembl.org/Gasterosteus_aculeatus/index.html). BLAST searches were performed to locate the 112 nine-spined stickleback microsatellite markers in the three-spined stickleback genome using the BLASTN tool in the Ensembl database. Initial searches were performed with the default conditions, and a locus was assigned to a genomic location if it provided a unique hit at E ≤ 1e−10. When a locus provided multiple matches at E ≤ 1e−10, it was unassigned unless the best hit had an E value at least 10 decimal places lower than the next best one. For ease of comparison, we numbered linkage groups (LGs) for the nine-spined stickleback linkage map in accordance with the syntenic LGs in the three-spined stickleback  − p(Ai)p(Bj), where p(Ai) is the frequency of allele Ai at locus A, p(Bj) is the frequency of allele Bj at locus B, and p(AiBj) is the frequency of haplotype AiBj in the population.Two different gametic LD measures, multiallelic D’ was estimated as  in each population and habitat type were generated in Microsoft Excel. The half-length of LD  in which population and habitat were treated as random and fixed factors, respectively, and associated D’ values were regarded as dependent variables, with physical distance between markers as a covariate. Furthermore, there were differences in marker density in different LGs. In order to corroborate the LD patterns observed in the genome-wide analyses, we examined LD patterns in four LGs with the greatest marker densities  estimated across all loci ranged from 1.50 (in Pyo) to 7.02 (in Lev), and from 0.085 (in Pyo) to 0.574 (in Kro), respectively .One-hundred nine microsatellite markers were successfully mapped to the three-spined stickleback genome. The basic indices of within-population genetic variability are given in ectively . Privateectively . The marulations . A high FST among population pairs varied greatly (FST = 0.003−0.724), most of which were significant . In general, FST values between inland freshwater populations were always greater than those between marine or coastal freshwater populations (Table S2). Principal component analysis revealed that the first and second axes accounted for 13.7% and 10.1% of variation in allele frequencies, respectively . The individuals from the inland freshwater populations clustered more tightly than those from the coastal freshwater and marine populations . Analysis of molecular variance analyses suggested that 7.4% of the total genetic variation was explained by geographic proximity (P < 0.001), whereas the factors of habitat type . No substructure was found within any of the populations at both the optimal K value  and the maximum tested K value . Thus, population substructuring was unlikely to account for the observed high levels of LD. The estimated pairwise relatedness coefficients were generally small  for 12 populations except Pyo (File S2), suggesting that most individuals should be unrelated; hence, family structure was not an explanation for the high LD values.The extent of population differentiation as measured by P = 0.03) under the TPM using the heterozygosity excess method. However, all populations except Pyo showed strong evidence for historical population bottlenecks using the M-ratio method, despite the differences in pre-bottleneck Ne (Table S3). Observed population-specific M-ratio values ranged from 0.670 to 0.898, and most  were lower than the corresponding Mc values (Table S3). It was unexpected that no bottleneck was detected in Pyo because this population had the lowest genetic diversity of all populations in this study . When different habitat types were considered, lake , pond , and river  habitats, showed significantly greater D’ values than the marine habitat. The greatest average D’ values were observed in the pond habitat , but significant differences were found among the lake  and pond  populations. When restricting the comparisons to LGs with high density markers . This supports the view that the relatively low number of microsatellite markers used in this study can indeed yield information about general patterns of genome-wide LD. When LD was measured with 2r, lower absolute values were observed (Table S4) compared with those of D’ .Overall, the levels of syntenic LD as measured by ely high , but var habitat . There wse of D’ . However2R < 0.01, P > 0.05; Table S6 and D’ and genomic distance. With regard to LD decay in different habitats, the dataset of all marine populations combined or all freshwater populations combined showed higher correlations and shorter LD half-length compared with the combined lake or pond datasets (Table S6). Interestingly, we found that the three coastal freshwater populations , and the level of LD in Por was independent of genomic distance (Table S6). This finding could be ascribable to stochasticity caused by the small number of marker pairs used to measure LD in each distance bin in these highly homozygous populations (Table S7).Comparison of the patterns of LD decay as a function of genomic distance revealed very weak and statistically nonsignificant  and comparable with the levels of haplotypic LD values (Table S4), indicating that observed high levels of LD were unlikely to be explainable as an effect of haplotype phasing. When the rare alleles were excluded, both haplotypic and composite D’ values were smaller, but the overall syntenic D’ value was still above 0.4 in almost all the populations (Table S8). On the contrary, both haplotypic and composite 2r values became larger without the rare alleles (Table S8). Notably, irrespective of whether inferred haplotypic data or unphased genotypic data were used and whether the rare alleles were involved in the analyses or not, the findings about the LD patterns among habitat types  based on combined data remained largely unchanged .The composite In general, low-to-moderate genetic diversity, strong genetic differentiation, and high levels of genome-wide LD were observed in Fennoscandian nine-spined stickleback populations. The extent and patterns of LD varied among populations and habitat types. Isolated and small freshwater populations tended to have greater LD compared with open marine populations. In the following, we will discuss these findings and their implications to our understanding of the factors influencing levels and extent of genomic LD in the wild.e.g., e.g., e.g., M-ratio tests indicated that historical bottlenecks most probably have contributed to the high magnitude of genome-wide LD. Given that the stickleback populations studied here have been colonized after the last glacial maximum , founder effects associated with postglacial recolonization also may account for the high LD. It should be noted that we have not taken recombination into account in our LD estimation due to its heterogeneity across the genome. Nevertheless, this should not affect the observed habitat or population differences in LD if the recombination hotspots are congruent in different populations, as has been reported for human populations . Likewise, the high proportion of rare alleles  and consequent loss of haplotypes in the populations may also yield high D’ values yet low 2r values , and gave consistent LD patterns in inter-habitat comparisons (Table S4). Thus, conclusions drawn from D’ values are qualitatively similar to those obtained using 2r values in respect to patterns of LD across habitat types.Different metrics have been developed to measure the degree of LD, and we employed both D’ values  in all of the 13 populations when the rare alleles were excluded. The differences in LD among habitat types  remained unchanged even if the rare alleles were excluded. Second, rare variants can convey important information in genome-wide genetic studies (Picea abies) populations  tend to elevate  studies , and a lOur findings of genomic LD and genetic variability have several important implications for gene mapping studies in nine-spined sticklebacks. First, given the high level of LD, a relatively small number of markers are required to cover a relatively large genomic region in QTL-mapping studies. Second, given the previous consideration, the mapping resolution will be relatively low because large genomic regions are likely to be inherited as linked clusters. Third, given the high frequency of rare alleles, nine-spined stickleback populations might prove to be suitable for rare variant mapping of complex traits. Nevertheless, although this study provides some preliminary insight on variation in LD across the nine-spined stickleback genome, one should bear in mind that the relatively low number of markers and their non-uniform distribution over the LGs and populations limit the inferences. Further exploration based on a larger number of markers, together with a high-density linkage map would pave the road for more refined inferences.To sum up, the results provide the first investigation of genome-wide LD patterns in the nine-spined stickleback, and also one of the most extensive studies exploring patterns of habitat related variation in LD in wild vertebrates. In general, high levels of LD were observed in most of the analyzed populations, and more interestingly, higher levels of LD were detected in inland freshwater than in costal populations. This habitat patterning in the levels of LD matches what we discovered—and what has been known from earlier studies—about habitat-specific differences in demographic history and effective population size in these populations. The levels of LD uncovered in present study also suggest that studies seeking to disclose the genetic basis of phenotypic traits using QTL-mapping approaches may face challenges, especially in inland freshwater populations which are low in genetic variability and exhibit high levels of LD: the few polymorphic markers segregating in those populations are likely to be associated for long stretches of linked genes.


Fritillaria tubiformis subsp. moggridgei Rix. is a rare alpine geophyte with shiny yellow flowers. This plant is sporadically distributed across the southwestern Alps where it is biogeographically close to F. tubiformis var. burnatii Planch. The latter has dark purple flowers and ranges in the majority of the Western and Central Alps. In order to develop appropriate strategies of conservation, a RAPD based analysis was conducted to study the genetic status of these taxa and the distribution of genetic variability of the subspecies by sampling seven populations distributed across the subspecies' range. Four populations of var. burnatii were chosen within this range and included in the genetic analysis. Some 264 individuals were analysed and 201 polymorphic loci were scored. Genetic diversity scored in the subspecies was in line with expectations for endemic species (He  = 0.194). F. tubiformis var. burnatii showed lower intraspecific diversity (He  = 0.173), notwithstanding a wider range than the subspecies. Most of the total phenotypic variation (about 83%) was allocated within populations, and significant lower proportions between taxa (6.45%) and between populations of the same taxon (10.64%). Moreover, PCoA analysis and Bayesian clustering separated populations into two genetically differentiated groups corresponding with the subspecific taxa. However, three populations ascribed to the subsp. moggridgei repeatedly showed genetic admixture with var. burnatii populations. Our findings suggest that: i) although the different flower colour, the two taxa are genetically very similar and share a consistent part of their gene pool, ii) the majority of genetic variability is allocated within populations rather than among them, iii) a representative amount of genetic diversity can be preserved by sampling from a restricted number of populations. The efficacy of RAPD markers in analysing genetic variation, and the contribution of the results to the preservation of biodiversity of the species, are discussed. Genetic erosion is one of the most important threats to the survival of many wild species. Nowadays these species are subjected to many stress factors, most of which are due to human activities: namely environment alteration, habitat fragmentation, urbanisation, pollution, irrational forest management and the introduction of allochthonous species. Current climate changes will probably determine a decline in growth and survival, and an alteration of genetic structure and diversity for many wild species. This pressure may be strengthened in marginal and peripheral populations of the species distribution, mainly in European southern populations of plants The characterisation of patterns of genetic diversity within species, and among populations, is a fundamental necessity to establish any program aimed at preservation of biodiversity. Genetic markers are basic for supplying information on the genetic structure of populations, to analyse the pattern of within-species variability distribution Fritillaria tubiformis subsp. moggridgei (Boiss. & Reut. ex Planchon) Rix (Liliaceae) is a rare taxon sporadically distributed along the Ligurian and Maritime Alps. The species is limited to a few main sites within the Parc National du Mercantour in France , and to seven populations of no more than 1000–10000 individuals found across Piedmont and Liguria, in Italy.Fritillaria tubiformis Gren. var. burnatii Planch. has a wider range, including the majority of the Western and Central Alps F. tubiformis Gren. & Godr. moggridgei are yellow instead of dark purple. Results of a phylogenetic analysis carried on combined DNA sequences of the partial matK gene and the rpl16 intron supported this view, and showed that subsp. moggridgei is genetically more closely related to var. burnatii than to any other species in the genus. However, in the same study, using plastid length-variable microsatellites a number of plastid haplotypes were found to be differently fixed in the two subspecies Conversely, moggridgei. In addition, we have used a data set based on DNA from four populations of F. tubiformis var. burnatii, sampled at the periphery of the subspecies distribution. A biosystematic study is also underway aimed at clarifying the exact phylogenetic relationships between the two taxa and their distribution range.Owing to this taxonomic uncertainty, our genetic analysis has been focused on the seven Italian populations of subsp. Random Amplified Polymorphic DNA (RAPD) technology is based on the amplification, using the Polymerase Chain Reaction (PCR), of short segments of DNA randomly chosen by the use of arbitrary, usually short (8–12 base pairs) primers. Despite having some drawbacks, the most important of which are the dominant expression and the low level of reproducibility Fritillaria tubiformis subsp. moggridgei populations from north-western Italy using RAPDs, and to evaluate possible effects of habitat fragmentation on genetic variation. The genetic relationship between the subspecies and Fritillaria tubiformis var. burnatii has also been investigated. Spatial structure and the expected association between genetic and geographical distances among populations were assessed, in order to provide recommendations for in situ preservation of the species.The aims of the present research were to estimate the amount and distribution of genetic diversity within and among F. tubiformis subsp. moggridgei were considered in the study, distributed across the Maritime and Ligurian Alps, which represent the natural range of the subspecies in Italy. In general, populations were of small size and individuals therein limited to several hundreds. In addition, four populations of F. t. var. burnatii were also considered and were sampled within an area that partly overlaps the distribution range of the former subspecies. Seven populations of Leaves were sampled from about 30 non-adjacent blooming individuals in each population, in order to avoid selection of clonal individuals. In total, 264 individuals were analysed. After collection, the vegetative material was frozen at −20°C until DNA extraction.Total DNA was extracted from frozen tissue using E.Z.N.A. SP Plant DNA Kit , and purified on columns following the manufacturer's protocol. Amplifications were carried out in a Perkin Elmer DNA thermal cycler (PE 9600). The optimal reaction for RAPD analysis was set using the following conditions: 25 µl volume containing 1× reaction buffer, 1 unit of Taq DNA polymerase (Promega), 2.5 mM magnesium chloride, 0.2 µM decamer primers, dNTPs mix 0.2 mM and 20 ng DNA. The amplification conditions were as follows: the first step at 95°C for 5 min., followed by 40 cycles of 1 min. at 94°C, 1 min. at 36°C and 2 min. at 72°C. The reaction was completed with a final run at 72°C for 8 min. We screened a total of 16 primers from Life Technologies. The amplification products were separated in 1.5% agarose gel with ethidium bromide in 0.5× TBE buffer. The banding patterns were visualised under UV light and acquired using a fluorimeter linked with a gel documentation system (GelDoc 2000). The bands were scored visually, and those of similar molecular size, scored for the same primer, were assumed to be homologous. Since RAPD markers are dominant it was assumed that each band was an independent locus with two alleles: presence or absence of the band. The reproducibility of the amplification products was tested twice for each sample and each primer.e) of alleles per locus, Nei's gene diversity e, that was adopted assuming the populations to be in Hardy-Weinberg equilibrium, although we were not able to investigate this since dominant markers were used), and Shannon's information index. The latter can be considered a measure of phenotypic diversity , where N is the number of plants analysed ST) ST value matrix according to Weir and Cockerham ST values under the hypothesis of no differences between the populations was also tested by using a permutational approach (10000 replicates).The distribution of genetic diversity within and among populations was assessed using Nei's genetic differentiation degree , to better understand genetic relationships among populations.Cluster analysis was performed on pairwise Fs UPGMA;  with theThe genetic structure of the populations was also explored using Bayesian clustering, performed using the software Structure moggridgei and 4 within var. burnatii). Five bands were unique to a single population: OPA05-19, OPA05-30, OPA05-21 and OPA09-27 in GAL, OPA05-22 in ROS. All bands fulfilled the Lynch and Milligan criterion Among the primers tested, OPA01, OPA02, OPA03, OPA4, OPA6, OPAF04, OPAF12 and OPAS18 gave non-reproducible amplification products and were therefore excluded from the analysis. The remaining eight selected primers showed reliable banding patterns and generated a total of 204 consistent and differential amplification products, ranging in size from 170 to 990 bp. Each primer amplified between 15 (OPA10) and 30 (OPA19) fragments, with a mean number of scored bands per primer of 25.5 . Almost Fritillaria tubiformis subsp. moggridgei showed an amount of internal genetic variability slightly higher than those of Fritillaria tubiformis var. burnatii.Genetic variation within populations was estimated for the 11 populations using various methods . The meaT  = 0.293), following Nei's statistics F. tubiformis subsp. moggridgei showed a genetic differentiation slightly higher than F. tubiformis var. burnatii . The hierarchical analysis of molecular variance (AMOVA) confirmed these findings, demonstrating that about 83% of the variation was within populations and the remaining variance was attributable partly to differences between groups (6.45%) and partly among populations (10.64%), providing corroborating evidence for the genetic structure predicted by the Nei's indices analysis  clustered together as a sister group to ROS  but also to SAC and SAM belonging to subsp. moggridgei. The second cluster grouped together the remaining populations belonging to subsp. moggridgei and precisely CRA and MAR  as a sister group to the populations FRO and PLU. No precise assignation was possible for the remaining two populations, SER and GAL, belonging respectively to var. burnatii and subsp. moggridgei.Cluster analysis, based on pairwise Fulations . PopulatThe PCoA analysis was performed to visualise the grouping of populations more clearly, providing a graphical representation of the relationships between them . Calculamoggridgei  clearly belong to cluster 1, whereas var. burnatii populations  are primarily composed by individuals from cluster 2. Exceptions are represented by two admixed populations belonging to subsp. moggridgei (SAC and SAM), that already in the UPGMA analysis were shown to be more similar to var. burnatii than to other populations of the same taxon.Following the method of Evanno et al. Fritillaria tubiformis subsp. moggridgei endemic of the South-Western Alps. Genetic structure in this subspecies was compared with four populations of Fritillaria tubiformis var. burnatii sampled within the range of the subspecies.This study investigated genetic variation of RAPD markers proved to be an efficient tool to analyse genetic variation both within and among populations. However, it must be underlined that RAPD variation cannot be linked with variation in adaptive genes F. tubiformisIn species with complex life histories, the choice of appropriate techniques of genetic analysis is not obvious. In a previous analysis run on chloroplast DNA with different type of sequence repeats, we showed that only insertion/deletion events were effective in discriminating genetically between yellow and dark purple flowered-morphotypes of F. tubiformis subsp. moggridgei displayed a level of genetic diversity in line with expectations. Expected heterozygosity (He  = 0.194) (pop reported by Nybom pop  = 0.19).= 0.194)  was simimoggridgei than in the Iranian endemic F. imperialis L. a much more widespread congeneric The average percentage of polymorphic loci (75–85%)  and totaF. tubiformis populations exhibited genetic divergence. Using the AMOVA procedure, we showed that although most of the phenotypic variation was allocated within populations (82.91%), a significant proportion was attributable to genetic differences between intraspecific taxa (6.45%), as well as between populations (10.64%)  .ST value (0.171) was significantly different from zero (P<0.001) (ST  = 17.4%), confirming the existence of a genetic structuring among populations.The overall FP<0.001) , providiF. imperialis and F. camschatcensis using RAPD markers, where variation within populations accounted for only 54% and 35.7% of the total variation, respectively moggridgei populations. This is supported by previous RAPD-based analyses which showed that long-lived, outcrossing or late successional species retain most of their genetic variability within populations F. tubiformis subsp. moggridgei has not experienced population bottlenecks, and the general low levels of inbreeding likewise suggest a predominantly outcrossing mating system for this taxon The results also indicate that most of the total genetic variation was present within populations. Patterns of high genetic diversity at both the species' and local population levels have already been documented in other mountain plants F. tubiformis subsp. moggridgei populations. SAM, for example, has little chance of gene flow. This is in fact a very small population at a relatively low altitude, which has suffered the impact of agricultural activities and human settlements. GAL and SAC populations, on the contrary, are isolated populations, which nevertheless might receive frequent visits of pollinators thanks to their position on the very summit of two mountains.Population size and geographical separation may be strong determinants of the spatial distribution of genetic variation in populations of neighboring species with fragmented distributions moggridgei showed repeated admixture with var. burnatii, leaving open the problem of their exact taxonomic attribution. The profile of the UPGMA tree supported a certain degree of genetic admixture across SAC and SAM populations, which positioned in the tree within populations of var. burnatii  than to any other Fritillaria species considered in the study Analyses of population genetic structure  showed that the studied populations belonged to two distinct genetic pools corresponding to the subspecific taxa. The varied geological, topographical and climatic conditions of the Alpine chain can boost isolation and divergence of populations even over short geographical distances, thus explaining the fragmented distribution and genetic complexity of some endemic species of the Maritime Alps burnatii . When anburnatii . These fmoggridgei and SER in the variety burnatii)  and SAM  demonstrated that population positioning in the UPGMA tree was almost geographically unrelated. In support of this finding, it has been observed that when analysed by RAPD markers, population diversity is little affected by geographical range urnatii) .moggridgei is ambiguous and it remains unclear whether they are a hybrid of recent origin between subsp. moggridgei and var. burnatiiThe position of the three populations of subsp. F. tubiformis sensu lato is increasingly rare in the wild, due to habitat loss, and some populations are already under threat because of human activities. In conclusion, this study has shown that F. tubiformis subsp. moggridgei has a substantial degree of genetic diversity, which may counteract genetic erosion and maintain high adaptive potential in the subspecies. Indeed, this potential is mainly determined by the species genetic diversity F. tubiformis subsp. moggridgei exhibits high level of genetic diversity and, in the perspective of a long-term conservation, a restricted number of its populations would be variable enough to represent the genetic variability of the subspecies.moggridgei and var. burnatii has shown that the taxonomic status of the Italian populations is uncertain F. tubiformis Gren. & Godr.), will provide information essential for the formulation of appropriate management strategies for the conservation of all these populations.A literature and herbarium-based survey of subsp. Dataset S1Fritillaria tubiformis subsp. moggridgei RAPD profile and Analysis of Molecular Variance (AMOVA). The pairwise FST matrix of all population comparisons and step by step pairwise distances along with population labels are also given and can be used to manually calculate AMOVA results.(XLSX)Click here for additional data file.


Genetic differentiation in natural populations is driven by geographic distance and by ecological or physical features within and between natural habitats that reduce migration. The primary population structure in wild barley differentiates populations east and west of the Zagros Mountains. Genetic differentiation between eastern and western populations is uneven across the genome and is greatest on linkage groups 2H and 5H. Genetic markers in these two regions demonstrate the largest difference in frequency between the primary populations and have the highest informativeness for assignment to each population. Previous cytological and genetic studies suggest there are chromosomal structural rearrangements (inversions or translocations) in these genomic regions. Environmental association analyses identified an association with both temperature and precipitation variables on 2H and with precipitation variables on 5H. The wild progenitors of major crops have long been recognized as a valuable genetic resource . NaturalHordeum vulgare ssp. spontaneum) have been used in barley breeding programs for cultivated barley improvement  and Anatolia (present day Turkey) to Central Asia, occurring across much of Southwestern Asia. rovement . RecentlExamination of population structure in crop wild progenitors can result in a better understanding of the number and geographic region of domestication events , which iA number of previous studies have examined wild barley genetic diversity . The assThe natural range of wild barley includes a variety of environmental conditions, from arid regions in Central Asia and the Syrian desert to coastal regions along the Mediterranean with relatively high rainfall, and from the cold environments in the Zagros Mountains, the western reaches of the Himalayas, and the Iranian plateau to relatively warm lowland Mediterranean coastal regions. By associating the geographic distribution of sequence polymorphisms in georeferenced samples with environmental variables, genomic regions and individual polymorphisms correlated with differences in drought or cold tolerance or other environmental factors may be identified for further investigation.The questions we seek to explore in this article are what ecological factors are most associated with observed population structure, how does the geographic distribution of populations relate to allele frequency differentiation genome-wide, and how can we use this information to improve conservation and utilization of wild barley genetic diversity? We report the examination of geographic structure and genetic differentiation using 3072 SNPs . The majority of accessions (77.4%) are from the Fertile Crescent, with the balance from Central Asia (15.7%), North Africa (3.8%), and the Caucasus region (2.8%). Individual accessions were self-fertilized for three generations to create inbred lines. Accession numbers, latitude, and longitude information for each sample are in Supporting Information, Table S1.The 318 sampled wild barley accessions are known as the Wild Barley Diversity Collection (WBDC) (The WBDC accessions were genotyped using the Illumina Golden Gate Genotyping Assay with two Barley Oligo Pool Assay (BOPA) chips (BOPA1 and BOPA2), each including 1536 SNPs  and wild barley WBDC218 and WBDC228 were trimmed of adapter contamination with Scythe  and then aligned to the Morex draft sequence (H. vulgare lines (∼1%) and between H. vulgare and H. bulbosum (∼3%) (http://www.broadinstitute.org/gatk/guide/best-practices). For realignment around indels, we used a set of high-confidence indels reported in Sanger resequencing datasets   as measured by r2  was calcFST (FST) than those affected only by demography  and six clusters identified by genetic assignment in STRUCTURE and calculated FST . The assmography .www.worldclim.org  (th percentile genome-wide) in the FST, environmental association, or SPA analyses can be considered candidate variants either causative or, more likely, linked to loci involved in local adaptation. Enrichment analysis of candidates among genic vs. nongenic and nonsynonymous vs. synonymous was performed by resampling the number of SNPs in each candidate list randomly from the genome 1000 times. Enrichment analyses included sets of SNPs genome-wide, centromeric regions, and the two regions that are putative structural rearrangements (inversions or translocations). This generated a distribution of ratios of the number of genic to the number of nongenic SNPs and ratios of the number of nonsynonymous to the number of synonymous SNPs in each analysis, which can be compared to the observed ratios from the data.SNPs that are outliers (above the 95Table S1). After all quality control measures, 2330 SNPs were assayed in each accession (Table S5).Initial screening of relatedness among accessions identified 30 wild barley accessions either with a large genetic distance from the majority of accessions or that appear to be duplicated within the sample. The large genetic distance was also associated with a high degree of identity to barley landraces genotyped on the same platform. The increase in genetic distance appears to result from ascertainment bias due to the discovery of barley SNPs primarily among cultivated samples, which results in a larger number of segregating polymorphisms in barley landrace accessions than in wild barley , consistent with isolation by distance among these wild barley accessions.K = 2 identified populations east and west of the Zagros Mountains. Samples from a broad portion of the range, including Central Asia (particularly east of the Caspian Sea), the Iranian Plateau, and most samples in Northern Mesopotamia (modern Northern Iraq and Syria) form an Eastern population, and samples west of the Zagros Mountains, including samples from the Levant, and around the Mediterranean to North Africa form a Western population (Table S2).STRUCTURE results based on single-SNP and 5-SNP haplotypes for pulation . There iK = 3, a group of accessions from the Syrian desert region becomes an independent cluster from the Western population. As K increases to 4, the samples along the east coast of the Mediterranean split into two groups, one in the north (Northern Levant) and the other in the south (Southern Levant). The Eastern population begins to differentiate as K is increased to 5. The samples in Central Asia are separated from samples from Northern Mesopotamia and those from west of the Caspian Sea. With K = 6, the seven samples from the Caspian Sea become an independent cluster .For STRUCTURE analysis based on haplotype data, when  cluster . For theFST analyses identify both a significant effect of individuals within subpopulations  and a significant effect of subpopulations within populations . Population structure is best explained by a six-subpopulation model (variance components = 17.7%); in contrast, the two-population model provides a poorer fit to the data (variance components = 10.6%). FST values are also higher at the subpopulation level than the population level  and three are in the Western population , constituting hierarchical population structure nested within the major Eastern and Western populations. The comparative on level . Within FST analysis indicates a higher mean FST value for this subpopulation (0.09) than for the other two Western subpopulations (0.05 and 0.04).In the PCA examination of population structure, the first PC separates all samples into the Eastern and Western populations. When adding the second PC, all six subpopulations are clearly differentiated. A PCA plot of genetic variation closely reflects geography in the population after a 42.44° counterclockwise rotation of the PCA plot using Procrustes analysis. The boundary of the Eastern and Western population approximately parallels the Zagros Mountains . The SyrFST between the Eastern and Western populations is 0.07. The boundary of these two populations is close to the Zagros Mountains (FST genome-wide is 0.06 and the correlation coefficient (r) between this partition based on the Zagros Mountains and the previous partition based on genetic assignment analysis is 0.473, which supports the hypothesis that the Zagros Mountains act as a natural barrier that bisects wild barley into the Eastern and Western populations. For the 29 microsatellite loci, the average RST between the Eastern and Western populations is 0.15.The average genome-wide ountains , which fFST values between the Eastern and Western populations (FSTs are 0.20 (56 SNPs) and 0.17 (32 SNPs), respectively, vs. a genome-wide FST = 0.07.SNPs with high ulations  or amongulations  are mostFST regions . Among all SNPs above the 95th and 99th percentiles (In = 0.31 and 0.42), 33% and 52% are in these two regions . However, when we perform genetic assignment analysis after masking these two high FST regions, the probability of assignment for each wild barley accession into the Eastern and Western population is nearly identical for all but one accession. This result reflects the relatively high informativeness for assignment observed for SNPs within pericentromeric regions on all linkage groups . Therefore, population structure in these two high FST regions is similar to the genome-wide pattern and the genome-wide pattern is driven by a high degree of differentiation in pericentromeric regions.Many SNPs or haplotypes genome-wide with high informativeness for assignment are in these two high Figure S2). Percent pairwise differences are lower in the Eastern population than in the Western population (vs. 86)  and the mean number of private alleles per locus  are higher in the Western than the Eastern population. The Southern Levant subpopulation has the highest values for the number of segregating sites and number of private SNPs, whereas the Caspian Sea subpopulation has the lowest values for these summary statistics (The joint unfolded site frequency spectrum demonstrates that there are more rare variants in the Western population than in the Eastern population (pulation . There a(vs. 86) . Becauseatistics .FST regions on 2H and 5H are potentially attributable to chromosomal structural variants. Population genetic variation, particularly patterns of LD, can suggest structural variation (FST region on 2H (r2 = 0.085) is higher than other regions of 2H (r2 = 0.018) and the adjacent regions with similar size left and right of the high FST region, where r2 = 0.021 for both regions. The average pairwise LD in the high FST region on 5H (r2 = 0.097) is also higher than other regions on 5H (r2 = 0.019) and the adjacent regions with similar size to the left and right of the high FST region (r2 = 0.017 and 0.020). The 5-SNP segment with the lowest haplotype number (4) on 2H is within the high FST region . As on other linkage groups, the segment with the lowest haplotype number on 2H and 5H is within the centromeric region . The centromeric region on 2H overlaps with the high FST region, whereas the centromere is distal to the high FST region on 5H . The observed LD, FST, and haplotype number patterns are consistent with recent positive selection and/or chromosome structural rearrangements. There are more unique haplotypes in the Western population than in the Eastern population . The first two PCs explain 80% of the total variance . The first PC includes most temperature variables, whereas most precipitation variables and altitude are in the second PC (Table S4B).Environmental association analysis was used to identify genetic polymorphisms potentially involved in local adaptation. PCA reveals two major clusters of environmental variables (th percentile) are distributed on all linkage groups  and 5H (7 out of 32) show strong geographic gradients in allele frequencies as their SPA scores are above the 95th percentile (2.72) (FST region on 2H. There are 24 SNPs with SPA score above the 99th percentile (3.75), and nearly half of these SNPs (11) are in these two high FST regions on 2H and 5H. The annotation information for the SNPs with SPA scores above the 95th percentile is in Table S8.SPA analysis reveals that ∼20% of the SNPs in the high e (2.72) . The SNPFigure S6A). There is no evidence of enrichment for nonsynonymous SNPs .Enrichment analysis reveals that the two putative chromosomal structural rearrangements are enriched for genic SNPs in the outliers of PC2 from environmental analysis and SPA analysis . There is a small but clear effect of ascertainment bias, which leads to an increased estimate of diversity in the Western population, because the Western population is more similar to the discovery panel. The size of a discovery panel is less important than the composition, as long as it is not extremely small (<4 chromosomes) . Environmental association analysis identifies multiple SNPs in these regions associated with both temperature and precipitation  in teosinte, the wild progenitor of maize  in a cold-regulated gene blt14  from the gene Two large pericentromeric regions on 2H and 5H make strong contributions to the population structure observed in wild barley. These two genomic regions are putative chromosome structural rearrangements that harbor variants that appear to contribute to environmental adaptation. In particular, the SNPs in these chromosomal regions are shown to be associated with temperature and precipitation variables. It will be important to determine how specific genetic variants within these rearrangements are associated with local adaptation. Nevertheless, the identification of genomic regions associated with environmental adaptation suggests an opportunity for crop improvement.


Ne) and higher genetic differentiation than central populations. We tested these hypotheses using eastern white pine (Pinus strobus) as a model for keystone, long-lived widely-distributed plants. All 614 eastern white pine trees, in a complete census of two populations each of marginal old-growth, central old-growth, and central second-growth, were genotyped at 11 microsatellite loci. The central populations had significantly higher allelic and genotypic diversity, latent genetic potential (LGP) and Ne than the marginal populations. However, heterozygosity and fixation index were similar between them. The marginal populations were genetically diverged from the central populations. Model testing suggested predominant north to south gene flow in the study area with curtailed gene flow to northern marginal populations. Signatures of natural selection were detected at three loci in the marginal populations; two showing divergent selection with directional change in allele frequencies, and one balancing selection. Contrary to the general belief, no significant differences were observed in genetic diversity, differentiation, LGP, and Ne between old-growth and second-growth populations. Our study provides information on the dynamics of migration, genetic drift and selection in central versus marginal populations of a keystone long-lived plant species and has broad evolutionary, conservation and adaptation significance.Marginal populations are expected to provide the frontiers for adaptation, evolution and range shifts of plant species under the anticipated climate change conditions. Marginal populations are predicted to show genetic divergence from central populations due to their isolation, and divergent natural selection and genetic drift operating therein. Marginal populations are also expected to have lower genetic diversity and effective population size ( Ne) and sub-optimal habitats in marginal populations Pinus contorta) Marginal populations are expected to provide the frontiers for adaptation, evolution and range shifts of plant species under the anticipated climate change conditions. Marginal populations are generally adapted to their sub-optimal habitats, and are predicted to be genetically diverged from central populations due to their isolation, and divergent natural selection and genetic drift operating therein in-situ natural marginal versus central populations of forest trees. Holliday et al. study While a number of studies have tested the central-marginal hypothesis in a diverse group of plants and animals, including forest trees In long-lived plants, such as conifers, there is a general belief that old-growth populations harbor higher genetic diversity than second-growth populations. However, empirical data to support or contradict this view is scarce Pinus strobus L.) provides an ideal organism to test the central- marginal and old-growth second-growth hypotheses. It is an ecologically important keystone species of white pine ecosystems, economically important for timber production, and long-lived (>400 years), with wide geographical distribution in North America Eastern white pine  is a predominantly outcrossing species Ne, and tested loci for signatures of natural selection.Here, we address the hypothesis that geographically marginal populations of eastern white pine have lower genetic diversity and higher genetic differentiation than and are genetically distinct from geographically central populations. We also asked the question whether old-growth populations have higher genetic diversity than second-growth populations. We undertook comprehensive genetic diversity and population structure analyses in a complete census of four old-growth and two second-growth populations of eastern white pine from its central and marginal distribution in northern Ontario, Canada. We also tested various models for gene flow between populations, estimated The studied eastern white pine populations are located on public lands in Ontario, Canada, which are not designated as protected areas. The field sampling was done by the employees of the Ontario Ministry of Natural Resources, which manages public forests there. Therefore, no specific permission was required for field sampling from the studied locations. Also, our study did not involve any endangered species.We studied six EWP natural populations from its natural distribution range in northern Ontario, Canada ; Table 1We chose to conduct an in-depth genetic analysis for a fewer populations from the same regional part of the natural distribution range of EWP in northern Ontario; first because the northern populations in this part of the EWP range are expected to shift range northwards and second the selection pressures in different parts of the marginal range of this species are likely to be different. Two populations in one area separated by 2 to 10 km, were studied as replicates of a population type , in order to minimize confounding effects of major eco-site and climate conditions by sampling far-distant populations from a different part of the species range. In order to minimize the effect of population age on central-marginal comparisons, we sampled two old-growth (OG) marginal, two old-growth central and two second-growth (SG) central populations. We could not locate second-growth marginal populations in our study area. We sampled all trees in a complete census of the marginal populations from the GL area Acer saccharum) and jack pine (Pinus banksiana) dominated stands .The two populations from the Rawhide Lake area are located 30 km north of Elliot Lake, Ontario, in the Rawhide Lake Conservation Reserve established in 2000 by Ontario Ministry of Natural Resources (OMNR). This area lies to the north of Mississagi Provincial Park. Classified as ecological site 5E, it is characterized by rocky landscape rich in glacial history and Moraine deposits. Old and ecologically mature pure eastern white pine stands are located in the western part of the reserve and are interspersed with sugar maple .The Galloway Lake old pine area, located roughly 100 km north of Sault Ste. Marie, Ontario, hosts the two old-growth EWP populations used in this study. This area, classified as ecosite 4E et al. et al.We genotyped 390 trees from the four central populations at 11 nuclear microsatellite loci  developeData analyses were performed in three sets: all six populations; central versus marginal populations; old-growth versus second-growth populations.FISStandard measures of genetic diversity were calculated using GENEPOP FST using Weir and Cockerham’s method FST between marginal populations was significantly higher than that between central populations, we applied one-sided and two-sided significance tests with 1000 permutations using FSTAT The genetic structure of the studied populations was examined using several complementary methods: single-locus and multilocus 5 bootstrap iterations for testing statistical support for clustering. Principal coordinate analysis (PCA) based on Nei’s Allele frequency heterogeneity among pairs of populations was tested using Fisher’s exact test in GENEPOP K) tested varied from 1 through 6 when all six populations were analyzed, and from 1 through 4 when only central OG and SG populations were analyzed. We discarded the first 105 steps of each run as burn-in, to allow the parameters to converge, prior to collecting the data for the next 105 steps. Different combinations of burn-ins and MCMC chains (between 0.25×105 and 1×105) were performed initially to find out the point of convergence for our data set and 1×105 steps of burn-in and MCMC chains each, per run were found to be sufficient. Twenty-five such runs for each putative value of K were performed to test for variance between the runs. To estimate the most optimal value of K for each level of population comparisons, we further processed the program output through STRUCTURE HARVESTER K ad-hoc statistic of 3 permutations for each of the 25 replicates of the chosen K using the Greedy algorithm in CLUMPP Bayesian analysis of the population structure was performed using the admixture model in STRUCTURE FST/1– FST using Mantel test over 1,000 permutations. Additionally, to account for the bias introduced by hierarchical population structure, stratified and partial Mantel tests were performed using genetic clusters and geography as covariates following Isolation by distance was tested by regressing the logarithm of pair-wise geographic distance with pair-wise FST outlier approach in LOSITAN FST was estimated and applied for 5×105 simulations to construct upper and lower 95% confidence limits. Markers with unusually high or low FST violating these thresholds were identified as candidates for selection. This analysis was done for three sets: all six populations, four central populations and three location-wise pooled populations. A built-in false positive correction was applied using a false discovery rate of 0.05.We employed F-statistics is described by FST as variance among populations in-total and FCT as variance among regions  F-statistics analysis following 5 coalescent simulations in ARLEQUIN 5 hierarchical fixation indices were used as null distributions to construct a 95% confidence interval and to identify loci outside the two-tailed significance threshold of P = 0.05, as outliers.Assuming the finite island model for detecting loci under selection can result in a large proportion of false positives due to confounding effects of shared history of populations and population structure or if the population samples are derived from hierarchically subdivided populations 5 steps followed by data collection for the next 105 steps. Posterior probability q values  were used to assign outlier status to markers. We performed a Genbank protein homology search (BlastX) to functionally annotate the sequences from which the markers found under natural selection were developed. The direction of selection on loci showing divergent selection was assessed by allele frequency changes in marginal populations.For comparison, we also employed a Bayesian method to detect outlier loci based on differences in allele frequencies between four central and two marginal populations using the software BAYESCAN Ne were estimated using MIGRATE software MNe) for the six individual populations were estimated from a separate MIGRATE analysis under the following conditions. Two loci, RPS-20 and RPS-39 were removed from the analysis due to their FST outlier status and 50 samples were randomly chosen per population. Ten short Markov chains and one long Markov chain were sampled per locus with the first 50,000 trees discarded as burn-in and the next 50,000 trees recorded over two replicates. Static heating was used and chains were allowed to swap. In addition, two replicate populations at each of the three sampling locations were combined into a pooled data set which was also analyzed with MIGRATE . The Ne was then estimated for individual and pooled populations from the θ parameter as follows: since θ = 4Neµ, therefore, Ne = θ/4×µ. The mutation rate (µ) was assumed to be constant at 10–3 for all microsatellite loci Bidirectional gene flow between pairs of populations and mutation-scaled FIS, LGP and Ne for individual populations and means for central and marginal, OG and SG populations are reported in Ho) and expected (He) heterozygosity (AP). The central populations had significantly (P<0.05) higher allelic diversity , AP, expected genotypic additivity, and LGP than the marginal populations. However, the marginal populations had higher heterozygosity than the central populations but the differences were not significant (P>0.05).The results on genetic diversity measures, zygosity . The cenFIS ranged from 0.084 in GL-B to 0.183 in RH-B, with a mean of 0.139 over all populations. On average, FIS was lower in the marginal than central populations, but the differences were not significant , whereas central OG and central SG populations showed statistically similar Ne  than in the marginal population (GL: 312). All Ne estimates were within the bounds of 95% confidence intervals, which again did not overlap between the central and marginal populations  was lower than that between two marginal populations (0.021), the differences were not statistically significant . The genetic structure and differentiation patterns from hierarchical AMOVA mirrored those observed from FST, confirming highly significant genetic differentiation between marginal and central populations  than among central populations (0.006 to 0.016) . The NJ K = 2)   among siK = 2) . IndividK = 2) . Resultsulations . When poclusters . Thus, wP = 0.06; stratified Mantel test: P = 0.09; Mantel test for hierarchical population structure using genetic clusters: P = 0.09; and partial Mantel test with geography as covariate: P = 0.07).Various Mantel tests for isolation by distance were not significant  based on the comparison of marginal likelihood Bayes factors  model had the highest probability ( factors . The numGAe), Ne, and LGP than the marginal populations but statistically similar heterozygosity and FIS. These patterns were consistent when two OG marginal populations were compared with two OG central or two SG central populations .Our results demonstrate that EWP central populations have significantly higher allelic diversity, genotypic diversity  The similar levels of heterozygosity in central and marginal populations may be due to longevity of EWP, loss of very little heterozygosity due to bottleneck and genetic drift over a few generations, and existence of moderate number of individuals in marginal populations. Genetic bottlenecks can disrupt the mutation-drift equilibrium in natural populations Ne between central and marginal populations are consistent with those recently reported for long-lived conifer, Thuja occidentaliset al. The patterns of genetic diversity  and Ne, inbreeding and LGP. These results are in contrast with the general belief and limited empirical evidence Ne observed in OG and SG populations. It may be possible that EWP goes through the processes that maintain high genetic diversity by age 100 years. EWP has predominantly outcrossing mating system The studied old-growth and second-growth EWP populations have similar levels of genetic diversity, We sampled central and marginal and old-growth and second-growth populations from a relatively small part of EWP range in northern Ontario in order to avoid major regional variation confounding our results. Study of populations from other parts of EWP range may yield different results. Also, a range-wide central-marginal population genetic study in EWP is needed.FST, AMOVA, genetic distance, NJ, and PCA consistently demonstrated that the marginal populations are genetically diverged and separated from the central populations. This may be a result of isolation, impeded gene flow, genetic drift and divergent selection and their synergistic effects. Although different Mantel tests were not significant, the isolation by distance (IBD) contributing to the genetic differentiation of the marginal populations from the central populations could not be completely ruled out due to low statistical power to detect IBD with a limited number of populations examined in the present study. Nevertheless, other genetic differentiation measures such as AMOVA attributed hierarchical population structure to the divergence of marginal populations where a change in allele frequencies towards fixation or purging was observed at loci RPS-1b, RPS-20, RPS-34b and RPS-39. Also curtailed gene flow to marginal populations  migration was the most likely model explaining the observed data. This pattern coincides with the prevalent north to south wind flow in the area. Geographical isolation of GL populations also may have resulted in restricted gene flow to them. Thus the observed pattern of migration is in agreement with the theoretical central-marginal expectation of curtailed gene flow to marginal populations despite abundant center. Coalescent methods, such as MIGRATE, estimate migration rates accurately when the population divergence time is deep. Because the time of population split between the studied central and marginal populations is likely to be very recent (<200 generations) based on the geological and fossil evidence, some assumptions have likely been violated in the gene flow analysis using MIGRATE. Thus caution must be exercised when interpreting the rates of migration between the study populations.de-novo leading edge of eastern white pine range expansion, a characteristic target of allele surfing Our study provides molecular evidence for divergent and balancing selection operating in the marginal populations of EWP in northern Ontario. Two alleles at RPS-20 and four alleles at RPS-39, candidate loci for divergent selection, showed sharp directional change in their frequencies in the marginal populations . Such a The divergent selection in the marginal populations is likely due to their local adaptation to different climatic and site conditions. The studied marginal and central EWP populations occur in two different ecoregions of Ontario. The GL populations occur in the ecoregion 4E (Tamagami Ecoregion) and the central RH and FR populations in the ecoregion 5E (Georgian Bay Ecoregion) Picea sitchensisBuscutella laevigataThe GL marginal populations demonstrated the presence of divergent selection at two candidate loci under both simple and hierarchical island models ; Fig. S4Ho was high in marginal populations. The balancing selection may be due to heterozygote advantage in marginal harsher climatic conditions Under balancing selection, heterozygotes of beneficial alleles may be maintained preferentially over homozygotes. Indeed, for RPS-12 consistently showing the signatures of balancing selection, Helianthus paradoxusin-situ natural marginal populations of long-lived and widely distributed plants.Signatures of selection in response to salt tolerance have been reported for microsatellite markers in Although SNPs in candidate genes and genomic elements/sequences make powerful markers to detect natural selection, microsatellites provide more precise information on genetic diversity, population structure and demography Ne and LGP than the central populations. The marginal populations have slightly higher but statistically similar heterozygosity to the central populations. The central and marginal EWP populations have similar levels of genetic divergence. The marginal EWP populations are genetically distinct from the central populations. Gene flow is asymmetrical with north to south migration fitting the observed data better than either south to north or panmixia models, consistent with the prevailing north-south wind pattern in the area. The marginal populations showed signatures of diversifying and balancing selection, probably in response to local adaptation. Curtailed gene flow and natural selection may be potential mechanisms underlying local adaptation of the GL marginal populations. The successional stage (old-growth and second-growth) of the populations apparently has no effect on the central-marginal population genetic patterns in EWP. The studied EWP old-growth and second-growth populations have similar genetic diversity and genetic constitution. Our study provides the original report on the dynamics of migration, genetic drift and selection in central and marginal populations of EWP, and perhaps of any long-lived plant species with wide geographical distribution. The results contribute to resolving the classical central-marginal debate and to the understanding of evolutionary genetic forces underlying local adaptation in marginal populations.Our study results conform to most of the theoretical expectations of central-marginal hypothesis. The marginal EWP populations have lower allelic diversity, Conservation value of marginal populations is debatable. We strongly caution against discounting the genetic conservation importance of the Galloway Lake area EWP populations, considering their genetic distinctness and potential for evolutionary change and local adaptation for future range expansion under anticipated climate change conditions. Our study calls for more extensive range-wide investigations of genome-wide and adaptive genetic variation and population structure in EWP, which could offer insights into local adaptation of the marginal populations and effectively address conservation genetic issues.Figure S1Neighbor-Joining tree showing genetic relationships among central and marginal populations of eastern white pine.(PDF)Click here for additional data file.Figure S2Principal coordinates plot showing genetic relationships of eastern white pine populations. Ordination of the eastern white pine populations on principal coordinates 1 (PC1) and 2 (PC2) based on their Nei (1972) genetic distances.(PDF)Click here for additional data file.Figure S3Log probability of data and ΔK estimates for all central and marginal eastern white pine populations.(PDF)Click here for additional data file.Figure S4FST outliers showing signatures of natural selection under simple island model.FST outlier graphs showing microsatellite loci putatively under selection in (a) central vs marginal populations and, (b) central old-growth vs central second-growth populations.(PDF)Click here for additional data file.Figure S5FST outlier test for old-growth populations under simple island model.FST outlier test for detection of candidate loci under selection in two old-growth marginal and two old-growth central populations.(PDF)Click here for additional data file.Figure S6FST outlier test for three pooled populations under simple island model.FST outlier test for detection of candidate loci under selection in three pooled central and marginal populations of eastern white pine.(PDF)Click here for additional data file.Figure S7Detection of natural selection using Bayesian FST method. Bayesian analysis for detection of candidate loci under selection in central and marginal populations. FST is plotted against log of posterior probability q values that indicate outlier status of markers. Filled circles represent markers under selection.(PDF)Click here for additional data file.Figure S8Effective population sizes and their 95% confidence intervals for eastern white pine populations. Effective population size estimates for six individual populations (A), and for three pooled populations (B). The K numbers indicate MCMC sweeps discarded as burn-in and recorded respectively.(PDF)Click here for additional data file.Table S1(DOCX)Click here for additional data file.Table S2(DOCX)Click here for additional data file.Table S3(DOCX)Click here for additional data file.Table S4(DOCX)Click here for additional data file.Microsatellite Genotyping Text S1(DOCX)Click here for additional data file.


Glossina fuscipes fuscipes is a tsetse species of high economic importance in Uganda where it is responsible for transmitting animal African trypanosomiasis (AAT) and both the chronic and acute forms of human African trypanosomiasis (HAT). We used genotype data from 17 microsatellites and a mitochondrial DNA marker to assess temporal changes in gene frequency for samples collected between the periods ranging from 2008 to 2014 in nine localities spanning regions known to harbor the two forms of HAT in northern Uganda.ST and Jost’s DEST between time points sampled for each site were generally low and ranged between 0.0019 and 0.1312 for both sets of indices. We observed the highest values of FST and DEST between time points sampled from Kitgum (KT), Karuma (KR), Moyo (MY) and Pader (PD), and the possible reasons for this are discussed. Effective population size (Ne) estimates using Waple’s temporal method ranged from 103 (95 % CI: 73–138) in Kitgum to 962 (95 % CI: 669–1309) in Oculoi (OC). Additionally, evidence of a bottleneck event was detected in only one population at one time point sampled; Aminakwach (AM-27) from December 2014 (P < 0.03889).Our findings suggest that the majority of the studied populations in both HAT foci are genetically stable across the time span sampled. Pairwise estimates of differentiation using standardized FFindings suggest general temporal stability of tsetse vectors in foci of both forms of HAT in northern Uganda. Genetic stability and the moderate effective population sizes imply that a re-emergence of vectors from local residual populations missed by control efforts is an important risk. This underscores the need for more sensitive sampling and monitoring to detect residual populations following control activities. The occurrence of the two diseases follows the restricted distribution of the tsetse flies across 37 countries, covering more than nine million kmTrypanosoma brucei rhodesiense, and the Gambian form that is restricted to central and western Africa and is caused by T. b. gambiense. The two trypanosome subspecies are identical morphologically but display stark differences in epidemiological features, and require different diagnosis and treatment methods [HAT is among the most debilitating diseases on the continent, with an estimated 70 million people at risk . The HAT methods , 6. AAT, methods . Overall methods .T. b. gambiense in the northwest and T. b. rhodesiense in the southeast [In Uganda, about two thirds of the total land area is infested with tsetse flies , and thioutheast . There ioutheast , and theoutheast , 14. Oveoutheast  will comoutheast , 17. ThiGlossina fuscipes fuscipes, where it is responsible for an estimated 90 % of all disease cases. Glossina f. fuscipes belongs to the Palpalis group of tsetse, which inhabits low bushes or forests at the edges of riverine habitats, and like other tsetse species, has a unique reproductive strategy where larvae develop in utero. Populations appear to respond to seasonal weather patterns, often disappearing during the bi-annual dry season from sites where they were previously abundant [in utero can stabilize population sizes during dry periods [The primary vector of both human and animal forms of trypanosomiasis in Uganda is the tsetse species abundant , which c periods , which w periods . Studyin periods . Tempora periods , and pro periods .G. f. fuscipes temporal population dynamics has focused on southern, central and southeastern Uganda and has demonstrated stability, with little evidence of seasonal variation in population size [Previous work on ion size , 25. Howion size –28, and ion size , 14. Thiion size . Southerion size . The uniG. f. fuscipes populations in areas north of Lake Kyoga by examining temporal samples collected across multiple locations in both the T. b. gambiense and T. b. rhodesiense disease belts. We do this by evaluating genetic stability at one mitochondrial marker and 17 microsatellite loci so as to contribute reliable scientific data around which sustainable vector control programs can be planned and implemented.In this study, we investigate temporal changes in nuclear allele and mitochondrial haplotype frequencies of G. f. fuscipes goes through approximately 8 generations per year [Tsetse flies were sampled using biconical traps  in nine per year , 33, ourG. f. fuscipes in previous studies [http://dna-analysis.yale.edu/). Alleles were scored using the program Genemarker v 2.4.0  with manual editing of the automatically scored peaks.We evaluated patterns of nuclear DNA (nDNA) genetic diversity using 17 microsatellite loci extensively evaluated for minimal allele dropouts and null alleles and optimized for use in  studies , 25, 27.IS) using the Markov chain method [O) and expected (HE) heterozygosity for each population. Significance values for all multiple tests and comparisons were adjusted using the Benjamini-Hochberg method in preference to the Bonferroni method because of lower incidence of false negatives [We evaluated microsatellite genotypes with Genepop v 4.2  to test n method  with 100n method  was usedegatives . We usedegatives  to calcuhttp://dna-analysis.research.yale.edu/). Sequence chromatograms were inspected by eye and sequences trimmed to remove poor quality data using Geneious v 6.1.8 . The forward and reverse strands were used to create a consensus sequence for each sample, and the sequences trimmed to a length of 404 bp and aligned in Geneious v 6.1.8. DnaSP v 5.10.01 [d) and nucleotide diversity (π).A 570 bp fragment of the region spanning across the mitochondrial DNA (mtDNA) COI and COII gene was PCR-amplified using the primers COII-F1 (5'- CCT CAA CAC TTT TTA GGT TTA G -3') and COI-R1 (5'- GGT TCT CTA ATT TCA TCA AGT A -3') as described by  with an  5.10.01  was usedWe determined the overall proportion of the variance attributable to differences in sampling dates for all 18 sampled points using the analysis of molecular variance (AMOVA) implemented in Arlequin v 3.5; first, we ran AMOVA with two groups containing all sites at generation zero pooled together to form one group and sites with multiple generations forming another. Then, we performed AMOVA with multiple groups by having each temporal sample  forming a separate group.ST values in Arlequin v 3.5, and Jost’s DEST [ST [ST values using the formulae developed by Hedricks [EST. P-values and confidence intervals for Jost’s DEST were also calculated based on 1000 bootstrap resamplings. To test for correlation of differentiation indices and time since first sampling, we ran a linear regression of standardized FST and DEST against number of generations separating time points sampled in JMP® v11.0 .Genetic differentiation between temporal samples from the same population was quantified by computing pairwise Ft’s DEST  using DEt’s DEST  in R [42DEST [ST , 43, 44.ΦST values between temporal samples in Arlequin v3.5, and used the same program to perform Fisher’s exact test of sample differentiation based on haplotype frequencies with 10,000 iterations and 1000 dememorization steps.We employed the same AMOVA strategy outlined above to determine the overall proportion of the variance attributable to differences in sampling dates for the mitochondrial data. We also quantified genetic differentiation between temporal samples from the same population by computing pairwise To provide an estimate of effective populations (Ne) of each site, we employed two methods using the nuclear microsatellite markers; the modified temporal method  based onEvidence of population bottleneck events was tested using two methods, both of which are implemented in the program BOTTLENECK . The firG. f. fuscipes tsetse flies were genotyped using 17 microsatellite loci. Allelic richness ranged from 3.45 in population AM to 4.50 in KR and was generally similar for samples of the same locality analyzed from different time points, except for KR which differed slightly between generation 0 (4.50) and generation 35 (4.08)  had the lowest observed (HO) and expected heterozygosity (HE) values, while site AR (generations 0 and 52) had the highest HO and HE values .A total of 404 d) was moderately high across samples; the number of haplotypes (h) ranged from 2 to 6. Nucleotide diversity (π) was highest in samples from KR (at generations 0 and 35), and lowest in samples from AM (at generation 27) and KT (at generation 22). However, both Hd and π remained similar for temporal samples except for KT and PD  for microsatellites between time points from the same site were generally low (Table P < 0.000); MY , PD  and KR . The moderately high values of F’ST estimates were in concordance with DEST estimates. Number of generations separating sampled time points was not clearly correlated with differentiation indices  and 0.0236 (P = 0.003) respectively, whereas other populations separated by fewer generations had similar or even higher values of differentiation indices to AR  and KR .The same trend was apparent for the mitochondrial DNA haplotype frequencies; some sampled localities gained and others lost haplotypes across time points Fig. . More crEstimates of effective population size (Ne) using the temporal method were all bound by a 95 % confidence interval and ranged from 103 (95 % CI: 73–138) in KT to 962 (95 % CI: 669–1309) in OC. Based on the linkage disequilibrium method, only two populations (AM-27 and KR-35) exhibited 95 % confidence intervals that excluded infinity. Ne estimates using the two methods generally agreed for most populations except for KR, OC and AP. Ne estimates for both KR and OC using the LD method were much lower than from the temporal method (five and eight times respectively). In contrast, Ne estimate for AP using the LD method was higher than the temporal method, and indistinguishable from infinity. For both methods, Ne estimates were not proportional to the number of generations separating temporal samples; for example, OC had the highest Ne estimate using the temporal method at 962 and was based on samples separated by 22 generations, while MY had a Ne estimate close to the overall mean at 313 (95 % CI: 227–412) and was based on samples separated by 38 generations  tested positive for bottleneck events at el Table ; however05 Table . Additio05 Table  approximG. f. fuscipes populations that harbor the two forms of HAT in northern Uganda. Generally, temporal population genetics studies are valuable in evaluating population stability and persistence, especially under changes in environment or following a perturbation [G.f. fuscipes from southern and central Uganda indicated population stability [G. pallidipes in Kenya [G. f. fuscipes in northern Uganda, the region where the two forms of HAT will likely merge in the near future.We carried out an evaluation of changes in gene frequencies at microsatellite loci and at an mtDNA marker to provide insight into the temporal stability of urbation . Tsetse tability , 25, thein Kenya . One limR, HO and HE), while MtDNA sequences show stable h, Hd, and π except for some populations such as PD and KT (Table IS estimates indicate mostly random mating within these populations. The pattern of reduced FIS values in the second sample at all sites except MY suggests a general increase in outbreeding in recent years, potentially driven by migration.Estimates of genetic diversity in both nDNA and mtDNA indicate stable populations that are currently under migration-drift equilibrium. Microsatellite results indicate relatively stable molecular diversity indices (AKT Table . These fST and Jost’s DEST. The moderate differentiation observed in the temporal sites KT and PD might be partially attributable to the geographic distance between trapping locations. Trapping of the generation 22 samples from KT was done 36.7 km from the original sample. Similarly, trapping of the generation 52 samples from PD was done 51.2 km from the original sample. Tsetse populations can be genetically distinct even at geographic scales of only 4–5 km, for example, as has been observed in G. pallidipes [G.palpalis gambiensis [G. f. fuscipes in Uganda [ST and ΦST values for these sites were highly significant (See Table Pairwise estimates of genetic divergence and AMOVA results in both the nDNA and the mtDNA analyses indicated temporal stability of sampled populations, with low estimates of genetic differentiation between temporal samples from the same locality. Exceptions included sites KT, MY and PD, which showed moderate differentiation using both standardized Fllidipes , G.palpambiensis , and even Uganda . Thus thG. palpalis palpalis in West Africa. Relatively large Ne estimates coupled with evidence of low temporal differentiation indicates genetic drift does not strongly impact these populations [Microsatellite-based population size estimates were variable and generally associated with large confidence intervals. Our Ne estimates were lower than most estimates from Uganda obtained by  but simiulations . Althougulations . Furtherulations . The appG. f. fuscipes to exist in discrete patches and the potential for seasonal fluctuations in population size presented by the climate of northern Uganda, there is little evidence of genetic bottlenecks in the populations we studied; only population AM at generation 27, sampled in December 2014, showed evidence of a bottleneck. Our finding of limited bottleneck events is congruent with previous studies in southern Uganda [in utero may help to relieve tsetse from harsh environmental conditions during reproduction, and hence stabilizes populations [Glossina f. fuscipes are known to generally disperse along waterways, following riverbeds or the edges of gallery forests, where they are able to survive low humidity conditions during dry seasons. Additionally, tsetse flies in northern Uganda have not been subjected to extensive control measures. A prolonged period of civil unrest in the region led to a disruption of control efforts and a breakdown in social infrastructure [Despite the tendency of n Uganda , 25, 33.ulations . Furthertructure , and lattructure . At the tructure , which pin utero helps to stabilize populations during dry periods, and as a result, decreases seasonal variation in tsetse number. Additionally, large populations of pupa, which develop in the ground over a period of weeks, may also help to ensure the continuity of tsetse populations and would contribute to reducing the variance in genetic changes over time [In summary, we find temporal stability in gene diversities of most tsetse populations sampled, which lends further credence to the hypothesis that larval development ver time . For norThe findings point to general temporal stability of tsetse vectors in both foci of the two forms of HAT in northern Uganda. Additionally, migration and gene flow is probably important in shaping the observed genetic stability, which suggests that area-wide control strategies are more desirable than localized efforts. Furthermore, genetic stability and moderate effective population sizes suggest there is a significant risk of re-emergence from local residual populations missed by control efforts. This underscores the need for more sensitive sampling techniques to detect residual populations when tsetse densities decrease due to vector control to avoid a population rebound effect when control and monitoring activities are relaxed. Our results, especially the Ne estimates, may also be useful in evaluating the effectiveness of future control efforts, for instance, by estimating reduction in Ne resulting from control.