Drosophila melanogaster. We used next-generation resequencing data from this experiment to examine genome-wide patterns of genetic variation over an evolutionary time-scale that approaches 1,000 generations. We also compared measures of variation within and differentiation between our populations to simulations based on a variety of evolutionary scenarios. Our analysis yielded no clear evidence of hard selective sweeps, whereby natural selection acts to increase the frequency of a newly-arising mutation in a population until it becomes fixed. We do find evidence for selection acting on standing genetic variation, as independent replicate populations exhibit similar population-genetic dynamics, without obvious fixation of candidate alleles under selection. A hidden-Markov model test for selection also found widespread evidence for selection. We found more genetic variation genome-wide, and less differentiation between replicate populations genome-wide, than arose in any of our simulated evolutionary scenarios.Experimental evolutionary genomics now allows biologists to test fundamental theories concerning the genetic basis of adaptation. We have conducted one of the longest laboratory evolution experiments with any sexually-reproducing metazoan,  The genetic basis of adaptation has historically been a major point of contention among evolutionary biologists1233Drosophila melanogaster, the more common sequencing strategy in E&R experiments has been to pool multiple individuals within or across evolving replicated laboratory populations1910In the case of outbreeding sexual species, such as et al.Genome-wide sequencing of genetic variation present in experimentally evolving sexual populations after many generations of selection remains of interest as a method for addressing the relative importance of selective sweeps, particularly from the standpoint of alleles being driven to fixation. Hitchhiking effects arising from successive selective sweeps are not expected to purge genetic variation genome-wide in sexual populations immediately7Here we show that such long-evolved moderate-sized sexual populations do not exhibit the general lack of genetic variation that is characteristic of long-evolved clonal populations. Instead, after sequencing five independent replicate populations sharing a common selection regime, and more than 900 generations of sustained directional selection, we find widespread maintenance of genetic variation genome-wide. While it could be argued we still do not have sufficient generations of selection or population sizes large enough, our study features the best data collected from laboratory evolution to date with respect to the long-term evolution of patterns of genetic variation in sexually reproducing populations of multicellular eukaryotes. Furthermore, we compare measures of variation within and differentiation between our populations to simulated data from a number of evolutionary scenarios. We consistently find that there is more variation maintained in our populations, and less differentiation between replicate populations, than is found in any of the evolutionary scenarios we simulated. Lastly, we look at patterns of genetic variation and the frequency distribution of genetic variation to test for selective sweeps.Drosophila melanogaster females obtained by Phillip Ives from his long-studied South Amherst, Massachusetts endemic populationwithin replicate populations, at temperatures of 23–25 degrees Celsius. Effective populations size (Ne) are around ~1000 based on calculations from demographic dataThe novel experimental material analyzed here is pooled genomic DNA obtained from each of the five “B” populations maintained in the Rose laboratory1416The B populations were pooled and sampled for sequencing at generation 785 from their founding in 1980, in March 2010, though this was after a total of 915 generations of lab domestication since Ives supplied the wild-caught females for founding the laboratory IV stock. From August of 1975 to June of 1981, the IV and B populations were cultured using corn meal based medium in 16 glass milk pint bottles per population, with 12 L:12D light exposure. From June 1981 to March 2010, the B populations sequenced here were cultured in 40 shell vials at densities of 60–80 eggs per vial, yielding 50–75 adults per vial. This equates to around a minimum census size of 2000 each generation. During this period, these B populations were cultured using banana-molasses mediumThe pooled genomic DNA was obtained by isolating 250 female flies from each of the five population replicates of the B flies, with harvesting ten days after the pupal stage, using the Gentra Puregene Cell Kit  according to standard protocol, after maceration of the fly tissues using Dounce Tissue Grinders . Genomic DNA concentrations and purities of the samples were assayed by DNA spectrophotometer. Size distributions were visualized by low agarose gel electrophoresis with DNA size markers. Genomic DNAs were stored at −20 °C before shipment to Beckman Coulter  for Illumina paired end sequencing. Reads were 76 bp in length. The fastq files used in our analyses are available through NCBI SRA (BioProject ID: PRJNA350701).D. melanogaster reference genome (version 5.55) using bwa memhttp://www.repeatmasker.org)D. melanogaster genome version 5.55.We mapped reads with BWA (version 0.7.8)1 through B5 respectively.A table with major and minor allele counts for each SNP in each population was then generated from this synchronized file. SNPs where discarded if coverage in any of the populations was less than 20X or greater than 150X. We also required a minimum minor allele frequency of 2% across all five populations. Based on these parameters, ~1.2 million SNPs were identified across the major chromosome arms. The average coverage at each called SNP was 62X, 65X, 57X, 66X, and 69X in BPoPoolationLocal depressions in genetic variation are considered one of the primary means of detecting selective sweeps from population level dataFST estimates were obtained using the formula: T is heterozygosity based on total population allele frequencies, and HS is the average subpopulation heterozygosity in each of the B populationsFST estimates were made at every polymorphic site in the data set. This was done to quantify the levels of differentiation between our five B populations, as well as between replicate populations in our simulated scenarios. FST estimates were calculated for the B populations along chromosome 3 R at every polymorphic site. This was also done for the simulated data, and once again all polymorphic positions along 3 R not present in the SNP table created from our real data set were discarded.FST value where N is the effective population size of each subpopulation and t is the time since divergence from their ancestral populationNm is equal to the number of migrants per generation27We also used the formula: https://sourceforge.net/projects/mimicree/)To perform our first set of simulations we used MimicrEE ( s), the dominance coefficient (h), and the nucleotide of the nonselected allele are provided (w11). The fitness of the heterozygous and homozygous individuals is given by: w11 = 1, w12 = 1 + hs, and w22 = 1 + sde novo mutations are considered, as its purpose is to simulate scenarios where adaptation results from selection on standing genetic variation. The simulated populations have non-overlapping generations and all individuals are hermaphrodites (though selfing is excluded). At each generation, matings are performed, where mating success (number of offspring) scales linearly with fitness, until the total number of offspring in the population equals the targeted population size (fecundity selection). Each parent contributes a single gamete to the offspring. Crossing-over events are introduced according to a user-specified recombination rate.For each selected locus, the selection coefficient 1) was defined as the beneficial allele in each case and was also defined as dominant (h = 0). For another set, the A2 allele was defined as beneficial and dominant (h = 1). And for a final set, all selected loci were codominant (h = 0.5). For each set, we simulated scenarios with selection coefficients ranging from 0.03 (low) to 0.1 (high)  . As we i1 (high) .ST across all polymorphic sites to compare to values observed in the B populations. We also looked at heterozygosity and FST over 50 kb windows, and calculated the variance between windows as a means of comparing spatial variation in heterozygosity and FST to what we observe in the B populations.From each set of 5 simulated populations under each scenario, we calculated average heterozygosity and average FD. melanogaster chromosome 3 R. An R program was created to simulate an initial population of F founder chromosomes expanded and used to found five populations that were then evolved for G generations at a population size of N gametes per population with M migrant gametes in the meta-population per generation. The simulation was accomplished by tracking founder segments and recombination breakpoints over time. So, the N gametes used to found a subpopulation initially consist of a random sample of size N drawn from the numbers 1 through F with replacement. Then each generation to create N new gametes (n = 1 … N) we draw two gametes with replacement from the previous generation and create a recombination breakpoint at position r = unif , if r < 0.63 (to simulate a chromosome of 63 cM) and n modulus 2 equal zero . Recombinant chromosomes are represented as a pair of vectors: a founder state vector, and a recombination breakpoint vector. So, for example, the nth gamete in a population might be Sn = {3, 17, 31, 3} and Bn = {0.20, 0.25, 0.60}, indicating that gamete has material from founder #3 from 0 to 20 cM, founder 17 from 20 to 25 cM, etc. This sampling scheme models drift and recombination in a Wright-Fisher population.Given how long our populations have been maintained in the lab, it is easy to imagine that there may have been some instances of migration due to accidental cross-contamination. Thus, in addition to the selection scenarios mentioned above, we performed simulations featuring migration. These simulations also feature optimizing selection, as opposed to our other simulations featuring directional and over dominant selection. We once again simulated an evolve-and-resequence experiment for a 63 cM long Q evenly spaced quantitative trait loci (QTL) on the chromosome, with a vector of effect sizes and starting allele frequencies (E and F), with QTL states randomly assigned via binomial sampling given Fq for each locus. The resulting quantitative trait has a heritability due to the QTL on 3 R of 12%, and an additional polygenic heritability of 38% due to the other chromosomes, and a total phenotypic variance of one. We then model a gamete’s phenotype as the sum of its effect sizes plus a random Gaussian deviate representing a polygenic component, plus a second random Gaussian deviate representing an environmental deviation, plus the current polygenic mean of the population. The fitness of each gamete is a standard Gaussian fitness function proportional to w = (pheno − NewOptimum)^2/(2*VarianceFitness), normalized to total fitness. Under this selection scheme each generation gametes are resampled proportional to w, resulting in allele frequency changes at the underlying QTL, and resulting changes in the mean phenotype due to those QTL. Furthermore, each generation the polygenic mean (the mean phenotype due to chromosomes other than 3 R) changes according to the Breeder’s equationa,i = 2piqiai2), a polygenic component with Gaussian variance Va;poly, and environmental variation (Ve). We held Ve/Vt constant, but allowed the ratio of Va to Va;poly to vary. Each generation an individual’s phenotypic value is the sum of allelic effects due to tracked loci, a Gaussian deviation due to the polygenic component, and a Gaussian environmental deviate. Those phenotypic values determines a vector of length N consisting of each individual’s average fitness based on the Gaussian fitness function, with N individual’s chosen with replacement from that vector to create the next generation, with the probability of being chosen proportional to average fitness. Between generations, the mean of the population then shifts due to changes in allele frequencies at both the tracked loci and untracked loci, as predicted by the Breeder’s equation: h2poly * S. In this context, h2poly is Va;poly/ and S is the observed selective differential . By modeling adaptation in this fashion, the population approaches the new optimum due to genetic changes at both tracked and untracked loci. This model thus accommodates adaptation at both the explicitly modeled chromosome arm and the remainder of the genome.Next we added selection to the simulation. We simulated Ve = 0.5 and Vp = 1.0 at generation zero, with a new optimum that is 15 phenotype standard deviations away from the population mean, a shift in optimum that was chosen to match experimental evolution experiments in Drosophila  3 R chromosomes from the DGRPN gametes for each SNP. Despite the fact that poolseq estimates allele frequency in the population based on a finite sample of gametes, with the accuracy of that estimate a function of coverage depth and number of gametes sampled for the Illumina library, we used the exact allele frequency estimates calculated using the method described above in our downstream calculations. Since libraries are made using a large number of individuals (>200) and the per site coverage approaches 60X, this simplification is likely acceptable.Each generation we simulated migration by randomly taking M pairs of gametes in the 5*N set being tracked and replacing the first member of the pair with the second. This corresponds to a one-way island model of migration. This means that if the entire population is 5*N gametes (where 5 is the number of populations), each generation k gametes are chosen and they essentially overwrite K other gametes. That is to say, “one-way” means gametes are not exchanged between populations and “island” means migration is equally likely between any two demes in any direction. We iterated this entire process for Using this framework, we simulated a number of evolutionary scenarios that involved varying the following: migration rates (M), number of selected QTL, effect sizes of selected QTL, and starting frequencies of selected QTL. Scenarios were simulated using groups of five populations to mimic our observed fly populations. Each scenario was simulated 300 times. All simulations ran for 800 generations (G) and all simulated populations consisted of 2000 gametes (N). We simulated scenarios with 0, 1 or 5 migration events per generation (M). We ran simulations with 0 , 3, 10 and 20 selected QTL. Selected QTL were evenly distributed across the chromosome arm. We also looked at the effects of the starting frequency of selected QTL (F) by running simulations where all selected alleles started at either 0.05 or 0.5. As we increased the number of QTL, we reduced their effect sizes (E) so that the sum of squared effect sizes was held constant. This was done to prevent changes in the heritability of the character. For simulations with 3 QTL, effect sizes were 1, 2, and 2. For simulations with 10 QTL, they were 1, 1, 1, 1, 1, 1, 1, 1, 0.71, and 0.71. And lastly, they were 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.5, 0.5, 0.5, and 0.5 for simulations with 20 QTL. [See ST across all polymorphic sites to compare with the values observed in the B populations. We then again looked at heterozygosity and FST over 100 kb windows and calculated the variance between windows as a means of comparing spatial variation in heterozygosity and FST to what we observe in the B populations.Once again, we calculated average heterozygosity and average Fet al.To test for footprints of selection across the genome, we relied on a hidden Markov Model developed with the intention to detect sweeps in pooled sequence data, developed by Boitard −10. The −k parameter is the per site transition probability q between neutral and selected states, which is an important tuning parameter for the hidden Markov model underlying this test. As q increases, less evidence is required for a transition to selection and more sweep candidates should be detected. We also ran tests under more (q = 10−11) and less stringent (q = 10−9) conditions, which only led to slight differences in the number of footprints detected (10(1 − p), where p is the maximum of the posterior probability of hidden state “Selection” within the window.Mpileups for individual populations were used as test inputs, since Pool-HMM can only process data from one population at a time. Scans were performed along each of the major chromosome arms using the following parameters: -n 500 (number of chromosomes in each pool), -pred , “Selected” (close to sweep site), or “Intermediate”  of each SNP), -C 150 , -r 5 (where 1/r is the proportion of sites that are used to estimate the background AFS), -theta ϴ , and −k 10detected . A confiWe applied this Pool-HMM test to results from our neutral simulations using the settings listed above, as a means of evaluating our false positive rate. For such tests of simulated data, we used 100 kb regions extracted from different runs of our neutral simulations. Essentially, we converted output from the simulations to mpileup files. Sequence and coverage variation were introduced based on what was found in the actual 3 R sequences from the B populations. In addition to the 100 kb regions just mentioned, we ran tests for selection on results for the entirety of 3 R from 15 simulated populations each taken from a different evolutionary scenario. We did this for populations taken from each of following scenarios: neutral evolution with migration rates M = 0, 5 and 20; 3 selected QTL’s with low starting frequencies with M = 0, 5 and 20; 3 selected QTL’s with high starting frequencies with M = 0, 5 and 20; 10 selected QTL’s with low starting frequencies with M = 0, 5 and 20; 10 selected QTL’s with high starting frequencies with M = 0, 5 and 20.et al.Plotting measures of genetic variation, heterozygosity and Watterson theta (ϴ), across 100 kb non-overlapping windows reveals depressions in genetic variation across all major chromosomes arms in the B populations . Many destarting base population used in our simulations  was 0.32. Average heterozygosity across 3 R was lower in the five B-population replicate data at 0.28, 0.29, 0.28, 0.27, and 0.27, respectively. We find lower levels of heterozygosity after 800 generations in all of our evolutionary scenarios  sites or weaker selection at 20 sites  .In our simulations with migration, we once again found that the addition of selection results in greater reductions in heterozygosity overall . IncreasFST across the 5 B populations was 0.08 across all chromosome arms, including 3 R individually. This value is far lower than what we would predict assuming no migration using the formula FSTshould be around 0.33 assuming N = 1,000 and t ~ 800 generations. Substituting our observed FST into this equation and instead solving for N suggests that in order to produce an FST estimate of 0.08, assuming no migration and random divergence, we would have to have an effective population size of around 4,700. To assesses how much migration would be required to produce this result, assuming random divergence and populations at equilibrium, we used the formula Nm, the number of migrants per generations, suggests our observed FST could be produced if there were 2.88 successful migrants per generation, each and every generation, if the assumptions of this model are met.Mean FSTwas also far lower than anything produced in the different evolutionary scenarios we simulated , for the single chromosome arm.Our observed imulated . In our FST  and there is no migration, there is some correspondence between the regions identified by pool-HMM and the locations of the actual selected QTL with pool-HMM identifying regions overlapping or adjacent to selected QTL . HoweverMigration also had a pronounced effect on pool-HMM’s ability to detected selected QTL. Across all the scenarios we tested, we found that increased migration rates resulted in reductions in the number of regions identified as being under selection . As mentIn summary, we found that both migration and the starting frequency of selected alleles affect the rate and accuracy at which pool-HMM identifies regions as being under selection. Consequently, the overall correspondence between regions identified by pool-HMM and the location of selected QTL in our simulated results was generally poor. Pool-HMM also detected far fewer regions in our simulated results than in any of the scans of our real data from the B population. This suggests there may be some other evolutionary factor(s) behind the allele frequency distributions in the evolved populations other than those we simulated . It is not entirely clear what this factor might be from our results. For instance, this discrepancy could be the result of some demographic factor acting on the B populations or some selective scenario not tested. Or perhaps a combination of the two.Drosophila populations, we found some depressions indicating reduced genetic polymorphism. But there are no regions where it was completely expunged. When comparing these results to the combined DGRP lines we used as base populations for our simulations, we found levels of variation in our populations to be lower on average. While there are clearly other factors at play, this disparity could also be due in large part to the nearly 1000 generations of evolutionary domestication that the experimental B populations have been subjected to, domestication that has featured both reduced effective population sizes as well as long-sustained stable patterns of selection. This hypothesis is supported by the localized reductions in polymorphism found within our populations, reductions which are consistent with adaptation involving allele frequencies moving part-way toward fixationApplying all our measures of genetic variation to the five observed Our tests for selection using pool-HMM are also suggestive of a widespread response to selection across the genome in our populations. However, it is unclear how many of these regions are indicative of a recent response to selection, or selection in the wild ancestral population sampled by Ives in 1975. We find a number of regions that are consistently implicated across all replicates, which is perhaps indicative of a parallel response to selection. However, further complicating matters, our tests on data from simulated populations suggest demographic factors and the starting frequencies of selected variants can have pronounced effects on pool-HMM’s ability to detect regions under selection. The role of the former in particular warrants further investigation. We found that migration produced large reductions in the number of regions identified as being under selection by pool-HMM across all scenarios we tested. Given the number of regions detected when pool-HMM is applied to the B population sequence data, it seems unlikely that migration between B populations is a major confounding factor. However, it is entirely possible that these results could be due to some other demographic factor or combinations of factors not explored in our simulations.e estimates of Mueller et al.We find that, after almost 1,000 generations of laboratory cultivation, the five replicate B populations studied here are not generally genetically depauperate. This is somewhat surprising, given the moderate Nde novo beneficial mutations in our simulations, given the values of our actual Ne and the number of generations under selection, there no doubt remains value in exploring a wider range of evolutionary scenarios. However, addressing these issues satisfactorily would constitute a considerable undertaking, well beyond the scope of this project.That being said, our simulations are far from perfect. Given the age of our system, we have no record of the starting genetic make-up of our populations. The populations featured in this study were derived from a single population that had been maintained for 130 generations under laboratory conditions. This population was in turn created from 200 gravid females collected in the wild brought into the lab. In contrast, the base population used in our simulations was created by essentially combining a hundred inbred lines from the DGRP. This difference alone represents a major confounding factor. Additionally, while we feel we are justified in excluding the potential for Drosophila have provided a variety of results and interpretations concerning the underlying mechanisms of adaptation. For instance, both Turner et al.et al.et al.et al.et al.Laboratory selection experiments with et al.et al.et al.8 and Tobler et al.et al.This discrepancy may be due to differences in experimental methods. For instance, the study of Turner et al.1) from the Burke et al. study also did not show clear signatures of completed selective sweeps, despite just over 600 generations of sustained selection.The only published study that is closely comparable to this one is that of Burke FST was in fact much lower than what would be predicted by classical theory assuming no migration and random divergence between subpopulation. To produce the level of FST we observe using this model would require an effective population size nearly five times greater than estimates made from empirical data by Mueller et al.FST, assuming random divergence and populations at equilibrium suggest that ~3 migrants per generation would be sufficient. However, given the nature of our system and its maintenance protocols, migration rates that high every generation seem unlikely. It is also worth noting we have no guarantee that the assumption of equilibrium conditions has been met in our populations, which could confound this estimateFST in simulated scenarios.The high degree of similarity in patterns of variation between our five replicate populations is another surprising aspect of our results. We found that our observed level of ST estimates to what we find in the five B populations. Increasing migration rates did produce reductions in FST, and it is likely that a drastic increase in rates of simulated migration per generation would produce something comparable to our observed values. However, once again, migration rates that high every generation do not seem likely, given that these populations are maintained independently and all migration is by definition accidental. It would also likely result in too much genetic variation being maintained, relative to the patterns in our genome-wide data, unless population sizes were also reduced. Our results could possibly be explained by parallel selection at a large number of loci, but more work would be required to test this hypothesis. And our initial findings suggest even this explanation may not be adequate.In our simulations, increasing the number of selected sites/QTLs and migration rates both failed to produce comparable FRegarding the relative importance of selective sweeps, our results are not conclusive. However, the high levels of genetic variation maintained in these populations in the face of relatively small population sizes, which foster genetic drift and background selectionHow to cite this article: Phillips, M. A. et al. Genome-wide analysis of long-term evolutionary domestication in Drosophila melanogaster. Sci. Rep.6, 39281; doi: 10.1038/srep39281 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Venturia inaequalis population types, virulent or avirulent towards Malus x domestica cultivars carrying Rvi6 gene, is maintained or broken several years after the first emergence of new virulent strains in Poland, and to investigate the relationship between ‘new’ and ‘native’ populations derived from the same commercial orchards. For this purpose, we investigated the genetic structure of populations of the apple scab fungus, occurring on apple tree cultivars containing Rvi6, Rvi1 or Rvi17 resistance gene or no resistance at all, based on microsatellite data obtained from 606 strains sampled in 10 orchards composed of various host cultivars.Tracking newly emergent virulent populations in agroecosystems provides an opportunity to increase our understanding of the co-evolution dynamics of pathogens and their hosts. On the one hand host plants exert selective pressure on pathogen populations, thus dividing them into subpopulations of different virulence, while on the other hand they create an opportunity for secondary contact between the two divergent populations on one tree. The main objectives of the study were to explore whether the previously reported structure between two Rvi6 gene from the Rvi6-avirulent populations and substructures within the Rvi6-group as a consequence of independent immigration events followed by rare, long-distance dispersals. We did not observe such a structuring effect of other genes determining apple scab resistance on any other populations, which in turn were genetically homogenous. However, in two orchards the co-occurrence of strains of different virulence pattern on the same trees was detected, blurring the genetic boundaries between populations.Application of genetic distance inferring and clustering methods allowed us to observe clear genetic distinctness of the populations virulent towards cultivars carrying Rvi6 exerted selective pressure on pathogens populations: those virulent toward Rvi6 hosts show unique and clear genetic and virulence pattern. For the first time in commercial Malus x domestica orchards, we reported secondary contacts between populations virulent and avirulent toward Rvi6 hosts. These two populations, first diverged in allopatry, second came into contact and subsequently began interbreeding, in such way that they show unambiguous footprints of gene flow today.Among several resistance genes studied, only The online version of this article (10.1186/s12862-018-1122-4) contains supplementary material, which is available to authorized users. Venturia inaequalis (Cooke) G. Winter is a haploid fungus from the Ascomycotina class that is responsible for the most damaging apple disease reported in almost all apple-growing regions - apple scab. Its annual cycle includes sexual reproduction on infected apple leaf litters in the winter followed by several cycles of asexual reproduction during the apple growing season, what makes disease management challenging. Disease control predominantly relies on leaf litter management and repeated fungicide applications in the spring [Fungal pathogens may affect plant physiology and reduce plant fitness by reducing the quality and quantity of seeds or fruits. Consequently, diseases caused by fungal pathogens are a vital component of global yield losses and therefore require intensive crop protection practices against pathogens, including intensive chemical use. However, the increasing demand for organic and chemical residue-free food requires the development of alternative protection practices, such as genetic pest control via plant breeding. This is even more important for agrosystems in which crop rotation is impossible, such as the perennial apple, grape or citrus tree crops. e spring , 2.V. inaequalis rely on gene-for-gene interactions [R gene) and a gene from the pathogen (avirulence gene). In this kind of interaction, the product of a given avirulence gene is recognized by the plant harbouring the matching resistance allele of the corresponding R gene. If a plant harbours the susceptibility allele or if the pathogen has the alternative virulence allele, infection occurs. To date, 17 main R genes that determine resistance against apple scab have been identified within the Malus species [R gene Rvi6  has been widely introgressed in cultivars of M. x domestica since that time, while the Rvi1  and Rvi17  cultivars have been registered in Poland in the 1990s. On the other hand, a number of apple resistance genes that were recently introgressed into M. x domestica cultivars from wild genetic resources of Malus were overcome by the V. inaequalis fungus, as reported from many areas of apple cultivation in Europe  were considered as free from V. inaequalis infections until the first report of apple scab symptoms in the Topaz cultivar in 2009 [Rvi6 cultivars, but the severity of the disease varied.Most genetic pest controls for ractions , such th species . For exaamed Vf, , originaamed Vg, , originamed Va1, , origina Europe (–7 and ot in 2009 . In the Rvi6) reportedly has a very strong effect on the structure of populations that infect both genotypes that can be generally maintained, even at the orchard scale [Rvi6 and non-Rvi6V. inaequalis populations diverged several thousand years ago  with no detectable gene flow between the two lineages. Based on extensive studies comprising populations at the European scale, Lemaire et al. found strong statistical support for a scenario in which the populations infecting the current Rvi6 varieties present in the agroecosystem emerged from existing populations present on the wild progenitor Japanese crabapple M. floribunda, which was further followed by several migration events of Rvi6-virulent individuals to European orchards. In a French orchard composed of both Rvi6 and non-Rvi6 hosts, Leroy et al. demonstrated that secondary gene flow between both populations is possible in nature, leading to both genetic and epidemiological changes [Previous population genetic studies have shown that apple resistance genes might induce specialization in pathogen populations, resulting in host-related adaptations , 10. Therd scale , 12. Recrd scale  have rev changes .Rvi6 cultivars, we concluded that this mechanism of resistance has also been overcome in Poland. Since little is known about the genetic structure of the newly observed pathogen populations affecting Rvi6 cultivars in Poland or their relationship with ‘native’ V. inaequalis populations, we wanted to assess the occurrence and characterize these pathogens in several organic orchards. One of our main goals was to assess the maintenance of reproductive isolation between populations in different commercial organic orchards, including those in which Rvi6 and non-Rvi6 cultivars occur sympatrically. This assessment would elucidate the evolutionary potential of fungal populations which diverged in allopatry for some period of time, what should be reflected in their genetic distinctness, yet now are observed in the same orchards, experiencing the secondary contacts. Moreover, we wanted to explore whether monogenic resistances other than the Rvi6 present in Malus x domestica cultivars in Poland could differentiate pathogen populations. We wanted to explore the genetic relationship between the Rvi6-virulent populations and other virulent pathogen populations, particularly those affecting apple cultivars containing Rvi17 (Va1), Rvi1 (Vg) or those without any known R genes by tracking the recombination possibilities between them. To reach this goal, we analysed 633 infected leaves, collected in 10 orchards across Poland and genotyped these samples at 11 microsatellite loci, since microsatellite markers were found as a good method used for V. inaequalis genotyping, suitable for inference about differentiation at fungal population level [Based on observations of disease symptoms in on level , 16.Rvi6-virulent strains were reported in Poland (Lublin). In all the orchard trees of Malus x domestica were sampled, and one population was collected from crab apple trees - F1 seedling of Malus x zumi var. ‘Colocarpa’ originated from open pollination . Infected leaves with clear scab lesions were randomly collected from mono-R-genic cultivars  and from cultivars without any known sources of apple scab resistance (hereafter Rvi0). Once the leaves were air dried, single and distinct scab lesions were cut from the leaves, placed into Eppendorf tubes and frozen at − 20 °C. prior to DNA extraction, the samples were submerged in liquid nitrogen and homogenized using a sterile mortar and pestle. The total DNA was extracted using the GeneMatrix Plant & Fungi DNA Purification Kit  according to the manufacturer’s instructions and eluted with 100 μl of the provided elution reagent. In total, DNA from 20 orchard populations, each comprising of 30–35 single scab lesions, were subjected to further analyses.During the late spring and early summer of 2012–2014, two commercial chemically controlled, six organic fungicide-free and two with mixed type of control apple tree orchards located in the central, southern and northern parts of Poland , polymerisation at 72 °C for 30 s and a final extension step at 72 °C for 5 min. The amplicons sizes were scored using an ABI PRISM 310 Genetic Analyzer with GeneScan™ 500 ROX™  as an internal size standard and analysed using Peak Scanner Software 1.0 (Thermo Fisher Scientific). Strains with profiles showing more than one clear signal per microsatellite marker were excluded, in order to avoid the possibility of genotyping of multiple infections in the same area of a leaf .The DNA from each sample was genotyped at 11 microsatellite loci. The amplifications were performed in one simplex and five multiplexes PCRs. Each forward primer in the applied primer sets  was calculated using the standard Bayesian criteria described by Rannala and Mountain [Individuals were assigned to regional sample groups using GeneClass 2.0 . The proMountain  and by sMountain . Individ4 iterations, 1 × 104 Monte Carlo Markov Chain schemes were performed for each run. No prior information regarding individual samples related to their location or their host was assumed, and a model with correlated allele frequencies and admixture among the populations was applied. The number of populations that best represented the observed data under the model implemented was determined by maximizing the estimated ln-likelihood of the results obtained for the different K values and the ΔK index, which is based on the rate of change in the ln-likelihood of the data between successive K values, to find the best K value estimates [The STRUCTURE 2.3.4 programme , which istimates  using thstimates  to obtaiAnother programme TESS 2.3.1, implementing an individually based spatially explicit Bayesian clustering algorithm for population genetic studies , 29, wasBased on the CLUMPAK outputs obtained from TESS and STRUCTURE Q-values, graphical presentations of the average individual membership coefficients were performed using R script available with TESS software . The estFor the purpose of comparison with Bayesian-based clustering algorithms, the clustering method based on distance matrix was applied. GDA program  was usedTo determine the most likely number of populations represented by the samples and to assess the levels of differentiation, five clustering and assignment methods were used.HD, [For each population, the mean number of alleles per locus and the number of private alleles  were estimated using the GDA, allelic richness  and unbiased average gene diversity  using FHD, , and allHD,  programmPopulation genetic structure was examined by means of a hierarchical analysis of molecular variance (AMOVA) as implemented in ARLEQUIN. Two-way AMOVA was conducted to estimate the variation associated with the differences among groups of populations, populations within groups and individuals within populations. For the comparison, one-way AMOVA was conducted by considering each group of populations separately to compare the distribution of genetic variation among vs within the populations within groups. GenAlEx was used to compute and test the statistical significance of Fi-statistics using a nonparametric approach based on 1000 random permutations of haplotypes.Rvi6-group, Rvi1-group, Rvi17-group, Rvi0-group or general non-Rvi6-group) were estimated using Weir and Cockerham’s pairwise estimator Ɵ [ST, and its statistical significance was assessed using a permutation test (1000 permutations) implemented in the GENETIX [The levels of differentiation between all populations and among populations within the assumed groups of populations  was estimated in GenAlEx for all populations, where Nm < 1 indicated low levels of gene flow, Nm = 1 indicated that the drift effects were exactly counterbalanced by the effects of gene flow so that populations neither diverged nor converged, and Nm > 1 suggested high levels of gene flow [Pairwise gene flow (ene flow .rBARd) [IA) and is independent from the number of loci included in the analysis. The null hypothesis of the random association among alleles at different loci (rBARd = 0), indicating random mating, was tested across all assumed groups of populations using the programme MULTILOCUS 1.2.2. [Allele associations among different loci were examined using a generalized measure of multilocus linkage equilibrium (rBARd) , 41, whiST by Slatkin or Rouset’s) and the pairwise Euclidean geographical distance matrix, both of which were log or not transformed.To test whether the genetic structures of the populations were influenced by Wright’s isolation by distance theory, the Mantel test was performed using the GenAlEx and Isolation By Distance Web Service 3.23  programmRvi6) population (Table 1aac3b to 32 for 1tc1g (mean = 15.6). For the groups of populations, the lowest allelic richness was observed for the Rvi6-group (3.563), while the values for the other groups were higher and at a similar level . The highest value for a population was for the ASI population , while the lowest was observed for the 1aac3b locus .The single, clear amplicons comprising 11 microsatellite loci were obtained for 633 samples. Clone correction using GenoType software revealed the presence of repeated haplotypes in 8 populations, while the highest clone percentage was observed for the END , while the lowest rate of accurate assignment was observed for the miscellaneous group comprising populations from cultivars without known apple scab resistance genes . Overall, the rate of misassignment  was high, as many individuals from one host cultivar group tended to be assigned with high probability to all the other host groups, but the individuals from the Rvi6-group were mostly assigned to the other populations from this group.The exclusion-based method implemented in GeneClass produced an accurate assignment rate  of 22.8% when all the individuals and populations were tested. Of the Rvi6 hosts. Indeed, most samples collected from the Rvi6 trees were assigned to a single group, while samples collected from the other trees  were grouped together. In both analyses, the algorithms detected some admixed individuals. From K = 3 to K = 5, STRUCTURE and TESS found evidence of additional groups, but they were irrespective from any other sources of resistances, suggesting that the algorithms actually indicated different levels of substructure within both the Rvi6 and the non-Rvi6 cluster. At K = 3, STRUCTURE found support for a substructure within the non-Rvi6 gene pool, while TESS found a third group within the Rvi6 cluster that preferentially comprised individuals of populations from Lublin and non-Rvi6 populations. At K = 4 using STRUCTURE, all the Rvi6 populations were divided into two clusters that each comprised populations from two distant geographical locations, whereas the rest of the populations were separated to two other clusters. At K = 4, the fourth cluster in TESS (green colour) favoured both Rvi6 and non-Rvi6-virulent populations from Lublin. Individual clustering observed for K = 5 in STRUCTURE was very similar to that at K = 4. Four subgroups among the Rvi6 populations were distinguished at K = 5 in TESS, corresponding with the geographical origin of the populations, whereas non-Rvi6 populations were in one heterogeneous cluster, with different levels of admixture for the individuals. For K = 7, indicated as the optimal number of clusters using TESS, some substructures were observed among the Rvi6 populations. However, generally most individuals were poorly assigned to the inferred clusters , individual-based Bayesian clustering analyses were performed using the STRUCTURE and TESS programmes. Using STRUCTURE, we observed the highest Evanno’s ΔK index for K = 4, but this index had low statistical support ΔK = 14.73)  . The largest genetic variation was partitioned between individuals within populations (85%), while variation among groups (7.71%) was comparable to those among populations within groups (6.97%). When each group of populations was considered separately, the largest genetic variation was again partitioned between individuals within populations (84.0% for the Rvi6-group and 96.5% for the non-Rvi6-group), but the variation among populations was much higher in the Rvi6-group (16%) than in the non-Rvi6-group (3.50%). The Fi-test conducted using GenAlEx confirmed a significant difference between these two groups .The two main groups of populations revealed in clustering methods (one containing only HD) calculated for all 20 populations was 0.613. The highest values were observed in ASI (0.762) from the Rvi17 populations and PAL (0.740) from the Rvi0 populations, and the lowest values were observed in BGJ (0.413) and GML (0.431) from the Rvi6 populations  from all the analysed population groups was noted in the Rvi6-group, while mean values for the other groups were determined to be 0.684 (Rvi17), 0.678 (Rvi1) and 0.698 (Rvi0).The overall unbiased gene diversity , the highest values of genetic distance between the groups were always observed for pairs comprising the Rvi6-group (0.141–0.173), while the genetic distances observed for the other pairs were much lower (0.020–0.027)  to 1 for populations sharing no common alleles  [ST or Ɵ) calculated among the 20 populations studied indicated medium  total differentiation among the populations. The pairwise estimates of FST values computed for all the pairs of populations were the lowest among the Rvi6 populations when these populations came from the same orchard. Interestingly, the distances between the Rvi6 and non-Rvi6 populations from the same orchard (Lublin and Nowy Dwor) were usually lower than those between the ARL & GML, END & RND Rvi6 populations and the Rvi6 populations from other orchards , while the values in the other groups were low .Falleles) . PairwisHD and Nei’s values and the FST estimates clearly showed that the Rvi6 populations were genetically closer to other Rvi6 populations, especially those derived from the same orchard. However, they were subdivided within the whole Rvi6-group. Moreover, the Rvi6 populations were clearly distanced from the non-Rvi6 populations that came from other orchards, which were in turn more closely related to each other. An exception was observed between the Rvi6 and non-Rvi6 populations occurring in sympatry, for which the FST values indicated very close (Lublin) to the closest (Nowy Dwor) genetic relationship among the populations.The observed Rvi6 and non-Rvi6 populations, the substructures within the Rvi6-group, and the genetic heterogeneity among the Rvi17, Rvi1 and Rvi0 groups of populations.These data are consistent with those from the clustering and assignment methods and explain the differences among the Nm) between all populations , derived from the same orchard. The mean Nm value between the Rvi6-group and the non-Rvi6-group was 2.441, while the mean values between the other groups  were between 7.063 and 8.495. These data may suggest that gene flow between the Rvi6 and non-Rvi6 populations can be limited but not excluded.Gene flow, considered as a movement of genes, can be detected in populations by observing the movement or dispersal of whole organisms or genomes from one population to another . PairwisrBARd = 0) from random mating can be rejected only when the value is significantly different from zero after 1000 randomizations. The null hypothesis of random mating could not be rejected for any of the tested groups of populations  or for the whole non-Rvi6-group. Thus, we assumed that among the populations within the groups, free recombination was possible. When the groups were tested in pairs, the null hypothesis was rejected only in pairs with the Rvi6-group, indicating that random mating did not occur between the Rvi6 and the non-Rvi6 populations. However, when the Rvi6 and non-Rvi6 populations sampled from the same orchard (Lublin or Nowy Dwor) were tested, the null hypothesis of random mating between these populations could not be rejected, indicating that random mating was possible between the sympatric populations.The index of association relies on the variance of genetic distances between pairs of individuals. Low variance occurs in a population with free recombination across loci, while high variance occurs when recombination is rare . The nulp > 0.05), the genetic variation among populations cannot be explained by the geographical origin of the populations. When the Rvi6 and non-Rvi6 groups of populations were tested separately, the same tendency was observed. This indicates that genetic population structures are not influenced by the geographic origin of the populations.Because the regression correlation analysis between genetic and geographic distances among all populations was not significant , the division of all populations into two main groups was evident. The Rvi6 populations formed a separate cluster or clusters depending on the algorithm used, which did not contain any non-Rvi6 populations. However, in each Rvi6 or non-Rvi6 cluster, individuals showing admixed ancestry from both clusters were observed . In parallel, the probability of assignment of Rvi6 individuals to non-Rvi6 populations was low . Second, the 1tc1g locus, which had the highest number of alleles in the Rvi6 populations, was represented by only 7 alleles out of a total of 32 alleles observed for this locus. However, these alleles were also present in almost all the other populations. Since the 1tc1g locus is considered to be linked to the AvrRvi6 locus of V. inaequalis [Rvi6-virulent populations is consistent with the genomic signature of the selective sweeps hypothesis [Rvi6 populations exhibited the lowest allelic richness, frequency of private alleles and mean genetic diversity across the 11 loci and the highest clonal fraction show that the genetic structure of these populations is the response to a main apple resistance gene breakdown. On the one hand, the observed reduced genetic diversity at the population level fits the primary concept of gene-for-gene interaction [Rvi6-virulent individuals can be external, and these individuals did not emerge from the selection of a mutated avrRvi6 allele in any of the sampled non-Rvi6 populations. They assumed that the Rvi6 populations derived from a single ancestral population after a substantial reduction of variability caused by a demographic bottleneck, and the current Rvi6 populations were founded by just a few related individuals. Lemaire et al. used demographic modelling to demonstrate that the two distinct Rvi6 and non-Rvi6 lineages diverged several thousands of generations ago without subsequent detectable gene flow [Rvi6 varieties emerged from pre-existing populations present on the non-agricultural progenitor M. floribunda, donor of Rvi6 gene, thus supporting the second scenario regarding the migration of virulence between ecosystems. Secondary contact between subpopulations is currently observed in some European apple tree orchards, representing the opportunity for adaptive pathogenic evolution [The pathosystem  overall . The pretructure , 10, 15.aequalis , the obspothesis . This hypothesis , 11. Theeraction . Based oene flow . Their svolution , includiRvi6 populations, we observed that the level of variation among these populations was almost five times higher than that within the non-Rvi6 group (AMOVA). Moreover, the Rvi6-group exhibited the highest FST values among groups, within itself, suggesting that Rvi6 populations are genetically subdivided. The lack of correlation between the genetic and geographic distances for the populations indicated that the genetic structure of the populations was not influenced by their geographic origin. However, some grouping related to geographic origin was observed in the segregation performed by the TESS, STRUCTURE (for K = 5) and PCoA algorithms. The strong evidence of substructures among the Rvi6 populations can be explained by the hypothesis assuming several recent instances of migration of virulent individuals from the non-agricultural to the agricultural pathosystem and then undergoing further distribution by rare, long-distance dispersal events. The orchards with Rvi6 cultivars are not very common in Poland; the distance between the examined neighbouring orchards containing Rvi6-cultivars is in range from 50 to 220 km, while the experimental orchard with various mono- and poli- R-gene apple cultivation is located in Dabrowice, in a distance from 18 to 220 km to the nearest commercial orchards containing Rvi6 cultivars. Therefore, we assume that there is a limited exchange of virulent strains between orchards, and the emergence of disease in Rvi6 hosts in previous decade is most likely a result of human activities regarding the transportation of infected apple tree leaves over long distances. In our study, it was possible to observe a structure related to the host only for the Rvi6 populations that was maintained over years because of impeded gene flow between immigrants and local populations. In the non-Rvi6 populations, neither significant population structure nor correlation between the assigned cluster and the host cultivars were detected. Even if the geographic distance between the non-Rvi6 populations was significant, they shared common genotypes. Here, free dispersal of the fungus through large apple tree areas both by wind or human-mediated actions together with gene flow lead to genetic homogeneity among populations within regions. Similarly, the lack of substructure linked to different hosts was observed for the populations derived from various host in single orchards [Rvi6 populations compared to the Rvi6 populations together with the small or absent clonal fractions support the hypothesis that non-Rvi6 populations are evolutionarily older than the Rvi6 populations [Rvi6 individuals observed currently in the agroecosystem are only a small representation of the whole Rvi6-virulent population.Whereas the overall level of genetic polymorphism was low in the orchards , 12. Theulations . The othRvi6 apple tree cultivars, which was detectable in most of the sampled orchards. The assignment test performed using GeneClass showed that the Rvi17, Rvi1 and Rvi0 groups exchanged individuals quite randomly, causing relatively low genetic distance between these groups as detected by other programmes, whereas the individuals from the Rvi6-group were frequently assigned to other populations within the same group. Genetic distance, inferred from Nei’s unbiased measure of genetic distance and FST values, was always the highest between the Rvi6-group and other groups of populations. The Rvi6 populations were clearly distanced from non-Rvi6 populations coming from other orchards and genetically closer to other Rvi6 populations, especially those derived from the same orchard. Moreover, the FST values estimated for the groups was the highest within the Rvi6-group, indicating higher differentiation between populations in the Rvi6-group than between populations in each of the non-Rvi6 groups, which agrees with the observed genetic subgroups among the Rvi6 populations. Conversely, the non-Rvi6 populations, even those derived from distant orchards, were genetically mixed but with an overall low level of genetic differentiation and variation. The random mating hypothesis testing showed that random mating was not possible between populations from the Rvi6-group and other groups, but was possible among individuals from the Rvi17, Rvi1 and Rvi0 groups. This probably explains why the clustering programmes assumed that non-Rvi6 populations originated from a single panmictic population. Our finding regarding random mating within the Rvi6-group differs from that of Guérin et al.; their linkage disequilibrium testing results were not consistent with random mating among Rvi6 populations [The results obtained in this study revealed clear genetic distinctness among populations that were virulent or non-virulent towards the ulations .Rvi6 in this study, led to a split of the pathogen population into two subpopulations. This phenomenon was previously observed in commercial orchards between infected M x domestica cultivars carrying or not carrying the Rvi6 gene on large scale [Rvi6 and non-Rvi6 hosts, what was observed between populations sampled at multiple sites on multiple host species and varieties [V. inaequalis strains virulent toward Rvi6 cultivars, that correlated with strong selection against immigrants in V. inaequalis populations. The strong genetic differentiation between the two populations may indicate, that the pathogenic capability of Rvi6-virulent individuals on non-Rvi6 host and their ability to compete with Rvi6-avirulent individuals on non-Rvi6 hosts may have been reduced. This in turn reduces the probability of meeting and mating within the host, resulting in detectable reproductive isolation between sympatric populations [Rvi6 and non-Rvi6 populations is usually maintained in agroecosystems, even when Rvi6 and non-Rvi6 cultivars are planted in the same orchard [The high structuring effect of the presence or absence of a single resistance gene in host plants, ge scale , 11, 13.arieties . Gladieuarieties  confirmeulations . Therefo orchard , 12.Rvi6 and non-Rvi6 populations was broken between the sympatric populations derived from orchards located in Nowy Dwor and Lublin, which was supported by five observations. First, the evidence of admixture between the two types of populations, expressed as Rvi6-virulent and avirulent individuals grouped in one cluster, was detected using TESS. This was most clear for populations from Lublin  and was slightly detected for populations from Nowy Dwor , which were most likely enhanced by human actions instead of selection of individuals from any of the sampled non-Rvi6 populations. The genetic structure of the Rvi6-virulent populations being clearly distinct from that of the non-Rvi6 populations is recognized as the host adaptation effect. Specifically, the selective pressure is exerted by the main apple scab resistance gene (Rvi6 in this study). However, we did not observe such an effect in the other V. inaequalis – apple tree interactions. Gene flow can occur between two divergent populations, leading to the evolution of hybrid swarms, especially when the representatives coexist in one orchard or tree. Such secondary contact may lead to evolutionary and epidemiological changes. Considering the ecological, economic and nutritional importance of apples in temperate countries, especially Poland, monitoring the emergence of new virulence is particularly important to allow for effective preventive strategies.Population structure analysis of Additional file 1:Fig. S1. Criteria used to determine the appropriate cluster solution. The left graph shows the delta K plot from the STRUCTURE 2.3.4. runs based on mean (|L``(K)|)/sd(L(K)) values, and the right graph shows the plot of the mean DIC value of each TESS 2.3.1. cluster solution. Plot lines were added to help visualize trends. (JPEG 49 kb)Additional file 2:Fig. S2. Ancestral proportion of Venturia inaequalis haplotypes for the K = 7 clusters inferred with TESS 2.3.1 software. In total, 606 strains are grouped according to the origin and cultivar of their host plant. Each haplotype is represented by a vertical line partitioned into seven segments. The dominant colour for each population indicates its affiliation to the inferred clusters. (TIFF 706 kb)Additional file 3:Fig. S3. a) Unweighted pair group method with arithmetic mean (UPGMA) and b) Neighbour-joining(NJ) dendrograms based on Nei’s genetic distance [V. inaequalis populations, genotyped within 11 microsatellite loci. Numbers at major nodes indicate the percentage of times the cluster to the right of the branch occurred among the sample of 1000-bootstrap-generated dendrograms. (TIFF 3745 kb)distance  between Additional file 4:Table S1. Measure of the pairwise comparisons of Nei’s unbiased genetic distance  and identity  estimated for the four assumed groups of V. inaequalis populations. (XLSX 9 kb)Additional file 5:Table S2. Measure of the pairwise comparisons of genetic distance (FST) estimated for 20 V. inaequalis populations. (XLSX 11 kb)Additional file 6:Table S3. Measure of the pairwise comparisons of gene flow (Nm) estimated for 20 V. inaequalis populations. (XLSX 11 kb)


Coluber constrictor flaviventris) and bullsnake (Pituophis catenifer sayi) in 3 major river valleys in southern Saskatchewan. Fixation indices (FST) showed that populations in river valleys were significantly differentiated for both species . Bayesian assignment (STRUCTURE) and ordination (DAPC) strongly supported genetically differentiated groups in the geographically distinct river valleys. Finer-scale subdivision of populations within river valleys was not apparent based on our data, but is a topic that should be investigated further. Our findings highlight the importance of major river valleys for snakes at the northern extent of their ranges, and raise the possibility that populations in each river valley may warrant separate management strategies.On the North American Great Plains, several snake species reach their northern range limit where they rely on sparsely distributed hibernacula located in major river valleys. Independent colonization histories for the river valleys and barriers to gene flow caused by the lack of suitable habitat between them may have produced genetically differentiated snake populations. To test this hypothesis, we used 10 microsatellite loci to examine the population structure of two species of conservation concern in Canada: the eastern yellow-bellied racer ( Rhinoplocephalus nigrescens; [Crotalus horridus, [Sistrurus catenatus catenatus; [Nerodia erythrogaster neglecta; [Vipera berus, [Coronella austriaca, [Crotalus triseriatus, [Nerodia erythrogaster neglecta, a habitat specialist) by hindering dispersal. It is clear that intrinsic and extrinsic factors may interact to produce constraints to dispersal and gene flow in snakes; however, most studies have addressed populations at the core of known geographic ranges , while tenatus; , Nerodiaeglecta; , Vipera a berus, ; Coronelstriaca, ; Crotalueriatus, ). The vaeriatus, . For exaeriatus,  showed tes (e.g. ). It is es e.g. –15) or i or iRhines (e.g. ) have beElaphe obsoleta obsoleta; [Gloydius halys; [Snake populations at northern range limits face ecological challenges that may affect dispersal, gene flow, and ultimately genetic population structure. For example, snakes at higher latitudes in North America and Eurasia rely heavily on a limited number of suitable hibernacula to survive harsh winters , and thebsoleta; , Gloydius halys; ). In adds halys; ) may be s halys; . Thus, gs halys; , 22–24).s halys; .Coluber constrictor flaviventris, hereafter racer) and the bullsnake (Pituophis catenifer sayi). In addition to being the range periphery for these species, the northern Great Plains is also one of the most human-altered landscapes in North America; over 70% of native grassland has been lost due to agriculture, and the region contains a high density of species at risk of extinction or extirpation [The Great Plains in central Canada represents the northern range limit for the eastern yellow-bellied racer . We capWe extracted DNA using DNeasy spin-column kits according to the manufacturer’s guidelines ; however, proteinase K digestions were extended to 8–12 hours at 56°C and we performed the optional RNase A treatment . DNA was quantified using a spectrophotometer . We genotyped all individuals  at 10 microsatellite loci that had been previously developed for each species , number of alleles per locus, and total number of private alleles. We examined deviations from Hardy-Weinberg Equilibrium (HWE) using GENEPOP v4.1.4 [IS; [http://dx.doi.org/10.5061/dryad.cc6r3).PCR products were size-fractionated using capillary electrophoresis on a DNA sequencer (Beckman-Coulter GeXP). A 600-bp in-lane size standard was used to determine the size of fragments with single base pair resolution . We scored microsatellite alleles with the aid of GENEMARKER v2.2.0 software  using default settings, with the exception that our bin width was expanded to ±1 base pair to reflect the resolution limits of the sequencer. All microsatellite profiles were visually inspected to confirm the accuracy of calls; a second observer independently verified the scoring, and both observers were blind to the geographic origin of samples. We quality checked microsatellite data sets and estimated the potential frequency of null alleles using MICRO-CHECKER . We usedP v4.1.4 . We esti1.4 [IS; ,37) usin1.4 [IS; . The comST) using Analysis of Molecular Variance  in t in tST)  , and one locus in bullsnakes (Piru15); these loci were removed from further analyses. Tests for HWE on the overall data set revealed that there was a significant excess of homozygotes at 7 of 9 loci for bullsnakes and 4 of 8 for racers after sequential Bonferroni correction. However, given the consistent and pervasive nature of the homozygote excess across loci and species, and the small number of individuals that failed to PCR amplify , we concluded that the deviation from HWE was most likely due to population structure in the data set (Wahlund effect). Thus, 9 and 8 loci were retained for subsequent analyses of population subdivision for bullsnakes and racers, respectively.st = 0.08, P = 0.001). Both areas also had high numbers of private alleles  for both the BMRV and FRV overall.Fixation index analysis based on user-defined populations showed significant differentiation between the FRV and BMRV . All three bullsnake populations also had a large number of private alleles, which further reinforces the distinction among river valleys  between sites to allow for population continuity. In addition, dispersal may be further restricted by human activities; both racers and bullsnakes avoid agricultural land ,23, whic natrix; ), but in natrix; ,15), ourIS values than those from the FRV and BMRV, suggesting that inbreeding may lead to more fine-scale structure. In addition, other genetic markers  might also enhance ability to identify fine-scale structure.Our analyses suggest that both racers and bullsnakes exhibit no, or only very weak, fine-scale structure within the FRV based on overwintering hibernacula (dens). Fine-scale population structure over short geographic distances is common in snakes, particularly species that are habitat specialists or exhibit philopatry to communal dens  at the same microsatellite loci in Kansas racers, while Canadian racers in our study had much lower allelic diversity and heterozygosity . Thus, populations at the northern range periphery are less genetically diverse than at the range core. Local biotic and abiotic conditions can drive adaptation in snakes , ,50, E = es , and alS1 TableC.c. flaviventris; bullsnake = P.c. sayi.To protect sensitive habitat and over-wintering hibernations sites for these species of conservation concern in Canada, exact locations have been offset by several hundred meters. Eastern yellow bellied racer = (DOCX)Click here for additional data file.


Ambystoma texanum) in western Ohio, where agriculture is now the dominant land use. Salamander tail tissue was collected from eight breeding pools. Three pools occur in the same forest; the other five are in forest patches at distances ranging from 250 m to 20 km from one another. Eight microsatellite loci were amplified by PCR and genotyped for allele size. Observed heterozygosities were lower than expected in all sampled populations; the two most isolated sites  had the highest inbreeding coefficients. Ha2 also had the lowest mean number of alleles and was found to be genetically differentiated from populations to which our data analysis indicates it was historically connected by gene flow. The most distant site (Ha1) had the highest number of private alleles and showed genetic differentiation from other populations both historically and currently. Geographic distance between pools was strongly correlated with the number of private alleles in a population. The results suggest that population isolation results in decreased genetic diversity and that a breakdown of metapopulation structure due to landscape change may contribute to differentiation between once‐connected populations.Habitat loss and fragmentation negatively impact the size and diversity of many natural populations. Woodland amphibians require connected aquatic and terrestrial habitats to complete their life cycle, and often rely on metapopulation structure for long‐term persistence. Wetland loss and deforestation fragment amphibian populations, which may result in population isolation and its negative effects. The aim of this research was to analyze the population genetic structure of small‐mouthed salamanders ( HabitatThe eastern North American landscape has been significantly altered in the last two hundred years, mainly through deforestation. In western Ohio today, small forest patches are common in an agricultural landscape Gordon, . These pAmphibian extinctions are occurring at an alarming rate around the globe  inhabit terrestrial areas that contain ephemeral breeding pools, to which adults exhibit high fidelity  in northeastern Ohio found that gene flow may occur in a highly fragmented landscape, possibly along riparian zones  at both the landscape and fine spatial scales in western Ohio. This species’ range extends throughout most of eastern North America, where it resides in forests that contain vernal pools  Genetic diversity would be lower  in isolated populations, as compared to those in forest patches with more than one breeding pool. (2) Populations in separate forest patches would show genetic differentiation from one another, while those in the same patch would not. (3) Migration is not occurring between populations in separate forest patches that were historically connected by forested habitat and presumably gene flow.22.1Salamanders were collected from eight breeding pools in five forest patches in rural Hardin County, Ohio Figure . Three pA. texanum were captured in screen funnel traps placed in breeding pools in March 2009 and 2010. Tail tissue from up to 20 individuals was preserved in ethanol, and salamanders were released back into the pools. For sites without 20 adult samples, larvae were used so that we had a total of 20 individuals for each pool. Ambystoma texanum larvae were collected with dip nets during the summer of 2008.Adult 2.2Ambystoma texanum larvae are difficult to distinguish from unisexual Ambystoma larvae . Brandon, , which aBrandon, . Larval Brandon, .A. texanum . The microsatellite loci were amplified by polymerase chain reaction, and fragment analysis of the products was carried out on an ABI 3100 and compared to a ROX 500 standard. PCR product size was determined using genescan v.3.1 and genotyper v.2.5 (Applied Biosystems).Eight polymorphic microsatellite loci were used as genetic markers in this study Table ; seven o2.3genepop v.4  between individuals within populations was performed in genalex v.6.3  for significant differences among mean population relatedness was performed, and 95% confidence intervals were derived for the expected range of rqg under the null hypothesis of random mating. This method determines whether populations with larval samples showed higher within‐population relatedness than those with only adult samples, which would be expected if siblings had inadvertently been collected.To rebut the possibility that larval samples from the same pool were siblings, Queller and Goodnight's  method o2.4genalex was used to determine population genetic measures for each population including mean number of alleles, number of private alleles, mean observed and expected heterozygosities, and inbreeding coefficients (FIS). Pairwise FST values were calculated in genalex for all population pairs with 999 permutations.structure 2.3.1  repetitions and a 10,000 burn‐in period assuming no admixture and independent allele frequencies and using sampling locations as priors. K was identified as the mean maximal value of ln P(D) and confirmed with calculation of ΔK   was performed (999 permutations) in genalex with populations assigned to regions based on the genetic clusters . The FST analog ΦST, which suppresses within‐individual variation, was used in order to detect subtle regional population structure.In order to determine the amount of variation explained by the genetic clusters identified in 2.5M was implemented to look for recent change in population size for sample populations. This method considers the number of alleles present in each population (for each locus) in relation to the expected number of alleles based on the difference between the largest and smallest allele size.The Garza and Williamson  method omigrate  from the equation ϴ = 4Neμ. Ne was calculated assuming a microsatellite mutation rate (μ) of 5 × 10−4  for all population pairs, rates were averaged for groups of populations in two ways. First, pools that are in the same forest patch  were combined. All of the populations that comprise region 3 from the AMOVA  were then combined in order to assess migration rates among the genetic clusters identified in structure.In addition to comparing bidirectional migration rates (2.6pasw v.18 (2009) that included FIS values, number of private alleles (NP), effective population sizes (Ne), forest patch area, pool area, distance to nearest sample pool, and average distance to other sample pools. Several linear univariate and multiple regressions with FIS, NP, or Ne as the dependent variable and the landscape traits as independent variables were performed. Distance to nearest sample pool and average distance to other sample pools were significantly correlated (p < .001), so they were not included together in a multiple regression model. In genalex, a Mantel test (999 permutations) was used to test for a relationship between geographic and population genetic distances.A bivariate correlation matrix was created in 3A. texanum from AjeD422.One hundred and sixty small‐mouthed salamanders were genotyped from eight breeding populations at eight highly polymorphic microsatellite loci Table . A total3.1micro‐checker found no evidence of large allele dropout in our data set, but did detect the possibility of null alleles for six loci due to homozygous excess  was not detected in populations with larval samples , but two of those were comprised of mainly adult samples. Hardin 10, which was a mixture of adult and larval samples, had the only negative mean rqg (−0.011).Increased relatedness . In all populations, HO was lower than expected, and inbreeding coefficients ranged from 0.201 (Ha11) to 0.461 (Ha2).A summary of genetic results by population is presented in Table 3.3FST values indicate that Ha1 has highly significant genetic differentiation relative to all other sites (p = .001), and Ha2 has significant genetic differentiation from all sites (p ≤ .009) except Ha10 (p = .148), which is the closest geographically  was found in Ha1; all other populations had 4 (Ha7 and Ha10) or fewer  = −5659.65 . The AMOVA detected significant structure at the regional and within‐population scale, but not among the populations in region 3 (Table 3.4Ne) estimates are shown in Table Ne = 600; all other sites had Ne estimates <500, with Ha2 having the lowest Ne = 429. Table M for each population, and all values are <0.71. Ha2 had the highest value (mean M = .707), while Ha1 had the lowest value (mean M = .499).The effective population size  to 1.56 (Ha7 to Ha2); all rates were low in general. Given the size of the populations, M = 1 translates into an Nm = 0.25 (1 migrant every 4 generations). All seven rates <0.70 were coming to or from Ha1. The seven highest rates were >1.20 and involved Ha2, Ha7, Ha10, Ha11, and all sites from Lawrence Woods .Bidirectional migration rates generated by Comparing migration rates at the forest patch scale shows that all rates to and from Ha1 are ≤0.91 Table . Rates tMigration rates were also compared among the regions from the AMOVA  Table . The mig3.6FIS values, or between number of private alleles and either forest patch area or pool area (Table p ≤ .001). Multiple regression models incorporating landscape variables were not significant for FIS values. For number of private alleles in a population, models combining forest patch area and pool area with either average distance to other sample sites or distance to nearest sample site were significant but did not improve upon the univariate models with distance measures. The Mantel test found a positive relationship between geographic and population genetic distances, with geographic distance explaining 86.1% of the genetic differentiation among sampled populations   Table .Ne as the dependent variable and landscape traits as independent variables found that pool area has a significant positive correlation with Ne , but forest patch area does not  . Pool area was combined with each distance measure into two separate multiple regression models, and both models showed improved fit .The regression analysis using 4Ambystoma population genetics have encountered the possibility of null alleles. Greenwald, Gibbs et al.  had the highest A. texanum to disperse, even within forest habitat. However, the presence of other unsampled pools within Lawrence Woods could serve as stepping stones that facilitate gene flow between our sites  are smaller than Ha1 and Ha2, but still ≥0.201. The pools sampled in Lawrence Woods are all about 900 m from each other, which is a substantial distance for A. maculatum in New York. Their study also detected higher than expected relatedness within ponds, although the range of rqg (0.02 to 0.17) was higher than the rqg values in this study (−0.011 to 0.040). The measure of relatedness is determined by comparing each population to the entire group of populations and therefore cannot be directly compared between studies. If a group of populations contains one or more populations with higher relatedness than the others, then they will have higher rqg values and the range of rqg values will be larger. The populations in this study may have relatedness measures that are more similar and therefore have rqg values that are lower and within a smaller range.The mean observed heterozygosity was 0.543, which was very similar to the value found by Zamudio and Wieczorek  of 0.534FST values of genetic differentiation between breeding populations are based on allelic variation and reflect the current population structure. It was hypothesized that populations in separate forest patches would show genetic differentiation, while those in the same forest would not. Ha1, which is 20 km distant from Lawrence Woods, is significantly different from all of the other sites. This degree of genetic difference was expected based on the low migration capability of this species. Ha2 is significantly different from all sites except Ha10, which is the site closest geographically. This may indicate that Ha2 has had more recent gene flow with Ha10 compared to the other populations. The FST values do reveal population structure within Lawrence Woods, but did not show significant differentiation between those sites and Ha10, Ha11, and Ha7, or among these latter three populations. The forest patch containing Ha 10 and 11 is separated from Lawrence Woods by a field that is about 250 m across, so migration between these two patches may be possible for A. texanum. The forest patch containing Ha7 is more than 1,800 meters from Lawrence Woods, and current salamander migration between these patches is unlikely.Pairwise FST values in indicating population structure within this forest patch.It was hypothesized that migration is not occurring between populations in separate forest patches due to landscape change. One measure of a population's genetic isolation from other sampled populations is the presence of private alleles, which are assumed to be the result of mutation events. If there is very little or no gene flow between populations, then these new alleles are not shared between populations , while no significant variation was explained at the population level for region 3. So while there may be some population structure among the Lawrence Woods sites, it is not great enough for structure to be able to assign individuals from those populations to separate genetic clusters. Ha2 is identified as a single cluster, but several individuals in its nearest population (Ha10) may belong to the Ha2 cluster. Ha2 and Ha10 also did not have a significant FST value. Ha1 is shown to contain two distinct genetic clusters, even although all individuals were sampled from the same pool. The FST values and number of private alleles show that Ha1 has highly significant genetic differentiation from the other sampled populations.The identification of genetic clusters by migrate provided estimates of historical gene flow and the opportunity to contrast migration rates prior to landscape change with current population structure detected with FST values and population assignment. It was hypothesized that all of the populations were historically connected by gene flow and that estimates of past migration rates would be similar among all sites, but the migrate results show that gene flow between Ha1 and all other sites was lower than among the rest of the populations. The genetic differentiation detected in this study between Ha1 and all other populations is probably not due to recent landscape change, but was present even historically. Ha1 may have been within a different metapopulation.The program The estimates of past gene flow for all of the other populations suggest that they had higher migration rates with one another than any had with Ha1 and therefore may indicate that they were all within the same metapopulation historically. The migration rate estimates among the Lawrence Woods sites were not greater than those between the Lawrence Woods populations and Ha2 or Ha7. Although it would not be expected that any individual salamander ever dispersed from the Ha2 pool to Ha7, gene flow could have occurred indirectly among populations in the metapopulation following the stepping stone model prior to fragmentation between the forest patches that are left today . The allelic diversity of Ha7 may be maintained by gene flow with this nearby unsampled population, so that Ha7's current allelic composition and diversity may remain similar to the Lawrence Woods populations even though migration may no longer occur between them. Four private alleles were found in Ha7, which supports recent lack of gene flow from this population to any of the others that were sampled.Based on migrate estimates bidirectional gene flow, it can be determined whether migration rates were symmetrical between populations or whether some populations were more often sources of migrants or metapopulation sinks. Overall, migration rates between the sampled populations were symmetrical, as the 95% confidence intervals around the maximum likelihood estimates overlap for most population pairs. Directionality was detected in two cases. Historically, Ha10 sent more migrants to both Ha3L5 and Ha11 than it received back from either. This appears to no longer be the case as four private alleles were detected in Ha10, suggesting dispersal of individuals from this population has been impaired. In addition, the rate of migration from Ha11 to Ha3L5 was greater than from Ha3L5 to Ha11. The migration rate estimates from Ha3L5 to the other sampled populations are generally lower than those from the other Lawrence Woods populations, Ha7, Ha10, Ha11, and Ha2.Since FST values. The migration rates between Ha2, Ha10, and Ha3L5 are three of the highest estimates from all populations, and this may be due to their spatial arrangement and the intervening landscape configuration. The breeding pools for these three populations are directly adjacent to and on the north side of County Road 200, which runs east to west through southern Hardin County. These pools may have been historically connected by a natural drainage route that would have provided a moist dispersal corridor between them.Concerning Ha3L5, the highest rates of gene flow from Ha3L5 go to Ha2 and Ha10; these rates are higher than those between Ha3L5 and the other sampled populations in Lawrence Woods. This suggests that although Ha3L1, 4, 5 are together in a patch today, their past (and probably present) connectivity may be less than would be expected, as indicated by their migrate also provided estimates of historical effective population sizes that may be most useful for comparisons between populations and are not necessarily accurate current population sizes. The range of effective population sizes calculated from the ϴ values generated by migrate is reasonable , while the other populations ranged from 429 (Ha2) to 498 (Ha3L4). Similar to the assignment of two genetic clusters to Ha1 in structure, migrate's larger estimate for population size is likely driven by within‐population structure.M  <0.70 indicate a recent reduction in population size. All of the sampled populations had mean M values below this threshold (except Ha2 with a mean M = 0.707). These low M values also indicate that the populations have lost alleles via genetic drift and suggest that the sampled populations have low connectivity to others. Migration among populations in a metapopulation should maintain allelic diversity by reintroducing alleles that have been lost. Ha1 had the lowest mean M (0.499) and the largest historical Ne estimate from migrate, which strongly suggests that this population was once larger in size and more genetically diverse than it is today.Population size is important in determining whether an isolated population's genetic diversity is depressed by inbreeding and genetic drift. According to Garza and Williamson , values Ambystoma species include the size of the breeding pool and/or size of the forest patch. The regression analysis found that neither forest patch area nor pool area was correlated with inbreeding coefficients; however, effective population size did have a significant positive relationship with pool area—a result supported by other amphibian studies .In the regression analysis, effective population size was also significantly correlated with the two landscape distance measures, although the relationship was positive and not expected. This positive correlation was driven by Ha1 having the highest NA. maculatum populations sampled in New York, those populations ≥4.8 km from each other were genetically independent. This may suggest that metapopulations for Ambystoma species operate within this scale, as populations within this distance were more similar. The importance of this scale for amphibians is supported by a study on Hyla arborea . Ha7 is geographically separated from the other populations sampled in this study by more than 1,800 m, but its relatively low FIS value may be due to the presence of other breeding pools in the same forest patch that was not sampled. Ha7 is not an isolated population and demonstrates that genetic diversity is maintained in populations that are near other populations.This study of the population genetic structure of breeding populations in highly agricultural Hardin County, Ohio, was initiated in order to understand the impact of habitat destruction and fragmentation on FST values show that Ha2 has become genetically differentiated from the others, except Ha10. Ha2 had the highest inbreeding coefficient, the lowest mean number of alleles, and the smallest estimated Ne. Ha1 also had a high inbreeding coefficient and is in a forest patch that contains only a single breeding pool. From aerial photographs and the past surveying of nearby forest patches and vernal pools, Ha1 appears to be an isolated population. The results of this study indicate that Ha1 has a larger population size than Ha2, which may explain why Ha1 had a higher average number of alleles.Ha2 may be an example of a small, isolated population. Estimates of historical gene flow suggest that this population was once connected by migration with the other populations, excluding Ha1. Pairwise Ambystoma breeding populations in many forest patches in this region may be experiencing inbreeding and genetic drift, especially if population sizes are small. Isolated populations lack the metapopulation structure that provides an avenue for movement of alleles, which enhances genetic diversity of populations and spreads adaptive alleles that are important for the persistence of a species in a changing environment. Numerous amphibian studies have detected reduced fitness in populations with decreased population genetic diversity  are rare to nonexistent in most counties. For this reason, The estimates of historical gene flow in and around Lawrence Woods suggest that most of the populations sampled were once part of a metapopulation in a continuously forested habitat that had numerous pools with subtle population structure. Migration between populations in the metapopulation may have occurred at different rates between some of the populations, as data from this study suggest. Varying gene flow rates could have been influenced by geographic distance between populations and landscape traits, such as topography and soil moisture that impact vegetative composition. It cannot be assumed that breeding populations in the same forest patch today necessarily have high rates of gene flow between them. The Lawrence Woods sites illustrate this because there was greater genetic differentiation among those populations than between any of the Lawrence Woods sites and Ha7, Ha10, or Ha11. Conservation efforts should protect networks of breeding pools in close proximity, while considering natural dispersal routes and potential migration barriers, factors were not investigated in this study.FST values , it is unlikely that gene flow between forest patches is occurring today because of both geographic distance and intervening land use, as supported by private alleles in Ha7 and Ha10. These populations may become genetically differentiated in the future, as mutations continue to introduce new alleles.Landscape change, including deforestation and potentially loss of vernal pools, has disrupted the metapopulation structure detected to be present historically among our populations (excluding Ha1). Ha3L5 today is significantly differentiated from Ha2; but in the past, these two populations were connected by a level of gene flow higher than between Ha3L5 and the other Lawrence Woods populations we sampled, demonstrating that gene flow may have been highest between Lawrence woods pools and those that today are outside the forest. Today, the landscape between these two sites is agricultural and likely inhospitable to migrating amphibians. Even among the sampled sites without significant pairwise None declared.E.R. and P.K.W. initiated and designed the project, and performed field sampling. E.R. performed the laboratory and data analyses, and wrote the first draft of the manuscript. C.K. advised on the laboratory and data analyses. All authors contributed to the final manuscript.Microsatellite genotypes will be submitted to Dryad for data archiving. Click here for additional data file.