Gorilla beringei graueri) and mountain gorillas (Gorilla beringei beringei), to directly infer temporal changes in genetic diversity within the last century. Using over 100 complete mitochondrial genomes, we observe a significant decline in haplotype and nucleotide diversity in Grauer’s gorillas. By including historical samples from now extinct populations we show that this decline can be attributed to the loss of peripheral populations rather than a decrease in genetic diversity within the core range of the species. By directly quantifying genetic changes in the recent past, our study shows that human activities have severely impacted eastern gorilla genetic diversity within only four to five generations. This rapid loss calls for dedicated conservation actions, which should include preservation of the remaining peripheral populations.Species and populations are disappearing at an alarming rate as a direct result of human activities. Loss of genetic diversity associated with population decline directly impacts species’ long-term survival. Therefore, preserving genetic diversity is of considerable conservation importance. However, to assist in conservation efforts, it is important to understand how genetic diversity is spatially distributed and how it changes due to anthropogenic pressures. In this study, we use historical museum and modern faecal samples of two critically endangered eastern gorilla taxa, Grauer’s ( The major contributors to species extinctions, changes in species assemblages and decrease in population size are hunting and habitat degradation due to climate change, agricultural and urban developments, and armed conflict8. However, direct quantitative assessments of the impact of human activities on natural populations remain limited, causing continued discussion of the magnitude of anthropogenic effects9.The rate at which species and populations disappear due to human activities has dramatically accelerated over the last centuries6. Reduction in population size frequently leads to a decrease in genetic diversity10. Populations with low genetic diversity have limited capacities to adapt to fast changing environments11, display lower fertility12, and are prone to infectious diseases13. Consequently, genetic diversity is considered one of the three forms of biodiversity that deserves global conservation attention14. However, species with long-term low genetic diversity may have developed adaptations to mitigate the negative effects of inbreeding or have experienced genetic purging, which removes strongly deleterious variants16. In addition, genetic diversity is strongly influenced by species’ life history and demography18. Hence, simply quantifying genetic diversity is insufficient to evaluate the conservation needs of a species. Therefore, it is imperative to distinguish long-term from short-term processes and to examine temporal changes in genetic diversity. Here, museum collections play an essential role, as they frequently span the critical period of the last few hundred years during which human influence on natural ecosystems has increased. By providing a window into the past, they thus help to disentangle long-term population processes from rapid, anthropogenically driven changes, offering a unique opportunity to directly quantify human impact21.Recently, attention has been drawn to the dramatic rate of population extinctions and population declineGorilla beringei graueri) and mountain gorillas (Gorilla beringei beringei). All four recognized gorilla taxa have experienced a continued long-term population decline over the last 100,000 years. However, this decline was particularly pronounced in the eastern species22, which is reflected in low estimates of genetic diversity in eastern compared to western gorillas23. Furthermore, the two eastern subspecies differ from each other in current population sizes25. Field-based studies have estimated up to 90% population decline in Grauer’s gorillas in the last two decades due to habitat loss and poaching, with reported current population size of less than 4,000 individuals26. The mountain gorilla population of the Virunga Volcanoes Massif counted only 250 individuals in the early 1980s27, but has recovered to around 480 individuals today28 thanks to major international conservation effort. Therefore, eastern gorillas represent an intriguing case of potentially rapid population decline over the last few generations that happened against the backdrop of slow long-term reduction in population size.Here we focus on two critically endangered eastern gorilla taxa, the Grauer’s  of eastern gorillas, we focus on complete mitochondrial genomes, a powerful and widely used marker in conservation genetics that is well suited to reflect recent demographic changes32. Due to the low effective population size of mtDNA (1/4th of the nuclear genome), nucleotide variants rapidly become fixed through genetic drift, allowing accurate resolution of demographic processes, even when the sampling period encompasses only a few generations35. Changes in genetic diversity can rapidly be detected as emergence or loss of unique genetic variants for this maternally inherited, non-recombining locus, whereas longer time periods (more generations) are needed before these processes are reflected in the nuclear genome38.Demographic analyses of gorillas have so far only considered modern samples and therefore have limited power to resolve how genetic diversity has changed in recent timesThe aims of our study are two-fold. First, we want to quantitatively assess how genetic diversity has changed over the course of only a few generations in two critically endangered gorilla subspecies that differ in their historical population sizes. Second, we aim to evaluate the relative contribution of decline in population size versus local population extinction to the genetic diversity of Grauer’s gorillas.39. Briefly, 69 historical Grauer’s gorilla samples (median collection year = 1950 [1910–1980]), 64 modern Grauer’s gorilla fecal samples (collected in 2014), and 22 historical mountain gorilla samples (median collection year = 1921 [1913–1956]) were included in this study  from two social groups: one social group from the Nkuba Research and Conservation Area (NK) in the Walikale territory, and one from the high altitude sector of the Kahuzi-Biega National Park (KBNP)40  to reduce confounding effects of having pre-dispersed maternally related individuals that share the same mitochondrial haplotype.An extensive description of methods for sample collection, extraction, hybridisation capture of complete mitochondrial genomes and sequencing procedures is presented indy Table . Historidy Table . Therefo40 Table . Approxi42. The libraries were subjected to 125 bp paired-end shotgun sequencing on the Illumina Hiseq2500 platform (High Output Mode). In addition, we captured complete mitochondrial genomes from all adult fecal samples (N = 56) and historical Grauer’s gorilla samples that were not shotgun-sequenced or for which we obtained insufficient mitochondrial genome coverage after shotgun sequencing (N = 59)39.Double-barcoded, double-indexed sequencing libraries were generated from 40 historical Grauer’s gorilla samples, all 22 mountain gorilla samples, and three extraction blanks following39. Complete mitochondrial sequences were obtained requiring at least 3x coverage for each nucleotide position39. We removed the mitochondrial D-loop before further analysis, as this hypervariable region is problematic to assemble in great apes, especially from degraded samples44, and frequently represents a major source of assembly error from shotgun data45. After combining data from identical individuals represented by multiple fecal samples , and removing genomes with more than 25% of missing sites, the newly generated dataset contained a total of 115 mitochondrial genomes: 68 historical Grauer’s individuals, 29 unique modern Grauer’s individuals, and 18 historical mountain gorilla individuals sequenced at an average depth of 120x , and quality filtered as described in0x Table , Fig. S1, and qua39), as is expected for archived museum samples47. As fecal samples were sonicated prior to library preparation and sequencing, no DNA damage analysis was performed for them39. Human contamination in all samples was low  and mtDNA sequences of all replicate samples (N = 48) were identical, demonstrating the reliability of the used method (see39). For all polymorphic sites in Grauer’s (N = 37) and mountain gorillas (N = 4) we manually confirmed that no stop-codons were introduced by these variants. In Grauer’s gorillas we identified one polymorphic site within a non-coding region, five polymorphic sites within rRNA and tRNA genes, with the remaining 31 variants residing within coding regions. Most of the identified polymorphisms in the coding regions were either synonymous (13 out of 31) or coded for an amino acid change commonly observed in nature . Transition accounted for the majority of substitutions (33 out of 37). In mountain gorillas, all polymorphic sites were transition within coding regions encoding for an amino acid change commonly observed in nature (PAM250 matrix ≥0).Sequence data from the historical samples showed low rates of typical ancient DNA damage patterns , a subspecies of the western gorillas with a large population size of ~140.000 individuals48 (Table 49. Minimum spanning haplotype networks were constructed in PopART V1.7 (http://popart.otago.ac.nz). We grouped all mitochondrial genomes by sub-species and sample type  and calculated haplotype statistics, molecular diversity indices, and neutrality tests in Arlequin3.550. We assessed the effect of unequal sample size by using permutation tests. To this end, we randomly subsampled mitochondrial genomes from each population to match the population with the smallest sample size  and repeatedly (n = 1000) recalculated haplotype and nucleotide diversity.We combined our data with published Grauer’s (N = 7) and mountain (N = 8) gorilla mitochondrial genomes44 Table . Additio48 Table . This wa48 Table . Geograp51. We used the estimated split date of western and eastern gorillas of ~1 million years ago obtained from nuclear data as calibration point53. To exclude confounding effects of different sample ages, only modern mitochondrial genomes were used. We first estimated genome wide mutation rate using the HKY-site model and enforcing a strict molecular clock. Birth-rate and clock-rate priors were set as gamma distribution with α = 0.001 and β = 1000. The prior for the split time between western and eastern gorillas was set as a log-normal distribution with M = 0.5 and S = 0.1, which corresponds to a divergence time of 0.848–1.18 Mya (95% CI). The Bayesian model was run for an MCMC length of 500 million and we used Tracer 1.654 to confirm run convergence and obtain probability distributions. We then partitioned the aligned mitochondrial genomes into coding genes (splitting triplets into 1st + 2nd and 3rd position), rRNAs, tRNAs, and non-coding regions in Geneious 10.1.2 using the annotated gorilla reference. The same BEAST model was then used to estimate partition-specific mutation rates. The obtained mutation rates were highly similar to those published for the human mitochondrial genome56  and historical (museum specimens).To account for the possibility of more recent demographic changes that could be bypassed by the skyline model, we also employed an approximate Bayesian computation approachST between the two temporal groups.We performed two inferences: (1) a model choice analysis with three competing models in which effective population size was constant, decreased or increased in a single recent event (30–110 years ago); and (2) a parameter estimation of two effective population sizes—present and historical. We ran 30 million simulations (10 million per model) for the model choice analysis and employed 10 acceptance proportions (0.005–0.05%) for assessing consistency of the estimated model likelihoods. For the parameter estimation analysis, we ran 1 billion simulations and applied rejection with a linear regression adjustment over the 0.001%  accepted simulations. In a dedicated analysis, we selected the following summary statistics: number of haplotypes, number of private haplotypes and genetic diversity for each of the two temporal groups as well as pairwise differences and F59 using the estimated gorilla mutation rate (1.28 × 10−8), no gamma parameter, and a Ts/Tv bias of 0.75 and 0.933 in mountain and Grauer’s gorillas, respectively (as identified by the selection model of molecular evolution carried out in MEGA 760).Finally, to discriminate between negative results that were due to lack of demographic change versus those due to limited information content of the data, we employed pseudo-observed datasets (PODs) to assess the statistical power of our samples and methodology. All analyses were performed separately for Grauer’s and Mountain gorilla mitochondrial genomes in the software BaySICS v1.9Sequence reads are available for download at the European Nucleotide Archive (ENA) under accession number PRJEB21370.Mitochondrial genome sequences are available at Genbank under accession numbers MH177628 - MH177754.Used python scripts are available upon request.western-Grauer’s < 0.001, Pwestern-mountain < 0.001). Consequently, haplotype diversity and nucleotide diversity for both eastern gorilla species was significantly lower than in western lowland gorillas , a result that is independent of sample size , one that was shared with the historical samples and one unique to the modern samples. We detected no significant difference in nucleotide and haplotype diversity between modern and historical samples  and 2.5x (95% CI 0.2x–68x) population size reduction in Grauer’s and mountain gorillas, respectively  and the bonobo (Pan paniscus)45, and around 100 times lower than in Sumatran orangutan (Pongo abelli)74. We also detected pronounced differences in mitochondrial diversity among gorilla species: western lowland gorillas harbor many highly diverse haplotypes, whereas only a few, closely related, haplotypes are found in the eastern species, particularly in mountain gorillas. These estimates are consistent with previous studies based on the mitochondrial hypervariable region77, nuclear data44, and match both current and historical census population size estimates48.Present-day mitochondrial diversity in the eastern gorillas is currently the lowest of all great apes: It is over 20 times lower than in Our temporal approach and the use of complete mitochondrial genomes constitute a powerful strategy for detecting recent population size changes in threatened and endangered species. The extremely low levels of genetic diversity found in eastern gorillas would make sequencing of only a small part of the mitochondrial genome prohibitively uninformative Fig. S.70, but also a general range contraction, accompanied by local population extirpation62. Because our historical sampling contains populations from both within and outside of the current species’ distribution range, it allowed us to effectively evaluate the effect of population size reduction compared to population loss. We find that genetic diversity within the Grauer’s gorilla core distribution range remained relatively stable over the study period , which is directly linked to the species’ evolutionary potential and hence long-term survival. Populations with low levels of standing genetic variation struggle to adapt in fast changing environments82 and instead identifies peripheral gorilla populations as important keepers of genetic diversity, highlighting their conservation importance. This observation is likely to apply to fragmented populations of many endangered species. Therefore, conservation actions aimed at preserving peripheral populations, improving habitat connectivity and hence facilitating gene flow, could have a disproportionally positive effect on maintaining overall species diversity.Our findings stand in contrast to the common notion that small peripheral populations contain few unique genetic variantsSupplementary figuresSupplementary tables


Phytophthora capsici is a devastating oomycete that affects solanaceous, cucurbitaceous, fabaceous, and other crops in the United States (US) and worldwide. The release of the P. capsici genome allows for design of robust markers for genetic studies. We identified and characterized microsatellites in the P. capsici transcriptome. A subset of 50 microsatellites were assayed in a diverse set of P. capsici isolates and evaluated for polymorphism. Polymorphic microsatellites were confirmed by fragment analysis, and 12 were used for population characterization of 50 P. capsici isolates from different states, hosts, and mating types. Analysis of genetic relationship among isolates revealed significant geographic structure by state. Our findings highlight the usefulness of these 12 microsatellites to characterize the population structure of P. capsici and potential transferability to closely-related Phytophthora spp. since markers are located in coding regions. Our markers will facilitate genetic characterization and complement phenotypic studies of P. capsici populations, which may assist in deployment of disease management strategies. Phytophthora capsici Leonian is a devastating hemibiotrophic pathogen that causes severe epidemics over a broad host range of crops, including peppers, cucurbits, tomatoes, snap beans, eggplants, and many other plant species in the United States (US) and worldwide6. P. capsici can produce numerous symptoms depending on the host, including foliar blighting, damping-off, wilting, and root, stem, and fruit rot4. In the field, disease management relies primarily on the application of fungicides, in combination with cultural practices that are unfavorable for disease development such as raised beds, plastic mulch, and drip irrigation3. However, the broad host range of P. capsici and its capability to undergo sexual recombination and produce thick-walled oospores as persistent survival structures7 has limited the efficacy of control strategies such as crop rotation9. Sexual recombination between the A1 and A2 mating types of this heterothallic oomycete can result in novel genotypes with increased virulence, pathogenicity, and the ability to overcome host resistance and fungicides3. P. capsici sporangia are infrequently dispersed among fields by wind10. Instead, surface water sources for irrigation and movement of infected plant material or infested soil are known to be key factors in local spread of P. capsici and in the development of epidemics11. Because of limited long-distance dispersal of P. capsici and its ability to quickly overcome resistant varieties and fungicides due to frequent sexual reproduction, a better understanding of regional pathogen population structure would allow for more effective deployment of resistant varieties and breeding for durable host resistance12.The oomycete P. capsici populations have been studied at a global scale within countries15 and at a local scale within states in the US19. Studies employed a wide range of molecular markers such as random amplified polymorphic DNA (RAPD)20, amplified fragment length polymorphism (AFLP)22, and single nucleotide polymorphism (SNP)13. Nonetheless, despite the benefits offered by microsatellites for population analyses23, only a few useful microsatellite loci have been developed to assess genetic diversity on P. capsici population studies18. Microsatellites, also known as simple sequence repeats (SSRs), are short DNA sequences consisting of tandemly repeated units, generally 1–6 base pairs in length25. Microsatellites can be found in either non-coding or coding regions and are highly polymorphic, making them desirable for population genetic analyses27. Microsatellites have been widely used in recent studies as a marker for diagnostics, population structure assessments, and epidemiological studies in oomycetes33. Genetic diversity in P. capsici has also been determined by microsatellites35, however, most of these studies relied on a set of microsatellites designed from a database of expressed sequence tags (ESTs)18. To date no microsatellite markers have been developed from the current P. capsici genome assembly, which could offer a set of standardized markers to be used by the scientific community35.The amount and distribution of genetic diversity in 37. The publication of the P. capsici genome, predicted transcriptome38, and the availability of microsatellite search tools, such as MISA (MIcroSAtellite identification tool)39, allow mining for microsatellites from whole genomes or transcriptomes. Identifying microsatellites in transcript sequences implies the identification of polymorphisms within coding regions, which represent annotated markers located in genes that may play important roles in pathogen virulence and survival40. This approach has been successfully applied to identify microsatellites in an increasing number of species, including Phytophthora species such as P. nicotianae28, P. ramorum42, P. sojae42, P. infestans42, P. plurivora32, P. multivora32, P. pini32, and plant pathogens such as Alternaria brassicicola43, Gaeumannomyces graminis44, Erysiphe necator45, Fusarium verticillioides46, F. oxysporum47, Anisogramma anomala48, and more recently Synchytrium endobioticum49. To date, development of microsatellite markers from transcript sequences in P. capsici has been unreported, despite the advantage of such studies and the economic importance of the pathogen. We aimed to: (i) identify and characterize microsatellites from the publicly available transcriptome of P. capsici using an in silico approach; (ii) determine microsatellite polymorphism in a diverse selection of P. capsici isolates and evaluate them in a population study of 50 isolates; and (iii) compare the distribution of microsatellites in the P. capsici transcriptome with the transcriptomes of two other plant pathogenic oomycetes (P. sojae and P. ramorum). The characterized polymorphic microsatellites in this study will help in understanding regional pathogen population structure, which will result in improved disease management strategies.Traditionally, the methodology for microsatellite mining involved enrichment of genomic DNA libraries for a few targeted microsatellite motifs, followed by screening and sequencing of clones through Sanger sequencing. The entire process is labor intensive, resource consuming, and usually yields a small number of polymorphic markersin silico search for microsatellites using MISA (MIcroSAtellite identification tool) examined 20.36 Mb of the transcriptome of P. capsici (v. 1.1). 8.17% of the sequences contained microsatellites , dinucleotides (9.33%), hexanucleotides (4.53%), and pentanucleiotides (1.62%). The P. capsici transcriptome is smaller than P. ramorum, and P. sojae, and it contains less microsatellites. The amount of microsatellites among these Phytophthora species seems to be highly correlated with their genome size . When accounting for the differences in length of examined sequences between species, the P. capsici transcriptome exhibited the lowest relative abundance (microsatellite/Mb) and relative density (bp/Mb) (91.09 microsatellite/Mb and 1280.05 bp/Mb respectively), compared to P. ramorum  and P. sojae   when compared with P. ramorum and P. sojae (AGCG/CGCT). Pentanucleotide motifs and hexanucleotides motifs showed the lowest frequencies (<1%) across the three species. In all species, regardless the motif, the microsatellites tended to repeat 3, 4, and 5 times more frequently, and very few microsatellites repeat >10 times  to flank microsatellites in the P. capsici isolates from different states in the US, exhibiting both mating types, and collected from different hosts . NC isolates showed the highest genetic diversity with Shannon-Weiner’s index of 3.091.The number of alleles per locus in the screened panel of isolates were, on average, 3.42 and ranged from 2 to 6 alleles. Heterozygosity varied from 0.38 to 0.79, with an average of 0.55. The evenness of alleles at each locus ranged from 0.63 to 1.0, with the mean evenness of 0.83 . Across states, nearly 13% of variation was attributed to differences among states, while more than 16% was attributed to variation between isolates within states, and 69% was attributed to variation within isolates. Only 6.38% of variation between isolates was attributed to differences due to host. Pairwise comparison (FST) between states also showed significant differentiation between P. capsici isolates from NC and all other states included in this study   Table . The STR5) Table . The del5) Table . Twenty .82 Fig. . P. capsP. capsici transcriptome to successfully identify 1,855 microsatellite loci using an in silico approach. The number of microsatellites detected in P. capsici is larger than those discovered in P. plurivora, P. multivora, P. pini, P. citricola, or P. alni using the 454-pyrosequencing-based method51. Our P. ramorum and P. sojae transcriptome survey and a P. nicotiana whole genome survey yielded higher numbers of microsatellites compared to what the P. capsici transcriptome yielded28. Relative abundance and relative density of microsatellites in P. capsici differs greatly to the observed values for P. ramorum and P. sojae; we hypothesize that such differences may be explained by the differences among genome sequencing approaches taken for these three species52. In fact, our results suggest high similarity among the frequencies, relative abundances, and densities of the same repeat unit across the three species. In the different transcriptome sequences analyzed here, trinucleotide microsatellites were the most common motif length, consistent with studies in other Phytophthora spp.41, fungal plant pathogens53, and plants54. The same studies also report lower abundance of di-, tetra-, and pentanucleiotides. Purifying selection for motifs that preserve reading frames explains the predominance of trinucleotide microsatellites in coding regions55.We exploited the et al.41 used transcriptome sequences of P. ramorum and P. sojae and had similar primer design success rate, greater than 80%, which is comparable with our findings. Insufficient flanking sequence adjacent to the microsatellite, such as a microsatellite close to the end of a contig, may explain the 20% primer design failure48. Gel electrophoresis screening of a set of 50 microsatellite primers in seven P. capsici isolates yielded 17 highly polymorphic microsatellite markers. We observed higher amplification efficiency (96.00%) than reported by Wang and Chilvers53 (85.00%) and Gagnon, et al.49 (79.03%) who examined whole genome sequences of F. virguliforme and S. endobioticum respectively. Amplification efficiency in our microsatellite primer screening might be attributed to the stability of coding regions with low selection pressure56. We identified a high number of polymorphic bands from trinucleotide microsatellites. Di-, tetra-, penta- and hexa-nucleotide microsatellites yielded the lowest number of polymorphic bands. The relatively low polymorphism implies that these repeat types are highly conserved across isolates used in this study in contrast with trinucleotides repeats, which may experience mutation and selection pressure for specific amino acids43. We further narrowed the list of microsatellites from 17 to 12 based on consistent amplification and polymorphism observed via gel electrophoresis, and confirmed the polymorphism via fragment length analysis.We designed primer pairs for more than 80% of the identified microsatellite loci. Our results reveal information about the reliability and quality of the genome assembly. Garnica, P. capsici isolates. Our Nei’s expected heterozygosity values are similar to the ones reported by Gagnon, et al.49 and Wallace and Quesada-Ocampo57 when they characterized microsatellites for S. endobioticum and Pseudoperonospora cubensis respectively. In a recent study, Gagnon, et al.58 used microsatellites mined from whole genome sequence data of P. ramorum isolates to assess genetic diversity. The markers developed in their study had an average evenness of 0.943 and were able to detect temporal and spatial distribution of P. ramorum in Canadian nurseries. In our study, the MLG curve reached a plateau with 11 microsatellite markers out of 12, implying that these markers are sufficient to depict all 50 multilocus genotypes. The P. capsici populations are known to stratify by geography since long distance movement is unlikely12. The Shannon-Wiener’s index confirmed the presence of high genetic diversity in our sample of 50 P. capsici isolates. NC isolates displayed more genetic diversity than P. capsici populations reported from 12 different sites in four regions of New York State16. Sampling for NC isolates encompassed 7 different sites distributed in three regions of North Carolina. Our results for diversity measurements suggest that the 12 polymorphic microsatellites produced enough resolution for genotyping genetically distinct individuals.Polymorphism indexes such as Nei’s expected heterozygosity (0.55) and evenness (0.83) indicated high potential for these markers to describe diversity in the panel of 50 59. The genetic distance tree detected some population structure based on geographic regions enabling us to differentiate isolates into two major clusters with 100% bootstrap support. One of the major clusters consisted of isolates mainly from NC while the other included isolates from all other states evaluated here. Such genetic differentiation can be explained by the fact that P. capsici exists in geographically isolated subpopulations in the US16. Our genetic distance tree was unable to separate isolates from SC, NY, TN, GA, and MI, therefore we performed a discriminatory analysis of principal components in order to infer the number of genetic clusters. Interestingly, the clustering of isolates based on the DAPC supported the hypothesis of the genetic distance tree. NC isolates collected across the state consistently formed a separate group relative to the other isolates in the DAPC. Unlike the genetic distance tree the relative position of SC isolates in the DAPC scatter plot suggests underlying population structure between SC and TN, NY, GA, and MI isolates. The clustering observed in the DAPC for NC and SC isolates showed significant genetic differentiation with FST values larger than 0.05 and in some cases larger than 0.25. Population differentiation statistics and the AMOVA analysis supported our hypothesis that the 12 microsatellite markers developed here are able to separate P. capsici isolates by geographic origin. Even though the AMOVA by host is significant, it only represents 6.38% of the variation between isolates. Two major clusters were detected in the STRUCTURE analysis using the admixture model. We observed some resemblance between the two major groups in the genetic distance tree and the two clusters revealed by STRUCTURE. All NC isolates clustered together in group 1 within the STRUCTURE analysis, supporting our hypothesis that the 12 microsatellite markers are robust and can differentiate isolates by state. We attribute the unresolved group 2 in the STRUCTURE analysis to the low number of isolates per field from SC, NY, TN, GA, and MI. Successful management of P. capsici requires a detailed knowledge of regional pathogen population structure and distribution3. Conducting surveys of local P. capsici populations and examining how they stratify can direct the deployment of management strategies in a more efficient way. The 12 microsatellite markers discovered here represent powerful and inexpensive tools for describing the population structure of P. capsici at a regional scale.As a first measure to detect population structure, we created a distance-based tree using Provesti’s distanceP. capsici is considered a pathogen with high evolutionary potential, because sexual reproduction increases the probability of developing genotypes that can overcome host resistance or are resistant to fungicides9. These novel genotypes could be leading to new epidemics that become isolated due to the soilborne dispersal mechanism of the pathogen3, and therefore understanding the phenotypic variation and genotypic structure in a particular region becomes highly desirable. Monitoring the genetic structure of the pathogen along with phenotypic monitoring of fungicide resistance, mating type, and virulence, allow for strategizing management. For example, if a genetic cluster was found to be more virulent to a particular host, it would be valuable to know the geographic location of isolates belonging to that genetic cluster so that one avoids planting a susceptible crop in that area60. Microsatellites as described by Cooke and Lees23 represent a powerful molecular marker for genetic analysis; they are affordable and reproducible among research groups. Even though genomic approaches are available now such as genotyping by sequencing, the P. infestans community still relies on microsatellite markers to understand the pathogen biology and its population dynamics63. In fact, Dunn, et al.16 emphasized the need for additional polymorphic markers to increase resolution and detect greater diversity in populations of P. capsici. Our study delivers a set of highly polymorphic microsatellite markers to the P. capsici community: these 12 microsatellites represent valuable tools to understand the structure and diversity of P. capsici populations, allowing for comparisons of datasets generated by different research groups. We foresee an additional application for our microsatellites as transferable markers across species complexes. Quesada-Ocampo, et al.12 used SNPs from several genomic regions to separate P. capsici and P. tropicalis in a STRUCTURE analysis, however, their SNP markers were unable to resolve an intermediate cluster containing both species. Microsatellites developed from coding regions are highly transferable to closely related species65. Since P. capsici is part of a species complex, we hypothesize that our microsatellites can distinguish among closely related species such as P. tropicalis and P. glovera. Further experiments should be completed to validate this hypothesis.P. capsici are highly polymorphic in a diverse panel of isolates, describe diversity of local populations, and may be transferable to species closely related to P. capsici. These microsatellites can be used to analyze the genetic structure of particular geographic regions and determine genotypes present in fields. The information generated by population studies using these markers may allow for better deployment of resistant cultivars if a correspondence between genetic cluster and virulence is found for a particular host. We are interested in using these markers to describe the until now unknown population structure of P. capsici in North Carolina and combining that knowledge with phenotypic information regarding fungicide resistance, mating type, and virulence.Taken together, our findings demonstrate that 12 microsatellites developed from the transcriptome sequences of P. capsici38, Pr-102 of P. ramorum52, and P6497 of P. sojae52 were downloaded as FASTA files from publically available databases at http://genome.jgi.doe.gov/Phyca11/Phyca11.home.html, http://genome.jgi.doe.gov/ramorum1/ramorum1.home.html, and http://genome.jgi.doe.gov/Physo3/Physo3.home.html. The transcriptome data was searched for microsatellite motifs of one to six nucleotides in length using the program MISA (MIcroSAtellite identification tool)39. Search criteria were set for identification of at least 10 repeat units for mono-nucleotides, six for di-nucleotides and five for tri-, tetra-, penta-, or hexa-nucleotides. Results were compared among species for abundance, frequency, distribution, and relative density of each repeat unit in the transcriptomes as described previously46.Predicted transcriptomes from genome sequences of isolates LT1534 of P. capsici transcriptome and generate input files for designing primers with the Primer3 program66. Primer3 identified primer pairs flanking each microsatellite loci with a melting temperature between 57 °C and 63 °C with an optimum at 60 °C, GC content between 20 and 80% with an optimum of 50%, and PCR products with expected length between 100 and 300 bp. The M13 sequence (5′-GACGGCCAGT-3′) was added at the 5′ end of each forward primer in order to allow for later fluorescent product labeling and fragment analysis as previously described67.The MISA output files and Perl programming were used to extract the coordinates of identified microsatellites in the P. capsici isolates were selected for primer validation via gel electrophoresis (Supplementary Table P. capsici genome sequencing project (LT1534). All isolates were transferred from long term storage cultures to V8 juice broth as described previously68. Mycelia were harvested through filtration, immediately lyophilized, and stored at −80 °C.A panel of seven single-spore g. The aqueous supernatant was transferred to a new tube and 0.8 volume of Isopropanol was added. Tubes were incubated at −20 °C overnight. After incubation, all samples were centrifuged for 15 min at 21,130 g, the supernatant was discarded and the pellet was precipitated with 80% Ethanol. The pellet was dissolved in 200 µl of sterile water. Quality and integrity of genomic DNA were estimated by measuring the 260/280 nm ratio in a NanoDrop ND-1000 spectrophotometer  and by electrophoresis in a 1% agarose gel in 0.5 × Tris-borate-EDTA buffer stained with ethidium bromide (5 µg/ml) for visualization.Genomic DNA was isolated using the following phenol-chloroform protocol: for each isolate, 200 mg of lyophilized mycelium were ground on an OMNI Bead Ruptor 24  at a speed of 5 m/s for 60 s, adding 2 mm glass beads. Subsequently, 600 µl of extraction buffer  was added to the powdered tissue and homogenized by vortexing. Samples were treated with 4 µl of RNAase A  and incubated for 10 min at room temperature with occasional gentle mixing. 500 µl of phenol was added, and the mixture was centrifuged for 10 min at 21,130 g. The supernatant was transferred to a fresh tube and an equal volume of Phenol:Chloroform was added and mixed by gentle inversion; the mixture was centrifuged for 10 min at 21,130 P. capsici isolates , 1 µl of each 10 µM primer, and 7 µl of sterile water. PCR cycling conditions included an initial denaturation at 94 °C for 3 min; followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 53 °C for 30 s and extension at 72 °C for 30 s; with a final extension at 72 °C for 5 min. PCR products were analyzed for polymorphism by electrophoresis in 4% agarose gels and visualized by ethidium bromide staining. A 100 bp DNA ladder  was used to estimate allele size.Ten microsatellite primer pairs each from di-, tri-, tetra-, penta-, and hexa-nucleotide motif length were randomly selected from the Primer3 output to detect polymorphism among 7 P. capsici isolates from different states, hosts, and mating types via fragment analysis . Genotyping reactions were subsequently performed by the NCSU Genomic Science Laboratory  on an ABI 3730xl DNA Analyzer .To determine the effectiveness of the microsatellite markers developed in this study for understanding population genetic structure and diversity, a subset of 12 microsatellite primers were selected on the basis of polymorphisms in agarose gels. These microsatellite markers were further analyzed across 50 P. capsici is a diploid organism belonging to the class Oomycota. Descriptive population statistics such as the number of alleles per locus (Na), heterozygosity (He), evenness, and the genotype accumulation curve were estimated using the functions “gac” and “locus_table” from the package poppr in RStudio 71.Allele sizes were called using the Microsatellite Plugin in Geneious version R9.0.5 . In order to reduce the risk of genotyping stutter peaks, we manually removed from the analysis peaks lower than 5% of the signal of the tallest peak at any given locus. We assumed two alleles to be present at each loci because 72. We built a genetic distance tree to assess genetic relatedness between isolates using Provesti’s distance with 1,000 bootstrap replicates59. Because the set of microsatellite markers discovered in this study are intended to elucidate population structure of P. capsici, we assessed whether the 50 P. capsici isolates significantly structure by state, host, or mating type by implementing the Analysis of Molecular Variance (AMOVA) included in the poppr package. To test for differentiation between P. capsici isolates from different states, pairwise FST values were estimated, and significance levels were tested with 999 permutations using GenAlEx 6.573. The presence of an underlining structure was examined using the Discriminant Analysis of Principal Components (DAPC) with the R package adegenet74, and the Bayesian Markov Chain Monte Carlo (MCMC) clustering model using the software STRUCTURE v2.3.475. The values for length of burnin, chain replication, and lambda were set at 50,000, 250,000, and 1 respectively. Isolates membership was inferred for one to 10 clusters and the optimal K was chosen by computing ΔK using STRUCTURE HARVESTER v.0.6.9476. Population structure figure defining the state of origin was obtained using Microsoft Excel (version 15.20).We employed the function “poppr” using the stratified clonecorrected data  to calculate the number of multilocus genotypes, the Shannon-Wiener index of genetic diversity and other summary statistics. The standardized index of association (Supplementary InformationSupplementary Table S2Supplementary Table S4


Vitex negundo var. heterophylla (Chinese chastetree) from adjacent habitat types at seven sampling sites. Using several functional traits, we detected a significant differentiation between habitat types. With amplified fragment length polymorphisms (AFLP) and methylation‐sensitive AFLP (MSAP), we found relatively high levels of genetic and epigenetic diversity but very low genetic and epigenetic differences between habitats within sites. Bayesian clustering showed a remarkable habitat‐related differentiation and more genetic loci associated with the habitat type than epigenetic, suggesting that the adaptation to the habitat is genetically based. However, we did not find any significant correlation between genetic or epigenetic variation and habitat using simple and partial Mantel tests. Moreover, we found no correlation between genetic and ecologically relevant phenotypic variation and a significant correlation between epigenetic and phenotypic variation. Although we did not find any direct relationship between epigenetic variation and habitat environment, our findings suggest that epigenetic variation may complement genetic variation as a source of functional phenotypic diversity associated with adaptation to the heterogeneous habitat in natural plant populations.Environmentally induced phenotypic plasticity is thought to play an important role in the adaption of plant populations to heterogeneous habitat conditions, and yet the importance of epigenetic variation as a mechanism of adaptive plasticity in natural plant populations still merits further research. In this study, we investigated populations of  These dominant markers provided a considerable number of anonymous loci, which generate powerful data for detecting the genetic and epigenetic structure across several heterogeneous habitats in a nonmodel species without a reference genome. We compared the patterns of genetic and epigenetic variation shaped by the habitat conditions to test the hypothesis that epigenetic variation plays a potential role independent of genetic variation in the adaptation of 22.1We selected plots for this study from seven sites in the south hills of Shandong Province, eastern China, separated by approximately 20–150 km , basal diameter (D), and number of resprouts (NR) were measured, and more than five fully expanded compound leaves were collected at different positions of the stem. Both leaf fresh weight (LFW) and leaf dry weight (LDW) were measured and leaf water content [LWC = (LFW − LDW)/LDW] was obtained. Leaflet area (Area) was determined with image processing program ImageJ  method  was combined with 10 μl double digestion mix containing 1 μl 10 × CutSmart Buffer , 2.5 μEcoRI (NEB), 0.5 μMseI or 2.5 μHpaII or 2.5 μMspI (NEB) in parallel reactions. The reaction was incubated at 37°C for 2 hr and inactivated at 80°C for 20 min. Then, the product was combined with 48 μT4 DNA ligase (NEB), 1.4 μl 10 × T4 DNA ligase buffer, 1 μl EcoRI adapter (5 μM), and 1 μl MseI adapter (50 μM) or 1 μl HpaII/MspI adapter (50 μM). The reaction was incubated at 16°C for 2 hr, inactivated at 65°C for 10 min, and diluted 1:5. For the preselective amplification (PCR1), 2 μl ligation product was combined with 13 μl PCR1 reaction mix containing 0.7 μl preselective primers (5 μM) each, 0.6 μl dNTPs , 1.5 μl 10× buffer (TIAGEN), 0.75 U polymerase (TIAGEN), and 9.2 μl H2O. The thermocycler protocol was 72.0°C (2 min) followed by 20 cycles of 94.0°C (20 s), 56.0°C (30 s), and 72.0°C (2 min) and a final extension at 60.0°C (30 min), performed on a Biometra TGradient. The PCR1 product was diluted 1:5. For the selective amplification (PCR2), 2 μl PCR1 product was combined with 13 μl PCR2 reaction mix containing 0.7 μl selective primers (5 μM) each, 0.6 μl dNTPs , 1.5 μl 10 × buffer (TIAGEN), 0.75 U polymerase (TIAGEN), and 9.2 μl H2O. The thermocycler protocol was 94.0°C (2 min) followed by 10 cycles of 94.0°C (20 s), 66.0°C  and 72.0°C (2 min) and 20 cycles of 94.0°C (20 s), 56.0°C (30 s) and 72.0°C (2 min), and a final extension at 60.0°C (30 min). Six selective primer combinations were chosen for the AFLP  and MSAP  analyses  with Rox‐500 internal size standard (Applied Biosystems) at the Shandong Academy of Agricultural Sciences. We used PEAKSCANNER v1.0 (Applied Biosystems) to analyze the AFLP and MSAP profiles. Binning of fragments was performed using a peak height threshold of 50 relative fluorescence units and a minimal size of 50 base pairs. Peak height data were scored with a binary code, zero for band absent, and one for band present. For every polymorphic locus, each allele must exist in more than two individuals .2.4K) present among individuals and assigns individuals to each K using Bayesian modeling. We tested 20 populations (k = 1–20), which are more than the maximum anticipated based on sampling location, with 20 runs at each k. We used both the log probability of observing the data (Ln Pr(x|k)) method of Structure and Delta K  or only the EcoRI–MspI fragment  as “1” and treating other conditions as “0.”All basic statistical analyses were carried out using the R environment. The MSAP profiles were analyzed with the R script MSAP_calc  and percentage of polymorphisms (PPL) for each population were calculated with GENALEX 6.5  = −2.51, p = .046], LWC  and SLA , and similar patterns of disturbance between two populations were revealed among sites other than Dafotou and Yaoxiang . We detected population structure at every level of hierarchy and found significant variation among regions (explaining 7% of the genetic variance) and among populations within region (2%), and the most (91%) among individuals within population , which indicated a high degree of intermixing between populations. These groups did not clearly reflect geographically based differences among seven sites  = −4.39, p = .005]  = −8.799, p < .001 for m‐MSAP; t(13) = −7.591, p < .001 for u‐MSAP], but two MSAP scoring approaches drew different conclusions comparing index H. Genetic H was lower than epigenetic H calculated with m‐MSAP data , but higher with u‐MSAP data . However, we failed to detect a significant correlation between genetic and epigenetic diversity. For u‐MSAP, hierarchical AMOVA detected little variation (1%) among populations within a site, but failed for m‐MSAP, and most variation existed within population. Moreover, MSAP datasets did not infer significant differences in genome‐wide cytosine methylation level  = −0.2443, p = .8151]. Only one MSAP locus was determined with habitat type through analysis with GLM.A total of 146 MSAP loci were analyzed, which were transformed into 146 u‐MSAP loci and 146 M‐MSAP. All 195 individuals displayed unique u‐MSAP and M‐MSAP genotypes. The epigenetic diversity was high based on both u‐MSAP (ranged from 0.449 to 0.493) and m‐MSAP (ranged from 0.495 to 0.566). There was also no significant difference in epigenetic diversity between understory and open habitats. We found higher levels of epigenetic than genetic diversity at the population level using index PPL [3.4r = .081, p = .0485). We also detected the geographic structure of genetic and epigenetic variation. At the level of population, we found a lower geographic regression slope for epigenetic structure than genetic structure using the Mantel method  between genetic and epigenetic variation when we controlled for the additional factor  between phenotypic and epigenetic variation when we controlled for genetic variation. Additionally, we detected the spatial structure of genetic  and epigenetic  diversity.Partial Mantel tests always displayed a significant correlation (r Figure . However4V. negundo (Su et al., V. negundo var. heterophylla. Although little differentiation between habitats within sites indicated strong gene flow, the AMOVA, Bayesian, and GLM analyses concluded congruently that genetic variation may play a more important role in habitat differentiation than epigenetic variation. The study of a floodplain herb emphasized the major role of environmentally induced epigenetic variation for adjustment to changing habitat conditions (Schulz et al., Consistent with the previous studies of Borrichia frutescens (Foust et al., The main distinction between understory and open habitat is the availability of light, which may have strong effects on a series of other biotic and abiotic environmental factors. In fact, there are many other obvious environmental differences in arbor coverage, human disturbance, elevation, slope aspect, and climate, which we cannot control for in natural conditions across seven sites. Moreover, the understory and open habitats are dynamic and could rapidly transform. Although epigenetic variation is considered as a very potential component in the adaption to the changing environment, relationships among genetic and epigenetic variation and habitat may show different patterns in varying species. When running simple and partial Mantel tests, we obtained the same pattern as for V. negundo var. heterophylla in our research region. We found phenotypic plasticity in response to controlled drought and shade treatment in our previous glasshouse experiments using V. negundo var. heterophylla (Du et al., The selected traits, including leaf water content, specific leaf area, level of resprout, and height‐diameter allometry, were plastic and could respond to the heterogeneous environment. For example, SLA, an indicator of ecophysiological characteristics with a strong phenotypic plasticity and substantial genetic effects (Scheepens, Frei, & Stöcklin, Amazingly, we found 15 AFLP loci statistically correlated with habitat type, and the Bayesian clustering suggested a parallel genetic divergence that may result in microevolution from independent origins. This was also supported by MMRR showing potential effect of habitat environment on genetic differentiation. Although strong gene flow has homogenized the genetic differentiation across the genome, adaptive loci will remain due to similar pressure (Trucchi, Frajman, Haverkamp, Schönswetter, & Paun, The epigenetic mechanism may be restricted when natural plant populations endure some discrete human‐caused disturbance, such as heavy metal pollution (Kim et al., V. negundo var. heterophylla with such high genetic diversity. While many previous studies took advantage of low genetic diversity clonal or inbred species to minimize possibilities for genetic control (Richards et al., It is a challenge to search for epigenetic components independent from genetic variation in Arabidopsis thaliana concluded that heritable epigenetic variation can cause substantial variation in ecologically important plant traits (Zhang et al., Finding direct casual links between epigenetic and variation and ecological phenotype is a key challenge in the study of epigenetic adaptation with nonmodel species (Richards et al., V. negundo var. heterophylla. Consistent with the results from a perennial herb (Herrera, Medrano, & Bazaga, According to the illustration of genetic and epigenetic isolation‐by‐distance scenarios along with hypothesized causes (Herrera et al., 5V. negundo var. heterophylla from an extensive variety of habitat environments. Two scoring approaches for MSAP marker data were conducted, and the results were consistent in most cases. The analysis demonstrated significant habitat‐related adaptation in phenotypic and genetic differentiation, suggesting an evident process of natural selection. However, we did not find a direct correlation between functional phenotypic and genetic variation, and Mantel tests and MMRR revealed a significant relationship between epigenetic and genetic or phenotypic variation. This result ultimately implied a potential intermediary role of epigenetic mechanisms in the adaption to heterogeneous habitats.Our study used functional traits, AFLP, and MSAP to analyze phenotypic, genetic, and epigenetic variation in natural populations of None declared.LL, WG, and ND conceived the idea and designed the study. LL, CP, and ND contributed to the field survey. CP and LL performed the molecular laboratory work. LL, XG, and WG analyzed and interpreted the data. LL drafted the manuscript and all authors participated in manuscript modifications and gave final approval for publication.


Maculinea alcon. The ecotypes occur in either hygric or xeric habitats, use different larval host plants and ant species, but no significant distinctive molecular traits have been revealed so far. We apply genome-wide RAD-sequencing to specimens originating from both habitats across Europe in order to get a view of the potential evolutionary processes at work. Our results confirm that genetic variation is mainly structured geographically but not ecologically — specimens from close localities are more related to each other than populations of each ecotype from distant localities. However, we found two loci for which the association with xeric versus hygric habitats is supported by segregating alleles, suggesting convergent evolution of habitat preference. Thus, ecological divergence between the forms probably does not represent an early stage of speciation, but may result from independent recurring adaptations involving few genes. We discuss the implications of these results for conservation and suggest preserving biotic interactions and main genetic clusters.Biotic interactions are often acknowledged as catalysers of genetic divergence and eventual explanation of processes driving species richness. We address the question, whether extreme ecological specialization is always associated with lineage sorting, by analysing polymorphisms in morphologically similar ecotypes of the myrmecophilous butterfly  Ecological relationships such as host-parasite, resource-consumer, mutualism or competition, affect the realized ecological niche of species and, consequently, their reaction to habitat changes3. The impact of such changes should be higher in ecological specialists, rather than generalists, as specialists are usually more constrained by their specific biotic and abiotic requirements4. Strong interactions and more specific needs may thus lead to faster isolation of populations and development of reproductive barriers. The evolution of different ecotypes may be associated with genetic differentiation, catalyzing lineage divergence and eventually driving the speciation processes8.Biotic interactions represent essential components of ecosystems11. However, in order to drive differential selection, the host-plant association character (most importantly adaptation to its defense secondary metabolites) should be inherited, variable, and conferring a local advantage. Whether or not host-plant association shifts occurred repeatedly within a species’ populations, or are associated with a deeper divergence of lineages, remains an open question in most herbivores showing multiple hosts13. So far, unraveling the evolutionary history of host-plant shifts has been rather limited due to the low resolution of classical genotyping and sequencing methods in a coalescent framework. However, with the advent of Next-Generation-Sequencing technologies, it is possible to analyse hundreds of loci from non-model species, and establish the extent to which host-plant association is linked with genetic differentiation, across the whole genome with a higher statistical power.Shifts in host-plant associations are found in many herbivorous insects, such as butterflies, in which it is often considered one of the main mechanisms producing their observed diversityMaculinea alcon , a «cuckoo» species of Myrmica ant colonies, feeding in the very early stages on a restricted number of plants and then imitating the ant brood and being fed by nurse ants. This strong ecological specialization potentially increases habitat patchiness and makes Maculinea a good model genus for the design of conservation strategies14, for understanding genetic variation structuring15, and for understanding the evolution of social parasitism16.Here, we apply a genome-wide approach to address this longstanding matter for the highly specialized lycaenid butterfly M. alcon sensu lato is known to oviposit predominantly on three main larval host plants  and to parasitize at least five Myrmica ant species19 in contrasting habitats, hygric versus xeric, with few populations occupying a spatially restricted xeric habitat at high elevations. Some authors referred to the hygric ecotypes as “pneumonanthe” and to the xeric as “cruciata”, based on the host plant20; however, here, we assign the names based on their habitat21. Furthermore, to distinguish the low and high altitude xeric ecotypes, we refer to the populations at high xeric elevations as the “alpine“ ecotype. At the very few known syntopic localities , the two low-elevation ecotypes also differ in their phenology, and their flight times only occasionally and partially overlap, limiting possibilities for gene flow24.In Europe, M. alcon and the xerophylous M. rebeli , but see Kudrna and Frič25 vs. Tartally et al.26 for nomenclatural issues regarding the taxon rebeli.Two taxa were previously recognized within this system and often treated as different species: the hygrophilous 27. Moreover, adults lack unambiguous significantly segregating morphological traits28, so that individuals and populations are usually classified based on the larval host plant and locality type. Furthermore, the re-examination of M. alcon populations occurring at high altitude in the Alps revealed a series of differences compared to lowland populations—flight period , larval host plants, ant hosts, wing pattern and genetic structure29— but these differences do not seem to justify the emergence of two distinct species20. Nevertheless, the different ecotypes and subspecies represent important evolutionary and conservation units20. We consider the existence of (at least) three ecotypes of M. alcon and provide the summary of their characteristics: 1) hygric , 2) xeric , and 3) “alpine” .It has been debated for nearly two decades whether these ecotypes are associated with genetic variation33, the morphological analyses revealed no or only very slight distinctive traits between the low-altitude ecotypes, with only the “alpine” ecotype differing by the wing pattern33. Similarly, classical molecular phylogenetic and population genetic studies from several localities of their area of occurrence, failed to reveal significant genetic differentiation between the low-altitude ecotypes35 despite their strong ecological differentiation. Minor ecotype-driven genetic differentiation in the few known syntopic/nearly syntopic populations was found, but it was not consistent with ecotype when other nearby populations were included36. The “alpine” population is usually referred to as «true rebeli». It shows a slight genetic differentiation and combined evidence based on a large number of phenotypic and genotypic markers suggested the existence of a subspecific differentiation20. The fact that no consistent genetic differences among the low-altitude ecotypes have been found so far does not necessarily mean they do not exist. Indeed, the current findings are based only on microsatellites, allozymes and classical sequencing of a reduced set of genes20. Theoretically, fine-grain differences could still be retrieved by performing genome-wide analyses. If differences among ecotypes are to be found, two hypotheses can be considered: (a) each of the forms is monophyletic and there is ongoing divergence between them, or (b) selection at a restricted number of loci occurred repeatedly with several independent evolutions of the ecotypes, indicating convergent evolution of habitat preference. Alternatively, if no genetic differences among ecotypes were found, even with a whole-genome sequencing approach, it would mean that the differences between the forms are likely caused by phenotypic plasticity or epigenetic variation only.In addition to the differences mentioned above, the ecotypes can be distinguished by different flight periods (differing by one to a few weeks), which, however, vary slightly between the localities and years and are dependent on the flowering period of the host plant. Despite these ecological differencesM. alcon, we investigated specimens of three ecotypes spanning the distribution of the species by applying Restriction site associated DNA sequencing (RAD-seq)37 and detecting single nucleotide polymorphisms (SNPs) at randomly distributed loci across the genome. We eventually discuss our results in light of conservation management.In order to test which scenario is at work in 38 . In the additional dataset, where Maculinea arion was used as outgroup in order to identify the earliest diverging lineage in M. alcon, 949 RAD loci were retained. This dataset contained many loci that were monomorphic for M. alcon and would have produced a phylogeny with branch lengths equal or close to zero for all M. alcon specimens . After removing those monomorphic loci, 156 loci remained for the phylogenetic analysis.The filtered dataset for 26 individuals  included 1,393 RAD loci. The SNP matrix is deposited in Zenodo (http://doi.org/10.5281/zenodo.997960)39 used to summarize the STRUCTURE outputs showed K = 4 as the most likely number of clusters. Three out of five runs for K = 4 showed the same pattern. We calculated mean cluster assignments from these three runs and used a threshold of 0.95 sample assignment to a given population . However, it allowed us to determine that the earliest diverging lineage within M. alcon was the Italian cluster (bootstrap support = 1).The phylogenetic tree reconstructed with PhyML revealed four main clades, corresponding to the clusters detected by STRUCTURE: Spanish, Italian, Serbian and Romanian Fig. . Hygric 2 = 0.492). Moreover, genetic distances were highly correlated with minimum path distance over land  while they did not show any correlation with habitat type .The multiple regression on dissimilarity matrices explained nearly half of the overall variance , as well as an mRNA coding for elongator complex protein 1 from Papilio machaon (XM_014513286.1); for sequence 629, blastn retrieved an anonymous mRNA from Bombyx mori (AK405939.1) as well as a putative uncharacterized protein from Papilio xuthus (XM_013315886.1).The BayeScan analysis revealed two significant SNP outliers suggesting the existence of ecotype-associated selection at those loci , originating from the same locality as studied by Tartally et al.30 did not cluster together, also suggesting a mixed origin of this population. Such a pattern is paralleled by observations in other species of the genus Maculinea, as for instance in M. arion, in which the two phenological forms differ in morphology and ecology but not genetically46.In this study, we investigated the genetic coherence of three ecotypes of the emblematic butterfly see Fig. . This suMaculinea species or forms depend on a single host ant and it was suggested that social parasitism and associated specific life cycle may chiefly affect the evolution, speciation and genetic background of these butterflies32. However, it has been subsequently shown that the usage of ant hosts may differ among localities, and may be adapted to exploiting multiple species or be switched to other species when the preferred host is not present or occurs only in low densities17. Therefore, the hypothesis regarding the influence of the ant host was revisited and some authors suggested that adaptation to the host plant (and corresponding habitat) is more likely to drive the evolution of Maculinea than would do adaptation to the ant species47. The host plants indeed represent allochronic resources as they usually flower at different times of the year24, because of the microclimatic conditions at the respective xeric and hygric habitats.Initially, it was thought that Maculinea/Lycaenidae), was established: (1) the RING finger and SPRY domain-containing protein is associated with oogenesis in Lepidoptera48 and (2) the elongator complex protein 1 is likely involved in acetylation of histones49 — although this has not been confirmed in invertebrates — and is recognized as a fast-evolving gene in insects50. Ecological differences in occupied biotopes, as well as in host plant phenology may explain the association with discrete differences in the oogenesis process. Overall, our results may be viewed as the footprint of multiple independent evolution of the two ecotypes, involving a few genes only. Similarly to Bereczki et al.20, we rule out a possible effect of Wolbachia infections in the segregation of ecotypes, given that all the samples analyzed were infected by the same bacterial strain.Our genome-wide study suggests that hygric and xeric ecotypes are statistically associated with different alleles at at least two loci (29 and 629). In one of the loci (29), homology to two types of mRNAs of the family Papilionidae  are slightly genetically differentiated from the other populations of M. alcon analyzed. Further investigation including more specimens from this ecotype is needed to identify putative genetic differentiation between this and other ecotypes of M. alcon.The evolutionary history of the third ecotype found in an alpine habitat cannot be retrieved from our data, although the fact that it clusters with geographically close populations from Italy may suggest a similar process of recurring evolution. Interestingly, a recent study36. Although we did not find evidence for lineage sorting or early-stage speciation, we found at least two statistically associated markers for the hygric and xeric ecotypes, at least at the scale of the current study. Ecotypic distinctiveness is therefore associated with population-level evolutionary processes rather than with ongoing divergence. This case raises the question how to treat the intra-specific variation from a conservation perspective. The massive use of molecular methods in the last decades enabled researchers to find even minor genetic variations within species or populations, but it also triggered questions concerning the minimal genetic variation and minimal taxonomic unit that should be considered to define conservation units52. Since the 1990s the idea that even intraspecific variation should be considered for conservation gained more and more support53. However, levels of within-species genetic differentiation should not be the only criterion used when planning conservation strategies54. Although the M. alcon ecotypes may only differ at few loci, they each have a different ecological strategy and very specific interactions with other species, and thus they may likely differ in the factors affecting their survival, the adequate management, and the impact of their potential extinction in the ecosystems.Several studies suggested the need to focus conservation efforts separately for each ecotype , and indicated a higher vulnerability of the alpine and xeric ecotypesMaculinea species have become a model group not only for the study of parasitism and for insect conservation but also for preserving biotic interactions55, and we strongly support this view. Accordingly, we advocate a strategy aiming at conserving these ecotypes (each associated with specific biotic interactions) as well as the main genetic clusters (four detected in our study) despite the extremely low divergence within the species, this approach being the sole that guarantees that all diversity is protected  would be interesting to further confirm that there is actually no significant genetic divergence driven by the ant host specificity.see Fig. . MoreoveM. alcon were collected from localities in Spain , Italy , Romania  and Serbia  between 2006 and 2014 . However, without further study of more individuals from this latter population and comparison to the nominotypic alpine populations of the taxon rebeli, we cannot ascertain that our high-altitude sample belongs to this taxon and thus conservatively denominate it as “alpine” ecotype. One hygric and one xeric sample originated from a well-known locality near Răscruci, Romania, where both ecotypes are syntopic  in Barcelona, Spain.For most of the samples, whole genomic DNA was extracted from the thorax or abdomen, using BioSprint 96 robot with BioSprint 96 DNA Blood Kit . The samples for which only legs were available  were extracted using the DNeasy Blood & Tissue Kit (Qiagen), in order to maximise the DNA yield. Obtained DNA isolates were diluted or concentrated to reach a final range of 20–50 ng/ul, based on measurements obtained with the PicoGreen dsDNA Assay kit  and FLUOstar Galaxy fluorescence microplate reader .37, with slight modifications. The whole genomic DNA was digested in 9 ul reaction with 1 U MseI  and 2 U SbfI-HF (NEB) restriction enzymes, 1x CutSmart buffer (NEB) and 6 ul of genomic DNA, at 37 °C for 3 hours. Single strand adapter oligonucleotides were annealed by heating to 95 °C and slow gradual cooling. Adapter ligation was performed at 16 °C for 3 hours in a 20 ul reaction comprising 9 ul of the digested DNA, 0.5 uM of both RAD-P1 and RAD-P2 . The 10 ul reaction mix contained 1 ul of adaptor-ligated DNA, 0.2 U Q5 Hot-start polymerase, 1x Q5 buffer (both NEB), 0.2 mM of each dNTP, 0.6 uM of each Illumina primer (Table The RAD-seq library preparation was performed using the double-digestion RAD protocolP2 Table  adapterser Table . Results57 and assembled de novo following the dDocent.FB pipeline (v.2.2.16)58. Final SNP data sets were filtered according to the dDocent filtering tutorial59: i.e. all loci present in less than 50% of the individuals, with Phred quality lower than 30 and minimum depth for a genotype call lower than 3, as well as samples having more than 90% of missing data, were removed. Minor allele frequency and allele balance at heterozygous sites filtering was applied. Additionally, we kept only biallelic loci, and discarded loci with missing information in more than 3 samples and all samples with more than 3 missing loci.Raw sequence reads were demultiplexed using the process_radtags program60 was used to convert the resulting VCF file to other formats. Phylogenetic analyses were performed with PhyML v. 3.3.2.061 with the following changes to the default settings: GTR substitution model, estimated base frequencies, best of NNI and SPR tree topology search. Two runs, one without outgroup and one with four samples of M. arion as outgroup were performed. The M. arion samples, collected in the same way as the M. alcon samples, are deposited at the same institution and the same protocols were used for library preparation and calling .PGD Spider v. 2.1.0.362, with the following settings: K = 2–10, burn-in = 100,000; number of MCMC repetitions after burn-in = 2,000,000; admixture model, 5 runs for each K. The results were visualized in Pophelper v1.0.1063.The genetic clustering of individual genotypes was assessed using the Structure software version 2.3.464 within RStudio v. 0.99.90365 using Provesti’s distance as implemented in the poppr package v. 2.2.166. A binary distance matrix was calculated for the ecological distance, attributing 0 to pairs of specimens belonging to the same ecotype, and 1 to those from different ecotypes. A geographical distance matrix encompassing the minimum path distance among specimens over land was produced—the minimum path distance was calculated by using the costDistance function as implemented in the gdistance R package (v. 1.2-1)67. The relative contribution of ecological and minimum path distances in explaining overall genetic distances was tested by multiple regression of dissimilarity matrix by using the MRM function implemented in the ecodist R package (v. 1.2.2)68. Since the variables were not normally distributed and the relationships not necessarily linear, a Spearman correlation was applied. P-values were calculated based on 1000 permutations.Genetic distances among specimens were calculated in R v. 3.3.141. The dataset was divided into three groups according to the three forms  and two runs including and excluding the single “alpine” sample were performed. The results were plotted in R v. 3.3.164 using the script plot_R.r implemented in BayeScan. FDR 0.5–0.10 was used, as the 0.10 threshold for q-values in BayeScan is more stringent than the same threshold for p-values in classical statistics41.Candidate loci diagnostic of ecotypes were investigated with BayeScan v. 2.1Maculinea butterflies are very often infected by Wolbachia bacteria, which is known to manipulate reproduction and to affect genetic variation structuring70, contigs obtained by dDocent for all specimens were blasted against available Wolbachia sequence data deposited in GenBank with the blastn tool40.Additionally, because The datasets supporting the conclusions are available in Zenodo (http://doi.org/10.5281/zenodo.997960).Supplementary InformationTable S1Table S2Table S3Table S4


Pocillopora, in particular within the Primary Species Hypothesis PSH09, identified by Gélin, Postaire, Fauvelot and Magalon (2017) using species delimitation methods  and which was found to split into three secondary species hypotheses  according to assignment tests using multi‐locus genotypes (13 microsatellites). From a large sampling  achieved in three marine provinces , genetic structuring analysis conducted with two clustering analyses (structure and DAPC) using 13 microsatellites revealed that SSH09a was restricted to the WIO while SSH09b and SSH09c were almost exclusively in the TSP and SEP. More surprisingly, each SSH split into two to three genetically differentiated clusters, found in sympatry at the reef scale, leading to a pattern of nested hierarchical levels (PSH > SSH > cluster), each level hiding highly differentiated genetic groups. Thus, rather than structured populations within a single species, these three SSHs, and even the eight clusters, likely represent distinct genetic lineages engaged in a speciation process or real species. The issue is now to understand which hierarchical level  corresponds to the species one. Several hypotheses are discussed on the processes leading to this pattern of mixed clusters in sympatry, evoking formation of reproductive barriers, either by allopatric speciation or habitat selection.Here, we examined the genetic variability in the coral genus  Nevertheless, the speciation process is slow and gradual. Thus, it is sometimes tricky to put a frontier between different units, notably when the speciation process is not achieved  tend to present significant genetic structure over small spatial scales while high dispersal abilities are not correlated with population subdivisions , they further revealed the occurrence of three secondary species hypotheses  independently of their corallum macromorphology, as it is not a diagnostic character in Pocillopora genus. So species identification was realized molecularly a posteriori of sampling and a priori of data analyses (see below), leading to a subset of 2,507 colonies corresponding to PSH09 sensu Gélin, Postaire, et al. . Genotyping was performed following the same protocol than in Gélin, Postaire, et al.  were identified using the R (R Development Core Team 2016) package structure  previously identified in Gélin, Postaire, et al. . To do snewhybrids v.1.1 . Once identified, the admixed colonies were not considered in the subsequent analyses.The colonies showing an assignment probability for each of the five runs >0.75 to a given SSH were assigned to this SSH . Colonies that did not show an assignment >0.75 for a given SSH were considered admixed, that is, assigned to more than one SSH (due to hybridization or shared ancestry or bad assignment due to missing data). For each admixed colony, it was assigned to the SSHs presenting an assignment probability >0.1 (a probability < 0.1 was considered as noise). Moreover, we used fstat v.2.9.3 both on the whole dataset and for each SSH separately  and the number of private alleles (Np) were estimated using fstat v.2.9.3  within each SSH, a Bayesian analysis was performed using Structure v.2.3.4  and minimized linkage disequilibrium (LD). To allow comparing the different outputs from the different types of analyses (see below), the colonies were assigned to clusters following the same rule than above  and the same rule was applied to constitute the dataset  for further analyses. As structure demands strong assumptions on the genetic groups identified, we performed in parallel a discriminant analysis of principal components   × ].In a hierarchical approach, these analyses were repeated on each cluster found in each SSH separately. Commonly, using FST . Finally, a hierarchical analysis of molecular variance (AMOVA) was performed using arlequin v.3.5  were assigned to both SSH09b and SSH09c (none to SSH09a), while almost all the colonies from the WIO  were assigned to SSH09a , except 12 and 15 that were assigned to SSH09b and SSH09c, respectively (Table n = 50) in Reunion Island, 44 colonies were assigned to SSH09a, three to SSH09b, and three to SSH09c. Noteworthy, all the colonies showed a probability of assignment to each SSH > 0.75, except six colonies (0.2%) that were found to be admixed between SSHs: one colony from New Caledonia (LOY6) was admixed between SSH09b and SSH09c, and five from North Madagascar were admixed: one (MAD8) between SSH09a and SSH09c, two colonies (MAD5 and MAD6) between SSH09a and SSH09c, and two (MAD6 and MAD7) between all the SSHs. Nevertheless, the multilocus genotypes of all these admixed colonies presented missing data (37% in average) and might just reflect a bad assignment. Additionally, newhybrids detected only 18 hybrids over 2,507 colonies (0.7%), and all were F2 hybrids. Moreover, a high proportion of colonies assigned to one of the three SSHs identified with structure  were assigned as pure lineages with newhybrids .Among the 2,507 , et al. ), each r al. n = ,075 wereFST estimates and from 0.376*** to 0.430*** for the Dest ones (Table Inter‐SSHs genetic differentiation varied from 0.158*** to 0.248*** for the FST per locus considering the whole dataset (Appendix FST ± SE = 0.188 ± 0.058). In addition, loci Pd11 and Pd13 showed a weaker amplification rate on colonies from SSH09a than from SSH09b and SSH09c (data not shown).The mean number of alleles (± standard error) per locus was high, varying from 13.00 ± 0.87 for SSH09b to 14.54 ± 1.27 for SSH09c Appendix . For eacAppendix  showed sNSSH09a = 1,403; NSSH09b = 323; NSSH09c = 781; Table Overall, this indicates a high genetic differentiation among the three SSHs, indicating restricted gene flow between these SSHs. Thus, from now, we will consider these three SSHs as independent genetic lineages and will perform the subsequent analyses on each SSH separately , DAPC showed nearly similar individual assignments than structure for both K = 2 and K = 3: for K = 2, we observed two clusters, SSH09a‐1 and SSH09a‐2, while for K = 3, SSH09a‐2 was further divided into two clusters  when re‐analyzed alone did split in three distinct subclusters with structure while with DAPC, two of these subclusters were overlapping. Then, reconsidering all the colonies and now K = 4, structure did find four clusters, but they did not correspond to the ones found using DAPC, nor to the ones found when treating the two‐first clusters independently .Considering all the colonies assigned to SSH09a  × ], we finally considered that all the colonies from SSH09a could be divided into three distinct clusters  were assigned to a unique cluster , and 93 colonies (6%) were admixed in several clusters: 29 colonies (2%) between SSH09a‐1 and SSH09a‐2, 10 colonies (0.7%) between SSH09a‐1 and SSH09a‐3, 29 colonies (2%) between SSH09a‐2 and SSH09a‐3, and 25 colonies (1.7%) among the three clusters. Nevertheless, only 12 of these 93 colonies presented no missing data, suggesting that the other admixed colonies (21% of missing data in average) might be the result of bad assignment. Comparing both methods, only 4.7% of the colonies were assigned differently. The minimum spanning tree retrieved the three clusters , for K = 2, colonies from the WIO, Chesterfield Islands, and French Polynesia were assigned in the first cluster , even when analyzing both clusters separately.Concerning SSH09b (n = 256) and SSH09b‐2 (n = 67); Figure K = 2 in structure, each colony was assigned identically among the five runs. Then, 307 colonies (95%) were assigned to a unique cluster (NSSH09b‐1 = 244 and NSSH09b‐2 = 63) and 16 colonies (5%) were admixed between SSH09b‐1 and SSH09b‐2. Nevertheless, only three colonies presented no missing data, suggesting that the 13 other admixed colonies (23% of missing data in average) might be the result of bad assignment. Only 2.9% of the colonies were assigned differently between both methods. The minimum spanning tree retrieved the two clusters . Global FST per locus ranged from 0.001 ns to 0.283*** for Poc40 , for K = 2, the structuring pattern nearly corresponded to a geographical pattern. Indeed, on one hand, colonies from Chesterfield Islands together with those of LOY4, LOY5  and the 15 colonies from the WIO were grouped in a first cluster  while colonies from Chesterfield Islands segregated in both SSH09c‐1 and SSH09c‐3. Using DAPC, the same partitioning was observed either for K = 2 or K = 3  were assigned to a unique cluster , and 25 colonies (3%) were admixed in several clusters: two colonies between SSH09c‐1 and SSH09c‐2, 11 colonies between SSH09c‐1 and SSH09c‐3, nine colonies between SSH09c‐2 and SSH09c‐3, and three colonies among the three clusters. Nevertheless, only four colonies presented no missing data, suggesting that the other admixed colonies (19% of missing data in average) might be the result of bad assignment. Only 1.9% of the colonies were assigned differently among the two methods. The three clusters were retrieved in the minimum spanning tree  or Dest (from 0.09*** to 0.31***) estimates  were mainly assigned in pure lineages with newhybrids .Additionally, within each SSH, only F2 hybrids were detected using Finally, considering this hierarchical clustering of the whole dataset , the AMOVA revealed that nearly 15% of the overall genetic variation was explained by the partition into three SSHs and 12% by the partition into clusters within SSH Appendix .44.1P. eydouxi name. In this previous analysis, based on microsatellite loci, Gélin, Postaire, et al. . Noteworthy, these clusters were as much genetically differentiated from each other as SSHs were, or as SSHs belonging to different PSHs were . The host–symbiont association should be further studied considering the clusters within the three SSHs to investigate the existence of specific associations at the different levels of genetic structuring in this host.Finally, host–symbiont associations could play a role in the structuring pattern observed in this study. The evolution of coral hosts and their endosymbionts remains unclear. As an illustration, studying 69 genera (20 families) of octocorals and their endosymbionts, Van Oppen, Mieog, Sánchez, and Fabricius  revealedTo date, we cannot favor one or another hypothesis and more investigations are needed to fully conclude regarding the origin of the genetically divergent clusters observed within PSH09. Several hypotheses imply an ancestral reduction in gene flow that created reproductive barriers or genome incompatibilities among the different clusters for each SSH, which contemporary gene flows have not homogenized yet.5Pocillopora eydouxi species hypothesis  revealed a nested partitioning of the different SSHs, obliging to think about the unit on which connectivity should be assessed. Whatever the causes, facing to this over‐partitioning of our dataset, the matter is not how to estimate connectivity but on what. As each SSH is a mix of several genetically differentiated clusters found in sympatry, we prefer considering the eight different clusters as our reference unit to assess genetic differentiation among populations.Examining the population structure of the Pocillopora genome seems now to be a fundamental key to improve the understanding of its history of divergence and would offer clues to favor one or another exposed hypothesis.Several hypotheses have been proposed to explain the observed pattern, but more investigations are needed to understand the structuring pattern of this species complex. Knowing more about None declared.https://doi.org/10.5281/zenodo.1042513Microsatellite genotypes: Zenodo HM, C.F. and P.G. designed research. H.M., C.F. and L.B. performed sampling. J.B. provided laboratory assistance. P.G. conducted molecular work and performed analyses. P.G., H.M. and C.F. wrote the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Acipenser fulvescens, it should be possible to discriminate between historical population structuring and contemporary gene flow and improve the broader understanding of anthropogenic influence. On the Winnipeg River, Manitoba, two hypotheses were tested: 1) Measureable quantities of former reservoir dwelling Lake Sturgeon now reside downstream of the Slave Falls Generating Station, and 2) genetically differentiated populations of Lake Sturgeon occur upstream and downstream, a result of historical structuring. Genetic methods based on ten microsatellite markers were employed, and simulations were conducted to provide context. With regards to contemporary upstream to downstream contributions, the inclusion of length-at-age data proved informative. Both pairwise relatedness and Bayesian clustering analysis substantiated that fast-growing outliers, apparently entrained after residing in the upstream reservoir for several years, accounted for ~15% of the Lake Sturgeon 525–750 mm fork length captured downstream. With regards to historical structuring, upstream and downstream populations were found to be differentiated , and heterozygosity metrics were higher for downstream versus upstream juveniles. Historical asymmetric (downstream) gene flow in the vicinity of the generating station was the most logical explanation for the observed genetic structuring. In this section of the Winnipeg River, construction of a major dam does not appear to have fragmented a previously panmictic Lake Sturgeon population, but alterations to habitat may be influencing upstream to downstream contributions in unexpected ways.Many hydroelectric dams have been in place for 50 - >100 years, which for most fish species means that enough generations have passed for fragmentation induced divergence to have accumulated. However, for long-lived species such as Lake Sturgeon,  Deforestation, urbanization, hydroelectric dams, weirs, water diversions, and linear transport infrastructure have altered terrestrial and aquatic environments, leading to widespread habitat fragmentation and threatening biological diversity –5. HowevPopulation fragmentation often results from habitat fragmentation, but the concepts are not equivocal. Contemporary habitat identified as fragmented may not preclude or restrict gene flow of a given species . ConversST) quantify differences in allele frequencies and heterozygosity among groups of individuals; the genetic signature of each individual reflects its entire lineage, and as such, these equilibrium metrics effectively synthesize generations of potentially non-random mating schemes, migration, and the impacts of chance events such as genetic mutations and demographic bottlenecks on patterns of between- and within-group variation ) and upstream juveniles (Ne = 383 [95% CI: 171 - ∞]) in the current study were similarly high.It has previously been suggested that population genetic results based on juveniles may be biased due to the Allendorf-Phelps effect ,129. Essoduction  in Lake oduction ,130; andST  were as hypothesized, with no direct evidence of any Allendorf-Phelps type bias [ST estimates of 0.0108 and 0.0114, respectively. Despite large sample sizes, genetic differentiation of upstream adults to upstream juveniles was not statistically significant and the value calculated was an order of magnitude lower (FST = 0.00142). No significant genetic differentiation was observed between slow-growing downstream juveniles and the fast-growing outliers, which seems to conflict with the contention that fast-growing outliers were upstream immigrants; if individuals sampled were drawn from the same population, they should be genetically differentiated from slow-growing downstream juveniles. Given the low degree of genetic differentiation observed between Lake Sturgeon upstream and downstream of the Slave Falls GS, this result is likely due to insufficient sample size . In simulations conducted by Kalinowski [ST was 0.01 and 16 highly polymorphic loci were available; the current study only had the benefit of ten loci with an average of 5.9 alleles per locus. However, when fast-growing outliers were excluded from upstream to downstream calculations, differences of mean estimates for key metrics  between upstream and downstream juvenile groups were more pronounced. Moreover, pairwise relatedness methods as well as Bayesian inference (STRUCTURE) were congruent in indicating inter-reservoir downstream contributions.Most basic comparisons of Fype bias ,129 due linowski , ~100 inOur results highlight the perhaps overlooked utility of combining genetic and biological data (such as length-at-age) when examining structure of fish populations  would not likely arise within <100 years. Furthermore, based on the results of the current and related studies [ST) between populations and also lower the level of equilibrium plateau. It should be noted that considerable uncertainty exists with regards to appropriate parameters for conducting genetic simulations of non-model species with overlapping generations. For example, the understanding of microsatellite mutation rates and repeat restrictions (which we did not incorporate) is incomplete. Also, the nature of regulation for populations at carrying capacity is uncertain and likely varies by species (or perhaps even among habitats inhabited by a given species) from the random approach to thinning that we prescribed in our simulations; in the case of Lake Sturgeon, thinning of juveniles might have different genetic trajectory consequences than would thinning of adults, particularly females, which are generally reputed to attain >75 years of age and be highly fecund late in life [Our second hypothesis examined the potential for historical population structuring in the study area. At the time of field data collection, only 82 years had passed since construction of the Slave Falls GS began in 1928, which corresponds to a small proportion of the post-glacial period –126. Our studies ,92, ther in life ,142,143.ST) can be present within two generations, but even in species with non-overlapping generations, in populations comprised of ≥500 individuals, the level of differentiation was consistently ≤0.002 and unlikely to be detectable without complete census sampling. Since mean Ne estimates in the study area were >350 in the Slave Falls Reservoir  and >700 downstream of the Slave Falls GS, and because Ne is typically a fraction of NC in fishes [Other modelling has also shown that 10–100 generations might be required for fragmentation-induced differentiation to become apparent in species, depending on the size of the fragmented population ,30. Lloyn fishes ,145, incn fishes , it is ln fishes ,87, wouln fishes ,147.ST of 0.1 was observed between Mattagami River (n = 40), Ontario and Rainy River (n = 27), Ontario/Minnesota localities based on the same suite of microsatellites employed herein [ST ranged from 0.04–0.12 [ST = 0. 013–0.014) between the population upstream and downstream of Slave Falls would be some degree of historical asymmetric gene flow. Previous studies have documented elevated genetic diversity in populations of white spotted-char, Salvelinus leucomaenis [Cottus gobio [O = 0.609, or 0.617 with putative upstream immigrants excluded, as per above) compared with those upstream (HO = 0.556); expected heterozygosity followed a similar pattern.Perhaps importantly, the degree of upstream to downstream differentiation observed in the current study was lower than has been observed for between-watershed pairings within the Hudson Bay drainage basin, isolated since glacial recession 7500–8500 years B.P. –126. An d herein . Conside.04–0.12 . Historicomaenis  and riveus gobio , locatedin situ recruitment in the downstream population) may have changed in the recent past, and could still be in flux; observed and expected heterozygosity were similar for both fast-growing and slow-growing downstream juveniles, and yet the combined downstream juvenile group did not exhibit a heterozygosity deficit, a result which would be consistent with a high-rate of upstream to downstream contribution via large juveniles being a recent phenomenon.Some degree of historical upstream to downstream dispersal in the study area seems likely, but the results of Scenario 2 simulations suggest that a 15% rate, ongoing since glacial recession, would not be compatible with the level of differentiation empirically observed if historical population sizes were ≥ 2,000 individuals. Therefore, the quantity of former upstream individuals captured downstream of the Slave Falls GS, in combination with Scenario 3 simulations, raises questions about contemporary persistence of genetic differentiation between upstream and downstream populations. Indeed, the total quantity of downstream juveniles of upstream origin might actually be higher than 15%, as length-at-age data would be unable to identify individuals which passed downstream early in life and subsequently grew at rates characteristic of the downstream sampling location; drifting larvae have been captured immediately upstream of the Slave Falls GS, 9 rkm downstream of the Pointe du Bois spawning site (Manitoba Hydro unpublished data). Given the rapid erosion of accumulated genetic differentiation following the onset of downstream dispersal in Scenario 3 simulations, it seems conceivable that our results may not actually reflect contemporary equilibrium conditions, but rather just a snap-shot of a converging genetic trajectory; a case could be made for population level habitat suitability for Lake Sturgeon having been improved by backwatering from the Slave Falls GS. If so, the Slave Falls Reservoir population may have already recovered from reductions due to historical harvest, and may now actually be approaching a carrying capacity that exceeds what the reach would have supported historically. Therefore, the quantity of upstream to downstream dispersal  were used to assess historical and contemporary genetic patterns in a small section of a large riverine system developed for hydroelectric power generation. A high rate of inter-reservoir downstream contribution via ongoing entrainment during middle life-stages was observed. Evidence of historical population structure was also revealed. Our results illustrate the importance of establishing a historical baseline, as Lake Sturgeon recovery plans have been focused on the premise of historical within-watershed panmixia and contemporary isolation by dams ,149–152,S1 Dataset(XLSX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table2 (and low Prob>χ2) indicate ability of the various estimators to resolve overall differences in relatedness distributions. Probabilities associated with multiple comparison tests  are also shown; however, all values were similarly low and therefore the metric largely uninformative. Estimators are ranked from left to right.High χ(DOCX)Click here for additional data file.S3 TableGroups that share the same score in the significance (Sig) columns did not have statistically different distributions, as determined by a Wilcoxon Rank-Sum test. Estimators are listed from left to right in order of performance ranking .(DOCX)Click here for additional data file.S4 TableData is summarized by mean relatedness (r) and standard deviation of relatedness (SD). Groups that share the same score in the significance (Sig) columns did not have statistically different distributions, as determined by Steel-Dwass All Pairs method. Estimators are listed from left to right in order of performance ranking .(DOCX)Click here for additional data file.