Forestry, Faculty of
Non UBC
Forest and Conservation Sciences, Department of
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Journal of Fungi 7 (2): 111 (2021)
Mullett, Martin S.
Drenkhan, Rein
Adamson, Kalev
Boroń, Piotr
Lenart-Boroń, Anna
Barnes, Irene
Tomšovský, Michal
Jánošíková, Zuzana
Adamčíková, Katarína
Ondrušková, Emília
Queloz, Valentin
Piškur, Barbara
Musolin, Dmitry L.
Davydenko, Kateryna
Georgieva, Margarita
Schmitz, Sophie
Kačergius, Audrius
Ghelardini, Luisa
Kranjec Orlović, Jelena
Müller, Michael
Oskay, Funda
Hauptman, Tine
Halász, Ágnes
Markovskaja, Svetlana
Solheim, Halvor
Vuorinen, Martti
Heinzelmann, Renate
Hamelin, Richard C.
Konečný, Adam
Dothistroma septosporum, the primary causal agent of Dothistroma needle blight, is one of the most significant foliar pathogens of pine worldwide. Its wide host and environmental ranges have led to its global success as a pathogen and severe economic damage to pine forests in many regions. This comprehensive global population study elucidated the historical migration pathways of the pathogen to reveal the Eurasian origin of the fungus. When over 3800 isolates were examined, three major population clusters were revealed: North America, Western Europe, and Eastern Europe, with distinct subclusters in the highly diverse Eastern European cluster. Modeling of historical scenarios using approximate Bayesian computation revealed the North American cluster was derived from an ancestral population in Eurasia. The Northeastern European subcluster was shown to be ancestral to all other European clusters and subclusters. The Turkish subcluster diverged first, followed by the Central European subcluster, then the Western European cluster, which has subsequently spread to much of the Southern Hemisphere. All clusters and subclusters contained both mating-types of the fungus, indicating the potential for sexual reproduction, although asexual reproduction remained the primary mode of reproduction. The study strongly suggests the native range of D. septosporum to be in Eastern Europe (i.e., the Baltic and Western Russia) and Western Asia.
FungiJournal ofArticleWorldwide Genetic Structure Elucidates the Eurasian Originand Invasion Pathways of Dothistroma septosporum, CausalAgent of Dothistroma Needle BlightMartin S. Mullett 1,* , Rein Drenkhan 2, Kalev Adamson 2, Piotr Boron´ 3 , Anna Lenart-Boron´ 4 ,Irene Barnes 5 , Michal Tomšovský 1 , Zuzana Jánošíková 6, Katarína Adamcˇíková 6 , Emília Ondrušková 6 ,Valentin Queloz 7, Barbara Piškur 8 , Dmitry L. Musolin 9 , Kateryna Davydenko 10,11 ,Margarita Georgieva 12 , Sophie Schmitz 13, Audrius Kacˇergius 14 , Luisa Ghelardini 15,16 ,Jelena Kranjec Orlovic´ 17 , Michael Müller 18 , Funda Oskay 19 , Tine Hauptman 8,20, Ágnes Halász 21,Svetlana Markovskaja 22 , Halvor Solheim 23, Martti Vuorinen 24, Renate Heinzelmann 25,Richard C. Hamelin 25,26 and Adam Konecˇný 27Citation: Mullett, M.S.; Drenkhan, R.;Adamson, K.; Boron´, P.; Lenart-Boron´,A.; Barnes, I.; Tomšovský, M.;Jánošíková, Z.; Adamcˇíková, K.;Ondrušková, E.; et al. WorldwideGenetic Structure Elucidates theEurasian Origin and InvasionPathways of Dothistroma septosporum,Causal Agent of Dothistroma NeedleBlight. J. Fungi 2021, 7, 111.https://doi.org/10.3390/jof7020111Academic Editor: David S. PerlinReceived: 8 January 2021Accepted: 26 January 2021Published: 3 February 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).1 Phytophthora Research Centre, Faculty of Forestry and Wood Technology, Department of Forest Protectionand Wildlife Management, Mendel University in Brno, Zemeˇdeˇlská 3, 61300 Brno, Czech Republic;michal.tomsovsky@mendelu.cz2 Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, 51006 Tartu, Estonia;rein.drenkhan@emu.ee (R.D.); kalev.adamson@emu.ee (K.A.)3 Department Forest Ecosystems Protection, University of Agriculture in Kraków, 31-425 Kraków, Poland;piotr.boron@urk.edu.pl4 Department of Microbiology and Biomonitoring, University of Agriculture in Kraków,30-059 Kraków, Poland; anna.lenart-boron@urk.edu.pl5 Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural BiotechnologyInstitute (FABI), University of Pretoria (UP), Pretoria 0002, South Africa; irene.barnes@fabi.up.ac.za6 Department of Plant Pathology and Mycology, Institute of Forest Ecology SAS, 949 01 Nitra, Slovakia;janosikova@ife.sk (Z.J.); katarina.adamcikova@ife.sk (K.A.); ondruskova@ife.sk (E.O.)7 Swiss Federal Research Institute WSL, Swiss Forest Protection, 8903 Birmensdorf, Switzerland;valentin.queloz@wsl.ch8 Department of Forest Protection, Slovenian Forestry Institute, Vecˇna pot 2, SI-1000 Ljubljana, Slovenia;barbara.piskur@gozdis.si (B.P.); tine.hauptman@bf.uni-lj.si (T.H.)9 Department of Forest Protection, Wood Science and Game Management, Saint Petersburg State ForestTechnical University, 194021 Saint Petersburg, Russia; musolin@gmail.com10 Department of Forest Protection, Ukrainian Research Institute of Forestry & Forest Melioration,61024 Kharkiv, Ukraine; kateryna.davydenko74@gmail.com11 Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences,75007 Uppsala, Sweden12 Department of Forest Entomology, Phytopathology and Game Fauna, Forest Research Institut—BulgarianAcademy of Sciences, 1756 Sofia, Bulgaria; margaritageorgiev@gmail.com13 Walloon Agricultural Research Centre, Department of Life Sciences, B-5030 Gembloux, Belgium;s.schmitz@cra.wallonie.be14 Voke˙ Branch of Lithuanian Research Centre for Agriculture and Forestry, LT-02232 Vilnius, Lithuania;audrius.kacergius@lammc.lt15 Department of Agricultural, Food, Environmental and Forest Sciences and Technologies (DAGRI),University of Florence, 50144 Firenze, Italy; luisa.ghelardini@unifi.it16 Institute for Sustainable Plant Protection (IPSP), National Research Council of Italy (CNR),50019 Sesto Fiorentino, Italy17 Faculty of Forestry, University of Zagreb, 10002 Zagreb, Croatia; jkranjec@sumfak.hr18 Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2,FI-00791 Helsinki, Finland; micms.muller@gmail.com19 Faculty of Forestry, Çankırı Karatekin University, 18200 Çankırı, Turkey; fundaoskay@karatekin.edu.tr20 Department of Forestry and Renewable Forest Resources, Biotechnical Faculty, University of Ljubljana,Vecˇna pot 83, SI-1000 Ljubljana, Slovenia21 Plant Health Diagnostic National Reference Laboratory, National Food Chain Safety Office,H-1118 Budapest, Hungary; halasza@nebih.gov.hu22 Nature Research Centre, Institute of Botany, Žaliu˛ju˛ Ežeru˛ Str. 49, LT-08406 Vilnius, Lithuania;svetlana.markovskaja@gamtc.lt23 Norwegian institute of Bioeconomy, P.O. Box 115, N-1431 Ås, Norway; halvor.solheim@nibio.noJ. Fungi 2021, 7, 111. https://doi.org/10.3390/jof7020111 https://www.mdpi.com/journal/jofJ. Fungi 2021, 7, 111 2 of 2824 Natural Resources Institute (LUKE), FI-77600 Suonenjoki, Finland; martti.vuorinen@luke.fi25 Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia,2424 Main Mall, Vancouver, BC V6T 1Z4, Canada; renate.heinzelmann@ubc.ca (R.H.);richard.hamelin@ubc.ca (R.C.H.)26 Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval/Pavillon Charles-Eugène Marchand,1030 Avenue de la Médecine, Québec City, QC G1V 0A6, Canada27 Department of Botany and Zoology, Faculty of Science, Masaryk University, 61137 Brno, Czech Republic;akonecny@sci.muni.cz* Correspondence: martinmullett@hotmail.comAbstract: Dothistroma septosporum, the primary causal agent of Dothistroma needle blight, is one ofthe most significant foliar pathogens of pine worldwide. Its wide host and environmental ranges haveled to its global success as a pathogen and severe economic damage to pine forests in many regions.This comprehensive global population study elucidated the historical migration pathways of thepathogen to reveal the Eurasian origin of the fungus. When over 3800 isolates were examined, threemajor population clusters were revealed: North America, Western Europe, and Eastern Europe, withdistinct subclusters in the highly diverse Eastern European cluster. Modeling of historical scenariosusing approximate Bayesian computation revealed the North American cluster was derived from anancestral population in Eurasia. The Northeastern European subcluster was shown to be ancestral toall other European clusters and subclusters. The Turkish subcluster diverged first, followed by theCentral European subcluster, then the Western European cluster, which has subsequently spread tomuch of the Southern Hemisphere. All clusters and subclusters contained both mating-types of thefungus, indicating the potential for sexual reproduction, although asexual reproduction remainedthe primary mode of reproduction. The study strongly suggests the native range of D. septosporum tobe in Eastern Europe (i.e., the Baltic and Western Russia) and Western Asia.Keywords: Mycosphaerella pini; biogeography; ABC; DNB; global spread; introduction pathways;invasive pathogen; global spread1. IntroductionDothistroma needle blight (DNB) is one of the most important and damaging diseasesof pines worldwide, affecting over 109 Pinaceae taxa [1]. The disease causes foliar necrosis,premature needle cast, reduction in growth, and in extreme cases, tree death [2]. DNBcauses large economic losses (e.g., NZD $24 million per year in New Zealand [3] and GBP£8.6 million per year in the UK [4]) due to timber losses from forest plantations, but alsonegatively affects the landscape and recreational value of forests and the esthetic valueof ornamentals, with non-timber losses estimated at GBP £50 million per year in the UKalone [5].The disease is caused by two species: Dothistroma septosporum (Doroguine) Morelet andD. pini Hulbary, which are only distinguishable using molecular methods [6]. Dothistromaseptosporum has a worldwide distribution, being present on all continents where availablehosts grow in habitats ranging from tropical to sub-arctic on a large host range [1]. On theother hand, D. pini has a much more restricted host and geographical range, being presentin parts of east–central USA and a limited number of European locations [1,7].DNB achieved notoriety in the 1950s and 1960s in the Southern Hemisphere, whereit caused and continues to cause extensive damage to non-native pine plantations [1,8].Since the 1990s, however, DNB has increased in incidence and severity in the NorthernHemisphere, with severe largescale outbreaks occurring in Canada, the UK, and France,and with the disease being reported for the first time from much of central and northernEurope [1]. Reasons for this increase in incidence and severity in the last 30 years has beenascribed to various aspects of the disease triangle: host genotype, pathogen genotype andenvironment. A number of studies have concluded that contributing factors to this diseaseescalation are increased host availability through the expansion of plantation forests, oftenJ. Fungi 2021, 7, 111 3 of 28of more susceptible non-native hosts, and changing climatic conditions, particularly above-average precipitation [1,9–11]. However, a comprehensive global population study toinvestigate the role of pathogen genotype and population has not been conducted to date.DNB has been observed in some regions for over 100 years, for example, Northeast-ern France [12], European Russia [13], Ukraine [1], and parts of the USA [14–16], withdendrochronological studies indicating its presence in British Columbia, Canada since1831 [10,17]. However, the origin of the genus and both species is still unknown, withthe native area hypothesized to be Central America [14], the Himalayas [18], and parts ofNorth America and Europe [14,19]. Recent genetic studies have not been able to resolvethe question of origin, partly due to a lack of substantial samples from Asia and CentralAmerica. These studies have, however, shown support for the pathogen being native toparts of both North America and Europe [20–23].Individual population studies have shown unexpected levels of diversity in manypopulations [20–29]. This diversity indicates that sexual reproduction is more common thanpreviously thought, particularly as the sexual stage (previously known as Mycosphaerellapini) has only rarely been observed, and the asexual stage is ubiquitous. Yet, it has alsoprovided support for the hypothesis that the fungus may be native to these areas. However,most of these studies have focused on a single country or region within a country, and it isunclear how these various populations relate to each other, including their pathways ofintroduction and migration. Only a few studies have included samples from more than onecountry, and only two [20,21] have explored intercontinental population patterns. Giventhe global distribution of D. septosporum, the objective of this study was to combine isolatesfrom the previous population studies with newly obtained isolates to provide a unifiedoverview and large-scale context of its populations and to investigate the role of pathogengenotype and population in contributing to the increase in serious outbreaks.Microsatellite data from an unprecedented number of samples were collected fromacross the worldwide range of D. septosporum, encompassing all but three countriesfrom which the pathogen has been reported. The data were modeled using approximateBayesian computation (ABC), a method used to understand population history, includinginvasion pathways of fungi, e.g., [30,31]. This likelihood-free technique manages anarbitrary number of populations and samples that are employed in complex evolutionaryscenarios and is particularly suited to inferences about introduction histories of invasivespecies [32]. The specific aims of this study were to (i) tie together and contextualizeprevious individual population studies by including isolates from previous populationstudies and newly obtained isolates, (ii) elucidate the phylogeographic relationships ofindividual regions and populations, (iii) investigate the mating-type and prevalence ofsexual recombination in these populations, (iv) determine whether the pathogen originslie in North America or Eurasia, and (v) determine the source of introduced populationsin the Southern Hemisphere.2. Materials and Methods2.1. Sample Collection and Fungal IsolationIn order to cover the largest geographical range possible and to facilitate comparisonwith previous D. septosporum population studies, isolates and/or DNA from earlier stud-ies [20–23,25,27–29,33] were included in this study. Additional needle samples with typicalsymptoms of DNB were collected from various Pinus species across six continents. Manysamples were opportunistically collected where infected pine trees were found. Singlespore isolations of D. septosporum were made using the methods outlined in [34].Isolates were grouped according to their country of origin and are listed in Table S1. Incases where large unsampled areas occurred between samples, distinct population groupswere created to reflect geographical population groups more accurately (e.g., multiplepopulation groups in Canada).J. Fungi 2021, 7, 111 4 of 282.2. Haplotype and Mating-Type DeterminationIsolates were grown in the dark for ca. two weeks at 20 ◦C on autoclaved cellophanediscs (Innovia Films, Wigton, UK) placed on Dothistroma medium [35] to obtain myceliumfor DNA extraction. DNA was extracted using a Kingfisher Flex magnetic particle proces-sor (Thermo Scientific, Waltham, MA, USA) using Kingfisher Plant DNA extraction kits(Thermo Scientific). Species-specific mating-type primers [36] were used to determine theDothistroma species and mating-type of each isolate as outlined in [27]. Eleven microsatellitemarkers developed by [37] were used for multilocus haplotyping. Multiplex PCR of themarkers (Doth_DS1, Doth_DS2, Doth_E, Doth_F, Doth_G, Doth_I, Doth_J, Doth_K, Doth_L,Doth_M, Doth_O) and fragment analysis were conducted as described by [27].Isolates with identical multilocus haplotypes (MLHs, i.e., alleles identical at all 11 loci)were considered clones. Two data sets were created: one containing all individuals (non-clone-corrected data set), another containing only one individual of each multilocus haplo-type per population (clone-corrected data set).2.3. Genetic Diversity and DifferentiationFive indices were used to evaluate genotypic diversity and were calculated in the Rpackages poppr [38] and vegan [39] using the non-clone-corrected dataset: (i) Shannon-Wiener index, H [40,41]; (ii) Stoddart and Taylor’s index, G [42]; (iii) Simpson’s index,λ [43]; (iv) genotypic richness, eMLG, the expected number of multilocus genotypes(eMLG) calculated by rarefaction to the smallest sample size (≥10); and (v) genotypicrichness, E5, an estimation of evenness which is equal to 0 when a single genotype isdominant and increases to 1 as genotypes become more equally represented [41]. Theproportion of isolates derived from clones, or asexual reproduction, is known as the clonalfraction (CF) and was calculated according to the method of [44].The clone-corrected dataset was used to calculate further indices: Nei’s gene diversity,Hexp [45], calculated in poppr; the total number of alleles, number of private alleles, andthe mean haploid genetic diversity (h) calculated in GENALEX 6.5 [46]; and allelic richness(AR) (i.e., the number of distinct alleles in a group) and private allele richness (PAR) (i.e.,the number of alleles unique to a particular group) calculated in ADZE [47]. The AR andPAR were computed using a rarefaction procedure to adjust them to a specific sample sizethat allowed comparison between populations having different sample sizes. Calculationswere standardized to a uniform size corresponding to the size of the smallest group.Pairwise FST values, used as a measure of population differentiation, and Nm, thepredicted number of migrants between population groups, were calculated in ARLEQUIN3.5 [48].2.4. Mating-Type Distribution and Sexual RecombinationAn equal proportion of mating-type idiomorphs indicates that sexual reproductioncould be frequent enough to maintain equilibrium. To determine whether groups differedsignificantly from the null hypothesis of a 1:1 ratio of mating-type idiomorphs, an exactbinomial test, using two-tailed p-values, was used [49].Poppr [38] was used to calculate the index of association (IA) together with its asso-ciated measure (rd). The IA is a measure of multilocus linkage disequilibrium and rd isa modification of it that facilitates comparisons between studies by removing the depen-dency on the number of loci used [50,51]. Sexual populations are expected to have linkageequilibrium due to no linkage among loci, while clonal populations are expected to havesignificant disequilibrium due to linkage among loci. The IA and rd from the observed datawere compared to values obtained after 1000 randomizations to simulate random mating.Both clone-corrected and non-clone-corrected data sets were used for mating tests inorder to reduce the chance of rejecting the null hypothesis of random mating that a smallerclone-corrected data set might carry [52].J. Fungi 2021, 7, 111 5 of 282.5. Population StructureThe population structure of the clone-corrected dataset was assessed using bothSTRUCTURE and DAPC. STRUCTURE 2.3.4 [53] implements a Bayesian, model-basedclustering algorithm to assign individuals to a specified number of clusters (K), mini-mizing linkage disequilibrium and maximizing Hardy–Weinberg equilibrium withinthe clusters [54]. To estimate the optimal number of clusters, 60 independent runs ofK = 1–15 were carried out in STRUCTURE using no priors (i.e., no information on geo-graphical location or host was provided). Each run had a burn-in of 100,000 iterationsfollowed by 500,000 data-collecting iterations, using a model of correlated allele fre-quencies and with admixture among populations allowed. CLUMPAK [55] was used todetermine the optimal value of K using the ∆K method of [56]. CLUMPAK was usedto align all optimum K STRUCTURE runs to the permutation with the highest H-value.The MCL threshold for similarity scores was set to 0.9. The DISTRUCT program [57]was used to visualize the CLUMPP output. Individual haplotypes were assigned to aparticular cluster if their membership probability to that cluster was ≥0.8. Additionally,a hierarchical STRUCTURE analysis was done in which the isolates from each of thethree major clusters were run in a separate STRUCTURE analysis, with the settingsidentical to those described above.To complement the Bayesian approach implemented in STRUCTURE, a multivariatetechnique that makes no assumptions regarding the population model or data structurewas used [58]. Discriminant analysis of principal components (DAPC) was conducted inthe R package ADEGENET [58,59]. It is particularly suited to identifying clusters (K) ofgenetically related individuals as it minimizes variation within groups and maximizesvariation between groups [58]. A sequential K-means procedure followed by an assessmentof the Bayesian information criterion (BIC) to assess the optimal number of clusters precedesthe DAPC analysis itself. Cross-validation was used to determine the optimal number ofprincipal components retained in the analysis [60].2.6. Modeling of Evolutionary HistoryThe STRUCTURE clusters were used to inform and develop historical scenariosdescribing the evolutionary relationships among populations. These scenarios wereinvestigated using approximate Bayesian computation (ABC) conducted in DIYABCv2.1.0 [61]. The real observed dataset of microsatellite haplotypes is compared withlarge numbers of simulated datasets (one million per scenario) based on competingfor evolutionary scenarios (models). The topology of the scenarios is designed as acomposition of events such as separation of one population from another, merging oftwo populations or change of effective population size. Furthermore, each scenario ischaracterized by a set of demographical parameters (time of events in the number ofgenerations, effective population size, admixture rate) and a mutational model. Modelselection (scenario comparison) is performed via relative posterior probabilities assignedto each scenario resulting from their relative vicinity (of the appropriate simulateddatasets) to the observed dataset in a multidimensional space of summary statistics (i.e.,usual population genetic characteristics such as gene diversity or Fst which decrease thecomplexity of the multilocus dataset).As the number of potential scenarios between a large number of populations is large,cumbersome, and computationally onerous, a stepwise procedure was adopted to buildevolutionary scenarios to address specific questions about relationships among two orthree STRUCTURE (sub)clusters, where the best scenario in the first step was used toinform the scenarios of the second step (sensu [62]).The first question about relationships among the North American (NA), WesternEuropean (WE) and Eastern European (EE) clusters (K = 3 STRUCTURE result) wasassessed via a set of 19 scenarios in ABC analysis 1. The scenarios tested whether allthree population clusters were derived separately from an ancestral population or if onecluster was derived from one of the other two, from an unsampled population, or fromJ. Fungi 2021, 7, 111 6 of 28an admixture event between populations. The dataset contained all samples from Canadaand USA, Western and Eastern Europe (243, 546 and 1041 clone-corrected samples for NA,WE and EE, respectively; Table S2).Subsequently, as the position of the Western European cluster was revealed, theevolutionary relationship between North American and Eastern European clusters wasaddressed in the fourteen scenarios of ABC analysis 2. These scenarios were based onthe premise that one sexual cycle per year was possible and that any genetic exchangebetween North America and Eurasia was either less than 500 years ago (i.e., the Europeandiscovery of America) or 11,000–30,000 years ago (i.e., across the Bering Land Bridge).Tested scenarios included the North American cluster being derived from the EasternEuropean cluster, and vice versa, either with a bottleneck or without, and either withrestrictions on the timing (up to 500 years ago or 11,000–30,000 years ago) or without.Further scenarios included both populations being derived from an unsampled ancestralpopulation, with and without a bottleneck, and with the restrictions on timing describedabove. Bottleneck events were allowed to range from 0 to 40 generations as continentaltranslocation could be expected to include a longer than usual bottleneck (see Table S2 fora detailed explanation and the historical interpretation of each scenario).The third question (analysis 3) regarded relationships among the three subclusters ofthe Eastern European cluster (K = 5 STRUCTURE result), i.e., Northeastern Europe (NEE,223 samples), Central Europe (CEE, 641 samples) and Turkey (TUR, 82 samples). The same19 scenarios as in analysis 1 were used.As the relationship of the Central European subcluster was clarified, analysis 4 cen-tered on determining the relationship between the Northeastern European subcluster andthe Turkish subcluster using five scenarios. The scenarios tested whether both populationswere derived from an ancestral population independently, if one derived from the other, orfrom admixture with an unsampled population.Once the relationship between the three Eastern European subclusters was clarified,the position of the Western European cluster was evaluated in analysis 5 using 11 sce-narios. The topology resulting from the previous analyses was retained, and scenariostested whether the Western European cluster derived from any of the three EasternEuropean subclusters or an unsampled population or from admixture between any twoof these populations.The final three analyses (6–8) aimed to determine the origins of the introducedpopulations (with over 10 MLHs) in the Southern Hemisphere. Twenty-two scenariostested whether the Southern Hemisphere population derived from any of five main pop-ulations delimited by STRUCTURE (North America, Western Europe, Central Europe,Northeastern Europe, Turkey) or an unsampled population, or from admixture betweenany two of these populations. As the Southern Hemisphere populations are knownto be recent introductions, the time of their formation was limited to between 1 and300 generations ago. Three Southern Hemisphere populations were considered—SouthAfrica Hogsback (n = 16; analysis 6), South Africa Tzaneen (n = 13, analysis 7), and Chile(n = 11, analysis 8).A list and full description of all scenarios is provided in Table S2. Demographic priorsof the tested scenarios included effective population size (10 to 10,000), the time of thesplit or admixture event (in the number of generations ago; 1 to 10,000), the duration ofthe bottleneck event (in number of generations; 0 to 20), the effective number of foundersof a population (2 to 100), and the rate of admixture (0 to 1), except where these differ asspecified above. One million datasets were simulated for each scenario. The generalizedstepwise model (GSM) was followed for the microsatellite loci, and the default DIYABCvalues for the priors of the mutation model parameters were used, with the exception ofthe mean mutation rate, which was extended to a minimum of 1 × 10−5. Only classicdinucleotide microsatellite markers fitting the GSM were used in the ABC datasets. Addi-tionally, the highly polymorphic dinucleotide marker L was excluded, resulting in sevenmarkers (DS1, DS2, F, G, I, J, and K) used in the analyses.J. Fungi 2021, 7, 111 7 of 28For each simulation, a number of commonly used genetic summary statistics (meannumber of alleles for one sample and between two samples, mean heterozygosity, Fstbetween two samples, mean index of classification between two samples, and (δµ)2distance between two samples) were used to compare it to the observed dataset usingEuclidian distances. The posterior probability of each scenario was then estimatedby polychotomous logistic regression on the 1% of simulated datasets closest to theobserved dataset [63,64]. Posterior distributions of parameters, model checking usingthe posterior based error and summary statistics not used in model selection, andconfidence in scenario choice using 1000 pseudo-observed test data sets were calculatedusing the options in DIYABC v2.1.0.3. Results3.1. Isolates and HaplotypesIn total, 3872 D. septosporum isolates from 44 countries on six continents were usedin this study (Table S1). The isolates were grouped into 56 population groups basedon geographical proximity (Figure 1a,b, Table S1). In the vast majority of cases, thiscorresponded to the county. In some cases, isolates from the same country were placedinto separate population groups (e.g., distant geographical groups separated by largeareas without isolates in Canada and Norway). Full details of isolates, including thehost, geographic location, and population group, are provided in Table S1. Based on the11 microsatellite markers, these isolates consisted of 1913 unique multilocus haplotypes.All loci were polymorphic with a total of 377 alleles, ranging from 6 at Doth_O to 97at Doth_L.J. Fungi 2021, 7, x FOR PEER REVIEW 7 of 29 stepwise model (GSM) was followed for the microsatellite loci, and the default DIYABC values for the priors of the mutation model parameters were used, with the exception of the mean mutation rate, which was extended to a minimum of 1 × 10−5. Only classic dinu-cleotide microsatellite markers fitting the GSM were used in the ABC datasets. Addition-ally, the highly polymorphic dinucleotide marker L was excluded, resulting in seven markers (DS1, DS2, F, G, I, J, and K) used in the analyses. For each simulation, a number of commonly used genetic summary statistics (mean number of alleles for one sample and between two samples, mean heterozygosity, Fst be-tween two samples, mean index of classification between two samples, and (δµ)2 distance between two samples) were used to compare it to the observed dataset using Euclidian distances. The posterior probability of each scenario was then estimated by polychoto-mous logistic regression on the 1% of simulated datasets closest to the observed dataset [63,64]. P sterior istributio s of parameters, model checking using the posterior based error and summ y statistics not used in model selection, and confidence in scen rio choice using 1000 pseudo-observed test data sets were calculated using the options in DI-YABC v2.1.0. 3. Results 3.1. Isolates and Haplotypes In total, 3872 D. septosporum isolates from 44 countries on six continents were used in this study (Table S1). The isolates were grouped into 56 population groups based on geo-graphical proximity (Figure 1a,b, Table S1). In the vast majority of cases, this corre-sponded to the county. In some cases, isolates from the same country were placed into separate population groups (e.g., distant geographical groups separated by large areas without isolates in Canada and Norway). Full details of isolates, including the host, geo-graphic location, and population group, are provided in Table S1. Based on the 11 mi-crosatellite markers, these isolates consisted of 1913 unique multilocus haplotypes. All loci were polymorphic with a total of 377 alleles, ranging from 6 at Doth_O to 97 at oth_L. (a) Figure 1. Cont.J. Fungi 2021, 7, 111 8 of 28J. Fungi 2021, 7, x FOR PEER REVIEW 8 of 29 (b) Figure 1. (a) World map of D. septosporum population groups and their STRUCTURE membership at K = 3; (b) European map of D. septosporum population groups and their STRUCTURE membership at K = 5. 3.2. Population Structure Assessment of the delta K statistic clearly indicated three clusters best explained the data from the STRUCTURE analysis (Figure S1). All 60 independent STRUCTURE runs were concordant (Figures 2 and S2a,b). K-means clustering and inspection of the BIC (Fig-ure S3) from the DAPC analysis also supported three clusters as the best split (Figure 3). The STRUCTURE and DAPC clusters were highly congruent. The clusters were named according to their major geographical occurrences as the North American, Western Euro-pean and Eastern European clusters. Higher values of K can also be biologically relevant and were examined to discern the substructuring of the populations. At K = 4, a clear subcluster formed of Central European isolates, and at K = 5, the Turkish isolates formed a distinct subcluster (Figure 2). The hieriarchical STRUCTURE analysis, in which each of the three major STRUCTURE clusters was run separately, produced patterns identical to those of the main run at higher values of K. The exception to this was the North American cluster, in which substructuring was seen in a roughly east-west pattern (Figure S4). Figure 1. (a) World map of D. septosporum population groups and their STRUCTURE embership at K = 3; (b) Europeanmap of D. septosporum population groups and their STRUCTURE membership at K = 5.3.2. Population StructurAssessment of the delta K statistic clearly indicated three clusters best explained thedata from the STRUCTURE analysis (Figure S1). All 60 independent STRUCTURE runswere concordant (Figure 2 and Figure S2a,b). K-means clustering and inspection of theBIC (Figure S3) from the DAPC analysis also supported three clusters as the best split(Figure 3). The STRUCTURE and DAPC clusters were highly congruent. The clusters werename according to their major geographical occurrences as the North Americ n, WesternEuropean and Eastern Eu opean clusters. Higher values of K can also be biologicallyrelevant and were xamined to discern the substructuring of the populations. At K = 4,a clear subcluster formed of Central European isolates, and at K = 5, the Turkish isolatesformed a distinct subcluster (Figure 2). The hieriarchical STRUCTURE analysis, in whicheach of the three major STRUCTURE clusters was run separately, produced patternsidentical to those of the main run at higher values of K. The exception to this was theNorth American cluster, in which substructuring was seen in a roughly east-west pattern(Figure S4).J. Fungi 2021, 7, 111 9 of 28J. Fungi 2021, 7, x FOR PEER REVIEW 9 of 29 Figure 2. Bayesian clustering of D. septosporum multilocus haplotypes inferred using the program STRUCTURE at K = 3, K = 4 and K = 5. Each multilocus haplotype is represented by avertical line partitioned into colored sections that represent the isolate’s estimated membership fractions in each cluster. Black lines separate isolates from different population groups.J. Fungi 2021, 7, 111 10 of 28J. Fungi 2021, 7, x FOR PEER REVIEW 10 of 29 Figure 2. Bayesian clustering of D. septosporum multilocus haplotypes inferred using the program STRUCTURE at K = 3, K = 4 and K = 5. Each multilocus haplotype is represented by a vertical line partitioned into colored sections that represent the isolate’s estimated membership fractions in each cluster. Black lines separate isolates from different population groups. Figure 3. Scatterplot of the discriminant analysis of principal components (DAPC) of D. septosporum multilocus haplotypes. Only the first two principal components of the DAPC are displayed. The first axis is the horizontal axis; the second axis is the vertical axis. Group 1 is equivalent to the Eastern European STRUCTURE cluster, group 2 to the Western European STRUCTURE cluster, and group 3 to the North American STRUCTURE cluster. Individual multilocus haplotypes are represented by dots and groups as inertia ellipses. At the top right, the PCA eigenvalues are represented, with the number of principal components used in the optimized analysis in black. Figure 3. Scatterplot of the discriminant analysis of principal c mponents (DAPC) of D. septos orum multilocus haplotypes.Only the first two principal components of the DAPC are displayed. The first axis is the horizontal axis; the second axis isthe vertical axis. Group 1 is equivalent to the Eastern European STRUCTURE cluster, group 2 to the Western EuropeanSTRUCTURE cluster, and group 3 to the North American STRUCTURE cluster. Individual multilocus haplotypes arerepresented by dots and groups as inertia ellipses. At the top right, the PCA eigenvalues are represented, with the numberof principal components used in the optimized analysis in black.3.3. Genetic DiversityGenotypic diversity measures (H, G, λ; Table 1a) showed the lowest diversity inSouth Croatia and Spain, while the highest was in Southern Poland and Canada WestBC. Genetic diversity measures (Hexp, h, AR; Table 1a), on the other hand, showed thehighest diversity in Lithuania, Latvia, and South Estonia while the lowest tended to be inSouth Croatia or in population groups in the Southern Hemisphere such as Australia andNew Zealand. While general trends can be seen from these values, they are influencedby the large variation in sample size between individual population groups; therefore, itis more informative to consider the diversity values for the STRUCTURE clusters (K-3)and subclusters (K = 4 and 5). The Eastern European cluster showed the highest level ofdiversity by almost all measures, both genotypic and genetic (eMLG, H, G, λ, Hexp, h,AR; Table 1b). The Western European and North American clusters had broadly similarlevels of diversity. In terms of subclusters, the Central European subcluster had the highestlevels of genotypic diversity (eMLG, H, G, lambda) as well as the highest total number ofalleles and private alleles, and Turkey had the lowest values for these measures (Table 1c).However, the Central European subcluster also had the greatest number of samples. Whenrarefied to an equal number of samples (n = 82), the Northeastern European subcluster hadthe highest allelic richness (AR), private allele richness (PAR), and mean haploid geneticdiversity (h) (Table 1c). Values of Fst varied greatly and ranged between 0 and 1, withlarger values between North American and European population groups and Nm (numberof migrants) ranging from 0 to infinity (Table S3).J. Fungi 2021, 7, 111 11 of 28Table 1. Number of D. septosporum isolates and summary statistics for (a) the population groups; (b) the three main STRUCTURE clusters; (c) the three Eastern European subclusters.(a)PopulationGroup N MLHeMLH±Standard Error H G λ E5 HexpClonalFraction Total AllelesPrivateAllelesh± StandardErrorAR±Standard ErrorPAR±Standard ErrorAustralia 4 2 NA 0.562 1.6 0.375 0.795 0.091 0.5 12 0 0.045 ± 0.045 NA NABelarus 1 1 NA NA NA NA NA NA NA NA NA NA NA NABelgium 2 2 NA 0.693 2 0.5 1 0.727 0 19 0 0.364 ± 0.07 NA NABhutan 12 11 9.32 ± 0.466 2.369 10.29 0.903 0.958 0.453 0.083 37 1 0.407 ± 0.096 2.357 ± 0.353 0.208 ± 0.112Bulgaria 8 8 NA 2.079 8 0.875 1 0.568 0 34 1 0.497 ± 0.069 2.531 ± 0.265 0.162 ± 0.098CanadaAlberta 27 26 9.87 ± 0.334 3.244 25.14 0.96 0.979 0.353 0.037 53 14 0.34 ± 0.108 2.11 ± 0.41 0.757 ± 0.392Canada GoldstreamRiver 1 1 NA NA NA NA NA NA NA NA NA NA NA NACanada Helen Lake 4 4 NA 1.386 4 0.75 1 0.394 0 22 0 0.295 ± 0.094 NA NACanadaPrince-George 55 41 9.38 ± 0.736 3.585 30.56 0.967 0.843 0.518 0.255 70 6 0.506 ± 0.077 2.46 ± 0.325 0.493 ± 0.282Canada West BC 239 168 9.81 ± 0.425 4.98 119.25 0.992 0.818 0.431 0.297 113 26 0.428 ± 0.088 2.215 ± 0.307 0.601 ± 0.239Chile 12 11 9.32 ± 0.466 2.369 10.29 0.903 0.958 0.506 0.083 36 0 0.453 ± 0.08 2.407 ± 0.296 0.099 ± 0.053CzechRepublic 91 68 9.66 ± 0.561 4.111 53.43 0.981 0.874 0.598 0.253 94 2 0.589 ± 0.071 2.7 ± 0.278 0.146 ± 0.06Denmark 43 10 4.02 ± 1.099 1.454 2.89 0.654 0.577 0.543 0.767 30 0 0.48 ± 0.075 2.366 ± 0.259 0.015 ± 0.013EasternAustria 51 48 9.89 ± 0.316 3.85 45.63 0.978 0.97 0.654 0.059 94 2 0.64 ± 0.062 2.938 ± 0.258 0.119 ± 0.05Ecuador andColombia 13 9 7.35 ± 0.764 2.032 6.26 0.84 0.794 0.278 0.308 26 0 0.247 ± 0.07 1.459 ± 0.145 0.062 ± 0.062England 596 195 8.76 ± 1.024 4.259 28.96 0.965 0.401 0.554 0.673 96 3 0.551 ± 0.074 2.522 ± 0.236 0.076 ± 0.038Finland 26 24 9.63 ± 0.57 3.131 21.12 0.953 0.919 0.644 0.077 72 2 0.617 ± 0.08 2.935 ± 0.321 0.203 ± 0.071FranceBrittany 282 82 7.55 ± 1.342 3.402 10.27 0.903 0.319 0.584 0.709 72 1 0.576 ± 0.076 2.656 ± 0.251 0.129 ± 0.053FranceCorsica 1 1 NA NA NA NA NA NA NA NA NA NA NA NAGeorgia 2 1 NA NA NA NA NA NA NA NA NA NA NA NAGreece 3 3 NA 1.099 3 0.667 1 0.455 0 19 0 0.303 ± 0.075 NA NAHungary 30 27 9.62 ± 0.582 3.245 23.68 0.958 0.92 0.571 0.1 64 0 0.549 ± 0.085 2.672 ± 0.313 0.094 ± 0.049Ireland 20 16 8.72 ± 0.893 2.649 11.76 0.915 0.819 0.552 0.2 36 2 0.515 ± 0.048 2.345 ± 0.151 0.104 ± 0.055Kenya 9 9 NA 2.197 9 0.889 1 0.349 0 22 0 0.31 ± 0.072 1.779 ± 0.218 0.145 ± 0.142Latvia 24 23 9.84 ± 0.369 3.12 22.15 0.955 0.977 0.704 0.042 70 0 0.672 ± 0.074 3.177 ± 0.307 0.144 ± 0.082Lithuania 16 14 9.09 ± 0.714 2.567 11.64 0.914 0.885 0.729 0.125 66 1 0.674 ± 0.065 3.295 ± 0.299 0.219 ± 0.092Montenegro 2 1 NA NA NA NA NA NA NA NA NA NA NA NANorthCroatia 16 3 2.62 ± 0.489 0.777 1.86 0.461 0.727 0.394 0.813 18 0 0.263 ± 0.078 NA NAJ. Fungi 2021, 7, 111 12 of 28Table 1. Cont.(a)PopulationGroup N MLHeMLH±Standard Error H G λ E5 HexpClonalFraction Total AllelesPrivateAllelesh± StandardErrorAR±Standard ErrorPAR±Standard ErrorNortheast Italy 6 5 NA 1.561 4.5 0.778 0.93 0.346 0.167 21 1 0.274 ± 0.085 1.764 ± 0.254 0.109 ± 0.07New Zealand 24 5 3.07 ± 0.865 0.873 1.71 0.417 0.512 0.164 0.792 16 0 0.131 ± 0.07 1.455 ± 0.247 0.043 ± 0.035Norway North 12 6 5.17 ± 0.665 1.35 2.67 0.625 0.583 0.591 0.5 28 0 0.462 ± 0.075 2.014 ± 0.185 0.054 ± 0.039Norway Southeast 29 23 9.33 ± 0.699 3.08 20.51 0.951 0.939 0.626 0.207 55 1 0.589 ± 0.083 2.855 ± 0.328 0.237 ± 0.12Norway West 52 23 7.41 ± 1.185 2.704 10.65 0.906 0.692 0.613 0.558 44 0 0.581 ± 0.053 2.623 ± 0.201 0.108 ± 0.049Northern Poland 96 44 8.32 ± 1.124 3.336 17.39 0.943 0.605 0.585 0.542 72 2 0.571 ± 0.083 2.704 ± 0.284 0.226 ± 0.084Romania 3 3 NA 1.099 3 0.667 1 0.606 0 23 0 0.404 ± 0.084 NA NARussia Far East 18 15 8.97 ± 0.785 2.63 12.46 0.92 0.89 0.521 0.167 40 1 0.48 ± 0.068 2.396 ± 0.263 0.16 ± 0.094Russia West 27 21 8.91 ± 0.896 2.917 15.51 0.936 0.83 0.695 0.222 66 1 0.661 ± 0.064 3.083 ± 0.275 0.123 ± 0.069Scotland 689 240 8.82 ± 1.048 4.502 28.22 0.965 0.305 0.56 0.652 146 22 0.557 ± 0.061 2.505 ± 0.209 0.157 ± 0.099SouthCroatia 8 2 NA 0.377 1.28 0.219 0.612 0.091 0.75 12 1 0.045 ± 0.045 NA NASoutheast Germany 64 46 9.22 ± 0.836 3.635 28.44 0.965 0.744 0.649 0.281 92 2 0.634 ± 0.079 2.98 ± 0.308 0.333 ± 0.125Serbia 9 9 9 ± 0 2.197 9 0.889 1 0.487 0 31 0 0.428 ± 0.084 2.331 ± 0.278 0.048 ± 0.031SouthEstonia 61 57 9.9 ± 0.306 4.02 53.93 0.981 0.968 0.684 0.066 116 6 0.672 ± 0.082 3.132 ± 0.335 0.218 ± 0.108South Italy 2 1 NA NA NA NA NA NA NA NA NA NA NA NASlovakia 274 157 9.54 ± 0.669 4.725 68 0.985 0.6 0.635 0.427 120 6 0.631 ± 0.068 2.857 ± 0.273 0.103 ± 0.041Slovenia 16 13 8.87 ± 0.743 2.513 11.64 0.914 0.938 0.62 0.188 52 0 0.572 ± 0.075 2.852 ± 0.316 0.17 ± 0.079South AfricaHogsback 20 16 8.72 ± 0.893 2.649 11.76 0.915 0.819 0.378 0.2 26 0 0.354 ± 0.064 1.822 ± 0.153 0.015 ± 0.011South Africa Tzaneen 14 13 9.51 ± 0.5 2.54 12.25 0.918 0.963 0.655 0.071 40 0 0.605 ± 0.051 2.714 ± 0.237 0.018 ± 0.012Spain 13 2 2 ± 0.059 0.54 1.55 0.355 0.768 0.727 0.846 19 0 0.364 ± 0.07 NA NASouthernPoland 602 317 9.7 ± 0.554 5.357 112.06 0.991 0.526 0.618 0.473 115 1 0.616 ± 0.073 2.768 ± 0.27 0.074 ± 0.029Sweden 8 4 NA 1.074 2.29 0.562 0.668 0.727 0.5 31 0 0.54 ± 0.071 NA NASwitzerland 68 28 6.86 ± 1.333 2.659 7.39 0.865 0.481 0.58 0.588 64 2 0.559 ± 0.068 2.665 ± 0.244 0.245 ± 0.085Turkey 108 82 9.41 ± 0.79 4.181 39.14 0.974 0.592 0.625 0.241 106 9 0.617 ± 0.083 2.871 ± 0.325 0.346 ± 0.093Ukraine 14 14 10 ± NaN 2.639 14 0.929 1 0.622 0 48 0 0.576 ± 0.052 2.713 ± 0.259 0.176 ± 0.117USAMontana and Idaho 7 5 NA 1.475 3.77 0.735 0.821 0.718 0.286 35 2 0.575 ± 0.046 3.182 ± 0.263 0.489 ± 0.345USA Oregon 1 1 NA NA NA NA NA NA NA NA NA NA NA NAWales 37 18 6.46 ± 1.293 2.288 5.21 0.808 0.475 0.557 0.514 45 0 0.525 ± 0.063 2.466 ± 0.214 0.036 ± 0.02Total 3872 1913 9.85 ± 0.394 6.864 266.29 0.996 0.277 0.762 0.506 377 NA NA NA NAJ. Fungi 2021, 7, 111 13 of 28Table 1. Cont.(b)Cluster N MLH eMLH± SE H G λ E5 Hexp ClonalFraction Total AllelesPrivateAlleles h± SEAR±Standard ErrorPAR±Standard ErrorWesternEuropean cluster 1570 571 190 ± 7.28 5.47 75 0.987 0.312 0.581 0.636 152 38 0.58 ± 0.072 11.026 ± 2.809 3.150 ± 1.721EasternEuropean cluster 1692 1009 268 ± 6.6 6.54 369 0.997 0.531 0.69 0.404 253 112 0.69 ± 0.068 16.265 ± 3.750 6.641 ± 2.524NorthAmerican cluster 331 241 241 ± 0 5.34 171 0.994 0.819 0.549 0.272 157 76 0.547 ± 0.083 14.241 ± 4.045 8.175 ± 3.316Total 3593 1821 267 ± 6.96 6.9 313 0.997 0.315 0.764 0.493 NA NA NA NA NA(c)EasternEuropeanSubclusterN MLH eMLH± SE H G λ E5 Hexp ClonalFraction Total AllelesPrivateAlleles h± SEAR±Standard ErrorPAR±Standard ErrorCentral Europe 1132 653 94.8 ± 3.39 6.11 239.1 0.996 0.53 0.703 0.423 191 44 0.634 ± 0.072 9.677 ± 2.015 1.669 ± 0.595Northeastern Europe 303 225 91.1 ± 3.28 5.18 111.8 0.991 0.624 0.635 0.257 174 35 0.7 ± 0.078 11.947 ± 2.904 3.557 ± 1.575Turkey 108 82 82 ± 0 4.18 39.1 0.974 0.592 0.625 0.241 106 21 0.617 ± 0.083 9.623 ± 1.798 3.062 ± 0.68Total 1543 957 98.8 ± 2.96 6.52 366.9 0.997 0.538 0.692 0.38 NA NA NA NA NAN = the number of isolates; MLH = the number of multilocus haplotypes; eMLH = the expected number of multilocus haplotypes, genotypic richness; H = Shannon–Wiener index; G = Stoddart and Taylor’s index;λ = Simpson’s index; E5 = estimation of evenness; Hexp = Nei’s gene diversity; h = mean haploid genetic diversity; AR = allelic richness; PAR = private allele richness. (a) Diversity indices not calculated for samplesizes less than 3. AR and PAR rarefied to the smallest sample size of 5, populations with less than 5 haplotypes excluded from the calculations. (b) Only individuals with a STRUCTURE membership probabilityof ≥0.8 to the respective cluster were allocated to the cluster and included in the calculation of diversity indices. AR and PAR were standardized to the smallest group size of 241. (c) The population CentralEurope contains population groups Southern Poland, Slovakia, Czech Republic, Hungary, Eastern Austria, Slovenia, Switzerland. The population Northeastern Europe contains population groups NorwaySoutheast, Sweden, Finland, Russia West, South Estonia, Latvia, Lithuania, Northern Poland, Belarus, Ukraine. Population Turkey contains only Turkey. AR and PAR rarefied to the smallest sample size of 82.J. Fungi 2021, 7, 111 14 of 283.4. Mating-Type Distribution and Sexual RecombinationThe vast majority of population groups contained both mating-types (Table 2a). Theexceptions to this were population groups with a low number of isolates or MLHs (<5) andtwo Southern Hemisphere population groups (New Zealand, Chile). Sexual recombinationis therefore possible in most population groups, and this was supported by the exactbinomial test on the mating-type ratios, using both non-clone-corrected and clone-correcteddatasets (Table 2a). However, the more sensitive IA and rd tests indicate linkage equilibrium,and therefore routine sexual reproduction, in far fewer population groups, particularlyusing the clone-corrected dataset.Of the main STRUCTURE clusters, only in the North American cluster was sexualrecombination frequently occurring according to the mating-type ratios (Table 2b). The IAand rd tests did not support frequent sexual recombination in any of the other STRUCTUREclusters. Regarding the STRUCTURE subclusters, frequent sexual recombination wassupported in the Turkish subcluster by both non-clone-corrected and clone-correctedmating-type ratios and in Central Europe by the clone-corrected IA and rd tests.3.5. Modeling of Evolutionary HistoryTo determine the evolutionary history of D. septosporum from the current set of isolatesrepresenting a global collection, 8 sets of scenarios were considered, each addressing aunique question regarding the evolution of D. septosporum populations. The STRUCTUREresults revealed three main population clusters (North American, Western European andEastern European) and the first set of ABC scenarios (analysis 1, Table S2) aimed to elucidatethe relationship between them. The posterior probabilities were highest for scenarios 1.12and 1.9 (p = 0.4012 and p = 0.3821, respectively; Figure S5, Table S2) in which the WesternEuropean cluster derived from the Eastern European cluster, either directly (scenario 1.9)or through a weak admixture event with an unsampled population (scenario 1.12), andboth the Eastern European and North American clusters derived independently from theancestral population. Posterior probabilities for scenario 1.9 indicate the Western Europeanpopulation cluster derived from the Eastern European cluster a median of 95.8 and amode of 45.2 generations ago. For scenario 1.12, an admixture of the Eastern Europeanpopulation cluster and an unsampled population occurred at a median of 46.1 and a modeof 18.7 generations ago (Table S4). Therefore, the posterior probabilities indicate the creationof the Western European cluster occurred around 18–96 generations ago.The next set of scenarios (analysis 2) was designed to elucidate the relationshipbetween the North American and Eastern European populations. The most supported sce-nario involved the North American population deriving from an unsampled/ancestral pop-ulation less than 500 generations ago and the Eastern European population deriving froman unsampled/ancestral population up to 50,000 generations ago (p = 0.7593; Table S2). Theinverse scenario, with Eastern Europe deriving from an unsampled/ancestral populationless than 500 generations ago and the North American population deriving from an un-sampled/ancestral population up to 50,000 generations ago was unsupported (p = 0.0031),as were scenarios where the North American and Eastern European populations weredirectly derived from each other or those where the genetic exchange between Eurasiaand North America corresponded to the presence of the Bering Land Bridge (p < 0.05).Posterior probabilities indicate the North American population derived from the ancestralpopulation a median of 316 and a mode of 434 generations ago with a strong bottleneck(few founders and long duration), whereas the Eastern European population derived fromthe ancestral population a median of 6460 and a mode of 3210 generations ago (Table S4).J. Fungi 2021, 7, 111 15 of 28Table 2. Mating type ratio and index of association tests for the D. septosporum isolates of (a) the population groups; (b) the three main STRUCTURE clusters; (c) the three Eastern Europeansubclusters. Bold p values (i.e., those that are non-significant) indicate random mating is supported by the test.(a)Non-Clone-Corrected Clone-CorrectedPopulation Group MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)Australia 0 4 NA NA NA NA 0 2 NA NA NA NABelarus 1 0 NA NA NA NA 1 0 NA NA NA NABelgium 0 2 NA NA NA NA 0 2 NA NA NA NABhutan 5 7 0.774 0.154 0.023 0.288 5 6 1 −0.035 −0.005 0.502Bulgaria 2 6 0.289 0.897 0.103 0.011 2 6 0.289 0.897 0.103 0.009Canada Alberta 13 14 1 0.491 0.075 0.009 12 14 0.845 0.43 0.067 0.014Canada GoldstreamRiver 0 1 NA NA NA NA 0 1 NA NA NA NACanada Helen Lake 0 4 NA NA NA NA 0 4 NA NA NA NACanada Prince-George 20 35 0.058 1.67 0.172 <0.001 10 31 0.001 1.55 0.164 <0.001Canada West BC 123 108 0.357 0.435 0.05 <0.001 83 77 0.693 0.28 0.032 <0.001Chile 0 12 <0.001 0.585 0.067 0.023 0 11 <0.001 0.326 0.037 0.127Czech Republic 28 60 <0.001 0.194 0.02 0.002 22 43 0.013 0.083 0.009 0.154Denmark 2 41 <0.001 4.3 0.51 <0.001 1 9 0.021 1.27 0.143 0.002Eastern Austria 22 29 0.401 0.627 0.064 <0.001 20 28 0.312 0.59 0.06 <0.001Ecuador andColombia 1 12 0.003 2.32 0.337 <0.001 1 8 0.039 2.25 0.329 0.002England 217 369 <0.001 0.506 0.054 <0.001 85 100 0.303 0.195 0.02 <0.001Finland 14 12 0.845 0.82 0.085 <0.001 14 10 0.541 0.506 0.052 0.002France Brittany 130 151 0.233 1.98 0.213 <0.001 31 50 0.045 0.406 0.043 <0.001France Corsica 0 1 NA NA NA NA 0 1 NA NA NA NAGeorgia 2 0 NA NA NA NA 1 0 NA NA NA NAGreece 2 1 NA NA NA NA 2 1 NA NA NA NAHungary 15 14 1 0.615 0.065 0.002 12 14 0.845 0.466 0.05 0.003Ireland 12 8 0.503 0.757 0.077 <0.001 8 8 1.196 0.468 0.047 0.007Kenya 6 3 0.508 0.272 0.039 0.146 6 3 0.508 0.272 0.039 0.147Latvia 10 14 0.541 0.45 0.046 0.32 10 13 0.678 0.406 0.041 0.474Lithuania 5 10 0.302 0.399 0.042 0.06 5 8 0.581 −0.197 −0.021 0.95J. Fungi 2021, 7, 111 16 of 28Table 2. Cont.(a)Non-Clone-Corrected Clone-CorrectedPopulation Group MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)Montenegro 1 1 NA NA NA NA 1 0 NA NA NA NANorth Croatia 12 4 0.077 3.26 0.664 <0.001 2 1 NA −0.2 −0.05 0.632Northeast Italy 2 4 0.688 0.807 0.164 0.023 2 3 1 0.533 0.109 0.158New Zealand 0 24 <0.001 0.134 0.071 0.224 0 5 0.063 −0.455 −0.228 0.933Norway North 1 11 0.006 4.17 0.528 <0.001 1 5 0.219 3.25 0.411 <0.001Norway Southeast 20 9 0.061 1.65 0.189 <0.001 15 8 0.21 1.45 0.165 <0.001Norway West 25 27 0.89 3.55 0.356 <0.001 10 13 0.678 2.14 0.215 <0.001Northern Poland 56 38 0.079 1.98 0.211 <0.001 26 16 0.164 1.33 0.139 <0.001Romania 3 0 NA NA NA NA 3 0 NA NA NA NARussia Far East 8 10 0.815 0.645 0.072 0.054 7 8 1 0.477 0.053 0.236Russia West 19 7 0.029 1.69 0.171 <0.001 14 6 0.115 0.808 0.082 <0.001Scotland 135 546 <0.001 1.67 0.168 <0.001 78 155 <0.001 0.477 0.048 <0.001South Croatia 7 1 0.07 NA NA NA 1 1 NA NA NA NASoutheasternGermany 30 33 0.801 0.631 0.067 <0.001 21 24 0.766 0.31 0.033 <0.001Serbia 5 4 1 0.908 0.115 0.032 5 4 1 0.908 0.115 0.028South Estonia 39 20 0.018 0.466 0.05 0.129 38 17 0.006 0.466 0.049 0.199South Italy 0 2 NA NA NA NA 0 1 NA NA NA NASlovakia 133 140 0.717 0.313 0.032 <0.001 77 79 0.936 0.095 0.01 0.011Slovenia 7 9 0.804 0.691 0.08 <0.001 6 7 1 0.1 0.012 0.324South Africa Hogsback 8 12 0.503 0.618 0.071 <0.001 8 8 1.196 0.386 0.044 0.009South Africa Tzaneen 8 6 0.791 0.903 0.092 <0.001 8 5 0.581 0.615 0.063 <0.001Spain 13 0 <0.001 7 1 <0.001 2 0 NA NA NA NASouthern Poland 255 332 0.002 0.512 0.052 <0.001 134 172 0.034 0.327 0.033 <0.001Sweden 6 2 0.289 4.72 0.526 <0.001 2 2 NA −0.545 −0.111 0.946Switzerland 39 25 0.103 2.77 0.287 <0.001 14 10 0.541 1.82 0.185 <0.001Turkey 55 53 0.923 0.906 0.104 <0.001 45 37 0.44 0.507 0.059 <0.001Ukraine 4 10 0.18 −0.011 −0.001 0.839 4 10 0.18 −0.011 −0.001 0.833USA Montana andIdaho 6 1 0.125 6.05 0.611 <0.001 4 1 0.375 4.5 0.514 <0.001USA Oregon 0 1 NA NA NA NA 0 1 NA NA NA NAWales 27 9 0.004 1.58 0.165 <0.001 9 8 1 −0.027 −0.003 0.544Total 1555 2259 <0.001 1.02 0.104 <0.001 868 1057 <0.001 0.969 0.099 <0.001J. Fungi 2021, 7, 111 17 of 28Table 2. Cont.(b)Non-Clone-Corrected Clone-CorrectedCluster MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)Western Europeancluster 566 987 <0.001 0.536 0.056 <0.001 220 335 <0.001 0.245 0.025 <0.001Eastern Europeancluster 767 895 0.002 0.299 0.031 <0.001 459 524 0.041 0.296 0.03 <0.001North American cluster 162 161 1 1.193 0.125 <0.001 106 127 0.19 1.101 0.115 <0.001Total 1554 2259 <0.001 1.105 0.113 <0.001 828 1031 <0.001 1.014 0.104 <0.001(c)Non-Clone-Corrected Clone-CorrectedEastern EuropeanSubcluster MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)MAT-1-1-1 MAT 1-2 p Value IA rdp Value(IA and rd)Central Europe 499 609 0.001 0.257 0.026 <0.001 279 354 0.003 0.325 0.033 0.141Northeastern Europe 175 122 0.002 0.498 0.051 <0.001 129 90 0.01 0.196 0.02 <0.001Turkey 55 53 0.923 0.906 0.104 <0.001 45 37 0.44 0.507 0.059 <0.001Total 729 784 0.165 0.31 0.032 <0.001 453 481 0.377 0.298 0.03 <0.001(b) Only individuals with a STRUCTURE membership probability of ≥0.8 to the respective cluster were allocated to the cluster and included in calculations. (c) Northeastern Europe contains population groupsNorway Southeast, Sweden, Finland, Russia West, South Estonia, Latvia, Lithuania, Northern Poland, Belarus, Ukraine. Central Europe contains population groups Southern Poland, Slovakia, Czech Republic,Hungary, Eastern Austria, Slovenia, Switzerland. Population Turkey contains only Turkey.J. Fungi 2021, 7, 111 18 of 28Subsequent sets of scenarios dealt with the relationship between subclusters of thethree main STRUCTURE clusters in Eastern Europe. Analysis 3 investigated whether theCentral European subcluster, Northeastern European subcluster and Turkish subclusterarose from the ancestral population separately or whether one derived from the other, froman unsampled population, or from admixture, possibly with an unsampled population.This analysis revealed that the Central European subcluster was clearly derived. In themost supported scenario (S3.16, p = 0.2218), the Central European subcluster derived fromthe Northeastern European subcluster. However, confidence intervals overlapped withtwo other scenarios, which were also well supported (S3.19, p = 0.2079 and S3.17, p = 0.1957,Figure 4, Table S2). The Central European subcluster was also derived in these scenarios,in the case of S3.19 from admixture between the Northeastern European subcluster and anunsampled population, or in the case of S3.17 from admixture between the NortheasternEuropean subcluster and the Turkish subcluster. Posterior probabilities for the formation ofthe Central European subcluster were a median of 232 and a mode of 136 generations agofor S3.16, a median of 152 and a mode of 71.2 generations ago for S3.19, and a median of 187and a mode of 109 generations ago for S3.17. Therefore, the Central European subclusteris likely to have arisen in the range of 70 to 190 generations ago with a weak bottleneckoccurring (short duration and a higher number of founders) (Table S4).J. Fungi 2021, 7, x FOR PEER REVIEW 13 of 29 contrast, both the South Africa Hogsback and Chile population groups originated from the Western European population (p = 0.3602 and p = 0.7727, respectively). Other popula-tion groups in the Southern Hemisphere contained too few multilocus haplotypes to con-fidently determine their origin using ABC analyses, yet most of them (Australia, New Zealand, Ecuador and Colombia) belonged strongly to the Western European STRUC-TURE cluster, as did Chile, suggesting they too were introduced from the Western Euro-pean population. Only Kenya differed in its STRUCTURE membership, which is almost identical to the South Africa Hogsback population at all values of K (Figure 2, Figure S2a,b), suggesting a similar origin. A graphical representation of relative timings of D. sept sporum p pulation diver-gence events is given in Figure 4, and a graphical repr sentation of geographical migra-tion events from all historical scenarios is given in Figure 5. Visual i pection of the model checking results confirmed all win ing scenarios and their priors fit the real observed da-taset well (Figure S6). Confidence i scenario choice for each analysis s given in Table S2, and all posterior distributi ns of parameters are given in Table S4. Figure 4. A graphical representation of the overall combined winning historical scenario showing relative timings of cluster and subcluster divergence and colonization events. Founder events and bottlenecks (f in legend) are indicated by thinner lines to indicate a reduction in effective popula-tion size. Ra is the rate of admixture and the thickness of lines that indicate the relative contribu-tion from each population. A is the ancestral population, NA the North American cluster, NEE the Northeast European subcluster, T the Turkish subcluster, CE the Central European subcluster, WE the Western European cluster, SATZ the South Africa Tzaneen population group, SAHG the South Africa Hogsback population group, CH the Chile population group. Figure 4. A graphical representation of the overall combined winning historical scenario showing relative timings of clusterand subcluster divergence and colonization events. Founder events and bottlenecks (f in legend) are indicated by thinnerlines to indicate a reduction in effective population size. Ra is the rate of admixture and the thickness of lines that indicatethe relative contribution from each population. A is the ancestral population, NA the North American cluster, NEE theNortheast European subcluster, T the Turkish subcluster, CE the Central European subcluster, WE the Western Europeancluster, SATZ the South Africa Tzaneen population group, SAHG the South Africa Hogsback population group, CH theChile popula ion group.J. Fungi 2021, 7, 111 19 of 28The next set of scenarios (analysis 4) considered only Northeastern Europe and Turkey(without the purely derived Central European subcluster) and revealed that the Turkishsubcluster was derived from the Northeastern European subcluster (p = 0.4116, Figure S5,Table S2). Posterior probabilities indicated the Turkish subcluster arose a median of 985and a mode of 444 generations ago with a strong bottleneck occurring (long duration and alow number of founders) (Table S4).Analysis 5 revealed that the Western European cluster derived from the North-eastern European subcluster directly and not from the Central European or Turkishsubclusters (p = 0.4318, Figure S5, Table S2). Posterior probabilities closely correspondedto those in other analyses and demonstrated that the youngest population to divergewas the Western European cluster, followed by the Central European subcluster andfinally the Turkish subcluster, which was the oldest population divergence event to takeplace (Figure 4, Table S4).Investigation of the origin of the Southern Hemisphere populations revealed that theSouth Africa Tzaneen population group originated from admixture between the CentralEuropean and Northeastern European populations (p = 0.4322, Figure S5, Table S2). Incontrast, both the South Africa Hogsback and Chile population groups originated from theWestern European population (p = 0.3602 and p = 0.7727, respectively). Other populationgroups in the Southern Hemisphere contained too few multilocus haplotypes to confidentlydetermine their origin using ABC analyses, yet most of them (Australia, New Zealand,Ecuador and Colombia) belonged strongly to the Western European STRUCTURE cluster,as did Chile, suggesting they too were introduced from the Western European population.Only Kenya differed in its STRUCTURE membership, which is almost identical to theSouth Africa Hogsback population at all values of K (Figure 2, Figure S2a,b), suggesting asimilar origin.A graphical representation of relative timings of D. septosporum population divergenceevents is given in Figure 4, and a graphical representation of geographical migration eventsfrom all historical scenarios is given in Figure 5. Visual inspection of the model checkingresults confirmed all winning scenarios and their priors fit the real observed dataset well(Figure S6). Confidence in scenario choice for each analysis is given in Table S2, and allposterior distributions of parameters are given in Table S4.J. Fungi 2021, 7, 111 20 of 28J. Fungi 2021, 7, x FOR PEER REVIEW 21 of 29 Figure 5. Graphical representation of D. septosporum migration pathways (marked by arrows) supported by STRUCTURE and DIYABC analyses. Ancestral popu-lation (A) gave rise to both the North American and Eastern European clusters and most likely resided in Eastern Europe and Western Asia. Colors of arrows and ellipses correspond to STRUCTURE cluster colors in Figure 2. Figure 5. Graphical representation of D. septosporum migration pathways (marked by arrows) supported by STRUCTURE and DIYABC analyses. Ancestral population (A) gave rise to boththe North American and Eastern European clusters and most likely resided in Eastern Europe and Western Asia. Colors of arrows and ellipses correspond to STRUCTURE cluster colors inFigure 2.J. Fungi 2021, 7, 111 21 of 284. DiscussionThis study constitutes the largest population study ever done on D. septosporum,encompassing new and all previously studied populations representing collections from44 countries, comprising all but three of the countries where the pathogen has been reportedfrom, with over 3800 isolates—with the aim of inferring global evolutionary histories.The population study on this large world-wide collection of D. septosporum revealed thepresence of three major population clusters (North America, Western Europe and EasternEurope) and their historical relationships, as well as more fine-scale substructuring ofthese populations and their interrelations. The North American population cluster wasthe most genetically distinct and geographically restricted, while the Western Europeanpopulation cluster has spread to much of the Southern Hemisphere. Analysis of historicalscenarios crucially revealed that D. septosporum is an Old World species being introducedinto North America from an ancestral population of Eurasian origin. Historical scenariosalso showed many of the European populations ultimately derived from the NortheasternEuropean subcluster indicating that Northeastern Europe and western Asia are likelyto be the center of origin of the pathogen. The Turkish subcluster is derived from theNortheastern European subcluster first (i.e., the oldest divergence event), followed bythe Central European subcluster and then the Western European cluster (the most recentdivergence event).DNB has been present in North America since at least 1917, when infected needleswere collected in Idaho, USA [6,15]. However, dendrochronological studies by [17] suggestthat DNB was causing a noticeable growth reduction of P. contorta Dougl. ex Loud. var.latifolia Engelm. (lodgepole pine) in British Columbia, Canada, as early as the 1830 s. InEurope, on the other hand, DNB was first recorded from needles collected in 1860 in France(as Hypostomum flichianum Vuill.), in 1878 in lower Austria (as Leptostroma pinastri in deThümen Mykotheca universalis n◦ 1278) (Holdenrieder and Queloz, personal commu-nication), in 1880 in Denmark (as Mycosphaerella pini Rostr. apud Munk) and in 1910 inEuropean Russia (as Cytosporina septospora Dorogin) [6]. The first records in North Americaand Europe are roughly concurrent; thus, it has long been debated whether Dothistroma andD. septosporum, in particular, is an Old or New World species. This question is especiallyrelevant as D. septosporum was long considered a non-native quarantine organism on theEPPO A2 list of quarantine organisms. However, since the end of 2019, due to its nowwidespread occurrence in Europe, it has been considered a Regulated Non-Quarantine pest(RNQP) in the European Union.Historical scenarios developed to elucidate the relationship between North Americanand Eurasian populations using DIYABC centered around the only known connectionsbetween the two continents, i.e., either ca. 11,000 to 30,000 years ago when the Bering LandBridge allowed human, plant, and animal contact between North America and Asia or ca.500 years ago till present when European rediscovery of North America again made thetransfer of the fungus possible. If the Eurasian population split from an ancestral populationless than 500 years ago, or between 11,000 and 30,000 years ago, the ancestral populationcould have been in the Americas. Conversely, if the North American population split froman ancestral population either less than 500 years ago, or between 11,000 and 30,000 yearsago, then the ancestral population could have been in Eurasia. The analysis revealed thatthe North American population cluster is relatively young and was clearly derived froman ancestral population less than 500 years ago (i.e., ca. 300 generations ago). The EasternEuropean population cluster was estimated to have derived from the ancestral populationin the order of several thousand years ago (i.e., three to seven thousand generations),making a New World ancestral population impossible. The data, therefore, demonstratethat D. septosporum is an Old World species. This is supported by the greater genetic andgenotypic diversity of the Eastern European population cluster compared to the NorthAmerican population cluster. Furthermore, all North American isolates formed a distinct,well-supported population cluster in STRUCTURE and DAPC analyses with no furthersubstructuring of the population even at very high values of K (up to K = 15). Conversely,J. Fungi 2021, 7, 111 22 of 28the Eastern European population cluster displayed clear substructuring and high geneticdiversity typical of native or long-established populations, whereas no substructuringwith lower diversity suggests a derived, or more recently established, population. Onlywhen a hierarchical STRUCTURE analysis was conducted, running the North Americanisolates independently, were distinct clusters delimited, which resembled those foundby [25] (Figure S4). This suggests the structuring of the North American populations ismuch weaker than that of the European populations.Nonetheless, the use of a larger number of markers, e.g., SNPs from genotyping-by-sequencing or whole-genome sequencing, and isolates from as yet unsampled regionsmay reveal new relationships and links. Particular progress could be made elucidating thetiming of divergence events. Such progress would help reconcile the findings of Capronet al. (2020), who found Canadian D. septosporum populations diverged from each otherbetween 31 and 7 thousand years ago, with the findings of the present study where theNorth American population diverged more recently.The distinct Western European population cluster dominates the range from north-western France through the British Isles and Ireland to western Norway. Even though thisis a European population, it is clearly differentiated from the Eastern European populationcluster, which occurs throughout the rest of Europe and Asia. Modeling of historicalscenarios showed that the Western European population arose from the Eastern Europeanpopulation cluster and had no connection with the North American population cluster.Such a connection between Western Europe (i.e., France and Britain) and its ex-coloniesin North America would not have been unexpected considering the close trade linksand movement of potentially infected material. However, the results using microsatel-lite markers indicate the Western European cluster derived relatively recently, less than100 generations ago, and if we consider one generation to occur in one year, then this isafter the first evidence of DNB in North America.Regardless of its recent origin, the Western European population cluster has beenspread to much of the Southern Hemisphere where it has caused, and in some countriescontinues to cause, severe damage to pine plantations [1,9]. Pine species are native to theNorthern Hemisphere, with only P. merkusii’s native range just crossing the equator [65],and have been introduced to the Southern Hemisphere primarily for timber production [66].Introductions often followed colonization routes, and afforestation programs began inmany areas from the 1870s onward (e.g., Australia, New Zealand, South Africa) [67–69]. In-troduction of pines was accompanied by or followed by the introduction of D. septosporum,and it is noteworthy that many of the Southern Hemisphere countries containing the West-ern European population cluster were old British colonies (i.e., Australia, New Zealand,South Africa, Kenya), demonstrating a clear route of introduction through increased tradevolumes with the colonial power and Western Europe in general. Nonetheless, SouthAmerican countries that were never British colonies also harbor the Western Europeanpopulation cluster (e.g., Chile, Ecuador and Colombia). Additionally, the Central Europeanand Northeastern European subclusters have been introduced to South Africa Tzaneen,showing that not only the Western European population has been introduced to the South-ern Hemisphere. It is clear that there have been at least two to three separate introductionsof D. septosporum to South Africa as the Hogsback population group originates from theWestern European cluster while the Tzaneen population group originates from admixturebetween the Central European and Northeastern European subclusters.Although the causal agent of DNB was first clearly described from European Russia in1911 [13], the disease only achieved notoriety in the 1950s and 1960s due to its severe impacton pine plantations in the Southern Hemisphere, particularly East Africa, New Zealandand Chile [1,8]. Sporadic reports of the disease occurred in Europe until the 1980s, yetwithout it causing significant damage [1]. It was not until the 1990s that the disease causedsevere damage in Europe—in France and Britain [2,70]. The areas where DNB has causedthe most damage in both the Southern and Northern Hemispheres are plantations of oftenexotic species, yet these areas also share the same population cluster of D. septosporum—J. Fungi 2021, 7, 111 23 of 28the Western European population cluster. This population is well-differentiated, basedon microsatellite markers, from the Eastern European population from which it derived,and differences doubtless occur throughout the genome. It is possible, given the severedamage this population inflicts on its host, that the Western European population clusterhas higher virulence compared to the other population clusters. The Western Europeanpopulation cluster is found on 37 host taxa (species, subspecies and varieties) while theEastern European population cluster has only been found on 23 host taxa and the NorthAmerican population cluster on only P. contorta and its varieties. This increased hostrange is also suggestive of increased virulence and, coupled with the era of colonizationand increased global trade from Western Europe to the Southern Hemisphere, may havecontributed to its global success. Variation in virulence levels of the populations should beinvestigated by rigorous artificial inoculation tests which would also help define the riskfurther introductions and spread of this population would pose to pine forests worldwide.At the edge of their distribution ranges, species are known to vary from their coredistribution range due to limited gene flow with the core population and increased geneticdrift within the edge population, resource limitation, new interspecific interactions, en-vironmental pressures, etc. [71–73]. Populations that colonize new habitats beyond theiroriginal distribution limits are likely to suffer strong selection pressure due to the intensifi-cation of these factors and exposure to new pressures and species competitions [74]. Thosepopulations that survive in new or edge habitats are likely to become locally adapted, withstudies of mollusks to mammals and plants showing populations adapted to their localenvironments [74–76], which in some cases are extremely, and unexpectedly, successfulwhen transplanted outside of their local environment [75].Dothistroma septosporum populations are by no means exempt from these pressuresand tendencies. Separate populations in new areas are exposed to new host taxa, differentfoliar microbial communities and competitors, and disparate environmental conditions.This sudden exposure to novel conditions, along with the associated genetic bottleneck,places the fungus under intense pressure to adapt rapidly. A shift to more sexual reproduc-tion in such challenging conditions is likely as sexual reproduction in many ascomycetefungi is associated with adverse conditions [77]. The generation of new genotypes bysexual recombination facilitates survival and adaptation, particularly advantageous in newor changing conditions [78,79]. We hypothesize that the novel host and environmentalconditions faced by D. septosporum in North America drove the population towards anincreased rate of sexual reproduction, as evidenced by the equal proportion of both mating-types, a situation not seen in the Western or Eastern European population clusters. Thisincreased rate of sexual recombination hastened local adaptation and divergence from theEurasian populations.This local adaptation may reflect a possible host preference of the North Americanpopulation group to lodgepole pine, the sole host on which this population cluster wasfound and a species native to North America. Although the majority of isolates from NorthAmerica were from lodgepole pine, the two non-lodgepole pine isolates originating fromNorth America did not cluster with the North American population cluster. Additionally,there is strong evidence that the North American population cluster has subsequently beenreintroduced into Europe, to Scotland, where it occurs almost exclusively on lodgepolepine [23,80,81]. Such specialization suggests the population cluster could be distinct enoughto form a geographical and physiological race, a term used by [80,81] when working withisolates of this population. Once again, artificial inoculation tests, based on the populationspresented here, would help clarify not only the virulence but also the degree of hostspecialization of the major D. septosporum global populations.While the affinity of the North American population group for lodgepole pine is clear,many of the other population clusters and subclusters present a preponderance for certainhost taxa. For example, the Western European population cluster is found predominantlyon P. nigra subsp. laricio, P. sylvestris and P. contorta, the Central European subcluster mainlyon P. nigra and P. mugo, the Northeastern European subcluster on P. sylvestris and P. nigraJ. Fungi 2021, 7, 111 24 of 28and the Turkish subcluster on P. brutia. This may indicate some degree of host adaptation,but it is much more likely to be a result of the geographical distribution of the subclustersand the hosts that are grown in these areas, either native species or the non-native speciesoften used in commercial forestry plantations where DNB thrives. Therefore, the presenceof D. septosporum on particular hosts is typically more influenced by the geographicaldistribution of the particular D. septosporum populations rather than the host specificationof the pathogen itself. To what extent specific populations exhibit local adaptation, andwhether this is more influenced by host species or environmental conditions (e.g., climate)will require extensive testing under controlled conditions.The Eastern European population cluster dominates the D. septosporum populationin much of Europe and Asia from northern Norway and Sweden throughout Europe toTurkey and across to Bhutan and the Russian Far East in Asia. This population clusterhas the highest genotypic and genetic diversity of all the population clusters and exhibitssignificant substructuring. The most prominent subcluster is that of Central Europe,which dominates the Czech Republic, Slovakia, southern Poland, Hungary, Switzerland,Eastern Austria, Slovenia, and Romania. Further subclustering reveals Turkey as a distinctsubcluster. Historical scenarios show the Central European subcluster to be derived fromthe Northeastern European subcluster, although with possible genetic contributions fromthe Turkish subcluster or an unsampled population. The Central European subcluster wasformed relatively recently, ca. 70–190 generations ago, and underwent a weak bottleneckevent. In contrast, the Turkish subcluster, clearly derived from the Northeastern Europeansubcluster, was formed long ago, ca. 400–1000 generations ago, and underwent a strongbottleneck event. This weak bottleneck and proximity to Northeastern Europe suggest theCentral European subcluster could be derived from the natural spread of the pathogen.The result that all European population clusters and subclusters (Western Europeancluster, Central European subcluster, Turkish subcluster) are derived from the NortheasternEuropean subcluster, along with this group’s high genetic diversity strongly suggests theregion is part of the center of origin of D. septosporum as proposed by [20]. However, thesame STRUCTURE population cluster is present in southern, particularly south-eastern,Europe. These population groups were not used for ABC modeling due to the lowernumbers of samples from this region. Therefore, this region could encompass the nativerange of the pathogen as well. This same population cluster is present in Bhutan andthe Russian Far East and, although no samples were obtained from much of central Asia,this population could occur throughout the boreal forests of Asia and Europe. The regionpossesses similar environmental conditions and abundant host species, particularly P.sylvestris, whose native range extends across the entire region. Dothistroma septosporumproduces only mild symptoms on P. sylvestris in the region [1,20,82], a situation typicalof co-evolved hosts and pathogens [83,84], further supporting the pathogen’s indigenousstatus in the region.Supplementary Materials: The following are available online at https://www.mdpi.com/2309-608X/7/2/111/s1, Figure S1: Delta K plot of the STRUCTURE analysis, showing K = 3 as the bestclustering of individuals, Figure S2: Bayesian clustering of D. septosporum multilocus haplotypesinferred using the program STRUCTURE at K = 6, K = 7 and K = 8 (a) and K = 9, K = 10 and K = 11 (b).Each multilocus haplotype is represented by a vertical line partitioned into colored sections thatrepresent the isolate’s estimated membership fractions in each cluster. Black lines separate isolatesfrom different population groups, Figure S3: Plot of the Bayesian information criterion (BIC) vs. thenumber of clusters from k-means clustering showing a break at K = 3, indicating three clusters bestdescribe the dataset for DAPC analysis, Figure S4: Bayesian clustering of only the North AmericanD. septosporum multilocus haplotypes inferred using the program STRUCTURE at K = 2, K = 3 andK = 4. Each multilocus haplotype is represented by a vertical line partitioned into colored sectionsthat represent the isolate’s estimated membership fractions in each cluster. Black lines separateisolates from different population groups. The inset shows a graph of delta K, Figure S5: Graphicalrepresentation of the winning scenarios of demographic history for each of the eight DIYABC analyses.Abbreviations used on time scales refer to time parameters used during simulations, Figure S6: ModelJ. Fungi 2021, 7, 111 25 of 28checking of the winning DIYABC scenarios for each of the eight conducted analyses, Table S1: Detailsof D. septosporum isolates used in this study including the host, geographic location, population group,microsatellite allele calls and mating-types, Table S2: Scenario descriptions, posterior probabilitieswith 95% credibility intervals of each scenario, and posterior predictive error of models, TableS3: Pairwise population FST (below the diagonal) and Nm, the estimated number of migrantsper generation (above the diagonal) for the D. septosporum population groups, Table S4: Posteriordistributions of parameters for winning scenarios of DIYABC analyses 1 to 8.Author Contributions: Conceptualization, M.S.M., A.K. (Adam Konecˇný), R.D., P.B. and I.B.; formalanalysis, M.S.M. and A.K. (Adam Konecˇný); writing—original draft preparation, M.S.M. and A.K.(Adam Konecˇný); writing—review and editing, M.S.M., A.K. (Adam Konecˇný), R.D., P.B., I.B., K.A.(Kalev Adamson), A.L.-B., M.T., Z.J., K.A. (Katarína Adamcˇíková), E.O., V.Q., B.P., D.L.M., K.D., M.G.,S.S., A.K. (Audrius Kacˇergius), L.G., J.K.O., M.M., F.O., T.H., Á.H., S.M., H.S., M.V., R.H., R.C.H. Allauthors have read and agreed to the published version of the manuscript.Funding: This research was funded by the Forestry Commission, UK, by the Estonian ResearchCouncil grant PSG136, and by the European Regional Development Fund, Project PhytophthoraResearch Centre Reg. No. CZ.02.1.01/0.0/0.0/15_003/0000453. Computational resources weresupplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the programProjects of Large Research, Development and Innovations Infrastructures.Institutional Review Board Statement: Not applicable.Data Availability Statement: The data presented in this study are available in Table S1.Acknowledgments: We would like to thank Angela Dale, Hanna Millberg, Kathy Lewis, Karli Ligi,Paolo Capretti, Nenad Kecˇa †, Tadeusz Kowalski, and Rasa Priedite for help with isolates. Computa-tional resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) providedwithin the program Projects of Large Research, Development and Innovations Infrastructures.Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the designof the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, orin the decision to publish the results.References1. 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Mycosphaerella pini
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global spread
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invasive pathogen
Worldwide Genetic Structure Elucidates the Eurasian Origin and Invasion Pathways of Dothistroma septosporum, Causal Agent of Dothistroma Needle Blight
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