Insights into the pathways of spread and potential origins of Dothistroma septosporum in Britain

Fungal Ecology 26 (2017) 85e98

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Insights into the pathways of spread and potential origins of Dothistroma septosporum in Britain M.S. Mullett a, b, *, A.V. Brown a, S. Fraser c, 1, R. Baden a, K.V. Tubby a a

Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berks, SL5 7PY, UK c University of Aberdeen, Institute of Biological and Environmental Sciences, St. Machar Drive, Aberdeen, AB24 3UU, UK b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 August 2016 Received in revised form 6 December 2016 Accepted 8 January 2017

Dothistroma needle blight (DNB) is a disease caused by two fungi, Dothistroma septosporum and Dothistroma pini, that has resulted in significant damage to pine forests worldwide. Analysis of 1194 British Dothistroma isolates revealed that only D. septosporum occurred in Britain; D. pini was not detected. The genetic diversity, population structure, and reproductive mode of D. septosporum in Britain were investigated using species-specific mating type markers and eleven microsatellite markers, revealing 382 multilocus haplotypes. Comparison of clustering methods (STRUCTURE, BAPS, DAPC) as well as spatial principal component analysis (sPCA) showed some differences between the methods but similar groupings. A clear north-south cline was found with attributes consistent with a native fungus. Other groups were most probably introduced, with one nearly exclusive lodgepole pine group exhibiting links with Canada. Evidence for the movement of specific multilocus haplotypes via nursery stock as well as across borders is provided and the implications discussed. Crown Copyright © 2017 Published by Elsevier Ltd. All rights reserved.

Corresponding Editor: Luke Barrett Keywords: Fungi Mycosphaerella pini Microsatellites SSR Forestry Sexual recombination Dothistroma pini Populations Pine

1. Introduction Dothistroma needle blight (DNB) is a severe foliar disease of pine caused by two species of Dothistroma: Dothistroma septosporum and Dothistroma pini. The disease has a worldwide distribution and affects at least 109 Pinaceae taxa, predominantly pines (Drenkhan et al., 2016). Although originally described from Russia in 1911 (Doroguine, 1911) the disease had initially only been important in the exotic pine plantations of the Southern Hemisphere during the 1950s and 60s (Gibson, 1972; Drenkhan et al., 2016). Since the 1990s however, DNB has caused severe outbreaks in the Northern Hemisphere in Canada, France and Britain, among others (Bradshaw, 2004; Drenkhan et al., 2016).

* Corresponding author. Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK. E-mail address: [email protected] (M.S. Mullett). 1 Present address: Department of Plant Science, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, South Africa. http://dx.doi.org/10.1016/j.funeco.2017.01.002 1754-5048/Crown Copyright © 2017 Published by Elsevier Ltd. All rights reserved.

It is unknown whether these recent outbreaks are due to changes in pathogen populations, forest management, distribution of susceptible host species, global climate change, or a combination of these and other factors (Woods et al., 2005; Drenkhan et al., 2016). Infection occurs primarily in the spring and early summer via rain-splash dispersed conidia which form in asexually produced acervuli on infected needles (Gibson, 1972; Karad
zic, 1989). Initial symptoms (i.e. chlorotic bands) can appear by late autumn but the characteristic red bands containing erumpent black fruit bodies typically develop the following spring (Brown and Webber, 2008). Affected needles are prematurely cast, often in the late summer or autumn of the year after infection (Karad
zi c, 1989). This repeated premature defoliation results in reduced growth and, in severe cases, tree death (Brown and Webber, 2008). The sexual stage of D. septosporum (previously known as the teleomorph Mycosphaerella pini) is rare and has not been reported in Britain. However, it has been found in several countries where outbreaks of the disease have also been severe (e.g. Canada and Serbia (Funk and Parker, 1966; Karad
zi c, 1989)) and in some nearby European

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countries (e.g. France, Germany, Denmark (Munk, 1957; Morelet, 1967; Butin and Richter, 1983)). Unlike conidia the sexually derived ascospores are reported to be wind dispersed and as the sexual stage is found on older, often senesced, needles it has been suggested it is saprotrophic (Butin, 1985; Karad
zi c, 2004). In Britain DNB was first recorded in the early 1950s in a nursery in Wareham, Dorset (Murray and Batko, 1962). The first recorded forest stand outbreak was in south Wales in 1958 and remained the only wider environment outbreak until another finding in south Wales in 1989 (Brown and Webber, 2008). Isolated records of the pathogen were also documented during forest fungal forays in the far north of Scotland in the mid-1980s, but these were not associated with disease outbreaks (British Mycological Society, 2014). From the late 1990s the number of reports of the disease increased dramatically (Brown and Webber, 2008). To establish the extent and severity of the disease large scale surveys of Corsican pine (Pinus nigra subsp. laricio) stands under 30 y old were carried out across Britain in 2006, and of this species and lodgepole pine (Pinus contorta) in Scotland in 2007. The surveys revealed that the disease was widespread across England and Wales, predominantly on Corsican pine, but less widely distributed across Scotland, where it was found mostly on lodgepole pine (Brown and Webber, 2008). Annual surveys have continued in Scotland since this time (incorporating Scots pine (Pinus sylvestris) from 2010), and in recent years DNB has become both more widespread and severe. Since 2011 the number of reports of the disease on Scots pine has also increased, raising concerns about the threat DNB poses both to native Caledonian pine woods and Scots pine plantations. Corsican pine is a valuable timber species planted widely across England, especially in the south east of England, but its susceptibility to DNB has led to a moratorium on planting this species on the Forestry Commission public forest estate throughout Britain. Nonetheless Corsican pine still makes up 13% of conifer area in England, while lodgepole pine constitutes 10% of conifer area in Scotland (Forestry Commission, 2015). Scots pine is planted widely in both England and Scotland, although less so in Wales and accounts for just under 17% of conifer area in Britain (Forestry Commission, 2015). Altogether the three pine species make up 28% of the conifer area in Britain, covering 364,000 ha (Forestry Commission, 2015), illustrating that the risk DNB poses to the forestry sector is very significant. The sudden emergence of devastating forest pathogens is usually due to the introduction of non-native agents. Examples include Ophiostoma ulmi and Ophiostoma novo-ulmi which cause Dutch elm disease, Hymenoscyphus fraxineus which causes ash dieback, Phytophthora ramorum the causal agent of sudden oak and larch death, and Cryphonectria parasitica the cause of chestnut blight (Brasier, 2008; Brasier and Webber, 2010; Gross et al., 2014). Invasion by a non-native pathogen comprises a number of stages: introduction, establishment and spread (Sakai et al., 2001) and multiple introductions can increase the success of a non-native, invasive species (Barrett and Husband, 1990). However, each individual founder event is generally caused by a small number of individuals, which constitutes a population bottleneck where genetic diversity is drastically reduced compared to the original source population (Sakai et al., 2001; Beebee and Rowe, 2004). Thus, a recently established population is likely to be less genetically diverse than the population from which it is derived (Barrett and Kohn, 1991), and consequently non-native populations are likely to be less genetically diverse than native populations. Some species have a rapid rate of population growth but there is often a lag phase between the establishment of an alien species and its spread (Crooks , 1999; Sakai et al., 2001). This lag phase can be attributed and Soule to the inherent nature of population growth and spread, occurrence of environmental factors needed for success of the alien (e.g. host

availability, optimum temperature), and/or genetic factors related to the fitness of the species (e.g. adaptation to a new habitat, evo, 1999; Sakai lution of invasive life-history traits) (Crooks and Soule et al., 2001). In general it has been assumed that D. septosporum is non-native to Britain, with the lag phase for its establishment unknown. Potentially the lag phase could have been the time between the establishment of the pathogen in Wareham in the 1950s and the widespread reports of the disease in the late 1990s, with the Wareham colonisation the only founder event. In this case the British population would be expected to be relatively homogeneous with low genetic diversity. Alternatively, multiple introductions of the pathogen could have occurred, which, given the success of the pathogen in Britain and large volumes of international trade in plant material, would not be surprising. Under such a scenario the overall genetic diversity should be higher but distinct populations would still be apparent. Another possibility is that D. septosporum is native to Britain, and has perhaps co-evolved with Scots pine as the native host. High variation and heritability of susceptibility to DNB suggest Scots pine has been exposed to D. septosporum longer than previously believed (Perry et al., 2016). The abundance of more susceptible exotic hosts coupled with changing climatic conditions may have favoured the spread and increase in intensity of the disease in recent decades. This scenario would present the highest level of genetic diversity with a Britain-wide population and weakly differentiated sub-populations present. The far-ranging extent of the disease in Britain, which could have been facilitated by the spread of wind-borne ascospores of the teleomorph, suggests sexual recombination may be occurring in Britain. This would allow novel combinations of genes to be generated which could then be rapidly and extensively disseminated by subsequent asexual reproduction (McDonald and Linde, 2002). Sexual reproduction in D. septosporum, therefore, has the potential to produce more virulent strains, or strains better suited to the local environment (e.g. climate, host species) and cause further damage to British pine forestry. A further threat to pine forests in Britain comes from the other causal agent of DNB, D. pini. Increasingly, this Dothistroma species is being detected in Europe as the molecular methods which readily differentiate it from D. septosporum become more widely adopted (Drenkhan et al., 2016). Potentially D. pini may be better adapted to some of the prevalent host species or environmental conditions in Britain compared with D. septosporum. The response of the two Dothistroma species to climatic changes is also likely to differ, thus having both species present could increase the pressure on hosts both immediately and in the future. The aim of this study was therefore to: (i) determine if D. pini was present in Britain; (ii) investigate the population structure of D. septosporum in Britain and critically assess a range of population clustering programmes in that process; (iii) investigate the possibility of sexual recombination of D. septosporum in Britain; and (iv) examine possible links to D. septosporum populations from France and Canada. 2. Materials and methods 2.1. Sample collection Samples were primarily selected from material collected during Forestry Commission DNB surveys undertaken between 2005 and 2013. In addition, a number of samples came from private sector surveys and from Forest Research experimental sites. Samples were selected to provide wide geographical coverage of Britain (Table 1) and single spore isolations of the pathogen were made using the methods outlined in Mullett et al. (2015). Samples were primarily

37

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87

from the major British pine species (Corsican, Scots and lodgepole pine), but also included a wide range of minor pine species (see Table 1 and Supplementary Table 1). Generally one to two isolates were obtained from each stand with more samples collected from a few Forest Research experimental sites (Supplementary Table 1). A total of 1194 British isolates were used in the study, their host species and geographical origin are summarized in Table 1. Two hundred and eighty two isolates from Brittany, France from the study of Mullett et al. (2015) and 23 isolates from British Columbia, Canada (10 from Kispiox and 13 from Nass Valley; courtesy of Kathy Lewis, University of Northern British Columbia) were also included in the study. 2.2. Haplotype and mating type determination Isolates were grown in the dark for c. 2 weeks at 20
C on autoclaved cellophane discs (Innovia Films, Wigton, UK) placed on Dothistroma Medium (Bradshaw et al., 2000) to obtain mycelium for DNA extraction. DNA was extracted using a KingFisher Flex magnetic particle processor (Thermo Scientific, MA, USA) using KingFisher Plant DNA Extraction kits (Thermo Scientific). Speciesspecific mating type primers (Groenewald et al., 2007) were used to determine the Dothistroma species and mating type of each isolate as outlined in Mullett et al. (2015). Eleven microsatellite markers developed by Barnes et al. (2008) were used for multilocus haplotyping. Multiplex PCR of the markers (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 was conducted as described by Mullett et al. (2015). Gene diversity was plotted against the number of loci using MULTILOCUS 1.3b (Agapow and Burt, 2001) in order to assess whether scoring more loci would increase gene diversity. Individuals 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 haplotype per population (clone-corrected data set).

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To examine the relationship between genetic and geographic distance (isolation by distance) a Mantel test was carried out in GENALEX 6.5 using Nei's genetic distance (Peakall and Smouse, 2012). A spatial principal component analysis (sPCA) was conducted using the adegenet package (Jombart, 2008) implemented in R (R Development Core Team, 2013). sPCA is a modification of PCA which relies on no specific population models or assignment of individuals to discrete subpopulations but rather takes into account both genetic variance between individuals and their spatial autocorrelation. A minimum distance neighbouring graph was chosen as suggested by Jombart et al. (2008). Significance of the spatial principal components was tested by the global and local Monte Carlo tests of Jombart et al. (2008) using 1000 permutations. The first two positive eigenvalue components of the sPCA were plotted on a geographic map to examine the spatial genetic structure.

Total

2.4. Population structure

Wales

Cowal & Trossachs Dumfries & Borders Galloway Inverness, Ross & Skye Lochaber Moray & Aberdeenshire North Highland Scottish Lowlands Tay West Argyll

Scotland

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e e e e e e Central England East England North England South England West England Yorkshire

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2.3. Isolation by distance and sPCA

England

Cedrus P. P. P. P. P. P. P. P. P. P. P. P. atlantica arizonica attenuata brutia contorta contorta coulteri elliottii gerardiana halepensis jeffreyi muricata nigra glauca var. murrayana

Table 1 Summary table of the number of British D. septosporum isolates by host species, country and Forest District.

P. nigra ssp. pallasiana

P. nigra ssp. salzmannii

P. P. P. P. P. P. Picea Picea Pseudotsuga Total nigra ponderosa radiata sabineana sylvestris torreyana abies sitchensis menziesii ssp. nigra

M.S. Mullett et al. / Fungal Ecology 26 (2017) 85e98

Population structure of the clone-corrected dataset was assessed using three different methods. Firstly, discriminant analysis of principal components (DAPC) was conducted in the R package adegenet (Jombart, 2008; Jombart et al., 2010). DAPC is a multivariate technique that makes no

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assumptions regarding the population model or data structure (Jombart et al., 2010). It is particularly suited to identifying clusters (K) of genetically related individuals (Jombart et al., 2010). DAPC uses a sequential K-means procedure followed by assessment of the Bayesian information criterion (BIC) to assess the optimal number of clusters. Cross-validation was used to determine the optimal number of principal components retained in the analysis (Jombart and Collins, 2015). Secondly, the Bayesian clustering method implemented in BAPS 6.0 (Corander et al., 2008) was used to cluster individuals. BAPS identifies clusters based on allele frequencies rather than partitioning isolates into clusters in Hardy-Weinberg equilibrium. The programme was run 4 times for each K ¼ 1e40, with a subset of K ¼ 17e20 run 20 times each. Thirdly, STRUCTURE 2.3.4 (Falush et al., 2003), one of the most popular population clustering programmes, was used to group isolates. STRUCTURE implements a Bayesian, model-based, clustering algorithm to assign individuals to a specified number of clusters (K), minimizing both linkage disequilibrium and deviation from Hardy-Weinberg equilibrium within the clusters (Pritchard et al., 2000). To estimate the optimal number of clusters, 20 independent runs of K ¼ 1e40 were carried out in STRUCTURE using no priors (i.e. no information on geographical location or host was provided). Each run had a burnin of 100,000 iterations followed by 500,000 data-collecting iterations, using a model of correlated allele frequencies and with admixture among populations allowed. CLUMPAK (Kopelman et al., 2015) was used to determine the optimal value of K using the DK method of Evanno et al. (2005) and the ln(Pr(XjK)) method suggested by Pritchard et al. (2000, 2010). CLUMPAK was used to align all optimum K STRUCTURE runs to the permutation with the highest H-value. The DISTRUCT programme (Rosenberg, 2004) was used to visualise the CLUMPP output. To explore possible links with the Brittany, France population (Mullett et al., 2015) and the Canadian population, data for these isolates were included in separate population analyses. DAPC, BAPS and STRUCTURE analyses were conducted as described above for the British data, the only difference being that two BAPS subsets were run: K ¼ 21 was run 20 times following a subset of K ¼ 16e24 run 9 times each. 2.5. Genetic diversity and differentiation Genotypic diversity was evaluated using four indices calculated in the R package poppr (Kamvar et al., 2014) and vegan (Oksanen et al., 2013): (i) Shannon-Wiener index, H (Shannon and Weaver, 1949; Grünwald et al., 2003); (ii) Stoddart and Taylor's index, G (Stoddart and Taylor, 1988); (iii) Genotypic richness, eMLG, the expected number of multilocus genotypes (eMLG) calculated by rarefaction to the smallest sample size; and (iv) Genotypic richness, E5, an estimation of evenness which is equal to 1 when all genotypes are equally represented and reduces toward 0 as a single genotype becomes more dominant (Grünwald et al., 2003). The clonal fraction (CF) is the proportion of isolates derived from clones, or asexual reproduction, and was calculated according to the method of Zhan et al. (2003). The clone-corrected dataset was used to calculate Nei's gene diversity, Hexp (Nei, 1978), in poppr, and allelic richness and private allele richness in ADZE (Szpiech et al., 2008). 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) were computed using a rarefaction procedure to adjust AR and PAR to a specific sample size, allowing comparisons between populations with different sample sizes. Calculations were standardised to a uniform size of 4 multilocus haplotypes corresponding to the size of the smallest group.

Pairwise FST values, used as a measure of population differentiation, and Nm, the predicted number of migrants between populations, were calculated in Arlequin 3.5 (Excoffier and Lischer, 2010). Hierarchical analysis of molecular variance (AMOVA) was carried out on the clone-corrected data set to test various hypotheses of population differentiation using GenAlEx 6.5 (Peakall and Smouse, 2012). The isolates were grouped in three ways: by host species; by Forest District (see Supplementary Fig. 1) the isolates originated from; and by the planting year of the forest stand the isolate came from. Host species AMOVA was restricted to isolates from the main hosts (Corsican pine, lodgepole pine, Scots pine) as other host species had limited numbers of individuals. AMOVA was also used to test for differentiation between isolates grouped into the same STRUCTURE clusters but originating from different countries, i.e. the Canadian isolates and the British ‘lodgepole pine group’. 2.6. Mating type and sexual recombination An exact binomial test, using two tailed p-values, was used to determine whether clusters differed significantly from the null hypothesis of a 1:1 ratio of mating type idiomorphs (http://www. biostathandbook.com/exactgof.html (McDonald, 2014)). An equal proportion of mating type idiomorphs indicates that sexual reproduction could be frequent enough to maintain equilibrium. The index of association (IA) together with its associated measure (rd ) were calculated in poppr (Kamvar et al., 2014). The IA is a measure of multilocus linkage disequilibrium and rd is a modification of it that removes dependency on the number of loci used, thus facilitating comparisons between studies (Brown et al., 1980; Agapow and Burt, 2001). Clonal populations are expected to have significant disequilibrium due to linkage among loci while sexual populations are expected to have linkage equilibrium due to no linkage among loci. The IA and rd from the observed data were 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 to reduce the chance of rejecting the null hypothesis of random mating that a smaller clone-corrected data set might carry (Milgroom, 1996). 3. Results 3.1. British isolates The British samples yielded a total of 1194 single spore isolates. Analysis of isolates using the species-specific mating type primer set showed that D. pini was not found and all were confirmed as D. septosporum. Of the total 1194 British isolates analysed, 382 unique multilocus haplotypes were identified based on the 11 microsatellite loci. All loci were polymorphic with a total of 135 different alleles detected. The number of alleles at each locus ranged from four at Doth_O to 33 at Doth_L, with an average value of 12.3 (±8.3 SD). A plot of gene diversity against number of loci showed that seven markers accounted for 97% of the variation, while ten markers accounted for 98% (data not shown), therefore the 11 markers used were deemed sufficient for population genetic analyses. 3.2. Isolation by distance and sPCA The Mantel test showed significant correlation between genetic and geographic distance (R2 ¼ 0.101, p  0.001) and therefore spatial patterns were further investigated using sPCA.

M.S. Mullett et al. / Fungal Ecology 26 (2017) 85e98

The sPCA revealed clear global structure (global test, max(t) ¼ 0.0086; p ¼ 0.006) and no local structure (local test, max(t) ¼ 0.0072; p ¼ 0.293) (Fig. 1 inset). Local structure refers to strong differences between neighbouring individuals while global structure relates to clines or patches of relatedness among individuals. The first spatial principal component (sPC) revealed a clear south (light yellowish-green) to north (darker green) cline, while the second sPC revealed a highly distinct group of individuals in Scotland (Fig. 1 brown points). Other slightly less distinct groups were also seen in both north Scotland (bright green points) and the south of England (yellow, olive brownish points, Fig. 1). 3.3. British population structure Sequential K-means clustering and the BIC indicated an optimum of 12 clusters in the British dataset and the DAPC shows these formed three groups of clusters (Fig. 2). BAPS indicated that 17 clusters best summarized the data with a probability of >0.999. The STRUCTURE analysis revealed three clusters when the DK method of Evanno et al. (2005) was used to choose the best K. However, using the ln(Pr(XjK)) suggested by Pritchard et al. (2000, 2010) nine clusters best explained the data (see Supplementary Fig. 2). While the DAPC, BAPS and STRUCTURE approaches to clustering individuals varied in the optimal number of K suggested, there was nevertheless considerable agreement across the methods for a number of the clusters, outlined below. 3.4. The ‘lodgepole pine group’ (clusters: DAPC 1; BAPS 1; STRUCTURE 1) This cluster of isolates was distinguished by all cluster analysis methods (DAPC, BAPS, STRUCTURE) and is the most clearly distinct

89

cluster in the sPCA analysis (Fig. 1 as brown points). The group was found almost exclusively on lodgepole pine with only six isolates (n ¼ 102) originating from another host (Scots pine), and these were all from either mixed Scots and lodgepole pine stands or Scots pine stands adjacent to lodgepole pine. It is the most highly clonal cluster (CF ¼ 0.96, Table 2), consisting of only four different MLHs out of 102 isolates, yet occurred over a relatively large area but only in Scotland (Fig. 3A). All measures of genotypic diversity were low (Table 2), and the lowest of all clusters. Gene diversity was low (Hexp ¼ 0.303) however private allele richness was the highest of all the clusters (PAR ¼ 0.813), highlighting its uniqueness. Values of FST between this group and all other clusters were high (FST  0.5) (Supplementary Table 2). Linkage disequilibrium was observed suggesting sexual recombination was not occurring in this cluster, only the exact test on the clone-corrected mating type ratio suggested mating could occur (Table 3). 3.5. The ‘northern Scottish group’ (clusters: DAPC 2; BAPS 2; STRUCTURE 2) A second clear group of isolates occurred predominantly towards the north of Scotland and is visible in the sPCA (Fig. 1, bright green points). All clustering analyses defined this group with highly similar clusters of isolates delimited in the analyses. This group consisted of BAPS cluster 2 and STRUCTURE cluster 2 (95% of BAPS cluster 2 isolates were included in STRUCTURE cluster 2). These two clusters were contained within DAPC cluster 2 which was a slightly larger cluster including additional isolates (94% of STRUCTURE cluster 2 isolates were included in DAPC cluster 2). Scots pine was the dominant host for this group (>60% of isolates from Scots pine). This group of isolates had moderate levels of genotypic diversity (Table 2), and high levels of clonality (CF ¼ 0.777 to 0.821; DAPC and BAPS clusters, respectively), the second highest after the ‘lodgepole pine group’. The group also had the lowest levels of gene diversity (Hexp ¼ 0.187 to 0.259; BAPS and DAPC clusters, respectively) and low levels of private allele richness (PAR ¼ 0.044 to 0.07 BAPS and DAPC/STRUCTURE clusters, respectively). Values of FST between this group and all other clusters were also high (generally FST  0.4, Supplementary Table 2). Although mating types were highly skewed to MAT 1-2 in both the non-clone-corrected and clone-corrected datasets the IA and rd on the clone-corrected dataset indicate that sexual recombination could be occurring in this group (Table 3). Only six isolates from this group (of the larger DAPC cluster of 236 isolates) occurred outside of Scotland in four locations (Fig. 3B). Interestingly this group also contained an isolate from Cedrus atlantica var. glauca, a recently reported non-pine host growing in an arboretum in northern Scotland (Mullett and Fraser, 2016), and the sole known representative of ITS haplotype Ds_HAP.2 (Barnes et al., 2016). 3.6. The ‘southern group’ (clusters: DAPC 3, 4, 5; BAPS 3, 4, 5, 6; STRUCTURE 3, 4)

Fig. 1. A map of Britain and the location of D. septosporum isolates used in the study. Colours of the points refer to the lagged scores of individuals based on the first two spatial principal components of the sPCA as shown in the colour legend. A gradual cline from south (light yellowish-green) to north (darker green) is apparent, as are distinct groups (e.g. brown points in Scotland). The inset shows a bar chart of eigenvalues of the sPCA.

A third group of overlapping clusters from the analyses was also evident, most clearly seen as yellowish to olive brownish points on the sPCA (Fig. 1). The group is mainly found on Corsican pine (generally  80% of isolates from Corsican pine) in the south of England, yet individuals occur across Britain to a limited extent (Fig. 3C). It is a less clearly delimited group than the previous two and consists of a number of overlapping clusters in each of the analysis methods; DAPC split the group into three clusters (DAPC 3, 4 and 5), BAPS into four clusters (BAPS 3, 4, 5, and 6), and STRUCTURE into only two clusters (STRUCTURE 3 and 4). FST values between the clusters comprising this group are among the lowest

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Fig. 2. Scatterplot of the discriminant analysis of principal components (DAPC) on British 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. The number and colours represent the 12 groups delineated by the K-means method (see main text for details). Individual multilocus haplotypes are represented by dots and clusters as inertia ellipses. At the top left the eigenvalues of the first 10 axes are represented.

between any clusters (Supplementary Table 2). The DAPC analysis shows the DAPC clusters of this group closely aligned (Fig. 2). The clusters comprising this group have low to mid-range levels of genotypic diversity, a generally high clonal fraction with high private allele richness, the highest after the ‘lodgepole pine group’ (Table 2). MAT 1-2 idiomorphs predominated in this group of clusters, exact tests on mating type ratios and the IA and rd tests have mixed results, suggesting sexual recombination in some clusters and not in others (Table 3). 3.7. The Wareham genotypes Ten isolates consisting of four MLHs, all from Corsican pine, were within 7 km of the original outbreak in the Wareham nurseries in southern England. Two of these MLHs were found only in the Wareham area, whereas the other two were found in the vicinity of some of the locations known to have received Wareham stock produced during the initial 1954 outbreak and in the vicinity of the pre-2000 forest disease outbreak records (Fig. 4), as well as in and around Bedgebury pinetum (southeast England) and East Anglia, a large Corsican pine growing region. The two widespread MLHs (n ¼ 90) were found predominantly on Corsican pine (88% of isolates) but one to two isolates were also found on a range of

minor exotic pine species growing in the pinetum (e.g. Pinus gerardiana, Pinus halepensis, Pinus torreyana). Three of the four MLHs belonged to the ‘southern group’; the fourth belonged to the remaining British clusters (DAPC cluster 10). 3.8. The remaining British clusters (clusters: DAPC 6e12; BAPS 7e17; STRUCTURE 5e9) Agreement of the remaining clusters discerned by the three clustering methods was low (i.e. there was low overlap of the clusters). They occur throughout Britain and do not follow a clear geographic (Fig. 3D) or host species pattern. The individuals from these clusters are well distributed along the cline of the sPCA (green points in Fig. 1). The clusters have a higher genotypic diversity and lower clonal fraction than the other clusters described (Table 2), and mating tests on the clone-corrected datasets support mating and sexual recombination in all but a few of the clusters (Table 3). 3.9. Relationships between French, Canadian and British isolates The British Columbian isolates had many distinctive alleles not found in Britain (21 private alleles) and at three loci (Doth_F, Doth_I, Doth_L) all alleles were private alleles. Inclusion of the

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Table 2 Number of D. septosporum isolates and summary statistics for (a) the British DAPC clusters; (b) the British BAPS clusters; (c) the British STRUCTURE clusters. Cluster

N

MLG

eMLG

SE

H

G

Lambda

E5

Clonal fraction

a) Number D. septosporum isolates and summary statistics for the British DAPC clusters 1 102 4 1.971 0.802 0.165 1.061 0.058 0.341 0.961 2 264 59 16.747 2.242 3.153 13.703 0.927 0.567 0.777 3 125 30 13.346 1.984 2.472 6.339 0.842 0.492 0.760 4 199 49 14.295 2.250 2.665 6.319 0.842 0.398 0.754 5 66 24 14.623 1.811 2.232 3.982 0.749 0.358 0.636 6 33 22 22.000 0.000 2.917 14.918 0.933 0.796 0.333 7 62 25 17.806 1.655 2.907 13.928 0.928 0.747 0.597 8 70 40 23.019 1.940 3.406 22.072 0.955 0.723 0.429 9 60 37 22.679 1.862 3.258 16.216 0.938 0.609 0.383 10 121 45 17.398 2.234 2.806 5.659 0.823 0.300 0.628 11 58 26 17.115 1.768 2.607 6.755 0.852 0.458 0.552 12 34 21 20.529 0.499 2.802 12.565 0.920 0.747 0.382 b) Number of D. septosporum isolates and summary statistics for the British BAPS clusters 1 102 4 1.294 0.510 0.165 1.061 0.058 0.341 0.961 2 224 40 7.033 1.219 2.813 10.559 0.905 0.610 0.821 3 56 6 2.645 0.770 0.858 1.794 0.443 0.585 0.893 4 109 21 4.006 1.204 1.593 2.669 0.625 0.426 0.807 5 69 20 5.350 1.300 2.040 3.964 0.748 0.443 0.710 6 73 43 9.105 0.854 3.563 28.497 0.965 0.803 0.411 7 46 22 8.180 1.058 2.883 14.493 0.931 0.800 0.522 8 89 25 5.047 1.354 1.979 3.132 0.681 0.342 0.719 9 22 13 7.180 1.113 2.287 7.333 0.864 0.716 0.409 10 68 40 8.608 1.061 3.360 18.645 0.946 0.635 0.412 11 27 13 6.506 1.182 2.179 6.231 0.840 0.668 0.519 12 39 15 5.861 1.254 2.067 4.460 0.776 0.502 0.615 13 34 12 6.458 1.101 2.184 6.800 0.853 0.736 0.647 14 104 34 6.280 1.410 2.551 4.957 0.798 0.335 0.673 15 50 30 8.034 1.186 3.002 12.255 0.918 0.589 0.400 16 7 4 4.000 0.000 1.154 2.579 0.612 0.728 0.429 17 75 40 7.823 1.277 3.138 10.797 0.907 0.444 0.467 c) Number of D. septosporum isolates and summary statistics for the British STRUCTURE clusters 1 102 4 3.176 0.765 0.165 1.061 0.058 0.341 0.961 2 250 48 24.579 2.602 2.987 12.362 0.919 0.603 0.808 3 142 29 18.633 2.168 2.058 3.983 0.749 0.437 0.796 4 163 58 33.636 2.794 3.195 10.522 0.905 0.407 0.644 5 115 32 24.817 1.946 2.565 5.708 0.825 0.393 0.722 6 150 53 33.066 2.659 3.071 8.041 0.876 0.343 0.647 7 74 51 51.000 0.000 3.750 33.390 0.970 0.780 0.311 8 114 58 43.043 2.414 3.738 30.364 0.967 0.716 0.491 9 84 49 44.183 1.476 3.372 13.263 0.925 0.436 0.417

French and Canadian isolates in population clustering analyses revealed interesting patterns. DAPC analysis of the combined dataset supported 13 clusters while BAPS supported 21 clusters with a probability of 0.977. The STRUCTURE analysis revealed three clusters when the DK method was used to choose the best K, and 14 clusters when the ln(Pr(XjK)) method was used (see Supplementary Fig. 2), however visual inspection of the STRUCTURE barplots suggested a maximum of six clusters would be realistic as additional clusters did not contain any individuals with high assignment probabilities. In both BAPS and DAPC analyses the Canadian isolates formed a distinct cluster, whereas STRUCTURE analysis grouped the Canadian isolates with the ‘lodgepole pine group’ of isolates from Scotland. This grouping occurred when both the DK method and the ln(Pr(XjK)) method of choosing the optimal number of clusters was used, as well as with the most realistic value of K chosen by visual inspection of the STRUCTURE plots (Fig. 5). However, their membership coefficient to the Canadian cluster was lower than the Canadian isolates. The remaining clusters (2, 6 or 13) contained the remaining British and French isolates. No shared MLHs occurred between Canada and any of the European locations. No uniquely French clusters were formed by any of the clustering methods. All clusters occurring in France also occurred in Britain. However, not all clusters occurring in Britain occurred in France - the ‘lodgepole pine group’ and northern Scotland group

Hexp

AR ± standard error

PAR ± standard error

0.303 0.259 0.421 0.367 0.347 0.364 0.428 0.431 0.432 0.36 0.329 0.397

1.667 1.376 1.763 1.678 1.797 1.633 1.798 1.787 1.806 1.713 1.680 1.808

± ± ± ± ± ± ± ± ± ± ± ±

0.236 0.120 0.201 0.232 0.220 0.193 0.228 0.215 0.203 0.214 0.159 0.243

0.813 0.072 0.258 0.138 0.366 0.088 0.141 0.056 0.101 0.132 0.087 0.105

± ± ± ± ± ± ± ± ± ± ± ±

0.214 0.032 0.109 0.054 0.178 0.040 0.044 0.022 0.029 0.055 0.033 0.071

0.303 0.187 0.188 0.498 0.301 0.374 0.415 0.392 0.434 0.419 0.488 0.293 0.408 0.376 0.383 0.242 0.424

1.667 1.199 1.444 1.980 1.749 1.673 1.660 1.827 1.795 1.719 1.843 1.546 1.790 1.710 1.637 1.444 1.827

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.236 0.066 0.248 0.191 0.187 0.184 0.144 0.181 0.169 0.134 0.195 0.209 0.197 0.133 0.145 0.242 0.165

0.790 0.044 0.242 0.246 0.299 0.234 0.042 0.030 0.142 0.111 0.202 0.065 0.110 0.022 0.015 0.140 0.071

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.217 0.019 0.163 0.122 0.178 0.132 0.032 0.027 0.087 0.059 0.102 0.043 0.045 0.014 0.009 0.137 0.025

0.303 0.206 0.375 0.425 0.457 0.44 0.502 0.448 0.42

1.667 1.261 1.835 1.808 1.898 1.799 2.020 1.847 1.896

± ± ± ± ± ± ± ± ±

0.236 0.087 0.212 0.195 0.208 0.179 0.169 0.159 0.142

0.842 0.071 0.443 0.309 0.156 0.141 0.253 0.070 0.132

± ± ± ± ± ± ± ± ±

0.207 0.028 0.181 0.130 0.045 0.082 0.082 0.027 0.049

were not present in the French data. The STRUCTURE results show clear similarities between the British ‘southern group’ and the French isolates; the British ‘southern group’ was retained largely intact and clustered with the majority of the French isolates in K ¼ 3, and showed the same range of clusters in K ¼ 6 and K ¼ 14 (Fig. 5). No shared MLHs occurred between Scotland and France. However, seven MLHs were shared between France and England, and one MLH occurred in both France and Wales.

3.10. AMOVA groupings and the impacts of host and planting year The hierarchical AMOVA clearly differentiated the Canadian population from the British ‘lodgepole pine group’ with 54% of variation occurring between the two groups (Table 4). The AMOVA also strongly supported grouping the isolates by host species (18% of variation between host species) and by Forest District (7% of variation between Forest Districts). Grouping isolates by the forest stand planting year was also supported as a valid way of grouping isolates. When grouped by the decade of the planting year (i.e. pre-1960, 1960s, 1970s, 1980s, 1990s, and 2000s) significant population differentiation occurred. Grouping into only three groups (pre-1980, 1980e1995, post-1995) allowed all stock produced post mid-1990s (after which reports of the disease increased dramatically) to be kept together. The AMOVA also supported population differentiation here, while explaining

Fig. 3. Maps of the distribution of the main British D. septosporum groups and DAPC clusters: A) the ‘lodgepole pine group’; B) the ‘northern Scottish group’; C) the ‘southern group’; D) the remaining British DAPC clusters.

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Table 3 Mating type ratio and index of association tests for the D. septosporum isolates of (a) the British DAPC clusters; (b) the British BAPS clusters; (c) the British STRUCTURE clusters. Bold p values (i.e. those that are non-significant) indicate random mating is supported by the test. Non-clone-corrected Cluster

MAT 1-1

MAT 1-2

p value

IA

Clone-corrected rd

p value (IA and rd )

MAT 1-1

MAT 1-2

a) Mating type ratio and index of association tests for the DAPC clusters of the British D. septosporum isolates 1 1 101 0.000 2.838 0.581 0.001 1 3 2 21 236 0.000 1.222 0.169 0.001 15 43 3 12 110 0.000 1.473 0.198 0.001 7 20 4 74 122 0.001 1.571 0.195 0.001 20 27 5 15 51 0.000 1.336 0.176 0.001 8 16 6 17 15 0.860 0.339 0.05 0.003 12 9 7 43 19 0.003 0.856 0.103 0.001 15 10 8 21 49 0.001 0.477 0.062 0.001 14 26 9 33 25 0.358 0.643 0.077 0.001 17 18 10 74 44 0.007 1.353 0.168 0.001 16 27 11 17 38 0.006 0.757 0.09 0.001 11 13 12 18 14 0.597 0.565 0.073 0.001 10 9 b) Mating type ratio and index of association tests for the BAPS clusters of the British D. septosporum isolates 1 1 101 0.000 2.838 0.581 0.001 1 3 2 8 209 0.000 0.499 0.072 0.001 6 33 3 0 54 2.000 1.242 0.311 0.001 0 4 4 9 100 0.000 2.917 0.338 0.001 6 15 5 14 55 0.000 1.468 0.198 0.001 8 12 6 29 41 0.188 0.135 0.014 0.049 21 20 7 34 12 0.002 0.495 0.063 0.001 14 8 8 65 22 0.000 2.614 0.315 0.001 13 11 9 2 20 0.000 0.914 0.103 0.001 2 11 10 28 40 0.182 0.244 0.028 0.001 16 24 11 5 22 0.002 1.502 0.189 0.001 5 8 12 10 28 0.005 0.871 0.153 0.001 5 9 13 20 14 0.392 1.349 0.177 0.001 6 6 14 65 37 0.007 1.228 0.135 0.001 13 19 15 30 17 0.079 0.409 0.046 0.001 14 13 16 6 1 0.125 1.624 0.542 0.001 3 1 17 20 51 0.000 1.226 0.141 0.001 13 24 c) Mating type ratio and index of association tests for the STRUCTURE clusters of the British D. septosporum isolates 1 1 101 0.000 2.838 0.581 0.001 1 3 2 11 233 0.000 0.603 0.079 0.001 9 39 3 16 126 0.000 1.924 0.261 0.001 9 20 4 35 124 0.000 1.065 0.116 0.001 25 30 5 91 22 0.000 1.368 0.165 0.001 18 12 6 87 62 0.049 1.113 0.129 0.001 23 30 7 35 34 1.000 0.254 0.028 0.001 22 24 8 48 64 0.156 0.4 0.043 0.001 23 33 9 22 58 0.000 0.667 0.069 0.001 16 30

slightly more of the intra group variation (5% vs 4%) (Table 4).

4. Discussion Examination of 1194 British Dothistroma isolates only revealed the presence of D. septosporum. D. pini was not detected in any samples, and as these represented a wide geographic range and mixture of host species and forest types (plantation forests, native pine woods, and also forest nurseries) it is therefore highly unlikely that D. pini is present in Britain. Although capable of causing significant damage to pine species in other countries (Drenkhan et al., 2016), it is certainly not the Dothistroma species causing the extensive and ongoing DNB damage to pine plantations in Britain. A number of clustering programmes, with differing underlying assumptions, were used to investigate population structure and each revealed a different optimal number of clusters. Such differences between programmes have been reported frequently, both in studies of natural populations (e.g. Rowe and Beebee, 2007; Frantz et al., 2009) and using simulated datasets (Latch et al., 2006; Frantz et al., 2009). Differences in interpretation can be accounted for by differences in the underlying models and assumptions. For instance, DAPC is based on PCA which maximises differences

p value

IA

rd

p value (IA and rd )

0.625 0 0.019 0.382 0.152 0.664 0.424 0.081 1 0.126 0.839 1

2.04 0.147 0.557 0.117 0.283 0.051 0.015 0.028 0.099 0.129 0.017 0.076

0.413 0.019 0.072 0.014 0.036 0.008 0.002 0.004 0.011 0.015 0.002 0.01

0.029 0.136 0.001 0.821 0.047 0.327 0.414 0.495 0.201 0.891 0.484 0.276

0.625 0 0.125 0.078 0.503 1 0.286 0.839 0.022 0.268 0.581 0.424 1.226 0.377 1 0.625 0.099

2.04 0.092 1.442 0.076 0.01 0.014 0.037 0.129 0.08 0.053 0.021 0.126 0.199 0.208 0.051 0.143 0.258

0.413 0.012 0.484 0.008 0.001 0.001 0.005 0.015 0.009 0.006 0.003 0.026 0.025 0.022 0.006 0.049 0.029

0.031 0.677 0.029 0.269 0.481 0.481 0.553 0.128 0.3 0.719 0.446 0.857 0.144 0.05 0.648 0.469 0.009

0.625 0 0.061 0.59 0.362 0.41 0.883 0.229 0.054

2.04 0.115 0.265 0.25 0.015 0.032 0.148 0.117 0.114

0.413 0.014 0.034 0.026 0.002 0.004 0.017 0.013 0.012

0.043 0.225 0.031 0.008 0.412 0.616 0.027 0.055 0.14

between clusters while minimizing differences within clusters; it is not based on any underlying population models (Jombart et al., 2010; Jombart and Collins, 2015). BAPS and STRUCTURE are both Bayesian methods and while STRUCTURE fits individuals into clusters which maximize Hardy-Weinberg and linkage equilibrium, BAPS clusters individuals based on allele frequencies (Frantz et al., 2009; Pritchard et al., 2010). The BAPS analysis generated the highest number of clusters (17), whereas the optimal number of clusters (K) suggested in the STRUCTURE analysis was the lowest of all methods used (three using DK and nine using ln(Pr(XjK))). Although STRUCTURE is the most widely used clustering programme (Puechmaille, 2016), it is questionable whether the underlying assumptions of the STRUCTURE model hold true for D. septosporum, a predominantly clonal, haploid fungus. Additionally, STRUCTURE performance is adversely affected by some issues present in this dataset, including uneven sample size between populations (Puechmaille, 2016), when isolation by distance is present (Frantz et al., 2009; Schwartz and McKelvey, 2009; Pritchard et al., 2010), and when the dataset contains close relatives (Anderson and Dunham, 2008; Pritchard et al., 2010; Rodríguez-Ramilo and Wang, 2012). For example, STRUCTURE's DK method of selecting K failed to identify the highly

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Fig. 4. The distribution in southwest England and south Wales of D. septosporum multilocus haplotypes found in Wareham, the location of the first recorded British DNB outbreak.

distinct ‘lodgepole pine group’ recognised by all other methods, probably because this group only contained four MLHs - a small sample of closely related individuals. Mantel tests and sPCA clearly showed that isolation by distance was significant. Nevertheless, the nine STRUCTURE clusters delineated using the ln(Pr(XjK)) method did closely match the clusters defined by the DAPC and BAPS methods. Overall, comparison of the composition of the clusters showed that many of the same, or similar, isolate groupings were delineated by the various methods. This increased confidence in the groupings and decreased the chances of anomalous results from reliance on a single method or due to chance alone. Identification of a number of clear groups also allowed inferences to be made about the origins and spread of the pathogen in Britain. The most distinctive group was the ‘lodgepole pine group’. Given its high private allele richness and clonality, along with low gene and genotypic diversity and high differentiation from all other clusters, this group is almost certainly an exotic introduction. The low diversity and reduced allelic richness suggests it derives from a founder event and subsequent genetic bottleneck (Dlugosch and Parker, 2008), as seen with many other pathogens introduced into new environments e.g. the poplar rust fungus Melampsora s et al., larici-populina introduced into Iceland and Canada (Barre 2008), Mycosphaerella fijiensis on banana crops in the Latin America-Caribbean area and Africa (Rivas et al., 2004; Robert et al., 2012), and the ash dieback fungus, H. fraxineus, in Europe (Gross et al., 2014). The near exclusivity of this group on lodgepole pine leads to speculation of it having been introduced into Britain along with this non-native conifer species. Lodgepole pine originates from western North America and has been extensively planted in Britain since the

1950s (Lines, 1996). No records suggest live plants were imported, but over 9000 kg of seed was purchased from western North America between 1920 and 1980 with over 64% of this coming from British Columbia (Lines, 1996). Although importation of seed material is generally considered to pose a lower phytosanitary risk, particularly for Dothistroma spp. which have not been detected on seed material, even using highly sensitive qPCR detection methods (Mullett, unpublished data) it is possible that some seed lots could have been contaminated with infected needle material. Previous studies illustrate D. septosporum can survive on such detached needle material for considerable periods of time (e.g. Mullett et al., 2016a). Alternatively the pathogen could have arrived on contaminated machinery or even visitors and associated belongings. The limited findings of the ‘lodgepole pine group’ on other hosts suggest high host specificity, possibly due to it being a separate lineage, coupled with environmental release. Phylogenetic studies would also reveal or rule out the occurrence of cryptic species, another possible explanation of the distinct group. Pathogenicity trials comparing the virulence of members of this group on other host species and comparing behaviour with other D. septosporum populations would help elucidate some of the reasons behind the patterns revealed in this study. The STRUCTURE analysis conducted on the combined British, French and Canadian isolates grouped the ‘lodgepole pine group’ and Canadian isolates together, further suggesting that this group may originate from western North America. However, BAPS and DAPC analyses separated the ‘lodgepole pine group’ from the Canadian isolates, and an AMOVA clearly supported their differentiation. Additionally, their membership coefficients to the Canadian cluster were lower than the Canadian isolates. Whilst the British

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Fig. 5. Bayesian clustering of D. septosporum multilocus haplotypes from Britain, Canada and France inferred using the programme STRUCTURE. A) The optimum number of clusters (K ¼ 3) determined by the DK method; B) the highest probable number of clusters determined by visual inspection of the STRUCTURE barplots; C) the optimum number of clusters (K ¼ 14) determined by the ln(Pr(XjK)) method. Each multilocus haplotype is represented by a vertical line partitioned into coloured sections that represent the isolate's estimated membership fractions in each cluster. Black lines separate isolates from the different groups discussed in the main text.

Table 4 Hierarchical analysis of molecular variance (AMOVA) for groupings of D. septosporum. Source of variation

Degrees of freedom

Est. variance

Percentage of variation

p-value

British lodgepole pine group vs British Columbia, Canada isolates Within groups Total

1 23 24

2.609 2.211 4.820

54% 46% 100%

0.001***

Host species: Corsican pine vs lodgepole pine vs Scots pine Within host species groups Total

2 1109 1111

0.597 2.759 3.356

18% 82% 100%

0.001***

Planting year: pre-1960 vs 1960s vs 1970s vs 1980s vs 1990s vs 2000s Within (the above) groups Within individual planting years Total

5 56 845 906

0.143 0.249 2.839 3.231

4% 8% 88% 100%

0.001*** 0.001*** 0.001***

Planting year: pre-1960 vs 1960e1995 vs post-1995 Within (the above) groups Within individual planting years Total

2 59 845 906

0.152 0.263 2.839 3.254

5% 8% 87% 100%

0.001*** 0.001*** 0.001***

Forest Districts (17 Forest Districts) Within Forest Districts Total

16 455 471

0.222 2.894 3.116

7% 93% 100%

0.001***

‘lodgepole pine group’ does not belong to the same population as the Canadian isolates, the geographic range of the British

Columbian isolates tested in this study (Kispiox and Nass Valley) was small compared to the extensive range of lodgepole pine,

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which stretches from the Yukon and Alaska in the north to Baja California in the south, and from the western coast to southern Colorado (Farjon, 2001). Investigation of additional isolates from across the host range (e.g. Peterson, 1981; Woods et al., 2005; Welsh et al., 2009) might clarify the of origin of the ‘lodgepole pine group’ found in Britain. Alternatively, the ‘lodgepole pine group’ could be the result of hybridisation between American and European Dothistroma isolates. The STRUCTURE plots suggest these isolates are admixed with c. 0.6 assignment probability to the Canadian cluster and c. 0.4 to the European clusters. The grouping of these isolates with the British and French ones by BAPS and DAPC analyses supports this hypothesis. The highly skewed mating type ratios may be due to reduced or selective viability of hybrids. The ‘northern Scottish group’ is unusual in that it had both low gene diversity and low private allele richness. Low private allele richness might result if the group were a sub-population of the larger British D. septosporum population rather than a recent exotic introduction which would have higher private allele richness. A slight genetic bottleneck and limited gene flow resulting from geographic restrictions would explain the low gene diversity and high clonality. However, these propositions remain difficult to verify. What was termed the ‘southern group’ had a more complex substructure and consisted of a number of clusters from various analysis methods. It was a less discrete group that is likely to contain subgroups, but is nonetheless separated from the bulk of the British isolates in the analyses. Five MLHs from this group were also present in the French dataset, along with two MLHs from other clusters, providing strong evidence for some degree of pathogen exchange between the two countries. It is, therefore, unsurprising that the same clusters, in all analysis methods, spanned both France and Britain. Natural spread of D. septosporum is known to occur over distances exceeding 1 km (Mullett et al., 2016b), but as England and France are separated by at least 33 km of host-free open water, human-mediated spread seems more probable than natural dispersal (Butin and Richter, 1983; Jankovský et al., 2004; 
rova  et al., 2006; Barnes et al., 2014). Although regulations Bedna exist to control such pathways (e.g. Forest Reproductive Material (Great Britain) Regulations 2002, implementing European Directive 1999/105/EC), there is increasing evidence of pests and pathogens being transported between and within countries by human activities including the plant trade (Brasier, 2008; Tubby and Webber, 2010; Santini et al., 2013). When the MLHs sampled from around the first observed British outbreak of DNB were analysed in detail they also suggested past transmission via infected nursery stock. The initial findings in 1954 came from two closely located nurseries in southern England (Wareham, Dorset) which continued to harbour sporadic outbreaks of the disease throughout the 1950s and 1960s (Murray and Batko, 1962). Eradication was attempted but some stock (presumed to be uninfected) was sent out to other nurseries and forests, chiefly in southern England and south Wales. Some of this stock was traced and inspected in 1958 and although no infection was found it is likely that DNB was never eradicated from the nurseries and it spread into, or from, the surrounding forest block. The same MLHs currently occurring around Wareham also occur in areas known to have received stock from Wareham at the time of the nursery outbreaks. Following such dissemination on infected plants a lag phase of several decades would be needed to allow the pathogen to build up significant inoculum loads to create noticeable problems. Forest nursery stock is inspected annually in Britain and infected plants are destroyed. However, diagnosing low levels of infection remains challenging even with modern molecular diagnostic techniques (Mullett et al., 2016b). Human-mediated dispersal may also account for the lack of geographical restrictions or pattern in

the remaining British clusters. Production of widely distributed cohorts of infected plants by forest nurseries during certain years would result in broadly similar D. septosporum populations in forest stands planted at similar times across wide geographical areas. Grouping isolates by the forest stand planting decade is statistically supported (Table 4), lending support to this hypothesis. Although anthropogenic transmission of disease has influenced the population structure, it does not fully explain the patterns seen in this study. While certain groups were clearly distinguished by all analytical methods (DAPC, BAPS, STRUCTURE, sPCA), others were less clear, with clustering programmes differing in their composition and allocation of individuals to clusters. As already mentioned, Bayesian methods underperform when isolation by distance is present (Frantz et al., 2009; Schwartz and McKelvey, 2009) as the Mantel test demonstrated was the case for the British dataset. Therefore, the model-free approach of DAPC is preferable in this case, and the results (Fig. 2) indicate a cline of closely related clusters following the interpretation of Jombart et al. (2010). Further examination of spatial relationships using sPCA reveal a clear north to south cline in addition to the three distinct groups already discussed (the ‘lodgepole pine’, ‘northern Scotland’ and ‘southern’ groups). Such a cline is likely to be the reason for the lack of concordance among clustering methods for these isolates and is more common for native or long-established populations than for recent introductions (e.g. the ‘lodgepole pine group’). The higher genotypic diversity, lower clonal fraction and support for sexual recombination and random mating in the clusters of this group (Table 2a) support the possibility of nativeness. A range of origins of Dothistroma have been proposed, including central America (Evans, 1984), the Himalayas (Ivory, 1994), and Canada (Welsh et al., 2009). However, recent population studies have suggested it may be native to certain parts of Europe (Drenkhan et al., 2013; Barnes et al., 2014). The current study suggests the native range extends to Britain and probably France, given the high degree of overlap between British and French clusters and the fact that some of the oldest records of Dothistroma originate from France (Fabre et al., 2012; Barnes et al., 2016). Overall, Britain's D. septosporum populations have a varied history. Some appear native to Britain, whereas evidence indicates others have been introduced and then spread across parts of the country. The overlap of clusters between Britain and Brittany, France and a number of shared MLHs between the regions demonstrates the apparent porosity of Britain's borders. Although it is not possible to say definitively whether this dissemination has occurred via natural or anthropogenic means, the apparent ease by which the pathogen has penetrated this physical and political border is of concern given the existence of a number of other extremely significant pathogens which are currently expanding their range across Europe. These include D. pini, which is found in many regions of France and Europe (Fabre et al., 2012; Drenkhan et al., 2016), the plane wilt pathogen Ceratocystis platani on Platarez-Sierra, 2015), and the pitch pine canker nus spp. (Tubby and Pe pathogen Fusarium circinatum on Pinus and Pseudotsuga menziesii (Wingfield et al., 2008, 2015). Britain's D. septosporum populations are distinct enough to inform opinions on their origins and movement and there is strong support for significant levels of sexual recombination within many of these populations. Recombination between current British populations or between these and newly introduced populations with unique genes would result in novel genotypes, some of which may be more virulent, or better suited to local climatic conditions or particular hosts. Reducing the potential for such adaptive events in the pathogen population would help combat the impact of the disease, if not necessarily reduce it. Therefore, limiting the introduction of new Dothistroma species and populations, while at the

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same time restricting the movement of current populations to new areas, should remain key to containing DNB damage to Britain's pine forests. Acknowledgments This study was financially supported by the Forestry Commission, UK and Imperial College London. The authors are grateful for the advice and support of Helen Hipperson, Irene Barnes, and Simon Archer. Kathy Lewis is thanked for providing the isolates from British Columbia, Canada. The authors also thank the anonymous reviewers whose comments helped improve the manuscript. This work was also partially supported by PROTREE, a project funded jointly by a grant from BBSRC, Defra, ESRC, the Forestry Commission, NERC and the Scottish Government, under the Tree Health and Plant Biosecurity Initiative, grant number BB/L012243/1. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.funeco.2017.01.002. References Agapow, P.-M., Burt, A., 2001. Indices of multilocus linkage disequilibrium. Mol. Ecol. Notes 1, 101e102. Anderson, E.C., Dunham, K.K., 2008. The influence of family groups on inferences made with the program structure. Mol. Ecol. Resour. 8, 1219e1229. http:// dx.doi.org/10.1111/j.1755-0998.2008.02355.x. Barnes, I., Cortinas, M.N., Wingfield, M.J., Wingfield, B.D., 2008. Microsatellite markers for the red band needle blight pathogen, Dothistroma septosporum. Mol. Ecol. Resour. 8, 1026e1029. Barnes, I., Van der Nest, A., Mullett, M.S., Crous, P.W., Drenkhan, R., 2016. Neotypification of Dothistroma septosporum and epitypification of D. pini, causal agents of Dothistroma needle blight of pine. For. Pathol. 46 (5), 407e488. http:// dx.doi.org/10.1111/efp.12304. Barnes, I., Wingfield, M.J., Carbone, I., Kirisits, T., Wingfield, B., 2014. Population structure and diversity of an invasive pine needle pathogen reflects anthropogenic activity. Ecol. Evol. 4, 3642e3661. http://dx.doi.org/10.1002/ece3.1200. s, B., Halkett, F., Dutech, C., Andrieux, A., Pinon, J., Frey, P., 2008. Genetic Barre structure of the poplar rust fungus Melampsora larici-populina: evidence for isolation by distance in Europe and recent founder effects overseas. Infect. Genet. Evol. 8, 577e587. http://dx.doi.org/10.1016/j.meegid.2008.04.005. Barrett, S.C.H., Husband, B.C., 1990. The genetics plant migration and colonization. In: Brown, A.H., Clegg, M.T., Kahler, A.L., Weir, B.S. (Eds.), Plant Population Genetics, Breeding and Genetic Resources. Sinauer, Sunderland, Massachusetts, pp. 254e277. Barrett, S.C.H., Kohn, J.R., 1991. Genetic and evolutionary consequences of small population size in plants: implications for conservation. In: Falk, D.A., Holsinger, K.E. (Eds.), Genetics and Conservation of Rare Plants. Oxford University Press, pp. 3e30. 
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