Ariska Van Der Nest

Michael J Wingfield

Dušan Sadiković

Martin S Mullett

Benoit Marçais

Valentin Queloz
email: valentin.queloz@wsl.ch

Katarina Adamčíková

Kateryna Davydenko

Irene Barnes
email: irene.barnes@fabi.up.ac.za

Hao Zhang

Guillaume J Bilodeau

Population structure and diversity of the needle pathogen Dothistroma pini suggests human-mediated movement in Europe

Keywords: Dothistroma needle blight, Dothistroma pini, microsatellites, mating types, pine needle pathogen, Mycosphaerella pini, red band needle blight

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Dothistroma needle blight (DNB) is recognized as one of the most important diseases of Pinus spp., both in planted and native forests, worldwide. The disease has a long history of having damaged plantations in the Southern Hemisphere dating back to the 1960s [START_REF] Gibson | Dothistroma blight of Pinus radiata[END_REF], but during the course of the last three decades, it has also increased in severity and incidence in the Northern Hemisphere [START_REF] Drenkhan | Recent invasion of foliage fungi of pines (Pinus spp.) to the Northern Baltics[END_REF][START_REF] Welsh | The outbreak history of Dothistroma needle blight: An emerging forest disease in northwestern British columbia, Canada[END_REF][START_REF] Fabre | Is the emergence of Dothistroma needle blight of pine in France caused by the cryptic species Dothistroma pini?[END_REF][START_REF] Boroń | The distribution of Dothistroma septosporum and its mating types in Poland[END_REF][START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF][START_REF] Ghelardini | Dothistroma Needle Blight in protected pine forests in Italy[END_REF]. Dothistroma needle blight has been reported on 113 taxa, of which 99 are in the genus Pinus [START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF][START_REF] Jánošíková-Hečková | The hosts and geographic range of Dothistroma needle blight in Slovakia[END_REF][START_REF] Barnes | Chapter 11 -Dothistroma needle blight[END_REF] and reports of the disease on new hosts and in new geographical regions are increasing [START_REF] Jánošíková-Hečková | The hosts and geographic range of Dothistroma needle blight in Slovakia[END_REF][START_REF] Matsiakh | Dothistroma spp. in western Ukraine and Georgia[END_REF][START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF][START_REF] Ondrušková | Needle blight caused by Dothistroma pini in Slovakia: Distribution, host range and mating types[END_REF][START_REF] Eppo | First report of Dothistroma pini in Germany[END_REF][START_REF] Mesanza | New hosts for Lecanosticta acicola and Dothistroma septosporum in newly established arboreta in Spain[END_REF]. The disease has been reported on Abies, Cedrus, Larix, Picea, and Pseudotsuga [START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF], although in most cases, infection has occurred when high inoculum load of the pathogen was present on Pinus species in close proximity to these hosts [START_REF] Barnes | Chapter 11 -Dothistroma needle blight[END_REF].

For many years, the identity of the causal agents of DNB was confused and strongly debated [START_REF] Barnes | Neotypification of Dothistroma septosporum and epitypification of D. pini, causal agents of Dothistroma needle blight of pine[END_REF]. This was due to a single distinct symptom (red bands on infected needles) and taxonomy reliant on morphological characteristics of the associated pathogen. Almost 110 years after the first description of DNB in France [START_REF] Vuillemin | Les Hypostomacées, nouvelle famille de champignons parasites[END_REF], it was conclusively shown that two distinct species can cause this disease. These include Dothistroma septosporum (Dorogin) M. Morelet and Dothistroma pini Hulbary that are most effectively distinguished based on molecular identification [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF][START_REF] Barnes | Neotypification of Dothistroma septosporum and epitypification of D. pini, causal agents of Dothistroma needle blight of pine[END_REF]. In an attempt to consolidate existing knowledge, an extensive collaboration of pathologists participating in the DIAROD (Determining Invasiveness And Risk Of Dothistroma: DIAROD, COST Action FP1102) project documented, as far as possible, the geographic distribution, hosts and mating type distribution of these two Dothistroma species [START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF].

Dothistroma septosporum has been the most extensively studied of the two DNB pathogens. This is at least in part due to its accidental introduction into various countries of the Southern Hemisphere where it became one of the most important constraints to plantation forestry based on non-native Pinus radiata [START_REF] Gibson | Dothistroma blight of Pinus radiata[END_REF]. Dothistroma septosporum has now been recorded in both the Southern and Northern Hemispheres in 48 countries [START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF][START_REF] Matsiakh | Dothistroma spp. in western Ukraine and Georgia[END_REF][START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF][START_REF] Ghelardini | Dothistroma Needle Blight in protected pine forests in Italy[END_REF] and its population structure and diversity in many of these areas is well understood [START_REF] Drenkhan | Genetic diversity of Dothistroma septosporum in Estonia, Finland and Czech republic[END_REF][START_REF] Barnes | Population structure and diversity of an invasive pine needle pathogen reflects anthropogenic activity[END_REF][START_REF] Mullett | Population structure and reproductive mode of Dothistroma septosporum in the Brittany peninsula of France[END_REF][START_REF] Adamson | Looking for relationships between the populations of Dothistroma septosporum in northern Europe and Asia[END_REF][START_REF] Oskay | Distribution and genetic diversity of Dothistroma septosporum in Pinus brutia forests of south-Western Turkey[END_REF][START_REF] Capron | Signatures of post-glacial genetic isolation and human-driven migration in the Frontiers in Genetics frontiersin[END_REF][START_REF] Mullett | Worldwide genetic structure elucidates the Eurasian origin and invasion pathways of Dothistroma septosporum, causal agent of Dothistroma needle blight[END_REF]. Several genomes of the pathogen have been sequenced and population genomics studies [START_REF] Ennos | Using genome resequencing to investigate racial structure, genetic diversity, sexual reproduction and hybridisation in the pine pathogen Dothistroma septosporum[END_REF], as well as investigations considering factors affecting its pathogenicity have been conducted [START_REF] Bradshaw | Global population genomics of the forest pathogen Dothistroma septosporum reveal chromosome duplications in high dothistromin-producing strains[END_REF][START_REF] Guo | DsEcp2-1 is a polymorphic effector that restricts growth of Dothistroma septosporum in pine[END_REF]. In contrast, very little is known regarding the biology or ecology of D. pini. Dothistroma pini is known only in the Northern Hemisphere where it has been recorded in 17 countries on 19 different Pinus hosts as well as Picea abies [START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF][START_REF] Jánošíková-Hečková | The hosts and geographic range of Dothistroma needle blight in Slovakia[END_REF][START_REF] Matsiakh | Dothistroma spp. in western Ukraine and Georgia[END_REF][START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF][START_REF] Ondrušková | Needle blight caused by Dothistroma pini in Slovakia: Distribution, host range and mating types[END_REF]. The pathogen was first described on non-native Pinus nigra J.F. Arnold collected in Michigan (1960s), Minnesota and Nebraska in the United States [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF]. At that time, it was thought to be restricted to the North American continent. Since then, D. pini has been reported in four additional states of the United States (Barnes et al., 2014a;[START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF].

Dothistroma pini was first discovered in Europe when it was found in the Ukraine and Russia in 2008 on non-native P. nigra subsp. pallasiana (Lamb.) Holmboe (Barnes et al., 2008b). However, molecular analysis of herbarium samples collected in France have shown that the pathogen has been present on the European continent at least since 1907 [START_REF] Fabre | Is the emergence of Dothistroma needle blight of pine in France caused by the cryptic species Dothistroma pini?[END_REF]. Since the first molecular identification of D. pini in Europe in 2008, the pathogen has also been confirmed as present in Belgium [START_REF] Schmitz | First detection of Dothistroma pini in Belgium[END_REF], Czech Republic [START_REF] Bergová | First occurrence of Dothistroma pini in the Czech republic[END_REF], France [START_REF] Ioos | Development, comparison, and validation of real-time and conventional PCR tools for the detection of the fungal pathogens causing Brown spot and red band needle blights of pine[END_REF], Georgia [START_REF] Matsiakh | Dothistroma spp. in western Ukraine and Georgia[END_REF], Germany (EPPO, 2019), Hungary [START_REF] Barnes | Needle blight of pine caused by two species of Dothistroma in Hungary[END_REF], Montenegro [START_REF] Lazarević | Dothistroma needle blight on high altitude pine forests in Montenegro[END_REF], Poland [START_REF] Wartalska | Dothistroma septosporum not detected in Pinus sylvestris seed trees from investigated stands in southern Poland[END_REF], Romania [START_REF] Barnes | Neotypification of Dothistroma septosporum and epitypification of D. pini, causal agents of Dothistroma needle blight of pine[END_REF], Serbia [START_REF] Pap | Forest health monitoring in Vojvodina in 2015[END_REF], Slovenia [START_REF] Piškur | Dothistroma needle blight in Slovenia is caused by two cryptic species: Dothistroma pini and Dothistroma septosporum[END_REF], Slovakia [START_REF] Ondrušková | Distribution and characterization of Dothistroma needle blight pathogens on Pinus mugo in Slovakia[END_REF], Spain [START_REF] Iturritxa | Spatial analysis of the risk of major forest diseases in Monterey pine plantations[END_REF] and Switzerland [START_REF] Queloz | First record of Dothistroma pini on Pinus nigra in Switzerland[END_REF].

Very little is known regarding the genetic diversity and population structure of D. pini. In a preliminary study testing 16 microsatellite markers developed for D. pini [START_REF] Siziba | Development of microsatellite markers for the pine needle blight pathogen, Dothistroma pini[END_REF], high levels of genetic diversity were found in populations of the pathogen in France, at least indicating the presence of the pathogen in that country for many years. In contrast, populations in other European countries such as Slovakia displayed low genetic diversity and strong signals of clonality, which suggests that D. pini was introduced into Slovakia [START_REF] Adamčíková | Population structure and genetic diversity suggest recent introductions of Dothistroma pini in Slovakia[END_REF].

Collections of D. pini made over a 12-year period, and including those obtained while documenting the presence of both this species and D. septosporum in Europe by the DIAROD cost action, has resulted in a collection of 345 isolates. This collection provided an opportunity to expand on previous, relatively small-scale studies [START_REF] Siziba | Development of microsatellite markers for the pine needle blight pathogen, Dothistroma pini[END_REF][START_REF] Adamčíková | Population structure and genetic diversity suggest recent introductions of Dothistroma pini in Slovakia[END_REF], and to more comprehensively consider the population structure and diversity of D. pini in Europe. The aims of this study were thus to 1) investigate the genetic diversity and population structure of the pathogen including countries or specific locations where the pathogen has been reported in Europe, and 2) determine its mode of reproduction and likely means of dispersal in Europe.

Materials and methods

Sample collection, fungal isolations and identifications

Pine needles that displayed DNB symptoms were collected between 2008 and 2019 from 30 locations in 11 countries of Europe (Supplementary Table S2, Figure 1). Additionally, the data generated for the 10 locations in Slovakia by [START_REF] Adamčíková | Population structure and genetic diversity suggest recent introductions of Dothistroma pini in Slovakia[END_REF] were incorporated in this study. For most samples, isolations were made from the collected samples as described by [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF]. Single germinating conidia were selected and plated onto 2% Dothistroma Sporulating Media (DSM: 5 g yeast extract (Biolab, Merck, Modderfontein, South Africa), 20 g malt extract (Biolab) and 15 g agar (BD Difco ™ , Sparks, MD)) per liter of distilled water with 100 mg/l streptomycin (Sigma-Aldrich, St Louis, MO). The plates were incubated for 4-6 weeks at 23 °C under natural day/night light cycles. All isolates are either maintained as cultures or freeze-dried material in the culture collection (CMW) of the Forestry and Agricultural Biotechnology Institute (FABI) in Pretoria, South Africa (Supplementary Table S2).

Fungal tissue was freeze dried and DNA extracted using a Zymo

Research ZR fungal/Bacterial DNA MiniPrep ™ kit (Irvine, CA) as described by [START_REF] Van Der Nest | Biodiversity of Lecanosticta pine-needle blight pathogens suggests a Mesoamerican Centre of origin[END_REF]. The identity of the isolates was determined by amplifying and sequencing the internal transcribed spacers (ITS) 1 and 2 and the 5.8 S rDNA region with the ITS1 and ITS4 primers [START_REF] White | Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics[END_REF]) and using the protocols described in [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF]. The PCR amplicons were sequenced in both directions using the BigDye Terminator v3.1 Cycle Sequencing Kit (Thermo Fisher Scientific) and the product was run on an ABI PRISM 3500xl capillary auto sequencer (Thermo Fisher Scientific). CLC Main workbench version 8.0 (CLC Bio, https://www. qiagenbioinformatics.com/products/clc-main-workbench/) was used to create consensus sequences using the forward and reverse sequences of the ITS region for each isolate. All consensus sequences were compared in a BLAST analysis against the GenBank database (NCBI; http://www.ncbi.nlm.nih.gov/genbank/) to confirm the identity of each isolate. To determine the ITS haplotype for each confirmed isolate of D. pini, sequences were compared to those reported in [START_REF] Barnes | Neotypification of Dothistroma septosporum and epitypification of D. pini, causal agents of Dothistroma needle blight of pine[END_REF]; [START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF] using MEGA 7.0.14 [START_REF] Kumar | MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets[END_REF].

Microsatellite amplification and haplotype determination

Sixteen labelled microsatellite markers [START_REF] Siziba | Development of microsatellite markers for the pine needle blight pathogen, Dothistroma pini[END_REF] were used to amplify all isolates considered in this study. An additional marker (Doth_A; Barnes et al. (2008a)) was included as an internal diagnostic marker. PCR reactions were performed, and where needed, optimized as described by [START_REF] Adamčíková | Population structure and genetic diversity suggest recent introductions of Dothistroma pini in Slovakia[END_REF] (see also Supplementary Table S3) to produce single PCR products. PCR reactions were carried out on an Applied Biosystems ® Veriti ® 96 well Thermal cycler (Thermo Fisher Scientific, Waltham, MA). The fragments were amplified using the same cycling conditions described by [START_REF] Barnes | Population structure and diversity of an invasive pine needle pathogen reflects anthropogenic activity[END_REF] with primer pair annealing temperatures as described by [START_REF] Adamčíková | Population structure and genetic diversity suggest recent introductions of Dothistroma pini in Slovakia[END_REF] (see also Supplementary Table S3). To determine amplification success, 5 µl PCR product was stained with 1 µl GelRed nucleic acid gel stain (Biotium), separated by gel electrophoresis on 2% SeaKem LE agarose gel (Lonza) for 15 min at 90 V and visualized under a UV light using a GelDoc EZ Imager (BioRad).

PCR products were pooled in two panels for fragment analysis as described by [START_REF] Siziba | Development of microsatellite markers for the pine needle blight pathogen, Dothistroma pini[END_REF] and with adjusted dilutions as indicated in Supplementary Table S3. In preparation for analysis, 1 μl of the pooled product was added to 0.14 μl GENESCAN ™ -500 LIZ ® (Life Technologies, Applied Biosystems, Warrington,

FIGURE 1

The 40 sampling locations of D. pini in Europe. The insert to the left indicates the proportion of isolates obtained per country in relation to other countries (numbered from 1-12). The countries are colour coded on the map to match the insert (original map obtained from https://www.vecteezy.com/free-vector/ europe-map) and each sampling location is indicated with a black circle.

United Kingdom) size standard and 12 μl formamide. Fragment analyses of the prepared reactions was conducted at the University of Pretoria in South Africa with an ABI PRISM 3500xl capillary auto sequencer (Thermo Fisher Scientific). Allele sizes were scored using GENEMAPPER ® Software version 5.0 (Applied Biosystems, Foster City, CA).

Alleles scored for each marker were combined to obtain a multilocus haplotype (MLH) for each isolate. Individual isolates were considered clones if they had the same combination of alleles for each marker analyzed. The R package poppr [START_REF] Kamvar | Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction[END_REF] was used to determine the number of MLHs in the dataset. Two datasets were generated for further analyses; the dataset that had not been clone-corrected included all individuals and the clone-corrected dataset contained single representatives of each unique MLH per population. Individuals from each particular country were grouped as populations.

Genetic diversity

The R package poppr [START_REF] Kamvar | Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction[END_REF] was used to calculate the number of MLHs, the expected number of MLHs based on rarefaction [START_REF] Hurlbert | The nonconcept of species diversity: A critique and alternative parameters[END_REF], the Shannon-Wiener Index [START_REF] Shannon | A mathematical theory of communication[END_REF], the Stoddart and Taylor's Index [START_REF] Stoddart | Genotypic diversity: Estimation and prediction in samples[END_REF], the Simpson's Index [START_REF] Simpson | Measurement of diversity[END_REF] and genotypic evenness [START_REF] Grünwald | Analysis of genotypic diversity data for populations of microorganisms[END_REF] for the populations using the non-clonecorrected dataset, as well as the genetic diversity [START_REF] Nei | Estimation of average heterozygosity and genetic distance from a small number of individuals[END_REF] per population using the clone-corrected dataset. The clonal fraction was calculated as in [START_REF] Barnes | Population structure and diversity of an invasive pine needle pathogen reflects anthropogenic activity[END_REF]. Furthermore, allelic richness (A R ) and private allelic richness (PA R ) were determined using ADZE [START_REF] Szpiech | ADZE: A rarefaction approach for counting alleles private to combinations of populations[END_REF] that uses rarefaction to allow for comparisons between populations with varying sample sizes. Calculations were standardized corresponding to the country with the smallest population size (Russia, N = 6). A minimum spanning network using Bruvo's genetic diversity [START_REF] Bruvo | A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level[END_REF] comparing the MLHs over 16 microsatellite loci was also drawn using the ismn function in the poppr package.

Population structure

The clone-corrected dataset was used to determine the most likely number of population clusters based on microsatellite allele sizes for all the individuals using STRUCTURE 2.3.4 [START_REF] Falush | Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies[END_REF]. The program assigns individuals to clusters (K) using a Bayesian clustering algorithm. Thirty independent runs of K = 1-20 were performed, with a burn-in value of 100,000 and 500,000 iterations. An admixture model with correlated allele frequencies was selected with no additional priors such as information on the host or location.

The optimal number of clusters was estimated with StructureSelector [START_REF] Li | StructureSelector: A web-based software to select and visualize the optimal number of clusters using multiple methods[END_REF]. StructureSelector implements the Evanno method that includes delta (K) and LnP (K) [START_REF] Evanno | Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study[END_REF] with the additional four Puechmaille methods (MAXMEAK, MAXMEDK, MEDMEDK and MEDMEAK) that provide a more accurate estimate of K in populations with uneven sizes [START_REF] Puechmaille | The program STRUCTURE does not reliably recover the correct population structure when sampling is uneven: Subsampling and new estimators alleviate the problem[END_REF]. In order to implement the Puechmaille methods, countries were assigned as populations in the dataset and the analysis was repeated twice. First a threshold of 0.5 was selected and second a threshold of 0.8 was selected to apply more stringent assignment of individuals into clusters. After the optimal K was determined, isolates were assigned into the optimal K clusters with a final STRUCTURE run with 30 independent runs, a burn-in value of 100,000 and 1,000 000 iterations. CLUMPAK [START_REF] Kopelman | Clumpak: A program for identifying clustering modes and packaging population structure inferences across K[END_REF] was used to converge all 30 runs of the optimal K and the output was visualized using the DISTRUCT program [START_REF] Rosenberg | Distruct: A program for the graphical display of population structure[END_REF]. Both CLUMPAK and DISTRUCT were implemented using the StructureSelector website (https://lmme.qdio.ac.cn/ StructureSelector/).

The adegenet package in R studio [START_REF] Jombart | Adegenet 1.3-1: new tools for the analysis of genomewide SNP data[END_REF]) was used to perform discriminant analysis of principal components (DAPC) [START_REF] Jombart | Discriminant analysis of principal components: A new method for the analysis of genetically structured populations[END_REF] to additionally visualize the population genetic structure of the European samples. The find.clusters function was used to determine the optimal number of clusters by assessment of the Bayesian information criterion (BIC). The optimal number of principal components retained in the analysis was determined by cross-validation using the xvalDapc function.

An Analysis of Molecular Variance (AMOVA) test was implemented in GENALEX version 6.5 [START_REF] Peakall | GenAlEx 6.5: Genetic analysis in excel. Population genetic software for teaching and research-an update[END_REF]. The test was used to evaluate if there was genetic differentiation among and within groups according to host species, countries and locations. One thousand permutations of the dataset were used to test significance. The null hypothesis of no genetic difference was rejected at p < 0.05.

Mating type determination and random mating

The mating type of the D. pini isolates was determined by using the primers of [START_REF] Groenewald | Characterization and distribution of mating type genes in the Dothistroma needle blight pathogens[END_REF] or in some cases the primer set of [START_REF] Janoušek | Development of microsatellite and mating type markers for the pine needle pathogen Lecanosticta acicola[END_REF]. Each reaction consisted of 2 μl template DNA (20 ng/μl concentration), 0.08 μl Faststart Taq DNA polymerase, 0.25 μl of each of the primers as specified by either [START_REF] Groenewald | Characterization and distribution of mating type genes in the Dothistroma needle blight pathogens[END_REF] or [START_REF] Janoušek | Development of microsatellite and mating type markers for the pine needle pathogen Lecanosticta acicola[END_REF], 0.6 μl of a mix of 200 mM dNTPs, 1.5 μl of 2.5 mM MgCl 2, 1.25 μl 10x PCR reaction buffer and the volume was adjusted to 12.5 μl with sterile SABAX water.

PCR reactions were carried out on an Applied Biosystems ® Veriti ® 96 well Thermal cycler (Thermo Fisher Scientific, Waltham, MA). The cycling conditions for all microsatellite fragments included an initial denaturation step at 95 °C for 4 min, 10 cycles consisting of 94 °C for 20 s, a 45 s annealing step with the temperature set according to the protocols by [START_REF] Groenewald | Characterization and distribution of mating type genes in the Dothistroma needle blight pathogens[END_REF] or [START_REF] Janoušek | Development of microsatellite and mating type markers for the pine needle pathogen Lecanosticta acicola[END_REF], and an elongation step of 45 s at 72 °C. This was followed by a further 25 cycles of 94 °C for 20 s, 45 s with a 5 s extension step per cycle at the annealing temperature, a 72 °C extension for 45 s and a final extension step of 72 °C for 30 min. The amplified products were visualized by staining 10 µl of each product with GelRed ™ nucleic acid gel stain. The fragments were separated on 2% SeaKem ® LE agarose gel for 50 min at 90 V and viewed under a UV light using the GelDoc ™ EZ Imager (BioRad, Hercules, CA). When using the [START_REF] Groenewald | Characterization and distribution of mating type genes in the Dothistroma needle blight pathogens[END_REF] primers, isolates that had an amplicon size of 820 bp were assigned as MAT1-1 and those with a size of 480 bp were assigned as MAT1-2. The [START_REF] Janoušek | Development of microsatellite and mating type markers for the pine needle pathogen Lecanosticta acicola[END_REF] primer sets produced amplicon sizes of approximately 560-634 bp for MAT1-1 and 288-323 bp for MAT1-2.

The possibility of sexual recombination was investigated using three methods. An exact binomial test, using two-tailed p-values (http://www.biostathandbook.com/exactgof.html) was used to test if [START_REF] Szpiech | ADZE: A rarefaction approach for counting alleles private to combinations of populations[END_REF]. The smallest country sample size considered was 6. g Privale allelic richness ± standard error [START_REF] Szpiech | ADZE: A rarefaction approach for counting alleles private to combinations of populations[END_REF]. The smallest country sample size considered was 6. h H: Shannon-Wiener Index of MLH, diversity [START_REF] Shannon | A mathematical theory of communication[END_REF]. i G: Stoddart and Taylor's Index of MLH, diversity [START_REF] Stoddart | Genotypic diversity: Estimation and prediction in samples[END_REF]). j Lambda: Simpson's Index [START_REF] Simpson | Measurement of diversity[END_REF] provides an estimation of the probability that two randomly selected genotypes are different: 0 = no genotypes different. 1 = all genotypes are different. k E.5: Genotypic evenness, [START_REF] Grünwald | Analysis of genotypic diversity data for populations of microorganisms[END_REF]. l D = [START_REF] Nei | Estimation of average heterozygosity and genetic distance from a small number of individuals[END_REF] the mating type ratios deviated from a 1:1 ratio (at p < 0.05) in the nonclone-corrected dataset, which provides evidence of random mating. The index of association (I A ) [START_REF] Brown | Multilocus structure of natural populations of Hordeum Spontaneum[END_REF][START_REF] Smith | How clonal are bacteria?[END_REF] and rBarD ( r d ) [START_REF] Agapow | Indices of multilocus linkage disequilibrium[END_REF] was used to test for linkage disequilibrium in the 16 microsatellite loci with both datasets using the R-package poppr [START_REF] Kamvar | Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction[END_REF]. The null hypothesis of alleles at different loci having no linkage due to sexual mating was rejected when p < 0.05.

R f PA R g H h G i Lambda j E.5 k D l Belgium 1 1 N/A N/A 16 0 N/A N/A N/A N/A N/A N/A N/A Czech Republic 1 1 N/A N/A 16 0 N/A N/A N/A N/A N/A N/A N/

Results

Sample collection, fungal isolations and identification

A total of 345 cultures included in this study were obtained from collections made in Europe. All of these isolates screened with the Doth_A marker [START_REF] Siziba | Development of microsatellite markers for the pine needle blight pathogen, Dothistroma pini[END_REF] produced an allele size of 111 bp and were thus confirmed as D. pini. These included representatives from 12 (Belgium, Czech Republic, France, Hungary, Romania, Western Russia, Serbia, Slovakia, Slovenia, Spain, Switzerland and Ukraine) of the 16 European countries where D. pini has been reported. The isolations were made from plant material obtained from 10 different Pinus species or sub-species with P. nigra being the most common of these (Supplementary Table S1).

Three of the six known D. pini ITS haplotypes [START_REF] Barnes | Neotypification of Dothistroma septosporum and epitypification of D. pini, causal agents of Dothistroma needle blight of pine[END_REF][START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF] were identified in the collection of isolates (Supplementary Table S1). Individuals having the ITS Haplotype 1 were the most abundant and were present in eight of the twelve countries (Czech Republic, France, Hungary, Slovakia, Slovenia, Spain, Switzerland, Ukraine) including 25 different locations. ITS Haplotype 2 was the second most abundant and was present in eight of the twelve countries (France, Romania, Western Russia, Serbia, Slovakia, Spain, Switzerland, Ukraine) and at 20 different locations. ITS Haplotype 4 individuals were present at nine locations in five countries (Belgium, France, Serbia, Slovakia and Spain). All three haplotypes were present in France, Spain and Slovakia.

Microsatellite amplification and haplotype determination

A total of 109 alleles were detected across the 16 polymorphic microsatellite loci. The number of alleles at each locus ranged from 2 at DP-MS4 and DP-MS18 to 19 at DP-MS12 (Supplementary Table S3). Isolates from Spain, Ukraine and Russia had the highest percentage (87.5%) of polymorphic loci (Supplementary Table S2) and those from Hungary had the lowest percentage (31.2%) of polymorphic loci (excluding countries for which only single isolates were available).

A total of 109 unique multilocus haplotypes (MLHs) were identified in the 345 isolates analyzed (Table 1; Figure 2, Supplementary Table S2) of which eight MLHs occurred in multiple, often non-bordering countries (Supplementary Figure S1). Some individuals sharing the same microsatellite MLH in different populations were of opposite mating type or of different ITS haplotypes, which suggests that they were not true clones. For example, MLH 52 (Supplementary Figure S1) occurred in isolates from four countries (Hungary, Slovakia, Slovenia and Ukraine) and at seven different locations, covering a distance of approximately 1500 km. This MLH was represented by individuals with the MAT1-1 idiomorph in Ukraine and the MAT1-2 idiomorph in the

Genetic diversity

Collections from France had the greatest number of MLHs, followed by the isolates from the Ukraine. When considering populations with a sample size of six and higher, Hungary had the fewest MLHs (five) followed by Russia, Slovenia and Switzerland, which had six each (Table 1). When comparing the approximate number of haplotypes that would be expected for the largest shared sample size (N = 6) based on rarefaction (eMLG), the genotypic richness was the highest in the populations from France and Spain (8.52 and 8.50). The populations from Slovenia and Switzerland had the lowest genetic diversity (3.45 and 3.57 respectively) (Table 1). The Slovenian and Slovakian populations had the highest clonal fractions (0.87 and 0.86) followed by those from Switzerland 0.75 (Table 1). The lowest clonal fraction was found in populations from Russia (0) followed by those from Spain (0.25) and France (0.43). For populations collected within France, the clonal fraction ranged from 0 (Nueng-sur-Beuvron) to 0.61 (Villefranche-sur-Cher). In isolates from Slovenia, the clonal fraction also ranged from 0 (Ribnica) to 0.90 (Panovec). The clonal fraction of 0.55 in Ukraine was due to the high clonal fraction (0.67) in Tsjurupinsk (Supplementary Table S4). The genetic diversity of isolates from all locations is summarized in Supplementary Table S4.

Varying levels of genotypic diversity and genotypic richness were observed for the isolates considered in this study (Table 1). Populations from France followed by Spain displayed the highest level of genetic diversity and richness, based on the Simpson index (H), Stoddart Taylor's index (G) and allelic richness (A R ) and rarefaction of MLGs. The genotypic evenness (E.5) observed in the populations from Russia and Spain were the closest to having equal abundance. Using Nei's unbiased gene diversity, the Russian population had the highest gene diversity (0.546) followed by those from Spain (0.494), Ukraine (0.379) and France (0.344). This could be due to the uneven sample sizes obtained at the different locations because the algorithm does not correct for small population sizes. Populations from Slovenia and Switzerland had the lowest genotypic diversity and genotypic richness. Countries for which only one or two isolates were available (i.e., Romania, Belgium and the Czech Republic) were not considered in the analyses.

The population from Spain had the highest number of private alleles (PA R ) (16.51%) followed by those from France (5.50%) and Slovakia (4.59%). Populations from Russia, Serbia, Slovenia and Switzerland had the lowest number of private alleles (0.90%). Within Slovakia, private alleles were from Arboretum Mlyňany, Jahodná, Košice and Zvolen and in France the private alleles were only from Souesmes (Supplementary Table S4).

Population structure

There was no consensus between different methods of determining the optimal number of clusters in the STRUCTURE analysis. The Evanno ΔK supported nineteen (K = 19) clusters, which indicates that this method failed to detect population structure. LnP (K) suggested K = 10 as the optimal scenario. The four Puechmaille methods suggested that 5-8 clusters are most likely the optimal number of clusters depending on the threshold that was set (Supplementary Figure S2). The STRUCTURE barplots for K = 2 to K = 9 for the major modes are illustrated in Supplementary Figure S3. The barplots for K = 5-8, together with the geographical distribution of the clusters are represented in Figure 3. In order to conduct the DAPC analysis, the find. clusters function in the adegenet package in R was used and this showed that K resides between 8 and 12. After several runs, K = 10 was proposed as the optimal scenario.

For both the K = 8 and K = 10 scenario, the DAPC (Figure 4) and STRUCTURE analysis (Figure 3) indicated that three or four major genetic groups reside between bordering countries in Western, Central, and Eastern Europe. Within these clusters, several smaller genetic groups were observed. The STRUCTURE analysis showed that populations in Western Europe (Belgium, Czech Republic, France and Switzerland) share a major cluster. In Central Europe, one cluster was shared between Hungary, Slovakia and Slovenia and a second genetic cluster was shared between Slovakia and Serbia. In Eastern Europe, isolates from Romania, Russia and Ukraine shared a cluster. Several smaller scattered genetic groups also resided among the populations and the Slovenian population, as well as the Spanish population, included unique genetic clusters.

The DAPC clusters (Figure 4) were mostly correlated with the geographic groups indicated by the STRUCTURE analysis with a Western group containing Cluster 1 (France, Spain, Switzerland), Cluster 3 (Czech Republic, France, Ukraine), Cluster 5 (Belgium, France and Spain) and Cluster 10 (France, Spain and Switzerland). A Central European group accommodated Cluster 4 (France, Hungary, Russia, Slovakia and Slovenia), Cluster 8 (Serbia and Slovakia) as well as a unique cluster (Cluster 2) having only individuals from Slovenia. The DAPC also indicated an Eastern European group with Cluster 6 (Russia, Ukraine), Cluster 7 (Romania, Russia and Ukraine) as well as Cluster 9 (Russia and Ukraine). The four distinct geographic groups suggested by both the STRUCTURE analysis and DAPC were also evident in a haplotype network drawn using Bruvo's genetic distance (Supplementary Figure S4).

The AMOVA results (Table 2) indicated significant population differentiation according to country (variance among individuals 47%, variance among countries 53%) and even more so by location within countries (variance among individuals 41%, variance among countries 59%). Although this explained less of the variance found among populations, AMOVA also strongly supported the grouping by host species (27% between species and 73% among individuals).

Mating type determination and random mating

The mating types were successfully amplified for all but two isolates, both from Slovakia (Table 3). Both mating type idiomorphs were detected in isolates from France, Serbia, Slovakia, Spain, Switzerland and Ukraine (Table 3). However, in Nueng-sur-Beuvron in France only MAT1-1 individuals were detected and in Serbia only MAT1-2 individuals were present in isolates from Subotica Sands. Similarly, although both mating types were present in the Slovakian collections, either MAT1-1 or MAT1-2 individuals were detected at each of the 10 locations sampled in this country. In Ukraine, the population from Nova Zburivka included only one individual that was MAT1-2 and in Mykolaiv Kinburn, only MAT1-1 individuals were detected. Although both mating types were found in these countries, random mating was statistically supported only in the populations from Spain, Switzerland and Ukraine as well as in the sub-populations from Souesmes and La Bouyale in France, Deliblato Sands in Serbia, and Hola Prystan, Tsjurupinsk and Mykolaiv Kinburn in Ukraine. In isolates from the Czech Republic, Romania and Russia only MAT1-1 individuals were present and in those from Belgium, Hungary, and Slovenia only MAT1-2 individuals were present (Table 3, Supplementary Table S2).

Testing linkage disequilibrium using the clone-corrected dataset, with the index of association and rbarD, provided evidence for sexual recombination only in the population from Spain (p-value of 0.144). Analysis of the non-clone-corrected dataset also supported evidence of sexual recombination in Serbia (p-values of 0.281). This result is however not plausible as the data for both Deliblato Sands and Subotica Sands in Serbia were pooled for this analysis and therefore do not reflect that single mating types were observed at each of these locations.

Discussion

This study provided the first insights into the population structure and genetic diversity of D. pini in Europe. Even though extensive sampling was conducted in the area over a 12-year period, due to the low incidence of D. pini, sampling was relatively unstructured and sample sizes were relatively small. This was also emphasized in reports in Switzerland [START_REF] Dubach | Nationales Monitoring von zwei besonders gefährlichen Föhrenkrankheiten[END_REF] as well as Spain [START_REF] De Urbina | Emerging needle blight diseases in Atlantic Pinus ecosystems of Spain[END_REF] where D. pini was less frequently detected than D. septosporum. Nonetheless, it was clear that D. pini is not new to the European continent and that movement of the pathogen was facilitated through anthropogenic activities.

Based on population structure analyses, the D. pini populations considered in this study grouped in four main geographic clusters including one in Western Europe, two in Central Europe, and one in Eastern Europe. Variable population diversity was observed between countries, with France, Spain and Ukraine having the highest levels of genetic diversity and the presence of both mating types. This suggests that D. pini has most likely been present in those countries for a long period of time and is in agreement with the identification of D. pini in France from herbarium specimens dating back to 1907 and 1965 [START_REF] Fabre | Is the emergence of Dothistroma needle blight of pine in France caused by the cryptic species Dothistroma pini?[END_REF]. In contrast, there were populations that were clonal and with a single mating type such as in Slovakia and Slovenia, suggesting more recent introductions. Additionally, the presence of the same MLHs over long distances suggests that human-mediated movement of D. pini is taking place in Europe, possibly through plant trade [START_REF] Pautasso | Network epidemiology and plant trade networks[END_REF].

Both mating types of D. pini were present in many populations considered in this study, but evidence for sexual recombination was supported only in the population from Spain. The fact that some isolates of the same MLHs were of different mating type suggests that sexual recombination could be occurring in other European populations of D. pini. This is not unusual and has been found in pathogens such as Teratosphaeria destructans [START_REF] Havenga | Genetic recombination in Teratosphaeria destructans causing a new disease outbreak in Malaysia[END_REF] as well as Verticillium dahliae, a clonally reproducing pathogen, having individuals of opposite mating types that were indicative of cryptic or ancestral sexual recombination events [START_REF] Milgroom | Recombination between clonal lineages of the asexual fungus Verticillium dahliae detected by genotyping by sequencing[END_REF][START_REF] Short | Maintenance of sex-related genes and the co-occurrence of both mating types in Verticillium dahliae[END_REF]. Dothistroma pini has a limited host range and is currently confined to a particular latitudinal geographical range both in Europe as well as in North America. The majority of the isolates in the present study were from several sub-species of Pinus nigra with few collections from P. coulteri, P. jeffreyi, P. mugo, P. ponderosa, P. schwerinii and P. sylvestris. Many of the single isolates from hosts other than P. nigra were from urban areas or arboreta and not from the native ranges of the host trees. This suggests that D. pini is most likely not native to the areas where it was collected in Europe and could have been introduced to the continent. This is in contrast to the more commonly occurring D. septosporum that is hypothesized to be native to the P. sylvestris forests in Northern Europe [START_REF] Adamson | Looking for relationships between the populations of Dothistroma septosporum in northern Europe and Asia[END_REF], Eastern Europe and Western Asia [START_REF] Mullett | Worldwide genetic structure elucidates the Eurasian origin and invasion pathways of Dothistroma septosporum, causal agent of Dothistroma needle blight[END_REF].

The results of this study have provided no clues to the possible center of origin of the pathogen. The only other area of the world where D. pini is known to occur is North America [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF]Barnes et al., 2014a;[START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF]. Dothistroma needle blight is widespread in the United States and has been reported in 35 states [START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF][START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF]. However, most of the reports were from the time before D. septosporum and D. pini were conclusively separated based on phylogenetic inference in 2004 [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF]. Thus, the presence of D. pini has been confirmed in only seven states in the Central regions of the United States [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF]Barnes et al., 2014a;[START_REF] Mullett | New country and regional records of the pine needle blight pathogens Lecanosticta acicola, Dothistroma septosporum and Dothistroma pini[END_REF] and D. septosporum in four states [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF][START_REF] Barnes | Neotypification of Dothistroma septosporum and epitypification of D. pini, causal agents of Dothistroma needle blight of pine[END_REF]. The techniques available to discriminate between the two species with relative ease [START_REF] Barnes | Multigene phylogenies reveal that red band needle blight of Pinus is caused by two distinct species of Dothistroma, D. septosporum and D. pini[END_REF][START_REF] Groenewald | Characterization and distribution of mating type genes in the Dothistroma needle blight pathogens[END_REF]Barnes et al., 2008a;[START_REF] Ioos | Development, comparison, and validation of real-time and conventional PCR tools for the detection of the fungal pathogens causing Brown spot and red band needle blights of pine[END_REF][START_REF] Schneider | Detection of pine needle diseases caused by Dothistroma septosporum, Dothistroma pini and Lecanosticta acicola using different methodologies[END_REF][START_REF] Aglietti | Rapid detection of pine pathogens Lecanosticta acicola, Dothistroma pini and D. septosporum on needles by probe-based LAMP assays[END_REF][START_REF] Myrholm | Development of a rapid loop-mediated isothermal amplification assay for the detection of Dothistroma septosporum[END_REF] should simplify efforts to collect isolates known to be those of D. pini from the United States, and potentially other unsampled areas such as Asia. This would facilitate an opportunity to compare populations across continents, using either microsatellite markers or whole genome comparisons, in an effort to understand global pathways of spread and potential native areas. The extensive data assembled in the present study will provide a solid foundation for these comparisons.

An intriguing question pertaining to DNB is why D. septosporum has spread from the Northern Hemisphere to many Southern Hemisphere countries but that the closely related D. pini has not done so. This could be related to host range where D. septosporum has mainly been a problem on P. radiata in the Southern Hemisphere [START_REF] Gibson | Dothistroma blight of Pinus radiata[END_REF][START_REF] Barnes | Population structure and diversity of an invasive pine needle pathogen reflects anthropogenic activity[END_REF][START_REF] Drenkhan | Global geographic distribution and host range of Dothistroma species: A comprehensive review[END_REF], although it has recently emerged as a serious constraint in plantations of P. tecunumanii in Colombia [START_REF] Rodas | Dothistroma needle blight: An emerging epidemic caused by Dothistroma septosporum in Colombia[END_REF]. Both Dothistroma species have relatively wide host ranges and as greater numbers of Pinus spp. are being tested and propagated in Southern Hemisphere countries, it seems plausible to suggest that D. pini poses an important threat to these resources. Based on experience with D. septosporum as well as the increasingly important pine needle pathogen Lecanosticta acicola (van der Nest et al., 2019a), and apparently D. pini as was found in this study, there is good reason to emphasize the importance of quarantine when moving Pinus germplasm between countries and continents.

  gene diversity. Data in bold indicates the total values for each of the summary statistics.

FIGURE 2

 2 FIGURE 2 Microsatellite haplotype diversity and mating type ratios of D. pini in each of the sampled countries in Europe. Each colour in the pie charts represents a different multilocus haplotype. The size of each MLH pie chart is proportional to the number of isolates per country where Belgium N = 1 and Slovakia N = 103. N = number of isolates, D = Nei's genetic diversity.

FIGURE 3

 3 FIGURE 3 Geographical patterns of population divisions observed using STRUCTURE analyses based on the most likely K values determined by the Puecemaille methods with (A) K = 5, (B) K = 6, (C) K = 7, (D) K = 8. The number of individuals belonging to each Cluster is represented as pie charts in each respective country. Four main genetic groups are spread throughout Western, Central and Eastern Europe with several smaller scattered genetic groups residing among the populations.

FIGURE 4

 4 FIGURE 4 Population structure of the European Dothistroma pini collection of isolates. (A) Scatterplot of the discriminant analysis of principal components (DAPC) on European Dothistroma pini multilocus haplotypes. The number and colours represent the 10 groups delineated by the K-means method. Individual multilocus haplotypes are represented by dots and clusters as ellipses. At the top left the eigenvalues of the first nine axes are represented. (B) The composition of the DAPC clusters. The columns and colours correspond to the inferred clusters and the rows correspond to the countries where the populations were sampled. The size of the squares is proportional to the number of individuals comprising each cluster. Cluster one for instance is comprised of individuals isolated from France, Spain and Switzerland with the majority of the individuals in this cluster isolated from France.

TABLE 1

 1 Summary diversity statistics of Dothistroma pini isolates within populations by country in Europe.

	Country a	N b	MLH c	eMLH d	CF e	Total no	Unique	A
						of alleles	alleles

TABLE 2

 2 Hierarchical analysis of molecular variance (AMOVA) of Dothistroma pini populations, grouped by countries, by locations and by host species. Mating type ratios and index of association tests for the Dothistroma pini populations collected in Europe.The index of association tests were conducted per country using both datasets.

	Source of variation	df	Sum of	Mean	Estimate of	Total	p-value
			squares	squares	variance	variation (%)
	Among Countries	8	1,127.55	140.94	1.96	53	0.01
	Among Individuals grouped by country	334	1,179.76	3.53	1.77	47
	Within Individuals	343	0.00	0.00	0.00	0
	Total	685	2,307.32		3.72	100
	Among Locations	27	1,372.24	50.82	2.05	59	0.01
	Among Individuals grouped by location	309	886.36	2.87	1.43	41
	Within Individuals	337	0.00	0.00	0.00	0
	Total	673	2,258.59		3.48	100
	Among Hosts	7	507.88	72.55	0.98	27	0.01
	Among Individuals grouped by hosts	333	1791.66	5.38	2.69	73
	Within Individuals	341	0.00	0.00	0.00	0
	Total	681	2,299.54		3.67	100

a Mating type ratios are indicated per country using the non-clone-corrected dataset. b

Frontiers in Genetics frontiersin.org

van der Nest et al. 10.3389/fgene.2023.1103331

Acknowledgments

We acknowledge that this manuscript was a research chapter of the PhD thesis of the leading author, A. van der Nest, and that the thesis will be electronically available in the University of Pretoria library repository as: van der Nest A. 2021. Research chapter 2: Population structure and diversity of the needle pathogen Dothistroma pini suggests human mediated movement in Europe. In: Species diversity of Lecanosticta and population genetics of Dothistroma species: important needle pathogens of Pinus. PhD(Genetics), Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria. Pp 73-115. We thank Dr Zuzana Janošíková-Hečková and Dr Emília Ondrušková from the Institute of Forest Ecology Slovak Academy of Sciences in Slovakia for their assistance in obtaining the large collection of D. pini isolates from Slovakia.

Data availability statement

The authors acknowledge that the data presented in this study must be deposited and made publicly available in an acceptable repository, prior to publication. Frontiers cannot accept a manuscript that does not adhere to our open data policies.

Author contributions

AN, MW, and IB contributed to conception and design of the study. IB, DS, MM, BM, VQ, KA, and KD contributed sample collections and KA contributed a dataset for inclusion. AN and IB conducted the lab work and organized the datasets. AN, DS, and IB did the data analysis and MW, MM, BM, VQ, KA, and KD made improvements to the analysis. AN wrote the first draft of the manuscript and MW and IB contributed significantly to editing the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

Financial support for this study was provided by the University of Pretoria, members of the Tree Protection Cooperative Program (TPCP), the National Research Foundation (Thuthuka Grant no 80670, and Grant no 95875), a Scarce Skills Doctoral Scholarship (Grant 89086) for A. van der Nest, and DIAROD: EU COST Action FP1102 DIAROD (Determining Invasiveness and Risk of Dothistroma, https://www.cost.eu/actions/FP1102/). The NRF acknowledge that opinions, findings, conclusions and/or recommendations expressed in any publication generated by the NRF supported research are that of the author (s), and that the NRF accepts no liability whatsoever in this regard. The NRF had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1103331/ full#supplementary-material

SUPPLEMENTARY FIGURE S1

The 109 multilocus haplotypes (MLH) derived from microsatellite data in each country and shared between countries. Eight MLHs, indicated by coloured arrows are shared between multiple, often non-bordering countries. France contains the highest number of MLHs, followed by Ukraine. MLH 52 (navy blue) occurred in four countries (Hungary, Slovakia, Slovenia, and Ukraine) in 7 locations, covering a distance of approximately 1500 km. The single individual from the Czech Republic shared MLH 83 (dark green) with isolates from different locations in France and 1480 km away as well as two isolates from Hola Prystan in Ukraine. MLH 56 (purple) was the second most occurring MLH and was shared by individuals in Souesmes (France), Diszel (Hungary) and Arborétum Mlyňany, Gabčikovo, Jahodna and Trstice in Slovakia. MLH47 (red) occurred in the Russian population (ITS Haplotype 2 individual) and 1150 km apart in the Hungarian population (ITS Haplotype 1 individual). MLH 11 (light blue) occurred in the population in Russia as well as in Hola Prystan in Ukraine (620 km apart). MLH 34 (lime green) was present in both Arborétum Mlyňany and Zvolen (Slovakia) as well as Delibratski Pesak and Subotička Pescara (Serbia), while individuals of MLH 32 (yellow) were detected in Kováčová (Slovakia) and Subotička Pescara (Serbia). MLH 59 (orange) was found in both Selles-Saint-Denis and La Ferté-Imbault (France) as well as in the population from Switzerland.

SUPPLEMENTARY FIGURE S2

The optimum number of clusters determined using STRUCTURESELECTOR. (A) The Evanno method (Delta K and LnP (K), suggested K= 19 and K = 10 respectively. When a threshold of 0.5 was set, the Puechmaille methods (B) determined the most optimal number of clusters as 7 or 8 and at a threshold of 0.8 (C), the most optimal number of clusters were determined to be 5 or 6.

SUPPLEMENTARY FIGURE S3

STRUCTURE results of D. pini populations per country using the clonecorrected dataset. The structure bar plots show the results for the major clustering modes from K=2 to K=9. The bar plots are divided according to geographical location.

SUPPLEMENTARY FIGURE S4

Haplotype network of D. pini collected in Europe drawn using Bruvo's genetic distance. Each circle represents a multilocus haplotype. The larger the circle, the more individuals have the same haplotype. The circle size for 1, 22 and 47 individuals of a particular haplotype are indicated on the left hand side for scale. The same four major clusters are observed as with the STRUCTURE analysis with the Western Europe individuals (blue oval), Central European clusters (yellow and pink ovals) and Eastern European cluster (dark green) clustering together.

SUPPLEMENTARY TABLE S1

Dothistroma pini collections from Europe used in this study.

SUPPLEMENTARY TABLE S2

Allele sizes for 345 individuals of Dothistroma pini in Europe based on 17 microsatellite markers.

SUPPLEMENTARY TABLE S3

Dothistroma pini microsatellite PCR annealing temperatures, MgCl2 adjustments, dilutions for fragment analysis for each microsatellite marker and number of alleles per microsatellite marker [START_REF] Adamčíková | Population structure and genetic diversity suggest recent introductions of Dothistroma pini in Slovakia[END_REF].

SUPPLEMENTARY TABLE S4

Summary diversity statistics of Dothistroma pini isolates within locations in Europe.


MD PhD; Marion Douplat
email: marion.douplat@chu-lyon.fr

MD Khadidja Daoud

MS Julien Berthiller

MD, PhD Anne-Marie Schott

MD Véronique Potinet

MD PhD Pierre Le Coz

MD PhD Karim Tazarourte

MD Laurent Jacquin

Poor Involvement of General Practitioners in Decisions of Withholding or Withdrawing Life-Sustaining Treatment in Emergency Departments

Keywords: general practitioners, emergency services, decision-making, terminal care, primary care

BACKGROUND: Decisions of withholding or withdrawing life-sustaining treatment are frequent in emergency departments (ED) and patients are often unable to communicate their wishes concerning end of life desires. OBJECTIVE: To evaluate the participation of general practitioners (GPs) during the decision-making process of withholding or withdrawing life-sustaining treatments in ED. DESIGN: Prospective observational multicenter study. PATIENTS: We included patients for whom a decision of withdrawing or withholding life-sustaining treatments was made in ED. For each patient, we enrolled one general practitioner. MAIN MEASURES: GPs were interviewed about their perception of end of life patient's management and the communication with ED and families. KEY RESULTS: There were 109 potential patient participants. We obtained answers from 54 (49.5%) of the patient's associated GPs. Only 4 (7.4%) GPs were involved during the decision-making process of withholding or withdrawing life-sustaining treatments. Among GPs, 29 (53.7%) were contacted by family after the decision, most often to talk about their difficult experience with the decision. A majority (94%) believed their involvements in these decisions were important and 68% wished to Balwaysp articipate in end of life decisions despite the fact that they usually don't participate in these decisions. Finally, 66% of GPs believed that management of end of life in the emergency department was a failure and should be anticipated. CONCLUSIONS: GPs would like to be more involved and barriers to GP involvement need to be overcome. We do not have any outcome data to suggest that routine involvement of GPs in all end of life patients improves their outcomes. Moreover, it requires major system and process-based changes to involve all primary care physicians in ED decision-making.

INTRODUCTION

Most deaths in emergency departments (ED) are preceded by a decision of withholding or withdrawing life-sustaining treatments and most of these patients are unable to communicate their wishes or participate in the decision. [START_REF] Conte | Withholding and withdrawing life-support therapy in an Emergency Department: prospective survey[END_REF][START_REF] Conte | Death in emergency departments: a multicenter cross-sectional survey with analysis of withholding and withdrawing life support[END_REF][START_REF] Damghi | Withholding and withdrawing life-sustaining therapy in a Moroccan Emergency Department: an observational study[END_REF] The context of emergency medicine, the lack of time, and the lack of a relationship with the patients add difficulties for physicians to initiate an informed decision of withdrawing or withholding life-sustaining treatments. Decisions of end of life have been shown to be difficult and complex for emergency physicians, [START_REF] Douplat | Physicians' experience in decisions of withholding and withdrawing life-sustaining treatments: a multicentre survey into emergency departments[END_REF] particularly in the absence of advance directives. [START_REF] Wall | Epidemiology of Advance Directives in Extended Care Facility Patients Presenting to the Emergency Department[END_REF] Indeed, a majority of patients have not written their advance directives beforehand. General practitioners (GPs) could be a source of information about wishes concerning patients' end of life. In a previous study, GPs were shown to be a good choice for third-party intervention in making end of life decisions for hospitalized patients. [START_REF] Ferrand | Participation of French general practitioners in end of life decisions for their hospitalised patients[END_REF] Unfortunately, medical advice is currently sought from GPs in less than 5% of cases. [START_REF] Conte | Death in emergency departments: a multicenter cross-sectional survey with analysis of withholding and withdrawing life support[END_REF] The prevalence of end of life discussions varies across countries. [START_REF] Evans | End of Life Communication: A Retrospective Survey of Representative General Practitioner Networks in Four Countries[END_REF] A recent study demonstrated poor communication in decision-making between practitioners and families at concerning non-communicating patients suffering from dementia but underlined that the decision-making process could be improved with better anticipation. [START_REF] Lamahewa | A qualitative study exploring the difficulties influencing decision making at the end of life for people with dementia[END_REF] Our study's purpose is to describe the involvement of GPs during the decisionmaking-process of withholding or withdrawing lifesustaining treatments in the ED.

METHODS

Study Design and Setting

We conducted a prospective observational multicenter study in 3 EDs of University hospitals in France between September 2015 and December 2016. The study was approved by the institutional ethics committee and follows the STROBE recommendations.

Selection of Participants

We enrolled adult patients unable to communicate and for whom a decision of withdrawing or withholding lifesustaining treatments was undertaken. For each patient, we included their general practitioner, defined as the physician who knew and followed the patient prior to the ED visit.

The decision-making process of these decisions was held as per usual by the emergency physician and GPs were contacted to inquire about their thoughts and their involvement after the fact. GPs were interviewed about their general perception of end of life patients' management and the communication with ED and families about a patient. The GPs survey is presented in the ESM. If the same GP had multiple patients the survey response only counted once.

Informed consent was obtained from relatives after the decision to withdraw or withhold life-sustaining treatments was made. Relatives were defined as all the individuals aged 18 and above who were present with the patient in the ED in the following order: spouse> adult child > other family or friend relationship. We excluded patients without relatives and GP.

Data Collection

Data about general practitioners included: age, sex, years of experience, environment of practice (classified with urban or rural), number of end of life patients followed per year, and end of life management training, which we defined as at least 10 h of classes over the two previous years.

Data concerning physicians' perception during the decision-making process were obtained with Likert scaled (« never », « sometimes », « often », or « always») and closed questions.

Definition

Patients unable to communicate in ED were described as acute loss of consciousness without a Glasgow score threshold, chronic vegetative state, or advanced dementia. Advanced dementia was defined by a mini-mental state (MMS) under 10. This evaluation was realized by the emergency physician who enrolled the patient.

End of life patient population in GP practice was defined as having a survival prognosis of less than 6 months.

Withdrawal was defined as a discontinuation of treatments that had previously been implemented, and withholding was defined as a predetermined decision not to implement therapies that would otherwise be deemed necessary because they were considered unlikely to modify the outcome in these particular instances. Therapies included mechanical ventilation, vasopressor, and inotropes, noninvasive ventilation, renal replacement therapy, intravenous fluid expansion, and oxygen at high rate. We did not include antibiotic which was not considered as an aggressive care.

Endpoints

General practitioners were interviewed by phone within two weeks of the sentinel ED visit. The interview was conducted by an emergency physician who did not participate in the care of included patients and 5 attempts were made to contact the general practitioners.

Statistical Analysis

Only general practitioners with a complete follow-up were considered for statistical analysis. Statistical analysis was performed using SPSS 22.0 (Chicago, IL). Quantitative variables are expressed as mean and standard deviation. Qualitative data are expressed as n and percentages.

RESULTS

Characteristics of General Practitioners

There were 109 potential patient participants. We obtained answers from 54 (49.5%) of the patient's associated general practitioners; 4 GPs had 2 patients enrolled. Our study's flow chart is provided in Figure 1. General Practitioners averaged 54 years of age and the majority worked in an urban environment. Table 1 shows the characteristics of participating general practitioners. Death in ED occurred for 47 patients (43.1%), and after 21 days, 84 (77.1 %) patients had died in hospital. Eight patients (7.3%) were not hospitalized and returned to their living place.

General Perception of End of Life Patients' Management

We asked GPs about their general perception of their implication in the decision of withholding or withdrawing lifesustaining treatment (Table 1). Less than half (46.0%) reported having Balways^discussed end of life with patients who had a chronic disease. More often, the subject was broached by the family. More than two-thirds of GPs (66.0%) thought that end of life in ED was a failure and nearly half (46.0%) felt that end of life patients should not be admitted to the hospital. The majority (94.0%) thought that their involvement in these decisions was important and 68.0% wished to Balways^participate in end of life decisions for their ED patients in the future; 68.0% of GPs felt that they would have sufficient time in their practice to participate in these decisions.

General Practitioner Communication with ED and Families of End of Life Patients

Few of the interviewed (9.3%) had sent these patients to the ED and less than half (44.0%) were aware of the patient's admission (Table 2). Most commonly, they were advised after patient discharge. About a fifth (20.4%) reported receiving a call from emergency physicians to get additional patient information; this was largely about the patient's medical history. There were few calls about the existence of advance directives or a trustworthy person. Finally, only 4 (7.4 %) GPs were involved during the decision-making process of withholding or withdrawing life-sustaining treatments, though most (88.9%) GPs agreed with the decision. Among the GPs, the majority (53.7%) were contacted by family members after the decision, most often to talk about their difficult experience with the decision.

DISCUSSION

We found that very few GPs were involved in the decision-making process of withholding or withdrawing life-sustaining treatments during the ED visit, although most of them would like to participate. Importantly, our GPs felt that time was not an obstacle. GPs can be a valuable source of information regarding the premorbid condition of the patients and their wills and wishes and can help ED providers to set appropriate treatment goals, especially in the context of end of life decisions. Our results are similar to previous studies. GPs were rarely involved in ED end of life decisions and GPs felt that their participation was important and that time would not be an obstacle for involvement. [START_REF] Conte | Death in emergency departments: a multicenter cross-sectional survey with analysis of withholding and withdrawing life support[END_REF][START_REF] Ferrand | Participation of French general practitioners in end of life decisions for their hospitalised patients[END_REF] The involvement of GPs is crucial in ED decision-making and numbers of guidelines recommend direct involvement of GPs. [START_REF] Feral -Pierssens | Withholding and withdrawing life-sustaining therapies from adult patients in emergency care[END_REF][START_REF]European Recommendations for End-of-Life Care for Adults in Departments of Emergency Medicine[END_REF][START_REF]End of Life Care for Adults in the Emergency Department[END_REF] But both specialties face challenges in end of life care. On the one hand, ED are not the most appropriate place to give end of life care [START_REF] Chan | End-of-life models and emergency department care[END_REF] even though many patients came to die in the ED. [START_REF] Conte | Withholding and withdrawing life-support therapy in an Emergency Department: prospective survey[END_REF][START_REF] Conte | Death in emergency departments: a multicenter cross-sectional survey with analysis of withholding and withdrawing life support[END_REF][START_REF] Olsen | Death in the emergency department[END_REF] Ethical aspects must be respected in end of life decisions such as the respect of the patient's autonomy and the non-malfeasance principle. [START_REF] Beauchamp | Principles of Biomedical Ethics (5e édition)[END_REF] Emergency physicians must take the best treatment option which gives most overall benefit and is least restrictive of the patient's future choices. GPs seem not to be identified by the ED as a source of information concerning patients' end of life wishes even if it was discussed previously. On the other hand, GPs should help emergency physicians to discuss the level of care for their patients. Even if it would not be reasonable for the GPs to be reachable in the middle of the night, they could be included in the decisionmaking in the following days especially for noncommunicating patients.

Another challenge between the two specialties is the end of life communication. GPs should discuss end of life wishes with concerned patients. Indeed, most patients didn't have advance directives before admission in the ED. [START_REF] Douplat | Difficulty of the decision-making process in emergency departments for end of life patients[END_REF] It could help to avoid sending them to the ED. It has been found that the main perceived barriers of GP-patient communication in palliative care are the lack of availability of GPs and also GPs' ambivalence to discuss 'bad prognosis. [START_REF] Slort | Perceived barriers and facilitators for general practitioner-patient communication in palliative care: a systematic review[END_REF][START_REF] Slort | Facilitators and barriers for GP-patient communication in palliative care: a qualitative study among GPs, patients, and end-of-life consultants[END_REF] Similarly, emergency physicians should ask patients or families whether they have made advance directives or any advance care plans with their GP.

GPs have also expressed dissatisfaction with ED communication including discharge letters not reaching GPs and deficiencies in the discharge information from the ED. [START_REF] Lane | From emergency department to general practitioner: evaluating emergency department communication and service to general practitioners[END_REF] Discharging dying patients from the ED may be appropriate and a care plan must be initiated and continued at home as it is recommended. [START_REF]European Recommendations for End-of-Life Care for Adults in Departments of Emergency Medicine[END_REF][START_REF]End of Life Care for Adults in the Emergency Department[END_REF] Moreover, copies of a discharge letter should be given to the family, care homes, and the GP. Previous studies have shown benefit to communicating with GPs. A call to the GP was found to significantly decrease the patients' ED waiting time and the studies avoided by this contact were found to result in significant cost savings. [START_REF] Montalto | Impact of general practitioners' referral letters to an emergency department[END_REF] The last challenge in the context of end of life decision is the communication between families and GPs. After a death, the care given to the patient's family has a major influence on how they grieve. [START_REF] Rubin | Sudden unexpected death: intervention with the survivors[END_REF][START_REF] Walters | Family grief in the emergency department[END_REF] The ED can be a difficult place to care for the bereaved family. Rapid time management constraints and the chaotic work environment in ED could impact communication with the family. Because of the lack of information concerning the decision-making process, it could be difficult for GPs to help the bereaved patients.

Based on our results, several recommendations can be proposed. Firstly, including GPs in the decision-making process could help emergency physicians to choose the best treatment option. Secondly, GPs and ED should initiate end of life care planning which should be clearly documented and communicated between the two specialties. Eventually, communication based on a multidisciplinary approach including physicians, nurses, psychologists in the ED, and GPs could help families to better endure their ED experience.

Our study has a number of strengths and weaknesses. Our study was a prospective multicenter trial. Limitations include the low participation rate of GPs, the relatively small sample size, and the lack of data on patient family satisfaction with the care received.

CONCLUSIONS

In conclusion, our study underlines the importance of improving involvement of GPs in the decision-making process of withholding or withdrawing life-sustaining treatments in emergency departments. GPs wish to participate in these decisions and could serve a central role in improving communication between ED providers, the GPs, and the patients' families.

Figure 1

 1 Figure 1 Trial profile of general practitioners during study period.

Table 1

 1 General Practitioner Characteristics and Perception of End of Life Patient's Management

	Characteristic	n = 50
	Age (mean ± SD)	53.6 ± 14.6
	Women	13 (26.0%)
	Years of experience	24.1 ± 11.6
	Environment of practice
	Urban	37 (74.0%)
	Rural	13 (26.0%)
	Number of end of life patients followed per year
	Less than 5	23 (46.0%)
	5 to 10 More than 10 End of life management training (yes)	8 (16.0%) 19 (38.0%) 16 (32.0%)	Table 2 General Practitioner Communication with Emergency Departments and Families of End of Life Patients
	General perception in end of life management End of life wishes was discussed with concerned patients		Communication with ED	n = 54
	Always	23 (46.0%)
	Often	0	Decision to admit patient in ED	5 (9.3%)
	Sometimes	24 (48.0%)	Aware of patient admission	24
	Never	3 (6.0%)		(44.0%)
	The way the subject was broached		The way GP was advised
	Family initiative	29 (58.0%)	By family	13
	Physician initiative	26 (52.0%)		(24.1%)
	Patient initiative	20 (40.0%)	By hospital discharge letter	7 (13.0%)
	Decision not to admit an end of life patient in hospital		By hospital call	5 (9.3%)
	Always	0	By GP initiative to call	3 (5.6%)
	Often	23 (46.0%)	Call received from emergency physician to get	11
	Sometimes	25 (50.0%)	information about the patient	(20.4%)
	Never	2 (4.0%)	Type of information asked by the emergency physician
	Management of end of life in ED was a failure	33 (66.0%)	Medical past history	8 (14.8%)
	Involvement in these decisions was important	47 (94.0%)	Autonomy/life quality of the patient	5 (9.3%)
	Physicians participating in decision must be present	11 (22.0%)	Existence of advance directives	2 (3.7%)
	Usual participation required in these decision		Existence of a trustworthy person	2 (3.7%)
	Always	0	Involved in the decision of WH/WD	4 (7.4%)
	Often	2 (4.0%)	Agreement with the decision of WH/WD	48
	Sometimes	20 (40.0%)		(88.9%)
	Never	28 (56.0%)	Communication with family	n = 54
	Wished participation in these decisions		Contact with the family following the decision	29
	Always	34 (68.0%)		(53.7%)
	Often	0	Reason the family talked about the decision
	Sometimes	14 (28%)	Difficult experience with the decision	11
	Never	2 (4.0%)		(20.4%)
	Practice leave enough time to participate in these	34 (68.0%)	Medical information about the patient	7 (13.0%)
	decisions		Misunderstanding of the decision	3 (5.6%)
	This participation must be valued	22 (44.0%)

Data are expressed as N (%), unless otherwise indicated Data are expressed as N (%), unless otherwise indicated WH withholding, WD withdrawing

Funding Information

The study was financed by the APICIL FOUNDATION. The institution BHospices Civils de Lyon^received the funds for data management and analysis.

Compliance with Ethical Standards:

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' Contributions MD, LJ conceived the study, designed the trial, and obtained research funding. MD, KD, LJ, and KT supervised the conduct of the trial and data collection. MD, LJ undertook recruitment of participating centers and patients and managed the data, including quality control. JB and LJ provided statistical advice on study design and analyzed the data. MD drafted the manuscript, and all authors contributed substantially to its revision. MD takes responsibility for the paper as a whole. All authors read and approved the final manuscript.

Conflict interest:

The authors declare that they do not have a conflict of interest.

Ethics Approval and Consent to Participate:

The study was approved by the ethics committee of the University Hospital of Lyon on March 12th, 2015.

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