Jean Carlier
email: jean.carlier@cirad.fr

Stéphanie Robert

Véronique Roussel

Yolande Chilin-Charles

Nadia Lubin-Adjanoh

Aude Gilabert

Catherine Abadie

Central American and Caribbean population history of the Pseudocercospora fijiensis fungus responsible for the latest worldwide pandemics on banana

Keywords: Emerging disease, fungal plant pathogen, population history, dispersal, Musa, Pseudocercospora fijiensis

Among the emerging fungal diseases threatening food security, the Pseudocercospora fijiensis fungus causing black leaf streak disease of banana is one of the most marked examples of a recent worldwide pandemic on a major crop. We assessed how this pathogen spread throughout the latest invaded region, i.e. Central America and the Caribbean. We retraced its population history combining detailed monitoring information on disease outbreaks and population genetic analyses based on large-scale sampling of P. fijiensis isolates from 121 locations throughout the region. The results first suggested that sexual reproduction was not lost during the P. fijiensis expansion, even in the insular Caribbean context, and a high level of genotypic diversity was maintained in all the populations studied. The population genetic structure of P. fijiensis and historical data showed that two disease waves swept northward and southward in all banana-producing countries in the study area from an initial entry point in Honduras, probably mainly through gradual stepwise spore dispersal. Serial founder events accompanying the northern and southern waves led to the establishment of two different genetic groups. A different population structure was detected on the latest invaded islands (Martinique, Dominica and Guadeloupe), revealing multiple introductions and admixture events that may have been partly due to human activities. The results of this study highlight the need to step up surveillance to limit the spread of other known emerging diseases of banana spread mainly by humans, but also to curb gene flow between established pathogen populations which could increase their evolutionary potential.

Introduction

Of all plant pathogens, fungi are a highly serious threat to crops [START_REF] Almeida | The Still Underestimated Problem of Fungal Diseases Worldwide[END_REF]. Global trade and large-scale standardized farming are conducive to fungal disease dispersal and establishment worldwide [START_REF] Mcdonald | Rapid emergence of pathogens in agro-ecosystems: global threats to agricultural sustainability and food security[END_REF]. Thus many so-called 'emerging plant diseases' are caused by pathogens that have increased their geographical range and become invasive in new and wide areas [START_REF] Anderson | Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers[END_REF]. Studying diseases that have recently emerged may help improve surveillance [START_REF] Parnell | Surveillance to Inform Control of Emerging Plant Diseases: An Epidemiological Perspective[END_REF] and they may also be useful models for understanding evolutionary and ecological processes involved in pathogen populations, such as dispersal, reproduction mode, population bottlenecks, admixture and changes in phenotypic traits [START_REF] Stukenbrock | The origins of plant pathogens in agro-ecosystems[END_REF]. It is first essential to reconstruct the history of pathogen populations during range expansions when documenting these processes in emerging diseases [START_REF] Gladieux | The population biology of fungal invasions[END_REF].

The detection dates of a given emerging disease across geographical areas could first be documented to retrace the history of pathogen populations. This spatiotemporal or historical data can be used to estimate the speed of past emergences, which is a key aspect for understanding and reacting to future invasive species [START_REF] Evans | The Speed of Invasion: Rates of Spread for Thirteen Exotic Forest Insects and Diseases[END_REF]. However, historical data are not informative enough to retrace the population history and identify the epidemiological and demographic processes that have accompanied the spread of emerging fungal diseases.

Population genetics are often a valuable complementary approach for gaining clearer insight into the population biology of a fungal invasion (review in [START_REF] Gladieux | The population biology of fungal invasions[END_REF][START_REF] Mcdonald | Rapid emergence of pathogens in agro-ecosystems: global threats to agricultural sustainability and food security[END_REF]. Such studies have notably shown that, although fungi can be dispersed naturally through spore production, humans also play a role in the spread of diseases via the transport of agricultural products or infected plant materials. This role seems most evident between continents because it is harder for pathogens to travel several thousand kilometres by natural means alone. Yet it is not easy to disentangle human-mediated introductions from natural dispersal on smaller geographical scales. Several demographic or evolutionary processes often accompany fungal invasions, which may promote adaptations such as population bottlenecks, or admixture between populations following multiple introductions. Lastly, changes in life history traits during fungal invasions have been described and the loss of sexual reproduction is one of the most frequent.

Banana-a major high-value crop produced in tropical regions-is threatened by several pathogens [START_REF] Guzman | Black leaf streak[END_REF], including the fungus Pseudocercopora fijiensis which causes black leaf streak disease (BLSD). This emerging disease has spread from Asia throughout the entire intertropical zone in the last 50 years and is considered to be one of the most serious threats to food security [START_REF] Pennisi | Armed and Dangerous[END_REF]. In some areas, over 50 fungicides are applied annually to control the disease, which obviously has a major impact on human health and the environment (de Lapeyre de [START_REF] De Lapeyre De Bellaire | Black Leaf Streak Disease is challenging the banana industry[END_REF].

P. fijiensis is a heterothallic ascomycete fungus that produces two types of spores. Conidia (from asexual reproduction) disperse over scales of only a few metres, whereas ascospores (from sexual reproduction) are easily wind-dispersed over an average distance of 200 m, but with a fat-tailed dispersal kernel that may extend several kilometres [START_REF] Rieux | Long-distance wind-dispersal of spores in a fungal plant pathogen: estimation of anisotropic dispersal kernels from an extensive field experiment[END_REF].

Sexual and asexual reproduction concomitantly prevail in banana plantations [START_REF] Guzman | Black leaf streak[END_REF]. Human-mediated introductions may also accelerate regionwide disease spread through the movement of infected leaf material (planting or packing materials, [START_REF] Churchill | Mycosphaerella fijiensis, the black leaf streak pathogen of banana: progress towards understanding pathogen biology and detection, disease development, and the challenges of control[END_REF]. In a continental context, sexual reproduction is not lost during invasion and-although genetic discontinuity indicative of some stochastic long-distance dispersal events has been reported-the genetic structure of P. fijiensis populations on a country scale suggest that the disease is spread mainly by gradual stepwise spore dispersal [START_REF] Halkett | Genetic discontinuities and disequilibria in recently established populations of the plant pathogenic fungus Mycosphaerella fijiensis[END_REF]. Supporting this observation, the rate of P. fijiensis spread estimated from the spatiotemporal data published in [START_REF] Halkett | Genetic discontinuities and disequilibria in recently established populations of the plant pathogenic fungus Mycosphaerella fijiensis[END_REF] was close to that predicted under certain conditions by a modelling approach accounting for both asexual and sexual reproduction [START_REF] Hamelin | Mate Finding, Sexual Spore Production, and the Spread of Fungal Plant Parasites[END_REF].

In the Americas, BLSD was first detected in Honduras in 1972 and was most probably introduced by humans [START_REF] Robert | Contrasting introduction scenarios among continents in the worldwide invasion of the banana fungal pathogen Mycosphaerella fijiensis[END_REF][START_REF] Robert | MULTIPLE INTRODUCTIONS AND ADMIXTURE AT THE ORIGIN OF THE CONTINENTAL SPREAD OF THE FUNGAL BANANA PATHOGEN MYCOSPHAERELLA FIJIENSIS IN CENTRAL AMERICA[END_REF], spreading northwards and southwards and then reaching the Caribbean in 1990 [START_REF] Guzman | Black leaf streak[END_REF]. Despite surveillance networks set up in the French West Indies [START_REF] Teycheney | Development of plant epidemiological surveillance networks, data exchanges and joint response strategies in the Caribbean: the french experience[END_REF], the last disease-free islands of Martinique, Dominica and Guadeloupe were reached and infested by BLSD from 2010 onwards [START_REF] Ioos | First Report of Black Sigatoka Disease in Banana Caused by Mycosphaerella fijiensis on Martinique Island[END_REF]. This situation offered an opportunity to monitor a recent fungal invasion in an insular context, which could provide new information on the population biology of such organisms.

This study set out to retrace the population history of P. fijiensis throughout Central America and the Caribbean to gain insight into the epidemiological and demographic processes that have accompanied its spread in this latest invaded region. To that end, we gathered detailed monitoring information on disease outbreaks within the region. At the same time, we conducted a population genetic analysis using large-scale sampling throughout all bananaproducing countries of mainland Central and South America and the Caribbean Basin. We used these data to address the following questions: What were the BLSD invasion routes throughout Central America and the Caribbean Islands? Was the invasion speed slower in the Caribbean insular context in comparison to mainland areas? Did successive bottlenecks accompany this invasion? Did the expansion have an impact on sexual reproduction when the fungus passed from island to island in the Carribean? Could this analysis generate insight into the potential invasion drivers, e.g. natural fungal dispersal by the wind, or human activities?

Materials and Methods

Historical data analysis

Historical P. fijiensis invasion data were collected in 12 Caribbean banana-producing countries. We noted the date and geographical coordinates (decimal units for latitude and longitude) for the locations corresponding to the first, middle and last detection of the disease in each country (Supplementary Dataset 1). The historical dataset was divided into two subsets corresponding to a northern front and a southern front. The northern front was represented by five countries (Cuba, Jamaica, Haiti, Dominican Republican, Puerto Rico) mainly distributed along a longitudinal east-west gradient and the southern front by seven countries (Trinidad, Grenada, St Vincent, St Lucia, Martinique, Dominica, Guadeloupe) mainly distributed along a latitudinal north-south gradient. The time of occurrence in each location was calculated as the time between the date of detection in the location and the date of the first appearance in the northern or southern Caribbean, i.e. the Esmeralda location in Cuba (21.850°N, -78.100°W) and Coromandel location in Trinidad (10.117°N, -61.800°W), respectively. These locations were considered as the origin of disease spread in the two Caribbean areas. A linear model between the occurrence time and geographical distance from the origin of each front was tested using the lm function of R 3.6.0 (http://www.Rproject.org). The Euclidian distance was chosen because the study locations for each of the fronts were more or less distributed along the same line (from west to east for the northern front and from south to north for the southern front). The speed of the invasion waves was calculated as the inverse of the regression slope.

Fungal material sampling and DNA extraction

Leaf samples were collected from most banana-growing countries in mainland tropical of Central, South America and the Caribbean Basin (Table1, Supplementary Dataset 1).

Overall, 2,188 fungal individuals from 121 different geographical locations and 19 countries were analysed. The host plants were susceptible clones of Cavendish banana and plantain cultivars. The samples from Central and South America (Mexico, Honduras, Costa Rica, Panama, Columbia and Venezuela) had been previously collected between 1991 and 2009 for the purposes of the study of [START_REF] Robert | Contrasting introduction scenarios among continents in the worldwide invasion of the banana fungal pathogen Mycosphaerella fijiensis[END_REF]. All of the other Caribbean samples were collected between 2009 and 2014 for this study. Overall, the samples first included 55 populations from 19 countries. The sampling effort was mainly focused on the Caribbean islands (46 populations), including three populations collected from three different locations per island, and even more were collected from two recently contaminated islands in the French West Indies (14 populations in Martinique and 6 in Guadeloupe). The populations consisted of around 18 to 60 fungal isolates collected simultaneously from different plants in the same banana cropfield. More intensive sampling was carried out in Martinique and Guadeloupe for the purpose of a landscape genetic analysis. About 397 isolates were collected from 27 locations in Guadeloupe and around 680 isolates were collected from 58 locations in Martinique (3 to 17 isolates per location) based on a two-dimensional sampling scheme designed to cover the two islands entirely (Supplementary Dataset 1).

Most of the populations were composed of fungal isolates grown in the laboratory. In this case, each individual corresponded to a strain derived from a single spore (sexual or asexual spores, depending on the case) isolated from an infected banana leaf. Growing mycelium resulting from spore germination was placed on solid medium (300 mL V8, 3 g CaCO3, 20 g/L agar, pH 6) for 2 weeks at 25°C, before being dried for 2 days at 55°C and ground. DNA was extracted from growing mycelium as described in [START_REF] Halkett | Genetic discontinuities and disequilibria in recently established populations of the plant pathogenic fungus Mycosphaerella fijiensis[END_REF].

For some populations, young lesions ("leaf streak") were cut from banana leaves, dried for a day at 55°C and stored at -20°C. Each lesion was considered to have been caused by a single fungal strain. DNA was directly extracted from the sampled lesions using the protocol described in [START_REF] Robert | Optimized genotyping with microsatellite markers in the fungal banana pathogen Mycosphaerella fijiensis (Mycosphaerellaceae)[END_REF].

Microsatellite marker genotyping

All individuals were genotyped using 16 microsatellite markers. These markers had already been used in other studies [START_REF] Robert | Optimized genotyping with microsatellite markers in the fungal banana pathogen Mycosphaerella fijiensis (Mycosphaerellaceae)[END_REF][START_REF] Zapater | Microsatellite markers for the fungal banana pathogens Mycosphaerella fijiensis, Mycosphaerella musicola and Mycosphaerella eumusae[END_REF]. They were combined in three multiplex panels of four markers for the first panel, and six markers for the other two panels (Table S1, Supplementary file 1). PCR amplification and genotyping were carried out as described in [START_REF] Robert | Optimized genotyping with microsatellite markers in the fungal banana pathogen Mycosphaerella fijiensis (Mycosphaerellaceae)[END_REF]. The genotype was obtained for at least 10 markers for all of the studied isolates, which was largely enough for genetic assignment [START_REF] Arthofer | Identifying the minimum number of microsatellite loci needed to assess population genetic structure: A case study in fly culturing[END_REF].

Linkage disequilibrium and genetic diversity analysis

Multi-locus gametic linkage equilibrium was estimated using the standardized index of association [START_REF] Agapow | Indices of multilocus linkage disequilibrium[END_REF] and tested with the poppr R-package [START_REF] Kamvar | Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction[END_REF]. Gametic linkage disequilibrium between all pairs of loci was also tested in all populations using Fisher's exact tests implemented in the genepop R-package with default parameters [START_REF] Rousset | genepop'007: a complete re-implementation of the genepop software for Windows and Linux[END_REF], and the false discovery rate (FDR) procedure implemented in the qvalue R-package version 2.12.0 (http://github.com/jdstorey/qvalue).

Genetic variation within the 55 populations with more than 18 isolates was estimated according to the number of polymorphic markers (out of 16), the number of multi-locus genotypes (MLG), and by 3 descriptive indices averaged across markers: gene diversity index (equivalent to the mean expected heterozygosity He [START_REF] Nei | Molecular Evolutionary Genetics[END_REF]), Simpson's index λ [START_REF] Hill | Diversity and evenness: a unifying notation and its consequences[END_REF] computed using the poppr R-package [START_REF] Kamvar | Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction[END_REF] and the mean allelic richness (Ar), calculated using the rarefaction method implemented in the hierfstat R-package [START_REF] Goudet | HIERFSTAT, a package for R to compute and test hierarchical F-statistics[END_REF]. The cumulative geographic distance from the Honduras population HND3

(close to the site where the epidemic was assumed to have started in the Americas) was calculated for each population following a point-by-point pathway northward and southward.

The correlation between that geographic distance and Ar or He was tested using the lm function of R 3.6.0 (http://www.R-project.org).

The BOTTLENECK program [START_REF] Piry | Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data[END_REF] was used to detect a potential signal of recent population bottlenecks based on the detection of excess heterozygosity (in the sense of [START_REF] Nei | Molecular Evolutionary Genetics[END_REF] gene diversity) compared to heterozygosity expected under the mutation-drift equilibrium assumption, from the observed number of alleles. The expected heterozygosity distribution was estimated based on 1000 replications assuming a two-phase mutation model (TPM: 70% stepwise mutation model (SMM), 30% IAM)), as recommended for microsatellite markers [START_REF] Dirienzo | MUTATIONAL PROCESSES OF SIMPLE-SEQUENCE REPEAT LOCI IN HUMAN-POPULATIONS[END_REF]. We then used the Wilcoxon significance test to determine whether loci displayed significant excess heterozygosity.

Population structure analyses

An admixture analysis was conducted by estimating the individual ancestral coefficients with the sNMF algorithm implemented in the LEA R-package [START_REF] Frichot | LEA: AnRpackage for landscape and ecological association studies[END_REF].

This approach is similar to Bayesian clustering programs, such as STRUCTURE [START_REF] Pritchard | Inference of population structure using multilocus genotype data[END_REF]. The K value representing the number of ancestral populations ranged from 1 to 10, and 10 independent runs were performed for each K value. The number of ancestral populations that best explained the genotypic data was chosen based on the cross-entropy criterion. This criterion evaluates the quality of fit of the statistical model to the data using a cross-validation technique [START_REF] Frichot | LEA: AnRpackage for landscape and ecological association studies[END_REF]. The maximum local contribution to ancestry was spatially interpolated at each geographical map point. Barplots were drawn up by taking the ancestry coefficient estimated for individuals or averaged for individuals belonging to the 55 populations presented in Table 2. Principal components analysis (PCA) of these populations was performed with the dudi.pca function using the R software ade4

package [START_REF] Dray | The ade4 package: Implementing the duality diagram for ecologists[END_REF].

To further evaluate the population structure and the degree of differentiation on different scales, we used an analysis of molecular variance (AMOVA) framework, as implemented in the HierFstat R-package [START_REF] Goudet | HIERFSTAT, a package for R to compute and test hierarchical F-statistics[END_REF]. The significance of the F-statistics estimated on different geographical levels was tested using 1,000 permutations between units. The 95% confidence intervals of these F-statistics were computed by boostrapping over loci 1,000 times. Pairwise Fst [START_REF] Weir | Estimating F-statistics for the analysis of population structure[END_REF] were also estimated and tested (Fisher exact test) using the Genepop R-package. Genetic distances between populations were estimated using the Cavalli-Sforza chord distance and represented using a neighbour-joining tree, as implemented in POPULATIONS1.2.31 software (www.bioinformatics.org/~tryphon/ populations/).

Approximate Bayesian Computation (ABC) model choice analyses

A model choice analysis was carried out using the ABC random forest (ABC-RF) methodology developed by [START_REF] Pudlo | Reliable ABC model choice via random forests[END_REF], to test scenarios of P. fijiensis introduction into Martinique. An ABC model choice analysis involves a comparison of observed data with data simulated under the evolutionary scenarios to be compared. Like [START_REF] Fraimout | Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest[END_REF],

we carried out two analyses with two different sets of populations as representatives of their genetic cluster (e.g. the Honduras ancestral population, the northern group, the southern group and the two populations from Martinique; Table S2, Supplementary File 1).

For each of the three scenarios tested (Fig. S6, Supplementary File 1; see results for a description), we simulated 60,000 datasets (20,000 per scenario) using DIYABC v2.1.0 software [START_REF] Cornuet | DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data[END_REF]. Parameters were drawn up from the prior distributions described in Table S3 (Supplementary File 1). Time was expressed as numbers of generations, assuming 10 successive generations per year [START_REF] Varlas | An analysis of the synoptic and dynamical characteristics of hurricane Sandy[END_REF]. Historical data were used to define the bounds of the introduction events. A generalized stepwise mutation model as implemented in the software was used to simulate mutations at microsatellite markers.

Observed and simulated datasets were summarized using all the one-sample and two-sample statistics for microsatellite markers, as well as the linear discriminant analysis (LDA) axes, calculated in DIYABC v2.1.0 [START_REF] Cornuet | DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data[END_REF]. The quality of the simulated models (scenarios and priors) was checked using PCA on the space of the summary statistics, as implemented in DIYABC v2.1.0 [START_REF] Cornuet | DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data[END_REF] (Fig. S7, Supplementary File 1).

After simulating the datasets, we carried out an ABC-RF model choice analysis using the abcrf R-package [START_REF] Pudlo | Reliable ABC model choice via random forests[END_REF]). An ABC-RF analysis provides a classification vote for competing scenarios where the number of times a scenario is selected represents the model best fitting the dataset from a forest of 900 trees, as well as the posterior probability of the best selected model. The consistency of the ABC-RF model choice analysis was ensured by replicating the simulations and ABC-RF analysis 10 times and calculating the prior error rate (Table S4, Supplementary File 1). The number of votes and the prior error rate for each scenario, as well as the posterior probability of the best selected model, were averaged over the 10 replicates. Preliminary analyses using different numbers of datasets per scenario were carried out to ensure that we used a sufficient number of datasets and of trees for the random forest analyses.

Results

Overall, 2,188 fungal individuals from 121 different geographical locations were isolated (Table 1, Fig. 1A). The isolates were genotyped using 16 microsatellites. Historical records were concurrently obtained from various locations (Table 1, Fig. 1A and Supplementary Dataset 1). These data were used to conduct statistical and population genetic analyses.

Random mating reproduction mode in all invasive populations

A within-population genetic analysis of 55 populations with more than 18 isolates suggested the existence of sexual reproduction and random mating in all populations studied (Table 2). Overall, 1,354 multi-locus genotypes (MLGs) were detected for 1,617 isolates.

Around 12.5% (170/1,354) of the total number of MLGs were repeated in the whole sample.

Of these repeated MLGs, 68.2% (116/170) were found only twice in the whole sample.

Around 37.6% (64/170) were repeated within and 62.4% (106/170) between populations.

Thus, most of the repeated MLGs were found only twice overall and were distributed in two different populations. Around 38.2% (65/170) of the repeated MLGs included at least one isolate which was cloned from ascospores derived from sexual reproduction. Taken together, these observations did not enable us to conclude that isolates having the same MGLs were clones. Due to the limited number of markers used, in most cases they may have corresponded to MLGs that had been sampled twice by chance even in a sexual reproduction setting. Regardless, the presence of repeated MLGs had little impact on the population structure observed and the number of MLGs detected in each population was very close to the total number of isolates sampled (Simpson's index > 0.93). Furthermore, in all the populations, no pairs of loci were found to be in significant linkage disequilibrium using Fisher's exact test. The multilocus association index rd was significantly not null in 42%

(23/55) of the populations and 16% (9/55) of the populations, at 5% and 1% FDR, respectively. However, rd was significantly and negatively correlated with the gene (r=-0.46, p=2.9 10 -4 ) and genotypic diversity index (r=-0.53, p=2.5 10 -5 ; Fig. S1, Supplementary File 1), suggesting that some linkage disequilibrium may have resulted from bottleneck effects, as described in theoretical studies [START_REF] Rogers | How population growth affects linkage disequilibrium[END_REF].

Two epidemic waves from Honduras with serial founder effects shaping the population structure

The chronology of the first disease occurrence dates in the different countries and locations (Table 1, Fig. 1A) suggested that P. fijiensis spread via two separate epidemic waves throughout the Central America-Caribbean Basin region. Starting from Honduras in 1972, a northern wave followed a route from Guatemala to Porto Rico (first detection in 2004), while a southern wave followed a route from Nicaragua to Guadeloupe (first detection in 2012). In the Caribbean, significant linear regressions were obtained between the occurrence times and the distances from the point considered as being the origin for each wave (R²=0.77 and 0.85 and p = 1.6e-05 and 1.9e-09, respectively, for the northern and southern waves, Fig. 2). The speed of invasion was calculated as the inverse of the slope of each linear regression and was very close for the two waves, with 6.8 km/month (81 km/year) for the northern wave and 6.7 km/month (80 km/year) for the southern wave.

The population genetic structure of P. fijiensis in America was found to be in accordance with historical data and showed that serial founder events accompanying the northern and southern waves had led to the establishment of different genetic groups. The highest levels of allelic richness (Ar) and Nei's gene diversity (He; [START_REF] Nei | Estimation of average heterozygosity and genetic distances from a small number of individuals[END_REF] were detected in Honduras, where the disease was first introduced in the area (Table 2). Significant signatures of bottlenecks on the distribution of allele frequencies were observed in 34% (19/55) and 16% (9/55) of the populations, at 5% and 1% FDR, respectively (Table 2). The number of microsatellite markers and sample sizes used may have been too low to have sufficient statistical power for tests on some samples [START_REF] Peery | Reliability of genetic bottleneck tests for detecting recent population declines[END_REF]. To further highlight the bottleneck effects, gene diversity in each location, as estimated by the Ar or He parameter, was plotted against the cumulative step-by-step geographical distance from Honduras following the migration waves (Fig. 3). A significant negative correlation was detected when jointly considering the northern and southern waves and genetic parameters. It was found that the population gene diversity decreased with the distance from Honduras.

Individual ancestry coefficients were first estimated using the sNMF algorithm [START_REF] Frichot | LEA: AnRpackage for landscape and ecological association studies[END_REF]. The cross-entropy curve suggested a K value of 3 to 4, which could be considered as representative of major ancestral populations (Fig. S2, Supplementary File 1).

The study populations were then considered to have derived from this ancestral gene pool and the ancestry coefficients gave the proportions of individual genomes originating from the different components of the pool. For a clear display of the results, barplots for the 55 populations with more than 18 isolates were drawn up from the mean values of the ancestry coefficients with K ranging from 3 to 9 (Fig. S3, Supplementary File 1). The major population structure in the study area was actually observed from a value of K=4 and a barplot of individual ancestry coefficients for this value is also presented in Fig. S4 (Supplementary File 1). To highlight the population genetic structure, the maximum value of the ancestry coefficients for each isolate was spatially interpolated (Fig. 1B), and a barplot of the mean values of the ancestry coefficients for K=4 and for 55 populations with more than 18 isolates is presented below (Fig. 1 C), as well as a neighbour-joining tree constructed from the genetic distance (Fig. 1 D). These analyses highlighted the existence of three major genetic groups corresponding to three geographical areas: a central group contained populations located in Central America, Colombia and Jamaica; a second northern group contained populations located in the northern Caribbean, from Cuba to Puerto Rico (there was some sub-structuring in this group); and a third, fairly uniform, southern group contained populations from Venezuela to the southern Caribbean islands to as far as Saint Lucia. PCA analysis revealed a very close structure (Fig. S5, Supplementary File 1).

F-statistics were estimated at different hierarchical levels using AMOVA, while excluding populations from Martinique, Dominica and Guadeloupe (Table 3). Three levels were considered: populations, countries and groups corresponding to the one defined from the clustering analysis. Significant values were obtained at all the levels tested. However, a larger proportion of the genetic diversity was distributed between groups (F=0.23) rather than between countries within groups (F=0.05). Genetic differentiation between populations within groups and within countries was similar (F=0.16 and 0.12, respectively). These estimations seemed to indicate that the groups were genetically quite homogeneous and different from each other.

Long-distance introduction and admixture events

We found a different population structure on islands consecutively located north of Saint Lucia (Martinique, Dominica and Guadeloupe), revealing multiple introductions and admixture events (Fig. 1). Based on some preliminary results, dense two-dimensional sampling was carried out in Martinique and Guadeloupe (Table 1, Fig. 1A), which enabled us to map the introduced genetic groups. Some individuals and populations from both ends of Martinique were related to the southern genetic group, while other samples located in the centre of the island were related to the northern genetic group, thus suggesting that there had been at least two independent introductions on that island (Fig. 1B and1C). Three populations from Martinique (MTQ2, MTQ3, MTQ13) were grouped on the middle branches of the tree presented in Fig. 1 D. Geographically, these samples were located at junctions between differentiated populations belonging to the northern and southern genetic groups and we suspected some admixtures between them. F-statistics between populations in Martinique belonging to different genetic groups were sometimes very high, with a maximum at F = 0.51 (Table 3), and the highest values were obtained when comparing populations from the ends of Martinique (MTQ1 and MTQ14) with all the others (Supplementary Dataset S1). The PCA projection of the populations from Martinique, Dominica and Guadeloupe were either close to one of the three major groups (southern, northern and central) or between these groups, thus supporting the idea that there have been multiple introductions and admixture events on these islands (Fig. S5, Supplementary File 1). Based on this descriptive analysis, three different scenarios for P. fijiensis introduction into Martinique were tested using an ABC procedure.

The first scenario assumed two independent introductions into Martinique from two populations belonging to the northern and southern migration waves (Fig. S6, Supplementary

File 1). Two other scenarios assumed a single introduction event from one population belonging to the northern or southern migration wave, with the second population from Martinique emerging from the first one after its establishment. In all the scenarios, Honduras was considered as the ancestral population and all the founded populations came through a bottleneck when introduced. In the first scenario, we considered that genetic differentiation between Martinican populations was the result of the combination of bottleneck effects and introductions from different origins, whereas in the other two scenarios genetic differentiation resulted only from bottleneck effects. The former scenario was found to be the most probable using two different sample sets (mean posterior probability of 0.95 ± 0.008; Table S4, Supplementary File 1).

Populations from Dominica and Guadeloupe showed intermediate patterns for ancestry coefficients (Fig. 1 C), and were all located in the middle of the tree in Fig. 1D, except population GLP5, which was more related to the southern genetic group. This distinct population had the lowest genetic diversity level in Guadeloupe (Table 2) and a very high level of genetic differentiation between all the other populations from Guadeloupe (0.27< F <0.41, p<0.0001; Supplementary Dataset 1). Thus, population GLP5 may have stemmed from a rare intense bottleneck and/or an independent introduction from the southern wave. It was not included in further analysis so as to be able to highlight the general trends. The range of pairwise F-statistics between populations from Guadeloupe and Dominica was lower or very close to that obtained between populations within countries in the central, northern or southern genetic groups (F<0.14, Table 3 and Supplementary Dataset 1). Taken together, all of the above observations suggested that the populations in Guadeloupe and Dominica were founded from an admixture between the northern and southern genetic groups.

The correlations between genetic diversity (estimated by Ar and He) and the cumulative geographical distance from Honduras for samples from Martinique, Dominica and Guadeloupe were significantly positive (Fig. 3), thus indicating that genetic diversity had increased on those islands, in contrast with samples from other countries. The estimated values of both parameters for the furthest samples from Honduras located in Guadeloupe were close to those obtained from Honduran samples. These results strongly indicated that there had been multiple introductions and/or admixtures on these islands.

Discussion

This study set out to gain insight into epidemiological and demographic processes that have accompanied the spread of the P. fijiensis fungus which causes black leaf streak disease of banana in the latest invaded region, i.e. Central America and the Caribbean. To that end, the population history of P. fijiensis in this region was retraced based on an analysis of successive recorded outbreaks and on the population genetic structure. Taken together, the findings of these analyses revealed that two different epidemic waves of P. fijiensis in the northern and southern Central America-Caribbean Basin region, starting from Honduras, shaped the P.

fijiensis population structure through serial founder events. This led to the formation of relatively homogenous genetic groups in the northern and southern Caribbean region, with lower genetic diversity compared to Central America and with genetic differentiation between them. The situation was unique in Martinique, Dominica and Guadeloupe. The results suggested independent introductions of P. fijiensis in Martinique from both the northern and southern origins and an admixture between these introductions accompanying the consecutive spread of the disease in Dominica and Guadeloupe.

In contrast with what is frequently observed in fungi [START_REF] Gladieux | The population biology of fungal invasions[END_REF], the genotypic diversity and linkage disequilibirum analysis showed that sexual reproduction was not lost in P. fijiensis during the invasion in the study region, above all following the serial founder events that occurred in the Caribbean region. Thus, all the populations probably kept the ability to produce wind-dispersed ascospores, which have been shown to disperse over long distances [START_REF] Rieux | Long-distance wind-dispersal of spores in a fungal plant pathogen: estimation of anisotropic dispersal kernels from an extensive field experiment[END_REF]. Ascospores might be essential for P. fijiensis to colonize new areas. Furthermore, the correlation between the occurrence time and the distance from a geographical point considered as the origin in each wave, and the close values obtained for the invasion speed between the two waves (about 80 km/year), suggested a similar and regular step-by-step migration trend. This speed was also close to that estimated in another study in a continental context (about 90 km/year) and using a modelling approach accounting for both sexual and asexual reproduction (about 100 km/year but with mate limitation; [START_REF] Hamelin | Mate Finding, Sexual Spore Production, and the Spread of Fungal Plant Parasites[END_REF]). Lastly, the existence of homogeneous genetic groups within each wave was also in favour of regular step-by-step migration. Taken together, all the data obtained in this study suggested that P. fijiensis spread throughout the Caribbean mainly via windborne spore dispersal. Although insular, the Caribbean geography formed by around 30 islands distributed along arcs and separated by 50 to 200 km might have made this spreading pattern possible. However, we cannot rule out that there may have been some impacts due to human activities, especially with regard to island-to-island dissemination. Indeed, for human activities as well as windborne spore dispersal, the shorter the distance between islands, the more likely exchanges were and both means of dispersal might have had a similar impact on the pathogen population structure.

The latest islands to be invaded (Martinique, Dominica and Guadeloupe) appeared to have been as a special case and the existence of two differentiated genetic groups between the northern and southern Caribbean led us to detect multiple introductions. What historical scenario could explain the genetic pattern observed? Through surveillance networks [START_REF] Teycheney | Development of plant epidemiological surveillance networks, data exchanges and joint response strategies in the Caribbean: the french experience[END_REF], we found that Dominica and Guadeloupe-located north of Martinique-were disease-free when BLSD was first detected in Martinique in 2010 (Table 1). Evidence of the introduction of BLSD in Martinique was the detection of an admixture between the northern and southern genetic groups in Dominica and Guadeloupe, while these groups were not found to be admixed in different locations in Martinique. Populations belonging to the southern genetic group might have been introduced into Martinique from Saint Lucia by windborne spore dispersal. However, an introduction into Martinique from the northern wave via wind would imply that the disease had first jumped to Guadeloupe and Dominica and then returned. This movement pattern would be contrary to the prevailing winds (east to west in the northern Caribbean) and hurricane trajectories [START_REF] Andraca-Gómez | A potential invasion route of Cactoblastis cactorum within the Caribbean region matches historical hurricane trajectories[END_REF][START_REF] Varlas | An analysis of the synoptic and dynamical characteristics of hurricane Sandy[END_REF]. Although the existence of rare and stochastic long-distance disease dispersal events cannot be ruled out, another likely introduction scenario for these islands that were invaded last might be: windborne introductions into Martinique from the south (via the south to north prevailing winds) or via human activities from the north. The disease could then have been introduced into Dominica and Guadeloupe from Martinique, likely by wind dispersal, accompanied by an admixture of the two introduced genetic groups.

The P. fijiensis fungus may have been at least partly introduced into Martinique by humans despite the surveillance network operating in the Caribbean [START_REF] Teycheney | Development of plant epidemiological surveillance networks, data exchanges and joint response strategies in the Caribbean: the french experience[END_REF]. Such events accompanying plant pathogen colonization on local or regional scales are difficult to detect and could be more frequent than imagined. Furthermore, the surveillance systems are not considered to be effective enough to avoid human-mediated introduction of crop diseases [START_REF] Carvajal-Yepes | A global surveillance system for crop diseases[END_REF]. Only an estimated 2 to 6% of all cargo entering a country can be effectively controlled [START_REF] Carvajal-Yepes | A global surveillance system for crop diseases[END_REF] and there is a lot of uncontrolled boat traffic in the Caribbean, especially small private boats illegally transporting vegetables between neighbouring islands. Our results supported this view and highlighted the need to reinforce surveillance to cope with other emerging crop diseases that have yet to be spread worldwide. The surveillance system in Australia has been efficient enough to eradicate BLSD eight times with the application of strict and intensive measures [START_REF] Guzman | Black leaf streak[END_REF] requiring full collaboration of many actors (from growers to the Ministry of Agriculture). On a global scale, strong international collaboration between such actors is key for preventing and excluding emerging plant diseases. Furthermore, reinforcing surveillance could have more evident impacts on the invasion patterns of pathogens mainly spread through human activities. In the case of banana, this could be crucial for limiting the spread of the very destructive soilborne Fusarium wilt tropical race 4 (Fusarium oxysporum fsp cubense TR4) fungus as a first outbreak in the Americas was suspected very recently in Colombia [START_REF] Stokstad | Banana fungus puts Latin America on alert[END_REF], and humans are considered to be the principal vector of dispersal [START_REF] Dita | Fusarium Wilt of Banana: Current Knowledge on Epidemiology and Research Needs Toward Sustainable Disease Management[END_REF][START_REF] Qazi | Banana bunchy top virus and the bunchy top disease[END_REF]. The same reasoning could be applied to the banana bunchy top virus, which is not present in the Americas and is thought to be spread over long distances through infected plant material [START_REF] Jacobsen | Seed degeneration of banana planting materials: strategies for improved farmer access to healthy seed[END_REF].

Even after a serial founder effect during its expansion in Central and South America and the Caribbean, all of the P. fijiensis populations studied had maintained a relatively high level of genetic diversity and sexual reproduction. It has been suggested that P. fijiensis is among the plant pathogens with the highest evolutionary potential [START_REF] Mcdonald | Pathogen population genetics, evolution potential, and durable resistance[END_REF] and this feature might remain true even in recently invaded regions. This assumption is supported by the erosion of quantitative resistance in newly disseminated hybrids on two Caribbean islands (Cuba and Dominican Republic), although local adaptation of P. fijiensis populations was demonstrated recently in cross-inoculation experiments [START_REF] Dumartinet | Pattern of local adaptation to quantitative host resistance in a major pathogen of a perennial crop[END_REF].

Furthermore, admixtures between different genetic backgrounds, as observed in Dominica and Guadeloupe, increase the genetic diversity upon which selection can act [START_REF] Lavergne | Increased genetic variation and evolutionary potential drive the success of an invasive grass[END_REF] and produce novel genotypes that may facilitate adaptation to novel habitats [START_REF] Rius | How important is intraspecific genetic admixture to the success of colonising populations?[END_REF], corresponding here to novel resistant varieties. This hypothesis could be tested by comparing the pathogenicity of non-admixed or admixed P. fijiensis genotypes on different hosts through cross-inoculation experiments. Furthermore, it can already be stressed that an effective surveillance system could be an integral part of sustainable strategies for plant resistance deployment, which can be seen as the most promising way of controlling emerging plant diseases [START_REF] Mundt | Durable resistance: a key to sustainable management of pathogens and pests[END_REF]. Indeed, a second outcome of such a system might be to restrain gene flows between pathogen populations, which might affect their evolutionary potential. [START_REF] Hill | Diversity and evenness: a unifying notation and its consequences[END_REF], 5 Nei's gene diversity [START_REF] Nei | Estimation of average heterozygosity and genetic distances from a small number of individuals[END_REF]

Fig. 1 .Fig. 2 .

 12 Fig. 1. Invasion and population genetic structure of Pseudocercospora fijiensis in the

Table 1

 1 Summary of the historical data and Pseudocercospora fijiensis samples collected. 702

		Country/Place	First official detection	Sampling
			(month/year)	(Number)
						Collected	Reference
				Locations	Isolates	years
		France/Guadeloupe	01/2012	27	397	This study
		Dominica	06/2012	3	91	This study
		France/Martinique	09/2010	58	680	This study
		Saint Lucia	01/2010	3	78	This study
		Saint Vincent	10/2009	3	94	This study
		Grenada	11/2005	1	25	This study
		Trinidad	11/2003	3	68	This study
		Venezuela	1991*	1	26	Robert et al., 2012
		Colombia	1981*	2	46	Robert et al., 2012
		Panama	1981*	1	31	Robert et al., 2012
		Costa Rica	1979*	1	30	Robert et al., 2012
		Honduras	1972*	3	80	Robert et al., 2012
		Mexico	1980*	1	11	Robert et al., 2012
		USA/Florida	1998*	1	60	This study
		Cuba	11/1989	3	138	This study
		Jamaica	08/1995	3	95	This study
		Haiti	01/2000	3	85	This study
		Dominican Rep.	09/1996	3	127	This study
		USA/Puerto Rico	08/2004	1	26	This study
		Total		121	2188
	703	*According to Jones (Jones, 2019)

Table 2

 2 Genetic statistics calculated in Pseudocercospora fijiensis populations. 704 Number of multi-locus genotypes (MLG), 2 Percentage of polymorphic loci, 3 Allelic richness, 4 Simpson's 705 index

	Country	Code	Number	Number	P 2	Ar 3	λ 4	He 5 Test of He	7
			of	of				excess 6
			isolates	MLG 1
	France/ Guadeloupe	GLP1	32	31	93.8 2.02 0.97 0.41 **	0.00
		GLP2	20	17	87.5 1.94 0.94 0.33 NS 8	0.02
		GLP3	25	24	87.5 1.90 0.96 0.38 **	0.02

1

Table 3

 3 ,6 Wilcoxon test implemented in Bottleneck software(Piry 706 et al., 1999), 7 Standardized index of association[START_REF] Agapow | Indices of multilocus linkage disequilibrium[END_REF]),8 Not significant, * Significant at a 707 false discovery rate (FDR) of 5%, ** Significant at a false discovery rate (FDR) of 1%. Genetic differentiation between Pseudocercospora fijiensis populations. 710

	708
	709
	Comparisons	F-statistic 1 CI-95% 2	Test
	Global analysis: AMOVA 3
	Between populations	0.36	0.30/0.43	Perm 4 ***

Acknowledgments

We thank all of the regional partners who sent either leaf samples or historical data: L.Perez Vicente from INISAV in Cuba, R. Anselm from the Ministry of Agriculture in Dominica, E. Prophète from the Ministry of Agriculture in Haiti, Debbie Henry and Janet Conie from the Ministry of Agriculture in Jamaica, K. Lombion, from the Ministry of Agriculture in Guadeloupe, T. Peter from the Ministry of Agriculture in Grenada, E. Husson from SICA-TG in Martinique, J. Iotti, from the Ministry of Agriculture in Martinique, M. Cortes from the Ministry of Agriculture in Porto Rico, L. Minier and J. Clase Salas, Secretary of Agriculture, Dominican Republic, L. Alexander from the Ministry of Agriculture in Saint Lucia, M. Richards from the Ministry of Agriculture in Saint Vincent, M. Fortune from the Ministry of Agriculture in Trinidad, M. Rivera from FHIA in Honduras, G. Rivas from CATIE in Costa Rica, and R. Ploetz from the University of Florida, USA. We would also like to thank Virginie Ravigné (CIRAD) for helpful discussions, Peter Biggins (CIRAD) and David Manley (freelance scientific translator) for their careful checking of the English.

Funding S. R.'s PhD was supported by a doctoral fellowship from CIRAD and the French Occitanie region, and A. G. by a post-doctoral fellowship from Agropolis Fondation (grant number 1504-004-E-Space). Experiments and sampling were supported by the Emerfundis-ANR project (grant number 07-BDIV-003), the Interreg IV Caribbean Programme (grant number 31409-Cabaré), the Prefecture de Guadeloupe (grant number FCR 2010/38), European Regional Development Fund and the Conseil Régional de Guadeloupe through the MALIN project.

Data availability

All the data are available in the manuscript or in the supplementary data.

Conclusions

The present study of the population history of the P. fijiensis fungus causing the latest major epidemic on bananas in the America-Caribbean area provided fresh insight into the epidemiological and demographic processes involved in this emerging plant disease. The results suggested that the fungus may have spread mainly through spores in this new area, which encompasses a large insular zone. However, probable human involvement in the spread of the disease was suspected on some islands. The evolutionary potential of new invasive P. fijiensis populations may remain high after serial founder events and could even increase following admixture, even on some islands. This study also highlighted that surveillance and quarantine measures need to be strengthened to cope with other emerging diseases of banana and potential gene flow between established pathogen populations. Possible pathogen adaptation following the release of new resistant hybrids should also be monitored in order to adjust resistance deployment strategies and hamper pathogen adaptation.

Declaration of competing interest

The authors declare that there is no conflict of interest.

Appendix A. Supplementary materials

Supplementary Dataset 1

Supplementary File 1