inoculated with *Melampsora medusae* f. sp. *deltoidae*, Coleman (1986) showed that the maximum level of susceptibility, measured by the number of lesions (uredinia) as a proxy for infection efficiency, is reached when the sugar availability is maximal, i.e., at a LPI of 5. This finding is inconsistent with our study, in which the infection efficiency did not vary with leaf maturity and the two responsive traits (uredinia size and sporulation rate) steadily increased from LPI 6 to 20. The discrepancy between Coleman (1986) experiments and our results may lie in differences in the growth conditions of poplar plants, which may lead to different levels of nutrient availability. The light quality available in growth chambers has been significantly improved during the last decades. In Coleman (1986), the total sugar content decreased from LPI 6 to 9 (the highest LPI measured). In our experiment, even though sugar contents displayed some variance among leaves, there was no consistent pattern along LPI. Our growth conditions may have led to sugar availability sufficient for steady infection success even at higher LPI. We therefore conclude that leaf sugar content may not be linked to leaf susceptibility *per se*.

Leaf nitrogen content also plays a role in fungal growth and sporulation (Bilgrami and Verma, 1978; Madan and Thind, 1998). Bilgrami and Verma (1978) stated that nitrogen sources are used by fungi for both structural and metabolic functions. These functions require the assimilation of nitrogen into amino acids, proteins, and some peptides. For example, Jensen and Munk (1997) and Robert et al. (2004) found that higher nitrogen fertilization enhanced barley powdery mildew and wheat leaf rust sporulation. In our experiment, although there was a substantial variance in leaf nitrogen contents [same order of magnitude as in Robert et al. (2004)], this variation did not significantly affect *M. larici-populina* development. The leaf nitrogen content only slightly correlated with latent period and sporulation capacity (urediniospores produced per day and per area).

Overall, we did not observe consistent variation in nutrient content along the leaf maturity gradient and, moreover, the variation in nutrient content was not correlated with the two responsive traits, uredinia size, and sporulation rate. Consequently, nutrient availability was apparently not the environmental factor best explaining fungal development in our experiment.

By contrast, the defense hypothesis, tested by assessing the constitutive level of phenolics (total and individual) appeared to better explain fungal development related to leaf age. Indeed, the total phenolic content decreased with leaf age and correlated negatively with uredinia size and sporulation rate. Although higher phenolic concentration in younger leaves is common in poplar and other plants (Coleman, 1986; Johnson and Kim, 2005; Royer et al., 2013; Ullah et al., 2017), the correlation between phenolic concentrations and resistance to fungal infection has not been demonstrated. Coleman (1986) and Coleman et al. (1987) did not detect a correlation between leaf phenolic content and the amount of uredinia produced by *M. medusae* on poplar trees. By contrast, in the same pathosystem, a negative correlation was observed between total phenolic content and urediniospore germination efficiency and hyphae length (Johnson and Kim, 2005). Other studies on black currant (*Ribes nigrum*) and willow

(*Salix myrsinifolia*) identified negative correlation between the concentrations of phenolic compounds and development traits of rusts (Hakulinen et al., 1999; Vagiri et al., 2017). By analyzing specific phenolics from poplar leaves, we were able to precisely identify groups of phenolic compounds that correlate with poplar resistance to rust. Among the four phenolic subclasses (from a total of 16 compounds), three subclasses (hydroxycinnamic acid esters, flavonols, and salicinoids) showed the same effect as total phenolic contents and correlated negatively with uredinia size and sporulation rate. The hydroxycinnamic acid ester chlorogenic acid has been shown to correlate to rust resistance in black currant (Vagiri et al., 2017). In addition, several studies highlighted the role of leaf surface flavonoids in the resistance to rust in poplar and birch (Shain and Miller, 1982; Valkama et al., 2005). In our study, we did not address the leaf location (inside or on the surface) of the two flavonols, rutin and quercetrin, however, the fact that they negatively correlate with rust development is in agreement with these previous studies. The only phenolic subclass that did not correlate with resistance to rust was flavan-3-ol which integrates catechin and proanthocyanidin dimers. A role of this phenolic subclass – especially catechin – in resistance to fungal pathogens, including rust fungi, has been proposed in several studies (Brignolas et al., 1998; Hakulinen et al., 1999; Vagiri et al., 2017). Moreover, a recent study on the *M. larici-populina* – *Populus nigra* pathosystem demonstrated that catechin is an effective chemical defense compound against rust (Ullah et al., 2017). This demonstration was based on the fact that (i) catechin is strongly induced upon rust infection, (ii) catechin exhibit a strong antifungal activity against *M. larici-populina* at *in planta* concentration, and (iii) poplar engineered to accumulate high or low levels of catechin were more resistant or more susceptible to rust, respectively. In our study, the concentrations of flavan-3-ols increased with leaf age. The same trend was observed in the study by Ullah et al. (2017) (see **Supplementary Figure S2**). The absence of a correlation between flavan-3-ol concentrations and rust resistance does not indicate that flavan-3-ols are not important factors in leaf rust resistance. Indeed, the compounds were assayed in uninfected leaves; hence reflect constitutive (i.e., non-induced) defense levels. In addition, the concentrations measured in our experiment (from 0.2 to 0.5 mg.g DW⁻¹) were below the concentrations proven to efficiently impair rust development on black poplar leaves (Ullah et al., 2017).

## CONCLUSION

*Melampsora larici-populina* development after single-spore inoculation onto poplar leaf disks was more constrained by the level of plant constitutive defenses than limited by nutrient availability. For the tested strain, uredinia size and sporulation rate, both components of the sporulation function, were the only traits responsive to reaction norms along a gradient of leaf maturity. This experiment – conducted on a single fungal isolate and on a single plant genotype – can nonetheless serve as a pilot study. We report the correlation of a fungal fitness component (sporulation) to an environmental characteristic imposed by the
leaf i.e., the constitutive level of phenolic content. In the future, it
would be worthwhile to assess the generality of this finding with
more fungal isolates and plant genotypes and decipher further the
interplay between constitutive and induced defense compound
levels. From a broader perspective, it would be interesting to
determine the plasticity and heritability of sporulation-related
traits to better assess how the complexity of leaf environment
shapes the evolutionary trajectory of a plant pathogen.

AUTHOR CONTRIBUTIONS

FH, PF, and BF designed the study. AM conducted the experiment and analyses. MP developed the single-spore inoculation and trait measurement methods. AA developed the qPCR method based on new primers and probe for *Melampsora larici-populina* in the rDNA ITS (Internal Transcribed Spacer) designed by CG and A-LB. RL conducted leaf phenolics analyses. AM and FH wrote the first draft of the manuscript. PF, MP, and RL contributed to the manuscript. All the authors approved the final version of the manuscript.

FUNDING

This work was supported by grants from the French National
Research Agency (Grant No. ANR-13-BSV7-0011, FunFit

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**Conflict of Interest Statement:** 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.

Copyright © 2018 Maupetit, Larbat, Pernaci, Andrieux, Guinet, Boutigny, Fabre, Frey and Halkett. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Adaptation of a Fungal Pathogen to Host Quantitative Resistance

Lise Frézal¹,², Guy Jacqua¹ and Claire Neema³*

¹ INRA-URPV, Guadeloupe, France, ² CNRS – ENS – INSERM, Institut de Biologie de l'École Normale Supérieure, Paris, France, ³ UMR BGPI, Montpellier SupAgro, Campus International de Baillarguet, Montpellier, France

Impact of host quantitative resistance on pathogen evolution is still poorly documented. In our study, we characterized the adaptation of the pathogenic fungus *Colletotrichum gloeosporioides*, to the quantitative resistance of its host, the water yam (*Dioscorea alata*). Genetic and pathogenic diversities of *C. gloeosporioides* populations were specified at the field scale. We used nuclear markers to describe fungal population structuring within and between six fields of three cultivars differently susceptible to the fungus. Strain aggressiveness was then quantified in the laboratory through cross-inoculation tests. The high level of genetic diversity and significant linkage disequilibrium revealed a significant influence of clonal reproduction in the *C. gloeosporioides* evolution. The recorded fungal migration between fields was weak (evidence for a dispersion mode via tubers rather than splashing dispersal), which provides the first molecular evidence for limited *C. gloeosporioides* migration via yam tuber exchanges. *C. gloeosporioides*'s populations are adapted to their host resistance. The aggressiveness of the fungal clones seems to have evolved toward an accumulation of components specific to each host cultivar. Despite the remaining marks of adaptation to the former widely cultivated host, adaptation to current cultivars was clearly depicted.

**Keywords:** local adaptation, aggressiveness, quantitative resistance, *Colletotrichum gloeosporioides*, *Dioscorea alata*

**OPEN ACCESS**

**Edited by:**

Josselin Montarry,
INRA Centre Bretagne-Normandie,
France

**Reviewed by:**

Fabien Halkett,
INRA Centre Nancy-Lorraine, France
Sébastien Guyader,
INRA Centre Antilles-Guyane, French
Guiana

**\*Correspondence:**

Claire Neema
claire.neema@supagro.fr

**Specialty section:**

This article was submitted to
Plant Microbe Interactions,
a section of the journal
Frontiers in Plant Science

**Received:** 17 June 2018

**Accepted:** 03 October 2018

**Published:** 31 October 2018

**Citation:**

Frézal L, Jacqua G and Neema C (2018) Adaptation of a Fungal Pathogen to Host Quantitative Resistance. *Front. Plant Sci.* 9:1554. doi: 10.3389/fpls.2018.01554

## INTRODUCTION

Discernment on the adaptive potential of plant pathogen species is essential to designing sustainable integrated pest management framework in which, durability of crop resistance is one crucial point. The adaptive potential of plant pathogen species highly depends on the forces shaping the pathogen evolution. Among these forces, the migration (spatial dispersal), the recombination (reproductive mode), genetic drift and above all, the selection by the host-plant resistances, are of particular importance (McDonald and Linde, 2002). Pathogen populations can either adapt to the most common host (general adaptation), or be divided into several entities, each adapted to one particular host genotype (local adaptation, Gandon et al., 1996; Gandon and Van Zandt, 1998). The theory of polygenic traits' evolution under divergent selection has not been much investigated, especially while investigating host pathogen interactions (Kawecki and Ebert, 2004). The few studies focusing on the host quantitative resistance impact on pathogen populations reported either the local adaptation of pathogen populations (Kaltz and Shykoff, 1998; Zhan et al., 2003), or a directional selection toward an increase in pathogen aggressiveness on all host cultivars (Andrivon, 1993; Andrivon et al., 2007; Montarry et al., 2007). In the present study, we described the population diversity of the plant pathogen *Colletotrichum gloeosporioides* in the Guadeloupean (French West Indies)
agrosystem. We also investigated the pathogen aggressiveness as an adaptive trait of the fungi to the quantitative resistance of its host, *Dioscorea alata*.

The filamentous ascomycete and plant pathogen *C. gloeosporioides* (Penz.) Penz. & Sacc. [telemorph: *Glomerella cingulata* (Stonem.) Spauld. & Schrenk] causes anthracnose disease on various crops (Bailey and Jeger, 1992; strawberry, Freeman et al., 1998; olive, Talhinhas et al., 2005; avocado, mango, stylo, Chakraborty et al., 1999; ornamental rain lupinines, Elmer et al., 2001; *Salsola tragus*, Berner et al., 2006; peach tree, Kim and Hong, 2008), among which the water yam (*D. alata*). The life cycle of this haploid and hemibiotroph fungus includes both sexual and asexual reproduction. If the two reproductive modes are observed on water yam, the asexual mode prevails during yam anthracnose epidemics. Conidia (asexual spores) disseminate from plant to plant by wind-directed-rain splashing (Green and Simons, 1994; Penet et al., 2014) which limits dispersal to several meters from the source (Ntahimpera et al., 1999).

Sexual reproduction can nevertheless occur at the end of epidemics, on dead leaves or stems, which—when left on the soil surface—may contribute to the primary infection of yam (Ripoche et al., 2007). Ascospores (sexual spores) are produced in dried-perithecia that can eventually be wind-dispersed on a long distance (Abang, 2003). Besides, *C. gloeosporioides* generates quiescent infections in yam tuber epidermis (Simons and Green, 1994); As farmers traditionally save tuber portions from a previous harvest for the new crop propagation, mycelia can pass from one crop season to the next. Thus, commercial exchanges of yam tubers and quiescent infections confer to *C. gloeosporioides* a capacity for long-distance migration and for clonal propagation (Green, 1994).

*Dioscorea alata* foliar anthracnose has been reported in Oceania and India (Winch et al., 1984; McDonald et al., 1998), in the West and Central Africa (Asiedu et al., 1998; Mignouna et al., 2001) and in the Caribbeans (Coursey, 1967; Green and Simons, 1994; Cowger and Mundt, 2002). In the West Indies, anthracnose disease became the most damaging disease on water yam at the end of the 1970s (McDonald et al., 1998) and was associated with serious yield losses reaching 80–100% for the common cultivars (*cv*) of this time, i.e., *cv* Pacala, in Guadeloupe (Green and Simons, 1994). In the 1970s, the cultivar Plimbite was introduced in the Guadeloupean islands and has been extensively cultivated until it became as susceptible to anthracnose as *cv* Pacala. After the late 1990s, farmers started to use more resistant cultivars such as *cv* Kabusah and *cv* Tahiti, but kept planting previous cultivars (Penet et al., 2016).

Genetic determinants of the interaction between *C. gloeosporioides* and *D. alata* are poorly understood. For a long time, this resistance was thought to be polygenic (Green and Simons, 1994; Abang et al., 2001). More recently, Petro et al. (2011) work based on the Boutou and Pyramide *D. alata* cultivars provided strong evidence for the quantitative inheritance of the components associated with the *D. alata* resistance to *C. gloeosporioides*. Next to this quantitative resistance, a gene-for-gene-like component (Flor, 1971) of the interaction between *C. gloeosporioides* and *D. alata* cultivars

has been unraveled (Mignouna et al., 2002; Petro et al., 2011). For instance, the single dominant locus (*Cdg1*) explains the specific resistance of *cv* TDa-95/00328 to moderately virulent *C. gloeosporioides* strains.

The genetic structure of *C. gloeosporioides* populations, causing yam anthracnose, has been mostly studied in Nigeria (Abang et al., 2001, 2004, 2005, 2006), which is the first yam producer in the world (71.5% of the world production, FAOstat 2007). There, *C. gloeosporioides* was shown to exhibit high genetic and phenotypic diversity from the lesion to the region scales (morphotypes, Vegetative Compatibility Groups, RAPD, MP-PCR) and a weak population structure according to the host or to the agroecological areas (Abang et al., 2004, 2006).

Yam breeding and the management of yam resistances in the French West Indies require knowledge about the influence of the migration, the reproductive mode and the yam quantitative resistance on *C. gloeosporioides* evolution. Moreover, the impact of the host quantitative resistance on the evolution of the pathogen aggressiveness has never been investigated. Aiming to document a part or all of these aspects, the objectives of this work were: (i) to estimate genetic diversity and its distribution among fields with neutral markers (AFLP); (ii) to infer the influence of recombination at the field scale; (iii) to study how the degree of host quantitative resistance to anthracnose influences the distribution of fungal aggressiveness diversity.

# MATERIALS AND METHODS

## Fungal Sampling and Isolation

A total of 222 *C. gloeosporioides* isolates were collected from December 2001 to January 2002, from six yam fields within a 5-km-diameter area of the Guadeloupe (FWI, between Morne à l'eau and Le Moule, 16°15N, 61°35O, **Figure 1** and **Supplementary Table S1**). Sampling was done on three yam (*D. alata*) cultivars with different levels of resistance to anthracnose: *cv* Pacala (highly susceptible), *cv* Kabusah (moderately susceptible) and *cv* Tahiti (resistant). The study was carried out in six production fields of approximately 0.5 ha, with identical soil composition, topology, crop management and climate (**Supplementary Table S1**). One month before harvest, leaves with typical anthracnose lesions were collected from 50 plots per field, each distant from 2 to 5 m. Fungus isolation was performed from freshly harvested infected yam leaves (on the same day). Circa 1 mm² of typical anthracnose lesions were plated on potato dextrose agar medium (PDA). *C. gloeosporioides* isolates were identified according to morphological characteristics (Sutton, 1992), then monoconidial cultures were obtained. The strains were grown on PDA-medium under photoperiodical conditions of 12 h day at 25°C and 12 h night at 23°C. Spores of 7-day-old cultures were suspended in distilled water to reach concentrations of 5.10⁵ conidia per mL. Mycelia were dried, thrown in liquid nitrogen and stored at -80°C for DNA extraction.
<figure><img src="image_31.png" /><figcaption>**FIGURE 1** | The locations of *Colletotrichum gloeosporioides* sampling sites in Guadeloupe (French West Indies); The relative geographical positions of the six yam (*Dioscorea alata*) fields are represented in the gray circle. The hosts cultivars were *D. alata* cv Kabusah (Kab), *cv Pacala* (Pac) and *cv Tahiti* (Tah). A, B were two locations where two fields were contiguous.</figcaption></figure>

## DNA Extraction and AFLP Markers
Genotyping

One milliliter of a suspension at 5.10⁵ conidia per mL was mixed with 30 mL of potato dextrose broth medium (PDB). After 72 h of incubation at 27°C, the mycelium was harvested, dried on sterile filter paper and stored at -80°C. Total genomic DNA was extracted from frozen mycelium with a phenol/chloroform protocol as described in Villaréal et al. (2002). DNA quality was checked by electrophoresis on 0.8% agarose gel and stored at -20°C in TE 0.1 (10 mM trisHCL, pH8; 0.1 mM EDTA). DNA digestion (*Mse*I and *Eco*RI), ligation, pre-amplification, selective amplification and electrophoresis were conducted as described in Villaréal et al. (2002). The primer combinations used for selective amplifications were Mse*I-0 +AT/Eco*RI-0 +AT, Mse*I-0 +AG/Eco*RI +AG and Mse*I-0 +TA/Eco*RI-0 +CAA. The 6%-bisacrylamide-urea gel was stained with silver nitrate as described in Chalhoub et al. (1997). Polymorphic bands between 100 and 500 pb were manually referenced as present ("1") or absent ("0"), which gave a 0-1 combination for each isolate. The AFLP patterns were checked for reproducibility with the two DNA replicates extracted and only clearly repeatable bands were used for further analyses. Co-migrating bands were assumed to be the same locus, and isolates having the same AFLP-pattern were assumed to be one single haplotype.

## Plant Material, Inoculation and *in vitro* Aggressiveness Assessment

In order to study the adaptive patterns of *C. gloeosporioides* on its host cv of origin, cross-infection experiments were performed. The diversity for aggressiveness was investigated on a 45-strain subsample composed of six to nine strains per field, randomly chosen in a cloned-corrected sample (i.e., sample containing only single-copy AFLP patterns).

### Host-Plant Material

*Colletotrichum gloeosporioides* strain aggressiveness was tested against the three host-plant cultivars from which they were isolated, i.e., water yam *cv Pacala* (high susceptibility), *cv Kabusah* (moderate susceptibility), *cv Tahiti* (low susceptibility), and also tested on one cultivar of reference for susceptibility to anthracnose, *cv Plimbite* (high susceptibility). Plants were grown from tuber pieces. All the yam tubers were produced in the INRA-URPV experimental center (Guadeloupe, French West Indies) which ensured the origin of the tubers as well as the sanitary control of their production. A total of 15-g tuber pieces were dipped for 24 h into fungicides (Chlorothalonil 550 g L⁻¹ and Carbendazim 100 g L⁻¹ supplied by Bayer CropScience), air-dried and covered with wood ashes. Tuber pieces were planted into humidified sterile compost in individual pots and put in a greenhouse under the natural conditions of Guadeloupe at the beginning of the wet season (25-30°C, >75% humidity, 12 h photoperiod). To prevent any leaf infection on the plants used in the *in vitro* tests, we applied pesticides (oxythioquinox at 10 mg/mL supplied by Bayer CropScience) to the compost every 3 weeks until the first leaf emergence. As advised by Simons and Green (1994), fully expanded young leaves were harvested on 3-month old plants. Leaves were then washed with sterilized water and their adaxial surfaces were placed on moistened filter-paper disks in Petri dishes (one leaf per petri dish). Leaves were inoculated on the entire abaxial surface, as described by Toribio and Jacqua (1978).

**Inoculation** Strains were grown on PDA-medium in 12 h day at 25°C and 12 h night at 23°C conditions. Spores of 7-day old cultures were suspended in distilled water to raise concentrations of 5.10⁵ conidia mL⁻¹. For each strain, 22.5 mL of the spore-suspension were sprayed on freshly cut leaves of each cultivar (five leaves per cultivar) using ecospray® atomizer (LCF labo chimie France, Aix en Provence). At the same time, non-inoculated leaves (sprayed with water) of each cultivar were used as controls. Leaves were incubated at 100% humidity and in photoperiodical conditions of 12 h day at 27°C, 12 h night at 22°C. To reproduce the natural conditions of infection of *D. alata* with *C. gloeosporioides*, inoculations were carried out just before night. For all host-strain combination and condition (control and inoculation), five independent repeats were performed simultaneously.

**Disease assessment** two different symptoms are commonly observed when inoculating *D. alata* leaves with *C. gloeosporioides*: the typical lesion (large necroses, dark brown, coalescent, high expansion rate) and the pinpoint lesion [punctual necroses of less than 2 mm in diameter, black, not coalescent;
Winch et al. (1984)]. Percentages of healthy, typical lesion and pinpoint lesion areas were visually estimated from the first to the 11th day after inoculation. In order to estimate strain aggressiveness, we use the analytical framework detailed in Frézal et al. (2012), which transforms the evolution of disease severity over time into one index, Ag. The construction of the Ag index is based the analysis of the symptom dynamics using a linear mixed-effects regression (PROC MIXED procedure; Laird and Ware, 1982) with two-knots, using SAS software (SAS Institute, 1987). The model is defined with a starting point at day 0 and two changes in slope (at day 6 and day 9). The typical lesion (Na) and total diseased areas (Da) were then defined as:

$$Da(t) = \alpha_{Da} \cdot t + \beta_{Da} (t - 6) + \gamma_{Da} (t - 9)$$

$$Na(t) = \alpha_{Na} \cdot t + \beta_{Na} (t - 6) + \gamma_{Na} (t - 9)$$

where, $(t-6) = (t-6)$ if $(t-6) > 0$, and 0 otherwise; where $(t-9) = (t-9)$ if $(t-9) > 0$, and 0 otherwise.

Strain aggressiveness index on each cultivar was defined as:

$$Ag = \frac{3}{10} \cdot 100 \left( \left( \alpha_{Da} \frac{6}{100} \right)^{\frac{1}{4}} \cdot \frac{Da (day 11)}{100} \right) + \frac{7}{10} \cdot 100 \left( \left( \alpha_{Na} \cdot \frac{6}{100} \right)^{\frac{1}{4}} \cdot \frac{Na (day 11)}{100} \right)$$

For each strain, four indices were calculated: Ag-K on yam cv Kabusah, Ag-P on cv Pacala, Ag-T on cv Tahiti and Ag-Pl on cv Plimbite. We also focused on the initial lesion expansion rates (i.e., $\alpha_{Na}$) and the typical lesion area on day 11 (Na) as this provides detailed insight on strain fitness and helps to imagine hypothetic outcomes of the competition between strains.

# Data Analysis

## Population Genetics Analyses

Diversity at each locus was estimated within subset of isolates grouped by field or cultivar. Unbiased gene diversity (Hd; Nei, 1987) was computed using GENETIX 4.05 (Belkhir et al., 1996/2004) on data sets containing only one copy of each haplotype per subset (i.e., clone-corrected data sets) (Chen and McDonald, 1996). Frequencies at each locus were estimated from each subset and used to calculate standard population statistics. In order to see whether the number of AFLP markers used in this study was sufficient to recover the maximal genotypic diversity, we plotted the genotypic diversity calculated from the multilocus genotype frequencies (Nei, 1987) versus the number of loci: our 23 polymorphic loci were discriminating and powerful enough (Supplementary Figure S1).

A minimum spanning tree based on the 23 ALFP markers was constructed for the full sample (222 isolates) using Bionumerics 5.1 with 100,000 resampling and partitioning of two majority trees. To determine population subdivision without any a priori knowledge, a second analysis was performed on the clone-corrected dataset using the Bayesian clustering method implemented in the software Structure version 2.3.3 (Pritchard et al., 2000). We used admixture and non-admixture

models to identify genetic groups, both with distinct allele frequencies (Falush et al., 2003), and we assumed uniform priors for the vector of proportion (qi) of the individual i's genome in each cluster. The scores of individuals in the genetic groups (i.e., the posterior estimates of the qi) correspond to the probability of ancestry in each one of them. We varied K from 1 to 10, with 10 replicates for each K, and with each simulation consisting in 800,000 Monte-Carlo Markov Chain (MCMC) iterations preceded by a burn-in period of 200,000 iterations. The most probable structure was determined by computing the posterior probability for each K using the distribution of maximum likelihoods and $\Delta K$ distribution (Evanno et al., 2005; Supplementary Figure S2), which is a quantity, related to the second order rate of change of the log probability of data with respect to the number of genetic clusters. Finally, to determine population subdivision without any a priori knowledge and without making any assumptions regarding the population genetics model, we described the genetic clusters from the clone-corrected dataset using the discriminant analysis of principal components (DAPC, Jombart et al., 2010) implemented in the R package adegenet 2.1.1 (Jombart and Ahmed, 2011; R Development Core Team, 2015).

Pairwise Weir and Cockerham (1984) values were calculated between all pairs of populations using GENETIX 4.05 (Belkhir et al., 1996/2004). Populations were defined as field samples and as subsets of individuals collected on the same yam cultivar, i.e., Kabusah, Tahiti and Pacala. Then, the significance of the pairwise-Fst was tested by 10,000 random permutations of the haplotypes (i.e., individuals) between the populations using Multilocus 1.3b software (Agapow and Burt, 2001).

The number of genotypes (Gs) was calculated in the whole sample and per filed from the clone-corrected dataset. As recommended by Arnaud-Haond et al. (2007) we calculated the modified index of clonal diversity, R from Dorken and Eckert (2001) as

$R=(G-1)/(N-1)$, with G, the number of identical multilocus genotypes and N, the number of individuals.

The degree of association between loci was estimated on the clone-corrected dataset by using the index of association, which is the ratio between the observed variance of the number of differences between pairs of strains and the expected variance under the hypothesis of absence of linkage disequilibrium (LD) (Agapow and Burt, 2001). We used a modified version of this index ($r_D$), corrected for the dependence to the number of loci used. $r_D$ has an expected value of 0 if there is no association of alleles at unlinked loci, as is expected in a randomly mating population. The significance of $r_D$ was tested by a randomization procedure (1,000 times) by comparing the observed value to that expected under the null hypothesis of complete random mating (Agapow and Burt, 2001). Value calculation and tests were performed by Multilocus 1.3b software (Agapow and Burt, 2001). To prevent any effect of the population structure or of the LD between markers, we tested the estimated and tested (LD) between markers and retained 16 loci with no residual LD. We estimated and tested the significance of $r_D$ within each genetic cluster using the 16 loci clone-corrected dataset.
## Aggressiveness Analysis

Analyses of variance were conducted for the factors host cultivar, field of origin, genetic group using the General Linear Models Procedure of SAS software (SAS Institute, Inc., Cary, NC, United States). A standard analysis of variance (ANOVA) of the aggressiveness indices on each of the four hosts was performed using the SAS software (SAS Institute, Inc., Cary, NC, United States) to test the effect of the host cultivar, the field and the genetic group on strain aggressiveness. Moreover, the mean-values of aggressiveness indices (i.e., Ag-K, Ag-T, Ag-P, Ag-Pl), of initial lesion expansion rate (α<sub>Na</sub>) and of final lesion area (Na) were compared using the Student's t-test. Means values were considered as different for a probability under the threshold of 0.01.

## RESULTS

### Genetic Diversity

A total of 222 monoconidial strains of *C. gloeosporioides* were isolated from three host cultivars (Figure 1). The AFLP analysis generated 89 clearly reproducible bands. Among the 89 clearly reproducible markers, 23 were polymorphic, independent and had a frequency between 0.05 and 0.95, i.e., non-rare alleles. All the markers were amplified from several isolates originating from at least three different fields. Among the 222 isolates, 197 haplotypes were identified, which correlated with high *R* ratios (i.e., modified *G/N* ratio, where the number of identical multilocus genotypes, *G*, is divided by the number of individuals, *N*) recorded for the entire dataset, 0.89, and in each field-population, from 0.83 to 0.93 (Table 1). Sixteen haplotypes were found in two, three or four copies, but the few isolates sharing the same haplotype were always recovered from a unique field (Supplementary Figure S3). Nei (1987) gene diversities were all superior to 0.20 whether they were calculated on field sample or on the entire dataset (Table 1).

### Population Structure

The population structure analysis with no *a priori*, displayed in the Minimum Spanning Tree (Figure 2A) was concordant with the structure analysis using both the Structure software (Figure 2B) and the DAPC—adegene package

(Supplementary Figures S4–S6). According to Evanno's method, the clone-corrected sample (197 isolates) was most likely divided into three genetic groups (Supplementary Figure S2). The group 1 (blue in Figure 2B, *k* = 3) was composed of more than 98% of strains isolated from *yam cv* Tahiti. This “Tahiti-exclusive” genetic group contained 78.8% of the strains collected on *cv* Tahiti, including 97% of the field Tah<sub>A</sub> sample and 61% of the field Tah<sub>B</sub> sample. For the groups 2 and 3 (green and orange in Figure 2B, *k* = 3) the clustering did not correspond to one host *cv* nor to one geographical location. The group 2 (green in Figure 2B, *k* = 3) was composed of isolates from the fields Pac<sub>C</sub> (50%), Kab<sub>A</sub> (33%), and Tah<sub>B</sub> (13.5%). The group 3 (orange in Figure 2B, *k* = 3) contained the entire Pac<sub>D</sub> sample and 91.6% of Kab<sub>B</sub> sample, which represented 93% of the total of strains contained in this genetic group. The genetic grouping was more complex for the group 2. Pac<sub>C</sub> strains belonged to one genetic group only, but Kab<sub>A</sub> strains belonged to two genetic groups, one common with strains originated from Tah<sub>B</sub> and one with strains from Pac<sub>C</sub>.

The computed pairwise-Fst proved that populations from different fields were significantly differentiated, whatever the host cultivar, *cv*, and the field (Table 2). The pairwise-Fst values were clearly consistent with the genetic clustering given by the analysis with *K* = 6. Indeed, the “Tahiti-private” group and the group 3, respectively, split into two genetic groups corresponding to distinct field of origin (i.e., Tah<sub>A</sub> vs Tah<sub>B</sub>; Pac<sub>D</sub> vs Kab<sub>B</sub>). The lowest Fst-value (0.161) was observed between the fields Kab<sub>A</sub> (host *cv* Kabusah) and Pac<sub>C</sub> (host *cv* Pacala) distant of 1.3 km, whereas higher Fst-values (0.406 and 0.404) were observed between two contiguous fields (Kab<sub>A</sub> and Tah<sub>A</sub>) and between the two *cv* Pacala fields (Pac<sub>C</sub> and Pac<sub>D</sub>).

Altogether, the populations sampled on the cultivar Tahiti tend to form a “Tahiti-specific” group, the populations from the fields Pac-D and Kab-B form two clearly distinct genetic groups. The sample from the Kab-A field does not form a uniform genetic group, as to a lesser extent, the sample from the fields Pac-C and Tah-B.

### Indices of Association

All indices of association values (*r*<sub>D</sub>) calculated were low (*r*<sub>D</sub>-values ranging from 0.003 to 0.103) but with significant departure to 0 for all populations except cluster 1 (Table 3).

<table><caption>TABLE 1 | Genetic diversity indexes within the pooled sample of *Colletotrichum gloeosporioides* and samples from each field.</caption><thead><tr><th colspan="2">Origin of the isolates</th><th># isolates</th><th># haplotypes</th><th># loci<sup>(a)</sup></th><th>H n.b.<sup>(b)</sup></th><th>R<sup>(c)</sup></th></tr><tr><th>Field</th><th>Host cv</th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>All</td><td>All</td><td>222</td><td>197</td><td>23</td><td>0.39 (0.12)</td><td>0.89</td></tr><tr><td>Kab<sub>A</sub></td><td>Kabusah</td><td>25</td><td>21</td><td>22</td><td>0.23 (0.19)</td><td>0.83</td></tr><tr><td>Kab<sub>B</sub></td><td>Kabusah</td><td>40</td><td>36</td><td>18</td><td>0.25 (0.16)</td><td>0.90</td></tr><tr><td>Pac<sub>C</sub></td><td>Pacala</td><td>35</td><td>32</td><td>22</td><td>0.20 (0.18)</td><td>0.91</td></tr><tr><td>Pac<sub>D</sub></td><td>Pacala</td><td>40</td><td>36</td><td>21</td><td>0.33 (0.15)</td><td>0.90</td></tr><tr><td>Tah<sub>A</sub></td><td>Tahiti</td><td>41</td><td>34</td><td>22</td><td>0.22 (0.18)</td><td>0.83</td></tr><tr><td>Tah<sub>B</sub></td><td>Tahiti</td><td>41</td><td>38</td><td>23</td><td>0.31 (0.17)</td><td>0.93</td></tr></tbody></table>

<footnote>(a) Number of polymorphic loci out of the 86 loci. (b) Non-biased Nei's (1987) diversity; standard errors are specified in brackets. (c) *R*, the debiased *G/N* index calculated as (G-1)/(N-1) with *G*, the number of identical multilocus genotypes and *N*, the number of individuals.</footnote>
A

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

B

<figure><img src="image_34.png" /></figure>

**FIGURE 2** | Genetic structure of the 222 *Colletotrichum gloeosporioides* strains sampled on six fields of three different cultivars. (A) The minimum-spanning tree based on ALFP markers was constructed using Bionumerics 5.1. Haplotypes collected on *cv* Tahiti are coded in blue, on *cv* Kabusah in green and on *cv* Pacala in Red (bottom right). The size of the circles corresponds to the number of replicates of the same haplotype (bottom right). Numbers of differences between haplotypes are coded by the type of line joining two circles (bottom right). (B) Structure software output based on 23 AFLP markers with the number of genetic groups (K) ranging from 2 to 6. Kab, Tah, Pac, respectively, stands for the host, *Dioscorea alata* cultivars Kabusah, Tahiti and Pacala. The letters A, B, C, D stand for the four geographical origins (see **Figure 1**). Gray arrows indicate the strains randomly chosen for the pathogenicity tests.

## Strain Aggressiveness Distribution

The diversity for strain aggressiveness was investigated on a 45-strain subsample. These strains belong to the main genetic groups described above (Figure 3 and Supplementary Figure S7). No symptom nor chlorosis appeared on control leaves 11 days after inoculation. We detected an effect of the genetic group (cluster) on strain aggressiveness on the cultivar Tahiti but none on the cultivars Kabusah, Pacala and Plimbite (Table 4). Besides, strain aggressiveness on yam *cv* Plimbite (taken as the

reference for susceptibility) and on yam *cv* Kabusah (moderate susceptibility) did not depend on the host nor on the field of origin (Table 4). Nevertheless, the Student's t-test showed that strains collected from *cv* Kabusah were more aggressive on their host *cv* Kabusah (Ag-index mean-value of 47.8) than the strains sampled from *cv* Tahiti (Ag-index mean-value of 20.3). More strikingly, strain aggressiveness, respectively, on the yam *cv* Tahiti (low susceptibility) and *cv* Pacala (high susceptibility), significantly depended on the host cultivar and the field of origin
<table><caption>**TABLE 2** | Pairwise-Fst among the six *Colletotrichum gloeosporioides* populations (222 isolates) collected from Yam (*Dioscorea alata*) fields in Morne-à-l'eau (French West Indies, Guadeloupe), estimated with 23 polymorphic AFLP-markers.</caption><thead><tr><th colspan="8">Origin of the strains</th></tr><tr><th>Host cv</th><th>Field</th><th>Kab<sub>A</sub></th><th>Kab<sub>B</sub></th><th>Pac<sub>C</sub></th><th>Pac<sub>D</sub></th><th>Tah<sub>A</sub></th><th>Tah<sub>B</sub></th></tr></thead><tbody><tr><td>Kabusah</td><td>Kab<sub>A</sub></td><td>-</td><td>0.0081</td><td>0.0035</td><td>0.0090</td><td>0.0054</td><td>0.0170</td></tr><tr><td>Kabusah</td><td>Kab<sub>B</sub></td><td>0.364*</td><td>-</td><td>0.0100</td><td>0.0017</td><td>0.0000</td><td>0.0046</td></tr><tr><td>Pacala</td><td>Pac<sub>C</sub></td><td>0.161*</td><td>0.463*</td><td>-</td><td>0.0076</td><td>0.0002</td><td>0.0250</td></tr><tr><td>Pacala</td><td>Pac<sub>D</sub></td><td>0.346*</td><td>0.282*</td><td>0.404*</td><td>-</td><td>0.0004</td><td>0.0045</td></tr><tr><td>Tahiti</td><td>Tah<sub>A</sub></td><td>0.406*</td><td>0.379*</td><td>0.244*</td><td>0.436*</td><td>-</td><td>0.0003</td></tr><tr><td>Tahiti</td><td>Tah<sub>B</sub></td><td>0.364*</td><td>0.341*</td><td>0.291*</td><td>0.282*</td><td>0.243*</td><td>-</td></tr></tbody></table>

Below the diagonal: Pairwise-Fst were calculated following the Weir and Cockerham (1984) indications. *indicates significant Fst (p < 0.001) after 1,000 permutations. Above the diagonal: Exact p-values of the exact test of sample differentiation based on haplotype frequencies with Markov chain length of 10,000 steps (Raymond and Rousset, 1995; Goudet et al., 1996).

<table><caption>**TABLE 3** | Standardized index of association within *Colletotrichum gloeosporioides* populations.</caption><thead><tr><th>Population</th><th>rD<sup>(a)</sup></th><th>rD<sup>(b)</sup></th></tr></thead><tbody><tr><td>All</td><td>0.031**</td><td></td></tr><tr><td>Kab<sub>A</sub></td><td>0.056**</td><td></td></tr><tr><td>Kab<sub>B</sub></td><td>0.103**</td><td></td></tr><tr><td>Pac<sub>C</sub></td><td>0.032**</td><td></td></tr><tr><td>Pac<sub>D</sub></td><td>0.036**</td><td></td></tr><tr><td>Tah<sub>A</sub></td><td>0.049**</td><td></td></tr><tr><td>Tah<sub>B</sub></td><td>0.029**</td><td></td></tr><tr><td>All</td><td>0.024***</td><td></td></tr><tr><td>Cluster 1</td><td>0.016</td><td></td></tr><tr><td>Cluster 2</td><td>0.03**</td><td></td></tr><tr><td>Cluster 3</td><td>0.028***</td><td></td></tr></tbody></table>

(Table 4). The mean aggressiveness on *cv* Tahiti was higher for the strains collected on this cultivar (mean Ag-index value of 44.7) than the strains from other host *cv* (mean Ag-index values of 16.6 and 11.7). In the same way, the strains collected on *cv* Pacala displayed the highest level of aggressiveness on *cv* Pacala than the other strains (Table 4). Finally, the effect of the host cultivar on the values for the initial lesion expansion rates (α<sub>Na</sub>; Figure 3) and the typical lesion area on day 11 (Na; Figure 3) was similar to the effect observed on the Ag-index (aggressiveness). The initial lesion expansion rate seemed critical to explain the adaptive evolution of the aggressiveness.

## Patterns of Strain Adaptation to Its Host Cultivar

The pairwise comparison of strain aggressiveness (Ag-indices) on the three host *cv* populations on the same inoculated *cv* (Table 4 and Figure 3) revealed clear pattern of local adaptation on *cv* Tahiti and *cv* Kabusah with high differences

in mean aggressiveness values. Local adaptation to the host *cv* Pacala (highly susceptible) was also shown, however, the mean aggressiveness values were all higher on *cv* Pacala than on the other inoculated cultivars which could be seen as a general adaptation to this cultivar. The patterns of local adaptation to the host cultivar were reinforced by the absence of any pattern of differential adaptation of the strains to the cultivar of reference, *cv* Plimbite. Finally, the pattern of *C. gloeosporioides* adaptation to its host cultivar, especially to the cultivar Tahiti, was similar to the pattern expected for locally adapted pathogen populations.

## DISCUSSION

Our purpose was to better understand how and in which proportions the migration, recombination and yam quantitative resistance influence the *C. gloeosporioides* evolution, and more particularly the evolution of *C. gloeosporioides* pathogenicity. Does the adaptive pattern of *C. gloeosporioides* correspond to a general adaptation to the most common host cultivar, or to a highly fragmented pathogen population resulting from the adaptation to each cultivar subpopulation (i.e., local adaptation; Gandon et al., 1996; Gandon and Van Zandt, 1998)? Furthermore, can the history of cultivars' succession in Guadeloupe be reflected by the recorded fungal pathogenicity distribution?

### *Colletotrichum gloeosporioides* Diversity and Sexual Reproduction Imprint

The AFLP analysis revealed high genetic diversity. Such levels for neutral molecular diversity were previously recorded for *C. gloeosporioides* collected from different yam hosts in three agro-ecological areas of Nigeria (Abang et al., 2006). The indirect evaluation of the reproductive mode, that is the standardized index of multilocus LD, was in accordance with a significant departure from total panmixia, but not reaching complete clonality. Our results thus revealed significant the impact of clonal reproduction on *C. gloeosporioides*'s evolution, which is in concordance with the role of conidia dispersal in the epidemic expansion and the presence of quiescent mycelia in tubers. However, the influence of recombination (sexuality) was not entirely excluded. Besides previous studies reported the presence of sexual reproductive structures of *C. gloeosporioides* on dead yam leaves at the end of the crop season (Toribio and Jacqu, 1978; Toribio et al., 1980; Degras et al., 1984; Abang et al., 2001, 2006). Therefore, it seems parsimonious to consider that recombination explains the high haplotype diversities observed in every subset. Altogether, the influence of sexual reproduction and the high levels of genetic diversity seem to be general traits in *C. gloeosporioides*'s biology as it was previously suggested for *C. gloeosporioides* populations damaging yams (Mignouna et al., 2002; Abang et al., 2005, 2006) and damaging strawberry (Xiao et al., 2004). To conclude, even if *C. gloeosporioides*, pathogenic on *D. alata*, undergoes weak recombination between two crop seasons, sexuality likely confers this fungus a great opportunity to evolve.

<footnote>(a) rD: standardized index of association calculated on the clone-corrected data set using 23 polymorphic markers, i.e., with the haplotypes (Haubold et al., 1998; Agapow and Burt, 2001). (b) rD*: standardized index of association calculated on the clone-corrected data set using 16 polymorphic markers where loci had no significant LD with each others; **indicates significant values with p-values below 0.01 after 1,000 random resampling, ***indicates significant values with p-values below 0.001 after 1,000 random resampling.</footnote>
<figure><img src="image_35.png" /></figure>

**FIGURE 3** | *C. gloeosporioides* local adaptation to its host resistance. A subsample of 45 *C. gloeosporioides* strains isolated from three *D. alata* host cultivars Kabusah (K, moderate susceptibility), Pacala (P, high susceptibility) and Tahiti (T, low susceptibility) were cross-inoculated on these cultivars and on a cultivar of reference (Plimbite, Ref.). (A) Boxplot for the necrosis area, Na. (B) Boxplot for the initial (i.e., day 0–day 6) necrosis expansion rate, α<sub>NA</sub>, were obtained for each cross-inoculation using R-software. (C) Pairwise comparison of the strain aggressiveness inoculated on their local and foreign hosts. Aggressiveness of the strains isolated from *cv* Kabusah are, respectively, represented with black circles (green dotted lines), from *cv* Pacala in black square (red dashed lines) and from *cv* Tahiti in gray triangles (blue lines). Arrows underline the loss or the gain of mean aggressiveness in comparison with the one of strains isolated on *cv* Pacala, the oldest and susceptible cultivar.

**Colletotrichum gloeosporioides**
**Migration Rate**

The haplotype distribution within one population gives
insights into its dispersal potential (McDonald, 1997). In the
present study, the AFLP genotyping of the haploid fungus
C. gloeosporioides revealed a strong population structure: each
field sample can be considered as one fungal population. The
number of genetic groups revealed with the no a priori analyses
and the high Fst-values recorded between field populations,
whatever their location or host cultivar, suggest a weak influence
of C. gloeosporioides migration between the fields studied during
the crop season. Indeed, no haplotypes were shared between
fields. Moreover, the strains from adjoining fields (KabA-TahA
and KabB-TahB; Figure 1 and (Supplementary Figure S3)

belonged to different genetic groups, whereas individuals from
the field PacC (Pacala) were genetically close to some of the
individuals from the field KabA (Kabusah). Furthermore, out
of the 18 multicopy haplotypes, only seven were sampled from
neighboring sampling positions whereas 11 were sampled
from dispersed sampling positions within the same field
(Supplementary Figure S3). In other words, for the fungal
populations studied here, the dispersal by splashing during the
crop season occurred but had a minor impact at a local scale (i.e.,
not beyond few meters).

In fact, the population structure recorded here (high diversity
and strong structure) corresponds to the one expected for a
primary inoculum. It is also consistent with the correlation
found between the presence of *C. gloeosporioides* mycelia in
the yam tubers planted and the subsequent leaf anthracnose
<table><caption>**TABLE 4** | Aggressiveness indices (Ag) of *Colletotrichum gloeosporioides* populations from six *D. alata* yam fields tested on four yam cultivars: Kabusah, Pacala, Tahiti and Plimbite (the cultivar of reference for the susceptibility to anthracnose).</caption><thead><tr><th rowspan="2">Levels</th><th rowspan="2">N</th><th colspan="6">Aggressiveness (Ag-index) estimated on four</th><th rowspan="2">D.alata</th><th rowspan="2">cultivars</th></tr><tr><th>cv Kabusah</th><th></th><th>cv Pacala</th><th></th><th>cv Tahiti</th><th></th></tr></thead><tbody><tr><td>host cv Kabusah</td><td>17</td><td>47.8</td><td>(7.5)</td><td>a</td><td>71.1</td><td>(6)</td><td>b</td><td>16.6</td><td>(5.2)</td><td>b</td><td>51.6</td><td>(7.2)</td><td>a</td></tr><tr><td>host cv Pacala</td><td>13</td><td>36.4</td><td>(9)</td><td>ab</td><td>91.5</td><td>(7.1)</td><td>a</td><td>11.7</td><td>(6.1)</td><td>b</td><td>56.6</td><td>(8.6)</td><td>a</td></tr><tr><td>host cv Tahiti</td><td>15</td><td>20.3</td><td>(8.3)</td><td>b</td><td>69.3</td><td>(6.4)</td><td>b</td><td>44.7</td><td>(5.5)</td><td>a</td><td>54</td><td>(7.7)</td><td>a</td></tr><tr><td>Kab<sub>A</sub> – field</td><td>8</td><td>41.4</td><td>(5.5)</td><td>ab</td><td>59.9</td><td>(8.2)</td><td>ab</td><td>12.1</td><td>(7.5)</td><td>b</td><td>43.4</td><td>(10.4)</td><td>a</td></tr><tr><td>Kab<sub>B</sub> – field</td><td>9</td><td>53.4</td><td>(6.3)</td><td>a</td><td>81.1</td><td>(7.7)</td><td>ab</td><td>20.7</td><td>(7)</td><td>b</td><td>59</td><td>(9.8)</td><td>a</td></tr><tr><td>Pac<sub>C</sub> – field</td><td>7</td><td>48</td><td>(5.9)</td><td>a</td><td>90</td><td>(8.2)</td><td>a</td><td>13.5</td><td>(7.5)</td><td>b</td><td>65.9</td><td>(10.4)</td><td>a</td></tr><tr><td>Pac<sub>D</sub> – field</td><td>6</td><td>13.3</td><td>(5.7)</td><td>b</td><td>94.5</td><td>(11.6)</td><td>a</td><td>8</td><td>(10.5)</td><td>b</td><td>38</td><td>(14.7)</td><td>a</td></tr><tr><td>Tah<sub>A</sub> – field</td><td>9</td><td>25.8</td><td>(10.8)</td><td>ab</td><td>58.8</td><td>(7.7)</td><td>b</td><td>37.1</td><td>(7)</td><td>a</td><td>59.7</td><td>(9.8)</td><td>a</td></tr><tr><td>Tah<sub>B</sub> – field</td><td>6</td><td>13</td><td>(12.4)</td><td>b</td><td>85</td><td>(9.5)</td><td>ab</td><td>56.2</td><td>(8.6)</td><td>a</td><td>45.5</td><td>(12)</td><td>a</td></tr><tr><td>Genetic cluster 1</td><td>13</td><td>23.3</td><td>(9.3)</td><td>a</td><td>66.2</td><td>(6.9)</td><td>a</td><td>43.4</td><td>(6.3)</td><td>a</td><td>53.3</td><td>(8.3)</td><td>a</td></tr><tr><td>Genetic cluster 2</td><td>15</td><td>42.4</td><td>(5.2)</td><td>a</td><td>73.2</td><td>(6.5)</td><td>ab</td><td>17.5</td><td>(5.9)</td><td>b</td><td>54</td><td>(7.7)</td><td>a</td></tr><tr><td>Genetic cluster 3</td><td>16</td><td>38.6</td><td>(6.1)</td><td>a</td><td>86.8</td><td>(6.3)</td><td>a</td><td>16.8</td><td>(5.7)</td><td>b</td><td>54</td><td>(7.5)</td><td>a</td></tr><tr><td>prob F, effect = host cultivar</td><td></td><td>0.061</td><td></td><td></td><td>0.048*</td><td></td><td></td><td>0.000***</td><td></td><td></td><td>0.908</td><td></td><td></td></tr><tr><td>prob F, effect = field of origin</td><td></td><td>0.075</td><td></td><td></td><td>0.019*</td><td></td><td></td><td>0.002***</td><td></td><td></td><td>0.472</td><td></td><td></td></tr><tr><td>prob F, effect = genetic group (cluster)</td><td></td><td>0.294</td><td></td><td></td><td>0.088</td><td></td><td></td><td>0.005***</td><td></td><td></td><td>0.998</td><td></td><td></td></tr></tbody></table>

Fields KabA/TahA, and KabB/TahB were contiguous (see Figure 1). a, b stands for the groups of significant differences of aggressiveness tested using Student's t-test with a p-value *p* < 0.01. Tests were performed considering separately each of the four inoculated cultivars and each of the fungal population grouping levels, host cv, field and genetic cluster. *,**,***, respectively, indicates significant values with p-values below 0.05 and 0.001. Standard errors are specified in brackets.

severity (Green and Simons, 1994). Ultimately, as farmers usually divide yam tubers into pieces (seeds) and save them for the next crop season, when a tuber is infected with one isolate, this isolate will be randomly dispersed within a field where seeds are planted. This fully explains the genetic diversity we observed within and between fungal field-populations. More importantly, the spatial distribution of the haplotypes seemed more likely related to seeds distribution than to fungus dispersal by splashing.

Finally, if dead stems and leaves left on the soil after the previous crop season are a potential source of primary inoculum (Ripoche et al., 2007), the impact of such event cannot be detected in this study, as for all the fields the previous crop was sugar cane. Although no strong influence of migration was detected in this study, the pathogen migration can occur between fields of severely damaged plants (Degras et al., 1984; Green, 1994).

## Colletotrichum Gloeosporioides Local Adaptive Response to Yam Resistances

Previous works already reported the ability of *C. gloeosporioides* to overcome host resistance (Miles and Lascano, 1997; Chakraborty et al., 1999; Abang et al., 2001; Abang, 2003) or to develop resistance to fungicides (Bayart and Pallas, 1994). The last point of our investigations focused on the adaptation of the field-structured populations to their host quantitative resistance. Are *C. gloeosporioides* field populations adapted to every host cultivar (general adaptation) or are they specifically adapted to their host cultivar?

We can also draw a parallel between the patterns of adaptation recorded and the “age” and intensity of each cultivar’s production in Guadeloupe. As a matter of fact, the cultivar Pacala was the most widespread in Guadeloupe until

the end of the 1960s, when its resistance to anthracnose was overcome. Despite its susceptibility to anthracnose, *cv* Pacala is still one of the most planted cultivars in Guadeloupe (Penet et al., 2016). The *cv* Plimbite, introduced during the 1970s, was widely cultivated in Guadeloupe but its prevalence decayed when its resistance was overcome in the early 1990s (Ano et al., 2002). The water yam *cv* Kabusah was the last cultivar introduced at the beginning of the 1990s and has been widely cultivated ever since. Finally, *cv* Tahiti has been cultivated from the 1960s but its prevalence increased in the 1990s.

The patterns of adaptation described in the present study are concordant with a general adaptation to the formerly highly prevalent hosts (*cv* Pacala and *cv* Plimbite) and a local adaptation (i.e., diversifying selection) to the cultivars which became abundant more recently (*cv* Kabusah and *cv* Tahiti; Table 4 and Figure 3). Surprisingly a pattern of local adaptation to the old but still widely planted *cv* Pacala was observed despite a strong general adaptation pattern, revealing an ongoing selective pressure of *cv* Pacala on *C. gloeosporioides* populations.

In our sample, strain virulence (not null aggressiveness) on the four yam cultivars prevailed. It is unclear whether this corresponds to an accumulation of virulences within *C. gloeosporioides* populations or to a core virulence component fixed in all populations. The first hypothesis seems more likely as a former survey on Brazilian *C. gloeosporioides* populations infecting *Stylosanthes* showed an increase of fungal race complexity within few years (Chakraborty et al., 2002). The overall virulence of the studied strains reveals that the recorded adaptation for aggressiveness does not depend on the virulence combination. The adaption of *C. gloeosporioides* to its yam host is quantitative. Besides, we did not find any correlation between strain aggressiveness (Ag-values) against the Kabusah, Tahiti
or Pacala cultivars (Supplementary Figure S8). This indicates a certain level of specificity in the *C. gloeosporioides-D. alata* interaction, even if it seems minor compared to the quantitative aspect. The evolutionary potential of the *C. gloeosporioides* populations pathogenic on *D. alata* reinforces the necessity to better understand the aggressiveness evolution in time and space in order to efficiently predict the durability of *D. alata* resistance to anthracnose. Further investigations of the in-field population dynamics are needed to evaluate the potential of aggressiveness evolution during one crop season. Finally, as previously mentioned, the yam tuber infection can be crucial for the parasite long-distance dispersal and its survival between crop seasons. The capacity of *C. gloeosporioides* to infect and survive in its host tuber is clearly a determinant aspect of its own fitness thus its evolutionary potential. The pattern of genetic diversity revealed by our study underlines the need to improve the control for seed quality at all cost.

## AUTHOR CONTRIBUTIONS

LF, GJ, and CN contributed to conception and design of the study. LF analyzed the results and wrote the first draft of the manuscript. CN supervised the work. All authors contributed to manuscript revision, read and approved the submitted version.

## FUNDING

This work was funded by the French National Institute of Agronomical Research (INRA) and the Antilles-Guyane region.

## ACKNOWLEDGMENTS

We particularly thank Bruno Le Cam, Bernard Tivoli, Jean Carlier, and Virginie Ravigné for their advices.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.01554/full#supplementary-material

**FIGURE S1** | Power of the number of markers used to detect genotypes and measure diversity.

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**FIGURE S3** | Detailed distribution of haplotypes at the field scale. The relative positions where the haplotypes where sampled are specified. The genetic groups obtained in the Structure analysis with *K* = 3, are color coded in orange, green, and blue. Strains belonging to several genetic groups are specified using dashed color code. The letters T, A, K, M, P, F correspond to the strains, respectively, originating from the fields specified in **Figure 1**, Tah-A, Tah-B, Kab-A, Kab-B, Pac-C, Pac-D. The strains found to be the same haplotype are specified using black boxes, the names of the duplicates are given with the letters a, b, c. No haplotypes were shared between fields.

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**Conflict of Interest Statement:** 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.

Copyright © 2018 Frézal, Jacqua and Neema. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Populations of the Beet Cyst Nematode *Heterodera schachtii* Exhibit Strong Differences in Their Life-History Traits Across Changing Thermal Conditions

Sylvain Fournet*, Lucile Pellan, Catherine Porte, Christophe Piriou, Eric Grenier and Josselin Montarry

IGEPP, INRA, Agrocampus Ouest, Université de Rennes 1, Le Rheu, France

It is widely accepted that climate has an essential influence on the distribution of species and that temperature is the major abiotic factor that affects their life-history traits. Species with very restricted active dispersal abilities and a wide geographical distribution are thus expected to encompass distinct populations adapted to contrasted local conditions. The beet cyst nematode *Heterodera schachtii* is a good biological model to study temperature adaptation in populations collected from different environments. Here, we tested the effect of temperature on *H. schachtii* life-history traits using seven field populations from Morocco, Spain, France, Germany, Austria, Poland and Ukraine. We tested hatching and multiplication rates of each population at different temperatures, as well as hatching rates of each population after storage at different temperatures – simulating survival conditions during the inter-cropping period. Results showed a strong temperature effect on the life-history traits explored. Temperature impact on hatching (at different temperatures and after storage at different temperatures) depended on the origin of populations, separating southern from northern ones. Surprisingly, low temperatures influenced hatching less in southern populations. However, for these populations, a storage period at low temperatures strongly reduce subsequent hatching. Conversely, nematode multiplication was not differentially affected by temperatures, as favorable conditions for the host are also favorable for the parasite. Finally, a significant correlation between the genetic diversity and the level of specialization showed that the less diverse populations were more specialized than the more diverse ones.

**Keywords:** adaptation, genetic diversity, hatching, specialist/generalist, survival

**OPEN ACCESS**

**Edited by:**
Brigitte Mauch-Mani,
University of Neuchâtel, Switzerland

**Reviewed by:**
Anna-Liisa Laine,
University of Helsinki, Finland
Pablo Castillo,
Consejo Superior de Investigaciones
Científicas (CSIC), Spain

*Correspondence:
Sylvain Fournet
sylvain.fournet@inra.fr

**Specialty section:**
This article was submitted to
Plant Microbe Interactions,
a section of the journal
Frontiers in Microbiology

**Received:** 02 July 2018
**Accepted:** 31 October 2018
**Published:** 16 November 2018

**Citation:**
Fournet S, Pellan L, Porte C,
Piriou C, Grenier E and Montarry J
(2018) Populations of the Beet Cyst
Nematode *Heterodera schachtii*
Exhibit Strong Differences in Their
Life-History Traits Across Changing
Thermal Conditions.
Front. Microbiol. 9:2801.
doi: 10.3389/fmicb.2018.02801

## INTRODUCTION

There is no doubt that climate has a major influence on the natural distribution of species (Pearson and Dawson, 2003) and that temperature is the major abiotic factor that affects species phenology (Scranton and Amarasekare, 2017). In a context of ongoing climate change, predicting the temperature impact on life-history traits, such as fecundity, development and survival, and thus on the distribution of species is a very challenging scientific question with implications for conservation biology, ecology, agronomy and pest management.

Species characterized by restricted active dispersal ability and a wide geographical distribution are expected to comprise distinct populations adapted to contrasted local conditions
(Davis and Shaw, 2001). Plant pathogens, being often distributed across a wide range of climates, are good biological models to study the temperature adaptation of populations from different environments. For instance, patterns of local adaptation to temperature have been highlighted in natural (Laine, 2008) and agricultural host-pathogen systems (Zhan and McDonald, 2011; Mboup et al., 2012; Mariette et al., 2016).

The life cycle of many plant pathogens can be split into two alternating phases: the epidemic period, onto or into the host, and the inter-epidemic period, often outside the host. Several investigations have shown the necessity to take into account the inter-epidemic period in order to better describe the evolutionary trajectories of plant pathogens (e.g., Pfender and Vollmer, 1999; Abang et al., 2006; Montarry et al., 2007; Sommerhalder et al., 2011; Suffert et al., 2015; Pasco et al., 2016). Interestingly, temperature regimes are likely to differ across locations much more during the inter-epidemic periods than during the epidemic periods, because optimal temperatures for host growth is quite similar whatever the geographical locality.

The beet cyst nematode *Heterodera schachtii*, which is regarded as the most important pest in sugar beet production worldwide (Müller, 1999; Amiri et al., 2002), is globally distributed with populations living in highly contrasted environments (see Subbotin et al., 2010 for a review). In European wild beets, *H. schachtii* is relatively rare in south of Spain and Portugal, but more frequent from the northern coast of Spain to Denmark (Gracianne et al., 2014). In European sugar beet fields, *H. schachtii* is widely present from east to west, from Portugal to Ukraine, and from south to north, from Morocco to Finland (Subbotin et al., 2010). Also, as for all plant parasitic nematodes, *H. schachtii* has only very limited active dispersal capabilities in the soil (Wallace, 1968), and temperature is one of the most influential environmental factor affecting key steps of its biological cycle (Trudgill et al., 2005; Kakaire et al., 2012; Kaczmarek et al., 2014; Vandenbossche et al., 2015). Indeed, temperature can act on all or part of life-history traits involved in embryogenesis, hatching and development after infection and survival (Wallace, 1955; Thomason and Fife, 1962; Jones, 1975; Griffin, 1988). For *H. schachtii*, such influence is well known and extensively described in many previous studies, but on a limited number of field populations coming from north European fields (e.g., Vandenbossche et al., 2015). Therefore, *H. schachtii* populations may be thus assumed to be differentially adapted to local temperatures, both for traits involved in development within their host plant (sugar beet), and for traits involved in their survival in the soil when plants are not present.

Here, we explored the effect of temperature on *H. schachtii* life-history traits using seven field populations from Morocco, Spain, France, Germany, Austria, Poland, and Ukraine. We tested the hatching and the multiplication rates of each population at different temperatures, as well as the hatching rate of each population after storage at different temperatures, simulating survival conditions during the inter-cropping period. Finally, to test the hypothesis that greater genetic diversity is associated with less ecological specialization, we calculated the level of specialization to the temperature and assessed the genetic diversity using microsatellite markers for each population.

# MATERIALS AND METHODS

## Nematode Populations

The seven *H. schachtii* populations were sampled between 2014 and 2016 in a single field in seven different countries and localities representing contrasted climatic zone: Morocco (Mor; Al Aroui), Spain (Spa; Lebridja), France (Fra; Prunay), Germany (Ger; Schlanstedt), Austria (Aus; Tadten), Poland (Pol; Radlowek), and Ukraine (Ukr; Teofipol). Each population corresponded to a single soil sample constituted of a minimum amount of 2 kg and a minimum of 10 elementary samples collected randomly in the field between 0 and 20 centimeters deep. The cysts extracted from each of these seven soil samples were multiplied one time in standardized conditions, (20°C, room chamber, 16:8) for two to four generations on the cultivar Ardan. Newly formed cysts were then stored at 5°C in moistened sandy soil.

## Genetic Diversity

The seven *H. schachtii* populations were genotyped using eight microsatellite markers developed by Montarry et al. (2015): Hs33, Hs36, Hs55, Hs56, Hs68, Hs84, Hs111, and Hs114. Between 21 and 39 individuals per population were successfully genotyped using only one larva (i.e., 1 s-stage juvenile) per cyst, in order to limit biases due to sibling relationships. DNA of each larva was extracted with a procedure using sodium hydroxide and proteinase K, following Boucher et al. (2013).

Three multiplex panels were defined to genotype the 207 individuals at the eight loci. PCR was performed in a 5 µL volume containing 1X of Type-it Microsatellite PCR kit, 0.4 µM of primer mix and 1 µL of template DNA. Cycling conditions included an initial denaturation at 95°C for 5 min, followed by 30 cycles of denaturation at 95°C for 30 s, annealing at 58°C for 90 s and extension at 72°C for 30 s, followed by a final extension at 60°C for 30 min. PCR products were then diluted to 1:10 in sterile water and 3 µL of this dilution were mixed with 0.05 µL of GeneScan 500 LIZ Size Standard (Applied Biosystems) and 5 µL of formamide (Applied Biosystems). Analyses of PCR products were conducted on a ABI Prism® 3130xl sequencer (Applied Biosystems).

Allele sizes were determined with the automatic calling and binning module of GeneMapper v4.1 (Applied Biosystems), and complemented by a manual examination of irregular results. Samples showing dubious genotypes were tested again.

Genetic diversity in each *H. schachtii* population was estimated using *GENETIX 4.05.2* (Belkhir et al., 2004) as the unbiased gene diversity (Hnb) (Nei, 1978). Hnb corresponds to the average probability across loci to draw at random different alleles in the same population.

## Hatching at Different Temperatures

For the seven *H. schachtii* populations, larvae emergence was investigated at three temperatures: 11, 17, and 23°C, which cover the range of temperatures encounter by European *H. schachtii* populations, from the North to the South, during the cropping period. For each population and each temperature, six
independent replicates were performed. Each replicate consisted of a pool of four cysts, calibrated in size between 450 and 500 µm. Cysts were placed on a sieve of 250 µm, allowing the active passage of larvae. Each sieve was put in one well of a 24-well plate containing root exudates of sugar beet (cv. Ardan). Plates were placed in the dark in three different climate chambers set at the respective temperatures. Temperature within each climate chamber was recorded every hour using thermo-tracers (ThermoTracer, Oceasoft, Montpellier, France – Supplementary Figure S1). The number of hatched larvae was counted after 1, 2, 3, 4, 7, 11, 15, 19, 23, 28, and 33 days, the exudate being changed at each date. The number of unhatched eggs was assessed at the end of the experiment in order to calculate the percentage of hatching.

## Multiplication at Different Temperatures

For the seven *H. schachtii* populations, *H. schachtii* multiplication was investigated at three temperatures: 11, 17, and 23°C. For each population and each temperature, 27 independent replicates were performed, i.e., 27 sugar beet plants of the cultivar Ardan. Plants were grown in a 4:1 sand-kaolin mixture watered with a nutrient solution (Hakaphos: NPK 15/10/15). Kaolin was used to aerate the sand and thus to promote the mobility of *H. schachtii* larvae. Each plant was inoculated at the two-leaf stage with 1 mL of a suspension of larvae concentrated at 500 larvae.mL⁻¹. Plants were placed in three different climate chambers set at the respective temperatures with a photoperiod of 16 h. Temperature within each climate chamber was recorded every hour using thermo-tracers (ThermoTracer, Oceasoft, Montpellier, France – Supplementary Figure S1). Plants were cut at the end of the cycle, when white females were well formed and contained eggs, i.e., after 4 weeks at 23°C, after 5 weeks at 17°C, and after 9 weeks at 11°C. Plants were kept several weeks in their climatic chamber until the maturation of cysts, i.e., three, four and six additional weeks at 23, 17, and 11°C, respectively. Newly formed cysts were extracted by filtering the soil through two sieves (800 and 250 µm) and counted under a stereomicroscope.

## Hatching Following Storage at Different Temperatures

Each *H. schachtii* population was multiplied one generation on the sugar beet cultivar Ardan and newly formed cysts were then stored during 2 months at four temperatures: -3, 4, 10, and 25°C, which cover the range of temperatures encounter by European *H. schachtii* populations, from the North to the South, during the inter-cropping period. For each population and each temperature, six independent replicates were performed. Each replicate consisted of a pool of four cysts, calibrated for their size between 450 and 500 µm, and placed in boxes containing 33 g of sand and 7 mL of water. Boxes were stored in the dark in incubators set at the respective temperatures. Temperature within each incubator was recorded every hour using thermo-tracers (ThermoTracer, Oceasoft, Montpellier, France – Supplementary Figure S1). After the storage period, each pool of four cysts was placed on a sieve of 250 µm, allowing the active passage of larvae, and each sieve was put in a 24-well plate containing root exudates of sugar beet (cv. Ardan). Plates

were placed in the dark at 20°C (± 1) in an incubator. The number of hatched larvae was counted after 1, 2, 3, 4, 7, 11, 15, 19, 23, and 28 days, the exudate being changed at each date. The number of unhatched eggs was assessed at the end of the experiment in order to calculate the percentage of hatching.

## Statistical Analysis

In order to take into account the hatching dynamic in its entirety, and not only the final point of the hatching curve, the Area Under the Hatching Curve (AUHC) was calculated for each replicate. For each population, and for each measured traits (i.e., AUHC, number of produced cysts and AUHC after storage), the temperature effect was tested through a one-way ANOVA and multiple comparisons of means were done with the Tukey contrasts test (α = 0.05). Normality and homogeneity of variances were checked using the Shapiro-Wilk and the Leven tests, respectively.

Standardized niche breadth ($B_A$) – a parameter estimating the level of ecological specialization of a species (or a population) – was calculated for each *H. schachtii* population using the standardized index developed by Levins (1968):

$$B_A = \frac{1}{n-1} \left[ \frac{1}{\sum p_i^2} - 1 \right]$$

where $p_i$ is the proportion of the measured fitness trait into the environment $i$ and $n$ the total number of tested environments. In this study, $n = 3$ for the hatching and the multiplication at different temperatures (11, 17, and 23°C) and $n = 4$ for the hatching following storage at different temperatures (-3, 4, 10, and 25°C). The standardized niche breadth varies from 0 to 1. A low niche breadth points to a high level of specialization (i.e., a specialist species) and an high niche breadth to a low level of specialization (i.e., a generalist species).

The relationships between the level of specialization ($B_A$) and the genetic diversity (Hnb) were tested with the Pearson's product-moment correlation. All statistical analyses were performed using the R software version 3.3.3 (R Development Core Team, 2017).

# RESULTS

## Hatching and Multiplication at Different Temperatures

ANOVAs revealed a very strong temperature effect for five *H. schachtii* populations, Fra, Ger, Aus, Pol, and Ukr (Table 1 and Figure 1), showing that the hatching pattern was very different at 11°C: larvae emergence was lower at 11°C than at 17 or 23°C, in terms of AUHC (Figure 1) but also in terms of cumulative hatching at the end of the experiment (Supplementary Figure S2). Conversely, the temperature effect was very small or even non-significant for the two remaining populations, Mor and Spa (Table 1 and Figure 1), for which larvae emergence (i.e., AUHC – Figure 1 – and cumulative hatching at the end of the experiment – Supplementary Figure S2) was similar whatever the temperature.
<table><caption>**TABLE 1** | Results from the analyses of variance (ANOVAS) assessing the temperature effect on the three explored life-history traits (AUHC at different temperatures, on the number of cysts produced at different temperatures and on AUHC after storage at different temperatures) for each *Heterodera schachtii* populations.</caption><thead><tr><th rowspan="2">Population</th><th rowspan="2">Source of variation</th><th colspan="3">AUHC</th><th colspan="3">Number of cysts</th><th colspan="3">AUHC after storage</th></tr><tr><th>df</th><th>F-value</th><th>P > F</th><th>df</th><th>F-value</th><th>P > F</th><th>df</th><th>F-value</th><th>P > F</th></tr></thead><tbody><tr><td rowspan="2">Mor</td><td>Temperature effect</td><td>2</td><td>2.00</td><td>0.1700</td><td>2</td><td>12.67</td><td>&lt;0.0001***</td><td>3</td><td>20.31</td><td>&lt;0.0001***</td></tr><tr><td>Error</td><td>15</td><td></td><td></td><td>74</td><td></td><td></td><td>20</td><td></td><td></td></tr><tr><td rowspan="2">Spa</td><td>Temperature effect</td><td>2</td><td>10.70</td><td>0.0013**</td><td>2</td><td>4.33</td><td>0.0166*</td><td>3</td><td>12.10</td><td>&lt;0.0001***</td></tr><tr><td>Error</td><td>15</td><td></td><td></td><td>75</td><td></td><td></td><td>20</td><td></td><td></td></tr><tr><td rowspan="2">Fra</td><td>Temperature effect</td><td>2</td><td>156.60</td><td>&lt;0.0001***</td><td>2</td><td>0.44</td><td>0.6480</td><td>3</td><td>11.84</td><td>0.0001***</td></tr><tr><td>Error</td><td>15</td><td></td><td></td><td>74</td><td></td><td></td><td>20</td><td></td><td></td></tr><tr><td rowspan="2">Ger</td><td>Temperature effect</td><td>2</td><td>407.30</td><td>&lt;0.0001***</td><td>2</td><td>11.15</td><td>&lt;0.0001***</td><td>3</td><td>0.99</td><td>0.4170</td></tr><tr><td>Error</td><td>15</td><td></td><td></td><td>74</td><td></td><td></td><td>20</td><td></td><td></td></tr><tr><td rowspan="2">Aus</td><td>Temperature effect</td><td>2</td><td>163.50</td><td>&lt;0.0001***</td><td>2</td><td>4.28</td><td>0.0174*</td><td>3</td><td>4.69</td><td>0.0122*</td></tr><tr><td>Error</td><td>15</td><td></td><td></td><td>76</td><td></td><td></td><td>20</td><td></td><td></td></tr><tr><td rowspan="2">Pol</td><td>Temperature effect</td><td>2</td><td>327.60</td><td>&lt;0.0001***</td><td>2</td><td>22.49</td><td>&lt;0.0001***</td><td>3</td><td>15.64</td><td>&lt;0.0001***</td></tr><tr><td>Error</td><td>15</td><td></td><td></td><td>74</td><td></td><td></td><td>20</td><td></td><td></td></tr><tr><td rowspan="2">Ukr</td><td>Temperature effect</td><td>2</td><td>69.29</td><td>&lt;0.0001***</td><td>2</td><td>11.16</td><td>&lt;0.0001***</td><td>3</td><td>9.00</td><td>0.0006***</td></tr><tr><td>Error</td><td>15</td><td></td><td></td><td>75</td><td></td><td></td><td>20</td><td></td><td></td></tr></tbody></table>

The statistically significant effects are indicated as *P* < 0.05, **P* < 0.01, and ***P* < 0.001.

<figure><img src="image_37.png" /><figcaption>**FIGURE 1** | Area Under the Hatching Curve (AUHC) at different temperatures (11, 17, and 23°C) for each *Heterodera schachtii* population (mean values ± standard deviations). Letters represent the homogenous groups identified by the Tukey contrasts test at the 5% threshold.</figcaption></figure>

Regarding the number of cysts produced on sugar beet plants at different temperatures, ANOVAs revealed a temperature effect for all populations, except for the French one, showing that *H. schachtii* populations produced more cysts at the intermediary temperature, i.e., 17°C (Table 1 and Figure 2).

## Hatching Following Storage at Different Temperatures

After a prior exposure for 2 months to different storage temperatures, the hatching experiment was performed at 20°C (±1) because at this temperature larvae of all populations hatched very well, as showed by the previous experiment (Figure 1). Larvae emergence was reduced after storage at -3°C for all populations except the German one (Table 1 and Figure 3).

The difference between the storage step at -3°C and the three other temperatures (4, 10, and 25°C) was much higher for the two Mediterranean populations (Mor and Spa) than for the five others (Fra, Ger, Aus, Pol, and Ukr), both for the AUHC (Figure 3) or the cumulative hatching at the end of the experiment (Supplementary Figure S3).

## Relationship Between Genetic Diversity and Ecological Specialization

Regarding the hatching (AUHC) at different temperatures, the standardized niche breadth $B_A$ – a parameter to measure how specialized a population is within a given environment – highlighted the generalist status of *H. schachtii* populations from Morocco and Spain (wide niche breath, 0.98 < $B_A$ < 0.99) and
<figure><img src="image_38.png" /><figcaption>**FIGURE 2** | Number of cysts obtained from a multiplication at different temperatures (11, 17, and 23°C) for each *H. schachtii* population (mean values ± standard deviations). Letters represent the homogenous groups identified by the Tukey contrasts test at the 5% threshold.</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>**FIGURE 3** | Area Under the Hatching Curve (AUHC) after storage at different temperatures (–3, 4, 10, and 25°C) for each *H. schachtii* population (mean values ± standard deviations). Letters represent the homogenous groups identified by the Tukey contrasts test at the 5% threshold.</figcaption></figure>

<table><caption>**TABLE 2** | Genetic diversity (Hnb) and standardized niche breadth (BA) calculated on AUHC at different temperatures, on the number of cysts produced at different temperatures and on AUHC after storage at different temperatures for each *H. schachtii* populations (Pop).</caption><thead><tr><th>Pop</th><th>n</th><th>Hnb</th><th>BA on AUHC</th><th>BA on the number of cysts</th><th>BA on AUHC after storage</th></tr></thead><tbody><tr><td>Mor</td><td>38</td><td>0.43</td><td>0.98</td><td>0.97</td><td>0.90</td></tr><tr><td>Spa</td><td>21</td><td>0.59</td><td>0.99</td><td>0.98</td><td>0.92</td></tr><tr><td>Fra</td><td>22</td><td>0.48</td><td>0.89</td><td>0.99</td><td>0.98</td></tr><tr><td>Ger</td><td>37</td><td>0.30</td><td>0.58</td><td>0.98</td><td>0.99</td></tr><tr><td>Aus</td><td>39</td><td>0.26</td><td>0.63</td><td>0.99</td><td>0.98</td></tr><tr><td>Pol</td><td>22</td><td>0.53</td><td>0.77</td><td>0.97</td><td>0.98</td></tr><tr><td>Ukr</td><td>28</td><td>0.37</td><td>0.83</td><td>0.98</td><td>0.99</td></tr></tbody></table>

n indicates the number of genotyped individuals.
<figure><img src="image_40.png" /><figcaption>**FIGURE 4** | Relationship between the genetic diversity (Hnb) and the standardized niche breadth on the AUHC at different temperatures (Bₐ on AUHC) calculated of each *H. schachtii* population.</figcaption></figure>

showed a lower niche breadth for the five other populations (0.58 < Bₐ < 0.89), when taking temperatures as different environments (Table 2). Moreover, there was a significant correlation between the genetic diversity and the level of specialization (cor = 0.758; *P* = 0.048), showing that the less diverse populations were more specialized than the more diverse ones, and vice versa (Figure 4).

Regarding the multiplication (number of cysts) at different temperatures and the hatching (AUHC) after storage at different temperatures, the standardized niche breadth Bₐ highlighted the generalist status of all *H. schachtii* populations (0.97 < Bₐ < 0.99 for the number of cysts and 0.92 < Bₐ < 0.99 for the AUHC, Table 2) and no correlation with the genetic diversity (cor = −0.373; *P* = 0.410 for the number of cysts and cor = −0.464; *P* = 0.294 for the AUHC).

DISCUSSION

Although the hatching and development of the beet cyst nematode *H. schachtii* have been already studied (e.g., Griffin, 1988), far less is known about the survival abilities of this species under different climate conditions that can be encountered during the inter-cropping season. The main objectives of the present work were thus to test the effect of temperature and to compare the level of specialization of different *H. schachtii* populations for key traits involved in survival and reproduction: hatching, development in the host plant and hatching after a survival period.

Low temperatures strongly reduced both the hatching pattern and the cumulative hatching rate of five populations (France, Germany, Austria, Poland, and Ukraine): at 11°C, hatching was slowly and was incomplete. Previous studies already showed the same behavior in populations from the same geographical areas (e.g., Vandenbossche et al., 2015). Conversely, temperature

had only a small effect on the two southern populations, from Spain and Morocco. This was a surprising result: one could imagine that northern populations should be less constrained by lower temperatures than southern ones, which faced much higher temperature levels over the year. In an unexpected way, temperature appeared thus to act differently according to the geographical origin of populations in the sequence leading to the hatch of larvae. Perry and Gaur (1996) showed that differences related to the host, for instance the age of the host plant, on which the nematodes multiplied could strongly affect their subsequent hatching rate. In our study, cysts all came from a single multiplication cycle on the same sugar beet cultivar, and experiments were performed in controlled conditions. The differences observed in hatching behavior could thus not be assigned to the conditions for obtaining cysts used in this study, but would rather be a clear answer of each population to the thermal conditions applied in the experiment. Indeed, cyst nematode species are all obligate parasites, and their reproductive success mainly depends on the synchronization between hatching and host presence. Consequently, it seems that natural selection has optimized the hatching response to enhance fitness in a different way for northern and southern populations (Spain and Morocco). For these last populations, the most important factor driving hatching may rather be the occurrence of favorable conditions (moisture and presence of root exudates), rather than temperature only.

Regarding the development in the host plant, thermal conditions did not affect each population differentially. In all cases, the number of newly formed cysts matched the usual shape of a thermal performance curve (Schulte et al., 2011), and all populations exhibited higher performances at 17°C. This is consistent with what was already described in the literature for field populations (e.g., Griffin, 1988) and for wild populations of *H. schachtii* (Gracianne et al., 2014). This optimum corresponds also to the optimal temperature for sugar beet cropping. Indeed,
even if sugar beet is grown throughout Europe, cropping is always
realized under conditions that ensure optimal development and
yields. These conditions correspond to the period from April
to September in the majority of European countries, but from
November to May in Morocco and in the south of Spain.

Most studies only considered the effect of temperature on the
multiplication of plant parasites during the epidemic phase, i.e.,
during the interaction with the host. Here, we also investigated
its impact on the survival of the progeny inside the cysts, because
the ability to survive when host plants were absent is also an
important fitness component submitted to selection (Roff, 1992).
In our case study, temperature during the inter-epidemic phases
are much more variable than those encountered during the
cropping period: they correspond either to very hot summer
conditions for the southern populations and to cold winter
conditions for the populations of northern and central Europe.
In both cases, these periods are characterized by unfavorable
conditions for the growth of host plants. We thus exposed cysts
of each *H. schachtii* population during 2 months to temperatures
from -3 to 25°C, which correspond to the range encountered
in natural conditions. The results confirmed the existence of
two distinct groups of populations: one (Morocco, Spain) for
which survival at a low temperature (-3°C) reduced strongly
the subsequent hatching rate, and a second one (all others
populations) where survival temperatures had only a limited
impact on subsequent hatching. At this point we could therefore
conclude, rightly as these populations never encountered such
low temperatures, that both southern populations were thus
clearly maladapted to survival under cold conditions, while
northern populations seemed adapted to survive over a larger
range of temperatures, including cold ones.

However, closer examination of the mean standard deviations
leads to a more nuanced conclusion. Indeed, southern
populations both exhibited greater variation for this trait
than northern ones (Figure 3 and Supplementary Figure S3)
and seemed to contain some individuals that could survive
cold conditions and others that could not (see Supplementary
Figure S4 showing the hatching curves for each independent
replicate). This observation makes sense in an evolutionary
context, where northern populations can be seen as the result
of a gradual northward (re)colonization process of Europe
after the Last Glacial Maximum (LGM), with the selection
among southern individuals of those able to survive the coldest
temperatures. Several studies make this hypothesis relevant.
First, the south of the Iberian Peninsula is well known as an
historical refuge, climatically stable during geological times
(Rodriguez-Sanchez et al., 2008) and therefore during the
LGM which corresponded to the latest coldest conditions
encountered in Europe. Second, two recent studies tend to
prove that wild populations of *H. schachtii* and of its wild hosts
*Beta maritima* recolonized the north of Europe following a
northward process of migration from the south of the Iberian
Peninsula (Leys et al., 2014; Gracianne, 2015; respectively). After
the LGM, the following huge increase of temperature pushes
northward favorable conditions to the development of host
plants and nematodes and make possible the recolonization of
northern Europe. While our study was performed with field

populations that share only part of their evolutionary history
with wild ones, our results matched this scenario rather well.
Indeed, we also observed a northward decrease of genetic
diversity in our field populations, as expected in the case of
sequential founder events associated to recolonization processes
and limited gene flow among population (Austerlitz et al.,
1997).

As a last challenge, we explored the link between genetic
diversity in populations and their ecological specialization,
testing the hypothesis that greater genetic diversity is associated
with less ecological specialization. Such a relationship was
only demonstrated so far between species: in 11 of 14 studies
identified by Li et al. (2014), the specialist species showed
lower genetic diversity than related generalists. Here, a similar
trend can be observed between populations: Spanish and
Morrocan populations were both generalists, with a high
niche breath and a higher genetic diversity, while Northern
populations appeared to be more specialized and less diverse. The
generalist character fitted the independence of the hatching and
development according to thermal conditions. The association of
this generalist trait with higher genetic diversity fitted also well
with Gracianne's scenario, which assumes that Spain and more
southern regions kept favorable climate conditions during the
LGM, promoting the maintenance of a huge genetic diversity in
these areas. The less diversity and the higher specialization to cold
climate conditions we observed in northern populations fitted
again well with this scenario. The northward recolonization of
Europe was associated with the selection for a better capability
to survive and a less susceptibility to hatch at low temperatures,
maximizing the synchronization between hatching and host
presence.

Further investigations are now needed, using wild *H. schachtii*
populations, to determine whether the counter-selection of the
capability to hatch at low temperatures and the selection of
the capability to survive at low temperatures are the result of
a fast evolution process in the cultivated compartment (i.e., an
adaptation to crop practices in northern Europe), or of a longer
process in the wild, associated to the northward recolonization
of Europe. Anyway, the high tolerance to contrasted survival
conditions observed in northern Europe field populations
explains well the presence of *H. schachtii* all around the world
where sugar beet is grown (Subbotin et al., 2010). Furthermore,
in the framework of global climate change, our data imply that
northern populations of *H. schachtii* should not be strongly
affected for both survival and development by global warming.

DATA AVAILABILITY

All data used in this article are available at data.inra.fr
(doi: 10.15454/ZKGTAH).

AUTHOR CONTRIBUTIONS

LP, CaP, and ChP performed the experiments according to a
protocol elaborated jointly by SF, EG, and JM. SF and JM analyzed
the data. SF, EG, and JM wrote the text and prepared the figures. All authors edited the paper and have approved the current version.

## ACKNOWLEDGMENTS

We gratefully acknowledge ITB, SES, KWS, Strube, and SYNGENTA who kindly provided the different *Heterodera*

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**Conflict of Interest Statement:** 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.

Copyright © 2018 Fournet, Pellan, Porte, Piriou, Grenier and Montarry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_41.png" /></figure>

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# Role of Temperature and Coinfection in Mediating Pathogen Life-History Traits

Elise Vaumourin* and Anna-Liisa Laine

Research Centre for Ecological Change, University of Helsinki, Helsinki, Finland

Understanding processes maintaining variation in pathogen life-history traits is a key challenge in disease biology, and of importance for predicting when and where risks of disease emergence are highest. Pathogens are expected to encounter tremendous levels of variation in their environment – both abiotic and biotic – and this variation may promote maintenance of variation in pathogen populations through space and time. Here, we measure life-history traits of an obligate fungal pathogen at both asexual and sexual stages under both single infection and coinfection along a temperature gradient. We find that temperature had a significant effect on all measured life-history traits while coinfection only had a significant effect on the number of sexual resting structures produced. The effect of temperature on life-history traits was both direct as well as mediated through a genotype-by-temperature interaction. We conclude that pathogen life-history traits vary in their sensitivity to abiotic and biotic variation in the environment.

**Keywords:** abiotic interactions, biotic interactions, life-history evolution, overwintering success, pathogen evolution, *Plantago lanceolata*, *Podosphaera plantaginis*

## OPEN ACCESS

**Edited by:**
Cindy E. Morris,
Genetics and Improvement of Fruit
and Vegetables (INRA), France

**Reviewed by:**
Remco Stam,
Technische Universität München,
Germany
Frédéric Suffert,
Institut National de la Recherche
Agronomique (INRA), France

**\*Correspondence:**
Elise Vaumourin
ellse.vaumourin@helsinki.fi

**Specialty section:**
This article was submitted to
Plant Microbe Interactions,
a section of the journal
Frontiers in Plant Science

**Received:** 15 June 2018
**Accepted:** 26 October 2018
**Published:** 20 November 2018

**Citation:**
Vaumourin E and Laine A-L (2018)
Role of Temperature and Coinfection
in Mediating Pathogen Life-History
Traits. Front. Plant Sci. 9:1670.
doi: 10.3389/fpls.2018.01670

## INTRODUCTION

Pathogens are a major threat to human health and food security (Strange and Scott, 2005; Jones et al., 2008). The success of pathogens may be attributed to their evolutionary potential which allows them to emerge, adapt, and persist in space and time within their host populations (Mundt, 2002; Friesen et al., 2006). Hence, understanding processes maintaining variation in pathogen life-history traits is a key question in evolutionary ecology and a major challenge for the design of disease control programs (Galvani, 2003; Grenfell et al., 2004). Environmental variation may be a powerful process maintaining variation in pathogen populations if infection outcomes depend on conditions directly or through genotype-by-environment interactions, and if variation is spatially and/or temporally structured (e.g., heterogeneous landscapes and seasonality). To understand the role of environmental variation in maintaining variation in pathogen populations, to date most studies have focused on the abiotic environment (e.g., climatic conditions, Harvell et al., 2002). Recently it has become evident that pathogens often occur simultaneously with other strains or species of pathogens colonizing the same host (Tollenaere et al., 2016). Thus, biotic variation in the co-occurring pathogen community may be an overlooked component of the processes maintaining variation in pathogen populations.
The abiotic environment has been shown to affect the evolutionary trajectories of pathogen life-histories (Blanford et al., 2003; Fels and Kaltz, 2006). Variation in temperature represents one of the most ubiquitous sources of environmental variation, and is known to greatly affect biochemical, physiological and behavioral processes of organisms. Pathogens with a free transmission stage are considered particularly vulnerable to variation in temperature (Truscott and Gilligan, 2003). In many pathosystems, temperature has been shown to affect pathogen ability to establish or maintain infection, its latency as well as its aggressiveness (e.g., Burdon, 1987; Thomas and Blanford, 2003; Fels and Kaltz, 2006). There is increasing evidence that the effect of temperature on pathogen fitness may be mediated through genotype-by-environment (G × E) interactions, suggesting that adaptation to biotic and abiotic habitats may be strongly linked (Ferguson and Read, 2002; Thomas and Blanford, 2003; Price et al., 2004; Mitchell et al., 2005; Fels and Kaltz, 2006). For example, it has been shown that the diversity *Arcobacter* populations is modulated by a temperature gradient (Fisher et al., 2014).

More recently, in addition to the surrounding abiotic environment, the biotic environment has been also highlighted to impact the maintenance of the variation in pathogen populations. For example, epidemic outcomes have been shown to change when multiple pathogens are present on a same host (Susi et al., 2015a). Strains under coinfection are generally expected to compete for the same limited resources provided by the host. If this process was acting alone, it could favor higher host exploitation rates (i.e., higher virulence) under coinfection than under single infection (Alizon, 2013). For many pathogens coinfection is also the pre-requisite for sexual reproduction, and thus changes in pathogen growth rates under coinfection may also represent facilitation to increase the probability of generating of new allelic combinations between the coinfecting strains (Otto, 2009; Carter et al., 2013). Thus, coinfection may largely contribute to maintain the genetic variation in pathogen life-history traits and can speed up the adaptation to environmental change (Becks and Agrawal, 2012).

Both abiotic and biotic environments may have strong impacts on infection outcomes, but little is known about their relative importance, and whether outcomes of coinfection are mediated by temperature (but see Marçais et al., 2017). In this study, we investigate how pathogen life-history traits that constitute both asexual and sexual stages are affected by the biotic environment (i.e., coinfection) and abiotic environment (i.e., temperature). Despite its ecological importance, the sexual stage is difficult to study for many pathosystems. Our study is focused on the interaction between *Podosphaera plantaginis* – *Plantago lanceolata* (powdery mildew – ribwort plantain). Given that this pathogen completes its entire life-cycle on the surface of the host plant, we are able to monitor the life-history traits visually in a non-destructive manner. Another advantage of this pathosystem is that *P. plantaginis* is a homothallic fungi (i.e., self-fertile, Tollenaere and Laine, 2013) so the production of the sexual structures (i.e., chasmothecia) can be achieved under both single strain infections as well as in coinfections. Thus, we can

evaluate the impact of coinfection and temperature on this critical life-history trait affecting overwinter survival.

# MATERIALS AND METHODS

## The Pathosystem: *Podosphaera plantaginis* – *Plantago lanceolata*

We focused our study on the powdery mildew, *Podosphaera plantaginis* (*Erysiphales*, Ascomycota), which is an obligate fungal pathogen naturally infecting host plant *Plantago lanceolata*, the ribwort plantain. The epidemic cycle of the powdery mildew starts with the germination of a spore on a susceptible host resulting in lesion where clonal spores which are wind-dispersed are produced. To survive the winter, resting structures (chasmothecia) are produced. Each resting structure contains eight sexually generated ascospores, which initiate new infections in the spring upon their release. In *P. plantaginis* chasmothecia production can be achieved via haploid selfing of pure strains as well as outcrossing between two different strains infecting the same host plant (Tollenaere and Laine, 2013).

The dynamics of *P. plantaginis* in its fragmented host population network have been intensively studied in the Åland Islands (50 km × 70 km area) in southwest Finland since 2001 (Ojanen et al., 2013). The study system consists of approximately 4 000 host populations that are surveyed annually for the presence of *P. plantaginis*. The powdery mildew infection is visually conspicuous as whitish mycelia on the leaves. Previous studies have shown that prevalence of the pathogen is low in this system, with 2–17% of the host populations being infected each year in the metapopulation. *Podosphaera plantaginis* persists as highly dynamic metapopulation with frequent extinctions and (re)colonizations (Laine and Hanski, 2006; Jousimo et al., 2014). Genotyping has revealed considerable genetic diversity in this pathogen metapopulation, with most of the strains found in only one or few localities with a small subset of strains being common across multiple host populations (Tollenaere et al., 2012). The three strains chosen for our experiment – S1 (genotype i1), S2 (genotype 4000) and S3 (genotype 876-1) – are temporally and spatially widely distributed in the Åland metapopulation (Supplementary Figure 1).

Prior to the experiment, we carried out repeated cycles of inoculations to obtain adequate stocks of sporulating fungal material for the inoculation trials. The inoculations were done on petri dishes on detached host leaves of a *P. lanceolata* genotype (490-15) that has been characterized as broadly susceptible during previous experimental and maintenance work. For the experiment we used conidial chains from lesions that were 16–18 days old. To have sufficient leaf material for the experiment, we produced multiple clones by placing mother plants growing in pots with a perforated bottom in pots filled with vermiculite. After 9 weeks the mother plant was cut from the roots it had developed in the pot of vermiculite. Roots in the vermiculite yielded new rosettes, which were planted into pots with sand-rich humus. The plants were grown under glasshouse conditions, with 16 h of light and a temperature of +22°C.
# Measuring Asexual and Sexual Pathogen Life-History Traits Under Single Versus Coinfection Along a Temperature Gradient

In order to investigate how pathogen life-history traits are impacted by the biotic environment (i.e., coinfection) and by the abiotic environment (i.e., temperature), we conducted an inoculation experiment where we recorded life-history traits of strains on their own (single infection) or with another strain (coinfection). We measured pathogen life-history traits under three different temperatures, +17°C, +20°C and +23°C. This range represent typical growing season temperatures that *P. plantaginis* experiences in the Åland archipelago (Laine, 2008). We measured two life-history traits that constitute the asexual stage (i.e., affecting pathogen growth): time from inoculation to germination and time from germination to sporulation. We also quantified three life-history traits of the sexual stage (i.e., affecting pathogen overwinter survival): time from sporulation to mature chasmothecia, total number of chasmothecia and proportion of viable chasmothecia. Given that this pathogen completes its entire life-cycle on the surface of the host plant, we were able to monitor life-history traits visually in a non-destructive manner. All inoculations were performed on the broadly susceptible host genotype (490-15).

To provide single vs. coinfection comparisons under different temperature conditions, each of the three strains was inoculated both individually as well as with another strain in a fully crossed design under the three different temperatures (+17°C,

+20°C and +23°C). A cross-inoculation of a strain with itself is considered as a single infection and a cross-inoculation of a strain with another strain is considered as a coinfection. *Plantago lanceolata* leaves were placed on a moist filter paper in a Ø 9 cm Petri dish. Three conidial spore chains of both strains (i.e., six conidial chains at each inoculation site) were carefully placed on the same spot of a *P. lanceolata* leaf by single hair inoculation technique (Tollenaere and Laine, 2013). On each leaf we inoculated three distinct spots resulting in 54 sites of inoculation for each strain pairing per temperature. All inoculated leaves inside Petri dishes were kept at their respective temperatures with a 16L/8D photoperiod in growth chambers. We included non-inoculated leaves as negative controls. All *P. lanceolata* leaves used in the experiment were harvested from same-aged clones that were maintained in the greenhouse.

For each inoculated site, using a dissecting microscope, we measured daily the life-history traits related to the pathogen growth. Then, 14 days after the appearance of the first mature chasmothecia, the number of total chasmothecia produced were counted under a dissecting microscope and the viability of approximately 10 mature randomly collected chasmothecia were measured. To measure their viability, we have used the vital stain fluorescein diacetate (FDA) method (Firstencel et al., 1990; Ficke et al., 2002; Vági et al., 2016). This method consists of gently crushing chasmothecia on microscope slides in a droplet of a solution containing FDA (10 µg/mL). Slides were then scored for fluorescing ascospores under an epifluorescence microscope. FDA is lipophilic, membrane-permeable, non-fluorescent and it is hydrolyzed onto polar fluorescent molecules in the cytoplasm

<figure><img src="image_42.png" /><figcaption>**FIGURE 1** | The mean and standard deviation of each asexual life-history trait of the three *Podosphaera plantaginis* strains in single vs. coinfection along a temperature gradient. Single infections are depicted by black lines and coinfection by red lines. The time is indicated in days.</figcaption></figure>
of living cells. These molecules remain in the cytoplasm of viable ascospores with intact cell membranes and exhibit intense green fluorescence when examined under blue light (465-495 nm) thereby signaling chasmothecia viability. A chasmothecium was scored as being viable if it contained any ascospores that exhibited intense green fluorescence.

## Statistical Analyses

To understand how temperature and coinfection affected variation in the asexual life-history traits - time from inoculation to germination, time from germination to sporulation and time from sporulation to mature chasmothecia - we used survival models as implemented in R-package survival (Therneau, 2015) with Cox Proportional Hazards model (Cox, 1972). All survival models had temperature, first strain identity, coinfection and their respective interactions with the temperature as fixed explanatory effects. The identity of the second coinfecting strain and the leaf were considered as random factors.

To understand how temperature and coinfection affected variation in the sexual life-history traits - total number of chasmothecia and proportion of viable produced chasmothecia - we fitted generalized linear mixed models. For the model estimating total number of produced chasmothecia we defined a normal error structure and an identity link function, and for the model analyzing proportion of viable chasmothecia we defined a binomial error structure and a logit link function. Then for each model, the temperature, first strain identity, coinfection and their respective interactions with the temperature were fixed explanatory effects and the second coinfecting strain and leaf were considered as random factors.

## RESULTS

We found that all measured life-history traits are significantly affected by temperature (Figures 1, 2 and Table 1). The average timing of each life-history event at each temperature and strain

<figure><img src="image_43.png" /><figcaption>**FIGURE 2** | The mean and standard deviation of the sexual life-history traits of the three *Podosphaera plantaginis* strains in single vs. coinfection along a temperature gradient. Single infections are depicted by black lines and coinfection by red lines. The time is indicated in days.</figcaption></figure>
<table><caption>**TABLE 1** | Results of the GLMMs analyzing infection development of *Podosphaera plantaginis* measured under single vs. coinfection under a temperature gradient.</caption><thead><tr><th>Source</th><th>χ<sup>2</sup></th><th>P</th></tr></thead><tbody><tr><td><strong>Time from inoculation to germination</strong></td><td></td><td></td></tr><tr><td>First strain identity</td><td>1.20</td><td>0.548</td></tr><tr><td>Coinfection</td><td>2.99</td><td>0.084</td></tr><tr><td>Temperature</td><td>14.57</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>First strain identity × Temperature</td><td>7.59</td><td>0.108</td></tr><tr><td>Temperature × Coinfection</td><td>0.31</td><td>0.858</td></tr><tr><td><strong>Time from first germination to first sporulation</strong></td><td></td><td></td></tr><tr><td>First strain identity</td><td>2.83</td><td>0.243</td></tr><tr><td>Coinfection</td><td>1.26</td><td>0.261</td></tr><tr><td>Temperature</td><td>13.31</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>First strain identity × Temperature</td><td>1.41</td><td>0.842</td></tr><tr><td>Temperature × Coinfection</td><td>4.08</td><td>0.130</td></tr><tr><td><strong>Time from first sporulation to first mature chasmothecia</strong></td><td></td><td></td></tr><tr><td>First strain identity</td><td>8.51</td><td><strong>0.014</strong></td></tr><tr><td>Coinfection</td><td>0.51</td><td>0.475</td></tr><tr><td>Temperature</td><td>149.41</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>First strain identity × Temperature</td><td>17.70</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>Temperature × Coinfection</td><td>2.85</td><td>0.241</td></tr><tr><td><strong>Total number of produced chasmothecia</strong></td><td></td><td></td></tr><tr><td>First strain identity</td><td>2.08</td><td>0.353</td></tr><tr><td>Coinfection</td><td>6.87</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>Temperature</td><td>386.85</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>First strain identity × Temperature</td><td>12.72</td><td><strong>0.013</strong></td></tr><tr><td>Temperature × Coinfection</td><td>1.70</td><td>0.428</td></tr><tr><td><strong>Proportion of viable chasmothecia</strong></td><td></td><td></td></tr><tr><td>First strain identity</td><td>57.06</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>Coinfection</td><td>1.00</td><td>0.317</td></tr><tr><td>Temperature</td><td>135.64</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>First strain identity × Temperature</td><td>25.77</td><td><strong>&lt;10<sup>-2</sup></strong></td></tr><tr><td>Temperature × Coinfection</td><td>0.39</td><td>0.822</td></tr></tbody></table>

Statistically significant results are shown in bold.

is summarized in Table 2 and in Supplementary Figure 2. At the lowest temperature, +17°C, we find all sexual life-history traits to perform better than at higher temperatures (Table 2). For example, for strain S1 the time from first sporulation to first mature chasmothecia is 2.1 times faster at +17°C than at +23°C, 2.1 times faster for strain S2 and 2.0 times faster for the strain S3. The total number of produced chasmothecia is 73.3 times higher at +17°C than at +23°C for strain S1, 13.8 times higher for strain S2, and 10.0 times higher for strain S3. The proportion of viable chasmothecia is 9.4 times higher at +17°C than at 23°C for the strain S1, 5.3 times higher for the strain S2 and the same for the strain S3. For the asexual life-history traits studied – time from inoculation to germination and time form germination to sporulation – the effect of temperature tended to be the opposite. Strains were typically faster to germinate and launch spore production at +20°C and +23°C than at +17°C (Table 2 and Figure 1).

Coinfection did not have a significant effect on the asexual life history traits measured. However, we found that under coinfection significantly more chasmothecia are produced

(Figure 2 and Tables 1, 3). For the strain S1 2.0% chasmothecia are produced more under coinfection, 45.6% more for the strain S2 and 63.4% more for the strain S3. The proportion of viable chasmothecia was not significantly different between single infection and coinfection (Figure 2 and Table 1). The interaction between temperature and coinfection was not significant for any of the measured life-history traits (Table 1).

We found significant variation among strains in the timing of chasmothecia production and their viability (Figure 2 and Table 1). We also found that for all traits measured, no strain can outperform all others in all temperatures (Figures 1, 2, Table 2, and Supplementary Figure 2). This trend was most pronounced when measuring the timing and total number of chasmothecia, as well as proportion of viable chasmothecia, as evidenced by the statistically significant strain identity × temperature interaction (Figure 2 and Table 1).

DISCUSSION

Understanding processes maintaining variation in pathogen life-history traits is a long-standing challenge in disease ecology as this variation underlies risks of pathogen emergence and risks of infection (Galvani, 2003; Grenfell et al., 2004). Here, we have measured life-history traits at both asexual and sexual stages of an obligate fungal pathogen. We find that when we simultaneously investigate the effect of abiotic and biotic environmental variation, only temperature had a consistent and significant impact on all measured pathogen life-history. For several of the measured traits, the pathogen strains also differed significantly in how they responded to variation in temperature. Coinfection had a significant direct effect on the number of produced chasmothecia. Our finding suggests that abiotic variation may be more important in maintaining variation in pathogen populations than biotic variation.

In line with a previous study (Laine, 2007), our results demonstrate that the asexual life-history traits leading to infection are significantly affected by temperature. For the asexual life-history traits the optimal temperature depended on the life-history trait as well as strain identity. In general, although the specific effects of temperature vary among pathosystems, it has been shown that temperature may impact all key pathogen life-history traits including the ability to establish infection, subsequent development as well as transmission (Burdon, 1987; Thomas and Blanford, 2003; Fels and Kaltz, 2006). Here, we also find that temperature significantly affects traits linked with the sexual stage. Our inoculation study revealed that at +17°C, the number and viability of produced chasmothecia was the highest for all three strains. This effect of the temperature is in line with the biology of the powdery mildew, as the production of the sexually produced resting structures may be partly triggered by lower temperatures toward the end of the growing season (Tollenaere and Laine, 2013).

Genotype-by-environment interactions are considered an important mechanism maintaining variation. In support of this, here we find that for nearly all measured life-history
<table><caption>**TABLE 2** | The means and standard deviations of the measured life-history traits for each temperature and strain under single infection.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">+17°C</th><th colspan="3">+20°C</th><th colspan="3">+23°C</th></tr><tr><th>S1</th><th>S2</th><th>S3</th><th>S1</th><th>S2</th><th>S3</th><th>S1</th><th>S2</th><th>S3</th></tr></thead><tbody><tr><td rowspan="2">Time from inoculation to germination</td><td>Mean</td><td><strong>5.90</strong></td><td>7.37</td><td>8.12</td><td>5.94</td><td>7.76</td><td><strong>5.42</strong></td><td>6.07</td><td>5.85</td><td><strong>5.39</strong></td></tr><tr><td>SD</td><td>3.71</td><td>4.34</td><td>6.55</td><td>2.70</td><td>6.41</td><td>2.93</td><td>3.87</td><td>2.76</td><td>3.69</td></tr><tr><td rowspan="2">Time from 1st germination to 1st sporulation</td><td>Mean</td><td>8.14</td><td>8.33</td><td><strong>7.88</strong></td><td>8.59</td><td><strong>5.18</strong></td><td>6.23</td><td>9.45</td><td>9.15</td><td><strong>8.12</strong></td></tr><tr><td>SD</td><td>4.21</td><td>6.84</td><td>6.46</td><td>6.47</td><td>5.03</td><td>4.27</td><td>5.55</td><td>7.54</td><td>5.56</td></tr><tr><td rowspan="2">Time from 1st sporulation to 1st mature chasmo</td><td>Mean</td><td>10.00</td><td><strong>9.47</strong></td><td>10.24</td><td>23.33</td><td><strong>14.00</strong></td><td>15.23</td><td>21.33</td><td>20.13</td><td><strong>20.07</strong></td></tr><tr><td>SD</td><td>2.98</td><td>4.24</td><td>5.26</td><td>6.28</td><td>6.44</td><td>7.76</td><td>5.33</td><td>7.29</td><td>7.25</td></tr><tr><td rowspan="2">Total number of produced chasmo</td><td>Mean</td><td><strong>824.19</strong></td><td>546.43</td><td>503.12</td><td>16.78</td><td><strong>25.00</strong></td><td>18.62</td><td>11.25</td><td>39.67</td><td><strong>50.33</strong></td></tr><tr><td>SD</td><td>719.76</td><td>432.12</td><td>648.03</td><td>11.40</td><td>36.73</td><td>27.52</td><td>13.29</td><td>74.88</td><td>65.66</td></tr><tr><td rowspan="2">Proportion of viable chasmo</td><td>Mean</td><td><strong>0.66</strong></td><td>0.58</td><td>0.12</td><td><strong>0.17</strong></td><td>0.04</td><td>0.00</td><td>0.07</td><td>0.11</td><td><strong>0.12</strong></td></tr><tr><td>SD</td><td>0.28</td><td>0.25</td><td>0.18</td><td>0.24</td><td>0.09</td><td>0.00</td><td>0.10</td><td>0.14</td><td>0.17</td></tr></tbody></table>

The time is indicated in days. The best performing strain at each temperature is indicated in bold.

<table><caption>**TABLE 3** | The means and standard deviations of the measured life-history for each temperature and strain under coinfection.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">+17°C</th><th colspan="3">+20°C</th><th colspan="3">+23°C</th></tr><tr><th>S1</th><th>S2</th><th>S3</th><th>S1</th><th>S2</th><th>S3</th><th>S1</th><th>S2</th><th>S3</th></tr></thead><tbody><tr><td rowspan="2">Time from inoculation to germination</td><td>Mean</td><td>6.37</td><td>7.06</td><td><strong>6.13</strong></td><td><strong>4.98</strong></td><td>6.07</td><td>5.53</td><td>5.59</td><td><strong>5.30</strong></td><td>5.36</td></tr><tr><td>SD</td><td>3.58</td><td>3.84</td><td>3.14</td><td>2.68</td><td>3.68</td><td>3.74</td><td>3.33</td><td>2.32</td><td>3.29</td></tr><tr><td rowspan="2">Time from 1st germination to 1st sporulation</td><td>Mean</td><td>9.25</td><td><strong>8.56</strong></td><td>9.16</td><td>6.46</td><td>6.69</td><td><strong>5.65</strong></td><td>6.81</td><td><strong>6.55</strong></td><td>7.26</td></tr><tr><td>SD</td><td>5.38</td><td>6.43</td><td>6.33</td><td>3.59</td><td>3.98</td><td>3.02</td><td>4.70</td><td>5.26</td><td>6.01</td></tr><tr><td rowspan="2">Time from 1st sporulation to 1st mature chasmo</td><td>Mean</td><td>9.08</td><td><strong>8.62</strong></td><td>9.07</td><td>20.00</td><td>19.50</td><td><strong>15.27</strong></td><td><strong>20.92</strong></td><td>23.00</td><td>23.34</td></tr><tr><td>SD</td><td>3.45</td><td>3.12</td><td>3.51</td><td>8.88</td><td>8.65</td><td>7.40</td><td>6.91</td><td>7.25</td><td>7.68</td></tr><tr><td rowspan="2">Total number of produced chasmo</td><td>Mean</td><td>765.22</td><td><strong>804.16</strong></td><td>794.07</td><td>17.56</td><td><strong>49.86</strong></td><td>46.67</td><td>86.42</td><td>35.67</td><td><strong>93.79</strong></td></tr><tr><td>SD</td><td>1211.90</td><td>784.58</td><td>1221.78</td><td>23.27</td><td>57.86</td><td>59.14</td><td>192.60</td><td>59.00</td><td>186.34</td></tr><tr><td rowspan="2">Proportion of viable chasmo</td><td>Mean</td><td>0.48</td><td><strong>0.52</strong></td><td>0.39</td><td><strong>0.17</strong></td><td>0.13</td><td>0.06</td><td>0.06</td><td><strong>0.12</strong></td><td>0.11</td></tr><tr><td>SD</td><td>0.36</td><td>0.33</td><td>0.35</td><td>0.15</td><td>0.14</td><td>0.11</td><td>0.09</td><td>0.18</td><td>0.19</td></tr></tbody></table>

The time is indicated in days. The best performing strain at each temperature is indicated in bold.

traits, the strains switch ranks along the temperature gradient. Hence, no strain outperforms all others across all temperatures. This trend was statistically significant when measuring all traits linked with sexual reproduction: the timing and number of produced chasmothecia, as well as their viability. These represent core fitness traits in *P. plantaginis*, as chasmothecia production is strongly linked with the ability to survive over winter (Tack and Laine, 2014). Although the experiment only included three strains of *P. plantaginis*, it is in line with previous work demonstrating significant temperature and genotype x temperature effects on asexual life-history traits using a diverse range of *P. plantaginis* strains (Laine, 2004, 2008). An interesting avenue of future work would be to test whether the results we find here – both direct and strain mediated sensitivity to temperature– also holds for a larger number of strains and under more extreme temperature variation.

For pathogens the biotic environment may vary depending on the community of coinfecting pathogens sharing the same host. Coinfection is a common phenomenon across plant pathosystems (Tollenaere et al., 2016). In the *P. plantaginis*

metapopulation, approximately half of the local pathogen populations support coinfection (Susi et al., 2015a). Theoretically coinfection has been proposed to change pathogen growth rates as strains compete for the same limited resources of the host, and there is some empirical support for this (Clement et al., 2012; Tollenaere et al., 2016; Suffert et al., 2018). Although previous studies on *P. plantaginis* have revealed coinfection to change both within- and between host dynamics (Susi et al., 2015a,b), here coinfection only had a significant effect on the number of chasmothecia produced. Higher production of resting spores under coinfection is expected to have profound fitness consequences as local pathogen populations go through a severe decline each winter (Tack and Laine, 2014), often resulting in local extinction (Jousimo et al., 2014). As coinfection varies spatially (Susi et al., 2015a), it can be a powerful mechanism maintaining diversity in this natural pathosystem.

Interestingly, we also did not detect a significant coinfection-by-temperature interaction for any of the measured life-history traits. It is likely that the effect of temperature in our experiment was so strong, that most of variation in these data is explained
by the temperature gradient. However, it should also be noted
that previous studies have shown strains to vary in how they
respond to coinfection (Laine and Mäkinen, 2018). Hence, it is
possible that the strains included in this study do not represent
those that respond strongly to coinfection. Moreover, the effects
of coinfection – either direct or mediated through an interaction
with temperature – may not become apparent in the development
of a single lesion but accumulate during the epidemic season (Susi
et al., 2015a,b). Hence, a future study using more strains and
allowing for several cycles of auto- and allo-infection would help
elucidate whether coinfection changes infections outcomes also
under pronounced abiotic variation.

CONCLUSION

Here, we find both abiotic and biotic environments to impact
pathogen life-history traits directly as well as through genotype
specific responses. Moreover, we find that the effect of
abiotic and biotic variation depends on pathogen life-history
traits. Temperature variation affected all studied pathogen life-
history traits and hence, we may expect it to be a powerful
mechanism maintaining diversity during the epidemic season.
We found coinfection to significantly affect the number of
produced chasmothecia which is expected to have far-reaching
consequences for pathogen survival from one epidemic season
to the next (Tack and Laine, 2014). For an epiphytic pathogen
such as *P. plantaginis*, it is not surprising to find high sensitivity
to ambient temperature. Temperature fluctuations may be
gradual (e.g., global warming), seasonal (e.g., summer vs. winter
resource availability), or continuously fluctuating (e.g., daily
temperatures). Our results suggest that *P. plantaginis* can cope
with such variation in the environment, as we documented
plastic responses in many life-history traits under temperature
variation. We conclude that environmental variation is expected

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DATA AVAILABILITY STATEMENT

All data associated with this study is available on the Dryad
Digital Repository: doi: 10.5061/dryad.fg25kk0.

AUTHOR CONTRIBUTIONS

EV and A-LL conceived and designed the experiments, analyzed
the data, and wrote the paper. EV performed the experiments.

FUNDING

This work was funded by grants from the Academy of Finland (296686), and the European Research Council (Consolidator Grant RESISTANCE 724508) to A-LL.

ACKNOWLEDGMENTS

Krista Raveala is acknowledged for assistance in the greenhouse
and laboratory.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fpls.2018.01670/
full#supplementary-material

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**Conflict of Interest Statement:** 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.

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# Striking Similarities Between *Botrytis cinerea* From Non-agricultural and From Agricultural Habitats

Marc Bardin*, Christel Leyronas, Claire Troulet and Cindy E. Morris

Pathologie Végétale, INRA, Montfavet, France

Investigations into life history of microorganisms that cause plant diseases have been limited mostly to contexts where they are in interaction with plants, and with cropped or otherwise managed vegetation. Therefore, knowledge about the diversity of plant pathogens, about potential reservoirs of inoculum and about the processes that contribute to their survival and adaptation is limited to these contexts. The agro-centric perspective of plant pathogen life histories is incoherent with respect to the capacity of many of them to persist as saprophytes on various substrates. In this context we have investigated the ubiquity of the broad host range necrotrophic fungus *Botrytis cinerea*, outside of agricultural settings and have determined if the populations in these natural habitats can be distinguished phenotypically and phylogenetically from populations isolated from diseased crops. Over a period of 5 years, we isolated *B. cinerea* from 235 samples of various substrates collected in France including rainfall, snowpack, river, and lake water, epilithic biofilms in mountain streams, leaf litter and plant debris, rock surfaces, bird feathers and healthy wild plants from outside of agricultural fields. All substrates except rock surfaces harbored *B. cinerea* leading us to establish a collection of purified strains that were compared to *B. cinerea* from diseased tomato, grapes and various other crops in France. Phylogenetic comparisons of 321 strains from crop plants and 100 strains from environmental substrates based on sequences of 9 microsatellite markers revealed that strains from crops and the environment could not be distinguished. Furthermore, the genetic diversity of strains outside of agriculture was just as broad as within agriculture. In tests to determine the aggressiveness of strains on tomato stems, the mean disease severity caused by strains from environmental substrates was statistically identical to the severity of disease caused by strains from tomato, but was significantly greater than the severity caused by strains from grape or other crops. Our results suggest that highly diverse populations of this plant pathogen persist outside of agriculture in association with substrates other than plants and that this part of their life history is compatible with its capacity to maintain its potential as plant pathogen.

**Keywords:** microbial ecology, epidemiology, gray mold, polyphagous, diversity, aggressiveness, fitness, selection

**OPEN ACCESS**

**Edited by:**
Jeremy Astier,
INRA UMR1347 Agroécologie, France

**Reviewed by:**
Renaud Travadon,
University of California, Davis,
United States
Michelina Ruocco,
Istituto per la Protezione Sostenibile
delle Piante (IPSP), Italy

**\*Correspondence:**
Marc Bardin
marc.bardin@inra.fr

**Specialty section:**
This article was submitted to Plant Microbe Interactions, a section of the journal Frontiers in Plant Science

**Received:** 28 August 2018
**Accepted:** 22 November 2018
**Published:** 05 December 2018

**Citation:**
Bardin M, Leyronas C, Troulet C and Morris CE (2018) Striking Similarities Between Botrytis cinerea From Non-agricultural and From Agricultural Habitats. Front. Plant Sci. 9:1820. doi: 10.3389/fpls.2018.01820
# INTRODUCTION

Investigations into the life history of microorganisms that can cause disease to plants have been limited mostly to contexts where they are in interaction with plants, and even more frequently with cropped or otherwise managed vegetation (Morris et al., 2009). Therefore, knowledge about the diversity of plant pathogens, about potential reservoirs of inoculum and about the processes that contribute to survival and adaptation of plant pathogens is limited to these contexts. By contrast, there are many examples of human pathogens for which habitats and biological activities are known outside the strictly medical context (Morris et al., 2009). The agro-centric perspective of plant pathogen life histories is incoherent with respect to the capacity of many plant pathogens to persist as saprophytes on various substrates. Thus, various plant pathogens, including bacteria, fungi and viruses have been detected in non-agricultural habitats or in the absence of host plants (Morris et al., 2009). For instance, recently the fungal species *Fusarium oxysporum*, a major fungal plant pathogen, has been isolated in tap water (Edel-Hermann et al., 2016). However, apart from the bacterial species *Pseudomonas syringae* (Morris et al., 2013), and the oomycete genera *Phytophthora* (Hansen et al., 2012), the ecology and evolutionary history of plant pathogens at spatiotemporal scales wider than their strict interaction with plants (either cultivated or wild) has not been extensively studied.

The aerially disseminated fungus *Botrytis cinerea* is considered to be a broad-host range necrotrophic pathogen able to attack about 586 plant genera, mostly dicots (Elad et al., 2016). It is also capable of growing as a saprophyte on dead and decaying plant matter (Williamson et al., 2007) and it probably has an underestimated facultative endophytic behavior, i.e., it survives in host plant tissue without generating disease symptoms (Van Kan et al., 2014). Additionally this fungus has been detected in non-agricultural contexts such as outdoor or indoor air in agricultural or urban settings (Bartlett et al., 2004; Lugauskas and Krikstaponis, 2004; Leyronas and Nicot, 2013), in clouds (Amato et al., 2007), in precipitation (Monteil et al., 2014), in agricultural or non-agricultural soils (Azmi and Seppelt, 1998; Leyronas et al., 2015c), on rocks and monuments (Gaylarde and Morton, 1999), in stocks of hydrocarbons (Gaylarde et al., 1999), on external or in internal parts of insects (Fermaud and Lemenn, 1989; Louis et al., 1996), on pelage of small mammals (Shchipanov et al., 2006) and human hairs (Ali-Shtayeh et al., 2001). However, to our knowledge, the genetic diversity and the pathogenic potential of saprophytic *B. cinerea* strains isolated from non-agricultural habitats have never been studied.

Recent research is revealing that natural environments harbor strains of microorganisms that have likely given rise to strains that have emerged in plant disease epidemics (Hansen et al., 2012; Monteil et al., 2016). In this context, we have assessed the ubiquity of the fungus *B. cinerea* outside of agricultural settings and have determined if the populations in these natural habitats can be distinguished phenotypically and phylogenetically from populations isolated from diseased crops. The objectives of the present study were (i) to determine the presence and the abundance of *B. cinerea* in various non-agricultural habitats,

(ii) to evaluate the genetic diversity and the phenotypic diversity of the strains collected, and (iii) to compare this diversity with strains collected from crops.

# MATERIALS AND METHODS

## Isolation of *B. cinerea* From Non-agricultural Habitats

Over a period of 5 years (2005–2010), we collected 235 samples of various substrates, including rainfall, snowpack, fresh water from rivers and lakes, epilithic biofilms in mountain streams, rocks, leaf litter and plant debris, healthy wild plants from outside of agricultural fields and feathers from the great tit (*Parus major*) (Table 1). Most of samples originated from the Provence-Alpes-Côte d’Azur region in France. Some of the strains obtained from rainfall and snowfall were part of a previous study (Monteil et al., 2014).

Each sample was plated on Petri plates containing the semi-selective *Botrytis Spore Trap Medium* (BSTM) (Edwards and Seddon, 2001). The Petri plates were sealed with parafilm and incubated at 20°C in daylight for 14 days to allow the development of fungal colonies. The colonies with mycelium resembling that of *B. cinerea* were individually sub-cultured on Potato Dextrose Agar (PDA) and their identity was confirmed 7 days after transplanting by observing characteristic asexual sporulation of *B. cinerea* (Barnett, 1998). Dry sterile cotton swabs were rubbed on sporulating plates to collect *B. cinerea* spores. The swabs were stored at –20°C until isolate purification.

All isolates were purified and single-spored in a classical way (Leyronas et al., 2012) prior to their genotypic and phenotypic characterization. Hereafter these characterized single spore isolates will be referred to as “environmental strains.”

## Strain Genotyping

A subset of 109 strains collected from non-agricultural habitats were genotyped (Table 1). They were compared to 327 agricultural strains sampled from lettuce and tomato plants grown in several greenhouses in the South of France (Leyronas et al., 2015a,b,c).

Genomic DNA was extracted from aliquots of 15 mg lyophilized fungal material (harvested from two-week old cultures on Potato Dextrose Agar), following the DNeasy Plant extraction Kit protocole (Qiagen). The nine microsatellite markers designed for *B. cinerea* by Fournier et al. (2002) were amplified following the protocol described by Leyronas et al. (2015b). To determine the size of the microsatellites, the PCR products were scanned with the help of an ABI 3730 sequencer (Applied Biosystems). GeneMapper software version 4.1 (Applied Biosystems) was then used for the microsatellite size analysis. Complete microsatellite size profiles (referred to as “haplotypes” hereafter) were obtained for 109 environmental strains and 327 agricultural strains.

## Genetic Characteristics of Strains

In a first step, the strains with the private allele at microsatellite locus BC6 associated with the cryptic species *B. pseudocinerea*
<table><caption>**TABLE 1** | Quantification of the number of *Botrytis cinerea* strains collected in non-agricultural habitats (environmental strains) and number of strains tested for different traits.</caption><thead><tr><th>Origin of samples</th><th>Number of samples collected</th><th>Number of samples containing <i>B. cinerea</i></th><th>Number of <i>B. cinerea</i> strains detected on medium</th><th>Number of genotyped strains</th><th>Number of strains tested for their aggressiveness on tomato</th><th>Number of strains tested for other phenotypic traits</th></tr></thead><tbody><tr><td>Precipitation</td><td>35</td><td>18</td><td>84</td><td>69</td><td>84</td><td>11</td></tr><tr><td>Snowpack</td><td>35</td><td>7</td><td>13</td><td>10</td><td>13</td><td>5</td></tr><tr><td>Fresh water</td><td>56</td><td>14</td><td>18</td><td>12</td><td>11</td><td>3</td></tr><tr><td>Epilithic biofilm</td><td>18</td><td>3</td><td>3</td><td>2</td><td>0</td><td>0</td></tr><tr><td>Litter</td><td>33</td><td>4</td><td>4</td><td>3</td><td>1</td><td>1</td></tr><tr><td>Rock surfaces</td><td>6</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Plant debris</td><td>9</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Asymptomatic wild plants</td><td>37</td><td>6</td><td>10</td><td>7</td><td>10</td><td>2</td></tr><tr><td>Bird feather</td><td>6</td><td>3</td><td>8</td><td>5</td><td>8</td><td>1</td></tr><tr><td>Total</td><td>235</td><td>56</td><td>141</td><td>109</td><td>128</td><td>24</td></tr></tbody></table>

(Walker et al., 2011) were removed from further analyses. This private allele is found only in *B. pseudocinerea* strains and never in *B. cinerea* strains. Then, in order to compare the *B. cinerea* strains sampled from the different reservoirs, several indices of genetic diversity were used. The software FSTAT version 2.9.3 (Goudet, 1995) was used to compute allelic richness (corrected for the smallest sample size) and unbiased gene diversity per locus. Unbiased gene diversity (Hnb) and allelic richness (average over the nine loci) were computed separately for the strains collected in the different reservoirs with the Genetix software (Belkhir et al., 1996–2004). The number of different multilocus haplotypes (MLH) was computed with GenClone 1.0 software (Arnaud-Haond and Belkhir, 2007). We used the index of haplotypic diversity (based on the number of individuals and the number of distinct MLH), which estimates the proportion of haplotypes present in a population and takes a value of 1 when a population is composed exclusively of unique haplotypes (Arnaud-Haond et al., 2007).

## Phylogenetic Relationships Between Strains From Non-agricultural Habitats and From Crops

To assess the relationships between the different strains, we computed a neighbor-joining (NJ) tree with the program POPULATIONS (version 1.2.32 provided by Olivier Langella, Quantitative Genetics and Evolution, Gif-sur-Yvette, France). The NJ-tree was based on the distances of Cavalli-Sforza and Edwards (1967), computed from our microsatellite loci. The tree was visualized and edited with TREEVIEW (Page, 1996). All analyses were conducted on data sets excluding clone replicates.

In addition, Arlequin version 3.5 (Excoffier et al., 2005) was used to assess genetic differentiation between strains collected from non-agricultural habitats (NH) and from crops by computing RST values as suggested by Slatkin (1995) for microsatellite data. The clonally corrected data set was used.

## Aggressiveness of Strains on Tomato Plants

The aggressiveness of a subset of 128 environmental strains (Table 1) was assessed on 8-week old tomato plants cv. Monalbo (INRA). Tomato plants were grown in a greenhouse and watered daily with a nutrient solution as described previously (Decognet et al., 2009). Each strain was inoculated on three plants. On each plant, three leaves were removed, leaving 1 cm petiole stubs on the stems and the wounds were inoculated with 10 µL aliquots of spore suspension. The spore suspensions were prepared from two-week old cultures on PDA and were adjusted to 10⁶ spores mL⁻¹. All plants were incubated in a growth chamber with a photoperiod of 14 h, with a light intensity of 162 µmol m⁻² s⁻¹, maintained at 21°C with a relative humidity above 90%. The length of resulting stem lesions was monitored daily from the 3rd to the 7th day after inoculation and these data were used to compute the area under the disease progress curves (AUDPC). Due to a limited place in the growth chamber, strains were tested in series of 10–15 strains, and in each series, strain BC1 was used as a reference to calculate an index of aggressiveness (IA) for each strain, relative to that of this strain (Decognet et al., 2009), as follows:

$$IA = 100 * (AUDPC_{\text{strain}}/AUDPC_{\text{BC1}})$$

with AUDPC<sub>strain</sub> being the value of AUDPC computed for the tested strain and AUDPC<sub>BC1</sub> the value of AUDPC obtained for the reference strain BC1. Two to three independent repetitions of the test were realized for each strain.

The aggressiveness of environmental strains was compared to that of 156 single-spored agricultural strains having a wide diversity regarding year, region of isolation and host plants. All these strains were collected from 1988 to 2008 with a majority collected after 2000 (108 strains). Regarding their geographical origin, 149 strains were sampled from France, of which 122 were from the southern part of the country. In addition, four strains came from Italy, two from Syria and one from Portugal. They were collected from various diseased plants: 101 from tomato, 33 from grape, and 22 from other plants including rose, cucumber,
strawberry, artichoke, pepper, carrot, onion, asparagus, peach fruit, cherry fruit, kiwifruit, hydrangea, gerbera, cyclamen, and poinsettia.

## Traits of *B. cinerea* in vitro

Mycelial growth, sporulation and sclerotia production were evaluated for a sub-sample of 31 agricultural strains and 24 environmental strains of *B. cinerea* (Table 1), which represent the full range of aggressiveness of the tested strains. Mycelial growth was evaluated by inoculating PDA with a 5 mm-diameter mycelial plug of each *B. cinerea* strain and incubating in a growth chamber (21°C, photoperiod 14 h, 114 µmol m⁻² s⁻¹). Two perpendicular measurements of the diameter of the mycelial colony were performed every day for 3 days and the rate of mycelial growth between the first and the third day of incubation was calculated for each strain. Three plates were inoculated for each strain and the whole experiment was repeated two times. The spore production was determined 14 days after inoculation on PDA medium (21°C, photoperiod 14 h, 114 µmol m⁻² s⁻¹). Spores were scrapped from the media, suspended in water and spore concentration was determined using a hemacytometer. The number of sclerotia produced by each strain of *B. cinerea* on PDA was recorded after five weeks of incubation at 21°C (1 week with a photoperiod of 14 h at 114 µmol m⁻² s⁻¹ and 4 weeks in the dark) and 5 weeks of incubation at 4°C in the dark. For the sporulation and sclerotia production experiments, three replicates were realized for each strain.

## Statistical Analyses

Statistical analyses were performed with Statistica (version 12, Statsoft). Non-parametric tests (Mann and Whitney) were used to determine significant differences between gene diversity and allelic richness of environmental and agricultural strains. To compare the different phenotypic traits evaluated, analysis of variance (ANOVA) were performed. In the case of a significant effect of the tested factor, a comparison of mean was realized using the Newman-Keuls test. The relationship between the various phenotypic traits tested (mycelial growth, sporulation, sclerotia production) and aggressiveness was realized using linear regressions. Statistical inferences were made at the 5% level of significance, unless indicated otherwise.

# RESULTS

## *B. cinerea* Is Ubiquitous in Non-agricultural Habitats

*Botrytis cinerea* was recovered in 56 samples out of the 235 samples collected (Table 1). From these 56 samples, we were able to purify 141 strains of *B. cinerea*. All substrates except rock surfaces harbored *B. cinerea*. The fungus was detected at a rate of 51% in precipitation, 50% from feathers of Great Tits, 21% in fresh water and 20% in snowpack. Detection rates were between 11 and 17% in the other substrates including epilithic biofilm, litter, plant debris and asymptomatic plants.

## Environmental and Agricultural Strains Cannot Be Significantly Differentiated Based on Their Genetic Diversity

Among the 109 genotyped environmental strains and 327 genotyped agricultural strains, 9 and 6 strains, respectively, carried the private allele at microsatellite locus BC6 associated with the cryptic species *B. pseudocinerea* (Walker et al., 2011) and were removed from further analyses. We thus compared the indices of genetic diversity based on the haplotypes obtained for the 100 and 321 remaining strains considered to be *B. cinerea* (Table 2). There was no significant difference between the number of alleles in each microsatellite locus and the gene diversity per locus of environmental strains and agricultural strains (respectively, $P_{Mann}$ and Whitney = 0.60, $P_{Mann}$ and Whitney = 0.79) (Table 3). The global gene diversity and the mean number of alleles per locus were in the same range for both population of strains (Table 2). However, environmental strains had higher haplotypic diversity than agricultural strains (Table 2). Two hundred and seventy different multilocus haplotypes (MLH) were found among the 423 strains analyzed but none of these was shared by environmental strains and agricultural strains.

The distribution of the strains collected from crops and those collected from non-agricultural habitats in the NJ tree shows that they were widely intermixed in the tree regardless of their origin (Figure 1). This result is supported by the low level of genetic differentiation ($R_{ST}$ = 0.021, *P* = 0.019) between these two groups of strains.

## Environmental Strains Represent a Greater Variability in Aggressiveness and an Overall Greater Mean Aggressiveness Than Agricultural Strains

Based on the lesion expansion on the stem of the tomato plants, different levels of aggressiveness were observed within the 128 environmental strains and the 156 agricultural strains of *B. cinerea* tested (Figure 2). For each of the population of strains (agricultural strains and environmental strains), significant differences were observed among strains (ANOVA, *P* < 0.0001). MOP7-4 isolated in rainfall in 2010, and H6 and BC25 isolated on diseased tomato plants in 1991 were not able to generate symptoms on the stem of the plant, suggesting that they are hypo-aggressive on tomato stems. However, these strains were able to infect the tomato petiole stubs (data not shown). The index of aggressiveness for the 284 strains of *B. cinerea* ranged from 0 to 144% on potted tomato plants with the wider range of aggressiveness for environmental strains compared to agricultural strains (Table 4). The mean disease severity caused by strains from environmental substrates was statistically identical to the severity of disease caused by strains from tomato, but was significantly greater than the severity caused by strains from grape or other crops. Globally, environmental strains are significantly more aggressive than agricultural strains on plants (58.9% vs. 50.4%, *P* = 0.009).