Elisabeth Fournier
email: elisabeth.fournier@supagro.inra.fr

Pierre Gladieux

Tatiana Giraud

The 'Dr Jekyll and Mr Hyde fungus': noble rot versus gray mold symptoms of Botrytis cinerea on grapes

Keywords: Ascomycete, Botrytis cinerea, genetic structure, gray mold, microsatellites, noble rot, population structure

Many cryptic species have recently been discovered in fungi, especially in fungal plant pathogens. Cryptic fungal species co-occurring in sympatry may occupy slightly different ecological niches, for example infecting the same crop plant but specialized on different organs or having different phenologies. Identifying cryptic species in fungal pathogens of crops and determining their ecological specialization are therefore crucial for disease management. Here, we addressed this question in the ascomycete Botrytis cinerea, the agent of gray mold on a wide range of plants. On grape, B. cinerea causes severe damage but is also responsible for noble rot used for processing sweet wines. We used microsatellite genotyping and clustering methods to elucidate whether isolates sampled on gray mold versus noble rot symptoms in three French regions belong to genetically differentiated populations. The inferred population structure matched geography rather than the type of symptom. Noble rot symptoms therefore do not seem to be caused by a specific B. cinerea population but instead seem to depend essentially on microclimatic conditions, which has applied consequences for the production of sweet wines.

Introduction

Since the advent of molecular biology, the use of genetic markers within the population genetics framework has revealed the existence of many cryptic species, that is, species previously unrecognized based on morphology alone, in a wide range of organisms (e.g., [START_REF] Taylor | Phylogenetic species recognition and species concepts in fungi[END_REF][START_REF] Gentetaki | Evidence for cryptic speciation in Carchesium polypinum Linnaeus, 1758 (Ciliophora: Petrichia) inferred from mitochondrial, nuclear, and morphological markers[END_REF][START_REF] Heimeier | Molecular species identification of Astrotoma agassizii from planktonic embryos: further evidence for a cryptic species complex[END_REF][START_REF] Jesse | Identification of a cryptic lineage within Potamon fluviatile (Crustacea: Brachyura: Potamidae)[END_REF][START_REF] Kreier | Phylogeny of the leafy liverwort Ptili-dium: cryptic speciation and shared haplotypes between the Northern and the Southern hemispheres[END_REF][START_REF] Nygren | Cryptic species of Notophyllum (Polychaeta: Phyllodocidae) in Scandinavian waters[END_REF]Gladieux et al. 2011a,b;[START_REF] Liu | DNA barcoding for the discrimination of Eurasian yews (Taxus L., Taxaceae) and the discovery of cryptic species[END_REF][START_REF] Piggott | Three fishes in one: cryptic species in an Amazonian floodplain forest specialist[END_REF] . This is especially true for microorganisms like fungi, because few morphological traits can be used to discriminate species and because inducing mating in vitro to test for interfertility is often impossible or less discriminant than other species criteria [START_REF] Koufopanou | Concordance of gene genealogies reveals reproductive isolation in the pathogenic fungus Coccidioides immitis[END_REF]Dettman et al. 2003a,b;[START_REF] Fournier | Characterization of Bc-hch, the Botrytis cinerea homolog of the Neurospora crassa het-c vegetative incompatibility locus, and its use as a population marker[END_REF]Le Gac et al. 2007a,b;[START_REF] Giraud | Speciation in fungi[END_REF]. Some of the recently discovered fungal cryptic species have been found to occur in allopatry, for instance on different continents, such as in Neurospora crassa (Dettman et al. 2003a), yeasts [START_REF] Kuehne | Allopatric divergence, secondary contact and genetic isolation in wild yeast[END_REF], or the crop path-ogen Fusarium graminearum [START_REF] O'donnell | Genealogical concordance between the mating type locus and seven other nuclear genes supports formal recognition of nine phylogenetically distinct species within the Fusarium graminearum clade[END_REF]). Other fungal cryptic species occur in sympatry and often display seemingly similar ecological niches [START_REF] Fitt | Coexistence of related pathogen species on arable crops in space and time[END_REF]Le Gac et al. 2007a;[START_REF] Giraud | Speciation in fungi[END_REF] . In this latter case, the sympatry between closely related species with apparently similar ecological niches can be explained by a recent secondary contact after speciation in allopatry or by different resources use [START_REF] Giraud | Speciation in fungi[END_REF]Gladieux et al. 2011b). For instance, in fungal plant pathogens, sibling species infecting the same crop plants are often specialized on different organs or have different phenologies [START_REF] Fitt | Coexistence of related pathogen species on arable crops in space and time[END_REF]. Beyond the fundamental interest in understanding the coexistence of sibling species in ecology, it is essential for disease management to recognize the different cryptic species in fungal pathogens of crops and their ecological specialization, as they can exhibit differences in fungicide resistance [START_REF] Fournier | Partition of the Botrytis cinerea complex in France using multiple gene genealogies[END_REF], phenologies [START_REF] Walker | Botrytis pseudocinerea, a new cryptic species causing grey mould in French vineyards in sympatry with Botrytis cinerea[END_REF], organ specialization [START_REF] Fitt | Coexistence of related pathogen species on arable crops in space and time[END_REF], host specialization (Le Gac et al. 2007a;[START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF], or ability to infect resistant plant varieties (Le Cam et al.

1

Evolutionary Applications

2002; [START_REF] Gladieux | Host-specific differentiation among populations of Venturia inaequalis causing scab on apple, pyracantha and loquat[END_REF]. Recognizing sibling pathogen species with such specialization has obviously important applied consequences for their control. An efficient control program demands good knowledge of the targeted species, in terms of fungicide resistance, phenology, and potential gene flow from related species (i.e., potential introgression of genes controlling fungicide resistance, aggressiveness, or virulence). For example, rapid and efficient diagnostics of Candida cryptic species allowed demonstration of differences in their prevalences in immunocompromised humans and in their susceptibility profiles to antifungal compounds [START_REF] Johnson | Rare and emerging Candida species[END_REF][START_REF] Peman | Epidemiology, species distribution and in vitro antifungal susceptibility of fungaemia in a Spanish multicentre prospective survey[END_REF]. Such discoveries help targeting treatments against the most harmful species. The recognition of cryptic species is also critical in pathogen species useful to humans. An example is the understanding of the environmental distributions of the entomopathogenic fungus Metarhizium anisopliae and its sister cryptic species, widely used as biological control agents for insects [START_REF] Schneider | A PCRbased tool for cultivation-independent detection and quantification of Metarhizium clade 1[END_REF].

Elucidating the population structure of pathogenic fungi is of uttermost importance when the targeted species or species complex can be at the same time a pest and a benefit, such as the ascomycete Botrytis cinerea. This 'Jekkyl and Hyde' fungus is indeed responsible for the gray mold disease that causes important damage on numerous host plants such as grapevine, strawberries, or tomatoes, but it is also responsible for the noble rot on grape berries used for processing sweet wines such as Sauternes. Despite its great economic importance, the difference between the noble rot and gray mold symptoms on grapevine has been little investigated. Grapevine noble rot, also called 'botrytization' or 'over maturation' of perfectly ripe berries, is a progressive process taking place at the end of the growing season, lasting 10-20 days and being restricted to particular 'terroirs' (i.e., particular combinations of pedo-climatic conditions) [START_REF] Rib Ereau-Gayon | Handbook of Enology. The Microbiology of Wine and Vinifications[END_REF]. The first stage of noble rot infection, called 'marron pourri plein', corresponds to the penetration of the fungus into ripe berries without altering the fruit skin. The further evolution of the botrytization process seems to depend on particular weather conditions-alternations of cold humid nights and sunny dry days. The fungus induces the decomposition of the fruit skin that becomes porous. Water losses by natural evaporation then lead to intense enzymatic maceration and increase sugar concentration in berries ('marron roti' stage). Botrytized berries are harvested one by one by trained people at the 'marron roti' stage when the fungus has been killed by the increase in sugar concentration. The chemical composition between botrytized and gray molded berries differs as regards amines, conjugated polyamines, and hydroxycinnamic acids [START_REF] Geny | Conjugated polyamines and hydroxycinnamic acids in grape berries during Botrytis cinerea disease development: differences between 'noble rot' and 'grey mould[END_REF][START_REF] Kiss | Study of amine composition of botrytized grape berries[END_REF]. The noble rot also has a direct impact on aroma precursors and volatile thiols [START_REF] Tominaga | Stereoisomeric distribution of 3-mercaptohexan-1-ol and 3-mercaptohexyl acetate in dry and sweet white wines made from Vitis vinifera (Var. Sauvignon Blanc and Semillon)[END_REF]Thi-bon et al. 2009), related to the characteristic exceptional range of aromas of sweet wines. At early stages of the botrytization process, a reversion of noble rot toward gray mold can be observed (G. Barbeau, Y. Brygoo, pers. com.). Moreover, both noble rot and gray mold symptoms are observed in sympatry, in the same vineyards, on the same plant, and sometimes within the same grapes. The question, however, remains whether the different symptoms are caused by genetically differentiated strains with contrasted metabolisms or by the same strains that would display different symptoms depending on environmental conditions, such as microclimatic temperature and humidity within the grape, maturity level of the infected berries, or presence of other microbial communities.

Botrytis cinerea (teleomorph Botryotinia fuckeliana) has a wide host range and can infect more than 220 eudicot plants. Botrytis cinerea has recently been shown to be a complex of two sibling species growing in sympatry [START_REF] Fournier | Partition of the Botrytis cinerea complex in France using multiple gene genealogies[END_REF]): B. cinerea (sensu stricto), aka 'B. cinerea Group II', is the predominant and most damageable species on grapevine, especially at vintage. The other species, initially designed B. cinerea Group I [START_REF] Fournier | Partition of the Botrytis cinerea complex in France using multiple gene genealogies[END_REF] and recently re-named B. pseudocinerea [START_REF] Walker | Botrytis pseudocinerea, a new cryptic species causing grey mould in French vineyards in sympatry with Botrytis cinerea[END_REF], is found at low frequencies in French populations [START_REF] Giraud | RFLP markers show genetic recombination in Botryotinita fuckeliana (Botrytis cinerea) and transposable elements reveal two sympatric species[END_REF][START_REF] Walker | Botrytis pseudocinerea, a new cryptic species causing grey mould in French vineyards in sympatry with Botrytis cinerea[END_REF]). Population genetics studies have revealed the existence of regular recombination within each species [START_REF] Giraud | RFLP markers show genetic recombination in Botryotinita fuckeliana (Botrytis cinerea) and transposable elements reveal two sympatric species[END_REF][START_REF] Fournier | Partition of the Botrytis cinerea complex in France using multiple gene genealogies[END_REF] and have shown a significant level of genetic differentiation between B. cinerea Group II populations occurring on different host plants [START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF][START_REF] Karchani-Balma | Geography, plants, and growing systems shape the genetic structure of Tunisian Botrytis cinerea populations[END_REF].

No attempt has been made so far to test the existence of genetic differentiation between strains causing noble rot and gray mold symptoms. This knowledge could yet have important agronomic applications. In case no genetic differentiation is found between noble rot and gray mold isolates, winegrowers should focus on the control of environmental parameters to optimize the development of noble rot. If the two symptoms were caused by two differentiated populations, noble rot could be distinguished from gray mold isolates, and specifically inoculated into targeted vineyards; this would allow the development of noble rot on more berries, increasing yield and saving the money spent to hire trained workers sorting the different kinds of infections berry by berry. It would also allow the development of new business aiming at producing noble rot inocula both for the domestic market and for export in wine-growing regions where botrytized berries are not used in the production of sweet wines. Such inoculations of 'noble rot isolates' could also allow producing sweet wines in a variety of regions where it could not be elaborated so far. Given the socioeconomic importance of sweet wines, this would have many applied consequences. Furthermore, various sweet wines are produced in different regions of France (e.g., Alsace and Bordelais), with debates regarding the involvement of noble rot in certain regions. The identification of genetically differentiated strains, between noble rot and gray mold or even between different regional types of noble rot, would have applied consequences, especially in terms of AOC ('appellation d'origine contrôl ee'), that is., authorization of labels such as 'noble rot'. Genetic tests could be designed and demanded for AOC labels if different types of strains are found, and further investigations could be conducted to determine the metabolic differences among noble rot regional types.

The aims of this study are therefore (i) to determine whether noble rot symptoms are caused by B. cinerea (sensu stricto, i.e., Group II) or by a cryptic distinct species, (ii) in case noble rot symptoms are caused by B. cinerea sensu stricto, to investigate whether genetic differentiation exists between populations of B. cinerea causing respectively gray mold and noble rot symptoms, and (iii) to investigate genetic differentiation between populations from different French vineyards used for sweet wine production. To achieve these goals, we sampled Botrytis on grapevine at fall in three French vineyards where sweet wines are produced: Alsace (Haut-Rhin, Eastern France), Anjou (Maine-et-Loire, central western France), and Bordelais (Gironde, south-western France), on berries displaying gray mold and on berries displaying noble rot symptoms in each vineyard, and we used multilocus microsatellite genotyping to investigate genetic structure.

Material and methods

Botrytis sampling

Isolates were sampled in September and October 2002 in three French regions: Alsace near the city of Colmar, Anjou near the town of Beaulieu-sur-Layon, and Bordelais near the town of Pessac (Table 1). In each region, several fields were sampled, the maximum distance between two fields never exceeding 2 km. In each field, Botrytis was sampled on grape (Vitis vinifera), both from gray mold symptoms ('Gray Mold' isolates) and from noble rot symptoms ('Noble Rot' isolates). All samples were taken from sporulating necroses on fruits using sterilized cotton buds. Samples were genetically purified by three consecutive cultures (3 days at 21°C on PDA medium) of uncontaminated growing mycelial colonies, as described in the study by [START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF]. Isolates were then grown toward sporulation, and spore suspensions were stored at À80°C in 20% glycerol.

DNA extraction and microsatellite amplification

For each isolate, genomic DNA was extracted using the Chelex (Bio-Rad laboratories, Hercules, CA, USA) protocol described in the study by [START_REF] Bucheli | Differences in population structure of the anther smut fungus Microbotryum violaceum on two closely related host species, Silene latifolia and S. dioica[END_REF]. Eight microsatellite markers [START_REF] Fournier | Characterization of nine polymorphic microsatellite loci in the phytopathogenic fungus Botrytis cinerea (Ascomycota)[END_REF] were amplified, with forward primers labeled with a fluorescent probe. PCR amplifications were performed as in [START_REF] Fournier | Characterization of nine polymorphic microsatellite loci in the phytopathogenic fungus Botrytis cinerea (Ascomycota)[END_REF] and gel visualization as in [START_REF] Giraud | Patterns of within population dispersion and mating of the fungus Microbotryum violaceum parasitising the plant Silene latifolia[END_REF]. The Bc6 locus carries a diagnostic allele for B. pseudocinerea [START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF][START_REF] Walker | Botrytis pseudocinerea, a new cryptic species causing grey mould in French vineyards in sympatry with Botrytis cinerea[END_REF].

Genic and genotypic diversities, and linkage disequilibrium

The software FSTAT version 2.9.3 [START_REF] Goudet | FSTAT (Version 1.2): a computer program to calculate F-statistics[END_REF] was used to estimate the unbiased genic diversity, H e [START_REF] Nei | Molecular Evolutionary Genetics[END_REF], and the mean number of alleles per locus, n a , over all loci in the total sample, in each of the six regionX symptom combination and in each cluster inferred using assignment methods. FSTAT was also used to calculate pairwise Weir and Cockerham's F ST [START_REF] Weir | Estimating F-statistics for the analysis of population structure[END_REF] between the clusters inferred using assignment methods and to evaluate their significance by random permutations and if necessary by progressive Bonferroni correction as described by [START_REF] Rice | Analyzing tables of statistical tests[END_REF]. The number of different multilocus genotypes, G, the haplotypic diversity, and the index of association, r D , were calculated using MULTILOCUS 1.3b (Agapow and Burt 2001). The r D index measures the multilocus linkage disequilibrium [START_REF] Brown | Multilocus structure of natural populations of Hordeum spontaneum[END_REF][START_REF] Maynard-Smith | How clonal are bacteria ?[END_REF][START_REF] Haubold | Detecting linkage disequilibrium in bacterial populations[END_REF]) and indicates whether two different individuals sharing the same allele at one locus are more likely to share the same allele at another locus, based on the variance of the number of loci with respect to which two individuals differ. r D is insensitive to the number of scored loci and varies from 0 (complete panmixia) to 1 (complete linkage disequilibrium). The null hypothesis of panmixia (r D = 0) was tested using the reshuffling procedure implemented in the software (500 randomizations).

Analyses of population subdivision

We used STRUCTURE version 2.3.1 [START_REF] Pritchard | Inference of population structure using multilocus genotype data[END_REF] to examine population subdivision. The model implemented allowed admixture and correlation in allele frequencies [START_REF] Falush | Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies[END_REF]. Burn-in length was set at 50 000 iterations followed by a run phase of 500 000 iterations. Analyses were conducted both using uniform priors and using the region of origin of isolates or type of symptoms as prior information to assist clustering (LocPrior model; [START_REF] Hubisz | Inferring weak population structure with the assistance of sample group information[END_REF]). We set the number of populations (K) from 1 to 8, but not higher, as preliminary analyses revealed that setting K values above 8 led to clusters with highly admixed ancestry, which is typical of too high a cluster number. For all analyses, we performed 30 independent runs for each value of K. Outputs were processed using CLUMPP version 1.1.2 [START_REF] Jakobsson | CLUMPP: a cluster machine and permutation program for dealing with label switching and multimodality in analysis of population structure[END_REF] to identify distinct modes in the replicated runs of each K ('modes' refer to distinct clustering solutions). We computed the rate of change in the log probability of data between successive K values (DK statistic; [START_REF] Evanno | Detecting the number of clusters of individuals using the software structure: a simulation study[END_REF].

We also used the discriminant analysis of principal components (DAPC, [START_REF] Jombart | Discriminant analysis of principal components: a new method for the analysis of genetically structured populations[END_REF]) as an alternative method to infer population subdivision. This multivariate method consists of a discriminant analysis on data transformed after a principal component analysis. We used the adegenet package (1.3-1) of the R 2.13.1 software (R Foundation for Statistical Computing, Vienna, Austria, http:// www.R-project.org) to perform DAPC analyses. The Kmeans procedure implemented in the function find.cluster allows determining the optimal number of clusters, C. We let C vary between 1 and 50 (with 5000 iterations for each value of C) and used the Bayesian information criterion (BIC) to determine the optimal value of C (corresponding to the value of C for which the BIC is minimal or at which the rate of change in the BIC abruptly changes).

Results

In total, 164 individual genotypes were analyzed, with sample sizes ranging between 18 and 38 in the different region 9 symptom combinations (Table 1). The data set presented 4.2% of missing data, corresponding to 55 individuals lacking information at one locus at most. On the basis of the diagnostic allele at the Bc6 locus, all individuals were identified as B. cinerea sensu stricto (Group II). Both in the full data set and in the region 9 symptom subsamples, most multilocus genotypes (MLGs) obtained with the eight microsatellites were each detected in a single individual, resulting in a G/N ratio of 0.93 in total and never lower than 0.94 within subsamples (G: number of different MLG, N: sample size). Haplotypic diversity was 0.998 overall and ranged from 0.993 to 1 in subsamples. Nei's genic diversity H e was 0.51 in the full data set and varied within subsamples between 0.37 (Bordelais) and 0.57 (Alsace). Multilocus linkage disequilibrium was low in all subsamples (r D ranging from 0 to 0.03) and never significantly different from 0 (except for the Anjou Gray subsample), indicating no detectable departure from panmixia.

STRUCTURE results are presented in Fig. 1. Only the membership coefficients for runs with 2 ≤ K ≤ 4 are shown, because higher values of K revealed no new clusters and only introduced some heterogeneity into membership coefficients. Analyses that made no use of prior population information or that used the type of symptoms (« Gray Mold » versus « Noble Rot ») to assist clustering revealed no clear pattern of clustering of genotypes (Figs 1A andB). The modal value of the DK statistic was observed at K = 2 for these models of population structure. However, the barplots showed pervasive admixture, providing little evidence for the existence of distinct clusters. In the case of the analysis using the type of symptom as prior information, the 1st cluster encompassed 65% of gray mold isolates and the 2nd cluster encompassed 57% of gray mold isolates. The assignment probability of individuals in the two inferred clusters depending on the type of symptom did not depart from random (v 2 = 0.59, P = 0.44, df = 1).

In contrast, analyses that used sampling locations as prior population information revealed a clear geographic pattern of clustering (Fig. 1C). Genotypes from Alsace, Anjou, and Bordelais had high membership values in distinct clusters (average AE standard deviation (%): 94.5 AE 3.8,73.1 AE 22.3,93.3 AE 10.5,respectively). The modal value of the DK statistic was observed at K = 3. The interclusters F ST ranged from 5% (between Anjou and Bordelais clusters) to 11-12% (between Alsace and the two other clusters). Hence, the partition of genetic diversity of B. cinerea populations is better explained by geography than by the type of symptom.

To confirm the population subdivision inferred using STRUCTURE, we performed a DAPC analysis, first without any a priori information on the origin of individuals or their type of symptoms (Fig. 2). The significant number of DAPC groups given by the K-means procedure was C = 4 (Fig. 2A). The pairwise F ST between these four DAPC groups ranged from 0.15 to 0.20. Individual assignments provided by DAPC differed from those by STRUCTURE: one of the STRUCTURE clusters was split into three DAPC groups, while the fourth DAPC group was composed of 55% of the individuals assigned to the second STRUCTURE cluster (Fig. 2B). The lack of congruence between the two approaches strengthens the conclusion that there is no strong pattern of population subdivision that would be detectable without using a priori knowledge to assist clustering. We then performed DAPC with three groups defined a priori on the basis of the geographic origin of individuals. The posterior assignments confirmed the relevance of the use of the geographic prior information: 80% of individuals sampled in Anjou were assigned to a first DAPC group, 84% of individuals sampled in Bordelais were assigned to the second DAPC group, and 72% of individuals sampled in Alsace were assigned to the third DAPC group (Fig. 3A). These DAPC assignments fitted well with the assignments obtained with the STRUCTURE analyses using geographic information to assist clustering (Fig. 3B).

The assignment methods using the geographic origin of isolates as prior information thus partitioned B. cinerea populations into three clusters strongly associated with the geographic origin of isolates (Table 2, Fig. 4). The first cluster, composed of 53 isolates exclusively coming from Bordelais, encompassed the highest proportion of repeated genotypes (G/N = 0.81) and was the less diverse (genic diversity H e = 0.414, mean number of alleles per locus n a = 3.5). The second cluster counted 46 isolates, all from the Anjou region, in which all MLGs but one were represented only once (G/N = 0.98). This cluster had a genic diversity H e of 0.448 and a mean number of alleles per locus n a of 3.6. The 42 isolates assigned to the third cluster came predominantly from Alsace (39/42) and were all unique MLGs. This 3rd cluster was the most diverse one (H e = 0.581, n a = 5.7). All three clusters were characterized by low values of multilocus linkage disequilibrium (r D ranging from 0.002 to 0.008). The Alsace cluster was also

(A) (B) (C)
Figure 1 Barplots of the STRUCTURE analysis without any prior information (A), with the symptom as prior information (B) or with the geographic origin as prior information (C).

(A) (B)

Figure 2 Scatter plot of the DAPC analysis without any prior information (A), and comparison of individual assignment between DAPC and STRUCTURE analyses without prior information (B). Individuals are considered assigned to a cluster if their posterior probability in that cluster is at least 0.75.

the most strongly differentiated from the two others (pairwise F ST : Alsace-Bordelais = 0.12, Alsace-Anjou = 0.11, Bordelais-Anjou = 0.05).

Discussion

Our study shows that noble rot symptoms on grapevine are caused by B. cinerea (sensu stricto) and not by B. pseudocinerea or by another cryptic species. The two clusters inferred using the STRUCTURE program without making use of sample group information to assist clustering were explained neither by the type of symptom nor by the geographic origin of isolates. This indicates weak population structure. Such limitations of the STRUCTURE method have recently been pinpointed: as this method considers all partitions as equally likely, a considerable amount of statistical evidence is needed to provide strong support for any particular partition, which is impossible to obtain when the data contain relatively little information [START_REF] Hubisz | Inferring weak population structure with the assistance of sample group information[END_REF]. This justifies the use of group information to assist the clustering algorithm. In the present case, prior knowledge on symptom caused by the sampled isolates did not reveal any structure. In contrast, making use of the geographic origin of individuals considerably improved the STRUCTURE clustering and led to a subdivision into three clusters clearly explained by geography. The confidence in this pattern of geographic subdivision was reinforced by the congruent results obtained using a DAPC analysis making use of sampling locations as prior information.

The cluster grouping isolates from Alsace was the most diverse one, showing the highest genetic diversity and the highest number of alleles per locus. The 'Alsace' cluster was also the most strongly differentiated from the two other ones. The 'Alsace' vineyard is geographically disconnected from the other French wine-producing regions, especially due to the Vosges Mountains, while being connected to West German vineyards. Anjou and Bordelais vineyards are in contrast more or less interconnected. The populations in Anjou and Bordelais experience different climatic conditions than those in Alsace. However, these differences in abiotic selective pressures are unlikely to be strong enough to lead to immigrant inviability or to the evolution of extrinsic postzygotic barriers to gene flow, which would be necessary to generate genetic differentiation at neutral loci [START_REF] Giraud | Linking emergence of fungal plant diseases and ecological speciation[END_REF]. The observed population subdivision

(A) (B)
Figure 3 Scatter plot of the DAPC analysis with the geographic origin of isolates as prior information (A), and comparison of individual assignment between DAPC and STRUCTURE analyses with geography as prior information (B). Individuals are considered assigned to a cluster if their posterior probability in that cluster is at least 0.75.

Table 2. Sizes of the clusters inferred using STRUCTURE, various indexes of genetic diversities and linkage disequilibrium within each cluster. Admixed individuals have posterior probabilities not higher than 0.75 in any of the clusters. The main geographic origin in the cluster gives the percentage of isolates assigned to this cluster (with a posterior probability not lower than 0.75) coming from a particular region. N: Number of isolates genotyped. G: number of observed multilocus genotypes. G/N: fraction of nonrepeated genotypes in the sample. H e : nonbiased genic diversity. n a : mean number of alleles per locus (standard deviation between brackets). r D : multilocus linkage disequilibrium (P: associated probability estimated after 500 randomizations).

Cluster

Main may rather be due to the lesser connectivity between Alsace and the two other regions. Also, as already outlined, significant population differentiation among host plants exists in B. cinerea [START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF], likely due to specialization to the host, but with significant gene flow. We therefore cannot exclude that the different geographic regions host populations of B. cinerea specialized on different plants, which would mix on different proportions into the population sampled on grape, therefore leading to significant differences in allelic frequencies in the analyzed samples due to host specialization instead of geographic isolation.

The overall level of genetic differentiation appears to be quite low, with pairwise F ST ranging from 5 to 12%. Such a pattern of weak genetic structure in B. cinerea has been previously reported [START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF] and indicates a history of recent continental spread, large scale contemporary gene flow and/or large effective population sizes. Our study also confirmed previous results showing that there was no linkage disequilibrium in B. cinerea populations [START_REF] Giraud | RFLP markers show genetic recombination in Botryotinita fuckeliana (Botrytis cinerea) and transposable elements reveal two sympatric species[END_REF][START_REF] Fournier | Sympatric genetic differentiation of a generalist pathogenic fungus, Botrytis cinerea, on two different host plants, grapevine and bramble[END_REF][START_REF] Karchani-Balma | Geography, plants, and growing systems shape the genetic structure of Tunisian Botrytis cinerea populations[END_REF]. Hence, although sexual reproduction has never been observed so far in natura, B. cinerea populations experience apparently regular events of recombination.

To conclude, gray mold and noble rot symptoms on grapevine are both caused by the same species, B. cinerea (sensu stricto), and there is no genetic differentiation, hence no barrier to gene flow, between isolates causing the different symptoms. This raises the question of the proximal mechanisms inducing the 'noble' pathway in B. cinerea. This fungus is able to attack a wide range of host species, if the conditions are met [START_REF] Elad | Microbial control of Botrytis spp[END_REF][START_REF] Choquer | Botrytis cinerea virulence factors: new insights into a necrotrophic and polyphageous pathogen[END_REF][START_REF] Williamson | Botrytis cinerea: the cause of grey mould disease[END_REF]. Besides encompassing a large arsenal of genes involved in necrotrophic processes (such as cell wall degradation or oxalic acid production), the genome of B. cinerea is enriched in secondary metabolism gene clusters and also harbors 5% (879/16400) genes encoding secreted proteins [START_REF] Amselem | Genomic analysis of the necrotrophic fungal pathogens Sclerotinia sclerotiorum and Botrytis cinerea[END_REF]). Recently, it has been shown that B. cinerea has a stimulating effect in the production of volatile thiols -compounds naturally produced by the grape berry that contribute strongly to the varietal aromas [START_REF] Thibon | Impact of noble rot on the aroma precursor of 3-sulfanylhexanol content in Vitis vinifera L. cv Sauvignon blanc and Semillon grape juice[END_REF][START_REF] Thibon | 3-Sulfanylhexanol precursor biogenesis in grapevine cells: the stimulating effect of Botrytis cinerea[END_REF]. More precisely, the 100-to 1000-fold enrichment of one particular cysteine S-conjugate precursors in botrytized berries was due to the production of metabolites by the fungus that were likely unstable in time and temperature-sensitive [START_REF] Thibon | 3-Sulfanylhexanol precursor biogenesis in grapevine cells: the stimulating effect of Botrytis cinerea[END_REF]. Therefore, this metabolic pathway involved in the botrytization process is likely to be modulated by microclimatic conditions. This hypothesis is in accordance with the field observation that the switch between gray mold and noble rot tightly depends on microclimatic conditions; a better description and understanding of the humidity and temperature conditions required for noble rot development would then be useful. We cannot rule out the existence of genetic differentiation at one (or a few) gene(s), possibly involved in the activation and/or regulation of this kind of metabolic pathways driving aromatic compounds biosynthesis. Further functional and transcriptomic studies are needed to investigate the existence of such genes and seek for a link between genotypes and phenotypes at these loci. Functional and transcriptomic studies could also help reveal which metabolic pathways are involved in the switch between the types of symptoms. Our study therefore strongly prompts for such studies: identifying a particular metabolic pathway and its mode of regulation could open the way for optimizing the production of sweet wines. Being able to foster noble rot symptoms and prevent the switch to gray mold would have important applied consequences and now appears the best route for optimizing the production of botrytized berries. Our study revealed the existence of genetic differentiation between regions, which may allow designing PCR-based tests on wines for checking the geographic origins in the context of AOC labels.

Figure 4

 4 Figure 4 Proportions of individuals assigned to the three clusters inferred with STRUCTURE using geographic prior information, in each of the three sampled regions. Light gray: cluster K1, dark gray: cluster K2, black: cluster K3, white: admixed individuals. The different French vineyards are indicated.

Table 1 .

 1 Sample sizes, various indexes of genetic diversities, and linkage disequilibrium, in total and for each region 9 symptom combination. N: Number of isolates genotyped. G: number of observed multilocus genotypes. G/N: fraction of nonrepeated genotypes in the sample. H e : nonbiased genic diversity. n a : mean number of alleles per locus (standard deviation between brackets). r D : multilocus linkage disequilibrium (P: associated probability estimated after 500 randomizations).

	Region	Symptom	N	G	G/N	H e	n a	r D
	Anjou	Gray mold	35	34	0.97	0.51	4.4 (2.1)	0.03 (P = 0.03)
		Noble rot	34	33	0.97	0.48	4.6 (2)	À0.02 (P = 0.66)
	Bordelais	Gray mold	38	36	0.95	0.46	4 (2)	0.02 (P = 0.08)
		Noble rot	18	17	0.94	0.37	2.7 (2)	0.02 (P = 0.22)
	Alsace	Gray mold	18	18	1	0.57	4.5 (2.1)	À0.01 (P = 0.65)
		Noble rot	21	21	1	0.56	4.1 (1.9)	0.01 (P = 0.24)
	Total		164	153	0.93	0.51	6.4 (3.1)	0.01 (P = 0.46)

© 2013 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

© 2013 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

© 2013 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

© 2013 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

© 2013 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

© 2013 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

Acknowledgements

We thank G. Barbeau, Y. Brygoo, M. Fermaud, D. Fortini, O. Jonot, C. L evis, F. Martinez, S. Merdinoglu, and R. Meziani for their help with population sampling, purification of isolates, DNA extraction, and microsatellite genotyping. PG is recipient of Marie Curie grant FP7-PEOPLE-2010-IOF-No. 273086. We acknowledge the grant ANR-07-BDIV-003 (Emerfundis Project).

Data archiving statement

Data deposited in the Dryad repository: doi:10.5061/dryad. 6mv07.

Literature cited


Mario Serrano

Bangjun Wang

Bibek Aryal

Christophe Garcion

Eliane Abou-Mansour

Silvia Heck

Markus Geisler

Felix Mauch

Christiane Nawrath

Jean-Pierre Métraux

Export of salicylic acid from the chloroplast requires the multidrug and toxin extrusion-like transporter EDS5

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


Nazzareno Diodato

Olga Petrucci

Gianni Bellocchi

Scale-invariant rainstorm hazard modelling for slopeland warning

Keywords: landslide-hazard, modelling, rainstorm, Southern Italy, threshold

Periods characterized by heavy rain and storms can trigger Multiple Damaging Hydrological Events (MDHE), in turn developing flooding, erosion, downpours and other phenomena affecting human habitats and ecosystems. However, approaches and applications to model MDHE and weather warning under specific environmental conditions are still poor. Based on the interpretation of the rainstorm -MDHE relationship, it was reconsidered and extended to a modelbased rainstorms hazard index (RHI) developed in a previous paper, where rainstorm-pulsing force and resistance state are combined. In this way, landscape response was achieved by individual formative rainfall events, while the specific sequence of events that can affect landscape was only assessed in a qualitative way. A retrospective evaluation of rainstorm hazard modelling, control runs, are also given for different precipitation durations (from 1 to 240 h), to be compared with MDHE at two test-sites of Southern Italy (Benevento, Campania region; Scilla, Calabria region). For these sites, the complete series of historical MDHE which occurred during a 10 year period (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) was gathered and used to validate the results of the rainfall model. The tool is relatively simple and potentially attractive to rapidly prediction of adverse consequences of rainstorms. Perspectives for real-time applications in emergency planning are ultimately given for an agenda in future research.

Introduction

Storms and hail are very f requent in all the provinces of the kingdom of Naples, (. . .). I t of ten happens that with the prevalence of a southwest wind, Naples and the west coast are deluged with rain f or whole months, while not a drop f alls in the eastern region; and on the other hand, when the Greek wind is blowing there may be much rain, and even abundance of snow (. . .), while the weather is perf ectly f ine in Naples. [START_REF] Henfrey | The Natural History of Europe: The Vegetation of Europe, its Conditions and Causes[END_REF] Since historical times, Mediterranean Europe has experienced what are now called 'Multiple Damaging Hydrological Events' (MDHEs: [START_REF] Petrucci | The use of historical data for the characterisation of multiple damaging hydrogeological events[END_REF], triggered by either short and intense storms or longer duration rainfall [START_REF] Grove | The "Little Ice Age" and its geomorphological consequences in Mediterranean Europe[END_REF]. In general, either floods or landslides occurring during MDHEs represent a source of economic loss. Over the last two decades, flash flooding (which is one of the most devastating hazards in terms of human life loss and infrastructures, [START_REF] Gruntfest | Dealing with flash floods, contemporary issues and future possibilities[END_REF], caused billions of Euros of damage in Europe (APFM-WMO, http://www.apfm.info/index.htm). As far as landslides, damage caused has been particularly severe in Italy (the focus of this study), where at least 6141 people were killed or injured between 1950 and 2008 by slope failures alone [START_REF] Brunetti | A prototype system to forecast rainfall induced landslides in Italy[END_REF]. In this country, centrally located in the Mediterranean basin, MDHEs usually cause victims, economical losses and major problems to activities of daily living. In general, the social and economic costs of MDHEs are not well documented, mainly because no agency is formally charged to gather data concerning them (IFRC, 2001). The great majority of available studies and databases [START_REF] Catenacci | Il dissesto idrogeologico e geoambientale in Italia dal dopoguerra al 1990. S.G.N., Memorie descrittive della[END_REF][START_REF] Guzzetti | Landslide fatalities and the evaluation of landslide risk in Italy[END_REF] report these losses as caused by different types of phenomena, without putting them in a global perspective of damaging events resulting from the simultaneous occurrence of floods and landslides.

Environmental hazards have been conventionally analysed in relation to their potential damage but, to date, not much research has been specifically focused on the study of extreme weather events for use in hydrological hazard forecasting and landscape responses [START_REF] Diodato | Storminess and environmental changes in the Mediterranean central area[END_REF]. Recent improvements include operational use of end-to-end forecasting systems in hydrology (e.g., MAP D-PHASE, [START_REF] Zappa | MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems[END_REF]. Advances made in recent decades have also demonstrated the capability of geo-computational tools and techniques [START_REF] Heneker | A point rainfall model for risk-based design[END_REF][START_REF] Dagan | Flow and transport in highly heterogeneous formations: 1. Conceptual framework and validity of first-order approximations[END_REF] and their potential for disaster mitigation (closely linked to the predictability of events and their consequences, [START_REF] Alexander | Information technology in real-time for monitoring and managing natural disasters[END_REF]. However, different aspects of hazards demonstrate that changes of seasonal predictability are difficult to quantify. Knowledge and technical ability are therefore required to accommodate this.

In addition, geo-computational hydrological models may need continual adjustments to meet either spatial or temporal variability [START_REF] Tucker | The channel-hillslope integrated landscape development model (CHILD)[END_REF][START_REF] Amengual | A hydrometeorological model intercomparison as a tool to quantify the forecast uncertainty in a medium size basin[END_REF], especially where this is important for rain forcing, land response and related economic loss for sensitive areas such as the Mediterranean region (as pointed out in Figure 1).

Geoscientists' efforts have primarily been addressed to detect regional relationships between rainfall and MDHEs (Petrucci andPolemio, 2003, 2009). For instance, specific geo-hazard methodologies were used at selected sites and calibrated according to natural disasters that have recently occurred [START_REF] Iiritano | Real-time estimation of hazard for landslides triggered by rainfall[END_REF]. In Italy, examples are the case of Crotone's flood, which happened on October 1996 [START_REF]Crotone e l'evento alluvionale del 14 Ottobre 1996[END_REF] or the mudflows that killed 137 people in Sarno, in May 1998 [START_REF] Cassetti | Il modello di soglia pluviometrica FLaIR[END_REF]. For an effective use of the information, environmental hazards can be better represented as an energetic process of the natural system deviating from its 'average' or 'normal' trend [START_REF] Haque | Mitigation of Natural Hazards and Disasters: International Perspectives[END_REF] and described by means of exceeding thresholds. A threshold is the value of a selected parameter that must be reached in order for a process to take place or a state to change [START_REF] White | Environmental Systems[END_REF]. [START_REF] Martina | A Bayesian decision approach to rainfall thresholds based flood warning[END_REF], for instance, modelled critical rainfall thresholds for a sub-basin of the Arno river (Central Italy), by using long hydrological time series combined with a Bayesian minimization function. Bayesian statistical techniques were used by [START_REF] Guzzetti | Rainfall thresholds for the initiation of landslides in central and southern Europe[END_REF], who reviewed worldwide-published rainfall thresholds for landslide initiation and proposed novel empirical rainfall thresholds for the Central European, Adriatic, Danubian and South-Eastern areas. Also, Monte Carlo simulations generated a considerable amount of research results but, with respect to flood risk mapping, harmonization is still incomplete [START_REF] Spachinger | Flood risk and flood hazard maps -visualisation of hydrological risks[END_REF].

Some authors sketched a global vision driving to a more complex research-based modelling, while also expanding monitoring and surveillance possibilities. One of the aims was to integrate standard GIS approaches and physically based hydrological models, either at sub-regional or regional scale (e.g., [START_REF] Lan | Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China[END_REF][START_REF] Knebl | Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event[END_REF]. Second, focus was put on threedimensional models for spatial-temporal estimation of hazard triggering factors (e.g., for shallow landslide hazard, [START_REF] Diodato | Spatio-temporal estimation of shallow landslide hazard triggered by rainfall using a three-dimensional model[END_REF]. Global thresholds are relevant where local or regional thresholds are not available, but they can result in (sometimes numerous) false occurrence predictions, i.e., predictions of damaging events that do not occur. Generally, regional and local thresholds perform reasonably well in the areas where they were developed, but cannot easily be extended to far away areas [START_REF] Crosta | A study of slope movements caused by heavy rainfall in Valtellina (Italy -July 1987)[END_REF].

To render comparable a series of rainfall thresholds assessed for different areas or regions, several investigators normalized rainfall intensity values via empirical parameters expressing some characteristics of the local climate. [START_REF] Wilson | Normalizing rainfall debris-flow thresholds along the U.S. Pacific coast for long-term variations in precipitation climate[END_REF] used the rainy-day normal, a climatic index that describes the occurrence of extreme storms that can trigger slope failures. Most commonly, normalization was carried out by dividing the rainfall intensity of an event by the mean annual precipitation (e.g., [START_REF] Wieczorek | Debris-flow hazards in the Blue Ridge of central Virginia[END_REF][START_REF] Aleotti | Soil slips triggered by October 13-16, 2000 flooding event in the Piedmont Region (North West Italy): critical analysis of rainfall data[END_REF][START_REF] Bacchini | Relations between rainfall and triggering of debris-flow: a case study of Cancia (Dolomites, Northeastern Italy)[END_REF]. However, in this case, the result is less reliable than that attainable using the rainyday normal.

MDHEs have no instrumentally determined magnitude scale (e.g., like that conventionally used for earthquakes), and this is why they are generally described in qualitative terms. For this reason, a quali-quantitative index (RHI, Rainstorm Hazard Index) that combines some attributes of hydrogeomorphological triggering mechanisms was recently developed as part of a model-based framework [START_REF] Diodato | Combining satellite and geospatial technologies for rainstorms hazard soft mapping[END_REF]. According to [START_REF] Casti | Would-be Worlds: How Computers Simulation is Changing the Frontiers of Science[END_REF], a model can profitably be used by societal decision-making systems if it possesses four characteristics: simplicity, clarity, is bias-free and has tractability. These properties should be self-evident for modellers, transparent for disaster managers, and easily interpretable for non-scientific audience. Tractability should be the most important characteristic for overall design of modern computer modelling, since it relates to the ability of the model to undertake the task efficiently, thus avoiding disproportionate computational costs [START_REF] Kelly | Spatial modelling of the terrestrial environment: the coupling of remote sensing with spatial models[END_REF].

According to these requirements, this study was set in a probabilistic, RHI-based framework within geospatial workflows to supply clues for defining hazard-warning operations in emergency management and civil defence purposes. The framework was also meant to identify critical thresholds based on the value of the ratio between pulsing forces perturbing the system (e.g., current rainstorm depth), and resistance forces represented by both storm temporal variability and antecedent weather conditions. This application was focused on Southern Italy, an area where MDHEs are frequently experienced and where the implementation of speed and inexpensive protection measures are desirable. Section 2 describes the modelling methodology, the areas under study and the data sources used to prepare the joint analysis of RHI and hydrogeological events. For inclusion in a geospatial probabilistic model (Section 3), RHI was calibrated against different precipitation durations (from 1 to 48 h) supplied by Met European Research Observatory (MetEROBS) time series (Benevento, Campania region). A control run was also made by re-running the model with a validation dataset (extrapolating until 240 h) and by comparing the results with MDHEs at an independent site (Scilla, Calabria region). Section 4 discusses and evaluates the assumptions adopted during the development of the methodology, and presents overall conclusions, areas of future development, and recommendations arising out of the findings of the study.

Methods, study areas and data

Spatial and time-invariant modelling

To take into account the rainstorms' erratic time pattern, storm hazards were preliminary estimated for hourly time-steps, for h = 1 to x hours of storm duration.

According to this scheme, an empirically controlled and conceptually based model was approached, in which the identification of the critical threshold value is a temporalspatial scale invariant indicator. For a given station and for each fixed rainstorm of duration h, hazardous events were defined based on a dimensionless ratio between rainfall amount and threshold-value. A knowledge-based system (expert system model) was designed, based on [START_REF] Diodato | Combining satellite and geospatial technologies for rainstorms hazard soft mapping[END_REF], to accomplish these logical conditions. For each rainy step of duration, h, at sampled location s o , the following power equation was developed:

RH I h(s α ) = max : 1 + RSD h f (Rc lim) 2 h(s α ) ∀ h = 1 . . . n h (1)
where RH I h is the rainstorm hazard, expressing the occurrence-and-magnitude of the storm compared to the expected rainy-climate; RSD h is the rainstorm depth (mm) representing the pulsing force that perturbs the system during each event of duration h, and:

f (Rclim) = Med(RSD h ) -16 - √ h × S wet (2)
is a function representing the system resistance state, that is the intrinsic ability of the system to resist to changes because of its recent and past history. Exponent 2 in Equation ( 1) corresponds to an empirical adaptation of the model for better discrimination of values. Med(RSD h ), the threshold value, is the median of the annual maximum rainfall (mm) of duration h, and the correction term (16-√ h)S wet (modified after Diodato and Ceccarelli, 2009) is a function calibrating the threshold value according to the current variations of soil humidity. In Equation ( 2), the term between brackets tends to be lower for short-duration rainstorms, and vice versa. The value of 16, whose unit is mm 0.5 , corresponds to a larger sensitivity to short-rather than long-duration rainstorms (when the historical memory gains more importance). It also marks the theoretical domain of validity of the equation (h = 256 mm). To express soil humidity, S wet (mm h -0.5 ) can assume four values: -0.5, 0.25, 0.5 and 1, for dry, dry mix (only just wet), wet and saturated-soil conditions respectively, preceding the event. The duration of rainstorm (h) under the square root is to explain major accommodations of the system to rainfall spanning over longer periods. The resistance function f (Rclim) includes the disorder state under which the rainstorm-pulsing forces act (after [START_REF] Sauchyn | Geoenvironmental mapping in southwestern Saskatchewan: rainfall erosion potential[END_REF], and its sensitivity degree depends upon the prior climate history (after [START_REF] Tucker | Drainage basin responses to climate change[END_REF]. In such respect, the response to a weather perturbation may depend on the degree to which the landscape retains 'memory' of past, ≈[Med(RSD h )], and more recent (and anomalous) conditions, ≈ [(16-√ h)S wet ]. These constraints drive the trajectories of the system within critical thresholds and RHI -equilibrium (repellers and attractors), their final destination and stability. In synthesis, for RH I > 1 (Equation (1)), the pulsing forces account for a larger percentage of the system resistance state, designing hazard increase according to a power function. In this way, RHI can be subject to large spatialtime fluctuations around critical values, and it can identify hazards in varying hydrological conditions. The climate across these regions is of Mediterranean type, but with important spatio-temporal differences in both precipitation and temperature patterns. The average annual precipitation ranges from 500 to 2000 mm. Long-term periods of seasonal climatic regime are similar for the two sites (Figure 2(b) and (c)), but rainfall extremes can be very different, as shown by the map in Figure 2(a).

Especially during and after the tillage period (September to November), erosive rainfall may be very hazardous because it is driven by a mixed regime of frontal rainstorms and localized showers and thunderstorms (Figure 2(b) and (c)). Important amounts of erosion, deposition and landslides affecting roads can therefore take place at and surrounding the Monte Pino-MetEROBS site in frequent events, but also during less frequent short events, such as those which occurred on May 2001 and June 2003. Especially during pulse phenomena, occurring in summer time, most soil losses on hillslopes are deposited before reaching the main stream at the outlet of the watershed. The Monte Pino area, in particular, shows sensitivity to both spring-summer and autumn high-intensity rainfall events.

In particular, tillage on steep slopes and flash erosivity regimes produce overland flows and high rates of sediment mobilization and transport. As with the Calabrian study area, a strip of territory was selected located on the southwest coast of the region, and extending through the municipalities of Scilla and Bagnara Calabra, for about 10 km in length and 3 km in width.

The historical series of MDHEs, for the Scilla area is long, starting from the beginning of 1900. The dates in which the events occur in this area often do not coincide with regional MDHEs. In practice, this area shows a high level of susceptibility to damage. In this zone, in fact, the presence of several elements at risk located at the slope foot (a highway, a railway and a state road, besides residential areas) ensure that almost each individual landslide or flood event affect some portion of them. The number of landslide phenomena (60% of cases) prevails on falls (14%) and flash floods affecting ravines located on the deep slopes of the area (26%). These last types of phenomena, often associated with landslides along the valley sides, can become destructive concentrated flows which run from the hills down to the populated areas located near the coast, as in the case of 31 May 2005, when a train derailed and the village of Favazzina was heavily damaged [START_REF] Bonavina | Le colate di fango e detrito lungo il versante costiero tra Bagnara Calabra, Scilla (Reggio Calabria): valutazioni di suscettibilità[END_REF].

Rainstorm hazard index-based joint analysis

Due to the partial availability of a historical series of sub-daily rainfall, a joint analysis of RHI and hydrogeological events was carried out covering the more recent period 1997-2010. MDHE data were inferred from technical and scientific publications [START_REF] Diodato | Local models for rainstorm-induced hazard analysis on Mediterranean river-torrential geomorphological systems[END_REF]; http://www.camilab.unical.it/oda/sito oda 4 003.htm), as well as from regional archives (IARC, http://www.sito. regione.campania.it/agricoltura).

MetEROBS was chosen as a reference site to calibrate RHI, because both RHI -data and MDHE series were available for this location. The RHI run depicted in Figure 3 enables examination of how the predicted hazard met the landscape response (i.e., MDHE) over 1997-2008. The period 2005-2007 was not analysed because of the mild weather conditions registered over this interval near the MetEROBS site. RHI based predicted values, estimated over the calibration set, were tested versus the observations, and scored by tallying results into a standard 2 × 2 contingency matrix of observed and expected pairs for maximizing the percentage of the hit rate that is equal to the ratio between the number of hit events (success events) and the total sampled events.

Hit rate was estimated to be 96% when accounting for the number of all rain events with > 20 mm day -1 , and 63% when only accounting for occurred or predicted events with MDHE. The results indicate a high percentage of success, significant at level α = 0.10. RHI graphs are also accompanied by daily or sub-daily rainstorms depth (black bars in Figure 3, right axis on an inverse scale), when almost 20 mm of rain has fallen.

The results show a good agreement between expected hazard (RHI bars) and occurred events around MetER-OBS over 1997-2008, with only episodic events of false alerts. It is also expected that extreme events can be predicted easily, as occurred in the peak of 13 November 1997, when rainfall recorded at MetEROBS was 111 mm, 86 mm of which fell in 3 h. In this event, remarkable erosion, sediment-laden floodwaters and landslides occurred in the whole Benevento province [START_REF] Diodato | Geostatistical uncertainty modelling for the environmental hazard assessment during single erosive rainstorm events[END_REF]. For this area, the calculation of historical precipitationfrequency data referred to as 3 h rainfall (by the Gumbel method) shows that a rain amount of 86 mm has a return period of 20 years. The most intense rainstorms, but with minor spatial extent compared to those in autumn, occurred between the end of spring and summer, including the second daily peak, which occurred in May 2001 when a heavy downpour hit the agricultural lands devastating the slopes around MetEROBS. However, this location showed sensitivity to both spring-summer and autumn high-intensity rainfall events. In particular, tillage on steep slopes and flash shower regimes produced overland flows and high rates of sediment mobilization and transport.

An independent location (the Scilla station) was used as the validation study site (Figure 4). Because of the level of high risk characterizing the area, which depends on both the high frequency of landslides and/or flood activations (on average, two events per year between 1973 and 2010), and the high density of vulnerable elements, the rain monitoring has been recently improved. Hence, only the period between 2001 and 2010 was considered for an evaluation of the effectiveness of RHIbased procedure via a two-by-two contingency table analysis (Table I)) based on the calculation of an array of relevant indices and their confidence intervals [START_REF] Fleiss | Statistical Methods for Rates and Proportions[END_REF] http://statpages.org/ctab2 × 2.html).

An Odds ratio largely greater than one indicates that the outcome of interest ('Outcome occurred') has a higher probability to come about with RHI > 1. A Kappa higher than 0.6 indicates a good agreement between risk factor and occurrence of the event, as was also confirmed by the other indices reported calculated in this study (Table I).

In Figure 4, expected hazards (RHI bars) and actual occurrences around Scilla confirm the performance already reflected in the calibration against MetEROBS data. Total and partial hit rates estimated at the validation stage result in values very similar to those observed in the calibration set (Figure 3). However, the validation set indicates that partial hit rates of failed alert are high (i.e., 53%). This discrepancy can be due to events characterized by prolonged rainfall (>96 h) which occurred at Scilla in the period evaluated but were not registered in the MetEROBS data used for calibration. This calls for the need of calibration work, which is consistent with the target area's short-and long-term needs.

As already noted (rainstorm timing of Equation ( 1), results of Figures 3 and4), the temporal pattern of hazard shows a remarkable complexity, which increases as rain concentration increases. This complexity is also reflected spatially, as it is possible to detect it from Figure 5(a), where extended and positive anomalies of rain amount in September occurred during the recent decade (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) compared to the climatological period .

The graph of Figure 5(b) also suggests that Southern Italy is subjected to increasing precipitation intensity and hazards in the month of September, spanning from 1948 to 2009. After 1996, rain rates have been observed to be unusually high, showing an abrupt change over the first decade of the twenty-first century. This can be important for the use of RHI modelling in operational procedures where climate information and human experience become essential for a successful implementation of an alert system.

Discussion and conclusions

Rainstorm hazards have many consequences, such as landslides, floods and intense erosion. Today, global climate change and consequent extreme weather events require a strong attention to these issues (also considering the greater risk posed by large urbanized areas). Rainstorm hazard awareness and preparedness as integral aspects of planning and development policy is needed in Southern Italy and it is receiving more and more attention by the community, scientists and engineers [START_REF] Cassetti | Il modello di soglia pluviometrica FLaIR[END_REF][START_REF] Wieczorek | Climatic factors influencing occurrence of debris flows[END_REF].

The complexity of hydrological system responses in the proximity of critical threshold values, however, pose major difficulties. As threshold phenomena are quite often associated with environmental hazards such as floods, soil erosion and contamination of shallow groundwater resources, it is of key importance to predict whether they might occur at all and, if so, then their intensity [START_REF] Zehe | Threshold behavior in hydrological systems and geo-ecosystems: manifestations, controls and implications for predictability[END_REF]. The present study was based on concepts (e.g., intermittence of phenomena and processes as characteristic feature of threshold behaviour in environmental systems) already outlined by other authors Figure 6. Scheme of the main hydrological forces approach to rain-thresholds for stable and unstable systems during rainstorm events (from Viles and Goudie, 2003, modified). This figure is available in colour online at wileyonlinelibrary.com/journal/met (e.g., [START_REF] Blöschl | On hydrological predictability[END_REF][START_REF] Rundle | Pattern dynamics, pattern hierarchies, and forecasting in complex multi-scale earth systems[END_REF]. Intermittence in space and/or time, in particular, poses problems for predictability. In general, a two-level mixed discrete-continuous prediction is approached, first predicting whether or not the phenomenon/process will occur and then, if so, its strength. Ever since the introduction of catastrophe theory (see, for instance, [START_REF] Thom | Structural Stability and Morphogenesis[END_REF] it is known that the accuracy of predictions of threshold phenomena/processes (i.e. does it occur or not?) depends on the current state of the system and the expected forcing/boundary condition. It was, therefore, suggested that the possibility to detect existence of threshold behaviour based on the intermittence of phenomena while predictability of system behaviour drastically decreases for certain combinations of states and expected stress conditions (Figure 6, upper panel), or in a range of states when a certain response is expected (Figure 6, lower panel). Figure 6 is a generic scheme of how clusters of rainstorm events can produce variability in geomorphic thresholds (e.g., overtopping), although it represents a simple non-linear view of landscape response which may be an oversimplification for many geomorphic systems.

When a system is stable in the phase of small disturbances it would become sensitive to larger perturbations. The search of the system threshold governs the behaviour that underlies much research on this topic [START_REF] Thomas | Landscape sensitivity in time and space -an introduction[END_REF]. Assuming that the system has adapted its hydrological regime to a minimum threshold at the lowest level below which damaging phenomena do not occur, then each sudden fluctuation in this regime, especially those exceeding the critical state above minimum threshold, may trigger damaging phenomena. Several such ideas on the clustering of events and complex responses to them can be presented as conceptual diagrams of the type of Figure 6, although multiple modalities may typically occur in which spatial complexity, chaos and stochastic perturbations govern the dynamics [START_REF] Scheffer | Early-warning signals for critical transitions[END_REF].

It must also be observed how extrinsic-intrinsic thresholds are exceeded, because the time distribution of rainstorms may show either different sequence patterns [START_REF] Ward | Environmental Hydrology[END_REF], or, in turn, different combinations of resistance and pulsing forces. Depending on the season, rainstorms may show time-varying intensity values. Winter rainfalls often have relatively low intensity, with little variation in intensity throughout the storm, spring or autumn storms may have the maximum intensity at intermediate or long durations. Furthermore, each station experiences a unique rainstorm temporal pattern and this complicates the model creation. However, while it is well known that rainstorm depth determines the occurrence of damaging hydrological events, rainstorm depth alone does not represent a sufficient condition for the triggering of flooding, erosion and other hydrological phenomena as those occurring during MDHEs [START_REF] Palecki | Storm precipitation in the United States. Part I: meteological characteristics[END_REF].

In MDHE-based studies, modelling and warning information are valuable sources of spatial information and they have proved useful on many occasions worldwide. However, there are many types of hazard experienced on both hourly and daily bases and their modelling solutions can vary. It may be appropriate, for instance, to provide a function of soil humidity with previous daily or hourly precipitation. Moreover, other factors that influence MDHEs could be assessed, e.g. friction angle of soil, soil suction, hydrological conditions, underground water flow, vegetation type, human factors as presence of narrow roads and houses. The paper does not cover the analysis of all different components with their respective weight on MDHEs. The proposed methodology seems, however, promising for application in real-time warning systems in areas where little data on (for example) streamflow and hillslope monitoring are available. One of the major advantages of Equation ( 1) is that the RHI assessed for a selected area can be compared to that of surrounding sites, because the results are scaled to indicator values for use in weather hazard maps, opposite to absolute threshold values [START_REF] Diodato | Spatial uncertainty modeling of climate processes for extreme hydrogeomorphological events hazard monitoring[END_REF]. It is evident, in Equation (1), that landscape response is achieved by individual formative events modelled in relation to the historical median rainfall for the same event durations. There are also specific events that can affect landscape within a sequence [START_REF] Brunsden | A critical assessment of the sensitivity concept in geomorphology[END_REF]. In the present study, combinations of these events, characterized by any frequency, magnitude, and duration, were only taken into Scale-invariant rainstorm hazard modelling 287 account in a qualitative way. An example is the coefficient of the soil water content in Equation 2, for which four attributes were designated and whose value can be derived in a variety of ways depending on the research context, e.g. from remote sensing data (after [START_REF] Ottlé | Remote sensing applications to hydrological modeling[END_REF]. In spite of such limitation, RHI is suitable to be included in a geospatial probabilistic model to be used for developing a soft geovisual communication in order to mitigate the uncertainty associated with and geocomputational tracking. The approach presented can be useful for the implementation of an early alert system, especially in regions where expensive systems are not at hand. The link of RHI flood-forecasting with the precipitation forecasts produced by atmospheric LAMs (Limited Area Models) could, for instance, be tested and installed in operational mode (e.g., [START_REF] Todini | An operational decision support system for flood risk mapping, forecasting and management[END_REF]. The RHI model may also be a tool for short-range weather forecasting, covering specific geographic areas (nowcasting) and for end-users when fine spatiotemporal resolution of precipitation forecast is usable.

Figure 1 .

 1 Figure 1. Global flood total economic loss risk distribution (by CIESIN World Data Center, NASA Socioeconomic Data and Applications Center SEDAC, http://sedac.ciesin.columbia.edu/wdc). This figure is available in colour online at wileyonlinelibrary.com/journal/met

  2.2. Test areas and data sets Two test areas, both located in Southern Italy, are considered in this study: Met European Research Observatory (MetEROBS) is located at Benevento-Monte Pino, in an inland valley of the Campano-Lucano Apennines; Scilla station is placed on the Thyrrhenian coast of Calabria (Figure 2(a)). MetEROBS (latitude 41.1 °N, longitude 14.1 °E, elevation 184 m a.s.l.) belongs to the TEMS-Terrestrial Ecosystem Monitoring Sites of FAO (http://www.fao.org/ gtos/tems/tsite show.jsp?TSITE ID=3730), resulting in accumulation and storage of a considerable amount of weather data of long duration and continuity. In 2009, MetEROBS was accredited by GEWEX-CEOP (Global Energy and Water Cycle Experiment-Coordinated Energy and Water Cycle Observations Project, http://www. gewex.org/ceop.htm) as part of the World Climate Research Programme (http://www.wmo.int/pages/prog/ wcrp). The area around the MetEROBS site comprises about 9 km 2 of cropland with sparse and dense vegetation, located in the Benevento district and bordered by the Serretelle valley (to the east side) and Taburno mountain (to the west side). Scilla raingauge station (latitude 38.1 °N, longitude 15.7 °E, elevation 73 m a.s.l.) is a telemetric raingauge located in the Calabria region and characterized by 20 years of sub-daily rainfall records 5, 10, 15, 20, and 60 min).

Figure 2 .Figure 3 .

 23 Figure 2. (a) Map of the annual maximum precipitations averaged upon the period 1961-1990 with the test-sites of MetEROBS and Scilla (from ISPRA-SCIA dataset http://www.isprambiente.it/site/it-IT), and (b and c) respective seasonal bioclimograms (shaded bands), and pluvio-evapotranspirative regimes (curves) as arranged by New LocClim-FAO software (http://www.fao.org/sd/dim en3/en3 051002 en.htm).This figure is available in colour online at wileyonlinelibrary.com/journal/met

Figure 4 .

 4 Figure 4. Daily rainstorms >20 mm day -1 (black bars), and simulated RHI (grey bars (blue in the online version)) with associated MDHE ( ) around Scilla station during 2001-2006. Empty triangles indicate lack alarm. Critical thresholds of 1 (Alert) and 3 (Warning) are drawn too.

Figure 5 .

 5 Figure 5. (a) Spatial pattern of the September rainfall anomalies (mm) during 2000-2008 compared to climatological rainfall (1950-2000) over Southern Italy (arranged from TRMM remote sensing by NASA Earth System Science Data and Services, http://nasadaacs.eos.nasa.gov); (b) September rain rate evolution from 1948 to 2009 for the region as in (a) (by National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Physical Sciences Division, http://www.esrl.noaa.gov). The figure is from Diodato (2010).

Table I .

 I Contingency table and relevant statistics for Scilla station (validation dataset).

	Scale-invariant rainstorm hazard modelling		285
	Contingency table		Outcome	Outcome did	Totals
			occurred		not occur
	Risk factor present (RH I > 1)	a = 13		b = 1	r 1 = 14
	Risk factor absent (RH I < 1)	c = 7		d = 132	r 2 = 139
	Totals		c 1 = 20		c 2 = 133	t = 153
	Relevant statistics	Index		Value	0.95 confidence interval
	Odds ratio, (a/b)/(c/d)			245.143	34.234	1632.214
	Relative risk, (a/r 1 )/(c/r 2 )		18.439	10.189	22.193
	Kappa, (observed-expected)/(1-expected)		0.736	0.547	0.790
	Overall fraction correct, (a + d)/t		0.948	0.910	0.958
	Mis-classification rate, = 1 -overall fraction correct	0.052	0.042	0.090
	Sensitivity, a/c 1			0.650	0.506	0.691
	Specificity, d/c 2			0.992	0.971	0.999
	Positive predictive value, a/r 1		0.929	0.724	0.987
	Negative predictive value, d/r 2		0.950	0.929	0.956

Copyright  2011 Royal Meteorological Society Meteorol. Appl. 19: 279-288 (2012)

Copyright  2011 Royal Meteorological Society Meteorol. Appl. 19: 279-288 (2012)