Research Article 
Corresponding author: Mireia GomezGallego ( mireia.gomezgallego@inrae.fr ) Academic editor: Christelle Robinet
© 2023 Elodie Muller, Miloň Dvořák, Benoit Marçais, Elsa Caeiro, Bernard Clot, MarieLaure DesprezLoustau, Björn Gedda, Karl Lundén, Duccio Migliorini, Gilles Oliver, Ana Paula Ramos, Daniel Rigling, Ondřej Rybníček, Alberto Santini, Salome Schneider, Jan Stenlid, Emma Tedeschini, Jaime Aguayo, Mireia GomezGallego.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Muller E, Dvořák M, Marçais B, Caeiro E, Clot B, DesprezLoustau ML, Gedda B, Lundén K, Migliorini D, Oliver G, Ramos AP, Rigling D, Rybníček O, Santini A, Schneider S, Stenlid J, Tedeschini E, Aguayo J, GomezGallego M (2023) Conditions of emergence of the Sooty Bark Disease and aerobiology of Cryptostroma corticale in Europe. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 319347. https://doi.org/10.3897/neobiota.84.90549

The sooty bark disease (SBD) is an emerging disease affecting sycamore maple trees (Acer pseudoplatanus) in Europe. Cryptostroma corticale, the causal agent, putatively native to eastern North America, can be also pathogenic for humans causing pneumonitis. It was first detected in 1945 in Europe, with markedly increasing reports since 2000. Pathogen development appears to be linked to heat waves and drought episodes. Here, we analyse the conditions of the SBD emergence in Europe based on a threedecadal timeseries data set. We also assess the suitability of aerobiological samples using a speciesspecific quantitative PCR assay to inform the epidemiology of C. corticale, through a regional study in France comparing twoyear aerobiological and epidemiological data, and a continental study including 12 air samplers from six countries (Czechia, France, Italy, Portugal, Sweden and Switzerland).
We found that an accumulated water deficit in spring and summer lower than 132 mm correlates with SBD outbreaks. Our results suggest that C. corticale is an efficient airborne pathogen which can disperse its conidia as far as 310 km from the site of the closest disease outbreak. Aerobiology of C. corticale followed the SBD distribution in Europe. Pathogen detection was high in countries within the host native area and with longer disease presence, such as France, Switzerland and Czech Republic, and sporadic in Italy, where the pathogen was reported just once. The pathogen was absent in samples from Portugal and Sweden, where the disease has not been reported yet. We conclude that aerobiological surveillance can inform the spatial distribution of the SBD, and contribute to early detection in pathogenfree countries.
Acer pseudoplatanus, aerobiology, airborne fungal spores, climate change, droughtinduced forest disease, heat wave, invasive pathogen, maple bark disease, quantitative speciesspecific PCR
Emerging infectious diseases threaten human health, agriculture and biodiversity (
Examples of forest diseases linked to climate extremes that are increasing in Europe are Diplodia tip blight in pine species (
The SBD spread is likely to be limited by the occurrence of drought and heat wave episodes, that promote the infection process of the introduced pathogen itself. Monitoring SBD presence therefore requires good surveillance methods that are not dependent on the identification of symptoms in the host as those occur mainly after extreme weather and in the advanced stages of the disease. The conidia of C. corticale have been speculated to disperse by wind (
The objectives of the present study are therefore: (1) to develop a realtime PCR assay for the detection of C. corticale spores in aerobiological samples; (2) to analyse the conditions of emergence of the SBD in Europe through the study of timeseries data of SBD occurrence and climatic data from France and Switzerland; (3) to analyse the dispersion of the pathogen C. corticale by wind at a regional scale, and (4) to study its presence on aerobiological samples at a continental scale.
Time series data collection
To analyse the emergence of SBD and its potential link to climate, we analysed complete timeseries data of disease occurrence in France and Switzerland during the last three decades, from 1990 to 2021 and modelled this occurrence as a function of different climatic variables. The French disease records during these three decades were obtained from the database of the French Forest Health Department (DSF, French acronym). This database contains annual records of forest health problems observed in France by a network of foresters trained for the diagnosis of abiotic, entomological or pathological damages. The Swiss data were obtained from the forest protection reports generated by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) (
$SR{R}_{ij}=\frac{NSB{D}_{ij}}{N{Ref}_{ij}}\xb7\frac{NRef}{NSBD}$ Eq. 1
where NSBD and NRef are, respectively, the total number of SBD and reference cases for the entire data set. Thus, a value of X for SRR_{ij} means that the report rate is X times the average report rate. Therefore, we assumed that a SRR_{ij} higher than 1 was an outbreak of the disease, as the reported number of cases exceeds the the basal level of the disease (considered to be the global average of our database, i.e. NSBD/NRef). The distribution of the SBD records in France and Switzerland are shown in Suppl. material
The climatic data were obtained from MétéoFrance (SAFRAN database) computed on a daily basis on an 8km resolution grid throughout France and Switzerland (except for the Tessin region, where these data were not available) (Suppl. material
The samples used as starting material in our aerobiological study consisted of microscope slides with a ca. 48mm portion of Melinex tape (corresponding to 24 h ± 2 h, depending on the sampling time) from Hirsttype volumetric air samplers used to monitor airborne pollen grains and fungal spores by the aerobiology networks of the involved European countries. The Hirsttype air samplers (
7day volumetric air sampler (Burkard Manufacturing Co Ltd, Hertfordshire, UK) in Brno (Czechia) installed on the roof of the University hospital, 15 m above ground to ensure landscapescale monitoring. Photo credit for Aneta Lukačevičová.
We undertook two studies, at a regional and a continental scale, to evaluate the use of permanent aerobiological networks to assess C. corticale epidemiological surveillance. The regional study focused on French samplers, while the continental study covered locations in six European countries over a wide latitudinal and longitudinal range: Czechia, France, Italy, Portugal, Sweden and Switzerland.
We selected samplers to cover the SBD outbreak in northeastern France in 2017 and 2018, following a twoyearspanned drought episode (from 2017 to 2018). We selected four samplers located in Mulhouse (the main focus of the outbreak), and in three locations at different distances from the main focus (with less records of the disease): Bart, Besançon and Strasbourg (Table
Selected air samplers, SBD records for years 2017 and 2018 considered for aerobiological sampling in 2017, and years 2018 and 2019 considered for 2018 sampling; and total sycamore maple basal area (m^{2}) in a 16×16 km grid.
Selected French air samplers for the regional study with different SBD incidence.
City  Code  GPS Coordinates  Year of the first record at < 50 km  Year of the first record at < 100 km  Year of the first record at < 180 km 

Mulhouse  MUL  47.7524, 7.3591  2010  2010  1992 
Bart  BAR  47.4856, 6.7694  no records  2010  1992 
Besançon  BES  47.2324, 6.0231  no records  2006  1992 
Strasbourg  STR  48.5833, 7.7500  no records  2010  2010 
Angoûleme  ANG  45.6494, 0.1645  no records  2016  1991 
Aurillac  AUR  44.9258, 2.4341  no records  no records  2014 
Avignon  AVI  43.9203, 4.8021  no records  no records  2002 
Gap  GAP  44.5575, 6.0761  no records  no records  2002 
In order to align the aerobiological data with the presence of the disease, we used the disease records from the DSF database (as described above). From 1989 to 2021, 1708 health reports were done on maples, of which 1351 were on A. pseudoplatanus and 172 corresponded to the SBD. We modelled the number of spores as a function of two variables: the distance to the disease and the maple basal area. We computed the distance to the closest disease record for each aerobiological sample (i.e. each captor) and year. We consider all the disease records taking place in both the year of the sampling and the following one, to capture the dispersion of the spores once the disease has been detected. Finally, we obtained host density data from the French National Forest Inventory (IFN, French acronym). We assigned to each sampler the sum of the total sycamore maple basal area in IFN plots in a radius of 50 km from each sampler, which is the reference area of influence of an aerobiological sampler (i.e. average distance at which the pollen is dispersed,
We selected a total of 12 air samplers across six European countries, spanning a large longitudinal and latitudinal range, in the axis northsouth from Sweden to Portugal, and in the axis westeast from Portugal to Czechia (Table
Locations of European air samplers for aerobiological samples analysed during the period from the 3^{rd} of June to the 25^{th} of September 2018, every 12 days (N = 10).
City  Code  GPS Coordinates  Country  Year first SBD record  Laboratory for DNA extraction 

Brno  BRN  49.20374, 16.61800  Czechia  2005^{1}  Mendel University (Czechia) 
Gap  GAP  44.55750, 6.07610  France  1950^{2}  INRAE Bordeaux (France) 
Pontivy  PON  48.06670, 2.96830  France  INRAE Bordeaux (France)  
Besançon  BES  47.23241, 6.02311  France  INRAE Bordeaux (France)  
Bordeaux  BOR  44.80670, 0.58960  France  INRAE Bordeaux (France)  
Bologna  BOL  44.49120, 11.36910  Italy  1952^{3}  IPSPCNR (Italy) 
Perugia  PER  43.10091, 12.39593  Italy  IPSPCNR (Italy)  
Gävle  GÄV  60.67959, 17.14330  Sweden  Not reported  SLU (Sweden) 
Visby  VIS  57.67336, 18.29269  Sweden  SLU (Sweden)  
Lisbon  LIS  38.823718, 9.176685  Portugal  Not reported  SLU (Sweden) 
Münsterlingen  MÜN  47.63040, 9.23679  Switzerland  1991^{4}  WSL (Switzerland) 
Payerne  PAY  46.81158, 6.94247  Switzerland  WSL (Switzerland) 
Slides for the regional study were extracted in the laboratory of Forest Pathology at INRAE Nancy (France). For the continental study, the slides were extracted in different laboratories (Table
The ITS region sequences with accurate identification were retrieved from GenBank for C. corticale and closely related species (Biscogniauxia nummularia, B. mediterranea, B. latirima, B. philippinensis, Obolarina dryophila, Graphostroma platystoma) to assure the specificity of the test. We also included, in the panel of species to be tested, species that are commonly found in Acer species, such as Alternaria alternata. Details of the included isolates are given in Suppl. material
Samples were run in triplicate in the regional study and in duplicate in the continental study, and both a negative (no template DNA) and a positive control (C. corticale mycelium DNA extract) were included in all series of reactions. Previous experience using spore traps has shown that qPCR Cq values can be below the detection limit of the assays, which means that the pathogen is present in the samples, but not at quantifiable concentrations (cf.
To quantify the spores on each aerobiological sample, we prepared 5fold serial dilutions of a spore solution obtained by adding purified water on the surface of a sporulating culture of a French C. corticale isolate, LSVM1510. Spore concentration was determined using a haemocytometer. We performed DNA extractions from of each of the five spore solutions spanning from 1144 to 2 spores/μl. We ran qPCR for the five DNA extracts in triplicate to obtain a standard curve. As both the initial volume and the final elution volume of the DNA extraction was 50 μl, to obtain the number of spores corresponding to each Cq, we multiplied the initial spore concentration per 2 μl used in the qPCR reaction. We then fitted a linear model with cycle threshold (Cq) as a function of the logarithm of the number of spores (P < 0.0001; R^{2} = 0.95; Cq = 37.0–1.2 log(number of spores/μl)). The same DNA extractions for spore quantification were used to perform two different standard curves at the Forest Pathology laboratory at INRAE Nancy (France) for the regional study and at the Mendel University (Czechia) for the continental study, where the respective qPCR assays of the samples were performed.
We have fitted Bayesian models to test the different hypotheses as follows. To analyse the effect of the climatic conditions on the emergence of the SBD, we modelled the SRR as a function of T_{X,summer}, PETP_{summer}, and PETP_{veg} that were calculated for one, two and three previous years (see section of data sources). We ran individual models due to the high collinearity between temperature and water balance. We then chose the model with lower deviance (comparing the 95% confidence interval of the deviance). The SRR followed a Poisson distribution (Eq. 2). We included a binomial process (Eq. 3) to account for zeros that arise in addition to those modelled by the Poisson process (i.e. failure to detect the disease in the field). Therefore, the model distinguished two potentially different processes that determine the occurrence of SBD: (1) the occurrence of conducive weather conditions so that the pathogen can develop and cause a number of disease cases, as a Poisson process, and (2) the detectability of the disease in the field which may depend on other factors such as the presence of inoculum (arrival of the exotic pathogen), as a binomial process. We compared models with and without the binomial process and chose the one with the lowest Deviance Information Criterion (DIC). Following Eq. 1 for the standardisation of the SBD records, and isolating the NSBD_{ij}, which is our response variable, we included the fraction $\frac{NRef}{NRe{f}_{ij}\xb7NSBD}$, as an offset term in the deterministic equation of the model (Eq. 4).
number of cases of SBD ~ Poisson (λ_{k} * d_{k}) Eq. 2
where k is the observation at a given sampler and date, λ_{k} is the number of spores, and d_{k} is the detectability of the disease, which follows a Bernoulli distribution (Eq. 3).
d_{k} ~ Bernoulli (p) Eq. 3
$\mathrm{log}\left({\lambda}_{k}\right)=alph{a}_{k}+bet{a}^{*}predicto{r}_{i}+\mathrm{log}\left(\frac{NRef}{NRe{f}_{ij}\xb7NSBD}\right)$ Eq. 4
where alpha is the intercept which varies for each year, j is the year, beta is the parameter estimate for the predictor, which can be any of the variables (cf. to the section ‘Time series data collection’).
We modelled the number of spores detected per week as a function of the distance to the closer disease report (model distance) and as a function of the total sycamore maple basal area in a radius of 50 km from the sampler (model host). We did not include the distance to the disease report and the total sycamore maple basal area as predictors in the same model because their high collinearity prevented model convergence. The two models followed a Poisson distribution (Eq. 5), with lambda varying for each observation following a Gamma distribution to deal with overdispersion (Eq. 6–8). We included a binomial process (Eq. 9) to account for zeros that arise in addition to those modelled by the Poisson process (i.e. sampler’s failure to capture spores even if they are present in the air). Therefore, the model distinguished two potentially different processes that determine the number of C. corticale spores in the air: (1) the sampler’s efficacy to capture spores, as a binomial process, and (2) the number of spores, as a Poisson process. Finally, we compared models with and without the binomial process and chose the one with the lowest Deviance Information Criterion (DIC). The number of samples per week (from 1 to 4) was added as an offset of the Poisson model (Eq. 10). In both cases, the best models were the ones including the binomial process, hence our data was zeroinflated.
number of spores ~ Poisson (λ_{k} * e_{k}) Eq. 5
where k is the observation at a given sampler and date, λ_{k} is the number of spores, and e_{k} is the efficacy of the sampler (probability of capturing any spores), which follows a Bernoulli distribution (Eq. 6):
λ_{k} ~ Gamma (a_{k}, b_{k}) Eq. 6
where a_{k} and b_{i} are the shape and rate of the Gamma distribution, which relate to the mean number of spores and to the standard deviation (sigma) as follows (Eq. 4–5):
a_{k} = spores_{k}^2 / sigma^2 Eq. 7
b_{k} = spores_{k} / sigma^2 Eq. 8
e_{k} ~ Bernoulli (p) Eq. 9
log(spores_{k}) = alpha + beta * predictor_{k} + log(Nsam_{k}) Eq. 10
where alpha is the intercept, beta is the parameter estimate for the predictor, which can be either the distance to the disease report (model distance) or the total sycamore maple basal area (model host), Nsam is the total number of samples analysed per week (offset term).
We modelled the probability of disease occurrence in a certain area of influence of the sampler (in a circumference of different radii, from 40 to 130 km of radius, by 10km intervals) as a function of the number of detected spores. The two models followed a Bernoulli distribution (Eq. 10). The deterministic part of the model is shown in Eq. 11.
Probability of disease occurrence in an area of 40 to 130 km radius ~ Bernoulli (p_{k}) Eq. 11
where k is the observation at a given sampler and date, and p_{i} is the presenceabsence of the disease at the given distance (40 to 130 km) from the sampler.
log(p_{k}) = alpha + beta * spores_{k} Eq. 12
where alpha and beta are the parameters estimated by the model, and spores is the number of spores detected by the sampler.
To validate our models, we simulated data based on the likelihood of each model. We then compared the means, the coefficients of variation and the sums of squares of the residuals of the original dataset with each simulated dataset. The histogram of the differences for each statistic should be zerocentred, with the proportion of negative (or positive) differences being lower than 0.85 for the model to be accepted.
All Bayesian models were implemented using a Markov chain Monte Carlo (MCMC) sampler (JAGS, Just Another Gibbs Sampler;
The selected primers and probe used in this study were ccITS2F (AGGTTGTGCTGTCCGGTG), reported in the study by
The climatic variable best explaining the standardised SBD report rate was the water balance (PETP) in the vegetative season (AprilAugust) of the year preceding the disease report (Table
Coefficient estimates for each climatic variable and their 95% credible intervals in brackets for models predicting the standardised SBD case rate per year. Estimates are generated from the posterior distributions of the variables in the Poisson model (Eq. 11). Each climatic variable is calculated for either the previous year (n1), two (n1 to n2) or three (n1 to n3) previous years. Rhat is the potential scale reduction factor and indicates whether the model has converged. Successful convergence is reached when Rhat values are < 1.1. T_{X}: average daily maximal temperature; T_{N}: average daily minimal temperature; PETP: Water balance as the sum of the daily difference between rainfall and PenmanMonteith evapotranspiration; n25: number of days per year where the temperature exceeds 25 °C; summer: JulyAugust; spring: AprilJune; winter: JanuaryMarch; veg: AprilAugust.
Variable  Years  Coefficient estimate [95% CI]  Rhat  Deviance [95% CI] 

T_{X},_{summer}  n1  1.19 [0.74, 1.68]  1.0012  209.1 [193.2, 228.3] 
T_{X},_{spring}  1.21 [0.77, 1.70]  1.0009  199.1 [183.7, 217.9]  
T_{Xveg}  1.15 [0.78, 1.53]  1.0009  201.0 [185.9, 218.8]  
T_{Nwinter}  0.66 [0.43, 0.89]  1.0009  199.0 [183.9, 217.1]  
PETP_{summer}  1.08 [1.38, 0.78]  1.0009  188.3 [172.7, 207.5]  
PETP_{veg}  1.15 [1.46, 0.85]  1.0009  183.6 [169.3, 200.5]  
PETP_{spring}  1.35 [1.85, 0.89]  1.0009  192.2 [178.1, 209.5]  
n25  1.16 [0.78, 1.58]  1.0009  208.0 [193.2, 227.1]  
T_{X},_{summer}  n1 to n2  1.37 [0.86, 1.98]  1.0009  197.9 [182.0, 217.9] 
T_{X},_{spring}  1.23 [0.77, 1.71]  1.0013  203.0 [187.8, 221.5]  
T_{Xveg}  1.25 [0.87, 1.66]  1.0009  196.1 [181.0, 214.3]  
T_{Nwinter}  0.64 [0.41, 0.87]  1.0009  202.8 [187.9, 221.0]  
PETP_{summer}  1.08 [1.50, 0.70]  1.0009  200.0 [185.8, 218.1]  
PETP_{veg}  1.07 [1.50, 0.68]  1.0001  208.6 [193.7, 227.1]  
PETP_{spring}  0.74 [1.26, 0.27]  1.0009  227.0 [212.3, 244.8]  
n25  1.40 [0.94, 1.90]  1.0010  191.0 [175.5, 210.7]  
T_{X},_{summer}  n1 to n3  0.83 [0.32, 1.43]  1.0009  222.2 [206.5, 241.2] 
T_{X},_{spring}  1.21 [0.78, 1.65]  1.0009  201.0 [186.6, 218.9]  
T_{Xveg}  1.09 [0.71, 1.49]  1.0009  205.8 [191.6, 222.8]  
T_{Nwinter}  0.72 [0.49, 0.95]  1.0009  201.5 [187.1, 218.6]  
PETP_{summer}  0.61 [1.10, 0.16]  1.0009  227.7 [212.5, 246.5]  
PETP_{veg}  0.50 [0.85, 0.16]  1.0009  225.9 [211.7, 244.6]  
PETP_{spring}  0.42 [0.80, 0.06]  1.0009  227.4 [214.2, 244.9]  
n25  1.16 [0.57, 1.88]  1.0009  211.3 [194.8, 231.4] 
The number of SBD cases increased exponentially with more negative water balance (Fig.
Model prediction of standardised SBD report rate as a function of water balance (measured as PETP) of the vegetative season (AprilAugust) of the year previous to the disease report (a). Evolution of standardised SBD reports from 1990 to 2021 in France and Switzerland (b), and model predictions (Eq. 2). A dotted grey line indicates a standardised record rate that equals 1, above which the number of cases of the SBD is higher than average, and hence considered an outbreak of the disease.
The number of spores detected per week was more abundant in samplers closer to disease reports (Fig.
Number of spores per day as a function of the distance to the closest disease report (a), and as a function of the total maple basal area (m^{2}) in a radius of 50 km from the sampler (b).
Parameter estimates and their 95% credible intervals in brackets for models describing spore detection as a function of distance to SBD reports and host density. Estimates are generated from the posterior distributions of the variables in the Poisson models (Eq. 6) with the variable response number of spores per week.
Response variable  Detected spores per week  

Parameter  Intercept estimate  Coefficient estimate  Probability of spore capture 
Distance to disease report  2.64 [2.07, 3.07]  0.41 [0.97, 0.06]  0.73 [0.61, 0.85] 
Total maple basal area  2.04 [1.41, 2.55]  0.04 [0.01, 0.10]  0.72 [0.60, 0.85] 
The two models (distance to the disease, and maple basal area) estimated a similar probability of spore capture (0.73 and 0.72, respectively, Bernoulli process in Eq. 8, Table
The probability of disease occurrence increased with the number of detected spores at a given distance (Fig.
The proportion of positive aerobiological samples based on the quantitative speciesspecific C. corticale PCR assay per year in Europe followed the reported presence of the disease in Europe (Fig.
Proportion of aerobiological samples that tested positive from May to September of 2018 (n = 10, except for Gap where n = 5) in the different European samplers following the natural distribution of the host Acer pseudoplatanus.
The present study aimed at analysing the emergence of SBD in Europe through the frequency of spore detection in aerobiological samples and timeseries of disease records. Our results show that the SBD disease is at an exponentially increasing phase in France and Switzerland with an increase in the magnitude of the number of disease cases that peaks following a marked water deficit. Those episodic disease peaks do not show a deceleration, but they continue to increase in magnitude the last peak is far higher than the precedent one (Fig.
Disease peaks increased exponentially in magnitude with time. C. corticale is an invasive pathogen, reported for the first time in continental Europe in 1952, in France. In Switzerland, the first known report was in 1991. The highest peaks of the standardized number of disease records in 2020 in both countries suggest that the pathogen, after several outbreaks of the disease, might have colonized more forest plots, where the disease was eventually able to develop after conducive weather conditions. Two processes may have then taken place that explain the exponential increase in the last decades in France and Switzerland: (1) the dispersion and the establishment of the exotic pathogen, and (2) an increasing frequency of low water balance and high temperatures which are conducive conditions for the SBD disease. The relative contribution of the two processes in the SBD emergence cannot be fully disentangled from our model. However, they are likely to have occurred additively, as the main dispersion events of the pathogen are highly dependent on drought conditions. Hence, the increased frequency of drought events and heat peaks might have led to higher dispersion rates. The estimation of the zeroinflation at 5% in our climate model suggests that the disease is mainly climatedriven (i.e. only 5% of disease absence is not explained by high water deficit in the vegetative season). Before 2005, there was no SRR higher than one, which implies an outbreak of the SBD (higher recording of SBD compared to average). The lack of SBD reports in early years may be due to the absence of inoculum (early phases of the invasive process) and the less frequent conducive conditions to the SBD. However, the lack of awareness of the disease by surveillance agents and hence little attention to symptoms in the field might have also resulted in fewer reports. Although the disease is detectable some months before sporulating lesions develop on the trunk (sooty appearance), those early symptoms are not specific to the SBD: wilting of leaves, presence of stool shoots and branch dieback (
The presence of C. corticale in aerobiological samples paralleled the presence of the disease SBD in Europe. At the continental level, monitoring of aerobiological samples shows a great potential as a largescale epidemiosurveillance method for the SBD in Europe. Especially, early aerial detection of C. corticale in diseasefree countries, such as Portugal and Sweden, could help implement special measures for SBD detection and eradication in the field. The advantage of aerobiological monitoring is that the aerobiological networks are already established and samples can be potentially obtained periodically. This method has already been proved for other forest pathogens such as Hymenoscyphus fraxineus, Heterobasidion annosum s.l., Erysiphe alphitoides, and Melampsora laricipopulina (
Distance tended to decrease the number of detected spores, but the magnitude of the effect was low and it was not significant (the confidence interval of parameter estimates contained 0). We detected C. corticale spores as far as 310 km from the closest disease report. This result suggests that the fungus can disperse long distances by wind. However, we cannot rule out the possibility of underreporting with unobserved SBD occurring closer to the samplers in urban settings. It is reasonable to assume that the main SBD foci in forests were registered in our database from 2017–2018 onwards, as the disease was well known at that time by the surveillance agents. But, outbreaks in parks or along roads may not have been as comprehensively included in our database. We did not sample locations at distances farther than 310 km. Therefore, we cannot establish the limit of aerial dispersal of the fungus. Other windborne pathogens have shorter dispersal distances, such as H. fraxineus, the causal agent of the ash dieback disease, whose spores can be detected up to 50–100 km from the disease front (
The relatively abundant number of spores of C. corticale detected in the surveyed air samplers, placed in cities, reveals a potential risk to human health. The spores of C. corticale cause the MBD (
This project has received funding from the European Union’s Horizon 2020 Programme for Research & Innovation under grant agreement No 771271 (HOMED project, “HOlistic Management of Emergent forest pests and Diseases”), and from the research project SIAMOIS (“Smart and Innovative Monitoring Of airborne fungal Invaders by molecular methods”) by the French laboratory of excellence LabEx ARBRE. We greatly appreciate the valuable assistance provided by Etienne BrejonLamartiniere, Laure Dubois, Julie Faivre d’Arcier, Manuela Branco Ferreira, Anaïs Gillet, Quirin Kupper, Aneta Lukačevičová, Miloslava Majerová, Sophie Strohecker, Fabrizio Cioldi. We thank the ARPAE Area Prevenzione Ambientale Metropolitana di Bologna, Italy, for providing part of the Italian samples analysed in this study. The mycology research unit of the ANSES Plant Health Laboratory is supported by a grant managed by the French National Research Agency as part of the French government’s ‘‘Investing for the Future’’ (PIA) programme (ANR11LABX000201, Laboratory of ExcellenceARBRE).
Records of SBD in France and Switzerland from 1990 to 2021 (red dots)
Data type: figure (word document)
Isolates which DNA was extracted and used to confirm the specificity of the primers ccITS2F and SBD3R and probe SBD5P
Data type: table (word document)
Standard curve and its correlation coefficient to determine the limit of detection for the realtime PCR assay in tenfolded DNA solutions of C. corticale mycelium (a) and total number of spores in the qPCR reaction (b)
Data type: figures (word document)
Zerocentred histogram of the residuals between simulated data and predictions of the model with the water balance (PETP) in the vegetative season (AprilAugust) of the year preceding disease report as a predictor of the standardized record rate of the SBD
Data type: figure (word document)
Zerocentred histogram of the residuals between simulated data and predictions of the model with the distance to the closest disease report as a predictor of the number of Cryptostroma corticale spores detected in aerobiological samples
Data type: figure (word document)
Zerocentred histogram of the residuals between simulated data and predictions of the model with the total sycamore maple basal area in a radius of 50 km from the sampler as a predictor of the number of Cryptostroma corticale spores detected in aerobiological samples
Data type: figure (word document)
Coefficient estimate for each variable of maple basal area computed for different radius and their 95% credible intervals in brackets for models predicting the number of spores detected per week
Data type: table (word document)
Probability of disease report in an area of 40km to 130km radius from the sampler as a function of the number of detected spores per day
Data type: figure (word document)