Assessing the Aerial Interconnectivity of Distant Reservoirs of Sclerotinia sclerotiorum

Abstract

Many phytopathogenic fungi are disseminated as spores via the atmosphere from short to long distances. The distance of dissemination determines the extent to which plant diseases can spread and novel genotypes of pathogens can invade new territories. Predictive tools including models that forecast the arrival of spores in areas where susceptible crops are grown can help to more efficiently manage crop health. However, such models are difficult to establish for fungi with broad host ranges because sources of inoculum cannot be readily identified. Sclerotinia sclerotiorum, the pandemic agent of white mold disease, can attack >400 plant species including economically important crops. Monitoring airborne inoculum of S. sclerotiorum in several French cropping areas has shown that viable ascospores are present in the air almost all the time, even when no susceptible crops are nearby. This raises the hypothesis of a distant origin of airborne inoculum. The objective of the present study was to determine the interconnectivity of reservoirs of S. sclerotiorum from distant regions based on networks of air mass movement. Viable airborne inoculum of S. sclerotiorum was collected in four distinct regions of France and 498 strains were genotyped with 16 specific microsatellite markers and compared among the regions. Air mass movements were inferred using the HYSPLIT model and archived meteorological data from the global data assimilation system (GDAS). The results show that up to 700 km could separate collection sites that shared the same haplotypes. There was low or no genetic differentiation between strains collected from the four sites. The rate of aerial connectivity between two sites varied according to the direction considered. The results also show that the aerial connectivity between sites is a better indicator of the probability of the incoming component (PIC) of inoculum at a given site from another one than is geographic distance. We identified the links between specific sites in the trajectories of air masses and we quantified the frequencies at which the directional links occurred as a proof-of-concept for an operational method to assess the arrival of airborne inoculum in a given area from distant origins.

Keywords: air-mass movement; airborne inoculum; contact network; polyphagous fungi; risk forecasting.

FIGURE 1

Aerobiological phases of spore dispersal and composition of aerial samples.

FIGURE 2

Strains collected from different regions and haplotypes they have in common (each color corresponds to one haplotype, the width of the rectangle indicates the number of strains bearing this haplotype). The arrows above columns indicate the aerial connectivity between regions (the larger the arrow, the more intense the connection).

FIGURE 3

Tropospheric connectivity in March that was inferred with HYSPLIT from archived meteorological data over the period 2008–2017.

FIGURE 4

Temporal variation in the tropospheric connectivity between sites.

FIGURE 5

Correlation across time between the tropospheric connectivity and the geographic distance.

FIGURE 6

Log-linear regression between PIC and tropospheric connectivity.

FIGURE 7

Log-linear regression between the PIC and the Earth surface distance.

FIGURE 8

Comparison of the inferred links between connectivity and probability of the incoming component (PIC) for different sampling schemes (top: 4 sites and 250 isolates per site; bottom: 10 sites and 100 isolates per site) when the maximum proportion of exogenous strains at the site level is 20%. Left: Cloud of points “connectivity × PIC” obtained for the 100 simulations and pointwise median and quantiles of order 0.025 and 0.975 of the linear regression line linking connectivity and PIC. Right: histogram of the estimate of the slope coefficient in the regression (a positive slope tends to show a positive link between connectivity and PIC). The proportion of positive estimated slopes and its 95%-confidence interval are given above the histogram.

FIGURE 9

Comparison of the inferred links between connectivity and probability of the incoming component (PIC) for different sampling efforts (top: 10 sites and 100 isolates per site; bottom: 10 sites and 1,000 isolates per site) when the maximum proportion of exogenous strains at the site level is 2%. Left: Cloud of points “connectivity × PIC” obtained for the 100 simulations and pointwise median and quantiles of order 0.025 and 0.975 of the linear regression line linking connectivity and PIC. Right: histogram of the estimate of the slope coefficient in the regression (a positive slope tends to show a positive link between connectivity and PIC). The proportion of positive estimated slopes and its 95%-confidence interval are given above the histogram.