Switching from a mechanistic model to a continuous model to study at different scales the effect of vine growth on the dynamic of a powdery mildew epidemic

International audience*Background and Aims : Epidemiological simulation models coupling plant growth with the dispersal and disease dynamics of an airborne plant pathogen were devised for a better understanding of host–pathogen dynamic interactions and of the capacity of grapevine development to modify the progress of powdery mildew epidemics. *Methods : The first model is a complex discrete mechanistic model (M-model) that explicitly incorporates the dynamics of host growth and the development and dispersion of the pathogen at the vine stock scale. The second model is a simpler ordinary differential equations (ODEs) compartmental SEIRT model (C-model) handling host growth (foliar surface) and the ontogenic resistance of the leaves. With the M-model various levels of vine development are simulated under three contrasting climatic scenarios and the relationship between host and disease variables are examined at key periods in the epidemic process. The ability of the C-model to retrieve the main dynamics of the disease for a range of vine growth given by the M-model is investigated. *Key Results : The M-model strengthens experimental results observed regarding the effect of the rate of leaf emergence and of the number of leaves at flowering on the severity of the disease. However, it also underlines strong variations of the dynamics of disease depending on the vigour and indirectly on the climatic scenarios. The C-model could be calibrated by using the M-model provided that different parameters before and after shoot topping and for various vigour levels and inoculation time are used. Biologically relevant estimations of the parameters that could be used for its extension to the vineyard scale are obtained. *Conclusions : The M-model is able to generate a wide range of growth scenarios with a strong impact on disease evolution. The C-model is a promising tool to be used at a larger scale

Effect of Crop Growth and Susceptibility on the Dynamics of a Plant Disease Epidemic: Powdery Mildew of Grapevine

Modéliser les interactions entre développement et architecture de la plante et épidémies de maladies fongiques aériennes, pour une gestion durable des cultures

Testing the spatial association of disease patterns between two dates in orchards

International audienceThe analysis of spatiotemporal patterns can provide clues about disease spread by assessing if the spatial pattern of diseased plants at one date is associated with the pattern of previously diseased plants. No generic statistical test was available to answer this question for spatiotemporal maps of binary data (healthy or diseased plants) in regular plantings (e.g., orchards). Here we describe a Monte Carlo test of the hypothesis that the location of newly diseased plants is independent of the location of previously diseased plants, even when the disease is spatially aggregated within each assessment period. This spatiotemporal test is designed to cope with the censorship arising on a lattice when plants are missing or cannot recover between the two dates. Expected patterns are simulated by shifting on a torus the whole pattern at the second date relatively to the pattern at the first date. For each simulation, we discard the censored points from observed and simulated data. In case of a positive association between disease patterns at two dates, the distances between newly and previously diseased trees should be smaller in the observed than in the simulated patterns. As an illustration, we analysed the dependence between patterns of trees showing Plum pox virus symptoms at two dates

Grapevine defence mechanisms when challenged by pathogenic fungi and oomycetes

International audienceTraditional cultivated grapevine (Vitis vinifera) is susceptible to many fungal and oomycete pathogens causing devastating diseases including powdery mildew, downy mildew, grey mould, black rot and trunk diseases. These infections trigger various defence mechanisms such as reinforcement of the cell wall structure, production of phytoalexins and patho- genesis-related proteins, and localized cell death. In V. vinifera susceptible varieties, these defences are not effective, while in resistant grapevine, recognition of the pathogen induces effective mechanisms that stop the infection. Breeding programmes are con- ducted to take advantage of this genetic resistance. Moreover, a range of exogenous defence stimulators can be used to obtain a so-called “induced resistance” in susceptible varieties. This chapter presents the recently acquired knowledge on the molecular mechanisms involved in genetic and induced resistances, and further consider other mechanisms such as ontogenic resistance. It also suggests how to exploit these resis- tances to durably protect vineyards against the different fungal diseases

Microbial association networks give relevant insights into plant pathobiomes

ABSTRACTInteractions between plant pathogens and other plant-associated microorganisms regulate disease. Deciphering the networks formed by these interactions, termed pathobiomes, is crucial to disease management. Our aim was to investigate whether microbial association networks inferred from metabarcoding data give relevant insights into pathobiomes, by testing whether inferred associations contain signals of ecological interactions. We used Poisson Lognormal Models to construct microbial association networks from metabarcoding data and then investigated whether some of these associations corresponded to interactions measurable in co-cultures or known in the literature, by using grapevine (Vitis vinifera) and the fungal pathogen causing powdery mildew (Erysiphe necator) as a model system. Our model suggested that the pathogen species was associated with 23 other fungal species, forming its putative pathobiome. These associations were not known as interactions in the literature, but one of them was confirmed by our co-culture experiments. The yeast Buckleyzyma aurantiaca impeded pathogen growth and reproduction, in line with the negative association found in the microbial network. Co-cultures also supported another association involving two yeast species. Together, these findings indicate that microbial networks can provide plausible hypotheses of ecological interactions that could be used to develop microbiome-based strategies for crop protection.</jats:p