Unité de Recherches de Pathologie Végétale INRAE PACA cm Domaine St Maurice BP 94 67, allée des chênes CS 60094 F84143 Montfavet cedex France Tel : 33 (0) 4.32.72.28.75 loup.rimbaud(a)inrae.fr

My publications in the open archive HAL INRAE http://orcid.org/0000-0002-8098-9984 http://www.researcherid.com/rid/N-8909-2017 Loup Rimbaud: using maths and biology to solve plant disease problems(INRAE Press, 2019/06/05)

I use spatiotemporal simulationmodels, complemented with laboratory and glasshouse experiments, as well as statistical analyses of epidemiological data. These experiments and field data result in the acquisition of crucial knowledge on the biology of the interactions between host plants, pathogens and possibly their vectors. Indeed, these knowledge give the possibility to calibrate model parameters or test model predictions, and can be very helpful to identify promising control methods. Finally, it is crucial for me to identify strategies that match with farmers’ needs, and communicate them in such a way that my researches have an impact on the real world.

1. Modelling control strategies of epidemics

Simulation models are very useful to optimise management strategies of epidemics, and circumvent the ethical, legal, logistical and economic constraints associated with experiments at large spatiotemporal scales. My models simulate the epidemiological dynamics of pathogens in cultivated landscapes under disease management, and aim at optimising management strategies. However, pathogens have an extraordinary evolutionary potential that allow them to overcome control methods employed in the field. This is particularly the case with the deployment of plant resistance. Thus, because they include pathogen evolution, the demo-genetic models I use are of great interest, and enable the identification of strategies that are both efficient and durable to manage plant diseases.

In collaboration with the BioSP unit (INRAE Avignon) and CSIRO (Canberra, Australia), I contributed to the development of the R package landsepi (Landscape Epidemiology and Evolution). This package allows the simulation of a panel of resistance deployment strategies against plant pathogens, and compare the following strategies:

• Gene pyramiding in the genome of a single cultivar
• Rotation of resistant cultivars on the same field
• Mixture of resistant cultivars in the same field
• Mosaic of resistant cultivars at the landscape scale

One of the first conclusions of this work is that there is no universal strategy for all pathogens and all situations: the optimal strategy depends, among others, on the considered pathosystems, the epidemiological and evolutionary context and the desired objectives (epidemiological efficiency, evolutionary durability, economic cost-effectiveness, …). We are currently developing a shiny interface for pedagogical purposes.

→ Click on the images to enlarge

Examples of simulated landscapes allocated with a 3-cultivar mosaic. An algorithm controls the relative proportion and degree of spatial aggregation of the different cultivars. (From Rimbaud L, Papaïx J, Rey JF, Barrett LG and Thrall PH (2018). Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens. PLoS Comput. Biol. 14:e1006067 https://doi.org/10.1371/journal.pcbi.1006067)

SEIR architecture of the model. Healthy hosts can be infected by propagules. Following a latent period, infectious hosts produce new propagules which may mutate and disperse across the landscape. At the end of the infectious period, infected hosts become epidemiologically inactive. (Adapted from Rimbaud L, Papaïx J, Barrett LG, Burdon JJ and Thrall PH (2018). Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance? Evol. Appl. 11(10):1791-1810. https://onlinelibrary.wiley.com/doi/full/10.1111/eva.12681)

2. Experimental calibration of models

A fine understanding of the biology of interaction between pathogens, hosts and vectors is necessary to identify relevant control strategies. Resistance is an interesting way to inhibit these interactions. Plant resistance is a decrease (possibly complete) in the ability of the parasite to infect, colonise or exploit the host for its own development. Numerous molecular mechanisms of plant resistance to pathogens have been elucidated, however very few data are available to understand the effect of such resistances on the main steps of parasitic infectious cycles. Such data are essential to calibrate simulations models (for example steps 1 to 4 of the SEIR architecture, see above) destined to evaluate different types of varietal resistance. Using  Potato virus Y (Potyviridae, potyvirus) and Cucumber mosaic virus (Bromoviridae, cucumovirus), my experiments aim at assessing different types of resistance to viruses in pepper (Capsicum annuum).

Our first results show that the aphid-mediated PVY infection rate (step 1) is slightly lower in a resistant accession (Perennial) than in a susceptible accession (Yolo Wonder). Nevertheless, this difference is too small to explain why Perennial is so resistant in the field; this accession must certainly affect other steps of the viral infectious cycle.

3. Transfer of knowledge

• Teaching (Master 2)
  • Université Paris-Saclay : De l’Agronomie à l’AgroEcologie (AAE).
  • Montpellier SupAgro : Protection des Plantes et Environnement (PPE). Amélioration des plantes et ingénierie des plantes tropicales et méditerranéennes (APIMET).
• Internship supervision (Master 2)
  • 2020 Pierre Mustin (main supervisor): Evaluation of plant resistance to viral transmission
  • 2020 Clarisse Vincent (co-supervisor) : Maintaining resistance durability to black sigatoka in new banana cultivar
  • 2019 Jean-Loup Gaussen (co-supervisor): Development of spatial tools for the R package landsepi
  • 2014 Samuel Marchat (co-supervisor): Development of an early detection protocol for the virus responsible of sharka in prunus trees