Postdoctoral position in Montpellier, France (18 months): Distribution modeling and surveillance of a plant disease at large scales: the case study of the Huanglongbing in Citrus farming.
We are looking to fill in an 18-months postdoctoral researcher position, starting in September of 2023 (starting date is flexible) to map large-scale agricultural risks and opportunities associated with bacterial plant diseases transmitted by insects. The work will focus on Citrus cultivation and risks associated to the HLB (Huanglongbing) disease in Europe over the next years and decades in the face of climate change.
The work will also deal with a larger issue of how to implement an iterative and replicable modelling framework that can be applied to large-scale epidemiological monitoring and control in various other agricultural models. This framework will potentially be applied to other organisms of concern for plant health in Europe. We are therefore looking for an individual with good skills in computational statistical ecology.
The economic stakes associated with epidemic crises are substantial in human, animal and plant health, in particular epidemics due to vector-borne pathogens. To limit the impact of these crises while reducing reliance on insecticides, there is a need to develop new surveillance strategies based on early prophylaxis, as we aim to do within the project BEYOND. A very useful decision support tool for policy makers would be a predictive epidemiological model, such as the mapping of the vector and the disease’s potential distributions, by using species distribution models (SDMs).
However, the reliability of these models heavily depends on the quality of the occurrence data that are used as input to map species distributions, as well as the biological knowledge-base that allows setting up realistic scenarios. On top of these general requirements, for the models to be useful in an epidemiological context, they need to be replicable, so that they can be easily updated with real-time monitoring information.
They also need to produce maps and risk indices at the scales that are relevant to feed into epidemiological surveillance schemes on the ground. We are therefore looking for an individual with skills in SDM implementation, but also some knowledge of the use of advanced computational tools such as containers and clusters. Knowledge on GIS and remote sensing tools are a plus, in order to incorporate relevant real-time predictors in the modelling process.
HLB is considered as one the main constraints on Citrus cultivation worldwide. It is a systemic bacterial disease vectored by at least two insect species. The disease is already present in most producing areas in other parts of the world, and is being closely followed by the French epidemiological surveillance system, particularly in the French overseas departments.
However, the disease is still absent from Europe, despite the recent arrival of one of its vectors in the Iberian Peninsula. Disease management mainly relies on vector biological control, intense surveillance and the culling of infective trees. Disposing of an optimized surveillance plan well in advance of disease arrival in a territory is thus utterly important.
The post-doctoral fellow will be in charge of jointly modelling the risks associated with the potential occurrence for the bacterial agent of HLB and Candidatus Liberibacter spp., and its psyllid vectors (Trioza erytreae and Diaphorina citri) in French Citrus growing areas.
He/she will be expected to seek for the complementary use of presence/absence data at contrasted scales (e.g., publicly available data at a global scale vs. official surveillance data at the landscape scale provided by the host team). He/she will also have to integrate proxies of the structure of the agricultural landscape as possible determinants of the risk of occurrence of HLB in a given landscape.
Eventually, risk maps will be integrated in a web application designed to help French surveillance agents define and improve their sampling campaigns. The work will be carried out in close collaboration with the French epidemiological surveillance platform, and the postdoctoral fellow will be based at the CBGP, near Montpellier.
The supervision team includes two experts on ecological modelling, as well as an expert on the biological system. His/her work will be integrated into a network of plant-health experts in France.
Requirements:
- Demonstrated strong expertise in advanced statistical analyses, use of R and GIS.
- Experience using Docker, Singularity, or other software for containerization.
- Some knowledge in ecology, epidemiology or plant-health is preferred, but we will consider applicants with statistical and / or computational backgrounds.
- Solid organizational skills: homogenizing and handling a database compiled from data collected from a variety of sources.
- Excellent relational abilities: Capacity to work within a large collaborative group, with both scientific partners and stakeholders. Note that we are an inclusive team; we therefore expect someone who can interact with a diverse group of people without prejudice of their backgrounds.
Salary: Wage will follow rules from the INRAE institute, and will depend notably on experience. French salaries include social benefits such as health insurance.
Application process: Applicants should send a single PDF file including a motivation letter (maximum 2 pages) that explains how your background fits the described profile and what your main contribution would be, a CV with the full list of publications, and the contacts for 3 references (please only include name, position, and email information, we will only contact them for recommendation letters if your profile seems to match our expectations), to Christine Meynard christine.meynard@inrae.fr before June 16th 2023. The CV should also contain an estimate of your wished starting date or any time constraints you may have to start your work with us. This material can be sent in English or in French.
Supervision: The postdoctoral researcher will be based at the CBGP in Montpellier (https://www6.montpellier.inrae.fr/cbgp/) , but with regular visits to PHIM (https://umr-phim.cirad.fr/), a few hundred meters away. He/she will be jointly advised by Christine MEYNARD (INRAE – CBGP), Virginie RAVIGNE (CIRAD – PHIM) and Nicolas SAUVION (INRAE – PHIM). In addition, the postdoctoral researcher will regularly interact with all members of the ANR project BEYOND (https://www6.inrae.fr/beyond/) , and the French Epidemio-surveillance platform (https://plateforme-esv.fr/).
References
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Facon, B., Hafsi, A., de la Masseliere, M.C., Robin, S., Massol, F., Dubart, M., et al. (2021). Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of species abundances in fruit flies. Ecol. Lett., 24, 1905– 1916.
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Meynard, C.N., Leroy, B. & Kaplan, D.M. (2019). Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing? Ecography, 42, 2021–2036.
Meynard, C.N. & Quinn, J.F. (2007). Predicting species distributions: a critical comparison of the most common statistical models using artificial species. J. Biogeogr., 34, 1455–1469.
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Narouei-Khandan, H. A., Halbert, S. E., Worner, S. P., & van Bruggen, A. H. (2016). Global climate suitability of citrus huanglongbing and its vector, the Asian citrus psyllid, using two correlative species distribution modeling approaches, with emphasis on the USA. European Journal of Plant Pathology, 144, 655-670.
Sauvion, N., Peccoud, J., Meynard, C. & Ouvrard, D. (2021). Occurrence data for the two cryptic species of Cacopsylla pruni (Hemiptera: Psylloidea). Biodiversity Data Journal, 9, e68860.
Silvie P., Cellier G., Gentzbitte L., Martin P., Milagros-Lopez M., et al.. (2019) Maladie du huanglongbing : Analyse du risque phytosanitaire pour l’Union européenne. Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail. 278 p. https://hal.science/hal-02272585