After graduating with a Bachelor's degree in Cell Biology and Animal Physiology in 2013 at Nantes University (France), I moved to a Master's degree in Bioinformatics (still in Nantes) as I have always had some interest in the world of computer science, and so what better way than to be able to merge two of my main interests: biology and computer science. During my Master's degree, I had the opportunity to do an internship at the LS2N (Laboratoire des Sciences du Numérique de Nantes, France) in collaboration with the Station Biologique de Roscoff (France), which allowed me to discover my strong attraction for the field of marine ecology issues and questions, leading to a PhD, still at the LS2N with Samuel Chaffron and Damien Eveillard.
Role in AtlantECO
In the context of ongoing global change, there is a need to analyze the nature and magnitude of changes in planktonic communities in response to environmental stressors. To address this issue, the statistical framework of species distribution modeling has emerged to model and predict the biogeography of planktonic species under different climate scenarios. However, this modeling approach does not explicitly take into account biotic interactions between species, even though they constitute the main drivers of planktonic community structure and dynamics and may be more relevant indicators of ecosystem health than individual species. With this in mind, my research involves adapting the distributional modeling framework to model and predict the biogeography of observed planktonic associations from metagenomic and metatranscriptomic datasets, on the basis that they may represent relevant biotic interactions. As such, we aim to identify the potential associations most vulnerable to climate change, and the potential impacts on ecosystem functions.
My work is mainly related to Work Package 5 (WP5) «advances in systems ecology», particularly in Task 5.2 «Construct cross-kingdoms interaction networks for microbiome and plastisphere communities» through the workflow of Task 5.1 «Review and improve statistical and computational methods for ecological network reconstruction». In the near future, we plan to collaborate with Work Package 2 (WP2) contributors Fabio Benedetti and Meike Vogt to use the computational pipeline for inferring species ecological networks from heterogeneous data types on the omics data compiled within WP2. It will allow the AtlantECO database to be enriched.
We have adapted the statistical distribution modeling framework to model and predict planktonic species associations with better overall accuracy than the classical species distribution modeling approach. We aim to identify the key associations and associated biomes potentially most subject to ecological variation under global change, which will lead to the identification of the main physico-chemical drivers of association dynamics. From these projections, we plan to infer local and global predicted networks from the projected associations to compare their topological characteristics as a potential indicator of ecosystem resilience and stability. In a subsequent step, we plan to assess the predicted impact of global change on ecosystem functions using genome-resolved information.