진학프로석·박사 채용 접수 플랫폼

CARRTEL is a research collective in limnology that aims to adopt a holistic approach to lake ecosystems. The joint research unit (UMR), based in Thonon-les-Bains and Le Bourget-du-Lac, has developed research in the field of functional ecology of lake environments at various spatial and temporal scales, thereby strengthening its national and international recognition in limnology. The overall objective of the research is to provide knowledge on biodiversity, the processes and interactions that govern lake ecosystem functioning, and the past, present, and future dynamics of lakes. This involves studying the lake itself as well as connected zones and watershed areas. Research focused on lakes aims, on one hand, to characterize lake biodiversity, biological assemblages, and their structural and functional organization, which underpin the key processes involved in ecosystem functions and services. On the other hand, it seeks to understand the impacts of disturbances affecting lakes—such as historical or emerging pollutants, nutrient resource levels, changes in physical conditions related to global warming, human management actions, and other local anthropogenic impacts. By employing a variety of disciplines and approaches (experimentation, observation, retrospective analysis, modeling, participatory science), this research contributes to a better understanding of the effects of both local and global anthropogenic changes on the ecological functions and biodiversity of lakes.

For this project, CARRTEL is associated to the Mathematics Laboratory (LAMA), a research unit funded by CNRS and Savoie Mont Blanc University, located on the scientific campus of Bourget-du-Lac, Savoy. The lab is organised in three research teams: EDPs2 (partial differential equations: deterministic and probabilistic studies), Géométrie (Geometry), and LIMD (Logic, Computer science, and Discrete Mathematics). This thematic diversity in one laboratory expresses the unity of mathematics in its three components: pure mathematics, applied mathematics, and mathematical computer science. The three teams share the excellence of foundational research on the one hand, and, on the other hand, the care devoted to applications. The latter cover for instance applications to other sciences (geophysics, physics, biology, mechanics, and computer science), to other areas of mathematics (e.g., geometry applied to control theory), or to pedagogy.

Recurrent algal and/or cyanobacterial blooms constitute environmental disturbances that can alter—or even threaten—entire lake ecosystems, including human health (Amorim & Moura 2021, Dorioz et al. 2023). These rapid and massive proliferations of certain phytoplankton taxa can negatively impact the environment, human health, and various ecosystem services (typically drinking water supply, recreational activities, or professional fishing). Although their dynamics and contributing factors are well known, their detection—a key element in risk prevention—remains challenging in some cases. Moreover, their effects on the environment, ecosystems, and consequently on future blooms, are still poorly understood.

Indeed, while some phytoplankton genera bloom at the surface and are thus easily detectable via satellite tools (Rahaghi et al. 2024), other taxa, sometimes toxic, develop at depth. The toxic filamentous cyanobacterium Planktothrix rubescens is a notable example in large European lakes (Jacquet et al. 2005, Knapp et al. 2021). Furthermore, recent environmental disturbances induced by climate change and human activity have increased the competitive advantage of cyanobacteria (Anneville et al. 2015, Paerl & Huisman 2008). As a result, blooms can now occur even in seemingly unfavorable environmental conditions (i.e., low-phosphorus regulated environments), as evidenced by the occurrence of blooms in recent decades in several oligotrophic or re-oligotrophicated lakes (Jacquet et al. 2014, Reinl et al. 2021, Suarez et al. 2023).

To limit impacts on ecosystem services and protect public health from these blooms, scientists and lake managers must deepen their knowledge, which has so far relied mostly on monitoring and various types of sampling and analysis. In this project, we propose a new approach based on mathematical modeling, offering new capabilities for both qualitative and quantitative understanding and forecasting. The objective of this project—designed and built around our two laboratories (CARRTEL and LAMA)—is to better understand the emergence of these phytoplankton blooms under changing environmental conditions, so that they can be detected more rapidly and effectively, and ideally anticipated and prevented. Furthermore, we believe it is important to understand the impact of such disturbances on ecosystems in order to develop tools that support the prevention of these natural risks.

This interdisciplinary project, bridging lake ecology and mathematical modeling, is fully aligned with one of the scientific priorities of the University of Savoie Mont-Blanc: human–environment interactions. It will rely on (i) CARRTEL's expertise in lake environments (particularly researchers specializing in the ecology of microalgae and cyanobacteria, and in lake monitoring), and on (ii) LAMA's skills in numerical simulation and mathematical modeling (especially researchers specializing in population dynamics and hydrodynamics).

Ultimately, the goal is to build a one-dimensional vertical mechanistic model (1DV) to represent or identify the ecological niche of representative species based on C, S, or R strategies, with threshold values for nutrients or abiotic factors. The model will focus on key mechanisms and must account for direct or indirect competition between species for nutrients, light, temperature, the influence of zooplankton or more generally higher trophic levels, as well as other parameters such as parasitism and allelopathy, if possible. The model can build on existing models [Awada et al., 2020] (see example of biological parameters for 4 phytoplankton species in the appendices) and draw inspiration from chemostat-type models to understand species assemblages and feedback loops between parameters [Rapaport et al., 2011]. We aim to test various scenarios (temperature gradients, surface light intensity, vertical water mass movement, phytoplankton community composition) and extract threshold values—e.g., for nitrogen and phosphorus—that will either favor or inhibit certain types of blooms.