Jointly led by UGB and IRD [Ile-de-France], this research explores the coupling of metabolic and ecological levels in biological systems.
Dorra Louati, researcher at UMMISCO (Université Gaston Berger de Saint-Louis Sénégal).
Thesis defended in 2017 at the doctoral school École doctorale Informatique, télécommunications et électronique de Paris (1992-....).
This research is the result of an international co-supervision between several partner institutions.
Referenced in the ABES/STAR network, this thesis meets the rigorous criteria of French higher education.
Research on the coupling of metabolic and ecological levels in complex biological systems opens interesting perspectives for modeling and simulation. By integrating formal models, this approach allows for a better understanding of the interactions between organisms and their environment. The DEVS (Discrete Event System Specification) formalism is used to develop a model that links the internal dynamics of organisms to their external interactions. This model is particularly relevant in the current context, where environmental challenges are becoming increasingly pressing. This raises crucial questions about how these interactions influence the survival and evolution of species, especially in a constantly changing world.
The developed multi-agent models, focused on sexual allocation and foraging, illustrate how the coupling between physiology and ecology can be applied. For example, in the case of fish populations, decisions made by an individual, influenced by its metabolism, can have repercussions on the ecosystem as a whole. If a fish chooses to feed on a certain prey due to its energy needs, this can lead to a decrease in that prey, thereby affecting the entire food chain. This prompts reflection on the importance of modeling in the management of natural resources and the conservation of biodiversity. Indeed, these models allow for anticipating the consequences of individual choices on the dynamics of the ecosystem.
It is crucial to ask how these models can be used to predict the impacts of environmental changes on populations. For example, in the context of climate change, coupled models could help forecast how variations in temperature and precipitation will affect species distribution and behavior. The research also highlights the need for an interdisciplinary approach, combining biology, ecology, and computer science, to address the complex challenges we face today. This integrated approach is essential for developing effective and sustainable conservation strategies.
In summary, this thesis proposes a significant advancement in the understanding of complex biological systems. It paves the way for practical applications in ecosystem management, species conservation, and resource optimization. For instance, the developed models could be used to formulate fishery management plans that take into account not only fish populations but also interactions with other species and the environment. Decision-makers must seize these tools to develop policies based on evidence and reliable models.
In West Africa, where biodiversity is rich but threatened by human activities such as deforestation and overfishing, the application of these models could have a significant impact. Countries in the region could benefit from simulations that predict the effects of resource exploitation on local ecosystems. This could also help raise awareness among local communities about the importance of conservation and sustainable resource management.
In conclusion, the coupling of multi-level models of complex systems represents a major advancement for research in biology and ecology. By integrating the internal dynamics of organisms with their external interactions, we can better understand and anticipate the challenges our ecosystems face. This not only allows us to protect biodiversity but also to ensure a sustainable future for generations to come.
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Sources and Access
Dorra Louati. Couplage de modèles multi-niveau de systèmes complexes : application aux systèmes biologiques. Modélisation et simulation. Université Pierre et Marie Curie - Paris VI; École Nationale des Sciences de l'Informatique (La Manouba, Tunisie), 2017. Français. ⟨NNT : 2017PA066630⟩. ⟨tel-01996580v2⟩