Ethan Cohen
Project
Génération de populations animales synthétiques par intelligence artificielle pour la modélisation écologique et l’adaptation aux changements climatiquesThe global biodiversity crisis, intensified by climate change, is driving a rapid decline in animal populations and disrupting ecosystems worldwide. Moreover, a large proportion of species remain poorly documented, which greatly limits our ability to model their dynamics, anticipate their responses to disturbance, and guide effective conservation strategies. This lack of data represents a major obstacle to understanding and managing biodiversity at broad spatial scales. In this context, recent advances in artificial intelligence (AI) offer new opportunities to address these data gaps. Machine learning approaches can integrate multiple sources of information and uncover complex relationships among ecological variables. This project aims to develop a methodological framework using AI to generate synthetic animal populations. These populations will simulate the dynamics, distribution, and resilience of real species, helping to better understand their demographic and spatial trajectories under different environmental scenarios. The approach combines generative models with species distribution models. The generative models produce individuals with realistic biological and ecological traits, while the distribution models incorporate spatial and environmental conditions to guide the generation process. Together, they create coherent virtual populations that are both functionally and spatially plausible. The framework will be applied to contrasting biological systems representative of Québec’s wildlife, such as bees in agricultural landscapes, frogs in wetland environments, and foxes in urban settings. These case studies will address complementary challenges, including mortality linked to environmental stress, vulnerability to habitat fragmentation, and interactions between wildlife and human activity. By bridging artificial intelligence and ecology, this project proposes a new way of representing biodiversity. Through the generation of virtual populations, it enables the exploration of potential species trajectories and the testing of adaptation scenarios under climate change.
