Climate change is having profound effects on the marine environment, with rising sea temperatures, acidification and alterations to salinity levels threatening marine biodiversity worldwide (Ruban et al. 2026). While climate fluctuations have always played a role in the evolution of life on Earth, warming rates exceed the adaptive capacity of most species (Voolstra et al. 2026). Consequently, it is critical to understand how marine ecosystems have responded to past climatic changes to be able to predict future trajectories. In that context, this project aims to develop and apply a multi-species framework to examine responses of marine ecosystems to climate fluctuations on a large temporal scale. The project will integrate a macrogenomic approach, ecological and climatic data as well as genome-informed simulations to predict future responses of marine ecosystems under contrasted climate change scenarios. My research will focus on Northeast Atlantic marine species spanning different trophic levels, leveraging modern and historical genomes to reconstruct past demography, track temporal patterns of genome erosion, and model future species distribution and genomic trajectories. To achieve this, I propose to carry out the following objectives: 1. Study the deep time demography of marine species from the Pleistocene to Holocene (2.5 My to 10 Ky BP) using publicly available high quality genomes 2. Examine recent changes in genome-wide diversity of marine species over the past ~300 years by comparing modern genomes with newly sequenced historical samples from museum collections for 8 North Atlantic species, representing different trophic levels 3. Predict future impacts of sea warming on ecosystem structure and genomic variation through the integration of genomic, ecological and climatic data to inform species distribution models and genome informed forward in time simulations. Through these objectives, I will address fundamental questions about the response of marine communities to environmental change and provide important data for conservation planning