Climate change is altering the environmental conditions of aquatic ecosystems worldwide at an unprecedented rate, increasing water temperatures and disrupting chemical regimes. Consequently, species can either adapt or experience reductions in fitness, contractions in their distributional ranges, or even extinction. Genetic adaptation can occur through both point mutations and structural variants, with growing evidence highlighting the pervasive role of structural variants in ecotype differentiation. These adaptive differences often follow clines matching the underlying and determining key environmental gradients such as salinity or temperature. Recent advances in genomic technologies have made it increasingly feasible to sequence large numbers of individuals at reduced costs and to reliably recover genomic data from modern as well as historical and ancient specimens. Comparing genomic data in a temporal framework thus enables the tracking of adaptive genomic changes across spatial gradients in real time with much higher precision than ever before. In this project, we propose to investigate fish species whose distributional ranges encompass environmental gradients related to both temperature and salinity. Our aim is to identify the key genomic adaptations that allow these species to thrive under distinct environmental conditions, to track adaptive allele frequency changes in both space and time and to predict the consequences of future climatic projections on population dynamics and adaptive responses of different species. We will focus on abundant and widely distributed foundation species of high ecological and economic value: the Atlantic herring, the Atlantic cod, and the European sardine. Using a combination of published genomic datasets from contemporary populations and newly generated historical/ancient sequences from samples sourced through a network of collaborating museums, we will pursue three main objectives. First, we will employ a comparative landscape genomics approach to consolidate and expand current knowledge on the genomic architecture (i.e., point mutations and structural variants) of adaptations to environmental gradients. Second, we will use a temporal genomic framework to assess whether there are correlations between genomic changes in key genes under selection and changes in environmental variables and to evaluate the consequences of these changes on population dynamics. Finally, based on the results obtained, we will apply simulation and landscape modeling approaches to predict how different climate change projections will impact the distribution, connectivity, abundance, and resilience of these species. Altogether, this project will generate critical knowledge to estimate the resilience of key fish species to climate change. We will do so by understanding how environmental gradients shape the genetic composition of these marine species, how they have historically adapted to environmental change, and how future climatic shifts may affect their survival and distribution, ultimately impacting ecosystem conservation and stock management.