Join us for an interdisciplinary session, where we will explore how cutting-edge omics technologies are transforming our understanding of ecosystems and their resilience in response to climatic change across all scales. Over billions of years, spatial and temporal shifts in environmental conditions have driven the evolution of diverse microbial, fungal, plant and animal species, shaping the ecosystems, atmosphere, and climate of Earth. Gaining insights into how these organisms and biomes function, adapt, and interact requires a deep understanding of their components and the complex feedback systems they form.
Technological innovations in measuring and interpreting “meta-omics” datasets are now providing unprecedented mechanistic insights across diverse organisms, scales, and environmental spheres. These advances also drive the development of next-generation models to predict ecosystem function. In this session, we bring together ecologists, geochemists, and evolutionary biologists to examine the available omics toolkits for studying organisms and communities and to discuss ongoing efforts to integrate this knowledge across biological and temporal scales to address pressing Earth system science questions.
By combining eco-evolutionary insights with ecosystem-level concepts like community traits and resilience, we aim to foster future ITS sessions that apply integrated omics approaches alongside geoscience techniques for a deeper, mechanistic understanding of ecosystems.
We welcome contributions studying all Earth’s spheres (Biosphere, Atmosphere, Hydrosphere, Cryosphere, Geosphere), using a wide range of omics datasets (metagenomics, metatranscriptomics, metabolomics, proteomics, lipidomics, spectranomics, ionomics, elementomics, and isotopomics) as well as other large datasets such as trait, phenotype, inventory, pollen, and fossil records. We are particularly interested in studies involving control experiments, long-term ecological surveys, or flux networks, as well as research that provides mechanistic insights and employs big data in Earth system models or machine learning to scale patterns across space and time.