Abstract. Increased human impacts on Earth systems are radically altering biogeochemical cycles. While long-term environmental observatories and Earth System Models (ESMs) provide valuable insights into the mechanisms of nutrient dynamics, their performance is limited at the fine spatial scales controlled by the functional diversity of plant and microbial communities. This gap in our understanding concerning the roles of microbial diversity and plant-microbial interactions in decomposition and nutrient dynamics extends across many global ecosystems. Recent advances in meta-omics technologies, including metagenomics, metatranscriptomics, metaproteomics, and metabolomics, offer a wide array of tools for assessing metabolic to genetic to evolutionary drivers of ecosystem functioning. Here, we explore the integration of meta-omics with traditional ecological approaches to examine responses to global environmental changes. We present case studies from diverse environments—soils, aquatic systems, clouds, and paleoarchives—demonstrating how meta-omics can unravel the roles of microbial diversity, metabolic pathways, and trait distributions critical to understanding greenhouse gas fluxes, nutrient cycling, and biogeochemistry. Although meta-omics is still beset with challenges including data heterogeneity arising from wide-ranging methods, omics-derived traits, kinetic parameters, and machine learning tools can be used to enhance ESM predictive capability. For example, emerging applications of meta-omics to ancient environmental DNA are extending our capacity to link historical patterns with future projections, offering a long-term perspective on ecosystem dynamics. This review highlights the potential of integrating omics with experimental manipulations alongside existing monitoring and modelling efforts to refine predictions of ecosystem responses to natural and anthropogenic-driven environmental changes. Because omics approaches cross a range of scientific domains, they could be used to foster collaboration and even integration within existing models, thus laying the foundation for informed conservation and ecosystem management strategies from local to global scales.
Abs et al. (Thu,) studied this question.