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Next-Generation Eco-Omics: Integrating Microbial Function Into Predictive Ecosystem Models
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DOI：10.1002/bab.70121.png)
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Abstract
En 中文
Microorganisms drive essential ecosystem functions by mediating carbon, nitrogen, sulfur, and phosphorus transformations that regulate productivity and shape climate feedbacks. Rapid methodological advances now allow precise linkage of microbial identity, in situ activity, and ecosystem processes across spatial and temporal scales. High-resolution approaches-including long-read metagenomics and Hi-C-generate near-complete metagenome-assembled genomes (MAGs) from diverse environments, enabling reconstruction of microbial and viral-host interaction networks. Activity-resolved tools such as quantitative stable isotope probing (qSIP) and bioorthogonal non-canonical amino acid tagging (BONCAT), combined with fluorescence-activated cell sorting (FACS), yield taxon-specific growth and substrate assimilation rates within hours. Single-cell isotope techniques, including Raman-SIP and nanoSIMS, deliver nanometer-scale metabolic insights. Spatial meta-omics platforms, such as MetaFISH and MALDI-MSI, map metabolites alongside microbial identities with micrometer-level precision. Meanwhile, autonomous sequencing systems, including environmental sample processors and nanopore adaptive sampling, enable real-time (<24 h) ecological surveillance. Integrating these multimodal datasets into trait-based frameworks has reduced uncertainty in carbon flux predictions by nearly 20%. This review synthesizes these innovations, outlines optimized analytical pipelines, and proposes a framework for embedding eco-omics into predictive ecosystem and climate models, supporting evidence-driven management aligned with Climate Action and Life on Land.
Keywords:
eco-omics integration
genome-resolved metagenomics
activity-resolved isotope probing
spatial metabolomics imaging
microbial modeling for SDG-aligned climate prediction
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