Next-Generation Eco-Omics: Integrating Microbial Function Into Predictive Ecosystem Models.

Area of Science:

• Microbial ecology
• Environmental science
• Genomics

Background:

• Microorganisms are crucial for ecosystem functions like nutrient cycling and climate regulation.
• Methodological advancements enable detailed study of microbial roles in ecosystems.

Purpose of the Study:

• To review innovations in eco-omics for linking microbial identity, activity, and ecosystem processes.
• To propose a framework for integrating eco-omics into predictive ecosystem and climate models.

Main Methods:

• High-resolution metagenomics (e.g., long-read, Hi-C) for near-complete genomes.
• Activity-resolved methods (qSIP, BONCAT, FACS) for growth and assimilation rates.
• Single-cell (Raman-SIP, nanoSIMS) and spatial (MetaFISH, MALDI-MSI) omics platforms.
• Autonomous sequencing for real-time ecological surveillance.

Main Results:

• Near-complete microbial genomes and interaction networks can be reconstructed.
• Taxon-specific metabolic rates are determined rapidly.
• Metabolites can be mapped with high spatial precision alongside microbial identities.
• Multimodal data integration reduced carbon flux prediction uncertainty by ~20%.

Conclusions:

• Eco-omics innovations offer unprecedented insights into microbial contributions to ecosystem functions.
• Integrating these tools into predictive models enhances ecological forecasting and management.
• This approach supports evidence-based strategies for climate action and land management.