Microbial communities play pivotal roles in ocean biogeochemistry, yet linking their composition to ecosystem functions remains a significant challenge. In this study, we demonstrate the predictive power of bacterioplankton taxonomic composition in explaining oxygen consumption during dissolved organic matter (DOM) degradation. Using 4 years of experimental data, we integrated 'omics with statistical modeling, applying feature selection and dimensionality reduction to develop high-performance linear regression models with strong predictive accuracy. Our framework also identifies key microbial groups driving oxygen consumption, including taxa known for their differential capabilities in DOM processing and recently shown to exhibit distinct respiration rates. Flavobacteriales emerge as central contributors to oxygen consumption, underscoring their ecological importance in nutrient-rich, highly productive coastal systems often referred to as 'green seas'. Their consistent dominance across varying oxygen consumption categories highlights their pivotal role in sustaining ecosystem functions in these environments. Beyond oxygen consumption, this framework provides a versatile tool for investigating microbially driven biogeochemical processes. By linking community composition with ecosystem functions, our study advances predictive microbial ecology. These findings deepen our understanding of microbial contributions to the ocean's carbon and oxygen cycles, improving our ability to anticipate their responses to environmental change.
Marine Bacterioplankton Composition Predicts Oxygen Consumption During Dissolved Organic Matter Degradation Experiments.
Cecília Alonso,Juan Zanetti,L. Griffero,Emiliano Pereira-Flores,B. González,Carolina Lescano,A. Pérez-Parada,Carolina Crisci,Rudolf I. Amann
Published 2025 in Environmental Microbiology
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- Publication year
2025
- Venue
Environmental Microbiology
- Publication date
2025-11-01
- Fields of study
Biology, Medicine, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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