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Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling
Robert L Sinsabaugh1, Stefano Manzoni, Daryl L Moorhead
1Biology Department, University of New Mexico, Albuquerque, NM 87131, USA. rlsinsab@unm.edu
Ecology Letters
|May 1, 2013
Summary
Microbial carbon use efficiency (CUE) is key for ecosystem models. This study highlights methodological differences causing discrepancies in CUE values between aquatic and terrestrial ecosystems, recommending a standard CUE of 0.30 for broad-scale models.
Area of Science:
- Microbial Ecology
- Ecosystem Modeling
- Biogeochemistry
Background:
- Carbon use efficiency (CUE) is crucial for understanding microbial roles in energy and carbon cycling.
- Theoretical maximum CUE is ~0.60, with natural systems expected around ~0.30 due to constraints.
- Discrepancies exist between reported CUE values for aquatic (~0.26) and terrestrial (~0.55) ecosystems.
Purpose of the Study:
- To investigate the reasons for differing CUE estimates in aquatic versus terrestrial ecosystems.
- To identify limitations in current ecological models regarding CUE calculations.
- To provide recommendations for more accurate CUE estimations in ecological modeling.
Main Methods:
- Comparative analysis of CUE estimation methodologies in aquatic and terrestrial environments.
- Review of thermodynamic and kinetic constraints on microbial growth.
- Evaluation of simulation models for their representation of microbial metabolism and CUE.
Main Results:
- Methodological differences in CUE estimation contribute to a twofold variation in reported values between ecosystem types.
- Soil CUE estimates may underestimate maintenance costs due to measurement challenges in water-limited conditions.
- Many models overestimate CUE by inadequately representing energy dissipation and stoichiometric limitations.
Conclusions:
- A standardized CUE value of 0.30 is recommended for broad-scale ecological models, adjusting for pervasive nutrient limitations.
- Finer-scale models require detailed consideration of resource quality, stoichiometry, and environmental factors for accurate CUE prediction.
- Addressing methodological and modeling limitations is essential for improving our understanding of microbial carbon cycling.
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