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Global patterns and drivers of soil microbial nitrogen and phosphorus use efficiency
Decai Gao1, Yakov Kuzyakov2,3, Manuel Delgado-Baquerizo4
1Qianyanzhou Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Nature Communications
|March 18, 2026
Summary
Microbial nutrient use efficiency, a key soil trait, shows global patterns. Nitrogen use efficiency is higher than phosphorus use efficiency, with soil organic carbon being a major predictor.
Area of Science:
- Microbial ecology
- Biogeochemistry
- Soil science
Background:
- Nutrient use efficiency (NUE) is a critical microbial functional trait, influencing nutrient cycling.
- Global patterns of microbial NUE and phosphorus use efficiency (PUE) are poorly understood.
- Understanding these efficiencies is vital for predicting soil carbon and nutrient dynamics.
Purpose of the Study:
- To estimate global microbial nitrogen use efficiency (NUE) and phosphorus use efficiency (PUE) across terrestrial ecosystems.
- To identify key environmental predictors of microbial NUE and PUE.
- To map potential nutrient cycling hotspots and inform large-scale ecological models.
Main Methods:
- Utilized the ecoenzymatic stoichiometric approach to estimate NUE (n=2012) and PUE (n=3419).
- Analyzed data from diverse terrestrial ecosystems worldwide.
- Applied spatial upscaling techniques to identify regional patterns.
Main Results:
- Globally, microbial NUE (mean 0.60) is significantly higher than PUE (mean 0.35).
- Soil organic carbon (SOC) is the strongest positive predictor for both NUE and PUE.
- Tundra and boreal forest soils exhibit lower NUE, indicating higher nitrogen acquisition investment in cold climates.
Conclusions:
- Microbial NUE and PUE exhibit distinct global patterns, influenced by factors like soil organic carbon.
- Cold ecosystems may have specialized nitrogen acquisition strategies.
- The findings provide crucial data for refining global biogeochemical models and identifying nutrient cycling hotspots.
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