Global drivers and patterns of microbial abundance in soil

环境科学 土壤碳 生物量(生态学) 生物地球化学循环 碳循环 全球变化 土壤科学 生态学 生态系统 土壤水分 气候变化 生物
作者
Héctor M. Serna‐Chavez,Noah Fierer,Peter M. van Bodegom
出处
期刊:Global Ecology and Biogeography [Wiley]
卷期号:22 (10): 1162-1172 被引量:321
标识
DOI:10.1111/geb.12070
摘要

Abstract Aim While soil microorganisms play key roles in E arth's biogeochemical cycles, methodological constraints and sparse data have hampered our ability to describe and understand the global distribution of soil microbial biomass. Here, we present a comprehensive quantification of the environmental drivers of soil microbial biomass. Location Global. Methods We used a comprehensive global dataset of georeferenced soil microbial biomass estimates and high‐resolution climatic and soil data. Results We show that microbial biomass carbon ( C Mic ) is primarily driven by moisture availability, with this single variable accounting for 34% of the global variance. For the microbial carbon‐to‐soil organic carbon ratio ( C Mic / C Org ), soil nitrogen content was an equally important driver as moisture. In contrast, temperature was not a significant predictor of microbial biomass patterns at a global scale, while temperature likely has an indirect effect on microbial biomass by influencing rates of evapotranspiration and decomposition. As our models explain an unprecedented 50% of the global variance of C Mic and C Mic / C Org , we were able to leverage gridded environmental information to build the first spatially explicit global estimates of microbial biomass and quantified the global soil microbial carbon pool to equal 14.6 Pg C . Main Conclusions Our unbiased models allowed us to build the first global spatially explicit predictions of microbial biomass. These patterns show that soil microbial biomass is not primarily driven by temperature, but instead, biomass is more heterogeneous through the effects of moisture availability and soil nutrients. Our global estimates provide important data for integration into large‐scale carbon and nutrient models that may imply a major step forward in our ability to predict the global carbon balance, now and in a future climate.
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