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Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale.

Decai GaoEdith BaiSiyu WangShengwei ZongZiping LiuXianlei FanChunhong ZhaoFrank Hagedorn
Published in: Global change biology (2022)
Soil microbial biomass and microbial stoichiometric ratios are important for understanding carbon and nutrient cycling in terrestrial ecosystems. Here, we compiled data from 12245 observations of soil microbial biomass from 1626 published studies to map global patterns of microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), microbial biomass phosphorus (MBP), and their stoichiometry using a random forest model. Concentrations of MBC, MBN, and MBP were most closely linked to soil organic carbon, while climatic factors were most important for stoichiometry in microbial biomass ratios. Modeled seasonal MBC concentrations peaked in summer in tundra and in boreal forests, but in autumn in subtropical and in tropical biomes. The global mean MBC/MBN, MBC/MBP, and MBN/MBP ratios were estimated to be 10, 48, and 6.7, respectively, at 0-30 cm soil depth. The highest concentrations, stocks, and microbial C/N/P ratios were found at high latitudes in tundra and boreal forests, probably due to the higher soil organic matter content, greater fungal abundance, and lower nutrient availability in colder than in warmer biomes. At 30-100 cm soil depth, concentrations of MBC, MBN, and MBP were highest in temperate forests. The MBC/MBP ratio showed greater flexibility at the global scale than did the MBC/MBN ratio, possibly reflecting physiological adaptations and microbial community shifts with latitude. The results of this study are important for understanding C, N, and P cycling at the global scale, as well as for developing soil C-cycling models including soil microbial C, N, and P as important parameters.
Keyphrases
  • microbial community
  • climate change
  • antibiotic resistance genes
  • wastewater treatment
  • anaerobic digestion
  • plant growth
  • high intensity
  • randomized controlled trial
  • risk assessment
  • machine learning
  • deep learning