Effects of multiple global change factors on soil microbial richness, diversity and functional gene abundances: A meta-analysis

物种丰富度 丰度(生态学) 生态系统 生态学 自行车 相对物种丰度 土壤微生物学 生物 土壤水分 地理 考古
作者
Yuqian Li,Junwei Ma,Yi Yu,Yijia Li,Xinyi Shen,Shouliang Huo,Xinghui Xia
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:815: 152737-152737 被引量:36
标识
DOI:10.1016/j.scitotenv.2021.152737
摘要

Soil microbial richness, diversity, and functional gene abundance are crucial factors affecting belowground ecosystem functions; however, there is still a lack of systematic understanding of their responses to global change. Here, we conducted a worldwide meta-analysis using 1071 observation data concerning the effects of global change factors (GCFs), including warming (W), increased precipitation (PPT+), decreased precipitation (PPT-), elevated CO2 concentration (eCO2), and nitrogen deposition (N), to evaluate their individual, combined, and interactive effects on soil microbial properties across different groups and ecosystems. Across the dataset, eCO2 increased microbial richness and diversity by 40.5% and 4.6%, respectively; warming and N addition decreased the abundance of denitrification functional genes (nirS, nirK, and nozS); N addition had a greater impact on soil C-cycling functional genes than on N-cycling ones. Long-term precipitation change was conducive to the increase in soil microbial richness, and fungal richness change was more sensitive than bacterial richness, but the sensitivity of bacteria richness to N addition was positively correlated with experimental duration. Soil microbial richness, diversity, and functional gene abundances could be significantly affected by individual or multiple GCF changes, and their interactions are mainly additive. W×eCO2 on microbial diversity, and N×PPT+ and W×N on N-cycling functional gene abundance showed synergistic interactions. Based on the limitations of the collected data and the findings, we suggest designing experiments with multiple GCFs and long experimental durations and incorporating the effects and interactions of multiple drivers into ecosystem models to accurately predict future soil microbial properties and functions under future global changes.
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