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Machine learning models reveal how biochar amendment affects soil microbial communities

Biochar 2023年第1期5卷 1527-1541页

作者： chaotang lei Tao Lu Haifeng Qian Yuxue Liu College of Environment Zhejiang University of TechnologyHangzhou 310032China State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products Institute of EnvironmentResourceSoil and FertilizersZhejiang Academy of Agricultural SciencesHangzhou 310021China Engineering Research Center of Biochar of Zhejiang Province Hangzhou 310021China

The biochar amendment plays a vital role in maintaining soil health largely due to its effects on soil microbial ***,individual cases and the variability in biochar properties are not sufficient to draw universal *** present study aimed to reveal how the biochar application affects soil microbial *** of 525 ITS and 128816S rRNA sequencing samples from previous studies were reanalyzed and machine learning models were applied to explore the dynamics of soil microbial communities under biochar *** results showed that biochar considerably changed the soil bacterial and fungal community composition and enhanced the relative abundances of Acidobacteriota,Firmicutes,Basidiomycota,and *** enhanced the robustness of the soil microbial community but decreased the interactions between fungi and *** random forest model combined with tenfold cross-validation were used to predict biomarkers of biochar response,indicating that potentially beneficial microbes,such as Gemmatimonadetes,Microtrichales,Candidatus_Kaiserbacteria,and Pyrinomonadales,were enriched in the soil with biochar amendment,which promoted plant growth and soil nutrient *** addition,the biochar amendment enhanced the ability of bacteria to biosynthesize and led to an increase in fungal nutrient patterns,resulting in an increase in the abundance and diversity of saprophytic fungi that enhance soil nutrient *** machine learning model more accurately revealed how biochar affected soil microbial community than previous independent *** study provides a basis for guiding the reasonable use of biochar in agricultural soil and minimizing its negative effects on soil microecosystem.

关键词： Biochar Soil health Soil microecosystem Machine learning Random forest Co-occurrence network

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Bacterial biogeography in China and its association to land use and soil organic carbon

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Soil Ecology Letters 2023年第4期5卷 29-38页

作者： Tao Lu Nuohan Xu chaotang lei Qi Zhang Zhenyan Zhang Liwei Sun Feng He Ning-Yi Zhou Josep Peñuelas Yong-Guan Zhu Haifeng Qian College of Environment Zhejiang University of TechnologyHangzhou 310032China State Key Laboratory of Microbial Metabolism School of Life Science&BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China CREAF Campus Universitat Autònoma de BarcelonaCerdanyola del VallèsBarcelona 08193CataloniaSpain CSIC Global Ecology Unit CREAF‐CSIC‐UABBarcelona 08193CataloniaSpain Key Laboratory of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-environmental SciencesChinese Academy of SciencesBeijing 100085China

●6102 high-quality sequencing results of soil bacterial samples were re-analyzed.●The type of land use was the principal driver of bacterial richness and diversity.●SOC content is positively correlated with key bacteria and total nitrogen *** organic carbon(SOC)is the largest pool of carbon in terrestrial ecosystems and plays a crucial role in regulating atmospheric CO_(2) *** the essential relationship between soil bacterial communities and SOC concentration is complicated because of many factors,one of which is *** systematically re-analyzed 6102 high-quality bacterial samples in China to delineate the bacterial biogeographic distribution of bacterial communities and identify key species associated with SOC concentration at the continental *** type of land use was the principal driver of bacterial richness and diversity,and we used machine learning to calculate its influence on microbial composition and their co-occurrence relationship with SOC *** land was much more complex than forest,grassland,wetland and wasteland,with high SOC concentrations tending to enrich bacteria such as Rubrobacter,Terrimonas and *** concentration was positively correlated with the amounts of soil total nitrogen and key bacteria Xanthobacteraceae,Streptomyces and Acidobacteria but was negatively correlated with soil pH,total phosphorus and *** study combined the SOC pool with bacteria and indicated that specific bacteria may be key factors affecting SOC concentration,forcing us to think about microbial communities associated with climate change in a new way.

关键词： biogeography soil organic carbon land-use type machine learning

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