Bacterial biogeography in China and its association to land use and soil organic carbon
Tao Lu, Nuohan Xu, Chaotang Lei, Qi Zhang, Zhenyan Zhang, Liwei Sun, Feng He, Ning-Yi Zhou, Josep Peñuelas, Yong-Guan Zhu, Haifeng Qian
Bacterial biogeography in China and its association to land use and soil organic carbon
● 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 content. |
Soil organic carbon (SOC) is the largest pool of carbon in terrestrial ecosystems and plays a crucial role in regulating atmospheric CO2 concentrations. Identifying the essential relationship between soil bacterial communities and SOC concentration is complicated because of many factors, one of which is geography. We 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 scale. The 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 concentration. Cultivated land was much more complex than forest, grassland, wetland and wasteland, with high SOC concentrations tending to enrich bacteria such as Rubrobacter, Terrimonas and Sphingomona. SOC 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 Micrococcaceae. Our 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.
biogeography / soil organic carbon / land-use type / machine learning
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