Biogeographic patterns of microbial association networks in paddy soil within Eastern China. (March 2020)

• Record Type: Journal Article
• Authors: Wan, Xiaoling; Gao, Qun; Zhao, Jianshu; Feng, Jiajie; van Nostrand, Joy D.; Yang, Yunfeng; Zhou, Jizhong
• Is Part Of: Soil biology and biochemistry. Volume 142(2020)
• Publication Date: 2020-03
• Subjects: Microbial network -- Biogeographic pattern -- Diazotroph -- Random matrix theory; Soil biochemistry -- Periodicals; Soil biology -- Periodicals; Sols -- Biochimie -- Périodiques; Sols -- Biologie -- Périodiques; Sols -- Microbiologie -- Périodiques; Bodembiologie; Biochemie; 631.46
• DOI: 10.1016/j.soilbio.2019.107696
• Languages: English
• ISSNs: 0038-0717

Abstract

Abstract: Understanding microbial associations and their responses to environmental changes are important topics in microbial ecology. Using an association network framework, here we compare the potential microbial association patterns within bacteria, fungi or diazotrophs in 198 paddy soil samples within Eastern China. All of three networks were non-random and possessed topological features of complex systems such as scale-free, small-world (i.e., high connectivity) and modularity, which could enable system stability and resilience. However, those networks exhibited distinct topological features. The fungal network was the most complex (based on average degree), the closest (based on average geodesic distance), the least modular (based on modularity) and contained the fewest positive links compared to bacterial and diazotrophic networks. In the fungal and diazotrophic networks, we detected 8 super-generalist OTUs (network hubs), which were the most important nodes in maintaining network structure. Further analyses showed that the bacterial network was mainly shaped by soil temperature, but the fungal network was mainly shaped by ammonia and the diazotrophic network was mainly shaped by volumetric water content, ammonia and soil temperature, signifying the importance of different environmental variables for each community. This network analysis approach provided new insights into microbial community responses to environmental perturbations by inferring bacterial, fungal and diazotrophic associations across a large spatial scale in paddy soils. Highlights: We used network tools to infer possible microbial associations in paddy soils. Associations within bacterial, fungal and diazotrophic communities differed. Fungal associations were the most complex and linked most to ammonia. Soil temperature was strongly linked to bacterial associations. Soil volumetric water content was strongly linked to diazotrophic associations.