Spatial and Temporal Distribution of Bacterioplankton Molecular Ecological Networks in the Yuan River under Different Human Activity Intensity

Abstract

Bacterioplankton communities play a crucial role in freshwater ecosystem functioning, but it is unknown how co-occurrence networks within these communities respond to human activity disturbances. This represents an important knowledge gap because changes in microbial networks could have implications for their functionality and vulnerability to future disturbances. Here, we compare the spatiotemporal and biogeographical patterns of bacterioplankton molecular ecological networks using high-throughput sequencing of Illumina HiSeq and multivariate statistical analyses from a subtropical river during wet and dry seasons. Results demonstrated that the lower reaches (high human activity intensity) network had less of an average degree (10.568/18.363), especially during the dry season, when compared with the upper reaches (low human activity intensity) network (10.685/37.552) during the wet and dry seasons, respectively. The latter formed more complexity networks with more modularity (0.622/0.556) than the lower reaches (high human activity intensity) network (0.505/0.41) during the wet and dry seasons, respectively. Bacterioplankton molecular ecological network under high human activity intensity became significantly less robust, which is mainly caused by altering of the environmental conditions and keystone species. Human activity altered the composition of modules but preserved their ecological roles in the network and environmental factors (dissolved organic carbon, temperature, arsenic, oxidation–reduction potential and Chao1 index) were the best parameters for explaining the variations in bacterioplankton molecular ecological network structure and modules. Proteobacteria, Actinobacteria and Bacteroidetes were the keystone phylum in shaping the structure and niche differentiations in the network. In addition, the lower reaches (high human activity intensity) reduce the bacterioplankton diversity and ecological niche differentiation, which deterministic processes become more important with increased farmland and constructed land area (especially farmland) with only 35% and 40% of the community variation explained by the neutral community model during the wet season and dry season, respectively. Keystone species in high human activity intensity stress habitats yield intense functional potentials and Bacterioplankton communities harbor keystone taxa in different human activity intensity stress habitats, which may exert their influence on microbiome network composition regardless of abundance. Therefore, human activity plays a crucial role in shaping the structure and function of bacterioplankton molecular ecological networks in subtropical rivers and understanding the mechanisms of this process can provide important information about human–water interaction processes, sustainable uses of freshwater as well as watershed management and conservation.

Anthropogenic Intensity-Determined Assembly and Network Stability of Bacterioplankton Communities in the Le’an River

“…When the stable environments in the upper reaches are disturbed by anthropogenic activity, niche-based selection strengthens and bacterioplankton communities become less characterized by stochasticity. Freshwater bacterioplankton can become exposed to lower physiological stress at low human activity intensity levels, and they can then grow and reproduce more freely, which results in the dominance of stochastic processes ( Wu B. et al, 2021 ). Jiao S. et al (2020) found that in low environmental stress ecosystems which experience lower environmental heterogeneity or under less competitive interactions between environmental generalists, stochastic assembly mechanisms can overrule deterministic processes.…”

“…We then construct Bacterioplankton molecular ecological networks using 16S rRNA and molecular ecological network methods ( Deng et al, 2012 ; Jiao S. et al, 2020 ; Wu B. et al, 2021 ). Phyla-level network analysis was performed to identify the relations between microbial taxa using Cytoscape version 3.4.0 combined with the CONET plug-in 3 ( Jiao S. et al, 2020 ; Xia et al, 2020 ; Mishra et al, 2021 ).…”

“…Understanding bacterioplankton community parameters (e.g., structure, composition, and distribution) is critical in accurately determining the mechanisms which drive microbial community assembly. Riverine ecosystems can be severely influenced by human-related activities which can lead to changes in species diversity, community composition, and biotic integrity at different trophic levels ( Vishnivetskaya et al, 2011 ; Ouyang et al, 2020 ; Wu B. et al, 2021 ). In particular, changes in land use can have dramatic effects on riverine microbial ecosystems ( Li et al, 2020 ).…”

Determinants and Assembly Mechanism of Bacterial Community Structure in Ningxia Section of the Yellow River

“…Additionally, there was a tight association among the major genera. Modules are groups of nodes that are well connected with one another, but less connected with nodes belonging to other modules [ 43 , 61 ]; nodes in the same module occupy similar ecological functions and niches [ 62 ]. Higher modularity indicated that the sediment bacterial community had more ecological niches and could maintain community stability through functional and ecological niche concentration, thus minimizing environmental impacts [ 63 ].…”