Network Analysis Reveals Seasonal Patterns of Bacterial Community Networks in Lake Taihu under Aquaculture Conditions

Abstract

Bacterial communities play essential roles in multiple ecological processes, such as primary production and nutrient recycling in aquatic systems. However, although the composition, diversity and function of bacterial communities have been well studied, little is known about the interactions and co-occurrence characteristics of these communities, let alone their seasonal patterns. To investigate the seasonal variations of bacterial community interactions, we collected water samples from four seasons in Lake Taihu and applied network analysis to reveal bacterial community interactions. Bacterial community networks were non-random in structure, and interactions among bacterial taxa in the networks varied markedly in different seasons. The autumnal bacterial network was the largest and most complex among obtained networks, whereas the spring correlation network was the simplest, having no module hubs or connectors. The important species of the networks were the dominant bacterial phyla/classes (e.g., Alphaproteobacteria and Bacteroidetes), although their relative abundance varied among seasons. The relationships between species and measured environmental variables changed over seasons; fewer environmental factors were correlated with bacterial species in the spring bacterial network, while we observed a greater number of species-environment correlations in the winter network. Our study highlights the seasonal differences in bacterial community interactions and expands our understanding of freshwater microbial ecology in systems affected by aquaculture.

The Seasonal Patterns, Ecological Function and Assembly Processes of Bacterioplankton Communities in the Danjiangkou Reservoir, China

“…The links, average clustering coefficient, and average connectivity for the autumn samples were all higher than those for the spring samples, indicating a more complex network association and higher susceptibility to interference from the external environment. Similarly, an analysis of the bacterioplankton network for the four seasons at Lake Taihu indicated that the network in autumn was the most complex and the network in spring was the simplest ( Lin et al, 2019 ). The reason may be due to the massive reproduction of phytoplankton in autumn.…”

Ocean acidification alters the benthic biofilm communities in intertidal soft sediments

“…For example, for the biotic networks in sands, OA increased the positive correlations but decreased the network modularity. Network modularity describes the strength of division of a network into modules (Lin et al, 2019). For the biotic networks in muds, OA increased the number of edges, average degree and network density.…”

Microbial co-occurrence networks as a biomonitoring tool for aquatic environments: a review

“…Usually, 'bottlenecks' are between nodes and get crossed by many pathways inside the network; they have a high betweenness centrality and high transitivity (Peura et al 2015;Delmas et al 2019). Common marine and freshwater taxa are typically 'hub' and or 'bottleneck' species likely due to their ability to adapt to environmental change (Peura et al 2015;Lin et al 2019). Species that have a large effect on the community are called 'keystone' species, they are less abundant than other organisms in the community, but the other nodes depend on their effect (Power and Scott Mills 1995;Berry and Widder 2014).…”

“…It measures how quickly a community can respond to a stimulus (Pavlopoulos et al 2011). The network constructed with river bacterial communities data changed diameter length according to seasons: spring had the shortest diameter, describing a very efficient and quick to respond community; and autumn had the longest diameter, indicating a slowing down of community collaborations (Lin et al 2019). These changes suggest that seasonality interferes with the structure of the bacterial network community and with the efficiency to collaborate (Lin et al 2019).…”