In our contemporary world, coastal area has gone through unprecedented development. Rapid urbanization and industrialization bring huge disturbance to the coastal environment. Marine microbes constantly interact among each other and with their environment, forming complex networks. These microbial communities play great roles in biogeochemical cycling of matter, they are also crucial to the transformation and metabolism of pollutants in coastal environment under anthropogenic disturbance. Metabolic interactions within the communities (i.e. competition or cooperation) can greatly influence the pollution response pattern of microbial communities. This influence gives rise to different community traits, e.g. life history strategies, which indicated by growth yield-resource acquisition-stress tolerance framework; and functional traits (e.g. functional redundancy, functional distribution). However, metabolic interactions are often overlooked when exploring microbial communities. In particular, we lack an understanding of interaction patterns under anthropogenic disturbance and their influence on the community traits.
While co-occurrence networks are common tools to model large-scale microbial community structure, these approaches are limited as correlations do not represent direct microbial interactions. In this case, genomic-scale metabolic models (GEMs) provide a way to disentangle metabolic interactions in natural environments. Thus, we constructed GEMs to explore the change of interaction patterns and community traits under anthropogenic disturbance, especially different disturbance intensity, using Hangzhou Bay in China as our study area. Intriguingly, disturbance intensity plays a key role in determining the interaction pattern. Moderate disturbance stimulates a cooperative pattern while severe disturbance stimulates a competitive pattern. We also discovered that metabolic interactions drive the change of life history traits. In competitive community microbes are more prone to resource acquisition and in cooperative community microbes are more prone to high yield growth. There is a more dissimilar distribution of functions among cooperative communities, proving the driving force of metabolic interactions in functional composition. Our integrated results deciphered the effects of different disturbance intensities on interaction patterns, which in turn changed the life history strategies and functional traits of microbes, and finally determined the pollution response patterns of communities.
 

coastal environment,anthropogenic disturbance,metabolic interactions,community traits