Bacteria versus fungi for predicting anthropogenic pollution in subtropical coastal sediments: Assembly process and environmental response. (January 2022) - Zhao, Zelong; Li, Hongjun; Sun, Yi; Zhan, Aibin; Lan, Wenlu; Woo, Sau Pinn; Shau-Hwai, Aileen Tan; Fan, Jingfeng
Graphical abstract: Highlights: Fungi showed a stronger distance-decay of similarities than bacteria. Stochastic processes were dominant to the bacteria and fungi community assembly. Bacterial assembly was more governed by environmental selection than fungi. Heavy metal pollution could be predicted using the benthic bacterial community data. Abstract: Coastal regions support the most threatened ecosystems on Earth, and anthropogenic activities have been significantly affecting both habitat structure and ecological quality. Understanding the dynamics of ecological response to multiple stresses is a precondition for management and restoration of largely disturbed coastal ecosystems. Among diverse taxa in coastal regions, benthic organisms are widely recognized as promising targets for assessing ecological causes and consequences of anthropogenic activity-derived stressors, such as environmental pollution. However, spatial and local environmental factors play important but different roles in shaping community structure of different benthic taxa, mainly owing to their distinct body size, mobility, and metabolic capacity. Here, we applied metabarcoding, coupled with physicochemical analyses, to determine the benthic microbial community composition in a typical subtropical coast area, Beibu Gulf in Southern China. Stochastic processes were found as the dominant ecological driver in shaping the community assembly of both bacteria and fungi. Moreover, environmental factors explained a considerable portion of variation in bacterial communities, while spatial factors were more influential in structuring larger body size and weak mobility fungal communities. Mantel tests and network analysis revealed significant relationships between several environmental variables and bacterial communities. More importantly, the concentrations of heavy metals, particularly Cr and Zn, could be predicted using the constructed random forest model based on bacterial communities. The results obtained here provide new insights into causes and consequences of various factors for influencing healthy coasts, thus further clearing the road to the integration of biological information into routine ecological monitoring of coastal ecosystems.