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Updated: May 12, 2026

Characterization of Aquatic Biofilms with Flow Cytometry
Published on: June 6, 2018
Pollutant biodegradation profile mediated by multi-trophic microbial dynamics in rivers
Joeselle M Serrana1,2, Run Tian2,3, Francisco J A Nascimento1,4,5
1Stockholm University Center for Circular and Sustainable Systems (SUCCeSS), Stockholm University, Stockholm 106 91, Sweden.
Abstract:
Microbial communities and environmental conditions are closely linked to ecosystem functions and directly govern the biodegradation of pollutants in aquatic environments. However, the role of multi-trophic interactions and their spatiotemporal dynamics in these processes remains poorly understood. Here, we examined how seasonal and spatial variations, mediated by trophic interactions within benthic microbial communities, influence their composition, functional capacity, and collective potential to degrade a diverse array of organic pollutants in rivers. By characterizing both prokaryotic (i.e. archaea and bacteria) and eukaryotic taxa (i.e. algae, fungi, protists, and metazoans), and inferring metabolic pathways, we explored the connections between community composition and pollutant degradation in wastewater-receiving rivers across four seasons. Mediation analysis revealed that variation in multi-trophic community structure statistically mediates the total effect of environmental factors on the biodegradation profiles of 96 organic pollutants, with prokaryotic communities explaining 60% of the total environmental influence. Eukaryotic groups also showed significant indirect mediation effects, with fungal, protistan, algal, and metazoan communities accounting for 56%, 53%, 26%, and 38% of the mediated effect, respectively. Across the two rivers studied, spatial variation explained more of the variance in community composition than seasonality did over the sampled year. Together, these results provide ecosystem-level insights into how multi-trophic microbial community organization is associated with pollutant biodegradation potential in dynamic river environments and support the development of predictive frameworks for sustainable water management.
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