Phototrophs as the central components of the plastisphere microbiome in coastal environments

Abstract

Upon entering the marine environment, plastics are colonized by a plethora of microorganisms to form a plastisphere, influencing the fate and transport of the plastic debris and the health of marine ecosystems. The assembly of marine plastisphere is generally believed to be dominated by stochastic processes. However, it remains elusive whether microbial interaction in the assembly of plastisphere microbial communities is conserved or not. We analyzed the plastisphere microbiomes of 137 plastic debris samples from intertidal zones at different geographical locations and habitats (seagrass, coral, mangrove, beach, and open ocean) and compared them with the surrounding sediment and seawater microbiomes. Microbial community structures of the plastisphere from different locations were more similar to each other but differed substantially from the surrounding sediment and water microbiomes, implying a common mechanism of plastisphere assembly. We used different machine learning algorithms (Multinomial Logistic Regression, Support Vector Machine, Decision Trees, Random Forest, and Artificial Neural Networks) to classify plastic debris samples with high sensitivity based on the microbiome composition. Eukaryotic and prokaryotic phototrophic organisms such as green algae, diatoms, and cyanobacteria, were found to be enriched on the plastic surfaces. Network analysis revealed the central role of the phototrophic organisms in the formation and sustenance of the plastispheres. We found that phototrophs served as core members interacting strongly with heterotrophic organisms in marine plastisphere, irrespective of the sampling location, habitats, and polymer types. This would explain the stochastic assembly of the plastisphere along with conserved properties driven by the phototrophs in the surrounding environment. Our results highlight the importance of phototrophic organisms in shaping the marine plastisphere microbial communities.

Keywords

• Plastisphere
• Microbiome
• Network analysis
• Cyanobacteria
• Machine learning
• Eukaryotes