Multifeature superposition analysis of the effects of microplastics on microbial communities in realistic environments

Abstract

Microplastic (MP) contamination has become an increasingly serious environmental problem. However, the risks of MP contamination in complex global climatic and geographic scenarios remain unclear. We established a multifeature superposition analysis boosting (MFAB) machine learning (ML) approach to address the above knowledge gap. MFAB-ML identified and predicted the importance, interaction networks and superposition effects of multiple features, including 34 characteristic variables (e.g., MP contamination and climatic and geographic variables), from 1354 samples distributed globally. MFAB-ML analysis achieved realistic and significant results, in some cases even opposite to those obtained using a single or a few features, revealing the importance of considering complicated scenarios. We found that the microbial diversity in East Asian seas will continually decrease due to the superposition effects of MPs with ocean warming; for example, the Chao1 index will decrease by 10.32% by 2065. The present work provides a powerful approach to identify and predict the multifeature superposition effects of pollutants on realistic environments in complicated climatic and geographic scenarios, overcoming the bias from general studies.

Keywords: Artificial intelligence; Climate change; Machine learning; Microbial community; Microplastics; Warming.