New insights into identifying sediment phosphorus sources in river-lake coupled system: A framework for optimizing microbial community fingerprints

Abstract

Identifying sediment phosphorus sources in river-lake coupled system is a question in developing preferential control strategies for phosphorus. As sediments adsorbed phosphorus and microbes would be transported with changing hydrodynamic, the phosphorus source-specific microbial community fingerprints shed light on determining the major sediment phosphorus sources. However, the identification of microbial community fingerprints is a challenge because both microbial succession and hydrological characteristics of river-lake systems would affect the stability of fingerprints. Therefore, this study provided a framework for optimizing phosphorus source-specific microbial community fingerprints, and attempted to identify the major sources of sediment phosphorus in river-lake coupled ecosystem. Meiliang Lake is one of the highly eutrophic area in Taihu Lake, where the sediments, bacterial communities, and phosphorus had a close relationship. Through analyzing the connectivity of microbes along water continuum, a microbial fingerprints candidate database was constructed. The phosphorus-related bacterial communities were screened and optimized by comparing the difference of predicted results between upstream and downstream, forming the stable microbial community fingerprints which consisted of Bacteroidia, Bacilli, Clostridia, and other species at the class level. SourceTracker results that based on the optimized phosphorus source-specific microbial community fingerprints indicated that the major sediment phosphorus sources to Meiliang Lake were Liangxi River, Wujingang River, and Donghuandi River, with the relative standard deviations ranging from 2.59% to 27.56%. The accuracy of phosphorus source apportionments was further confirmed based on the composite pollution index and hydrodynamic condition. This study put forward suggestions on how to improve the stability of microbial community fingerprints, and would help to improve the understanding of applying microbial source tracking method to identify the sources of abiotic pollution like sediment phosphorus.