Source identification of phosphorus in the river-lake interconnected system using microbial community fingerprints.

Phosphorus, one of the primary limiting factors for eutrophication, plays a crucial role in the ecology health of aquatic ecosystems. However, understanding phosphorus bioavailability and source contributions in contaminated lake sediments which could help develop effective eutrophication management plans is limited largely due to the lack of appropriate methods in large catchments with a complex arrangement of sources. Based on the significant relationships between sediment, phosphorus and microbial community, source-specific microbial community fingerprints formed by machine-learning classification SourceTracker might shed light on determining dominant phosphorus sources in the river-lake systems in the era of high-throughput sequencing. This study was conducted in Dongting Lake that suffered accelerated eutrophication due to considerable phosphorus input from the inflow-rivers. The results of phosphorus fractionation according to the Standards, Measurements and Testing harmonized procedure indicated that sediments in the central lake had a higher concentration of non-apatite inorganic phosphorus (Mann-Whitney U test), which deserves greater attention on the risk of phosphorus release. The significant relationships between phosphorus fractionations, sediment and bacterial community were established with the spearman correlation and network analysis. SourceTracker analysis indicated that the major inflow-rivers of phosphorus sources to Dongting Lake were the Songzi, Miluo, and Xinqiang Rivers. The effects of sediment diffusion distance on phosphorus source apportionment were further confirmed. Taken together, our results contribute to an improved understanding of phosphorus fractionations and source contributions in the river-lake systems and its potential impact to eutrophication management plans.

New Insights into Sediment Transport in Interconnected River-Lake Systems Through Tracing Microorganisms.

A growing awareness of the wider environmental significance of diffuse sediment pollution in interconnected river-lake systems has generated the need for reliable provenance information. Owing to their insufficient ability to distinguish between multiple sources, common sediment source apportionment methods would rarely be a practical solution. On the basis of the inseparable relationships between sediment and adsorbed microorganisms, community-based microbial source tracking may be a novel method of identifying dominant sediment sources in the era of high-throughput sequencing. Dongting Lake was selected as a study area as it receives considerable sediment import from its inflowing rivers during the flood season. This study was conducted to characterize the bacterial community composition of sediment samples from the inflow-river estuaries and quantify their sediment microbe contributions to the central lake. Metagenomic analysis revealed that the community compositions of source sediment samples were significantly different, allowing specific sources to be identified with the machine learning classification program SourceTracker. A modified analysis using SourceTracker found that the major contributors to three major lake districts were the Songzi, Zishui, and Xinqiang Rivers. The impacts of hydrodynamic conditions on source apportionment were further verified and suggested the practicability of this method to offer a systematic and comprehensive understanding of sediment sources, pathways, and transport dynamics. Finally, a novel framework for sediment source-tracking was established to develop effective sediment management and control strategies in river-lake systems.