Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, PR China.
Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, PR China.
Environ Res. 2020 Jul;186:109498. doi: 10.1016/j.envres.2020.109498. Epub 2020 Apr 9.
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.
磷是富营养化的主要限制因素之一，在水生生态系统的生态健康中起着至关重要的作用。然而，由于缺乏适当的方法，对于受污染的湖泊沉积物中的磷生物有效性和来源贡献的理解在很大程度上受到限制，这些沉积物中存在大量的污染源，且其排列复杂。基于沉积物、磷和微生物群落之间的显著关系，通过机器学习分类 SourceTracker 形成的特定于来源的微生物群落指纹图谱可能有助于确定河流-湖泊系统中主要的磷源，这在高通量测序时代尤为重要。本研究在洞庭湖进行，由于流入河流的磷输入量增加，洞庭湖加速富营养化。根据标准化、测量和测试协调程序进行的磷分级结果表明，湖心沉积物的非磷灰石无机磷浓度较高（曼-惠特尼 U 检验），这需要更加关注磷释放的风险。磷分级、沉积物和细菌群落之间的显著关系通过 Spearman 相关性和网络分析建立。SourceTracker 分析表明，对洞庭湖磷源的主要入流河流是松滋河、汨罗河和新墙河。沉积物扩散距离对磷源分配的影响也得到了进一步证实。总之，我们的研究结果有助于更好地理解河流-湖泊系统中的磷分级和来源贡献及其对富营养化管理计划的潜在影响。
