Periphytic algae is frequently utilized as a health indicator for ecosystems. Many research studies have been conducted in China on the periphytic algae community, but none has compared the periphytic algae community structure at inflow rivers among different lakes in the Yangtze river basin. The periphytic algae were investigated at 94 sites in inflow rivers of Dianchi Lake, Danjiangkou Reservoir, Dongtinghu Lake, Poyanghu Lake, Chaohu Lake, and Taihu Lake. Based on microscopic research, eight phyla and 126 genera of periphytic algae were found in the inflow river of six lakes, with Cyanobacteria and Bacillariophyta dominating. The CCA (Canonical Correspondence Analysis) was used to analyze the association between the periphytic algae community and environmental factors in the inflow river of six lakes, and the LefSe (Linear discriminant analysis effect size) analysis was used to find enriched species in the inflow river of six lakes. We discovered that TN (total nitrogen) and TP (total phosphorus) were the driving environment variables at the basin scale based on the combined results of the CCA and the Mantel Test. The TITAN (Threshold Indicator Taxa Analysis) analysis also revealed the indicator species and their TN and TP concentration thresholds. Finally, we assessed the ecosystem health of the inflow river at six lakes; biotic and abiotic indices yielded conflicting results, but utilizing both indices to assess ecosystem health using the Random Forest algorithm will yield objective and comprehensive results.

中文翻译：

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长江流域六湖入流河流附生藻类环境驱动及水生生态系统评价

附生藻类经常被用作生态系统的健康指标。国内对附生藻类群落进行了多项研究，但尚未对长江流域不同湖泊入流河流的附生藻类群落结构进行比较。对滇池、丹江口水库、洞庭湖、鄱阳湖、巢湖、太湖流入河流的94个点进行了附生藻类调查。经显微镜研究，在6个湖泊的流入河流中发现了8门126属的附生藻类，以蓝藻和芽孢杆菌门为主。采用CCA（Canonical Correspondence Analysis）分析了六湖入流河中附生藻类群落与环境因子的关联，并采用LefSe（线性判别分析效应大小）分析来寻找六个湖泊流入河流中的富集物种。基于CCA和Mantel测试的综合结果，我们发现TN（总氮）和TP（总磷）是流域尺度的驱动环境变量。TITAN（阈值指标分类分析）分析还揭示了指标物种及其 TN 和 TP 浓度阈值。最后，我们评估了六个湖泊流入河流的生态系统健康状况；生物和非生物指数产生了相互矛盾的结果，但利用这两个指数使用随机森林算法评估生态系统健康将产生客观和全面的结果。基于CCA和Mantel测试的综合结果，我们发现TN（总氮）和TP（总磷）是流域尺度的驱动环境变量。TITAN（阈值指标分类分析）分析还揭示了指标物种及其 TN 和 TP 浓度阈值。最后，我们评估了六个湖泊流入河流的生态系统健康状况；生物和非生物指数产生了相互矛盾的结果，但利用这两个指数使用随机森林算法评估生态系统健康将产生客观和全面的结果。基于CCA和Mantel测试的综合结果，我们发现TN（总氮）和TP（总磷）是流域尺度的驱动环境变量。TITAN（阈值指标分类分析）分析还揭示了指标物种及其 TN 和 TP 浓度阈值。最后，我们评估了六个湖泊流入河流的生态系统健康状况；生物和非生物指数产生了相互矛盾的结果，但利用这两个指数使用随机森林算法评估生态系统健康将产生客观和全面的结果。TITAN（阈值指标分类分析）分析还揭示了指标物种及其 TN 和 TP 浓度阈值。最后，我们评估了六个湖泊流入河流的生态系统健康状况；生物和非生物指数产生了相互矛盾的结果，但利用这两个指数使用随机森林算法评估生态系统健康将产生客观和全面的结果。TITAN（阈值指标分类分析）分析还揭示了指标物种及其 TN 和 TP 浓度阈值。最后，我们评估了六个湖泊流入河流的生态系统健康状况；生物和非生物指数产生了相互矛盾的结果，但利用这两个指数使用随机森林算法评估生态系统健康将产生客观和全面的结果。