基于宏条形码技术的白洋淀水华藻类识别及其驱动因子分析

浮游藻类是引起水华暴发的主要原因.为筛选潜在水华藻类，评估白洋淀水华风险区域，于2020年8月对白洋淀373点位展开浮游藻类调查.利用宏条形码技术分析，解析水华藻类群落组成，同时采用显微镜计数法统计藻密度.根据总藻密度对白洋淀不同区域的水华程度进行评估，同时进一步针对水华藻类群落，耦合淀区水质条件，探究白洋淀不同区域水华藻类群落空间差异驱动因子，以甄别影响水华藻类群落结构关键环境因子.结果表明，95%以上采样区域无水华风险(藻类密度<2×10⁶个·L^-1),仅5个样点存在轻微水华风险.但水华藻类群落分析共检测到了90种水华藻类，其中优势水华藻种有20种，隶属于以绿藻门、蓝藻门和裸藻门为主.水华藻类群落结构在不同区域上具有显著空间异质性(P<0.05).关键驱动因子解析结果表明，总磷(TP)、总氮(TN)和氨氮(NH⁺₄-N)是造成水华藻类群落结构差异的关键因子.其中，门水平上，蓝藻门水华藻类与以上关键因子显著正相关；种水平上，硅藻门和绿藻门水华藻类与关键因子响应更显著.因此，水华藻类群落...

Metabarcoding Profiling of Phytoplankton Communities Associated with Algal Blooms and Determining Related Drivers in Baiyangdian Lake

Phytoplankton are the main cause of algal blooms. To identify bloom algae and assess the risks of the algal blooms in Baiyangdian Lake, a survey on 373 sites was conducted in August 2020. The phytoplankton were studied via both morphological-based density counting and metabarcoding profiling. Then, the bloom degree was classed according to algae density, and the relationship between the community of bloom algae and environmental variables were modeled to determine key factors constraining spatial variation in bloom algae communities. The results showed that more than 95% of the sampling sites were free from the risk of algal blooms(phytoplankton density<2×10⁶ cells·L^-1), and only five sites had a slight risk of algal blooms. A total of 90 species with potential of algal blooming were detected, including 20 dominant species, which were mainly affiliated with Chlorophyta, Cyanophyta, and Euglenophyta. Communities of bloom algae significantly varied among different regions(P<0.05). Total phosphorus(TP), total nitrogen(TN), and ammonia nitrogen(NH₄⁺-N) were the key factors significantly affecting the spatial variation in algal bloom communities. At the phylum level, these key factors were significantly positively correlated with Chlorophyta, whereas at the species level, species in Bacillariophyta and Chlorophyta responded significantly to these key factors. Thus, our findings suggested that nutrient levels were significantly related to bloom algae communities, and we proposed that controlling the input of nutrients such as nitrogen and phosphorus and regulating the hydrological process of the lake would be effective management techniques to prevent algal blooms in Baiyangdian Lake.