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

Abstract

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.