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A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis
Published on: August 26, 2021
Harmful algal bloom species identified in the East China Sea through metabarcoding analysis of large-scale sampling
Yang Chen1, Shuya Liu2, Nansheng Chen2
1CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; Laboratory for Marine Ecology and Environmental Science, Qingdao Marine Science and Technology Center, Qingdao 266200, China; College of Marine Science, University of Chinese Academy of Sciences, Beijing 100039, China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China; Research Center for Marine Science, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China; Hebei Key Laboratory of Ocean Dynamics, Resources and Environments, Qinhuangdao 066004, China.
Abstract:
East China Sea (ECS) is known for its frequent occurrences of harmful algal blooms (HABs) especially in spring, many of which are toxigenic. In this study, we identified HAB species through metabarcoding analysis of 178 water samples collected from 44 sampling sites at different depths in the continental shelf of ECS in spring of 2019. Through amplicon sequence variant (ASV)-based metabarcoding analysis, we identified 3896 ASVs representing 32 classes in six phytoplankton phyla/divisions, among which Dinoflagellata was the most dominant phylum. Among 244 phytoplankton species annotated in this study, 67 HAB species were identified, of which 29 species having the potential to produce toxin. Many of these identified HAB species displayed unique spatial distribution patterns. Phylogenetic network analysis revealed rich molecular diversity of Prorocentrum, Alexandrium and Pseudo-nitzschia genera. Temperature and NO3- both showed significant correlations with most phytoplankton phyla (p < 0.01), while PO43- exhibited significant correlations with the dominant HAB species Chaetoceros tenuissimus (p < 0.05) and Scrippsiella trochoidea (p < 0.01). Clearly, these environmental factors play a key role in influencing the distribution of phytoplankton species in ECS. This study represents the first large-scale attempt in identifying HAB species in ECS in spring through ASV-based metabarcoding analysis. Results from this study will facilitate further comparative research on the molecular diversity and biogeography of HAB species, and monitoring HAB events in ECS.
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