Key Laboratory of Applied Marine Biotechnology, School of Marine Science, Ministry of education, Ningbo University, Ningbo 315832, China; Ningbo institute of Oceanography, Ningbo 315832, China.
Key Laboratory of Applied Marine Biotechnology, School of Marine Science, Ministry of education, Ningbo University, Ningbo 315832, China.
Sci Total Environ. 2020 Mar 20;709:135951. doi: 10.1016/j.scitotenv.2019.135951. Epub 2019 Dec 5.
Shifts in bacterioplankton communities during algal blooms have been widely investigated, but our understanding of their succession over the continuous course of paralytic shellfish poisoning producing Gymnodinium catenatum blooms and diatom (Skeletonema costatum and Chaetoceros curvisetus) blooms in natural bays is highly understudied. Here, we used high-throughput sequencing of bacterioplankton 16S rRNA genes to investigate the composition and successional patterns of bacterioplankton communities during Gymnodinium-diatom bloom cycles. Changes in community compositional patterns were then evaluated in context of environmental and phytoplankton community variation. Bacterioplankton α-diversity significantly decreased during the emergence of the algal blooms, with Flavobacteriaceae, Rhodobacteraceae, Cryomorphaceae, and Saprospiraceae as the dominant bacterial families in waters during the blooms. Bacterioplankton community compositions could be separated into three successive stages according to bloom dynamics, wherein the succession of bacterioplankton communities was correlated with changes in algal species. Environmental variables, and particularly pH, salinity, and nutrient concentrations (e.g., of nitrite, nitrate, and ammonium) were strongly associated with variation in bacterioplankton community structures. Variance partitioning analysis indicated that phytoplankton effects alone could explain more variance than only environmental effects. Moreover, LEfSe analysis was used to identify special bacterioplankton genera as "biomarkers" for bloom stages, such as Tepidisphaera and Pseudarcicella, whose abundances were significantly associated with different stages of the phytoplankton blooms. The phylotype "biomarkers" that were identified hold significant potential as indicators for phytoplankton bloom successional dynamics. Overall, these results may contribute to the understanding of the ecological processes shaping microbial communities during successive Gymnodinium-diatom blooms.
在藻类大量繁殖期间，浮游细菌群落的变化已被广泛研究，但我们对其在连续产生麻痹性贝类毒素的 Gymnodinium catenatum 藻华和硅藻（Skeletonema costatum 和 Chaetoceros curvisetus）藻华期间的演替过程的理解还远远不够。在这里，我们使用高通量测序技术对浮游细菌 16S rRNA 基因进行测序，以研究 Gymnodinium-硅藻藻华周期中浮游细菌群落的组成和演替模式。然后根据环境和浮游植物群落变化评估群落组成模式的变化。在藻类大量繁殖期间，浮游细菌的 α 多样性显著降低，在藻华期间水中的优势细菌科为黄杆菌科、红杆菌科、Cryomorphaceae 和 Saprospiraceae。根据藻华动态，浮游细菌群落组成可分为三个连续阶段，其中浮游细菌群落的演替与藻类物种的变化相关。环境变量，特别是 pH 值、盐度和营养浓度（例如亚硝酸盐、硝酸盐和铵盐）与浮游细菌群落结构的变化密切相关。方差分解分析表明，浮游植物的影响单独可以解释比仅环境影响更多的方差。此外，使用 LEfSe 分析确定了特殊的浮游细菌属作为藻华阶段的“生物标志物”，例如 Tepidisphaera 和 Pseudarcicella，其丰度与浮游植物藻华的不同阶段显著相关。鉴定出的“生物标志物”在指示浮游植物藻华演替动态方面具有重要潜力。总体而言，这些结果可能有助于理解在连续的 Gymnodinium-硅藻藻华期间塑造微生物群落的生态过程。
