Harmful algal blooms, including those caused by the toxic diatom Pseudo-nitzschia, can have significant impacts on human health, ecosystem functioning and ultimately food security. In the current study we characterized a bloom of species of Pseudo-nitzschia that occurred in a south-eastern Australian oyster-growing estuary in 2019. Using light microscopy, combined with molecular (ITS/5.8S and LSU D1-D3 rDNA regions) and toxicological evidence, we observed the bloom to consist of multiple species of Pseudo-nitzschia including P. cf. cuspidata, P. hasleana, P. fraudulenta and P. multiseries, with P. cf. cuspidata being the only species that produced domoic acid (3.1 pg DA per cell). As several species of Pseudo-nitzschia co-occurred, only one of which produced DA, we developed a rapid, sensitive and efficient quantitative real-time polymerase chain reaction (qPCR) assay to detect only species belonging to the P. pseudodelicatissima complex Clade I, to which P. cf. cuspidata belongs, and this indicated that P. cuspidata or closely related strains may have dominated the Pseudo-nitzschia community at this time. Finally, using high resolution water temperature and salinity sensor data, we modeled the relationship between light microscopy determined abundance of P. delicatissima group and environmental variables (temperature, salinity, rainfall) at two sites within the estuary. A total of eight General Linear Models (GLMs) explaining between 9 and 54% of the deviance suggested that the temperature (increasing) and/or salinity (decreasing) data were generally more predictive of high cell concentrations than the rainfall data at both sites, and that overall, cell concentrations were more predictive at the more oceanic site than the more upstream site, using this method. We conclude that the combination of rapid molecular methods such as qPCR and real-time sensor data modeling, can provide a more rapid and effective early warning of harmful algal blooms of species of Pseudo-nitzschia, resulting in more beneficial regulatory and management outcomes.

有害藻华，包括由有毒硅藻Pseudo-nitzschia引起的藻华，会对人类健康、生态系统功能以及最终的粮食安全产生重大影响。在目前的研究中，我们描述了2019 年发生在澳大利亚东南部牡蛎生长河口的假尼茨夏属物种的大量繁殖。使用光学显微镜，结合分子（ITS/5.8S 和 LSU D1-D3 rDNA 区域）和毒理学证据，我们观察到的绽放包括多个物种的拟菱形藻，包括P.比照 cuspidata、P. hasleana、P. fakeulenta和P. multiseries，与P. cf. 尖刺是唯一产生软骨藻酸的物种（每个细胞 3.1 pg DA）。由于几种Pseudo-nitzschia 共生，其中只有一种产生 DA，我们开发了一种快速、灵敏和有效的定量实时聚合酶链反应 (qPCR) 测定法，以仅检测属于P.pseudodelicatissima复合体进化枝 I 的物种，其中P. cf. cuspidata属于，这表明P. cuspidata或密切相关的菌株此时可能主导了Pseudo-nitzschia群落。最后，使用高分辨率水温和盐度传感器数据，我们模拟了光学显微镜确定的P. delicatissima丰度之间的关系河口内两个地点的组和环境变量（温度、盐度、降雨量）。总共 8 个通用线性模型 (GLM) 解释了 9% 到 54% 的偏差，表明温度（增加）和/或盐度（减少）数据通常比两个站点的降雨数据更能预测高细胞浓度，总体而言，使用这种方法，与更上游的站点相比，更多海洋站点的细胞浓度更具预测性。我们得出的结论是，将 qPCR 等快速分子方法与实时传感器数据建模相结合，可以对Pseudo-nitzschia物种的有害藻华提供更快速有效的早期预警，从而产生更有益的监管和管理结果。