Phycocyanin-rich Synechococcus dominates the blooms in a tropical estuary lake

Mao, Feijian; Li, Wenxuan; Sim, Zhi Yang; He, Yiliang; Chen, Qiuwen; Gin, Karina Yew-Hoong

doi:10.1016/j.jenvman.2022.114889

Cited by 9 publications

(1 citation statement)

References 48 publications

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“…However, it is a relatively expensive technique due to it being time‐consuming and requiring specialist taxonomic expertise, resulting in a lack of long‐term, high temporal resolution phytoplankton datasets that are needed to identify physicochemical controls on phytoplankton blooms (Dubelaar et al 2004; Rolland et al 2009). In recent years, flow cytometry techniques have been developed that are able to enumerate and phenotypically characterize the phytoplankton community at high throughput and low cost, allowing river phytoplankton concentrations to be monitored at high temporal resolutions (Dubelaar et al 2004; Moorhouse et al 2018; Mao et al 2022).…”

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confidence: 99%

Predicting river phytoplankton blooms and community succession using ecological niche modeling

Bowes,

Hutchins,

Nicholls

et al. 2024

Limnology & Oceanography

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Excessive phytoplankton concentrations in rivers can result in the loss of plant and invertebrate communities, and threaten drinking water supplies. Whilst the physicochemical controls on algal blooms have been identified previously, how these factors combine to control the initiation, size, and cessation of blooms in rivers is not well understood. We applied flow cytometry to quantify diatom, chlorophyte, and cyanobacterial group abundances in the River Thames (UK) at weekly intervals from 2011 to 2022, alongside physicochemical data. A niche modeling approach was used to identify thresholds in water temperature, flow, solar radiation, and soluble reactive phosphorus (SRP) concentrations required to produce periods of phytoplankton growth, with blooms only occurring when all thresholds were met. The thresholds derived from the 2011 to 2018 dataset were applied to a test data set (2019–2022), which predicted the timing and duration of blooms at accuracies of > 80%. Diatoms and nano‐chlorophyte blooms were initiated by flow and water temperature, and usually terminated due to temperature and flow going out of the threshold range, or SRP and Si becoming limiting. Cyanobacterial bloom dynamics were primarily controlled by water temperature and solar radiation. This simple methodology provides a key understanding of phytoplankton community succession and inter‐annual variation and can be applied to any river with similar water quality and phytoplankton data. It provides early warnings of algal and cyanobacterial bloom timings, which support future catchment management decisions to safeguard water resources, and provides a basis for modeling changing phytoplankton bloom risk due to future climate change.

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confidence: 99%