Decoding environmental regimes and spring phytoplankton bloom occurrence in the central Yellow Sea

Area of Science:

• Marine Ecology
• Oceanography
• Climate Science

Background:

• Phytoplankton blooms are crucial indicators of climate-driven ocean changes.
• Shifts in bloom timing and magnitude significantly impact marine ecosystems.
• Environmental factors governing phytoplankton bloom development are not well understood.

Purpose of the Study:

• To analyze long-term environmental data from the Central Yellow Sea (CYS) to identify drivers of the spring phytoplankton bloom (SPB).
• To define critical thresholds for SPB developmental stages using machine learning.
• To classify SPB types based on phenology and environmental characteristics.

Main Methods:

• Analysis of long-term environmental data (2003-2023) from the CYS.
• Application of a machine-learning decision tree (DT) model.
• Identification of quantitative critical thresholds for chlorophyll-a (Chl-a) changes.

Main Results:

• The SPB initial stage was linked to low light in early April.
• The peak stage, with rapid Chl-a increase, was driven by high light intensity in mid-April.
• Bloom decline and termination stages correlated with increasing sea surface temperature (SST), with termination occurring above 17.27 °C.

Conclusions:

• SPB peak timing is determined by the interplay of SST, mixing, and light.
• Atmospheric inputs modulate the magnitude of phytoplankton blooms.
• A phenology-threshold approach offers a framework for interpreting bloom dynamics and environmental conditions.