Zhu, Yuanli, Hughes, David J., Feng, Yuanyuan, Browning, Thomas J. , Du, Ping, Meng, Qicheng, Wang, Shengqiang, Wang, Bing, Li, Dewang, Jiang, Zhibing and Zeng, Jiangning (2024) Assessing Phytoplankton Primary Productivity Variability in the Changjiang Estuary, East China Sea From Coupled Fast Repetition Rate (FRR) Fluorometry and Chlorophyll‐ a Measurements. Journal of Geophysical Research: Biogeosciences, 129 (1). e2023JG007709. DOI 10.1029/2023JG007709.

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Abstract

Phytoplankton primary productivity (PP) varies significantly over environmental gradients, particularly in physically‐dynamic systems such as estuaries and coastal seas. During summer, runoff peaks in the Changjiang River driving large environmental gradients in both the Changjiang estuary and adjacent East China Sea (ECS), likely driving significant variability in PP. As satellite models of PP often underperform in coastal waters, we aimed to develop a novel approach for assessing net PP variability in such a dynamic environment. Parallel in situ measurements of Fast Repetition Rate (FRR) fluorometry and carbon (C) uptake rates were conducted for the first time in this region during two summer cruises in 2019 and 2021. A series of 13 C‐incubations ( n = 31) were performed, with measured PP ranging from ∼6 to 1,700 mgC m −3 d −1 . Net PP values were significantly correlated with salinity ( r = 0.45), phytoplankton chlorophyll a (Chl‐ a , r = 0.88), Photosystem II (PSII) functional absorption cross‐section ( σ PSII , r = −0.76) and maximum PSII quantum yield ( F v / F m , r = 0.59). Stepwise regression analysis showed that Chl‐ a and σ PSII were the strongest predictors of net PP. A generalized additive model (GAM) was also used to estimate net PP considering nonlinear effects of Chl‐ a and σ PSII . We demonstrate that GAM outperforms linear modeling approaches in estimating net PP in this study, as evidenced by a lower root mean square error (∼140 vs. 250 mgC m −3 d −1 ). Our novel approach provides a valuable tool to examine carbon cycling dynamics in this important region.

Plain Language Summary

The East China Sea has a complex current system that creates a highly dynamic physical environment for phytoplankton, particularly during the summer months. Net primary productivity (PP) is highly variable in this region, yet characterizing these spatial patterns in PP is difficult due to the lack of a high‐resolution data collecting method. Therefore, a strong need exists for a quick and easily implemented method for monitoring PP in this dynamic system. Based on parallel measurements of phytoplankton biomass and photophysiology, we present a novel approach that allows us to rapidly and easily assess regional PP at a high resolution. The high data volume potentially afforded by our net PP estimation method could not only contribute to a better understanding of PP variations in such a dynamic environment, but also help fill the large gaps in field data needed for validating satellite‐based PP models.

Key Points

Parallel in situ measurements of net primary productivity (PP) and Fast Repetition Rate fluorometry were conducted in the Changjiang estuary
Productivity was highest at stations with high Chl and low σ PSII , typically located along the Chiangjiang river plume front
A generalized additive model was developed to estimate net PP, providing an approach for assessing regional C‐cycling dynamics

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