Spatio-temporal dynamics of phytoplankton functional groups in the South China Sea and their relative contributions to marine primary production

Abstract

The sea-surface distribution of four phytoplankton functional groups in the South China Sea (SCS) namely Coccolithophores (Coc), Diatoms (Dia), Cyanobacteria (Cya) and Chlorophytes (Chlo) was studied across spatial and temporal scales. The time-series datasets derived from the National Aeronautic and Space Administration (NASA) Ocean Biogeochemical Model and retrieved via the Giovanni Portal were analysed statistically with well-known methods including Pearson’s correlation coefficient, ordinary least square regression (OLR), Maximum Entropy Spectral Analysis (MESA) and Principal Components Analysis (PCA). The data were analysed for relative abundance, seasonality, cyclicity, long-term trends, spatial variability, inter-relationships, and relative contributions of individual phytoplankton groups to primary production (PP, using Chl-a as a proxy). The results reveal that the numerically dominant phytoplankton group in terms of relative abundance is Cya, comprising about 57% of the planktonic biomass, followed closely by Coc (about 40%), while Dia constitutes almost 3% of the total abundance. Dia was found to have the strongest annual cycle, and hence displayed the highest rate of seasonal variability. Clear spatial segregation patterns have also been uncovered; Coc and Dia are strongly correlated in the Northern SCS (NSCS) while Cya dominate in Southern SCS (SSCS). Coc and Dia display a strong positive correlation with PP, whereas the seasonal relationships between Cya and Chlo with PP are non-linear, and hence appear statistically non-significant. The rise in the trend of Cya and decrease in Coc, since 2004, may be indicative of changes in successional patterns of phytoplankton functional groups in the SCS. This study, therefore, sets the precedence for more robust research to uncover the immediate and remote causes of the observed spatiotemporal changes in marine phytoplankton dynamics and to develop predictive models of marine primary productivity (MPP) and ecological health.