Diel Patterns of Variable Fluorescence and Carbon Fixation of Picocyanobacteria Prochlorococcus-Dominated Phytoplankton in the South China Sea Basin

Abstract

The various photosynthetic apparatus and light utilization strategies of phytoplankton are among the critical factors that regulate the distribution of phytoplankton and primary productivity in the ocean. Active chlorophyll fluorescence has been a powerful technique for assessing the nutritional status of phytoplankton by studying the dynamics of photosynthesis. Further studies of the energetic stoichiometry between light absorption and carbon fixation have enhanced understanding of the ways phytoplankton adapt to their niches. To explore the ecophysiology of a Prochlorococcus-dominated phytoplankton assemblage, we conducted studies of the diel patterns of variable fluorescence and carbon fixation by phytoplankton in the oligotrophic South China Sea (SCS) basin in June 2017. We found that phytoplankton photosynthetic performance at stations SEATS and SS1 were characterized by a nocturnal decrease, dawn maximum, and midday decrease of the maximum quantum yield of PSII (Fvfalse(′false)/Fmfalse(′false), which has been denoted as both Fv/Fm and Fv′/Fm′) in the nutrient-depleted surface layer. That these diel patterns of Fvfalse(′false)/Fmfalse(′false) were similar to those in the tropical Pacific Ocean suggests macro-nutrient and potentially micro-nutrient stress. However, the fact that variations were larger in the central basin than at the basin's edge implied variability in the degree of nutrient limitation in the basin. The estimated molar ratio of gross O2 production to net production of carbon (GOP:NPC) of 4.9:1 was similar to ratios reported across the world's oceans. The narrow range of the GOP:NPC ratios is consistent with the assumption that there is a common strategy for photosynthetic energy allocation by phytoplankton. That photo-inactivated photosystems or nonphotochemical quenching rather than GOP accounted for most of the radiation absorbed by phytoplankton explains why the maximum quantum yield of carbon fixation was rather low in the oligotrophic SCS.

Primary Productivity Dynamics in the Summer Arctic Ocean Confirms Broad Regulation of the Electron Requirement for Carbon Fixation by Light-Phytoplankton Community Interaction

“…We similarly recently reported that variance of K C , derived from parallel measures of FRRf-derived ETRs and C-uptake rates, could be generally explained by patterns in light availability (Zhu et al, 2016), but importantly was much improved when considering differences in phytoplankton size structures (Zhu et al, 2017). In fact, changes in predominant species within phytoplankton communities appears to be a factor increasingly important in explaining patterns of K C (e.g., Kulk et al, 2018;Xie et al, 2018), likely reflecting selection of taxa by environmental factors that are not specifically measured as part of the assessment exercise of interest (see Hughes et al, 2018a). Patterns of K C variability over space and time therefore remain problematic to fully resolve.…”

Physiological and Ecological Responses of Photosynthetic Processes to Oceanic Properties and Phytoplankton Communities in the Oligotrophic Western Pacific Ocean

“…The PSII operating efficiency (F q ′/F v ′) quantified the fraction of functional RCII and accounted for the extent of photochemical quenching/(energy conversion) by PSII (i.e., the efficiency of charge separation in RCII) ( Suggett et al, 2003 ; Melrose et al, 2006 ). NPQ at given light level was derived from normalized Stern–Volmer quenching coefficient, defined as NPQ NSV [NPQ NSV = F o ′/F v ′, where F o ′ represented the minimum F yield in the presence of NPQ NSV , was estimated as F o ′ = F o /(F v /F m + F o /F m ′)] ( Müller et al, 2001 ; Moore et al, 2003 ; Xie et al, 2018 ; Wei et al, 2019b ).…”

“…Our FRRF measurement protocol allowed for reliable estimation of σ PSII ′ in the existence of NPQ NSV . The instantaneous RCII normalized ETR RCII (mol e – mol RCII –1 s –1 ) for each light level was calculated as the product of PAR (E, μmol quanta m –2 s –1 ), σ PSII ′ at E, F q ′/F v ′ and the constant value (6.022 × 10 –3 ) for converting μmol quanta to quanta and Å 2 (10 –20 m 2 ) to m 2 according to biophysical sigma-based algorithm ( Suggett et al, 2003 ; Schuback et al, 2015 ; Xie et al, 2018 ):…”

“…Thereafter, the ecologically relevant rates of carbon fixation can be converted by FRRF-derived ETR RCII through a conversion factor, i.e., the effective electron requirement for carbon fixation ( Melrose et al, 2006 ; Zhu et al, 2016 ; Schuback et al, 2017 ; Morelle and Claquin, 2018 ). Additionally, the applicability of FRRF-based measurements to estimate marine primary production, alone or in combination with other techniques, are potentially limited since the light absorption characteristics and electron transport vary significantly in response to environmental constraints or combinations thereof and changes in species taxonomy and physiology ( Lawrenz et al, 2013 ; Jin et al, 2016 ; Schuback et al, 2017 ; Xie et al, 2018 ; Wei et al, 2019b ; Zhu et al, 2019 ). As such, more recent studies have sought to better characterize the extent and nature of variation between these photosynthetic processes and environmental/biological variables ( Moore et al, 2003 ; Suggett et al, 2009 ; Schuback et al, 2017 , etc.).…”

Contrasting responses of intertidal microphytobenthos and phytoplankton biomass and taxonomic composition to the nutrient loads in the Jiulong River Estuary

“…() proposed another approach to obtain a reliable CHEMTAX result, and in the present study, we followed a modified procedure (Xie et al . ) as follows. With the average (Avg ), maximum ( Max ), and minimum ( Min ) seed ratios in Table , we randomly generated 60 seed ratio matrices with the equation SR = Avg + D × R , where R is the random number between −1 and 1, and D is Max − Avg or Avg − Min depending on whether R is positive or negative.…”