Fast repetition rate fluorometry (FRRF) derived phytoplankton primary productivity in the Bay of Bengal

Abstract

The approach of fast repetition rate fluorometry (FRRF) requires a conversion factor (φe: C/nPSII) to derive ecologically-relevant carbon uptake rates (PPz,t). However, the required φe:C/nPSII is commonly measured by 14C assimilation and varies greatly across phytoplankton taxonomy and environmental conditions. Consequently, the use of FRRF to estimate gross primary productivity (GPz,t), alone or in combination with other approaches, has been restricted by both inherent conversion and procedural inconsistencies. Within this study, based on a hypothesis that the non-photochemical quenching (NPQNSV) can be used as a proxy for the variability and magnitude of φe:C/nPSII, we thus proposed an independent field model coupling with the NPQNSV-based φe:C/nPSII for FRRF-derived carbon, without the need for additional φe:C/nPSII in the Bay of Bengal (BOB). Therewith, this robust algorithm was verified by the parallel measures of electron transport rates and 14C-uptake PPz,t. NPQNSV is theoretically caused by the effects of excess irradiance pressure, however, it showed a light and depth-independent response on large spatial scales of the BOB. Trends observed for the maximum quantum efficiency (Fv/Fm), the quantum efficiency of energy conversion (Fq'/Fm') and the efficiency of charge separation (Fq'/Fv') were similar and representative, which displayed a relative maximum at the subsurface and were collectively limited by excess irradiance. In particular, most observed values of Fv/Fm in the BOB were only about half of the values expected for nutrient replete phytoplankton. FRRF-based estimates of electron transport at PSII (ETRRCII) varied significantly, from 0.01 to 8.01 mol e- mol RCII-1 s-1, and showed profound responses to depth and irradiance across the BOB, but fitting with the logistic model. N, P, and irradiance are key environmental drivers in explaining the broad-scale variability of photosynthetic parameters. Furthermore, taxonomic shifts and physiological changes may be better predictors of photosynthetic parameters, and facilitate the selection of better adapted species to optimize photosynthetic efficiency under any particular set of ambient light condition.