Abstract
Predicting conversion of photosynthetic electron transport to inorganic carbon uptake rates (the so-called electron requirement for carbon fixation, KC) is central to the broad scale deployment of Fast Repetition Rate fluorometry (FRRf) for primary productivity studies. However, reconciling variability of KC over space and time to produce robust algorithms remains challenging given the large number of factors that influence KC. We have previously shown that light appears a proximal driver of Kc in several ocean regions and therefore examined whether and how light similarly regulated KC variability in the Arctic Ocean during a summer cruise 2016. Sampling transited ice-free and ice-covered waters, with temperature, salinity and Chl-a concentrations all higher for the ice-free than ice covered surface waters. Micro- and pico-phytoplankton generally dominated the ice-free and ice-covered waters respectively. Values of KC, determined from parallel measures of daily integrated electron transport rates and 14C-uptake, were overall lower for the ice-covered versus ice-free stations. As in our previous studies, KC was strongly linearly correlated to daily PAR (r=0.68, n=46, p<0.001) and this relationship could be further improved (r=0.84, n=46, p<0.001) by separating samples into ice-free (micro-phytoplankton dominated) versus ice-covered (Nano- and Pico-phytoplankton dominated water. We subsequently contrasted the PAR-KC relationship form the Arctic waters with the previous relationships from the Ariake Bay and East China Sea, and revealed that these various PAR-KC relationships can be systematically explained across regions by phytoplankton community size structure. Specifically, the value of the linear slope describing PAR-KC decreases as water bodies have an increasing fraction of larger phytoplankton. We propose that this synoptic trend reflects how phytoplankton community structure integrates past and immediate environmental histories and hence may be better broad-scale predictor of KC than specific environmental factors such as temperature and nutrients. We provide a novel algorithm that may enable broad-scale retrieval of CO2 uptake from FRRf with knowledge of light and phytoplankton community size information.
Highlights
Accurate estimation of ocean primary productivity (PP) is central to understanding marine carbon geochemical cycling and the transfer of energy through food webs (Hancke et al, 2015)
Summer has been a recent focus of study within the Arctic Ocean because of the rapid reduction of sea ice that appears to drive a series of dramatic changes in ocean biogeochemistry
Sea ice melting results in warming and freshening of surface waters (Screen and Simmonds, 2010), which subsequently enhances the stratification of the upper Arctic Ocean
Summary
Accurate estimation of ocean primary productivity (PP) is central to understanding marine carbon geochemical cycling and the transfer of energy through food webs (Hancke et al, 2015). Compared to traditional incubation-based PP methods (Regaudie-de-Gioux et al, 2014), in situ bio-optical active chlorophylla fluorescence methods, notably Fast Repetition Rate fluorometry (FRRf, Kolber et al, 1998), afford unprecedented high spatial and temporal resolution needed to understand how the environment continually fine-tunes primary productivity (e.g., Behrenfeld et al, 2006; Suggett et al, 2009b). Chlorophylla fluorescence measurements inherently quantify PP through the activity of photosystem II (PSII) light reactions, resulting in a “photosynthetic currency” of PSII photosynthetic electron transfer rate (ETRPSII, mol e− mol RCII s−1, Suggett et al, 2009a; Hughes et al, 2018b). Developing approaches to model KC, and account for KC variability, are critical for efforts utilizing FRRf-based measurements for highly resolute CO2 uptake rate retrieval
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