Gross community production and metabolic balance in the South Pacific Gyre, using a non intrusive bio-optical method

Abstract

Abstract. The very clear waters of the South Pacific Gyre likely constitute an end-member of oligotrophic conditions which remain essentially unknown with respect to its impact on carbon fixation and exportation. We describe a non-intrusive bio-optical method to quantify the various terms of a production budget (Gross community production, community losses, net community production) in this area. This method is based on the analysis of the diel cycle in Particulate Organic Carbon (POC), derived from high frequency measurements of the particle attenuation coefficient cp. We report very high integrated rates of Gross Community Production within the euphotic layer (average of 846±484 mg C m−2 d−1 for 17 stations) that are far above any rates determined using incubation techniques for such areas. Furthermore we show that the daily production of POC is essentially balanced by the losses so that the system cannot be considered as net heterotrophic. Our results thus agree well with geochemical methods, but not with incubation studies based on oxygen methods. We stress to the important role of deep layers, below the euphotic layer, in contributing to carbon fixation when incident irradiance at the ocean surface is high (absence of cloud coverage). These deep layers, not considered up to know, might fuel part of the heterotrophic processes in the upper layer, including through dissolved organic carbon. We further demonstrate that, in these extremely clear and stratified waters, integrated gross community production is proportional to the POC content and surface irradiance via an efficiency index ψ GCP*, the water column cross section for Gross Community Production. We finally discuss our results in the context of the role of oligotrophic gyre in the global carbon budget and of the possibility of using optical proxies from space for the development of growth community rather than primary production global models.