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Project leader: Prof.dr. G.J. Herndl
Financier: NWO Council for Earth and Life Sciences
The North Atlantic Deep Water (NADW) formation in the Greenland-Island-Norwegian (GIN) Sea is one of the major driving forces of the oceanic conveyor belt system, which in turn, substantially influences the global climate. Despite the generally recognized importance of the NADW for the oceanic circulation and the oceanic carbon cycling, there is hardly information available on the biological transformation of the dissolved organic matter (DOM) pool in deep waters. In a collaborative effort using recent advances in analytical techniques, we propose to study the structural changes in the bacterioplankton community and DOM pool in the NADW from its formation in the GIN Sea along 2 transects following its two major branches covering approximately the first 50 years of the NADW in the oceanic conveyor belt system. The recently developed approaches used to link the phylogenetic characterization of the NADW bacterial community with its function in the transfer of DOM are based 1) on the capillary electrophoretic separation of the bacterial community alive to the species to group level and subsequent activity measurements and 2) on MICRO-FISH. The DOM pool will be characterized by its bulk composition (C, N, P) and by compound-specific analyses such as muramic acid, lignin and enantiomeric amino acids. Additionally the oxidation stage of the major macromolecular compounds, i.e. carbohydrates, proteins and lipids, will be determined in the NADW and its overlying water masses. Using this suite of new approaches which have been tested in recent cruises in deep water environments, we will able to determine the biogeographic variability of the richness and evenness of the bacterioplankton community and its relation to the expected alterations in the DOM pool of the NADW. The approach to study the progress of NADW along the conveyor belt is a natural experiment on DOM degradation and bacterioplankton dynamics, which we need to know and mechanistically understand to more accurately model oceanic carbon cycles.