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Dryad

AQUACOSM VIMS-Ehux – Core data

Data files

Apr 15, 2020 version files 111.94 MB
Jul 30, 2020 version files 111.95 MB
Mar 03, 2021 version files 111.95 MB
Feb 28, 2024 version files 111.95 MB

Abstract

The cosmopolitan coccolithophore Emiliania huxleyi is a unicellular alga that forms massive oceanic blooms covering thousands of square kilometers (Tyrrell & Merico 2004). The intricate calcite exoskeleton of E. huxleyi accounts for ~1/3 of total marine CaCO3 production (Monteiro et al. 2016). E. huxleyi blooms are an important source of DMS, which is, by far, the most abundant volatile sulfur compound in the surface ocean and the best studied aerosol precursor (Simó 2001) with a significant climate-regulating role that enhances cloud formation (Alcolombri et al. 2015; Simó 2001). Biotic interactions that regulate the fate of these blooms play a profound role in determining carbon and nutrient cycling in the ocean and feedback to the atmosphere. Annual E. huxleyi spring blooms are frequently terminated following infection by a specific large dsDNA virus (EhV) that belongs to the Coccolithovirus group (Schroeder et al. 2002). Despite the huge ecological importance of host-virus interactions, the ability to assess their ecological impact is limited to questions that focus mainly on quantification of viral abundance and diversity in a reductionist manner.

The project in which this dataset was collected is a holistic approach to untangle the complexity in alga-virus-bacterium interactions during an E. huxleyi bloom, their effect on the metabolome of the phycosphere, and their possible implications to C and S cycles. The project took place for 24 days, including daily sampling for various biological and physiochemical parameters. Flow cytometry was used to monitor different populations of phytoplankton, bacteria and virus-like particles (VLP). Additionally, physiochemical properties of the water such as salinity, temperature and nutrient concentrations were acquired, as well as viral abundances estimated by qPCR. These data compose the contextual data for various scientific papers.