Description: Microzooplankton form an integral component of pelagic food chains in the East Greenland Sea. Using experimental techniques to determine rate processes between microzooplankton, their predators and prey, it was possible to demonstrate a close coupling of the three communities, which characterized pelagic food web dynamics during summer and autumn in the investigation area. High rates of microzooplankton herbivory did not lead to increases in their standing stocks, but served rather as a link of phytoplankton carbon to higher trophic levels. Although they constituted less than 4 % of total biomass in the euphotic zone, their high turnover rates established them as dominant members of the pelagic food web in the investigation area. The species composition of microzooplankton in the East Greenland Sea was dominated by naked ciliates and heterotrophic dinoflagellates, with tintinnids comprising less than 10 % of total number. Although the same genera were represented in both June/July 1989 and August/September 1990, species diversity was significantly higher during the latter investigation. Copepod nauplii, present among the microzooplanton in August/September 1990, were absent in June/July 1989. Rates of herbivory of microzooplankton were measured by the seawater dilution technique of Landry and Hassett (1982), and amounted to between 21 - 39 % and 10 -39 % of chl a standing stocks on a daily basis. A modification of this technique using HPLC analysis of algal pigments in August 1990 showed processes of selective grazing among the algae. High rates of consumption of dinoflagellates and pryrnnesiophytes were seen. Additionally, the rate of consumption of microprotozoans by copepods of the dominant species, Ca/anus hyperboreus, was investigated, and amounted to ea. 18 % of microprotozoan standing stocks per day in the upper 25 m of the water column. In controlled laboratory experiments the degradation of algal pigments by microprotozoan grazers was investigated using HPLC analysis. A rapid and complete degradation of chl a was seen in all experiments, accompanied by the transitional accumulation of intermediate breakdown products. Degradation of algal carotenoids occurred at rates different from those for chl a, altering the Chl:Carotenoid ratio during the experiments. It is postulated that microprotozoans could be a major pathway for the loss of chl a in marine environments.

Description: Earth System Sciences have been generating increasingly larger amounts of heterogeneous data in recent years. We identify the need to combine Earth System Sciences with Data Sciences, and give our perspective on how this could be accomplished within the sub-field of Marine Sciences. Marine data hold abundant information and insights that Data Science techniques can reveal. There is high demand and potential to combine skills and knowledge from Marine and Data Sciences to best take advantage of the vast amount of marine data. This can be accomplished by establishing Marine Data Science as a new research discipline. Marine Data Science is an interface science that applies Data Science tools to extract information, knowledge, and insights from the exponentially increasing body of marine data. Marine Data Scientists need to be trained Data Scientists with a broad basic understanding of Marine Sciences and expertise in knowledge transfer. Marine Data Science doctoral researchers need targeted training for these specific skills, a crucial component of which is co-supervision from both parental sciences. They also might face challenges of scientific recognition and lack of an established academic career path. In this paper, we, Marine and Data Scientists at different stages of their academic career, present perspectives to define Marine Data Science as a distinct discipline. We draw on experiences of a Doctoral Research School, MarDATA, dedicated to training a cohort of early career Marine Data Scientists. We characterize the methods of Marine Data Science as a toolbox including skills from their two parental sciences. All of these aim to analyze and interpret marine data, which build the foundation of Marine Data Science.