(Table 1) Annual gross and new primary production rates obtained for areas centered on the AWI-HAUSGARTEN mooring (2000-2005)

The lack of extended dataset has so far prevented an inclusive understanding of the long-term relationships between primary production (PP) and vertical export in the Arctic Ocean. It is urgent to investigate these connections as Arctic ecosystems are on the verge of climate-related shifts, which could be caused by the combined effects of increase in Pacific and Atlantic inflow, climate warming, and sea ice decline. For a period of 6 years we investigated the degree of coupling between PP and export by making use of modelled PP rates and vertical particle fluxes collected with sediment traps moored at ~300 m depth in the eastern Fram Strait. Our analyses indicate that total and new simulated PP averaged for different areas centered on the mooring location (5-200 km radius) explain at best 20-44% of the observed biogenic particle fluxes at 300 m, when applying extended time-lags (55-90 days) between PP and vertical fluxes. Based on this phasing, we define a conceptual framework that presents the temporal dimension as a prime determinant of the maximum strength of the PP-export coupling at a given depth. Our results support that planktonic food webs in the Fram Strait process heavily biogenic material in the epipelagic zone, but we further suggest that Atlantic-Arctic water interactions induce a particular ecological setting responsible for the extended turn-over. In conclusion, we hypothesize that global warming could promote a transition toward a more retentive ecosystem in the Fram Strait region despite the likely increase of pelagic PP in the Arctic Ocean.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.