Water temperature data from southern elephant seal from Marion Island with links to datasets

As the Antarctic Circumpolar Current crosses the South-West Indian Ocean Ridge, it creates an extensive eddy field characterised by high sea level anomaly variability. We investigated the diving behaviour of female southern elephant seals from Marion Island during their post-moult migrations in relation to this eddy field in order to determine its role in the animals' at-sea dispersal. Most seals dived within the region significantly more often than predicted by chance, and these dives were generally shallower and shorter than dives outside the eddy field. Mixed effects models estimated reductions of 44.33 ± 3.00 m (maximum depth) and 6.37 ± 0.10 min (dive duration) as a result of diving within the region, along with low between-seal variability (maximum depth: 5.5 % and dive duration: 8.4 %). U-shaped dives increased in frequency inside the eddy field, whereas W-shaped dives with multiple vertical movements decreased. Results suggest that Marion Island's adult female elephant seals' dives are characterised by lowered cost-of-transport when they encounter the eddy field during the start and end of their post-moult migrations. This might result from changes in buoyancy associated with varying body condition upon leaving and returning to the island. Our results do not suggest that the eddy field is a vital foraging ground for Marion Island's southern elephant seals. However, because seals preferentially travel through this area and likely forage opportunistically while minimising transport costs, we hypothesise that climate-mediated changes in the nature or position of this region may alter the seals' at-sea dispersal patterns.

At surface behaviour at location on spot of southern elephant seals from Marion Island in 2004 with links to datasets

A novel classification system was applied to the sea level anomaly (SLA) environment around Marion Island. We classified the SLA seascape into habitat types and calculated percentage of habitat use of ten juvenile southern elephant seals (SES). Movements were compared to SLA and SLA slope values indicative of ocean eddy features. This classification provides a measure of habitat change due to seasonal fluctuations in SLA. Some of the seals made two migrations in different seasons, each of similar duration and proportions of potential foraging behaviour. The seals in this study did not use any intense eddy features, but their behaviours varied with SLA class. Potential foraging behaviour was positively influenced by negative SLA values (i.e. areas of below average sea surface height). Searching behaviour during the winter was more likely at eddy edges where high SLA slope values correlated with low SLA values. Though the seals did not forage within newly spawned eddies, they did forage near the sub-Antarctic front. Plankton and other biological resources transported by eddies formed at the subtropical convergence zone are evidently concentrated in this region and enhance the food chain there, forming a foraging ground for juvenile SES from Marion Island.

30 years of synoptic observations from Neumayer Station with links to datasets

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.