Calcareous green alga Halimeda tolerates ocean acidification conditions at tropical carbon dioxide seeps

We investigated ecological, physiological, and skeletal characteristics of the calcifying green alga Halimeda grown at CO2 seeps (pHtotal ? 7.8) and compared them to those at control reefs with ambient CO2 conditions (pHtotal ? 8.1). Six species of Halimeda were recorded at both the high CO2 and control sites. For the two most abundant species Halimeda digitata and Halimeda opuntia we determined in situ light and dark oxygen fluxes and calcification rates, carbon contents and stable isotope signatures. In both species, rates of calcification in the light increased at the high CO2 site compared to controls (131% and 41%, respectively). In the dark, calcification was not affected by elevated CO2 in H. digitata, whereas it was reduced by 167% in H. opuntia, suggesting nocturnal decalcification. Calculated net calcification of both species was similar between seep and control sites, i.e., the observed increased calcification in light compensated for reduced dark calcification. However, inorganic carbon content increased (22%) in H. digitata and decreased (-8%) in H. opuntia at the seep site compared to controls. Significantly, lighter carbon isotope signatures of H. digitata and H. opuntia phylloids at high CO2 (1.01 per mil [parts per thousand] and 1.94 per mil, respectively) indicate increased photosynthetic uptake of CO2 over HCO3- potentially reducing dissolved inorganic carbon limitation at the seep site. Moreover, H. digitata and H. opuntia specimens transplanted for 14 d from the control to the seep site exhibited similar delta13C signatures as specimens grown there. These results suggest that the Halimeda spp. investigated can acclimatize and will likely still be capable to grow and calcify in inline image conditions exceeding most pessimistic future CO2 projections.

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.