2 resultados para Multidimensional data analysis
em Publishing Network for Geoscientific
Resumo:
Ocean acidification may stimulate primary production through increased availability of inorganic carbon in the photic zone, which may in turn change the biogenic flux of dissolved organic carbon (DOC) and the growth potential of heterotrophic bacteria. To investigate the effects of ocean acidification on marine bacterial assemblages, a two-by-three factorial mescosom experiment was conducted using surface sea water from the East Greenland Current in Fram Strait. Pyrosequencing of the V1-V2 region of bacterial 16S ribosomal RNA genes was used to investigate differences in the endpoint (Day 9) composition of bacterial assemblages in mineral nutrient-replete mesocosms amended with glucose (0 µm, 5.3 µm and 15.9 µm) under ambient (250 µatm) or acidified (400 µatm) partial pressures of CO2 (pCO2). All mesocosms showed low richness and diversity by Chao1 estimator and Shannon index, respectively, with general dominance by Gammaproteobacteria and Flavobacteria. Nonmetric multidimensional scaling analysis and two-way analysis of variance of the Jaccard dissimilarity matrix (97% similarity cut-off) demonstrated that the significant community shift between 0 µm and 15.9 µm glucose addition at 250 µatm pCO2 was eliminated at 400 µatm pCO2. These results suggest that the response potential of marine bacteria to DOC input may be altered under acidified conditions.
Resumo:
During the SINOPS project, an optimal state of the art simulation of the marine silicon cycle is attempted employing a biogeochemical ocean general circulation model (BOGCM) through three particular time steps relevant for global (paleo-) climate. In order to tune the model optimally, results of the simulations are compared to a comprehensive data set of 'real' observations. SINOPS' scientific data management ensures that data structure becomes homogeneous throughout the project. Practical work routine comprises systematic progress from data acquisition, through preparation, processing, quality check and archiving, up to the presentation of data to the scientific community. Meta-information and analytical data are mapped by an n-dimensional catalogue in order to itemize the analytical value and to serve as an unambiguous identifier. In practice, data management is carried out by means of the online-accessible information system PANGAEA, which offers a tool set comprising a data warehouse, Graphical Information System (GIS), 2-D plot, cross-section plot, etc. and whose multidimensional data model promotes scientific data mining. Besides scientific and technical aspects, this alliance between scientific project team and data management crew serves to integrate the participants and allows them to gain mutual respect and appreciation.