Swath sonar bathymetry during POLARSTERN cruise ARK-XXII/1c (PS70) with links to multibeam raw data files

Multibeam data were measured as part of the project HERMES during R/V Polarstern cruise ARK-XXII/1 (2007-05-29 to 2007-07-25) along transits and survey profiles and partly during stationary work. Data were achieved mainly in the coastal areas of northern Norway, at the Hakon Mosby Mud Volcano at the continental margin approx. 200 nm off the norwegian coast and the AWI-Hausgarten area approx. 150 nm west of Svalbard. A number of surveys were carried out in the coastal areas of northern Norway (Sula Reef, Roest Reef, Traena area, Floholmen area, Sotbakken area) and around the area of the Hakon Mosby Mud Volcano. The multibeam sonar system Atlas Hydrosweep DS-2 (Atlas Hydrographic, http://www.atlashydro.com) was operated using 59 beams and 90° aperture angle. The refraction correction was achieved using CTD profiles measured during this cruise or, during transits, utilizing the system's own cross fan calibration. The quality of data might be reduced during bad weather periods or adverse sea ice conditions (only in the AWI-Hausgarten area). This dataset contains raw data that are not processed and thus may contain errors and blunders in depth and position.

Global macrozoobenthos production and energy budget data base V150731, with link to database

I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.