The role of microbial sulfate reduction on organic matter oxidation was studied quantitatively in temperate intertidal surface sediments of the German Wadden Sea (southern North Sea) on a seasonal base in the years 1998-2007. The sampling sites represent the range of sediments found in the back-barrier tidal area of Spiekeroog Island: sands, mixed and muddy flats. The correspondingly different contents in organic matter, metals, and porosities lead to significant differences in the activity of sulfatereducing bacteria with volumetric sulfate reduction rates (SRR) in the top 15 cm of up to 1.4 μmol cm −3 day −1 .Depth-integrated areal SRR ranged between 0.9 and 106 mmol m −2 day −1 , with the highest values found in the mudflat sediments and lower rates measured in sands at the same time, demonstrating the impact of both temperature and organic matter load. According to a modeling approach for a 154-km 2 large tidal area, about 39, 122, and 285 tons of sulfate are reduced per day, during winter, spring/ autumn, and summer, respectively. Hence, the importance of areal benthic organic matter mineralization by microbial sulfate reduction increases during spring/autumn and summer by factors of about 2 and 7, respectively, when compared to winter time. The combined results correspond to an estimated benthic organic carbon mineralization rate via sulfate reduction of 78 g C m −2 year −1 .
Boulders provide ecologically important hard grounds in shelf seas, and form protected habitats under the European Habitats Directive. Boulders on the seafloor can usually be recognized in backscatter mosaics due to a characteristic pattern of high backscatter intensity followed by an acoustic shadow. The manual identification of boulders on mosaics is tedious and subjective, and thus could benefit from automation. In this study, we train an object detection framework, RetinaNet, based on a neural network backbone, ResNet, to detect boulders in backscatter mosaics derived from a sidescan-sonar operating at 384 kHz. A training dataset comprising 4617 boulders and 2005 negative examples similar to boulders was used to train RetinaNet. The trained model was applied to a test area located in the Kriegers Flak area (Baltic Sea), and the results compared to mosaic interpretation by expert analysis. Some misclassification of water column noise and boundaries of artificial plough marks occurs, but the results of the trained model are comparable to the human interpretation. While the trained model correctly identified a higher number of boulders, the human interpreter had an advantage at recognizing smaller objects comprising a bounding box of less than 7 × 7 pixels. Almost identical performance between the best model and expert analysis was found when classifying boulder density into three classes (0, 1-5, more than 5) over 10,000 m 2 areas, with the best performing model reaching an agreement with the human interpretation of 90%.
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