Scottish Association for Marine Science

About: Scottish Association for Marine Science is a facility organization based out in Oban, United Kingdom. It is known for research contribution in the topics: Sea ice & Benthic zone. The organization has 524 authors who have published 1765 publications receiving 70783 citations. The organization is also known as: SAMS & Scottish Marine Station for Scientific Research.

Sulphur cycling in organic‐rich marine sediments from a Scottish fjord

Abstract: In this study, the biogeochemical transformations of sulphur in organic-rich marine sediments in a Scottish fjord are investigated by a combination of pore water and sediment geochemistry with sulphide diffusive gradient thin-film probes and sulphate isotopic data (δ34S and δ18O). Particular attention is paid to sulphur cycling in the upper sediment profile where sulphate reduction occurs but free sulphide is below the detection limits of conventional pore water geochemical analysis but quantifiable by sulphide diffusive gradient thin film. In the uppermost part of the sediment core, δ18O sulphate decreased from near-sea water values to +7‰, indicating that anoxic sulphide oxidation dominated during this interval. Sulphate δ34S remained unchanged as there was no net sulphate reduction (i.e. reduction was balanced by re-oxidation). Below 4 cm depth, there was a slight increase in sulphate δ34S from 20‰ to 23‰ associated with minor accumulation of iron sulphide. The δ18O of the sulphate also increased, to around +10‰ at 10 cm depth, as a result of the isotopic exchange of sulphate–oxygen with pore water and/or sulphur disproportionation reactions mediated during sulphur cycling. These processes continued to increase the δ18O of the sulphate to 14‰ at 20 cm depth with no further change in the δ34S of the sulphate. Below 20 cm depth, free sulphide is detectable in pore waters and both the δ34S of the sulphate and sulphide increase with depth with an offset controlled by kinetic fractionation during bacterial sulphate reduction. The δ34S of the sedimentary organic fraction shifted towards lower, more bacteriogenic, values with depth in the profile, without any increase in the size of this sulphur pool. Thus, the organic sulphur fraction was open to interaction with bacteriogenic sulphide without the occurrence of net addition. Therefore, caution should be exercised when using sulphur isotopic compositions to infer simple net addition of bacteriogenic sulphide to the organic sulphur fraction.

Improving predictive mapping of deep-water habitats: Considering multiple model outputs and ensemble techniques

Abstract: In the deep sea, biological data are often sparse; hence models capturing relationships between observed fauna and environmental variables (acquired via acoustic mapping techniques) are often used to produce full coverage species assemblage maps. Many statistical modelling techniques are being developed, but there remains a need to determine the most appropriate mapping techniques. Predictive habitat modelling approaches (redundancy analysis, maximum entropy and random forest) were applied to a heterogeneous section of seabed on Rockall Bank, NE Atlantic, for which landscape indices describing the spatial arrangement of habitat patches were calculated. The predictive maps were based on remotely operated vehicle (ROV) imagery transects, high-resolution autonomous underwater vehicle (AUV) sidescan backscatter maps and ship-based multibeam bathymetry. Area under the curve (AUC) and accuracy indicated similar performances for the three models tested, but performance varied by species assemblage, with the transitional species assemblage showing the weakest predictive performances. Spatial predictions of habitat suitability differed between statistical approaches, but niche similarity metrics showed redundancy analysis and random forest predictions to be most similar. As one statistical technique could not be found to outperform the others when all assemblages were considered, ensemble mapping techniques, where the outputs of many models are combined, were applied. They showed higher accuracy than any single model. Different statistical approaches for predictive habitat modelling possess varied strengths and weaknesses and by examining the outputs of a range of modelling techniques and their differences, more robust predictions, with better described variation and areas of uncertainties, can be achieved. As improvements to prediction outputs can be achieved without additional costly data collection, ensemble mapping approaches have clear value for spatial management.