Remote Sensing of Sea Surface Temperatures for Aquaculture Planning in Northern Norway

A major limitation for salmon (Salmo salar L.) farming in arctic environments is the low winter temperatures influencing the salmon's growth rates, mortality and quality. A detailed knowledge of the sea temperature variations in a region can help to avoid the establishment of fish farms in areas that are less suitable. In order to supply local fish farmers and planning authorities with such information, a satellite survey of sea surface temperatures in a late winter situation was conducted in northern Norway. Landsat Thematic Mapper data were calibrated with in situ measurements. The relationship between sea surface temperatures and other factors in the physical environment was visualized in a very comprehensive way. Temperature zones were found to be consistent with information in literature and of relevance to the fish farming industry. New, potentially suitable sites for fish farming could be indicated in many areas where no historical data were available.Key words: remote sensing, sea surface temperatures, aquaculture planning, northern NorwayMots clés: télédétection, températures de la surface marine, planification de l’aquaculture, Norvege septentrional

CASCADE-The Circum-Arctic Sediment CArbon DatabasE

Biogeochemical cycling in the semi-enclosed Arctic Ocean is strongly influenced by land–ocean transport of carbon and other elements and is vulnerable to environmental and climate changes. Sediments of the Arctic Ocean are an important part of biogeochemical cycling in the Arctic and provide the opportunity to study present and historical input and the fate of organic matter (e.g., through permafrost thawing). Comprehensive sedimentary records are required to compare differences between the Arctic regions and to study Arctic biogeochemical budgets. To this end, the Circum-Arctic Sediment CArbon DatabasE (CASCADE) was established to curate data primarily on concentrations of organic carbon (OC) and OC isotopes (δ13C, Δ14C) yet also on total N (TN) as well as terrigenous biomarkers and other sediment geochemical and physical properties. This new database builds on the published literature and earlier unpublished records through an extensive international community collaboration. This paper describes the establishment, structure and current status of CASCADE. The first public version includes OC concentrations in surface sediments at 4244 oceanographic stations including 2317 with TN concentrations, 1555 with δ13C-OC values and 268 with Δ14C-OC values and 653 records with quantified terrigenous biomarkers (high-molecular-weight n-alkanes, n-alkanoic acids and lignin phenols). CASCADE also includes data from 326 sediment cores, retrieved by shallow box or multi-coring, deep gravity/piston coring, or sea-bottom drilling. The comprehensive dataset reveals large-scale features of both OC content and OC sources between the shelf sea recipients. This offers insight into release of pre-aged terrigenous OC to the East Siberian Arctic shelf and younger terrigenous OC to the Kara Sea. Circum-Arctic sediments thereby reveal patterns of terrestrial OC remobilization and provide clues about thawing of permafrost. CASCADE enables synoptic analysis of OC in Arctic Ocean sediments and facilitates a wide array of future empirical and modeling studies of the Arctic carbon cycle. The database is openly and freely available online (https://doi.org/10.17043/cascade; Martens et al., 2021), is provided in various machine-readable data formats (data tables, GIS shapefile, GIS raster), and also provides ways for contributing data for future CASCADE versions. We will continuously update CASCADE with newly published and contributed data over the foreseeable future as part of the database management of the Bolin Centre for Climate Research at Stockholm University

Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project

Effective ocean management requires integrated and sustainable ocean observing systems enabling us to map and understand ecosystem properties and the effects of human activities. Autonomous subsurface and surface vehicles, here collectively referred to as “gliders”, are part of such ocean observing systems providing high spatiotemporal resolution. In this paper, we present some of the results achieved through the project “Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach—GLIDER”. In this project, three autonomous surface and underwater vehicles were deployed along the Lofoten–Vesterålen (LoVe) shelf-slope-oceanic system, in Arctic Norway. The aim of this effort was to test whether gliders equipped with novel sensors could effectively perform ecosystem surveys by recording physical, biogeochemical, and biological data simultaneously. From March to September 2018, a period of high biological activity in the area, the gliders were able to record a set of environmental parameters, including temperature, salinity, and oxygen, map the spatiotemporal distribution of zooplankton, and record cetacean vocalizations and anthropogenic noise. A subset of these parameters was effectively employed in near-real-time data assimilative ocean circulation models, improving their local predictive skills. The results presented here demonstrate that autonomous gliders can be effective long-term, remote, noninvasive ecosystem monitoring and research platforms capable of operating in high-latitude marine ecosystems. Accordingly, these platforms can record high-quality baseline environmental data in areas where extractive activities are planned and provide much-needed information for operational and management purposes

Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p

Benthic fauna in soft sediments from the Barents and Pechora Seas

Benthic infaunal abundance data from 138 stations in the Barents Sea and surrounding waters are provided in a public database. All samples were collected with a 0.1 m2 van Veen grab and identification was carried out by professional taxonomists. Most abundance data are presented at the species level