Modelling seasonal and geographical n-3 polyunsaturated fatty acid contents in marine fish from the Northeast Atlantic Ocean.

Demand for n-3 polyunsaturated fatty acids (n-3 PUFAs) exceeds supply. Large-scale studies on effects of season and geography of n-3 PUFAs in marine fish from the Northeast Atlantic Ocean (NEAO) may be used to optimize utilization and improve nutrition security. Using a sinusoid model, seasonal cycles of n-3 PUFAs were determined and found to be species-specific and clearly pronounced for the pelagic zooplankton feeding species. The Greenland halibut showed very little seasonal variation. The n-3 PUFA content in North Sea autumn-spawning (NSAS) herring peaked in summer, while Norwegian spring-spawning (NSS) herring and mackerel had their peak in autumn. A time shift of peaks in n-3 PUFAs between the two herring stocks was detected, likely due to different spawning strategies in addition to a delay of n-3 PUFAs flux in the northern regions of the NEAO. This study demonstrates that consideration of nutrient contents, such as n-3 PUFAs, when organizing and structuring fishery approaches may improve overall nutritional yield. Based on total annual Norwegian fish landings and seasonal variation in n-3 PUFA contents, n-3 PUFAs yield could theoretically be increased from 13.79 kilo ton per year from the current fishing tactics, to 15.54 if the pelagic species were only caught during the time of their seasonal n-3 PUFA peaks. Pelagic fish is a good source for dietary n-3 PUFAs, but harvest timing will also influence n-3 PUFAs intake by human consumers. One portion of fatty fish harvested during winter/spring may not meet the weekly intake reference nutritional guidelines for n-3 PUFAs. Marine n-3 PUFAs yields also varied geographically and decreased southwards, with the lowest values in Skagerrak. This study can serve as a model to understand patterns of reproductive cycles and geographical distribution of n-3 PUFAs in fatty fish from the NEAO and the novel approach may be useful to support sustainable, seasonal fishing programmes for optimization of n-3 PUFAs yields.

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions.

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species-the bubblegum coral Paragorgia arborea-in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring.

This paper presents the technological developments and the policy contexts for the project "Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring" (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining.

Modular AUV System with Integrated Real-Time Water Quality Analysis.

This paper describes the concept, the technical implementation and the practical application of a miniaturized sensor system integrated into an autonomous underwater vehicle (AUV) for real-time acquisition of water quality parameters. The main application field of the presented system is the analysis of the discharge of nitrates into Norwegian fjords near aqua farms. The presented system was developed within the research project SALMON (Sea Water Quality Monitoring and Management) over a three-year period. The development of the sensor system for water quality parameters represented a significant challenge for the research group, as it was to be integrated in the payload unit of the autonomous underwater vehicle in compliance with the underwater environmental conditions. The German company -4H- JENA engineering GmbH (4HJE), with experience in optical in situ-detection of nutrients, designed and built the measurement system. As a carrier platform, the remotely operated vehicle (ROV) "CWolf" from Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung - Institutsteil Angewandte Systemtechnik (IOSB-AST) modified to an AUV was deployed. The concept presented illustrates how the measurement system can be integrated easily into the vehicle with a minimum of hard- and software technical interfaces.