Acoustic-trawl surveys are an important tool for marine stock management and environmental monitoring of marine life. Correctly assigning the acoustic signal to species or species groups is a challenge, and recently trawl camera systems have been developed to support interpretation of acoustic data. Examining images from known positions in the trawl track provides high resolution ground truth for the presence of species. Here, we develop and deploy a deep learning neural network to automate the classification of species present in images from the Deep Vision trawl camera system. To remedy the scarcity of training data, we developed a novel training regime based on realistic simulation of Deep Vision images. We achieved a classification accuracy of 94% for blue whiting, Atlantic herring, and Atlantic mackerel, showing that automatic species classification is a viable and efficient approach, and further that using synthetic data can effectively mitigate the all too common lack of training data.

One of the leading causes of overfishing is the catch of unwanted fish and marine life in commercial fishing gears. Echosounders are nowadays routinely used to detect fish schools and make qualitative estimates of the amount of fish and species present. However, the problem of estimating sizes using acoustic systems is still largely unsolved, with only a few attempts at real-time operation and only at demonstration level. This paper proposes a novel image-based method for individual fish detection, targeted at drastically reducing catches of undersized fish in commercial trawling. The proposal is based on the processing of stereo images acquired by the Deep Vision imaging system, directly placed in the trawl. The images are pre-processed to correct for nonlinearities of the camera response. Then, a Mask R-CNN architecture is used to localize and segment each individual fish in the images. This segmentation is subsequently refined using local gradients to obtain an accurate estimate of the boundary of every fish. Testing was conducted with two representative datasets, containing in excess of 2600 manually annotated individual fish, and acquired using distinct artificial illumination setups. A distinctive advantage of this proposal is the ability to successfully deal with cluttered images containing overlapping fish.

Small offshore banks may be sites of intense feeding by upper trophic level predators. We studied the distribution of cetaceans, seabirds, pelagic fish, euphausiids and zooplankton over a 9 × 15 km bank to determine the conditions and processes that concentrated prey there and to examine the relative importance of bottom-up or top-down controls. Euphausiids were the primary prey during most foraging activity. While these were widespread in subsurface waters, foraging was concentrated on dense surface swarms that formed during daylight hours over 2 small crests. Internal wave passage resulted in upward movement and concentration of euphausiids in these areas through a coupling of physical processes and euphausiid behavior, resulting in surface swarms. Thus, internal waves appear to provide a critical mechanism enhancing trophic energy transfer. The formation of dense, localized and accessible prey concentrations was more important to foraging than was the overall available prey biomass. The estimated maximum daily consumption of euphausiids by cetaceans, seabirds and herring combined was < 0.4% of the estimated instantaneous euphausiid biomass, and top-down control was unlikely to have substantially influenced euphausiid biomass at this site. Some predator species that do not prey extensively on euphausiids or herring were more prevalent in off-bank waters. The scales of predictability and the temporal dynamics of such features determine the manner in which populations of upper trophic level organisms utilize a variable environment.KEY WORDS: Foraging ecology · Euphausiid · Internal wave · Consumption · Cetacean · SeabirdResale or republication not permitted without written consent of the publisher

Funding has come from Scantrol AS in cooperation with a variety of sources within the Research Council of Norway. Specifically, Naerings-PhD programme project # 194968 (Automatisk klassifisering og sortering of fisk I trål), MAROFF programme project # 187336 (Automatisk klassifisering og sortering of fisk I trål), HAVKYST programme project # 178432 (Commercial mid-water trawling for cod, haddock and saithe: Shifting effort to reduce impact on bottom fauna of bottom trawling) and the Centre for Research Innovation (SFI) programme project # 203477 Centre for

Ecosystem surveys are carried out annually in the Barents Sea by Russia and Norway to monitor the spatial distribution of ecosystem components and to study population dynamics. One component of the survey is mapping the upper pelagic zone using a trawl towed at several depths. However, the current technique with a single codend does not provide fine-scale spatial data needed to directly study species overlaps. An in-trawl camera system, Deep Vision, was mounted in front of the codend in order to acquire continuous images of all organisms passing. It was possible to identify and quantify of most young-of-the-year fish (e.g. Gadus morhua, Boreogadus saida and Reinhardtius hippoglossoides) and zooplankton, including Ctenophora, which are usually damaged in the codend. The system showed potential for measuring the length of small organisms and also recorded the vertical and horizontal positions where individuals were imaged. Young-of-the-year fish were difficult to identify when passing the camera at maximum range and to quantify during high densities. In addition, a large number of fish with damaged opercula were observed passing the Deep Vision camera during heaving; suggesting individuals had become entangled in meshes farther forward in the trawl. This indicates that unknown numbers of fish are probably lost in forward sections of the trawl and that the heaving procedure may influence the number of fish entering the codend, with implications for abundance indices and understanding population dynamics. This study suggests modifications to the Deep Vision and the trawl to increase our understanding of the population dynamics.