Description: The genus Sebastes is a morphologically and ecologically diverse genus of rockfish characterized by high longevity, late-maturity, and low natural mortality. On the northwest Atlantic continental shelf, the Acadian redfish (Sebastes fasciatus) is the most common rockfish species above 300 m depth. This species has been widely exploited resulting in the depletion or collapse of most of its stocks. Management of long-lived species with intricate life-history characteristics is challenging and requires highly integrated biological and oceanographic monitoring, which allow the identification of environmental drivers and demographic and behavioural trends. The present study uses high temporal resolution imaging and environmental data, acquired with an autonomous lander deployed for 10-months at the Sambro Bank Sponge Conservation Area (Scotian Shelf) to elucidate S. fasciatus temporal dynamics and behavioural trends in response to near-bed environmental conditions. S. fasciatus, mostly displayed passive locomotion and static behaviours, in common with other shelf-dwelling Sebastes species. Hydrodynamics appear to act as a synchronizing factor conditioning its swimming behaviour. S. fasciatus total counts exhibited a seasonal shift in rhythm's phase likely reflecting changes in lifestyle requirements.

Description: Data from a weather station (Garraf natural park) deployed at the Catalan coast from 2013 to 2014. The station at Garraf natural park provides solar irradiance and rain intensity. Data from the Garraf natural park station has been obtained through the Servei Meteorològic de Catalunya (Catalan Meteorological Service). An automatic quality control check has been applied to the data.

Description: Data from two CTD sensors (SBE37 and SBE16) deployed at OBSEA seafloor observatory from 2013 to 2014. OBSEA station is located 4km off-the-coast of Vilanova i la Geltrú (Catalonia, Spain) at a depth of 20 meters. Measured variables are sea water temperature, conductivity, pressure, salinity and sound velocity. Data from the CTD sensors has been acquired every 10 seconds, then a quality control procedure has been applied following QARTOD guidelines. Afterwards the quality controlled data has been averaged in periods of 30 minutes (discarding data flagged as bad data). Every data point has an associated a quality control flag and standard deviation value. The quality control flag values are 1: good data, 2: not applied, 3: suspicious data, 4: bad data, 9: missing data. The standard deviation provides a measure of the variability of the data within the 30min time window used in the average.

Description: Data from a weather station (Vilanova i la Geltrú) deployed at the Catalan coast from 2013 to 2014. The station at Vilanova i la Geltrú provides air temperature, wind speed and wind direction. Data from the Vilanova i la Geltrú weather station has been acquired every minute, then a quality control procedure has been applied following QARTOD guidelines. Afterwards the quality controlled data has been averaged in periods of 30 minutes (discarding data flagged as bad data). Every data point has an associated a quality control flag and standard deviation value. The quality control flag values are 1: good data, 2: not applied, 3: suspicious data, 4: bad data, 9: missing data. The standard deviation provides a measure of the variability of the data within the 30min time window used in the average.

Description: Underwater images and abundances of different fish taxa detected at the OBSEA Cabled Observatory from January 2013 to December 2014. OBSEA station is located 4km off-the-coast of Vilanova i la Geltrú (Catalonia, Spain) at a depth of 20 meters. Two cameras were used (Sony SNC-RZ25N from 2013-01-01 to 2014-12-11 and Axis P1346-E from 2014-12-11 to 2014-12-31). 29 fish taxa have been visually identified and classified according to their taxa name.

Description: Underwater images and manual tags of different fish taxa detected from the image repository of the OBSEA Cabled Observatory from January 2013 to December 2014. OBSEA station is located 4km off-the-coast of Vilanova i la Geltrú (Catalonia, Spain) at a depth of 20 meters . Tags are rectangles marking the fish position in a picture (region of interest), identified by their x/y vertix values (in pixels).The aim of this dataset is to train Artificial Intelligence (AI) algorithms to automatically detect and or classify fish specimens from underwater pictures.Two cameras were used (Sony SNC-RZ25N from 2013-01-01 to 2014-12-11 and Axis P1346-E from 2014-12-11 to 2014-12-31). 29 fish taxa have been visually identified and classified according to their taxa name. Unclassified fish have been marked as Unknown fish.The fish taxa recognized in the dataset are: Coris julis, Chromis chromis, Diplodus vulgaris, Diplodus sargus, Oblada melanura, Spondyliosoma cantharus, Diplodus cervinus, Diplodus annularis, Spicara maena, Dentex dentex, Diplodus puntazzo, Conger conger, Symphodus mediterraneus, Atherina sp., Scorpaena sp., Pagrus pagrus, Pagellus sp., Serranus cabrilla, Sciaena umbra, Sarpa salpa, Trachurus sp., Sparus aurata, Apogon sp., Seriola dumerili, Thalassoma pavo, Epinephelus marginatus, Boops boops, Symphodus cinereus, Symphodus tinca.

Description: Here we present fish observation data obtained during the first EGIM deployment at the OBSEA observatory, which led to the development of a methodology for correlating fish activity and biologically relevant oceanographic variables. In addition to salinity, temperature, sound speed, water depth and dissolved oxygen data measured by the EGIM, two underwater digital cameras were deployed to provide ecological observations: a CAM1 camera rotating 360° and five metres apart, and a fixed CAM2 camera pointed at an artificial reef where fish activity is observed. For the analysis, the two close cameras were considered as one. For each image, the total number of fish was counted. All visible individuals were taxonomically classified as detailed as possible, using the latest scientific nomenclature from FISHBase.

Description: 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.

Description: 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.

Description: Increasing interest in the acquisition of biotic and abiotic resources from within the deep sea (e.g., fisheries, oil–gas extraction, and mining) urgently imposes the development of novel monitoring technologies, beyond the traditional vessel-assisted, time-consuming, high-cost sampling surveys. The implementation of permanent networks of seabed and water-column-cabled (fixed) and docked mobile platforms is presently enforced, to cooperatively measure biological features and environmental (physicochemical) parameters. Video and acoustic (i.e., optoacoustic) imaging are becoming central approaches for studying benthic fauna (e.g., quantifying species presence, behavior, and trophic interactions) in a remote, continuous, and prolonged fashion. Imaging is also being complemented by in situ environmental-DNA sequencing technologies, allowing the traceability of a wide range of organisms (including prokaryotes) beyond the reach of optoacoustic tools. Here, we describe the different fixed and mobile platforms of those benthic and pelagic monitoring networks, proposing at the same time an innovative roadmap for the automated computing of hierarchical ecological information on deep-sea ecosystems (i.e., from single species’ abundance and life traits to community composition, and overall biodiversity).