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The development and use of FerryBox systems as a cost-effective instrument for continuous observations of the marine environment has been well established since more than 15 years. The systems have evolved to maturity and are since widely used around the coastal ocean of Europe. The availability of newly developed sensors allows the extension of FerryBox measurements to more biogeochemical parameters which are of interest for the requirements of the Marine Strategy Framework Directive (MSFD). The FerryBox community initially formed from the partners of an EU funded FerryBox project provides mutual exchange of experience and is now organized within EuroGOOS as a so called FerryBox Task Team (www.ferrybox.org). Within the EU funded infrastructure projects JERICO and JERICO-NEXT the technical harmonization as well as the developing of best practise guides for FerryBox systems have been a step further to high quality environmental data products. Within JERICO-NEXT it has been decided to build up a common FerryBox database and data portal in order to make the FerryBox data more available and visible. Furthermore this database will be function as a close link to the Copernicus Marine Environmental Monitoring Services (CMEMS) and the EMODnet portal.

The development and use of FerryBox systems as a cost-effective instrument for continuous observations of the marine environment has been well established since more than 15 years. The systems have evolved to maturity and are since widely used around the coastal ocean of Europe. The availability of newly developed sensors allows the extension of FerryBox measurements to more biogeochemical parameters which are of interest for the requirements of the Marine Strategy Framework Directive (MSFD). The FerryBox community initially formed from the partners of an EU funded FerryBox project provides mutual exchange of experience and is now organized within EuroGOOS as a so called FerryBox Task Team (www.ferrybox.org). Within the EU funded infrastructure projects JERICO and JERICO-NEXT the technical harmonization as well as the developing of best practise guides for FerryBox systems have been a step further to high quality environmental data products. Within JERICO-NEXT it has been decided to build up a common FerryBox database and data portal in order to make the FerryBox data more available and visible. Furthermore this database will be function as a close link to the Copernicus Marine Environmental Monitoring Services (CMEMS) and the EMODnet portal.

We show the vertical distributions of temperature, salinity, nutrients, oxygen, CO2 fugacity (fCO2), pH and chlorophyll-a at the end of the winter 1984 in the northeast sector of the Atlantic Ocean between 40°N and 47°N. The intense vertical mixing due to winter cooling resulting in homogeneous water column layers as deep as 300 m at 47°N and, getting shallower further south to 100 m at 40°N. This northward increasing of the winter mixed layer was accompanied with a northward increasing trend in nutrients and CO2 and a decreasing in temperature, salinity, oxygen, pH and chlorophyll-a. Taking into account these observations, we analyse the winter mixing effect on the CO2 uptake of the newly formed water masses. The mixed-layer CO2 values were very close to the expected values supposing that the new water masses were formed under conditions of CO2 atmospheric equilibrium and were subsequently subjected to convective mixing with aged waters from below the main pycnocline. We also show the occurrence of a significant phytoplankton biomass through the entire winter mixed layer. This phenomenon, previously coined as ‘phyto-convection’ [Backhaus, J.O., Wehde, H., Hegseth, E.N., Kämpf, J., 1999. ‘Phyto-convection’: the role of oceanic convection in primary production. Mar. Ecol., Prog. Ser. 189, 77–92] affects the nutrient and CO2 levels of the newly formed water masses and it is an indicator of the extremely fast vertical velocities through the winter mixed layer.

This paper explores the uptake processes that different communities use with regards to their standard operating procedures and best practices Across our five case studies, we noted what made uptake processes successful or challenging and distilled a set of recommendations to further develop the OBPS’s approach to supporting communities in developing and converging methods into best practices.

This paper explores the uptake processes that different communities use with regards to their standard operating procedures and best practices Across our five case studies, we noted what made uptake processes successful or challenging and distilled a set of recommendations to further develop the OBPS’s approach to supporting communities in developing and converging methods into best practices.

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.

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.

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.

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.

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.

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.

Report on data and connectivity upgrading, documenting system improvements to integrate observing systems, improve network infrastructure, near-real-time delivery of data and best practice for integrated observations to provide data useful for Marine Services and other users

Report on data and connectivity upgrading, documenting system improvements to integrate observing systems, improve network infrastructure, near-real-time delivery of data and best practice for integrated observations to provide data useful for Marine Services and other users

Report on data and connectivity upgrading, documenting system improvements to integrate observing systems, improve network infrastructure, near-real-time delivery of data and best practice for integrated observations to provide data useful for Marine Services and other users

Report on data and connectivity upgrading, documenting system improvements to integrate observing systems, improve network infrastructure, near-real-time delivery of data and best practice for integrated observations to provide data useful for Marine Services and other users