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By creating a dependable, transparent, and cost-effective system for forecasting and ongoing environmental impact monitoring of exploration and exploitation activities in the deep sea, TRIDENT seeks to contribute to the sustainable exploitation of seabed mineral resources. In order to operate autonomously in remote locations under harsh conditions and send real-time data to authorities in charge of granting licenses and providing oversight, this system will create and integrate new technology and innovative solutions. The efficient monitoring and inspection system that will be created will abide by national and international legal frameworks. At the sea surface, mid-water, and the bottom, TRIDENT will identify all pertinent physical, chemical, geological, and biological characteristics that must be monitored. It will also look for data gaps and suggest procedures for addressing them. These are crucial actions to take in order to produce accurate indicators of excellent environmental status, statistically robust environmental baselines, and thresholds for significant impact, allowing for the standardization of methods and tools. In order to monitor environmental parameters on mining and reference areas at representative spatial and temporal scales, the project consortium will thereafter develop and test an integrated system of stationary and mobile observatory platforms outfitted with the most recent automatic sensors and samplers. The system will incorporate high-capacity data processing pipelines able to gather, transmit, process, and display monitoring data in close to real-time to facilitate prompt actions for preventing major harm to the environment. Last but not least, it will offer systemic and technological solutions for predicting probable impacts of applying the developed monitoring and mitigation techniques

The ocean takes up approximately 25% of the carbon dioxide that humans emit to the atmosphere, it absorbs most of the excess heat trapped in the Earth system by greenhouse gas emissions, thus regulating climate and life on Earth, and also provides a remarkable number of resources for humanity. Most geo-hazards occur in oceanic areas. High-quality systematic ocean observations are necessary to improvour knowledge and understanding of the complex environmental processes and to serve as early warning systems of great socio-economic impact. Research Infrastructures (RIs) are large-scale facilities that provide resources and services for scientific communities to conduct high-level research and foster innovation. RIs promote knowledge, outreach and education to public, private, and policy stakeholders, as well as providing crucial information to operational services such as Copernicus. In Europe several marine RIs have been established, which are maintained by national and European Union (EU) resources. This paper describes the significance of the marine RIs in the European Marine Observation Landscape, their status in terms of cooperation, coordination and integration. It highlights the socio-economic benefits for this integration process, being a significant pillar of the European Ocean Observing System (EOOS)

The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods.

At 1440 utc (1540 local time) on Saturday 18 June 2022, when low tide had passed and the tide was due to rise, the water drained from the harbours of Union Hall and nearby Courtmacsherry on the south coast of Ireland (see Figure 1a). Eyewitnesses captured the event on video,1 with observers remarking that it reminded them of reports of the tide receding ahead of the tsunami in Japan in 2011. The event was captured by the Irish Marine Institute’s tide gauge in Union Hall, which recorded a drop in water level of 70cm in 5min. Compared to the normal ebb and flow of tides of ~1cm per minute, this was a dramatic event. The event impacted most of the Irish south coast, from Castletownbere to Dunmore East and onwards to Britain, with reports of unusual tidal behaviour in Pembrokeshire in South Wales (see Sibley, 2022, this issue).

The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods

The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods., Peer Reviewed, Article signat per 31 autors/es: Jacopo Aguzzi, Damianos Chatzievangelou, Nathan J. Robinson, Nixon Bahamon, Alan Berry, Marc Carreras, Joan Batista Company, Corrado Costa, Joaquin del Rio Fernandez, Ahmad Falahzadeh, Spyros Fifas, Sascha Flögel, Jordi Grinyó, Jonás Pall Jonasson, Patrik Jonsson, Colm Lordan, Mathieu Lundy, Simone Marini, Michela Martinelli, Ivan Masmitja, Luca Mirimin, Atif Naseer, Joan Navarro, Narcís Palomeras, Giacomo Picardi, Cristina Silva, Sergio Stefanni , Maria Vigo, Yolanda Vila, Adrian Weetman i Jennifer Doyle., Postprint (published version)

Cabled observatories are marine infrastructures equipped with biogeochemical and oceanographic sensors as well as High-Definition video and audio equipment, hence providing unprecedented opportunities to study marine biotic and abiotic components. Additionally, non-invasive monitoring approaches such as environmental DNA (eDNA) metabarcoding have further enhanced the ability to characterize marine life. Although the use of non-invasive tools beholds great potential for the sustainablemonitoring of biodiversity and declining natural resources, such techniques are rarely used in parallel and understanding their limitations is challenging. Thus, this study combined Underwater Video (UV) with eDNA metabarcoding data to produce marine fish community profiles over a 2 months period in situ at a cabled observatory in the northeast Atlantic (SmartBay Ireland). By combining both approaches, an increased number of fish could be identified to the species level (total of 22 species), including ecologically and economically important species such as Atlantic cod, whiting, mackerel and monkfish

Seafloor multiparametric fibre-optic-cabled video observatories are emerging tools for standardized monitoring programmes, dedicated to the production of real-time fishery-independent stock assessment data. Here, we propose that a network of cabled cameras can be set up and optimized to ensure representative long-term monitoring of target commercial species and their surrounding habitats. We highlight the importance of adding the spatial dimension to fixed-point-cabled monitoring networks, and the need for close integration with Artificial Intelligence pipelines, that are necessary for fast and reliable biological data processing. We then describe two pilot studies, exemplary of using video imagery and environmental monitoring to derive robust data as a foundation for future ecosystem-based fish-stock and biodiversity management. The first example is from the NE Pacific Ocean where the deep-water sablefish (Anoplopoma fimbria) has been monitored since 2010 by the NEPTUNE cabled observatory operated by Ocean Networks Canada. The second example is from the NE Atlantic Ocean where the Norway lobster (Nephrops norvegicus) is being monitored using the SmartBay observatory developed for the European Multidisciplinary Seafloor and water column Observatories. Drawing from these two examples, we provide insights into the technological challenges and future steps required to develop full-scale fishery-independent stock assessments., This work was funded by the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring; MartTERA ERA-Net Cofound), ARCHES (Autonomous Robotic Networks to Help Modern Societies; German Helmholtz Association), RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades, Spanish Government), RESNEP (CTM2017-82991-C2-1-R; Ministerio de Ciencia, Innovación y Universidades, Spanish Government), and SmartLobster (EMSO-LINK Trans National Access-TNA). The EMSO_SmartBay cabled observatory was funded by Science Foundation Ireland (SFI) as part of a SFI Research Infrastructure Award Grant No. 12/RI/2331., Peer Reviewed, Postprint (author's final draft)

Cabled video-observatories offer new opportunities to monitor fish species at frequencies and durations never attained before, quantifying the behavioural activities of their individuals, and providing ancillary data to inform stock assessment (in a fishery-independent manner). In this context, our objective was to improve the ecological monitoring capability of SmartBay observatory (20 m depth, Galway Bay, Ireland), through a pilot study dedicated to tracking of fish counts (as a proxy of populations activity rhythms), in a context where species behaviour and consequent community turnover may occur at different temporal cycles (i.e. tidal versus day-night). In order to understand how animals can regulate their behavioural activity upon those cycles, we enforced a time-lapse (1 h interval) image collection and concomitant multiparametric oceanographic plus acoustic data acquisition continuously during 24 h, over 30 days in August 2018 (when turbidity is at minimum). For each image, we classified and then counted all visible fish and derived count time series. Periodogram and waveform analyses were used to calculate their fluctuations' periodicity (i.e. the ruling cycle) and phase (i.e. peak timing in relation to the cycle). A total of 12 marine teleost species were pictured with Trisopterus minutus, Trachurus trachurus and Chelidonichthys lucerna characterized by day-night related rhythms, while others, such as Trisopterus luscus and Gadus morhua, were influenced by the tidal cycle. 24 h count patterns were compared together and investigated for time-based ecological niche-partitioning in a wave and current-affected soundscape. These findings were discussed in relation to the ecology of species and the feasibility of promising observatory-based monitoring applications in fishery assessment practices, when targeted species have commercial value., Dr. J. Aguzzi and J. del Rio are members of the CSIC-UPC Associated Unit “Tecnoterra”, managing the OBSEA platform, an EMSO testing-site. This work was developed within the framework of the Projects ADVANCE (H2020 INFRAIA-2014-2015 grant agreement 654410, Jerico-Next TNA), RESBIO (TEC2017-87861-R) and ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring; MarTERA ERA-Net Cofound). The SmartBay cabled observatory was funded by Science Foundation Ireland (SFI) as part of an SFI Research Infrastructure Award under Grant No. 12/RI/233., Peer Reviewed, Postprint (author's final draft)

Seafloor multiparametric fibre-optic-cabled video observatories are emerging tools for standardized monitoring programmes, dedicated to the production of real-time fishery-independent stock assessment data. Here, we propose that a network of cabled cameras can be set up and optimized to ensure representative long-term monitoring of target commercial species and their surrounding habitats. We highlight the importance of adding the spatial dimension to fixed-point-cabled monitoring networks, and the need for close integration with Artificial Intelligence pipelines, that are necessary for fast and reliable biological data processing. We then describe two pilot studies, exemplary of using video imagery and environmental monitoring to derive robust data as a foundation for future ecosystem-based fish-stock and biodiversity management. The first example is from the NE Pacific Ocean where the deep-water sablefish (Anoplopoma fimbria) has been monitored since 2010 by the NEPTUNE cabled observatory operated by Ocean Networks Canada. The second example is from the NE Atlantic Ocean where the Norway lobster (Nephrops norvegicus) is being monitored using the SmartBay observatory developed for the European Multidisciplinary Seafloor and water column Observatories. Drawing from these two examples, we provide insights into the technological challenges and future steps required to develop full-scale fishery-independent stock assessments

Cabled video-observatories offer new opportunities to monitor fish species at frequencies and durations never attained before, quantifying the behavioural activities of their individuals, and providing ancillary data to inform stock assessment (in a fishery-independent manner). In this context, our objective was to improve the ecological monitoring capability of SmartBay observatory (20 m depth, Galway Bay, Ireland), through a pilot study dedicated to tracking of fish counts (as a proxy of populations activity rhythms), in a context where species behaviour and consequent community turnover may occur at different temporal cycles (i.e. tidal versus day-night). In order to understand how animals can regulate their behavioural activity upon those cycles, we enforced a time-lapse (1 h interval) image collection and concomitant multiparametric oceanographic plus acoustic data acquisition continuously during 24 h, over 30 days in August 2018 (when turbidity is at minimum). For each image, we classified and then counted all visible fish and derived count time series. Periodogram and waveform analyses were used to calculate their fluctuations' periodicity (i.e. the ruling cycle) and phase (i.e. peak timing in relation to the cycle). A total of 12 marine teleost species were pictured with Trisopterus minutus, Trachurus trachurus and Chelidonichthys lucerna characterized by day-night related rhythms, while others, such as Trisopterus luscus and Gadus morhua, were influenced by the tidal cycle. 24 h count patterns were compared together and investigated for time-based ecological niche-partitioning in a wave and current-affected soundscape. These findings were discussed in relation to the ecology of species and the feasibility of promising observatory-based monitoring applications in fishery assessment practices, when targeted species have commercial value