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Life on our planet likely evolved in the ocean, and thus exo-oceans are key habitats to search for extraterrestrial life. We conducted a data-driven bibliographic survey on the astrobiology literature to identify emerging research trends with marine science for future synergies in the exploration for extraterrestrial life in exo-oceans. Based on search queries, we identified 2592 published items since 1963. The current literature falls into three major groups of terms focusing on (1) the search for life on Mars, (2) astrobiology within our Solar System with reference to icy moons and their exo-oceans, and (3) astronomical and biological parameters for planetary habitability. We also identified that the most prominent research keywords form three key-groups focusing on (1) using terrestrial environments as proxies for Martian environments, centred on extremophiles and biosignatures, (2) habitable zones outside of “Goldilocks” orbital ranges, centred on ice planets, and (3) the atmosphere, magnetic field, and geology in relation to planets’ habitable conditions, centred on water-based oceans.

Peer Reviewed

This paper presents a software infrastructure based on Deep Learning aimed at identifying habitats of certain species of crustacean that colonize areas of the marine Mesopelagic zone. Determining their presense is done, in this case, from the detection of holes in the sand that form sets of burrow structures. Preliminar inferencing models are obtained from images captured in the North Sea by trawled UWTV (undewater TV) stations, offering quite significant detection success ratios., Peer Reviewed

To assess conservation efforts and follow the effects of anthropogenic pressures, it is crucial to monitor the ecological status of benthic habitats. Monitoring requires measurements that are made on-site, reproduced over a vast region, and capable of capturing their spatial evolution over time. This calls for the expensively efficient development of tools and protocols that deliver reliable, high-quality data. This article introduces the PLOME multiparametric benthic landers, a new tool and protocol for non-extractively assessing the status of fishes and benthic habitats. By combining independent and wirelessly connected benthic stations, AUVs, and USVs, PLOME creates a spatially adjustable, non-invasive, modular platform. Lander stations will be able to continuously deliver high-rate data while AUVs can provide comprehensive spatial measurements

Numerous studies have focused on the scavenger communities that feed on the carcasses of large marine animals, such as whales, in deep-sea habitats. Yet, there are far fewer studies in shallow water ecosystems and especially in the Mediterranean. Here, we performed an artificial cetacean fall in shallow waters in the northwestern Mediterranean. The cetacean carcass was monitored by via 30-min time-lapse photos using a fixed camera. We observed that bony fish were the main scavenger taxa. In addition, different species arrived at different times perhaps reflecting their role as scavengers or active predators

To improve our understanding of how marine ecosystems function, it is crucial to quantify their processes using proper spatio-temporal multiparametric monitoring techniques. Science and innovative technologies must play a central role in developing the Blue Growth in a sustainable manner, where advances in enabling technologies such as remote sensing, modelling, AI and autonomous systems, will enhance our capacity to monitor and predict, assess and manage ecosystems. The PLOME project proposes a spatially adaptive, non-invasive, modular platform of independent and wirelessly connected benthic stations and AUVs to intelligently observe, monitor and map marine ecosystems, during long-lasting periods with real-time supervision. The monitoring solution has a simple deployment and is easy-to-move from an experimental site to another, without any cable installation, for coastal and deep water environments. Stations provide continuous and intensive temporal observation, while AUVs can provide such intensive measurement at spatial level, when they undock for a mission from a station in which they previously recharged batteries and transmitted information. The PLOME project will demonstrate the proposed concept in two scenarios. The first one, involves testing independent capabilities in a real deep-sea scenario, while the second one entails a oneweek demonstration in shallow water, where an AUV will be operated from a docking station. This paper describes the Girona 1000 AUV from the Universitat de Girona that will be used for the deep tests , conducted at depths ranging from 200 to 400 meters. The AUV will be used in cooperation with two fixed stations developed by the Universitat Politècnica de Catalunya. Acoustic communications and ranges between the AUV and the stations will be used to coordinate the AUV’s work and to improve its navigation. Optical communications will be used to transmit data to the stations gathered from the AUV observations. The AUV will integrate a, Peer Reviewed

N. norvegicus supports one of the most commercially-important fisheries in the Mediterranean, and there is considerable interest in developing non-invasive sampling stock assessment methods. Currently, stock assessments are conducted via trawling or by UnderWater TeleVision (UWTV) surveys with limited capacity to provide direct population data due to the burrowing behavior of the species. Here, we used acoustic telemetry to characterize the burrow emergence and movement patterns of N. norvegicus in relation to internal tides and inertial currents in deep-sea habitats of the northwestern Mediterranean. We deployed acoustic tags on 25 adults between May and June 2019, and collected up to 4 months of detection data from each tag. Tagged lobsters spent approximately 12% of their time in door-keeping (i.e., detections at burrow entrance with no displacements) but differences existed among the different behavioral rhythms identified. We observed that mixed day-night, tidal and inertial rhythms in field settings were similar to those observed in laboratory tests. The presence of mixed day-night and tidal periodicity poses the question of why N. norvegicus exhibits flexible responses to environmental cues other than photoperiod. It is possible that tidal regulation of locomotor activity could reduce energy expenditure in relation to hydrodynamic drag. Inertial periodicity occurs in animals with no clear burrowing-oriented activity (always present on the seabed). Possibly, inertial-related movements are the result of a disruption of the biological clock. Our results are discussed in the context of how burrow emergence rhythms may bias UWTV surveys and how novel in situ monitoring approaches address these biases

Animal detection, classification and tracking as edge computing functionalities of mobile robotic platforms are increasingly relevant in marine ecosystem monitoring (Aguzzi et al., 2020; 2022). Time-series of geo-referenced counts for different species are crucial to train AI-based data processing algorithms. These will be executed on-board underwater robotic platforms, to deliver real-time, remote information on abundances and biodiversity. Crawlers are emerging mobile robotic platforms, either tethered to permanent infrastructures like cabled observatories, offshore industrial rigs, and mariculture assets (Danovaro et al., 2019), or moving autonomously along the seafloor for extended periods. Bearing cameras and complex sets of oceanographic and geochemical sensors, they can be used to consistently expand the radius of ecological monitoring of fixed cabled observatories by video-sweeping large seabed surfaces (Chatzievangelou et al., 2020), the benthic boundary layer (as the benthic-pelagic ecotone; Chatzievangelou et al., 2021), and the overlaying water column. Our objective is the automated, real-time image processing to classify and track multiple species, opening the pathway toward the creation of crawler on-board video-intelligence. Accordingly, manual classification of animals in videos acquired by the crawler Wally at the Barkley Canyon hydrates (900 m depth; NE Pacific) site of Ocean Networks Canada’s NEPTUNE observatory is ongoing, as a necessary step to create groundtruth datasets to train AI algorithms. Examples of key species (Figure 1) and AI tracking and classification (Figure 2) are provided

The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler “Wally” (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers.

Over half a century ago, following the development and integration of the adequate technology, the deep sea transitioned from being the last frontier for exploration on our planet to the subject of industrial-level exploitation. While the main impacts in the deep sea are currently the product of large-scale fisheries and offshore energy production (mainly oil and gas), the expected transition of the global economy towards greener (or, in this case, bluer) solutions will likely include the use of the marine domain as a setting for renewable energy infrastructure (e.g., floating wind farms loosely anchored to the seabed) and seabed mining for rare minerals that are fundamental to the renewable energy technologies. Despite the risks posed by these impacts, the development of robust management guidelines for deep-sea habitats and resources has not kept pace with technological progress and growing economic forces. As a result, this highlights an opportunity to develop standardized methodologies, goals and overall strategies. To achieve this, it is imperative for scientists and managing authorities to reach a high level of consensus in both data acquisition and treatment, as well as in reliable ecological indicators to track both natural and human-induced ecosystem changes. [...]

Deep-sea fishing has been carried out on an industrial scale since the 1950s, and this has had a variety of effects on the environment and its biota. Most benthic species experience a decline in abundance or a constant decline in abundance as a result of direct disturbance of the seafloor, such as its plowing and scraping by hauled nets, with overall impacts on regional biodiversity [1]. Sediment has lost some of its biogenic habitat complexity, and sessile epifauna-provided microhabitat has been destroyed or disrupted [2] and marine protected areas (MPAs) have been widely implemented to address this decline. Marine fish mobility, which is crucial for ecosystem function and is increasingly being researched with acoustic telemetry, has an impact on how well no-take MPAs (i.e., marine reserves) work in terms of protecting and repopulating fish populations [3], [4] Therefore, it is necessary to continuously monitor periodic changes in commercially exploited deep-sea ecosystems in order to gather baseline information, give accurate environmental impact assessments, and derive sound biological indicators for restoration. Using a fixed acoustic ultra-short baseline (USBL) receiver on benthic lander and miniature bidirectional acoustic tags [5], we address three key questions: How far can fish move? Does connectivity exist between adjacent MPAs? Does existing MPA size match the spatial scale of fish movements?, Peer Reviewed

Nowadays, there is a growing awareness on the social and economic importance of the ocean. In this context, being able to carry out a diverse range of operations underwater is of paramount importance for many industrial sectors as well as for marine science and to enforce restoration and mitigation actions. Underwater robots allowed us to venture deeper and for longer time into the remote and hostile marine environment. However, traditional design concepts such as propeller driven remotely operated vehicles, autonomous underwater vehicles, or tracked benthic crawlers, present intrinsic limitations, especially when a close interaction with the environment is required. An increasing number of researchers are proposing legged robots as a bioinspired alternative to traditional designs, capable of yielding versatile multi-terrain locomotion, high stability, and low environmental disturbance. In this work, we aim at presenting the new field of underwater legged robotics in an organic way, discussing the prototypes in the state-of-the-art and highlighting technological and scientific challenges for the future. First, we will briefly recap the latest developments in traditional underwater robotics from which several technological solutions can be adapted, and on which the benchmarking of this new field should be set. Second, we will the retrace the evolution of terrestrial legged robotics, pinpointing the main achievements of the field. Third, we will report a complete state of the art on underwater legged robots focusing on the innovations with respect to the interaction with the environment, sensing and actuation, modelling and control, and autonomy and navigation. Finally, we will thoroughly discuss the reviewed literature by comparing traditional and legged underwater robots, highlighting interesting research opportunities, and presenting use case scenarios derived from marine science applications

The use of marine cabled video observatories with multiparametric environmental data collection capability is becoming relevant for ecological monitoring strategies. Their ecosystem surveying can be enforced in real time, remotely, and continuously, over consecutive days, seasons, and even years. Unfortunately, as most observatories perform such monitoring with fixed cameras, the ecological value of their data is limited to a narrow field of view, possibly not representative of the local habitat heterogeneity. Docked mobile robotic platforms could be used to extend data collection to larger, and hence more ecologically representative areas. Among the various state-of-the-art underwater robotic platforms available, benthic crawlers are excellent candidates to perform ecological monitoring tasks in combination with cabled observatories. Although they are normally used in the deep sea, their high positioning stability, low acoustic signature, and low energetic consumption, especially during stationary phases, make them suitable for coastal operations. In this paper, we present the integration of a benthic crawler into a coastal cabled observatory (OBSEA) to extend its monitoring radius and collect more ecologically representative data. The extension of the monitoring radius was obtained by remotely operating the crawler to enforce back-and-forth drives along specific transects while recording videos with the onboard cameras. The ecological relevance of the monitoring-radius extension was demonstrated by performing a visual census of the species observed with the crawler’s cameras in comparison to the observatory’s fixed cameras, revealing non-negligible differences. Additionally, the videos recorded from the crawler’s cameras during the transects were used to demonstrate an automated photo-mosaic of the seabed for the first time on this class of vehicles. In the present work, the crawler travelled in an area of 40 m away from the OBSEA, producing an extension of the mo

The use of marine cabled video observatories with multiparametric environmental data collection capability is becoming relevant for ecological monitoring strategies. Their ecosystem surveying can be enforced in real time, remotely, and continuously, over consecutive days, seasons, and even years. Unfortunately, as most observatories perform such monitoring with fixed cameras, the ecological value of their data is limited to a narrow field of view, possibly not representative of the local habitat heterogeneity. Docked mobile robotic platforms could be used to extend data collection to larger, and hence more ecologically representative areas. Among the various state-of-the-art underwater robotic platforms available, benthic crawlers are excellent candidates to perform ecological monitoring tasks in combination with cabled observatories. Although they are normally used in the deep sea, their high positioning stability, low acoustic signature, and low energetic consumption, especially during stationary phases, make them suitable for coastal operations. In this paper, we present the integration of a benthic crawler into a coastal cabled observatory (OBSEA) to extend its monitoring radius and collect more ecologically representative data. The extension of the monitoring radius was obtained by remotely operating the crawler to enforce back-and-forth drives along specific transects while recording videos with the onboard cameras. The ecological relevance of the monitoring-radius extension was demonstrated by performing a visual census of the species observed with the crawler’s cameras in comparison to the observatory’s fixed cameras, revealing non-negligible differences. Additionally, the videos recorded from the crawler’s cameras during the transects were used to demonstrate an automated photo-mosaic of the seabed for the first time on this class of vehicles. In the present work, the crawler travelled in an area of 40 m away from the OBSEA, producing an extension of the mo, This research was partially funded by JERICO-S3 project (Joint European Research Infrastructure of Coastal Observatories: Science, Service, Sustainability, Call: H2020-INFRAIA-2019-1, Project ID: 871153) and BITER project (grant agreement PID2020-114732RB-C32, financially supported by the Ministerio de Ciencia e Innovación). A.F. was funded by the pre-doctoral fellowship from AGAUR ref. BDNS 474817., Peer Reviewed, Postprint (published version)

The burrowing crustacean decapod Nephrops norvegicus is a significant species in European Atlantic and Mediterranean fisheries. Research over the decades has mainly focused on behavioral and physiological aspects related to the burrowing lifestyle, since animals can only be captured by trawls when engaged in emergence on the seabed. Here, we performed a global bibliographic survey of all the scientific literature retrieved in SCOPUS since 1965, and terminology maps were produced with the VOSviewer software to reveal established and emerging research areas. We produced three term-map plots: term clustering, term citation, and term year. The term clustering network showed three clusters: fishery performance, assessment, and management; biological cycles in growth, reproduction, and behavior; and finally, physiology and ecotoxicology, including food products. The term citation map showed that intense research is developed on ecotoxicology and fishery management. Finally, the term year map showed that the species was first studied in its morphological and physiological aspects and more recently in relation to fishery and as a food resource. Taken together, the results indicate scarce knowledge on how burrowing behavior and its environmental control can alter stock assessment, because of the poor use of current and advanced monitoring technologies

In the last decades, stock assessment surveys targeting the Norway lobster Nephrops norvegicus have been gradually moving from physical/extracting sampling (e.g., trawling surveys) towards video/imaging-based solutions such as UnderWater TeleVision (UWTV) surveys with towed sledges. However, both approaches still face operational challenges which may introduce bias when translating the basic data (individuals captured by trawling or burrow systems filmed by UWTV) to accurate densities. In the future, an ecosystem-based monitoring and assessment plan should: improve the currently used equation “1 burrow system ≈ 1 animal”; include activity rhythms in sampling; derive other ecological indicators (e.g., biodiversity); and increase automa-tion in image/data processing. This requires deploying intelligent monitoring networks consist-ing of stationary and mobile platforms with distinct focus and capabilities, while integrating novel sampling methodologies (i.e., eDNA/eRNA; opto-acoustic mapping, etc.). In parallel, pow-erful Artificial Intelligence algorithms should be integrated to streamline data analysis and assist the extraction of ecological information in the form of hierarchically computed indicators, from animal counts and size all the way to ecosystem functioning. This rationale was detailed in a 2022 publication led by ICM-CSIC (Aguzzi et al., 2022), with the participation of several WGNEPS members

The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler “Wally” (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers

The Working Group on Nephrops Surveys (WGNEPS) is the international coordination group for Nephrops underwater television and trawl surveys within ICES. This report summarizes the national contributions on the results of the surveys conducted in 2022 together with time series covering all survey years, problems encountered, data quality checks and technological improvements as well as the planning for survey activities for 2023. In total, 21 surveys covering 26 functional units (FU’s) in the ICES area and 1 geographical subarea (GSA) in the Adriatic Sea were discussed and further improvements in respect to survey design and data analysis standardization and the use of most recent technology were reviewed. The first exploratory UWTV survey on the FU 25 Nephrops grounds was also presented to the group. The results of the evaluation of reference sets for FU3&4 Skagerrak/Kattegat were accepted following the process set down by the 2018 workshop (WKNEPS). An alternative method estimate Nephrops abundance was shown to the group using the recently published R package sdmTMB. The group agreed to hold a workshop in 2025 to address burrow size estimations to update correction factors and terms of reference for this to be agreed at next meeting. Automatic burrow detection based on deep learning methods continues to show promising results where datasets from multiple institutes were used. Plans are being progressed for an international Nephrops UWTV database to be established at the ICES data centre with a sub-group

To improve our understanding of how marine ecosystems function, it is crucial to quantify their processes using proper spatio-temporal multiparametric monitoring techniques. Science and innovative technologies must play a central role in developing the Blue Growth in a sustainable manner, where advances in enabling technologies such as remote sensing, modelling, AI and autonomous systems, will enhance our capacity to monitor and predict, assess and manage ecosystems. The PLOME project proposes a spatially adaptive, non-invasive, modular platform of independent and wirelessly connected benthic stations and AUVs to intelligently observe, monitor and map marine ecosystems, during long-lasting periods with real-time supervision. The monitoring solution has a simple deployment and is easy-to-move from an experimental site to another, without any cable installation, for coastal and deep water environments. Stations provide continuous and intensive temporal observation, while AUVs can provide such intensive measurement at spatial level, when they undock for a mission from a station in which they previously recharged batteries and transmitted information. The PLOME project will demonstrate the proposed concept in two scenarios. The first one, involves testing independent capabilities in a real deep-sea scenario, while the second one entails a oneweek demonstration in shallow water, where an AUV will be operated from a docking station. This paper describes the Girona 1000 AUV from the Universitat de Girona that will be used for the deep tests , conducted at depths ranging from 200 to 400 meters. The AUV will be used in cooperation with two fixed stations developed by the Universitat Politècnica de Catalunya. Acoustic communications and ranges between the AUV and the stations will be used to coordinate the AUV’s work and to improve its navigation. Optical communications will be used to transmit data to the stations gathered from the AUV observations. The AUV will integrate a

Deep-sea fishing has been carried out on an industrial scale since the 1950s, and this has had a variety of effects on the environment and its biota. Most benthic species experience a decline in abundance or a constant decline in abundance as a result of direct disturbance of the seafloor, such as its plowing and scraping by hauled nets, with overall impacts on regional biodiversity [1]. Sediment has lost some of its biogenic habitat complexity, and sessile epifauna-provided microhabitat has been destroyed or disrupted [2] and marine protected areas (MPAs) have been widely implemented to address this decline. Marine fish mobility, which is crucial for ecosystem function and is increasingly being researched with acoustic telemetry, has an impact on how well no-take MPAs (i.e., marine reserves) work in terms of protecting and repopulating fish populations [3], [4] Therefore, it is necessary to continuously monitor periodic changes in commercially exploited deep-sea ecosystems in order to gather baseline information, give accurate environmental impact assessments, and derive sound biological indicators for restoration. Using a fixed acoustic ultra-short baseline (USBL) receiver on benthic lander and miniature bidirectional acoustic tags [5], we address three key questions: How far can fish move? Does connectivity exist between adjacent MPAs? Does existing MPA size match the spatial scale of fish movements?

This paper presents a software infrastructure based on Deep Learning aimed at identifying habitats of certain species of crustacean that colonize areas of the marine Mesopelagic zone. Determining their presense is done, in this case, from the detection of holes in the sand that form sets of burrow structures. Preliminar inferencing models are obtained from images captured in the North Sea by trawled UWTV (undewater TV) stations, offering quite significant detection success ratios

The use of technology as a low-impacting monitoring method is becoming widespread across the oceans. Furthermore, the declared United Nation (UN) Decade of Ocean Science for Sustainable Development highlighted the importance of the technological development for the protection and sustainable use of marine resources. In particular, the use of seafloor cabled observatories in an integrated way, with a multitude of sensors and cameras, will be fundamental in the future management decisions of marine resources. Cameras in such infrastructures could be used as biological sensors extracting meaningful information on number of individuals per marine species. [...]

Recent advances in robotic design, autonomy and sensor integration create solutions for the exploration of deep-sea environments,transferable to the oceans of icy moons. Marine platforms do not yet have the mission autonomy capacity of their space counterparts (e.g., the state of the art Mars Perseverance rover mission), although different levels of autonomous navigation and mapping, as well as sampling, are an extant capability. In this setting their increasingly biomimicked designs may allow access to complex environmental scenarios, with novel, highly-integrated life-detecting, oceanographic and geochemical sensor packages. Here, we lay an outlook for the upcoming advances in deep-sea robotics through synergies with space technologies within three major research areas: biomimetic structure and propulsion (including power storage and generation), artificial intelligence and cooperative networks, and life-detecting instrument design. New morphological and material designs, with miniaturized and more diffuse sensor packages, will advance robotic sensing systems. Artificial intelligence algorithms controlling navigation and communications will allow the further development of the behavioral biomimicking by cooperating networks. Solutions will have to be tested within infrastructural networks of cabled observatories, neutrino telescopes, and off-shore industry sites with agendas and modalities that are beyond the scope of our work, but could draw inspiration on the proposed examples for the operational combination of fixed and mobile platforms.

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.

Coordinated systems composed by fixed and mobile robotic platforms are used for the ecological monitoring of megafauna across ecological gradients (oceanographic, geomorphological and substrate characteristics across the bathymetry of the slope and through water column layers) continuously at a high temporal frequency. None of these platforms is branching into estuarine and river areas, providing the coupling of ecological gradients that encompass marine and inland waters. Here, we propose a conceptual scheme to bring forward that integration, by establishing the principles of a network of fixed platforms working not only with HD imaging but also with advanced optoacoustic tools that works in turbid waters. This sensor dotation may be expanded by tools for augmented species presence detection beyond the HD and optoacoustic imaging, such as Passive Acoustic Monitoring and omics approaches (eDNA). Detection by acoustic and molecular markers can be cross-validated against libraries of images. The use of land-docked crawlers may allow stepping-stone observations between patches to spatially scale local data, extending the ecological representativeness of local observations. Those infrastructures may be of a special relevance for legally prescribed monitoring of endangered European eel (Anguilla anguilla), but also for other species, including anadromous Salmo salar in increasingly impacted coastal-river areas

Our understanding of marine ecosystem functioning and processes relies on adequate spatio-temporal multiparametric monitoring procedures. Over the next 3 years, the Project PLOME (Platforms for Long-lasting Observation of Marine Ecosystems) will implement a spatially adaptive and autonomous network of easy-to-use benthic landers with dockable Autonomous Underwater Vehicles (AUVs)ñ This network will be used to intelligently video-monitor and map marine ecosystems and their environment from coastal to deep-sea areas. All platforms will be connected via acoustic or optical communication and will operate over periods of weeks to months with real-time supervision. Stations will provide continuous and intensive temporal observations, while dockable AUVs (with battery recharge and data downloading capability) will provide intensive measurements at various spatial scales, using intelligent and adaptive trajectories to explore surrounding areas. Biological, geochemical and oceanographic data will be generated by an array of sensors including acoustic receivers and cameras. Images will be processed in real-time for species classification and tracking, using advanced data analysis and Deep Learning techniques. Metadata will be communicated between landers and AUVs and transmitted opportunistically whenever an Unmanned Surface Vehicle (USV) connects the platform via aerial communications (i.e. GSM and satellite communications, depending on form distance to shore). The unattended operation will also be possible with an innovation of pop-up buoys that will allow data transfer to the surface from landers and UAVs to be relayed once the pop-up buoys reach the surface. Complex ecological indicators for ecosystem management will be computed from the collected data, by applying advanced computer vision techniques to classify, count and size individuals in video images and to generate multimodal maps of the seabed. A pipeline for automated data treatment will be tailored for multiparam

Ocean Networks Canada (ONC) operates large seafloor cabled observatories in the Arctic, Atlantic and Pacific, with some of its long-term observations surpassing 16 years. We present snapshot results from long-term video time-series observations and in-situ experiments studying the benthic boundary layer in two coastal and one continental margin setting of Canada¿s Atlantic and Pacific Oceans. Using video imagery spanning for 7 years (2013-2020) we studied the deep-sea pink urchin Strongylocentrotus fragilis with respect to the expanding oxygen minimum zone in Barkley Canyon (420 m), NE Pacific. In a second case study, we analyzed 6 months of hourly videos from the newly installed Holyrood observatory in Conception Bay, Newfoundland, Atlantic, to investigate benthic-pelagic coupling following the onset of the 2021 spring bloom. From a series of short-term experiments, we combined video and acoustic imagery (dual-frequency identification sonars) and passive acoustics data to better understand poorly understood fish vocalizations, overall temporal changes in benthic abundance and diversity, and behavioural responses to artificial lighting. In a first experiment, in turbid waters of the Fraser River Delta (150 m), Strait of Georgia, the acoustic camera proved to be the most efficient device for measuring faunal densities, while the video was more efficient in detecting a moderately diverse assemblage of fish and invertebrates. Light avoidance behaviour was detected in a large number of species while light attraction was verified for the spotted ratfish Hydrolagus colliei. In the second and third experiments, deployed at 640 m depth adjacent to Barkley Canyon, sequential bait-introduction was employed for the study of benthic successional patterns of deep-sea scavenger communities under limiting dissolved oxygen conditions. Lastly, we present an example of machine learning using a deep learning neural network applied to the automatic detection of commercially harvested s

Ocean observatories collect large volumes of video data, with some data archives now spanning well over a few decades, and bringing the challenges of analytical capacity beyond conventional processing tools. The analysis of such vast and complex datasets can only be achieved with appropriate machine learning and Artificial Intelligence (AI) tools. The implementation of AI monitoring programs for animal tracking and classification becomes necessary in the particular case of deep-sea cabled observatories, as those operated by Ocean Networks Canada (ONC), where Petabytes of data are now collected each and every year since their installation. Here, we present a machine-learning and computer vision automated pipeline to detect and count sablefish (Anoplopoma fimbria), a key commercially exploited species in the N-NE Pacific. We used 651 hours of video footage obtained from three long-term monitoring sites in the NEPTUNE cabled observatory, in Barkley Canyon, on the nearby slope, and at depths ranging from 420 to 985 m. Our proposed AI sablefish detection and classification pipeline was tested and validated for an initial 4.5 month period (Sep 18 2019-Jan 2 2020), and was a first step towards validation for future processing of the now decade-long video archives from Barkley Canyon. For the validation period, we trained a YOLO neural network on 2917 manually annotated frames containing sablefish images to obtain an automatic detector with a 92% Average Precision (AP) on 730 test images, and a 5-fold cross-validation AP of 93% (± 3.7%). We then ran the detector on all video material (i.e., 651 hours from a 4.5 month period), to automatically detect and annotate sablefish. We finally applied a tracking algorithm on detection results, to approximate counts of individual fishes moving on scene and obtain a time series of proxy sablefish abundance. Those proxy abundance estimates are among the first to be made using such a large volume of video data from deep-sea settings. We

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

Scientific, industrial and societal needs call urgently for the development and establishment of intelligent, cost-effective and ecologically sustainable monitoring protocols and robotic platforms for the continuous exploration of marine ecosystems. Internet Operated Vehicles (IOVs) such as crawlers, provide a versatile alternative to conventional observing and sampling tools, being tele-operated, (semi-) permanent mobile platforms capable of operating on the deep and coastal seafloor. Here we present outstanding observations made by the crawler “Wally” in the last decade at the Barkley Canyon (BC, Canada, NE Pacific) methane hydrates site, as a part of the NEPTUNE cabled observatory. The crawler followed the evolution of microhabitats formed on and around biotic and/or abiotic structural features of the site (e.g., a field of egg towers of buccinid snails, and a colonized boulder). Furthermore, episodic events of fresh biomass input were observed (i.e., the mass transport of large gelatinous particles, the scavenging of a dead jellyfish and the arrival of macroalgae from shallower depths). Moreover, we report numerous faunal behaviors (i.e., sablefish rheo- and phototaxis, the behavioral reactions and swimming or resting patterns of further fish species, encounters with octopuses and various crab intra- and interspecific interactions). We report on the observed animal reactions to both natural and artificial stimuli (i.e., crawler’s movement and crawler light systems). These diverse observations showcase different capabilities of the crawler as a modern robotic monitoring platform for marine science and offshore industry. Its long deployments and mobility enable its efficiency in combining the repeatability of long-term studies with the versatility to opportunistically observe rarely seen incidents when they occur, as highlighted here. Finally, we critically assess the empirically recorded ecological footprint and the potential impacts of crawler operations on the

This work proposes the experimentation of an innovative hydraulic dredge for clam fishing (Chamelea gallina) in the Adriatic Sea (Italy). This innovative gear aimed at increasing the selectivity of the typical hydraulic dredge used currently, while at the same reducing the impact on benthos through the conception, installation, and experimentation of innovative technological solutions, consisting mainly of a vibrating bottom panel on the dredge and a “warning device” on the dredge mouth. Comparative experiments of the traditional vs. the modified gear, employing two boats fishing in parallel on the northern coast of Abruzzi (Adriatic Sea) and contrasting the catch with both paired comparisons and through modelling, showed that the innovative hydraulic dredge retains fewer undersize clams while yielding similar amounts of commercial product, moreover of higher quality; at the same time, it takes on board less discard, and catches significantly less vagile fauna. In short, the innovative gear is gaining five times over a list of six parameters considered as positive and/or advantageous for the clam fishery. The results allow proposals of potential improvements to clam-fishing instruments to make the selection processes more effective while promoting a lower impacting fishery, which is essential for clam management