The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

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 ReviewedPostprint (author's final draft

Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures

17 pages, 1 figure, 1 tableDeep-sea ecosystems are reservoirs of biodiversity that are largely unexplored, but their exploration and biodiscovery are becoming a reality thanks to biotechnological advances (e.g., omics technologies) and their integration in an expanding network of marine infrastructures for the exploration of the seas, such as cabled observatories. While still in its infancy, the application of environmental DNA (eDNA) metabarcoding approaches is revolutionizing marine biodiversity monitoring capability. Indeed, the analysis of eDNA in conjunction with the collection of multidisciplinary optoacoustic and environmental data, can provide a more comprehensive monitoring of deep-sea biodiversity. Here, we describe the potential for acquiring eDNA as a core component for the expanding ecological monitoring capabilities through cabled observatories and their docked Internet Operated Vehicles (IOVs), such as crawlers. Furthermore, we provide a critical overview of four areas of development: (i) Integrating eDNA with optoacoustic imaging; (ii) Development of eDNA repositories and cross-linking with other biodiversity databases; (iii) Artificial Intelligence for eDNA analyses and integration with imaging data; and (iv) Benefits of eDNA augmented observatories for the conservation and sustainable management of deep-sea biodiversity. Finally, we discuss the technical limitations and recommendations for future eDNA monitoring of the deep-sea. It is hoped that this review will frame the future direction of an exciting journey of biodiscovery in remote and yet vulnerable areas of our planet, with the overall aim to understand deep-sea biodiversity and hence manage and protect vital marine resourcesThis research has been funded within the framework of the following project activities: ARIM (Autonomous Robotic Sea-Floor Infrastructure for Benthopelagic Monitoring; MarTERA ERA-Net Cofound); RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades); JERICO-S3: (Horizon 2020; Grant Agreement no. 871153); ENDURUNS (Research Grant Agreement H2020-MG-2018-2019-2020 n.824348); Slovenian Research Agency (Research Core Funding Nos. P1-0237 and P1-0255 and project ARRS-RPROJ-JR-J1-3015). We also profited of the funding from the Spanish Government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) and Italian Ministry of Education (MIUR) under the “Bando premiale FOE 2015” (nota prot. N. 850, dd. 27 ottobre 2017) with the project EarthCruisers “EARTH’s CRUst Imagery for Investigating Seismicity, Volcanism, and Marine Natural Resources in the Sicilian Offshore”. Ocean Networks Canada was funded through Canada Foundation for Innovation-Major Science Initiative (CFI-MSI) fund 30199Peer reviewe

Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures

Deep-sea ecosystems are reservoirs of biodiversity that are largely unexplored, but their exploration and biodiscovery are becoming a reality thanks to biotechnological advances (e.g., omics technologies) and their integration in an expanding network of marine infrastructures for the exploration of the seas, such as cabled observatories. While still in its infancy, the application of environmental DNA (eDNA) metabarcoding approaches is revolutionizing marine biodiversity monitoring capability. Indeed, the analysis of eDNA in conjunction with the collection of multidisciplinary optoacoustic and environmental data, can provide a more comprehensive monitoring of deep-sea biodiversity. Here, we describe the potential for acquiring eDNA as a core component for the expanding ecological monitoring capabilities through cabled observatories and their docked Internet Operated Vehicles (IOVs), such as crawlers. Furthermore, we provide a critical overview of four areas of development: (i) Integrating eDNA with optoacoustic imaging ; (ii) Development of eDNA repositories and cross-linking with other biodiversity databases ; (iii) Artificial Intelligence for eDNA analyses and integration with imaging data ; and (iv) Benefits of eDNA augmented observatories for the conservation and sustainable management of deep-sea biodiversity. Finally, we discuss the technical limitations and recommendations for future eDNA monitoring of the deep-sea. It is hoped that this review will frame the future direction of an exciting journey of biodiscovery in remote and yet vulnerable areas of our planet, with the overall aim to understand deep-sea biodiversity and hence manage and protect vital marine resources

Olive flowering in South Italy

Phenological investigations that adopt aerobiological monitoring methodologies are frequently used for species that rely on the wind for pollen grain dispersion, such as the olive in the Mediterranean basin. The present study of olive flowering dates was carried out in the Calabria region (southern Italy). These were calculated on the basis of a phenological study of pollen levels in the atmosphere in three typical olive-growing areas over an 11 year study period (1999–2009). This phenological method provides olive flowering maps that are based on temperatures (as the growing degree days: GDDs), which are highly correlated with the release of the pollen grains. According to the model developed, the average GDDs corresponding to the flowering dates were calculated for the baseline period of 1981–2000. Moreover, with the use of meteorological data derived from the Intergovernmental Panel on Climate Change scenarios, the future olive flowering dates are estimated for the 20 year period from 2081 to 2100. The close relationships between the spring temperature trends and the reproductive phenological phases in the olive are highly sensitive to climatic change, which has implications in terms of potential latitude and altitude shifts in the olive cultivation areas. In some cultivation areas in southern Italy, the present particular combination of microclimate, soil status and level of erosion is considered as limiting to regular vegetative plant development. However, the use of olive cultivars that are specifically adapted to extremely stressful environments, in terms of high temperatures and water deficit, might represent the main solution for the mitigation of the consequences of climatic change

Machine learning applied to big data from marine cabled observatories: A case study of sablefish monitoring in the NE Pacific

Ideas for this paper resulted from discussions during the international workshop “Marine cabled observatories: moving towards applied monitoring for fisheries management, ecosystem function and biodiversity”, funded by Ocean Networks Canada and co-hosted by ICM-CSIC, in Barcelona, Spain on 4–5 October 2018.-- 15 pages, 8 figures, 1 table, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2022.842946/full#supplementary-material.-- Data availability statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. We made all training computations on the Google Colab notebook12. To repeat the training, please clone the Google Drive repository containing the annotated data at13. All detection, tracking, and time-series analyses are freely available for reproduction atOcean 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 discuss our AI results for application on a decade-long video monitoring program, and particularly with potential for complementing fisheries management practices of a commercially important speciesThis work was developed within the framework of the Research Unit Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring; MarTERA ERA-Net Cofound); RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades); PLOME (PLEC2021-007525/AEI/10.13039/501100011033; Ministerio de Ciencia, Innovación y Universidades); JERICO-S3: (Horizon 2020; Grant Agreement no. 871153); ENDURUNS (Research Grant Agreement H2020-MG-2018-2019-2020 n.824348). We also profited from the funding of the Spanish Government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). [...] Ocean Networks Canada is funded through Canada Foundation for Innovation-Major Science Initiative (CFI-MSI) fund 3019Peer reviewe

Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna

Measurement(s) Coastal Benthic Megafauna Technology Type(s) Autonomous Imaging Device Factor Type(s) None Sample Characteristic - Organism Sterechinus neumayeri • Odontaster • Trematomus • Polynoidae • Diplasterias • Ophiuridae • Ammothea • Neobuccinum eatoni • Staurocucumis • Flabegraviera mundata Sample Characteristic - Environment coastal sea water Sample Characteristic - Location East Antarctic

High-throughput video and acoustic imaging from seafloor cabled observatories for benthic ecosystem monitoring in coastal and deep-sea settings

4th Marine Imaging Workshop, 3-6 October 2022, Brest, FranceOcean 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 sablefish, Anoplopoma fimbria. With an 92% average precision detection algorithm, we applied results to more than 650 hours of video imagery, and discuss its applications for stock-related assessment metrics and monitoringPeer reviewe

The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

15 pages, 4 figures, supplementary material https://doi.org/10.1093/icesjms/fsaa169.-- There are no new data associated with this article. No new datawere generated or analysed in support of this researchSeafloor 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 assessmentsThis 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/2331With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data