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  • 1
    Online Resource
    Online Resource
    Integrating Diel Vertical Migrations of Bioluminescent Deep Scattering Layers Into Monitoring Programs
    Frontiers Media SA ; 2021
    In:  Frontiers in Marine Science Vol. 8 ( 2021-5-28)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 8 ( 2021-5-28)
    Abstract: The deep sea (i.e., & gt;200 m depth) is a highly dynamic environment where benthic ecosystems are functionally and ecologically connected with the overlying water column and the surface. In the aphotic deep sea, organisms rely on external signals to synchronize their biological clocks. Apart from responding to cyclic hydrodynamic patterns and periodic fluctuations of variables such as temperature, salinity, phytopigments, and oxygen concentration, the arrival of migrators at depth on a 24-h basis (described as Diel Vertical Migrations; DVMs), and from well-lit surface and shallower waters, could represent a major response to a solar-based synchronization between the photic and aphotic realms. In addition to triggering the rhythmic behavioral responses of benthic species, DVMs supply food to deep seafloor communities through the active downward transport of carbon and nutrients. Bioluminescent species of the migrating deep scattering layers play a not yet quantified (but likely important) role in the benthopelagic coupling, raising the need to integrate the efficient detection and quantification of bioluminescence into large-scale monitoring programs. Here, we provide evidence in support of the benefits for quantifying and continuously monitoring bioluminescence in the deep sea. In particular, we recommend the integration of bioluminescence studies into long-term monitoring programs facilitated by deep-sea neutrino telescopes, which offer photon counting capability. Their Photo-Multiplier Tubes and other advanced optical sensors installed in neutrino telescope infrastructures can boost the study of bioluminescent DVMs in concert with acoustic backscatter and video imagery from ultra-low-light cameras. Such integration will enhance our ability to monitor proxies for the mass and energy transfer from the upper ocean into the deep-sea Benthic Boundary Layer (BBL), a key feature of the ocean biological pump and crucial for monitoring the effects of climate-change. In addition, it will allow for investigating the role of deep scattering DVMs in the behavioral responses, abundance and structure of deep-sea benthic communities. The proposed approach may represent a new frontier for the study and discovery of new, taxon-specific bioluminescence capabilities. It will thus help to expand our knowledge of poorly described deep-sea biodiversity inventories and further elucidate the connectivity between pelagic and benthic compartments in the deep-sea.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2021.661809
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2757748-X
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  • 2
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    High-Frequency Patterns in the Abundance of Benthic Species near a Cold-Seep – An Internet Operated Vehicle Application
    Public Library of Science (PLoS) ; 2016
    In:  PLOS ONE Vol. 11, No. 10 ( 2016-10-12), p. e0163808-
    Details
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 11, No. 10 ( 2016-10-12), p. e0163808-
    Type of Medium: Online Resource
    ISSN: 1932-6203
    URL: Article
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    DOI: 10.1371/journal.pone.0163808
    DOI: 10.1371/journal.pone.0163808.g001
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    DOI: 10.1371/journal.pone.0163808.s005
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2016
    detail.hit.zdb_id: 2267670-3
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  • 3
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    The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms
    MDPI AG ; 2020
    In:  Sensors Vol. 20, No. 6 ( 2020-03-21), p. 1751-
    Details
    In: Sensors, MDPI AG, Vol. 20, No. 6 ( 2020-03-21), p. 1751-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s20061751
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 4
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    A New Coastal Crawler Prototype to Expand the Ecological Monitoring Radius of OBSEA Cabled Observatory
    MDPI AG ; 2023
    In:  Journal of Marine Science and Engineering Vol. 11, No. 4 ( 2023-04-18), p. 857-
    Details
    In: Journal of Marine Science and Engineering, MDPI AG, Vol. 11, No. 4 ( 2023-04-18), p. 857-
    Abstract: 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 monitoring field of view (FOV), and covering an area approximately 230 times larger than OBSEA’s camera. The analysis of the videos obtained from the crawler’s and the observatory’s cameras revealed differences in the species observed. Future implementation scenarios are also discussed in relation to mission autonomy to perform imaging across spatial heterogeneity gradients around the OBSEA.
    Type of Medium: Online Resource
    ISSN: 2077-1312
    URL: Article
    DOI: 10.3390/jmse11040857
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2738390-8
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  • 5
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    Video_2.mp4
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 9 ( 2022-8-26)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 9 ( 2022-8-26)
    Abstract: 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 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 species.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
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    DOI: 10.3389/fmars.2022.842946
    DOI: 10.3389/fmars.2022.842946.s001
    DOI: 10.3389/fmars.2022.842946.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
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  • 6
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    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 9 ( 2022-8-29)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 9 ( 2022-8-29)
    Abstract: 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 benthic ecosystem of the Barkley Canyon hydrates site, together with potential solutions to mitigate them into the future.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    URL: Article
    DOI: 10.3389/fmars.2022.833617
    DOI: 10.3389/fmars.2022.833617.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
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  • 7
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    Seasonal monitoring of deep-sea megabenthos in Barkley Canyon cold seep by internet operated vehicle (IOV)
    Public Library of Science (PLoS) ; 2017
    In:  PLOS ONE Vol. 12, No. 5 ( 2017-5-30), p. e0176917-
    Details
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 12, No. 5 ( 2017-5-30), p. e0176917-
    Type of Medium: Online Resource
    ISSN: 1932-6203
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    DOI: 10.1371/journal.pone.0176917
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    DOI: 10.1371/journal.pone.0176917.s004
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2017
    detail.hit.zdb_id: 2267670-3
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  • 8
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    Deep learning based deep-sea automatic image enhancement and animal species classification
    Springer Science and Business Media LLC ; 2023
    In:  Journal of Big Data Vol. 10, No. 1 ( 2023-03-24)
    Details
    In: Journal of Big Data, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2023-03-24)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2196-1115
    URL: Article
    DOI: 10.1186/s40537-023-00711-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2780218-8
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  • 9
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    Research Trends and Future Perspectives in Marine Biomimicking Robotics
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 11 ( 2021-05-29), p. 3778-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 11 ( 2021-05-29), p. 3778-
    Abstract: Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950–2020), evidencing a sharp research increase in 2003–2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21113778
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2052857-7
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  • 10
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    DataSheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 9 ( 2022-9-9)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 9 ( 2022-9-9)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    URL: Article
    DOI: 10.3389/fmars.2022.969071
    DOI: 10.3389/fmars.2022.969071.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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