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  • Bonofiglio, Federico  (3)
  • 2020-2024  (3)
  • 1
    Online Resource
    Online Resource
    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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  • 2
    Online Resource
    Online Resource
    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
    URL: Article
    URL: Article
    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
    detail.hit.zdb_id: 2757748-X
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  • 3
    Online Resource
    Online Resource
    Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 8 ( 2022-1-12)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 8 ( 2022-1-12)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2021.797140
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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