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Knights AM, Lemasson AJ, Firth LB, Beaumont N, Birchenough S, Claisse J, Coolen JWP, Copping A, De Dominicis M, Degraer S, Elliott M, Fernandes PG, Fowler AM, Frost M, Henry LA, Hicks N, Hyder K, Jagerroos S, Love M, Lynam C, Macreadie PI, McLean D, Marlow J, Mavraki N, Montagna PA, Paterson DM, Perrow MR, Porter J, Bull AS, Schratzberger M, Shipley B, van Elden S, Vanaverbeke J, Want A, Watson SCL, Wilding TA, Somerfield PJ. To what extent can decommissioning options for marine artificial structures move us toward environmental targets? JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 350:119644. [PMID: 38000275 DOI: 10.1016/j.jenvman.2023.119644] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/20/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023]
Abstract
Switching from fossil fuels to renewable energy is key to international energy transition efforts and the move toward net zero. For many nations, this requires decommissioning of hundreds of oil and gas infrastructure in the marine environment. Current international, regional and national legislation largely dictates that structures must be completely removed at end-of-life although, increasingly, alternative decommissioning options are being promoted and implemented. Yet, a paucity of real-world case studies describing the impacts of decommissioning on the environment make decision-making with respect to which option(s) might be optimal for meeting international and regional strategic environmental targets challenging. To address this gap, we draw together international expertise and judgment from marine environmental scientists on marine artificial structures as an alternative source of evidence that explores how different decommissioning options might ameliorate pressures that drive environmental status toward (or away) from environmental objectives. Synthesis reveals that for 37 United Nations and Oslo-Paris Commissions (OSPAR) global and regional environmental targets, experts consider repurposing or abandoning individual structures, or abandoning multiple structures across a region, as the options that would most strongly contribute toward targets. This collective view suggests complete removal may not be best for the environment or society. However, different decommissioning options act in different ways and make variable contributions toward environmental targets, such that policy makers and managers would likely need to prioritise some targets over others considering political, social, economic, and ecological contexts. Current policy may not result in optimal outcomes for the environment or society.
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Affiliation(s)
- Antony M Knights
- University of Plymouth, School of Biological and Marine Sciences, Drake Circus, Plymouth, PL4 8AA, UK.
| | - Anaëlle J Lemasson
- University of Plymouth, School of Biological and Marine Sciences, Drake Circus, Plymouth, PL4 8AA, UK
| | - Louise B Firth
- University of Plymouth, School of Biological and Marine Sciences, Drake Circus, Plymouth, PL4 8AA, UK
| | - Nicola Beaumont
- Plymouth Marine Laboratory, Prospect Place, Devon, PL1 3DH, UK
| | - Silvana Birchenough
- Centre for Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft, Suffolk, NR33 0HT, UK
| | - Jeremy Claisse
- Department of Biological Sciences, California State Polytechnic University, Pomona, CA, 91768, USA; Vantuna Research Group, Occidental College, Los Angeles, CA, 90041, USA
| | - Joop W P Coolen
- Wageningen Marine Research, Ankerpark 27, 1781, AG, Den Helder, the Netherlands
| | - Andrea Copping
- Pacific Northwest National Laboratory and University of Washington, Seattle, USA
| | | | - Steven Degraer
- Royal Belgian Institute of Natural Sciences, Operational Directory Natural Environment, Marine Ecology and Management, Brussels, Belgium
| | - Michael Elliott
- School of Environmental Sciences, University of Hull, HU6 7RX, UK; International Estuarine & Coastal Specialists (IECS) Ltd., Leven, HU17 5LQ, UK
| | - Paul G Fernandes
- Heriot-Watt University, The Lyell Centre, Research Avenue South, Edinburgh, EH14 4AP, UK
| | - Ashley M Fowler
- New South Wales Department of Primary Industries, Sydney Institute of Marine Science, Mosman, NSW, 2088, Australia
| | - Matthew Frost
- Plymouth Marine Laboratory, Prospect Place, Devon, PL1 3DH, UK
| | - Lea-Anne Henry
- School of GeoSciences, University of Edinburgh, King's Buildings Campus, James Hutton Road, EH9 3FE, Edinburgh, UK
| | - Natalie Hicks
- School of Life Sciences, University of Essex, Colchester, Essex, UK
| | - Kieran Hyder
- Centre for Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft, Suffolk, NR33 0HT, UK; School of Environmental Sciences, University of East Anglia, Norwich, UK
| | - Sylvia Jagerroos
- King Abdullah University of Science & Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Milton Love
- Marine Science Institute, University of California Santa Barbara, USA
| | - Chris Lynam
- Centre for Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft, Suffolk, NR33 0HT, UK
| | - Peter I Macreadie
- Deakin University, School of Life and Environmental Sciences, Burwood, Australia
| | - Dianne McLean
- Australian Institute of Marine Science (AIMS), Perth, Australia; The UWA Oceans Institute, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Joseph Marlow
- Scottish Association for Marine Science (SAMS), Oban, UK
| | - Ninon Mavraki
- Wageningen Marine Research, Ankerpark 27, 1781, AG, Den Helder, the Netherlands
| | - Paul A Montagna
- Texas A&M University-Corpus Christi, Corpus Christi, TX, USA
| | - David M Paterson
- School of Biology, University of St Andrews, St Andrews, KY16 8LB, UK
| | - Martin R Perrow
- Department of Geography, University College London, Gower Street, London, WC1E 6BT, UK
| | - Joanne Porter
- International Centre Island Technology, Heriot-Watt University, Orkney Campus, Stromness, Orkney, UK
| | | | - Michaela Schratzberger
- Centre for Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft, Suffolk, NR33 0HT, UK
| | - Brooke Shipley
- Texas Parks and Wildlife Department, Coastal Fisheries - Artificial Reef Program, USA
| | - Sean van Elden
- School of Biological Sciences, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Jan Vanaverbeke
- Royal Belgian Institute of Natural Sciences, Operational Directory Natural Environment, Marine Ecology and Management, Brussels, Belgium
| | - Andrew Want
- Energy and Environment Institute, University of Hull, HU6 7RX, UK
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Fast and accurate mapping of fine scale abundance of a VME in the deep sea with computer vision. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Recent Trends in AI-Based Intelligent Sensing. ELECTRONICS 2022. [DOI: 10.3390/electronics11101661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by the future research directions pertaining to this exciting and fast-moving field.
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McLaren DEK, Rawlins AJ. Occurrence of alkylphenols and alkylphenol ethoxylates in North Sea sediment samples collected across oil and gas fields. MARINE POLLUTION BULLETIN 2022; 178:113655. [PMID: 35461021 DOI: 10.1016/j.marpolbul.2022.113655] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/06/2022] [Accepted: 04/08/2022] [Indexed: 06/14/2023]
Abstract
Alkylphenol ethoxylates (APEs) have been used in several offshore oil and gas production applications including as emulsifiers in drilling mud formulations, which may have resulted in their disposal to sea. Despite concern over the endocrine disrupting potential of their alkylphenol (AP) degradation products, information on the presence of AP/APEs in marine sediments in the vicinity of oil and gas production facilities is scarce. This paper presents the occurrence of AP/APEs in marine sediment in North Sea oil and gas fields. The concentrations of octylphenol, nonylphenol and their ethoxylates near offshore installations were broadly comparable to, or higher than those of coastal and estuarine point source discharges. When compared to environmental assessment criteria, the NPCA Class V threshold values for octylphenol and nonylphenol were exceeded within 100 m and 500 m of infrastructure respectively, with higher concentrations of AP/APEs reported in fields that came online prior to 1986.
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Affiliation(s)
- Diana E K McLaren
- Fugro, 1-9 The Curve, 32 Research Avenue North, Heriot-Watt University, Riccarton, Edinburgh EH14 4AP, UK
| | - Andrew J Rawlins
- Fugro, 1-9 The Curve, 32 Research Avenue North, Heriot-Watt University, Riccarton, Edinburgh EH14 4AP, UK.
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Bagi A, Knapik K, Baussant T. Abundance and diversity of n-alkane and PAH-degrading bacteria and their functional genes - Potential for use in detection of marine oil pollution. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 810:152238. [PMID: 34896501 DOI: 10.1016/j.scitotenv.2021.152238] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/26/2021] [Accepted: 12/03/2021] [Indexed: 06/14/2023]
Abstract
Monitoring environmental status through molecular investigation of microorganisms in the marine environment is suggested as a potentially very effective method for biomonitoring, with great potential for automation. There are several hurdles to that approach with regards to primer design, variability across geographical locations, seasons, and type of environmental pollution. Here, qPCR analysis of genes involved in the initial activation of aliphatic and aromatic hydrocarbons were used in a laboratory setup mimicking realistic oil leakage at sea. Seawater incubation experiments were carried out under two different seasons with two different oil types. Degenerate primers targeting initial oxygenases (alkane 1-monooxygenase; alkB and aromatic-ring hydroxylating dioxygenase; ARHD) were employed in qPCR assays to quantify the abundance of genes essential for oil degradation. Shotgun metagenomics was used to map the overall community dynamics and the diversity of alkB and ARHD genes represented in the microbial community. The amplicons generated through the qPCR assays were sequenced to reveal the diversity of oil-degradation related genes captured by the degenerate primers. We identified a major mismatch between the taxonomic diversity of alkB and ARHD genes amplified by the degenerate primers and those identified through shotgun metagenomics. More specifically, the designed primers did not amplify the alkB genes of the two most abundant alkane degraders that bloomed in the experiments, Oceanobacter and Oleispira. The relative abundance of alkB sequences from shotgun metagenomics and 16S rRNA-based Oleispira-specific qPCR assay were better signals for oil in water than the tested qPCR alkB assay. The ARHD assay showed a good agreement with PAHs degradation despite covering only 25% of the top 100 ARHD genes and missing several abundant Cycloclasticus sequences that were present in the metagenome. We conclude that further improvement of the degenerate primer approach is needed to rely on the use of oxygenase-related qPCR assays for oil leakage detection.
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Affiliation(s)
- Andrea Bagi
- NORCE Norwegian Research Centre, Bergen, Norway.
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Management and Sustainable Exploitation of Marine Environments through Smart Monitoring and Automation. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2022. [DOI: 10.3390/jmse10020297] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Monitoring of aquatic ecosystems has been historically accomplished by intensive campaigns of direct measurements (by probes and other boat instruments) and indirect extensive methods such as aero-photogrammetry and satellite detection. These measurements characterized the research in the last century, with significant but limited improvements within those technological boundaries. The newest advances in the field of smart devices and increased networking capabilities provided by emerging tools, such as the Internet of Things (IoT), offer increasing opportunities to provide accurate and precise measurements over larger areas. These perspectives also correspond to an increasing need to promptly respond to frequent catastrophic impacts produced by drilling stations and intense transportation activities of dangerous materials over ocean routes. The shape of coastal ecosystems continuously varies due to increasing anthropic activities and climatic changes, aside from touristic activities, industrial impacts, and conservation practices. Smart buoy networks (SBNs), autonomous underwater vehicles (AUVs), and multi-sensor microsystems (MSMs) such as smart cable water (SCW) are able to learn specific patterns of ecological conditions, along with electronic “noses”, permitting them to set innovative low-cost monitoring stations reacting in real time to the signals of marine environments by autonomously adapting their monitoring programs and eventually sending alarm messages to prompt human intervention. These opportunities, according to multimodal scenarios, are dramatically changing both the coastal monitoring operations and the investigations over large oceanic areas by yielding huge amounts of information and partially computing them in order to provide intelligent responses. However, the major effects of these tools on the management of marine environments are still to be realized, and they are likely to become evident in the next decade. In this review, we examined from an ecological perspective the most striking innovations applied by various research groups around the world and analyzed their advantages and limits to depict scenarios of monitoring activities made possible for the next decade.
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7
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Greening the Artificial Intelligence for a Sustainable Planet: An Editorial Commentary. SUSTAINABILITY 2021. [DOI: 10.3390/su132413508] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) is one of the most popular and promising technologies of our time [...]
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8
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Sanganyado E, Chingono KE, Gwenzi W, Chaukura N, Liu W. Organic pollutants in deep sea: Occurrence, fate, and ecological implications. WATER RESEARCH 2021; 205:117658. [PMID: 34563929 DOI: 10.1016/j.watres.2021.117658] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/04/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
The deep sea - an oceanic layer below 200 m depths - has important global biogeochemical and nutrient cycling functions. It also receives organic pollutants from anthropogenic sources, which threatens the ecological function of the deep sea. In this Review, critically examined data on the distribution of organic pollutants in the deep sea to outline the role of biogeochemical and geophysical factors on the global distribution and regional chemodynamics of organic pollutants in the deep sea. We found that the contribution of deep water formation to the influx of perfluorinated compounds reached a maximum, following peak emission, faster in young deep waters (< 10 years) compared to older deep waters (> 100 years). For example, perfluorinated compounds had low concentrations (< 10 pg L-1) and vertical variations in the South Pacific Ocean where the ocean currents are old (< 1000 years). Steep geomorphologies of submarine canyons, ridges, and valleys facilitated the transport of sediments and associated organic pollutants by oceanic currents from the continental shelf to remote deep seas. In addition, we found that, even though an estimated 1.2-4.2 million metric tons of plastic debris enter the ocean through riverine discharge annually, the role of microplastics as vectors of organic pollutants (e.g., plastic monomers, additives, and attached organic pollutants) in the deep sea is often overlooked. Finally, we recommend assessing the biological effects of organic pollutants in deep sea biota, large-scale monitoring of organic pollutants, reconstructing historical emissions using sediment cores, and assessing the impact of deep-sea mining on the ecosystem.
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Affiliation(s)
- Edmond Sanganyado
- Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Science, Shantou University, Shantou, Guangdong 515063, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511458, China.
| | | | - Willis Gwenzi
- Department of Soil Science and Agricultural Engineering, Biosystems and Environmental Engineering Research Group, University of Zimbabwe, Harare, Zimbabwe
| | - Nhamo Chaukura
- Department of Physical and Earth Sciences, Sol Plaatje University, Kimberley, South Africa
| | - Wenhua Liu
- Guangdong Provincial Key Laboratory of Marine Biotechnology, Institute of Marine Science, Shantou University, Shantou, Guangdong 515063, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511458, China
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9
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Autonomous Underwater Vehicles and Field of View in Underwater Operations. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2021. [DOI: 10.3390/jmse9030277] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Submarine inspections and surveys require underwater vehicles to operate in deep waters efficiently, safely and reliably. Autonomous Underwater Vehicles employing advanced navigation and control systems present several advantages. Robust control algorithms and novel improvements in positioning and navigation are needed to optimize underwater operations. This paper proposes a new general formulation of this problem together with a basic approach for the management of deep underwater operations. This approach considers the field of view and the operational requirements as a fundamental input in the development of the trajectory in the autonomous guidance system. The constraints and involved variables are also defined, providing more accurate modelling compared with traditional formulations of the positioning system. Different case studies are presented based on commercial underwater cameras/sonars, analysing the influence of the main variables in the measurement process to obtain optimal resolution results. The application of this approach in autonomous underwater operations ensures suitable data acquisition processes according to the payload installed onboard.
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Aracri S, Giorgio-Serchi F, Suaria G, Sayed ME, Nemitz MP, Mahon S, Stokes AA. Soft Robots for Ocean Exploration and Offshore Operations: A Perspective. Soft Robot 2021; 8:625-639. [PMID: 33450174 PMCID: PMC8713554 DOI: 10.1089/soro.2020.0011] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The ocean and human activities related to the sea are under increasing pressure
due to climate change, widespread pollution, and growth of the offshore energy
sector. Data, in under-sampled regions of the ocean and in the offshore patches
where the industrial expansion is taking place, are fundamental to manage
successfully a sustainable development and to mitigate climate change. Existing
technology cannot cope with the vast and harsh environments that need monitoring
and sampling the most. The limiting factors are, among others, the spatial
scales of the physical domain, the high pressure, and the strong hydrodynamic
perturbations, which require vehicles with a combination of persistent autonomy,
augmented efficiency, extreme robustness, and advanced control. In light of the
most recent developments in soft robotics technologies, we propose that the use
of soft robots may aid in addressing the challenges posed by abyssal and
wave-dominated environments. Nevertheless, soft robots also allow for fast and
low-cost manufacturing, presenting a new potential problem: marine pollution
from ubiquitous soft sampling devices. In this study, the technological and
scientific gaps are widely discussed, as they represent the driving factors for
the development of soft robotics. Offshore industry supports increasing energy
demand and the employment of robots on marine assets is growing. Such expansion
needs to be sustained by the knowledge of the oceanic environment, where large
remote areas are yet to be explored and adequately sampled. We offer our
perspective on the development of sustainable soft systems, indicating the
characteristics of the existing soft robots that promote underwater
maneuverability, locomotion, and sampling. This perspective encourages an
interdisciplinary approach to the design of aquatic soft robots and invites a
discussion about the industrial and oceanographic needs that call for their
application.
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Affiliation(s)
- Simona Aracri
- Scottish Microelectronics Centre, Institute for Integrated Micro and Nano Systems, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
| | - Francesco Giorgio-Serchi
- Scottish Microelectronics Centre, Institute for Integrated Micro and Nano Systems, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
| | - Giuseppe Suaria
- Institute of Marine Sciences-National Research Council (ISMAR-CNR), La Spezia, Italy
| | - Mohammed E Sayed
- Scottish Microelectronics Centre, Institute for Integrated Micro and Nano Systems, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
| | - Markus P Nemitz
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.,Robotics Engineering Program, Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
| | - Stephen Mahon
- Scottish Microelectronics Centre, Institute for Integrated Micro and Nano Systems, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
| | - Adam A Stokes
- Scottish Microelectronics Centre, Institute for Integrated Micro and Nano Systems, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
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Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy. REMOTE SENSING 2020. [DOI: 10.3390/rs12132117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Scientific exploration of seabed substrata has significantly progressed in the last few years. Hydroacoustic methods of seafloor investigation, including multibeam echosounder measurements, allow us to map large areas of the seabed with unprecedented precision. Through time-series of hydroacoustic measurements, it was possible to determine areas with distinct characteristics in the inlets of the Lagoon of Venice, Italy. Their temporal variability was investigated. Monitoring the changes was particularly relevant, considering the presence at the channel inlets of mobile barriers of the Experimental Electromechanical Module (MoSE) project installed to protect the historical city of Venice from flooding. The detection of temporal and spatial changes was performed by comparing seafloor maps created using object-based image analysis and supervised classifiers. The analysis included extraction of 25 multibeam echosounder bathymetry and backscatter features. Their importance was estimated using an objective approach with two feature selection methods. Moreover, the study investigated how the accuracy of classification could be affected by the scale of object-based segmentation. The application of the classification method at the proper scale allowed us to observe habitat changes in the tidal inlet of the Venice Lagoon, showing that the sediment substrates located in the Chioggia inlet were subjected to very dynamic changes. In general, during the study period, the area was enriched in mixed and muddy sediments and was depleted in sandy deposits. This study presents a unique methodological approach to predictive seabed sediment composition mapping and change detection in a very shallow marine environment. A consistent, repeatable, logical site-specific workflow was designed, whose main assumptions could be applied to other seabed mapping case studies in both shallow and deep marine environments, all over the world.
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12
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Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring. REMOTE SENSING 2020. [DOI: 10.3390/rs12101572] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The number and areal extent of marine protected areas worldwide is rapidly increasing as a result of numerous national targets that aim to see up to 30% of their waters protected by 2030. Automated seabed classification algorithms are arising as faster and objective methods to generate benthic habitat maps to monitor these areas. However, no study has yet systematically compared their repeatability. Here we aim to address that problem by comparing the repeatability of maps derived from acoustic datasets collected on consecutive days using three automated seafloor classification algorithms: (1) Random Forest (RF), (2) K–Nearest Neighbour (KNN) and (3) K means (KMEANS). The most robust and repeatable approach is then used to evaluate the change in seafloor habitats between 2012 and 2015 within the Greater Haig Fras Marine Conservation Zone, Celtic Sea, UK. Our results demonstrate that only RF and KNN provide statistically repeatable maps, with 60.3% and 47.2% agreement between consecutive days. Additionally, this study suggests that in low-relief areas, bathymetric derivatives are non-essential input parameters, while backscatter textural features, in particular Grey Level Co-occurrence Matrices, are substantially more effective in the detection of different habitats. Habitat persistence in the test area between 2012 and 2015 was 48.8%, with swapping of habitats driving the changes in 38.2% of the area. Overall, this study highlights the importance of investigating the repeatability of automated seafloor classification methods before they can be fully used in the monitoring of benthic habitats.
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Knapik K, Bagi A, Krolicka A, Baussant T. Metatranscriptomic Analysis of Oil-Exposed Seawater Bacterial Communities Archived by an Environmental Sample Processor (ESP). Microorganisms 2020; 8:E744. [PMID: 32429288 PMCID: PMC7284936 DOI: 10.3390/microorganisms8050744] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/12/2020] [Accepted: 05/14/2020] [Indexed: 11/17/2022] Open
Abstract
The use of natural marine bacteria as "oil sensors" for the detection of pollution events can be suggested as a novel way of monitoring oil occurrence at sea. Nucleic acid-based devices generically called genosensors are emerging as potentially promising tools for in situ detection of specific microbial marker genes suited for that purpose. Functional marker genes are particularly interesting as targets for oil-related genosensing but their identification remains a challenge. Here, seawater samples, collected in tanks with oil addition mimicking a realistic oil spill scenario, were filtered and archived by the Environmental Sample Processor (ESP), a fully robotized genosensor, and the samples were then used for post-retrieval metatranscriptomic analysis. After extraction, RNA from ESP-archived samples at start, Day 4 and Day 7 of the experiment was used for sequencing. Metatranscriptomics revealed that several KEGG pathways were significantly enriched in samples exposed to oil. However, these pathways were highly expressed also in the non-oil-exposed water samples, most likely as a result of the release of natural organic matter from decaying phytoplankton. Temporary peaks of aliphatic alcohol and aldehyde dehydrogenases and monoaromatic ring-degrading enzymes (e.g., ben, box, and dmp clusters) were observed on Day 4 in both control and oil-exposed and non-exposed tanks. Few alkane 1-monooxygenase genes were upregulated on oil, mostly transcribed by families Porticoccaceae and Rhodobacteraceae, together with aromatic ring-hydroxylating dioxygenases, mostly transcribed by Rhodobacteraceae. Few transcripts from obligate hydrocarbonoclastic genera of Alcanivorax, Oleispira and Cycloclasticus were significantly enriched in the oil-treated exposed tank in comparison to control the non-exposed tank, and these were mostly transporters and genes involved in nitrogen and phosphorous acquisition. This study highlights the importance of seasonality, i.e., phytoplankton occurrence and senescence leading to organic compound release which can be used preferentially by bacteria over oil compounds, delaying the latter process. As a result, such seasonal effect can reduce the sensitivity of genosensing tools employing bacterial functional genes to sense oil. A better understanding of the use of natural organic matter by bacteria involved in oil-biodegradation is needed to develop an array of functional markers enabling the rapid and specific in situ detection of anthropogenic pollution.
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Affiliation(s)
| | | | | | - Thierry Baussant
- NORCE Environment, NORCE Norwegian Research Centre AS, 4070 Randaberg, Norway; (K.K.); (A.B.); (A.K.)
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A framework for the development of a global standardised marine taxon reference image database (SMarTaR-ID) to support image-based analyses. PLoS One 2019; 14:e0218904. [PMID: 31891586 PMCID: PMC6938304 DOI: 10.1371/journal.pone.0218904] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 12/09/2019] [Indexed: 02/08/2023] Open
Abstract
Video and image data are regularly used in the field of benthic ecology to document biodiversity. However, their use is subject to a number of challenges, principally the identification of taxa within the images without associated physical specimens. The challenge of applying traditional taxonomic keys to the identification of fauna from images has led to the development of personal, group, or institution level reference image catalogues of operational taxonomic units (OTUs) or morphospecies. Lack of standardisation among these reference catalogues has led to problems with observer bias and the inability to combine datasets across studies. In addition, lack of a common reference standard is stifling efforts in the application of artificial intelligence to taxon identification. Using the North Atlantic deep sea as a case study, we propose a database structure to facilitate standardisation of morphospecies image catalogues between research groups and support future use in multiple front-end applications. We also propose a framework for coordination of international efforts to develop reference guides for the identification of marine species from images. The proposed structure maps to the Darwin Core standard to allow integration with existing databases. We suggest a management framework where high-level taxonomic groups are curated by a regional team, consisting of both end users and taxonomic experts. We identify a mechanism by which overall quality of data within a common reference guide could be raised over the next decade. Finally, we discuss the role of a common reference standard in advancing marine ecology and supporting sustainable use of this ecosystem.
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Zhang T, Liu S, He X, Huang H, Hao K. Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles. SENSORS (BASEL, SWITZERLAND) 2019; 20:E102. [PMID: 31878003 PMCID: PMC6982770 DOI: 10.3390/s20010102] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 12/10/2019] [Accepted: 12/18/2019] [Indexed: 11/28/2022]
Abstract
In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.
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Affiliation(s)
- Tiedong Zhang
- National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China; (T.Z.)
| | - Shuwei Liu
- National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China; (T.Z.)
| | - Xiao He
- National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China; (T.Z.)
| | - Hai Huang
- National Key Laboratory of Science and Technology on Underwater Vehicle, Harbin Engineering University, Harbin 150001, China; (T.Z.)
| | - Kangda Hao
- School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China
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Benoist NM, Morris KJ, Bett BJ, Durden JM, Huvenne VA, Le Bas TP, Wynn RB, Ware SJ, Ruhl HA. Monitoring mosaic biotopes in a marine conservation zone by autonomous underwater vehicle. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2019; 33:1174-1186. [PMID: 30859604 PMCID: PMC6850053 DOI: 10.1111/cobi.13312] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 02/19/2019] [Accepted: 03/04/2019] [Indexed: 05/19/2023]
Abstract
The number of marine protected areas (MPAs) has increased dramatically in the last decade and poses a major logistic challenge for conservation practitioners in terms of spatial extent and the multiplicity of habitats and biotopes that now require assessment. Photographic assessment by autonomous underwater vehicle (AUV) enables the consistent description of multiple habitats, in our case including mosaics of rock and sediment. As a case study, we used this method to survey the Greater Haig Fras marine conservation zone (Celtic Sea, northeast Atlantic). We distinguished 7 biotopes, detected statistically significant variations in standing stocks, species density, species diversity, and faunal composition, and identified significant indicator species for each habitat. Our results demonstrate that AUV-based photography can produce robust data for ecological research and practical marine conservation. Standardizing to a minimum number of individuals per sampling unit, rather than to a fixed seafloor area, may be a valuable means of defining an ecologically appropriate sampling unit. Although composite sampling represents a change in standard practice, other users should consider the potential benefits of this approach in conservation studies. It is broadly applicable in the marine environment and has been successfully implemented in deep-sea conservation and environmental impact studies. Without a cost-effective method, applicable across habitats, it will be difficult to further a coherent classification of biotopes or to routinely assess their conservation status in the rapidly expanding global extent of MPAs.
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Affiliation(s)
- Noëlie M.A. Benoist
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
- University of SouthamptonSouthamptonSO14 3ZHU.K.
| | - Kirsty J. Morris
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
| | - Brian J. Bett
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
| | - Jennifer M. Durden
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
- University of SouthamptonSouthamptonSO14 3ZHU.K.
- University of HawaiiHonoluluHI96822U.S.A.
| | - Veerle A.I. Huvenne
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
| | - Tim P. Le Bas
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
| | - Russell B. Wynn
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
| | - Suzanne J. Ware
- Centre for Environment Fisheries and Aquaculture ScienceLowestoftNR33 0HTU.K.
| | - Henry A. Ruhl
- Ocean Biogeochemistry and EcosystemsNational Oceanography CentreSouthamptonSO14 3ZHU.K.
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