Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies
The patchy nature and overall scarcity of available scientific data poses a challenge to holistic ecosystem-based management that considers the whole range of ecological, social, and economic aspects that affect ecosystem health and productivity in the deep sea. In particular, the evaluation of, for...
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Online Access: | http://dx.doi.org/10.3389/fmars.2023.1139425 https://www.frontiersin.org/articles/10.3389/fmars.2023.1139425/full |
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crfrontiers:10.3389/fmars.2023.1139425 2024-02-11T10:06:32+01:00 Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies Callery, Oisín Grehan, Anthony Science Foundation Ireland Horizon 2020 Framework Programme Marine Institute 2023 http://dx.doi.org/10.3389/fmars.2023.1139425 https://www.frontiersin.org/articles/10.3389/fmars.2023.1139425/full unknown Frontiers Media SA https://creativecommons.org/licenses/by/4.0/ Frontiers in Marine Science volume 10 ISSN 2296-7745 Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography journal-article 2023 crfrontiers https://doi.org/10.3389/fmars.2023.1139425 2024-01-26T10:09:15Z The patchy nature and overall scarcity of available scientific data poses a challenge to holistic ecosystem-based management that considers the whole range of ecological, social, and economic aspects that affect ecosystem health and productivity in the deep sea. In particular, the evaluation of, for instance, the impact of human activities/climate change, the adequacy and representativity of MPA networks, and the valuation of ecosystem goods and services is hampered by the lack of detailed seafloor habitat maps and a univocal classification system. To maximize the use of current evidence-based management decision tools, this paper investigates the potential application of a supervised machine learning methodology to expand a well-established habitat classification system throughout an entire ocean basin. A multi-class Random Forest habitat classification model was built using the predicted distributions of 6 deep-sea fish and 6 cold-water corals as predictor variables (proxies). This model, found to correctly classify the area covered by an existing European seabed habitat classification system with ~90% accuracy, was used to provide a univocal deep-sea habitat classification for the North Atlantic. Until such time as global seabed mapping projects are complete, supervised machine learning approaches, as described here, can provide the full coverage classified maps and preliminary habitat inventories needed to underpin marine management decision making. Article in Journal/Newspaper North Atlantic Frontiers (Publisher) Frontiers in Marine Science 10 |
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Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography |
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Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography Callery, Oisín Grehan, Anthony Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies |
topic_facet |
Ocean Engineering Water Science and Technology Aquatic Science Global and Planetary Change Oceanography |
description |
The patchy nature and overall scarcity of available scientific data poses a challenge to holistic ecosystem-based management that considers the whole range of ecological, social, and economic aspects that affect ecosystem health and productivity in the deep sea. In particular, the evaluation of, for instance, the impact of human activities/climate change, the adequacy and representativity of MPA networks, and the valuation of ecosystem goods and services is hampered by the lack of detailed seafloor habitat maps and a univocal classification system. To maximize the use of current evidence-based management decision tools, this paper investigates the potential application of a supervised machine learning methodology to expand a well-established habitat classification system throughout an entire ocean basin. A multi-class Random Forest habitat classification model was built using the predicted distributions of 6 deep-sea fish and 6 cold-water corals as predictor variables (proxies). This model, found to correctly classify the area covered by an existing European seabed habitat classification system with ~90% accuracy, was used to provide a univocal deep-sea habitat classification for the North Atlantic. Until such time as global seabed mapping projects are complete, supervised machine learning approaches, as described here, can provide the full coverage classified maps and preliminary habitat inventories needed to underpin marine management decision making. |
author2 |
Science Foundation Ireland Horizon 2020 Framework Programme Marine Institute |
format |
Article in Journal/Newspaper |
author |
Callery, Oisín Grehan, Anthony |
author_facet |
Callery, Oisín Grehan, Anthony |
author_sort |
Callery, Oisín |
title |
Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies |
title_short |
Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies |
title_full |
Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies |
title_fullStr |
Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies |
title_full_unstemmed |
Extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies |
title_sort |
extending regional habitat classification systems to ocean basin scale using predicted species distributions as proxies |
publisher |
Frontiers Media SA |
publishDate |
2023 |
url |
http://dx.doi.org/10.3389/fmars.2023.1139425 https://www.frontiersin.org/articles/10.3389/fmars.2023.1139425/full |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Frontiers in Marine Science volume 10 ISSN 2296-7745 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3389/fmars.2023.1139425 |
container_title |
Frontiers in Marine Science |
container_volume |
10 |
_version_ |
1790604313146425344 |