Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean
Soundscape analyses provide an integrative approach to studying the presence and complexity of sounds within long-term acoustic data sets. Acoustic metrics (AMs) have been used extensively to describe terrestrial habitats but have had mixed success in the marine environment. Novel approaches are nee...
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Online Access: | http://dx.doi.org/10.3389/fmars.2021.749802 https://www.frontiersin.org/articles/10.3389/fmars.2021.749802/full |
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crfrontiers:10.3389/fmars.2021.749802 2024-09-09T19:31:46+00:00 Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean Pegg, Nicole Roca, Irene T. Cholewiak, Danielle Davis, Genevieve E. Van Parijs, Sofie M. U.S. Fleet Forces Command Bureau of Ocean Energy Management 2021 http://dx.doi.org/10.3389/fmars.2021.749802 https://www.frontiersin.org/articles/10.3389/fmars.2021.749802/full unknown Frontiers Media SA https://creativecommons.org/licenses/by/4.0/ Frontiers in Marine Science volume 8 ISSN 2296-7745 journal-article 2021 crfrontiers https://doi.org/10.3389/fmars.2021.749802 2024-08-27T04:04:10Z Soundscape analyses provide an integrative approach to studying the presence and complexity of sounds within long-term acoustic data sets. Acoustic metrics (AMs) have been used extensively to describe terrestrial habitats but have had mixed success in the marine environment. Novel approaches are needed to be able to deal with the added noise and complexity of these underwater systems. Here we further develop a promising approach that applies AM with supervised machine learning to understanding the presence and species richness (SR) of baleen whales at two sites, on the shelf and the slope edge, in the western North Atlantic Ocean. SR at both sites was low with only rare instances of more than two species (out of six species acoustically detected at the shelf and five at the slope) vocally detected at any given time. Random forest classification models were trained on 1-min clips across both data sets. Model outputs had high accuracy (>0.85) for detecting all species’ absence in both sites and determining species presence for fin and humpback whales on the shelf site (>0.80) and fin and right whales on the slope site (>0.85). The metrics that contributed the most to species classification were those that summarized acoustic activity (intensity) and complexity in different frequency bands. Lastly, the trained model was run on a full 12 months of acoustic data from on the shelf site and compared with our standard acoustic detection software and manual verification outputs. Although the model performed poorly at the 1-min clip resolution for some species, it performed well compared to our standard detection software approaches when presence was evaluated at the daily level, suggesting that it does well at a coarser level (daily and monthly). The model provided a promising complement to current methodologies by demonstrating a good prediction of species absence in multiple habitats, species presence for certain species/habitat combinations, and provides higher resolution presence information for ... Article in Journal/Newspaper baleen whale baleen whales North Atlantic Frontiers (Publisher) Frontiers in Marine Science 8 |
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Soundscape analyses provide an integrative approach to studying the presence and complexity of sounds within long-term acoustic data sets. Acoustic metrics (AMs) have been used extensively to describe terrestrial habitats but have had mixed success in the marine environment. Novel approaches are needed to be able to deal with the added noise and complexity of these underwater systems. Here we further develop a promising approach that applies AM with supervised machine learning to understanding the presence and species richness (SR) of baleen whales at two sites, on the shelf and the slope edge, in the western North Atlantic Ocean. SR at both sites was low with only rare instances of more than two species (out of six species acoustically detected at the shelf and five at the slope) vocally detected at any given time. Random forest classification models were trained on 1-min clips across both data sets. Model outputs had high accuracy (>0.85) for detecting all species’ absence in both sites and determining species presence for fin and humpback whales on the shelf site (>0.80) and fin and right whales on the slope site (>0.85). The metrics that contributed the most to species classification were those that summarized acoustic activity (intensity) and complexity in different frequency bands. Lastly, the trained model was run on a full 12 months of acoustic data from on the shelf site and compared with our standard acoustic detection software and manual verification outputs. Although the model performed poorly at the 1-min clip resolution for some species, it performed well compared to our standard detection software approaches when presence was evaluated at the daily level, suggesting that it does well at a coarser level (daily and monthly). The model provided a promising complement to current methodologies by demonstrating a good prediction of species absence in multiple habitats, species presence for certain species/habitat combinations, and provides higher resolution presence information for ... |
author2 |
U.S. Fleet Forces Command Bureau of Ocean Energy Management |
format |
Article in Journal/Newspaper |
author |
Pegg, Nicole Roca, Irene T. Cholewiak, Danielle Davis, Genevieve E. Van Parijs, Sofie M. |
spellingShingle |
Pegg, Nicole Roca, Irene T. Cholewiak, Danielle Davis, Genevieve E. Van Parijs, Sofie M. Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean |
author_facet |
Pegg, Nicole Roca, Irene T. Cholewiak, Danielle Davis, Genevieve E. Van Parijs, Sofie M. |
author_sort |
Pegg, Nicole |
title |
Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean |
title_short |
Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean |
title_full |
Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean |
title_fullStr |
Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean |
title_full_unstemmed |
Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean |
title_sort |
evaluating the efficacy of acoustic metrics for understanding baleen whale presence in the western north atlantic ocean |
publisher |
Frontiers Media SA |
publishDate |
2021 |
url |
http://dx.doi.org/10.3389/fmars.2021.749802 https://www.frontiersin.org/articles/10.3389/fmars.2021.749802/full |
genre |
baleen whale baleen whales North Atlantic |
genre_facet |
baleen whale baleen whales North Atlantic |
op_source |
Frontiers in Marine Science volume 8 ISSN 2296-7745 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3389/fmars.2021.749802 |
container_title |
Frontiers in Marine Science |
container_volume |
8 |
_version_ |
1809900596325515264 |