Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data
Understanding the spatiotemporal distributions of migratory marine species at marine renewable energy sites is a crucial step towards assessing the potential impacts of tidal stream turbines and related infrastructure upon these species. However, the dynamic marine conditions that make tidal channel...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.851757/full |
| _version_ | 1818012908777373696 |
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| author | Charles W. Bangley Daniel J. Hasselman Joanna Mills Flemming Fredrick G. Whoriskey Joel Culina Lilli Enders Rod G. Bradford |
| author_facet | Charles W. Bangley Daniel J. Hasselman Joanna Mills Flemming Fredrick G. Whoriskey Joel Culina Lilli Enders Rod G. Bradford |
| author_sort | Charles W. Bangley |
| collection | DOAJ |
| description | Understanding the spatiotemporal distributions of migratory marine species at marine renewable energy sites is a crucial step towards assessing the potential impacts of tidal stream turbines and related infrastructure upon these species. However, the dynamic marine conditions that make tidal channels attractive for marine renewable power development also make it difficult to identify and follow species of marine fishes with existing technologies such as hydroacoustics and optical cameras. Acoustic telemetry can resolve some of these problems. Acoustic tags provide unique individual ID codes at an ultrasonic frequency, which are then detected and recorded by acoustic receivers deployed in the area of interest. By matching detection locations of fish species with environmental conditions at proposed sites for tidal energy infrastructure, species distribution models can be developed to predict the probability of species occurrence at sites of current and planned tidal power development. This information can be used to develop statistically robust encounter rate models to aid in quantifying the risk of tidal power development to migratory fish species. We used this approach to develop a predictive model of striped bass (Morone saxatilis) distribution within Minas Passage in the upper Bay of Fundy, Nova Scotia. Model results suggested increased probability of striped bass presence in Minas Passage during late ebb tide conditions and at relatively high water temperatures. We demonstrate the potential utility of species distribution modeling of acoustic tag detections in predicting interactions with renewable energy infrastructure, and show the importance of physical oceanographic variables influencing species distributions in a highly dynamic marine environment. |
| first_indexed | 2024-04-14T06:26:51Z |
| format | Article |
| id | doaj.art-124aa03bbc7542b2b31a5ae45233bbf8 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-14T06:26:51Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-124aa03bbc7542b2b31a5ae45233bbf82022-12-22T02:07:46ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-07-01910.3389/fmars.2022.851757851757Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry DataCharles W. Bangley0Daniel J. Hasselman1Joanna Mills Flemming2Fredrick G. Whoriskey3Joel Culina4Lilli Enders5Rod G. Bradford6Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, CanadaFundy Ocean Research Centre for Energy, Dartmouth, NS, CanadaDepartment of Mathematics and Statistics, Dalhousie University, Halifax, NS, CanadaOcean Tracking Network, Dalhousie University, Halifax, NS, CanadaFundy Ocean Research Centre for Energy, Dartmouth, NS, CanadaAcadia University, Wolfville, NS, CanadaBedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, NS, CanadaUnderstanding the spatiotemporal distributions of migratory marine species at marine renewable energy sites is a crucial step towards assessing the potential impacts of tidal stream turbines and related infrastructure upon these species. However, the dynamic marine conditions that make tidal channels attractive for marine renewable power development also make it difficult to identify and follow species of marine fishes with existing technologies such as hydroacoustics and optical cameras. Acoustic telemetry can resolve some of these problems. Acoustic tags provide unique individual ID codes at an ultrasonic frequency, which are then detected and recorded by acoustic receivers deployed in the area of interest. By matching detection locations of fish species with environmental conditions at proposed sites for tidal energy infrastructure, species distribution models can be developed to predict the probability of species occurrence at sites of current and planned tidal power development. This information can be used to develop statistically robust encounter rate models to aid in quantifying the risk of tidal power development to migratory fish species. We used this approach to develop a predictive model of striped bass (Morone saxatilis) distribution within Minas Passage in the upper Bay of Fundy, Nova Scotia. Model results suggested increased probability of striped bass presence in Minas Passage during late ebb tide conditions and at relatively high water temperatures. We demonstrate the potential utility of species distribution modeling of acoustic tag detections in predicting interactions with renewable energy infrastructure, and show the importance of physical oceanographic variables influencing species distributions in a highly dynamic marine environment.https://www.frontiersin.org/articles/10.3389/fmars.2022.851757/fullspecies distribution analysistidal stream energy impactacoustic telemetryminas passageboosted regression tree (BRT) modelsstriped bass (Morone saxatilis) |
| spellingShingle | Charles W. Bangley Daniel J. Hasselman Joanna Mills Flemming Fredrick G. Whoriskey Joel Culina Lilli Enders Rod G. Bradford Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data Frontiers in Marine Science species distribution analysis tidal stream energy impact acoustic telemetry minas passage boosted regression tree (BRT) models striped bass (Morone saxatilis) |
| title | Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data |
| title_full | Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data |
| title_fullStr | Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data |
| title_full_unstemmed | Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data |
| title_short | Modeling the Probability of Overlap Between Marine Fish Distributions and Marine Renewable Energy Infrastructure Using Acoustic Telemetry Data |
| title_sort | modeling the probability of overlap between marine fish distributions and marine renewable energy infrastructure using acoustic telemetry data |
| topic | species distribution analysis tidal stream energy impact acoustic telemetry minas passage boosted regression tree (BRT) models striped bass (Morone saxatilis) |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.851757/full |
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