AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations
Abstract Every autumn on the south coast of Victoria Island (Nunavut, Canada), endangered Dolphin and Union (DU) caribou (Rangifer tarandus groenlandicus x pearyi) wait for sea ice to form before continuing their southwards migration to the mainland. Delayed freeze‐up, less stable ice conditions and...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Ecological Solutions and Evidence |
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| Online Access: | https://doi.org/10.1002/2688-8319.70034 |
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| author | Ellen Bowler James Byrne Lisa‐Marie Leclerc Amélie Roberto‐Charron Martin S. J. Rogers Rachel D. Cavanagh Jason Harasimo Melanie L. Lancaster Ryan S. Y. Chan Oliver Strickson Jeremy Wilkinson Rod Downie J. Scott Hosking Tom R. Andersson |
| author_facet | Ellen Bowler James Byrne Lisa‐Marie Leclerc Amélie Roberto‐Charron Martin S. J. Rogers Rachel D. Cavanagh Jason Harasimo Melanie L. Lancaster Ryan S. Y. Chan Oliver Strickson Jeremy Wilkinson Rod Downie J. Scott Hosking Tom R. Andersson |
| author_sort | Ellen Bowler |
| collection | DOAJ |
| description | Abstract Every autumn on the south coast of Victoria Island (Nunavut, Canada), endangered Dolphin and Union (DU) caribou (Rangifer tarandus groenlandicus x pearyi) wait for sea ice to form before continuing their southwards migration to the mainland. Delayed freeze‐up, less stable ice conditions and ice‐breaking by vessels are putting migrating caribou at risk, but unpredictable freeze‐up times pose challenges for conservation planning. Having early warning of when the caribou sea ice crossing is likely to take place could guide more targeted measures (e.g., ice‐breaking vessel management). In this case study, we use a multi‐stakeholder approach to explore the potential of using observed and forecast sea ice concentration (SIC) to predict when DU caribou are likely to cross the sea ice. We examine links between caribou movement records and coincident satellite observations of SIC collected between 1996–2005 and 2015–2019. We establish probabilistic “percent‐crossed” metrics to convert SIC freeze‐up profiles into anticipated sea ice crossing‐start date ranges and maps. Finally, we assess the potential of using IceNet, an AI‐based 25 km resolution SIC forecast model, to predict these crossing‐start ranges in 2020–2022. We identify a clear link between SIC freeze‐up profiles and crossing‐start times, with median SIC reaching 98.8% (IQR = 94.1%, 100%) when caribou start their crossings. Our percent‐crossed metrics are effective in converting SIC records into crossing‐start date maps which can guide human experts. IceNet results show promise, predicting crossing‐start ranges comparable to those observed in 2022 up to three weeks before the first observed sea ice crossing. In 2021, IceNet's predicted ranges are systematically early, but improve between three‐ to one‐week lead times. Practical implication: AI sea ice forecasts could provide early warning of DU caribou sea ice crossing times, informing mitigation of ice‐breaking vessels and providing a blueprint applicable to other ice‐dependent species. Our case study contributes practical considerations, limitations and areas for future research to drive innovation in this emerging field forward. Ultimately, forecasts could be integrated into human‐expert centred decision‐support tools, guiding dynamic conservation and management for Arctic species. |
| format | Article |
| id | doaj-art-0e26c491aaf246f9beb0b3f38a775bee |
| institution | DOAJ |
| issn | 2688-8319 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecological Solutions and Evidence |
| spelling | doaj-art-0e26c491aaf246f9beb0b3f38a775bee2025-08-20T03:15:31ZengWileyEcological Solutions and Evidence2688-83192025-04-0162n/an/a10.1002/2688-8319.70034AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrationsEllen Bowler0James Byrne1Lisa‐Marie Leclerc2Amélie Roberto‐Charron3Martin S. J. Rogers4Rachel D. Cavanagh5Jason Harasimo6Melanie L. Lancaster7Ryan S. Y. Chan8Oliver Strickson9Jeremy Wilkinson10Rod Downie11J. Scott Hosking12Tom R. Andersson13British Antarctic Survey Cambridge UKBritish Antarctic Survey Cambridge UKGovernment of Nunavut, Department of Environment Kugluktuk Nunavut CanadaGovernment of Nunavut, Department of Environment Kugluktuk Nunavut CanadaBritish Antarctic Survey Cambridge UKBritish Antarctic Survey Cambridge UKWWF‐Canada Iqaluit Nunavut CanadaWWF Global Arctic Programme Solna SwedenThe Alan Turing Institute London UKThe Alan Turing Institute London UKBritish Antarctic Survey Cambridge UKWWF Woking UKBritish Antarctic Survey Cambridge UKBritish Antarctic Survey Cambridge UKAbstract Every autumn on the south coast of Victoria Island (Nunavut, Canada), endangered Dolphin and Union (DU) caribou (Rangifer tarandus groenlandicus x pearyi) wait for sea ice to form before continuing their southwards migration to the mainland. Delayed freeze‐up, less stable ice conditions and ice‐breaking by vessels are putting migrating caribou at risk, but unpredictable freeze‐up times pose challenges for conservation planning. Having early warning of when the caribou sea ice crossing is likely to take place could guide more targeted measures (e.g., ice‐breaking vessel management). In this case study, we use a multi‐stakeholder approach to explore the potential of using observed and forecast sea ice concentration (SIC) to predict when DU caribou are likely to cross the sea ice. We examine links between caribou movement records and coincident satellite observations of SIC collected between 1996–2005 and 2015–2019. We establish probabilistic “percent‐crossed” metrics to convert SIC freeze‐up profiles into anticipated sea ice crossing‐start date ranges and maps. Finally, we assess the potential of using IceNet, an AI‐based 25 km resolution SIC forecast model, to predict these crossing‐start ranges in 2020–2022. We identify a clear link between SIC freeze‐up profiles and crossing‐start times, with median SIC reaching 98.8% (IQR = 94.1%, 100%) when caribou start their crossings. Our percent‐crossed metrics are effective in converting SIC records into crossing‐start date maps which can guide human experts. IceNet results show promise, predicting crossing‐start ranges comparable to those observed in 2022 up to three weeks before the first observed sea ice crossing. In 2021, IceNet's predicted ranges are systematically early, but improve between three‐ to one‐week lead times. Practical implication: AI sea ice forecasts could provide early warning of DU caribou sea ice crossing times, informing mitigation of ice‐breaking vessels and providing a blueprint applicable to other ice‐dependent species. Our case study contributes practical considerations, limitations and areas for future research to drive innovation in this emerging field forward. Ultimately, forecasts could be integrated into human‐expert centred decision‐support tools, guiding dynamic conservation and management for Arctic species.https://doi.org/10.1002/2688-8319.70034Arctic Northwest PassageArtificial Intelligence (AI)Dolphin and Union cariboudynamic conservationGPS trackingmigration |
| spellingShingle | Ellen Bowler James Byrne Lisa‐Marie Leclerc Amélie Roberto‐Charron Martin S. J. Rogers Rachel D. Cavanagh Jason Harasimo Melanie L. Lancaster Ryan S. Y. Chan Oliver Strickson Jeremy Wilkinson Rod Downie J. Scott Hosking Tom R. Andersson AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations Ecological Solutions and Evidence Arctic Northwest Passage Artificial Intelligence (AI) Dolphin and Union caribou dynamic conservation GPS tracking migration |
| title | AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations |
| title_full | AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations |
| title_fullStr | AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations |
| title_full_unstemmed | AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations |
| title_short | AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations |
| title_sort | ai sea ice forecasts for arctic conservation a case study predicting the timing of caribou sea ice migrations |
| topic | Arctic Northwest Passage Artificial Intelligence (AI) Dolphin and Union caribou dynamic conservation GPS tracking migration |
| url | https://doi.org/10.1002/2688-8319.70034 |
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