New AI tool aids caribou conservation in a changing Arctic

Artificial intelligence sea ice forecasting systems could help predict and protect the migration routes of endangered caribou in the Canadian Arctic, according to a new study. The research, led by British Antarctic Survey (BAS) scientists in partnership with The Alan Turing Institute, WWF and the Government of Nunavut, demonstrates how this technology could assist local agencies in protecting critical migration routes which cross areas of land and sea ice.

The technology, called IceNet, could help prevent ice-breaking vessels from disrupting these routes at key moments in the migration of caribou, ensuring they can safely travel south across areas of sea ice from their summer calving grounds.

The study, published in Ecological Solutions and Evidence combines satellite observations, GPS tracking, AI forecasting, and local expertise to create AI-informed migration early-warning systems for Dolphin and Union caribou (Rangifer tarandus groenlandicus x pearyi).
Dr Ellen Bowler, a machine learning research scientist at BAS and lead author of the study, explained:

“Many Arctic animals’ life histories and movement patterns are intrinsically linked to sea ice, which grows and retreats with the seasons. Climate change is causing Arctic sea ice to recede and become less stable – which also means the region is becoming more accessible for ships. This combination of threats could have fatal consequences for iconic animals like caribou.

This new technique combines our AI sea-ice forecasts with historic tracking data from the caribou, so we can predict when they are likely to start migrating based on when the sea ice forms. This means that local teams can take short-term action to support the migration by protecting sea ice migration routes from ice-breaking vessels. It’s exciting that AI could give such a positive real-world outcome.”

Dolphin and Union caribou migrate twice a year across sea ice which forms seasonally in the waterways between mainland Canada and Victoria Island. This transit sees the caribou gather on the banks of Victoria Island every autumn, waiting for sea ice to reform before they can migrate back south. They begin crossing once sufficient ice has formed, which varies annually.

Unfortunately, increasing numbers of caribou perish during these crossings due to lower sea ice quality, and ice-breaking vessels which pose a further threat by creating open-water leads, potentially blocking crucial migration routes. Local organisations are exploring how they can mitigate shipping impacts to protect the herd, which are vital to the lives and livelihoods of local Inuit and Inuvialuit people.

The authors developed a model combining caribou tracking data and satellite observations of sea ice to establish connections between sea ice formation and the timing of the autumn migration. The AI then forecasts when sea ice conditions triggering the autumn migration will be met, to give early-warnings of peak migration times which can be updated on an annual basis.

This ecological research builds on an existing machine learning model, IceNet, a probabilistic model shown to accurately predict sea ice extents up to three months in advance. IceNet is trained on satellite-derived observations and outperforms state-of-the-art traditional models, including in seasonal forecasts of summer sea ice. Developed by BAS and The Alan Turing Institute, IceNet is also set to aid navigation by the British Antarctic Survey research ship RRS Sir David Attenborough.

The information provided by the AI model can support local agencies in making more informed and dynamic decisions – such as when and where to recommend short-term limits on ice-breaking activity by nearby ships. The AI model’s development and operation benefit greatly from invaluable local knowledge.

Dr Scott Hosking, co-author and Mission Director for Environmental Forecasting at The Alan Turing Institute added:

“The Arctic is warming at four times the rate of the rest of the planet, resulting in rapid changes. In the hands of local conservation experts, and combined with their existing knowledge and practices, this tool could help mitigate the threats posed to wildlife and communities in the Arctic.”

 The team are working to explore applications for other Arctic species, including iconic species such as polar bears and walrus. This could include predicting when polar bears are most likely to arrive on land near to communities, to mitigate against human-polar bear conflict.

Dr Melanie Lancaster Senior Specialist, Arctic Species, for the WWF Global Arctic Programme, said:

“The climate crisis is causing the Arctic to transform and become less predictable. Nature, once superbly in balance, is struggling to cope in many parts of the region. Tools like IceNet can help remove some of this unpredictability through forecasting and understanding changes in sea ice, which is essential habitat for many animals, including whales, walrus, polar bears and even some caribou. This information can in turn be used to inform decision-making to give these species the best chance to adapt.”

‘AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations’ is published today in Ecological Solutions and Evidence. https://doi.org/10.1002/2688-8319.70034

The research was supported by a grant from WWF [WWF-UK Arctic IceNet grant GB085600].
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For further information:

Andrea Norgren | Sr. Manager Communications, WWF Global Arctic Programme | andrea.norgren@wwf.se