Biologging reveals rapid movements of harbour seals between freshwater and marine habitats in the subarctic
Bibliographic record
Abstract
Biologging tools can provide invaluable information on the movement and behaviour of animals, facilitating the elucidation of ecological dynamics, especially for wide-ranging species, and supporting conservation and management efforts. Harbour seals (Phoca vitulina) exhibit extensive habitat plasticity in their vast range across the northern hemisphere, with likely recent increases in abundance at northern latitudes, yet details of their movement behaviour in subarctic areas remain largely unknown. We used satellite-telemetry data, including nearly 5,000 locations and over 12,000 dives, obtained from six harbour seals tagged in western Hudson Bay from 2021 to 2023, to address the knowledge gap on their movement behaviour between marine and freshwater habitats in subarctic regions. We document the behavioural patterns, transit speeds, and diverse aquatic system usage, including detailed records of a harbour seal track traversing over 170 km upriver on three separate trips along the Seal River, Canada. Notably, we observed a rapid downstream transit from the Seal River to Hudson Bay, covering 214 km within a single day. Additionally, we highlight the prevalence of short dive durations in the Seal and Churchill Rivers, in contrast to longer dive durations in Hudson Bay. These insights complement existing evidence of harbour seal occurrences and river use at northern latitudes, as well as enhance our understanding of harbour seal movement ecology within Hudson Bay which can be used to better inform conservation and management strategies between connected freshwater and marine environments in the Arctic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".