Arctic terns from circumpolar breeding colonies share common migratory routes
Bibliographic record
Abstract
The Arctic tern is an iconic seabird, famous for its annual migrations between the Arctic and the Antarctic. Its wide geographical range has impeded knowledge of potential population bottlenecks during its annual bi-hemispheric movements. Although Arctic terns breed in the Pacific, Atlantic, and Arctic coasts of North America, few tracking studies have been conducted on North American Arctic terns, and none in Canada, which represents a significant proportion of their circumpolar breeding range. Using light-level geolocators, we tracked 53 Arctic terns from 5 breeding colonies across a wide latitudinal and longitudinal range within North America. We compared the routes taken by birds in our study and migration timing to those previously tracked from Greenland, Iceland, The Netherlands, Sweden, Norway, Maine (USA), and S. Alaska (USA). Most Arctic terns tracked globally used one of 3 southbound migration routes: (1) Atlantic West Africa; (2) Atlantic Brazil; and (3) Pacific coastal, and one of 2 northbound migration routes: (1) Mid-ocean Atlantic and (2) Mid-ocean Pacific. Some other trans-equatorial seabirds also used these migration routes, suggesting that Arctic tern routes may be important for other species. The migration timing for southbound and northbound migrations was generally different between tracked tern colonies worldwide but generally fell within a 1-2 mo window. Our research suggests that conservation management of Arctic terns during their migration should dynamically adapt with the times of the year that terns use parts of their route. Future identification of common multi-species seabird flyways could aid the international negotiations required to conserve pelagic seabirds such as Arctic terns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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 teacher head, 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".