Multi‐colony tracking of two pelagic seabirds with contrasting flight capability illustrates how windscapes shape migratory movements at an ocean‐basin scale
Bibliographic record
Abstract
Migration is a common trait among many animals allowing the exploitation of spatiotemporally variable resources. It often implies high energetic costs to cover large distances, for example between breeding and wintering grounds. For flying or swimming animals, the adequate use of winds and currents can help reduce the associated energetic costs. Migratory seabirds are good models because they dwell in habitats characterized by strong winds while undertaking very long migrations. We tested the hypothesis that seabirds migrate through areas with favourable winds. To that end, we used the SEATRACK dataset, a multi‐colony geolocator tracking dataset, for two North Atlantic seabirds with contrasting flight capabilities, the black‐legged kittiwake Rissa tridactyla and the Atlantic puffin Fratercula arctica , and wind data from the ERA5 climate reanalysis model. Both species had on average positive wind support during migration. Their main migratory routes were similar and followed seasonally prevailing winds. The general migratory movement had a loop‐shape at the scale of the North Atlantic, with an autumn route (southward) along the east coast of Greenland, and a spring route (northward) closer to the British Isles. While migrating, both species had higher wind support in spring than in autumn. Kittiwakes migrated farther and benefited from higher wind support than puffins on average. The variation in wind conditions encountered while migrating was linked to the geographical location of the colonies. Generally, northernmost colonies had a better wind support in autumn while the southernmost colonies had a better wind support in spring, with some exceptions. Our study helps understanding how the physical environment shapes animal migration, which is crucial to further predict how migrants will be impacted by ongoing environmental changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".