Climate change drives migratory range shift via individual plasticity in shearwaters
Bibliographic record
Abstract
How individual animals respond to climate change is key to whether populations will persist or go extinct. Yet, few studies investigate how changes in individual behavior underpin these population-level phenomena. Shifts in the distributions of migratory animals can occur through adaptation in migratory behaviors, but there is little understanding of how selection and plasticity contribute to population range shift. Here, we use long-term geolocator tracking of Balearic shearwaters ( Puffinus mauretanicus ) to investigate how year-to-year changes in individual birds’ migrations underpin a range shift in the post-breeding migration. We demonstrate a northward shift in the post-breeding range and show that this is brought about by individual plasticity in migratory destination, with individuals migrating further north in response to changes in sea-surface temperature. Furthermore, we find that when individuals migrate further, they return faster, perhaps minimizing delays in return to the breeding area. Birds apparently judge the increased distance that they will need to migrate via memory of the migration route, suggesting that spatial cognitive mechanisms may contribute to this plasticity and the resulting range shift. Our study exemplifies the role that individual behavior plays in populations’ responses to environmental change and highlights some of the behavioral mechanisms that might be key to understanding and predicting species persistence in response to climate change.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".