Breeding seabirds increase foraging range in response to an extreme marine heatwave
Bibliographic record
Abstract
Marine heatwaves are increasing in frequency and can disrupt marine ecosystems non-linearly. In this study, we examined the effect of the North Pacific warming event of 2014, the largest long-term sea surface anomaly on record, on black-legged kittiwake Rissa tridactyla foraging trips before, during, and after the event. We assessed foraging trip characteristics (trip distance and duration), the dispersal of foraging locations, and the persistence of foraging areas within and among years. Foraging trip characteristics, foraging area size, and location varied from year to year. Kittiwake foraging was more dispersed, direct, and farther from the colony in years immediately after and during the warming event. A third of the foraging area used pre-heatwave (2012) was important in subsequent years, which indicates that this area was, and may still be, a perennial foraging hot spot. During the chick-rearing stage, black-legged kittiwakes increased their speed and reduced the proportion of resting compared to the incubation stage. We conclude that marine heatwaves may have a strong impact on seabird foraging, extending foraging ranges, and that those impacts may be nonlinear with a strong lag.
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 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 | 0.000 | 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".