Circumpolar dynamics of a marine top‐predator track ocean warming rates
Bibliographic record
Abstract
Global warming is a nonlinear process, and temperature may increase in a stepwise manner. Periods of abrupt warming can trigger persistent changes in the state of ecosystems, also called regime shifts. The responses of organisms to abrupt warming and associated regime shifts can be unlike responses to periods of slow or moderate change. Understanding of nonlinearity in the biological responses to climate warming is needed to assess the consequences of ongoing climate change. Here, we demonstrate that the population dynamics of a long-lived, wide-ranging marine predator are associated with changes in the rate of ocean warming. Data from 556 colonies of black-legged kittiwakes Rissa tridactyla distributed throughout its breeding range revealed that an abrupt warming of sea-surface temperature in the 1990s coincided with steep kittiwake population decline. Periods of moderate warming in sea temperatures did not seem to affect kittiwake dynamics. The rapid warming observed in the 1990s may have driven large-scale, circumpolar marine ecosystem shifts that strongly affected kittiwakes through bottom-up effects. Our study sheds light on the nonlinear response of a circumpolar seabird to large-scale changes in oceanographic conditions and indicates that marine top predators may be more sensitive to the rate of ocean warming rather than to warming itself.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".