Arctic‐breeding black‐legged kittiwakes show individual variation in foraging responses to glacial conditions without consequences for reproductive output
Bibliographic record
Abstract
Behavioural plasticity is likely to influence how individuals continue to access resources under rapid climate change. Plasticity will be particularly important at highly dynamic, prey‐rich foraging areas such as upwelling fronts of marine‐terminating glaciers in the high Arctic, where profitability varies significantly across space and time. Understanding individual variation in plasticity and its adaptive potential is crucial to understand a populations flexibility to future climate scenarios. By analysing GPS data from 186 black‐legged kittiwakes Rissa tridactyla breeding in the high Arctic over six years, we quantified individual variation in behavioural plasticity in use of glacial fronts and its relationship with the number of chicks produced. Variation in the relationship between glacial use and levels of discharged meltwater was primarily explained by differences in food availability between years. Whereas there was no significant relationship between discharge rates and glacier use in years of low zooplankton biomass, the probability of glacial front use and time spent at glaciers decreased in years when food was more abundant, despite high discharge and likely good conditions at the front. Interestingly, neither glacial use nor plasticity in foraging during the breeding season correlated with the number of surviving chicks, suggesting that all individuals still obtained enough food for reproduction. Understanding the complex nature of individual variation in plasticity and when it is likely to be adaptive will be the first step in highlighting when plasticity can be used to predict how species will respond to rapidly changing environments.
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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".