Multiannual environmental forcing shapes breeding phenology and success in a subantarctic seabird
Bibliographic record
Abstract
Abstract Climate-driven phenological mismatches threaten avian reproduction by disrupting food availability during critical breeding stages. In marine ecosystems, time lags between environmental changes and their effects on food webs are challenging to model, yet they can have a profound impact on top-predator reproduction. We disentangle how oceanic and climatic variability influence the breeding phenology and success of a keystone seabird of the Southern Ocean drawing on 24 years of data from 17,000 marked king penguins. We document an exceptional 19-day advancement in breeding phenology, alongside increased breeding success (44% in 2000, 62% in 2023). A sliding-window analysis reveals that sea temperature and primary production in key foraging zones predict both phenology and breeding success, with lags ranging from several weeks to two years. While king penguins appear to be keeping pace with current changes, their dependence on multi-year environmental conditions underscores the vulnerability of top predators to unpredictable, fast-changing, and more frequent extreme conditions.
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 | 0.001 |
| 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".