Effect of breeding performance on the distribution and activity budgets of a predominantly resident population of black‐browed albatrosses
Bibliographic record
Abstract
Abstract Pelagic seabirds breeding at high latitudes generally split their annual cycle between reproduction, migration, and wintering. During the breeding season, they are constrained in their foraging range due to reproduction while during winter months, and they often undertake long‐distance migrations. Black‐browed albatrosses (Thalassarche melanophris) nesting in the Falkland archipelago remain within 700 km from their breeding colonies all year‐round and can therefore be considered as resident. Accordingly, at‐sea activity patterns are expected to be adjusted to the absence of migration. Likewise, breeding performance is expected to affect foraging, flying, and floating activities, as failed individuals are relieved from reproduction earlier than successful ones. Using geolocators coupled with a saltwater immersion sensor, we detailed the spatial distribution and temporal dynamics of at‐sea activity budgets of successful and failed breeding black‐browed albatrosses nesting in New Island, Falklands archipelago, over the breeding and subsequent nonbreeding season. The 90% monthly kernel distribution of failed and successful breeders suggested no spatial segregation. Both groups followed the same dynamics of foraging effort both during daylight and darkness all year, except during chick‐rearing, when successful breeders foraged more intensively. Failed and successful breeders started decreasing flying activities during daylight at the same time, 2–3 weeks after hatching period, but failed breeders reached their maximum floating activity during late chick‐rearing, 2 months before successful breeders. Moon cycle had a significant effect on activity budgets during darkness, with individuals generally more active during full moon. Our results highlight that successful breeders buffer potential reproductive costs during the nonbreeding season, and this provides a better understanding of how individuals adjust their spatial distribution and activity budgets according to their breeding performance in absence of migration.
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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 | 0.000 | 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".