Within-individual behaviour of razorbills Alca torda during the non-breeding season is consistent but not related to personality
Bibliographic record
Abstract
Within-individual consistency and between-individual variation in migratory and foraging behaviour have been observed in many species, including seabirds. Migratory differences among individuals within species have been linked to factors such as age, sex and animal personality, and may have fitness consequences. Therefore, we aimed to investigate individual consistency in distribution (i.e. location, distance from colony), foraging dive depth and dietary metrics for razorbills Alca torda during non-breeding seasons across years and whether these behaviours were related to a personality trait. Between 2017 and 2023, we deployed light-level geolocator (global location sensor, GLS) and combination geolocator temperature-depth recorder (GLS/TDR) tags on personality-tested razorbills (docile-aggressive continuum) during chick-rearing in coastal Newfoundland, Canada, and collected feathers (head, belly, secondary) to quantify stable isotope ratios of nitrogen (δ 15 N) as a proxy of trophic level. Razorbills displayed high repeatability ( R ) in their non-breeding locations and the distances travelled north ( R = 0.592) and south ( R = 0.564) from the colony. Razorbills also displayed low but significant repeatability in dive depth ( R = 0.133) and δ 15 N values ( R = 0.128). However, these metrics were not related to docility. Overall, the small but consistent differences in non-breeding location and foraging behaviour among individuals suggest that environmental conditions causing negative fitness consequences may be experienced by certain subsets of the population, thereby minimising the impacts of localised threats at the population-level. Although relationships between our measured personality trait (docility) and non-breeding behaviours were not observed, we suggest future studies investigate personality to understand the between-individual variation shown in a growing number of studies.
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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 | 0.001 | 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".