Personality predicts foraging site fidelity and trip repeatability in a marine predator
Bibliographic record
Abstract
Animal populations are often comprised of both foraging specialists and generalists. For instance, some individuals show higher foraging site fidelity (spatial specialization) than others. Such individual differences in degree of specialization can persist over time-scales of months or even years in long-lived animals, but the mechanisms leading to these different individual strategies are not fully understood. There is accumulating evidence that individual variation in foraging behaviour is shaped by animal personality traits, such as boldness. Despite this, the potential for boldness to drive differences in the degree of specialization is unknown. In this study, we used novel object tests to measure boldness in black-legged kittiwakes (Rissa tridactyla) breeding at four colonies in Svalbard and deployed GPS loggers to examine their at-sea foraging behaviour. We estimated the repeatability of foraging trips and used a hidden Markov model to identify locations of foraging sites in order to quantify individual foraging site fidelity. Across the breeding season, bolder birds were more repeatable than shy individuals in the distance and range of their foraging trips, and during the incubation period (but not chick rearing), bolder individuals were more site-faithful. Birds exhibited these differences while showing high spatial similarity in foraging areas, indicating that site selection was not driven by personality-dependent spatial partitioning. We instead suggest that a relationship between boldness and site fidelity may be driven by differences in behavioural flexibility between bold and shy individuals. Together, these results provide a potential mechanism by which widely reported individual differences in foraging specialization may emerge.
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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 | 0.002 |
| 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".