Individual risk-taking behaviour in black-capped chickadees (<i>Poecile atricapillus</i>) does not predict annual survival
Bibliographic record
Abstract
Within species, individuals often show repeatable differences in behaviours, called ‘animal personality’. One behaviour that has been widely studied is how quickly an individual resumes feeding after a disturbance, referred to as boldness or risk-taking. Depending on the mechanism(s) shaping risk-taking behaviour, risk-taking could be positively, negatively, or not associated with differences in overall survival. We studied risk-taking and survival in a population of free-living black-capped chickadees (Poecile atricapillus) in which we previously showed repeatable among-individual differences in risk-taking over the course of several months. We found no evidence that variation in risk-taking is associated with differences in annual survival. We suggest that variation in risk-taking is likely shaped by multiple mechanisms simultaneously, such that the net effect on survival is small or null. For example, among-individual differences in energy demand may favour greater risk-taking without imposing an overall mortality cost if higher energy demand covaries with escape flight performance. We propose directions for future work, including using a multi-trait, multi-year approach to study risk-taking, to allow for stronger inferences regarding the mechanisms shaping these behavioural decisions.
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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 | 0.001 | 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".