Does metabolic rate predict risk‐taking behaviour? A field experiment in a wild passerine bird
Bibliographic record
Abstract
Summary Individuals often show consistent differences in risk‐taking behaviours; behaviours that increase resource acquisition at the expense of an increased risk of mortality. Recently, basal metabolic rate (BMR) has been suggested as a potentially important state variable underlying adaptive individual differences in a range of behaviours, including risk‐taking. We tested the relationship between BMR and risk‐taking in free‐living great tits (Parus major) using experimental manipulations of perceived predation risk. We compared the latency of individuals to return to feeders following control (human) and predator (model sparrowhawk, Accipiter nisus) disturbances at fixed feeder locations. We predicted that if variation in risk‐taking is shaped by energetic constraints, high BMR individuals should return to feeders sooner following both disturbance types and show smaller changes in risk‐taking as a function of predation danger. Higher BMR tended to be associated with lower risk‐taking following control disturbances but greater risk‐taking following predator disturbances, resulting in a significant interaction between BMR and treatment. Within‐individual changes in risk‐taking as a function of ambient temperature (a proxy for within‐individual changes in energetic constraints) mirrored these results. Lower temperatures tended to be associated with lower risk‐taking following control disturbances, but greater risk‐taking following predator disturbances, resulting in a significant interaction between temperature and treatment. The effects of BMR and temperature on variation in risk‐taking as a function of perceived predation danger were qualitatively similar, suggesting that energetic constraints play a role in shaping risk‐taking. However, the hypothesized mechanism (energetic requirements directly influence the optimal expression of risk‐taking behaviour) is insufficient to account for the observed negative relationship between energetic constraint and risk‐taking following control disturbances. We conclude that variation in risk‐taking is associated with differences in energetic constraints, including BMR and temperature, but that the relationship is context‐specific, here high vs. low perceived predation risk. Further studies are needed to elucidate potential mechanisms that could generate context‐specific relationships between energetic constraints and risk‐taking.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".