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Record W2066451979 · doi:10.1111/1365-2435.12318

Does metabolic rate predict risk‐taking behaviour? A field experiment in a wild passerine bird

2014· article· en· W2066451979 on OpenAlex
Kimberley J. Mathot, Marion Nicolaus, Yimen G. Araya‐Ajoy, Niels J. Dingemanse, Bart Kempenaers

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFunctional Ecology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMax-Planck-GesellschaftDeutscher Akademischer Austauschdienst
KeywordsBiologyPasserinePredationParusPredatorBasal metabolic rateEcologyAccipiterDemography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.226
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it