Among-individual variation in thermal plasticity of fish metabolic rates causes profound variation in temperature-specific trait repeatability, but does not co-vary with behavioural plasticity
Bibliographic record
Abstract
Conspecifics of the same age and size differ consistently in the pace with which they expend energy. This among-individual variation in metabolic rate is thought to influence behavioural variation, since differences in energy requirements should motivate behaviours that facilitate energy acquisition, such as being bold or active in foraging. While there is evidence for links between metabolic rate and behaviour in constant environments, we know little about whether metabolic rate and behaviour change together when the environment changes—that is, if metabolic and behavioural plasticity co-vary. We investigated this using a fish that becomes dormant in winter and strongly reduces its activity when the environment cools, the cunner ( Tautogolabrus adspersus ). We found strong and predictable among-individual variation in thermal plasticity of metabolic rates, from resting to maximum levels, but no evidence for among-individual variation in thermal plasticity of movement activity, meaning that these key physiological and behavioural traits change independently when the environment changes. The strong among-individual variation in metabolic rate plasticity resulted in much higher repeatability (among-individual consistency) of metabolic rates at warm than cold temperatures, indicating that the potential for metabolic rate to evolve under selection is temperature-dependent, as repeatability can set the upper limit to heritability. This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".