Fight‐flight or freeze‐hide? Personality and metabolic phenotype mediate physiological defence responses in flatfish
Bibliographic record
Abstract
Survival depends on appropriate behavioural and physiological responses to danger. In addition to active 'fight-flight' defence responses, a passive 'freeze-hide' response is adaptive in some contexts. However, the physiological mechanisms determining which individuals choose a given defence response remain poorly understood. We examined the relationships among personality, metabolic performance and physiological stress responses across an environmental gradient in the olive flounder, Paralichthys olivaceus. We employed four behavioural assays to document the existence of two distinct behavioural types ('bold' and 'shy') in this species. We found consistent metabolic differences between individuals of a given behavioural type across an environmental gradient: shy individuals had overall lower aerobic scope, maximum metabolic rate and standard metabolic rate than bold individuals in both high (25 ppt) and low (3 ppt) salinity. These behavioural and metabolic differences translated into divergent physiological responses during acute stress: shy individuals adopted a passive 'freeze-hide' response by reducing their oxygen consumption rates (akin to shallow breathing) whereas bold individuals adopted an active 'fight-flight' response by increasing their rates of respiration. These distinct defence strategies were repeatable within individuals between salinity treatments. Although it has been suggested theoretically, this is the first empirical evidence that the metabolic response to stressful situations differs between bold and shy individuals. Our results emphasize the importance of incorporating physiological measures to understand the mechanisms driving persistent inter-individual differences in animals.
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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 | 0.000 |
| 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".