Behavioural and kinematic components of the fast-start escape response in fish: individual variation and temporal repeatability
Bibliographic record
Abstract
Inter-individual variation in physiological performance traits, which is stable over time, can be of potential ecological and evolutionary significance. The fish escape response is interesting in this regard because it is a performance trait for which inter-individual variation may determine individual survival. The temporal stability of such variation is, however, largely unexplored. We quantified individual variation of various components of the escape response in a population of European sea bass (Dicentrarchus labrax), considering both non-locomotor (responsiveness and latency) and locomotor (speed, acceleration, turning rate, turning angle and distance travelled in a fixed time, D(esc)) variables. We assessed whether variation in performance was temporally stable and we searched for any trade-offs among the components of the response that might explain why the variation persisted in the population. The coefficient of variation was high for all components, from 23% for turning rate to 41% for D(esc), highlighting the non-stereotypic nature of the response. Individual performance for all variables was significantly repeatable over five sequential responses at 30 min intervals, and also repeatable after a 30 day interval for most of the components. This indicates that the variation is intrinsic to the individuals, but there was no evidence for trade-offs amongst the components of the response, suggesting that, if trade-offs exist, they must be against other ecologically important behavioural or performance traits.
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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) | 0.000 | 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".