Does thermal plasticity affect susceptibility to capture in fish? Insights from a simulated trap and trawl fishery
Bibliographic record
Abstract
In fishes, physiological and behavioural traits can correlate with vulnerability to capture with fishing gears, highlighting the capacity of fisheries selection to drive phenotypic change in exploited populations. There remains a paucity of information regarding how different fishing gears may select on phenotypic traits and how relationships between individual traits and capture vulnerability change across environmental gradients. By simulating the capture process in a trawl and trap using wild minnows (Phoxinus phoxinus) acclimated to different temperatures, we investigated how contrasting fishing gears select on behavioural and physiological traits and how this selection is modulated by temperature. Despite similar risk of capture in each gear, selection differed between traps and trawls. Fish exhibiting low spontaneous activity were at greater capture risk in the trawl across all temperatures, while traps showed no selection except at 24 °C. No relationships between physiological traits and capture vulnerability were found, except between swim performance and trap capture vulnerability at 24 °C. This study demonstrates that fisheries selection on individual traits is likely context-specific, depending on both fishing gear type and environment.
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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 | 0.001 |
| 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".