Fish vulnerability to capture by trapping is modulated by individual parasite density
Bibliographic record
Abstract
Commercial fishery harvest is a powerful evolutionary agent, but we know little about whether environmental stressors affect harvest-associated selection. We test how parasite infection relates to trapping vulnerability through selective processes underlying capture. We used fish naturally infected with parasites, including trematodes causing black spots under fish skin. We first assessed how individual parasite density related to standard metabolic rate (SMR), maximum metabolic rate (MMR) and absolute aerobic scope (AAS)-then used laboratory fishing simulations to test how capture vulnerability was related to parasite density. We further explored group-trapping dynamics using experimental shoals containing varying proportions of infected fish (groups of six with either 0, 2, 4 or 6 infected individuals). At the individual level, we found a positive relationship between parasite presence and SMR, but not MMR or AAS. While we saw no relationship between individual metabolic capacity and vulnerability to trapping, we found the length of time fish spent in traps increased with increasing parasite density, a predictor of trapping-related capture probability. At the group level, the number of infected individuals in a shoal did not affect overall group trapping vulnerability. Our results suggest that parasite infection has some capacity to shift individual vulnerability patterns in fisheries, and potentially influence the evolutionary outcomes of fisheries-induced evolution.
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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".