Does capture method or the presence of aquatic protected areas influence the selective harvest of behavioural types in largemouth bass?
Bibliographic record
Abstract
Selectively removing fish based on particular traits, such as body size, may shift trait abundance in the remaining population, resulting in a phenomenon called fisheries-induced evolution. Recently, there is growing interest in evaluating the effects of fisheries-induced evolution on fish behaviour. Aquatic protected areas (APAs) have been designated in some habitats in efforts to prohibit harvesting and maintain natural ranges of phenotypic variation for impacted species. Here, we attempted to test whether APAs that prohibit all forms of fishing have an evolutionary influence on adult largemouth bass (Micropterus salmoides) behaviour by investigating the relationship between capture method and behavioural type. Fish, caught via active (angling) and passive (hoop net) capture techniques in both protected (70+ year old APAs in eastern Ontario) and adjacent nonprotected areas, were subjected to standard tests of boldness (refuge emergence, general activity, and flight-initiation-distance). A behavioural syndrome characterized by consistent within-individual variation and correlation of boldness behaviours (activity and refuge emergence) was present. Our results provide evidence that APAs may promote behavioural diversification and protect traits selectively targeted by recreational angling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".