The understudied and underappreciated role of predation in the mortality of fish released from fishing gears
Bibliographic record
Abstract
Abstract The assumption that animals released from fishing gears survive has frequently been scrutinized by researchers in recent years. Mortality estimates from these research efforts can be incorporated into management models to ensure the sustainability of fisheries and the conservation of threatened species. Post‐release mortality estimates are typically made by holding the catch in a tank, pen or cage for short‐term monitoring (e.g. 48 h). These estimates may be inaccurate in some cases because they fail to integrate the challenges of the wild environment. Most obvious among these challenges is predator evasion. Stress and injury from a capture experience can temporarily impair physiological capacity and alter behaviour in released animals, a period during which predation risk is likely elevated. In large‐scale commercial fisheries, predators have adapted their behaviour to capitalize on impaired fishes being discarded, while in recreational catch‐and‐release fisheries, exercise and air exposure can similarly impede the capacity for released fish to evade opportunistic predators. Owing to the indirect and often cryptic nature of this source of mortality, very few studies have attempted to document it. A survey of the literature demonstrated that <2% of the papers in the combined realms of bycatch and catch‐and‐release have directly addressed or considered post‐release predation. Future research should combine field telemetry and laboratory studies using both natural and simulated predation encounters and incorporate physiological and behavioural endpoints. Quite simply, predation is an understudied and underappreciated contributor to the mortality of animals released from fishing gears.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".