Release mortality in Pacific salmon fisheries along the homing migration and recommended best practices to maximize welfare and survival
Bibliographic record
Abstract
Release or discard of captured fish commonly occurs in commercial and recreational fisheries and can result in immediate fish mortality during capture or delayed mortality upon release. This review synthesizes data from Pacific salmon ( Oncorhynchus spp.) fisheries examining intrinsic and extrinsic factors affecting Pacific salmon individual release mortality (RM) across species and fishing sectors as adult fish mature and transit from marine, to estuarine, to fresh water. RM risk was high (26–45 % observed mortality) in all fisheries and environments when captured fish were bleeding, had high levels of scale loss, had fin or eye damage, and were exposed to low oxygen from net crowding and exhaustion. Highest RM risk (>45 % observed mortality) was associated with gill net and purse seine fisheries. Air exposure and handling duration contributed to high RM when water temperatures in any environment exceeded 18°C. Estuarine and lower river environments have elevated RM risk due to osmotic, maturation, and temperature changes. Short to medium term (≤24 h) observations were poor predictors of longer-term RM, and observations of at least 5–10 days were needed to assess more complete RM rates. RM mechanisms were environment, fishery sector, and life-stage specific. Our best practice recommendations for modifying current fishing practices are gear- and location-specific and aim to minimise stress, injury, and bycatch, which could result in improvements to fish welfare, reductions in RM, and associated conservation benefits.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".