Recapturing escaped fish from marine aquaculture is largely unsuccessful: alternatives to reduce the number of escapees in the wild
Bibliographic record
Abstract
Abstract Farmed fish that escape and mix with wild fish populations can have significant ecological and genetic consequences. To reduce the number of escaped fish in the wild, recapture is often attempted. Here, we review the behaviours of escapees post‐escape, and how recapture success varies with escaped fish size, the size of the initial escape event and recapture methods. Success rates of fishing gears varied among species, with gill‐nets and coastal barrier nets most effective for recapture of salmonids. Recapture success was strongly negatively correlated with both fish size and the number of fish escaped, regardless of species. Recapture success was universally low across all studied species (8%). Numerous tracking studies of escaped fish indicate that recapture efforts should be initiated within 24 h of an escape incident for highest recapture success. However, most large escape events are due to storms, which mean recapture efforts rarely start within this timeframe. Recapture of escaped fish is broadly ineffective in marine habitats, with rare exception. High bycatch rates during ineffective recapture attempts imply that large‐scale recapture efforts should be weighed against the possibility of affecting wild fish populations negatively. We suggest three alternative approaches to reduce escapee numbers in wild habitats: (i) protect populations of predatory fish around sea‐cage farms from fishing, as they prey upon smaller escapees; (ii) construct impact offset programmes to target recapture in habitats where escapees can be efficiently caught; and (iii) ensure technical standards are legislated so that fish farmers invest in preventative technologies to minimize escapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".