Effects of aquaculture on wild fish populations: a synthesis of data
Bibliographic record
Abstract
The potential adverse environmental effects of aquaculture have been the subject of considerable attention in both the media and the scientific literature. We undertook a synthesis of the published scientific literature, primarily concerning Atlantic salmon (Salmo salar), to assess the current data available regarding these potential effects. No data are available to test for the direct effects of aquaculture organisms on the demographics of wild fish populations. However, seven studies show that escaped salmon in the wild have lower fitness, as measured by survival and reproductive success, than native salmon. Thirteen other studies, encompassing 91 different traits, provide strong evidence of phenotypic differences between farmed and wild salmon, presumably because of artificial selection in the aquaculture environment. An additional 10 studies have documented significant genetic differences between farmed salmon and the wild fish with which they will interact, or potentially interact. Given the paucity of data regarding actual population consequences of escaped farmed fish on wild populations, and the documented differences between the two types of fish, it seems prudent to treat farmed fish as exotic species with potentially negative consequences for wild populations, particularly when the latter are of conservation concern.Key words: aquaculture, Atlantic salmon, artificial selection, fitness, introgression.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".