Genetic and ecological effects of salmon farming on wild salmon: modelling from experimental results
Bibliographic record
Abstract
Abstract Cultured salmonids are released or escape into the wild in large numbers and may make up significant proportions of wild salmonid populations in fresh- and saltwater, causing considerable concern for the fitness and productivity of these populations. This paper focuses on the effects of escaped farmed Atlantic salmon (Salmo salar) on wild salmon. Farmed salmon have been under artificial selection for growth and other economically important traits for 30 years and are genetically different in their origin at the molecular and quantitative genetic levels. Escaped farmed salmon spawn in the wild with limited success. Their offspring outgrow those of wild origin but suffer higher mortality. Whole-river experiments in Ireland and Norway have shown that the lifetime success of farmed salmon is reduced relative to wild salmon. Based on data from these experiments, we model the future of wild salmon populations experiencing invasions of escaped farmed salmon. Simulations with a fixed intrusion rate of 20% escaped farmed salmon at spawning suggest that substantial changes take place in wild salmon populations within ten salmon generations (∼40 years). Low-invasion scenarios suggest that farmed offspring are unlikely to become established in the population, whereas high-invasion scenarios suggest that populations are eventually mixtures of hybrid and farmed descendants. Recovery of the wild population is not likely under all circumstances, even after many decades without further intrusion. Managers of wild salmon will have difficulty in obtaining broodstock of the original wild population after a few generations of high intrusion. We conclude that further measures to reduce escapes of farmed salmon and their spawning in wild populations are urgently needed.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".