Model-based evaluation of the genetic impacts of farm-escaped Atlantic salmon on wild populations
Bibliographic record
Abstract
Genetic interactions (i.e. hybridization) between wild and escaped Atlantic salmon Salmo salar from aquaculture operations have been widely documented, yet the ability to incorporate predictions of risk into aquaculture siting advice has been limited. Here we demonstrate a model-based approach to assessing these potential genetic interactions using a salmon aquaculture expansion scenario in southern Newfoundland as an example. We use an eco-genetic individual-based Atlantic salmon model (IBSEM) parameterized for southern Newfoundland populations, with regional environmental data and field-based estimates of survival, to explore how the proportion of escapees relative to the size of wild populations could potentially influence genetic and demographic changes in wild populations. Our simulations suggest that both demographic decline and genetic change are predicted when the percentage of escapees in a river relative to wild population size is equal to or exceeds 10% annually. The occurrence of escapees in southern Newfoundland rivers under a proposed expansion scenario was predicted using river and site locations and models of dispersal for early and late escapees. Model predictions of escapee dispersal suggest that under the proposed expansion scenario, the number of escapees is expected to increase by 49% and the highest escapee concentrations will shift westward, consistent with the location of proposed expansion (20 rivers total >10% escapees, max 24%). Our results identify susceptible rivers and potential impacts predicted under the proposed aquaculture expansion scenario and illustrate how model-based predictions of both escapee dispersal and genetic impacts can be used to inform both aquaculture management decisions and wild salmon conservation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".