Evaluation of potential direct genetic effects of the proposed Atlantic salmon (Salmo salar) aquaculture site expansion in southern Newfoundland
Bibliographic record
Abstract
In 2019 a proponent applied for aquaculture licenses at various sites located on the south coast of Newfoundland and the request was referred to DFO for siting advice including examination of the potential for genetic interactions with wild Atlantic salmon. Here we examine the potential genetic interactions resulting from the proposed finfish expansion involving thirteen sites (1M individuals/site) in southern Newfoundland using a combination of empirical data, and both individual-based and dispersal modeling. 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 aquaculture parr survival, to explore how the proportion of escapees relative to the size of wild populations influences genetic and demographic change in the wild. Our simulations suggest that both demographic decline and genetic change are predicted when the proportion of escapees relative to wild population size exceeds 10% annually. The occurrence of escapees in southern Newfoundland rivers (estimated population size ~22,000 individuals), both at present and under the proposed expansion scenario were predicted using river and site locations, simple models of dispersal for early and late escapees, and the best available data from Canada and Europe. Model predictions of escapee dispersal suggest that under the present regime, rivers characterized by the largest proportion of escapees relative to wild population size are located in the head of Fortune Bay and Bay d’Espoir (19 rivers total > 10% escapees, max 15.6%) consistent with recent empirical evidence of escapees and hybridization. Under the proposed expansion, the number of escapees in southern Newfoundland rivers is predicted to increase by 49% (1.5X) and the rivers characterized by the greatest proportion of escapees relative to wild population size are predicted to occur in the Bay d’Espoir area (20 rivers total >10% escapees, max 24%).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".