Genetic risks of supplementing trout populations with native stocks: a simulation case study from current Pyrenean populations
Bibliographic record
Abstract
The risks of supplementation must be examined to assess the genetic effects to native wild populations before full implementation or exclusion of programs that involve captive breeding and release. Real genetic data can be applied to simulations of genetic changes in populations of interest and subsequently used in risk assessment. Ancestral Mediterranean brown trout (Salmo trutta) lineages exhibit complex population structure among native populations. Genetically divergent Atlantic stocks were maintained and released in the Mediterranean rivers as recreational fish, which resulted in hybridization and introgression with local populations. Therefore, we designed a new supplementation program based on native stocks and evaluated the genetic risks associated with releasing native fish in recreational fisheries. Our simulation was delimited by the observed population genetic structure and available hatchery facilities in the study region. Supplementation with native stocks maintained estimates of gene diversity indexes (total diversity (HT), local diversity (HS), and population differentiation (GST)). However, simulations indicated that long-term supplementation significantly reduced genetic diversity among locations because of a homogenizing effect of populations along each management unit. Therefore, such reinforcements compromised the conservation of local genetic variation. Nevertheless, replacement of current foreign stocks with native stocks can be an important step towards promoting the value of preserving local diversity among anglers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".