Genetic monitoring of supportive breeding in brown trout (<i>Salmo trutta</i>L.), using microsatellite DNA markers
Bibliographic record
Abstract
Stocking with offspring of local wild fish, so-called supportive breeding, is often advocated as an alternative to stocking domesticated fish. However, it is important to ensure that supportive breeding does not result in inbreeding and loss of genetic variability. We analysed eight microsatellite loci in samples of wild and hatchery-reared brown trout (Salmo trutta) from three populations subject to supportive breeding. For calibrating statistical procedures, we included two test samples of reared offspring for which the precise number of parent fish was known and a sample from a further wild reference population. Three different statistical procedures were used to detect population bottlenecks and loss of variability: (i) a randomization test for comparing allelic diversity between samples; (ii) estimates of effective number of breeders from gametic-phase disequilibrium; and (iii) a test for assessing population bottlenecks based on detecting deviations from mutation-drift equilibrium. All three procedures were useful but they also exhibited different strengths and limitations, with the test for population bottlenecks probably being the single most useful procedure for routine monitoring. In two populations subject to supportive breeding, there were strong indications of reduced effective population sizes, and significant genetic differentiation was observed between different samples from the same population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".