Population genetic diversity and structure of wild and hatchery-raised populations of European abalone <i>Haliotis tuberculata tuberculata</i>: guidelines for future restocking and stock-enhancement programs
Bibliographic record
Abstract
Populations of Haliotis tuberculata tuberculata declined sharply over the last two decades, mainly due to pathogenic bacteria, Vibrio Harveyi. In this context, restocking or stock-enhancement operations based on hatchery juveniles might be relevant to restore populations and ensure sustainable fisheries. Maintaining the genetic diversity of wild populations and hatchery individuals is a primary concern in supplementation programs. Here, the genetic diversity of 14 hatchery samples and 10 wild populations was assessed using 158 nuclear SNPs. Genetic diversity was comparable between wild and hatchery samples, and even slightly higher in the hatchery. However, high genetic differentiation and small effective population sizes suggested strong genetic drift in the hatchery. Pooling hatchery samples decreased differentiation levels with wild samples, suggesting that released juveniles should be composed of several cohorts and/or generations to limit the genetic heterogeneity between seed and wild populations. Moreover, reduced connectivity was detected between northwestern and southeastern populations, suggesting that restocking broodstock should be chosen depending on the locality where it would be released. Overall, this study provides useful guidelines for future restocking programs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".