Loss of genetic integrity correlates with stocking intensity in brook charr (Salvelinus fontinalis)
Bibliographic record
Abstract
Supportive breeding and stocking performed with non-native or domesticated fish to support sport fishery industry is a common practice throughout the world. Such practices are likely to modify the genetic integrity of natural populations depending on the extent of genetic differences between domesticated and wild fish and on the intensity of stocking. The purpose of this study is to assess the effects of variable stocking intensities on patterns of genetic diversity and population differentiation among nearly 2000 brook charr (Salvelinus fontinalis) from 24 lakes located in two wildlife reserves in Québec, Canada. Our results indicated that the level of genetic diversity was increased in more intensively stocked lakes, mainly due to the introduction of new alleles of domestic origin. As a consequence, the population genetic structure was strongly homogenized by intense stocking. Heavily stocked lakes presented higher admixture levels and lower levels of among lakes genetic differentiation than moderately and un-stocked lakes. Moreover, the number of stocking events explained the observed pattern of population genetic structure as much as hydrographical connections among lakes in each reserve. We discuss the implications for the conservation of exploited fish populations and the management of stocking practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".