Does human‐induced hybridization have long‐term genetic effects? Empirical testing with domesticated, wild and hybridized fish populations
Bibliographic record
Abstract
Current conservation practices exclude human-generated hybridized populations from protection, as the genetic effects of hybridization in the wild have been observed to be long-lasting based on neutral genetic markers and are considered potentially irreversible. Theory, however, predicts otherwise for genes under selection. We transplanted combinations of wild, domesticated and hybridized populations of a fish species to new environments. We then compared survival, phenotypic variation and plasticity to determine whether hybridization affects adaptive potential after multiple generations of selection in the wild. Although the fitness of our hybridized populations at the onset of hybridization cannot be assessed, our results suggest that within five to eleven generations, selection can remove introduced foreign genes from wild populations that have hybridized with domesticated conspecifics. The end result is hybridized populations that, in terms of survival, phenotypic plasticity, mean trait expression and overall general responses to environmental change, closely resemble neighbouring wild populations. These results have important implications for considering the potential conservation value of hybridized populations and illustrate the effectiveness of selection in a local environment.
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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 | 0.004 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".