Introgression of domesticated alleles into a wild trout genotype and the impact on seasonal survival in natural lakes
Bibliographic record
Abstract
We tested the fitness consequences of introgression of fast-growing domesticated fish into a wild population. Fry from wild and domesticated rainbow trout (Oncorhynchus mykiss) crosses, F1 hybrids, and first- and second-generation backcrosses were released into two natural lakes. Parentage analysis using microsatellite loci facilitated the identification of survivors, so fitness was estimated in nature from the first-feeding stage. Results indicated that under certain conditions, domesticated fish survived at least as well as wild fish within the same environment. Relative growth and survival of the crosses, however, were highly dependent on environment. During the first summer, fastest-growing crosses had the highest survival, but this trend was reversed after one winter and another summer. Although the F1 hybrids showed evidence of outbreeding depression because of the disruption of local adaptation, there was little evidence of outbreeding depression in the backcrosses, and the second-generation backcrosses exhibited a wild-type phenotype. This information is relevant for assessing the multigenerational risk of escaped or released domesticated fish should they successfully interbreed with wild populations and provides information on how to minimize detrimental impacts of a conservation breeding and/or management programme. These data also further understanding of the selection pressures in nature that maintain submaximal rates of growth.
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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 | 0.000 | 0.000 |
| 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".