Genetic and maternal effects on juvenile survival and fitness-related traits in three populations of Atlantic salmon
Bibliographic record
Abstract
Although studies addressing natural selection have primarily focused on additive genetic effects because of their direct relationship with responses to selection, nonadditive genetic and maternal effects can also significantly influence phenotypes. We partitioned the phenotypic variance of survival and fitness-related traits in juvenile Atlantic salmon (Salmo salar) from three allopatric populations (LaHave, Sebago, and Saint-Jean) into additive genetic, nonadditive genetic, and maternal environmental effects using a full-factorial breeding design. We also modelled the potential increase in offspring performance if nonrandom mating (e.g., mate choice) is considered instead of random mating. The three populations exhibited significant differences in trait values as well as the genetic architecture of the traits. Nevertheless, nonadditive genetic and maternal environmental effects tended to be larger than the additive genetic effects. There was also a shift from maternal environmental to genetic effects during development in two of the populations. That is, maternal environmental effects were larger at early (egg and alevin) life stages, whereas nonadditive effects were larger at the later (fry) life stage. The amount of additive genetic effects was small, suggesting the traits will respond slowly to selection. We discuss how different maternal environmental effects across years may influence the genetic architecture of offspring traits.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".