Quantitative genetic and translocation experiments reveal genotype‐by‐environment effects on juvenile life‐history traits in two populations of Chinook salmon (<i>Oncorhynchus tshawytscha</i>)
Bibliographic record
Abstract
Understanding the genetic architecture of phenotypic plasticity is required to assess how populations might respond to heterogeneous or changing environments. Although several studies have examined population-level patterns in environmental heterogeneity and plasticity, few studies have examined individual-level variation in plasticity. Here, we use the North Carolina II breeding design and translocation experiments between two populations of Chinook salmon to detail the genetic architecture and plasticity of offspring survival and growth. We followed the survival of 50,800 offspring through the larval stage and used parentage analysis to examine survival and growth through freshwater rearing. In one population, we found that additive genetic, nonadditive genetic and maternal effects explained 25%, 34% and 55% of the variance in larvae survival, respectively. In the second population, these effects explained 0%, 24% and 61% of the variance in larvae survival. In contrast, fry survival was regulated primarily by additive genetic effects, which indicates a shift from maternal to genetic effects as development proceeds. Fry growth also showed strong additive genetic effects. Translocations between populations revealed that offspring survival and growth varied between environments, the degree of which differed among families. These results indicate genetic differences among individuals in their degree of plasticity and consequently their ability to respond to environmental variation.
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 | 0.000 | 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".