Data from: Egg size and the adaptive capacity of early life history traits in Chinook salmon (Oncorhynchus tshawytscha)
Bibliographic record
Abstract
Offspring traits are greatly influenced by maternal effects and these maternal effects may provide an important pathway through which populations can adapt to changing thermal environments. We investigated the effect of egg size on the among and within population variation in early life history traits among introduced Great Lakes Chinook salmon (Oncorhynchus tshawytscha) populations under varying thermal conditions. We reared Chinook salmon from three populations in a common garden hatchery study at 6.5°C, 9.4°C, and 15.2°C, and measured a variety of fitness-related traits during development. We found that most of the among population variation in early life history traits was explained by egg size. However, the contribution of egg size to the among population variation decreased with an increase in temperature suggesting that other effects, such as genetic, contribute at high temperature. Within populations, egg size explained much of the dam variance and maternal effect for traits in every temperature, whereas egg size generally had little to no influence on the sire variance and heritability. Overall, our results demonstrate the significant contribution egg size makes to shaping early life history phenotypes among and within populations, and suggests that egg size is an important pathway through which offspring phenotypes can evolve on contemporary timescales.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".